diff --git a/.coveragerc b/.coveragerc index 5179611ae..cba539ca8 100644 --- a/.coveragerc +++ b/.coveragerc @@ -4,6 +4,7 @@ omit = */contrib/* */test/* include=libpysal/* +disable_warnings=include-ignored [report] omit = __init__.py diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 000000000..f847a0dac --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +*.ipynb linguist-language=Python +libpysal/_version.py export-subst diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml new file mode 100644 index 000000000..c63ae133b --- /dev/null +++ b/.github/workflows/build_docs.yml @@ -0,0 +1,60 @@ + + name: Build Docs + on: + push: + # Sequence of patterns matched against refs/tags + tags: + - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10 + workflow_dispatch: + inputs: + version: + description: Manual Doc Build Reason + default: test + required: false + + jobs: + docs: + name: Build & Push Docs + runs-on: ${{ matrix.os }} + timeout-minutes: 90 + strategy: + matrix: + os: ['ubuntu-latest'] + environment-file: [ci/310.yaml] + experimental: [false] + defaults: + run: + shell: bash -l {0} + + steps: + - name: Checkout repo + uses: actions/checkout@v3 + + - name: Setup micromamba + uses: mamba-org/provision-with-micromamba@main + with: + environment-file: ${{ matrix.environment-file }} + micromamba-version: 'latest' + + - name: Make Docs + run: cd docs; make html + + - name: Commit Docs + run: | + git clone https://github.com/ammaraskar/sphinx-action-test.git --branch gh-pages --single-branch gh-pages + cp -r docs/_build/html/* gh-pages/ + cd gh-pages + git config --local user.email "action@github.com" + git config --local user.name "GitHub Action" + git add . + git commit -m "Update documentation" -a || true + # The above command will fail if no changes were present, + # so we ignore the return code. + + - name: push to gh-pages + uses: ad-m/github-push-action@master + with: + branch: gh-pages + directory: gh-pages + github_token: ${{ secrets.GITHUB_TOKEN }} + force: true diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml index 27253b3ae..c780d4874 100644 --- a/.github/workflows/release_and_publish.yml +++ b/.github/workflows/release_and_publish.yml @@ -7,66 +7,59 @@ # under the user's name, not the organzation. #-------------------------------------------------- - name: release_and_publish - + name: Release & Publish + on: push: # Sequence of patterns matched against refs/tags tags: - - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10 - + - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10 + jobs: build: + name: Create release & publish to PyPI runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: '3.x' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install setuptools wheel twine jupyter urllib3 pandas pyyaml - python setup.py sdist bdist_wheel - - name: Run Changelog - run: | - jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=-1 --ExecutePreprocessor.kernel_name=python3 tools/gitcount.ipynb - - name: Cat Changelog - uses: pCYSl5EDgo/cat@master - id: changetxt - with: - path: ./tools/changelog.md - env: - TEXT: ${{ steps.changetxt.outputs.text }} - - name: Create Release - id: create_release - uses: actions/create-release@v1 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # token is provided by GHA, DO NOT create - with: - tag_name: ${{ github.ref }} - release_name: Release ${{ github.ref }} - body: ${{ steps.changetxt.outputs.text }} - draft: false - prerelease: false - - name: Get Asset name - run: | - export PKG=$(ls dist/) - set -- $PKG - echo "name=$1" >> $GITHUB_ENV - - name: Upload Release Asset - id: upload-release-asset - uses: actions/upload-release-asset@v1 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - upload_url: ${{ steps.create_release.outputs.upload_url }} # This pulls from the CREATE RELEASE step above, referencing it's ID to get its outputs object, which include a `upload_url`. See this blog post for more info: https://jasonet.co/posts/new-features-of-github-actions/#passing-data-to-future-steps - asset_path: dist/${{ env.name }} - asset_name: ${{ env.name }} - asset_content_type: application/zip - - name: Publish distribution 📦 to PyPI - uses: pypa/gh-action-pypi-publish@master - with: - user: __token__ - password: ${{ secrets.PYPI_PASSWORD }} + - name: Checkout repo + uses: actions/checkout@v3 + + - name: Set up python + uses: actions/setup-python@v4 + with: + python-version: "3.x" + + - name: Install Dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine jupyter urllib3 pandas pyyaml + python setup.py sdist bdist_wheel + + - name: run Changelog + run: | + jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=-1 --ExecutePreprocessor.kernel_name=python3 tools/gitcount.ipynb + + - name: cat Changelog + uses: pCYSl5EDgo/cat@master + id: changetxt + with: + path: ./tools/changelog.md + env: + TEXT: ${{ steps.changetxt.outputs.text }} + + - name: Get the tag name + run: echo "TAG=${GITHUB_REF/refs\/tags\//}" >> $GITHUB_ENV + + - name: Release + uses: softprops/action-gh-release@v1 + with: + body: ${{ steps.changetxt.outputs.text }} + body_path: ${{ steps.changetxt.outputs.path }} + name: Release ${{ env.TAG }} + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + - name: Publish distribution 📦 to PyPI + uses: pypa/gh-action-pypi-publish@master + with: + user: __token__ + password: ${{ secrets.PYPI_PASSWORD }} diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml index 2b7a61743..1c8deb966 100644 --- a/.github/workflows/unittests.yml +++ b/.github/workflows/unittests.yml @@ -10,46 +10,56 @@ jobs: unittests: - name: conda (${{ matrix.os }}, ${{ matrix.environment-file }}) + name: ${{ matrix.os }}, ${{ matrix.environment-file }} runs-on: ${{ matrix.os }} - # timeout-minutes: 25 + env: + FETCH_EXAMPLES: python -c 'import libpysal; libpysal.examples.fetch_all()' + RUN_TEST: pytest -v -n auto libpysal --cov libpysal --cov-config .coveragerc --cov-report xml --color yes --cov-append --cov-report term-missing + #timeout-minutes: 25 strategy: matrix: - os: ['macos-latest', 'ubuntu-latest', 'windows-latest'] - environment-file: [ci/36.yaml, ci/37.yaml, ci/38.yaml] - exclude: - - environment-file: ci/36.yaml + os: ['ubuntu-latest'] + environment-file: [ + ci/38-minimal.yaml, + ci/39.yaml, + ci/310.yaml, + ci/311.yaml, + ci/311-dev.yaml + ] + include: + - environment-file: ci/311.yaml + os: macos-latest + - environment-file: ci/311.yaml os: windows-latest - defaults: - run: - shell: bash -l {0} + fail-fast: false + steps: - - uses: actions/checkout@v2 - - uses: actions/cache@v2 - env: - CACHE_NUMBER: 0 - with: - path: ~/conda_pkgs_dir - key: ${{ matrix.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles(matrix.environment-file) }} - - uses: conda-incubator/setup-miniconda@v2 + - name: checkout repo + uses: actions/checkout@v3 + + - name: setup micromamba + uses: mamba-org/provision-with-micromamba@main with: - miniconda-version: 'latest' - mamba-version: '*' - channels: conda-forge - channel-priority: true - auto-update-conda: false - auto-activate-base: false - environment-file: ${{ matrix.environment-file }} - activate-environment: test - use-only-tar-bz2: true - - run: mamba info --all - - run: mamba list - - run: conda config --show-sources - - run: conda config --show - - run: python -c 'import libpysal; libpysal.examples.fetch_all()' - - run: py.test -v libpysal --cov=libpysal --cov-report=xml - - name: codecov (${{ matrix.os }}, ${{ matrix.environment-file }}) - uses: codecov/codecov-action@v1 + environment-file: ${{ matrix.environment-file }} + micromamba-version: 'latest' + channel-priority: 'flexible' + + - name: run tests - bash + shell: bash -l {0} + run: | + ${{ env.FETCH_EXAMPLES }} + ${{ env.RUN_TEST }} + if: matrix.os != 'windows-latest' + + - name: run tests - powershell + shell: powershell + run: | + ${{ env.FETCH_EXAMPLES }} + ${{ env.RUN_TEST }} + if: matrix.os == 'windows-latest' + + - name: codecov + uses: codecov/codecov-action@v3 with: token: ${{ secrets.CODECOV_TOKEN }} file: ./coverage.xml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 062139579..39677e9ea 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: -- repo: https://github.com/ambv/black - rev: stable - hooks: - - id: black - language_version: python3.8 +- repo: https://github.com/psf/black + rev: 22.10.0 + hooks: + - id: black + language_version: python3 diff --git a/MANIFEST.in b/MANIFEST.in index 9b9838046..8cf15845b 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,2 +1,4 @@ include authors.txt LICENSE.txt THANKS.txt CHANGELOG.md MANIFEST.in pysal/COPYING requirements_dev.txt requirements_plus.txt requirements.txt requirements_plus_conda.txt requirements_plus_pip.txt requirements_docs.txt +include versioneer.py +include libpysal/_version.py diff --git a/ci/310.yaml b/ci/310.yaml new file mode 100644 index 000000000..6712c4f34 --- /dev/null +++ b/ci/310.yaml @@ -0,0 +1,30 @@ +name: test +channels: + - conda-forge +dependencies: + - python=3.10 + - platformdirs + - beautifulsoup4 + - jinja2 + - pandas + - scipy + - xarray + # testing + - codecov + - matplotlib + - pytest + - pytest-cov + - pytest-xdist + # optional + - geopandas + - joblib + - networkx + - numba + - packaging + - zstd + # for docs build action (this env only) + - nbsphinx + - numpydoc + - sphinx + - sphinxcontrib-bibtex + - sphinx_bootstrap_theme diff --git a/ci/311-dev.yaml b/ci/311-dev.yaml new file mode 100644 index 000000000..c5c3a5b83 --- /dev/null +++ b/ci/311-dev.yaml @@ -0,0 +1,33 @@ +name: test +channels: + - conda-forge +dependencies: + - python=3.11 + - platformdirs + - beautifulsoup4 + - jinja2 + - pandas + - scipy + - xarray + # testing + - codecov + - matplotlib + - pytest + - pytest-cov + - pytest-xdist + # optional + - geopandas + - joblib + - networkx + - packaging + - zstd + - Cython + - pip + - pip: + # dev versions of packages + - --pre --extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple + - scipy + - pandas + - git+https://github.com/shapely/shapely.git@main + - git+https://github.com/geopandas/geopandas.git@main + - git+https://github.com/pydata/xarray.git@main diff --git a/ci/311.yaml b/ci/311.yaml new file mode 100644 index 000000000..7eb2e7b00 --- /dev/null +++ b/ci/311.yaml @@ -0,0 +1,25 @@ +name: test +channels: + - conda-forge +dependencies: + - python=3.11 + - platformdirs + - beautifulsoup4 + - jinja2 + - pandas + - scipy + - xarray + # testing + - codecov + - matplotlib + - pytest + - pytest-cov + - pytest-xdist + # optional + - geopandas>=0.12.0 + - joblib + - networkx + - packaging + - shapely>=2.0b1 + - xarray + - zstd diff --git a/ci/311_shapely_dev.yaml b/ci/311_shapely_dev.yaml new file mode 100644 index 000000000..8b68a0d6d --- /dev/null +++ b/ci/311_shapely_dev.yaml @@ -0,0 +1,31 @@ +name: test +channels: + - conda-forge +dependencies: + - python=3.11 + - platformdirs + - beautifulsoup4 + - jinja2 + - pandas>=1.0 + - scipy>=1.0 + - xarray + # testing + - codecov + - matplotlib + - pytest + - pytest-cov + - pytest-xdist + # optional + - geopandas>=0.12.0 + - joblib + - networkx + - packaging + - shapely>=2.0b1 + - xarray + - zstd + # for docs build action (this env only) + - nbsphinx + - numpydoc + - sphinx + - sphinxcontrib-bibtex + - sphinx_bootstrap_theme diff --git a/ci/36.yaml b/ci/36.yaml deleted file mode 100644 index 7af1f515c..000000000 --- a/ci/36.yaml +++ /dev/null @@ -1,18 +0,0 @@ -name: test -channels: - - conda-forge -dependencies: - - python=3.6 - - beautifulsoup4 - - pandas>=1.0 - - scipy>=1.0 - - xarray - # testing - - codecov - - matplotlib - - pytest - - pytest-cov - # optional - - geopandas>=0.7.0 - - numba - - zstd diff --git a/ci/37.yaml b/ci/37.yaml deleted file mode 100644 index 2d3f3023c..000000000 --- a/ci/37.yaml +++ /dev/null @@ -1,18 +0,0 @@ -name: test -channels: - - conda-forge -dependencies: - - python=3.7 - - beautifulsoup4 - - pandas>=1.0 - - scipy>=1.0 - - xarray - # testing - - codecov - - matplotlib - - pytest - - pytest-cov - # optional - - geopandas>=0.7.0 - - numba - - zstd diff --git a/ci/38-minimal.yaml b/ci/38-minimal.yaml new file mode 100644 index 000000000..e3e24e3f8 --- /dev/null +++ b/ci/38-minimal.yaml @@ -0,0 +1,27 @@ +name: test +channels: + - conda-forge +dependencies: + - python=3.8 + - beautifulsoup4=4.10 + - jinja2=3.0 + - pandas=1.3 + - scipy=1.8 + - xarray=0.18 + # testing + - codecov + - matplotlib + - pytest + - pytest-cov + - pytest-xdist + # optional + - geopandas>=0.10.0 + - shapely=1.8 + - joblib + - networkx=2.7 + - numba=0.54 + - packaging + - zstd + - pip + - pip: + - platformdirs==2.0.2 diff --git a/ci/38.yaml b/ci/39.yaml similarity index 60% rename from ci/38.yaml rename to ci/39.yaml index 3fcbc99d3..4323aacbf 100644 --- a/ci/38.yaml +++ b/ci/39.yaml @@ -2,19 +2,23 @@ name: test channels: - conda-forge dependencies: - - python=3.8 + - python=3.9 + - platformdirs - beautifulsoup4 - - pandas>=1.0 - - scipy>=1.0 + - jinja2 + - pandas + - scipy - xarray # testing - codecov - matplotlib - pytest - pytest-cov + - pytest-xdist # optional - - geopandas>=0.7.0 - - numba - - xarray + - geopandas - joblib + - networkx + - numba + - packaging - zstd diff --git a/codecov.yml b/codecov.yml index 3f7277717..f6567d60a 100644 --- a/codecov.yml +++ b/codecov.yml @@ -1,6 +1,6 @@ codecov: notify: - after_n_builds: 9 + after_n_builds: 6 coverage: range: 50..95 round: nearest @@ -18,5 +18,5 @@ coverage: comment: layout: "reach, diff, files" behavior: once - after_n_builds: 9 + after_n_builds: 6 require_changes: true diff --git a/docsrc/Makefile b/docs/Makefile similarity index 79% rename from docsrc/Makefile rename to docs/Makefile index dfe6c3b5e..45b306f40 100644 --- a/docsrc/Makefile +++ b/docs/Makefile @@ -4,7 +4,7 @@ # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build -SPHINXPROJ = PACKAGE_NAME +SPHINXPROJ = libpysal SOURCEDIR = . BUILDDIR = _build @@ -17,15 +17,18 @@ help: # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile + @rsync -r --exclude '.ipynb_checkpoints/' ../notebooks/ ./notebooks/ @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) github: @make html sync: - @rsync -avh --exclude '.nojekyll' _build/html/ ../docs/ --delete + @rsync -avh _build/html/ ../docs/ --delete @make clean + touch .nojekyll clean: rm -rf $(BUILDDIR)/* rm -rf auto_examples/ + rm -rf generated/ diff --git a/docs/_images/notebooks_Raster_awareness_API_17_1.png b/docs/_images/notebooks_Raster_awareness_API_17_1.png deleted file mode 100644 index 2f5bf9635..000000000 Binary files a/docs/_images/notebooks_Raster_awareness_API_17_1.png and /dev/null differ diff --git a/docs/_images/notebooks_Raster_awareness_API_29_1.png b/docs/_images/notebooks_Raster_awareness_API_29_1.png deleted file mode 100644 index 8d819fb25..000000000 Binary files a/docs/_images/notebooks_Raster_awareness_API_29_1.png and /dev/null differ diff --git a/docs/_images/notebooks_Raster_awareness_API_33_0.png b/docs/_images/notebooks_Raster_awareness_API_33_0.png deleted file mode 100644 index bc873a709..000000000 Binary files a/docs/_images/notebooks_Raster_awareness_API_33_0.png and /dev/null differ diff --git a/docs/_images/notebooks_Raster_awareness_API_37_2.png b/docs/_images/notebooks_Raster_awareness_API_37_2.png deleted file mode 100644 index fbb67f786..000000000 Binary files a/docs/_images/notebooks_Raster_awareness_API_37_2.png and /dev/null differ diff --git a/docs/_images/notebooks_Raster_awareness_API_39_1.png b/docs/_images/notebooks_Raster_awareness_API_39_1.png deleted file mode 100644 index 314727db2..000000000 Binary files a/docs/_images/notebooks_Raster_awareness_API_39_1.png and /dev/null differ diff --git a/docs/_images/notebooks_Raster_awareness_API_6_1.png b/docs/_images/notebooks_Raster_awareness_API_6_1.png deleted file mode 100644 index 3053db34e..000000000 Binary files a/docs/_images/notebooks_Raster_awareness_API_6_1.png and /dev/null differ diff --git a/docs/_images/notebooks_examples_22_1.png b/docs/_images/notebooks_examples_22_1.png deleted file mode 100644 index 815ff76c0..000000000 Binary files a/docs/_images/notebooks_examples_22_1.png and /dev/null differ diff --git a/docs/_images/notebooks_voronoi_10_1.png b/docs/_images/notebooks_voronoi_10_1.png deleted file mode 100644 index 2a69766a6..000000000 Binary files a/docs/_images/notebooks_voronoi_10_1.png and /dev/null differ diff --git a/docs/_images/notebooks_voronoi_15_1.png b/docs/_images/notebooks_voronoi_15_1.png deleted file mode 100644 index 99df9a64d..000000000 Binary files a/docs/_images/notebooks_voronoi_15_1.png and /dev/null differ diff --git a/docs/_images/notebooks_voronoi_16_1.png b/docs/_images/notebooks_voronoi_16_1.png deleted file mode 100644 index 6cea96537..000000000 Binary files a/docs/_images/notebooks_voronoi_16_1.png and /dev/null differ diff --git a/docs/_images/notebooks_voronoi_19_0.png b/docs/_images/notebooks_voronoi_19_0.png deleted file mode 100644 index 232a198ee..000000000 Binary files a/docs/_images/notebooks_voronoi_19_0.png and /dev/null differ diff --git a/docs/_images/notebooks_weights_12_0.png b/docs/_images/notebooks_weights_12_0.png deleted file mode 100644 index 5a56851d2..000000000 Binary files a/docs/_images/notebooks_weights_12_0.png and /dev/null differ diff --git a/docs/_images/notebooks_weights_21_0.png b/docs/_images/notebooks_weights_21_0.png deleted file mode 100644 index b1db5032e..000000000 Binary files a/docs/_images/notebooks_weights_21_0.png and /dev/null differ diff --git a/docs/_images/notebooks_weights_28_1.png b/docs/_images/notebooks_weights_28_1.png deleted file mode 100644 index 407c4c1e6..000000000 Binary files a/docs/_images/notebooks_weights_28_1.png and /dev/null differ diff --git a/docs/_images/notebooks_weights_31_0.png b/docs/_images/notebooks_weights_31_0.png deleted file mode 100644 index 17a7bc4db..000000000 Binary files a/docs/_images/notebooks_weights_31_0.png and /dev/null differ diff --git a/docs/_images/notebooks_weights_38_0.png b/docs/_images/notebooks_weights_38_0.png deleted file mode 100644 index 203ad1cf4..000000000 Binary files a/docs/_images/notebooks_weights_38_0.png and /dev/null differ diff --git a/docs/_images/notebooks_weights_41_0.png b/docs/_images/notebooks_weights_41_0.png deleted file mode 100644 index 58b445166..000000000 Binary files a/docs/_images/notebooks_weights_41_0.png and /dev/null differ diff --git a/docs/_images/notebooks_weights_57_0.png b/docs/_images/notebooks_weights_57_0.png deleted file mode 100644 index b2572fb12..000000000 Binary files a/docs/_images/notebooks_weights_57_0.png and /dev/null differ diff --git a/docs/_images/notebooks_weights_58_0.png b/docs/_images/notebooks_weights_58_0.png deleted file mode 100644 index 5c8a58518..000000000 Binary files a/docs/_images/notebooks_weights_58_0.png and /dev/null differ diff --git a/docs/_images/notebooks_weights_7_0.png b/docs/_images/notebooks_weights_7_0.png deleted file mode 100644 index 50b900db0..000000000 Binary files a/docs/_images/notebooks_weights_7_0.png and /dev/null differ diff --git a/docs/_modules/index.html b/docs/_modules/index.html deleted file mode 100644 index 066d71b63..000000000 --- a/docs/_modules/index.html +++ /dev/null @@ -1,165 +0,0 @@ - - - - - - - Overview: module code — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

All modules for which code is available

- - -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/cg/alpha_shapes.html b/docs/_modules/libpysal/cg/alpha_shapes.html deleted file mode 100644 index 6ee5dbc6a..000000000 --- a/docs/_modules/libpysal/cg/alpha_shapes.html +++ /dev/null @@ -1,869 +0,0 @@ - - - - - - - libpysal.cg.alpha_shapes — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.cg.alpha_shapes

-"""
-Computation of alpha shape algorithm in 2-D based on original implementation
-by Tim Kittel (@timkittel) available at:
-
-    https://github.com/timkittel/alpha-shapes
-
-Author(s):
-    Dani Arribas-Bel daniel.arribas.bel@gmail.com
-"""
-
-import numpy as np
-import scipy.spatial as spat
-
-from ..common import requires, jit, HAS_JIT
-
-if not HAS_JIT:
-    from warnings import warn
-
-    NUMBA_WARN = (
-        "Numba not imported, so alpha shape construction may be slower than expected."
-    )
-
-try:
-    import pygeos
-
-    HAS_PYGEOS = True
-except ModuleNotFoundError:
-    HAS_PYGEOS = False
-
-
-EPS = np.finfo(float).eps
-
-__all__ = ["alpha_shape", "alpha_shape_auto"]
-
-
-@jit
-def nb_dist(x, y):
-    """numba implementation of distance between points `x` and `y`
-
-    Parameters
-    ----------
-
-    x : ndarray
-        Coordinates of point `x`
-
-    y : ndarray
-        Coordinates of point `y`
-
-    Returns
-    -------
-
-    dist : float
-        Distance between `x` and `y`
-
-    Examples
-    --------
-
-    >>> x = np.array([0, 0])
-    >>> y = np.array([1, 1])
-    >>> dist = nb_dist(x, y)
-    >>> dist
-    1.4142135623730951
-
-    """
-    sum = 0
-    for x_i, y_i in zip(x, y):
-        sum += (x_i - y_i) ** 2
-    dist = np.sqrt(sum)
-    return dist
-
-
-@jit(nopython=True)
-def r_circumcircle_triangle_single(a, b, c):
-    """Computation of the circumcircle of a single triangle
-
-    Parameters
-    ----------
-
-    a : ndarray
-        (2,) Array with coordinates of vertex `a` of the triangle
-    b : ndarray
-        (2,) Array with coordinates of vertex `b` of the triangle
-    c : ndarray
-        (2,) Array with coordinates of vertex `c` of the triangle
-
-    Returns
-    -------
-
-    r : float
-        Circumcircle of the triangle
-
-    Notes
-    -----
-
-    Source for equations:
-
-    > https://www.mathopenref.com/trianglecircumcircle.html
-
-    [Last accessed July 11th. 2018]
-
-    Examples
-    --------
-
-    >>> a = np.array([0, 0])
-    >>> b = np.array([0.5, 0])
-    >>> c = np.array([0.25, 0.25])
-    >>> r = r_circumcircle_triangle_single(a, b, c)
-    >>> r
-    0.2500000000000001
-
-    """
-    ab = nb_dist(a, b)
-    bc = nb_dist(b, c)
-    ca = nb_dist(c, a)
-
-    num = ab * bc * ca
-    den = np.sqrt((ab + bc + ca) * (bc + ca - ab) * (ca + ab - bc) * (ab + bc - ca))
-    if den == 0:
-        return np.array([ab, bc, ca]).max() / 2.0
-    else:
-        return num / den
-
-
-@jit(nopython=True)
-def r_circumcircle_triangle(a_s, b_s, c_s):
-    """Computation of circumcircles for a series of triangles
-
-    Parameters
-    ----------
-
-    a_s : ndarray
-        (N, 2) array with coordinates of vertices `a` of the triangles
-    b_s : ndarray
-        (N, 2) array with coordinates of vertices `b` of the triangles
-    c_s : ndarray
-        (N, 2) array with coordinates of vertices `c` of the triangles
-
-    Returns
-    -------
-
-    radii : ndarray
-        (N,) array with circumcircles for every triangle
-
-    Examples
-    --------
-
-    >>> a_s = np.array([[0, 0], [2, 1], [3, 2]])
-    >>> b_s = np.array([[1, 0], [5, 1], [2, 4]])
-    >>> c_s = np.array([[0, 7], [1, 3], [4, 2]])
-    >>> rs = r_circumcircle_triangle(a_s, b_s, c_s)
-    >>> rs
-    array([3.53553391, 2.5       , 1.58113883])
-
-    """
-    len_a = len(a_s)
-    r2 = np.zeros((len_a,))
-    for i in range(len_a):
-        r2[i] = r_circumcircle_triangle_single(a_s[i], b_s[i], c_s[i])
-    return r2
-
-
-@jit
-def get_faces(triangle):
-    """Extract faces from a single triangle
-
-    Parameters
-    ----------
-
-    triangles : ndarray
-        (3,) array with the vertex indices for a triangle
-
-    Returns
-    -------
-
-    faces : ndarray
-        (3, 2) array with a row for each face containing the indices of the two
-        points that make up the face
-
-    Examples
-    --------
-
-    >>> triangle = np.array([3, 1, 4], dtype=np.int32)
-    >>> faces = get_faces(triangle)
-    >>> faces
-    array([[3., 1.],
-           [1., 4.],
-           [4., 3.]])
-
-    """
-    faces = np.zeros((3, 2))
-    for i, (i0, i1) in enumerate([(0, 1), (1, 2), (2, 0)]):
-        faces[i] = triangle[i0], triangle[i1]
-    return faces
-
-
-@jit
-def build_faces(faces, triangles_is, num_triangles, num_faces_single):
-    """Build facing triangles
-
-    Parameters
-    ----------
-
-    faces : ndarray
-        (num_triangles * num_faces_single, 2) array of zeroes in int form
-
-    triangles_is : ndarray
-        (D, 3) array, where D is the number of Delaunay triangles, with the
-        vertex indices for each triangle
-
-    num_triangles : int
-        Number of triangles
-
-    num_faces_single : int
-        Number of faces a triangle has (i.e. 3)
-
-    Returns
-    -------
-
-    faces : ndarray
-        Two dimensional array with a row for every facing segment containing
-        the indices of the coordinate points
-
-    Examples
-    --------
-
-    >>> import scipy.spatial as spat
-    >>> pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])
-    >>> triangulation = spat.Delaunay(pts)
-    >>> triangulation.simplices
-    array([[3, 1, 4],
-           [1, 2, 4],
-           [2, 1, 0]], dtype=int32)
-    >>> num_faces_single = 3
-    >>> num_triangles = triangulation.simplices.shape[0]
-    >>> num_faces = num_triangles * num_faces_single
-    >>> faces = np.zeros((num_faces, 2), dtype=np.int_)
-    >>> mask = np.ones((num_faces,), dtype=np.bool_)
-    >>> faces = build_faces(faces, triangulation.simplices, num_triangles, num_faces_single)
-    >>> faces
-    array([[3, 1],
-           [1, 4],
-           [4, 3],
-           [1, 2],
-           [2, 4],
-           [4, 1],
-           [2, 1],
-           [1, 0],
-           [0, 2]])
-
-    """
-    for i in range(num_triangles):
-        from_i = num_faces_single * i
-        to_i = num_faces_single * (i + 1)
-        faces[from_i:to_i] = get_faces(triangles_is[i])
-    return faces
-
-
-@jit
-def nb_mask_faces(mask, faces):
-    """ Run over each row in `faces`, if the face in the following row is the
-    same, then mark both as False on `mask`
-
-    Parameters
-    ----------
-
-    mask : ndarray
-        One-dimensional boolean array set to True with as many observations as
-        rows in `faces`
-
-    faces : ndarray
-        Sorted sequence of faces for all triangles (ie. triangles split by each
-        segment)
-
-    Returns
-    -------
-
-    masked : ndarray
-         Sequence of outward-facing faces
-
-    Examples
-    --------
-
-    >>> import numpy as np
-    >>> faces = np.array([[0, 1], [0, 2], [1, 2], [1, 2], [1, 3], [1, 4], [1, 4], [2, 4], [3, 4]])
-    >>> mask = np.ones((faces.shape[0], ), dtype=np.bool_)
-    >>> masked = nb_mask_faces(mask, faces)
-    >>> masked
-    array([[0, 1],
-           [0, 2],
-           [1, 3],
-           [2, 4],
-           [3, 4]])
-
-    """
-    for k in range(faces.shape[0] - 1):
-        if mask[k]:
-            if np.all(faces[k] == faces[k + 1]):
-                mask[k] = False
-                mask[k + 1] = False
-    return faces[mask]
-
-
-def get_single_faces(triangles_is):
-    """Extract outward facing edges from collection of triangles
-
-    Parameters
-    ----------
-
-    triangles_is : ndarray
-        (D, 3) array, where D is the number of Delaunay triangles, with the
-        vertex indices for each triangle
-
-    Returns
-    -------
-
-    single_faces : ndarray
-
-    Examples
-    --------
-
-    >>> import scipy.spatial as spat
-    >>> pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])
-    >>> alpha = 0.33
-    >>> triangulation = spat.Delaunay(pts)
-    >>> triangulation.simplices
-    array([[3, 1, 4],
-           [1, 2, 4],
-           [2, 1, 0]], dtype=int32)
-    >>> get_single_faces(triangulation.simplices)
-    array([[0, 1],
-           [0, 2],
-           [1, 3],
-           [2, 4],
-           [3, 4]])
-
-    """
-    num_faces_single = 3
-    num_triangles = triangles_is.shape[0]
-    num_faces = num_triangles * num_faces_single
-    faces = np.zeros((num_faces, 2), dtype=np.int_)
-    mask = np.ones((num_faces,), dtype=np.bool_)
-
-    faces = build_faces(faces, triangles_is, num_triangles, num_faces_single)
-
-    orderlist = ["x{}".format(i) for i in range(faces.shape[1])]
-    dtype_list = [(el, faces.dtype.str) for el in orderlist]
-    # Arranging each face so smallest vertex is first
-    faces.sort(axis=1)
-    # Arranging faces in ascending way
-    faces.view(dtype_list).sort(axis=0)
-    # Masking
-    single_faces = nb_mask_faces(mask, faces)
-    return single_faces
-
-
-@requires("geopandas", "shapely")
-def alpha_geoms(alpha, triangles, radii, xys):
-    """Generate alpha-shape polygon(s) from `alpha` value, vertices of
-    `triangles`, the `radii` for all points, and the points themselves
-
-    Parameters
-    ----------
-
-    alpha : float
-        Alpha value to delineate the alpha-shape
-
-    triangles : ndarray
-         (D, 3) array, where D is the number of Delaunay triangles, with the
-         vertex indices for each triangle
-
-    radii : ndarray
-        (N,) array with circumcircles for every triangle
-
-    xys : ndarray
-        (N, 2) array with one point per row and coordinates structured as X and Y
-
-    Returns
-    -------
-
-    geoms : GeoSeries
-        Polygon(s) resulting from the alpha shape algorithm. The GeoSeries
-        object remains so even if only a single polygon is returned. There is
-        no CRS included in the object.
-
-    Examples
-    --------
-
-    >>> import scipy.spatial as spat
-    >>> pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])
-    >>> alpha = 0.33
-    >>> triangulation = spat.Delaunay(pts)
-    >>> triangles = pts[triangulation.simplices]
-    >>> triangles
-    array([[[6, 7],
-            [3, 5],
-            [9, 3]],
-    <BLANKLINE>
-           [[3, 5],
-            [4, 1],
-            [9, 3]],
-    <BLANKLINE>
-           [[4, 1],
-            [3, 5],
-            [0, 1]]])
-    >>> a_pts = triangles[:, 0, :]
-    >>> b_pts = triangles[:, 1, :]
-    >>> c_pts = triangles[:, 2, :]
-    >>> radii = r_circumcircle_triangle(a_pts, b_pts, c_pts)
-    >>> geoms = alpha_geoms(alpha, triangulation.simplices, radii, pts)
-    >>> geoms
-    0    POLYGON ((0.00000 1.00000, 3.00000 5.00000, 4....
-    dtype: geometry
-
-    """
-    from shapely.geometry import LineString
-    from shapely.ops import polygonize
-    from geopandas import GeoSeries
-
-    triangles_reduced = triangles[radii < 1 / alpha]
-    outer_triangulation = get_single_faces(triangles_reduced)
-    face_pts = xys[outer_triangulation]
-    geoms = GeoSeries(list(polygonize(list(map(LineString, face_pts)))))
-    return geoms
-
-
-
[docs]@requires("geopandas", "shapely")
-def alpha_shape(xys, alpha):
-    """Alpha-shape delineation (Edelsbrunner, Kirkpatrick & Seidel, 1983) from a collection of points
-
-    Parameters
-    ----------
-
-    xys : ndarray
-        (N, 2) array with one point per row and coordinates structured as X and
-        Y
-
-    alpha : float
-        Alpha value to delineate the alpha-shape
-
-    Returns
-    -------
-
-    shapes : GeoSeries
-         Polygon(s) resulting from the alpha shape algorithm. The GeoSeries
-         object remains so even if only a single polygon is returned. There is
-         no CRS included in the object.
-
-    Examples
-    --------
-
-    >>> pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])
-    >>> alpha = 0.1
-    >>> poly = alpha_shape(pts, alpha)
-    >>> poly
-    0    POLYGON ((0.00000 1.00000, 3.00000 5.00000, 6....
-    dtype: geometry
-    >>> poly.centroid
-    0    POINT (4.69048 3.45238)
-    dtype: geometry
-
-
-    References
-    ----------
-
-    Edelsbrunner, H., Kirkpatrick, D., & Seidel, R. (1983). On the shape of
-        a set of points in the plane. IEEE Transactions on information theory,
-        29(4), 551-559.
-
-    """
-    if not HAS_JIT:
-        warn(NUMBA_WARN)
-    if xys.shape[0] < 4:
-        from shapely import ops, geometry as geom
-
-        return ops.cascaded_union([geom.Point(xy) for xy in xys]).convex_hull.buffer(0)
-    triangulation = spat.Delaunay(xys)
-    triangles = xys[triangulation.simplices]
-    a_pts = triangles[:, 0, :]
-    b_pts = triangles[:, 1, :]
-    c_pts = triangles[:, 2, :]
-    radii = r_circumcircle_triangle(a_pts, b_pts, c_pts)
-    del triangles, a_pts, b_pts, c_pts
-    geoms = alpha_geoms(alpha, triangulation.simplices, radii, xys)
-    return geoms

-
-
-def _valid_hull(geoms, points):
-    """Sanity check within ``alpha_shape_auto()`` to verify the generated alpha
-    shape actually contains the original set of points (xys).
-
-    Parameters
-    ----------
-
-    geoms : GeoSeries
-        See alpha_geoms()
-
-    points : list
-        xys parameter cast as shapely.geometry.Point objects
-
-    Returns
-    -------
-
-    flag : bool
-        Valid hull for alpha shape [True] or not [False]
-
-    """
-    flag = True
-    # if there is not exactly one polygon
-    if geoms.shape[0] != 1:
-        return False
-    # if any (xys) points do not intersect the polygon
-    if HAS_PYGEOS:
-        return pygeos.intersects(pygeos.from_shapely(geoms[0]), points).all()
-    else:
-        for point in points:
-            if not point.intersects(geoms[0]):
-                return False
-        return True
-
-
-
[docs]@requires("geopandas", "shapely")
-def alpha_shape_auto(
-    xys, step=1, verbose=False, return_radius=False, return_circles=False
-):
-    """Computation of alpha-shape delineation with automated selection of alpha.
-
-    This method uses the algorithm proposed by  Edelsbrunner, Kirkpatrick &
-    Seidel (1983) to return the tightest polygon that contains all points in
-    `xys`. The algorithm ranks every point based on its radious and iterates
-    over each point, checking whether the maximum alpha that would keep the
-    point and all the other ones in the set with smaller radii results in a
-    single polygon. If that is the case, it moves to the next point;
-    otherwise, it retains the previous alpha value and returns the polygon
-    as `shapely` geometry.
-
-    Parameters
-    ----------
-
-    xys : ndarray
-        Nx2 array with one point per row and coordinates structured as X and Y
-
-    step : int
-        [Optional. Default=1] Number of points in `xys` to jump ahead after
-        checking whether the largest possible alpha that includes the point and
-        all the other ones with smaller radii
-
-    verbose : Boolean
-        [Optional. Default=False] If True, it prints alpha values being tried at every step.
-
-    Returns
-    -------
-    poly : shapely.Polygon
-         Tightest alpha-shape polygon containing all points in `xys`
-
-    Examples
-    --------
-
-    >>> pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])
-    >>> poly = alpha_shape_auto(pts)
-    >>> poly.bounds
-    (0.0, 1.0, 9.0, 7.0)
-    >>> poly.centroid.x, poly.centroid.y
-    (4.690476190476191, 3.4523809523809526)
-
-    References
-    ----------
-
-    Edelsbrunner, H., Kirkpatrick, D., & Seidel, R. (1983). On the shape of
-        a set of points in the plane. IEEE Transactions on information theory,
-        29(4), 551-559.
-
-    """
-    if not HAS_JIT:
-        warn(NUMBA_WARN)
-    from shapely import geometry as geom
-
-    if return_circles:
-        return_radius = True
-    if xys.shape[0] < 4:
-        from shapely import ops
-
-        if xys.shape[0] == 3:
-            multipoint = ops.cascaded_union([geom.Point(xy) for xy in xys])
-            alpha_shape = multipoint.convex_hull.buffer(0)
-        else:
-            alpha_shape = geom.Polygon([])
-        if xys.shape[0] == 1:
-            if return_radius:
-                if return_circles:
-                    out = [alpha_shape, 0, alpha_shape]
-                return alpha_shape, 0
-            return alpha_shape
-        elif xys.shape[0] == 2:
-            if return_radius:
-                r = spat.distance.euclidean(xys[0], xys[1]) / 2
-                if return_circles:
-                    circle = _construct_centers(xys[0], xys[1], r)
-                    return [alpha_shape, r, circle]
-                return [alpha_shape, r]
-            return alpha_shape
-        elif return_radius:  # this handles xys.shape[0] == 3
-            radius = r_circumcircle_triangle_single(xys[0], xys[1], xys[2])
-            if return_circles:
-                circles = construct_bounding_circles(alpha_shape, radius)
-                return [alpha_shape, radius, circles]
-            return [alpha_shape, radius]
-        return alpha_shape
-    triangulation = spat.Delaunay(xys)
-    triangles = xys[triangulation.simplices]
-    a_pts = triangles[:, 0, :]
-    b_pts = triangles[:, 1, :]
-    c_pts = triangles[:, 2, :]
-    radii = r_circumcircle_triangle(a_pts, b_pts, c_pts)
-    radii[np.isnan(radii)] = 0  # "Line" triangles to be kept for sure
-    del triangles, a_pts, b_pts, c_pts
-    radii_sorted_i = radii.argsort()
-    triangles = triangulation.simplices[radii_sorted_i][::-1]
-    radii = radii[radii_sorted_i][::-1]
-    geoms_prev = alpha_geoms((1 / radii.max()) - EPS, triangles, radii, xys)
-    if HAS_PYGEOS:
-        points = pygeos.points(xys)
-    else:
-        points = [geom.Point(pnt) for pnt in xys]
-    if verbose:
-        print("Step set to %i" % step)
-    for i in range(0, len(radii), step):
-        radi = radii[i]
-        alpha = (1 / radi) - EPS
-        if verbose:
-            print("%.2f%% | Trying a = %f" % ((i + 1) / radii.shape[0], alpha))
-        geoms = alpha_geoms(alpha, triangles, radii, xys)
-        if _valid_hull(geoms, points):
-            geoms_prev = geoms
-            radi_prev = radi
-        else:
-            break
-    if verbose:
-        print(geoms_prev.shape)
-    if return_radius:
-        out = [geoms_prev[0], radi_prev]
-        if return_circles:
-            out.append(construct_bounding_circles(out[0], radi_prev))
-        return out
-    # Return a shapely polygon
-    return geoms_prev[0]

-
-
-def construct_bounding_circles(alpha_shape, radius):
-    """Construct the bounding circles for an alpha shape, given the radius
-    computed from the `alpha_shape_auto` method.
-
-    Arguments
-    ---------
-    alpha_shape : shapely.Polygon
-        An alpha-hull with the input radius.
-
-    radius : float
-        The radius of the input alpha_shape.
-
-    Returns
-    -------
-    center : numpy.ndarray of shape (n,2)
-        The centers of the circles defining the alpha_shape.
-
-    """
-    coordinates = list(alpha_shape.boundary.coords)
-    n_coordinates = len(coordinates)
-    centers = []
-    for i in range(n_coordinates - 1):
-        a, b = coordinates[i], coordinates[i + 1]
-        centers.append(_construct_centers(a, b, radius))
-    return centers
-
-
-@jit(nopython=True)
-def _construct_centers(a, b, radius):
-    midpoint_x = (a[0] + b[0]) * 0.5
-    midpoint_y = (a[1] + b[1]) * 0.5
-    d = ((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2) ** 0.5
-    if b[0] - a[0] == 0:
-        m = np.inf
-        axis_rotation = np.pi / 2
-    else:
-        m = (b[1] - a[1]) / (b[0] - a[0])
-        axis_rotation = np.arctan(m)
-    # altitude is perpendicular bisector of AB
-    interior_angle = np.arccos(0.5 * d / radius)
-    chord = np.sin(interior_angle) * radius
-
-    dx = chord * np.sin(axis_rotation)
-    dy = chord * np.cos(axis_rotation)
-
-    up_x = midpoint_x - dx
-    up_y = midpoint_y + dy
-    down_x = midpoint_x + dx
-    down_y = midpoint_y - dy
-
-    # sign gives us direction of point, since
-    # shapely shapes are clockwise-defined
-    sign = np.sign((b[0] - a[0]) * (up_y - a[1]) - (b[1] - a[1]) * (up_x - a[0]))
-    if sign == 1:
-        return up_x, up_y
-    else:
-        return down_x, down_y
-
-
-if __name__ == "__main__":
-
-    import matplotlib.pyplot as plt
-    import time
-    import geopandas as gpd
-
-    plt.close("all")
-    xys = np.random.random((1000, 2))
-    t0 = time.time()
-    geoms = alpha_shape_auto(xys, 1)
-    t1 = time.time()
-    print("%.2f Seconds to run algorithm" % (t1 - t0))
-    f, ax = plt.subplots(1)
-    gpd.GeoDataFrame({"geometry": [geoms]}).plot(ax=ax, color="orange", alpha=0.5)
-    ax.scatter(xys[:, 0], xys[:, 1], s=0.1)
-    plt.show()
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/cg/kdtree.html b/docs/_modules/libpysal/cg/kdtree.html deleted file mode 100644 index 873bf7cde..000000000 --- a/docs/_modules/libpysal/cg/kdtree.html +++ /dev/null @@ -1,461 +0,0 @@ - - - - - - - libpysal.cg.kdtree — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.cg.kdtree

-"""
-KDTree for PySAL: Python Spatial Analysis Library.
-
-Adds support for Arc Distance to scipy.spatial.KDTree.
-"""
-import math
-import scipy.spatial
-import numpy
-from scipy import inf
-from . import sphere
-from .sphere import RADIUS_EARTH_KM
-
-__author__ = "Charles R Schmidt <schmidtc@gmail.com>"
-
-__all__ = ["DISTANCE_METRICS", "FLOAT_EPS", "KDTree"]
-
-DISTANCE_METRICS = ["Euclidean", "Arc"]
-FLOAT_EPS = numpy.finfo(float).eps
-
-
-
[docs]def KDTree(data, leafsize=10, distance_metric="Euclidean", radius=RADIUS_EARTH_KM):
-    """kd-tree built on top of kd-tree functionality in scipy. If using scipy
-    0.12 or greater uses the scipy.spatial.cKDTree, otherwise uses
-    scipy.spatial.KDTree. Offers both Arc distance and Euclidean distance. Note
-    that Arc distance is only appropriate when points in latitude and
-    longitude, and the radius set to meaningful value (see docs below).
-
-    Parameters
-    ----------
-    data : array
-        The data points to be indexed. This array is not copied, and so
-        modifying this data will result in bogus results. Typically nx2.
-
-    leafsize : int
-        The number of points at which the algorithm switches over to brute-force. Has to be positive. Optional, default is 10.
-
-    distance_metric : string
-        Options: "Euclidean" (default) and "Arc".
-
-    radius : float
-        Radius of the sphere on which to compute distances. Assumes data in
-        latitude and longitude. Ignored if distance_metric="Euclidean". Typical
-        values: pysal.cg.RADIUS_EARTH_KM (default) pysal.cg.RADIUS_EARTH_MILES
-
-    """
-
-    if distance_metric.lower() == "euclidean":
-        if (
-            int(scipy.version.version.split(".")[1]) < 12
-            and int(scipy.version.version.split(".")[0]) == 0
-        ):
-            return scipy.spatial.KDTree(data, leafsize)
-        else:
-            return scipy.spatial.cKDTree(data, leafsize)
-    elif distance_metric.lower() == "arc":
-        return Arc_KDTree(data, leafsize, radius)

-
-
-# internal hack for the Arc_KDTree class inheritance
-if (
-    int(scipy.version.version.split(".")[1]) < 12
-    and int(scipy.version.version.split(".")[0]) == 0
-):
-    temp_KDTree = scipy.spatial.KDTree
-else:
-    temp_KDTree = scipy.spatial.cKDTree
-
-
-class Arc_KDTree(temp_KDTree):
-    def __init__(self, data, leafsize=10, radius=1.0):
-        """KDTree using Arc Distance instead of Euclidean Distance.
-
-        Returned distances are based on radius. For Example, pass in the radius
-        of earth in miles to get back miles. Assumes data are Lng/Lat, does not
-        account for geoids.
-
-        For more information see docs for scipy.spatial.KDTree
-
-        Examples
-        --------
-        >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
-        >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
-        >>> d,i = kd.query((90,0), k=4)
-        >>> d
-        array([10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801])
-        >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM
-        >>> round(d[0],5) == round(circumference/4.0,5)
-        True
-        """
-        self.radius = radius
-        self.circumference = 2 * math.pi * radius
-        temp_KDTree.__init__(self, list(map(sphere.toXYZ, data)), leafsize)
-
-    def _toXYZ(self, x):
-        if not issubclass(type(x), numpy.ndarray):
-            x = numpy.array(x)
-        if len(x.shape) == 2 and x.shape[1] == 3:  # assume point is already in XYZ
-            return x
-        if len(x.shape) == 1 and x.shape[0] == 3:  # assume point is already in XYZ
-            return x
-        elif len(x.shape) == 1:
-            x = numpy.array(sphere.toXYZ(x))
-        else:
-            x = list(map(sphere.toXYZ, x))
-        return x
-
-    def count_neighbors(self, other, r, p=2):
-        """See scipy.spatial.KDTree.count_neighbors
-
-        Parameters
-        ----------
-        p: ignored, kept to maintain compatibility with scipy.spatial.KDTree
-
-        Examples
-        --------
-        >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
-        >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
-        >>> kd.count_neighbors(kd,0)
-        4
-        >>> circumference = 2.0*math.pi*sphere.RADIUS_EARTH_KM
-        >>> kd.count_neighbors(kd,circumference/2.0)
-        16
-        """
-        if r > 0.5 * self.circumference:
-            raise ValueError(
-                "r, must not exceed 1/2 circumference of the sphere (%f)."
-                % self.circumference
-                * 0.5
-            )
-        r = sphere.arcdist2linear(r, self.radius)
-        return temp_KDTree.count_neighbors(self, other, r)
-
-    def query(self, x, k=1, eps=0, p=2, distance_upper_bound=inf):
-        """See scipy.spatial.KDTree.query
-
-        Parameters
-        ----------
-        x : array-like, last dimension self.m
-            query points are lng/lat.
-        p: ignored
-            kept to maintain compatibility with scipy.spatial.KDTree
-
-        Examples
-        --------
-        >>> import numpy as np
-        >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
-        >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
-        >>> d,i = kd.query((90,0), k=4)
-        >>> d
-        array([10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801])
-        >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM
-        >>> round(d[0],5) == round(circumference/4.0,5)
-        True
-        >>> d,i = kd.query(kd.data, k=3)
-        >>> d2,i2 = kd.query(pts, k=3)
-        >>> (d == d2).all()
-        True
-        >>> (i == i2).all()
-        True
-
-        """
-        eps = sphere.arcdist2linear(eps, self.radius)
-        if distance_upper_bound != inf:
-            distance_upper_bound = sphere.arcdist2linear(
-                distance_upper_bound, self.radius
-            )
-        d, i = temp_KDTree.query(
-            self, self._toXYZ(x), k, eps=eps, distance_upper_bound=distance_upper_bound
-        )
-        dims = len(d.shape)
-        r = self.radius
-        if dims == 0:
-            return sphere.linear2arcdist(d, r), i
-        if dims == 1:
-            # TODO: implement linear2arcdist on numpy arrays
-            d = [sphere.linear2arcdist(x, r) for x in d]
-        elif dims == 2:
-            d = [[sphere.linear2arcdist(x, r) for x in row] for row in d]
-        return numpy.array(d), i
-
-    def query_ball_point(self, x, r, p=2, eps=0):
-        """See scipy.spatial.KDTree.query_ball_point
-
-        Parameters
-        ----------
-        p : ignored
-            kept to maintain compatibility with scipy.spatial.KDTree
-
-        Examples
-        --------
-        >>> import numpy as np
-        >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
-        >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
-        >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM
-        >>> kd.query_ball_point(pts, circumference/4.)
-        array([list([0, 1, 2]), list([0, 1, 3]), list([0, 2, 3]), list([1, 2, 3])],
-              dtype=object)
-        >>> kd.query_ball_point(pts, circumference/2.)
-        array([list([0, 1, 2, 3]), list([0, 1, 2, 3]), list([0, 1, 2, 3]),
-               list([0, 1, 2, 3])], dtype=object)
-
-        """
-        eps = sphere.arcdist2linear(eps, self.radius)
-        # scipy.sphere.KDTree.query_ball_point appears to ignore the eps argument.
-        # we have some floating point errors moving back and forth between cordinate systems,
-        # so we'll account for that be adding some to our radius, 3*float's eps value.
-        if r > 0.5 * self.circumference:
-            raise ValueError(
-                "r, must not exceed 1/2 circumference of the sphere (%f)."
-                % self.circumference
-                * 0.5
-            )
-        r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3
-        return temp_KDTree.query_ball_point(self, self._toXYZ(x), r, eps=eps)
-
-    def query_ball_tree(self, other, r, p=2, eps=0):
-        """See scipy.spatial.KDTree.query_ball_tree
-
-        Parameters
-        ----------
-        p : ignored
-            kept to maintain compatibility with scipy.spatial.KDTree
-
-        Examples
-        --------
-        >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
-        >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
-        >>> kd.query_ball_tree(kd, kd.circumference/4.) == [[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]]
-        True
-        >>> kd.query_ball_tree(kd, kd.circumference/2.) == [[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]]
-        True
-
-        """
-        eps = sphere.arcdist2linear(eps, self.radius)
-        # scipy.sphere.KDTree.query_ball_point appears to ignore the eps argument.
-        # we have some floating point errors moving back and forth between cordinate systems,
-        # so we'll account for that be adding some to our radius, 3*float's eps value.
-        if self.radius != other.radius:
-            raise ValueError("Both trees must have the same radius.")
-        if r > 0.5 * self.circumference:
-            raise ValueError(
-                "r, must not exceed 1/2 circumference of the sphere (%f)."
-                % self.circumference
-                * 0.5
-            )
-        r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3
-        return temp_KDTree.query_ball_tree(self, other, r, eps=eps)
-
-    def query_pairs(self, r, p=2, eps=0):
-        """See scipy.spatial.KDTree.query_pairs
-
-        Parameters
-        ----------
-        p : ignored
-            kept to maintain compatibility with scipy.spatial.KDTree
-
-        Examples
-        --------
-        >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
-        >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
-        >>> kd.query_pairs(kd.circumference/4.) == set([(0, 1), (1, 3), (2, 3), (0, 2)])
-        True
-        >>> kd.query_pairs(kd.circumference/2.) == set([(0, 1), (1, 2), (1, 3), (2, 3), (0, 3), (0, 2)])
-        True
-
-        """
-        if r > 0.5 * self.circumference:
-            raise ValueError(
-                "r, must not exceed 1/2 circumference of the sphere (%f)."
-                % self.circumference
-                * 0.5
-            )
-        r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3
-        return temp_KDTree.query_pairs(self, r, eps=eps)
-
-    def sparse_distance_matrix(self, other, max_distance, p=2):
-        """See scipy.spatial.KDTree.sparse_distance_matrix
-
-        Parameters
-        ----------
-        p : ignored
-            kept to maintain compatibility with scipy.spatial.KDTree
-
-        Examples
-        --------
-        >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
-        >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
-        >>> kd.sparse_distance_matrix(kd, kd.circumference/4.).todense()
-        matrix([[    0.        , 10007.54339801, 10007.54339801,     0.        ],
-                [10007.54339801,     0.        ,     0.        , 10007.54339801],
-                [10007.54339801,     0.        ,     0.        , 10007.54339801],
-                [    0.        , 10007.54339801, 10007.54339801,     0.        ]])
-        >>> kd.sparse_distance_matrix(kd, kd.circumference/2.).todense()
-        matrix([[    0.        , 10007.54339801, 10007.54339801, 20015.08679602],
-                [10007.54339801,     0.        , 20015.08679602, 10007.54339801],
-                [10007.54339801, 20015.08679602,     0.        , 10007.54339801],
-                [20015.08679602, 10007.54339801, 10007.54339801,     0.        ]])
-
-        """
-        if self.radius != other.radius:
-            raise ValueError("Both trees must have the same radius.")
-        if max_distance > 0.5 * self.circumference:
-            raise ValueError(
-                "max_distance, must not exceed 1/2 circumference of the sphere (%f)."
-                % self.circumference
-                * 0.5
-            )
-        max_distance = sphere.arcdist2linear(max_distance, self.radius) + FLOAT_EPS * 3
-        D = temp_KDTree.sparse_distance_matrix(self, other, max_distance)
-        D = D.tocoo()
-        # print D.data
-        a2l = lambda x: sphere.linear2arcdist(x, self.radius)
-        # print map(a2l,D.data)
-        return scipy.sparse.coo_matrix((list(map(a2l, D.data)), (D.row, D.col))).todok()
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/cg/locators.html b/docs/_modules/libpysal/cg/locators.html deleted file mode 100644 index bb09d62a7..000000000 --- a/docs/_modules/libpysal/cg/locators.html +++ /dev/null @@ -1,971 +0,0 @@ - - - - - - - libpysal.cg.locators — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.cg.locators

-"""
-Computational geometry code for PySAL: Python Spatial Analysis Library.
-"""
-
-__author__ = "Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow"
-__credits__ = "Copyright (c) 2005-2011 Sergio J. Rey"
-
-import math
-import copy
-import warnings
-from .rtree import *
-from .standalone import *
-from .shapes import *
-
-__all__ = ["Grid", "BruteForcePointLocator", "PointLocator", "PolygonLocator"]
-
-dep_msg = "is deprecated and will be reoved in libpysal 4.4.0"
-
-
-
[docs]class Grid:
-    """
-    Representation of a binning data structure.
-    """
-
-
[docs]    def __init__(self, bounds, resolution):
-        """
-        Returns a grid with specified properties.
-
-        __init__(Rectangle, number) -> Grid
-
-        Parameters
-        ----------
-        bounds      : the area for the grid to encompass
-        resolution  : the diameter of each bin
-
-        Examples
-        --------
-        TODO: complete this doctest
-        >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
-        """
-        warnings.warn("Grid " + dep_msg, DeprecationWarning)
-        if resolution == 0:
-            raise Exception("Cannot create grid with resolution 0")
-        self.res = resolution
-        self.hash = {}
-        self.x_range = (bounds.left, bounds.right)
-        self.y_range = (bounds.lower, bounds.upper)
-        try:
-            self.i_range = int(
-                math.ceil((self.x_range[1] - self.x_range[0]) / self.res)
-            )
-            self.j_range = int(
-                math.ceil((self.y_range[1] - self.y_range[0]) / self.res)
-            )
-        except Exception:
-            raise Exception(
-                "Invalid arguments for Grid(): ("
-                + str(x_range)
-                + ", "
-                + str(y_range)
-                + ", "
-                + str(res)
-                + ")"
-            )

-
-
[docs]    def in_grid(self, loc):
-        """
-        Returns whether a 2-tuple location _loc_ lies inside the grid bounds.
-
-        Test tag: <tc>#is#Grid.in_grid</tc>
-        """
-        return (
-            self.x_range[0] <= loc[0] <= self.x_range[1]
-            and self.y_range[0] <= loc[1] <= self.y_range[1]
-        )

-
-    def __grid_loc(self, loc):
-        i = min(self.i_range, max(int((loc[0] - self.x_range[0]) / self.res), 0))
-        j = min(self.j_range, max(int((loc[1] - self.y_range[0]) / self.res), 0))
-        return (i, j)
-
-
[docs]    def add(self, item, pt):
-        """
-        Adds an item to the grid at a specified location.
-
-        add(x, Point) -> x
-
-        Parameters
-        ----------
-        item  : the item to insert into the grid
-        pt : the location to insert the item at
-
-        Examples
-        --------
-
-        >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
-        >>> g.add('A', Point((4.2, 8.7)))
-        'A'
-        """
-        if not self.in_grid(pt):
-            raise Exception(
-                "Attempt to insert item at location outside grid bounds: " + str(pt)
-            )
-        grid_loc = self.__grid_loc(pt)
-        if grid_loc in self.hash:
-            self.hash[grid_loc].append((pt, item))
-        else:
-            self.hash[grid_loc] = [(pt, item)]
-        return item

-
-
[docs]    def remove(self, item, pt):
-        """
-        Removes an item from the grid at a specified location.
-
-        remove(x, Point) -> x
-
-        Parameters
-        ----------
-        item : the item to remove from the grid
-        pt : the location the item was added at
-
-        Examples
-        --------
-
-        >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
-        >>> g.add('A', Point((4.2, 8.7)))
-        'A'
-        >>> g.remove('A', Point((4.2, 8.7)))
-        'A'
-        """
-        if not self.in_grid(pt):
-            raise Exception(
-                "Attempt to remove item at location outside grid bounds: " + str(pt)
-            )
-        grid_loc = self.__grid_loc(pt)
-        self.hash[grid_loc].remove((pt, item))
-        if self.hash[grid_loc] == []:
-            del self.hash[grid_loc]
-        return item

-
-
[docs]    def bounds(self, bounds):
-        """
-        Returns a list of items found in the grid within the bounds specified.
-
-        bounds(Rectangle) -> x list
-
-        Parameters
-        ----------
-        item     : the item to remove from the grid
-        pt       : the location the item was added at
-
-        Examples
-        --------
-
-        >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
-        >>> g.add('A', Point((1.0, 1.0)))
-        'A'
-        >>> g.add('B', Point((4.0, 4.0)))
-        'B'
-        >>> g.bounds(Rectangle(0, 0, 3, 3))
-        ['A']
-        >>> g.bounds(Rectangle(2, 2, 5, 5))
-        ['B']
-        >>> sorted(g.bounds(Rectangle(0, 0, 5, 5)))
-        ['A', 'B']
-        """
-        x_range = (bounds.left, bounds.right)
-        y_range = (bounds.lower, bounds.upper)
-        items = []
-        lower_left = self.__grid_loc((x_range[0], y_range[0]))
-        upper_right = self.__grid_loc((x_range[1], y_range[1]))
-        for i in range(lower_left[0], upper_right[0] + 1):
-            for j in range(lower_left[1], upper_right[1] + 1):
-                if (i, j) in self.hash:
-                    items.extend(
-                        [
-                            item[1]
-                            for item in [
-                                item
-                                for item in self.hash[(i, j)]
-                                if x_range[0] <= item[0][0] <= x_range[1]
-                                and y_range[0] <= item[0][1] <= y_range[1]
-                            ]
-                        ]
-                    )
-        return items

-
-
[docs]    def proximity(self, pt, r):
-        """
-        Returns a list of items found in the grid within a specified distance of a point.
-
-        proximity(Point, number) -> x list
-
-        Parameters
-        ----------
-        pt : the location to search around
-        r  : the distance to search around the point
-
-        Examples
-        --------
-        >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
-        >>> g.add('A', Point((1.0, 1.0)))
-        'A'
-        >>> g.add('B', Point((4.0, 4.0)))
-        'B'
-        >>> g.proximity(Point((2.0, 1.0)), 2)
-        ['A']
-        >>> g.proximity(Point((6.0, 5.0)), 3.0)
-        ['B']
-        >>> sorted(g.proximity(Point((4.0, 1.0)), 4.0))
-        ['A', 'B']
-        """
-        items = []
-        lower_left = self.__grid_loc((pt[0] - r, pt[1] - r))
-        upper_right = self.__grid_loc((pt[0] + r, pt[1] + r))
-        for i in range(lower_left[0], upper_right[0] + 1):
-            for j in range(lower_left[1], upper_right[1] + 1):
-                if (i, j) in self.hash:
-                    items.extend(
-                        [
-                            item[1]
-                            for item in [
-                                item
-                                for item in self.hash[(i, j)]
-                                if get_points_dist(pt, item[0]) <= r
-                            ]
-                        ]
-                    )
-        return items

-
-
[docs]    def nearest(self, pt):
-        """
-        Returns the nearest item to a point.
-
-        nearest(Point) -> x
-
-        Parameters
-        ----------
-        pt : the location to search near
-
-        Examples
-        --------
-        >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
-        >>> g.add('A', Point((1.0, 1.0)))
-        'A'
-        >>> g.add('B', Point((4.0, 4.0)))
-        'B'
-        >>> g.nearest(Point((2.0, 1.0)))
-        'A'
-        >>> g.nearest(Point((7.0, 5.0)))
-        'B'
-        """
-        search_size = self.res
-        while self.proximity(pt, search_size) == [] and (
-            get_points_dist((self.x_range[0], self.y_range[0]), pt) > search_size
-            or get_points_dist((self.x_range[1], self.y_range[0]), pt) > search_size
-            or get_points_dist((self.x_range[0], self.y_range[1]), pt) > search_size
-            or get_points_dist((self.x_range[1], self.y_range[1]), pt) > search_size
-        ):
-            search_size = 2 * search_size
-        items = []
-        lower_left = self.__grid_loc((pt[0] - search_size, pt[1] - search_size))
-        upper_right = self.__grid_loc((pt[0] + search_size, pt[1] + search_size))
-        for i in range(lower_left[0], upper_right[0] + 1):
-            for j in range(lower_left[1], upper_right[1] + 1):
-                if (i, j) in self.hash:
-                    items.extend(
-                        [
-                            (get_points_dist(pt, item[0]), item[1])
-                            for item in self.hash[(i, j)]
-                        ]
-                    )
-        if items == []:
-            return None
-        return min(items)[1]

-
-
-class BruteForcePointLocator:
-    """
-    A class which does naive linear search on a set of Point objects.
-    """
-
-    def __init__(self, points):
-        """
-        Creates a naive index of the points specified.
-
-        __init__(Point list) -> BruteForcePointLocator
-
-        Parameters
-        ----------
-        points : a list of points to index (Point list)
-
-        Examples
-        --------
-        >>> pl = BruteForcePointLocator([Point((0, 0)), Point((5, 0)), Point((0, 10))])
-        """
-        warnings.warn("BruteForcePointLocator " + dep_msg, DeprecationWarning)
-        self._points = points
-
-    def nearest(self, query_point):
-        """
-        Returns the nearest point indexed to a query point.
-
-        nearest(Point) -> Point
-
-        Parameters
-        ----------
-        query_point : a point to find the nearest indexed point to
-
-        Examples
-        --------
-        >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
-        >>> pl = BruteForcePointLocator(points)
-        >>> n = pl.nearest(Point((1, 1)))
-        >>> str(n)
-        '(0.0, 0.0)'
-        """
-        return min(self._points, key=lambda p: get_points_dist(p, query_point))
-
-    def region(self, region_rect):
-        """
-        Returns the indexed points located inside a rectangular query region.
-
-        region(Rectangle) -> Point list
-
-        Parameters
-        ----------
-        region_rect : the rectangular range to find indexed points in
-
-        Examples
-        --------
-        >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
-        >>> pl = BruteForcePointLocator(points)
-        >>> pts = pl.region(Rectangle(-1, -1, 10, 10))
-        >>> len(pts)
-        3
-        """
-        return [
-            p
-            for p in self._points
-            if get_rectangle_point_intersect(region_rect, p) is not None
-        ]
-
-    def proximity(self, origin, r):
-        """
-        Returns the indexed points located within some distance of an origin point.
-
-        proximity(Point, number) -> Point list
-
-        Parameters
-        ----------
-        origin  : the point to find indexed points near
-        r       : the maximum distance to find indexed point from the origin point
-
-        Examples
-        --------
-        >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
-        >>> pl = BruteForcePointLocator(points)
-        >>> neighs = pl.proximity(Point((1, 0)), 2)
-        >>> len(neighs)
-        1
-        >>> p = neighs[0]
-        >>> isinstance(p, Point)
-        True
-        >>> str(p)
-        '(0.0, 0.0)'
-        """
-        return [p for p in self._points if get_points_dist(p, origin) <= r]
-
-
-
[docs]class PointLocator:
-    """
-    An abstract representation of a point indexing data structure.
-    """
-
-
[docs]    def __init__(self, points):
-        """
-        Returns a point locator object.
-
-        __init__(Point list) -> PointLocator
-
-        Parameters
-        ----------
-        points : a list of points to index
-
-        Examples
-        --------
-        >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
-        >>> pl = PointLocator(points)
-        """
-        warnings.warn("PointLocator " + dep_msg, DeprecationWarning)
-        self._locator = BruteForcePointLocator(points)

-
-
[docs]    def nearest(self, query_point):
-        """
-        Returns the nearest point indexed to a query point.
-
-        nearest(Point) -> Point
-
-        Parameters
-        ----------
-        query_point : a point to find the nearest indexed point to
-
-        Examples
-        --------
-        >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
-        >>> pl = PointLocator(points)
-        >>> n = pl.nearest(Point((1, 1)))
-        >>> str(n)
-        '(0.0, 0.0)'
-        """
-        return self._locator.nearest(query_point)

-
-
[docs]    def region(self, region_rect):
-        """
-        Returns the indexed points located inside a rectangular query region.
-
-        region(Rectangle) -> Point list
-
-        Parameters
-        ----------
-        region_rect : the rectangular range to find indexed points in
-
-        Examples
-        --------
-        >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
-        >>> pl = PointLocator(points)
-        >>> pts = pl.region(Rectangle(-1, -1, 10, 10))
-        >>> len(pts)
-        3
-        """
-        return self._locator.region(region_rect)

-
-    overlapping = region
-
-
[docs]    def polygon(self, polygon):
-        """
-        Returns the indexed points located inside a polygon
-        """

-
-        # get points in polygon bounding box
-
-        # for points in bounding box, check for inclusion in polygon
-
-
[docs]    def proximity(self, origin, r):
-        """
-        Returns the indexed points located within some distance of an origin point.
-
-        proximity(Point, number) -> Point list
-
-        Parameters
-        ----------
-        origin  : the point to find indexed points near
-        r       : the maximum distance to find indexed point from the origin point
-
-        Examples
-        --------
-        >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
-        >>> pl = PointLocator(points)
-        >>> len(pl.proximity(Point((1, 0)), 2))
-        1
-        """
-        return self._locator.proximity(origin, r)

-
-
-
[docs]class PolygonLocator:
-    """
-    An abstract representation of a polygon indexing data structure.
-    """
-
-
[docs]    def __init__(self, polygons):
-        """
-        Returns a polygon locator object.
-
-        __init__(Polygon list) -> PolygonLocator
-
-        Parameters
-        ----------
-        polygons : a list of polygons to index
-
-        Examples
-        --------
-        >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
-        >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
-        >>> pl = PolygonLocator([p1, p2])
-        >>> isinstance(pl, PolygonLocator)
-        True
-        """
-        warnings.warn("PolygonLocator " + dep_msg, DeprecationWarning)
-        self._locator = polygons
-        # create and rtree
-        self._rtree = RTree()
-        for polygon in polygons:
-            x = polygon.bounding_box.left
-            y = polygon.bounding_box.lower
-            X = polygon.bounding_box.right
-            Y = polygon.bounding_box.upper
-            self._rtree.insert(polygon, Rect(x, y, X, Y))

-
-
[docs]    def inside(self, query_rectangle):
-        """
-        Returns polygons that are inside query_rectangle
-
-        Examples
-        --------
-
-        >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
-        >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
-        >>> p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))])
-        >>> pl = PolygonLocator([p1, p2, p3])
-        >>> qr = Rectangle(0, 0, 5, 5)
-        >>> res = pl.inside( qr )
-        >>> len(res)
-        1
-        >>> qr = Rectangle(3, 7, 5, 8)
-        >>> res = pl.inside( qr )
-        >>> len(res)
-        0
-        >>> qr = Rectangle(10, 10, 12, 12)
-        >>> res = pl.inside( qr )
-        >>> len(res)
-        0
-        >>> qr = Rectangle(0, 0, 12, 12)
-        >>> res = pl.inside( qr )
-        >>> len(res)
-        3
-
-        Notes
-        -----
-
-        inside means the intersection of the query rectangle and a
-        polygon is not empty and is equal to the area of the polygon
-        """
-        left = query_rectangle.left
-        right = query_rectangle.right
-        upper = query_rectangle.upper
-        lower = query_rectangle.lower
-
-        # rtree rect
-        qr = Rect(left, lower, right, upper)
-        # bb overlaps
-        res = [r.leaf_obj() for r in self._rtree.query_rect(qr) if r.is_leaf()]
-
-        qp = Polygon(
-            [
-                Point((left, lower)),
-                Point((right, lower)),
-                Point((right, upper)),
-                Point((left, upper)),
-            ]
-        )
-        ip = []
-        GPPI = get_polygon_point_intersect
-        for poly in res:
-            flag = True
-            lower = poly.bounding_box.lower
-            right = poly.bounding_box.right
-            upper = poly.bounding_box.upper
-            left = poly.bounding_box.left
-            p1 = Point((left, lower))
-            p2 = Point((right, upper))
-            if GPPI(qp, p1) and GPPI(qp, p2):
-                ip.append(poly)
-        return ip

-
-
[docs]    def overlapping(self, query_rectangle):
-        """
-        Returns list of polygons that overlap query_rectangle
-
-        Examples
-        --------
-
-        >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
-        >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
-        >>> p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))])
-        >>> pl = PolygonLocator([p1, p2, p3])
-        >>> qr = Rectangle(0, 0, 5, 5)
-        >>> res = pl.overlapping( qr )
-        >>> len(res)
-        2
-        >>> qr = Rectangle(3, 7, 5, 8)
-        >>> res = pl.overlapping( qr )
-        >>> len(res)
-        1
-        >>> qr = Rectangle(10, 10, 12, 12)
-        >>> res = pl.overlapping( qr )
-        >>> len(res)
-        0
-        >>> qr = Rectangle(0, 0, 12, 12)
-        >>> res = pl.overlapping( qr )
-        >>> len(res)
-        3
-        >>> qr = Rectangle(8, 3, 9, 4)
-        >>> p1 = Polygon([Point((2, 1)), Point((2, 3)), Point((4, 3)), Point((4,1))])
-        >>> p2 = Polygon([Point((7, 1)), Point((7, 5)), Point((10, 5)), Point((10, 1))])
-        >>> pl = PolygonLocator([p1, p2])
-        >>> res = pl.overlapping(qr)
-        >>> len(res)
-        1
-
-        Notes
-        -----
-        overlapping means the intersection of the query rectangle and a
-        polygon is not empty and is no larger than the area of the polygon
-        """
-        left = query_rectangle.left
-        right = query_rectangle.right
-        upper = query_rectangle.upper
-        lower = query_rectangle.lower
-
-        # rtree rect
-        qr = Rect(left, lower, right, upper)
-
-        # bb overlaps
-        res = [r.leaf_obj() for r in self._rtree.query_rect(qr) if r.is_leaf()]
-        # have to check for polygon overlap using segment intersection
-
-        # add polys whose bb contains at least one of the corners of the query
-        # rectangle
-
-        sw = (left, lower)
-        se = (right, lower)
-        ne = (right, upper)
-        nw = (left, upper)
-        pnts = [sw, se, ne, nw]
-        cs = []
-        for pnt in pnts:
-            c = [r.leaf_obj() for r in self._rtree.query_point(pnt) if r.is_leaf()]
-            cs.extend(c)
-
-        cs = list(set(cs))
-
-        overlapping = []
-
-        # first find polygons with at least one vertex inside query rectangle
-        remaining = copy.copy(res)
-        for polygon in res:
-            vertices = polygon.vertices
-            for vertex in vertices:
-                xb = vertex[0] >= left
-                xb *= vertex[0] < right
-                yb = vertex[1] >= lower
-                yb *= vertex[1] < upper
-                if xb * yb:
-                    overlapping.append(polygon)
-                    remaining.remove(polygon)
-                    break
-
-        # for remaining polys in bb overlap check if vertex chains intersect
-        # segments of the query rectangle
-        left_edge = LineSegment(Point((left, lower)), Point((left, upper)))
-        right_edge = LineSegment(Point((right, lower)), Point((right, upper)))
-        lower_edge = LineSegment(Point((left, lower)), Point((right, lower)))
-        upper_edge = LineSegment(Point((left, upper)), Point((right, upper)))
-        for polygon in remaining:
-            vertices = copy.copy(polygon.vertices)
-            if vertices[-1] != vertices[0]:
-                vertices.append(vertices[0])  # put on closed cartographic form
-            nv = len(vertices)
-            for i in range(nv - 1):
-                head = vertices[i]
-                tail = vertices[i + 1]
-                edge = LineSegment(head, tail)
-                li = get_segments_intersect(edge, left_edge)
-                if li:
-                    overlapping.append(polygon)
-                    break
-                elif get_segments_intersect(edge, right_edge):
-                    overlapping.append(polygon)
-                    break
-                elif get_segments_intersect(edge, lower_edge):
-                    overlapping.append(polygon)
-                    break
-                elif get_segments_intersect(edge, upper_edge):
-                    overlapping.append(polygon)
-                    break
-        # check remaining for explicit containment of the bounding rectangle
-        # cs has candidates for this check
-        sw = Point(sw)
-        se = Point(se)
-        ne = Point(ne)
-        nw = Point(nw)
-        for polygon in cs:
-            if get_polygon_point_intersect(polygon, sw):
-                overlapping.append(polygon)
-                break
-            elif get_polygon_point_intersect(polygon, se):
-                overlapping.append(polygon)
-                break
-            elif get_polygon_point_intersect(polygon, ne):
-                overlapping.append(polygon)
-                break
-            elif get_polygon_point_intersect(polygon, nw):
-                overlapping.append(polygon)
-                break
-        return list(set(overlapping))

-
-
[docs]    def nearest(self, query_point, rule="vertex"):
-        """
-        Returns the nearest polygon indexed to a query point based on
-        various rules.
-
-        nearest(Polygon) -> Polygon
-
-        Parameters
-        ----------
-        query_point  : a point to find the nearest indexed polygon to
-
-        rule         : representative point for polygon in nearest query.
-                 vertex -- measures distance between vertices and query_point
-                 centroid -- measures distance between centroid and
-                 query_point
-                 edge   -- measures the distance between edges and query_point
-
-        Examples
-        --------
-        >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
-        >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
-        >>> pl = PolygonLocator([p1, p2])
-        >>> try: n = pl.nearest(Point((-1, 1)))
-        ... except NotImplementedError: print("future test: str(min(n.vertices())) == (0.0, 1.0)")
-        future test: str(min(n.vertices())) == (0.0, 1.0)
-        """
-        raise NotImplementedError

-
-
[docs]    def region(self, region_rect):
-        """
-        Returns the indexed polygons located inside a rectangular query region.
-
-        region(Rectangle) -> Polygon list
-
-        Parameters
-        ----------
-        region_rect  : the rectangular range to find indexed polygons in
-
-        Examples
-        --------
-        >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
-        >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
-        >>> pl = PolygonLocator([p1, p2])
-        >>> n = pl.region(Rectangle(0, 0, 4, 10))
-        >>> len(n)
-        2
-        """
-        n = self._locator
-        for polygon in n:
-            points = polygon.vertices
-            pl = BruteForcePointLocator(points)
-            pts = pl.region(region_rect)
-            if len(pts) == 0:
-                n.remove(polygon)
-        return n

-
-
[docs]    def contains_point(self, point):
-        """
-        Returns polygons that contain point
-
-
-        Parameters
-        ----------
-        point: point (x,y)
-
-        Returns
-        -------
-        list of polygons containing point
-
-        Examples
-        --------
-        >>> p1 = Polygon([Point((0,0)), Point((6,0)), Point((4,4))])
-        >>> p2 = Polygon([Point((1,2)), Point((4,0)), Point((4,4))])
-        >>> p1.contains_point((2,2))
-        1
-        >>> p2.contains_point((2,2))
-        1
-        >>> pl = PolygonLocator([p1, p2])
-        >>> len(pl.contains_point((2,2)))
-        2
-        >>> p2.contains_point((1,1))
-        0
-        >>> p1.contains_point((1,1))
-        1
-        >>> len(pl.contains_point((1,1)))
-        1
-        >>> p1.centroid
-        (3.3333333333333335, 1.3333333333333333)
-        >>> pl.contains_point((1,1))[0].centroid
-        (3.3333333333333335, 1.3333333333333333)
-
-        """
-        # bbounding box containment
-        res = [r.leaf_obj() for r in self._rtree.query_point(point) if r.is_leaf()]
-        # explicit containment check for candidate polygons needed
-        return [poly for poly in res if poly.contains_point(point)]

-
-
[docs]    def proximity(self, origin, r, rule="vertex"):
-        """
-        Returns the indexed polygons located within some distance of an
-        origin point based on various rules.
-
-        proximity(Polygon, number) -> Polygon list
-
-        Parameters
-        ----------
-        origin  : the point to find indexed polygons near
-        r       : the maximum distance to find indexed polygon from the origin point
-
-        rule    : representative point for polygon in nearest query.
-                vertex -- measures distance between vertices and query_point
-                centroid -- measures distance between centroid and
-                query_point
-                edge   -- measures the distance between edges and query_point
-
-        Examples
-        --------
-        >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
-        >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
-        >>> pl = PolygonLocator([p1, p2])
-        >>> try:
-        ...     len(pl.proximity(Point((0, 0)), 2))
-        ... except NotImplementedError:
-        ...     print("future test: len(pl.proximity(Point((0, 0)), 2)) == 2")
-        future test: len(pl.proximity(Point((0, 0)), 2)) == 2
-        """
-        raise NotImplementedError

-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/cg/shapes.html b/docs/_modules/libpysal/cg/shapes.html deleted file mode 100644 index 602f5b43d..000000000 --- a/docs/_modules/libpysal/cg/shapes.html +++ /dev/null @@ -1,2251 +0,0 @@ - - - - - - - libpysal.cg.shapes — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.cg.shapes

-"""
-Computational geometry code for PySAL: Python Spatial Analysis Library.
-
-"""
-
-__author__ = "Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow, Hu Shao"
-
-import math
-from .sphere import arcdist
-
-from typing import Union
-
-__all__ = [
-    "Point",
-    "LineSegment",
-    "Line",
-    "Ray",
-    "Chain",
-    "Polygon",
-    "Rectangle",
-    "asShape",
-]
-
-
-
[docs]def asShape(obj):
-    """Returns a PySAL shape object from ``obj``, which
-    must support the ``__geo_interface__``.
-    
-    Parameters
-    ----------
-    obj : {libpysal.cg.{Point, LineSegment, Line, Ray, Chain, Polygon}
-        A geometric representation of an object.
-    
-    Raises
-    ------
-    TypeError
-        Raised when ``obj`` is not a supported shape.
-    NotImplementedError
-        Raised when ``geo_type`` is not a supported type.
-    
-    Returns
-    -------
-    obj : {libpysal.cg.{Point, LineSegment, Line, Ray, Chain, Polygon}
-        A new geometric representation of the object.
-    
-    """
-
-    if isinstance(obj, (Point, LineSegment, Line, Ray, Chain, Polygon)):
-        pass
-    else:
-        if hasattr(obj, "__geo_interface__"):
-            geo = obj.__geo_interface__
-        else:
-            geo = obj
-
-        if hasattr(geo, "type"):
-            raise TypeError("%r does not appear to be a shape object." % (obj))
-
-        geo_type = geo["type"].lower()
-
-        # if geo_type.startswith('multi'):
-        #    raise NotImplementedError, "%s are not supported at this time."%geo_type
-
-        if geo_type in _geoJSON_type_to_Pysal_type:
-
-            obj = _geoJSON_type_to_Pysal_type[geo_type].__from_geo_interface__(geo)
-        else:
-            raise NotImplementedError("%s is not supported at this time." % geo_type)
-
-    return obj

-
-
-class Geometry(object):
-    """A base class to help implement ``is_geometry``
-    and make geometric types extendable.
-    
-    """
-
-    def __init__(self):
-        pass
-
-
-
[docs]class Point(Geometry):
-    """Geometric class for point objects.
-
-    Parameters
-    ----------
-    loc : tuple
-        The point's location (number :math:`x`-tuple, :math:`x` > 1).
-    
-    Examples
-    --------
-    
-    >>> p = Point((1, 3))
-    
-    """
-
-
[docs]    def __init__(self, loc):
-
-        self.__loc = tuple(map(float, loc))

-
-    @classmethod
-    def __from_geo_interface__(cls, geo):
-        return cls(geo["coordinates"])
-
-    @property
-    def __geo_interface__(self):
-        return {"type": "Point", "coordinates": self.__loc}
-
-    def __lt__(self, other) -> bool:
-        """Tests if the point is less than another object.
-
-        Parameters
-        ----------
-        other : libpysal.cg.Point
-            An object to test equality against.
-
-        Examples
-        --------
-        
-        >>> Point((0, 1)) < Point((0, 1))
-        False
-        
-        >>> Point((0, 1)) < Point((1, 1))
-        True
-        
-        """
-
-        return (self.__loc) < (other.__loc)
-
-    def __le__(self, other) -> bool:
-        """Tests if the point is less than or equal to another object.
-
-        Parameters
-        ----------
-        other : libpysal.cg.Point
-            An object to test equality against.
-
-        Examples
-        --------
-        
-        >>> Point((0, 1)) <= Point((0, 1))
-        True
-        
-        >>> Point((0, 1)) <= Point((1, 1))
-        True
-        
-        """
-
-        return (self.__loc) <= (other.__loc)
-
-    def __eq__(self, other) -> bool:
-        """Tests if the point is equal to another object.
-
-        Parameters
-        ----------
-        other : libpysal.cg.Point
-            An object to test equality against.
-        
-        Examples
-        --------
-        
-        >>> Point((0, 1)) == Point((0, 1))
-        True
-        
-        >>> Point((0, 1)) == Point((1, 1))
-        False
-        
-        """
-
-        try:
-            return (self.__loc) == (other.__loc)
-        except AttributeError:
-            return False
-
-    def __ne__(self, other) -> bool:
-        """Tests if the point is not equal to another object.
-
-        Parameters
-        ----------
-        other : libpysal.cg.Point
-            An object to test equality against.
-
-        Examples
-        --------
-        
-        >>> Point((0, 1)) != Point((0, 1))
-        False
-        
-        >>> Point((0, 1)) != Point((1, 1))
-        True
-        
-        """
-
-        try:
-            return (self.__loc) != (other.__loc)
-        except AttributeError:
-            return True
-
-    def __gt__(self, other) -> bool:
-        """Tests if the point is greater than another object.
-
-        Parameters
-        ----------
-        other : libpysal.cg.Point
-            An object to test equality against.
-
-        Examples
-        --------
-        
-        >>> Point((0, 1)) > Point((0, 1))
-        False
-        
-        >>> Point((0, 1)) > Point((1, 1))
-        False
-        
-        """
-
-        return (self.__loc) > (other.__loc)
-
-    def __ge__(self, other) -> bool:
-        """Tests if the point is greater than or equal to another object.
-
-        Parameters
-        ----------
-        other : libpysal.cg.Point
-            An object to test equality against.
-
-        Examples
-        --------
-        
-        >>> Point((0, 1)) >= Point((0, 1))
-        True
-        
-        >>> Point((0, 1)) >= Point((1, 1))
-        False
-        
-        """
-
-        return (self.__loc) >= (other.__loc)
-
-    def __hash__(self) -> int:
-        """Returns the hash of the point's location.
-
-        Examples
-        --------
-        
-        >>> hash(Point((0, 1))) == hash(Point((0, 1)))
-        True
-        
-        >>> hash(Point((0, 1))) == hash(Point((1, 1)))
-        False
-        
-        """
-
-        return hash(self.__loc)
-
-    def __getitem__(self, *args) -> Union[int, float]:
-        """Return the coordinate for the given dimension.
-        
-        Parameters
-        ----------
-        *args : tuple
-            A singleton tuple of :math:`(i)` with :math:`i`
-            as the index of the desired dimension.
-
-        Examples
-        --------
-        
-        >>> p = Point((5.5, 4.3))
-        >>> p[0] == 5.5
-        True
-        >>> p[1] == 4.3
-        True
-        
-        """
-
-        return self.__loc.__getitem__(*args)
-
-    def __getslice__(self, *args) -> slice:
-        """Return the coordinates for the given dimensions.
-
-        Parameters
-        ----------
-        *args : tuple
-            A tuple of :math:`(i,j)` with :math:`i` as the index to the start
-            slice and :math:`j` as the index to end the slice (excluded).
-
-        Examples
-        --------
-        
-        >>> p = Point((3, 6, 2))
-        >>> p[:2] == (3, 6)
-        True
-        
-        >>> p[1:2] == (6,)
-        True
-        
-        """
-
-        return self.__loc.__getslice__(*args)
-
-    def __len__(self) -> int:
-        """ Returns the dimensions of the point.
-
-        Examples
-        --------
-        
-        >>> len(Point((1, 2)))
-        2
-        
-        """
-
-        return len(self.__loc)
-
-    def __repr__(self) -> str:
-        """Returns the string representation of the ``Point``.
-
-        Examples
-        --------
-        
-        >>> Point((0, 1))
-        (0.0, 1.0)
-        
-        """
-
-        return str(self)
-
-    def __str__(self) -> str:
-        """Returns a string representation of a ``Point`` object.
-
-        Examples
-        --------
-        
-        >>> p = Point((1, 3))
-        >>> str(p)
-        '(1.0, 3.0)'
-        
-        """
-
-        return str(self.__loc)

-        # return "POINT ({} {})".format(*self.__loc)
-
-
-
[docs]class LineSegment(Geometry):
-    """Geometric representation of line segment objects.
-
-    Parameters
-    ----------
-    start_pt : libpysal.cg.Point
-        The point where the segment begins.
-    end_pt : libpysal.cg.Point
-        The point where the segment ends.
-
-    Attributes
-    ----------
-    p1 : libpysal.cg.Point
-        The starting point of the line segment.
-    p2 : Point
-        The ending point of the line segment.
-    bounding_box : libpysal.cg.Rectangle
-        The bounding box of the segment.
-    len : float
-        The length of the segment.
-    line : libpysal.cg.Line
-        The line on which the segment lies.
-
-    Examples
-    --------
-    
-    >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
-
-    """
-
-
[docs]    def __init__(self, start_pt, end_pt):
-
-        self._p1 = start_pt
-        self._p2 = end_pt
-        self._reset_props()

-
-    def __str__(self):
-        return "LineSegment(" + str(self._p1) + ", " + str(self._p2) + ")"
-        # return "LINESTRING ({} {}, {} {})".format(
-        #    self._p1[0], self._p1[1], self._p2[0], self._p2[1]
-        # )
-
-    def __eq__(self, other) -> bool:
-        """Returns ``True`` if ``self`` and ``other`` are the same line segment.
-
-        Examples
-        --------
-        
-        >>> l1 = LineSegment(Point((1, 2)), Point((5, 6)))
-        >>> l2 = LineSegment(Point((5, 6)), Point((1, 2)))
-        >>> l1 == l2
-        True
-        
-        >>> l2 == l1
-        True
-        
-        """
-
-        eq = False
-
-        if not isinstance(other, self.__class__):
-            pass
-        else:
-            if other.p1 == self._p1 and other.p2 == self._p2:
-                eq = True
-            elif other.p2 == self._p1 and other.p1 == self._p2:
-                eq = True
-
-        return eq
-
-
[docs]    def intersect(self, other) -> bool:
-        """Test whether segment intersects with other segment (``True``) or
-        not (``False``). Handles endpoints of segments being on other segment.
-        
-        Parameters
-        ----------
-        other : libpysal.cg.LineSegment
-            Another line segment to check against.
-        
-        Examples
-        --------
-
-        >>> ls = LineSegment(Point((5, 0)), Point((10, 0)))
-        >>> ls1 = LineSegment(Point((5, 0)), Point((10, 1)))
-        >>> ls.intersect(ls1)
-        True
-        
-        >>> ls2 = LineSegment(Point((5, 1)), Point((10, 1)))
-        >>> ls.intersect(ls2)
-        False
-        
-        >>> ls2 = LineSegment(Point((7, -1)), Point((7, 2)))
-        >>> ls.intersect(ls2)
-        True
-        
-        """
-
-        ccw1 = self.sw_ccw(other.p2)
-        ccw2 = self.sw_ccw(other.p1)
-        ccw3 = other.sw_ccw(self.p1)
-        ccw4 = other.sw_ccw(self.p2)
-
-        intersects = ccw1 * ccw2 <= 0 and ccw3 * ccw4 <= 0
-
-        return intersects

-
-    def _reset_props(self):
-        """**HELPER METHOD. DO NOT CALL.**
-        Resets attributes which are functions of other attributes.
-        The getters for these attributes (implemented as properties)
-        then recompute their values if they have been reset since
-        the last call to the getter.
-
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
-        >>> ls._reset_props()
-        
-        """
-
-        self._bounding_box = None
-        self._len = None
-        self._line = False
-
-    def _get_p1(self):
-        """**HELPER METHOD. DO NOT CALL.**
-        Returns the ``p1`` attribute of the line segment.
-
-        Returns
-        -------
-        self._p1 : libpysal.cg.Point
-            The ``_p1`` attribute.
-
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
-        >>> r = ls._get_p1()
-        >>> r == Point((1, 2))
-        True
-        
-        """
-
-        return self._p1
-
-    def _set_p1(self, p1):
-        """**HELPER METHOD. DO NOT CALL.**
-        Sets the ``p1`` attribute of the line segment.
-        
-        Parameters
-        ----------
-        p1 : libpysal.cg.Point
-            A point.
-        
-        Returns
-        -------
-        self._p1 : libpysal.cg.Point
-            The reset ``p1`` attribute.
-
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
-        >>> r = ls._set_p1(Point((3, -1)))
-        >>> r == Point((3.0, -1.0))
-        True
-        
-        """
-
-        self._p1 = p1
-        self._reset_props()
-
-        return self._p1
-
-    p1 = property(_get_p1, _set_p1)
-
-    def _get_p2(self):
-        """**HELPER METHOD. DO NOT CALL.**
-        Returns the ``p2`` attribute of the line segment.
-
-        Returns
-        -------
-        self._p2 : libpysal.cg.Point
-            The ``_p2`` attribute.
-
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
-        >>> r = ls._get_p2()
-        >>> r == Point((5, 6))
-        True
-        
-        """
-
-        return self._p2
-
-    def _set_p2(self, p2):
-        """**HELPER METHOD. DO NOT CALL.**
-        Sets the ``p2`` attribute of the line segment.
-
-        Parameters
-        ----------
-        p2 : libpysal.cg.Point
-            A point.
-        
-        Returns
-        -------
-        self._p2 : libpysal.cg.Point
-            The reset ``p2`` attribute.
-
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
-        >>> r = ls._set_p2(Point((3, -1)))
-        >>> r == Point((3.0, -1.0))
-        True
-        
-        """
-
-        self._p2 = p2
-        self._reset_props()
-
-        return self._p2
-
-    p2 = property(_get_p2, _set_p2)
-
-
[docs]    def is_ccw(self, pt) -> bool:
-        """Returns whether a point is counterclockwise of the
-        segment (``True``) or not (``False``). Exclusive.
-
-        Parameters
-        ----------
-        pt : libpysal.cg.Point
-            A point lying ccw or cw of a segment.
-
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((0, 0)), Point((5, 0)))
-        >>> ls.is_ccw(Point((2, 2)))
-        True
-        
-        >>> ls.is_ccw(Point((2, -2)))
-        False
-        
-        """
-
-        v1 = (self._p2[0] - self._p1[0], self._p2[1] - self._p1[1])
-        v2 = (pt[0] - self._p1[0], pt[1] - self._p1[1])
-
-        return v1[0] * v2[1] - v1[1] * v2[0] > 0

-
-
[docs]    def is_cw(self, pt) -> bool:
-        """Returns whether a point is clockwise of the
-        segment (``True``) or not (``False``). Exclusive.
-
-        Parameters
-        ----------
-        pt : libpysal.cg.Point
-            A point lying ccw or cw of a segment.
-
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((0, 0)), Point((5, 0)))
-        >>> ls.is_cw(Point((2, 2)))
-        False
-        
-        >>> ls.is_cw(Point((2, -2)))
-        True
-        
-        """
-
-        v1 = (self._p2[0] - self._p1[0], self._p2[1] - self._p1[1])
-        v2 = (pt[0] - self._p1[0], pt[1] - self._p1[1])
-
-        return v1[0] * v2[1] - v1[1] * v2[0] < 0

-
-
[docs]    def sw_ccw(self, pt):
-        """Sedgewick test for ``pt`` being ccw of segment.
-
-        Returns
-        -------
-        is_ccw : bool
-            ``1`` if turn from ``self.p1`` to ``self.p2`` to ``pt`` is ccw.
-            ``-1`` if turn from ``self.p1`` to ``self.p2`` to ``pt`` is cw.
-            ``-1`` if the points are collinear and ``self.p1`` is in the middle.
-            ``1`` if the points are collinear and ``self.p2`` is in the middle.
-            ``0`` if the points are collinear and ``pt`` is in the middle.
-
-        """
-
-        p0 = self.p1
-        p1 = self.p2
-        p2 = pt
-
-        dx1 = p1[0] - p0[0]
-        dy1 = p1[1] - p0[1]
-        dx2 = p2[0] - p0[0]
-        dy2 = p2[1] - p0[1]
-
-        if dy1 * dx2 < dy2 * dx1:
-            is_ccw = 1
-        elif dy1 * dx2 > dy2 * dx1:
-            is_ccw = -1
-        elif dx1 * dx2 < 0 or dy1 * dy2 < 0:
-            is_ccw = -1
-        elif dx1 * dx1 + dy1 * dy1 >= dx2 * dx2 + dy2 * dy2:
-            is_ccw = 0
-        else:
-            is_ccw = 1
-
-        return is_ccw

-
-
[docs]    def get_swap(self):
-        """Returns a ``LineSegment`` object which has its endpoints swapped.
-
-        Returns
-        -------
-        line_seg : libpysal.cg.LineSegment
-            The ``LineSegment`` object which has its endpoints swapped.
-
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
-        >>> swap = ls.get_swap()
-        >>> swap.p1[0]
-        5.0
-        
-        >>> swap.p1[1]
-        6.0
-        
-        >>> swap.p2[0]
-        1.0
-        
-        >>> swap.p2[1]
-        2.0
-        
-        """
-
-        line_seg = LineSegment(self._p2, self._p1)
-
-        return line_seg

-
-    @property
-    def bounding_box(self):
-        """Returns the minimum bounding box of a ``LineSegment`` object.
-        
-        Returns
-        -------
-        self._bounding_box : libpysal.cg.Rectangle
-            The bounding box of the line segment.
-        
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
-        >>> ls.bounding_box.left
-        1.0
-        
-        >>> ls.bounding_box.lower
-        2.0
-        
-        >>> ls.bounding_box.right
-        5.0
-        
-        >>> ls.bounding_box.upper
-        6.0
-        
-        """
-
-        # If LineSegment attributes p1, p2 changed, recompute
-        if self._bounding_box is None:
-            self._bounding_box = Rectangle(
-                min([self._p1[0], self._p2[0]]),
-                min([self._p1[1], self._p2[1]]),
-                max([self._p1[0], self._p2[0]]),
-                max([self._p1[1], self._p2[1]]),
-            )
-        return Rectangle(
-            self._bounding_box.left,
-            self._bounding_box.lower,
-            self._bounding_box.right,
-            self._bounding_box.upper,
-        )
-
-    @property
-    def len(self) -> float:
-        """Returns the length of a ``LineSegment`` object.
-
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((2, 2)), Point((5, 2)))
-        >>> ls.len
-        3.0
-        
-        """
-
-        # If LineSegment attributes p1, p2 changed, recompute
-        if self._len is None:
-            self._len = math.hypot(self._p1[0] - self._p2[0], self._p1[1] - self._p2[1])
-
-        return self._len
-
-    @property
-    def line(self):
-        """Returns a ``Line`` object of the line on which the segment lies.
-
-        Returns
-        -------
-        self._line : libpysal.cg.Line
-            The ``Line`` object of the line on which the segment lies.
-
-        Examples
-        --------
-        
-        >>> ls = LineSegment(Point((2, 2)), Point((3, 3)))
-        >>> l = ls.line
-        >>> l.m
-        1.0
-        
-        >>> l.b
-        0.0
-        
-        """
-
-        if self._line == False:
-            dx = self._p1[0] - self._p2[0]
-            dy = self._p1[1] - self._p2[1]
-
-            if dx == 0 and dy == 0:
-                self._line = None
-            elif dx == 0:
-                self._line = VerticalLine(self._p1[0])
-            else:
-                m = dy / float(dx)
-                # y - mx
-                b = self._p1[1] - m * self._p1[0]
-                self._line = Line(m, b)
-
-        return self._line

-
-
-class VerticalLine(Geometry):
-    """Geometric representation of verticle line objects.
-    
-    Parameters
-    ----------
-    x : {int, float}
-        The :math:`x`-intercept of the line. ``x`` is also an attribute.
-    
-    Examples
-    --------
-    
-    >>> ls = VerticalLine(0)
-    >>> ls.m
-    inf
-    
-    >>> ls.b
-    nan
-    
-    """
-
-    def __init__(self, x):
-
-        self._x = float(x)
-        self.m = float("inf")
-        self.b = float("nan")
-
-    def x(self, y) -> float:
-        """Returns the :math:`x`-value of the line at a particular :math:`y`-value.
-
-        Parameters
-        ----------
-        y : {int, float}
-            The :math:`y`-value at which to compute :math:`x`.
-
-        Examples
-        --------
-        
-        >>> l = VerticalLine(0)
-        >>> l.x(0.25)
-        0.0
-        
-        """
-
-        return self._x
-
-    def y(self, x) -> float:
-        """Returns the :math:`y`-value of the line at a particular :math:`x`-value.
-
-        Parameters
-        ----------
-        x : {int, float}
-            The :math:`x`-value at which to compute :math:`y`.
-
-        Examples
-        --------
-        
-        >>> l = VerticalLine(1)
-        >>> l.y(1)
-        nan
-        
-        """
-
-        return float("nan")
-
-
-
[docs]class Line(Geometry):
-    """Geometric representation of line objects.
-
-    Parameters
-    ----------
-    m : {int, float}
-        The slope of the line. ``m`` is also an attribute.
-    b : {int, float}
-        The :math:`y`-intercept of the line. ``b`` is also an attribute.
-    
-    Raises
-    ------
-    ArithmeticError
-        Raised when infinity is passed in as the slope.
-    
-    Examples
-    --------
-    
-    >>> ls = Line(1, 0)
-    >>> ls.m
-    1.0
-    
-    >>> ls.b
-    0.0
-    
-    """
-
-
[docs]    def __init__(self, m, b):
-
-        if m == float("inf"):
-            raise ArithmeticError("Slope cannot be infinite.")
-
-        self.m = float(m)
-        self.b = float(b)

-
-
[docs]    def x(self, y: Union[int, float]) -> float:
-        """Returns the :math:`x`-value of the line at a particular :math:`y`-value.
-
-        Parameters
-        ----------
-        y : {int, float}
-            The :math:`y`-value at which to compute :math:`x`.
-        
-        Raises
-        ------
-        ArithmeticError
-            Raised when ``0.`` is passed in as the slope.
-        
-        Examples
-        --------
-        
-        >>> l = Line(0.5, 0)
-        >>> l.x(0.25)
-        0.5
-        
-        """
-
-        if self.m == 0:
-            raise ArithmeticError("Cannot solve for 'x' when slope is zero.")
-
-        return (y - self.b) / self.m

-
-
[docs]    def y(self, x: Union[int, float]) -> float:
-        """Returns the :math:`y`-value of the line at a particular :math:`x`-value.
-
-        Parameters
-        ----------
-        x : {int, float}
-            The :math:`x`-value at which to compute :math:`y`.
-
-        Examples
-        --------
-        
-        >>> l = Line(1, 0)
-        >>> l.y(1)
-        1.0
-        
-        """
-
-        if self.m == 0:
-            return self.b
-
-        return self.m * x + self.b

-
-
-
[docs]class Ray:
-    """Geometric representation of ray objects.
-
-    Parameters
-    ----------
-    origin : libpysal.cg.Point
-        The point where the ray originates.
-    second_p :
-        The second point specifying the ray (not ``origin``.)
-
-    Attributes
-    ----------
-    o : libpysal.cg.Point
-        The origin (point where ray originates). See ``origin``.
-    p : libpysal.cg.Point
-        The second point on the ray (not the point where the
-        ray originates). See ``second_p``.
-    
-    Examples
-    --------
-    
-    >>> l = Ray(Point((0, 0)), Point((1, 0)))
-    >>> str(l.o)
-    '(0.0, 0.0)'
-    
-    >>> str(l.p)
-    '(1.0, 0.0)'
-    
-    """
-
-
[docs]    def __init__(self, origin, second_p):
-
-        self.o = origin
-        self.p = second_p

-
-
-
[docs]class Chain(Geometry):
-    """Geometric representation of a chain, also known as a polyline.
-    
-    Parameters
-    ----------
-    vertices : list
-        A point list or list of point lists.
-
-    Attributes
-    ----------
-    vertices : list
-        The list of points of the vertices of the chain in order.
-    len : float
-        The geometric length of the chain.
-    
-    Examples
-    --------
-    
-    >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])
-    
-    """
-
-
[docs]    def __init__(self, vertices: list):
-
-        if isinstance(vertices[0], list):
-            self._vertices = [part for part in vertices]
-        else:
-            self._vertices = [vertices]
-        self._reset_props()

-
-    @classmethod
-    def __from_geo_interface__(cls, geo: dict):
-        if geo["type"].lower() == "linestring":
-            verts = [Point(pt) for pt in geo["coordinates"]]
-        elif geo["type"].lower() == "multilinestring":
-            verts = [list(map(Point, part)) for part in geo["coordinates"]]
-        else:
-            raise TypeError("%r is not a Chain." % geo)
-        return cls(verts)
-
-    @property
-    def __geo_interface__(self) -> dict:
-        if len(self.parts) == 1:
-            return {"type": "LineString", "coordinates": self.vertices}
-        else:
-            return {"type": "MultiLineString", "coordinates": self.parts}
-
-    def _reset_props(self):
-        """**HELPER METHOD. DO NOT CALL.** Resets attributes which are
-        functions of other attributes. The ``getter``s for these attributes
-        (implemented as ``properties``) then recompute their values if they
-        have been reset since the last call to the ``getter``.
-
-        """
-
-        self._len = None
-        self._arclen = None
-        self._bounding_box = None
-
-    @property
-    def vertices(self) -> list:
-        """Returns the vertices of the chain in clockwise order.
-
-        Examples
-        --------
-        
-        >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])
-        >>> verts = c.vertices
-        >>> len(verts)
-        4
-        
-        """
-
-        return sum([part for part in self._vertices], [])
-
-    @property
-    def parts(self) -> list:
-        """Returns the parts (lists of ``libpysal.cg.Point`` objects) of the chain.
-
-        Examples
-        --------
-       
-        >>> c = Chain(
-        ...     [
-        ...         [Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))],
-        ...         [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))]
-        ...     ]
-        ... )
-        >>> len(c.parts)
-        2
-        
-        """
-
-        return [[v for v in part] for part in self._vertices]
-
-    @property
-    def bounding_box(self):
-        """Returns the bounding box of the chain.
-
-        Returns
-        -------
-        self._bounding_box : libpysal.cg.Rectangle
-            The bounding box of the chain.
-        
-        Examples
-        --------
-        
-        >>> c = Chain([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))])
-        >>> c.bounding_box.left
-        0.0
-        
-        >>> c.bounding_box.lower
-        0.0
-        
-        >>> c.bounding_box.right
-        2.0
-        
-        >>> c.bounding_box.upper
-        1.0
-        
-        """
-
-        if self._bounding_box is None:
-            vertices = self.vertices
-            self._bounding_box = Rectangle(
-                min([v[0] for v in vertices]),
-                min([v[1] for v in vertices]),
-                max([v[0] for v in vertices]),
-                max([v[1] for v in vertices]),
-            )
-
-        return self._bounding_box
-
-    @property
-    def len(self) -> int:
-        """Returns the geometric length of the chain.
-
-        Examples
-        --------
-        
-        >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])
-        >>> c.len
-        3.0
-        
-        >>> c = Chain(
-        ...     [
-        ...         [Point((0, 0)), Point((1, 0)), Point((1, 1))],
-        ...         [Point((10, 10)), Point((11, 10)), Point((11, 11))]
-        ...     ]
-        ... )
-        >>> c.len
-        4.0
-        
-        """
-
-        def dist(v1: tuple, v2: tuple) -> Union[int, float]:
-            return math.hypot(v1[0] - v2[0], v1[1] - v2[1])
-
-        def part_perimeter(p: list) -> Union[int, float]:
-            return sum([dist(p[i], p[i + 1]) for i in range(len(p) - 1)])
-
-        if self._len is None:
-            self._len = sum([part_perimeter(part) for part in self._vertices])
-
-        return self._len
-
-    @property
-    def arclen(self) -> Union[int, float]:
-        """Returns the geometric length of the chain
-        computed using 'arcdistance' (meters).
-        
-        """
-
-        def part_perimeter(p: list) -> Union[int, float]:
-            return sum([arcdist(p[i], p[i + 1]) * 1000.0 for i in range(len(p) - 1)])
-
-        if self._arclen is None:
-            self._arclen = sum([part_perimeter(part) for part in self._vertices])
-
-        return self._arclen
-
-    @property
-    def segments(self) -> list:
-        """Returns the segments that compose the chain."""
-
-        return [
-            [LineSegment(a, b) for (a, b) in zip(part[:-1], part[1:])]
-            for part in self._vertices
-        ]

-
-
-class Ring(Geometry):
-    """Geometric representation of a linear ring. Linear rings must be
-    closed, the first and last point must be the same. Open rings will
-    be closed. This class exists primarily as a geometric primitive to
-    form complex polygons with multiple rings and holes. The ordering
-    of the vertices is ignored and will not be altered.
-
-    Parameters
-    ----------
-    vertices : list
-        A list of vertices.
-
-    Attributes
-    ----------
-    vertices : list
-        A list of points with the vertices of the ring.
-    len : int
-        The number of vertices.
-    perimeter : float
-        The geometric length of the perimeter of the ring.
-    bounding_box : libpysal.cg.Rectangle
-        The bounding box of the ring.
-    area : float
-        The area enclosed by the ring.
-    centroid : {tuple, libpysal.cg.Point}
-        The centroid of the ring defined by the 'center of gravity'
-        or 'center or mass'.
-    _quad_tree_structure : libpysal.cg.QuadTreeStructureSingleRing
-        The quad tree structure for the ring. This structure helps
-        test if a point is inside the ring.
-    
-    """
-
-    def __init__(self, vertices):
-        if vertices[0] != vertices[-1]:
-            vertices = vertices[:] + vertices[0:1]
-            # msg = "Supplied vertices do not form a closed ring, "
-            # msg += "the first and last vertices are not the same."
-            # raise ValueError(msg)
-
-        self.vertices = tuple(vertices)
-        self._perimeter = None
-        self._bounding_box = None
-        self._area = None
-        self._centroid = None
-        self._quad_tree_structure = None
-
-    def __len__(self) -> int:
-        return len(self.vertices)
-
-    @property
-    def len(self) -> int:
-        return len(self)
-
-    @staticmethod
-    def dist(v1, v2) -> Union[int, float]:
-
-        return math.hypot(v1[0] - v2[0], v1[1] - v2[1])
-
-    @property
-    def perimeter(self) -> Union[int, float]:
-
-        if self._perimeter is None:
-            dist = self.dist
-            v = self.vertices
-            self._perimeter = sum(
-                [dist(v[i], v[i + 1]) for i in range(-1, len(self) - 1)]
-            )
-        return self._perimeter
-
-    @property
-    def bounding_box(self):
-        """Returns the bounding box of the ring.
-
-        Returns
-        -------
-        self._bounding_box : libpysal.cg.Rectangle
-            The bounding box of the ring.
-        
-        Examples
-        --------
-        
-        >>> r = Ring(
-        ...     [
-        ...         Point((0, 0)),
-        ...         Point((2, 0)),
-        ...         Point((2, 1)),
-        ...         Point((0, 1)),
-        ...         Point((0, 0))
-        ...     ]
-        ... )
-        
-        >>> r.bounding_box.left
-        0.0
-        
-        >>> r.bounding_box.lower
-        0.0
-        
-        >>> r.bounding_box.right
-        2.0
-        
-        >>> r.bounding_box.upper
-        1.0
-        
-        """
-
-        if self._bounding_box is None:
-            vertices = self.vertices
-            x = [v[0] for v in vertices]
-            y = [v[1] for v in vertices]
-            self._bounding_box = Rectangle(min(x), min(y), max(x), max(y))
-
-        return self._bounding_box
-
-    @property
-    def area(self) -> Union[int, float]:
-        """Returns the area of the ring.
-
-        Examples
-        --------
-        
-        >>> r = Ring(
-        ...     [
-        ...         Point((0, 0)),
-        ...         Point((2, 0)),
-        ...         Point((2, 1)),
-        ...         Point((0, 1)),
-        ...         Point((0, 0))
-        ...     ]
-        ... )
-        >>> r.area
-        2.0
-        
-        """
-
-        return abs(self.signed_area)
-
-    @property
-    def signed_area(self) -> Union[int, float]:
-        if self._area is None:
-            vertices = self.vertices
-            x = [v[0] for v in vertices]
-            y = [v[1] for v in vertices]
-            N = len(self)
-
-            A = 0.0
-            for i in range(N - 1):
-                A += (x[i] + x[i + 1]) * (y[i] - y[i + 1])
-            A = A * 0.5
-            self._area = -A
-
-        return self._area
-
-    @property
-    def centroid(self):
-        """Returns the centroid of the ring.
-
-        Returns
-        -------
-        self._centroid : libpysal.cg.Point
-            The ring's centroid.
-
-        Notes
-        -----
-        
-        The centroid returned by this method is the geometric centroid.
-        Also known as the 'center of gravity' or 'center of mass'.
-
-        Examples
-        --------
-        
-        >>> r = Ring(
-        ...     [
-        ...         Point((0, 0)),
-        ...         Point((2, 0)),
-        ...         Point((2, 1)),
-        ...         Point((0, 1)),
-        ...         Point((0, 0))
-        ...     ]
-        ... )
-        >>> str(r.centroid)
-        '(1.0, 0.5)'
-        
-        """
-
-        if self._centroid is None:
-            vertices = self.vertices
-            x = [v[0] for v in vertices]
-            y = [v[1] for v in vertices]
-            A = self.signed_area
-            N = len(self)
-            cx = 0
-            cy = 0
-            for i in range(N - 1):
-                f = x[i] * y[i + 1] - x[i + 1] * y[i]
-                cx += (x[i] + x[i + 1]) * f
-                cy += (y[i] + y[i + 1]) * f
-            cx = 1.0 / (6 * A) * cx
-            cy = 1.0 / (6 * A) * cy
-            self._centroid = Point((cx, cy))
-
-        return self._centroid
-
-    def build_quad_tree_structure(self):
-        """Build the quad tree structure for this polygon. Once
-        the structure is built, speed for testing if a point is
-        inside the ring will be increased significantly.
-        
-        """
-
-        self._quad_tree_structure = QuadTreeStructureSingleRing(self)
-
-    def contains_point(self, point):
-        """Point containment using winding number. The implementation is based on
-        `this <http://www.engr.colostate.edu/~dga/dga/papers/point_in_polygon.pdf>`_.
-        
-        Parameters
-        ----------
-        point : libpysal.cg.Point
-            The point to test for containment.
-        
-        Returns
-        -------
-        point_contained : bool
-            ``True`` if ``point`` is contained within the polygon, otherwise ``False``.
-        
-        """
-
-        point_contained = False
-
-        if self._quad_tree_structure is None:
-            x, y = point
-
-            # bbox checks
-            bbleft = x < self.bounding_box.left
-            bbright = x > self.bounding_box.right
-            bblower = y < self.bounding_box.lower
-            bbupper = y > self.bounding_box.upper
-
-            if bbleft or bbright or bblower or bbupper:
-                pass
-            else:
-                rn = len(self.vertices)
-                xs = [self.vertices[i][0] - point[0] for i in range(rn)]
-                ys = [self.vertices[i][1] - point[1] for i in range(rn)]
-                w = 0
-
-                for i in range(len(self.vertices) - 1):
-                    yi = ys[i]
-                    yj = ys[i + 1]
-                    xi = xs[i]
-                    xj = xs[i + 1]
-                    if yi * yj < 0:
-                        r = xi + yi * (xj - xi) / (yi - yj)
-                        if r > 0:
-                            if yi < 0:
-                                w += 1
-                            else:
-                                w -= 1
-                    elif yi == 0 and xi > 0:
-                        if yj > 0:
-                            w += 0.5
-                        else:
-                            w -= 0.5
-                    elif yj == 0 and xj > 0:
-                        if yi < 0:
-                            w += 0.5
-                        else:
-                            w -= 0.5
-                if w == 0:
-                    pass
-                else:
-                    point_contained = True
-        else:
-            point_contained = self._quad_tree_structure.contains_point(point)
-
-        return point_contained
-
-
-
[docs]class Polygon(Geometry):
-    """Geometric representation of polygon objects.
-    Returns a polygon created from the objects specified.
-
-    Parameters
-    ----------
-    vertices : list
-        A list of vertices or a list of lists of vertices.
-    holes : list
-        A list of sub-polygons to be considered as holes.
-        Default is ``None``.
-
-    Attributes
-    ----------
-    vertices : list
-        A list of points with the vertices of the polygon in clockwise order.
-    len : int
-        The number of vertices including holes.
-    perimeter : float
-        The geometric length of the perimeter of the polygon.
-    bounding_box : libpysal.cg.Rectangle
-        The bounding box of the polygon.
-    bbox : list
-        A list representation of the bounding box in the 
-        form ``[left, lower, right, upper]``.
-    area : float
-        The area enclosed by the polygon.
-    centroid : tuple
-        The 'center of gravity', i.e. the mean point of the polygon.
-    
-    Examples
-    --------
-    
-    >>> p1 = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
-    
-    """
-
-
[docs]    def __init__(self, vertices, holes=None):
-
-        self._part_rings = []
-        self._hole_rings = []
-
-        def clockwise(part: list) -> list:
-            if standalone.is_clockwise(part):
-                return part[:]
-            else:
-                return part[::-1]
-
-        vl = list(vertices)
-        if isinstance(vl[0], list):
-            self._part_rings = list(map(Ring, vertices))
-            self._vertices = [clockwise(part) for part in vertices]
-        else:
-            self._part_rings = [Ring(vertices)]
-            self._vertices = [clockwise(vertices)]
-        if holes is not None and holes != []:
-            if isinstance(holes[0], list):
-                self._hole_rings = list(map(Ring, holes))
-                self._holes = [clockwise(hole) for hole in holes]
-            else:
-                self._hole_rings = [Ring(holes)]
-                self._holes = [clockwise(holes)]
-        else:
-            self._holes = [[]]
-        self._reset_props()

-
-    @classmethod
-    def __from_geo_interface__(cls, geo: dict):
-        """While PySAL does not differentiate polygons and multipolygons
-        GEOS, Shapely, and geoJSON do. In GEOS, etc, polygons may only
-        have a single exterior ring, all other parts are holes.
-        MultiPolygons are simply a list of polygons.
-        
-        """
-
-        geo_type = geo["type"].lower()
-        if geo_type == "multipolygon":
-            parts = []
-            holes = []
-            for polygon in geo["coordinates"]:
-                verts = [[Point(pt) for pt in part] for part in polygon]
-                parts += verts[0:1]
-                holes += verts[1:]
-            if not holes:
-                holes = None
-            return cls(parts, holes)
-        else:
-            verts = [[Point(pt) for pt in part] for part in geo["coordinates"]]
-            return cls(verts[0:1], verts[1:])
-
-    @property
-    def __geo_interface__(self) -> dict:
-        """Return ``__geo_interface__`` information lookup."""
-
-        if len(self.parts) > 1:
-            geo = {
-                "type": "MultiPolygon",
-                "coordinates": [[part] for part in self.parts],
-            }
-            if self._holes[0]:
-                geo["coordinates"][0] += self._holes
-            return geo
-        if self._holes[0]:
-            return {"type": "Polygon", "coordinates": self._vertices + self._holes}
-        else:
-            return {"type": "Polygon", "coordinates": self._vertices}
-
-    def _reset_props(self):
-        """Resets the geometric properties of the polygon."""
-        self._perimeter = None
-        self._bounding_box = None
-        self._bbox = None
-        self._area = None
-        self._centroid = None
-        self._len = None
-
-    def __len__(self) -> int:
-        return self.len
-
-    @property
-    def len(self) -> int:
-        """Returns the number of vertices in the polygon.
-
-        Examples
-        --------
-        
-        >>> p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])
-        >>> p1.len
-        4
-        
-        >>> len(p1)
-        4
-        
-        """
-
-        if self._len is None:
-            self._len = len(self.vertices)
-        return self._len
-
-    @property
-    def vertices(self) -> list:
-        """Returns the vertices of the polygon in clockwise order.
-
-        Examples
-        --------
-        
-        >>> p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])
-        >>> len(p1.vertices)
-        4
-        
-        """
-
-        return sum([part for part in self._vertices], []) + sum(
-            [part for part in self._holes], []
-        )
-
-    @property
-    def holes(self) -> list:
-        """Returns the holes of the polygon in clockwise order.
-
-        Examples
-        --------
-        
-        >>> p = Polygon(
-        ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
-        ...     [Point((1, 2)), Point((2, 2)), Point((2, 1)), Point((1, 1))]
-        ... )
-        >>> len(p.holes)
-        1
-        
-        """
-
-        return [[v for v in part] for part in self._holes]
-
-    @property
-    def parts(self) -> list:
-        """Returns the parts of the polygon in clockwise order.
-
-        Examples
-        --------
-        
-        >>> p = Polygon(
-        ...     [
-        ...         [Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))],
-        ...         [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))]
-        ...     ]
-        ... )
-        >>> len(p.parts)
-        2
-        
-        """
-
-        return [[v for v in part] for part in self._vertices]
-
-    @property
-    def perimeter(self) -> Union[int, float]:
-        """Returns the perimeter of the polygon.
-
-        Examples
-        --------
-        
-        >>> p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
-        >>> p.perimeter
-        4.0
-        
-        """
-
-        def dist(v1: Union[int, float], v2: Union[int, float]) -> float:
-            return math.hypot(v1[0] - v2[0], v1[1] - v2[1])
-
-        def part_perimeter(part) -> Union[int, float]:
-            return sum([dist(part[i], part[i + 1]) for i in range(-1, len(part) - 1)])
-
-        sum_perim = lambda part_type: sum([part_perimeter(part) for part in part_type])
-
-        if self._perimeter is None:
-            self._perimeter = sum_perim(self._vertices) + sum_perim(self._holes)
-
-        return self._perimeter
-
-    @property
-    def bbox(self):
-        """Returns the bounding box of the polygon as a list.
-        
-        Returns
-        -------
-        self._bbox : list
-            The bounding box of the polygon as a list.
-        
-        See Also
-        --------
-        
-        libpysal.cg.bounding_box
-        
-        """
-
-        if self._bbox is None:
-            self._bbox = [
-                self.bounding_box.left,
-                self.bounding_box.lower,
-                self.bounding_box.right,
-                self.bounding_box.upper,
-            ]
-        return self._bbox
-
-    @property
-    def bounding_box(self):
-        """Returns the bounding box of the polygon.
-
-        Returns
-        -------
-        self._bounding_box : libpysal.cg.Rectangle
-            The bounding box of the polygon.
-
-        Examples
-        --------
-        
-        >>> p = Polygon([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))])
-        >>> p.bounding_box.left
-        0.0
-        
-        >>> p.bounding_box.lower
-        0.0
-        
-        >>> p.bounding_box.right
-        2.0
-        
-        >>> p.bounding_box.upper
-        1.0
-        
-        """
-
-        if self._bounding_box is None:
-            vertices = self.vertices
-            self._bounding_box = Rectangle(
-                min([v[0] for v in vertices]),
-                min([v[1] for v in vertices]),
-                max([v[0] for v in vertices]),
-                max([v[1] for v in vertices]),
-            )
-        return self._bounding_box
-
-    @property
-    def area(self) -> float:
-        """Returns the area of the polygon.
-
-        Examples
-        --------
-        
-        >>> p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
-        >>> p.area
-        1.0
-        
-        >>> p = Polygon(
-        ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
-        ...     [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))]
-        ... )
-        >>> p.area
-        99.0
-        
-        """
-
-        def part_area(pv: list) -> float:
-            __area = 0
-            for i in range(-1, len(pv) - 1):
-                __area += (pv[i][0] + pv[i + 1][0]) * (pv[i][1] - pv[i + 1][1])
-            __area = __area * 0.5
-            if __area < 0:
-                __area = -area
-            return __area
-
-        sum_area = lambda part_type: sum([part_area(part) for part in part_type])
-        _area = sum_area(self._vertices) - sum_area(self._holes)
-
-        return _area
-
-    @property
-    def centroid(self) -> tuple:
-        """Returns the centroid of the polygon.
-
-        Notes
-        -----
-        
-        The centroid returned by this method is the geometric
-        centroid and respects multipart polygons with holes.
-        Also known as the 'center of gravity' or 'center of mass'.
-
-        Examples
-        --------
-        
-        >>> p = Polygon(
-        ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
-        ...     [Point((1, 1)), Point((1, 2)), Point((2, 2)), Point((2, 1))]
-        ... )
-        >>> p.centroid
-        (5.0353535353535355, 5.0353535353535355)
-        
-        """
-
-        CP = [ring.centroid for ring in self._part_rings]
-        AP = [ring.area for ring in self._part_rings]
-        CH = [ring.centroid for ring in self._hole_rings]
-        AH = [-ring.area for ring in self._hole_rings]
-
-        A = AP + AH
-        cx = sum([pt[0] * area for pt, area in zip(CP + CH, A)]) / sum(A)
-        cy = sum([pt[1] * area for pt, area in zip(CP + CH, A)]) / sum(A)
-
-        return cx, cy
-
-
[docs]    def build_quad_tree_structure(self):
-        """Build the quad tree structure for this polygon. Once
-        the structure is built, speed for testing if a point is
-        inside the ring will be increased significantly.
-        
-        """
-
-        for ring in self._part_rings:
-            ring.build_quad_tree_structure()
-        for ring in self._hole_rings:
-            ring.build_quad_tree_structure()
-        self.is_quad_tree_structure_built = True

-
-
[docs]    def contains_point(self, point):
-        """Test if a polygon contains a point.
-        
-        Parameters
-        ----------
-        point : libpysal.cg.Point
-            A point to test for containment.
-        
-        Returns
-        -------
-        contains : bool
-            ``True`` if the polygon contains ``point`` otherwise ``False``.
-        
-        Examples
-        --------
-        
-        >>> p = Polygon(
-        ...     [Point((0,0)), Point((4,0)), Point((4,5)), Point((2,3)), Point((0,5))]
-        ... )
-        >>> p.contains_point((3,3))
-        1
-        
-        >>> p.contains_point((0,6))
-        0
-        
-        >>> p.contains_point((2,2.9))
-        1
-        
-        >>> p.contains_point((4,5))
-        0
-        
-        >>> p.contains_point((4,0))
-        0
-
-        Handles holes.
-
-        >>> p = Polygon(
-        ...     [Point((0, 0)), Point((0, 10)), Point((10, 10)), Point((10, 0))],
-        ...     [Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))]
-        ... )
-        >>> p.contains_point((3.0, 3.0))
-        False
-        
-        >>> p.contains_point((1.0, 1.0))
-        True
-
-        Notes
-        -----
-        
-        Points falling exactly on polygon edges may yield unpredictable results.
-        
-        """
-
-        searching = True
-
-        for ring in self._hole_rings:
-            if ring.contains_point(point):
-                contains = False
-                searching = False
-                break
-
-        if searching:
-            for ring in self._part_rings:
-                if ring.contains_point(point):
-                    contains = True
-                    searching = False
-                    break
-            if searching:
-                contains = False
-
-        return contains

-
-
-
[docs]class Rectangle(Geometry):
-    """Geometric representation of rectangle objects.
-
-    Attributes
-    ----------
-    left : float
-        Minimum x-value of the rectangle.
-    lower : float
-        Minimum y-value of the rectangle.
-    right : float
-        Maximum x-value of the rectangle.
-    upper : float
-        Maximum y-value of the rectangle.
-    
-    Examples
-    --------
-    
-    >>> r = Rectangle(-4, 3, 10, 17)
-    >>> r.left #minx
-    -4.0
-    
-    >>> r.lower #miny
-    3.0
-    
-    >>> r.right #maxx
-    10.0
-    
-    >>> r.upper #maxy
-    17.0
-    
-    """
-
-
[docs]    def __init__(self, left, lower, right, upper):
-
-        if right < left or upper < lower:
-            raise ArithmeticError("Rectangle must have positive area.")
-        self.left = float(left)
-        self.lower = float(lower)
-        self.right = float(right)
-        self.upper = float(upper)

-
-    def __bool__(self):
-        """Rectangles will evaluate to False if they have zero area.
-        ``___nonzero__`` is used "to implement truth value
-        testing and the built-in operation ``bool()``"
-        ``-- http://docs.python.org/reference/datamodel.html
-
-        Examples
-        --------
-        
-        >>> r = Rectangle(0, 0, 0, 0)
-        >>> bool(r)
-        False
-        
-        >>> r = Rectangle(0, 0, 1, 1)
-        >>> bool(r)
-        True
-        
-        """
-
-        return bool(self.area)
-
-    def __eq__(self, other):
-        if other:
-            return self[:] == other[:]
-        return False
-
-    def __add__(self, other):
-        x, y, X, Y = self[:]
-        x1, y2, X1, Y1 = other[:]
-
-        return Rectangle(
-            min(self.left, other.left),
-            min(self.lower, other.lower),
-            max(self.right, other.right),
-            max(self.upper, other.upper),
-        )
-
-    def __getitem__(self, key):
-        """
-        
-        Examples
-        --------
-        
-        >>> r = Rectangle(-4, 3, 10, 17)
-        >>> r[:]
-        [-4.0, 3.0, 10.0, 17.0]
-        
-        """
-
-        l = [self.left, self.lower, self.right, self.upper]
-
-        return l.__getitem__(key)
-
-
[docs]    def set_centroid(self, new_center):
-        """Moves the rectangle center to a new specified point.
-
-        Parameters
-        ----------
-        new_center : libpysal.cg.Point
-            The new location of the centroid of the polygon.
-
-        Examples
-        --------
-        
-        >>> r = Rectangle(0, 0, 4, 4)
-        >>> r.set_centroid(Point((4, 4)))
-        >>> r.left
-        2.0
-        
-        >>> r.right
-        6.0
-        
-        >>> r.lower
-        2.0
-        
-        >>> r.upper
-        6.0
-        
-        """
-
-        shift = (
-            new_center[0] - (self.left + self.right) / 2,
-            new_center[1] - (self.lower + self.upper) / 2,
-        )
-
-        self.left = self.left + shift[0]
-        self.right = self.right + shift[0]
-        self.lower = self.lower + shift[1]
-        self.upper = self.upper + shift[1]

-
-
[docs]    def set_scale(self, scale):
-        """Rescales the rectangle around its center.
-
-        Parameters
-        ----------
-        scale : int, float
-            The ratio of the new scale to the old
-            scale (e.g. 1.0 is current size).
-
-        Examples
-        --------
-        
-        >>> r = Rectangle(0, 0, 4, 4)
-        >>> r.set_scale(2)
-        >>> r.left
-        -2.0
-        >>> r.right
-        6.0
-        >>> r.lower
-        -2.0
-        >>> r.upper
-        6.0
-        
-        """
-
-        center = ((self.left + self.right) / 2, (self.lower + self.upper) / 2)
-
-        self.left = center[0] + scale * (self.left - center[0])
-        self.right = center[0] + scale * (self.right - center[0])
-        self.lower = center[1] + scale * (self.lower - center[1])
-        self.upper = center[1] + scale * (self.upper - center[1])

-
-    @property
-    def area(self) -> Union[int, float]:
-        """Returns the area of the Rectangle.
-
-        Examples
-        --------
-        
-        >>> r = Rectangle(0, 0, 4, 4)
-        >>> r.area
-        16.0
-        
-        """
-
-        return (self.right - self.left) * (self.upper - self.lower)
-
-    @property
-    def width(self) -> Union[int, float]:
-        """Returns the width of the Rectangle.
-
-        Examples
-        --------
-        
-        >>> r = Rectangle(0, 0, 4, 4)
-        >>> r.width
-        4.0
-        
-        """
-
-        return self.right - self.left
-
-    @property
-    def height(self) -> Union[int, float]:
-        """Returns the height of the Rectangle.
-
-        Examples
-        --------
-        
-        >>> r = Rectangle(0, 0, 4, 4)
-        >>> r.height
-        4.0
-        
-        """
-
-        return self.upper - self.lower

-
-
-_geoJSON_type_to_Pysal_type = {
-    "point": Point,
-    "linestring": Chain,
-    "multilinestring": Chain,
-    "polygon": Polygon,
-    "multipolygon": Polygon,
-}
-
-# moving this to top breaks unit tests !
-from . import standalone
-from .polygonQuadTreeStructure import QuadTreeStructureSingleRing
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/cg/sphere.html b/docs/_modules/libpysal/cg/sphere.html deleted file mode 100644 index ee8d82e0a..000000000 --- a/docs/_modules/libpysal/cg/sphere.html +++ /dev/null @@ -1,814 +0,0 @@ - - - - - - - libpysal.cg.sphere — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.cg.sphere

-"""
-sphere: Tools for working with spherical geometry.
-
-Author(s):
-    Charles R Schmidt schmidtc@gmail.com
-    Luc Anselin luc.anselin@asu.edu
-    Xun Li xun.li@asu.edu
-
-"""
-
-__author__ = (
-    "Charles R Schmidt <schmidtc@gmail.com>,"
-    "Luc Anselin <luc.anselin@asu.edu,"
-    "Xun Li <xun.li@asu.edu"
-)
-
-import math
-import numpy
-import scipy.spatial
-import scipy.constants
-from scipy.spatial.distance import euclidean
-from math import pi, cos, sin
-
-
-__all__ = [
-    "RADIUS_EARTH_KM",
-    "RADIUS_EARTH_MILES",
-    "arcdist",
-    "arcdist2linear",
-    "brute_knn",
-    "fast_knn",
-    "fast_threshold",
-    "linear2arcdist",
-    "toLngLat",
-    "toXYZ",
-    "lonlat",
-    "harcdist",
-    "geointerpolate",
-    "geogrid",
-]
-
-
-RADIUS_EARTH_KM = 6371.0
-RADIUS_EARTH_MILES = (RADIUS_EARTH_KM * scipy.constants.kilo) / scipy.constants.mile
-
-
-
[docs]def arcdist(pt0, pt1, radius=RADIUS_EARTH_KM):
-    """Arc distance between two points on a sphere.
-
-    Parameters
-    ----------
-    pt0 : tuple
-        A point assumed to be in form (longitude,latitude).
-    pt1 : tuple
-        A point assumed to be in form (longitude,latitude).
-    radius : float
-        The radius of a sphere. Default is Earth's radius in
-        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's
-        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)
-        is also an option.
-        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html
-
-    Returns
-    -------
-    dist : float
-        The arc distance between ``pt0`` and ``pt1`` using supplied ``radius``.
-
-    Examples
-    --------
-    
-    >>> pt0 = (0, 0)
-    >>> pt1 = (180, 0)
-    >>> d = arcdist(pt0, pt1, RADIUS_EARTH_MILES)
-    >>> d == math.pi * RADIUS_EARTH_MILES
-    True
-    
-    """
-
-    dist = linear2arcdist(euclidean(toXYZ(pt0), toXYZ(pt1)), radius)
-
-    return dist

-
-
-
[docs]def arcdist2linear(arc_dist, radius=RADIUS_EARTH_KM):
-    """Convert an arc distance (spherical earth)
-    to a linear distance (R3) in the unit sphere.
-    
-    Parameters
-    ----------
-    arc_dist : float
-        The arc distance to convert.
-    radius : float
-        The radius of a sphere. Default is Earth's radius in
-        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's
-        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)
-        is also an option.
-        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html
-    
-    Returns
-    -------
-    linear_dist : float
-        The linear distance conversion of ``arc_dist``.
-    
-    Examples
-    --------
-    
-    >>> pt0 = (0, 0)
-    >>> pt1 = (180, 0)
-    >>> d = arcdist(pt0, pt1, RADIUS_EARTH_MILES)
-    >>> d == math.pi * RADIUS_EARTH_MILES
-    True
-    
-    >>> arcdist2linear(d, RADIUS_EARTH_MILES)
-    2.0
-    
-    """
-
-    circumference = 2 * math.pi * radius
-    linear_dist = (
-        2 - (2 * math.cos(math.radians((arc_dist * 360.0) / circumference)))
-    ) ** (0.5)
-
-    return linear_dist

-
-
-
[docs]def linear2arcdist(linear_dist, radius=RADIUS_EARTH_KM):
-    """Convert a linear distance in the unit sphere
-    (R3) to an arc distance based on supplied radius.
-    
-    Parameters
-    ----------
-    linear_dist : float
-        The linear distance to convert.
-    radius : float
-        The radius of a sphere. Default is Earth's radius in
-        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's
-        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)
-        is also an option.
-        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html
-    
-    Returns
-    -------
-    arc_dist : float
-        The arc distance conversion of ``linear_dist``.
-    
-    Raises
-    ------
-    ValueError
-        Raised when ``linear_dist`` exceeds the diameter of the unit sphere.
-    
-    Examples
-    --------
-    
-    >>> pt0 = (0, 0)
-    >>> pt1 = (180, 0)
-    >>> d = arcdist(pt0, pt1, RADIUS_EARTH_MILES)
-    >>> d == linear2arcdist(2.0, radius=RADIUS_EARTH_MILES)
-    True
-    
-    """
-
-    if linear_dist == float("inf"):
-        arc_dist = linear_dist
-    elif linear_dist > 2.0:
-        msg = "'linear_dist', must not exceed the diameter of the unit sphere, 2.0."
-        raise ValueError(msg)
-    else:
-        circumference = 2 * math.pi * radius
-        a2 = linear_dist ** 2
-        theta = math.degrees(math.acos((2 - a2) / (2.0)))
-        arc_dist = (theta * circumference) / 360.0
-
-    return arc_dist

-
-
-
[docs]def toXYZ(pt):
-    """Convert a point's latitude and longitude to x,y,z.
-    
-    Parameters
-    ----------
-    pt : tuple
-        A point assumed to be in form (lng,lat).
-
-    Returns
-    -------
-    x, y, z : tuple
-        A point in form (x, y, z).
-    
-    """
-
-    phi, theta = list(map(math.radians, pt))
-    phi, theta = phi + pi, theta + (pi / 2)
-    x = 1 * sin(theta) * cos(phi)
-    y = 1 * sin(theta) * sin(phi)
-    z = 1 * cos(theta)
-
-    return x, y, z

-
-
-
[docs]def toLngLat(xyz):
-    """Convert a point's x,y,z to latitude and longitude.
-    
-    Parameters
-    ----------
-    xyz : tuple
-        A point assumed to be in form (x,y,z).
-    
-    Returns
-    -------
-    phi, theta : tuple
-        A point in form (phi, theta) [y,x].
-    
-    """
-
-    x, y, z = xyz
-    if z == -1 or z == 1:
-        phi = 0
-    else:
-        phi = math.atan2(y, x)
-        if phi > 0:
-            phi = phi - math.pi
-        elif phi < 0:
-            phi = phi + math.pi
-    theta = math.acos(z) - (math.pi / 2)
-
-    return phi, theta

-
-
-
[docs]def brute_knn(pts, k, mode="arc", radius=RADIUS_EARTH_KM):
-    """Computes a brute-force :math:`k` nearest neighbors.
-    
-    Parameters
-    ----------
-    pts : list
-        A list of :math:`x,y` pairs.
-    k :  int
-        The number of points to query.
-    mode : str
-        The mode of distance. Valid modes are ``'arc'``
-        and ``'xyz'``. Default is ``'arc'``.
-    radius : float
-        The radius of a sphere. Default is Earth's radius in
-        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's
-        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)
-        is also an option.
-        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html
-    
-    Returns
-    -------
-    w : dict
-        A neighbor ID lookup.
-    
-    """
-
-    n = len(pts)
-    full = numpy.zeros((n, n))
-
-    for i in range(n):
-        for j in range(i + 1, n):
-            if mode == "arc":
-                lng0, lat0 = pts[i]
-                lng1, lat1 = pts[j]
-                dist = arcdist(pts[i], pts[j], radius=radius)
-            elif mode == "xyz":
-                dist = euclidean(pts[i], pts[j])
-            full[i, j] = dist
-            full[j, i] = dist
-
-    w = {}
-    for i in range(n):
-        w[i] = full[i].argsort()[1 : k + 1].tolist()
-
-    return w

-
-
-
[docs]def fast_knn(pts, k, return_dist=False, radius=RADIUS_EARTH_KM):
-    """Computes :math:`k` nearest neighbors on a sphere.
-
-    Parameters
-    ----------
-    pts : list
-        A list of :math:`x,y` pairs.
-    k :  int
-        The number of points to query.
-    return_dist : bool
-        Return distances in the ``wd`` container object (``True``).
-        Default is ``False``.
-    radius : float
-        The radius of a sphere. Default is Earth's radius in
-        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's
-        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)
-        is also an option.
-        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html
-
-    Returns
-    -------
-    wn : dict
-        A neighbor ID lookup.
-    wd : dict
-        A neighbor distance lookup (optional).
-
-    """
-
-    pts = numpy.array(pts)
-    kd = scipy.spatial.KDTree(pts)
-    d, w = kd.query(pts, k + 1)
-    w = w[:, 1:]
-    wn = {}
-
-    for i in range(len(pts)):
-        wn[i] = w[i].tolist()
-
-    if return_dist:
-        d = d[:, 1:]
-        wd = {}
-        for i in range(len(pts)):
-            wd[i] = [linear2arcdist(x, radius=radius) for x in d[i].tolist()]
-        return wn, wd
-    return wn

-
-
-
[docs]def fast_threshold(pts, dist, radius=RADIUS_EARTH_KM):
-    """Find all neighbors on a sphere within a threshold distance.
-
-    Parameters
-    ----------
-    pointslist : list
-        A list of lat-lon tuples. This **must** be a list, even for one point.
-    dist: float
-        The threshold distance.
-    radius : float
-        The radius of a sphere. Default is Earth's radius in
-        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's
-        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)
-        is also an option.
-        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html
-
-    Returns
-    -------
-    wd : dict
-        A neighbor distance lookup where the key is the ID
-        of a point and the value is a list of IDs for other
-        points within ``dist`` of the key point,
-
-    """
-
-    d = arcdist2linear(dist, radius)
-    kd = scipy.spatial.KDTree(pts)
-    r = kd.query_ball_tree(kd, d)
-    wd = {}
-
-    for i in range(len(pts)):
-        l = r[i]
-        l.remove(i)
-        wd[i] = l
-
-    return wd

-
-
-
[docs]def lonlat(pointslist):
-    """Converts point order from lat-lon tuples to lon-lat (x,y) tuples.
-
-    Parameters
-    ----------
-    pointslist : list
-        A list of lat-lon tuples. This **must** be a list, even for one point.
-
-    Returns
-    -------
-    newpts : list
-        A list with tuples of points in lon-lat order.
-
-    Examples
-    --------
-    
-    >>> points = [
-    ...     (41.981417, -87.893517), (41.980396, -87.776787), (41.980906, -87.696450)
-    ... ]
-    >>> newpoints = lonlat(points)
-    >>> newpoints
-    [(-87.893517, 41.981417), (-87.776787, 41.980396), (-87.69645, 41.980906)]
-
-    """
-
-    newpts = [(i[1], i[0]) for i in pointslist]
-
-    return newpts

-
-
-def haversine(x):
-    """Computes the haversine formula.
-
-    Parameters
-    ----------
-    x : float
-        The angle in radians.
-
-    Returns
-    -------
-    haversine_dist : float
-        The square of sine of half the radian (the haversine formula).
-
-    Examples
-    --------
-    
-    >>> haversine(math.pi) # is 180 in radians, hence sin of 90 = 1
-    1.0
-
-    """
-
-    x = math.sin(x / 2)
-
-    haversine_dist = x * x
-
-    return haversine_dist
-
-
-# Lambda functions
-
-# degree to radian conversion
-d2r = lambda x: x * math.pi / 180.0
-
-# radian to degree conversion
-r2d = lambda x: x * 180.0 / math.pi
-
-
-def radangle(p0, p1):
-    """Radian angle between two points on a sphere in lon-lat (x,y).
-
-    Parameters
-    ----------
-    p0 : tuple
-        The first point in (lon,lat) format.
-    p1 : tuple
-        The second point in (lon,lat) format.
-
-    Returns
-    -------
-    d : float
-        Radian angle in radians.
-
-    Examples
-    --------
-    
-    >>> p0 = (-87.893517, 41.981417)
-    >>> p1 = (-87.519295, 41.657498)
-    >>> radangle(p0, p1)
-    0.007460167953189258
-
-    Notes
-    -----
-    
-    Uses haversine formula, function haversine and degree to radian
-    conversion lambda function ``d2r``.
-
-    """
-
-    x0, y0 = d2r(p0[0]), d2r(p0[1])
-    x1, y1 = d2r(p1[0]), d2r(p1[1])
-    d = 2.0 * math.asin(
-        math.sqrt(haversine(y1 - y0) + math.cos(y0) * math.cos(y1) * haversine(x1 - x0))
-    )
-
-    return d
-
-
-
[docs]def harcdist(p0, p1, lonx=True, radius=RADIUS_EARTH_KM):
-    """Alternative the arc distance function, uses the haversine formula.
-
-    Parameters
-    ----------
-    p0 : tuple
-        The first point decimal degrees.
-    p1 : tuple
-        The second point decimal degrees.
-    lonx : bool
-        The method to assess the order of the coordinates.
-        ``True`` for (lon,lat); ``False`` for (lat,lon).
-        Default is ``True``.
-    radius : float
-        The radius of a sphere. Default is Earth's radius in
-        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's
-        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)
-        is also an option. Set to ``None`` for radians.
-        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html
-
-    Returns
-    -------
-    harc_dist : harc_dist
-        The distance in units specified, km, miles or radians.
-
-    Examples
-    --------
-    
-    >>> p0 = (-87.893517, 41.981417)
-    >>> p1 = (-87.519295, 41.657498)
-    >>> harcdist(p0, p1)
-    47.52873002976876
-    
-    >>> harcdist(p0, p1, radius=None)
-    0.007460167953189258
-
-    Notes
-    -----
-    
-    Uses the ``radangle`` function to compute radian angle.
-
-    """
-
-    if not (lonx):
-        p = lonlat([p0, p1])
-        p0 = p[0]
-        p1 = p[1]
-
-    harc_dist = radangle(p0, p1)
-
-    if radius is not None:
-        harc_dist = harc_dist * radius
-
-    return harc_dist

-
-
-
[docs]def geointerpolate(p0, p1, t, lonx=True):
-    """Finds a point on a sphere along the great circle distance between
-    two points on a sphere also known as a way point in great circle navigation.
-
-    Parameters
-    ----------
-    p0 : tuple
-        The first point decimal degrees.
-    p1 : tuple
-        The second point decimal degrees.
-    t : float
-        The proportion along great circle distance between ``p0``
-        and ``p1`` (e.g., :math:`\mathtt{t}=0.5` would find the mid-point).
-    lonx : bool
-        The method to assess the order of the coordinates.
-        ``True`` for (lon,lat); ``False`` for (lat,lon).
-        Default is ``True``.
-
-    Returns
-    -------
-    newpx, newpy : tuple
-        The new point in decimal degrees of (lon-lat) by
-        default or (lat-lon) if ``lonx`` is set to ``False``.
-
-    Examples
-    --------
-    
-    >>> p0 = (-87.893517, 41.981417)
-    >>> p1 = (-87.519295, 41.657498)
-    >>> geointerpolate(p0, p1, 0.1)             # using lon-lat
-    (-87.85592403438788, 41.949079912574796)
-    
-    >>> p3 = (41.981417, -87.893517)
-    >>> p4 = (41.657498, -87.519295)
-    >>> geointerpolate(p3, p4, 0.1, lonx=False) # using lat-lon
-    (41.949079912574796, -87.85592403438788)
-
-    """
-
-    if not (lonx):
-        p = lonlat([p0, p1])
-        p0 = p[0]
-        p1 = p[1]
-
-    d = radangle(p0, p1)
-    k = 1.0 / math.sin(d)
-    t = t * d
-    A = math.sin(d - t) * k
-    B = math.sin(t) * k
-
-    x0, y0 = d2r(p0[0]), d2r(p0[1])
-    x1, y1 = d2r(p1[0]), d2r(p1[1])
-
-    x = A * math.cos(y0) * math.cos(x0) + B * math.cos(y1) * math.cos(x1)
-    y = A * math.cos(y0) * math.sin(x0) + B * math.cos(y1) * math.sin(x1)
-    z = A * math.sin(y0) + B * math.sin(y1)
-
-    newpx = r2d(math.atan2(y, x))
-    newpy = r2d(math.atan2(z, math.sqrt(x * x + y * y)))
-
-    if not lonx:
-        return newpy, newpx
-
-    return newpx, newpy

-
-
-
[docs]def geogrid(pup, pdown, k, lonx=True):
-    """Computes a :math:`k+1` by :math:`k+1` set of grid
-    points for a bounding box in lat-lon. Uses ``geointerpolate``.
-
-    Parameters
-    ----------
-    pup : tuple
-        The lat-lon or lon-lat for the upper left corner of the bounding box.
-    pdown : tuple
-        The lat-lon or lon-lat for The lower right corner of The bounding box.
-    k : int
-        The number of grid cells (grid points will be one more).
-    lonx : bool
-        The method to assess the order of the coordinates.
-        ``True`` for (lon,lat); ``False`` for (lat,lon).
-        Default is ``True``.
-
-    Returns
-    -------
-    grid : list
-        A list of tuples with (lat-lon) or (lon-lat) for grid points,
-        row by row, starting with the top row and moving to the bottom;
-        coordinate tuples are returned in same order as input.
-
-    Examples
-    --------
-    
-    >>> pup = (42.023768, -87.946389)       # Arlington Heights, IL
-    >>> pdown = (41.644415, -87.524102)     # Hammond, IN
-    >>> geogrid(pup,pdown, 3, lonx=False)
-    [(42.023768, -87.946389),
-     (42.02393997819538, -87.80562679358316),
-     (42.02393997819538, -87.66486420641684),
-     (42.023768, -87.524102),
-     (41.897317, -87.94638900000001),
-     (41.8974888973743, -87.80562679296166),
-     (41.8974888973743, -87.66486420703835),
-     (41.897317, -87.524102),
-     (41.770866000000005, -87.94638900000001),
-     (41.77103781320412, -87.80562679234043),
-     (41.77103781320412, -87.66486420765956),
-     (41.770866000000005, -87.524102),
-     (41.644415, -87.946389),
-     (41.64458672568646, -87.80562679171955),
-     (41.64458672568646, -87.66486420828045),
-     (41.644415, -87.524102)]
-
-    """
-
-    if lonx:
-        corners = [pup, pdown]
-    else:
-        corners = lonlat([pup, pdown])
-
-    tpoints = [float(i) / k for i in range(k)[1:]]
-    leftcorners = [corners[0], (corners[0][0], corners[1][1])]
-    rightcorners = [(corners[1][0], corners[0][1]), corners[1]]
-    leftside = [leftcorners[0]]
-    rightside = [rightcorners[0]]
-
-    for t in tpoints:
-        newpl = geointerpolate(leftcorners[0], leftcorners[1], t)
-        leftside.append(newpl)
-        newpr = geointerpolate(rightcorners[0], rightcorners[1], t)
-        rightside.append(newpr)
-    leftside.append(leftcorners[1])
-    rightside.append(rightcorners[1])
-
-    grid = []
-    for i in range(len(leftside)):
-        grid.append(leftside[i])
-        for t in tpoints:
-            newp = geointerpolate(leftside[i], rightside[i], t)
-            grid.append(newp)
-        grid.append(rightside[i])
-    if not (lonx):
-        grid = lonlat(grid)
-
-    return grid

-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/cg/standalone.html b/docs/_modules/libpysal/cg/standalone.html deleted file mode 100644 index 68f6026c9..000000000 --- a/docs/_modules/libpysal/cg/standalone.html +++ /dev/null @@ -1,1492 +0,0 @@ - - - - - - - libpysal.cg.standalone — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.cg.standalone

-"""
-Helper functions for computational geometry in PySAL.
-
-"""
-
-__author__ = "Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow"
-__credits__ = "Copyright (c) 2005-2009 Sergio J. Rey"
-
-import doctest
-import math
-import copy
-import random
-from .shapes import *
-from itertools import islice
-import scipy.spatial
-import numpy as np
-
-EPSILON_SCALER = 3
-
-
-__all__ = [
-    "bbcommon",
-    "get_bounding_box",
-    "get_angle_between",
-    "is_collinear",
-    "get_segments_intersect",
-    "get_segment_point_intersect",
-    "get_polygon_point_intersect",
-    "get_rectangle_point_intersect",
-    "get_ray_segment_intersect",
-    "get_rectangle_rectangle_intersection",
-    "get_polygon_point_dist",
-    "get_points_dist",
-    "get_segment_point_dist",
-    "get_point_at_angle_and_dist",
-    "convex_hull",
-    "is_clockwise",
-    "point_touches_rectangle",
-    "get_shared_segments",
-    "distance_matrix",
-]
-
-
-
[docs]def bbcommon(bb, bbother):
-    """Old Stars method for bounding box overlap testing.
-    Also defined in ``pysal.weights._cont_binning``.
-
-    Parameters
-    ----------
-    bb : list
-        A bounding box.
-    bbother : list
-        The bounding box to test against.
-    
-    Returns
-    -------
-    chflag : int
-        ``1`` if ``bb`` overlaps ``bbother``, otherwise ``0``.
-
-    Examples
-    --------
-
-    >>> b0 = [0, 0, 10, 10]
-    >>> b1 = [10, 0, 20, 10]
-    >>> bbcommon(b0, b1)
-    1
-    
-    """
-
-    chflag = 0
-
-    if not ((bbother[2] < bb[0]) or (bbother[0] > bb[2])):
-        if not ((bbother[3] < bb[1]) or (bbother[1] > bb[3])):
-            chflag = 1
-
-    return chflag

-
-
-
[docs]def get_bounding_box(items):
-    """Find bounding box for a list of geometries.
-
-    Parameters
-    ----------
-    items : list
-        PySAL shapes.
-
-    Returns
-    -------
-    rect = libpysal.cg.Rectangle
-        The bounding box for a list of geometries.
-
-    Examples
-    --------
-    
-    >>> bb = get_bounding_box([Point((-1, 5)), Rectangle(0, 6, 11, 12)])
-    >>> bb.left
-    -1.0
-    
-    >>> bb.lower
-    5.0
-    
-    >>> bb.right
-    11.0
-    
-    >>> bb.upper
-    12.0
-    
-    """
-
-    def left(o):
-        # Polygon, Ellipse
-        if hasattr(o, "bounding_box"):
-            return o.bounding_box.left
-        # Rectangle
-        elif hasattr(o, "left"):
-            return o.left
-        # Point
-        else:
-            return o[0]
-
-    def right(o):
-        # Polygon, Ellipse
-        if hasattr(o, "bounding_box"):
-            return o.bounding_box.right
-        # Rectangle
-        elif hasattr(o, "right"):
-            return o.right
-        # Point
-        else:
-            return o[0]
-
-    def lower(o):
-        # Polygon, Ellipse
-        if hasattr(o, "bounding_box"):
-            return o.bounding_box.lower
-        # Rectangle
-        elif hasattr(o, "lower"):
-            return o.lower
-        # Point
-        else:
-            return o[1]
-
-    def upper(o):
-        # Polygon, Ellipse
-        if hasattr(o, "bounding_box"):
-            return o.bounding_box.upper
-        # Rectangle
-        elif hasattr(o, "upper"):
-            return o.upper
-        # Point
-        else:
-            return o[1]
-
-    rect = Rectangle(
-        min(list(map(left, items))),
-        min(list(map(lower, items))),
-        max(list(map(right, items))),
-        max(list(map(upper, items))),
-    )
-
-    return rect

-
-
-
[docs]def get_angle_between(ray1, ray2):
-    """Returns the angle formed between a pair of rays which share an origin.
-
-    Parameters
-    ----------
-    ray1 : libpysal.cg.Ray
-        A ray forming the beginning of the angle measured.
-    ray2 : libpysal.cg.Ray
-        A ray forming the end of the angle measured.
-    
-    Returns
-    -------
-    angle : float
-        The angle between ``ray1`` and ``ray2``.
-    
-    Raises
-    ------
-    ValueError
-        Raised when rays do not have the same origin.
-    
-    Examples
-    --------
-    
-    >>> get_angle_between(
-    ...     Ray(Point((0, 0)), Point((1, 0))),
-    ...     Ray(Point((0, 0)), Point((1, 0)))
-    ... )
-    0.0
-    
-    """
-
-    if ray1.o != ray2.o:
-        raise ValueError("Rays must have the same origin.")
-
-    vec1 = (ray1.p[0] - ray1.o[0], ray1.p[1] - ray1.o[1])
-    vec2 = (ray2.p[0] - ray2.o[0], ray2.p[1] - ray2.o[1])
-
-    rot_theta = -math.atan2(vec1[1], vec1[0])
-    rot_matrix = [
-        [math.cos(rot_theta), -math.sin(rot_theta)],
-        [math.sin(rot_theta), math.cos(rot_theta)],
-    ]
-
-    rot_vec2 = (
-        rot_matrix[0][0] * vec2[0] + rot_matrix[0][1] * vec2[1],
-        rot_matrix[1][0] * vec2[0] + rot_matrix[1][1] * vec2[1],
-    )
-
-    angle = math.atan2(rot_vec2[1], rot_vec2[0])
-
-    return angle

-
-
-
[docs]def is_collinear(p1, p2, p3):
-    """Returns whether a triplet of points is collinear.
-
-    Parameters
-    ----------
-    p1 : libpysal.cg.Point
-        A point.
-    p2 : libpysal.cg.Point
-        A point.
-    p3 : libpysal.cg.Point
-        A point.
-
-    Returns
-    -------
-    collinear : bool
-        ``True`` if ``{p1, p2, p3}`` are collinear, otherwise ``False``.
-
-    Examples
-    --------
-    
-    >>> is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 5)))
-    True
-    
-    >>> is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 0)))
-    False
-    
-    """
-
-    eps = np.finfo(type(p1[0])).eps
-
-    slope_diff = abs(
-        (p2[0] - p1[0]) * (p3[1] - p1[1]) - (p2[1] - p1[1]) * (p3[0] - p1[0])
-    )
-
-    very_small_dist = EPSILON_SCALER * eps
-
-    collinear = slope_diff < very_small_dist
-
-    return collinear

-
-
-
[docs]def get_segments_intersect(seg1, seg2):
-    """Returns the intersection of two segments if one exists.
-
-    Parameters
-    ----------
-    seg1 : libpysal.cg.LineSegment
-        A segment to check for an intersection.
-    seg2 : libpysal.cg.LineSegment
-        The segment to check against ``seg1`` for an intersection.
-
-    Returns
-    -------
-    intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, None}
-        The intersecting point or line between ``seg1`` and
-        ``seg2`` if an intersection exists or ``None`` if
-        ``seg1`` and ``seg2`` do not intersect.
-
-    Examples
-    --------
-    
-    >>> seg1 = LineSegment(Point((0, 0)), Point((0, 10)))
-    >>> seg2 = LineSegment(Point((-5, 5)), Point((5, 5)))
-    >>> i = get_segments_intersect(seg1, seg2)
-    >>> isinstance(i, Point)
-    True
-    
-    >>> str(i)
-    '(0.0, 5.0)'
-    
-    >>> seg3 = LineSegment(Point((100, 100)), Point((100, 101)))
-    >>> i = get_segments_intersect(seg2, seg3)
-    
-    """
-
-    p1 = seg1.p1
-    p2 = seg1.p2
-    p3 = seg2.p1
-    p4 = seg2.p2
-    a = p2[0] - p1[0]
-    b = p3[0] - p4[0]
-    c = p2[1] - p1[1]
-    d = p3[1] - p4[1]
-    det = float(a * d - b * c)
-
-    intersection = None
-
-    if det == 0:
-        if seg1 == seg2:
-            intersection = LineSegment(seg1.p1, seg1.p2)
-        else:
-            a = get_segment_point_intersect(seg2, seg1.p1)
-            b = get_segment_point_intersect(seg2, seg1.p2)
-            c = get_segment_point_intersect(seg1, seg2.p1)
-            d = get_segment_point_intersect(seg1, seg2.p2)
-            if a and b:  # seg1 in seg2
-                intersection = LineSegment(seg1.p1, seg1.p2)
-            if c and d:  # seg2 in seg1
-                intersection = LineSegment(seg2.p1, seg2.p2)
-            if (a or b) and (c or d):
-                p1 = a if a else b
-                p2 = c if c else d
-                intersection = LineSegment(p1, p2)
-    else:
-        a_inv = d / det
-        b_inv = -b / det
-        c_inv = -c / det
-        d_inv = a / det
-        m = p3[0] - p1[0]
-        n = p3[1] - p1[1]
-        x = a_inv * m + b_inv * n
-        y = c_inv * m + d_inv * n
-        intersect_exists = 0 <= x <= 1 and 0 <= y <= 1
-
-        if intersect_exists:
-            intersection = Point(
-                (p1[0] + x * (p2[0] - p1[0]), p1[1] + x * (p2[1] - p1[1]))
-            )
-
-    return intersection

-
-
-
[docs]def get_segment_point_intersect(seg, pt):
-    """Returns the intersection of a segment and point.
-
-    Parameters
-    ----------
-    seg : libpysal.cg.LineSegment
-        A segment to check for an intersection.
-    pt : libpysal.cg.Point
-        A point to check ``seg`` for an intersection.
-
-    Returns
-    -------
-    pt : {libpysal.cg.Point, None}
-        The intersection of a ``seg`` and ``pt`` if one exists, otherwise ``None``.
-
-    Examples
-    --------
-    
-    >>> seg = LineSegment(Point((0, 0)), Point((0, 10)))
-    >>> pt = Point((0, 5))
-    >>> i = get_segment_point_intersect(seg, pt)
-    >>> str(i)
-    '(0.0, 5.0)'
-    
-    >>> pt2 = Point((5, 5))
-    >>> get_segment_point_intersect(seg, pt2)
-    
-    """
-
-    eps = np.finfo(type(pt[0])).eps
-
-    if is_collinear(pt, seg.p1, seg.p2):
-        if get_segment_point_dist(seg, pt)[0] < EPSILON_SCALER * eps:
-            pass
-        else:
-            pt = None
-    else:
-        vec1 = (pt[0] - seg.p1[0], pt[1] - seg.p1[1])
-        vec2 = (seg.p2[0] - seg.p1[0], seg.p2[1] - seg.p1[1])
-
-        if abs(vec1[0] * vec2[1] - vec1[1] * vec2[0]) < eps:
-            pass
-        else:
-            pt = None
-
-    return pt

-
-
-
[docs]def get_polygon_point_intersect(poly, pt):
-    """Returns the intersection of a polygon and point.
-
-    Parameters
-    ----------
-    poly : libpysal.cg.Polygon
-        A polygon to check for an intersection.
-    pt : libpysal.cg.Point
-        A point to check ``poly`` for an intersection.
-
-    Returns
-    -------
-    ret : {libpysal.cg.Point, None}
-        The intersection of a ``poly`` and ``pt`` if one exists, otherwise ``None``.
-
-    Examples
-    --------
-    
-    >>> poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
-    >>> pt = Point((0.5, 0.5))
-    >>> i = get_polygon_point_intersect(poly, pt)
-    >>> str(i)
-    '(0.5, 0.5)'
-    
-    >>> pt2 = Point((2, 2))
-    >>> get_polygon_point_intersect(poly, pt2)
-    
-    """
-
-    def pt_lies_on_part_boundary(p, vx):
-        vx_range = range(-1, len(vx) - 1)
-        seg = lambda i: LineSegment(vx[i], vx[i + 1])
-        return [i for i in vx_range if get_segment_point_dist(seg(i), p)[0] == 0] != []
-
-    ret = None
-
-    # Weed out points that aren't even close
-    if get_rectangle_point_intersect(poly.bounding_box, pt) is None:
-        pass
-    else:
-        if [vxs for vxs in poly._vertices if pt_lies_on_part_boundary(pt, vxs)] != []:
-            ret = pt
-        elif [vxs for vxs in poly._vertices if _point_in_vertices(pt, vxs)] != []:
-            ret = pt
-        if poly._holes != [[]]:
-            if [vxs for vxs in poly.holes if pt_lies_on_part_boundary(pt, vxs)] != []:
-                # pt lies on boundary of hole.
-                pass
-            if [vxs for vxs in poly.holes if _point_in_vertices(pt, vxs)] != []:
-                # pt lines inside a hole.
-                ret = None
-            # raise NotImplementedError,
-            # 'Cannot compute containment for polygon with holes'
-
-    return ret

-
-
-
[docs]def get_rectangle_point_intersect(rect, pt):
-    """Returns the intersection of a rectangle and point.
-
-    Parameters
-    ----------
-    rect : libpysal.cg.Rectangle
-        A rectangle to check for an intersection.
-    pt : libpysal.cg.Point
-        A point to check ``rect`` for an intersection.
-
-    Returns
-    -------
-    pt : {libpysal.cg.Point, None}
-        The intersection of a ``rect`` and ``pt`` if one exists, otherwise ``None``.
-
-    Examples
-    --------
-    
-    >>> rect = Rectangle(0, 0, 5, 5)
-    >>> pt = Point((1, 1))
-    >>> i = get_rectangle_point_intersect(rect, pt)
-    >>> str(i)
-    '(1.0, 1.0)'
-    
-    >>> pt2 = Point((10, 10))
-    >>> get_rectangle_point_intersect(rect, pt2)
-    
-    """
-
-    if rect.left <= pt[0] <= rect.right and rect.lower <= pt[1] <= rect.upper:
-        pass
-    else:
-        pt = None
-
-    return pt

-
-
-
[docs]def get_ray_segment_intersect(ray, seg):
-    """Returns the intersection of a ray and line segment.
-
-    Parameters
-    ----------
-    ray : libpysal.cg.Ray
-        A ray to check for an intersection.
-    seg : libpysal.cg.LineSegment
-        A segment to check for an intersection against ``ray``.
-
-    Returns
-    -------
-    intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, None}
-        The intersecting point or line between ``ray`` and
-        ``seg`` if an intersection exists or ``None`` if
-        ``ray`` and ``seg`` do not intersect.
-    
-    See Also
-    --------
-    
-    libpysal.cg.get_segments_intersect
-    
-    
-    Examples
-    --------
-    
-    >>> ray = Ray(Point((0, 0)), Point((0, 1)))
-    >>> seg = LineSegment(Point((-1, 10)), Point((1, 10)))
-    >>> i = get_ray_segment_intersect(ray, seg)
-    >>> isinstance(i, Point)
-    True
-    
-    >>> str(i)
-    '(0.0, 10.0)'
-    
-    >>> seg2 = LineSegment(Point((10, 10)), Point((10, 11)))
-    >>> get_ray_segment_intersect(ray, seg2)
-    
-    """
-
-    # Upper bound on origin to segment dist (+1)
-    d = (
-        max(
-            math.hypot(seg.p1[0] - ray.o[0], seg.p1[1] - ray.o[1]),
-            math.hypot(seg.p2[0] - ray.o[0], seg.p2[1] - ray.o[1]),
-        )
-        + 1
-    )
-    ratio = d / math.hypot(ray.o[0] - ray.p[0], ray.o[1] - ray.p[1])
-    ray_seg = LineSegment(
-        ray.o,
-        Point(
-            (
-                ray.o[0] + ratio * (ray.p[0] - ray.o[0]),
-                ray.o[1] + ratio * (ray.p[1] - ray.o[1]),
-            )
-        ),
-    )
-
-    intersection = get_segments_intersect(seg, ray_seg)
-
-    return intersection

-
-
-
[docs]def get_rectangle_rectangle_intersection(r0, r1, checkOverlap=True):
-    """Returns the intersection between two rectangles.
-
-    Parameters
-    ----------
-    r0 : libpysal.cg.Rectangle
-        A rectangle to check for an intersection.
-    r1 : libpysal.cg.Rectangle
-        A rectangle to check for an intersection against ``r0``.
-    checkOverlap : bool
-        Call ``bbcommon(r0, r1)`` prior to complex geometry
-        checking. Default is ``True``. Prior to setting as
-        ``False`` see the Notes section. 
-    
-    Returns
-    -------
-    intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, libpysal.cg.Rectangle, None}
-        The intersecting point, line, or rectangle between 
-        `r0`` and ``r1`` if an intersection exists or ``None``
-        if ``r0`` and ``r1`` do not intersect.
-
-    Notes
-    -----
-    
-    The algorithm assumes the rectangles overlap. The keyword
-    ``checkOverlap=False`` should be used with extreme caution.
-
-    Examples
-    --------
-    
-    >>> r0 = Rectangle(0,4,6,9)
-    >>> r1 = Rectangle(4,0,9,7)
-    >>> ri = get_rectangle_rectangle_intersection(r0,r1)
-    >>> ri[:]
-    [4.0, 4.0, 6.0, 7.0]
-    
-    >>> r0 = Rectangle(0,0,4,4)
-    >>> r1 = Rectangle(2,1,6,3)
-    >>> ri = get_rectangle_rectangle_intersection(r0,r1)
-    >>> ri[:]
-    [2.0, 1.0, 4.0, 3.0]
-    
-    >>> r0 = Rectangle(0,0,4,4)
-    >>> r1 = Rectangle(2,1,3,2)
-    >>> ri = get_rectangle_rectangle_intersection(r0,r1)
-    >>> ri[:] == r1[:]
-    True
-    
-    """
-
-    intersection = None
-    common_bb = True
-
-    if checkOverlap:
-        if not bbcommon(r0, r1):
-            # raise ValueError, "Rectangles do not intersect"
-            common_bb = False
-
-    if common_bb:
-        left = max(r0.left, r1.left)
-        lower = max(r0.lower, r1.lower)
-        right = min(r0.right, r1.right)
-        upper = min(r0.upper, r1.upper)
-
-        if upper == lower and left == right:
-            intersection = Point((left, lower))
-        elif upper == lower:
-            intersection = LineSegment(Point((left, lower)), Point((right, lower)))
-        elif left == right:
-            intersection = LineSegment(Point((left, lower)), Point((left, upper)))
-        else:
-            intersection = Rectangle(left, lower, right, upper)
-
-    return intersection

-
-
-
[docs]def get_polygon_point_dist(poly, pt):
-    """Returns the distance between a polygon and point.
-
-    Parameters
-    ----------
-    poly : libpysal.cg.Polygon
-        A polygon to compute distance from.
-    
-    pt : libpysal.cg.Point
-        a point to compute distance from
-
-    Returns
-    -------
-    dist : float
-        The distance between ``poly`` and ``point``.
-    
-    Examples
-    --------
-    
-    >>> poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
-    >>> pt = Point((2, 0.5))
-    >>> get_polygon_point_dist(poly, pt)
-    1.0
-    
-    >>> pt2 = Point((0.5, 0.5))
-    >>> get_polygon_point_dist(poly, pt2)
-    0.0
-    
-    """
-
-    if get_polygon_point_intersect(poly, pt) is not None:
-        dist = 0.0
-    else:
-        part_prox = []
-        for vertices in poly._vertices:
-            vx_range = range(-1, len(vertices) - 1)
-            seg = lambda i: LineSegment(vertices[i], vertices[i + 1])
-            _min_dist = min([get_segment_point_dist(seg(i), pt)[0] for i in vx_range])
-            part_prox.append(_min_dist)
-        dist = min(part_prox)
-
-    return dist

-
-
-
[docs]def get_points_dist(pt1, pt2):
-    """Returns the distance between a pair of points.
-
-    Parameters
-    ----------
-    pt1 : libpysal.cg.Point
-        A point.
-    
-    pt2 : libpysal.cg.Point
-        The other point.
-    
-    Returns
-    -------
-    dist : float
-        The distance between ``pt1`` and ``pt2``.
-    
-    Examples
-    --------
-    
-    >>> get_points_dist(Point((4, 4)), Point((4, 8)))
-    4.0
-    
-    >>> get_points_dist(Point((0, 0)), Point((0, 0)))
-    0.0
-    
-    """
-
-    dist = math.hypot(pt1[0] - pt2[0], pt1[1] - pt2[1])
-
-    return dist

-
-
-
[docs]def get_segment_point_dist(seg, pt):
-    """Returns (1) the distance between a line segment and point
-    and (2) the distance along the segment to the closest location on the
-    segment from the point as a ratio of the length of the segment.
-
-    Parameters
-    ----------
-    seg : libpysal.cg.LineSegment
-        A line segment to compute distance from.
-    pt : libpysal.cg.Point
-        A point to compute distance from.
-    
-    Returns
-    -------
-    dist : float
-        The distance between ``seg`` and ``pt``.
-    ratio : float
-        The distance along ``seg`` to the closest location on
-        ``seg`` from ``pt`` as a ratio of the length of ``seg``.
-
-    Examples
-    --------
-    
-    >>> seg = LineSegment(Point((0, 0)), Point((10, 0)))
-    >>> pt = Point((5, 5))
-    >>> get_segment_point_dist(seg, pt)
-    (5.0, 0.5)
-    
-    >>> pt2 = Point((0, 0))
-    >>> get_segment_point_dist(seg, pt2)
-    (0.0, 0.0)
-    
-    """
-
-    src_p = seg.p1
-    dest_p = seg.p2
-
-    # Shift line to go through origin
-    points_0 = pt[0] - src_p[0]
-    points_1 = pt[1] - src_p[1]
-    points_2 = 0
-    points_3 = 0
-    points_4 = dest_p[0] - src_p[0]
-    points_5 = dest_p[1] - src_p[1]
-
-    segment_length = get_points_dist(src_p, dest_p)
-
-    # Meh, robustness...
-    # maybe should incorporate this into a more general approach later
-    if segment_length == 0:
-        dist, ratio = get_points_dist(pt, src_p), 0
-
-    else:
-        u_x = points_4 / segment_length
-        u_y = points_5 / segment_length
-
-        inter_x = u_x * u_x * points_0 + u_x * u_y * points_1
-        inter_y = u_x * u_y * points_0 + u_y * u_y * points_1
-
-        src_proj_dist = get_points_dist((0, 0), (inter_x, inter_y))
-        dest_proj_dist = get_points_dist((inter_x, inter_y), (points_4, points_5))
-
-        if src_proj_dist > segment_length or dest_proj_dist > segment_length:
-            src_pt_dist = get_points_dist((points_2, points_3), (points_0, points_1))
-            dest_pt_dist = get_points_dist((points_4, points_5), (points_0, points_1))
-
-            if src_pt_dist < dest_pt_dist:
-                dist, ratio = src_pt_dist, 0
-            else:
-                dist, ratio = dest_pt_dist, 1
-        else:
-            dist = get_points_dist((inter_x, inter_y), (points_0, points_1))
-            ratio = src_proj_dist / segment_length
-
-    return dist, ratio

-
-
-
[docs]def get_point_at_angle_and_dist(ray, angle, dist):
-    """Returns the point at a distance and angle relative to the origin of a ray.
-
-    Parameters
-    ----------
-    ray : libpysal.cg.Ray
-        The ray to which ``angle`` and ``dist`` are relative.
-    angle : float
-        The angle relative to ``ray`` at which ``point`` is located.
-    dist : float
-        The distance from the origin of ``ray`` at which ``point`` is located.
-    
-    Returns
-    -------
-    point : libpysal.cg.Point
-        The point at ``dist`` and ``angle`` relative to the origin of ``ray``.
-
-    Examples
-    --------
-    
-    >>> ray = Ray(Point((0, 0)), Point((1, 0)))
-    >>> pt = get_point_at_angle_and_dist(ray, math.pi, 1.0)
-    >>> isinstance(pt, Point)
-    True
-    
-    >>> round(pt[0], 8)
-    -1.0
-    
-    >>> round(pt[1], 8)
-    0.0
-    
-    """
-
-    v = (ray.p[0] - ray.o[0], ray.p[1] - ray.o[1])
-    cur_angle = math.atan2(v[1], v[0])
-    dest_angle = cur_angle + angle
-
-    point = Point(
-        (ray.o[0] + dist * math.cos(dest_angle), ray.o[1] + dist * math.sin(dest_angle))
-    )
-
-    return point

-
-
-
[docs]def convex_hull(points):
-    """Returns the convex hull of a set of points.
-    
-    Parameters
-    ----------
-    points : list
-        A list of points for computing the convex hull.
-
-    Returns
-    -------
-    stack : list
-        A list of points representing the convex hull.
-    
-    Examples
-    --------
-    
-    >>> points = [Point((0, 0)), Point((4, 4)), Point((4, 0)), Point((3, 1))]
-    >>> convex_hull(points)
-    [(0.0, 0.0), (4.0, 0.0), (4.0, 4.0)]
-    
-    """
-
-    def right_turn(p1, p2, p3) -> bool:
-        """Returns if ``p1`` -> ``p2`` -> ``p3`` forms a 'right turn'."""
-        vec1 = (p2[0] - p1[0], p2[1] - p1[1])
-        vec2 = (p3[0] - p2[0], p3[1] - p2[1])
-        _rt = vec2[0] * vec1[1] - vec2[1] * vec1[0] >= 0
-        return _rt
-
-    points = copy.copy(points)
-    lowest = min(points, key=lambda p: (p[1], p[0]))
-
-    points.remove(lowest)
-    points.sort(key=lambda p: math.atan2(p[1] - lowest[1], p[0] - lowest[0]))
-
-    stack = [lowest]
-
-    for p in points:
-        stack.append(p)
-        while len(stack) > 3 and right_turn(stack[-3], stack[-2], stack[-1]):
-            stack.pop(-2)
-
-    return stack

-
-
-
[docs]def is_clockwise(vertices):
-    """Returns whether a list of points describing
-    a polygon are clockwise or counterclockwise.
-    
-    Parameters
-    ----------
-    vertices : list
-        A list of points that form a single ring.
-
-    Returns
-    -------
-    clockwise : bool
-        ``True`` if ``vertices`` are clockwise, otherwise ``False``.
-    
-    See Also
-    --------
-    
-    libpysal.cg.ccw
-    
-    Examples
-    --------
-    
-    >>> is_clockwise([Point((0, 0)), Point((10, 0)), Point((0, 10))])
-    False
-    
-    >>> is_clockwise([Point((0, 0)), Point((0, 10)), Point((10, 0))])
-    True
-    
-    >>> v = [
-    ...     (-106.57798, 35.174143999999998),
-    ...     (-106.583412, 35.174141999999996),
-    ...     (-106.58417999999999, 35.174143000000001),
-    ...     (-106.58377999999999, 35.175542999999998),
-    ...     (-106.58287999999999, 35.180543),
-    ...     (-106.58263099999999, 35.181455),
-    ...     (-106.58257999999999, 35.181643000000001),
-    ...     (-106.58198299999999, 35.184615000000001),
-    ...     (-106.58148, 35.187242999999995),
-    ...     (-106.58127999999999, 35.188243),
-    ...     (-106.58138, 35.188243),
-    ...     (-106.58108, 35.189442999999997),
-    ...     (-106.58104, 35.189644000000001),
-    ...     (-106.58028, 35.193442999999995),
-    ...     (-106.580029, 35.194541000000001),
-    ...     (-106.57974399999999, 35.195785999999998),
-    ...     (-106.579475, 35.196961999999999),
-    ...     (-106.57922699999999, 35.198042999999998),
-    ...     (-106.578397, 35.201665999999996),
-    ...     (-106.57827999999999, 35.201642999999997),
-    ...     (-106.57737999999999, 35.201642999999997),
-    ...     (-106.57697999999999, 35.201543000000001),
-    ...     (-106.56436599999999, 35.200311999999997),
-    ...     (-106.56058, 35.199942999999998),
-    ...     (-106.56048, 35.197342999999996),
-    ...     (-106.56048, 35.195842999999996),
-    ...     (-106.56048, 35.194342999999996),
-    ...     (-106.56048, 35.193142999999999),
-    ...     (-106.56048, 35.191873999999999),
-    ...     (-106.56048, 35.191742999999995),
-    ...     (-106.56048, 35.190242999999995),
-    ...     (-106.56037999999999, 35.188642999999999),
-    ...     (-106.56037999999999, 35.187242999999995),
-    ...     (-106.56037999999999, 35.186842999999996),
-    ...     (-106.56037999999999, 35.186552999999996),
-    ...     (-106.56037999999999, 35.185842999999998),
-    ...     (-106.56037999999999, 35.184443000000002),
-    ...     (-106.56037999999999, 35.182943000000002),
-    ...     (-106.56037999999999, 35.181342999999998),
-    ...     (-106.56037999999999, 35.180433000000001),
-    ...     (-106.56037999999999, 35.179943000000002),
-    ...     (-106.56037999999999, 35.178542999999998),
-    ...     (-106.56037999999999, 35.177790999999999),
-    ...     (-106.56037999999999, 35.177143999999998),
-    ...     (-106.56037999999999, 35.175643999999998),
-    ...     (-106.56037999999999, 35.174444000000001),
-    ...     (-106.56037999999999, 35.174043999999995),
-    ...     (-106.560526, 35.174043999999995),
-    ...     (-106.56478, 35.174043999999995),
-    ...     (-106.56627999999999, 35.174143999999998),
-    ...     (-106.566541, 35.174144999999996),
-    ...     (-106.569023, 35.174157000000001),
-    ...     (-106.56917199999999, 35.174157999999998),
-    ...     (-106.56938, 35.174143999999998),
-    ...     (-106.57061499999999, 35.174143999999998),
-    ...     (-106.57097999999999, 35.174143999999998),
-    ...     (-106.57679999999999, 35.174143999999998),
-    ...     (-106.57798, 35.174143999999998)
-    ... ]
-    >>> is_clockwise(v)
-    True
-    
-    """
-
-    clockwise = True
-
-    if not len(vertices) < 3:
-        area = 0.0
-        ax, ay = vertices[0]
-        for bx, by in vertices[1:]:
-            area += ax * by - ay * bx
-            ax, ay = bx, by
-        bx, by = vertices[0]
-        area += ax * by - ay * bx
-
-        clockwise = area < 0.0
-
-    return clockwise

-
-
-def ccw(vertices):
-    """Returns whether a list of points is counterclockwise.
-    
-    Parameters
-    ----------
-    vertices : list
-        A list of points that form a single ring.
-
-    Returns
-    -------
-    counter_clockwise : bool
-        ``True`` if ``vertices`` are counter clockwise, otherwise ``False``.
-    
-    See Also
-    --------
-    
-    libpysal.cg.is_clockwise
-    
-    Examples
-    --------
-    
-    >>> ccw([Point((0, 0)), Point((10, 0)), Point((0, 10))])
-    True
-    
-    >>> ccw([Point((0, 0)), Point((0, 10)), Point((10, 0))])
-    False
-    
-    """
-
-    counter_clockwise = True
-
-    if is_clockwise(vertices):
-        counter_clockwise = False
-
-    return counter_clockwise
-
-
-def seg_intersect(a, b, c, d):
-    """Tests if two segments (a,b) and (c,d) intersect.
-    
-    Parameters
-    ----------
-    a : libpysal.cg.Point
-        The first vertex for the first segment.
-    b : libpysal.cg.Point
-        The second vertex for the first segment.
-    c : libpysal.cg.Point
-        The first vertex for the second segment.
-    d : libpysal.cg.Point
-        The second vertex for the second segment.
-    
-    Returns
-    -------
-    segments_intersect : bool
-        ``True`` if segments ``(a,b)`` and ``(c,d)``, otherwise ``False``.
-    
-    Examples
-    --------
-    
-    >>> a = Point((0,1))
-    >>> b = Point((0,10))
-    >>> c = Point((-2,5))
-    >>> d = Point((2,5))
-    >>> e = Point((-3,5))
-    >>> seg_intersect(a, b, c, d)
-    True
-    
-    >>> seg_intersect(a, b, c, e)
-    False
-    
-    """
-
-    segments_intersect = True
-
-    acd_bcd = ccw([a, c, d]) == ccw([b, c, d])
-
-    abc_abd = ccw([a, b, c]) == ccw([a, b, d])
-
-    if acd_bcd or abc_abd:
-        segments_intersect = False
-
-    return segments_intersect
-
-
-def _point_in_vertices(pt, vertices):
-    """**HELPER METHOD. DO NOT CALL.** Returns whether a point
-    is contained in a polygon specified by a sequence of vertices.
-
-    Parameters
-    ----------
-    pt : libpysal.cg.Point
-        A point.
-    vertices : list
-        A list of vertices representing as polygon.
-    
-    Returns
-    -------
-    pt_in_poly : bool
-        ``True`` if ``pt`` is contained in ``vertices``, otherwise ``False``.
-
-    Examples
-    --------
-    
-    >>> _point_in_vertices(
-    ...     Point((1, 1)),
-    ...     [Point((0, 0)), Point((10, 0)), Point((0, 10))]
-    ... )
-    True
-    
-    """
-
-    def neg_ray_intersect(p1, p2, p3) -> bool:
-        """Returns whether a ray in the negative-x
-        direction from ``p3`` intersects the segment between.
-        """
-
-        if not min(p1[1], p2[1]) <= p3[1] <= max(p1[1], p2[1]):
-            nr_inters = False
-        else:
-            if p1[1] > p2[1]:
-                vec1 = (p2[0] - p1[0], p2[1] - p1[1])
-            else:
-                vec1 = (p1[0] - p2[0], p1[1] - p2[1])
-
-            vec2 = (p3[0] - p1[0], p3[1] - p1[1])
-
-            nr_inters = vec1[0] * vec2[1] - vec2[0] * vec1[1] >= 0
-
-        return nr_inters
-
-    vert_y_set = set([v[1] for v in vertices])
-    while pt[1] in vert_y_set:
-        # Perturb the location very slightly
-        pt = pt[0], pt[1] + -1e-14 + random.random() * 2e-14
-
-    inters = 0
-    for i in range(-1, len(vertices) - 1):
-        v1 = vertices[i]
-        v2 = vertices[i + 1]
-        if neg_ray_intersect(v1, v2, pt):
-            inters += 1
-
-    pt_in_poly = inters % 2 == 1
-
-    return pt_in_poly
-
-
-
[docs]def point_touches_rectangle(point, rect):
-    """Returns ``True`` (``1``) if the point is in the rectangle
-    or touches it's boundary, otherwise ``False`` (``0``).
-
-    Parameters
-    ----------
-    point : {libpysal.cg.Point, tuple}
-        A point or point coordinates.
-    rect : libpysal.cg.Rectangle
-        A rectangle.
-    
-    Returns
-    -------
-    chflag : int
-        ``1`` if ``point`` is in (or touches
-        boundary of) ``rect``, otherwise ``0``.
-    
-    Examples
-    --------
-    
-    >>> rect = Rectangle(0, 0, 10, 10)
-    >>> a = Point((5, 5))
-    >>> b = Point((10, 5))
-    >>> c = Point((11, 11))
-    >>> point_touches_rectangle(a, rect)
-    1
-    
-    >>> point_touches_rectangle(b, rect)
-    1
-    
-    >>> point_touches_rectangle(c, rect)
-    0
-    
-    """
-
-    chflag = 0
-    if point[0] >= rect.left and point[0] <= rect.right:
-        if point[1] >= rect.lower and point[1] <= rect.upper:
-            chflag = 1
-
-    return chflag

-
-
-
[docs]def get_shared_segments(poly1, poly2, bool_ret=False):
-    """Returns the line segments in common to both polygons.
-
-    Parameters
-    ----------
-    poly1 : libpysal.cg.Polygon
-        A Polygon.
-    poly2 : libpysal.cg.Polygon
-        A Polygon.
-    bool_ret : bool
-        Return only a ``bool``. Default is ``False``.
-    
-    Returns
-    -------
-    common : list
-        The shared line segments between ``poly1`` and ``poly2``.
-    _ret_bool : bool
-        Whether ``poly1`` and ``poly2`` share a
-        segment (``True``) or not (``False``).
-
-    Examples
-    --------
-    
-    >>> from libpysal.cg.shapes import Polygon
-    >>> x = [0, 0, 1, 1]
-    >>> y = [0, 1, 1, 0]
-    >>> poly1 = Polygon(list(map(Point, zip(x, y))) )
-    >>> x = [a+1 for a in x]
-    >>> poly2 = Polygon(list(map(Point, zip(x, y))) )
-    >>> get_shared_segments(poly1, poly2, bool_ret=True)
-    True
-
-    """
-
-    # get_rectangle_rectangle_intersection inlined for speed.
-    r0 = poly1.bounding_box
-    r1 = poly2.bounding_box
-    wLeft = max(r0.left, r1.left)
-    wLower = max(r0.lower, r1.lower)
-    wRight = min(r0.right, r1.right)
-    wUpper = min(r0.upper, r1.upper)
-
-    segmentsA = set()
-    common = list()
-    partsA = poly1.parts
-
-    for part in poly1.parts + [p for p in poly1.holes if p]:
-        if part[0] != part[-1]:  # not closed
-            part = part[:] + part[0:1]
-        a = part[0]
-
-        for b in islice(part, 1, None):
-            # inlining point_touches_rectangle for speed
-            x, y = a
-            # check if point a is in the bounding box intersection
-            if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper:
-                x, y = b
-                # check if point b is in the bounding box intersection
-                if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper:
-                    if a > b:
-                        segmentsA.add((b, a))
-                    else:
-                        segmentsA.add((a, b))
-            a = b
-
-    _ret_bool = False
-
-    for part in poly2.parts + [p for p in poly2.holes if p]:
-        if part[0] != part[-1]:  # not closed
-            part = part[:] + part[0:1]
-        a = part[0]
-
-        for b in islice(part, 1, None):
-            # inlining point_touches_rectangle for speed
-            x, y = a
-            if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper:
-                x, y = b
-                if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper:
-                    if a > b:
-                        seg = (b, a)
-                    else:
-                        seg = (a, b)
-                    if seg in segmentsA:
-                        common.append(LineSegment(*seg))
-                        if bool_ret:
-                            _ret_bool = True
-                            return _ret_bool
-            a = b
-
-    if bool_ret:
-        if len(common) > 0:
-            _ret_bool = True
-        return _ret_bool
-
-    return common

-
-
-
[docs]def distance_matrix(X, p=2.0, threshold=5e7):
-    """Calculate a distance matrix.
-
-    Parameters
-    ----------
-    X : numpy.ndarray
-        An :math:`n \\times k` array where :math:`n` is the number
-        of observations and :math:`k` is the number of dimensions
-        (2 for :math:`x,y`).
-    p : float
-        Minkowski `p`-norm distance metric parameter where
-        :math:`1<=\mathtt{p}<=\infty`. ``2`` is Euclidean distance and
-        ``1`` is Manhattan distance. Default is ``2.0``.
-    threshold : int
-        If :math:`(\mathtt{n}**2)*32 > \mathtt{threshold}` use
-        ``scipy.spatial.distance_matrix`` instead of working in RAM,
-        this is roughly the amount of RAM (in bytes) that will be used.
-        Must be positive. Default is ``5e7``.
-
-    Returns
-    -------
-    D : numpy.ndarray
-        An n by :math:`m` :math:`p`-norm distance matrix.
-    
-    Raises
-    ------
-    TypeError
-        Raised when an invalid dimensional array is passed in.
-    
-    Notes
-    -----
-    
-    Needs optimization/integration with other weights in PySAL.
-    
-    Examples
-    --------
-    
-    >>> x, y = [r.flatten() for r in np.indices((3, 3))]
-    >>> data = np.array([x, y]).T
-    >>> d = distance_matrix(data)
-    >>> np.array(d)
-    array([[0.        , 1.        , 2.        , 1.        , 1.41421356,
-            2.23606798, 2.        , 2.23606798, 2.82842712],
-           [1.        , 0.        , 1.        , 1.41421356, 1.        ,
-            1.41421356, 2.23606798, 2.        , 2.23606798],
-           [2.        , 1.        , 0.        , 2.23606798, 1.41421356,
-            1.        , 2.82842712, 2.23606798, 2.        ],
-           [1.        , 1.41421356, 2.23606798, 0.        , 1.        ,
-            2.        , 1.        , 1.41421356, 2.23606798],
-           [1.41421356, 1.        , 1.41421356, 1.        , 0.        ,
-            1.        , 1.41421356, 1.        , 1.41421356],
-           [2.23606798, 1.41421356, 1.        , 2.        , 1.        ,
-            0.        , 2.23606798, 1.41421356, 1.        ],
-           [2.        , 2.23606798, 2.82842712, 1.        , 1.41421356,
-            2.23606798, 0.        , 1.        , 2.        ],
-           [2.23606798, 2.        , 2.23606798, 1.41421356, 1.        ,
-            1.41421356, 1.        , 0.        , 1.        ],
-           [2.82842712, 2.23606798, 2.        , 2.23606798, 1.41421356,
-            1.        , 2.        , 1.        , 0.        ]])
-    
-    """
-
-    if X.ndim == 1:
-        X.shape = (X.shape[0], 1)
-
-    if X.ndim > 2:
-        msg = "Should be 2D point coordinates: %s dimensions present." % X.ndim
-        raise TypeError(msg)
-
-    n, k = X.shape
-
-    if (n ** 2) * 32 > threshold:
-        D = scipy.spatial.distance_matrix(X, X, p)
-    else:
-        M = np.ones((n, n))
-        D = np.zeros((n, n))
-        for col in range(k):
-            x = X[:, col]
-            xM = x * M
-            dx = xM - xM.T
-            if p % 2 != 0:
-                dx = np.abs(dx)
-            dx2 = dx ** p
-            D += dx2
-        D = D ** (1.0 / p)
-
-    return D

-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/cg/voronoi.html b/docs/_modules/libpysal/cg/voronoi.html deleted file mode 100644 index 73b28ff89..000000000 --- a/docs/_modules/libpysal/cg/voronoi.html +++ /dev/null @@ -1,492 +0,0 @@ - - - - - - - libpysal.cg.voronoi — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.cg.voronoi

-"""
-Voronoi tesslation of 2-d point sets.
-
-Adapted from https://gist.github.com/pv/8036995
-
-"""
-
-import numpy as np
-from scipy.spatial import Voronoi
-
-__author__ = "Serge Rey <sjsrey@gmail.com>"
-
-__all__ = ["voronoi_frames"]
-
-
-def voronoi(points, radius=None):
-    """Determine finite Voronoi diagram for a 2-d point set.
-    See also ``voronoi_regions()``.
-
-    Parameters
-    ----------
-    points : array_like
-        An nx2 array of points.
-    radius : float (optional)
-        The distance to 'points at infinity'. Default is ``None.``
-
-    Returns
-    -------
-    vor : tuple
-        A two-element tuple consisting of a list and an array. Each element of
-        the list contains the sequence of the indices of Voronoi vertices
-        composing a Voronoi polygon (region), whereas the array contains
-        the Voronoi vertex coordinates.
-    
-    Examples
-    --------
-    
-    >>> points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]
-    >>> regions, coordinates = voronoi(points)
-    >>> regions
-    [[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]
-    
-    >>> coordinates
-    array([[  4.21783296,   4.08408578],
-           [  7.51956025,   3.51807539],
-           [  9.4642193 ,  19.3994576 ],
-           [ 14.98210684, -10.63503022],
-           [ -9.22691341,  -4.58994414],
-           [ 14.98210684, -10.63503022],
-           [  1.78491801,  19.89803294],
-           [  9.4642193 ,  19.3994576 ],
-           [  1.78491801,  19.89803294],
-           [ -9.22691341,  -4.58994414]])
-    
-    """
-
-    vor = voronoi_regions(Voronoi(points), radius=radius)
-
-    return vor
-
-
-def voronoi_regions(vor, radius=None):
-    """Finite voronoi regions for a 2-d point set. See also ``voronoi()``.
-
-    Parameters
-    ----------
-    vor : scipy.spatial.Voronoi
-        A planar Voronoi diagram.
-    radius : float (optional)
-        Distance to 'points at infinity'. Default is ``None.``
-    
-    Returns
-    -------
-    regions_vertices : tuple
-        A two-element tuple consisting of a list of finite voronoi regions
-        and an array Voronoi vertex coordinates.
-    
-    """
-
-    new_regions = []
-    new_vertices = vor.vertices.tolist()
-
-    center = vor.points.mean(axis=0)
-    if radius is None:
-        radius = vor.points.ptp().max() * 2
-
-    all_ridges = {}
-    for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):
-        all_ridges.setdefault(p1, []).append((p2, v1, v2))
-        all_ridges.setdefault(p2, []).append((p1, v1, v2))
-
-    for p1, region in enumerate(vor.point_region):
-        vertices = vor.regions[region]
-
-        if all(v >= 0 for v in vertices):
-            new_regions.append(vertices)
-            continue
-
-        ridges = all_ridges[p1]
-        new_region = [v for v in vertices if v >= 0]
-
-        for p2, v1, v2 in ridges:
-            if v2 < 0:
-                v1, v2 = v2, v1
-            if v1 >= 0:
-                continue
-
-            t = vor.points[p2] - vor.points[p1]
-            t /= np.linalg.norm(t)
-            n = np.array([-t[1], t[0]])
-
-            midpoint = vor.points[[p1, p2]].mean(axis=0)
-            direction = np.sign(np.dot(midpoint - center, n)) * n
-            far_point = vor.vertices[v2] + direction * radius
-
-            new_region.append(len(new_vertices))
-            new_vertices.append(far_point.tolist())
-
-        vs = np.asarray([new_vertices[v] for v in new_region])
-        c = vs.mean(axis=0)
-        angles = np.arctan2(vs[:, 1] - c[1], vs[:, 0] - c[0])
-        new_region = np.array(new_region)[np.argsort(angles)]
-
-        new_regions.append(new_region.tolist())
-
-    regions_vertices = new_regions, np.asarray(new_vertices)
-
-    return regions_vertices
-
-
-def as_dataframes(regions, vertices, points):
-    """Helper function to store finite Voronoi regions and
-    originator points as ``geopandas`` (or ``pandas``) dataframes.
-
-    Parameters
-    ----------
-    regions : list
-        Each element of the list contains sequence of the indexes of
-        voronoi vertices composing a vornoi polygon (region).
-    vertices : array_like
-        The coordinates of the vornoi vertices.
-    points : array_like
-        The originator points.
-
-    Returns
-    -------
-    region_df : geopandas.GeoDataFrame
-        Finite Voronoi polygons as geometries.
-    points_df : geopandas.GeoDataFrame
-        Originator points as geometries.
-    
-    Raises
-    ------
-    ImportError
-        Raised when ``geopandas`` is not available.
-    ImportError
-        Raised when ``shapely`` is not available.
-    
-    """
-
-    try:
-        import geopandas as gpd
-    except ImportError:
-        gpd = None
-
-    try:
-        from shapely.geometry import Polygon, Point
-    except ImportError:
-        from .shapes import Polygon, Point
-
-    if gpd is not None:
-        region_df = gpd.GeoDataFrame()
-        region_df["geometry"] = [Polygon(vertices[region]) for region in regions]
-
-        point_df = gpd.GeoDataFrame()
-        point_df["geometry"] = gpd.GeoSeries(Point(pnt) for pnt in points)
-    else:
-        import pandas as pd
-
-        region_df = pd.DataFrame()
-        region_df["geometry"] = [
-            Polygon(vertices[region].tolist()) for region in regions
-        ]
-        point_df = pd.DataFrame()
-        point_df["geometry"] = [Point(pnt) for pnt in points]
-
-    return region_df, point_df
-
-
-
[docs]def voronoi_frames(points, radius=None, clip="extent"):
-    """Composite helper to return Voronoi regions and
-    generator points as individual dataframes.
-
-    Parameters
-    ----------
-    points : array_like
-        The originator points.
-    radius : float
-        The distance to 'points at infinity' used in building voronoi cells.
-        Default is ``None``.
-    clip : {str, shapely.geometry.Polygon}
-        An overloaded option about how to clip the voronoi cells.
-        Default is ``'extent'``. Options are as follows.
-        
-        * ``'none'``/``None`` -- No clip is applied. Voronoi cells may be arbitrarily larger that the source map. Note that this may lead to cells that are many orders of magnitude larger in extent than the original map. Not recommended.
-        * ``'bbox'``/``'extent'``/``'bounding box'`` -- Clip the voronoi cells to the bounding box of the input points.
-        * ``'chull``/``'convex hull'`` -- Clip the voronoi cells to the convex hull of the input points.
-        * ``'ashape'``/``'ahull'`` -- Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alphashape, using ``libpysal.cg.alpha_shape_auto``).
-        * Polygon -- Clip to an arbitrary Polygon.
-    
-    tolerance : float
-        The percent of map width to use to buffer the extent of the map,
-        if clipping (default: ``.01``, or 1%).
-
-    Returns
-    -------
-    reg_vtx : tuple
-        Two ``geopandas.GeoDataFrame`` (or ``pandas.DataFrame`` if ``geopandas``
-        is unavailable) objects--``(region_df, points_df)``--of finite 
-        Voronoi polygons and the originator points as geometries.
-
-    Notes
-    -----
-
-    If ``geopandas`` is not available the return types will be
-    ``pandas.DataFrame`` objects, each with a geometry column populated
-    with PySAL shapes. If ``geopandas`` is available, return types are
-    ``pandas.GeoDataFrame`` objects with a geometry column populated
-    with shapely geometry types.
-
-    Examples
-    --------
-    
-    >>> points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]
-    >>> regions_df, points_df = voronoi_frames(points)
-    >>> regions_df.shape
-    (4, 1)
-    
-    >>> regions_df.shape == points_df.shape
-    True
-
-    """
-
-    regions, vertices = voronoi(points, radius=radius)
-    regions, vertices = as_dataframes(regions, vertices, points)
-    if clip:
-        regions = clip_voronoi_frames_to_extent(regions, vertices, clip=clip)
-
-    reg_vtx = regions, vertices
-    return reg_vtx

-
-
-def clip_voronoi_frames_to_extent(regions, vertices, clip="extent"):
-    """Generate a geopandas.GeoDataFrame of Voronoi cells clipped to
-    a specified extent.
-    
-    Parameters
-    ----------
-    regions : geopandas.GeoDataFrame
-        A (geo)dataframe containing voronoi cells to clip.
-    vertices : geopandas.GeoDataFrame
-        A (geo)dataframe containing vertices used to build voronoi cells.
-    clip : str, shapely.geometry.Polygon
-        An overloaded option about how to clip the voronoi cells.
-        The options are:
-          - 'none'/None: No clip is applied. Voronoi cells may be arbitrarily
-            larger that the source map. Note that this may lead to cells that
-            are many orders of magnitude larger in extent than
-            the original map. Not recommended.
-          - 'bbox'/'extent'/'bounding box': Clip the voronoi cells to the
-            bounding box of the input points.
-          - 'chull'/'convex hull': Clip the voronoi cells to the
-            convex hull of the input points.
-          - 'ashape'/'ahull': Clip the voronoi cells to the tightest hull that
-            contains all points (e.g. the smallest alphashape,
-            using ``libpysal.cg.alpha_shape_auto``).
-          - Polygon: Clip to an arbitrary Polygon.
-    
-    Returns
-    -------
-    clipped_regions : geopandas.GeoDataFrame
-        A ``geopandas.GeoDataFrame`` of clipped voronoi regions.
-    
-    Raises
-    ------
-    ImportError
-        Raised when ``shapely`` is not available.
-    ImportError
-        Raised when ``geopandas`` is not available.
-    ValueError
-        Raised when in invalid value for ``clip`` is passed in.
-    
-    """
-    try:
-        from shapely.geometry import Polygon
-    except ImportError:
-        raise ImportError("Shapely is required to clip voronoi regions.")
-    try:
-        import geopandas
-    except ImportError:
-        raise ImportError("Geopandas is required to clip voronoi regions.")
-
-    if isinstance(clip, Polygon):
-        clipper = geopandas.GeoDataFrame(geometry=[clip])
-    elif clip is None:
-        return regions
-    elif clip.lower() == "none":
-        return regions
-    elif clip.lower() in ("bounds", "bounding box", "bbox", "extent"):
-        min_x, min_y, max_x, max_y = vertices.total_bounds
-        bounding_poly = Polygon(
-            [
-                (min_x, min_y),
-                (min_x, max_y),
-                (max_x, max_y),
-                (max_x, min_y),
-                (min_x, min_y),
-            ]
-        )
-        clipper = geopandas.GeoDataFrame(geometry=[bounding_poly])
-    elif clip.lower() in ("chull", "convex hull", "convex_hull"):
-        clipper = geopandas.GeoDataFrame(
-            geometry=[vertices.geometry.unary_union.convex_hull]
-        )
-    elif clip.lower() in (
-        "ahull",
-        "alpha hull",
-        "alpha_hull",
-        "ashape",
-        "alpha shape",
-        "alpha_shape",
-    ):
-        from .alpha_shapes import alpha_shape_auto
-        from ..weights.distance import get_points_array
-
-        coordinates = get_points_array(vertices.geometry)
-        clipper = geopandas.GeoDataFrame(geometry=[alpha_shape_auto(coordinates)])
-    else:
-        raise ValueError(
-            "Clip type '{}' not understood. Try one "
-            " of the supported options: [None, 'extent', "
-            "'chull', 'ahull'].".format(clip)
-        )
-    clipped_regions = geopandas.overlay(regions, clipper, how="intersection")
-    return clipped_regions
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/examples.html b/docs/_modules/libpysal/examples.html deleted file mode 100644 index 8baed1607..000000000 --- a/docs/_modules/libpysal/examples.html +++ /dev/null @@ -1,193 +0,0 @@ - - - - - - - libpysal.examples — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.examples

-""" The :mod:`libpysal.examples` module includes a number of small built-in
-    example datasets as well as functions to fetch larger datasets.
-"""
-
-from .base import example_manager
-from .remotes import datasets as remote_datasets
-from .remotes import download as fetch_all
-from .builtin import datasets as builtin_datasets
-
-
-from typing import Union
-
-__all__ = ["get_path", "available", "explain", "fetch_all"]
-
-example_manager.add_examples(remote_datasets)
-example_manager.add_examples(builtin_datasets)
-
-
-
[docs]def available() -> str:
-    """List available datasets."""
-
-    return example_manager.available()

-
-
-
[docs]def explain(name: str) -> str:
-    """Explain a dataset by name."""
-
-    return example_manager.explain(name)

-
-
-def load_example(example_name: str) -> Union[base.Example, builtin.LocalExample]:
-    """Load example dataset instance."""
-
-    return example_manager.load(example_name)
-
-
-
[docs]def get_path(file_name: str) -> str:
-    """Get the path for a file by searching installed datasets."""
-
-    installed = example_manager.get_installed_names()
-    for name in installed:
-        example = example_manager.datasets[name]
-        pth = example.get_path(file_name, verbose=False)
-        if pth:
-            return pth
-    print("{} is not a file in any installed dataset.".format(file_name))

-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/io/fileio.html b/docs/_modules/libpysal/io/fileio.html deleted file mode 100644 index 1e08722b6..000000000 --- a/docs/_modules/libpysal/io/fileio.html +++ /dev/null @@ -1,616 +0,0 @@ - - - - - - - libpysal.io.fileio — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.io.fileio

-"""
-FileIO: Module for reading and writing various file types in a Pythonic way.
-This module should not be used directly, instead...
-
-```
-import pysal.core.FileIO as FileIO
-```
-
-Readers and Writers will mimic python file objects.
-    .seek(n) seeks to the n'th object
-    .read(n) reads n objects, default == all
-    .next() reads the next object
-
-"""
-
-__author__ = "Charles R Schmidt <schmidtc@gmail.com>"
-
-__all__ = ["FileIO"]
-
-import os.path
-from warnings import warn
-from ..common import MISSINGVALUE
-
-from typing import Union
-
-
-class FileIO_MetaCls(type):
-    """This meta class is instantiated when the class is first defined. All
-    subclasses of `FileIO` also inherit this meta class, which registers
-    their abilities with the FileIO registry. Subclasses must contain
-    ``FORMATS`` and ``MODES`` (both are ``type(list)``).
-    
-    Raises
-    ------
-    TypeError
-        FileIO subclasses must have ``FORMATS`` and ``MODES`` defined.
-    
-    """
-
-    def __new__(mcs, name, bases, dict):
-
-        cls = type.__new__(mcs, name, bases, dict)
-
-        if name != "FileIO" and name != "DataTable":
-            if "FORMATS" in dict and "MODES" in dict:
-                # msg = "Registering %s with FileIO.\n\tFormats: %r\n\tModes: %r"
-                # msg = msg % (name, dict["FORMATS"], dict["MODES"])
-                FileIO._register(cls, dict["FORMATS"], dict["MODES"])
-            else:
-                raise TypeError(
-                    "FileIO subclasses must have 'FORMATS' and 'MODES' defined."
-                )
-
-        return cls
-
-
-
[docs]class FileIO(object, metaclass=FileIO_MetaCls):  # should be a type?
-    """Metaclass for supporting spatial data file read and write.
-
-    How this works:
-    
-    ``FileIO.open(\\*args) == FileIO(\\*args)``
-    
-    When creating a new instance of `FileIO` the ``.__new__`` method intercepts.
-    ``.__new__`` parses the filename to determine the ``fileType``. Next,
-    ``.__registry`` and checked for that type. Each type supports one or more modes
-    (``['r', 'w', 'a', etc.]``). If we support the type and mode, an instance of the
-    appropriate handler is created and returned. All handlers must inherit from this
-    class, and by doing so are automatically added to the ``.__registry`` and are
-    forced to conform to the prescribed API. The metaclass takes care of the
-    registration by parsing the class definition. It doesn't make much sense to
-    treat weights in the same way as shapefiles and dbfs, so...
-    
-    * ... for now we'll just return an instance of `W` on ``mode='r'``.
-    * ... on ``mode='w'``, ``.write`` will expect an instance of `W`.
-    
-    """
-
-    __registry = {}  # {'shp':{'r':[OGRshpReader,pysalShpReader]}}
-
-    def __new__(cls, dataPath="", mode="r", dataFormat=None):
-        """Intercepts the instantiation of ``FileIO`` and dispatches
-        to the correct handler. If no suitable handler is found a
-        python file object is returned.
-        """
-
-        if cls is FileIO:
-            try:
-                newCls = object.__new__(
-                    cls.__registry[cls.getType(dataPath, mode, dataFormat)][mode][0]
-                )
-            except KeyError:
-                return open(dataPath, mode)
-            return newCls
-        else:
-            return object.__new__(cls)
-
-
[docs]    @staticmethod
-    def getType(dataPath: str, mode: str, dataFormat=None) -> str:
-        """Parse the ``dataPath`` and return the data type."""
-
-        if dataFormat:
-            ext = dataFormat
-        else:
-            ext = os.path.splitext(dataPath)[1]
-            ext = ext.replace(".", "")
-            ext = ext.lower()
-        if ext == "txt":
-            f = open(dataPath, "r")
-            l1 = f.readline()
-            l2 = f.readline()
-            if ext == "txt":
-                try:
-                    n, k = l1.split(",")
-                    n, k = int(n), int(k)
-                    fields = l2.split(",")
-                    assert len(fields) == k
-                    return "geoda_txt"
-                except:
-                    return ext
-
-        return ext

-
-    @classmethod
-    def _register(cls, parser, formats, modes):
-        """This method is called automatically via the Metaclass of `FileIO` subclasses
-        This should be private, but that hides it from the Metaclass.
-        """
-
-        assert cls is FileIO
-
-        for format in formats:
-            if not format in cls.__registry:
-                cls.__registry[format] = {}
-            for mode in modes:
-                if not mode in cls.__registry[format]:
-                    cls.__registry[format][mode] = []
-                cls.__registry[format][mode].append(parser)
-        # cls.check()
-
-
[docs]    @classmethod
-    def check(cls):
-        """Prints the contents of the registry."""
-
-        print("PySAL File I/O understands the following file extensions:")
-
-        for key, val in list(cls.__registry.items()):
-            print("Ext: '.%s', Modes: %r" % (key, list(val.keys())))

-
-
[docs]    @classmethod
-    def open(cls, *args, **kwargs):
-        """Alias for ``FileIO()``."""
-
-        return cls(*args, **kwargs)

-
-    class _By_Row:
-        def __init__(self, parent):
-            self.p = parent
-
-        def __repr__(self) -> str:
-            if not self.p.ids:
-                return "keys: range(0,n)"
-            else:
-                return "keys: " + list(self.p.ids.keys()).__repr__()
-
-        def __getitem__(self, key) -> Union[list, str]:
-            if type(key) == list:
-                r = []
-                if self.p.ids:
-                    for k in key:
-                        r.append(self.p.get(self.p.ids[k]))
-                else:
-                    for k in key:
-                        r.append(self.p.get(k))
-                return r
-            if self.p.ids:
-                return self.p.get(self.p.ids[key])
-            else:
-                return self.p.get(key)
-
-        __call__ = __getitem__
-
-
[docs]    def __init__(self, dataPath="", mode="r", dataFormat=None):
-        self.dataPath = dataPath
-        self.dataObj = ""
-        self.mode = mode
-        # pos Should ALWAYS be in the range 0,...,n
-        # for custom IDs set the ids property.
-        self.pos = 0
-        self.__ids = None  # {'id':n}
-        self.__rIds = None
-        self.closed = False
-        self._spec = []
-        self.header = []

-
-    def __getitem__(self, key):
-        return self.by_row.__getitem__(key)
-
-    @property
-    def by_row(self):
-        return self._By_Row(self)
-
-    def __getIds(self):
-        return self.__ids
-
-    def __setIds(self, ids: Union[list, dict, None]):
-        """Property method for ``.ids``. Takes a list of ids and maps then
-        to a 0-based index. Need to provide a method to set ID's based on
-        a ``fieldName`` preferably without reading the whole file.
-        
-        Raises
-        ------
-        AssertionError
-            Raised when IDs are not unique.
-        
-        """
-
-        if isinstance(ids, list):
-            try:
-                assert len(ids) == len(set(ids))
-            except AssertionError:
-                raise KeyError("IDs must be unique.")
-            # keys: ID values: i
-            self.__ids = {}
-            # keys: i values: ID
-            self.__rIds = {}
-            for i, id in enumerate(ids):
-                self.__ids[id] = i
-                self.__rIds[i] = id
-        elif isinstance(ids, dict):
-            self.__ids = ids
-            self.__rIds = {}
-            for id, n in list(ids.items()):
-                self.__rIds[n] = id
-        elif not ids:
-            self.__ids = None
-            self.__rIds = None
-
-    ids = property(fget=__getIds, fset=__setIds)
-
-    @property
-    def rIds(self) -> Union[dict, None]:
-        return self.__rIds
-
-    def __iter__(self):
-        self.seek(0)
-        return self
-
-    @staticmethod
-    def _complain_ifclosed(closed):
-        """From `StringIO`.
-        
-        Raises
-        ------
-        ValueError
-            Raised when a file is already closed.
-        
-        """
-        if closed:
-            raise ValueError("I/O operation on closed file.")
-
-
[docs]    def cast(self, key, typ):
-        """Cast ``key`` as ``typ``.
-        
-        Raises
-        ------
-        TypeError
-            Raised when a cast object in not callable.
-        KeyError
-            Raised when a key is not present.
-        
-        """
-        if key in self.header:
-            if not self._spec:
-                self._spec = [lambda x: x for k in self.header]
-            if typ is None:
-                self._spec[self.header.index(key)] = lambda x: x
-            else:
-                try:
-                    assert hasattr(typ, "__call__")
-                    self._spec[self.header.index(key)] = typ
-                except AssertionError:
-                    raise TypeError("Cast objects must be callable.")
-        else:
-            raise KeyError("%s" % key)

-
-    def _cast(self, row) -> list:
-        """
-        
-        Raises
-        ------
-        ValueError
-            Raised when a value could not be cast a particular type.
-        
-        """
-        if self._spec and row:
-            try:
-                return [f(v) for f, v in zip(self._spec, row)]
-            except ValueError:
-                r = []
-                for f, v in zip(self._spec, row):
-                    try:
-                        if not v and f != str:
-                            raise ValueError
-                        r.append(f(v))
-                    except ValueError:
-                        msg = "Value '%r' could not be cast to %s, "
-                        msg += "value set to MISSINGVALUE."
-                        msg = msg % (v, str(f))
-                        warn(msg, RuntimeWarning)
-                        r.append(MISSINGVALUE)
-                return r
-
-        else:
-            return row
-
-    def __next__(self) -> list:
-        """A `FileIO` object is its own iterator, see `StringIO`.
-        
-        Raises
-        ------
-        StopIteration
-            Raised at the EOF.
-            
-        """
-
-        self._complain_ifclosed(self.closed)
-        r = self.__read()
-        if r is None:
-            raise StopIteration
-
-        return r
-
-
[docs]    def close(self):
-        """Subclasses should clean themselves up and then call this method."""
-
-        if not self.closed:
-            self.closed = True
-            del self.dataObj, self.pos

-
-
[docs]    def get(self, n: int) -> list:
-        """Seeks the file to ``n`` and returns ``n``. If ``.ids`` is set
-        ``n`` should be an id, else, ``n`` should be an offset.
-        """
-
-        prevPos = self.tell()
-        self.seek(n)
-        obj = self.__read()
-        self.seek(prevPos)
-
-        return obj

-
-
[docs]    def seek(self, n: int):
-        """Seek the `FileObj` to the beginning of the ``n``'th record.
-        If IDs are set, seeks to the beginning of the record at ID, ``n``.
-        """
-
-        self._complain_ifclosed(self.closed)
-        self.pos = n

-
-
[docs]    def tell(self) -> int:
-        """Return ID (or offset) of next object."""
-
-        self._complain_ifclosed(self.closed)
-
-        return self.pos

-
-
[docs]    def read(self, n=-1) -> Union[list, None]:
-        """Read at most ``n`` objects, less if read hits EOF.
-        If size is negative or omitted read all objects until EOF.
-        Returns ``None`` if EOF is reached before any objects.
-        
-        Raises
-        ------
-        StopIteration
-            Raised at the EOF.
-        
-        """
-
-        self._complain_ifclosed(self.closed)
-
-        if n < 0:
-            # return list(self)
-            result = []
-            while 1:
-                try:
-                    result.append(self.__read())
-                except StopIteration:
-                    break
-            return result
-        elif n == 0:
-            return None
-        else:
-            result = []
-            for i in range(0, n):
-                try:
-                    result.append(self.__read())
-                except StopIteration:
-                    break
-            return result

-
-    def __read(self) -> list:
-        """Gets one row from the file handler, and if necessary casts it's objects.
-        
-        Raises
-        ------
-        StopIteration
-            Raised at the EOF.
-        
-        """
-
-        row = self._read()
-        if row is None:
-            raise StopIteration
-        row = self._cast(row)
-
-        return row
-
-    def _read(self):
-        """Must be implemented by subclasses that support 'r' subclasses.
-        Should increment ``.pos`` and redefine this doc string.
-        
-        Raises
-        ------
-        NotImplementedError
-        
-        """
-
-        self._complain_ifclosed(self.closed)
-        raise NotImplementedError
-
-
[docs]    def truncate(self, size=None):
-        """Should be implemented by subclasses and redefine this doc string.
-        
-        Raises
-        ------
-        NotImplementedError
-        
-        """
-
-        self._complain_ifclosed(self.closed)
-        raise NotImplementedError

-
-
[docs]    def write(self, obj):
-        """Must be implemented by subclasses that support 'w' subclasses
-        Should increment ``.pos``. Subclasses should also check if ``obj``
-        is an instance of type(list) and redefine this doc string.
-        
-        Raises
-        ------
-        NotImplementedError
-        
-        """
-
-        self._complain_ifclosed(self.closed)
-        "Write obj to dataObj"
-        raise NotImplementedError

-
-
[docs]    def flush(self):
-        """
-        
-        Raises
-        ------
-        NotImplementedError
-        
-        """
-
-        self._complain_ifclosed(self.closed)
-        raise NotImplementedError

-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/weights/contiguity.html b/docs/_modules/libpysal/weights/contiguity.html deleted file mode 100644 index 350a7b9c9..000000000 --- a/docs/_modules/libpysal/weights/contiguity.html +++ /dev/null @@ -1,780 +0,0 @@ - - - - - - - libpysal.weights.contiguity — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.weights.contiguity

-import itertools
-
-import numpy
-
-from ..cg import voronoi_frames
-from ..io.fileio import FileIO
-from ._contW_lists import ContiguityWeightsLists
-from .util import get_ids, get_points_array
-from .weights import WSP, W
-from .raster import da2W, da2WSP
-
-try:
-    from shapely.geometry import Point as shapely_point
-    from ..cg.shapes import Point as pysal_point
-
-    point_type = (shapely_point, pysal_point)
-except ImportError:
-    from ..cg.shapes import Point as point_type
-
-WT_TYPE = {"rook": 2, "queen": 1}  # for _contW_Binning
-
-__author__ = "Sergio J. Rey <srey@asu.edu> , Levi John Wolf <levi.john.wolf@gmail.com>"
-
-__all__ = ["Rook", "Queen", "Voronoi"]
-
-
-
[docs]class Rook(W):
-    """
-    Construct a weights object from a collection of pysal polygons that share at least one edge.
-
-    Parameters
-    ----------
-    polygons    : list
-                a collection of PySAL shapes to build weights from
-    ids         : list
-                a list of names to use to build the weights
-    **kw        : keyword arguments
-                optional arguments for :class:`pysal.weights.W`
-
-    See Also
-    ---------
-    :class:`libpysal.weights.weights.W`
-    """
-
-
[docs]    def __init__(self, polygons, **kw):
-        criterion = "rook"
-        ids = kw.pop("ids", None)
-        polygons, backup = itertools.tee(polygons)
-        first_shape = next(iter(backup))
-        if isinstance(first_shape, point_type):
-            polygons, vertices = voronoi_frames(get_points_array(polygons))
-            polygons = list(polygons.geometry)
-        neighbors, ids = _build(polygons, criterion=criterion, ids=ids)
-        W.__init__(self, neighbors, ids=ids, **kw)

-
-
[docs]    @classmethod
-    def from_shapefile(cls, filepath, idVariable=None, full=False, **kwargs):
-        """
-        Rook contiguity weights from a polygon shapefile.
-
-        Parameters
-        ----------
-
-        shapefile : string
-                    name of polygon shapefile including suffix.
-        sparse    : boolean
-                    If True return WSP instance
-                    If False return W instance
-
-        Returns
-        -------
-
-        w          : W
-                     instance of spatial weights
-
-        Examples
-        --------
-        >>> from libpysal.weights import Rook
-        >>> import libpysal
-        >>> wr=Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"), "POLYID")
-        >>> "%.3f"%wr.pct_nonzero
-        '8.330'
-        >>> wr=Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"), sparse=True)
-        >>> pct_sp = wr.sparse.nnz *1. / wr.n**2
-        >>> "%.3f"%pct_sp
-        '0.083'
-
-        Notes
-        -----
-
-        Rook contiguity defines as neighbors any pair of polygons that share a
-        common edge in their polygon definitions.
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.contiguity.Rook`
-        """
-        sparse = kwargs.pop("sparse", False)
-        if idVariable is not None:
-            ids = get_ids(filepath, idVariable)
-        else:
-            ids = None
-        w = cls(FileIO(filepath), ids=ids, **kwargs)
-        w.set_shapefile(filepath, idVariable=idVariable, full=full)
-        if sparse:
-            w = w.to_WSP()
-        return w

-
-
[docs]    @classmethod
-    def from_iterable(cls, iterable, sparse=False, **kwargs):
-        """
-        Construct a weights object from a collection of arbitrary polygons. This
-        will cast the polygons to PySAL polygons, then build the W.
-
-        Parameters
-        ----------
-        iterable    : iterable
-                      a collection of of shapes to be cast to PySAL shapes. Must
-                      support iteration. Can be either Shapely or PySAL shapes.
-        **kw        : keyword arguments
-                      optional arguments for  :class:`pysal.weights.W`
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.contiguity.Rook`
-        """
-        new_iterable = iter(iterable)
-        w = cls(new_iterable, **kwargs)
-        if sparse:
-            w = WSP.from_W(w)
-        return w

-
-
[docs]    @classmethod
-    def from_dataframe(
-        cls, df, geom_col=None, idVariable=None, ids=None, id_order=None, **kwargs
-    ):
-        """
-        Construct a weights object from a pandas dataframe with a geometry
-        column. This will cast the polygons to PySAL polygons, then build the W
-        using ids from the dataframe.
-
-        Parameters
-        ----------
-        df          : DataFrame
-                      a :class: `pandas.DataFrame` containing geometries to use
-                      for spatial weights
-        geom_col    : string
-                      the name of the column in `df` that contains the
-                      geometries. Defaults to active geometry column.
-        idVariable  : string
-                      the name of the column to use as IDs. If nothing is
-                      provided, the dataframe index is used
-        ids         : list
-                      a list of ids to use to index the spatial weights object.
-                      Order is not respected from this list.
-        id_order    : list
-                      an ordered list of ids to use to index the spatial weights
-                      object. If used, the resulting weights object will iterate
-                      over results in the order of the names provided in this
-                      argument. 
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.contiguity.Rook`
-        """
-        if geom_col is None:
-            geom_col = df.geometry.name
-        if id_order is not None:
-            if id_order is True and ((idVariable is not None) or (ids is not None)):
-                # if idVariable is None, we want ids. Otherwise, we want the
-                # idVariable column
-                id_order = list(df.get(idVariable, ids))
-            else:
-                id_order = df.get(id_order, ids)
-        elif idVariable is not None:
-            ids = df.get(idVariable).tolist()
-        elif isinstance(ids, str):
-            ids = df.get(ids).tolist()
-        return cls.from_iterable(
-            df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
-        )

-
-
[docs]    @classmethod
-    def from_xarray(
-        cls,
-        da,
-        z_value=None,
-        coords_labels={},
-        k=1,
-        include_nodata=False,
-        n_jobs=1,
-        sparse=True,
-        **kwargs,
-    ):
-        """
-        Construct a weights object from a xarray.DataArray with an additional
-        attribute index containing coordinate values of the raster
-        in the form of Pandas.Index/MultiIndex.
-
-        Parameters
-        ----------
-        da : xarray.DataArray
-            Input 2D or 3D DataArray with shape=(z, y, x)
-        z_value : int/string/float
-            Select the z_value of 3D DataArray with multiple layers.
-        coords_labels : dictionary
-            Pass dimension labels for coordinates and layers if they do not
-            belong to default dimensions, which are (band/time, y/lat, x/lon)
-            e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"}
-            Default is {} empty dictionary.
-        sparse : boolean
-            type of weight object. Default is True. For libpysal.weights.W, sparse = False
-        k : int
-            Order of contiguity, this will select all neighbors upto kth order.
-            Default is 1.
-        include_nodata : boolean
-            If True, missing values will be assumed as non-missing when
-            selecting higher_order neighbors, Default is False
-        n_jobs : int
-            Number of cores to be used in the sparse weight construction. If -1,
-            all available cores are used. Default is 1.
-        **kwargs : keyword arguments
-            optional arguments passed when sparse = False
-
-        Returns
-        -------
-        w : libpysal.weights.W/libpysal.weights.WSP
-            instance of spatial weights class W or WSP with an index attribute 
-
-        Notes
-        -----
-        1. Lower order contiguities are also selected.
-        2. Returned object contains `index` attribute that includes a 
-        `Pandas.MultiIndex` object from the DataArray.
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.weights.WSP`
-        """
-        if sparse:
-            w = da2WSP(da, "rook", z_value, coords_labels, k, include_nodata)
-        else:
-            w = da2W(da, "rook", z_value, coords_labels, k, include_nodata, **kwargs)
-        return w

-
-
-
[docs]class Queen(W):
-    """
-    Construct a weights object from a collection of pysal polygons that share at least one vertex.
-
-    Parameters
-    ----------
-    polygons    : list
-                  a collection of PySAL shapes to build weights from
-    ids         : list
-                  a list of names to use to build the weights
-    **kw        : keyword arguments
-                  optional arguments for :class:`pysal.weights.W`
-
-    See Also
-    --------
-    :class:`libpysal.weights.weights.W`
-    """
-
-
[docs]    def __init__(self, polygons, **kw):
-        criterion = "queen"
-        ids = kw.pop("ids", None)
-        polygons, backup = itertools.tee(polygons)
-        first_shape = next(iter(backup))
-        if isinstance(first_shape, point_type):
-            polygons, vertices = voronoi_frames(get_points_array(polygons))
-            polygons = list(polygons.geometry)
-        neighbors, ids = _build(polygons, criterion=criterion, ids=ids)
-        W.__init__(self, neighbors, ids=ids, **kw)

-
-
[docs]    @classmethod
-    def from_shapefile(cls, filepath, idVariable=None, full=False, **kwargs):
-        """
-        Queen contiguity weights from a polygon shapefile.
-
-        Parameters
-        ----------
-
-        shapefile   : string
-                      name of polygon shapefile including suffix.
-        idVariable  : string
-                      name of a column in the shapefile's DBF to use for ids.
-        sparse      : boolean
-                      If True return WSP instance
-                      If False return W instance
-        Returns
-        -------
-
-        w            : W
-                       instance of spatial weights
-
-        Examples
-        --------
-        >>> from libpysal.weights import Queen
-        >>> import libpysal
-        >>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"))
-        >>> "%.3f"%wq.pct_nonzero
-        '9.829'
-        >>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"),"POLYID")
-        >>> "%.3f"%wq.pct_nonzero
-        '9.829'
-        >>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"), sparse=True)
-        >>> pct_sp = wq.sparse.nnz *1. / wq.n**2
-        >>> "%.3f"%pct_sp
-        '0.098'
-
-        Notes
-
-        Queen contiguity defines as neighbors any pair of polygons that share at
-        least one vertex in their polygon definitions.
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.contiguity.Queen`
-        """
-        sparse = kwargs.pop("sparse", False)
-        if idVariable is not None:
-            ids = get_ids(filepath, idVariable)
-        else:
-            ids = None
-        w = cls(FileIO(filepath), ids=ids, **kwargs)
-        w.set_shapefile(filepath, idVariable=idVariable, full=full)
-        if sparse:
-            w = w.to_WSP()
-        return w

-
-
[docs]    @classmethod
-    def from_iterable(cls, iterable, sparse=False, **kwargs):
-        """
-        Construct a weights object from a collection of arbitrary polygons. This
-        will cast the polygons to PySAL polygons, then build the W.
-
-        Parameters
-        ----------
-        iterable    : iterable
-                      a collection of of shapes to be cast to PySAL shapes. Must
-                      support iteration. Contents may either be a shapely or PySAL shape.
-        **kw        : keyword arguments
-                      optional arguments for  :class:`pysal.weights.W`
-        See Also
-        ---------
-        :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.contiguiyt.Queen`
-        """
-        new_iterable = iter(iterable)
-        w = cls(new_iterable, **kwargs)
-        if sparse:
-            w = WSP.from_W(w)
-        return w

-
-
[docs]    @classmethod
-    def from_dataframe(cls, df, geom_col=None, **kwargs):
-        """
-        Construct a weights object from a pandas dataframe with a geometry
-        column. This will cast the polygons to PySAL polygons, then build the W
-        using ids from the dataframe.
-
-        Parameters
-        ----------
-        df          : DataFrame
-                      a :class: `pandas.DataFrame` containing geometries to use
-                      for spatial weights
-        geom_col    : string
-                      the name of the column in `df` that contains the
-                      geometries. Defaults to active geometry column
-        idVariable  : string
-                      the name of the column to use as IDs. If nothing is
-                      provided, the dataframe index is used
-        ids         : list
-                      a list of ids to use to index the spatial weights object.
-                      Order is not respected from this list.
-        id_order    : list
-                      an ordered list of ids to use to index the spatial weights
-                      object. If used, the resulting weights object will iterate
-                      over results in the order of the names provided in this
-                      argument. 
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.contiguity.Queen`
-        """
-        idVariable = kwargs.pop("idVariable", None)
-        ids = kwargs.pop("ids", None)
-        id_order = kwargs.pop("id_order", None)
-        if geom_col is None:
-            geom_col = df.geometry.name
-        if id_order is not None:
-            if id_order is True and ((idVariable is not None) or (ids is not None)):
-                # if idVariable is None, we want ids. Otherwise, we want the
-                # idVariable column
-                ids = list(df.get(idVariable, ids))
-                id_order = ids
-            elif isinstance(id_order, str):
-                ids = df.get(id_order, ids)
-                id_order = ids
-        elif idVariable is not None:
-            ids = df.get(idVariable).tolist()
-        elif isinstance(ids, str):
-            ids = df.get(ids).tolist()
-        w = cls.from_iterable(
-            df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
-        )
-        return w

-
-
[docs]    @classmethod
-    def from_xarray(
-        cls,
-        da,
-        z_value=None,
-        coords_labels={},
-        k=1,
-        include_nodata=False,
-        n_jobs=1,
-        sparse=True,
-        **kwargs,
-    ):
-        """
-        Construct a weights object from a xarray.DataArray with an additional
-        attribute index containing coordinate values of the raster
-        in the form of Pandas.Index/MultiIndex.
-
-        Parameters
-        ----------
-        da : xarray.DataArray
-            Input 2D or 3D DataArray with shape=(z, y, x)
-        z_value : int/string/float
-            Select the z_value of 3D DataArray with multiple layers.
-        coords_labels : dictionary
-            Pass dimension labels for coordinates and layers if they do not
-            belong to default dimensions, which are (band/time, y/lat, x/lon)
-            e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"}
-            Default is {} empty dictionary.
-        sparse : boolean
-            type of weight object. Default is True. For libpysal.weights.W, sparse = False
-        k : int
-            Order of contiguity, this will select all neighbors upto kth order.
-            Default is 1.
-        include_nodata : boolean
-            If True, missing values will be assumed as non-missing when
-            selecting higher_order neighbors, Default is False
-        n_jobs : int
-            Number of cores to be used in the sparse weight construction. If -1,
-            all available cores are used. Default is 1.
-        **kwargs : keyword arguments
-            optional arguments passed when sparse = False
-
-        Returns
-        -------
-        w : libpysal.weights.W/libpysal.weights.WSP
-            instance of spatial weights class W or WSP with an index attribute 
-
-        Notes
-        -----
-        1. Lower order contiguities are also selected.
-        2. Returned object contains `index` attribute that includes a 
-        `Pandas.MultiIndex` object from the DataArray.
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.weights.WSP`
-        """
-        if sparse:
-            w = da2WSP(da, "queen", z_value, coords_labels, k, include_nodata)
-        else:
-            w = da2W(da, "queen", z_value, coords_labels, k, include_nodata, **kwargs)
-        return w

-
-
-
[docs]def Voronoi(points, criterion="rook", clip="ahull", **kwargs):
-    """
-    Voronoi weights for a 2-d point set
-
-
-    Points are Voronoi neighbors if their polygons share an edge or vertex.
-
-
-    Parameters
-    ----------
-
-    points      : array
-                  (n,2)
-                  coordinates for point locations
-    kwargs      : arguments to pass to Rook, the underlying contiguity class.
-
-    Returns
-    -------
-
-    w           : W
-                  instance of spatial weights
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from libpysal.weights import Voronoi
-    >>> np.random.seed(12345)
-    >>> points= np.random.random((5,2))*10 + 10
-    >>> w = Voronoi(points)
-    >>> w.neighbors
-    {0: [2, 3, 4], 1: [2], 2: [0, 1, 4], 3: [0, 4], 4: [0, 2, 3]}
-    """
-    from ..cg.voronoi import voronoi_frames
-
-    region_df, _ = voronoi_frames(points, clip=clip)
-    if criterion.lower() == "queen":
-        cls = Queen
-    elif criterion.lower() == "rook":
-        cls = Rook
-    else:
-        raise ValueError(
-            "Contiguity criterion {} not supported. "
-            'Only "rook" and "queen" are supported.'.format(criterion)
-        )
-    return cls.from_dataframe(region_df, **kwargs)

-
-
-def _from_dataframe(df, **kwargs):
-    """
-    Construct a voronoi contiguity weight directly from a dataframe. 
-    Note that if criterion='rook', this is identical to the delaunay
-    graph for the points. 
-
-    If the input dataframe is of any other geometry type than "Point",
-    a value error is raised. 
-
-    Arguments
-    ---------
-    df          :   pandas.DataFrame
-                    dataframe containing point geometries for a 
-                    voronoi diagram.
-
-    Returns
-    -------
-    w           :   W
-                    instance of spatial weights.
-    """
-    try:
-        x, y = df.geometry.x.values, df.geometry.y.values
-    except ValueError:
-        raise NotImplementedError(
-            "Voronoi weights are only"
-            " implemented for point geometries. "
-            "You may consider using df.centroid."
-        )
-    coords = numpy.column_stack((x, y))
-    return Voronoi(coords, **kwargs)
-
-
-Voronoi.from_dataframe = _from_dataframe
-
-
-def _build(polygons, criterion="rook", ids=None):
-    """
-    This is a developer-facing function to construct a spatial weights object. 
-
-    Parameters
-    ---------
-    polygons    : list
-                  list of pysal polygons to use to build contiguity
-    criterion   : string
-                  option of which kind of contiguity to build. Is either "rook" or "queen" 
-    ids         : list
-                  list of ids to use to index the neighbor dictionary
-
-    Returns
-    -------
-    tuple containing (neighbors, ids), where neighbors is a dictionary
-    describing contiguity relations and ids is the list of ids used to index
-    that dictionary. 
-
-    NOTE: this is different from the prior behavior of buildContiguity, which
-          returned an actual weights object. Since this just dispatches for the
-          classes above, this returns the raw ingredients for a spatial weights
-          object, not the object itself. 
-    """
-    if ids and len(ids) != len(set(ids)):
-        raise ValueError(
-            "The argument to the ids parameter contains duplicate entries."
-        )
-
-    wttype = WT_TYPE[criterion.lower()]
-    geo = polygons
-    if issubclass(type(geo), FileIO):
-        geo.seek(0)  # Make sure we read from the beginning of the file.
-
-    neighbor_data = ContiguityWeightsLists(polygons, wttype=wttype).w
-
-    neighbors = {}
-    # weights={}
-    if ids:
-        for key in neighbor_data:
-            ida = ids[key]
-            if ida not in neighbors:
-                neighbors[ida] = set()
-            neighbors[ida].update([ids[x] for x in neighbor_data[key]])
-        for key in neighbors:
-            neighbors[key] = set(neighbors[key])
-    else:
-        for key in neighbor_data:
-            neighbors[key] = set(neighbor_data[key])
-    return (
-        dict(
-            list(zip(list(neighbors.keys()), list(map(list, list(neighbors.values())))))
-        ),
-        ids,
-    )
-
-
-def buildContiguity(polygons, criterion="rook", ids=None):
-    """
-    This is a deprecated function.
-
-    It builds a contiguity W from the polygons provided. As such, it is now
-    identical to calling the class constructors for Rook or Queen. 
-    """
-    # Warn('This function is deprecated. Please use the Rook or Queen classes',
-    #        UserWarning)
-    if criterion.lower() == "rook":
-        return Rook(polygons, ids=ids)
-    elif criterion.lower() == "queen":
-        return Queen(polygons, ids=ids)
-    else:
-        raise Exception('Weights criterion "{}" was not found.'.format(criterion))
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/weights/distance.html b/docs/_modules/libpysal/weights/distance.html deleted file mode 100644 index 5858a82fe..000000000 --- a/docs/_modules/libpysal/weights/distance.html +++ /dev/null @@ -1,1098 +0,0 @@ - - - - - - - libpysal.weights.distance — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.weights.distance

-__all__ = ["KNN", "Kernel", "DistanceBand"]
-__author__ = "Sergio J. Rey <srey@asu.edu>, Levi John Wolf <levi.john.wolf@gmail.com>"
-
-
-from ..cg.kdtree import KDTree
-from .weights import W, WSP
-from .util import (
-    isKDTree,
-    get_ids,
-    get_points_array_from_shapefile,
-    get_points_array,
-    WSP2W,
-)
-
-import copy
-from warnings import warn as Warn
-from scipy.spatial import distance_matrix
-import scipy.sparse as sp
-import numpy as np
-
-
-def knnW(data, k=2, p=2, ids=None, radius=None, distance_metric="euclidean"):
-    """
-    This is deprecated. Use the pysal.weights.KNN class instead.
-    """
-    # Warn('This function is deprecated. Please use pysal.weights.KNN', UserWarning)
-    return KNN(data, k=k, p=p, ids=ids, radius=radius, distance_metric=distance_metric)
-
-
-
[docs]class KNN(W):
-    """
-    Creates nearest neighbor weights matrix based on k nearest
-    neighbors.
-
-    Parameters
-    ----------
-    kdtree      : object
-                  PySAL KDTree or ArcKDTree where KDtree.data is array (n,k)
-                  n observations on k characteristics used to measure
-                  distances between the n objects
-    k           : int
-                  number of nearest neighbors
-    p           : float
-                  Minkowski p-norm distance metric parameter:
-                  1<=p<=infinity
-                  2: Euclidean distance
-                  1: Manhattan distance
-                  Ignored if the KDTree is an ArcKDTree
-    ids         : list
-                  identifiers to attach to each observation
-
-    Returns
-    -------
-
-    w         : W
-                instance
-                Weights object with binary weights
-
-    Examples
-    --------
-    >>> import libpysal
-    >>> import numpy as np
-    >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
-    >>> kd = libpysal.cg.KDTree(np.array(points))
-    >>> wnn2 = libpysal.weights.KNN(kd, 2)
-    >>> [1,3] == wnn2.neighbors[0]
-    True
-    >>> wnn2 = KNN(kd,2)
-    >>> wnn2[0]
-    {1: 1.0, 3: 1.0}
-    >>> wnn2[1]
-    {0: 1.0, 3: 1.0}
-
-    now with 1 rather than 0 offset
-
-    >>> wnn2 = libpysal.weights.KNN(kd, 2, ids=range(1,7))
-    >>> wnn2[1]
-    {2: 1.0, 4: 1.0}
-    >>> wnn2[2]
-    {1: 1.0, 4: 1.0}
-    >>> 0 in wnn2.neighbors
-    False
-
-    Notes
-    -----
-
-    Ties between neighbors of equal distance are arbitrarily broken.
-
-    See Also
-    --------
-    :class:`libpysal.weights.weights.W`
-    """
-
-
[docs]    def __init__(
-        self,
-        data,
-        k=2,
-        p=2,
-        ids=None,
-        radius=None,
-        distance_metric="euclidean",
-        **kwargs
-    ):
-        if radius is not None:
-            distance_metric = "arc"
-        if isKDTree(data):
-            self.kdtree = data
-            self.data = self.kdtree.data
-        else:
-            self.kdtree = KDTree(data, radius=radius, distance_metric=distance_metric)
-            self.data = self.kdtree.data
-        self.k = k
-        self.p = p
-        this_nnq = self.kdtree.query(self.data, k=k + 1, p=p)
-
-        to_weight = this_nnq[1]
-        if ids is None:
-            ids = list(range(to_weight.shape[0]))
-
-        neighbors = {}
-        for i, row in enumerate(to_weight):
-            row = row.tolist()
-            row.remove(i)
-            row = [ids[j] for j in row]
-            focal = ids[i]
-            neighbors[focal] = row
-        W.__init__(self, neighbors, id_order=ids, **kwargs)

-
-
[docs]    @classmethod
-    def from_shapefile(cls, filepath, *args, **kwargs):
-        """
-        Nearest neighbor weights from a shapefile.
-
-        Parameters
-        ----------
-
-        data       : string
-                     shapefile containing attribute data.
-        k          : int
-                     number of nearest neighbors
-        p          : float
-                     Minkowski p-norm distance metric parameter:
-                     1<=p<=infinity
-                     2: Euclidean distance
-                     1: Manhattan distance
-        ids        : list
-                     identifiers to attach to each observation
-        radius     : float
-                     If supplied arc_distances will be calculated
-                     based on the given radius. p will be ignored.
-
-        Returns
-        -------
-
-        w         : KNN
-                    instance; Weights object with binary weights.
-
-        Examples
-        --------
-
-        Polygon shapefile
-        >>> import libpysal
-        >>> from libpysal.weights import KNN
-        >>> wc=KNN.from_shapefile(libpysal.examples.get_path("columbus.shp"))
-        >>> "%.4f"%wc.pct_nonzero
-        '4.0816'
-        >>> set([2,1]) == set(wc.neighbors[0])
-        True
-        >>> wc3=KNN.from_shapefile(libpysal.examples.get_path("columbus.shp"),k=3)
-        >>> set(wc3.neighbors[0]) == set([2,1,3])
-        True
-        >>> set(wc3.neighbors[2]) == set([4,3,0])
-        True
-
-
-        Point shapefile
-
-        >>> w=KNN.from_shapefile(libpysal.examples.get_path("juvenile.shp"))
-        >>> w.pct_nonzero
-        1.1904761904761905
-        >>> w1=KNN.from_shapefile(libpysal.examples.get_path("juvenile.shp"),k=1)
-        >>> "%.3f"%w1.pct_nonzero
-        '0.595'
-
-        Notes
-        -----
-
-        Ties between neighbors of equal distance are arbitrarily broken.
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        """
-        return cls(get_points_array_from_shapefile(filepath), *args, **kwargs)

-
-
[docs]    @classmethod
-    def from_array(cls, array, *args, **kwargs):
-        """
-        Creates nearest neighbor weights matrix based on k nearest
-        neighbors.
-
-        Parameters
-        ----------
-        array       : np.ndarray
-                      (n, k) array representing n observations on
-                      k characteristics used to measure distances
-                      between the n objects
-        **kwargs    : keyword arguments, see Rook
-
-        Returns
-        -------
-        w         : W
-                    instance
-                    Weights object with binary weights
-
-        Examples
-        --------
-        >>> from libpysal.weights import KNN
-        >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
-        >>> wnn2 = KNN.from_array(points, 2)
-        >>> [1,3] == wnn2.neighbors[0]
-        True
-        >>> wnn2 = KNN.from_array(points,2)
-        >>> wnn2[0]
-        {1: 1.0, 3: 1.0}
-        >>> wnn2[1]
-        {0: 1.0, 3: 1.0}
-
-        now with 1 rather than 0 offset
-
-        >>> wnn2 = KNN.from_array(points, 2, ids=range(1,7))
-        >>> wnn2[1]
-        {2: 1.0, 4: 1.0}
-        >>> wnn2[2]
-        {1: 1.0, 4: 1.0}
-        >>> 0 in wnn2.neighbors
-        False
-
-        Notes
-        -----
-
-        Ties between neighbors of equal distance are arbitrarily broken.
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        """
-        return cls(array, *args, **kwargs)

-
-
[docs]    @classmethod
-    def from_dataframe(cls, df, geom_col=None, ids=None, *args, **kwargs):
-        """
-        Make KNN weights from a dataframe.
-
-        Parameters
-        ----------
-        df      :   pandas.dataframe
-                    a dataframe with a geometry column that can be used to
-                    construct a W object
-        geom_col :  string
-                    the name of the column in `df` that contains the
-                    geometries. Defaults to active geometry column.
-        ids     :   string or iterable
-                    if string, the column name of the indices from the dataframe
-                    if iterable, a list of ids to use for the W
-                    if None, df.index is used.
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        """
-        if geom_col is None:
-            geom_col = df.geometry.name
-        pts = get_points_array(df[geom_col])
-        if ids is None:
-            ids = df.index.tolist()
-        elif isinstance(ids, str):
-            ids = df[ids].tolist()
-        return cls(pts, *args, ids=ids, **kwargs)

-
-
[docs]    def reweight(self, k=None, p=None, new_data=None, new_ids=None, inplace=True):
-        """
-        Redo K-Nearest Neighbor weights construction using given parameters
-
-        Parameters
-        ----------
-        new_data    : np.ndarray
-                      an array containing additional data to use in the KNN
-                      weight
-        new_ids     : list
-                      a list aligned with new_data that provides the ids for
-                      each new observation
-        inplace     : bool
-                      a flag denoting whether to modify the KNN object
-                      in place or to return a new KNN object
-        k           : int
-                      number of nearest neighbors
-        p           : float
-                      Minkowski p-norm distance metric parameter:
-                      1<=p<=infinity
-                      2: Euclidean distance
-                      1: Manhattan distance
-                      Ignored if the KDTree is an ArcKDTree
-
-        Returns
-        -------
-        A copy of the object using the new parameterization, or None if the
-        object is reweighted in place.
-        """
-
-        if new_data is not None:
-            new_data = np.asarray(new_data).reshape(-1, 2)
-            data = np.vstack((self.data, new_data)).reshape(-1, 2)
-            if new_ids is not None:
-                ids = copy.deepcopy(self.id_order)
-                ids.extend(list(new_ids))
-            else:
-                ids = list(range(data.shape[0]))
-        elif (new_data is None) and (new_ids is None):
-            # If not, we can use the same kdtree we have
-            data = self.kdtree
-            ids = self.id_order
-        elif (new_data is None) and (new_ids is not None):
-            Warn("Remapping ids must be done using w.remap_ids")
-        if k is None:
-            k = self.k
-        if p is None:
-            p = self.p
-        if inplace:
-            self._reset()
-            self.__init__(data, ids=ids, k=k, p=p)
-        else:
-            return KNN(data, ids=ids, k=k, p=p)

-
-
-
[docs]class Kernel(W):
-    """
-    Spatial weights based on kernel functions.
-
-    Parameters
-    ----------
-
-    data        : array
-                  (n,k) or KDTree where KDtree.data is array (n,k)
-                  n observations on k characteristics used to measure
-                  distances between the n objects
-    bandwidth   : float
-                  or array-like (optional)
-                  the bandwidth :math:`h_i` for the kernel.
-    fixed       : binary
-                  If true then :math:`h_i=h \\forall i`. If false then
-                  bandwidth is adaptive across observations.
-    k           : int
-                  the number of nearest neighbors to use for determining
-                  bandwidth. For fixed bandwidth, :math:`h_i=max(dknn) \\forall i`
-                  where :math:`dknn` is a vector of k-nearest neighbor
-                  distances (the distance to the kth nearest neighbor for each
-                  observation).  For adaptive bandwidths, :math:`h_i=dknn_i`
-    diagonal    : boolean
-                  If true, set diagonal weights = 1.0, if false (default),
-                  diagonals weights are set to value according to kernel
-                  function.
-    function    : {'triangular','uniform','quadratic','quartic','gaussian'}
-                  kernel function defined as follows with
-
-                  .. math::
-
-                      z_{i,j} = d_{i,j}/h_i
-
-                  triangular
-
-                  .. math::
-
-                      K(z) = (1 - |z|) \\ if |z| \\le 1
-
-                  uniform
-
-                  .. math::
-
-                      K(z) = 1/2 \\ if |z| \\le 1
-
-                  quadratic
-
-                  .. math::
-
-                      K(z) = (3/4)(1-z^2) \\ if |z| \\le 1
-
-                  quartic
-
-                  .. math::
-
-                      K(z) = (15/16)(1-z^2)^2 \\ if |z| \\le 1
-
-                  gaussian
-
-                  .. math::
-
-                      K(z) = (2\\pi)^{(-1/2)} exp(-z^2 / 2)
-
-    eps         : float
-                  adjustment to ensure knn distance range is closed on the
-                  knnth observations
-
-    Attributes
-    ----------
-    weights : dict
-              Dictionary keyed by id with a list of weights for each neighbor
-
-    neighbors : dict
-                of lists of neighbors keyed by observation id
-
-    bandwidth : array
-                array of bandwidths
-
-    Examples
-    --------
-    >>> from libpysal.weights import Kernel
-    >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
-    >>> kw=Kernel(points)
-    >>> kw.weights[0]
-    [1.0, 0.500000049999995, 0.4409830615267465]
-    >>> kw.neighbors[0]
-    [0, 1, 3]
-    >>> kw.bandwidth
-    array([[20.000002],
-           [20.000002],
-           [20.000002],
-           [20.000002],
-           [20.000002],
-           [20.000002]])
-    >>> kw15=Kernel(points,bandwidth=15.0)
-    >>> kw15[0]
-    {0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}
-    >>> kw15.neighbors[0]
-    [0, 1, 3]
-    >>> kw15.bandwidth
-    array([[15.],
-           [15.],
-           [15.],
-           [15.],
-           [15.],
-           [15.]])
-
-    Adaptive bandwidths user specified
-
-    >>> bw=[25.0,15.0,25.0,16.0,14.5,25.0]
-    >>> kwa=Kernel(points,bandwidth=bw)
-    >>> kwa.weights[0]
-    [1.0, 0.6, 0.552786404500042, 0.10557280900008403]
-    >>> kwa.neighbors[0]
-    [0, 1, 3, 4]
-    >>> kwa.bandwidth
-    array([[25. ],
-           [15. ],
-           [25. ],
-           [16. ],
-           [14.5],
-           [25. ]])
-
-    Endogenous adaptive bandwidths
-
-    >>> kwea=Kernel(points,fixed=False)
-    >>> kwea.weights[0]
-    [1.0, 0.10557289844279438, 9.99999900663795e-08]
-    >>> kwea.neighbors[0]
-    [0, 1, 3]
-    >>> kwea.bandwidth
-    array([[11.18034101],
-           [11.18034101],
-           [20.000002  ],
-           [11.18034101],
-           [14.14213704],
-           [18.02775818]])
-
-    Endogenous adaptive bandwidths with Gaussian kernel
-
-    >>> kweag=Kernel(points,fixed=False,function='gaussian')
-    >>> kweag.weights[0]
-    [0.3989422804014327, 0.2674190291577696, 0.2419707487162134]
-    >>> kweag.bandwidth
-    array([[11.18034101],
-           [11.18034101],
-           [20.000002  ],
-           [11.18034101],
-           [14.14213704],
-           [18.02775818]])
-
-    Diagonals to 1.0
-
-    >>> kq = Kernel(points,function='gaussian')
-    >>> kq.weights
-    {0: [0.3989422804014327, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 0.3989422804014327, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 0.3989422804014327, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 0.3989422804014327, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 0.3989422804014327, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 0.3989422804014327]}
-    >>> kqd = Kernel(points, function='gaussian', diagonal=True)
-    >>> kqd.weights
-    {0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]}
-
-    """
-
-
[docs]    def __init__(
-        self,
-        data,
-        bandwidth=None,
-        fixed=True,
-        k=2,
-        function="triangular",
-        eps=1.0000001,
-        ids=None,
-        diagonal=False,
-        distance_metric="euclidean",
-        radius=None,
-        **kwargs
-    ):
-        if radius is not None:
-            distance_metric = "arc"
-        if isKDTree(data):
-            self.kdtree = data
-            self.data = self.kdtree.data
-            data = self.data
-        else:
-            self.kdtree = KDTree(data, distance_metric=distance_metric, radius=radius)
-            self.data = self.kdtree.data
-        self.k = k + 1
-        self.function = function.lower()
-        self.fixed = fixed
-        self.eps = eps
-        if bandwidth:
-            try:
-                bandwidth = np.array(bandwidth)
-                bandwidth.shape = (len(bandwidth), 1)
-            except:
-                bandwidth = np.ones((len(data), 1), "float") * bandwidth
-            self.bandwidth = bandwidth
-        else:
-            self._set_bw()
-
-        self._eval_kernel()
-        neighbors, weights = self._k_to_W(ids)
-        if diagonal:
-            for i in neighbors:
-                weights[i][neighbors[i].index(i)] = 1.0
-        W.__init__(self, neighbors, weights, ids, **kwargs)

-
-
[docs]    @classmethod
-    def from_shapefile(cls, filepath, idVariable=None, **kwargs):
-        """
-        Kernel based weights from shapefile
-
-        Parameters
-        ----------
-        shapefile   : string
-                      shapefile name with shp suffix
-        idVariable  : string
-                      name of column in shapefile's DBF to use for ids
-
-        Returns
-        -------
-        Kernel Weights Object
-
-        See Also
-        ---------
-        :class:`libpysal.weights.weights.W`
-        """
-        points = get_points_array_from_shapefile(filepath)
-        if idVariable is not None:
-            ids = get_ids(filepath, idVariable)
-        else:
-            ids = None
-        return cls.from_array(points, ids=ids, **kwargs)

-
-
[docs]    @classmethod
-    def from_array(cls, array, **kwargs):
-        """
-        Construct a Kernel weights from an array. Supports all the same options
-        as :class:`libpysal.weights.Kernel`
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        """
-        return cls(array, **kwargs)

-
-
[docs]    @classmethod
-    def from_dataframe(cls, df, geom_col=None, ids=None, **kwargs):
-        """
-        Make Kernel weights from a dataframe.
-
-        Parameters
-        ----------
-        df      :   pandas.dataframe
-                    a dataframe with a geometry column that can be used to
-                    construct a W object
-        geom_col :  string
-                    the name of the column in `df` that contains the
-                    geometries. Defaults to active geometry column.
-        ids     :   string or iterable
-                    if string, the column name of the indices from the dataframe
-                    if iterable, a list of ids to use for the W
-                    if None, df.index is used.
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        """
-        if geom_col is None:
-            geom_col = df.geometry.name
-        pts = get_points_array(df[geom_col])
-        if ids is None:
-            ids = df.index.tolist()
-        elif isinstance(ids, str):
-            ids = df[ids].tolist()
-        return cls(pts, ids=ids, **kwargs)

-
-    def _k_to_W(self, ids=None):
-        allneighbors = {}
-        weights = {}
-        if ids:
-            ids = np.array(ids)
-        else:
-            ids = np.arange(len(self.data))
-        for i, neighbors in enumerate(self.kernel):
-            if len(self.neigh[i]) == 0:
-                allneighbors[ids[i]] = []
-                weights[ids[i]] = []
-            else:
-                allneighbors[ids[i]] = list(ids[self.neigh[i]])
-                weights[ids[i]] = self.kernel[i].tolist()
-        return allneighbors, weights
-
-    def _set_bw(self):
-        dmat, neigh = self.kdtree.query(self.data, k=self.k)
-        if self.fixed:
-            # use max knn distance as bandwidth
-            bandwidth = dmat.max() * self.eps
-            n = len(dmat)
-            self.bandwidth = np.ones((n, 1), "float") * bandwidth
-        else:
-            # use local max knn distance
-            self.bandwidth = dmat.max(axis=1) * self.eps
-            self.bandwidth.shape = (self.bandwidth.size, 1)
-            # identify knn neighbors for each point
-            nnq = self.kdtree.query(self.data, k=self.k)
-            self.neigh = nnq[1]
-
-    def _eval_kernel(self):
-        # get points within bandwidth distance of each point
-        if not hasattr(self, "neigh"):
-            kdtq = self.kdtree.query_ball_point
-            neighbors = [
-                kdtq(self.data[i], r=bwi[0]) for i, bwi in enumerate(self.bandwidth)
-            ]
-            self.neigh = neighbors
-        # get distances for neighbors
-        bw = self.bandwidth
-
-        kdtq = self.kdtree.query
-        z = []
-        for i, nids in enumerate(self.neigh):
-            di, ni = kdtq(self.data[i], k=len(nids))
-            if not isinstance(di, np.ndarray):
-                di = np.asarray([di] * len(nids))
-                ni = np.asarray([ni] * len(nids))
-            zi = np.array([dict(list(zip(ni, di)))[nid] for nid in nids]) / bw[i]
-            z.append(zi)
-        zs = z
-        # functions follow Anselin and Rey (2010) table 5.4
-        if self.function == "triangular":
-            self.kernel = [1 - zi for zi in zs]
-        elif self.function == "uniform":
-            self.kernel = [np.ones(zi.shape) * 0.5 for zi in zs]
-        elif self.function == "quadratic":
-            self.kernel = [(3.0 / 4) * (1 - zi ** 2) for zi in zs]
-        elif self.function == "quartic":
-            self.kernel = [(15.0 / 16) * (1 - zi ** 2) ** 2 for zi in zs]
-        elif self.function == "gaussian":
-            c = np.pi * 2
-            c = c ** (-0.5)
-            self.kernel = [c * np.exp(-(zi ** 2) / 2.0) for zi in zs]
-        else:
-            print(("Unsupported kernel function", self.function))

-
-
-
[docs]class DistanceBand(W):
-    """
-    Spatial weights based on distance band.
-
-    Parameters
-    ----------
-
-    data        : array
-                  (n,k) or KDTree where KDtree.data is array (n,k)
-                  n observations on k characteristics used to measure
-                  distances between the n objects
-    threshold  : float
-                 distance band
-    p          : float
-                 Minkowski p-norm distance metric parameter:
-                 1<=p<=infinity
-                 2: Euclidean distance
-                 1: Manhattan distance
-    binary     : boolean
-                 If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0
-                 If false wij=dij^{alpha}
-    alpha      : float
-                 distance decay parameter for weight (default -1.0)
-                 if alpha is positive the weights will not decline with
-                 distance. If binary is True, alpha is ignored
-
-    ids         : list
-                  values to use for keys of the neighbors and weights dicts
-
-    build_sp    : boolean
-                  True to build sparse distance matrix and false to build dense
-                  distance matrix; significant speed gains may be obtained
-                  dending on the sparsity of the of distance_matrix and
-                  threshold that is applied
-    silent      : boolean
-                  By default libpysal will print a warning if the
-                  dataset contains any disconnected observations or
-                  islands. To silence this warning set this
-                  parameter to True.
-
-    Attributes
-    ----------
-    weights : dict
-              of neighbor weights keyed by observation id
-
-    neighbors : dict
-                of neighbors keyed by observation id
-
-    Examples
-    --------
-    >>> import libpysal
-    >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
-    >>> wcheck = libpysal.weights.W({0: [1, 3], 1: [0, 3], 2: [], 3: [0, 1], 4: [5], 5: [4]})
-
-    WARNING: there is one disconnected observation (no neighbors)
-    Island id:  [2]
-    >>> w=libpysal.weights.DistanceBand(points,threshold=11.2)
-
-    WARNING: there is one disconnected observation (no neighbors)
-    Island id:  [2]
-    >>> libpysal.weights.util.neighbor_equality(w, wcheck)
-    True
-    >>> w=libpysal.weights.DistanceBand(points,threshold=14.2)
-    >>> wcheck = libpysal.weights.W({0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 2, 1], 5: [4]})
-    >>> libpysal.weights.util.neighbor_equality(w, wcheck)
-    True
-
-    inverse distance weights
-
-    >>> w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False)
-
-    WARNING: there is one disconnected observation (no neighbors)
-    Island id:  [2]
-    >>> w.weights[0]
-    [0.1, 0.08944271909999159]
-    >>> w.neighbors[0].tolist()
-    [1, 3]
-
-    gravity weights
-
-    >>> w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False,alpha=-2.)
-
-    WARNING: there is one disconnected observation (no neighbors)
-    Island id:  [2]
-    >>> w.weights[0]
-    [0.01, 0.007999999999999998]
-
-    Notes
-    -----
-
-    This was initially implemented running scipy 0.8.0dev (in epd 6.1).
-    earlier versions of scipy (0.7.0) have a logic bug in scipy/sparse/dok.py
-    so serge changed line 221 of that file on sal-dev to fix the logic bug.
-
-    """
-
-
[docs]    def __init__(
-        self,
-        data,
-        threshold,
-        p=2,
-        alpha=-1.0,
-        binary=True,
-        ids=None,
-        build_sp=True,
-        silence_warnings=False,
-        distance_metric="euclidean",
-        radius=None,
-    ):
-        """Casting to floats is a work around for a bug in scipy.spatial.
-        See detail in pysal issue #126.
-
-        """
-        if ids is not None:
-            ids = list(ids)
-        if radius is not None:
-            distance_metric = "arc"
-        self.p = p
-        self.threshold = threshold
-        self.binary = binary
-        self.alpha = alpha
-        self.build_sp = build_sp
-        self.silence_warnings = silence_warnings
-
-        if isKDTree(data):
-            self.kdtree = data
-            self.data = self.kdtree.data
-        else:
-            if self.build_sp:
-                try:
-                    data = np.asarray(data)
-                    if data.dtype.kind != "f":
-                        data = data.astype(float)
-                    self.kdtree = KDTree(
-                        data, distance_metric=distance_metric, radius=radius
-                    )
-                    self.data = self.kdtree.data
-                except:
-                    raise ValueError("Could not make array from data")
-            else:
-                self.data = data
-                self.kdtree = None
-        self._band()
-        neighbors, weights = self._distance_to_W(ids)
-        W.__init__(
-            self, neighbors, weights, ids, silence_warnings=self.silence_warnings
-        )

-
-
[docs]    @classmethod
-    def from_shapefile(cls, filepath, threshold, idVariable=None, **kwargs):
-        """
-        Distance-band based weights from shapefile
-
-        Parameters
-        ----------
-        shapefile   : string
-                      shapefile name with shp suffix
-        idVariable  : string
-                      name of column in shapefile's DBF to use for ids
-
-        Returns
-        --------
-        Kernel Weights Object
-
-        """
-        points = get_points_array_from_shapefile(filepath)
-        if idVariable is not None:
-            ids = get_ids(filepath, idVariable)
-        else:
-            ids = None
-        return cls.from_array(points, threshold, ids=ids, **kwargs)

-
-
[docs]    @classmethod
-    def from_array(cls, array, threshold, **kwargs):
-        """
-        Construct a DistanceBand weights from an array. Supports all the same options
-        as :class:`libpysal.weights.DistanceBand`
-
-        """
-        return cls(array, threshold, **kwargs)

-
-
[docs]    @classmethod
-    def from_dataframe(cls, df, threshold, geom_col=None, ids=None, **kwargs):
-        """
-        Make DistanceBand weights from a dataframe.
-
-        Parameters
-        ----------
-        df      :   pandas.dataframe
-                    a dataframe with a geometry column that can be used to
-                    construct a W object
-        geom_col :  string
-                    the name of the column in `df` that contains the
-                    geometries. Defaults to active geometry column.
-        ids     :   string or iterable
-                    if string, the column name of the indices from the dataframe
-                    if iterable, a list of ids to use for the W
-                    if None, df.index is used.
-
-        """
-        if geom_col is None:
-            geom_col = df.geometry.name
-        pts = get_points_array(df[geom_col])
-        if ids is None:
-            ids = df.index.tolist()
-        elif isinstance(ids, str):
-            ids = df[ids].tolist()
-        return cls(pts, threshold, ids=ids, **kwargs)

-
-    def _band(self):
-        """Find all pairs within threshold."""
-        if self.build_sp:
-            self.dmat = self.kdtree.sparse_distance_matrix(
-                self.kdtree, max_distance=self.threshold, p=self.p
-            ).tocsr()
-        else:
-            if str(self.kdtree).split(".")[-1][0:10] == "Arc_KDTree":
-                raise TypeError(
-                    "Unable to calculate dense arc distance matrix;"
-                    ' parameter "build_sp" must be set to True for arc'
-                    " distance type weight"
-                )
-            self.dmat = self._spdistance_matrix(self.data, self.data, self.threshold)
-
-    def _distance_to_W(self, ids=None):
-        if self.binary:
-            self.dmat[self.dmat > 0] = 1
-            self.dmat.eliminate_zeros()
-            tempW = WSP2W(
-                WSP(self.dmat, id_order=ids), silence_warnings=self.silence_warnings
-            )
-            neighbors = tempW.neighbors
-            weight_keys = list(tempW.weights.keys())
-            weight_vals = list(tempW.weights.values())
-            weights = dict(list(zip(weight_keys, list(map(list, weight_vals)))))
-            return neighbors, weights
-        else:
-            weighted = self.dmat.power(self.alpha)
-            weighted[weighted == np.inf] = 0
-            weighted.eliminate_zeros()
-            tempW = WSP2W(
-                WSP(weighted, id_order=ids), silence_warnings=self.silence_warnings
-            )
-            neighbors = tempW.neighbors
-            weight_keys = list(tempW.weights.keys())
-            weight_vals = list(tempW.weights.values())
-            weights = dict(list(zip(weight_keys, list(map(list, weight_vals)))))
-            return neighbors, weights
-
-    def _spdistance_matrix(self, x, y, threshold=None):
-        dist = distance_matrix(x, y)
-        if threshold is not None:
-            zeros = dist > threshold
-            dist[zeros] = 0
-        return sp.csr_matrix(dist)

-
-
-def _test():
-    import doctest
-
-    # the following line could be used to define an alternative to the '<BLANKLINE>' flag
-    # doctest.BLANKLINE_MARKER = 'something better than <BLANKLINE>'
-    start_suppress = np.get_printoptions()["suppress"]
-    np.set_printoptions(suppress=True)
-    doctest.testmod()
-    np.set_printoptions(suppress=start_suppress)
-
-
-if __name__ == "__main__":
-    _test()
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/weights/raster.html b/docs/_modules/libpysal/weights/raster.html deleted file mode 100644 index 573c49736..000000000 --- a/docs/_modules/libpysal/weights/raster.html +++ /dev/null @@ -1,983 +0,0 @@ - - - - - - - libpysal.weights.raster — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.weights.raster

-from .util import lat2SW
-from .weights import WSP, W
-import numpy as np
-from warnings import warn
-import os
-import sys
-from scipy import sparse
-
-if os.path.basename(sys.argv[0]) in ("pytest", "py.test"):
-
-    def jit(*dec_args, **dec_kwargs):
-        """
-        decorator mimicking numba.jit
-        """
-
-        def intercepted_function(f, *f_args, **f_kwargs):
-            return f
-
-        return intercepted_function
-
-
-else:
-    from ..common import jit
-
-__author__ = "Mragank Shekhar <yesthisismrshekhar@gmail.com>"
-
-__all__ = ["da2W", "da2WSP", "w2da", "wsp2da", "testDataArray"]
-
-
-
[docs]def da2W(
-    da,
-    criterion="queen",
-    z_value=None,
-    coords_labels={},
-    k=1,
-    include_nodata=False,
-    n_jobs=1,
-    **kwargs,
-):
-    """
-    Create a W object from xarray.DataArray with an additional
-    attribute index containing coordinate values of the raster
-    in the form of Pandas.Index/MultiIndex.
-
-    Parameters
-    ----------
-    da : xarray.DataArray
-        Input 2D or 3D DataArray with shape=(z, y, x)
-    criterion : {"rook", "queen"}
-        Type of contiguity. Default is queen.
-    z_value : int/string/float
-        Select the z_value of 3D DataArray with multiple layers.
-    coords_labels : dictionary
-        Pass dimension labels for coordinates and layers if they do not
-        belong to default dimensions, which are (band/time, y/lat, x/lon)
-        e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"}
-        Default is {} empty dictionary.
-    k : int
-        Order of contiguity, this will select all neighbors upto kth order.
-        Default is 1.
-    include_nodata : boolean
-        If True, missing values will be assumed as non-missing when
-        selecting higher_order neighbors, Default is False
-    n_jobs : int
-        Number of cores to be used in the sparse weight construction. If -1,
-        all available cores are used. Default is 1.
-    **kwargs : keyword arguments
-        Optional arguments for :class:`libpysal.weights.W`
-
-    Returns
-    -------
-    w : libpysal.weights.W
-       instance of spatial weights class W with an index attribute
-
-    Notes
-    -----
-    1. Lower order contiguities are also selected.
-    2. Returned object contains `index` attribute that includes a
-    `Pandas.MultiIndex` object from the DataArray.
-
-    Examples
-    --------
-
-    >>> from libpysal.weights.raster import da2W, testDataArray
-    >>> da = testDataArray().rename(
-            {'band': 'layer', 'x': 'longitude', 'y': 'latitude'})
-    >>> da.dims
-    ('layer', 'latitude', 'longitude')
-    >>> da.shape
-    (3, 4, 4)
-    >>> da.coords
-    Coordinates:
-        * layer      (layer) int64 1 2 3
-        * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0
-        * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0
-    >>> da.attrs
-    {'nodatavals': (-32768.0,)}
-    >>> coords_labels = {
-        "z_label": "layer",
-        "y_label": "latitude",
-        "x_label": "longitude"
-    }
-    >>> w = da2W(da, z_value=2, coords_labels=coords_labels)
-    >>> "%.3f"%w.pct_nonzero
-    '30.000'
-    >>> w[(2, 90.0, 180.0)] == {(2, 90.0, 60.0): 1, (2, 30.0, 180.0): 1}
-    True
-    >>> len(w.index)
-    10
-    >>> w.index[:2]
-    MultiIndex([(2, 90.0,  60.0),
-                (2, 90.0, 180.0)],
-               names=['layer', 'latitude', 'longitude'])
-
-    See Also
-    --------
-    :class:`libpysal.weights.weights.W`
-    """
-    warn(
-        "You are trying to build a full W object from "
-        "xarray.DataArray (raster) object. This computation "
-        "can be very slow and not scale well. It is recommended, "
-        "if possible, to instead build WSP object, which is more "
-        "efficient and faster. You can do this by using da2WSP method."
-    )
-    wsp = da2WSP(da, criterion, z_value, coords_labels, k, include_nodata, n_jobs)
-    w = wsp.to_W(**kwargs)
-
-    # temp addition of index attribute
-    w.index = wsp.index
-    return w

-
-
-
[docs]def da2WSP(
-    da,
-    criterion="queen",
-    z_value=None,
-    coords_labels={},
-    k=1,
-    include_nodata=False,
-    n_jobs=1,
-):
-    """
-    Create a WSP object from xarray.DataArray with an additional
-    attribute index containing coordinate values of the raster
-    in the form of Pandas.Index/MultiIndex.
-
-    Parameters
-    ----------
-    da : xarray.DataArray
-        Input 2D or 3D DataArray with shape=(z, y, x)
-    criterion : {"rook", "queen"}
-        Type of contiguity. Default is queen.
-    z_value : int/string/float
-        Select the z_value of 3D DataArray with multiple layers.
-    coords_labels : dictionary
-        Pass dimension labels for coordinates and layers if they do not
-        belong to default dimensions, which are (band/time, y/lat, x/lon)
-        e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"}
-        Default is {} empty dictionary.
-    k : int
-        Order of contiguity, this will select all neighbors upto kth order.
-        Default is 1.
-    include_nodata : boolean
-        If True, missing values will be assumed as non-missing when
-        selecting higher_order neighbors, Default is False
-    n_jobs : int
-        Number of cores to be used in the sparse weight construction. If -1,
-        all available cores are used. Default is 1.
-
-    Returns
-    -------
-    wsp : libpysal.weights.WSP
-       instance of spatial weights class WSP with an index attribute
-
-    Notes
-    -----
-    1. Lower order contiguities are also selected.
-    2. Returned object contains `index` attribute that includes a
-    `Pandas.MultiIndex` object from the DataArray.
-
-    Examples
-    --------
-    >>> from libpysal.weights.raster import da2WSP, testDataArray
-    >>> da = testDataArray().rename(
-            {'band': 'layer', 'x': 'longitude', 'y': 'latitude'})
-    >>> da.dims
-    ('layer', 'latitude', 'longitude')
-    >>> da.shape
-    (3, 4, 4)
-    >>> da.coords
-    Coordinates:
-        * layer      (layer) int64 1 2 3
-        * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0
-        * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0
-    >>> da.attrs
-    {'nodatavals': (-32768.0,)}
-    >>> coords_labels = {
-        "z_label": "layer",
-        "y_label": "latitude",
-        "x_label": "longitude"
-    }
-    >>> wsp = da2WSP(da, z_value=2, coords_labels=coords_labels)
-    >>> wsp.n
-    10
-    >>> pct_sp = wsp.sparse.nnz *1. / wsp.n**2
-    >>> "%.3f"%pct_sp
-    '0.300'
-    >>> print(wsp.sparse[4].todense())
-    [[0 0 1 0 0 1 1 1 0 0]]
-    >>> wsp.index[:2]
-    MultiIndex([(2, 90.0,  60.0),
-                (2, 90.0, 180.0)],
-               names=['layer', 'latitude', 'longitude'])
-
-    See Also
-    --------
-    :class:`libpysal.weights.weights.WSP`
-    """
-    z_id, coords_labels = _da_checker(da, z_value, coords_labels)
-    shape = da.shape
-    if z_id:
-        slice_dict = {}
-        slice_dict[coords_labels["z_label"]] = 0
-        shape = da[slice_dict].shape
-        slice_dict[coords_labels["z_label"]] = slice(z_id - 1, z_id)
-        da = da[slice_dict]
-
-    ser = da.to_series()
-    dtype = np.int32 if (shape[0] * shape[1]) < 46340 ** 2 else np.int64
-    if "nodatavals" in da.attrs and da.attrs["nodatavals"]:
-        mask = (ser != da.attrs["nodatavals"][0]).to_numpy()
-        ids = np.where(mask)[0]
-        id_map = _idmap(ids, mask, dtype)
-        ser = ser[ser != da.attrs["nodatavals"][0]]
-    else:
-        ids = np.arange(len(ser), dtype=dtype)
-        id_map = ids.copy()
-
-    n = len(ids)
-
-    try:
-        import numba
-    except (ModuleNotFoundError, ImportError):
-        warn(
-            "numba cannot be imported, parallel processing "
-            "and include_nodata functionality will be disabled. "
-            "falling back to slower method"
-        )
-        include_nodata = False
-        # Fallback method to build sparse matrix
-        sw = lat2SW(*shape, criterion)
-        if "nodatavals" in da.attrs and da.attrs["nodatavals"]:
-            sw = sw[mask]
-            sw = sw[:, mask]
-
-    else:
-        k_nas = k if include_nodata else 1
-
-        if n_jobs != 1:
-            try:
-                import joblib
-            except (ModuleNotFoundError, ImportError):
-                warn(
-                    f"Parallel processing is requested (n_jobs={n_jobs}),"
-                    f" but joblib cannot be imported. n_jobs will be set"
-                    f" to 1.",
-                    stacklevel=2,
-                )
-                n_jobs = 1
-
-        if n_jobs == 1:
-            sw_tup = _SWbuilder(
-                *shape, ids, id_map, criterion, k_nas, dtype
-            )  # -> (data, (row, col))
-        else:
-            if n_jobs == -1:
-                n_jobs = os.cpu_count()
-            # Parallel implementation
-            sw_tup = _parSWbuilder(
-                *shape, ids, id_map, criterion, k_nas, dtype, n_jobs
-            )  # -> (data, (row, col))
-
-        sw = sparse.csr_matrix(sw_tup, shape=(n, n), dtype=np.int8,)
-
-    # Higher_order functionality, this uses idea from
-    # libpysal#313 for adding higher order neighbors.
-    # Since diagonal elements are also added in the result,
-    # this method set the diagonal elements to zero and
-    # then eliminate zeros from the data. This changes the
-    # sparcity of the csr_matrix !!
-    if k > 1 and not include_nodata:
-        sw = sum(map(lambda x: sw ** x, range(1, k + 1)))
-        sw.setdiag(0)
-        sw.eliminate_zeros()
-        sw.data[:] = np.ones_like(sw.data, dtype=np.int8)
-
-    index = ser.index
-    wsp = WSP(sw, index=index)
-    return wsp

-
-
-
[docs]def w2da(data, w, attrs={}, coords=None):
-    """
-    Creates xarray.DataArray object from passed data aligned with W object.
-
-    Parameters
-    ---------
-    data : array/list/pd.Series
-        1d array-like data with dimensionality conforming to w
-    w : libpysal.weights.W
-        Spatial weights object aligned with passed data
-    attrs : Dictionary
-        Attributes stored in dict related to DataArray, e.g. da.attrs
-        Default is {} empty dictionary.
-    coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates
-        Coordinates corresponding to DataArray, e.g. da.coords
-
-    Returns
-    -------
-    da : xarray.DataArray
-        instance of xarray.DataArray
-
-    Examples
-    --------
-    >>> from libpysal.raster import da2W, testDataArray, w2da
-    >>> da = testDataArray()
-    >>> da.shape
-    (3, 4, 4)
-    >>> w = da2W(da, z_value=2)
-    >>> data = np.random.randint(0, 255, len(w.index))
-    >>> da1 = w2da(data, w)
-
-    """
-    if not isinstance(w, W):
-        raise TypeError("w must be an instance of weights.W")
-    if hasattr(w, "index"):
-        da = _index2da(data, w.index, attrs, coords)
-    else:
-        raise AttributeError(
-            "This method requires `w` object to include `index` attribute that is built as a `pandas.MultiIndex` object."
-        )
-    return da

-
-
-
[docs]def wsp2da(data, wsp, attrs={}, coords=None):
-    """
-    Creates xarray.DataArray object from passed data aligned with WSP object.
-
-    Parameters
-    ---------
-    data : array/list/pd.Series
-        1d array-like data with dimensionality conforming to wsp
-    wsp : libpysal.weights.WSP
-        Sparse weights object aligned with passed data
-    attrs : Dictionary
-        Attributes stored in dict related to DataArray, e.g. da.attrs
-        Default is {} empty dictionary.
-    coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates
-        coordinates corresponding to DataArray, e.g. da.coords
-
-    Returns
-    -------
-    da : xarray.DataArray
-        instance of xarray.DataArray
-
-    Examples
-    --------
-    >>> from libpysal.raster import da2WSP, testDataArray, wsp2da
-    >>> da = testDataArray()
-    >>> da.shape
-    (3, 4, 4)
-    >>> wsp = da2WSP(da, z_value=2)
-    >>> data = np.random.randint(0, 255, len(wsp.index))
-    >>> da1 = w2da(data, wsp)
-
-    """
-    if not isinstance(wsp, WSP):
-        raise TypeError("wsp must be an instance of weights.WSP")
-    if hasattr(wsp, "index"):
-        da = _index2da(data, wsp.index, attrs, coords)
-    else:
-        raise AttributeError(
-            "This method requires `wsp` object to include `index` attribute that is built as a `pandas.MultiIndex` object."
-        )
-    return da

-
-
-
[docs]def testDataArray(shape=(3, 4, 4), time=False, rand=False, missing_vals=True):
-    """
-    Creates 2 or 3 dimensional test xarray.DataArray object
-
-    Parameters
-    ---------
-    shape : tuple
-        Tuple containing shape of the DataArray aligned with
-        following dimension = (lat, lon) or (layer, lat, lon)
-        Default shape = (3, 4, 4)
-    time : boolean
-        Type of layer, if True then layer=time else layer=band
-        Default is False.
-    rand : boolean
-        If True, creates a DataArray filled with unique and random data.
-        Default is false (generates seeded random data)
-    missing_vals : boolean
-        Create a DataArray filled with missing values. Default is True.
-
-    Returns
-    -------
-    da : xarray.DataArray
-        instance of xarray.DataArray
-    """
-    try:
-        from xarray import DataArray
-    except ImportError:
-        raise ModuleNotFoundError("xarray must be installed to use this functionality")
-    if not rand:
-        np.random.seed(12345)
-    coords = {}
-    n = len(shape)
-    if n != 2:
-        layer = "time" if time else "band"
-        dims = (layer, "y", "x")
-        if time:
-            layers = np.arange(
-                np.datetime64("2020-07-30"), shape[0], dtype="datetime64[D]"
-            )
-        else:
-            layers = np.arange(1, shape[0] + 1)
-        coords[dims[-3]] = layers
-    else:
-        dims = ("y", "x")
-    coords[dims[-2]] = np.linspace(90, -90, shape[-2])
-    coords[dims[-1]] = np.linspace(-180, 180, shape[-1])
-    data = np.random.randint(0, 255, shape)
-    attrs = {}
-    if missing_vals:
-        attrs["nodatavals"] = (-32768.0,)
-        miss_ids = np.where(np.random.randint(2, size=shape) == 1)
-        data[miss_ids] = attrs["nodatavals"][0]
-    da = DataArray(data, coords, dims, attrs=attrs)
-    return da

-
-
-def _da_checker(da, z_value, coords_labels):
-    """
-    xarray.dataarray checker for raster interface
-
-    Parameters
-    ----------
-    da : xarray.DataArray
-        Input 2D or 3D DataArray with shape=(z, y, x)
-    z_value : int/string/float
-        Select the z_value of 3D DataArray with multiple layers.
-    coords_labels : dictionary
-        Pass dimension labels for coordinates and layers if they do not
-        belong to default dimensions, which are (band/time, y/lat, x/lon)
-        e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"}
-        Default is {} empty dictionary.
-
-    Returns
-    -------
-    z_id : int
-        Returns the index of layer
-    dims : dictionary
-        Mapped dimensions of the DataArray
-    """
-    try:
-        from xarray import DataArray
-    except ImportError:
-        raise ModuleNotFoundError("xarray must be installed to use this functionality")
-
-    if not isinstance(da, DataArray):
-        raise TypeError("da must be an instance of xarray.DataArray")
-    if da.ndim not in [2, 3]:
-        raise ValueError("da must be 2D or 3D")
-    if not (
-        np.issubdtype(da.values.dtype, np.integer)
-        or np.issubdtype(da.values.dtype, np.floating)
-    ):
-        raise ValueError("da must be an array of integers or float")
-
-    # default dimensions
-    def_labels = {
-        "x_label": coords_labels["x_label"]
-        if "x_label" in coords_labels
-        else ("x" if hasattr(da, "x") else "lon"),
-        "y_label": coords_labels["y_label"]
-        if "y_label" in coords_labels
-        else ("y" if hasattr(da, "y") else "lat"),
-    }
-
-    if da.ndim == 3:
-        def_labels["z_label"] = (
-            coords_labels["z_label"]
-            if "z_label" in coords_labels
-            else ("band" if hasattr(da, "band") else "time")
-        )
-
-        z_id = 1
-        if z_value is None:
-            if da.sizes[def_labels["z_label"]] != 1:
-                warn("Multiple layers detected. Using first layer as default.")
-        else:
-            z_id += tuple(da[def_labels["z_label"]]).index(z_value)
-    else:
-        z_id = None
-    return z_id, def_labels
-
-
-def _index2da(data, index, attrs, coords):
-    """
-    Creates xarray.DataArray object from passed data
-
-    Parameters
-    ---------
-    data : array/list/pd.Series
-        1d array-like data with dimensionality conforming to index
-    index : pd.MultiIndex
-        indices of the DataArray when converted to pd.Series
-    attrs : Dictionary
-        Attributes stored in dict related to DataArray, e.g. da.attrs
-    coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates
-        coordinates corresponding to DataArray, e.g. da[n-1:n].coords
-
-    Returns
-    -------
-    da : xarray.DataArray
-        instance of xarray.DataArray
-    """
-    try:
-        from xarray import DataArray
-    except ImportError:
-        raise ModuleNotFoundError("xarray must be installed to use this functionality")
-
-    data = np.array(data).flatten()
-    idx = index
-    dims = idx.names
-    indexer = tuple(idx.codes)
-    shape = tuple(lev.size for lev in idx.levels)
-
-    if coords is None:
-        missing = np.prod(shape) > idx.shape[0]
-        if missing:
-            if "nodatavals" in attrs:
-                fill_value = attrs["nodatavals"][0]
-            else:
-                min_data = np.min(data)
-                fill_value = min_data - 1 if min_data < 0 else -1
-                attrs["nodatavals"] = tuple([fill_value])
-            data_complete = np.full(shape, fill_value, data.dtype)
-        else:
-            data_complete = np.empty(shape, data.dtype)
-        data_complete[indexer] = data
-        coords = {}
-        for dim, lev in zip(dims, idx.levels):
-            coords[dim] = lev.to_numpy()
-    else:
-        fill = attrs["nodatavals"][0] if "nodatavals" in attrs else 0
-        data_complete = np.full(shape, fill, data.dtype)
-        data_complete[indexer] = data
-        data_complete = data_complete[:, ::-1]
-
-    da = DataArray(data_complete, coords=coords, dims=dims, attrs=attrs)
-    return da.sortby(dims[-2], False)
-
-
-@jit(nopython=True, fastmath=True)
-def _idmap(ids, mask, dtype):
-    """
-    Utility function computes id_map of non-missing raster data
-
-    Parameters
-    ----------
-    ids : ndarray
-        1D array containing ids of non-missing raster data
-    mask : ndarray
-        1D array mask array
-    dtype : type
-        Data type of the id_map array
-
-    Returns
-    -------
-    id_map : ndarray
-        1D array containing id_maps of non-missing raster data
-    """
-    id_map = mask * 1
-    id_map[ids] = np.arange(len(ids), dtype=dtype)
-    return id_map
-
-
-@jit(nopython=True, fastmath=True)
-def _SWbuilder(
-    nrows, ncols, ids, id_map, criterion, k, dtype,
-):
-    """
-    Computes data and orders rows, cols, data for a single chunk
-
-    Parameters
-    ----------
-    nrows : int
-        Number of rows in the raster data
-    ncols : int
-        Number of columns in the raster data
-    ids : ndarray
-        1D array containing ids of non-missing raster data
-    id_map : ndarray
-        1D array containing id_maps of non-missing raster data
-    criterion : str
-        Type of contiguity.
-    k : int
-        Order of contiguity, Default is 1
-    dtype : type
-        Data type of the id_map array
-
-    Returns
-    -------
-    data : ndarray
-        1D ones array containing weight of each neighbor
-    rows : ndarray
-        1D ones array containing row value of each id
-        in the sparse weight object
-    cols : ndarray
-        1D ones array containing columns value of each id
-        in the sparse weight object
-    """
-    rows, cols = _compute_chunk(nrows, ncols, ids, id_map, criterion, k, dtype)
-    data = np.ones_like(rows, dtype=np.int8)
-    return (data, (rows, cols))
-
-
-@jit(nopython=True, fastmath=True, nogil=True)
-def _compute_chunk(
-    nrows, ncols, ids, id_map, criterion, k, dtype,
-):
-    """
-    Computes rows cols for a single chunk
-
-    Parameters
-    ----------
-    nrows : int
-        Number of rows in the raster data
-    ncols : int
-        Number of columns in the raster data
-    ids : ndarray
-        1D array containing ids of non-missing raster data
-    id_map : ndarray
-        1D array containing id_maps of non-missing raster data
-    criterion : str
-        Type of contiguity.
-    k : int
-        Order of contiguity, Default is 1
-    dtype : type
-        Data type of the rows and cols array
-
-    Returns
-    -------
-    rows : ndarray
-        1D ones array containing row value of each id
-        in the sparse weight object
-    cols : ndarray
-        1D ones array containing columns value of each id
-        in the sparse weight object
-    ni : int
-        Number of rows and cols
-    """
-    n = len(ids)
-    # Setting d which is used for row, col preallocation
-    d = 4 if criterion == "rook" else 8
-    if k > 1:
-        d = int((k / 2) * (2 * 8 + (k - 1) * 8))
-    rows = np.empty(d * n, dtype=dtype)
-    cols = np.empty_like(rows)
-    ni = 0  # -> Pointer to store rows and cols in array
-    for order in range(1, k + 1):
-        condition = (
-            (order - 1)
-            if criterion == "queen"
-            else ((k - order) if ((k - order) < order) else (order - 1))
-        )
-        for i in range(n):
-            id_i = ids[i]
-            og_id = id_map[id_i]
-
-            if ((id_i + order) % ncols) >= order:
-                # east neighbor
-                id_neighbor = id_map[id_i + order]
-                if id_neighbor:
-                    rows[ni], cols[ni] = og_id, id_neighbor
-                    ni += 1
-                    rows[ni], cols[ni] = id_neighbor, og_id
-                    ni += 1
-                # north-east to south-east neighbors
-                for j in range(condition):
-                    if (id_i // ncols) < (nrows - j - 1):
-                        id_neighbor = id_map[(id_i + order) + (ncols * (j + 1))]
-                        if id_neighbor:
-                            rows[ni], cols[ni] = og_id, id_neighbor
-                            ni += 1
-                            rows[ni], cols[ni] = id_neighbor, og_id
-                            ni += 1
-                    if (id_i // ncols) >= j + 1:
-                        id_neighbor = id_map[(id_i + order) - (ncols * (j + 1))]
-                        if id_neighbor:
-                            rows[ni], cols[ni] = og_id, id_neighbor
-                            ni += 1
-                            rows[ni], cols[ni] = id_neighbor, og_id
-                            ni += 1
-
-            if (id_i // ncols) < (nrows - order):
-                # south neighbor
-                id_neighbor = id_map[id_i + (ncols * order)]
-                if id_neighbor:
-                    rows[ni], cols[ni] = og_id, id_neighbor
-                    ni += 1
-                    rows[ni], cols[ni] = id_neighbor, og_id
-                    ni += 1
-                # south-west to south-east neighbors
-                for j in range(condition):
-                    if (id_i % ncols) >= j + 1:
-                        id_neighbor = id_map[id_i + (ncols * order) - j - 1]
-                        if id_neighbor:
-                            rows[ni], cols[ni] = og_id, id_neighbor
-                            ni += 1
-                            rows[ni], cols[ni] = id_neighbor, og_id
-                            ni += 1
-                    if ((id_i + j + 1) % ncols) >= j + 1:
-                        id_neighbor = id_map[id_i + (ncols * order) + j + 1]
-                        if id_neighbor:
-                            rows[ni], cols[ni] = og_id, id_neighbor
-                            ni += 1
-                            rows[ni], cols[ni] = id_neighbor, og_id
-                            ni += 1
-
-                if criterion == "queen" or ((k / order) >= 2.0):
-                    if (id_i % ncols) >= order:
-                        # south-west neighbor
-                        id_neighbor = id_map[id_i + (ncols * order) - order]
-                        if id_neighbor:
-                            rows[ni], cols[ni] = og_id, id_neighbor
-                            ni += 1
-                            rows[ni], cols[ni] = id_neighbor, og_id
-                            ni += 1
-                    if ((id_i + order) % ncols) >= order:
-                        # south-east neighbor
-                        id_neighbor = id_map[id_i + (ncols * order) + order]
-                        if id_neighbor:
-                            rows[ni], cols[ni] = og_id, id_neighbor
-                            ni += 1
-                            rows[ni], cols[ni] = id_neighbor, og_id
-                            ni += 1
-    return rows[:ni], cols[:ni]
-
-
-@jit(nopython=True, fastmath=True)
-def _chunk_generator(
-    n_jobs, starts, ids,
-):
-    """
-    Construct chunks to iterate over within numba in parallel
-
-    Parameters
-    ----------
-    n_jobs : int
-        Number of cores to be used in the sparse weight construction. If -1,
-        all available cores are used.
-    starts : ndarray
-        (n_chunks+1,) array of positional starts for ids chunk
-    ids : ndarray
-        1D array containing ids of non-missing raster data
-
-    Yields
-    ------
-    ids_chunk : numpy.ndarray
-        (n_chunk,) array containing the chunk of non-missing raster data
-    """
-    chunk_size = starts[1] - starts[0]
-    for i in range(n_jobs):
-        start = starts[i]
-        ids_chunk = ids[start : (start + chunk_size)]
-        yield (ids_chunk,)
-
-
-def _parSWbuilder(
-    nrows, ncols, ids, id_map, criterion, k, dtype, n_jobs,
-):
-    """
-    Computes data and orders rows, cols, data in parallel using numba
-
-    Parameters
-    ----------
-    nrows : int
-        Number of rows in the raster data
-    ncols : int
-        Number of columns in the raster data
-    ids : ndarray
-        1D array containing ids of non-missing raster data
-    id_map : ndarray
-        1D array containing id_maps of non-missing raster data
-    criterion : str
-        Type of contiguity.
-    k : int
-        Order of contiguity, Default is 1
-    dtype : type
-        Data type of the rows and cols array
-    n_jobs : int
-        Number of cores to be used in the sparse weight construction. If -1,
-        all available cores are used.
-
-    Returns
-    -------
-    data : ndarray
-        1D ones array containing weight of each neighbor
-    rows : ndarray
-        1D ones array containing row value of each id
-        in the sparse weight object
-    cols : ndarray
-        1D ones array containing columns value of each id
-        in the sparse weight object
-    """
-    from joblib import Parallel, delayed, parallel_backend
-
-    n = len(ids)
-    chunk_size = n // n_jobs + 1
-    starts = np.arange(n_jobs + 1) * chunk_size
-    chunk = _chunk_generator(n_jobs, starts, ids)
-    with parallel_backend("threading"):
-        worker_out = Parallel(n_jobs=n_jobs)(
-            delayed(_compute_chunk)(nrows, ncols, *ids, id_map, criterion, k, dtype)
-            for ids in chunk
-        )
-    rows, cols = zip(*worker_out)
-    rows = np.concatenate(rows)
-    cols = np.concatenate(cols)
-    data = np.ones_like(rows, dtype=np.int8)
-    return (data, (rows, cols))
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/weights/set_operations.html b/docs/_modules/libpysal/weights/set_operations.html deleted file mode 100644 index e466327c2..000000000 --- a/docs/_modules/libpysal/weights/set_operations.html +++ /dev/null @@ -1,669 +0,0 @@ - - - - - - - libpysal.weights.set_operations — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.weights.set_operations

-"""
-Set-like manipulation of weights matrices.
-"""
-
-__author__ = "Sergio J. Rey <srey@asu.edu>, Charles Schmidt <schmidtc@gmail.com>, David Folch <david.folch@asu.edu>, Dani Arribas-Bel <darribas@asu.edu>"
-
-import copy
-from .weights import W, WSP
-from scipy.sparse import isspmatrix_csr
-from numpy import ones
-
-__all__ = [
-    "w_union",
-    "w_intersection",
-    "w_difference",
-    "w_symmetric_difference",
-    "w_subset",
-    "w_clip",
-]
-
-
-
[docs]def w_union(w1, w2, **kwargs):
-    """
-    Returns a binary weights object, w, that includes all neighbor pairs that
-    exist in either w1 or w2.
-
-    Parameters
-    ----------
-
-    w1                      : W
-                              object
-    w2                      : W
-                              object
-    **kwargs                : keyword arguments
-                              optional arguments for :class:`pysal.weights.W`
-
-    Returns
-    -------
-
-    w       : W
-              object
-
-    Notes
-    -----
-    ID comparisons are performed using ==, therefore the integer ID 2 is
-    equivalent to the float ID 2.0. Returns a matrix with all the unique IDs
-    from w1 and w2.
-
-    Examples
-    --------
-
-    Construct rook weights matrices for two regions, one is 4x4 (16 areas)
-    and the other is 6x4 (24 areas). A union of these two weights matrices
-    results in the new weights matrix matching the larger one.
-
-    >>> from libpysal.weights import lat2W, w_union
-    >>> w1 = lat2W(4,4)
-    >>> w2 = lat2W(6,4)
-    >>> w = w_union(w1, w2)
-    >>> w1[0] == w[0]
-    True
-    >>> w1.neighbors[15]
-    [11, 14]
-    >>> w2.neighbors[15]
-    [11, 14, 19]
-    >>> w.neighbors[15]
-    [19, 11, 14]
-
-    """
-    neighbors = dict(list(w1.neighbors.items()))
-    for i in w2.neighbors:
-        if i in neighbors:
-            add_neigh = set(neighbors[i]).union(set(w2.neighbors[i]))
-            neighbors[i] = list(add_neigh)
-        else:
-            neighbors[i] = copy.copy(w2.neighbors[i])
-    return W(neighbors, **kwargs)

-
-
-
[docs]def w_intersection(w1, w2, w_shape="w1", **kwargs):
-    """
-    Returns a binary weights object, w, that includes only
-    those neighbor pairs that exist in both w1 and w2.
-
-    Parameters
-    ----------
-
-    w1                      : W
-                              object
-    w2                      : W
-                              object
-    w_shape                 : string
-                              Defines the shape of the returned weights matrix. 'w1' returns a
-                              matrix with the same IDs as w1; 'all' returns a matrix with all
-                              the unique IDs from w1 and w2; and 'min' returns a matrix with
-                              only the IDs occurring in both w1 and w2.
-    **kwargs                : keyword arguments
-                              optional arguments for :class:`pysal.weights.W`
-
-    Returns
-    -------
-
-    w       : W
-              object
-
-    Notes
-    -----
-    ID comparisons are performed using ==, therefore the integer ID 2 is
-    equivalent to the float ID 2.0.
-
-    Examples
-    --------
-
-    Construct rook weights matrices for two regions, one is 4x4 (16 areas)
-    and the other is 6x4 (24 areas). An intersection of these two weights
-    matrices results in the new weights matrix matching the smaller one.
-
-    >>> from libpysal.weights import lat2W, w_intersection
-    >>> w1 = lat2W(4,4)
-    >>> w2 = lat2W(6,4)
-    >>> w = w_intersection(w1, w2)
-    >>> w1[0] == w[0]
-    True
-    >>> w1.neighbors[15]
-    [11, 14]
-    >>> w2.neighbors[15]
-    [11, 14, 19]
-    >>> w.neighbors[15]
-    [11, 14]
-
-    """
-
-    if w_shape == "w1":
-        neigh_keys = list(w1.neighbors.keys())
-    elif w_shape == "all":
-        neigh_keys = set(w1.neighbors.keys()).union(set(w2.neighbors.keys()))
-    elif w_shape == "min":
-        neigh_keys = set(w1.neighbors.keys()).intersection(set(w2.neighbors.keys()))
-    else:
-        raise Exception("invalid string passed to w_shape")
-
-    neighbors = {}
-    for i in neigh_keys:
-        if i in w1.neighbors and i in w2.neighbors:
-            add_neigh = set(w1.neighbors[i]).intersection(set(w2.neighbors[i]))
-            neighbors[i] = list(add_neigh)
-        else:
-            neighbors[i] = []
-
-    return W(neighbors, **kwargs)

-
-
-
[docs]def w_difference(w1, w2, w_shape="w1", constrained=True, **kwargs):
-    """
-    Returns a binary weights object, w, that includes only neighbor pairs
-    in w1 that are not in w2. The w_shape and constrained parameters
-    determine which pairs in w1 that are not in w2 are returned.
-
-    Parameters
-    ----------
-
-    w1                      : W
-                              object
-    w2                      : W
-                              object
-    w_shape                 : string
-                              Defines the shape of the returned weights matrix. 'w1' returns a
-                              matrix with the same IDs as w1; 'all' returns a matrix with all
-                              the unique IDs from w1 and w2; and 'min' returns a matrix with
-                              the IDs occurring in w1 and not in w2.
-    constrained             : boolean
-                              If False then the full set of neighbor pairs in w1 that are
-                              not in w2 are returned. If True then those pairs that would
-                              not be possible if w_shape='min' are dropped. Ignored if
-                              w_shape is set to 'min'.
-    **kwargs                : keyword arguments
-                              optional arguments for :class:`pysal.weights.W`
-
-    Returns
-    -------
-
-    w       : W
-              object
-
-    Notes
-    -----
-    ID comparisons are performed using ==, therefore the integer ID 2 is
-    equivalent to the float ID 2.0.
-
-    Examples
-    --------
-
-    Construct rook (w2) and queen (w1) weights matrices for two 4x4 regions
-    (16 areas). A queen matrix has all the joins a rook matrix does plus joins
-    between areas that share a corner. The new matrix formed by the difference
-    of rook from queen contains only join at corners (typically called a
-    bishop matrix). Note that the difference of queen from rook would result
-    in a weights matrix with no joins.
-
-    >>> from libpysal.weights import lat2W, w_difference
-    >>> w1 = lat2W(4,4,rook=False)
-    >>> w2 = lat2W(4,4,rook=True)
-    >>> w = w_difference(w1, w2, constrained=False)
-    >>> w1[0] == w[0]
-    False
-    >>> w1.neighbors[15]
-    [10, 11, 14]
-    >>> w2.neighbors[15]
-    [11, 14]
-    >>> w.neighbors[15]
-    [10]
-
-    """
-
-    if w_shape == "w1":
-        neigh_keys = list(w1.neighbors.keys())
-    elif w_shape == "all":
-        neigh_keys = set(w1.neighbors.keys()).union(set(w2.neighbors.keys()))
-    elif w_shape == "min":
-        neigh_keys = set(w1.neighbors.keys()).difference(set(w2.neighbors.keys()))
-        if not neigh_keys:
-            raise Exception("returned an empty weights matrix")
-    else:
-        raise Exception("invalid string passed to w_shape")
-
-    neighbors = {}
-    for i in neigh_keys:
-        if i in w1.neighbors:
-            if i in w2.neighbors:
-                add_neigh = set(w1.neighbors[i]).difference(set(w2.neighbors[i]))
-                neighbors[i] = list(add_neigh)
-            else:
-                neighbors[i] = copy.copy(w1.neighbors[i])
-        else:
-            neighbors[i] = []
-
-    if constrained or w_shape == "min":
-        constrained_keys = set(w1.neighbors.keys()).difference(set(w2.neighbors.keys()))
-        island_keys = set(neighbors.keys()).difference(constrained_keys)
-        for i in island_keys:
-            neighbors[i] = []
-        for i in constrained_keys:
-            neighbors[i] = list(set(neighbors[i]).intersection(constrained_keys))
-
-    return W(neighbors, **kwargs)

-
-
-
[docs]def w_symmetric_difference(w1, w2, w_shape="all", constrained=True, **kwargs):
-    """
-    Returns a binary weights object, w, that includes only neighbor pairs
-    that are not shared by w1 and w2. The w_shape and constrained parameters
-    determine which pairs that are not shared by w1 and w2 are returned.
-
-    Parameters
-    ----------
-
-    w1                      : W
-                              object
-    w2                      : W
-                              object
-    w_shape                 : string
-                              Defines the shape of the returned weights matrix. 'all' returns a
-                              matrix with all the unique IDs from w1 and w2; and 'min' returns
-                              a matrix with the IDs not shared by w1 and w2.
-    constrained             : boolean
-                              If False then the full set of neighbor pairs that are not
-                              shared by w1 and w2 are returned. If True then those pairs
-                              that would not be possible if w_shape='min' are dropped.
-                              Ignored if w_shape is set to 'min'.
-    **kwargs                : keyword arguments
-                              optional arguments for :class:`pysal.weights.W`
-
-    Returns
-    -------
-
-    w       : W
-              object
-
-    Notes
-    -----
-    ID comparisons are performed using ==, therefore the integer ID 2 is
-    equivalent to the float ID 2.0.
-
-    Examples
-    --------
-
-    Construct queen weights matrix for a 4x4 (16 areas) region (w1) and a rook
-    matrix for a 6x4 (24 areas) region (w2). The symmetric difference of these
-    two matrices (with w_shape set to 'all' and constrained set to False)
-    contains the corner joins in the overlap area, all the joins in the
-    non-overlap area.
-
-    >>> from libpysal.weights import lat2W, w_symmetric_difference
-    >>> w1 = lat2W(4,4,rook=False)
-    >>> w2 = lat2W(6,4,rook=True)
-    >>> w = w_symmetric_difference(w1, w2, constrained=False)
-    >>> w1[0] == w[0]
-    False
-    >>> w1.neighbors[15]
-    [10, 11, 14]
-    >>> w2.neighbors[15]
-    [11, 14, 19]
-    >>> set(w.neighbors[15]) == set([10, 19])
-    True
-
-    """
-
-    if w_shape == "all":
-        neigh_keys = set(w1.neighbors.keys()).union(set(w2.neighbors.keys()))
-    elif w_shape == "min":
-        neigh_keys = set(w1.neighbors.keys()).symmetric_difference(
-            set(w2.neighbors.keys())
-        )
-    else:
-        raise Exception("invalid string passed to w_shape")
-
-    neighbors = {}
-    for i in neigh_keys:
-        if i in w1.neighbors:
-            if i in w2.neighbors:
-                add_neigh = set(w1.neighbors[i]).symmetric_difference(
-                    set(w2.neighbors[i])
-                )
-                neighbors[i] = list(add_neigh)
-            else:
-                neighbors[i] = copy.copy(w1.neighbors[i])
-        elif i in w2.neighbors:
-            neighbors[i] = copy.copy(w2.neighbors[i])
-        else:
-            neighbors[i] = []
-
-    if constrained or w_shape == "min":
-        constrained_keys = set(w1.neighbors.keys()).difference(set(w2.neighbors.keys()))
-        island_keys = set(neighbors.keys()).difference(constrained_keys)
-        for i in island_keys:
-            neighbors[i] = []
-        for i in constrained_keys:
-            neighbors[i] = list(set(neighbors[i]).intersection(constrained_keys))
-
-    return W(neighbors, **kwargs)

-
-
-
[docs]def w_subset(w1, ids, **kwargs):
-    """
-    Returns a binary weights object, w, that includes only those
-    observations in ids.
-
-    Parameters
-    ----------
-
-    w1                      : W
-                              object
-    ids                     : list
-                              A list containing the IDs to be include in the returned weights
-                              object.
-    **kwargs                : keyword arguments
-                              optional arguments for :class:`pysal.weights.W`
-
-    Returns
-    -------
-
-    w       : W
-              object
-
-    Examples
-    --------
-
-    Construct a rook weights matrix for a 6x4 region (24 areas). By default
-    PySAL assigns integer IDs to the areas in a region. By passing in a list
-    of integers from 0 to 15, the first 16 areas are extracted from the
-    previous weights matrix, and only those joins relevant to the new region
-    are retained.
-
-    >>> from libpysal.weights import lat2W, w_subset
-    >>> w1 = lat2W(6,4)
-    >>> ids = range(16)
-    >>> w = w_subset(w1, ids)
-    >>> w1[0] == w[0]
-    True
-    >>> w1.neighbors[15]
-    [11, 14, 19]
-    >>> w.neighbors[15]
-    [11, 14]
-
-    """
-
-    neighbors = {}
-    ids_set = set(list(ids))
-    for i in ids:
-        if i in w1.neighbors:
-            neigh_add = ids_set.intersection(set(w1.neighbors[i]))
-            neighbors[i] = list(neigh_add)
-        else:
-            neighbors[i] = []
-
-    return W(neighbors, id_order=list(ids), **kwargs)

-
-
-
[docs]def w_clip(w1, w2, outSP=True, **kwargs):
-    """
-    Clip a continuous W object (w1) with a different W object (w2) so only cells where
-    w2 has a non-zero value remain with non-zero values in w1.
-
-    Checks on w1 and w2 are performed to make sure they conform to the
-    appropriate format and, if not, they are converted.
-
-    Parameters
-    ----------
-    w1                      : W
-                              W, scipy.sparse.csr.csr_matrix
-                              Potentially continuous weights matrix to be clipped. The clipped
-                              matrix wc will have at most the same elements as w1.
-    w2                      : W
-                              W, scipy.sparse.csr.csr_matrix
-                              Weights matrix to use as shell to clip w1. Automatically
-                              converted to binary format. Only non-zero elements in w2 will be
-                              kept non-zero in wc. NOTE: assumed to be of the same shape as w1
-    outSP                   : boolean
-                              If True (default) return sparse version of the clipped W, if
-                              False, return W object of the clipped matrix
-    **kwargs                : keyword arguments
-                              optional arguments for :class:`pysal.weights.W`
-
-    Returns
-    -------
-    wc      : W
-              W, scipy.sparse.csr.csr_matrix
-              Clipped W object (sparse if outSP=Ture). It inherits ``id_order`` from w1.
-
-    Examples
-    --------
-    >>> from libpysal.weights import lat2W
-
-    First create a W object from a lattice using queen contiguity and
-    row-standardize it (note that these weights will stay when we clip the
-    object, but they will not neccesarily represent a row-standardization
-    anymore):
-
-    >>> w1 = lat2W(3, 2, rook=False)
-    >>> w1.transform = 'R'
-
-    We will clip that geography assuming observations 0, 2, 3 and 4 belong to
-    one group and 1, 5 belong to another group and we don't want both groups
-    to interact with each other in our weights (i.e. w_ij = 0 if i and j in
-    different groups). For that, we use the following method:
-
-    >>> import libpysal
-    >>> w2 = libpysal.weights.block_weights(['r1', 'r2', 'r1', 'r1', 'r1', 'r2'])
-
-    To illustrate that w2 will only be considered as binary even when the
-    object passed is not, we can row-standardize it
-
-    >>> w2.transform = 'R'
-
-    The clipped object ``wc`` will contain only the spatial queen
-    relationships that occur within one group ('r1' or 'r2') but will have
-    gotten rid of those that happen across groups
-
-    >>> wcs = libpysal.weights.w_clip(w1, w2, outSP=True)
-
-    This will create a sparse object (recommended when n is large).
-
-    >>> wcs.sparse.toarray()
-    array([[0.        , 0.        , 0.33333333, 0.33333333, 0.        ,
-            0.        ],
-           [0.        , 0.        , 0.        , 0.        , 0.        ,
-            0.        ],
-           [0.2       , 0.        , 0.        , 0.2       , 0.2       ,
-            0.        ],
-           [0.2       , 0.        , 0.2       , 0.        , 0.2       ,
-            0.        ],
-           [0.        , 0.        , 0.33333333, 0.33333333, 0.        ,
-            0.        ],
-           [0.        , 0.        , 0.        , 0.        , 0.        ,
-            0.        ]])
-
-
-    If we wanted an original W object, we can control that with the argument
-    ``outSP``:
-
-    >>> wc = libpysal.weights.w_clip(w1, w2, outSP=False)
-    >>> wc.full()[0]
-    array([[0.        , 0.        , 0.33333333, 0.33333333, 0.        ,
-            0.        ],
-           [0.        , 0.        , 0.        , 0.        , 0.        ,
-            0.        ],
-           [0.2       , 0.        , 0.        , 0.2       , 0.2       ,
-            0.        ],
-           [0.2       , 0.        , 0.2       , 0.        , 0.2       ,
-            0.        ],
-           [0.        , 0.        , 0.33333333, 0.33333333, 0.        ,
-            0.        ],
-           [0.        , 0.        , 0.        , 0.        , 0.        ,
-            0.        ]])
-
-    You can check they are actually the same:
-
-    >>> wcs.sparse.toarray() == wc.full()[0]
-    array([[ True,  True,  True,  True,  True,  True],
-           [ True,  True,  True,  True,  True,  True],
-           [ True,  True,  True,  True,  True,  True],
-           [ True,  True,  True,  True,  True,  True],
-           [ True,  True,  True,  True,  True,  True],
-           [ True,  True,  True,  True,  True,  True]])
-
-    """
-
-    from .util import WSP2W
-
-    if not w1.id_order:
-        w1.id_order = None
-    id_order = w1.id_order
-    if not isspmatrix_csr(w1):
-        w1 = w1.sparse
-    if not isspmatrix_csr(w2):
-        w2 = w2.sparse
-    w2.data = ones(w2.data.shape)
-    wc = w1.multiply(w2)
-    wc = WSP(wc, id_order=id_order)
-    if not outSP:
-        wc = WSP2W(wc, **kwargs)
-    return wc

-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/weights/spatial_lag.html b/docs/_modules/libpysal/weights/spatial_lag.html deleted file mode 100644 index 1648f9d28..000000000 --- a/docs/_modules/libpysal/weights/spatial_lag.html +++ /dev/null @@ -1,388 +0,0 @@ - - - - - - - libpysal.weights.spatial_lag — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.weights.spatial_lag

-"""
-Spatial lag operations.
-"""
-__author__ = "Sergio J. Rey <srey@asu.edu>, David C. Folch <david.folch@asu.edu>, Levi John Wolf <ljw2@asu.edu"
-__all__ = ["lag_spatial", "lag_categorical"]
-
-import numpy as np
-
-
-
[docs]def lag_spatial(w, y):
-    """
-    Spatial lag operator.
-
-    If w is row standardized, returns the average of each observation's neighbors;
-    if not, returns the weighted sum of each observation's neighbors.
-
-    Parameters
-    ----------
-
-    w                   : W
-                          libpysal spatial weightsobject
-    y                   : array
-                          numpy array with dimensionality conforming to w (see examples)
-
-    Returns
-    -------
-
-    wy                  : array
-                          array of numeric values for the spatial lag
-
-    Examples
-    --------
-
-    Setup a 9x9 binary spatial weights matrix and vector of data; compute the
-    spatial lag of the vector.
-
-    >>> import libpysal
-    >>> import numpy as np
-    >>> w = libpysal.weights.lat2W(3, 3)
-    >>> y = np.arange(9)
-    >>> yl = libpysal.weights.lag_spatial(w, y)
-    >>> yl
-    array([ 4.,  6.,  6., 10., 16., 14., 10., 18., 12.])
-
-    Row standardize the weights matrix and recompute the spatial lag
-
-    >>> w.transform = 'r'
-    >>> yl = libpysal.weights.lag_spatial(w, y)
-    >>> yl
-    array([2.        , 2.        , 3.        , 3.33333333, 4.        ,
-           4.66666667, 5.        , 6.        , 6.        ])
-
-
-    Explicitly define data vector as 9x1 and recompute the spatial lag
-
-    >>> y.shape = (9, 1)
-    >>> yl = libpysal.weights.lag_spatial(w, y)
-    >>> yl
-    array([[2.        ],
-           [2.        ],
-           [3.        ],
-           [3.33333333],
-           [4.        ],
-           [4.66666667],
-           [5.        ],
-           [6.        ],
-           [6.        ]])
-
-
-    Take the spatial lag of a 9x2 data matrix
-
-    >>> yr = np.arange(8, -1, -1)
-    >>> yr.shape = (9, 1)
-    >>> x = np.hstack((y, yr))
-    >>> yl = libpysal.weights.lag_spatial(w, x)
-    >>> yl
-    array([[2.        , 6.        ],
-           [2.        , 6.        ],
-           [3.        , 5.        ],
-           [3.33333333, 4.66666667],
-           [4.        , 4.        ],
-           [4.66666667, 3.33333333],
-           [5.        , 3.        ],
-           [6.        , 2.        ],
-           [6.        , 2.        ]])
-
-    """
-    return w.sparse * y

-
-
-
[docs]def lag_categorical(w, y, ties="tryself"):
-    """
-    Spatial lag operator for categorical variables.
-
-    Constructs the most common categories of neighboring observations, weighted
-    by their weight strength.
-
-    Parameters
-    ----------
-
-    w                   : W
-                          PySAL spatial weightsobject
-    y                   : iterable
-                          iterable collection of categories (either int or
-                          string) with dimensionality conforming to w (see examples)
-    ties                : str
-                          string describing the method to use when resolving
-                          ties. By default, the option is "tryself",
-                          and the category of the focal observation
-                          is included with its neighbors to try
-                          and break a tie. If this does not resolve the tie,
-                          a winner is chosen randomly. To just use random choice to
-                          break ties, pass "random" instead.
-    Returns
-    -------
-    an (n x k) column vector containing the most common neighboring observation
-
-    Notes
-    -----
-    This works on any array where the number of unique elements along the column
-    axis is less than the number of elements in the array, for any dtype.
-    That means the routine should work on any dtype that np.unique() can
-    compare.
-
-    Examples
-    --------
-
-    Set up a 9x9 weights matrix describing a 3x3 regular lattice. Lag one list of
-    categorical variables with no ties.
-
-    >>> import libpysal
-    >>> import numpy as np
-    >>> np.random.seed(12345)
-    >>> w = libpysal.weights.lat2W(3, 3)
-    >>> y = ['a','b','a','b','c','b','c','b','c']
-    >>> y_l = libpysal.weights.lag_categorical(w, y)
-    >>> np.array_equal(y_l, np.array(['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b']))
-    True
-
-    Explicitly reshape y into a (9x1) array and calculate lag again
-
-    >>> yvect = np.array(y).reshape(9,1)
-    >>> yvect_l = libpysal.weights.lag_categorical(w,yvect)
-    >>> check = np.array( [ [i] for i in  ['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b']] )
-    >>> np.array_equal(yvect_l, check)
-    True
-
-    compute the lag of a 9x2 matrix of categories
-
-    >>> y2 = ['a', 'c', 'c', 'd', 'b', 'a', 'd', 'd', 'c']
-    >>> ym = np.vstack((y,y2)).T
-    >>> ym_lag = libpysal.weights.lag_categorical(w,ym)
-    >>> check = np.array([['b', 'd'], ['a', 'c'], ['b', 'c'], ['c', 'd'], ['b', 'd'], ['c', 'c'], ['b', 'd'], ['c', 'd'], ['b', 'c']])
-    >>> np.array_equal(check, ym_lag)
-    True
-
-    """
-    if isinstance(y, list):
-        y = np.array(y)
-    orig_shape = y.shape
-    if len(orig_shape) > 1:
-        if orig_shape[1] > 1:
-            return np.vstack([lag_categorical(w, col) for col in y.T]).T
-    y = y.flatten()
-    output = np.zeros_like(y)
-    labels = np.unique(y)
-    normalized_labels = np.zeros(y.shape, dtype=np.int)
-    for i, label in enumerate(labels):
-        normalized_labels[y == label] = i
-    for focal_name, neighbors in w:
-        focal_idx = w.id2i[focal_name]
-        neighborhood_tally = np.zeros(labels.shape)
-        for neighb_name, weight in list(neighbors.items()):
-            neighb_idx = w.id2i[neighb_name]
-            neighb_label = normalized_labels[neighb_idx]
-            neighborhood_tally[neighb_label] += weight
-        out_label_idx = _resolve_ties(
-            focal_idx, normalized_labels, neighborhood_tally, neighbors, ties, w
-        )
-        output[focal_idx] = labels[out_label_idx]
-    return output.reshape(orig_shape)

-
-
-def _resolve_ties(idx, normalized_labels, tally, neighbors, method, w):
-    """
-    Helper function to resolve ties if lag is multimodal
-
-    first, if this function gets called when there's actually no tie, then the
-    correct value will be picked.
-
-    if 'random' is selected as the method, a random tiebeaker is picked
-
-    if 'tryself' is selected, then the observation's own value will be used in
-    an attempt to break the tie, but if it fails, a random tiebreaker will be
-    selected.
-
-    Arguments
-    ---------
-    idx                 : int
-                          index (aligned with `normalized_labels`) of the 
-                          current observation being resolved.
-    normalized_labels   : (n,) array of ints
-                          normalized array of labels for each observation
-    tally               : (p,) array of floats
-                          current tally of neighbors' labels around `idx` to resolve.
-    neighbors           : dict of (neighbor_name : weight)
-                          the elements of the weights object, identical to w[idx]
-    method              : string
-                          configuration option to use a specific tiebreaking method. 
-                          supported options are:
-                          1. tryself: Use the focal observation's label to tiebreak.
-                                      If this doesn't successfully break the tie, 
-                                      (which only occurs if it induces a new tie),
-                                      decide randomly. 
-                          2. random: Resolve the tie randomly amongst winners.
-                          3. lowest: Pick the lowest-value label amongst winners.
-                          4. highest: Pick the highest-value label amongst winners.
-    w                   : pysal.W object
-                          a PySAL weights object aligned with normalized_labels. 
-
-    Returns
-    -------
-    integer denoting which label to use to label the observation.
-    """
-    (ties,) = np.where(tally == tally.max())  # returns a tuple for flat arrays
-    if len(tally[tally == tally.max()]) <= 1:  # no tie, pick the highest
-        return np.argmax(tally).astype(int)
-    elif method.lower() == "random":  # choose randomly from tally
-        return np.random.choice(np.squeeze(ties)).astype(int)
-    elif method.lower() == "lowest":  # pick lowest tied value
-        return ties[0].astype(int)
-    elif method.lower() == "highest":  # pick highest tied value
-        return ties[-1].astype(int)
-    elif (
-        method.lower() == "tryself"
-    ):  # add self-label as observation, try again, random if fail
-        mean_neighbor_value = np.mean(list(neighbors.values()))
-        tally[normalized_labels[idx]] += mean_neighbor_value
-        return _resolve_ties(idx, normalized_labels, tally, neighbors, "random", w)
-    else:
-        raise KeyError("Tie-breaking method for categorical lag not recognized")
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/weights/spintW.html b/docs/_modules/libpysal/weights/spintW.html deleted file mode 100644 index e7dd3527f..000000000 --- a/docs/_modules/libpysal/weights/spintW.html +++ /dev/null @@ -1,426 +0,0 @@ - - - - - - - libpysal.weights.spintW — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.weights.spintW

-"""
-Spatial weights for spatial interaction including contiguity OD weights (ODW),
-network based weights (netW), and distance-decay based vector weights (vecW).
-
-"""
-
-__author__ = "Taylor Oshan  <tayoshan@gmail.com> "
-
-from scipy.sparse import kron
-from .weights import W, WSP
-from .distance import DistanceBand
-from collections import OrderedDict
-
-
-
[docs]def ODW(Wo, Wd, transform="r", silence_warnings=True):
-    """
-    Constructs an o*d by o*d origin-destination style spatial weight for o*d
-    flows using standard spatial weights on o origins and d destinations. Input
-    spatial weights must be binary or able to be sutiably transformed to binary.
-
-    Parameters
-    ----------
-    Wo          : W object for origin locations
-                  o x o spatial weight object amongst o origins
-
-    Wd          : W object for destination locations
-                  d x d spatial weight object amongst d destinations
-
-    transform   : Transformation for standardization of final OD spatial weight; default
-                  is 'r' for row standardized
-
-    Returns
-    -------
-    W           : spatial contiguity W object for assocations between flows
-                 o*d x o*d spatial weight object amongst o*d flows between o
-                 origins and d destinations
-
-    Examples
-    --------
-
-    >>> import libpysal
-    >>> O = libpysal.weights.lat2W(2,2)
-    >>> D = libpysal.weights.lat2W(2,2)
-    >>> OD = libpysal.weights.ODW(O,D)
-    >>> OD.weights[0]
-    [0.25, 0.25, 0.25, 0.25]
-    >>> OD.neighbors[0]
-    [5, 6, 9, 10]
-    >>> OD.full()[0][0]
-    array([0.  , 0.  , 0.  , 0.  , 0.  , 0.25, 0.25, 0.  , 0.  , 0.25, 0.25,
-           0.  , 0.  , 0.  , 0.  , 0.  ])
-
-    """
-    if Wo.transform != "b":
-        try:
-            Wo.tranform = "b"
-        except:
-            raise AttributeError(
-                "Wo is not binary and cannot be transformed to "
-                "binary. Wo must be binary or suitably transformed to binary."
-            )
-    if Wd.transform != "b":
-        try:
-            Wd.tranform = "b"
-        except:
-            raise AttributeError(
-                "Wd is not binary and cannot be transformed to "
-                "binary. Wd must be binary or suitably transformed to binary."
-            )
-    Wo = Wo.sparse
-    Wo.eliminate_zeros()
-    Wd = Wd.sparse
-    Wd.eliminate_zeros()
-    Ww = kron(Wo, Wd, format="csr")
-    Ww.eliminate_zeros()
-    Ww = WSP(Ww).to_W(silence_warnings=silence_warnings)
-    Ww.transform = transform
-    return Ww

-
-
-
[docs]def netW(link_list, share="A", transform="r", **kwargs):
-    """
-    Create a network-contiguity based weight object based on different nodal
-    relationships encoded in a network.
-
-    Parameters
-    ----------
-    link_list   : list
-                  of tuples where each tuple is of the form (o,d) where o is an
-                  origin id and d is a destination id
-
-    share       : string
-                  denoting how to define the nodal relationship used to determine neighboring edges; defualt is 'A' for any shared nodes between two network edges; options include: O a shared origin node; D a shared destination node; OD; a shared origin or a shared destination node; C a shared node that is the destination of the first edge and the origin of the second edge - i.e., a directed chain is formed moving from edge one to edge two.
-
-    transform   : Transformation for standardization of final OD spatial weight; default
-                  is 'r' for row standardized
-    **kwargs    : keyword arguments
-                  optional arguments for :class:`pysal.weights.W`
-
-
-    Returns
-    -------
-     W          : nodal contiguity W object for networkd edges or flows
-                  W Object representing the binary adjacency of the network edges
-                  given a definition of nodal relationshilibpysal.weights.spintW.
-
-    Examples
-    --------
-    >>> import libpysal
-    >>> links = [('a','b'), ('a','c'), ('a','d'), ('c','d'), ('c', 'b'), ('c','a')]
-    >>> O = libpysal.weights.netW(links, share='O')
-    >>> O.neighbors[('a', 'b')]
-    [('a', 'c'), ('a', 'd')]
-    >>> OD = libpysal.weights.netW(links, share='OD')
-    >>> OD.neighbors[('a', 'b')]
-    [('a', 'c'), ('a', 'd'), ('c', 'b')]
-    >>> any_common = libpysal.weights.netW(links, share='A')
-    >>> any_common.neighbors[('a', 'b')]
-    [('a', 'c'), ('a', 'd'), ('c', 'b'), ('c', 'a')]
-
-    """
-    neighbors = {}
-    neighbors = OrderedDict()
-    edges = link_list
-    for key in edges:
-        neighbors[key] = []
-        for neigh in edges:
-            if key == neigh:
-                continue
-            if share.upper() == "OD":
-                if key[0] == neigh[0] or key[1] == neigh[1]:
-                    neighbors[key].append(neigh)
-            elif share.upper() == "O":
-                if key[0] == neigh[0]:
-                    neighbors[key].append(neigh)
-            elif share.upper() == "D":
-                if key[1] == neigh[1]:
-                    neighbors[key].append(neigh)
-            elif share.upper() == "C":
-                if key[1] == neigh[0]:
-                    neighbors[key].append(neigh)
-            elif share.upper() == "A":
-                if (
-                    key[0] == neigh[0]
-                    or key[0] == neigh[1]
-                    or key[1] == neigh[0]
-                    or key[1] == neigh[1]
-                ):
-                    neighbors[key].append(neigh)
-            else:
-                raise AttributeError(
-                    "Parameter 'share' must be 'O', 'D'," " 'OD', or 'C'"
-                )
-    netW = W(neighbors, **kwargs)
-    netW.tranform = transform
-    return netW

-
-
-
[docs]def vecW(
-    origin_x,
-    origin_y,
-    dest_x,
-    dest_y,
-    threshold,
-    p=2,
-    alpha=-1.0,
-    binary=True,
-    ids=None,
-    build_sp=False,
-    **kwargs
-):
-    """
-    Distance-based spatial weight for vectors that is computed using a
-    4-dimensional distance between the origin x,y-coordinates and the
-    destination x,y-coordinates
-
-    Parameters
-    ----------
-    origin_x   : list or array
-                 of vector origin x-coordinates
-    origin_y   : list or array
-                 of vector origin y-coordinates
-    dest_x     : list or array
-                 of vector destination x-coordinates
-    dest_y     : list or array
-                 of vector destination y-coordinates
-    threshold  : float
-                 distance band
-    p          : float
-                 Minkowski p-norm distance metric parameter:
-                 1<=p<=infinity
-                 2: Euclidean distance
-                 1: Manhattan distance
-    binary     : boolean
-                 If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0
-                 If false wij=dij^{alpha}
-    alpha      : float
-                 distance decay parameter for weight (default -1.0)
-                 if alpha is positive the weights will not decline with
-                 distance. If binary is True, alpha is ignored
-
-    ids         : list
-                  values to use for keys of the neighbors and weights dicts
-    build_sp    : boolean
-                  True to build sparse distance matrix and false to build dense
-                  distance matrix; significant speed gains may be obtained
-                  dending on the sparsity of the of distance_matrix and
-                  threshold that is applied
-    **kwargs    : keyword arguments
-                  optional arguments for :class:`pysal.weights.W`
-
-
-    Returns
-    ------
-    W           : DistanceBand W object that uses 4-dimenional distances between
-                  vectors origin and destination coordinates.
-
-    Examples
-    --------
-    >>> import libpysal
-    >>> x1 = [5,6,3]
-    >>> y1 = [1,8,5]
-    >>> x2 = [2,4,9]
-    >>> y2 = [3,6,1]
-    >>> W1 = libpysal.weights.vecW(x1, y1, x2, y2, threshold=999)
-    >>> list(W1.neighbors[0])
-    [1, 2]
-    >>> W2 = libpysal.weights.vecW(x1, y2, x1, y2, threshold=8.5)
-    >>> list(W2.neighbors[0])
-    [1, 2]
-
-    """
-    data = list(zip(origin_x, origin_y, dest_x, dest_y))
-    W = DistanceBand(
-        data,
-        threshold=threshold,
-        p=p,
-        binary=binary,
-        alpha=alpha,
-        ids=ids,
-        build_sp=False,
-        **kwargs
-    )
-    return W

-
-
-
[docs]def mat2L(edge_matrix):
-    """
-    Convert a matrix denoting network connectivity (edges or flows) to a list
-    denoting edges
-
-    Parameters
-    ----------
-    edge_matrix   : array
-                    where rows denote network edge origins, columns denote
-                    network edge destinations, and non-zero entries denote the
-                    existence of an edge between a given origin and destination
-
-    Returns
-    -------
-     edge_list    : list
-                    of tuples where each tuple is of the form (o,d) where o is an
-                    origin id and d is a destination id
-
-    """
-    if len(edge_matrix.shape) != 2:
-        raise AttributeError(
-            "Matrix of network edges should be two dimensions"
-            "with edge origins on one axis and edge destinations on the"
-            "second axis with non-zero matrix entires denoting an edge"
-            "between and origin and destination"
-        )
-    edge_list = []
-    rows, cols = edge_matrix.shape
-    for row in range(rows):
-        for col in range(cols):
-            if edge_matrix[row, col] != 0:
-                edge_list.append((row, col))
-    return edge_list

-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/weights/user.html b/docs/_modules/libpysal/weights/user.html deleted file mode 100644 index e4aa1248d..000000000 --- a/docs/_modules/libpysal/weights/user.html +++ /dev/null @@ -1,300 +0,0 @@ - - - - - - - libpysal.weights.user — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.weights.user

-"""
-Convenience functions for the construction of spatial weights based on
-contiguity and distance criteria.
-"""
-
-__author__ = "Sergio J. Rey <srey@asu.edu> "
-
-from .util import get_points_array_from_shapefile, min_threshold_distance
-from ..io.fileio import FileIO as ps_open
-from .. import cg
-import numpy as np
-
-__all__ = [
-    "min_threshold_dist_from_shapefile",
-    "build_lattice_shapefile",
-    "spw_from_gal",
-]
-
-
-
[docs]def spw_from_gal(galfile):
-    """
-    Sparse scipy matrix for w from a gal file.
-
-    Parameters
-    ----------
-
-    galfile  : string
-               name of gal file including suffix
-
-    Returns
-    -------
-
-    spw      : sparse_matrix
-               scipy sparse matrix in CSR format
-
-    ids      : array
-               identifiers for rows/cols of spw
-
-    Examples
-    --------
-    >>> import libpysal
-    >>> spw = libpysal.weights.spw_from_gal(libpysal.examples.get_path("sids2.gal"))
-    >>> spw.sparse.nnz
-    462
-
-    """
-
-    return ps_open(galfile, "r").read(sparse=True)

-
-
-
[docs]def min_threshold_dist_from_shapefile(shapefile, radius=None, p=2):
-    """
-    Get the maximum nearest neighbor distance between observations in the
-    shapefile.
-
-    Parameters
-    ----------
-    shapefile  : string
-                 shapefile name with shp suffix.
-    radius     : float
-                 If supplied arc_distances will be calculated
-                 based on the given radius. p will be ignored.
-    p          : float
-                 Minkowski p-norm distance metric parameter:
-                 1<=p<=infinity
-                 2: Euclidean distance
-                 1: Manhattan distance
-
-    Returns
-    -------
-    d          : float
-                 Maximum nearest neighbor distance between the n
-                 observations.
-
-    Examples
-    --------
-    >>> import libpysal
-    >>> md = libpysal.weights.min_threshold_dist_from_shapefile(libpysal.examples.get_path("columbus.shp"))
-    >>> md
-    0.6188641580768541
-    >>> libpysal.weights.min_threshold_dist_from_shapefile(libpysal.examples.get_path("stl_hom.shp"), libpysal.cg.sphere.RADIUS_EARTH_MILES)
-    31.846942936393717
-
-    Notes
-    -----
-    Supports polygon or point shapefiles. For polygon shapefiles, distance is
-    based on polygon centroids. Distances are defined using coordinates in
-    shapefile which are assumed to be projected and not geographical
-    coordinates.
-
-    """
-    points = get_points_array_from_shapefile(shapefile)
-    if radius is not None:
-        kdt = cg.kdtree.Arc_KDTree(points, radius=radius)
-        nn = kdt.query(kdt.data, k=2)
-        nnd = nn[0].max(axis=0)[1]
-        return nnd
-    return min_threshold_distance(points, p)

-
-
-
[docs]def build_lattice_shapefile(nrows, ncols, outFileName):
-    """
-    Build a lattice shapefile with nrows rows and ncols cols.
-
-    Parameters
-    ----------
-
-    nrows       : int
-                  Number of rows
-    ncols       : int
-                  Number of cols
-    outFileName : str
-                  shapefile name with shp suffix
-
-    Returns
-    -------
-    None
-
-    """
-    if not outFileName.endswith(".shp"):
-        raise ValueError("outFileName must end with .shp")
-    o = ps_open(outFileName, "w")
-    dbf_name = outFileName.split(".")[0] + ".dbf"
-    d = ps_open(dbf_name, "w")
-    d.header = ["ID"]
-    d.field_spec = [("N", 8, 0)]
-    c = 0
-    for i in range(ncols):
-        for j in range(nrows):
-            ll = i, j
-            ul = i, j + 1
-            ur = i + 1, j + 1
-            lr = i + 1, j
-            o.write(cg.Polygon([ll, ul, ur, lr, ll]))
-            d.write([c])
-            c += 1
-    d.close()
-    o.close()

-
-
-def _test():
-    import doctest
-
-    # the following line could be used to define an alternative to the '<BLANKLINE>' flag
-    # doctest.BLANKLINE_MARKER = 'something better than <BLANKLINE>'
-    start_suppress = np.get_printoptions()["suppress"]
-    np.set_printoptions(suppress=True)
-    doctest.testmod()
-    np.set_printoptions(suppress=start_suppress)
-
-
-if __name__ == "__main__":
-    _test()
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/weights/util.html b/docs/_modules/libpysal/weights/util.html deleted file mode 100644 index 5beaaaaeb..000000000 --- a/docs/_modules/libpysal/weights/util.html +++ /dev/null @@ -1,1820 +0,0 @@ - - - - - - - libpysal.weights.util — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.weights.util

-from ..io.fileio import FileIO as psopen
-from .weights import W, WSP
-from .set_operations import w_subset
-import numpy as np
-from scipy import sparse
-from scipy.spatial import KDTree
-import copy
-import scipy.spatial
-import os
-import scipy
-from warnings import warn
-import numbers
-from collections import defaultdict
-from itertools import tee
-from ..common import requires
-from distutils.version import LooseVersion
-
-try:
-    import geopandas as gpd
-
-    GPD_08 = str(gpd.__version__) >= LooseVersion("0.8.0")
-except ImportError:
-    warn("geopandas not available. Some functionality will be disabled.")
-
-__all__ = [
-    "lat2W",
-    "block_weights",
-    "comb",
-    "order",
-    "higher_order",
-    "shimbel",
-    "remap_ids",
-    "full2W",
-    "full",
-    "WSP2W",
-    "insert_diagonal",
-    "fill_diagonal",
-    "get_ids",
-    "get_points_array_from_shapefile",
-    "min_threshold_distance",
-    "lat2SW",
-    "w_local_cluster",
-    "higher_order_sp",
-    "hexLat2W",
-    "neighbor_equality",
-    "attach_islands",
-    "nonplanar_neighbors",
-    "fuzzy_contiguity",
-]
-
-
-KDTREE_TYPES = [scipy.spatial.KDTree, scipy.spatial.cKDTree]
-
-
-
[docs]def hexLat2W(nrows=5, ncols=5, **kwargs):
-    """
-    Create a W object for a hexagonal lattice.
-
-    Parameters
-    ----------
-    nrows      : int
-                 number of rows
-    ncols      : int
-                 number of columns
-    **kwargs   : keyword arguments
-                 optional arguments for :class:`pysal.weights.W`
-
-
-    Returns
-    -------
-    w : W
-        instance of spatial weights class W
-
-    Notes
-    -----
-    Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.
-
-    Construction is based on shifting every other column of a regular lattice
-    down 1/2 of a cell.
-
-    Examples
-    --------
-    >>> from libpysal.weights import lat2W, hexLat2W
-    >>> w = lat2W()
-    >>> w.neighbors[1]
-    [0, 6, 2]
-    >>> w.neighbors[21]
-    [16, 20, 22]
-    >>> wh = hexLat2W()
-    >>> wh.neighbors[1]
-    [0, 6, 2, 5, 7]
-    >>> wh.neighbors[21]
-    [16, 20, 22]
-    """
-
-    if nrows == 1 or ncols == 1:
-        print("Hexagon lattice requires at least 2 rows and columns")
-        print("Returning a linear contiguity structure")
-        return lat2W(nrows, ncols)
-
-    n = nrows * ncols
-    rid = [i // ncols for i in range(n)]
-    cid = [i % ncols for i in range(n)]
-    r1 = nrows - 1
-    c1 = ncols - 1
-
-    w = lat2W(nrows, ncols).neighbors
-    for i in range(n):
-        odd = cid[i] % 2
-        if odd:
-            if rid[i] < r1:  # odd col index above last row
-                # new sw neighbor
-                if cid[i] > 0:
-                    j = i + ncols - 1
-                    w[i] = w.get(i, []) + [j]
-                # new se neighbor
-                if cid[i] < c1:
-                    j = i + ncols + 1
-                    w[i] = w.get(i, []) + [j]
-
-        else:  # even col
-            # nw
-            jnw = [i - ncols - 1]
-            # ne
-            jne = [i - ncols + 1]
-            if rid[i] > 0:
-                w[i]
-                if cid[i] == 0:
-                    w[i] = w.get(i, []) + jne
-                elif cid[i] == c1:
-                    w[i] = w.get(i, []) + jnw
-                else:
-                    w[i] = w.get(i, []) + jne
-                    w[i] = w.get(i, []) + jnw
-
-    return W(w, **kwargs)

-
-
-
[docs]def lat2W(nrows=5, ncols=5, rook=True, id_type="int", **kwargs):
-    """
-    Create a W object for a regular lattice.
-
-    Parameters
-    ----------
-
-    nrows      : int
-                 number of rows
-    ncols      : int
-                 number of columns
-    rook       : boolean
-                 type of contiguity. Default is rook. For queen, rook =False
-    id_type    : string
-                 string defining the type of IDs to use in the final W object;
-                 options are 'int' (0, 1, 2 ...; default), 'float' (0.0,
-                 1.0, 2.0, ...) and 'string' ('id0', 'id1', 'id2', ...)
-    **kwargs   : keyword arguments
-                 optional arguments for :class:`pysal.weights.W`
-
-
-    Returns
-    -------
-
-    w : W
-        instance of spatial weights class W
-
-    Notes
-    -----
-
-    Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.
-
-    Examples
-    --------
-
-    >>> from libpysal.weights import lat2W
-    >>> w9 = lat2W(3,3)
-    >>> "%.3f"%w9.pct_nonzero
-    '29.630'
-    >>> w9[0] == {1: 1.0, 3: 1.0}
-    True
-    >>> w9[3] == {0: 1.0, 4: 1.0, 6: 1.0}
-    True
-    """
-    n = nrows * ncols
-    r1 = nrows - 1
-    c1 = ncols - 1
-    rid = [i // ncols for i in range(n)]  # must be floor!
-    cid = [i % ncols for i in range(n)]
-    w = {}
-    r = below = 0
-    for i in range(n - 1):
-        if rid[i] < r1:
-            below = rid[i] + 1
-            r = below * ncols + cid[i]
-            w[i] = w.get(i, []) + [r]
-            w[r] = w.get(r, []) + [i]
-        if cid[i] < c1:
-            right = cid[i] + 1
-            c = rid[i] * ncols + right
-            w[i] = w.get(i, []) + [c]
-            w[c] = w.get(c, []) + [i]
-        if not rook:
-            # southeast bishop
-            if cid[i] < c1 and rid[i] < r1:
-                r = (rid[i] + 1) * ncols + 1 + cid[i]
-                w[i] = w.get(i, []) + [r]
-                w[r] = w.get(r, []) + [i]
-            # southwest bishop
-            if cid[i] > 0 and rid[i] < r1:
-                r = (rid[i] + 1) * ncols - 1 + cid[i]
-                w[i] = w.get(i, []) + [r]
-                w[r] = w.get(r, []) + [i]
-
-    neighbors = {}
-    weights = {}
-    for key in w:
-        weights[key] = [1.0] * len(w[key])
-    ids = list(range(n))
-    if id_type == "string":
-        ids = ["id" + str(i) for i in ids]
-    elif id_type == "float":
-        ids = [i * 1.0 for i in ids]
-    if id_type == "string" or id_type == "float":
-        id_dict = dict(list(zip(list(range(n)), ids)))
-        alt_w = {}
-        alt_weights = {}
-        for i in w:
-            values = [id_dict[j] for j in w[i]]
-            key = id_dict[i]
-            alt_w[key] = values
-            alt_weights[key] = weights[i]
-        w = alt_w
-        weights = alt_weights
-    return W(w, weights, ids=ids, id_order=ids[:], **kwargs)

-
-
-
[docs]def block_weights(regimes, ids=None, sparse=False, **kwargs):
-    """
-    Construct spatial weights for regime neighbors.
-
-    Block contiguity structures are relevant when defining neighbor relations
-    based on membership in a regime. For example, all counties belonging to
-    the same state could be defined as neighbors, in an analysis of all
-    counties in the US.
-
-    Parameters
-    ----------
-    regimes     : list, array
-                  ids of which regime an observation belongs to
-    ids         : list, array
-                  Ordered sequence of IDs for the observations
-    sparse      : boolean
-                  If True return WSP instance
-                  If False return W instance
-    **kwargs    : keyword arguments
-                  optional arguments for :class:`pysal.weights.W`
-
-
-    Returns
-    -------
-
-    W : spatial weights instance
-
-    Examples
-    --------
-    >>> from libpysal.weights import block_weights
-    >>> import numpy as np
-    >>> regimes = np.ones(25)
-    >>> regimes[range(10,20)] = 2
-    >>> regimes[range(21,25)] = 3
-    >>> regimes
-    array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2.,
-           2., 2., 2., 1., 3., 3., 3., 3.])
-    >>> w = block_weights(regimes)
-    >>> w.weights[0]
-    [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
-    >>> w.neighbors[0]
-    [1, 2, 3, 4, 5, 6, 7, 8, 9, 20]
-    >>> regimes = ['n','n','s','s','e','e','w','w','e']
-    >>> n = len(regimes)
-    >>> w = block_weights(regimes)
-    >>> w.neighbors == {0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8], 6: [7], 7: [6], 8: [4, 5]}
-    True
-    """
-    rids = np.unique(regimes)
-    neighbors = {}
-    NPNZ = np.nonzero
-    regimes = np.array(regimes)
-    for rid in rids:
-        members = NPNZ(regimes == rid)[0]
-        for member in members:
-            neighbors[member] = members[NPNZ(members != member)[0]].tolist()
-    w = W(neighbors, **kwargs)
-    if ids is not None:
-        w.remap_ids(ids)
-    if sparse:
-        w = WSP(w.sparse, id_order=ids)
-    return w

-
-
-
[docs]def comb(items, n=None):
-    """
-    Combinations of size n taken from items
-
-    Parameters
-    ----------
-
-    items : list
-            items to be drawn from
-    n     : integer
-            size of combinations to take from items
-
-    Returns
-    -------
-
-    implicit : generator
-               combinations of size n taken from items
-
-    Examples
-    --------
-    >>> x = range(4)
-    >>> for c in comb(x, 2):
-    ...     print(c)
-    ...
-    [0, 1]
-    [0, 2]
-    [0, 3]
-    [1, 2]
-    [1, 3]
-    [2, 3]
-
-    """
-    items = list(items)
-    if n is None:
-        n = len(items)
-    for i in list(range(len(items))):
-        v = items[i : i + 1]
-        if n == 1:
-            yield v
-        else:
-            rest = items[i + 1 :]
-            for c in comb(rest, n - 1):
-                yield v + c

-
-
-
[docs]def order(w, kmax=3):
-    """
-    Determine the non-redundant order of contiguity up to a specific
-    order.
-
-    Parameters
-    ----------
-
-    w       : W
-              spatial weights object
-
-    kmax    : int
-              maximum order of contiguity
-
-    Returns
-    -------
-
-    info : dictionary
-           observation id is the key, value is a list of contiguity
-           orders with a negative 1 in the ith position
-
-    Notes
-    -----
-    Implements the algorithm in :cite:`Anselin1996b`.
-
-    Examples
-    --------
-    >>> from libpysal.weights import order, Rook
-    >>> import libpysal
-    >>> w = Rook.from_shapefile(libpysal.examples.get_path('10740.shp'))
-
-    WARNING: there is one disconnected observation (no neighbors)
-    Island id:  [163]
-    >>> w3 = order(w, kmax = 3)
-    >>> w3[1][0:5]
-    [1, -1, 1, 2, 1]
-
-    """
-
-    ids = w.id_order
-    info = {}
-    for id_ in ids:
-        s = [0] * w.n
-        s[ids.index(id_)] = -1
-        for j in w.neighbors[id_]:
-            s[ids.index(j)] = 1
-        k = 1
-        while k < kmax:
-            knext = k + 1
-            if s.count(k):
-                # get neighbors of order k
-                js = [ids[j] for j, val in enumerate(s) if val == k]
-                # get first order neighbors for order k neighbors
-                for j in js:
-                    next_neighbors = w.neighbors[j]
-                    for neighbor in next_neighbors:
-                        nid = ids.index(neighbor)
-                        if s[nid] == 0:
-                            s[nid] = knext
-            k = knext
-        info[id_] = s
-    return info

-
-
-
[docs]def higher_order(w, k=2, **kwargs):
-    """
-    Contiguity weights object of order k.
-
-    Parameters
-    ----------
-
-    w        : W
-               spatial weights object
-    k        : int
-               order of contiguity
-    **kwargs : keyword arguments
-               optional arguments for :class:`pysal.weights.W`
-
-    Returns
-    -------
-
-    implicit : W
-               spatial weights object
-
-    Notes
-    -----
-    Proper higher order neighbors are returned such that i and j are k-order
-    neighbors iff the shortest path from i-j is of length k.
-
-    Examples
-    --------
-    >>> from libpysal.weights import lat2W, higher_order
-    >>> w10 = lat2W(10, 10)
-    >>> w10_2 = higher_order(w10, 2)
-    >>> w10_2[0] ==  {2: 1.0, 11: 1.0, 20: 1.0}
-    True
-    >>> w5 = lat2W()
-    >>> w5[0] ==  {1: 1.0, 5: 1.0}
-    True
-    >>> w5[1] == {0: 1.0, 2: 1.0, 6: 1.0}
-    True
-    >>> w5_2 = higher_order(w5,2)
-    >>> w5_2[0] == {10: 1.0, 2: 1.0, 6: 1.0}
-    True
-    """
-    return higher_order_sp(w, k, **kwargs)

-
-
-
[docs]def higher_order_sp(
-    w, k=2, shortest_path=True, diagonal=False, lower_order=False, **kwargs
-):
-    """
-    Contiguity weights for either a sparse W or W for order k.
-
-    Parameters
-    ----------
-    w             : W
-                            sparse_matrix, spatial weights object or
-                            scipy.sparse.csr.csr_instance
-    k             : int
-                    Order of contiguity
-    shortest_path : boolean
-                    True: i,j and k-order neighbors if the
-                    shortest path for i,j is k.
-                    False: i,j are k-order neighbors if there
-                    is a path from i,j of length k.
-    diagonal      : boolean
-                    True:  keep k-order (i,j) joins when i==j
-                    False: remove k-order (i,j) joins when i==j
-    lower_order   : boolean
-                    True: include lower order contiguities
-                    False: return only weights of order k
-    **kwargs      : keyword arguments
-                    optional arguments for :class:`pysal.weights.W`
-
-    Returns
-    -------
-    wk : W
-             WSP, type matches type of w argument
-
-
-    Examples
-    --------
-
-    >>> from libpysal.weights import lat2W, higher_order_sp
-    >>> w25 = lat2W(5,5)
-    >>> w25.n
-    25
-    >>> w25[0] == {1: 1.0, 5: 1.0}
-    True
-    >>> w25_2 = higher_order_sp(w25, 2)
-    >>> w25_2[0] == {10: 1.0, 2: 1.0, 6: 1.0}
-    True
-    >>> w25_2 = higher_order_sp(w25, 2, diagonal=True)
-    >>> w25_2[0] ==  {0: 1.0, 10: 1.0, 2: 1.0, 6: 1.0}
-    True
-    >>> w25_3 = higher_order_sp(w25, 3)
-    >>> w25_3[0] == {15: 1.0, 3: 1.0, 11: 1.0, 7: 1.0}
-    True
-    >>> w25_3 = higher_order_sp(w25, 3, shortest_path=False)
-    >>> w25_3[0] == {1: 1.0, 3: 1.0, 5: 1.0, 7: 1.0, 11: 1.0, 15: 1.0}
-    True
-    >>> w25_3 = higher_order_sp(w25, 3, lower_order=True)
-    >>> w25_3[0] == {5: 1.0, 7: 1.0, 11: 1.0, 2: 1.0, 15: 1.0, 6: 1.0, 10: 1.0, 1: 1.0, 3: 1.0}
-    True
-
-    """
-    id_order = None
-    if issubclass(type(w), W) or isinstance(w, W):
-        if np.unique(np.hstack(list(w.weights.values()))) == np.array([1.0]):
-            id_order = w.id_order
-            w = w.sparse
-        else:
-            raise ValueError("Weights are not binary (0,1)")
-    elif scipy.sparse.isspmatrix_csr(w):
-        if not np.unique(w.data) == np.array([1.0]):
-            raise ValueError(
-                "Sparse weights matrix is not binary (0,1) weights matrix."
-            )
-    else:
-        raise TypeError(
-            "Weights provided are neither a binary W object nor "
-            "a scipy.sparse.csr_matrix"
-        )
-
-    if lower_order:
-        wk = sum(map(lambda x: w ** x, range(2, k + 1)))
-        shortest_path = False
-    else:
-        wk = w ** k
-
-    rk, ck = wk.nonzero()
-    sk = set(zip(rk, ck))
-
-    if shortest_path:
-        for j in range(1, k):
-            wj = w ** j
-            rj, cj = wj.nonzero()
-            sj = set(zip(rj, cj))
-            sk.difference_update(sj)
-
-    if not diagonal:
-        sk = set([(i, j) for i, j in sk if i != j])
-
-    if id_order:
-        d = dict([(i, []) for i in id_order])
-        for pair in sk:
-            k, v = pair
-            k = id_order[k]
-            v = id_order[v]
-            d[k].append(v)
-        return W(neighbors=d, **kwargs)
-    else:
-        d = {}
-        for pair in sk:
-            k, v = pair
-            if k in d:
-                d[k].append(v)
-            else:
-                d[k] = [v]
-        return WSP(W(neighbors=d, **kwargs).sparse)

-
-
-
[docs]def w_local_cluster(w):
-    r"""
-    Local clustering coefficients for each unit as a node in a graph.
-
-    Parameters
-    ----------
-
-    w   : W
-          spatial weights object
-
-    Returns
-    -------
-
-    c     : array
-            (w.n,1)
-            local clustering coefficients
-
-    Notes
-    -----
-
-    The local clustering coefficient :math:`c_i` quantifies how close the
-    neighbors of observation :math:`i` are to being a clique:
-
-    .. math::
-
-       c_i = | \{w_{j,k}\} |/ (k_i(k_i - 1)): j,k \in N_i
-
-    where :math:`N_i` is the set of neighbors to :math:`i`, :math:`k_i =
-    |N_i|` and :math:`\{w_{j,k}\}` is the set of non-zero elements of the
-    weights between pairs in :math:`N_i` :cite:`Watts1998`.
-
-    Examples
-    --------
-    >>> from libpysal.weights import lat2W, w_local_cluster
-    >>> w = lat2W(3,3, rook=False)
-    >>> w_local_cluster(w)
-    array([[1.        ],
-           [0.6       ],
-           [1.        ],
-           [0.6       ],
-           [0.42857143],
-           [0.6       ],
-           [1.        ],
-           [0.6       ],
-           [1.        ]])
-
-    """
-
-    c = np.zeros((w.n, 1), float)
-    w.transformation = "b"
-    for i, id in enumerate(w.id_order):
-        ki = max(w.cardinalities[id], 1)  # deal with islands
-        Ni = w.neighbors[id]
-        wi = w_subset(w, Ni).full()[0]
-        c[i] = wi.sum() / (ki * (ki - 1))
-    return c

-
-
-
[docs]def shimbel(w):
-    """
-    Find the Shimbel matrix for first order contiguity matrix.
-
-    Parameters
-    ----------
-    w     : W
-            spatial weights object
-
-    Returns
-    -------
-
-    info  : list
-            list of lists; one list for each observation which stores
-            the shortest order between it and each of the the other observations.
-
-    Examples
-    --------
-    >>> from libpysal.weights import lat2W, shimbel
-    >>> w5 = lat2W()
-    >>> w5_shimbel = shimbel(w5)
-    >>> w5_shimbel[0][24]
-    8
-    >>> w5_shimbel[0][0:4]
-    [-1, 1, 2, 3]
-    """
-
-    info = {}
-    ids = w.id_order
-    for i in ids:
-        s = [0] * w.n
-        s[ids.index(i)] = -1
-        for j in w.neighbors[i]:
-            s[ids.index(j)] = 1
-        k = 1
-        flag = s.count(0)
-        while flag:
-            p = -1
-            knext = k + 1
-            for j in range(s.count(k)):
-                neighbor = s.index(k, p + 1)
-                p = neighbor
-                next_neighbors = w.neighbors[ids[p]]
-                for neighbor in next_neighbors:
-                    nid = ids.index(neighbor)
-                    if s[nid] == 0:
-                        s[nid] = knext
-            k = knext
-            flag = s.count(0)
-        info[i] = s
-    return info

-
-
-
[docs]def full(w):
-    """
-    Generate a full numpy array.
-
-    Parameters
-    ----------
-    w        : W
-               spatial weights object
-
-    Returns
-    -------
-    (fullw, keys) : tuple
-                    first element being the full numpy array and second element
-                    keys being the ids associated with each row in the array.
-
-    Examples
-    --------
-    >>> from libpysal.weights import W, full
-    >>> neighbors = {'first':['second'],'second':['first','third'],'third':['second']}
-    >>> weights = {'first':[1],'second':[1,1],'third':[1]}
-    >>> w = W(neighbors, weights)
-    >>> wf, ids = full(w)
-    >>> wf
-    array([[0., 1., 0.],
-           [1., 0., 1.],
-           [0., 1., 0.]])
-    >>> ids
-    ['first', 'second', 'third']
-    """
-    return w.full()

-
-
-
[docs]def full2W(m, ids=None, **kwargs):
-    """
-    Create a PySAL W object from a full array.
-
-    Parameters
-    ----------
-    m          : array
-                 nxn array with the full weights matrix
-    ids        : list
-                 User ids assumed to be aligned with m
-    **kwargs   : keyword arguments
-                 optional arguments for :class:`pysal.weights.W`
-
-
-    Returns
-    -------
-    w       : W
-              PySAL weights object
-
-    Examples
-    --------
-    >>> from libpysal.weights import full2W
-    >>> import numpy as np
-
-    Create an array of zeros
-
-    >>> a = np.zeros((4, 4))
-
-    For loop to fill it with random numbers
-
-    >>> for i in range(len(a)):
-    ...     for j in range(len(a[i])):
-    ...         if i!=j:
-    ...             a[i, j] = np.random.random(1)
-
-    Create W object
-
-    >>> w = full2W(a)
-    >>> w.full()[0] == a
-    array([[ True,  True,  True,  True],
-           [ True,  True,  True,  True],
-           [ True,  True,  True,  True],
-           [ True,  True,  True,  True]])
-
-    Create list of user ids
-
-    >>> ids = ['myID0', 'myID1', 'myID2', 'myID3']
-    >>> w = full2W(a, ids=ids)
-    >>> w.full()[0] == a
-    array([[ True,  True,  True,  True],
-           [ True,  True,  True,  True],
-           [ True,  True,  True,  True],
-           [ True,  True,  True,  True]])
-    """
-    if m.shape[0] != m.shape[1]:
-        raise ValueError("Your array is not square")
-    neighbors, weights = {}, {}
-    for i in range(m.shape[0]):
-        # for i, row in enumerate(m):
-        row = m[i]
-        if ids:
-            i = ids[i]
-        ngh = list(row.nonzero()[0])
-        weights[i] = list(row[ngh])
-        ngh = list(ngh)
-        if ids:
-            ngh = [ids[j] for j in ngh]
-        neighbors[i] = ngh
-    return W(neighbors, weights, id_order=ids, **kwargs)

-
-
-
[docs]def WSP2W(wsp, **kwargs):
-
-    """
-    Convert a pysal WSP object (thin weights matrix) to a pysal W object.
-
-    Parameters
-    ----------
-    wsp                     : WSP
-                              PySAL sparse weights object
-    **kwargs                : keyword arguments
-                              optional arguments for :class:`pysal.weights.W`
-
-    Returns
-    -------
-    w       : W
-              PySAL weights object
-
-    Examples
-    --------
-    >>> from libpysal.weights import lat2W, WSP, WSP2W
-
-    Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells
-    (rook contiguity), then construct a PySAL sparse weights object (wsp).
-
-    >>> sp = lat2SW(2, 5)
-    >>> wsp = WSP(sp)
-    >>> wsp.n
-    10
-    >>> wsp.sparse[0].todense()
-    matrix([[0, 1, 0, 0, 0, 1, 0, 0, 0, 0]], dtype=int8)
-
-    Convert this sparse weights object to a standard PySAL weights object.
-
-    >>> w = WSP2W(wsp)
-    >>> w.n
-    10
-    >>> print(w.full()[0][0])
-    [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
-
-
-    """
-    wsp.sparse
-    indices = wsp.sparse.indices
-    data = wsp.sparse.data
-    indptr = wsp.sparse.indptr
-    id_order = wsp.id_order
-    if id_order:
-        # replace indices with user IDs
-        indices = [id_order[i] for i in indices]
-    else:
-        id_order = list(range(wsp.n))
-    neighbors, weights = {}, {}
-    start = indptr[0]
-    for i in range(wsp.n):
-        oid = id_order[i]
-        end = indptr[i + 1]
-        neighbors[oid] = indices[start:end]
-        weights[oid] = data[start:end]
-        start = end
-    ids = copy.copy(wsp.id_order)
-    w = W(neighbors, weights, ids, **kwargs)
-    w._sparse = copy.deepcopy(wsp.sparse)
-    w._cache["sparse"] = w._sparse
-    return w

-
-
-def insert_diagonal(w, val=1.0, wsp=False):
-    warn("This function is deprecated. Use fill_diagonal instead.")
-    return fill_diagonal(w, val=val, wsp=wsp)
-
-
-
[docs]def fill_diagonal(w, val=1.0, wsp=False):
-    """
-    Returns a new weights object with values inserted along the main diagonal.
-
-    Parameters
-    ----------
-    w        : W
-               Spatial weights object
-
-    diagonal : float, int or array
-               Defines the value(s) to which the weights matrix diagonal should
-               be set. If a constant is passed then each element along the
-               diagonal will get this value (default is 1.0). An array of length
-               w.n can be passed to set explicit values to each element along
-               the diagonal (assumed to be in the same order as w.id_order).
-
-    wsp      : boolean
-               If True return a thin weights object of the type WSP, if False
-               return the standard W object.
-
-    Returns
-    -------
-    w        : W
-               Spatial weights object
-
-    Examples
-    --------
-    >>> from libpysal.weights import lat2W
-    >>> import numpy as np
-
-    Build a basic rook weights matrix, which has zeros on the diagonal, then
-    insert ones along the diagonal.
-
-    >>> w = lat2W(5, 5, id_type='string')
-    >>> w_const = insert_diagonal(w)
-    >>> w['id0'] ==  {'id5': 1.0, 'id1': 1.0}
-    True
-    >>> w_const['id0'] == {'id5': 1.0, 'id0': 1.0, 'id1': 1.0}
-    True
-
-    Insert different values along the main diagonal.
-
-    >>> diag = np.arange(100, 125)
-    >>> w_var = insert_diagonal(w, diag)
-    >>> w_var['id0'] == {'id5': 1.0, 'id0': 100.0, 'id1': 1.0}
-    True
-
-    """
-
-    w_new = copy.deepcopy(w.sparse)
-    w_new = w_new.tolil()
-    if issubclass(type(val), np.ndarray):
-        if w.n != val.shape[0]:
-            raise Exception("shape of w and diagonal do not match")
-        w_new.setdiag(val)
-    elif isinstance(val, numbers.Number):
-        w_new.setdiag([val] * w.n)
-    else:
-        raise Exception("Invalid value passed to diagonal")
-    w_out = WSP(w_new, copy.copy(w.id_order))
-    if wsp:
-        return w_out
-    else:
-        return WSP2W(w_out)

-
-
-
[docs]def remap_ids(w, old2new, id_order=[], **kwargs):
-    """
-    Remaps the IDs in a spatial weights object.
-
-    Parameters
-    ----------
-    w        : W
-               Spatial weights object
-
-    old2new  : dictionary
-               Dictionary where the keys are the IDs in w (i.e. "old IDs") and
-               the values are the IDs to replace them (i.e. "new IDs")
-
-    id_order : list
-               An ordered list of new IDs, which defines the order of observations when
-               iterating over W. If not set then the id_order in w will be
-               used.
-    **kwargs : keyword arguments
-               optional arguments for :class:`pysal.weights.W`
-
-
-    Returns
-    -------
-
-    implicit : W
-               Spatial weights object with new IDs
-
-    Examples
-    --------
-    >>> from libpysal.weights import lat2W
-    >>> w = lat2W(3,2)
-    >>> w.id_order
-    [0, 1, 2, 3, 4, 5]
-    >>> w.neighbors[0]
-    [2, 1]
-    >>> old_to_new = {0:'a', 1:'b', 2:'c', 3:'d', 4:'e', 5:'f'}
-    >>> w_new = remap_ids(w, old_to_new)
-    >>> w_new.id_order
-    ['a', 'b', 'c', 'd', 'e', 'f']
-    >>> w_new.neighbors['a']
-    ['c', 'b']
-
-    """
-
-    if not isinstance(w, W):
-        raise Exception("w must be a spatial weights object")
-    new_neigh = {}
-    new_weights = {}
-    for key, value in list(w.neighbors.items()):
-        new_values = [old2new[i] for i in value]
-        new_key = old2new[key]
-        new_neigh[new_key] = new_values
-        new_weights[new_key] = copy.copy(w.weights[key])
-    if id_order:
-        return W(new_neigh, new_weights, id_order, **kwargs)
-    else:
-        if w.id_order:
-            id_order = [old2new[i] for i in w.id_order]
-            return W(new_neigh, new_weights, id_order, **kwargs)
-        else:
-            return W(new_neigh, new_weights, **kwargs)

-
-
-
[docs]def get_ids(in_shps, idVariable):
-    """
-    Gets the IDs from the DBF file that moves with a given shape file or
-    a geopandas.GeoDataFrame.
-
-    Parameters
-    ----------
-    in_shps      : str or geopandas.GeoDataFrame
-                   The input geographic data. Either
-                   (1) a path to a shapefile including suffix (str); or
-                   (2) a geopandas.GeoDataFrame.
-    idVariable   : str
-                   name of a column in the shapefile's DBF or the
-                   geopandas.GeoDataFrame to use for ids.
-
-    Returns
-    -------
-    ids          : list
-                   a list of IDs
-
-    Examples
-    --------
-    >>> from libpysal.weights.util import get_ids
-    >>> import libpysal
-    >>> polyids = get_ids(libpysal.examples.get_path("columbus.shp"), "POLYID")
-    >>> polyids[:5]
-    [1, 2, 3, 4, 5]
-
-    >>> from libpysal.weights.util import get_ids
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> gdf = gpd.read_file(libpysal.examples.get_path("columbus.shp"))
-    >>> polyids = gdf["POLYID"]
-    >>> polyids[:5]
-    0    1
-    1    2
-    2    3
-    3    4
-    4    5
-    Name: POLYID, dtype: int64
-
-    """
-
-    try:
-        if type(in_shps) == str:
-            dbname = os.path.splitext(in_shps)[0] + ".dbf"
-            db = psopen(dbname)
-            cols = db.header
-            var = db.by_col[idVariable]
-            db.close()
-        else:
-            cols = list(in_shps.columns)
-            var = list(in_shps[idVariable])
-        return var
-
-    except IOError:
-        msg = (
-            'The shapefile "%s" appears to be missing its DBF file. '
-            + ' The DBF file "%s" could not be found.' % (in_shps, dbname)
-        )
-        raise IOError(msg)
-    except (AttributeError, KeyError):
-        msg = (
-            'The variable "%s" not found in the DBF/GDF. The the following '
-            + "variables are present: %s." % (idVariable, ",".join(cols))
-        )
-        raise KeyError(msg)

-
-
-def get_points_array(iterable):
-    """
-    Gets a data array of x and y coordinates from a given iterable
-    Parameters
-    ----------
-    iterable      : iterable
-                    arbitrary collection of shapes that supports iteration
-
-    Returns
-    -------
-    points        : array
-                    (n, 2)
-                    a data array of x and y coordinates
-
-    Notes
-    -----
-    If the given shape file includes polygons,
-    this function returns x and y coordinates of the polygons' centroids
-
-    """
-    first_choice, backup = tee(iterable)
-    try:
-        data = np.vstack([np.array(shape.centroid) for shape in first_choice])
-    except AttributeError:
-        data = np.vstack([shape for shape in backup])
-    return data
-
-
-
[docs]def get_points_array_from_shapefile(shapefile):
-    """
-    Gets a data array of x and y coordinates from a given shapefile.
-
-    Parameters
-    ----------
-    shapefile     : string
-                    name of a shape file including suffix
-
-    Returns
-    -------
-    points        : array
-                    (n, 2)
-                    a data array of x and y coordinates
-
-    Notes
-    -----
-    If the given shape file includes polygons,
-    this function returns x and y coordinates of the polygons' centroids
-
-    Examples
-    --------
-    Point shapefile
-
-    >>> import libpysal
-    >>> from libpysal.weights.util import get_points_array_from_shapefile
-    >>> xy = get_points_array_from_shapefile(libpysal.examples.get_path('juvenile.shp'))
-    >>> xy[:3]
-    array([[94., 93.],
-           [80., 95.],
-           [79., 90.]])
-
-
-    Polygon shapefile
-
-    >>> xy = get_points_array_from_shapefile(libpysal.examples.get_path('columbus.shp'))
-    >>> xy[:3]
-    array([[ 8.82721847, 14.36907602],
-           [ 8.33265837, 14.03162401],
-           [ 9.01226541, 13.81971908]])
-    """
-
-    f = psopen(shapefile)
-    data = get_points_array(f)
-    return data

-
-
-
[docs]def min_threshold_distance(data, p=2):
-    """
-    Get the maximum nearest neighbor distance.
-
-    Parameters
-    ----------
-
-    data    : array
-              (n,k) or KDTree where KDtree.data is array (n,k)
-              n observations on k attributes
-    p       : float
-              Minkowski p-norm distance metric parameter:
-              1<=p<=infinity
-              2: Euclidean distance
-              1: Manhattan distance
-
-    Returns
-    -------
-    nnd    : float
-             maximum nearest neighbor distance between the n observations
-
-    Examples
-    --------
-    >>> from libpysal.weights.util import min_threshold_distance
-    >>> import numpy as np
-    >>> x, y = np.indices((5, 5))
-    >>> x.shape = (25, 1)
-    >>> y.shape = (25, 1)
-    >>> data = np.hstack([x, y])
-    >>> min_threshold_distance(data)
-    1.0
-
-    """
-    if issubclass(type(data), scipy.spatial.KDTree):
-        kd = data
-        data = kd.data
-    else:
-        kd = KDTree(data)
-    nn = kd.query(data, k=2, p=p)
-    nnd = nn[0].max(axis=0)[1]
-    return nnd

-
-
-
[docs]def lat2SW(nrows=3, ncols=5, criterion="rook", row_st=False):
-    """
-    Create a sparse W matrix for a regular lattice.
-
-    Parameters
-    ----------
-
-    nrows   : int
-              number of rows
-    ncols   : int
-              number of columns
-    rook    : {"rook", "queen", "bishop"}
-              type of contiguity. Default is rook.
-    row_st  : boolean
-              If True, the created sparse W object is row-standardized so
-              every row sums up to one. Defaults to False.
-
-    Returns
-    -------
-
-    w : scipy.sparse.dia_matrix
-        instance of a scipy sparse matrix
-
-    Notes
-    -----
-
-    Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.
-    This method directly creates the W matrix using the strucuture of the contiguity type.
-
-    Examples
-    --------
-
-    >>> from libpysal.weights import lat2SW
-    >>> w9 = lat2SW(3,3)
-    >>> w9[0,1] == 1
-    True
-    >>> w9[3,6] == 1
-    True
-    >>> w9r = lat2SW(3,3, row_st=True)
-    >>> w9r[3,6] == 1./3
-    True
-    """
-
-    n = nrows * ncols
-    diagonals = []
-    offsets = []
-    if criterion == "rook" or criterion == "queen":
-        d = np.ones((1, n))
-        for i in range(ncols - 1, n, ncols):
-            d[0, i] = 0
-        diagonals.append(d)
-        offsets.append(-1)
-
-        d = np.ones((1, n))
-        diagonals.append(d)
-        offsets.append(-ncols)
-
-    if criterion == "queen" or criterion == "bishop":
-        d = np.ones((1, n))
-        for i in range(0, n, ncols):
-            d[0, i] = 0
-        diagonals.append(d)
-        offsets.append(-(ncols - 1))
-
-        d = np.ones((1, n))
-        for i in range(ncols - 1, n, ncols):
-            d[0, i] = 0
-        diagonals.append(d)
-        offsets.append(-(ncols + 1))
-    data = np.concatenate(diagonals)
-    offsets = np.array(offsets)
-    m = sparse.dia_matrix((data, offsets), shape=(n, n), dtype=np.int8)
-    m = m + m.T
-    if row_st:
-        m = sparse.spdiags(1.0 / m.sum(1).T, 0, *m.shape) * m
-    return m

-
-
-def write_gal(file, k=10):
-    f = open(file, "w")
-    n = k * k
-    f.write("0 %d" % n)
-    for i in range(n):
-        row = i / k
-        col = i % k
-        neighs = [i - i, i + 1, i - k, i + k]
-        neighs = [j for j in neighs if j >= 0 and j < n]
-        f.write("\n%d %d\n" % (i, len(neighs)))
-        f.write(" ".join(map(str, neighs)))
-    f.close()
-
-
-
[docs]def neighbor_equality(w1, w2):
-    """
-    Test if the neighbor sets are equal between two weights objects
-
-    Parameters
-    ----------
-
-    w1 : W
-        instance of spatial weights class W
-
-    w2 : W
-        instance of spatial weights class W
-
-    Returns
-    -------
-    Boolean
-
-
-    Notes
-    -----
-    Only set membership is evaluated, no check of the weight values is carried out.
-
-
-    Examples
-    --------
-    >>> from libpysal.weights.util import neighbor_equality
-    >>> from libpysal.weights import lat2W, W
-    >>> w1 = lat2W(3,3)
-    >>> w2 = lat2W(3,3)
-    >>> neighbor_equality(w1, w2)
-    True
-    >>> w3 = lat2W(5,5)
-    >>> neighbor_equality(w1, w3)
-    False
-    >>> n4 = w1.neighbors.copy()
-    >>> n4[0] = [1]
-    >>> n4[1] = [4, 2]
-    >>> w4 = W(n4)
-    >>> neighbor_equality(w1, w4)
-    False
-    >>> n5 = w1.neighbors.copy()
-    >>> n5[0]
-    [3, 1]
-    >>> n5[0] = [1, 3]
-    >>> w5 = W(n5)
-    >>> neighbor_equality(w1, w5)
-    True
-
-    """
-    n1 = w1.neighbors
-    n2 = w2.neighbors
-    ids_1 = set(n1.keys())
-    ids_2 = set(n2.keys())
-    if ids_1 != ids_2:
-        return False
-    for i in ids_1:
-        if set(w1.neighbors[i]) != set(w2.neighbors[i]):
-            return False
-    return True

-
-
-def isKDTree(obj):
-    """
-    This is a utility function to determine whether or not an object is a
-    KDTree, since KDTree and cKDTree have no common parent type
-    """
-    return any([issubclass(type(obj), KDTYPE) for KDTYPE in KDTREE_TYPES])
-
-
-
[docs]def attach_islands(w, w_knn1, **kwargs):
-    """
-    Attach nearest neighbor to islands in spatial weight w.
-
-    Parameters
-    ----------
-
-    w            : libpysal.weights.W
-                   pysal spatial weight object (unstandardized).
-    w_knn1       : libpysal.weights.W
-                   Nearest neighbor pysal spatial weight object (k=1).
-    **kwargs     : keyword arguments
-                   optional arguments for :class:`pysal.weights.W`
-
-
-    Returns
-    -------
-                 : libpysal.weights.W
-                   pysal spatial weight object w without islands.
-
-    Examples
-    --------
-    >>> from libpysal.weights import lat2W, Rook, KNN, attach_islands
-    >>> import libpysal
-    >>> w = Rook.from_shapefile(libpysal.examples.get_path('10740.shp'))
-    >>> w.islands
-    [163]
-    >>> w_knn1 = KNN.from_shapefile(libpysal.examples.get_path('10740.shp'),k=1)
-    >>> w_attach = attach_islands(w, w_knn1)
-    >>> w_attach.islands
-    []
-    >>> w_attach[w.islands[0]]
-    {166: 1.0}
-
-    """
-
-    neighbors, weights = copy.deepcopy(w.neighbors), copy.deepcopy(w.weights)
-    if not len(w.islands):
-        print("There are no disconnected observations (no islands)!")
-        return w
-    else:
-        for island in w.islands:
-            nb = w_knn1.neighbors[island][0]
-            if type(island) is float:
-                nb = float(nb)
-            neighbors[island] = [nb]
-            weights[island] = [1.0]
-            neighbors[nb] = neighbors[nb] + [island]
-            weights[nb] = weights[nb] + [1.0]
-        return W(neighbors, weights, id_order=w.id_order, **kwargs)

-
-
-
[docs]def nonplanar_neighbors(w, geodataframe, tolerance=0.001, **kwargs):
-    """
-    Detect neighbors for non-planar polygon collections
-
-
-    Parameters
-    ----------
-
-    w:   pysal W
-         A spatial weights object with reported islands
-
-
-    geodataframe: GeoDataframe
-                  The polygon dataframe from which w was constructed.
-
-    tolerance: float
-               The percentage of the minimum horizontal or vertical extent (minextent) of
-               the dataframe to use in defining  a buffering distance to allow for fuzzy
-               contiguity detection. The buffering distance is equal to tolerance*minextent.
-    **kwargs:  keyword arguments
-               optional arguments for :class:`pysal.weights.W`
-
-
-    Attributes
-    ----------
-
-    non_planar_joins : dictionary
-               Stores the new joins detected. Key is the id of the focal unit, value is a list of neighbor ids.
-
-    Returns
-    -------
-
-    w: pysal W
-       Spatial weights object that encodes fuzzy neighbors.
-       This will have an attribute `non_planar_joins` to indicate what new joins were detected.
-
-    Notes
-    -----
-
-    This relaxes the notion of contiguity neighbors for the case of shapefiles
-    that violate the condition of planar enforcement. It handles three types
-    of conditions present in such files that would result in islands when using
-    the regular PySAL contiguity methods. The first are edges for nearby
-    polygons that should be shared, but are digitized separately for the
-    individual polygons and the resulting edges do not coincide, but instead
-    the edges intersect. The second case is similar to the first, only the
-    resultant edges do not intersect but are "close". The final case arises
-    when one polygon is "inside" a second polygon but is not encoded to
-    represent a hole in the containing polygon.
-
-    The buffering check assumes the geometry coordinates are projected.
-
-    Examples
-    --------
-
-    >>> import geopandas as gpd
-    >>> import libpysal
-    >>> df = gpd.read_file(libpysal.examples.get_path('map_RS_BR.shp'))
-    >>> w = libpysal.weights.Queen.from_dataframe(df)
-    >>> w.islands
-    [0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374]
-    >>> wnp = libpysal.weights.nonplanar_neighbors(w, df)
-    >>> wnp.islands
-    []
-    >>> w.neighbors[0]
-    []
-    >>> wnp.neighbors[0]
-    [23, 59, 152, 239]
-    >>> wnp.neighbors[23]
-    [0, 45, 59, 107, 152, 185, 246]
-
-    Also see `nonplanarweights.ipynb`
-
-    References
-    ----------
-
-    Planar Enforcement: http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6
-
-
-    """
-
-    gdf = geodataframe
-    assert (
-        gdf.sindex
-    ), "GeoDataFrame must have a spatial index. Please make sure you have `libspatialindex` installed"
-    islands = w.islands
-    joins = copy.deepcopy(w.neighbors)
-    candidates = gdf.geometry
-    fixes = defaultdict(list)
-
-    # first check for intersecting polygons
-    for island in islands:
-        focal = gdf.iloc[island].geometry
-        neighbors = [
-            j
-            for j, candidate in enumerate(candidates)
-            if focal.intersects(candidate) and j != island
-        ]
-        if len(neighbors) > 0:
-            for neighbor in neighbors:
-                if neighbor not in joins[island]:
-                    fixes[island].append(neighbor)
-                    joins[island].append(neighbor)
-                if island not in joins[neighbor]:
-                    fixes[neighbor].append(island)
-                    joins[neighbor].append(island)
-
-    # if any islands remain, dilate them and check for intersection
-    if islands:
-        x0, y0, x1, y1 = gdf.total_bounds
-        distance = tolerance * min(x1 - x0, y1 - y0)
-        for island in islands:
-            dilated = gdf.iloc[island].geometry.buffer(distance)
-            neighbors = [
-                j
-                for j, candidate in enumerate(candidates)
-                if dilated.intersects(candidate) and j != island
-            ]
-            if len(neighbors) > 0:
-                for neighbor in neighbors:
-                    if neighbor not in joins[island]:
-                        fixes[island].append(neighbor)
-                        joins[island].append(neighbor)
-                    if island not in joins[neighbor]:
-                        fixes[neighbor].append(island)
-                        joins[neighbor].append(island)
-
-    w = W(joins, **kwargs)
-    w.non_planar_joins = fixes
-    return w

-
-
[docs]@requires('geopandas')
-def fuzzy_contiguity(gdf, tolerance=0.005, buffering=False, drop=True, buffer=None, predicate='intersects', **kwargs):
-    """
-    Fuzzy contiguity spatial weights
-
-    Parameters
-    ----------
-
-    gdf:   GeoDataFrame
-
-    tolerance: float
-               The percentage of the length of the minimum side of the bounding rectangle for the GeoDataFrame to use in determining the buffering distance.
-
-    buffering: boolean
-               If False (default) joins will only be detected for features that intersect (touch, contain, within).
-               If True then features will be buffered and intersections will be based on buffered features.
-
-    drop: boolean
-          If True (default), the buffered features are removed from the GeoDataFrame. If False, buffered features are added to the GeoDataFrame.
-
-    buffer : float
-             Specify exact buffering distance. Ignores `tolerance`.
-
-    predicate : {'intersects', 'within', 'contains', 'overlaps', 'crosses', 'touches'}
-                The predicate to use for determination of neighbors. Default is 'intersects'. If None is passed, neighbours are determined based on
-                the intersection of bounding boxes.
-
-    **kwargs: keyword arguments
-              optional arguments for :class:`pysal.weights.W`
-
-
-    Returns
-    -------
-
-    w:  PySAL W
-        Spatial weights based on fuzzy contiguity. Weights are binary.
-
-    Examples
-    --------
-
-    >>> import libpysal
-    >>> from libpysal.weights import fuzzy_contiguity
-    >>> import geopandas as gpd
-    >>> rs = libpysal.examples.get_path('map_RS_BR.shp')
-    >>> rs_df = gpd.read_file(rs)
-    >>> wq = libpysal.weights.Queen.from_dataframe(rs_df)
-    >>> len(wq.islands)
-    29
-    >>> wq[0]
-    {}
-    >>> wf = fuzzy_contiguity(rs_df)
-    >>> wf.islands
-    []
-    >>> wf[0] == dict({239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0})
-    True
-
-    Example needing to use buffering
-
-    >>> from shapely.geometry import Polygon
-    >>> p0 = Polygon([(0,0), (10,0), (10,10)])
-    >>> p1 = Polygon([(10,1), (10,2), (15,2)])
-    >>> p2 = Polygon([(12,2.001), (14, 2.001), (13,10)])
-    >>> gs = gpd.GeoSeries([p0,p1,p2])
-    >>> gdf = gpd.GeoDataFrame(geometry=gs)
-    >>> wf = fuzzy_contiguity(gdf)
-    >>> wf.islands
-    [2]
-    >>> wfb = fuzzy_contiguity(gdf, buffering=True)
-    >>> wfb.islands
-    []
-    >>> wfb[2]
-    {1: 1.0}
-
-    Example with a custom index
-
-    >>> rs_df_ix = rs_df.set_index("NM_MUNICIP")
-    >>> wf_ix = fuzzy_contiguity(rs_df)
-    >>> wf_ix.neighbors["TAVARES"]
-    ['SÃO JOSÉ DO NORTE', 'MOSTARDAS']
-
-    Notes
-    -----
-
-    This relaxes the notion of contiguity neighbors for the case of feature
-    collections that violate the condition of planar enforcement. It handles
-    three types of conditions present in such collections that would result in
-    islands when using the regular PySAL contiguity methods. The first are
-    edges for nearby polygons that should be shared, but are digitized
-    separately for the individual polygons and the resulting edges do not
-    coincide, but instead the edges intersect. The second case is similar to
-    the first, only the resultant edges do not intersect but are "close". The
-    final case arises when one polygon is "inside" a second polygon but is not
-    encoded to represent a hole in the containing polygon.
-
-    Detection of the second case will require setting buffering=True and exploring different values for tolerance.
-
-    The buffering check assumes the geometry coordinates are projected.
-
-
-    References
-    ----------
-
-    Planar Enforcement: http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6
-
-
-    """
-    if buffering:
-        if not buffer:
-            # buffer each shape
-            minx, miny, maxx, maxy = gdf.total_bounds
-            buffer = tolerance * 0.5 * abs(min(maxx - minx, maxy - miny))
-        # create new geometry column
-        new_geometry = gdf.geometry.buffer(buffer)
-        gdf["_buffer"] = new_geometry
-        old_geometry_name = gdf.geometry.name
-        gdf.set_geometry('_buffer', inplace=True)
-
-    neighbors = {}
-    if GPD_08:
-        # query tree based on set predicate
-        inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)
-        # remove self hits
-        itself = inp == res
-        inp = inp[~itself]
-        res = res[~itself]
-
-        # extract index values of neighbors
-        for i, ix in enumerate(gdf.index):
-            ids = gdf.index[res[inp == i]].tolist()
-            neighbors[ix] = ids
-    else:
-        if predicate != 'intersects':
-            raise ValueError(f'Predicate `{predicate}` requires geopandas >= 0.8.0.')
-        tree = gdf.sindex
-        for i, (ix, geom) in enumerate(gdf.geometry.iteritems()):
-            hits = list(tree.intersection(geom.bounds))
-            hits.remove(i)
-            possible = gdf.iloc[hits]
-            ids = possible[possible.intersects(geom)].index.tolist()
-            neighbors[ix] = ids
-
-    if buffering:
-        gdf.set_geometry(old_geometry_name, inplace=True)
-        if drop:
-            gdf.drop(columns=["_buffer"], inplace=True)
-
-    return W(neighbors, **kwargs)

-
-
-if __name__ == "__main__":
-
-    from libpysal.weights import lat2W
-
-    assert (lat2W(5, 5).sparse.todense() == lat2SW(5, 5).todense()).all()
-    assert (lat2W(5, 3).sparse.todense() == lat2SW(5, 3).todense()).all()
-    assert (
-        lat2W(5, 3, rook=False).sparse.todense() == lat2SW(5, 3, "queen").todense()
-    ).all()
-    assert (
-        lat2W(50, 50, rook=False).sparse.todense() == lat2SW(50, 50, "queen").todense()
-    ).all()
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_modules/libpysal/weights/weights.html b/docs/_modules/libpysal/weights/weights.html deleted file mode 100644 index f86bf0a9c..000000000 --- a/docs/_modules/libpysal/weights/weights.html +++ /dev/null @@ -1,1655 +0,0 @@ - - - - - - - libpysal.weights.weights — libpysal v4.4.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for libpysal.weights.weights

-"""
-Weights.
-"""
-__author__ = "Sergio J. Rey <srey@asu.edu>"
-
-import copy
-from os.path import basename as BASENAME
-import math
-import warnings
-import numpy as np
-import scipy.sparse
-from scipy.sparse.csgraph import connected_components
-
-# from .util import full, WSP2W resolve import cycle by
-# forcing these into methods
-from . import adjtools
-from ..io.fileio import FileIO as popen
-
-__all__ = ["W", "WSP"]
-
-
-
[docs]class W(object):
-    """
-    Spatial weights class. Class attributes are described by their
-    docstrings. to view, use the ``help`` function.
-
-    Parameters
-    ----------
-    
-    neighbors : dict
-        Key is region ID, value is a list of neighbor IDS.
-        For example, ``{'a':['b'],'b':['a','c'],'c':['b']}``.
-    weights : dict
-       Key is region ID, value is a list of edge weights.
-       If not supplied all edge weights are assumed to have a weight of 1.
-       For example, ``{'a':[0.5],'b':[0.5,1.5],'c':[1.5]}``.
-    id_order : list
-       An ordered list of ids, defines the order of observations when
-       iterating over ``W`` if not set, lexicographical ordering is used
-       to iterate and the ``id_order_set`` property will return ``False``.
-       This can be set after creation by setting the ``id_order`` property.
-    silence_warnings : bool
-       By default ``libpysal`` will print a warning if the dataset contains
-       any disconnected components or islands. To silence this warning set this
-       parameter to ``True``.
-    ids : list
-        Values to use for keys of the neighbors and weights ``dict`` objects.
-
-    Attributes
-    ----------
-    
-    asymmetries
-    cardinalities
-    component_labels
-    diagW2
-    diagWtW
-    diagWtW_WW
-    histogram
-    id2i
-    id_order
-    id_order_set
-    islands
-    max_neighbors
-    mean_neighbors
-    min_neighbors
-    n
-    n_components
-    neighbor_offsets
-    nonzero
-    pct_nonzero
-    s0
-    s1
-    s2
-    s2array
-    sd
-    sparse
-    trcW2
-    trcWtW
-    trcWtW_WW
-    transform
-
-    Examples
-    --------
-    
-    >>> from libpysal.weights import W
-    >>> neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}
-    >>> weights = {0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1, 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1]}
-    >>> w = W(neighbors, weights)
-    >>> "%.3f"%w.pct_nonzero
-    '29.630'
-
-    Read from external `.gal file <https://geodacenter.github.io/workbook/4a_contig_weights/lab4a.html#gal-weights-file>`_.
-
-    >>> import libpysal
-    >>> w = libpysal.io.open(libpysal.examples.get_path("stl.gal")).read()
-    >>> w.n
-    78
-    >>> "%.3f"%w.pct_nonzero
-    '6.542'
-
-    Set weights implicitly.
-
-    >>> neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}
-    >>> w = W(neighbors)
-    >>> round(w.pct_nonzero,3)
-    29.63
-    >>> from libpysal.weights import lat2W
-    >>> w = lat2W(100, 100)
-    >>> w.trcW2
-    39600.0
-    >>> w.trcWtW
-    39600.0
-    >>> w.transform='r'
-    >>> round(w.trcW2, 3)
-    2530.722
-    >>> round(w.trcWtW, 3)
-    2533.667
-
-    Cardinality Histogram:
-    
-    >>> w.histogram
-    [(2, 4), (3, 392), (4, 9604)]
-
-    Disconnected observations (islands):
-
-    >>> from libpysal.weights import W
-    >>> w = W({1:[0],0:[1],2:[], 3:[]})
-
-    UserWarning: The weights matrix is not fully connected:
-    There are 3 disconnected components.
-    There are 2 islands with ids: 2, 3.
-
-    """
-
-
[docs]    def __init__(
-        self, neighbors, weights=None, id_order=None, silence_warnings=False, ids=None
-    ):
-        self.silence_warnings = silence_warnings
-        self.transformations = {}
-        self.neighbors = neighbors
-        if not weights:
-            weights = {}
-            for key in neighbors:
-                weights[key] = [1.0] * len(neighbors[key])
-        self.weights = weights
-        self.transformations["O"] = self.weights.copy()  # original weights
-        self.transform = "O"
-        if id_order is None:
-            self._id_order = list(self.neighbors.keys())
-            self._id_order.sort()
-            self._id_order_set = False
-        else:
-            self._id_order = id_order
-            self._id_order_set = True
-        self._reset()
-        self._n = len(self.weights)
-        if not self.silence_warnings and self.n_components > 1:
-            message = (
-                "The weights matrix is not fully connected: "
-                "\n There are %d disconnected components." % self.n_components
-            )
-            ni = len(self.islands)
-            if ni == 1:
-                message = message + "\n There is 1 island with id: " "%s." % (
-                    str(self.islands[0])
-                )
-            elif ni > 1:
-                message = message + "\n There are %d islands with ids: %s." % (
-                    ni,
-                    ", ".join(str(island) for island in self.islands),
-                )
-            warnings.warn(message)

-
-    def _reset(self):
-        """Reset properties."""
-        self._cache = {}
-
-
[docs]    def to_file(self, path="", format=None):
-        """
-        Write a weights to a file. The format is guessed automatically 
-        from the path, but can be overridden with the format argument. 
-
-        See libpysal.io.FileIO for more information. 
-
-        Arguments
-        ---------
-        path    :   string
-                    location to save the file
-        format  :   string
-                    string denoting the format to write the weights to. 
-
-
-        Returns
-        -------
-        None
-        """
-        f = popen(dataPath=path, mode="w", dataFormat=format)
-        f.write(self)
-        f.close()

-
-
[docs]    @classmethod
-    def from_file(cls, path="", format=None):
-        """
-        Read a weights file into a W object. 
-
-        Arguments
-        ---------
-        path    :   string
-                    location to save the file
-        format  :   string
-                    string denoting the format to write the weights to. 
-
-        Returns
-        -------
-        W object
-        """
-        f = popen(dataPath=path, mode="r", dataFormat=format)
-        w = f.read()
-        f.close()
-        return w

-
-
[docs]    @classmethod
-    def from_shapefile(cls, *args, **kwargs):
-        # we could also just "do the right thing," but I think it'd make sense to
-        # try and get people to use `Rook.from_shapefile(shapefile)` rather than
-        # W.from_shapefile(shapefile, type=`rook`), otherwise we'd need to build
-        # a type dispatch table. Generic W should be for stuff we don't know
-        # anything about.
-        raise NotImplementedError(
-            "Use type-specific constructors, like Rook,"
-            " Queen, DistanceBand, or Kernel"
-        )

-
-
[docs]    @classmethod
-    def from_WSP(cls, WSP, silence_warnings=True):
-        return WSP2W(WSP, silence_warnings=silence_warnings)

-
-
[docs]    @classmethod
-    def from_adjlist(
-        cls, adjlist, focal_col="focal", neighbor_col="neighbor", weight_col=None
-    ):
-        """
-        Return an adjacency list representation of a weights object.
-
-        Parameters
-        ----------
-        
-        adjlist : pandas.DataFrame
-            Adjacency list with a minimum of two columns.
-        focal_col : str
-            Name of the column with the "source" node ids.
-        neighbor_col : str
-            Name of the column with the "destination" node ids.
-        weight_col : str
-            Name of the column with the weight information. If not provided and
-            the dataframe has no column named "weight" then all weights
-            are assumed to be 1.
-        """
-        if weight_col is None:
-            weight_col = "weight"
-        try_weightcol = getattr(adjlist, weight_col)
-        if try_weightcol is None:
-            adjlist = adjlist.copy(deep=True)
-            adjlist["weight"] = 1
-        all_ids = set(adjlist[focal_col].tolist())
-        all_ids |= set(adjlist[neighbor_col].tolist())
-        grouper = adjlist.groupby(focal_col)
-        neighbors = grouper[neighbor_col].apply(list).to_dict()
-        weights = grouper[weight_col].apply(list).to_dict()
-        neighbors.update({k: [] for k in all_ids.difference(list(neighbors.keys()))})
-        weights.update({k: [] for k in all_ids.difference(list(weights.keys()))})
-        return cls(neighbors=neighbors, weights=weights)

-
-
[docs]    def to_adjlist(
-        self,
-        remove_symmetric=False,
-        focal_col="focal",
-        neighbor_col="neighbor",
-        weight_col="weight",
-    ):
-        """
-        Compute an adjacency list representation of a weights object.
-
-        Parameters
-        ----------
-        remove_symmetric : bool
-            Whether or not to remove symmetric entries. If the ``W``
-            is symmetric, a standard directed adjacency list will contain
-            both the forward and backward links by default because adjacency
-            lists are a directed graph representation. If this is ``True``,
-            a ``W`` created from this adjacency list **MAY NOT BE THE SAME**
-            as the original ``W``. If you would like to consider (1,2) and
-            (2,1) as distinct links, leave this as ``False``.
-        focal_col : str
-            Name of the column in which to store "source" node ids.
-        neighbor_col : str
-            Name of the column in which to store "destination" node ids.
-        weight_col : str
-            Name of the column in which to store weight information.
-        """
-        try:
-            import pandas as pd
-        except ImportError:
-            raise ImportError("pandas must be installed to use this method")
-        n_islands = len(self.islands)
-        if n_islands > 0 and (not self.silence_warnings):
-            warnings.warn(
-                "{} islands in this weights matrix. Conversion to an "
-                "adjacency list will drop these observations!"
-            )
-        adjlist = pd.DataFrame(
-            ((idx, n, w) for idx, neighb in self for n, w in list(neighb.items())),
-            columns=("focal", "neighbor", "weight"),
-        )
-        return adjtools.filter_adjlist(adjlist) if remove_symmetric else adjlist

-
-
[docs]    def to_networkx(self):
-        """Convert a weights object to a ``networkx`` graph.
-
-        Returns
-        -------
-        A ``networkx`` graph representation of the ``W`` object.
-        """
-        try:
-            import networkx as nx
-        except ImportError:
-            raise ImportError("NetworkX is required to use this function.")
-        G = nx.DiGraph() if len(self.asymmetries) > 0 else nx.Graph()
-        return nx.from_scipy_sparse_matrix(self.sparse, create_using=G)

-
-
[docs]    @classmethod
-    def from_networkx(cls, graph, weight_col="weight"):
-        """Convert a ``networkx`` graph to a PySAL ``W`` object.
-
-        Parameters
-        ----------
-        graph : networkx.Graph
-            The graph to convert to a ``W``.
-        weight_col : string
-            If the graph is labeled, this should be the name of the field
-            to use as the weight for the ``W``.
-        
-        Returns
-        -------
-        w : libpysal.weights.W
-            A ``W`` object containing the same graph as the ``networkx`` graph.
-        """
-        try:
-            import networkx as nx
-        except ImportError:
-            raise ImportError("NetworkX is required to use this function.")
-        sparse_matrix = nx.to_scipy_sparse_matrix(graph)
-        w = WSP(sparse_matrix).to_W()
-        return w

-
-    @property
-    def sparse(self):
-        """Sparse matrix object. For any matrix manipulations required for w,
-        ``w.sparse`` should be used. This is based on ``scipy.sparse``.
-        """
-        if "sparse" not in self._cache:
-            self._sparse = self._build_sparse()
-            self._cache["sparse"] = self._sparse
-        return self._sparse
-
-    @property
-    def n_components(self):
-        """Store whether the adjacency matrix is fully connected.
-        """
-        if "n_components" not in self._cache:
-            self._n_components, self._component_labels = connected_components(
-                self.sparse
-            )
-            self._cache["n_components"] = self._n_components
-            self._cache["component_labels"] = self._component_labels
-        return self._n_components
-
-    @property
-    def component_labels(self):
-        """Store the graph component in which each observation falls.
-        """
-        if "component_labels" not in self._cache:
-            self._n_components, self._component_labels = connected_components(
-                self.sparse
-            )
-            self._cache["n_components"] = self._n_components
-            self._cache["component_labels"] = self._component_labels
-        return self._component_labels
-
-    def _build_sparse(self):
-        """Construct the sparse attribute.
-        """
-
-        row = []
-        col = []
-        data = []
-        id2i = self.id2i
-        for i, neigh_list in list(self.neighbor_offsets.items()):
-            card = self.cardinalities[i]
-            row.extend([id2i[i]] * card)
-            col.extend(neigh_list)
-            data.extend(self.weights[i])
-        row = np.array(row)
-        col = np.array(col)
-        data = np.array(data)
-        s = scipy.sparse.csr_matrix((data, (row, col)), shape=(self.n, self.n))
-        return s
-
-    @property
-    def id2i(self):
-        """Dictionary where the key is an ID and the value is that ID's
-        index in ``W.id_order``.
-        """
-        if "id2i" not in self._cache:
-            self._id2i = {}
-            for i, id_i in enumerate(self._id_order):
-                self._id2i[id_i] = i
-            self._id2i = self._id2i
-            self._cache["id2i"] = self._id2i
-        return self._id2i
-
-    @property
-    def n(self):
-        """Number of units.
-        """
-        if "n" not in self._cache:
-            self._n = len(self.neighbors)
-            self._cache["n"] = self._n
-        return self._n
-
-    @property
-    def s0(self):
-        r"""``s0`` is defined as
-
-        .. math::
-
-               s0=\sum_i \sum_j w_{i,j}
-
-        """
-        if "s0" not in self._cache:
-            self._s0 = self.sparse.sum()
-            self._cache["s0"] = self._s0
-        return self._s0
-
-    @property
-    def s1(self):
-        r"""``s1`` is defined as
-
-        .. math::
-
-               s1=1/2 \sum_i \sum_j \Big(w_{i,j} + w_{j,i}\Big)^2
-
-        """
-        if "s1" not in self._cache:
-            t = self.sparse.transpose()
-            t = t + self.sparse
-            t2 = t.multiply(t)  # element-wise square
-            self._s1 = t2.sum() / 2.0
-            self._cache["s1"] = self._s1
-        return self._s1
-
-    @property
-    def s2array(self):
-        """Individual elements comprising ``s2``.
-
-        See Also
-        --------
-        s2
-
-        """
-        if "s2array" not in self._cache:
-            s = self.sparse
-            self._s2array = np.array(s.sum(1) + s.sum(0).transpose()) ** 2
-            self._cache["s2array"] = self._s2array
-        return self._s2array
-
-    @property
-    def s2(self):
-        r"""``s2`` is defined as
-
-        .. math::
-
-                s2=\sum_j \Big(\sum_i w_{i,j} + \sum_i w_{j,i}\Big)^2
-
-        """
-        if "s2" not in self._cache:
-            self._s2 = self.s2array.sum()
-            self._cache["s2"] = self._s2
-        return self._s2
-
-    @property
-    def trcW2(self):
-        """Trace of :math:`WW`.
-
-        See Also
-        --------
-        diagW2
-
-        """
-        if "trcW2" not in self._cache:
-            self._trcW2 = self.diagW2.sum()
-            self._cache["trcw2"] = self._trcW2
-        return self._trcW2
-
-    @property
-    def diagW2(self):
-        """Diagonal of :math:`WW`.
-
-        See Also
-        --------
-        trcW2
-
-        """
-        if "diagw2" not in self._cache:
-            self._diagW2 = (self.sparse * self.sparse).diagonal()
-            self._cache["diagW2"] = self._diagW2
-        return self._diagW2
-
-    @property
-    def diagWtW(self):
-        """Diagonal of :math:`W^{'}W`.
-
-        See Also
-        --------
-        trcWtW
-
-        """
-        if "diagWtW" not in self._cache:
-            self._diagWtW = (self.sparse.transpose() * self.sparse).diagonal()
-            self._cache["diagWtW"] = self._diagWtW
-        return self._diagWtW
-
-    @property
-    def trcWtW(self):
-        """Trace of :math:`W^{'}W`.
-
-        See Also
-        --------
-        diagWtW
-
-        """
-        if "trcWtW" not in self._cache:
-            self._trcWtW = self.diagWtW.sum()
-            self._cache["trcWtW"] = self._trcWtW
-        return self._trcWtW
-
-    @property
-    def diagWtW_WW(self):
-        """Diagonal of :math:`W^{'}W + WW`.
-        """
-        if "diagWtW_WW" not in self._cache:
-            wt = self.sparse.transpose()
-            w = self.sparse
-            self._diagWtW_WW = (wt * w + w * w).diagonal()
-            self._cache["diagWtW_WW"] = self._diagWtW_WW
-        return self._diagWtW_WW
-
-    @property
-    def trcWtW_WW(self):
-        """Trace of :math:`W^{'}W + WW`.
-        """
-        if "trcWtW_WW" not in self._cache:
-            self._trcWtW_WW = self.diagWtW_WW.sum()
-            self._cache["trcWtW_WW"] = self._trcWtW_WW
-        return self._trcWtW_WW
-
-    @property
-    def pct_nonzero(self):
-        """Percentage of nonzero weights.
-        """
-        if "pct_nonzero" not in self._cache:
-            self._pct_nonzero = 100.0 * self.sparse.nnz / (1.0 * self._n ** 2)
-            self._cache["pct_nonzero"] = self._pct_nonzero
-        return self._pct_nonzero
-
-    @property
-    def cardinalities(self):
-        """Number of neighbors for each observation.
-        """
-        if "cardinalities" not in self._cache:
-            c = {}
-            for i in self._id_order:
-                c[i] = len(self.neighbors[i])
-            self._cardinalities = c
-            self._cache["cardinalities"] = self._cardinalities
-        return self._cardinalities
-
-    @property
-    def max_neighbors(self):
-        """Largest number of neighbors.
-        """
-        if "max_neighbors" not in self._cache:
-            self._max_neighbors = max(self.cardinalities.values())
-            self._cache["max_neighbors"] = self._max_neighbors
-        return self._max_neighbors
-
-    @property
-    def mean_neighbors(self):
-        """Average number of neighbors.
-        """
-        if "mean_neighbors" not in self._cache:
-            self._mean_neighbors = np.mean(list(self.cardinalities.values()))
-            self._cache["mean_neighbors"] = self._mean_neighbors
-        return self._mean_neighbors
-
-    @property
-    def min_neighbors(self):
-        """Minimum number of neighbors.
-        """
-        if "min_neighbors" not in self._cache:
-            self._min_neighbors = min(self.cardinalities.values())
-            self._cache["min_neighbors"] = self._min_neighbors
-        return self._min_neighbors
-
-    @property
-    def nonzero(self):
-        """Number of nonzero weights.
-        """
-        if "nonzero" not in self._cache:
-            self._nonzero = self.sparse.nnz
-            self._cache["nonzero"] = self._nonzero
-        return self._nonzero
-
-    @property
-    def sd(self):
-        """Standard deviation of number of neighbors.
-        """
-        if "sd" not in self._cache:
-            self._sd = np.std(list(self.cardinalities.values()))
-            self._cache["sd"] = self._sd
-        return self._sd
-
-    @property
-    def asymmetries(self):
-        """List of id pairs with asymmetric weights.
-        """
-        if "asymmetries" not in self._cache:
-            self._asymmetries = self.asymmetry()
-            self._cache["asymmetries"] = self._asymmetries
-        return self._asymmetries
-
-    @property
-    def islands(self):
-        """List of ids without any neighbors.
-        """
-        if "islands" not in self._cache:
-            self._islands = [i for i, c in list(self.cardinalities.items()) if c == 0]
-            self._cache["islands"] = self._islands
-        return self._islands
-
-    @property
-    def histogram(self):
-        """Cardinality histogram as a dictionary where key is the id and
-        value is the number of neighbors for that unit.
-        """
-        if "histogram" not in self._cache:
-            ct, bin = np.histogram(
-                list(self.cardinalities.values()),
-                list(range(self.min_neighbors, self.max_neighbors + 2)),
-            )
-            self._histogram = list(zip(bin, ct))
-            self._cache["histogram"] = self._histogram
-        return self._histogram
-
-    def __getitem__(self, key):
-        """Allow a dictionary like interaction with the weights class.
-
-        Examples
-        --------
-        >>> from libpysal.weights import lat2W
-        >>> w = lat2W()
-
-        >>> w[0] == dict({1: 1.0, 5: 1.0})
-        True
-        """
-        return dict(list(zip(self.neighbors[key], self.weights[key])))
-
-    def __iter__(self):
-        """
-        Support iteration over weights.
-
-        Examples
-        --------
-        >>> from libpysal.weights import lat2W
-        >>> w=lat2W(3,3)
-        >>> for i,wi in enumerate(w):
-        ...     print(i,wi[0])
-        ...
-        0 0
-        1 1
-        2 2
-        3 3
-        4 4
-        5 5
-        6 6
-        7 7
-        8 8
-        >>>
-        """
-        for i in self._id_order:
-            yield i, dict(list(zip(self.neighbors[i], self.weights[i])))
-
-
[docs]    def remap_ids(self, new_ids):
-        """
-        In place modification throughout ``W`` of id values from
-        ``w.id_order`` to ``new_ids`` in all.
-
-        Parameters
-        ----------
-
-        new_ids : list, numpy.ndarray
-            Aligned list of new ids to be inserted. Note that first
-            element of ``new_ids`` will replace first element of
-            ``w.id_order``, second element of ``new_ids`` replaces second
-            element of ``w.id_order`` and so on.
-
-        Examples
-        --------
-
-        >>> from libpysal.weights import lat2W
-        >>> w = lat2W(3, 3)
-        >>> w.id_order
-        [0, 1, 2, 3, 4, 5, 6, 7, 8]
-        >>> w.neighbors[0]
-        [3, 1]
-        >>> new_ids = ['id%i'%id for id in w.id_order]
-        >>> _ = w.remap_ids(new_ids)
-        >>> w.id_order
-        ['id0', 'id1', 'id2', 'id3', 'id4', 'id5', 'id6', 'id7', 'id8']
-        >>> w.neighbors['id0']
-        ['id3', 'id1']
-        """
-
-        old_ids = self._id_order
-        if len(old_ids) != len(new_ids):
-            raise Exception(
-                "W.remap_ids: length of `old_ids` does not match \
-            that of new_ids"
-            )
-        if len(set(new_ids)) != len(new_ids):
-            raise Exception("W.remap_ids: list `new_ids` contains duplicates")
-        else:
-            new_neighbors = {}
-            new_weights = {}
-            old_transformations = self.transformations["O"].copy()
-            new_transformations = {}
-            for o, n in zip(old_ids, new_ids):
-                o_neighbors = self.neighbors[o]
-                o_weights = self.weights[o]
-                n_neighbors = [new_ids[old_ids.index(j)] for j in o_neighbors]
-                new_neighbors[n] = n_neighbors
-                new_weights[n] = o_weights[:]
-                new_transformations[n] = old_transformations[o]
-            self.neighbors = new_neighbors
-            self.weights = new_weights
-            self.transformations["O"] = new_transformations
-
-            id_order = [self._id_order.index(o) for o in old_ids]
-            for i, id_ in enumerate(id_order):
-                self.id_order[id_] = new_ids[i]
-
-            self._reset()

-
-    def __set_id_order(self, ordered_ids):
-        """Set the iteration order in w. ``W`` can be iterated over. On 
-        construction the iteration order is set to the lexicographic order of 
-        the keys in the ``w.weights`` dictionary. If a specific order
-        is required it can be set with this method.
-
-        Parameters
-        ----------
-
-        ordered_ids : sequence
-            Identifiers for observations in specified order.
-
-        Notes
-        -----
-
-        The ``ordered_ids`` parameter is checked against the ids implied
-        by the keys in ``w.weights``. If they are not equivalent sets an
-        exception is raised and the iteration order is not changed.
-
-        Examples
-        --------
-
-        >>> from libpysal.weights import lat2W
-        >>> w=lat2W(3,3)
-        >>> for i,wi in enumerate(w):
-        ...     print(i, wi[0])
-        ...
-        0 0
-        1 1
-        2 2
-        3 3
-        4 4
-        5 5
-        6 6
-        7 7
-        8 8
-        >>> w.id_order
-        [0, 1, 2, 3, 4, 5, 6, 7, 8]
-        >>> w.id_order=range(8,-1,-1)
-        >>> list(w.id_order)
-        [8, 7, 6, 5, 4, 3, 2, 1, 0]
-        >>> for i,w_i in enumerate(w):
-        ...     print(i,w_i[0])
-        ...
-        0 8
-        1 7
-        2 6
-        3 5
-        4 4
-        5 3
-        6 2
-        7 1
-        8 0
-
-        """
-
-        if set(self._id_order) == set(ordered_ids):
-            self._id_order = ordered_ids
-            self._id_order_set = True
-            self._reset()
-        else:
-            raise Exception("ordered_ids do not align with W ids")
-
-    def __get_id_order(self):
-        """Returns the ids for the observations in the order in which they
-        would be encountered if iterating over the weights.
-        """
-        return self._id_order
-
-    id_order = property(__get_id_order, __set_id_order)
-
-    @property
-    def id_order_set(self):
-        """ Returns ``True`` if user has set ``id_order``, ``False`` if not.
-
-        Examples
-        --------
-        >>> from libpysal.weights import lat2W
-        >>> w=lat2W()
-        >>> w.id_order_set
-        True
-        """
-        return self._id_order_set
-
-    @property
-    def neighbor_offsets(self):
-        """
-        Given the current ``id_order``, ``neighbor_offsets[id]`` is the
-        offsets of the id's neighbors in ``id_order``.
-
-        Returns
-        -------
-        neighbor_list : list
-            Offsets of the id's neighbors in ``id_order``.
-
-        Examples
-        --------
-        >>> from libpysal.weights import W
-        >>> neighbors={'c': ['b'], 'b': ['c', 'a'], 'a': ['b']}
-        >>> weights ={'c': [1.0], 'b': [1.0, 1.0], 'a': [1.0]}
-        >>> w=W(neighbors,weights)
-        >>> w.id_order = ['a','b','c']
-        >>> w.neighbor_offsets['b']
-        [2, 0]
-        >>> w.id_order = ['b','a','c']
-        >>> w.neighbor_offsets['b']
-        [2, 1]
-        """
-
-        if "neighbors_0" not in self._cache:
-            self.__neighbors_0 = {}
-            id2i = self.id2i
-            for j, neigh_list in list(self.neighbors.items()):
-                self.__neighbors_0[j] = [id2i[neigh] for neigh in neigh_list]
-            self._cache["neighbors_0"] = self.__neighbors_0
-
-        neighbor_list = self.__neighbors_0
-
-        return neighbor_list
-
-
[docs]    def get_transform(self):
-        """Getter for transform property.
-
-        Returns
-        -------
-        transformation : str, None
-            Valid transformation value. See the ``transform`` 
-            parameters in ``set_transform()`` for a detailed description.
-            
-        Examples
-        --------
-        >>> from libpysal.weights import lat2W
-        >>> w=lat2W()
-        >>> w.weights[0]
-        [1.0, 1.0]
-        >>> w.transform
-        'O'
-        >>> w.transform='r'
-        >>> w.weights[0]
-        [0.5, 0.5]
-        >>> w.transform='b'
-        >>> w.weights[0]
-        [1.0, 1.0]
-
-        See also
-        --------
-        set_transform
-
-        """
-
-        return self._transform

-
-
[docs]    def set_transform(self, value="B"):
-        """Transformations of weights.
-
-        Parameters
-        ----------
-        transform : str
-            This parameter is not case sensitive. The following are
-            valid transformations.
-            
-            * **B** -- Binary
-            * **R** -- Row-standardization (global sum :math:`=n`)
-            * **D** -- Double-standardization (global sum :math:`=1`)
-            * **V** -- Variance stabilizing
-            * **O** -- Restore original transformation (from instantiation)
-        
-        Notes
-        -----
-
-        Transformations are applied only to the value of the weights at
-        instantiation. Chaining of transformations cannot be done on a ``W``
-        instance.
-
-        
-        Examples
-        --------
-        >>> from libpysal.weights import lat2W
-        >>> w=lat2W()
-        >>> w.weights[0]
-        [1.0, 1.0]
-        >>> w.transform
-        'O'
-        >>> w.transform='r'
-        >>> w.weights[0]
-        [0.5, 0.5]
-        >>> w.transform='b'
-        >>> w.weights[0]
-        [1.0, 1.0]
-        """
-        value = value.upper()
-        self._transform = value
-        if value in self.transformations:
-            self.weights = self.transformations[value]
-            self._reset()
-        else:
-            if value == "R":
-                # row standardized weights
-                weights = {}
-                self.weights = self.transformations["O"]
-                for i in self.weights:
-                    wijs = self.weights[i]
-                    row_sum = sum(wijs) * 1.0
-                    if row_sum == 0.0:
-                        if not self.silence_warnings:
-                            print(("WARNING: ", i, " is an island (no neighbors)"))
-                    weights[i] = [wij / row_sum for wij in wijs]
-                weights = weights
-                self.transformations[value] = weights
-                self.weights = weights
-                self._reset()
-            elif value == "D":
-                # doubly-standardized weights
-                # update current chars before doing global sum
-                self._reset()
-                s0 = self.s0
-                ws = 1.0 / s0
-                weights = {}
-                self.weights = self.transformations["O"]
-                for i in self.weights:
-                    wijs = self.weights[i]
-                    weights[i] = [wij * ws for wij in wijs]
-                weights = weights
-                self.transformations[value] = weights
-                self.weights = weights
-                self._reset()
-            elif value == "B":
-                # binary transformation
-                weights = {}
-                self.weights = self.transformations["O"]
-                for i in self.weights:
-                    wijs = self.weights[i]
-                    weights[i] = [1.0 for wij in wijs]
-                weights = weights
-                self.transformations[value] = weights
-                self.weights = weights
-                self._reset()
-            elif value == "V":
-                # variance stabilizing
-                weights = {}
-                q = {}
-                k = self.cardinalities
-                s = {}
-                Q = 0.0
-                self.weights = self.transformations["O"]
-                for i in self.weights:
-                    wijs = self.weights[i]
-                    q[i] = math.sqrt(sum([wij * wij for wij in wijs]))
-                    s[i] = [wij / q[i] for wij in wijs]
-                    Q += sum([si for si in s[i]])
-                nQ = self.n / Q
-                for i in self.weights:
-                    weights[i] = [w * nQ for w in s[i]]
-                weights = weights
-                self.transformations[value] = weights
-                self.weights = weights
-                self._reset()
-            elif value == "O":
-                # put weights back to original transformation
-                weights = {}
-                original = self.transformations[value]
-                self.weights = original
-                self._reset()
-            else:
-                raise Exception("unsupported weights transformation")

-
-    transform = property(get_transform, set_transform)
-
-
[docs]    def asymmetry(self, intrinsic=True):
-        r"""
-        Asymmetry check.
-
-        Parameters
-        ----------
-        intrinsic : bool
-            Default is ``True``. Intrinsic symmetry is defined as
-            
-            .. math::
-                
-                w_{i,j} == w_{j,i}
-            
-            If ``intrinsic`` is ``False`` symmetry is defined as
-            
-            .. math:: 
-            
-                i \in N_j \ \& \ j \in N_i
-                
-            where :math:`N_j` is the set of neighbors for :math:`j`.
-
-        Returns
-        -------
-        asymmetries : list
-            Empty if no asymmetries are found if asymmetries, then a
-            ``list`` of ``(i,j)`` tuples is returned.
-
-        Examples
-        --------
-
-        >>> from libpysal.weights import lat2W
-        >>> w=lat2W(3,3)
-        >>> w.asymmetry()
-        []
-        >>> w.transform='r'
-        >>> w.asymmetry()
-        [(0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7)]
-        >>> result = w.asymmetry(intrinsic=False)
-        >>> result
-        []
-        >>> neighbors={0:[1,2,3], 1:[1,2,3], 2:[0,1], 3:[0,1]}
-        >>> weights={0:[1,1,1], 1:[1,1,1], 2:[1,1], 3:[1,1]}
-        >>> w=W(neighbors,weights)
-        >>> w.asymmetry()
-        [(0, 1), (1, 0)]
-        """
-
-        if intrinsic:
-            wd = self.sparse.transpose() - self.sparse
-        else:
-            transform = self.transform
-            self.transform = "b"
-            wd = self.sparse.transpose() - self.sparse
-            self.transform = transform
-
-        ids = np.nonzero(wd)
-        if len(ids[0]) == 0:
-            return []
-        else:
-            ijs = list(zip(ids[0], ids[1]))
-            ijs.sort()
-            return ijs

-
-
[docs]    def symmetrize(self, inplace=False):
-        """Construct a symmetric KNN weight. This ensures that the neighbors
-        of each focal observation consider the focal observation itself as
-        a neighbor. This returns a generic ``W`` object, since the object is no
-        longer guaranteed to have ``k`` neighbors for each observation.
-        """
-        if not inplace:
-            neighbors = copy.deepcopy(self.neighbors)
-            weights = copy.deepcopy(self.weights)
-            out_W = W(neighbors, weights, id_order=self.id_order)
-            out_W.symmetrize(inplace=True)
-            return out_W
-        else:
-            for focal, fneighbs in list(self.neighbors.items()):
-                for j, neighbor in enumerate(fneighbs):
-                    neighb_neighbors = self.neighbors[neighbor]
-                    if focal not in neighb_neighbors:
-                        self.neighbors[neighbor].append(focal)
-                        self.weights[neighbor].append(self.weights[focal][j])
-            self._cache = dict()
-            return

-
-
[docs]    def full(self):
-        """Generate a full ``numpy.ndarray``.
-
-        Parameters
-        ----------
-        self : libpysal.weights.W
-            spatial weights object
-
-        Returns
-        -------
-        (fullw, keys) : tuple
-            The first element being the full ``numpy.ndarray`` and second
-            element keys being the ids associated with each row in the array.
-
-        Examples
-        --------
-        >>> from libpysal.weights import W, full
-        >>> neighbors = {'first':['second'],'second':['first','third'],'third':['second']}
-        >>> weights = {'first':[1],'second':[1,1],'third':[1]}
-        >>> w = W(neighbors, weights)
-        >>> wf, ids = full(w)
-        >>> wf
-        array([[0., 1., 0.],
-               [1., 0., 1.],
-               [0., 1., 0.]])
-        >>> ids
-        ['first', 'second', 'third']
-        """
-        wfull = np.zeros([self.n, self.n], dtype=float)
-        keys = list(self.neighbors.keys())
-        if self.id_order:
-            keys = self.id_order
-        for i, key in enumerate(keys):
-            n_i = self.neighbors[key]
-            w_i = self.weights[key]
-            for j, wij in zip(n_i, w_i):
-                c = keys.index(j)
-                wfull[i, c] = wij
-        return (wfull, keys)

-
-
[docs]    def to_WSP(self):
-        """Generate a ``WSP`` object.
-
-        Returns
-        -------
-
-        implicit : libpysal.weights.WSP
-            Thin ``W`` class
-
-        Examples
-        --------
-        >>> from libpysal.weights import W, WSP
-        >>> neighbors={'first':['second'],'second':['first','third'],'third':['second']}
-        >>> weights={'first':[1],'second':[1,1],'third':[1]}
-        >>> w=W(neighbors,weights)
-        >>> wsp=w.to_WSP()
-        >>> isinstance(wsp, WSP)
-        True
-        >>> wsp.n
-        3
-        >>> wsp.s0
-        4
-
-        See also
-        --------
-        WSP
-
-        """
-        return WSP(self.sparse, self._id_order)

-
-
[docs]    def set_shapefile(self, shapefile, idVariable=None, full=False):
-        """
-        Adding metadata for writing headers of ``.gal`` and ``.gwt`` files.
-
-        Parameters
-        ----------
-        shapefile : str
-            The shapefile name used to construct weights.
-        idVariable : str
-            The name of the attribute in the shapefile to associate
-            with ids in the weights.
-        full : bool
-            Write out the entire path for a shapefile (``True``) or
-            only the base of the shapefile without extension (``False``).
-            Default is ``True``.
-        """
-
-        if full:
-            self._shpName = shapefile
-        else:
-            self._shpName = BASENAME(shapefile).split(".")[0]
-
-        self._varName = idVariable

-
-
[docs]    def plot(
-        self, gdf, indexed_on=None, ax=None, color="k", node_kws=None, edge_kws=None
-    ):
-        """Plot spatial weights objects. **Requires** ``matplotlib``, and
-        implicitly requires a ``geopandas.GeoDataFrame`` as input.
-
-        Parameters
-        ----------
-        gdf : geopandas.GeoDataFrame
-            The original shapes whose topological relations are modelled in ``W``.
-        indexed_on : str 
-            Column of ``geopandas.GeoDataFrame`` that the weights object uses
-            as an index. Default is ``None``, so the index of the 
-            ``geopandas.GeoDataFrame`` is used.
-        ax : matplotlib.axes.Axes
-            Axis on which to plot the weights. Default is ``None``, so
-            plots on the current figure.
-        color : str
-            ``matplotlib`` color string, will color both nodes and edges
-            the same by default.
-        node_kws : dict
-            Keyword arguments dictionary to send to ``pyplot.scatter``,
-            which provides fine-grained control over the aesthetics
-            of the nodes in the plot.
-        edge_kws : dict
-            Keyword arguments dictionary to send to ``pyplot.plot``,
-            which provides fine-grained control over the aesthetics
-            of the edges in the plot.
-
-        Returns
-        -------
-        f : matplotlib.figure.Figure
-            Figure on which the plot is made.
-        ax : matplotlib.axes.Axes
-            Axis on which the plot is made.
-        
-        Notes
-        -----
-        If you'd like to overlay the actual shapes from the 
-        ``geopandas.GeoDataFrame``, call ``gdf.plot(ax=ax)`` after this.
-        To plot underneath, adjust the z-order of the plot as follows: 
-        ``gdf.plot(ax=ax,zorder=0)``.
-
-        Examples
-        --------
-
-        >>> from libpysal.weights import Queen
-        >>> import libpysal as lp
-        >>> import geopandas
-        >>> gdf = geopandas.read_file(lp.examples.get_path("columbus.shp"))
-        >>> weights = Queen.from_dataframe(gdf)
-        >>> tmp = weights.plot(gdf, color='firebrickred', node_kws=dict(marker='*', color='k'))
-        """
-        try:
-            import matplotlib.pyplot as plt
-        except ImportError:
-            raise ImportError(
-                "W.plot depends on matplotlib.pyplot, and this was"
-                "not able to be imported. \nInstall matplotlib to"
-                "plot spatial weights."
-            )
-        if ax is None:
-            f = plt.figure()
-            ax = plt.gca()
-        else:
-            f = plt.gcf()
-        if node_kws is not None:
-            if "color" not in node_kws:
-                node_kws["color"] = color
-        else:
-            node_kws = dict(color=color)
-        if edge_kws is not None:
-            if "color" not in edge_kws:
-                edge_kws["color"] = color
-        else:
-            edge_kws = dict(color=color)
-
-        for idx, neighbors in self:
-            if idx in self.islands:
-                continue
-            if indexed_on is not None:
-                neighbors = gdf[gdf[indexed_on].isin(neighbors)].index.tolist()
-                idx = gdf[gdf[indexed_on] == idx].index.tolist()[0]
-            centroids = gdf.loc[neighbors].centroid.apply(lambda p: (p.x, p.y))
-            centroids = np.vstack(centroids.values)
-            focal = np.hstack(gdf.loc[idx].geometry.centroid.xy)
-            seen = set()
-            for nidx, neighbor in zip(neighbors, centroids):
-                if (idx, nidx) in seen:
-                    continue
-                ax.plot(*list(zip(focal, neighbor)), marker=None, **edge_kws)
-                seen.update((idx, nidx))
-                seen.update((nidx, idx))
-        ax.scatter(
-            gdf.centroid.apply(lambda p: p.x),
-            gdf.centroid.apply(lambda p: p.y),
-            **node_kws
-        )
-        return f, ax

-
-
-
[docs]class WSP(object):
-    """Thin ``W`` class for ``spreg``.
-
-    Parameters
-    ----------
-
-    sparse : scipy.sparse.{matrix-type}
-        NxN object from ``scipy.sparse``
-
-    Attributes
-    ----------
-
-    n           : int
-                  description
-    s0          : float
-                  description
-    trcWtW_WW   : float
-                  description
-
-    Examples
-    --------
-
-    From GAL information
-
-    >>> import scipy.sparse
-    >>> from libpysal.weights import WSP
-    >>> rows = [0, 1, 1, 2, 2, 3]
-    >>> cols = [1, 0, 2, 1, 3, 3]
-    >>> weights =  [1, 0.75, 0.25, 0.9, 0.1, 1]
-    >>> sparse = scipy.sparse.csr_matrix((weights, (rows, cols)), shape=(4,4))
-    >>> w = WSP(sparse)
-    >>> w.s0
-    4.0
-    >>> w.trcWtW_WW
-    6.395
-    >>> w.n
-    4
-
-    """
-
-
[docs]    def __init__(self, sparse, id_order=None, index=None):
-        if not scipy.sparse.issparse(sparse):
-            raise ValueError("must pass a scipy sparse object")
-        rows, cols = sparse.shape
-        if rows != cols:
-            raise ValueError("Weights object must be square")
-        self.sparse = sparse.tocsr()
-        self.n = sparse.shape[0]
-        self._cache = {}
-        if id_order:
-            if len(id_order) != self.n:
-                raise ValueError(
-                    "Number of values in id_order must match shape of sparse"
-                )
-            else:
-                self._id_order = id_order
-                self._cache["id_order"] = self._id_order
-        # temp addition of index attribute
-        import pandas as pd  # will be removed after refactoring is done
-        if index is not None:
-            if not isinstance(index, (pd.Index, pd.MultiIndex, pd.RangeIndex)):
-                raise TypeError("index must be an instance of pandas.Index dtype")
-            if len(index) != self.n:
-                raise ValueError(
-                    "Number of values in index must match shape of sparse"
-                )
-        else:
-            index = pd.RangeIndex(self.n)
-        self.index = index

-
-    @property
-    def id_order(self):
-        """An ordered list of ids, assumed to match the ordering in ``sparse``.
-        """
-        # Temporary solution until the refactoring is finished
-        if "id_order" not in self._cache:
-            if hasattr(self, "index"):
-                self._id_order = self.index.tolist()
-            else:
-                self._id_order = list(range(self.n))
-            self._cache["id_order"] = self._id_order
-        return self._id_order
-
-    @property
-    def s0(self):
-        r"""``s0`` is defined as:
-
-        .. math::
-
-               s0=\sum_i \sum_j w_{i,j}
-
-        """
-        if "s0" not in self._cache:
-            self._s0 = self.sparse.sum()
-            self._cache["s0"] = self._s0
-        return self._s0
-
-    @property
-    def trcWtW_WW(self):
-        """Trace of :math:`W^{'}W + WW`.
-        """
-        if "trcWtW_WW" not in self._cache:
-            self._trcWtW_WW = self.diagWtW_WW.sum()
-            self._cache["trcWtW_WW"] = self._trcWtW_WW
-        return self._trcWtW_WW
-
-    @property
-    def diagWtW_WW(self):
-        """Diagonal of :math:`W^{'}W + WW`.
-        """
-        if "diagWtW_WW" not in self._cache:
-            wt = self.sparse.transpose()
-            w = self.sparse
-            self._diagWtW_WW = (wt * w + w * w).diagonal()
-            self._cache["diagWtW_WW"] = self._diagWtW_WW
-        return self._diagWtW_WW
-
-
[docs]    @classmethod
-    def from_W(cls, W):
-        """Constructs a ``WSP`` object from the ``W``'s sparse matrix.
-
-        Parameters
-        ----------
-        W : libpysal.weights.W
-            A PySAL weights object with a sparse form and ids.
-
-        Returns
-        -------
-        A ``WSP`` instance.
-        """
-        return cls(W.sparse, id_order=W.id_order)

-
-
[docs]    def to_W(self, silence_warnings=False):
-        """
-        Convert a pysal WSP object (thin weights matrix) to a pysal W object.
-
-        Parameters
-        ----------
-        self : WSP
-            PySAL sparse weights object.
-        silence_warnings : bool
-            Switch to ``True`` to turn off print statements for every
-            observation with islands. Default is ``False``, which does
-            not silence warnings.
-
-        Returns
-        -------
-        w : W
-            PySAL weights object.
-
-        Examples
-        --------
-        >>> from libpysal.weights import lat2SW, WSP, WSP2W
-
-        Build a 10x10 ``scipy.sparse`` matrix for a rectangular 2x5
-        region of cells (rook contiguity), then construct a ``libpysal``
-        sparse weights object (``self``).
-
-        >>> sp = lat2SW(2, 5)
-        >>> self = WSP(sp)
-        >>> self.n
-        10
-        >>> print(self.sparse[0].todense())
-        [[0 1 0 0 0 1 0 0 0 0]]
-
-        Convert this sparse weights object to a standard PySAL weights object.
-
-        >>> w = WSP2W(self)
-        >>> w.n
-        10
-        >>> print(w.full()[0][0])
-        [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
-
-        """
-
-        indices = list(self.sparse.indices)
-        data = list(self.sparse.data)
-        indptr = list(self.sparse.indptr)
-        id_order = self.id_order
-        if id_order:
-            # replace indices with user IDs
-            indices = [id_order[i] for i in indices]
-        else:
-            id_order = list(range(self.n))
-        neighbors, weights = {}, {}
-        start = indptr[0]
-        for i in range(self.n):
-            oid = id_order[i]
-            end = indptr[i + 1]
-            neighbors[oid] = indices[start:end]
-            weights[oid] = data[start:end]
-            start = end
-        ids = copy.copy(self.id_order)
-        w = W(neighbors, weights, ids, silence_warnings=silence_warnings)
-        w._sparse = copy.deepcopy(self.sparse)
-        w._cache["sparse"] = w._sparse
-        return w

-
- -
- -
-
- - - \ No newline at end of file diff --git a/docs/_sources/api.rst.txt b/docs/_sources/api.rst.txt deleted file mode 100644 index 01da37c71..000000000 --- a/docs/_sources/api.rst.txt +++ /dev/null @@ -1,248 +0,0 @@ -.. _api_ref: - -.. currentmodule:: libpysal - - -libpysal API reference -====================== - -Spatial Weights ---------------- - -.. autosummary:: - :toctree: generated/ - - libpysal.weights.W - -Distance Weights -++++++++++++++++ -.. autosummary:: - :toctree: generated/ - - libpysal.weights.DistanceBand - libpysal.weights.Kernel - libpysal.weights.KNN - -Contiguity Weights -++++++++++++++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.weights.Queen - libpysal.weights.Rook - libpysal.weights.Voronoi - libpysal.weights.W - -spint Weights -+++++++++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.weights.WSP - libpysal.weights.netW - libpysal.weights.mat2L - libpysal.weights.ODW - libpysal.weights.vecW - -Weights tools to interface with rasters -+++++++++++++++++++++++++++++++++++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.weights.da2W - libpysal.weights.da2WSP - libpysal.weights.w2da - libpysal.weights.wsp2da - libpysal.weights.testDataArray - -Weights Util Classes and Functions -++++++++++++++++++++++++++++++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.weights.block_weights - libpysal.weights.lat2W - libpysal.weights.comb - libpysal.weights.order - libpysal.weights.higher_order - libpysal.weights.shimbel - libpysal.weights.remap_ids - libpysal.weights.full2W - libpysal.weights.full - libpysal.weights.WSP2W - libpysal.weights.get_ids - libpysal.weights.get_points_array_from_shapefile - libpysal.weights.fill_diagonal - -Weights user Classes and Functions -++++++++++++++++++++++++++++++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.weights.min_threshold_distance - libpysal.weights.lat2SW - libpysal.weights.w_local_cluster - libpysal.weights.higher_order_sp - libpysal.weights.hexLat2W - libpysal.weights.attach_islands - libpysal.weights.nonplanar_neighbors - libpysal.weights.fuzzy_contiguity - libpysal.weights.min_threshold_dist_from_shapefile - libpysal.weights.build_lattice_shapefile - libpysal.weights.spw_from_gal - libpysal.weights.neighbor_equality - - -Set Theoretic Weights -+++++++++++++++++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.weights.w_union - libpysal.weights.w_intersection - libpysal.weights.w_difference - libpysal.weights.w_symmetric_difference - libpysal.weights.w_subset - libpysal.weights.w_clip - - -Spatial Lag -+++++++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.weights.lag_spatial - libpysal.weights.lag_categorical - - -cg: Computational Geometry --------------------------- - -alpha_shapes -++++++++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.cg.alpha_shape - libpysal.cg.alpha_shape_auto - -voronoi -+++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.cg.voronoi_frames - - -sphere -++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.cg.RADIUS_EARTH_KM - libpysal.cg.RADIUS_EARTH_MILES - libpysal.cg.arcdist - libpysal.cg.arcdist2linear - libpysal.cg.brute_knn - libpysal.cg.fast_knn - libpysal.cg.fast_threshold - libpysal.cg.linear2arcdist - libpysal.cg.toLngLat - libpysal.cg.toXYZ - libpysal.cg.lonlat - libpysal.cg.harcdist - libpysal.cg.geointerpolate - libpysal.cg.geogrid - -shapes -++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.cg.Point - libpysal.cg.LineSegment - libpysal.cg.Line - libpysal.cg.Ray - libpysal.cg.Chain - libpysal.cg.Polygon - libpysal.cg.Rectangle - libpysal.cg.asShape - -standalone -++++++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.cg.bbcommon - libpysal.cg.get_bounding_box - libpysal.cg.get_angle_between - libpysal.cg.is_collinear - libpysal.cg.get_segments_intersect - libpysal.cg.get_segment_point_intersect - libpysal.cg.get_polygon_point_intersect - libpysal.cg.get_rectangle_point_intersect - libpysal.cg.get_ray_segment_intersect - libpysal.cg.get_rectangle_rectangle_intersection - libpysal.cg.get_polygon_point_dist - libpysal.cg.get_points_dist - libpysal.cg.get_segment_point_dist - libpysal.cg.get_point_at_angle_and_dist - libpysal.cg.convex_hull - libpysal.cg.is_clockwise - libpysal.cg.point_touches_rectangle - libpysal.cg.get_shared_segments - libpysal.cg.distance_matrix - - -locators -++++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.cg.Grid - libpysal.cg.PointLocator - libpysal.cg.PolygonLocator - - -kdtree -++++++ - -.. autosummary:: - :toctree: generated/ - - libpysal.cg.KDTree - - -io --- - -.. autosummary:: - :toctree: generated/ - - libpysal.io.open - libpysal.io.fileio.FileIO - - -examples --------- - - -.. autosummary:: - :toctree: generated/ - - libpysal.examples.available - libpysal.examples.explain - libpysal.examples.get_path diff --git a/docs/_sources/generated/libpysal.cg.Chain.rst.txt b/docs/_sources/generated/libpysal.cg.Chain.rst.txt deleted file mode 100644 index f91f22f1f..000000000 --- a/docs/_sources/generated/libpysal.cg.Chain.rst.txt +++ /dev/null @@ -1,33 +0,0 @@ -libpysal.cg.Chain -================= - -.. currentmodule:: libpysal.cg - -.. autoclass:: Chain - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Chain.__init__ - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~Chain.arclen - ~Chain.bounding_box - ~Chain.len - ~Chain.parts - ~Chain.segments - ~Chain.vertices - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.Grid.rst.txt b/docs/_sources/generated/libpysal.cg.Grid.rst.txt deleted file mode 100644 index c7b6aa23b..000000000 --- a/docs/_sources/generated/libpysal.cg.Grid.rst.txt +++ /dev/null @@ -1,28 +0,0 @@ -libpysal.cg.Grid -================ - -.. currentmodule:: libpysal.cg - -.. autoclass:: Grid - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Grid.__init__ - ~Grid.add - ~Grid.bounds - ~Grid.in_grid - ~Grid.nearest - ~Grid.proximity - ~Grid.remove - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.KDTree.rst.txt b/docs/_sources/generated/libpysal.cg.KDTree.rst.txt deleted file mode 100644 index 020fb9649..000000000 --- a/docs/_sources/generated/libpysal.cg.KDTree.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.KDTree -================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: KDTree \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.Line.rst.txt b/docs/_sources/generated/libpysal.cg.Line.rst.txt deleted file mode 100644 index b04ae005b..000000000 --- a/docs/_sources/generated/libpysal.cg.Line.rst.txt +++ /dev/null @@ -1,24 +0,0 @@ -libpysal.cg.Line -================ - -.. currentmodule:: libpysal.cg - -.. autoclass:: Line - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Line.__init__ - ~Line.x - ~Line.y - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.LineSegment.rst.txt b/docs/_sources/generated/libpysal.cg.LineSegment.rst.txt deleted file mode 100644 index dba1ae0b8..000000000 --- a/docs/_sources/generated/libpysal.cg.LineSegment.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -libpysal.cg.LineSegment -======================= - -.. currentmodule:: libpysal.cg - -.. autoclass:: LineSegment - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~LineSegment.__init__ - ~LineSegment.get_swap - ~LineSegment.intersect - ~LineSegment.is_ccw - ~LineSegment.is_cw - ~LineSegment.sw_ccw - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~LineSegment.bounding_box - ~LineSegment.len - ~LineSegment.line - ~LineSegment.p1 - ~LineSegment.p2 - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.Point.rst.txt b/docs/_sources/generated/libpysal.cg.Point.rst.txt deleted file mode 100644 index 133d91fbd..000000000 --- a/docs/_sources/generated/libpysal.cg.Point.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -libpysal.cg.Point -================= - -.. currentmodule:: libpysal.cg - -.. autoclass:: Point - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Point.__init__ - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.PointLocator.rst.txt b/docs/_sources/generated/libpysal.cg.PointLocator.rst.txt deleted file mode 100644 index 1a65fb787..000000000 --- a/docs/_sources/generated/libpysal.cg.PointLocator.rst.txt +++ /dev/null @@ -1,27 +0,0 @@ -libpysal.cg.PointLocator -======================== - -.. currentmodule:: libpysal.cg - -.. autoclass:: PointLocator - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~PointLocator.__init__ - ~PointLocator.nearest - ~PointLocator.overlapping - ~PointLocator.polygon - ~PointLocator.proximity - ~PointLocator.region - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.Polygon.rst.txt b/docs/_sources/generated/libpysal.cg.Polygon.rst.txt deleted file mode 100644 index a0fab3ee9..000000000 --- a/docs/_sources/generated/libpysal.cg.Polygon.rst.txt +++ /dev/null @@ -1,38 +0,0 @@ -libpysal.cg.Polygon -=================== - -.. currentmodule:: libpysal.cg - -.. autoclass:: Polygon - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Polygon.__init__ - ~Polygon.build_quad_tree_structure - ~Polygon.contains_point - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~Polygon.area - ~Polygon.bbox - ~Polygon.bounding_box - ~Polygon.centroid - ~Polygon.holes - ~Polygon.len - ~Polygon.parts - ~Polygon.perimeter - ~Polygon.vertices - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.PolygonLocator.rst.txt b/docs/_sources/generated/libpysal.cg.PolygonLocator.rst.txt deleted file mode 100644 index 482bc76b9..000000000 --- a/docs/_sources/generated/libpysal.cg.PolygonLocator.rst.txt +++ /dev/null @@ -1,28 +0,0 @@ -libpysal.cg.PolygonLocator -========================== - -.. currentmodule:: libpysal.cg - -.. autoclass:: PolygonLocator - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~PolygonLocator.__init__ - ~PolygonLocator.contains_point - ~PolygonLocator.inside - ~PolygonLocator.nearest - ~PolygonLocator.overlapping - ~PolygonLocator.proximity - ~PolygonLocator.region - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_KM.rst.txt b/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_KM.rst.txt deleted file mode 100644 index 001acab4d..000000000 --- a/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_KM.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.RADIUS\_EARTH\_KM -============================= - -.. currentmodule:: libpysal.cg - -.. autodata:: RADIUS_EARTH_KM \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_MILES.rst.txt b/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_MILES.rst.txt deleted file mode 100644 index 36c27f184..000000000 --- a/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_MILES.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.RADIUS\_EARTH\_MILES -================================ - -.. currentmodule:: libpysal.cg - -.. autodata:: RADIUS_EARTH_MILES \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.Ray.rst.txt b/docs/_sources/generated/libpysal.cg.Ray.rst.txt deleted file mode 100644 index 69efdb894..000000000 --- a/docs/_sources/generated/libpysal.cg.Ray.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -libpysal.cg.Ray -=============== - -.. currentmodule:: libpysal.cg - -.. autoclass:: Ray - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Ray.__init__ - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.Rectangle.rst.txt b/docs/_sources/generated/libpysal.cg.Rectangle.rst.txt deleted file mode 100644 index aefa995d1..000000000 --- a/docs/_sources/generated/libpysal.cg.Rectangle.rst.txt +++ /dev/null @@ -1,32 +0,0 @@ -libpysal.cg.Rectangle -===================== - -.. currentmodule:: libpysal.cg - -.. autoclass:: Rectangle - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Rectangle.__init__ - ~Rectangle.set_centroid - ~Rectangle.set_scale - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~Rectangle.area - ~Rectangle.height - ~Rectangle.width - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.alpha_shape.rst.txt b/docs/_sources/generated/libpysal.cg.alpha_shape.rst.txt deleted file mode 100644 index d7b00aabd..000000000 --- a/docs/_sources/generated/libpysal.cg.alpha_shape.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.alpha\_shape -======================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: alpha_shape \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.alpha_shape_auto.rst.txt b/docs/_sources/generated/libpysal.cg.alpha_shape_auto.rst.txt deleted file mode 100644 index eabc9198d..000000000 --- a/docs/_sources/generated/libpysal.cg.alpha_shape_auto.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.alpha\_shape\_auto -============================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: alpha_shape_auto \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.arcdist.rst.txt b/docs/_sources/generated/libpysal.cg.arcdist.rst.txt deleted file mode 100644 index f3ebba8ee..000000000 --- a/docs/_sources/generated/libpysal.cg.arcdist.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.arcdist -=================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: arcdist \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.arcdist2linear.rst.txt b/docs/_sources/generated/libpysal.cg.arcdist2linear.rst.txt deleted file mode 100644 index e3afcf98a..000000000 --- a/docs/_sources/generated/libpysal.cg.arcdist2linear.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.arcdist2linear -========================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: arcdist2linear \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.asShape.rst.txt b/docs/_sources/generated/libpysal.cg.asShape.rst.txt deleted file mode 100644 index 2bd215bed..000000000 --- a/docs/_sources/generated/libpysal.cg.asShape.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.asShape -=================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: asShape \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.bbcommon.rst.txt b/docs/_sources/generated/libpysal.cg.bbcommon.rst.txt deleted file mode 100644 index 3a774e5c5..000000000 --- a/docs/_sources/generated/libpysal.cg.bbcommon.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.bbcommon -==================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: bbcommon \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.brute_knn.rst.txt b/docs/_sources/generated/libpysal.cg.brute_knn.rst.txt deleted file mode 100644 index 549e67cb3..000000000 --- a/docs/_sources/generated/libpysal.cg.brute_knn.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.brute\_knn -====================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: brute_knn \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.convex_hull.rst.txt b/docs/_sources/generated/libpysal.cg.convex_hull.rst.txt deleted file mode 100644 index 18c1c230c..000000000 --- a/docs/_sources/generated/libpysal.cg.convex_hull.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.convex\_hull -======================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: convex_hull \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.distance_matrix.rst.txt b/docs/_sources/generated/libpysal.cg.distance_matrix.rst.txt deleted file mode 100644 index d4e57bc66..000000000 --- a/docs/_sources/generated/libpysal.cg.distance_matrix.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.distance\_matrix -============================ - -.. currentmodule:: libpysal.cg - -.. autofunction:: distance_matrix \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.fast_knn.rst.txt b/docs/_sources/generated/libpysal.cg.fast_knn.rst.txt deleted file mode 100644 index 7155f9186..000000000 --- a/docs/_sources/generated/libpysal.cg.fast_knn.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.fast\_knn -===================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: fast_knn \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.fast_threshold.rst.txt b/docs/_sources/generated/libpysal.cg.fast_threshold.rst.txt deleted file mode 100644 index f86c422fd..000000000 --- a/docs/_sources/generated/libpysal.cg.fast_threshold.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.fast\_threshold -=========================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: fast_threshold \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.geogrid.rst.txt b/docs/_sources/generated/libpysal.cg.geogrid.rst.txt deleted file mode 100644 index 2f375ae00..000000000 --- a/docs/_sources/generated/libpysal.cg.geogrid.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.geogrid -=================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: geogrid \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.geointerpolate.rst.txt b/docs/_sources/generated/libpysal.cg.geointerpolate.rst.txt deleted file mode 100644 index 73f93e37c..000000000 --- a/docs/_sources/generated/libpysal.cg.geointerpolate.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.geointerpolate -========================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: geointerpolate \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_angle_between.rst.txt b/docs/_sources/generated/libpysal.cg.get_angle_between.rst.txt deleted file mode 100644 index 9e5c317bd..000000000 --- a/docs/_sources/generated/libpysal.cg.get_angle_between.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_angle\_between -=============================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_angle_between \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_bounding_box.rst.txt b/docs/_sources/generated/libpysal.cg.get_bounding_box.rst.txt deleted file mode 100644 index 089f0fa7b..000000000 --- a/docs/_sources/generated/libpysal.cg.get_bounding_box.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_bounding\_box -============================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_bounding_box \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_point_at_angle_and_dist.rst.txt b/docs/_sources/generated/libpysal.cg.get_point_at_angle_and_dist.rst.txt deleted file mode 100644 index 66a05376b..000000000 --- a/docs/_sources/generated/libpysal.cg.get_point_at_angle_and_dist.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_point\_at\_angle\_and\_dist -============================================ - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_point_at_angle_and_dist \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_points_dist.rst.txt b/docs/_sources/generated/libpysal.cg.get_points_dist.rst.txt deleted file mode 100644 index e19e232cd..000000000 --- a/docs/_sources/generated/libpysal.cg.get_points_dist.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_points\_dist -============================= - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_points_dist \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_polygon_point_dist.rst.txt b/docs/_sources/generated/libpysal.cg.get_polygon_point_dist.rst.txt deleted file mode 100644 index 3d53d2328..000000000 --- a/docs/_sources/generated/libpysal.cg.get_polygon_point_dist.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_polygon\_point\_dist -===================================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_polygon_point_dist \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_polygon_point_intersect.rst.txt b/docs/_sources/generated/libpysal.cg.get_polygon_point_intersect.rst.txt deleted file mode 100644 index 1cd7529f3..000000000 --- a/docs/_sources/generated/libpysal.cg.get_polygon_point_intersect.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_polygon\_point\_intersect -========================================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_polygon_point_intersect \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_ray_segment_intersect.rst.txt b/docs/_sources/generated/libpysal.cg.get_ray_segment_intersect.rst.txt deleted file mode 100644 index cfe51618c..000000000 --- a/docs/_sources/generated/libpysal.cg.get_ray_segment_intersect.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_ray\_segment\_intersect -======================================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_ray_segment_intersect \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_rectangle_point_intersect.rst.txt b/docs/_sources/generated/libpysal.cg.get_rectangle_point_intersect.rst.txt deleted file mode 100644 index b31e074e5..000000000 --- a/docs/_sources/generated/libpysal.cg.get_rectangle_point_intersect.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_rectangle\_point\_intersect -============================================ - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_rectangle_point_intersect \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst.txt b/docs/_sources/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst.txt deleted file mode 100644 index bcdbf5ef2..000000000 --- a/docs/_sources/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_rectangle\_rectangle\_intersection -=================================================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_rectangle_rectangle_intersection \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_segment_point_dist.rst.txt b/docs/_sources/generated/libpysal.cg.get_segment_point_dist.rst.txt deleted file mode 100644 index fd32bb258..000000000 --- a/docs/_sources/generated/libpysal.cg.get_segment_point_dist.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_segment\_point\_dist -===================================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_segment_point_dist \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_segment_point_intersect.rst.txt b/docs/_sources/generated/libpysal.cg.get_segment_point_intersect.rst.txt deleted file mode 100644 index c54747be1..000000000 --- a/docs/_sources/generated/libpysal.cg.get_segment_point_intersect.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_segment\_point\_intersect -========================================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_segment_point_intersect \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_segments_intersect.rst.txt b/docs/_sources/generated/libpysal.cg.get_segments_intersect.rst.txt deleted file mode 100644 index a0325a346..000000000 --- a/docs/_sources/generated/libpysal.cg.get_segments_intersect.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_segments\_intersect -==================================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_segments_intersect \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.get_shared_segments.rst.txt b/docs/_sources/generated/libpysal.cg.get_shared_segments.rst.txt deleted file mode 100644 index 23d6c5174..000000000 --- a/docs/_sources/generated/libpysal.cg.get_shared_segments.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.get\_shared\_segments -================================= - -.. currentmodule:: libpysal.cg - -.. autofunction:: get_shared_segments \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.harcdist.rst.txt b/docs/_sources/generated/libpysal.cg.harcdist.rst.txt deleted file mode 100644 index fd1897c42..000000000 --- a/docs/_sources/generated/libpysal.cg.harcdist.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.harcdist -==================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: harcdist \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.is_clockwise.rst.txt b/docs/_sources/generated/libpysal.cg.is_clockwise.rst.txt deleted file mode 100644 index 02e3491bb..000000000 --- a/docs/_sources/generated/libpysal.cg.is_clockwise.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.is\_clockwise -========================= - -.. currentmodule:: libpysal.cg - -.. autofunction:: is_clockwise \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.is_collinear.rst.txt b/docs/_sources/generated/libpysal.cg.is_collinear.rst.txt deleted file mode 100644 index 35252dea5..000000000 --- a/docs/_sources/generated/libpysal.cg.is_collinear.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.is\_collinear -========================= - -.. currentmodule:: libpysal.cg - -.. autofunction:: is_collinear \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.linear2arcdist.rst.txt b/docs/_sources/generated/libpysal.cg.linear2arcdist.rst.txt deleted file mode 100644 index 25004601e..000000000 --- a/docs/_sources/generated/libpysal.cg.linear2arcdist.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.linear2arcdist -========================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: linear2arcdist \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.lonlat.rst.txt b/docs/_sources/generated/libpysal.cg.lonlat.rst.txt deleted file mode 100644 index 21197e585..000000000 --- a/docs/_sources/generated/libpysal.cg.lonlat.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.lonlat -================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: lonlat \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.point_touches_rectangle.rst.txt b/docs/_sources/generated/libpysal.cg.point_touches_rectangle.rst.txt deleted file mode 100644 index 19475cfc2..000000000 --- a/docs/_sources/generated/libpysal.cg.point_touches_rectangle.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.point\_touches\_rectangle -===================================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: point_touches_rectangle \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.toLngLat.rst.txt b/docs/_sources/generated/libpysal.cg.toLngLat.rst.txt deleted file mode 100644 index 32a85fc4a..000000000 --- a/docs/_sources/generated/libpysal.cg.toLngLat.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.toLngLat -==================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: toLngLat \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.toXYZ.rst.txt b/docs/_sources/generated/libpysal.cg.toXYZ.rst.txt deleted file mode 100644 index cd18210ec..000000000 --- a/docs/_sources/generated/libpysal.cg.toXYZ.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.toXYZ -================= - -.. currentmodule:: libpysal.cg - -.. autofunction:: toXYZ \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.cg.voronoi_frames.rst.txt b/docs/_sources/generated/libpysal.cg.voronoi_frames.rst.txt deleted file mode 100644 index 5270c27a4..000000000 --- a/docs/_sources/generated/libpysal.cg.voronoi_frames.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.cg.voronoi\_frames -=========================== - -.. currentmodule:: libpysal.cg - -.. autofunction:: voronoi_frames \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.examples.available.rst.txt b/docs/_sources/generated/libpysal.examples.available.rst.txt deleted file mode 100644 index 263a1b59c..000000000 --- a/docs/_sources/generated/libpysal.examples.available.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.examples.available -=========================== - -.. currentmodule:: libpysal.examples - -.. autofunction:: available \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.examples.explain.rst.txt b/docs/_sources/generated/libpysal.examples.explain.rst.txt deleted file mode 100644 index 3964e1189..000000000 --- a/docs/_sources/generated/libpysal.examples.explain.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.examples.explain -========================= - -.. currentmodule:: libpysal.examples - -.. autofunction:: explain \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.examples.get_path.rst.txt b/docs/_sources/generated/libpysal.examples.get_path.rst.txt deleted file mode 100644 index 8750fd88d..000000000 --- a/docs/_sources/generated/libpysal.examples.get_path.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.examples.get\_path -=========================== - -.. currentmodule:: libpysal.examples - -.. autofunction:: get_path \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.io.fileio.FileIO.rst.txt b/docs/_sources/generated/libpysal.io.fileio.FileIO.rst.txt deleted file mode 100644 index 822071cd6..000000000 --- a/docs/_sources/generated/libpysal.io.fileio.FileIO.rst.txt +++ /dev/null @@ -1,42 +0,0 @@ -libpysal.io.fileio.FileIO -========================= - -.. currentmodule:: libpysal.io.fileio - -.. autoclass:: FileIO - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~FileIO.__init__ - ~FileIO.cast - ~FileIO.check - ~FileIO.close - ~FileIO.flush - ~FileIO.get - ~FileIO.getType - ~FileIO.open - ~FileIO.read - ~FileIO.seek - ~FileIO.tell - ~FileIO.truncate - ~FileIO.write - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~FileIO.by_row - ~FileIO.ids - ~FileIO.rIds - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.io.open.rst.txt b/docs/_sources/generated/libpysal.io.open.rst.txt deleted file mode 100644 index 28508b74f..000000000 --- a/docs/_sources/generated/libpysal.io.open.rst.txt +++ /dev/null @@ -1,42 +0,0 @@ -libpysal.io.open -================ - -.. currentmodule:: libpysal.io - -.. autoclass:: open - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~open.__init__ - ~open.cast - ~open.check - ~open.close - ~open.flush - ~open.get - ~open.getType - ~open.open - ~open.read - ~open.seek - ~open.tell - ~open.truncate - ~open.write - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~open.by_row - ~open.ids - ~open.rIds - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.DistanceBand.rst.txt b/docs/_sources/generated/libpysal.weights.DistanceBand.rst.txt deleted file mode 100644 index 528076369..000000000 --- a/docs/_sources/generated/libpysal.weights.DistanceBand.rst.txt +++ /dev/null @@ -1,75 +0,0 @@ -libpysal.weights.DistanceBand -============================= - -.. currentmodule:: libpysal.weights - -.. autoclass:: DistanceBand - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~DistanceBand.__init__ - ~DistanceBand.asymmetry - ~DistanceBand.from_WSP - ~DistanceBand.from_adjlist - ~DistanceBand.from_array - ~DistanceBand.from_dataframe - ~DistanceBand.from_file - ~DistanceBand.from_networkx - ~DistanceBand.from_shapefile - ~DistanceBand.full - ~DistanceBand.get_transform - ~DistanceBand.plot - ~DistanceBand.remap_ids - ~DistanceBand.set_shapefile - ~DistanceBand.set_transform - ~DistanceBand.symmetrize - ~DistanceBand.to_WSP - ~DistanceBand.to_adjlist - ~DistanceBand.to_file - ~DistanceBand.to_networkx - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~DistanceBand.asymmetries - ~DistanceBand.cardinalities - ~DistanceBand.component_labels - ~DistanceBand.diagW2 - ~DistanceBand.diagWtW - ~DistanceBand.diagWtW_WW - ~DistanceBand.histogram - ~DistanceBand.id2i - ~DistanceBand.id_order - ~DistanceBand.id_order_set - ~DistanceBand.islands - ~DistanceBand.max_neighbors - ~DistanceBand.mean_neighbors - ~DistanceBand.min_neighbors - ~DistanceBand.n - ~DistanceBand.n_components - ~DistanceBand.neighbor_offsets - ~DistanceBand.nonzero - ~DistanceBand.pct_nonzero - ~DistanceBand.s0 - ~DistanceBand.s1 - ~DistanceBand.s2 - ~DistanceBand.s2array - ~DistanceBand.sd - ~DistanceBand.sparse - ~DistanceBand.transform - ~DistanceBand.trcW2 - ~DistanceBand.trcWtW - ~DistanceBand.trcWtW_WW - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.KNN.rst.txt b/docs/_sources/generated/libpysal.weights.KNN.rst.txt deleted file mode 100644 index 7eca1d31d..000000000 --- a/docs/_sources/generated/libpysal.weights.KNN.rst.txt +++ /dev/null @@ -1,76 +0,0 @@ -libpysal.weights.KNN -==================== - -.. currentmodule:: libpysal.weights - -.. autoclass:: KNN - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~KNN.__init__ - ~KNN.asymmetry - ~KNN.from_WSP - ~KNN.from_adjlist - ~KNN.from_array - ~KNN.from_dataframe - ~KNN.from_file - ~KNN.from_networkx - ~KNN.from_shapefile - ~KNN.full - ~KNN.get_transform - ~KNN.plot - ~KNN.remap_ids - ~KNN.reweight - ~KNN.set_shapefile - ~KNN.set_transform - ~KNN.symmetrize - ~KNN.to_WSP - ~KNN.to_adjlist - ~KNN.to_file - ~KNN.to_networkx - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~KNN.asymmetries - ~KNN.cardinalities - ~KNN.component_labels - ~KNN.diagW2 - ~KNN.diagWtW - ~KNN.diagWtW_WW - ~KNN.histogram - ~KNN.id2i - ~KNN.id_order - ~KNN.id_order_set - ~KNN.islands - ~KNN.max_neighbors - ~KNN.mean_neighbors - ~KNN.min_neighbors - ~KNN.n - ~KNN.n_components - ~KNN.neighbor_offsets - ~KNN.nonzero - ~KNN.pct_nonzero - ~KNN.s0 - ~KNN.s1 - ~KNN.s2 - ~KNN.s2array - ~KNN.sd - ~KNN.sparse - ~KNN.transform - ~KNN.trcW2 - ~KNN.trcWtW - ~KNN.trcWtW_WW - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.Kernel.rst.txt b/docs/_sources/generated/libpysal.weights.Kernel.rst.txt deleted file mode 100644 index fce92dedc..000000000 --- a/docs/_sources/generated/libpysal.weights.Kernel.rst.txt +++ /dev/null @@ -1,75 +0,0 @@ -libpysal.weights.Kernel -======================= - -.. currentmodule:: libpysal.weights - -.. autoclass:: Kernel - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Kernel.__init__ - ~Kernel.asymmetry - ~Kernel.from_WSP - ~Kernel.from_adjlist - ~Kernel.from_array - ~Kernel.from_dataframe - ~Kernel.from_file - ~Kernel.from_networkx - ~Kernel.from_shapefile - ~Kernel.full - ~Kernel.get_transform - ~Kernel.plot - ~Kernel.remap_ids - ~Kernel.set_shapefile - ~Kernel.set_transform - ~Kernel.symmetrize - ~Kernel.to_WSP - ~Kernel.to_adjlist - ~Kernel.to_file - ~Kernel.to_networkx - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~Kernel.asymmetries - ~Kernel.cardinalities - ~Kernel.component_labels - ~Kernel.diagW2 - ~Kernel.diagWtW - ~Kernel.diagWtW_WW - ~Kernel.histogram - ~Kernel.id2i - ~Kernel.id_order - ~Kernel.id_order_set - ~Kernel.islands - ~Kernel.max_neighbors - ~Kernel.mean_neighbors - ~Kernel.min_neighbors - ~Kernel.n - ~Kernel.n_components - ~Kernel.neighbor_offsets - ~Kernel.nonzero - ~Kernel.pct_nonzero - ~Kernel.s0 - ~Kernel.s1 - ~Kernel.s2 - ~Kernel.s2array - ~Kernel.sd - ~Kernel.sparse - ~Kernel.transform - ~Kernel.trcW2 - ~Kernel.trcWtW - ~Kernel.trcWtW_WW - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.ODW.rst.txt b/docs/_sources/generated/libpysal.weights.ODW.rst.txt deleted file mode 100644 index bd6e87340..000000000 --- a/docs/_sources/generated/libpysal.weights.ODW.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.ODW -==================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: ODW \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.Queen.rst.txt b/docs/_sources/generated/libpysal.weights.Queen.rst.txt deleted file mode 100644 index 36858f566..000000000 --- a/docs/_sources/generated/libpysal.weights.Queen.rst.txt +++ /dev/null @@ -1,76 +0,0 @@ -libpysal.weights.Queen -====================== - -.. currentmodule:: libpysal.weights - -.. autoclass:: Queen - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Queen.__init__ - ~Queen.asymmetry - ~Queen.from_WSP - ~Queen.from_adjlist - ~Queen.from_dataframe - ~Queen.from_file - ~Queen.from_iterable - ~Queen.from_networkx - ~Queen.from_shapefile - ~Queen.from_xarray - ~Queen.full - ~Queen.get_transform - ~Queen.plot - ~Queen.remap_ids - ~Queen.set_shapefile - ~Queen.set_transform - ~Queen.symmetrize - ~Queen.to_WSP - ~Queen.to_adjlist - ~Queen.to_file - ~Queen.to_networkx - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~Queen.asymmetries - ~Queen.cardinalities - ~Queen.component_labels - ~Queen.diagW2 - ~Queen.diagWtW - ~Queen.diagWtW_WW - ~Queen.histogram - ~Queen.id2i - ~Queen.id_order - ~Queen.id_order_set - ~Queen.islands - ~Queen.max_neighbors - ~Queen.mean_neighbors - ~Queen.min_neighbors - ~Queen.n - ~Queen.n_components - ~Queen.neighbor_offsets - ~Queen.nonzero - ~Queen.pct_nonzero - ~Queen.s0 - ~Queen.s1 - ~Queen.s2 - ~Queen.s2array - ~Queen.sd - ~Queen.sparse - ~Queen.transform - ~Queen.trcW2 - ~Queen.trcWtW - ~Queen.trcWtW_WW - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.Rook.rst.txt b/docs/_sources/generated/libpysal.weights.Rook.rst.txt deleted file mode 100644 index 364b3b2cb..000000000 --- a/docs/_sources/generated/libpysal.weights.Rook.rst.txt +++ /dev/null @@ -1,76 +0,0 @@ -libpysal.weights.Rook -===================== - -.. currentmodule:: libpysal.weights - -.. autoclass:: Rook - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Rook.__init__ - ~Rook.asymmetry - ~Rook.from_WSP - ~Rook.from_adjlist - ~Rook.from_dataframe - ~Rook.from_file - ~Rook.from_iterable - ~Rook.from_networkx - ~Rook.from_shapefile - ~Rook.from_xarray - ~Rook.full - ~Rook.get_transform - ~Rook.plot - ~Rook.remap_ids - ~Rook.set_shapefile - ~Rook.set_transform - ~Rook.symmetrize - ~Rook.to_WSP - ~Rook.to_adjlist - ~Rook.to_file - ~Rook.to_networkx - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~Rook.asymmetries - ~Rook.cardinalities - ~Rook.component_labels - ~Rook.diagW2 - ~Rook.diagWtW - ~Rook.diagWtW_WW - ~Rook.histogram - ~Rook.id2i - ~Rook.id_order - ~Rook.id_order_set - ~Rook.islands - ~Rook.max_neighbors - ~Rook.mean_neighbors - ~Rook.min_neighbors - ~Rook.n - ~Rook.n_components - ~Rook.neighbor_offsets - ~Rook.nonzero - ~Rook.pct_nonzero - ~Rook.s0 - ~Rook.s1 - ~Rook.s2 - ~Rook.s2array - ~Rook.sd - ~Rook.sparse - ~Rook.transform - ~Rook.trcW2 - ~Rook.trcWtW - ~Rook.trcWtW_WW - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.Voronoi.rst.txt b/docs/_sources/generated/libpysal.weights.Voronoi.rst.txt deleted file mode 100644 index ed097d5c9..000000000 --- a/docs/_sources/generated/libpysal.weights.Voronoi.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.Voronoi -======================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: Voronoi \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.W.rst.txt b/docs/_sources/generated/libpysal.weights.W.rst.txt deleted file mode 100644 index 21e1d5c00..000000000 --- a/docs/_sources/generated/libpysal.weights.W.rst.txt +++ /dev/null @@ -1,73 +0,0 @@ -libpysal.weights.W -================== - -.. currentmodule:: libpysal.weights - -.. autoclass:: W - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~W.__init__ - ~W.asymmetry - ~W.from_WSP - ~W.from_adjlist - ~W.from_file - ~W.from_networkx - ~W.from_shapefile - ~W.full - ~W.get_transform - ~W.plot - ~W.remap_ids - ~W.set_shapefile - ~W.set_transform - ~W.symmetrize - ~W.to_WSP - ~W.to_adjlist - ~W.to_file - ~W.to_networkx - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~W.asymmetries - ~W.cardinalities - ~W.component_labels - ~W.diagW2 - ~W.diagWtW - ~W.diagWtW_WW - ~W.histogram - ~W.id2i - ~W.id_order - ~W.id_order_set - ~W.islands - ~W.max_neighbors - ~W.mean_neighbors - ~W.min_neighbors - ~W.n - ~W.n_components - ~W.neighbor_offsets - ~W.nonzero - ~W.pct_nonzero - ~W.s0 - ~W.s1 - ~W.s2 - ~W.s2array - ~W.sd - ~W.sparse - ~W.transform - ~W.trcW2 - ~W.trcWtW - ~W.trcWtW_WW - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.WSP.rst.txt b/docs/_sources/generated/libpysal.weights.WSP.rst.txt deleted file mode 100644 index 8192bd7d1..000000000 --- a/docs/_sources/generated/libpysal.weights.WSP.rst.txt +++ /dev/null @@ -1,33 +0,0 @@ -libpysal.weights.WSP -==================== - -.. currentmodule:: libpysal.weights - -.. autoclass:: WSP - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~WSP.__init__ - ~WSP.from_W - ~WSP.to_W - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~WSP.diagWtW_WW - ~WSP.id_order - ~WSP.s0 - ~WSP.trcWtW_WW - - \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.WSP2W.rst.txt b/docs/_sources/generated/libpysal.weights.WSP2W.rst.txt deleted file mode 100644 index 0b05884a3..000000000 --- a/docs/_sources/generated/libpysal.weights.WSP2W.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.WSP2W -====================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: WSP2W \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.attach_islands.rst.txt b/docs/_sources/generated/libpysal.weights.attach_islands.rst.txt deleted file mode 100644 index 49cb88ab5..000000000 --- a/docs/_sources/generated/libpysal.weights.attach_islands.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.attach\_islands -================================ - -.. currentmodule:: libpysal.weights - -.. autofunction:: attach_islands \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.block_weights.rst.txt b/docs/_sources/generated/libpysal.weights.block_weights.rst.txt deleted file mode 100644 index 30a79c630..000000000 --- a/docs/_sources/generated/libpysal.weights.block_weights.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.block\_weights -=============================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: block_weights \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.build_lattice_shapefile.rst.txt b/docs/_sources/generated/libpysal.weights.build_lattice_shapefile.rst.txt deleted file mode 100644 index c742797cf..000000000 --- a/docs/_sources/generated/libpysal.weights.build_lattice_shapefile.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.build\_lattice\_shapefile -========================================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: build_lattice_shapefile \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.comb.rst.txt b/docs/_sources/generated/libpysal.weights.comb.rst.txt deleted file mode 100644 index fd4fc858b..000000000 --- a/docs/_sources/generated/libpysal.weights.comb.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.comb -===================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: comb \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.da2W.rst.txt b/docs/_sources/generated/libpysal.weights.da2W.rst.txt deleted file mode 100644 index 5dacd87c0..000000000 --- a/docs/_sources/generated/libpysal.weights.da2W.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.da2W -===================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: da2W \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.da2WSP.rst.txt b/docs/_sources/generated/libpysal.weights.da2WSP.rst.txt deleted file mode 100644 index e5bfd8c6f..000000000 --- a/docs/_sources/generated/libpysal.weights.da2WSP.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.da2WSP -======================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: da2WSP \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.fill_diagonal.rst.txt b/docs/_sources/generated/libpysal.weights.fill_diagonal.rst.txt deleted file mode 100644 index 84d067071..000000000 --- a/docs/_sources/generated/libpysal.weights.fill_diagonal.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.fill\_diagonal -=============================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: fill_diagonal \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.full.rst.txt b/docs/_sources/generated/libpysal.weights.full.rst.txt deleted file mode 100644 index cd584af47..000000000 --- a/docs/_sources/generated/libpysal.weights.full.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.full -===================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: full \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.full2W.rst.txt b/docs/_sources/generated/libpysal.weights.full2W.rst.txt deleted file mode 100644 index 8cd2e8c77..000000000 --- a/docs/_sources/generated/libpysal.weights.full2W.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.full2W -======================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: full2W \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.fuzzy_contiguity.rst.txt b/docs/_sources/generated/libpysal.weights.fuzzy_contiguity.rst.txt deleted file mode 100644 index e8d510ec9..000000000 --- a/docs/_sources/generated/libpysal.weights.fuzzy_contiguity.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.fuzzy\_contiguity -================================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: fuzzy_contiguity \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.get_ids.rst.txt b/docs/_sources/generated/libpysal.weights.get_ids.rst.txt deleted file mode 100644 index c49fa48e6..000000000 --- a/docs/_sources/generated/libpysal.weights.get_ids.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.get\_ids -========================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: get_ids \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.get_points_array_from_shapefile.rst.txt b/docs/_sources/generated/libpysal.weights.get_points_array_from_shapefile.rst.txt deleted file mode 100644 index 3098243ae..000000000 --- a/docs/_sources/generated/libpysal.weights.get_points_array_from_shapefile.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.get\_points\_array\_from\_shapefile -==================================================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: get_points_array_from_shapefile \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.hexLat2W.rst.txt b/docs/_sources/generated/libpysal.weights.hexLat2W.rst.txt deleted file mode 100644 index aeac82591..000000000 --- a/docs/_sources/generated/libpysal.weights.hexLat2W.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.hexLat2W -========================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: hexLat2W \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.higher_order.rst.txt b/docs/_sources/generated/libpysal.weights.higher_order.rst.txt deleted file mode 100644 index d51b29b23..000000000 --- a/docs/_sources/generated/libpysal.weights.higher_order.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.higher\_order -============================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: higher_order \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.higher_order_sp.rst.txt b/docs/_sources/generated/libpysal.weights.higher_order_sp.rst.txt deleted file mode 100644 index b7e831e72..000000000 --- a/docs/_sources/generated/libpysal.weights.higher_order_sp.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.higher\_order\_sp -================================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: higher_order_sp \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.lag_categorical.rst.txt b/docs/_sources/generated/libpysal.weights.lag_categorical.rst.txt deleted file mode 100644 index 1a2fdd7e2..000000000 --- a/docs/_sources/generated/libpysal.weights.lag_categorical.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.lag\_categorical -================================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: lag_categorical \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.lag_spatial.rst.txt b/docs/_sources/generated/libpysal.weights.lag_spatial.rst.txt deleted file mode 100644 index 204f5d807..000000000 --- a/docs/_sources/generated/libpysal.weights.lag_spatial.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.lag\_spatial -============================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: lag_spatial \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.lat2SW.rst.txt b/docs/_sources/generated/libpysal.weights.lat2SW.rst.txt deleted file mode 100644 index b0b48ae9b..000000000 --- a/docs/_sources/generated/libpysal.weights.lat2SW.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.lat2SW -======================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: lat2SW \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.lat2W.rst.txt b/docs/_sources/generated/libpysal.weights.lat2W.rst.txt deleted file mode 100644 index 93724cf5f..000000000 --- a/docs/_sources/generated/libpysal.weights.lat2W.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.lat2W -====================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: lat2W \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.mat2L.rst.txt b/docs/_sources/generated/libpysal.weights.mat2L.rst.txt deleted file mode 100644 index 8d1808c3e..000000000 --- a/docs/_sources/generated/libpysal.weights.mat2L.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.mat2L -====================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: mat2L \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst.txt b/docs/_sources/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst.txt deleted file mode 100644 index 8944feb4d..000000000 --- a/docs/_sources/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.min\_threshold\_dist\_from\_shapefile -====================================================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: min_threshold_dist_from_shapefile \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.min_threshold_distance.rst.txt b/docs/_sources/generated/libpysal.weights.min_threshold_distance.rst.txt deleted file mode 100644 index 6a2941498..000000000 --- a/docs/_sources/generated/libpysal.weights.min_threshold_distance.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.min\_threshold\_distance -========================================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: min_threshold_distance \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.neighbor_equality.rst.txt b/docs/_sources/generated/libpysal.weights.neighbor_equality.rst.txt deleted file mode 100644 index 714af0a7c..000000000 --- a/docs/_sources/generated/libpysal.weights.neighbor_equality.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.neighbor\_equality -=================================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: neighbor_equality \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.netW.rst.txt b/docs/_sources/generated/libpysal.weights.netW.rst.txt deleted file mode 100644 index 492b0d729..000000000 --- a/docs/_sources/generated/libpysal.weights.netW.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.netW -===================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: netW \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.nonplanar_neighbors.rst.txt b/docs/_sources/generated/libpysal.weights.nonplanar_neighbors.rst.txt deleted file mode 100644 index d19855dd8..000000000 --- a/docs/_sources/generated/libpysal.weights.nonplanar_neighbors.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.nonplanar\_neighbors -===================================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: nonplanar_neighbors \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.order.rst.txt b/docs/_sources/generated/libpysal.weights.order.rst.txt deleted file mode 100644 index 77e9ed195..000000000 --- a/docs/_sources/generated/libpysal.weights.order.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.order -====================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: order \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.remap_ids.rst.txt b/docs/_sources/generated/libpysal.weights.remap_ids.rst.txt deleted file mode 100644 index 4899fc477..000000000 --- a/docs/_sources/generated/libpysal.weights.remap_ids.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.remap\_ids -=========================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: remap_ids \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.shimbel.rst.txt b/docs/_sources/generated/libpysal.weights.shimbel.rst.txt deleted file mode 100644 index ed151c58c..000000000 --- a/docs/_sources/generated/libpysal.weights.shimbel.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.shimbel -======================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: shimbel \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.spw_from_gal.rst.txt b/docs/_sources/generated/libpysal.weights.spw_from_gal.rst.txt deleted file mode 100644 index fc4592a3e..000000000 --- a/docs/_sources/generated/libpysal.weights.spw_from_gal.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.spw\_from\_gal -=============================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: spw_from_gal \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.testDataArray.rst.txt b/docs/_sources/generated/libpysal.weights.testDataArray.rst.txt deleted file mode 100644 index ded0f3098..000000000 --- a/docs/_sources/generated/libpysal.weights.testDataArray.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.testDataArray -============================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: testDataArray \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.vecW.rst.txt b/docs/_sources/generated/libpysal.weights.vecW.rst.txt deleted file mode 100644 index 8baf549d2..000000000 --- a/docs/_sources/generated/libpysal.weights.vecW.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.vecW -===================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: vecW \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.w2da.rst.txt b/docs/_sources/generated/libpysal.weights.w2da.rst.txt deleted file mode 100644 index 6a11417f1..000000000 --- a/docs/_sources/generated/libpysal.weights.w2da.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.w2da -===================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: w2da \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.w_clip.rst.txt b/docs/_sources/generated/libpysal.weights.w_clip.rst.txt deleted file mode 100644 index d1731b1e1..000000000 --- a/docs/_sources/generated/libpysal.weights.w_clip.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.w\_clip -======================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: w_clip \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.w_difference.rst.txt b/docs/_sources/generated/libpysal.weights.w_difference.rst.txt deleted file mode 100644 index 6f0210d95..000000000 --- a/docs/_sources/generated/libpysal.weights.w_difference.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.w\_difference -============================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: w_difference \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.w_intersection.rst.txt b/docs/_sources/generated/libpysal.weights.w_intersection.rst.txt deleted file mode 100644 index bf25a21e0..000000000 --- a/docs/_sources/generated/libpysal.weights.w_intersection.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.w\_intersection -================================ - -.. currentmodule:: libpysal.weights - -.. autofunction:: w_intersection \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.w_local_cluster.rst.txt b/docs/_sources/generated/libpysal.weights.w_local_cluster.rst.txt deleted file mode 100644 index 56e84ef20..000000000 --- a/docs/_sources/generated/libpysal.weights.w_local_cluster.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.w\_local\_cluster -================================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: w_local_cluster \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.w_subset.rst.txt b/docs/_sources/generated/libpysal.weights.w_subset.rst.txt deleted file mode 100644 index 2164fd6fd..000000000 --- a/docs/_sources/generated/libpysal.weights.w_subset.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.w\_subset -========================== - -.. currentmodule:: libpysal.weights - -.. autofunction:: w_subset \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.w_symmetric_difference.rst.txt b/docs/_sources/generated/libpysal.weights.w_symmetric_difference.rst.txt deleted file mode 100644 index c06c0b5f3..000000000 --- a/docs/_sources/generated/libpysal.weights.w_symmetric_difference.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.w\_symmetric\_difference -========================================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: w_symmetric_difference \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.w_union.rst.txt b/docs/_sources/generated/libpysal.weights.w_union.rst.txt deleted file mode 100644 index 278a9c33f..000000000 --- a/docs/_sources/generated/libpysal.weights.w_union.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.w\_union -========================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: w_union \ No newline at end of file diff --git a/docs/_sources/generated/libpysal.weights.wsp2da.rst.txt b/docs/_sources/generated/libpysal.weights.wsp2da.rst.txt deleted file mode 100644 index 7b71f7f45..000000000 --- a/docs/_sources/generated/libpysal.weights.wsp2da.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -libpysal.weights.wsp2da -======================= - -.. currentmodule:: libpysal.weights - -.. autofunction:: wsp2da \ No newline at end of file diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt deleted file mode 100644 index 8040ac2e3..000000000 --- a/docs/_sources/index.rst.txt +++ /dev/null @@ -1,133 +0,0 @@ -.. libpysal documentation master file - -libpysal: Python Spatial Analysis Library Core -============================================== - -.. image:: https://github.com/pysal/libpysal/workflows/.github/workflows/unittests.yml/badge.svg - :target: https://github.com/pysal/libpysal/actions?query=workflow%3A.github%2Fworkflows%2Funittests.yml - -.. image:: https://badges.gitter.im/pysal/pysal.svg - :target: https://gitter.im/pysal/pysal - -.. image:: https://badge.fury.io/py/libpysal.svg - :target: https://badge.fury.io/py/libpysal - -.. raw:: html - -
-
-
-
-
- - -
-
Weights for nonplanar enforced geometries
-
- -
-
-
-
-
- - -************ -Introduction -************ - -**libpysal** offers four modules that form the building blocks in many upstream packages in the `PySAL family `_: - -- Spatial Weights: libpysal.weights -- Input-and output: libpysal.io -- Computational geometry: libpysal.cg -- Built-in example datasets libpysal.examples - - -Examples demonstrating some of **libpysal** functionality are available in the `tutorial `_. - -Details are available in the `libpysal api `_. - -For background information see :cite:`pysal2007`. - -*********** -Development -*********** - -libpysal development is hosted on github_. - -.. _github : https://github.com/pysal/libpysal - -Discussions of development occurs on the -`developer list `_ -as well as gitter_. - -.. _gitter : https://gitter.im/pysal/pysal? - -**************** -Getting Involved -**************** - -If you are interested in contributing to PySAL please see our -`development guidelines `_. - - -*********** -Bug reports -*********** - -To search for or report bugs, please see libpysal's issues_. - -.. _issues : http://github.com/pysal/libpysal/issues - - -*************** -Citing libpysal -*************** - -If you use PySAL in a scientific publication, we would appreciate citations to the following paper: - - `PySAL: A Python Library of Spatial Analytical Methods `_, *Rey, S.J. and L. Anselin*, Review of Regional Studies 37, 5-27 2007. - - Bibtex entry:: - - @Article{pysal2007, - author={Rey, Sergio J. and Anselin, Luc}, - title={{PySAL: A Python Library of Spatial Analytical Methods}}, - journal={The Review of Regional Studies}, - year=2007, - volume={37}, - number={1}, - pages={5-27}, - keywords={Open Source; Software; Spatial} - } - - - -******************* -License information -******************* - -See the file "LICENSE.txt" for information on the history of this -software, terms & conditions for usage, and a DISCLAIMER OF ALL -WARRANTIES. - - -libpysal -======== - -Core components of the Python Spatial Analysis Library (`PySAL`_) - - - -.. toctree:: - :hidden: - :maxdepth: 4 - :caption: Contents: - - Installation - Tutorial - API - References - -.. _PySAL: https://github.com/pysal/pysal diff --git a/docs/_sources/installation.rst.txt b/docs/_sources/installation.rst.txt deleted file mode 100644 index 8c6ea63cb..000000000 --- a/docs/_sources/installation.rst.txt +++ /dev/null @@ -1,57 +0,0 @@ -.. Installation - -Installation -============ - -libpysal supports python >= `3.6`_ only. Please make sure that you are -operating in a python 3 environment. - -Installing released version ---------------------------- - -conda -+++++ - -libpysal is available through conda:: - - - conda install -c conda-forge libpysal - - -pypi -++++ - - -libpysal is available on the `Python Package Index`_. Therefore, you can either -install directly with `pip` from the command line:: - - pip install -U libpysal - - -or download the source distribution (.tar.gz) and decompress it to your selected -destination. Open a command shell and navigate to the decompressed folder. -Type:: - - pip install . - -Installing development version ------------------------------- - -Potentially, you might want to use the newest features in the development -version of libpysal on github - `pysal/libpysal`_ while have not been incorporated -in the Pypi released version. You can achieve that by installing `pysal/libpysal`_ -by running the following from a command shell:: - - pip install git+https://github.com/pysal/libpysal.git - -You can also `fork`_ the `pysal/libpysal`_ repo and create a local clone of -your fork. By making changes -to your local clone and submitting a pull request to `pysal/libpysal`_, you can -contribute to libpysal development. - -.. _3.6: https://docs.python.org/3.6/ -.. _3.7: https://docs.python.org/3.7/ -.. _3.8: https://docs.python.org/3.8/ -.. _Python Package Index: https://pypi.org/project/libpysal/ -.. _pysal/libpysal: https://github.com/pysal/libpysal -.. _fork: https://help.github.com/articles/fork-a-repo/ diff --git a/docs/_sources/notebooks/Raster_awareness_API.ipynb.txt b/docs/_sources/notebooks/Raster_awareness_API.ipynb.txt deleted file mode 100644 index a38dc049d..000000000 --- a/docs/_sources/notebooks/Raster_awareness_API.ipynb.txt +++ /dev/null @@ -1,754 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Raster awareness API\n", - "\n", - "This notebook will give an overview of newly developed raster interface. We'll cover \n", - "basic usage of the functionality offered by the interface which mainly involves:\n", - "1. converting `xarray.DataArray` object to the PySAL's weights object (`libpysal.weights.W`/`WSP`).\n", - "2. going back to the `xarray.DataArray` from weights object.\n", - "\n", - "using different datasets:\n", - "- with missing values.\n", - "- with multiple layers.\n", - "- with non conventional dimension names." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "from libpysal.weights import Rook, Queen, raster\n", - "import matplotlib.pyplot as plt\n", - "from splot import libpysal as splot\n", - "import numpy as np\n", - "import xarray as xr\n", - "import pandas as pd\n", - "from esda import Moran_Local" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading Data\n", - "\n", - "*The interface only accepts `xarray.DataArray`*, this can be easily obtained from raster data\n", - "format using `xarray`'s I/O functionality which can read from a variety of data formats some of them are listed below: \n", - "- [GDAL Raster Formats](https://svn.osgeo.org/gdal/tags/gdal_1_2_5/frmts/formats_list.html) via `open_rasterio` method.\n", - "- [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) via `open_dataset` method.\n", - "\n", - "In this notebook we'll work with `NetCDF` and `GeoTIFF` data. \n", - "\n", - "### Using xarray example dataset\n", - "First lets load up a `netCDF` dataset offered by xarray." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[3869000 values with dtype=float32]\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]\n" - ] - } - ], - "source": [ - "ds = xr.tutorial.open_dataset(\"air_temperature.nc\") # -> returns a xarray.Dataset object\n", - "da = ds[\"air\"] # we'll use the \"air\" data variable for further analysis\n", - "print(da)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`xarray`'s data structures like `Dataset` and `DataArray` provides `pandas` like functionality for multidimensional-array or ndarray. \n", - "\n", - "In our case we'll mainly deal with `DataArray`, we can see above that the `da` holds the data for air temperature, it has 2 dims coordinate dimensions `x` and `y`, and it's layered on `time` dimension so in total 3 dims (`time`, `lat`, `lon`).\n", - "\n", - "We'll now group `da` by month and take average over the `time` dimension\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n" - ] - } - ], - "source": [ - "da = da.groupby('time.month').mean()\n", - "print(da.coords) # as a result time dim is replaced by month " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2EAAAKACAYAAAAVY3CsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9e7wsWV3fjX++VX3Zl7PPbeacM1ccwBEELwMMiGIMt3iLD/CoIBr8DZE8kwR+CmKIaMwjJiFBUR41iSYT0WeMqCDXEeXmKGNABGdwZICBoFzHOcwwlzPnsm/dXd/nj+7d67Nq1+pd++zuPrv3/rxfr/3aq6tX1VpV9a1VVV3d76+5O4QQQgghhBBCTIfsQndACCGEEEIIIfYTugkTQgghhBBCiCmimzAhhBBCCCGEmCK6CRNCCCGEEEKIKaKbMCGEEEIIIYSYIroJE0IIIYQQQogpopuwPY6ZHTazF9Prp5rZOyfQzrvN7NQkli0EMJ1YNrNrzOxDZvYJM/uYmf3AOJcvxAZTiuevMrPbzOz2QUz/i3EuX4gNpnWtMVj2QTP7ezP7L5NYvhDTQjdhe5/DAF68VaUx8FoAPzyFdsT+5TAmH8vLAP5/7v5YAN8J4JfN7PCE2xT7k8OYfDyfBPAt7n4NgG8C8Eozu2zCbYr9yWFM51oDAP49gFum1JYQE0M3YbsEM7vKzD5lZr9hZh83szeY2TPN7INm9hkze9Kg3lEze/vgU/q/NLNvGEx/lZn9ppm938w+a2Y/Nlj0awA8cvBJ6GsH0w6Y2ZsH7b3BzGyn/Xf3mwGc2elyxOwzy7Hs7v/b3T8zKN8N4F4Ax3ayTDHbzHg8r7v72uBlGzrn73tmOZ4H7T8BwAkA793psoS44Li7/nbBH4CrAHQBfD36J8rbAPwmAAPwbABvH9T7zwB+dlB+OoDbB+VXAfgL9E+0FwO4H0BzsNyPUztPBfAQgCsG7XwIwLdW9OcVAG6v+PvVEevwVADvvNDbUn8X9m8vxPJgvicBuBNAdqG3qf4u3N+sxzOAKwF8DP2nvC+50NtTfxf2b5bjebCc9w9i+oUA/suF3p76099O/hoQu4nPufsdAGBmnwBws7u7md2B/gAHAN8K4PsAwN3/1MwuMrNDg/f+yPufeq6Z2b3of1pUxUfc/a5BO7cPlv0BruDur0X/K4ZCnA8zHctmdimA/wngOncvtjOv2JPMbDy7+5cAfMPga4hvN7M3u/s9decXe5JZjecXA/hjd//SGB6qCXHB0U3Y7mKNygW9LhD2VdXI4xXz95Dev1vWM7NXAPgnFfP+ubv/WMV0IZiZjWUzOwjgjwD8jLv/ZaJdsb+Y2XgedsT97sEF9z8A8OZUPbEvmNV4/mYA/8D6ApADAFpmdtbdX5loX4hdjW7CZo8/R3/A+vdm9lQA97n76RGfCp0BsLTdRvQkTEyBXRfLZtYC8DYAv+3uf7DdtsS+ZjfG8xUA7nf3FTM7AuApAF633TbFvmTXxbO7D2/WzOyFAK7VDZiYZXQTNnu8CsBvmdnGd/yvG1XZ3e8f/OD24wDehf4n/GPHzP4XgEej/0PcuwC8yN3fM4m2xJ7hVdh9sfw8AN8G4KLBSR4AXujut0+gLbG3eBV2Xzx/LYBfMjNH/8nGL258DU2ILXgVdl88C7GnMHffupYQQgghhBBCiLEgXa0QQgghhBBCTBHdhAkhhBBCCCHEFNFNmBBCCCGEEEJMEd2ECSGEEEIIIcQUmRk7Yqux6HPtw5umGxJiEU+8SHlIzstPkmo71V6NftRlbHkKrbLYf21blj2j6VR2mhzV4ekj1iF6y6vLVoQXxnV4esHbPFEehVWv3+nlk/e5+7F6C9lMqzmIZ16fUfGR6m9qnXYUXz7i5RhjeNJYIraj6aXPobIasU2zeI2EoXEsxxvNiur3oritE8OpfZFa79Lr06tfPu94brYWfW7+yMg6ljiGzyuexjX27fb4PR8S+3vTWFtjfE6SGINRVE+38ti13XiO+lTjmMbO4rkxt+jtpaOb39huvNSpP6l8w9F5ZfvzjKvt88Kqy9FiozE4Me8oePtE1w40PRnPvJwJXdOV4vnMmb/f0fXGtPmOpy36/Q/0ptrmbR9be4+7f+dUGx0DM3MTNtc+jCc/9p9vmm6JQdy6dAQVVO7xhQ7XOY8jiOfnfnS71F6iH6l5mVEXeNmYHmLycvLSMhsUHq3msFi0qTxP5bl8WO7Oh3JvLiy31wrrVDTSJ/9oAKR9lq9ReTVUaqyGAz5fCds/W+mE5azTfumWBgjeB3zx0gzbwOdC+b0f/XdfwA6Yax/Gk7/un5cG/fAiil8A6PWq3+t0K+tEcVfnhnPUDWpiue6J42e7xtUaNzK1oQtKy0MMpmIZrVY0e7HQpnKo11sI8/coznvN6puzqEu0+bJuvG1yittsjWJ4meJ2jcqroYwOlXuleNmgQdug2Yze8nZYp/d8/D+edzzPzR/B45/Sz6nKFyiW+DDEuonpvXpxE23nUTcaG1USF00j26tzU7tb4G2Q8/gaNlTRioOz16Z6TapHIcI3Z7yfMgq7fD3EXb5C4/Eyj8HrUds8JmOdynze5PEk+jCEj+lQ5lgGdhbP7aWjePRzXg6gfH0RinGcY8s6KUbeHKduLlJtJPqUvGkYMX9UZ7vrx8dYzVOBJ25AOB75eqFHw3ZBY3CPh7jyeMwhRaGWUXg2VkOlBsczlfm6I+vwebm8bRPnxMSH2E7HKx/HAPCnf/bTO7remDb3P9DDR97zsKm2mV/6mYun2uCY0NcRhRBCCCGEEGKKzMyTMCGEEEIIIcTuxQEUSHw7Q0ToSZgQQgghhBBCTBE9CRNCCCGEEEKMAUfP9SSsDnoSJoQQQgghhBBTZHaehGWG3mJz8/SEiYcNctahMpveEgbFyJq1yRSXsM7xcuuY4urYDYvSJwlk9PPuWijXsaSx6YpNcWT/i6YDQE4KobWgEMqWQ9+zh6ptVU22CraqDYO9+bi97mJ43ZkPbXTnQ987C7QepEnL10LbrTNhOS3qX+PUSph3NTZ2RaY5skRazuawHOPEG/HynNbHmiXTUpfqkpEJZFRKxm0qHlN20KJkjmTLIB8nqWVFxwXZFCPLIscyxVn5vYS11NiYRnHLRkTnY4EtbCuroZzFFqrsobCsbC6YEhvtUPb5oOYqKJ6LVmi7aLNBkS11cXu9g3wMUNvd0EZkAT0T1iM/Q/F8NpSjdV1nTVppzCET5E7wzCID6rC5pCmRKkW22vMw1LJZM3UuSGnUGzVtjMZjTsKWR7EW2R/5PFQez5mE4TC2Pyb6QW1n1EaeWia2H6s8Pxvdum0234Zy50BYZr4Wn7cbK9UW0OxMOKfZSihH1kTw2ETHevnctQMcgA8WnUxBUccqeD52xAR14jml9Y8zQsQNpuyIWcIcmuwHL4bioygPC6n1TVgXs3WvLOdrKYNidRkAiui4oqb51ErXF12y4GZLdK1B/WisNKgcH98p821kQebTJs3ba+n5yH5hdm7CxJ6Gb8CEEEIIIcTs0Rdz7MVEjONHt9tCCCGEEEIIMUX0+EEIIYQQQggxFqSor4eehAkhhBBCCCHEFNGTMCGEEEIIIcSOcTh6KSmdiNCTMCGEEEIIIYSYIjPzJMxzoHugEau0ESs+WZmadVjJThrvLiu9WSvMKm1SDPdiXXdkWN3unb5Vq4ejcjbCWdvg/pJXlTTqrKeOlOBRG6xgZ8V8KRxIM4+E1t5Z257SRa8HxXC2HNTDOfW7BaB3YC7MfyyUO6yuX6rW3zbWWN9M089RpVHbmXXxpCbvHZoflteOzWNsZIailUXxbCndMOK4i1TVtC+9lhOZY7tacx05e0fMX6tOtH6cHoKUvSVFPSvrLZVygbcBx2mTfMWcBiJLHXulz6E4tjkmaAzh9WOtdk7qYaf6BcX1+uFYC79+sDoVA8dCsxGmZ5RuI1upVvMjp5lbYXsUSwtR252LxxTPBvTa/T7G43GizONrNGbU9HUzUXxVjz8pytr26D1L6KwTfeSUCZZTn2jfWY9jqLyA6n45jZGeODektPnRNiir1nn1aH80OnTsnaNFUf0eqbu7i6HcWSRdfZNithcfY81zYZ429au1SuMAZVyI0l9kpKWfC7HdPUznjp1iQW9eJ563LYE7jzCPwo7i4HwOmRQcR0VRPRbF5erGPYrl+L2CX0eHfnX6hfiY5r5WNh31r7FaepOSB/Ox1AvZQNCbo3Q4PFTz8d0L5ebZUKX9ULw92nw9ul5K+7LRj5zH/7Bx1g/NzKW52CETfRJmZo8ys9vp77SZvczMjprZ+8zsM4P/RybZD7H7iW7AhBBCCCHETFLAp/o3q0z0JszdP+3u17j7NQCeAGAZwNsAvBLAze5+NYCbB6+FEEIIIYQQYs8zzWeezwDwd+7+BTN7NoCnDqbfCOD9AH5yin0RQgghhBBCjBEH0Jvhp1PTZJpijucD+L1B+YS7nwSAwf/jVTOY2fVmdquZ3dpZP1dVRYiZgeN5XfEsZphobF5TLIvZhuO5u6J4FkJMh6nchJlZC8CzAPzBduZz9xvc/Vp3v7bZWpxM54SYEhzPLcWzmGGisbmtWBazDcdzY17xLMRO0W/C6jGtJ2HfBeCj7n7P4PU9ZnYpAAz+3zulfgghhBBCCCHEBWVavwn7QYSvIgLATQCuA/Cawf93bLWAomk4d2KzOjutQQ7ljBS8WYfrV+vBU9MBwEgRna9XK+5ZR2prVCZVu5EKmHXdKBJaeSDWb1PZ2uRYZcUw6+rX10O5QxuBNfadWBUeLXeeVMkLYT/0DpDOfa46nPKVsNz8bPDG2hpNf2gZxVIwJLKmubtA2lj+kJI+/GguU/ls2BeNZdoGpNPvPOxo1MfVo2F7rh4hVexSqNOhMt6JHVHkhrUjzUjny9rdTXEXxTDHZ0JPnVIXcxu96uVYKQ1EpLXnfkUpIVg/T8cFx1dKG15eV1ZSc0xSPINjmOuz7p7TKmTVqRhYcw0AxTzF8yLp3Vuc5oKU3ufCcZUthzIfu3xcrB2Ox7DVI3Rc0VutM6GNfJ22M40n3qY4PRLiee1wk8ph+esH46DoHKAXf4LzxnNgfanfTkqZHcd2zbQMNT7cTKnvs251nSiWy+2lzgG0v+NjhhYbaeU5PQqrrRPpIRCfP6L2+NyQVbfBum2n9no03vXm4s9boz7SuuYrPHbSuSQaW2g8XgjLXTtE6UPosGqwbh5AY7l6x/YW6HxD55XOgXD8sLp77RDFNo/NQP9X5ueJZ0B3kM0hGZ8cz8kFobJStPbno5ivMU/yWqhbqhcNz5z6pE4bHM+JtktjO2fPcM7YkIVl8RhSNDjmeXr1crgfecge0n/NwzOfNvlagyTNPD5y2w261mANvpcufzqUvmH9IKXZ4euZA9XXNt0ZfxjrgJI112TiT8LMbAHAPwLwVpr8GgD/yMw+M3jvNZPuh9jd8A2YEEIIIYQQe5mJPwlz92UAF5Wm3Y++LVEIIYQQQggh9hVKyy2EEEIIIYQYC8XWVQSmq6gXQgghhBBCiH2PnoQJIYQQQgghdozDlay5JnoSJoQQQgghhBBTZOSTMDP73hrLWHX3Px5Tf5IUDWD5uG3Ws7Iqlm+86QupWY91vKgsR8vheUtKV9aDswI1X2OlNGnsV0n5uxL8qTmp0201uFNtfYTSm5X1GW8I8gG3aZ4e6/GpvV5Cg98opQBoBWWwN0nrnZGuO9LUFlQ/1CnavNxgQbTFeP06B0N7y8fCPKukdWFtbPMsdX0lLKt1irYnqdNXTywMy6eujtXkZ64K8xdHSTve5P2BsVE0gOWL80h9O1I9HKnlQ5nTL6T6l1Lfc2xnrEEv67NTGnA6FjKK+SzSbbOTONW/EUp81np3qtM9RBp7XlZ0vNDnTXn6syduI1vb+jMqPha8FY6RgtT3qxeHuD53abzM9UPULdIdt06HcuMsqcLpGFs9Pj8sP/SI0PaZr+JYDsd91qLAweYMGOdLkQHrG8cla6cT6Rf4jSi2yvGRUIJnPHzRrs8pa0GkrY5SLqRXOj42qI959bERnT94enRIUnt5Yl4AWc5e7kS6CKvW0kerFJ33SPtdOqa7rP5mjX47bLheEQYnXqf1pTB97bBRGZX1y4p6Pj8yq8eDlv7MlSGez10e6nSO0g5vcx6C8Q3OngPrB89v3mQ3Utcpo9IkpNIsRDPQolLpTjgDTumKL6WyT7bH83q1dz95firVixdGVax6uUhcu0Wqe1q/eLSLG+HjqkeZEXokaS5COMbHFY85q+nzL6cfWT4R2l69JCysOEjXZc099Csqj4ZEMYKtvo74P9DP4TXqdP1tACZ+Eyb2NnwDJoQQQgghxF5mq5uwd7n7j4yqYGa/M8b+CCGEEEIIIWYQh+yIdRn5fRt3f8FWC6hTRwghhBBCCCFEn9p2RDP7FgBX8Tzu/tsT6JMQQgghhBBC7AgzmwPw5wDa6N/DvNndf9bMjgJ4I/r3Np8H8Dx3f3Awz08BeBH6Py/8MXd/zyT6VusmzMz+J4BHArgd4feODkA3YUIIIYQQQggAht5IlcTUWQPwdHc/a2ZNAB8ws3cB+F4AN7v7a8zslQBeCeAnzewxAJ4P4LEALgPwJ2b2Ne6+2feyQ+o+CbsWwGPcyxozIYQQQgghhNh9DO5dNnzazcGfA3g2gKcOpt8I4P0AfnIw/ffdfQ3A58zsbwE8CcCHxt23ujdhHwdwCYCT4+5AXTwDevOorwlnXWtKw5pSsiZ0q0Csu89IiRwpjbvhp3Y5qb+by2FzN85ReSWYATNS1Fsn/mljpOhmzTy7QKM9Sv7zdmjDiuqfTLJuG4iV297My9U3tZ2vhT7xZmNFffdAWGa3HX9SsnYotL9ynNTHR0i5PU8a/AYpYFd4hwd1NzyUV46FOuceVtoGx0K+gTapj4teVlneMRnQa2+eNmyr1JQnNr9xPKZiOxHPUbnN9WtqvKN0DaHDWbdaxW00netw3PTr0T7m9W5TcBd0zNBxgW7i58CsAI8UyKUBhRX8a6QPpnpFK6cyxcdc2IgdinNWFbOSvv+a2pvnsYX7SzvHQzkZzxTLcxzLpf1a9Mb0SaWF/RTFGtubuSlWStP+3TzW0nup8TlxPuDlFqRg5/KodCeRij6Z4oTTkoDKnKKheiOUj+eCxldLaPA5BllRP+JwrVwOEK9fj8bhLsVgj9OS0KHH4/Tq0TB9/XC1pn/zhg4Ls2OhzIr75ctoXSmeW3Mhnns0Hvu4YhkADCgGGSay6ESWrj/sR+q6I6WlLxvceTU4RhKpHziOUueIkcdLlNKA6tG1Dad+sFTGlsTmL48xUcqfGrr61LaKzl2c3YEyz3hJuBxtH9qIfB5mRX13obqDfH5knb6V1rVDaQ5Wj1PqkyNhsJjKtcYFwBFl2pgWF5vZrfT6Bne/YeOFmeUAbgPw1QD+q7t/2MxOuPtJAHD3k2Z2fFD9cgB/Scu6azBt7GyVJ+wP0d+eSwA+aWYfQf+xHgDA3Z81iU6J/Qef2IUQQgghhKjJfe5+berNwVcJrzGzwwDeZmZfN2JZVR8rTOS2cqsnYb84iUaFEEIIIYQQe49d9puwIe5+yszeD+A7AdxjZpcOnoJdCuDeQbW7AFxJs10B4O5J9GcrRf0t7n4LgO/eKPO0SXRICCGEEEIIIXaKmR0bPAGDmc0DeCaATwG4CcB1g2rXAXjHoHwTgOebWdvMHg7gagAfmUTf6v4m7B+h/2M15rsqpgkhhBBCCCH2IY5d9yTsUgA3Dn4XlgF4k7u/08w+BOBNZvYiAF8E8FwAcPdPmNmbAHwSQBfASyZhRgS2/k3YvwTwYgCPNLOP0VtLAD44iQ4JIYQQQgghxE5x948BeFzF9PsBPCMxz6sBvHrCXdvySdjvAngXgP+Evj9/gzPu/sDEeiWEEEIIIYQQe5SRN2Hu/pCZnQHw9e7+hSn1qbovGdBd3CwniVTELVJbt0kT3KD5WCO6Hn4SZ+vVqlHW0ANAthbeYy2xkXo11niH+p3VUG6dCeXmOdJ7dxpUjtc3I/Uxa72tS09JE0pjxgtWKKe8uyUirTSpgVPqb6t+FO2sYW/SNlh2rB2s/olipK7mXbkY+n7uYWFZy5dR29zeHC1onl258Y8jO2t0WLAWNx+fHMcN6M6X4pdUuWU9bvSa4pzjOaMYzlYpTUIwPCOjmG2sgOpUa7iBeJ+xHjmn2M5XSdcdKY2rddsc2xwHQKz4jrTQiV+wFh0+fii2e3wwkKa8SfGbpb8yESnW8+r5C5rutB6dBapD2uRNfiWO5wWK5yspni+h+rQNevMUz4u9qipYX0sP8Vle89jfAs+A7sLgRUKfzfHbozQTSI3NADIan7PV6nE3X+VydR2m4EO7tOt5f/O4ndZqcx3W1YcVz9coBUGv3vjBxwwfJ2WF/7BLUTzSdNbY5/HK8naIYpjHI8r0UbRCnfUDNL3NdarHqOW5uO3Vi6sP5ILO2VigeM7CcrsdXsFQtDHKdT0Dirn+wqNsLhzbFLd8reF8jihoTE1cX1hRCsJEOh1OgVPua2XbvG2K9NjO9Vgpztczztc5PLbXUPBvugyIjrHqZfE6pdJZRNcRjeo6RVnZz5k+eH5S2XfnORUJ7VeK7WIh1F+7KJGfAHFcZJRagcfdHqUzcpp/XGPzhaScEkVUs+XQ5e4FgL8xs4edTwNmdtjM3mxmnzKzO83sm83sqJm9z8w+M/h/5HyWLfYOqRswIYQQQggh9hp1xRyXAvjEIE/YuY2JNfOE/QqAd7v795tZC8ACgJ8GcLO7v8bMXon+Vx0l+RBCCCGEEGJG2YVijl1L3ZuwnzufhZvZQQDfBuCFAODu6wDWzezZAJ46qHYjgPdDN2FCCCGEEEKIfUCtmzB3v8XMTgB44mDSR9z93lHzDHgEgK8A+C0z+0YAtwF4KYAT7n5ysOyTZna8amYzux7A9QDQOKJvLIrZhuO5eVDxLGaXaGw+pFgWsw3Hc65rDSF2hMPQ2/rXTgI1fhMGAGb2PPQTlT0XwPMAfNjMvr/GrA0Ajwfw6+7+OPS/yvjK0bME3P0Gd7/W3a/NFhfrzibEroTjOZ9XPIvZJYrlBcWymG2ieD6geBZCTIe6X0f8NwCeuPH0y8yOAfgTAG/eYr67ANzl7h8evH4z+jdh95jZpYOnYJcCqPNUTQghhBBCCCFmnro3YVnp64f3o55Z8ctm9iUze5S7fxr9pGifHPxdB+A1g//v2LIHuaN7qLtZRUta1qwZnK7NdlCCthrVbt8eqVu7veAzLUgh210tbaKz4XXzIVYRUx22Ls9RmZS/3Tbp2RfDchrLYebGSqw0zklz22uTgpy0vUaK7kjXTf5Zz8jdyg7ZstKeX5LuOFJ8N0hvzHUidX31MvP18GLhvl5kSGRlemOZlfOkYyZtbKSQZQU26Y0t4xUq6WRZG8wx1aDtOUZFfdEEVk94pDouWNc9HytqWXHbaFF6AnIDF72wbVh92yWFO5Ypfh8McdCmrH+srt/Ub1Zbk3q9R/GcUuKzuj7SEJcOao4pI/X3Jr3yRv1m9VCURXp8ihWK+bK6O9YjW+X0lNaelcgFHxccvqWUFxznPdYmN2ll5/nYDZMtoaQuaN9z/OaNUkyNS1HfANYvKjb1z5vUKUoPkbdDudGKU0UwUTxTDHe7tKGXaew8HcqtU6FKFM8JhT4Q76doPSjmWRvO8VhYdazw8cKa8U26+mhoqo6vqG2en9Mv8PHZqO5T+XV8LNL8OZ9vaDqX+VTCmnEOzbk4zooFXpHqttl5Hqm7s+qxOR+n0jtz9DZSPlA/+HgzartJ8cz9iMZmWgeOay4DcZxjNWxcPxfKnIrEaXtECvc8MW6WNlOkmY8yelTHjpHOnVM3pFL0lFNyRMcDLSuaP0oNQvNG42t1nUhpXzqMomVF5y7aT22+QOHl0vRoXOPjMG4vs+prj1Q853SdmmdS1O8X6t6EvdvM3gPg9wavfwDAH9ec90cBvGFgRvwsgH+K/qHyJjN7EYAvov81R7GPkaJeCCGEEELsF+qKOV5hZt8H4Cno3+/f4O5vqznv7QCurXjrGXU7KYQQQgghhNjdSFFfn7pPwuDubwHwlgn2RQghhBBCCCH2PLVuwszsewH8PIDj6D8JMwDu7gcn2DchhBBCCCHEzGDolX+IKiqp+yTsFwD8H+5+5yQ7I4QQQgghhBB7nbo3YffoBkwIIYQQQgiRwgEUStZci7o3Ybea2RsBvB3AUEDt7m+dRKcqyR2NA51I6VmGtbis6F4jFT2rQlkh2m4GR2rWDnpQnyfHNoCV+eBVXW8FVy/rka3DTurq/tpiqNNhjTfp2Ftn4x82ts6Qtp309ZGSmpTe+SqpzDuRf3ZYZN22lRX1BKu8izzxg0teLumRiya7n6k6dWnuVIEe1Yt0u7Rc47QCC6RQZqU76WdZLx3/TjS9rglD9HhpOHpHO3F8jGiXFfodSpvA2nwjrW2D0jVklK6hWAyO9PWFEMu9hbDRWqfijmQJtXz0bQNaDVYXs06ZVeHNc4nlADBS27KWm2Oby5EJl2OwqNY0p1Tkm+Zn1XheHZuxsry6f6yhz1fj5opoBOYVIY11Vp1ywROhw2rskRkoijEFeu4oDg92etR29TFWkKp9vRfirpwCIopn0oBzuoaCx9EDYWMuL4bYbp6h8bGUIiBFrIOvLuc0bvNyOdb4x+mRArxbOsa66fFoWCeKtVSchjKP2XUV9ZEefNRxUtEnp/Nej1KJWDm3BO9nqw5iS+i9OSZsxLXAjsiAbL4fY6m0JKlrjYL2S5ZIj8Ia+1Y5RQOlsenMh5253g7xXKzQTubsO4lUA86Dc3l1ePNTTEbK+OoMP9Gu43Gex/9R7WWJ4yqqznGaV5eLhKJ+xCk+uaxUOh3wWJmnYnNEg8lLJt+qitjj1L0JOwhgGcC30zQHML2bMLGn4RswIYQQQggh9jJ1FfX/dNT7ZvZT7v6fxtMlIYQQQgghxCwiRX09xvWlTSVbFkIIIYQQQoga1M4TtgW65RVCCCGEEGIf4y5FfV3GtZUm9AtZIYQQQgghhNhb6EmYEEIIIYQQYiwUui2oxbhuwv5gTMtJYhioXUv7lTXzWVZS4W7My3bRRJ0U7UaskG0fCK/PkGp2tRF09XYmbNZ8pVqPzNZeVqwWS1QOVtr+azIINs+G6RmpgVn7ms+FtvN1UvsmdMhlkzAriosWKedJPx8Zhnv8olqDz8u0kqmYdd3cF95uGWmhLWzyGNbGNmhBpJbNGvHKRqkLuEz7eLuxMxIDrFkkFbflVAwp3fd2Nc2ciqF9OJSXKd3CajsOvMZDIUBZM5+vUSXeNPR8vTdHVaLUAaSuX44P6iiOeLGk6c/XabuxTjmKR5qZYovjt9cqtR2lUKjW4LP6m9MhcCVP6KKzkpGatyFvn4g68Uxxanl1HJVjZWzxbI6sMdjYfHxz26n4TWjJy8tKEcUzlVdaYeevz4V4zs+FWM5W4y+CRGMLj328X+jQ4Njm1ANJDT6va2nTG8V2pLLnFA0cz7SpeNxMxVC5vUhhzhpwq64TacC5SiIdRaQQL/eJz9nN0DGO4SyRuoPP96x6b+QJx/n5YD48P5glxuRtKsdT8V+GY3iuFQJplVKOrDQotQilK7E1SgvQqe6gN+J+OKXjAcWzr9Oy1qvP90XiGInG41IajOi6Z5ta+rgSlTltx4jvdqXSlCSXm9plfBzTWJuXrimiMZlnT6TxyKPz/xivNcSuptbXEc3sF8zsoJk1zexmM7vPzF6w8b67/8fJdVHsB4pxfRwghBBCCCEuCA6gh2yqf7NK3Z5/u7ufBvA9AO4C8DUAXjGxXgkhhBBCCCHEHqXuTdjGw+bvBvB77v7AhPojhBBCCCGEEHuaul8C+0Mz+xSAFQAvNrNjAFa3mEcIIYQQQgixb5Civi61tpK7vxLANwO41t07AJYBPHuSHRNCCCGEEEKIvUitJ2FmtgDgJQAeBuB6AJcBeBSAd06ua0IIIYQQQohZwQEUMyzLmCZ1v474WwBuA/Atg9d3oa+ln95NmG3WdgOxyrPZCM5TS2ltyVPKdZwWzY9RraQybZIK9+DCKtULC1jJguu1Z7SJV0gln1DIRrrhTYp6rke67oQeOaf5c9bXsiaWtPdlDTErkbOE1j7SzydUxzw90oOTEhcAOgtUj/Tzke44Uh9vT8/OythGM/bjxrrjonJ6MxufBtnMkZf6kLFavKQ0TimO+Zhg5bjTDiiozMvh9VxcDEG0XNLjdij9QnGGdPXnWBlPfY3iK5SddfUUm2UzZr5aresuEmkdeNPE34Cg5dCmTtcHCtbPc5oFPlyjeA7lHuvLOX5HjLIc21G9qJPVY0UUz+2wgqzrZu0xx3L/vfFokM1CygeOu5QePxnLpek8vycO9VQ854uhvELq6PV2CMjOarxjWFnPiu9Ibc1jJO2W3hylXKA0DkldfQlL6OcjtzWn7UjUj+IpkSahTDRWJ3T3yeUiMZ3XoVdqvKjuDCu+W3Qu53M8n495PM63mapjFMN0OOXpWfX4nIrnlHLck370eKxu0TwLrTDActurpPjvtsJO8m5WWUZpO2WUyiGnctEO8/SWQyAk1fd07jIaQ6104KZS1EQxkYp/VE/3xL7fFKdcLzo2UoNLog4XE7EMxOMrX5umYpWvTccZz2J3U/cm7JHu/gNm9oMA4O4rZuXbk2rM7PMAzgDoAei6+7VmdhTAGwFcBeDzAJ7n7g9us+9iD8E3YEIIIYQQYjbpjfiwQQTqPi9cN7N5DD4DMLNHAlgbPUvE09z9Gne/dvD6lQBudverAdw8eC2EEEIIIYQQe566T8J+FsC7AVxpZm8A8BQAL9xBu88G8NRB+UYA7wfwkztYnhBCCCGEEOIC4rCZTqA8TWrdhLn7+8zsowCejP43ZV/q7vfVbMMBvNf6X5z+7+5+A4AT7n5ysOyTZna8akYzux59EQgaFx+q2ZwQuxPFs9grKJbFXkLxLIS4ENS6VR38/uu7ADzB3d8JYMHMnlSzjae4++MH87/EzL6tbufc/QZ3v9bdr80PLdadTYhdSRTPBxXPYnZRLIu9hOJZCHEhqPt1xF9D3830dAD/Dn3RxlsAPHGrGd397sH/e83sbQCeBOAeM7t08BTsUgD3nk/nhRBCCCGEELuHQsmaa1H3Juyb3P3xZvbXAODuD5pZa6uZzGwRQObuZwblb0f/Ju4mANcBeM3g/zu2WlZmjnars0ndaZF+fmv1cR7pbquVs6yJHaWTXWiuV5ZPt4OX+FQzaP+6Z0n1SjrkSFfPOuTS3mGtt5PqvXEuTM9IFZ51qQ5rv0lv3KEP/bolQyGrmRvL1TrySD+f0IZHmnFap07pA8fOEtVrk546tQtIixvp8dnI26jWeM+3aSUAtCOFbFFZbth4lN5AP/ZarW4Uz1ZDe1wmUpDXSL+QokV64oVW7NU+Nxden1sI7vX1c9XxnC9zbFM/aL9wrHkpNQKnVsi6oWxRyoRqpXGUxoGWw/Ny/KK0S5Mq7oQ2metzyoXuPPeJD4a4vdSyorYjtTIp2VvV8VwnloExxrMFvXgddXfKq1vuXyr9QoqMdlKLxoZF0nt3FsJGXlmnYAGwth4Gp/UVem85TLd1OjdQG5xSoyBdfaS9T6RJKL8XKbCjOKiub3SMMCmd/uaK1fU45ULRokp5jbF5lBKftN4cC5xiYK4ZBo5WlHqm+pydj3lsbre6m9qLy1svp05qnLrzN2gbzNP4XCyEhXV7YYd3ihAs51ZCcK4txzHvPVLZF9QenSuN4q63XhqshzOHYtHla430ykaxk0ppkNLVJ879EeVrxTwxDtcJHYpZ5/7xxkGccobjeb7ZpemUfoHjGVuPnWLvUfcmrGNmOYId8Rjqhe4JAG8b2OwbAH7X3d9tZn8F4E1m9iIAXwTw3G33XOwp+AZMCCGEEELMHg5IzFGTujdhvwrgbQCOm9mrAXw/gJ/ZaiZ3/yyAb6yYfj+AZ2yjn0IIIYQQQgixJ9jyJszMMgCfA/Cv0b9xMgDPcfc7J9w3IYQQQgghxIzgMCVrrsmWN2HuXpjZL7n7NwP41BT6JIQQQgghhBB7lrpf2nyvmX3fQFUvhBBCCCGEEOI8qfubsJcDWATQNbNV9L+S6O5+cGI9E0IIIYQQQswUhcQctah1E+buF9xdZ+ZoN3qbNcYJ/TwrYRsJvXGR+M5qSlcPAB3ysLNGtEXa0UYntBfpTNdZV0waV1LUN1ZD9bJ6tbvoleUir56fdfWsK2btNyu9e6RZLs/Dyu1IFd6rnp5aTqw9juv15lj5TNuQFN/WSEg5aVvlzdCRNunVF+bCBplrxF7nVk4K2aw6dsatqJ9rdZMq2nK6BY7tlAac0ynwdO53QW7e9W44/Hn5zSzekaw7PuvkYWdtL6cIaJB2l1IjgHT1rHPfFHecYoDmieKW1fWJFAhRSgfW44/ajYnUCgwfM5xGglX03HZB2wOteKGsOY++Z0B65YziuTUXVpzjeZ6V3hTLkeZ6xNi5Eww+1IinlN51UimU+8Njb7QeefVA06M4zaKxudrhfs7jAagoqo8f7lWUToTKvfmwTr0FOt4oJqJ5S+rulL4+Ov2krmm8uhxtzmKEKpzik+PZaZ9xnIJTKdB4HCnAueMlVTjrz+fmQwwvtqtTvqTGPo7nDOOJ5Y02NhT5qRQiqTE4dUylYnlUv7sJ9zqPzxzzazT4dbphJ0XhUZSWeS7U662EcnEgjCeRrn6OzsvlZW1A4/mm9CgJ5bxlie0QpQuqrpIiuUzEaRK4nOXVB5AnFPVZk9IqtOPULhzPcw0en7dW1KeuWcXeo9ZNmJk9vmLyQwC+4O6JLCVC1IdvwIQQQgghxOzhDvSUrLkWdb+O+GsAHg/gjsHrrwfwNwAuMrN/4e7vnUTnhBBCCCGEEGKvUfdW9fMAHufuT3D3JwC4BsDHATwTwC9MpmtCCCGEEEKI2cFQTPlvVql7E/Zod//Exgt3/yT6N2WfnUy3hBBCCCGEEGJvUvfriJ82s18H8PuD1z8A4H+bWRvRz+2FEEIIIYQQ+xGHfhNWl7pb6YUA/hbAywD8OIDPDqZ1ADxtAv0SQgghhBBCiD1JXUX9ipn9GoB3uvunS2+fHX+3NpOZY7G1Vjl9g0ZCZcvqzyyhk00qZEvK2fVejipSalrWuLKimPXXrGAvutSP0jPGfCW81z0Q5uktkhK5Rbp6qs9KcGO9d0ppjNgmy1puULlImFR789S/A+RfnmOlcUmZTbrjNmm5G1Sv2QjTU8raNtXhmGEtfaukYU/pjhtZtU52p2TmWGitb5pW1YcyViPm6yx3PaHuLi9ntdusrBd1kdS+vYVI6h2qrFTHP2u8AaC7QHNTF6Pjh3dfSitv1eVIMV/zY6gofcLi1vHMquMmTc9LMc/xnNK7zze7VA4xE6XFSKTkGDWWjSvlQp55pGOuoo7eu9yfVPqGVAoJHsO5zHX409mipG0vejRWs7Z9KaxbNw/Bk61S8PCyeD1IV9/jtCTrpcDjVffq4yQi9ROIxHjupW3JWnos0BhH4y7HcMYpEzieOS1MQgmelXTbqXjm8ZljIRXDqTo7JcscB9prI5dba6xNjLv8+5XyOM/zsKI+imda71W6hOPYZkU976O5A/E11FoW4tmXw7KKVVouxQGnFKizvxt5vTEmdb7jderRMVYU1dsmNbaUX6dSZjQSsc2pALo0TnD9A6Xr07lE2ptU7MRjeCLfj9hz1LoEMbNnAbgdwLsHr68xs5sm2C+xz8hS+b+EEEIIIcTM0EM21b9ZpW7PfxbAkwCcAgB3vx3AVRPpkRBCCCGEEELsYeqKObru/pDZ7GoghRBCCCGEEJPDYdHXREWaujdhHzezHwKQm9nVAH4MwF9MrltCCCGEEEIIsTep+3XEHwXwWABrAH4PwGn0TYlCCCGEEEIIAUC/CatLXTviMoB/M/gTQgghhBBCCHGejLwJM7M/BNKubHd/Vp1GzCwHcCuAv3f37zGzowDeiL7c4/MAnufuD45aRmaOhUZns2a5hpY+rl+tHI/1xqQqLjmCWSNaJDzBR+aXh+XTB4IbfnWZtLHn6M6dFtMlvXe+Gi+fdd2smS/mQ5+8HaZ3GqENY4Uya4yrLeX9atU2/lh9TKrjgnTd2YHg119cDFrbNumJWbENxDp5fo/3ZTS9huq1RRst1gfH+zWvoTgepY3fLrk5FpvrtVIpjCLSdSdiM6XwbXnYll1OpVBue25lWDy31BqWz3Qphim2+UOp3gGKTYrHjGO7dBhxHEXm70hRn4jnhKI+UtGP2LQcz876+QXSai+QJj4Rz/OtEP/thKoYiFMllN8bTq+hLj4f3Xz5GDhfzBxzjc6wPOzTNtMnlNchtT1S83draKsbrdVhubMYD3CnqNzp0Fjd4PnD9l9fplwH58KplMfarBX2fU4G2O5a3LYX1eeSaHoi5QLoWHA+Llgr34rjpjUX+tWmPnLKhCieEyr57Y7N5XnqxHOdsXBcsQz0leUHBuubTIFT41xQ51qjWyROsojHcE6Nw8s6mIdzJvfvAQt5Pta6ITYbrfiEP9cO49SZZrhW6Z4NsV3QON+iFAaH6bzAfWXlO8dKmdQ2XO2F/nYS2yf1eyOOm7w81lJ8LjTCes/loZzqU+p8ypTjNJ0aidNqVF9r1rke2c044nUTabbaSr8I4JcAfA7ACoD/Mfg7C+Dj22jnpQDupNevBHCzu18N4ObBa7GPqcoBJ4QQQgghxF5k5E2Yu9/i7rcAeJy7/4C7/+Hg74cAfGudBszsCgD/GMBv0ORnA7hxUL4RwHO23XMhhBBCCCGEmEHq2hGPmdkj3P2zAGBmDwdwrOa8vwzgXwNYomkn3P0kALj7STM7XnNZQgghhBBCiF2JoZf4uY6IqXsT9uMA3m9mnx28vgrA9VvNZGbfA+Bed7/NzJ663c6Z2fUb7cydWNqithC7G8Wz2CtwLLePK5bFbMPxPH/iwAXujRBiv1DXjvjuQX6wRw8mfcrd6/yI5ykAnmVm3w1gDsBBM/sdAPeY2aWDp2CXArg30e4NAG4AgEOPPjHbv1QU+54onh+leBazC8fy0qMuUSyLmYbj+fCjjyuehdgBEnPUZ+RWMrPHb5Tdfc3d/2bwt1ZVp4y7/5S7X+HuVwF4PoA/dfcXALgJwHWDatcBeMcO1kEIIYQQQgghZoatnoT91uBrhKO+3Pl6AI/bZruvAfAmM3sRgC8CeO5WM+RW4GBrdaSulvWkzaxacZ7SyqfolXSkBS13PaFPPdomhe/Foe3PU9+X7w8KWVtP9Sm+R7YVUhHzW82wXCOFrDdJ2dyrboN1ygnzar9et1oJjnnSGB8K+ueLls4Ny0faQWXLmljWxwPAYiNokOukGMipDn//OEe1DnYUdTTIKTXt+ZCb42Az/TB5cyqG6niuO/8GUSqGSIHcqJwOAAcz6uehh4bFu6nOmWw+vODtxGpm4+MllItGqa8LpK3m+TuJz4y4u6yrT33ElIplAJgLbc8dCPF45EBIO3GwHeJ8jvTzXF5ohG3WjHTbcTzyfmpaaDv1KWJKb5yqM4pOKgfFNsmtwFKF3TTWMpPKPFJ311OR14n5Iq9Wf3dZA03LueJAiGUAWCAN+4MrYXzu0RiZk/b9HJXXWCWfh+mczoDTc6w0SG8PoFds/akx6/9ZXd9dzyun56SlX5gP/QCAg/MhhnnfcQzzfmKNd+rcWndfMuMaw8cVy8BgbKZUBsN+JJTj273WiK4paLsCcdzy/Hxtw9cdPP2S+TPD8gGK5fsplst9aiY0+nxkcGoF1tKfWDgblps4r2xOO1GdkoDXO9b50/ia0N1zGzzutkvXF81Eqg/WwXdqHId1n/CkfhOV1xjL9sLvqfbCOkyDrW7CDgG4DaNvwr5SpyF3fz+A9w/K9wN4Rp35xP6Ab8CEEEIIIYTYy4y8CRt8jVAIIYQQQgghRuJu+k1YTbSVhBBCCCGEEGKK6CZMCCGEEEIIIaZI3TxhQgghhBBCCDGSnr6OWItaW8n6vMDM/u/B64eZ2ZMm2zUhhBBCCCGE2HvUfRL2awAKAE8H8O8AnAHwFgBPnFC/NpFbgUPNlU3TU/pn1pyygrSsnK+Cdamd0t08/9iQ2+smdPXH54LGdeFE0NHefzBoY8+tt4bls6vtYXnlobloWettUhG3wjo15oOKtdEKZWdtLCnqPaFh9ZIJuOhRvdAtNOdCG0uLQeV7bDGsK6/3UjPUKWtjU/A+y7C1ojil+q5LSj/Pyx2nBjmzAguN9SiG6qrM6yhuGY7hLq0DHwusVl4v0sPC0XZQtc8dDfvy7FKIYd6WK50w/b6HFkM/5oKiO2vG+6vZro7hWqudOLyj+KdlNkttL5G6++hCWFdebx6HOJ5TYw7H7yh1d56I87q67w3qplIYVzxn5jgwSLfAfW0k4rnOmA2k45zVxxyrHM8ppTeP3+UUGZctdKh8OiyLdNarvRC3XzxzZFh+MAvtzbfDcljp3aA6a+34GFujFBGsq+dtwqr81U7oR56H5bZIg3+gHYyznFYBAA63Qr/YTFveJsM2aqjo68Z8XvN42GD6Y7NjMe9vE973dc4rdVKDMOV+ryeuVaL90g0n40gNT+WLWiFFzLF2UNfPj1Dif7EZ4vlAO6jelyiVyqV0XCw1Qkxx22sjzh987PN56RytU4NSK3B/F/PQj1Tcta1TWafcx17i+UMj3/61wyQYZzqcC4Fj++mg9it1b8K+yd0fb2Z/DQDu/qCZtbaaSQghhBBCCCFETN2bsI6Z5Rh8Fm1mx4AajyeEEEIIIYQQ+wTTb8JqUncr/SqAtwE4bmavBvABAP9xYr0SQgghhBBCiD1KrSdh7v4GM7sNwDPQ/9XFc9z9zon2TAghhBBCCCH2ICOfhJnZ0Y0/APcC+D0AvwvgnsE0IYQQQgghhOiLOdym+jcKM7vSzP7MzO40s0+Y2UsH07/RzD5kZneY2R+a2UGa56fM7G/N7NNm9h2T2lZbPQm7Df3taQAeBuDBQfkwgC8CePikOiaEEEIIIYQQO6AL4Cfc/aNmtgTgNjN7H4DfAPCv3P0WM/sRAK8A8G/N7DEAng/gsQAuA/AnZvY17j52febImzB3fzgAmNl/A3CTu//x4PV3AXjmuDszitwcBxurm7SjdbSxuVU7RPiHg6yK5WU2SgpZvuPm97rUBqs5G8a61aACPtIKyusV0h6fXg9a+rvnhjflAIAzy+E93g4ZqY+NPhDIs8R6JxTIm+uFhbF2+bIDQVN7Yj7ob1khy2rZ1D4q7xeuxxrk7SrZmd55aFKLxA9K59CpnH4+ZMN4DutZV98f6+qr9zHHc0bK4MzDvBzLkV66FBNdTstA++IgpR5g5TWvx7lekKiy/neFFNtzjfR6r3fDeuRZaNsidXfoX5Pa4DrdXlhOi9o7MR9SKQDxcbnYCPHM61Qnnnk75SNSD6Soo2dPxWlUf0SdJsZzPsnNh5pzHu/Gqe+PVNz0BQ7eFxzzrN6uk0qk3D7v+0taYbzjNliDf2puflg+QErvWJVPp9uSW5iPsdUuHa9Wfbyeo9QPPM5fPBfil1ODzGfx2NXOtx5reF/yONMkbXudOB81fnM87yTmm2O8NsqtGI5tqW2wk7QR0bUG0ufAVDwvUGzydL7u4GuKw5RS49LWQ8n2WMd/bi4o44+S7n4hC9cwddIClJ9QpOL5AKVJ4G2+lHN6mxDDHCscH9yncqzUHXtT829Feazl/cHbmetxHT6W9oLePZUG4ELg7icBnByUz5jZnQAuB/AoAH8+qPY+AO8B8G8BPBvA77v7GoDPmdnfAngSgA+Nu291t9ITN27AAMDd3wXgH467M2L/cj65vYQQQgghxL7nYjO7lf6ur6pkZlcBeByADwP4OIBnDd56LoArB+XLAXyJZrtrMG3s1FXU32dmPwPgd9D/euILANw/iQ4JIYQQQgghZg/H1r/TmgD3ufu1oyqY2QEAbwHwMnc/PfgK4q+a2f8N4CYAG49kqzp//l/JGkHdJ2E/COAY+pr6twM4PpgmhBBCCCGEELsSM2uifwP2Bnd/KwC4+6fc/dvd/Qnoiwf/blD9LoSnYgBwBYC7J9Gvuor6BwC8dBIdEEIIIYQQQuwNil30mzAzMwCvB3Cnu7+Oph9393vNLAPwMwD+2+CtmwD8rpm9Dn0xx9UAPjKJvtW6CTOzP0PFozh3f/rYeySEEEIIIYQQO+cpAH4YwB1mdvtg2k8DuNrMXjJ4/VYAvwUA7v4JM3sTgE+ib1Z8ySTMiED934T9KyrPAfg+9DsmhBBCCCGEELsOd/8Aqn/nBQC/kpjn1QBePbFODaj7dcTbSpM+aGa3bDWfmc2hr39sD9p6s7v/7CDR8xsBXAXg8wCe5+4PjlpW03o43jpdWxuaUuTW0ZmmFKIAsFoE9WtWhOWyjjmlR26SSrhDWu0WzXu0GRTDcyWN8F35odBHmn+u2amczoruxWZQwEZ6e9o2rTx9o79ACtnL504Nyxc3g+I7tc3j6fU0v1yPVacpJXsdHSr/ULRcP9VeFBdj1MY2rYdjrTPJ98v6/pReNxW3rKLn7dz2EFNrCXV9o2Sq7CZUxFliH3N6gsMUzxzn964shfZKqRQ41lZJtcz7r8WabFo/1iyzHpzn5f6daAf9OBArkccZzym2++Pl7cbmqDrbVTCnaFgPF7f640Bqe9TR94/S6fM4zPXalJNjzUOsrFEbDVacUwyez/pf2jo1LLPR9a78SGiPYnCBYm2tF463Tllnzeec1tYx0aFzDCvjjzaDTvwApQxJpWkpE52vIj371vEfL6fe2M7EsV29DbjOKB35TmhYD8ebp0fWSan16+j0ud+dUsqERk7pF5yuFzj1A6fWyarHgyK6zkmPAZc2wnqyAv7eTkiPw1r6Q3kYz3k9VunYq0sqJQH3YzFbq6yTSi9U51qhXG8nY/Aotq2432b6kd2MO9CbvphjJqm1p83sKP1dPMgefUmNWdcAPN3dvxHANQC+08yeDOCVAG5296sB3Dx4LYQQQgghhBB7nrpfR7wN/d+EGfpfQ/wcgBdtNZO7O4CNRyXNwZ+jnwjtqYPpNwJ4P4CfrNkXIYQQQgghxC7kAijqZ5K6N2Ff6+6rPMHM2qnKpXo5+jdxXw3gv7r7h83sxCCDNdz9pJkd306nhRBCCCGEEGJWqfvF07+omPahOjO6e8/dr0Hfs/8kM/u6mm3CzK7fyH599oH1rWcQYhfD8XzuQcWzmF04lpcfrP7dhhCzQjQ261pDiB3RT9acTfVvVhnZczO7xMyeAGDezB5nZo8f/D0VwMJ2GnL3U+h/7fA7AdxjZpcO2rgUwL2JeW5w92vd/doDR1vbaU6IXQfH8+IRxbOYXTiWF47U+lKEELuWaGzWtYYQYkps9XXE7wDwQvSfYr2Opp9B37E/EjM7BqDj7qfMbB7AMwH8PPqJ0K4D8JrB/3dsu+dCCCGEEEKIXcU4bdJ7mZE3Ye5+I4Abzez73P0t57H8Swfz5+g/dXuTu7/TzD4E4E1m9iIAXwTw3K0WlKGIVKlVbFdfzj8c7HjYFKtU7hTxJpojfWpKa85qX9anZmCNd6hzAOHrPAca4ad3i434az7L3aCBPbU2H5bVCH1irf2BZpj/eDvo0FPbkVXHQFoZfbRxjsqkqKf1KxLbo642lklt57ivpHdN1kmnHoj07qzOpb6P84emmRVYyleT6vn6et1q/TDH3XIvPKnoIEzntAqsqC+vZ+o91lDzPubyocZKZXucxoE19ACwRHF/cSvEGm8r1m8v5OuVdViBHB2HVOdQHvoHAEtZ/HqD7cZzXY1xSuPL7aXithft+2rlcs+qY6X/3njiObcChxp9dTVv86ZVp7yok26hTKT1ZjU2pQxpgNIW5NVjEacGKbeX6he3x/v74mYYUx/shi+GnOnODcusqL+oFcbKspo8eSxl1ek4y+elDTjFwgLpvcvxmDqOW1bdXiqtQJaI/5yWWY7xWuOzVcd51A9qu1P75+1bk6PAgXy1cvqwTzUU53xNwWNzk3K+rqJZmqc61rKMdPzUdjYinqs4S7EJAJ1G6ONh0s8/1AvXF8tFeDJ4ceNMZf0zRVgu76/yGNCk+GoZpxkpqqcn4iseD6r3ffl4LhLjZYrU2Mztla8j4vlrtFFj3BZ7m5Ejl5m9wN1/B8BVZvby8vvu/rqK2fj9jwF4XMX0+wE8Y5t9FXuYuhetQgghhBBCzDpbfXy0OPh/oOK97WcmFUIIIYQQQuxJHFLU12WrryP+90HxT9z9g/yemT1lYr0SQgghhBBCiD1K3S9S/2cAj68xTQghhBBCCLEvsZnWxk+TrX4T9s0AvgXAsdJvwg4CyKvnEkIIIYQQQgiRYqsnYS30fw/WALBE008D+P5JdUoIIYQQQggxe4wyR4rAVr8JuwXALWb2/7r7F6bUp0oycyxma0llaZk6GtxOjYd5eRb7R1g1y7rcIjGdVaopLTArf1np+tVz90RtLxwNKu47Tl8+LD9Auvq5VlDAPmLhvmH5RPOhyrZZSdy2oFPur1O1JnuR1MesQU7pjZn66u5EWoGoT3SQU5HbYJ3sqNgprHpdIyX4GA2OORxL+UpS/Vxe/6SanPq9RrpuVusvkM6d1da8TFZmc8wCQBtdmqd6+7OSOlKC07I4Bg8dDHrjL60ejdpb6QUlMqdpuLR1KkynuONtyCkkWIec0hjPWZyuIZWCIkU91XE99Xo6lUaYvs7jSUJjHI1xvPxS/XH9cDpHMVT98zaLVeZbj5Xlk/Y6H7teVJepDY7t1LjbptCuq23nZT3UCyp6judHz58clj+3dmxYPk1KcNaeP6x9P+rA4ysfY6zm5+3GY3hKAV6mTgqQViLdQErjzcuZK7WdGp9TKQ1S6Ql4Oby/dkpmXpmqIqXdTynLWUWfOg5HadRT681jNcdzFPOJ/bjmsRL/ZOfwsHxF64FQbobyfd2DwzLHP4+vx0hdv57Q7APxuM37m8esPJFmJJV3Kn2tUEpDQ9skFfNRWgGvPve3vDpO10vnTR6fU+l7onOt7ln2JXV/E7ZsZq8F8FgAw7OKuz99Ir0SQgghhBBCzBTu6Q9oREzdj/bfAOBTAB4O4OcAfB7AX02oT0IIIYQQQgixZ6l7E3aRu78eQMfdb3H3HwHw5An2SwghhBBCCCH2JHW/jrjxRfOTZvaPAdwN4IrJdEkIIYQQQggxi0hRX4+6N2H/wcwOAfgJ9PODHQTwskl1SgghhBBCCCH2KrVuwtz9nYPiQwCeBgBm9rIJ9UkIIYQQQggxYzhsbPbdvU7dJ2FVvBzAL4+pH1uSwbGQrW16xJnSjmak6i0SSu+UgTrS3ZbqZOQRZX19Wes9rJNQA7OetR1ptcP0o/nZaJ6vWgzK+WYW9LAfuO+rh+V10tcukRL5suaD1Nfq3c465HLfm+hVTmfq1In17+nH1etg3W5CTZvQyrPWthVpcKu1wgDQo3lSOtlxkluBpWw1il/uXzOr1uACaXUxd7UTaYJJg5+I2YymZ15P9xypvykeOTa57YUs6OC/qhFieSkLcQoAn1y+bFheI+34HB3TJxpBD877iLXJHI+ROppigvtXJpXSIJVKINL3J1IblGO53rI4hUWXpm8zFUN5VROK++2SW4HDeT/lQMqIxXrvlPq/rHiO0jew+j61XyiGc9LYR3E+Yn8znKogpXfn/XVlKyjned47lq8cls/2gq4+b8XLZMV3NA5Y9Zjay6qV3in4WCgzKjVGFVEMJ66z4nEtXib3hcfnKP0Cz1PnWm58hno0EOI5pcRn7X6HlOWp8ZyPT15mWcsfxWoiLUMK3q68zecS1xfl9jjtwVWtrwzLrJX/UueiYZl19VzneB5iuQyP4XEaCB7Dq9c1eVEfpadJB0udsTo6Z1iYzukyUqlBmh6n6InGfb4GrXGtUec4FHuDndyE6TZXCCGEEEIIMUTJmuuxk9vtMX72JIQQQgghhBD7g5FPwszsDKpvtgzA/ER6JIQQQgghhBB7mJE3Ye6+NK2OCCGEEEIIIWYXx4jf8IkI/fpPCCGEEEIIIabITsQcQgghhBBCCDFEyZrrMdGbMDO7EsBvA7gEfdn7De7+K2Z2FMAbAVwF4PMAnufuD6aWAwAGR8t6WC3t2Dyhby7NPISVoHkWyqse9KysB23lJe1opBplBWm1kjrVV9azLmUrw/IonSxrYL9u7q5h+cFDi8Py3507Nix/duXiYfmK1gPD8kWkkF31VuXyAWDRglI80vYTKV30akKDP0f68vLjalbF1tHEpzTgHdLb8/7KRjwdz6JUB6G87tV1dorBMZd1Ii0zt5vSYpfJovgP8cLqYdbrskY3Faeb00AktOOs2qX+chyxEpxju0XH6iNa90bL5b5/ZuXEsHzX+tFhmRXKRwc66f681akKmFQsl+HYPleE4yQaH6JjtFq3zXr28rbkdAC8/9gstU7DdBHFaUo7zvX5OCodb2M6SWZwzA3Gik7ilMJjM28P3k4txERae1J055G6O9RJff0lpYouH2Op8TmOZ+oH1WG19VXNkH6hMx/W7xPLlw/LX1gLYzMQpxC5snE6zJ9IfTJKxV1F+Vjg8x0zR+vK80T6+BrjcaRLL9Xn8Zm71aJVCmeeOE6bVq1hTwv4t0//WqPiWoL6GsUmaddZ884pILgOx2/5GEylYuD1novSVFDKnMR4zMvhNCFlUqkOLqF0ILzen107Pix/uXt4WL6qGdI1XFK6flp11tLTGGfV43avRjqEZdrmPG/5+onnj1MPbH0dkYo7rlO+PuBjIFWvSX2KYwdinzDpW9UugJ9w968F8GQALzGzxwB4JYCb3f1qADcPXot9zHYvKoQQQgghxC7D+8map/k3q0z0JszdT7r7RwflMwDuBHA5gGcDuHFQ7UYAz5lkP4QQQgghhBBitzC134SZ2VUAHgfgwwBOuPtJoH+jZmbHR80rhBBCCCGE2N04lKy5LlP55ZyZHQDwFgAvc/fTW9Wn+a43s1vN7NbTDyR+7yXEjKB4FnsFjuWHFMtixlE8CyEuBBO/CTOzJvo3YG9w97cOJt9jZpcO3r8UwL1V87r7De5+rbtfe/CoRI5itlE8i70Cx/IhxbKYcRTPQogLwURvwszMALwewJ3u/jp66yYA1w3K1wF4xyT7IYQQQgghhJg8EnPUY9If+TwFwA8DuMPMbh9M+2kArwHwJjN7EYAvAnjuVgsyOJrWRXOEMp5h3WqqTkFa1Myr9aybdd2kJ3XWjlbPz0rXOVK+83LmSF/LOmSeDgCL9HqpGZTzxw/fOiz/RevqYfnzqxcNyw90g8ae+83luWasr80y7jspphPq71gtW63vj5T2vP3h8YGUUOx2Eup7VoVH9VlXz3rvko6Xl8tboazNHhcbWu851jJ7tYq8Xz/0txXpchN6XVpW20mPzLp0mpfrp1TFI/sRaXdDrCxGymtSJdOxsFjSJn/TwmdDPXrvZOfwsHyuaFN7vcrpB7PVYZmPpRapg0fp6jmeO9G6UioF21qRHo8NpbEoMU7xOrHmn+M52meRip6WGaV9iNuu1HCfBxmc9nPY3xwTvG4cX1EqCiuNtdG4QWkWaNss0NEaaacT7cX9jqen4nkuitVE+gWKr4NU5/FzX6js990Uy0Cspz5jc8PyOU4hQv3gY6zO2LwpzmnVOUZi/Tlrw6vH4/jcysp42nfl7c9doUOmSKZ+CHDMJ1PS7BAzH+6rVNqPVHqPLOPjkOO/OpZ7pZifQ4ijncQzpwPh7crTgTi2U/EcX3eE9AvMV7pLoa+03udKYfdAL8R2Mzpns/I/lFuJsYzrFwllf7O0naI0Qnw8RKkHepXTo9QgNa41+n2p3mfRtQan6IjGn3ppVMTsM9GbMHf/ANIZD54xybbFbDHLn2QIIYQQQoiBmEPXdLVQSmshhBBCCCGEmCL6BaoQQgghhBBiLOhJWD30JEwIIYQQQgghpoiehAkhhBBCCCF2jGO2jYXTRE/ChBBCCCGEEGKKzMyTMAPQRG+TnjWlimVGKbc3YFV4SvUNjFLR12mDVOGkIGXVLmtVWaVdfo/5qkZQIh9bumNY/szckWGZ9bOnioVh+Uxvfli+vxc0swBwugjvPbL5lWF5kTS3ZwrW7VYr7ZN+zEgNW9qX9B7rlZvGSurq/RTrbkM/WAdblDqVW7WYvkf7e5xKZNYgb5dYB0/TWUVcSm+wAat268ZypKTmtik+8yilQNj+KaX93IhteZQU30+ZD7r6L7cOVC73jAftMWudTxdh+imEmL+yEdI7LJT2waki7Ps82vfU9ygVQ9gBBSmKUykyyjHUpHI07iSWlSf2WVk/X0U+Qse/EziW88S2QSJOR42brO+O1d005tCnrYtZ9fqljtty6pJWNA6H/Z2K81Fj9QZHaaz8B/OfG5a/3JqP6uXR+BzeY3U97+KcVe358rDMx/05b1L9eDun4pnhNUrt11glX53GYdU5ykv7PKGr70W6+sruIY8U4OOL7Y1rjU3tWSK2ibKmfFg/EcvlGKwzPnOc8nm9iPTs1fr3TeNPYmzh6fxEY4mW+/Wte4blUzSm8rxf7oZxF4hjgVOInKNx+3DOqUXCstYTqYa4vWZljT5FpLivTpXD8DmKtwGvQ+pao1+PxqnEfs0TY2QTk0m/IHYfM3MTJvY2dW6mhRBCCCHE7qb8QbeoRle+QgghhBBCCDFF9CRMCCGEEEIIsXNcivq66EmYEEIIIYQQQkwRPQkTQgghhBBC7BiHnoTVRU/ChBBCCCGEEGKKmPtktMXjxsy+AuAcgPsudF8uEBdjf677bl3vr3L3Y+c78z6P5926TyfNbl7v847nQSx/Abt7/SaJ1nv3oXg+f7Teu48dXW9Mm4OPOuFP/G//ZKpt/unT/5/b3P3aqTY6Bmbm64jufszMbp3FjTwO9uu679X13s/xrPXeW2xcHOzV9dsKrffeQvGs9RZiWujriEIIIYQQQggxRWbmSZgQQgghhBBi9+IwiTlqMmtPwm640B24gOzXdd/L672X120UWu+9yV5fvxRa773JXl+/FFpvIabEzIg5hBBCCCGEELuXpUdd4k/4tRdMtc1bnvlLMynmmLUnYUIIIYQQQggx0+g3YUIIIYQQQoixUEC/CauDnoQJIYQQQgghxBTRTZgQQgghhBBCTBF9HVEIIYQQQgixY9whRX1N9CRMCCGEEEIIIaaInoQJIYQQQgghxoLrSVgt9CRMCCGEEEIIIaaInoQJIYQQQgghxoDpN2E10ZOwPY6ZHTazF9Prp5rZOyfQTs/Mbh/83TTu5QsxxVh+mJm918zuNLNPmtlV425DiGnEs5k9jcbl281s1cyeM842hACmOj7/gpl9YjA+/6qZ6WpfzCy6Cdv7HAbw4q0qjYEVd79m8PesKbQn9h+HMZ1Y/m0Ar3X3rwXwJAD3TqFNsf84jAnHs7v/2ca4DODpAJYBvHeSbYp9y2FMOJ7N7FsAPAXANwD4OgBPBPAPJ9mmOD/cbap/s4puwnYJZnaVmX3KzH7DzD5uZm8ws2ea2QfN7DNm9qRBvaNm9nYz+5iZ/aWZfcNg+qvM7DfN7P1m9lkz+7HBol8D4JGDT0FfO5h2wMzePGjvDfokSYyTWY5lM3sMgIa7vw8A3P2suy/vZJlitpnleC7x/QDepXje38x4PDuAOQAtAG0ATQD37HCZQlww9Juw3cVXA3gugOsB/BWAHwLwrQCeBeCnATwHwM8B+Gt3f46ZPR39T+2vGcz/aABPA7AE4NNm9usAXgng6wafhMLMngrgcQAeC+BuAB9E/5OlD3BHzOwVAP5JRR//3N1/rGL6nJndCqAL4DXu/vZtrrvYW8xqLH8NgFNm9lYADwfwJwBe6e697W8CsYeY1Xhmng/gdbXXWOxlZjKe3f1DZvZnAE4CMAD/xd3vPL9NIMSFRzdhu4vPufsdAGBmnwBws7u7md0B4KpBnW8F8H0A4O5/amYXmdmhwXt/5O5rANbM7F4AJxLtfMTd7xq0c/tg2dHA6O6vBfDaTXOmeZi7321mjwDwp2Z2h7v/3TbmF3uLWY3lBoB/gP7FwxcBvBHACwG8vub8Ym8yq/GMwbIuBfD1AN6znfnEnmUm49nMvhrA1wK4YjDpfWb2be7+53XmF9PBoWTNddFN2O5ijcoFvS4Q9lVVZHvF/D2k9++W9bb7aau73z34/1kzez/6F7G6Cdu/zGos34X+p7+fHcz7dgBPhm7C9juzGs8bPA/A29y9k3hf7C9mNZ7/TwB/6e5nB/O+C/3xWTdhYibRTdjs8efoD1j/fvC4/z53Pz3iq9Zn0P/KwLbY5qdTRwAsu/uamV2M/lcOfmG7bYp9x66LZfS/mnPEzI65+1fQlxncut02xb5kN8bzBj8I4Ke225bY1+zGeP4igP/LzP4T+jeJ/xDAL2+3TTFhHHDfuprQTdgs8ioAv2VmH0PfdHXdqMrufv/gB7cfB/AuAH80gT59LYD/bmYF+rKX17j7JyfQjthbvAq7LJbdvWdm/wrAzYMfkd8G4H+Mux2xJ3kVdlk8A30RA4ArAdwyieWLPcursPvi+c3ofzB2B/pP5d7t7n84gXaEmArmul0VQgghhBBC7JDFqy/1R//qj0y1zY9+93+8zd2vnWqjY0CKeiGEEEIIIYSYIvo6ohBCCCGEEGLHODDTCZSniZ6ECSGEEEIIIcQU0U2YEEIIIYQQYs9hZlea2Z+Z2Z1m9gkze+lg+jVm9pdmdruZ3WpmT6J5fsrM/tbMPm1m3zGpvs3M1xHzxUVvHjl6fjOP0z1S5wnrdturWT/ZdJ35J7UNqOyJ6bWhPhr3t6iebjQ9OW+qXCbR32id6COL5fvuus/dj41Y4kjypUVvXHQkXWFTX3fwaN/2oHwnWqUxfu0hta22G9vJGLRa9aLY5qbrxLMlqpSbpnhev+v84zlfWvTGxSNieVNHxshOdn3teSfR+RGNT6U5r3wv1StPxXCt6aWu1B2TtyJ17sEO4/kAj80X8itV1fto35LaBlYvlqNQ44Aptp5uifLIOK/qa7lPqWup0uORtbt3dr0xfWy3JWvuAvgJd/+omS0BuM3M3od+KqWfc/d3mdl3D14/1cweA+D5AB4L4DIAf2JmX+PuvXF3bGZuwppHjuKKH335yCMrNbgnD5oUIy646sRVrX4gMf08Tl7Jm5Ht3qRs6hhV43JOZYqgoknlRnX9qPEiLDQrpRDN16m8Wl1u8PT1sNysw2VquhixspT7pIjWKUzvtkP5o7/x8i+kF7Y1jYuO4JJ/+2PRhh25X1IXOCmim8cwQzzon8cVUI0DgBe703E42cXUxd92j/XydwFy2lZUtgaVMz4jJ7rXC294NzRi63GD1qFjYC2U83WKiy51l8qp8SQKFY7l0mjfa4d1+uwrfuK847lx8RFc+qofHTSYqESd2qmQ11IxnBy3Exdpo+K/Tr3txjYfFyNuxqP3at7YVEKhFsUsAOPYpvEhta5FLyysoHgGl9c5znlsj9c1ivlUPHM3Ehen0fmmFff7cz++g3i+6Agu/TcvHTS4zZ280xvoGrFdawwf5+dSO1jWpmN91AdSlY1TbFLM8jid5UVlnXIs83FVdCie18MFiq2G6fkKl8O8fN2R0XUKj9MAkKXGZ45hujYqWqHca8fz/O+f3dn1xn7H3U8CODkonzGzOwFcjn5EHhxUOwTg7kH52QB+393XAHzOzP4WwJMAfGjcfZuZmzAhhBBCCCHE7ma3Zr8a5E18HIAPA3gZgPeY2S+i/7HVtwyqXQ7gL2m2uwbTxo5+EyaEEEIIIYSYVS4e/K5r4+/6cgUzOwDgLQBe5u6nAfxLAD/u7lcC+HEAr9+oWrH8idxW6kmYEEIIIYQQYixcAEX9faOSNZtZE/0bsDe4+1sHk68DMPjuMf4AwG8MyncBuJJmvwLhq4pjRU/ChBBCCCGEEHsOMzP0n3Ld6e6vo7fuBvAPB+WnA/jMoHwTgOebWdvMHg7gagAfmUTf9CRMCCGEEEIIsRd5CoAfBnCHmd0+mPbTAP4vAL9iZg0AqwCuBwB3/4SZvQnAJ9E3K75kEmZEYIZuwtwGBqRRTzj5vTpWNZ71fL7tmbIV1tD21jEdbrKf8Xts4qljgjwflXyij2y04rCMLG4Ja6LnZEmj57BeNrclLFi9uVDuroVyg81ykckodLyxwusQ73Dz6o0VGbiovGNsw+S0w68Zb9cwVepD9TJHNrh1e3WaKKqPi/7rOuvEZQ7ORP1R7SXqJYeTnBphsxyVs0Y4ENmy6M34oGaLYq9Nxq61aksXKOZzKhtbQHn5bN0rb5txfltksO4jjYNbNDvKGJj27icWbNX7JdpHebwvMn4vKpN9jbuRMBr2CtqPbMkseFBL+6x5G9Yx053PD+Atsa6W2G45x3OLxkdav4LjtxO0b71V1uPGFlCwgY5NcwmzXASvd7bDcZQxwBsV8ZwycdZhu1bAEe1ZwhiYivNRx2TKxOm0/XmsjsbtGieQcvzWGR/qfI0tXtcwPadjOivFBB/HHMO9+bCA7jzF7QKVl7kc6jeW6VqDri8ARGN1xtdJfB2XUtTPzJV5Ne4X5OuISdz9A0gH7BMS87wawKsn1qkB+jqiEEIIIYQQQkyRGb/fFkIIIYQQQuwWdlmy5l2LnoQJIYQQQgghxBTRkzAhhBBCCCHEWNityZp3G3oSJoQQQgghhBBTRE/ChBBCCCGEEGNhN9kRdzMzdRNmxWh1bVK9ntKzsyKd1KmjYidpWGW9a6SjJZWqV05Oq+uLuCOR2pSsv5HJtkYmg0hVXdeWy21zGymDL9VnlXbUj7KROqGsL6jca9MMi6HYJb1x4xzNe7Zaid8o94n63iN9bWcxlNeXMD4yRz7fjZS9kVL3PNS+qV2ZbKMmBcVhEcV5tTY8NfhGSm/SdXvpmK6lRN7JVx1G6KKTX6Hgvner+8SLYoUyEup6ADBO39AKB1bRCgd4N+eDPRq0hsVIAk5NF4lUEf02xvN9kbxR4PDR/kGXkwY6S6iqs0Qsl3/IzfuiQCLuapA6dvLSdO47lxtWPZ1hLf1aL2z0tW4od7qkuS7S65CzGj6vbpu3LW+bSI9fVE8vv8fbvaDcIL1O9RdlovGEFOl5I+Qo4VjuNOLLjF5Or6lto3LqKzpOgb6hkQeAojGeWAb6x+iBi5Y3Tc+jVAXnn0bgfC5OU+1l0XTaL+cxQHIcdSkOuj2KW5oenQsS8WxlTXyiv1xOpXuIzgup9BAcv6VrIaOx0xJjdbNNMdwMC+jyeNwKA6lHY3O8DaKQXEclUfodurbpzuu7fPuFiX4d0cweZWa3099pM3uZmR01s/eZ2WcG/49Msh9i97Mph5EQQgghhBB7lIle+rr7p939Gne/Bv2EaMsA3gbglQBudverAdw8eC2EEEIIIYSYURwG9+n+zSrTfP7wDAB/5+5fAPBsADcOpt8I4DlT7IcQQgghhBBCXDCm+Zuw5wP4vUH5hLufBAB3P2lmx6tmMLPrAVwPAI0j+saimG2ieL740AXujRDnD8dy89jBC9wbIXZGHM8am4XYKfpVWz2m8iTMzFoAngXgD7Yzn7vf4O7Xuvu12eLi1jMIsYuJ4vmg4lnMLhzLjUOKZTHbcDznBxcudHeEEPuEaT0J+y4AH3X3ewav7zGzSwdPwS4FcO+U+iGEEEIIIYSYBC5FfV2mdRP2gwhfRQSAmwBcB+A1g//v2HIJVkOpzAbrlO47WU7rwZltZwHnQIw071ZZRpfqlB7oOmua6RlmZFqmPRpp5dmsnFDJjyKykTerp6Panh21kdH6ZR2qU9LJ9iK1NqmI24kdQPrZjHSwvRb1KQ+dWsvjndydo3nmaTqpYntj1MY28x4uOXo6qZ1OqbDrwirgRkK3zXVS2nAA6JImuEuBt07qYtZys1Y4KifUw0VJb5zUbEf6YarTJY1xL3WApyYX6beSVB/T3DaXbUSXMlKQF5F7unoeVnT3opilY4RivmiF5Xs7XtesXSOfRQ1aeRdXHjwFII61FFlCpT0qBrPEQDVqnq0oK/ELr9Zv12mDjws+XlbpuODjpXyBwnrwOnrx6DgkDT54PKF4apM+Hohjks9p3MfV9TDQ97pb6+obFMt8fup1owQKKOjc7KS4j+J5geKZxn+neLYm6ftbOxsvmVbewxWHT/XbrnEhmRpH6+zHuvFbp16UaoDKqdgs1+sl4j/SxEfTq+ftRSr5ctqJVLqT6jpRegnqel5DdV+MSMuQukGI0hBw2PKQ38l5hrDM8jUFfUHA6RvbfE1RzFPczofxOJ+Lj1exd5n4TZiZLQD4RwD+OU1+DYA3mdmLAHwRwHMn3Q8hhBBCCCHEhNGPwmox8Zswd18GcFFp2v3o2xKFEEIIIYQQYl+hFLlCCCGEEEIIMUWmqagXQgghhBBC7GEk5qiHnoQJIYQQQgghxBTRkzAhhBBCCCHEWNi2SXyfMvImzMy+t8YyVt39j8fUnzS5ww92YXlJKU2KW8u2udcTj0uLhGoaANBL6LBZk5pXa1VZF+OkS2XFtnVIz9qN24407qyZT622V5cjPf4os2/qabJX12EtMacIiPpnIx5RU72CItNbXlnmbRWp70lRz611DoTy2tF4o/WOhgU0FsMCGg3Su6d7vm1aWYHLFk+jkVVrwlmXvfk9ih1aQ9YPp9TKKXUxT2+UgqKVhz7OWdhOGacOqKFEXi+C2pdV2D1Pa5M9oUpeJS33OimDu6TDZo09H28cZ17S4ztrjRNxHsFDAM96HiegaDxZo5QLqzQ+0JjA6Rp6S2G/tJZC/C62Qx6IvDR2svb689vvbrScA821TcuMldk7+9JFSh9f1EgqwPHcooFivtEp1dv6WOT2OOVCl2K7IFX1QiPsi7r68kg/TwPhcjco4891Qh4CjrVur1onjtgSH20TVsvzsRjty8RmTunZo+N7PW7cVsJrPhcVlELBlsK+WVxaC+V29fYs89nkO1uTm+PgIJ5TY9l2Uxik6mxOk5CIbZo9imcam+P0I9Undo5ToDQmUzlKS1Kc/7FbHts7HBd8buDpvbCyHRrPWT+fkUq+1QzboEnn0/LYsN4JxxIfJxldq7Sb1Wr46LiiMdjWq/cRAHQXaXw+SvF8ZGVYPrK4XNn3MjuJZ7G72eq68n+gn8Nr1Jnu2wBM/iZM7G30qYkQQgghxEzj0G/C6rLVTdi73P1HRlUws98ZY3+EEEIIIYQQYk8z8ibM3V+w1QLq1BFCCCGEEELscRzJn/uImNo/czGzbwFwFc/j7r89gT4JIYQQQgghxJ6l1k2Ymf1PAI8EcDuAjV8POgDdhAkhhBBCCCHENqj7JOxaAI9xl3RSCCGEEEIIUY3uFupR9ybs4wAuAXBygn0ZjTnydm+zj5111imPLiupPa0UrQWr17kvND1vkzKV9KmsiI61wqRnJV19We1brJOqer1aW12HyF6b0Nj3KybKNH/C6hyp+dlwWzRIM9tEDOt5WX9OWnrQti0arIjm5WSV0zsHaJlHYz11mzTIrUZoI6l8HhOsoGbt+movPjRZ59shlXCk/E30j3XYnCGAY5CnzzXjbXPIVoflg20qN1ZRRY+Chddvrcfr16Q6cZxHKmhaFuubW6TrPo250DYHWyI2LbImx0Eff85UI0dD6hipGSucViOjdAhFi8ptGuMo5ov5UKd5gLT0CxTLpLAunxTr6N3rYNic1gAAur61/pqnr3XjmO8mtPYp1TuPxw1SWC+Qip4V9Yt52E4AcIBep1T7ax7ijuO5k9GY49XHZHOEQpw1+Ge67WH5LKnoT6+FOOdt1Uuc91i9XVahr5H6uyjoWKTp3fXQhlN3OU65jSaNm7wvuq3Sfp2jGKa+23zYNwcOhrHl0EJQerPGm8fB3g406qNIpd5YT6jWuQ5P5zLHVp6VUu4kVPZ8fLUbYTvN5aG81AzbbD6Lx/ANOqVjimN1hcdkiu3UscukFPzlsZ2P146F96L5KVyK1tZjVOpYLV8PplKf8Hies/6/UX0C6SzQscfXawulioth/qUjQUV/8YGzwzKPTXzO75S2m9i7bJUn7A/Rj9AlAJ80s48AGJ6p3P1Zk+2e2DfoR5xCCCGEELOPnoTVYqsnYb84lV4IIYQQQgghxD5hK0X9LQBgZj/v7j/J75nZzwO4ZYJ9E0IIIYQQQswMpmTNNan7Rep/VDHtu8bZESGEEEIIIYTYD2z1m7B/CeDFAB5pZh+jt5YAfHCSHRNCCCGEEEKIvchWvwn7XQDvAvCfALySpp9x9wcm1ishhBBCCCHE7CExRy22+k3YQ2Z2BsDXu/sXptSnSsyALC82K1yN65C6OKFMZU0p60WT318tTWdVLytQWTXdIC39wlxQR7MumumwfpwUwZ1WrCntdEgVu0b6YFoPsK6eN0HB6lbSOvdoHUYcNNFm4C+x5olKtLDUpo0t7B61H82TKBtrvElpv3agukFrktJ7rhu9x/GS0tJn2WYN9/nSg+FstxVNS2mPgVh9HMUwK5R71d8ublBqhJyCghXlvWid4/nnG9W64wbpopuUq6CgAOHprE1ey0OZ1chArLXndWWF73pCb8zHZNGhdeLtyeNEI96nHBeWSEfhiTGE24vyMnBcl7XJdPxlOem+F8L26bVIM0/zc/qLdjv0m7dHKm0BAOSjDvht4AjxmUWa+NA/VrJ3rVq/3M3i+OWxMMKq90s0Lx1Lq1TnQJO2d2neNunrWVXdY603q9rpuMooZjOvVmaPYo3mX6Zx4aHV+WH5wXOhzPp4o3Fpfi7EzWI7nHvmSscwH/sr6+VcIX28RvoXVsyzKp/HnMXFOJVFpx32DbfRapJ6vRmPz8N5E1r6sup9J3A88/5jvTqr4bNG9f5eT6QS4W2fla5UeT1SscNx26J+8Pi62AjpFnIK2h7SinpO2cDxuNIL8cjrVCTOSam0IgCptZFOr9KkbbDYCnPMkZqflf2pc8RaKc2L1TjHp9J2LLTCsdQ4EtpePcCpIuJtyykbFml+3n/c3yiNge5g9g1b/ibM3QsAf2NmDzufBszssJm92cw+ZWZ3mtk3m9lRM3ufmX1m8P/I+Sxb7B3GdD0ohBBCCCEuFN6/uZ3m36xSV8xxKYBPmNnNZnbTxl/NeX8FwLvd/dEAvhHAneh/tfFmd78awM2Iv+oohBBCCCGEEHuWrX4TtsHPnc/CzewggG8D8EIAcPd1AOtm9mwATx1UuxHA+wH85OYlCCGEEEIIIWYGfbupFrWehA3yhX0KfSviEoA7N3KIbcEjAHwFwG+Z2V+b2W+Y2SKAE+5+crDskwCOV81sZteb2a1mdmvv9Lk6XRVi18LxvH5q5UJ3R4jzhmN57dTq1jMIsYuJ41ljsxBiOtS6CTOz5wH4CIDnAngegA+b2ffXmLUB4PEAft3dHwfgHLbx1UN3v8Hdr3X3a/ODi3VnE2JXwvHcOjy/9QxC7FI4ltuH5y50d4TYEXE8a2wWYufYlP9mk7pfR/w3AJ7o7vcCgJkdA/AnAN68xXx3AbjL3T88eP1m9G/C7jGzS939pJldCuDe7XddCCGEEEIIIWaPujdh2cYN2ID7Uc+s+GUz+5KZPcrdPw3gGQA+Ofi7DsBrBv/fUacTlnmk/QSABmnfjW6Gm1n1dKZbVCtWmV5J+71GauDOKqm0SVXdZZV8M0xnRT33m7W0rGddz2PtbkbK7JzU2gX1MSqTrj5SdJMWu6AmrKwhpnqp7/d66hW/Qds2paF3ACBFN5edyqnPOzKOi2rjcjIOgJKKmDqZ0z5oJFIMnA+9wnBmvR3FKVNWb7M+O6VEZsUtq3ZZt82cW28Py6ukA2c9cXlZjcSy5vNqBS8r8XusLkb19gaAFq1rShkcHa/0IIbX9Wwe1m+djluPtPDx+rRapMluBeUz69y7qZQSTdI3syqf00OUU15EGvAwnfvFMWgJPXsqJQerjvMsrjOueC7chlr1FsUzK+pZZ83TD1CZtd8A0CU9der45OnL3XDgr1C5oPLp9RAs83msbed0Cny8peB+8LjNGvu6H9DWSanCPGRhPfgcxfX5vDLfjNeV99NSM2znlFa9k5jOaTUYjs2ybp5fl9NhVMHHSLRc0u6n0r/slNRyeRzk8Ypjm8cM3n6cbqRMl+pxPLPKPFayhzpreagz72E8zvm6yOL1iWKeTpAc/3yccP/4PNGhdeI6rdJxxNuzSWU+Xj1xrPM2j1IERDERpnfLaV5a4XUqzlP7httu0rjECv3y2F7n3MV9bycU/GJvU/cm7N1m9h4Avzd4/QMA/rjmvD8K4A1m1gLwWQD/FP0buDeZ2YsAfBH9rzmK/Uxe42wshBBCCCF2N7qkq0WtmzB3f4WZfR+Ap6D/2d4N7v62mvPeDuDaireeUbeTQgghhBBCCLFXqPskDO7+FgBvmWBfhBBCCCGEELOMnoTVoq4d8XvN7DNm9pCZnTazM2Z2etKdE0IIIYQQQoi9Rt0nYb8A4P9w9zsn2RkhhBBCCCHEjOKIzWsiSa0nYQDu0Q2YEEIIIYQQQuycuk/CbjWzNwJ4O4Chk9Pd3zqJTlVh5sizItLulmFVL6ujWQOaZax7DvOmNPHWjL/YOkeq3+Vmq7JtboO18r1IAR/6x6ro+WZQy8414k8SVvOgcU2pgVl/GumzSZvfo+lJjT0QKepZJTxK9V5VH7zevfTMkYo+r1Zxp9T3kdY2r97+jJWmc6/qqL93jqGb2IdArJsH4vXjWOVypCNnhTKnRqDpkUqeltMtpWVokf6fFb6n1kNS07NZ0MHPkdJ4MVLXhzZ6Iz4la7N+mNaJVcncxoFmGMYOkjL4dDv0iXX8rC4u6/vbpM9OpQ9g3fR6RlpoUtrztuXjfq0bD7lrHVLnU71opMir9zeTUtfzsVpOezCu2HbYUPkcKdVJq81xk1I0t0rbO/Uea8AjPTXFzSrpy7k9rlOOwbO9ECOs32ZdNyu9c3bR06J4/Of6o7Y39/FAHmL4YGN1WD7cWhmWTy2EY4/15Qxvs7JqPUuMcQ3qe4OOpZQym9s+2wnnwzWaXlZ3MxyTKRV9NE5RP6K+jlHpbbS81D7jeO72wno3Cu5f9bjbiuKp1G86JfAYxyp6bnuulGZhA47lPBpD16N6C/R6jvrb8+p0JwzXWSmq932npHxfbIT2FqjMWvtUWoY64xWfLxqlsTKlsmdi/T+NX3TOSB07o5aVGvNS5+PJXXdMjzrpJ0T9m7CDAJYBfDtNcwBTuwkTexuXol4IIYQQQuwT6irq/+mo983sp9z9P42nS0IIIYQQQgixd6n7m7CtULJlIYQQQggh9js+5b8ZZVw3YdKgCCGEEEIIIUQNaidr3oIZvg8VQgghhBBCjAUp6muhJ2FCCCGEEEIIMUXG9STsD8a0nJGY+UjdLauB80S1LKFy5uWyUrpRsvaxhpp11uvdap0pK6iXu0HjyjrquXZY5pHF5WF5qRkUwQDQJM0wa2BT+tMOKdBXGqS3J119l3T1vZKaPFJmk9Kdte+sIeVt6KSiL3i5rKj3aiV3fwI78ctvDhcQ+kT7qdEkNTPp1VPpCYB0XETa94Tufiewpj7SzZcVtYn+MZFKmLZtt6jeTqzrZp1vOf0BK9kfWg1q7PtY20vb6UA7xO3x+XPD8qFmUGyzmrksWeZlsR6cFfcNUn83il5lHWahUa2PH6Ux5uOYy+ukoU5p7Hk5fHw28rg93pfrnerhmFNYtCmeU/FiCQVyWW9fXvdxUEc/n1JQp6YDwLqTwprinHXfXGY9OOuvGdZ+A8ByN2i9OY5Yq320GeKZVfJMh/rKSvV2Vq0TB4C1IvSFx+24H2uV5RQF6cFZuQ9gmFKgXC+LVOoUd7RtudyjfXGmMzcsR+krSF3fb4PiIpE+ow6TUtQ7KMZoqI3juVpxniXOHXVpJFIx8DjTScTzCmvsKZZ5/y41V6N5OIXCgdJ7VfNzfLD6vuNh3jNZiANW1wPxdUtqG+aJ7cYxvJJQ9kfPFUqLicf9reOO4389odAfFYO8rQrf3vOO1HlsltgDlv2pUCsyzOwXzOygmTXN7GYzu8/MXrDxvrv/x8l1UQghhBBCCCH2DnVvz7/d3U8D+B4AdwH4GgCvmFivhBBCCCGEELPFtM2IM/zUre5N2Maz3+8G8Hvu/sCE+iOEEEIIIYQQe5q6vwn7QzP7FIAVAC82s2MAqr9ALIQQQgghhBAiSa2bMHd/pZn9PIDT7t4zs2UAz55s14QQQgghhBCzg0lRX5O6Yo4FAC8B8OuDSZcBuHZSnRJCCCGEEEKIvUrdryP+FoDbAHzL4PVd6Gvp37nVjGb2eQBnAPQAdN39WjM7CuCNAK4C8HkAz3P3B7daVpUinHXM/H4dzXhK9T1Sgx8tixTRzTC9RzrrSP3dCJrTDqnhWZ3Oy59rxArXOYTXZzuktialMbfHWtss2k5ht3dImd0tK+pZN03bjZXZrHpnXX1B24CV/zy9vPVZiZ+C+xSp7xP7krftPKUU4HUAYr1sFDuJ8jhIxR+wWSee0h3bCN16FSm9bsuqYwUAGqRk71JMtUi7m1KTx9NZ103q+SI9DLFKOI+WS31KKNlZwV9k1f0rq4AjRb2RlpjmXyCpPuvSeV35mOxGiud42/LrcxT+PD6k6s+Rdj+l+m6MSHswTq33Rnymjpfz0eGnYr7OccjzptT1B/L1aJ5GIt0Aa7wPNUKahUN5SCfCfVomLXekqx/x63Hub2obcloHVsPnqI7nlFocANoZn6PC+i3kIaY4DQTDcR4pzknZ34jOPYvR/JzugeN2Lk+kkTCOYU5DszMdfB34+E7uo8T0ZqRBr96WZVL7kj8uzxJ14nQenGqDrw/ifvA8C1k4HloWpq9S+oQedYT3fdMTuvly3NGYuuaUlqHY+nkA932epncozvMobtLrmhpDeFvxcld64ZjuJnTz5fH0fPb/Vv2bKfbAKkyDumKOR7r7L2CQ0sfdV7C9BM1Pc/dr3H3j6dkrAdzs7lcDuHnwWuxj6tyACSGEEEIIsReoexO2bmbzGNzbmtkjAWydLTLNswHcOCjfCOA5O1iWEEIIIYQQYjcgRX0t6t6E/SyAdwO40szegP7Tq39dc14H8F4zu83Mrh9MO+HuJwFg8P/4NvoshBBCCCGEEDNLXTvi+8zsowCejP7XEF/q7vfVbOMp7n63mR0H8L6B6r4Wg5u26wGgeexQ3dmE2JVwPLeOH7zAvRHi/OFYnjuxdIF7I8TO4HieP3HgAvdGCLFfqGtHNADfBeAJ7v5OAAtm9qQ687r73YP/9wJ4G4AnAbjHzC4dLPtSAPcm5r3B3a9192vzgwt1mhNi18Lx3DykeBazC8dy6/D81jMIsYvheG4rnoXYOfo6Yi3qfh3x1wB8M4AfHLw+A+C/bjWTmS2a2dJGGcC3A/g4gJsAXDeodh2Ad2yjz0IIIYQQQggxs9RV1H+Tuz/ezP4aANz9QTNrbTUTgBMA3tZ/kIYGgN9193eb2V8BeJOZvQjAFwE8d6sFZeZoNbubVLRJLX1C8ZkndPCjdOHlflSVFzJS+7L6mzSlrLtd7gY96+m1uWF5rRd2yWo33j0HmkEhe7AZvCjrOenuE+vBuu7lnJSzrXAf3unFWmzWwfP2aeasDa9WT/O8rHTl6eX2Vjthfdc71aHptM8KMip6j1ThpF9mhX6LlP+LzVhP3Uro/FMq4p1i5mjn3fPS4Sc14NtUjqeWM2exbpjVwKyRZh0wx+0y6Xw55ruk6z6YrQ7LrA7uz0P6bNYgW7Xyt1GQujirVgyvkQZ/lLqb16mXVWuXU/U7pDc+jXBML6+F462c/iJPpJToWfXnY1yfU1gcaIR4Tqm+R63rTsjhWBjo8neiVh6VLqCOrp7nZ9U01znYCHF3SfuhaFknGqcr22DlPCu6Wem9mIXxeIlicLloVy6zTNNCPR5TF2gsY939WiKtQ5SKBKy0j+OJNfN8/B1qBO3+nIU4OlOEeH6wE5Tz3CeGl8nnHgBYJ005x+pSI2zDeUofUEctzuu6UzJzLAzaT8VXSo/PdZpRqoh6ivJmSmVOq8fXPIu0zS5unhmWD1P6hHUPsXK2F/ZjGdbSH83PDsu9PMQOx3O07xNy4/L69PJQcY3U93zO4FhNxTDHb50YBOLjlY8xnp/HcG5vnuJ0pRf6zee0Vrk9mqdNx1KzRixMKuXC1HAoWXNN6t6EdcwsR7AjHgO2HvXc/bMAvrFi+v0AnrGNfoo9zmripksIIYQQQoi9Rt0r319F//dcx83s1QC+H8DPTKxXQgghhBBCiJljL+SbngZb3oSZWQbgc+gr6Z+B/oPn57j7nRPumxBCCCGEEELsOba8CXP3wsx+yd2/GUBtvbwQQgghhBBin6EnYbWoa0d8r5l930BVL4QQQgghhBDiPKn7m7CXA1gE0DWzVfS/kujuroyzQgghhBBCiH3F4OHUFe7+pfOZv9ZNmLsvnc/Cx4mZY67RjRTNQKzy5PdYE8/q7iLlUk0ss0xqfm6PtbarPVZj25bl5fWgP13vxZv96HzQzh5sBTVtSq8e9Y+1x41OZR1WyZf7VUef3i2yyvI6aV95OuvZW40ultdJU1tUb0+eh/d3j5Y71wrrd3AuKKmPtFdCnZI2uayXrWp7nGTmWGyup3XzpTivo58vEkpYXm4Ud6iOwfI68+t1UmPHauZqZTOnYngQIUE1b+8DeYhlINb58nKbCV09q4eZSD1M5XgbxDHfq6HVjY9d0t3TevMx2aKUDuV9FCnFaR+3m2H7sNL46FyI4QOkp2ZVdVJzXWJcsZ1ZgYWBIj+lX05pvM+vPdawU4oA5/Ensb89/eUPPh4ipTQp1c+Q4julxl7Kwz5qJfbFemLefnvVYxErs+N4Tqdc2C4cO3WOPe4H12eld9EM2wOIde187HO5SeMDt5da13yM333KrcDhQZ9T27NJ4zPHCpPU6Y/YR3ycROMz7/vE7LxtWEvPmvfy+jzUDWPyKinjs1box0WNoKvPs7AvV53O13SMcQqb3ojjbTUL7fEY3ktcY8UpGpqVdZjydRzHFMdLNF4musttr+bVbS+UzmOc6qXOccn7qe4YLi487u5m9nYATzif+WvdhJnZ4ysmPwTgC+6+dTIdIbaAb8CEEEIIIYTYKWZ2JYDfBnAJ+um1bnD3XzGzNwJ41KDaYQCn3P2awTw/BeBFAHoAfszd3zOiib80sye6+19tt291v474awAeD+COweuvB/A3AC4ys3/h7u/dbsNCCCGEEEKIvcUuU9R3AfyEu3/UzJYA3GZm73P3H9ioYGa/hP7DJZjZYwA8H8BjAVwG4E/M7GvcPfWI8mkA/rmZfQHAOYSfbH3DVh2rexP2eQAvcvdPUAdfAeDfA3grAN2ECSGEEEIIIXYN7n4SwMlB+YyZ3QngcgCfBIa/63oegKcPZnk2gN939zUAnzOzvwXwJAAfSjTxXefbt7o3YY/euAEDAHf/pJk9zt0/K2GiEEIIIYQQAgBQ4/fVY+ZiM7uVXt/g7jeUK5nZVQAeB+DDNPkfALjH3T8zeH05gL+k9+8aTCsv66C7nwZw5nw7Xfcm7NNm9usAfn/w+gcA/G8zawOo/lWqEEIIIYQQQkyW+9z92lEVzOwAgLcAeNng5mmDHwTwe1y1YvaqL1j+LoDvAXBb6X0bvH7EVp2uexP2QgAvBvCywcI/AOBfoX8D9rSayxBCCCGEEELsVRy7LlmzmTXRvwF7g7u/laY3AHwvYrvhXQCupNdXALi7vEx3/57B/4eb2VEAVwOYK9cbRV1F/YqZ/RqAd7r7p0tvn62aZ9zk5jjQWtuk6k4pvpPlhFY7RVktyupjVrqnFOKrpIpd74V5WZF+fCFswtONdiivxfvy1Mp8ZRvH58P8rAZO6babllbWpuiy7juxDYostMcqcyT8mbz92/Pdbe+/lGKdt+3BZlDUt2l6o6SATWncJ6Wob1iBw+2VZDqEzZp40jEntgfD2yYVBxzLvL/Kuv52vrUAdT4LcXesHeJxuRd0vue6Iba5vSKPH6YvkOL7QB72Hyt8U7DGO6UjT6nrgVipzCppnn62164sM4dIy83l8vHGxyKrmXnftGl/pFT0o7TXkya3Akdb5wblGmNqjf0IpI/JZH2KqW5O6nQ6Rg43Q5qPY434GyRH8xC3i1msm95gqRfi8SvdkELkTFF93mVd/RypzOdKLmzebqwKr5NShWOzl3Bsz5U06hwvfAyc6YVzzBotl+e/vP1gWE5Cqx0pvT1WevM8czRujEoNs0Gd7bFTGtbDRa3NlzVROpzEOTRPpGI4H4V+J3HOzTK6jsg4lQKNB7SNFyiWF7KyRj3Mfx/F88nO4co+sa5+yUJsl1N9pOBzESvuO1n1mMyqfdbSswY/pXNPpZAASsdiYhumxqkO9YnTSfByyqTORXy81jm/ifNj8Juv1wO4091fV3r7mQA+5e530bSbAPyumb0OfTHH1QA+MmL5/wzAS9G/WbsdwJMB/AWAZ2zVt1pHjpk9a7Dgdw9eX2NmN9WZV4g6TOpmRwghhBBC7FueAuCHATzdzG4f/H334L3nI/4qIgYOjDehL+54N4CXjDAjAv0bsCein7braej/5uy+Oh2r+3XEn0XfDPL+QQdvH/y4TQghhBBCCCH67KLP1d39A6j+nRfc/YWJ6a8G8OqaTay6+6qZwcza7v4pM3vU1rPVvwnruvtDMiEKIYQQQgghBADgLjM7DODtAN5nZg+i4jdkVdS9Cfu4mf0QgNzMrgbwY+h/31EIIYQQQgghAOy6ZM0Txd3/z0HxVWb2ZwAOYfDzra2o92tK4EfRzxy9hv53J0+jb0oUQgghhBBCiH2Nu9/i7je5+3qd+nXtiMsA/s3gTwghhBBCCCE2s4+ehO2EkTdhZvaHGLEp3f1ZdRoxsxzArQD+3t2/Z+DTfyOAqwB8HsDz3P3B9BL6Ct+DzbVNFr1GFoQljRrq9WZiOqtvIx1sqX5B7a1TPVaQsuJ7oRFuhs+yojuhqz+wGBSy5XW979xiWNZ6WNZlCyHn3IlWKMe67rB+KaXrKJaLoJNdIe14ymrIiu2itbUuHYhV3Kn9lIL13qwDTunVR2myWbc7qr87IbcCR5vLUXzxtqyj+gbi9Uj1m/dFpEGvcSwAsc7/oua5YZn3F8+zkIWYb7eDtvfBbojfe9eDDrnMkUZo45LGKep7WD/WBDO83iltMivAyxpjPja4zPHf5OM7D+vKCmvWFbepPEpDHK9f6BfHMyuRUzpypoj05ZZ8byc0rMCRRl/9nlJ3nw9R37162/C4VmSUMqQI+66bSEnQLmnbl7Kgn2eFdYvi4HDGyvmw7z+7fnxYfqi3EJZJivqDtPzyvuD92skorYlXn2N433M8NxH6ym2Xlfsch6wK523A+4/n5zZS8HjAywfiddqulpuP+9TxslMaVuDiQfqCaExO9DWVHqJe+pD4GIyOaVpsk647lrukdudUFs0Qs2UV/Qat0rn/RPOhyrbvXT84LN9nYaxmRf3hLKR7YHjcLZ9nOV1Bk2RzPH25oOsk2t8cz6k4TR0jZaJ4pmMvlXKBSaUxKachKB/jVfN3kFdOr6v8F7PPVnv6FwH8EoDPAVgB8D8Gf2cBfHwb7bwUwJ30+pUAbnb3qwHcPHgt9jHtbOtcVEIIIYQQYpfjU/6bUUbehA2+23gLgMe5+w+4+x8O/n4IwLfWacDMrgDwjwH8Bk1+NoAbB+UbATxn2z0XQgghhBBCiBmk7jPPY2b2iI0XZvZwAMdqzvvLAP41oofrOOHuJwFg8P94xXxCCCGEEEIIseeoq6j/cQDvN7PPDl5fBeD6rWYys+8BcK+732ZmT91u58zs+o12Fk4c2O7sQuwqOJ4PXLKwRW0hdi8cy4cunb/AvRFiZ3A8H7msvUVtIcQozPeXon4n1LUjvnuQH+zRg0mfcvfqX37GPAXAs8zsuwHMAThoZr8D4B4zu9TdT5rZpQDuTbR7A4AbAODoo49rl4qZhuP5+GMuUjyLmYVj+fLHHlYsi5mG4/lhX3dQ8SyEmAojv45oZo/fKLv7mrv/zeBvrapOGXf/KXe/wt2vAvB8AH/q7i8AcBOA6wbVrgPwjh2sgxBCCCGEEGI34DbdvxllqydhvzX4GuGoNXw9gMdts93XAHiTmb0IwBcBPHerGTIrcKC5ukndzRpYNuyxup7niRTWiR3HquPNWmdSvWdBL3quG2t4NzjaOldZvm8tfL2S+3qAlNfduVi7u9oNGteFZqh3oh209A9r3x/qk/b1bG+usn+jNNJrRbVOttng/oYHoqxrZWVwSvtd1grHitZqjS9vf9YHp/Ylq31TatkyZW1wqr87oWE9XNQ6G8VvXb13St2d2mYditO1IhzyvC+4vTPdOFZWeqRnp1i7uHmmsn+s9z6cn6uczv1byOOH6lc2Qwxf0gjtsSb+dFEdzylYe9yz0Dar5Pv9Ddpl1ivz/McQ1pv1xkxKpz8q5rdLOZVAZT8Sancg3g47IbcCR0ldvUEdvXfUn9Jngjz+8DjM68F1uIleHuqf6oTtf996GHfvaR6K2mP9Nve3RUr2OdrfrLmeK+nuN2CN9zE6FlZL++JMEbbVetQeHdMJLX2P6rCC/KI8rM9SSVkej5dB/c1jO5NKd8LnmDhNSCBD3DavUy2NO7WdO22nHajuR9G0Ho41No9tdVIucD9SY0B0DVI6Bnm/cnspXfrZXhiX+Fjgfb8+4rzFccvxzMcup9g4moXlXt7geA7rcY5iokzuRWW5KCi2UZ0agcdmPq5S6WZGneNZJ1/nOiJVJ5Wip1wvuo6kYnz8VKvrxd5mq5uwQwBuw+ibsK/Uacjd3w/g/YPy/QCeUWc+sT/YycWoEEIIIYTYJehLvbUYeRM2+BqhEEIIIYQQQogxoccPQgghhBBCCDFF6irqhRBCCCGEEGIkUtTXQ0/ChBBCCCGEEGKK1LoJsz4vMLP/e/D6YWb2pMl2TQghhBBCCDFT+JT/ZpS6X0f8NfQFwE8H8O8AnAHwFgBPnFC/NpGZ40C+jnYeK6EbCf35KD3pBin1OatCV3qxbrWg95rUBiuR10kDzkrwI42gVX30gS/T9KB6Zebzi6PXCw3StTaDVpi19KxxTSl8I+VvYjoQq9BZRX+I1oMVsrw9V0doakMDodi0XqSejhXHdISRp7POPk5phfMRR205LcEGxRgNjg0rcKRxLo7ZEZrlWHcctk6kq6+h8eY4Zw0up3fo5rFum9MvfHntIM0T6l3RfGBY/prWPcPyYYqPL2arw/JSHsqssQeAS/KgpZ+j7dPK+FgP/U3FWqRnt7CuS3m1AhkAliz0i4/1OrG2Gm3n6mNs03K2md6E46BXIzdKB9UpCYDxpVxoWA9HB2PYdlX0ka67NP7wsuKxgXTRpPhm5f5CFlJ4rOVhG5zrBqX3368didrjeR7dvntYvqwRYqJJO+ych+msNF+gmLqIYnshUo6Xx5+wrHOUiiFSVdMsnHblIB9LGWu8wzmirNJOpTfgep3k+ZHStNBxmCXGx3Jamag92sfl/V/VHsNtswJ+p+QohmNSSqHPpHT6PP5w6gA+L/OYMQoeazlOH+wuDMv3dA5SnRCDV7WCwPqqxqlouRltWt7+67Q9L28+OCxfQuf+Q5T6ZIm201wR2j5XGmPyxD5etbAdOJ4XLCzrIJ0/Usr+uK3zuDKvMx7XHLM5dQTv5ThGEtevY0y5IHY3dUeub3L3x5vZXwOAuz9oZtWJsYQ4D8aZg0sIIYQQQlwAXL8Jq0vdj/Y7ZpZj8FmcmR0DdKsuhBBCCCGEENul7pOwXwXwNgDHzezVAL4fwM9MrFdCCCGEEEKI2UNPwmpR6ybM3d9gZrcBeAb634h9jrvfOdGeCSGEEEIIIcQeZORNmJkdpZf3Avg9fs/dH9g8lxBCCCGEEEKIFFs9CbsN/YeKBuBhAB4clA8D+CKAh0+yc0IIIYQQQogZQl9HrMXImzB3fzgAmNl/A3CTu//x4PV3AXjm5LsXyK3AwcZKpNIGgDlSt6Z05Cm1OKtvWeOdObuA43m6ZPFjvfIBBG3sCmlhWFfPOtkm6bYvys8Oy0t50AqXubgZ6h1thDIra1l/y1pcViiv1zQRsr6bVb1lrXfVcpukXmXzIZdZu9tGZ6wK+K1IxQowQkW/TZ34KHIrcDhfjhTNo2AFc4vUzKwSTqnrWX0bHT/UNKuw57NYUc/pF1Z6QYr6hZWLhmXWAT+W9N4X59yPU8Py5Y2goV8o7Ytl6vs50s/P0XpfRCr5VQ/97SV00a2E0piPnf56hL6s0rZdpDoc57zdeCyaQ7wNU/QSsZbUdbMq3BLz1lxmHe1+HXI4lrL0uDWqH9G2LGnGef14X/Ixw2NIlJKA9stBUsxznJbTj3xm5cSwzGPco5th7Lw4D2P4GQ+67p4HjfccHWOcYqFDsblYGkuatB6c+mQ1oV5fLkgNT9vjMOm954wV/3GDLXqPNeVRPaqT0n1Hx5tVK+2BtE6c54/SpUQxTGMILSvS7Ft8XbATMnMs2PrIOhzDvA587cDH13q0HyndQkkhx2M1xzwf0wt56BuP+RzPX1wLYzOnA/n61qmovSNZSNmw3AipbuZo/Y9S2ps56m9BMdGmdcoooHpFPMZwHDVrjD+c4oTTOhS02SLNfs0L/zrzpFIj1K6T7Et1ypJelNZEdzD7hbpXvU/cuAEDAHd/F4B/OJkuif3ING/AhBBCCCHEZDCf7t+sUteOeJ+Z/QyA30H//v4FAO4fPYsQQgghhBBCiDJ1Hz/8IIBj6Gvq3w7g+GCaEEIIIYQQQohtUFdR/wCAl064L0IIIYQQQgix56l1E2Zmf4aKnxm6+9PH3iMhhBBCCCHEbDLDv9OaJnV/E/avqDwH4PsAjE9HJIQQQgghhBD7hLpfR7ytNOmDZnbLVvOZ2RyAPwfQHrT1Znf/2UES6DcCuArA5wE8z50cvxU0rYfjrdORwrUurMtlDXLGetGM65MS1+P21kg5zxrkPA+3/Q3Sz3PbvNxVUuKvkob7quy+sMxWrHD9+07Inc0K5aOkuOc+lZXPGzS9+v65rMvNsEzvVetke9G24rarVbvxvLHelftbjNMHvwMi7XVNtX8dchSbtN68/pu3WbeyHmudeZtFCn5OHUDHT2HV25hjGYjjuVvwPg59fKAbJO5f7h0clr+aVPSX5iHOHyo4zUG5HxxrrO4mvTF9zJbxOlHXuf5BUi6vj/gpLB+vkSbYqpXgrKLPIyU478sRsUxvcawVCdV+hmqNcXQsUXEu0af+6/HEc2bFcDyqsw7xJ6TpPvD2zJ3Wg8ddWhinCOAQ4pifa4U6nSJum7fHPZ1Dw/LfU1qGSyjlwrEs1H+A2lilc8RB0okvVp9uAAAtOhYXScPO+49jvpOxpjzMyzEfx2/cXlKBTfWi9qJ9SZWibC50ruS4LjUexzNrx7mNaqLzkLMmfnwYfJhmIO5T9bk8xXp0Pqv383tevxaN+TxWL9Cismaoz2l2esmxOR4DLqEx+YpGODbuL8IWXS5C/CMnZTytUyc6D1WP00AcnxzzSxbOhRxrPH8exRQqp9e9bOhRt4pErMaxWV1npEo+Sv1QnW4jpemXLXr/UGtPm9lR+rvYzL4DwCU1Zl0D8HR3/0YA1wD4TjN7MoBXArjZ3a8GcPPgtdjHpG4YhRBCCCHEjDBlPf1+UNTfhv5nXob+R/KfA/CirWZydwew8ZimOfhzAM8G8NTB9BsBvB/AT9bsixBCCCGEEELMLHVvwr7W3Vd5gpm1U5VL9XL0b+K+GsB/dfcPm9kJdz8JAO5+0syOb6fTQgghhBBCiF3IDD+dmiZ1v3j6FxXTPlRnRnfvufs1AK4A8CQz+7qabcLMrjezW83s1rMPdraeQYhdDMfzQw/IayNmF47lU/eP8xc5QkyfKJ4fUDwLIabDyJswM7vEzJ4AYN7MHmdmjx/8PRXAwnYacvdT6H/t8DsB3GNmlw7auBTAvYl5bnD3a9392gNHmlVVhJgZOJ4PHdVv4MTswrF8+KLxCWuEuBBE8XxU8SzEjvEp/80oW10JfgeAF6L/FOt1NP0MgJ/eauFmdgxAx91Pmdk8gGcC+HkANwG4DsBrBv/fse2eCyGEEEIIIcQMMvImzN1vBHCjmX2fu7/lPJZ/6WD+HP2nbm9y93ea2YcAvMnMXgTgiwCeu9WCcitwOF8eqahnTTMrYWPNeLU6nfXErHot0yRdd+YJva5lldOzhMLlK92lYfmSxqlheSmLfoaHJmmyzxRzoV4e9K5zVv21zUgln5H6mdSyWenjhBZt6yxS0NIDVNKwrrOifoR6elinZEQsK+uH0xMPbIuE9vV8SLVd0L5kTfZO6WuQO9F+4RQB65v04aw7rtbSR+pbsIq++lioE8tAvC+beZhnMRHPn18/NixfSfF8WR76fYZmPVPEccDa3pSim2FNMOuKOSJYjdyjdS3rs7ntjI6lTmI/Meu8nUiJX96e0Ty0n3m5SQ049W/76vrJfQV2o83ceHytXode1FeO5fj44vmbVj1ur9F+4bQMka6e4HG+3YjrpHTTn1k/MSxfkn9xWF6wsKxTxfyw/KXORcPyajOkHLm6eWpYbpXSQ/RY653QxDdpe5A0HOcSKu1oOaWY7SU/Oa6OwegYS6jvezU09v33AikF/HbpTVnpzefDHh33O02tklqPOql55nJKo5FQn/9993D0+hGNkBlojuL5XBF+8n935wj1I7TxiEZ8fbIBtzxXivOcXnOKoDZVW+ZrNI7naDyobm9UFERbJErlQPPzeTCqz/FcfYyU43fUuL+dOrOIYbaNhdNk5E2Ymb3A3X8HwFVm9vLy++7+uorZ+P2PAXhcxfT7ATxjm30Ve5jUTZAQQgghhBB7ja2+jriR5e9AxXu6zxVCCCGEEEKIbbLV1xH/+6D4J+7+QX7PzJ4ysV4JIYQQQgghZg89pqlF3S9S/+ea04QQQgghhBBCjGCr34R9M4BvAXCs9Juwg0AN84IQQgghhBBif+ASc9Rlq9+EtdD/PVgDwBJNPw3g+yfVKSGEEEIIIYTYq2z1m7BbANxiZv+vu39hSn2qJEdfUV8mpU5n9TOrzHn6mler6HNSuJc16qw45nKsGi8qp6c42wu6+c+sXTIsX93+clTvksZDw/IDveBK+XLn8LC8mK0Ny0fzs8MyK5u5T6y0b43Q4KYU0axY5WWldMNRugBLP0yN6qF6O7N6vQ5sYCxrxiO9Pu3znITKdTTBdcnMMWedeFuC9b3bT+YcbRvW/yZ03byPOqyJL+26BdKtx4r7oCvmY+yhXtB13756xbC83LonLJP6dH9vEcyXu4eG5eON08PyI0jxzZrsVe5TtF9DmSNlLtK8p62cOamIc6tWdPeifVb90V8nkQqjv1wav7xaM5+TFnq76vpoiC+lWMhpv+4Egw/V1fF4x6ksqscPZtP0SN9P+nmE2InHahonEmNDm+ctKfGzaB+H95aLIIT/1PrhYfmRFI+cToT13n+9clXoH740LD+6eX/UNiu3OwnNfFw/vLHI8RitA+uz0x9Lc1qHuFp16odo3Ego5s9HPV/nXJmq0xpj+gWDD/d/Qeee+LjfHlH6nBHryXEOrpdQzvN5PYv2F6eeCeXV0jXPF2izXdEIa3URXTv8LV2TfHj5q4flc3MUz61TwzKr5Mu9zqNydVzMcUqVaJ0SceTVdTgVSZnUscH9jeM5uSiqU047sXXcR4p7TrexacvNIHoSVou6V3rLZvZaAI8FMLxjcPenT6RXQgghhBBCCLFHqfso4Q0APgXg4QB+DsDnAfzVhPokhBBCCCGEEHuWujdhF7n76wF03P0Wd/8RAE+eYL+EEEIIIYQQs4ZP+W9Gqft1xI0vHp80s38M4G4AV4yoL4QQQgghhBCigro3Yf/BzA4B+An084MdBPCySXVKCCGEEEIIMXtIUV+PWjdh7v7OQfEhAE8DADN72YT6JIQQQgghhBB7lu17sAMvB/DLY+rHluRWYClb2aSoLRIaUFZ8d2rklWa966j6GatfvVovnSV0sgzr3BnWNN/fW4ree2Tz3sr5/2b1YcPyA6T7XspXhuWLsnOVbbRItpuVPrpg5WrTYnnrBp2kbjqhqCf9bMtj0W+kPqb9x33sWbXStQ6sxC/Py9pyVvWvFtVpDHaKoa+o5/VkTf55KZdplXKKzXVOs0B1OGabea9yOpBW83Occ2qE6Fiitu+leL4qeyC0V4qVv1s7MSzfQ7r6o5Si4so86NVZ59upoQWeS6mOAayT1rhIqrvPn/K2zMBa+uoUD7G6mIi05qzSpmM1iqN4uO95PXX4VmRwzA3a4bFz3eNaGzRRravflCIjEas8hnCcN2k8KRLbj7d/XhqnOY44nnkeXi6nRriMxtrLmg8Oy39x5urQD5r3kjykXgCAr2qEttfAxy5r0UM5SPOBNm2PIjr26gVtj6qlFN1MStfNCvdo+5fOEWWV93B+43QDIVZ5v7Ro1lXfyeVLGkNYF1b+dxJpaIoonQoq6yQprwOtXyr9DscjXwfweMxjAKdPKF93xOkGwnKvaoR5vkTx/JdnHzks8/Y/nt8xLD+8EZa5VpL5c0x2+NqD6szReZqvSTi2WT8fbeXUdKTTNHD8x5FKbxhvp+r0C+Xrp56nj4EN8ui6c3vXM7sePQmrxU72+njO4EKgfi4ZIYQQQgghZp2dfJSk+1whhBBCCCFEnxk3Fk6TkTdhZnYG1ZvSAMxPpEdCCCGEEEIIsYcZeRPm7kuj3hdCCCGEEEIIsT0m88tWIYQQQgghxL5Divp67DEdixBCCCGEEELsbib6JMzMrgTw2wAuQd8YeoO7/4qZHQXwRgBXAfg8gOe5+4Op5QD9H6E10dvkZCxIzcxqbFZ8F9G95tbqb1bLsgIZiHXHrHtNK0hpWay3p75Geu+EZrbfRlj5gzTPieZDw/KdK5cPy19av2hYvnw+1DmWh3l7Iz6t6JCWlVWq3I9I30zLOpfQB8+xMru0LzuRSrpa78t1eP5epAmuTjGQRyrzuE4vUoWHer1ITTu+w2VDg8zC4FS6BWBUPAe436w35n3EKvMUo9T/vKwm9Sm36njmOosWtPIMK5QB4Hgz6LtPrh8elj+5dtmwfNH852j+0N9zBR9v1evAlLXFPA9vqQ5Vi+M/vNFkRTfV522zyQIa1QvlLJX6gY8R1j/X0NX3No0nW8fCduH1S8VpK6F8L4+hkaLbUtsjpTv3yjo8vVlKA5Enjhke55vR+Mznm9DGwxoh/cLdc/cNyxzLt1NaEQA4tvh3w/LRLAjoHyqqU5nw1kipu+MtFm+n+IgLcAxnkXKeF0XLovbWa4xL/WWlxmdWgod9kxp366aV2Qms00+Ni6mxNqNtE11TUHkOJWV8oo06unseD6I2KH6XsngM5tQs3AKnPbiqGWL47vaRYfmezsFh+fa1K4flY/nnh+VDWZzi5YxXx3POqWdqGB24/mpRvW3KqUiyaGwKJNX1ifNx6lqonHohuoahJlLn4FRKgplFT8JqMek93QXwE+7+tQCeDOAlZvYYAK8EcLO7Xw3g5sFrsY/pbDPnlxBCCCGEELPKRJ+EuftJACcH5TNmdieAywE8G8BTB9VuBPB+AD85yb4IIYQQQgghJot+E1aPqT1+MLOrADwOwIcBnBjcoG3cqB1PzHO9md1qZreeur9XVUWImSGK5wcUz2J24Vh+8IGtvyolxG5G8SyEuBBM5SbMzA4AeAuAl7n76a3qb+DuN7j7te5+7eGLJvOdbyGmRRTPRxXPYnbhWD5yVF8lFrON4lmIMeNT/ptRJj7amFkT/RuwN7j7WweT7zGzSwfvXwrg3kn3QwghhBBCCCF2A5O2IxqA1wO4091fR2/dBOA6AK8Z/H/H1ktz5FZsMi2xqWY9YUpkCxZbx5rWG1po5tCJ5i8Gogg2CwFpC2KKcn/nsmo7UMoix9PnonL4OttjW19Otv9QbwEA8FerV+Hq1j0AgFNFvF683ofz4M2ai2yOoHK1NYvtboetgzPF5vAq3KK+b9iFcuvF1jk2BbF1iIpFZD0Ly2wh/qpftF8Ha5KXzGhch01bbLuaBHPWGa5307pY9WCT6tSK52qDXOb1vlJTpIxfCRtXK7LGVdsO50rbdoGMXHNRnPeXtZivRPF1eOF/D8ufaFwyLD/QPTAsf6kb8siXLXe8DS/Jzw7LS1l/nXpwNAfbPIPVMmUxPOYUDrRt8/GQW4jtDD60JebwkkGw2rQVjRsjLI8b5rgcPoyFpvXiOBrEcx3D2k5hI1jT1iPhDpvfNvrStG7aOkrrzcdnaj3KZjmu10T1cdyKbJ/VdZqRaY6XyeeeYEi8srE2jKPvXfoEzhSDfi0Cn1hPxfN8mE5j86ki1DmWnxuWD2ccg46mbYyjFhkS6+xxNnmOirUNOIYzC/O3UAwNiZn5cHzOy8dUNP6T1Zbjg+bJrVM5Puclk/Aoq+t2yQbH9BzZlDM+zuncw9Pn0A3vWXxMc//YfpcljvVUnVHHMccqnwejPiZiGIlx6asawXZ7dOnjUXu3r106LG/E8x+evRqPaf/9cPoijf+rPjcsH6NrjaXIjIkkvcg4aYPlZzhDhsSNbd5xYDGxrDrjfJmiYpYWCrqG8SiGo+sTHucTcdSKxp9e0s4o9haTTtb8FAA/DOAOM7t9MO2n0b/5epOZvQjAFwE8d8L9qIQHuaobsHGzkxuw82HjBgzA8AYMqHcDtlOqbsD6bW++ASuXx0nVDdhugtc7dQM2KbZ7A3Y+bHUDBoz3cfxWN2AAhjdgQL2L1FFU3YABZXVx9fSdwjcvfJFWdQN2IdjqBqxfZzKnoO3egO2UVkKxPbwBQ/oGbKc0ue1t3oCdD6nYZkV9Wde9E1IfkEV92iV23Uhpv90bsB2y3RuwnVJ1AwYgeQO2U6puwABU3oAB6Ruw86HqBqzc3jgNzzN/AzbjXxGcJpO2I34A6c81njHJtoUQQgghhBBiNzLpJ2FCCCGEEEKIfYCh1jebBaaoqBdCCCGEEEIIoSdhQgghhBBCiHGh34TVQk/ChBBCCCGEEGKKzMyTMEOF6hYAUrpusO6W7TlsKQrfWp2jL7CWNfRsNmoljIUpvTFb7rgOG7vKlq7YZhTaayVMSkdJV/zkubuH5VMFaZ1p2325F0xGZwpSIxcHsJStVM7DprkFaq9DNijW3S5lW5sdm/Dh/GV1N5PT9oj1+GwWrA7lsvaVzXGREpysUSkl/iQU37yN2Vo5Z52kUSsVz8xCpDqu3q6j9Pvcr9RxlUqf0Ezp7Wk5oz79OUG5Do7OnRyWv1JUz/Vl0tWfKoIRlPd12ZD3yOapYXkpI0sqabDYeLeU7DDHYPVHfxltftbVA3FqhfWErptpRmbBMJ33S6uUrmE9YdqcRPqFlFo/FRNljX3lGI/NqUI24LGynA6kaplcH4iPdTa38j5L9akOl+Vh3S6bvxcd6uNXaPOnxucvdw8Ny6eLoPfm7XF1FMthX58p4v0bxTOPD9HwUB3PqbGZt9Ncad/zuWE1EYO8zzoUw+mxudosOO5Y3ogLjpeWUxvR9QKfL7h/nFZk++eOOvGcohnp++OUGs2E0XU9MX4xJ7Jwnn3qfEiNk4plAPj7Ho3PZGzmsZrHZx6bD1E8r7KmHzw213uWsEppW3rR2BngJfH1TMc4lqvHq/I+To3hbK5cN75GY6Or2C/MzE3YhWLa6tvUDdg04BuwadBJ6HwnReoGbDdyIZXLO7nonAVSN2DTIKU6nhSpG7DdQp0bsEkxTo16HVI3YNOAb8CmQZ0bMDF9mjbdY4xvwMT+YsqhNrPo64hCCCGEEEIIMUV0EyaEEEIIIYQYDz7lvxGY2ZVm9mdmdqeZfcLMXkrv/aiZfXow/Rdo+k+Z2d8O3vuOHW6NJPo6ohBCCCGEEGIv0gXwE+7+UTNbAnCbmb0PwAkAzwbwDe6+ZmbHAcDMHgPg+QAeC+AyAH9iZl/j7mP/IrmehAkhhBBCCCHGwy56EubuJ939o4PyGQB3ArgcwL8E8Bp3Xxu8d+9glmcD+H13X3P3zwH4WwBP2sHWSKKbMCGEEEIIIcSscrGZ3Up/11dVMrOrADwOwIcBfA2Af2BmHzazW8zsiYNqlwP4Es1212Da2NHXEcWuYJSiXgghhBBCzAB+QeyI97n7taMqmNkBAG8B8DJ3P21mDQBHADwZwBMBvMnMHgFUXoxOZI1m5ibsU3es3/fkqz5/DsB9F7ovF4iLsT/Xfbeu91ftZOZ9Hs+7dZ9Omt283ucdz3fe0bnvCV/1pS9gd6/fJNF67z7OO54/eUfnvmu+6i7F81g4uXWV3cNu3t87ut4QgJk10b8Be4O7v3Uw+S4Ab3V3B/ARMyvQj4O7AFxJs18B4G5MgJm5CXP3Y2Z261Z3unuV/brue3W993M8a733Fu5+DNi767cVWu+9heJZ6y32FmZmAF4P4E53fx299XYATwfwfjP7GgAt9G/EbwLwu2b2OvTFHFcD+Mgk+jYzN2FCCCGEEEKIXc7uStb8FAA/DOAOM7t9MO2nAfwmgN80s48DWAdw3eCp2CfM7E0APom+WfElkzAjAroJE0IIIYQQQuxB3P0DqP6dFwC8IDHPqwG8emKdGjBrN2E3XOgOXED267rv5fXey+s2Cq333mSvr18KrffeZK+vXwqtt9gxF0DMMZNY/8mbEEIIIYQQQpw/C8ev9Ec99+VTbfP2X3v5bbP4m75ZexImhBBCCCGE2K3o+U4tlKxZCCGEEEIIIaaInoQJIYQQQgghxoJ+E1YPPQkTQgghhBBCiCmimzAhhBBCCCGEmCL6OqIQQgghhBBi5zgk5qiJnoQJIYQQQgghxBTRkzAhhBBCCCHEeNCTsFroSZgQQgghhBBCTBHdhO1xzOywmb2YXj/VzN45gXZ+3sw+Pvj7gXEvX+xPphi/7zazU+Vlm9nDzezDZvYZM3ujmbXG3bbYP+yCeP7/m9nfmpmb2cXjblfsL3ZBPL/BzD49uO74TTNrjrttsX0MfUX9NP9mFd2E7X0OA3jxVpV2gpn9YwCPB3ANgG8C8AozOzjJNsW+4TAmHL8DXgvghyum/zyA/8fdrwbwIIAXTaEvYu9yGBc2nj8I4JkAvjCFPoi9z2Fc2Hh+A4BHA/h6APMA/tkU+iLE2NBN2C7BzK4ys0+Z2W8MPtV5g5k908w+OPgU/kmDekfN7O1m9jEz+0sz+4bB9FcNPgl6v5l91sx+bLDo1wB4pJndbmavHUw7YGZvHrT3BjOzHXb/MQBucfeuu58D8DcAvnOHyxQzxIzHL9z9ZgBnSutkAJ4O4M2DSTcCeM5O2xK7n70Yz4Ppf+3un9/p8sVssYfj+Y99AICPALhip22JMeFT/ptRJObYXXw1gOcCuB7AXwH4IQDfCuBZAH4a/QvAnwPw1+7+HDN7OoDfRv8JFND/ROhpAJYAfNrMfh3AKwF8nbtfA/S/LgDgcQAeC+Bu9D8ZfQqAD3BHzOwVAP5JRR//3N1/rDTtbwD8rJm9DsDCoA+f3P7qixlnVuM3xUUATrl7d/D6LgCX15xXzD57LZ7F/mbPxvPga4g/DOCl251XiAuJbsJ2F59z9zsAwMw+AeBmd3czuwPAVYM63wrg+wDA3f/UzC4ys0OD9/7I3dcArJnZvQBOJNr5iLvfNWjn9sGyo0HS3V+L/lcAtsTd32tmTwTwFwC+AuBDALqj5xJ7kJmM3xFUfYI7w5+5iW2y1+JZ7G/2cjz/Gvo3cP9rjMsUYuLoJmx3sUblgl4XCPtq1IUhz99Dev9uWW+7n1S5+6sBvHow7+8C+EyibbF3mdn4TXAfgMNm1hg8DbsC/U93xf5gr8Wz2N/syXg2s58FcAzAP9/OfGKymOvzyjroJmz2+HP0B69/P3j0f5+7nx7xtesz6H99YFts55MqM8sBHHb3+wffIf8GAO/dbptiX7Dr4nfEMtzM/gzA9wP4fQDXAXjHTpYp9hwzE89C1GCm4tnM/hmA7wDwDHcvdro8IaaNbsJmj1cB+C0z+xiAZfQvDJMMbow+aGYfB/AuAH80gT41AfyvwUB9GsAL6Hc0QjCvwu6LX5jZ/0L/Nw8HzOwuAC9y9/cA+EkAv29m/wHAXwN4/STaFzPLqzBD8TwQKvxrAJcA+JiZ/bG7yygnNngVZiieAfw39E2fHxpcf7zV3f/dJPogtsGMyzKmibkeGQohhBBCCCF2yOLFV/rXPvvHp9rmbb/5E7e5+7VTbXQM6EmYEEIIIYQQYizMcgLlaaI8YUIIIYQQQggxRXQTJoQQQgghhBBTRF9HFEIIIYQQQowHfR2xFjNzE5YfWPTG0aOb30js6FrfR61TJ2lm3Rk+oeXWYaff1Y36zuXMq6en1rXcDxbMFmEmo+mpMi8rOX1U2wxVjKrRc+Pl++66z92PjVjKSBrzi948dDS9L8Y5gPH6jDHuptL3cZHaBqXt4Vl1ORnn0cxUieO0R7NSedN73bDcOIapve1u22hd45Xl7p49/ffnHc/N9qK3F+uPzVH3pi2GqtvcTrZzaswZ2V6NBrlKYsHO+ziK5dK+T8R56tiIjnVevyi2KX6jWC6tW0HBnYrtqLtWPZ3XtRTbp1dOnnc8t5qLPjd3ePMb27ymsGkMhMkm6rZdJ0JrjD87Po5T+5jL4UUc5zw9MW9pnvhagPoeXV8kYpjjNzW9vFwmFbeJdQKA02v37Oh6Q+xeZuYmrHH0KC57xctgRenIqnGBbr3qq+rUiWXURWvywmybdzbJi+GRg0j6vepGqHpie6RuWDYtKg/lohkqOpfbtDAqW8aN0zJ7tDFX42/GZsuhwcY5o3Ko01ih8irNux76lHeoab5IGJVRhK+l8/Ci1w7Tb3v9T3xhxBK2pHnoKL76BS8fEbOlLqVuPgmOD95f0b5LTB91c5ZqO7qhSK1HnWvLctvbje0a90ROI11B5d5cPE+XXvcWwoJ7bWqkldgBndBgvhLiuXk6TG8/FM/SftDpvbBBm+dCOVsLZb64TRKtd+iH5/GG7bXCe7e855XnHc/txaP4xme8bPMbdBGSvIgvEnVK9ZJsN1ZGjAHRhda4bsJSF3jlC7TUdvDq/nriBuv/a+/dwyTJyjr/7xt5rayqvk5PT88FehgHENEdcGRR9ueiKKvoAquIsI86rDwP63oDFRUvq+667uINLz9XfzuKu6OwCC4gyCKIs4ILKjDgwMww3ISZYaCZe3dXd3VV3t7fHxmV53ui42RFd1dlV2Z9P89TT52MPBFxTsQbJzIyIj/vsBkO6kE7VOovxONrbzHM0+vQGNek5dL4wMd6jcba5kp5/LYe7ob6KzQDAFulXL49GqB5+/OH0IwaUg9lb9CB3KA6AN75kV8873hut/fhKU/6/rOmp74YieImiq/ExeYk0vm4ytc98NLp27W+ZP8GqQvrCe2ILjooPms0ZvH41Qz7e9jiMsU8lYfNuG98LrfEmFBbC/2or4YsO7VTIWbtNMXzmVD2tTjO0Utk6amFNhrHcJMOvjZ92ADwjk//6gV93rgYSMxRDf0mTAghhBBCCCGmyMzcCRNCCCGEEELscHQnrBK6EyaEEEIIIYQQU0R3woQQQgghhBAXjus3YVXRnTAhhBBCCCGEmCKzcycscwwXB5PrRNYtVtyVq6MjDXoFw9rEilU07J5YX6rdVW1hCUujJxeVMHEVzJORoS+hJY5VbGQyYkESGeeyRpi51iAzVhvwYZh/sBDqrS8Fm1B/NdSpn+JyWFSDbIo4HRrCNsVaN94iSfPYApnHGlvndx82gNXLPZ1ioSjgS8VqKm65DzWKCZaIRaZP3mHpOLB+eC+j3ZcFGRpqVOY6VdILjN5MNCWl5S6fXAnrxa9ZIGiJA2hAG87bZC7shE4NFqjcCfV7y/H3Xt09YR3rJ8Jw3DoRdlTzZFgWWxNrZ4J9K1ujdpC1zHthfcN2bJDzhfj1+TKsA6uXZvm6+Y1QjK2abHfj6fFyk7bCc0y5EK2bhGWRWa7Yrqgf5e2IDGtsN4z6FxbEKQhQ1LZHDU5MpnmydbLP9mjDDcrPkWyWA4DBUrCvre9vjMtrB0JMrO8NDekvhHl7S6G8vi/UOXNJWEfzUFhO+3hsemueDDuhfiIMFtlpMtCt0yDSp53Wo37TMofNLfwoYzY27nlKf87VOQ765fs+FR/FwSuZsiFh2YwCleMusRyfYEOM5qE4si7FFBv/IrNlwhibFb7nJ/Nh0szI283L+5TxvGTMZKvgsEO2QQCDxfC6vxhins2h/f0hjtb3hXJ9PczbWAkHQ/1EMCJmj5C6GQBWwocSX18vLUemy0ZokzdDWcw3s3MRJuYavgATQgghhBAzih5HrIQ++QohhBBCCCHEFNGdMCGEEEIIIcQFY5CYoyq6EyaEEEIIIYQQU0R3woQQQgghhBBbQ0oyIyJ0J0wIIYQQQgghpshM3QkzA7xoQk1o3yOdLCm6USclKGu8eebo0rTokK3Q0ISW3gdUZuU4Tx+kOjGBZNs3J+pd8ZuLc9TrR03qksabtObDOimzG4WZa6RzrtOOXgzlYSvM3yXl9qBJmlrS12a07nqwyU78lmZYD/P0SFHfXU7Ocl54lk4DcNauZ818QtvOWnpkXjrdsyrfThX0/extp33mdd6vFMM0qrC6PlLdR5rlwtpTWvpICZ5sLq2cqlB4DGvl08vaskFtjdX81G+aPmzStonKobH9ZjyARfr6RYrnFm1P0jzXSE1eJx12tp7QRbeC6njYipXN3b1bo6iHAYPcDF2jYyxLaekTNmvPiq8TevAKinpLxArHvxVScvA4FSvFy5cVHa9ch9Mc1Ggb8wYpKOorp0jZoBWWa8NwwEV6/EitX1gf1eO0B14rjzue3qWUI8NOKPdJXd9fDPUHxdQItF+NlPPZGg0W3HYuk4reO0FH3ruEHPoXiGdAvzNqczJVAddPnX5Zz87pCcDq+sKyOK1DlTsJHGw85g9TgVOAx0g6sAxhn/F4HrWJGzvgdrA+vvARk4+HWuLDSqTK52OGypy2oEtj36nV0IzCYrOFELi1g3vD6i5fHJfXSFHfW+LxICytdTz0YZG2f2uFP2DEKvrBqVMoo8bboxHWPdzTLqk9W+g3YdXY1jthZvY4M7uV/k6a2cvM7ICZvcvMPpX/37+d7RAzQC3xyUwIIYQQQog5Y1svwtz9E+5+nbtfB+ArAawCeDOAVwC42d2vBXBz/loIIYQQQggh5p5p/ibsGQD+0d3vBvAcADfl028C8NwptkMIIYQQQgix1fhF+JtRpnkR9gIAr8vLh939GADk/y8tm8HMXmJmt5jZLYNTp6fUTCG2hyieTyuexezCsdw/o1gWsw3Hc6+reBZCTIepXISZWRPAswH86bnM5+43uvv17n59bWlx8xmE2MFE8byoeBazC8dyfUGxLGYbjudGU/EsxIViw+n+zSrTuhP2zQA+7O735a/vM7MjAJD/v39K7RBCCCGEEEKIi8q0FPUvRHgUEQDeCuAGAK/M/79l0yUYRpruorM3pfUmP6Yl1N1RHVKAZ7XyOgBgPE8FC2xsdCU9O+mQfVCucy8qlKPnXlPrjky4VGnAy6XFcJ1CXyO1Oan9vVZeTrWDFfxRm9a5Exm8ERrGKul6KyiUa6Su7/G2PUNKXWo368i7pJxd2x9rk3sJ1XJvT1jJYIm0uBeIOZB1DRnZdc/n2xyOeY/U6xRfvL+yCkFbrMLqY15fnXcAFTnlQmqEidJDTGhKFEc0O223jDX/BJm70afyMJitMWjH8ctpE6LDgfrEivqsS8cVp2IgBfiwQwtqFxpL6xvQOoYnI3d0Kf2loJ/vHgydWtsXAmFtP6nFg5UZQBzb0eh8Hlh+aGR0iLDOOk6TQDNWfJY/0tUnvjpMps6I0jhY6XQAbA5HrUvjz1p52Vg7Hh2HfOzR4uvhRdaPO15bD3GRdUPZ+jQm0gnHKSVHv1Onctg4A0rnMax6pk+d1CLNfygPOtw+lFZqnogXxX1njf5gKWi5+5eFfCDr+0Lj1/ZTSgdKGdLdU2jvXxU7UJ1h3cbHUGpMjmONPjtw/NMYEI1RnhhjiuvjNAlRaoTy+aO0Cpy+guNpEHeI28hExxillUGDArpHqRF61PEeDc6sjwcA55wlFDCsq2+Ecc3blGKjHVJsOLUp69Gxc/JMWM7JlXjdpIy3bmgvp1rpLYfyOjm7szArmidDuX6a+rNS0NCTnr9+9NHjcv+yfePy6cMh5jm21/cWjsO/x+wxw7/TmibbfifMzDoAvhHAm2jyKwF8o5l9Kn/vldvdDrGz4QswIYQQQggh5pltvxPm7qsADhamPYSRLVEIIYQQQggxJyhZczWmaUcUQgghhBBCiF2PLsKEEEIIIYQQYopMS8whhBBCCCGEmGccsZVOJJl4EWZm31ZhGWvu/vYtao8QQgghhBBCzDWb3Qn7fYz08ZO81l8LYNsvwixzNDtdDAvadp/kty6D60e6+XL1fPFintfn1BbLgt2vThr1Rp30qVRnSLr6Xj9oXwekqx/046dFI/X3kD3sXInqRH58qjPkOkjiCbW/TbA8l62OFemRAbmgsY9091Qvq6XMiawKD1Nr66VVsHaAypcWOn7p2ri4Z2/Q3B5oFBS7OfckWlQVz4DBwhDDBsVQKt0CikrkRJmpoASPUxUk6hdeR+VUugbOSEC67kjfnFg3EKvoI/U92ZEHrJ9PaPqHkZaelPFNrl9Iy5CKT3rBza1i/OftYWeldCjfgazgr6+T6p10yqsHQwdXrqLpV4YNWD8Y4rrZjFMsNLE1eAb08xQPA9L/c1CkYpBjIitkgKiSsiGKoyjdQ3lahig+gv36bLLy4zI6Tqx8etS+xLHKxwUw0qKP5+mUn5Z53/dbVCYtfW8hTOd94XFGjriNqXGAtxUti7fbsFk+ZkfjN43NAFA7Qwc4xciZI2ElJ64OKz/16NAoOxjG5kZ761KGMJ4B3eWzD+woBQjFs1EgRGk0Il09L4jnLaTAoXqcJiFOyVGurueYz7rl8c86d6CQZiEFbwo+vvk4puUal7uFfTQoP1Cc0jd4M8T/cCEE22AhTOf0C9y+2r7QwPqpOOk2p3voUb3VS8K6WUvfWwwdbFEqkuapsJzaQ6Sl7xfSjzzqyLj40HX7xuXj15IG/3DYPtYs/6wxq0jMUY3NLsL+wt2/d1IFM3vNFrZH7FKS+caEEEIIIYSYMyZehLn7d222gCp1hBBCCCGEELsAfa9eicpiDjP7GgBHeR53/6NtaJMQQgghhBBCzC2VLsLM7I8BXAPgVgAbD746AF2ECSGEEEIIIcQ5UPVO2PUAnuAu56QQQgghhBDibAwSc1SlarLm2wFctp0NEUIIIYQQQojdwGZ5wv4co8cOlwF8zMw+AGAsAHf3Z29v87gtjkZjEOncAWBIVtCkrj7SytPkYbnm1BI69tE6UPpeRvO0SDW61Ap+3mYt6Ei5rX3y3fYHoX/dQdxX7vt6j3StPdK7sgE22afyrygm6v7pvSGr8kmpD5oebbd65McvXbwV2puR5r/G+n/S1Q8XSM28h7YNbTdW+PaWw4uMlPQAcGhfUM0uNMI+65ILvduf4Hk+V2xkhLRheWwWvx4ZZuX7jOdnvbEndNFcTtUp6qyH/LoZGuncJtr3A9JvW5Om87FDXvmsG8ddjZTWvP+4HYM2KeNJP+8cazXWNKOUs9Ti7LvnfcNq/gbFIC/XytdnTVLGN2ONMadfWN8bZlpbY396mJ5R2or1faHG2uVhbOkcXA3ldtiYxbGz+Pp88Qzo5zbooo58XCel946U3PE8vKyUJr7YjvAise8mpRSIUn2UL7ffptgmTTy3ifXgtS7p1flwacQN6e8NGyilmefUClH6BT6L8zaYkAaC+5RarkfrSBxvNB4Y1enuC/Oursdx5vWwQt7/awdpnqvCG61LQzwvtMK5leO3P6j6ffLmuAGDvIlWmL4Ba/qj8bJC+gXusw3iOOB6vI9qnC6Ax3zS0vOyMhr7ahz/9cIBkLhbYZEGv7wcbY8WH+Cs1o8Dz3oJRX0jNJKXNaTprNoHnWP4WOrScTS4PE7CwbHdp2OMtfTdvRznoa1dOvmcupw/X1wayoND0fpOHQkrPPkYWvflIYdOk9IscEqiYX/r4vmi4K5kzRXZ7HHEX5tKK4Q4x3RvQgghhBBCzCqbKerfAwBm9svu/pP8npn9MoD3bGPbhBBCCCGEEDOEfhNWjar3PL+xZNo3b2VDhBBCCCGEEGI3sNlvwv4dgO8HcI2ZfZTeWgbwvu1smBBCCCGEEGLG0J2wSmz2m7D/CeAvAPwXAK+g6Svu/vC2tUoIIYQQQggh5pTNfhN2wsxWAHy5u989pTYJIYQQQgghxNyyabJmdx+a2UfM7FHufs+5rsDM9gH4AwBPxOgG5fcC+ASA1wM4CuAuAM9390cmLSczR7vZi3Tlo/YFrV6jTlrbelB/Nmphei+hgGfFLS+zqG0fkB52SH7qGqmmW9SODunOO/Wg12VYf9qnZfYKivr1ethdzUboX5d09SlVLyv0M/rF5JD6NyjMm7qb7MPyeSKtKiulWY8/4VeIsf6f2kjbxHjbLgTVa/fSML27jz3Loa2tZthmizRvcX1r/eAfHvA+Tmjiz4vMgc4Awy53mt5vxSrzZifETp22Abev3w39HvYSXm5O0UD7y3ppPaU3aP+RkjqjYzE6TvgYGZRPZ51yUZvMr1mt7ZGint5YoHKNvdylxZjiLuVxgLcJ94/jgJX4FEOp9bFeGojV0wvLIW1CtxVi9dRhjmdqRoOPhRAfPDawxnswjA8+Hi8vBK8Bvb2j7c5xFGnQeX8thvVm1IaztMykNrc10vSvh3XU1kK5TlknMjq8oxjKystArAeP4Tgonxwp6llfXqd20/DPqnsA6O4hZfZeqtcJ5WGUtSCRsoKOqxptg9qZYr3ytrOWfkAq+mS6h2H5RrA9obNrnTjO1i5PpEZYCPUaFM98zl/nMY6OyWwLLQDeAM4czpdLMcGK80GH0lRQ/6zBOTVo26yFPmeJWAbifVZfLd+XHEecooTbWqPzSv0M1enF2ym12VhRn1GaBZ7f6ITtpIwf1sp18wBg/YTunsb8QZvGrGb5SBop9HkI5jQt7XiefofSAi2E6XxcpdJf9PeEBZ94bFjOytH0B5roHLUYdg5/HurTGMefq2wrP2tcJCTmqEZVMccRAHeY2c1m9taNv4rz/haAd7j74wH8EwB3YvRo483ufi2AmxE/6ih2IUopIYQQQgghdgub3gnL+Q/ns3Az2wPgawG8CADcvQuga2bPAfD0vNpNAN4N4CfPXoIQQgghhBBiJnAAQ32zXoVKd8LyfGEfx8iKuAzgzo0cYpvwGAAPAPjvZvYPZvYHZrYI4LC7H8uXfQzApZMWIoQQQgghhBDzQqWLMDN7PoAPAPgOAM8H8H4ze16FWesAngzg99z9SQBO4xwePTSzl5jZLWZ2S//katXZhNiRcDwPVk5f7OYIcd5EsXxKsSxmmyieTyuehbhgfMp/M0rV34T9DICvcvcb3P17ADwFwL+vMN+9AO519/fnr/8XRhdl95nZEQDI/99fNrO73+ju17v79fU9nbIqQswMHM+15cWL3RwhzpsolpcUy2K2ieJ5UfEshJgOVS/CMnfnC6WHqszr7l8E8Dkze1w+6RkAPgbgrQBuyKfdAOAtFdshhBBCCCGE2KGYT/dvVqkq5niHmb0TwOvy198J4O0V5/0hAK81syaAzwD4NxhdwL3BzF4M4B6MHnOcSJY5lpvrGBaUpRndh2Qt/Z5GcLp26kETn5GDtEvO2e6ANO/k4F3tsxcYONVthfJ6c1xmjTqb/lg5z0rdJmnz66Tg5fr9LFb7plT7/UZw1rKGuqik3iClqO8X6rOOP6XtZyX+gNS0ke7eyzWzjBlgtB0yUiJnWbk3lpXxS21y+LbXS2qn5z37PUo3UCtPdXChWM3R2rMebUvW5LcasS97sRVieKkZ+sf7klMu9DjtAU0/Q+kMVs+EWO6thjj3XhwHRvvCSAFfb5KambanR4p6igleLunIPSukgWBF/SARO9xETr9QJ002t5v18RPCcUDtGpI/3vvlqn1EoUntpnWn/fjx8VejNrbb4ZjOFro4FzimODVFqx4vp15L+JjPlbrDD3bzdYfJHBNNipWFdmjHAsW5Fc6kHM+chmOdyt3TIW57J0K5eTzsxzo/yU6r8IIpfUBD/TBR5iHZ+uXTBxRgvI60Aj9WY1timHHWxDcTynjWhlM+kKyQHaXGOn96+i6jEBm0WBtOQbxIenbeZTzO077MmoUOtcrHjfgYJY03xQGPdxy/W5VuARil5OgfzjdEIjUF96lDx+pCK5S5D5wqIkopU0jLsH6G45lS0jxC8Uz7K4oVHmdojKp1OY1DfIzVaH/X1ql/UbxQG3m/9lnHT1Wo+qBVSHvTKR8MPZqHPl+0OKaqtJvGnJV4HfHxR7Hdoqk0znPZKRWJk3q+36GYLX7c4vGMPw4lVPQ1Sv9S28J4FjubShdh7v7jZvbtAJ6GUfTe6O5vrjjvrQCuL3nrGVUbKeYfS1xoCSGEEEIIMW9UvRMGd38jgDduY1uEEEIIIYQQs4ySv1aiqh3x28zsU2Z2wsxOmtmKmZ3c7sYJIYQQQgghxPlgZleZ2V+b2Z1mdoeZvTSf/gtm9nkzuzX/exbN81Nm9mkz+4SZ/YvtalvVO2G/AuBfuvud29UQIYQQQgghxGyzw2QZfQA/5u4fNrNlAB8ys3fl7/2Gu/8aVzazJwB4AYAvA3A5gL8ys8e6+5b/WK+qHfE+XYAJIYQQQgghZgV3P+buH87LKwDuBHDFhFmeA+BP3H3d3T8L4NMYpebacqreCbvFzF4P4M8AjNVs7v6m7WiUEEIIIYQQYsa4OAmULzGzW+j1je5+Y7GSmR0F8CQA78dINviDZvY9AG7B6G7ZIxhdoP09zXYvJl+0nTdVL8L2AFgF8Eya5gCmdhGWmWOh0UPdYoseK56HpB1ltXyXdN1Ncgk3ST/OGvsG1dlXUOKfarTH5eNUXh+Ub8pIIU7t4Ha36+XtKNJn7biX38Tk5fL6WPfMdVhlXiso6lP6dFbfxxrpUE4qy4dpXzdr6VnRzvrhosY6TA9lVslz/WzC/fEaxRWnDIjKtnUGx8yGWGx3o/Vy/HpB67/eD/HF25/7ynG7UA+O4WYz+KiHC2G5K+3g5j3ZDrG8th6nZeB9xspzVgbzdq6xJp7246AZ2t1bD/0ZFjTNkUacUyOwrp6bwfFFKmgMqE0NSn/QCNtpUkx4FKu87kSbhon2ceqFCakR+Lg0Z/Vxubqb4fiPNN607lrBQFp8fb5Y5mgtjOItlU6C92Ok6Kbxo9geVpAvNEM8czqKfifEzonFhXF5neK5f4LWsb55ugwgVsbzvmTNdUaZMFiZzfOyrp514kVlPL/Ham1e7oDORf1FLtNYy6chDsfCITZkdT63i9XfvG4qd8FpHHgdtKEoXQmr9YFCGolU6ohEPNdq6XjeKixzNBZ7Z7UvUsDTZE7HcnotpK1J6fRbFMuLC3EfBoshqE4thPF5rRXiuR7FMzUpEdocj6yrB4DamVBunKIUOqdoO9M6hg2KZzoPZV1ayRZ++E6ecml8pExDMAqirB83pHWCxmFOl+JZWTEKyCFp7Dktg9G6vTA2p1KhRJ9JOI2KDNEXyoPuXmZiH2NmSxgJBl/m7ifN7PcA/CJGUfuLAH4dwPeiPLHMtlxWVlXU/5tJ75vZT7n7f9maJondSFbblvgWQgghhBBTwhB/kbgTMLMGRhdgr914is/d76P3fx/A2/KX9wK4ima/EsAXtqNdVX8TthmbJlsWQgghhBBCiGlho1ukrwZwp7u/iqYfoWr/CsDtefmtAF5gZi0zuxrAtQA+sB1tq5wnbBOqPeMhhBBCCCGEENPhaQC+G8BtZnZrPu2nAbzQzK7D6FHDuwD8WwBw9zvM7A0APoaRWfEHtsOMCGzdRdjOuu8ohBBCCCGEmD476Cdu7v5elN8sevuEeX4JwC9tW6NytupxRN0JE0IIIYQQQogKbNWdsD/douUIIYQQQgghZpSdJubYqVS6CDOzXwHwnwCcAfAOAP8EI8XjawDA3f/ztrWQyOBnKaVZwZzSTWdgZfO5Pda5UOslX7epfLIXFLKnekFTywr41V65vrYzDP7fS9rBgbxY0NUPyEHLunqG+8d1uuRxXSOdPqv8e4PyZQJpHfAazZ9ZKPdJ28t6atZwF4/RSONL2v5mBUU96+NZ255SzBdjJRk7rBneQkW92ahtvHxOn5DqZ7FNtQl9KqND6nous9L+eH0hmmflTIjtXpdU+ZHmNxRrrdCPzkKI4XYzlNdpX/d6cdylUiNEcUTxNSRNfEpj77TrLKG8LswCy8r7x3Eb7Sd2REfq7fR+idpCscppGZoUz7WErj5LrC8VK0B8bFwIBj9rO47aurmKfFL7UopnZqkV/NmLFF8Pkwb8ZKszLg9PhDGqdiZ+EIRV3HEghOKgTeUwnGNI82apLCO8ukJIsKI+KvNxxaeiVVpUxipt2vek0y/inMmBFd+8C9g6Tm2PVfu07jpVovhlJT0A1DlFRFZ+LNYSaR1ScV6rMPZVxoAsbwuHYBale0iliqC2Jto9iYUGfb7YG3bgCdpmq40wPg9PhJ1XP016dooVS+xHAOgthfKwwWVKg3Ka+pH4+MS6eqPUIDbh41Zq6BzWeBylIscdxTb3aUhpHLzw6dZ75QMKp2KohWwu0bJ424DT79Spr/V4H0epSbheIrY5Fcl2pV8QO4+qjyM+091PAvhWjNSNjwXw49vWKrHrqJd8iBNCCCGEEDOEX4S/GaXqRdjG9wDPAvA6d394m9ojhBBCCCGEEHNN1d+E/bmZfRyjxxG/38wOAVjbZB4hhBBCCCGEEAUqXYS5+yvM7JcBnHT3gZmtAnjO9jZNCCGEEEIIMTv42T/6F6VUehzRzDoAfgDA7+WTLgdw/XY1SgghhBBCCCHmlaq/CfvvALoAviZ/fS9GtkQhhBBCCCGEADCyX07zb1ap+puwa9z9O83shQDg7mfMqgiEATO7C8AKgAGAvrtfb2YHALwewFEAdwF4vrs/MnE58E2VyqyiZyVsRo5W1oA3zlFXDwAN8qQuN8LP4lKq8S5p4vukcR2SezWlTl/IYj0+6+fXSTnPKnruayOxXK7D27RbUNR3Exp8pk1qc9aqsoo+paFu12OH8iSFfBnDhPo+2hek926S17ZZ2Pe8TVh3HMfUFirq4ZFKH4j1xkUdfkpZy/07V4U+p1iot9Lbno/0UxmlX6iFGGRlPKumuU+cdoDLqzX2/wK9fnnccTsGrKj3xFCUCKFIdT+M5+W+N1r90nniNtE+o37Xa5xuINQvrq/Ksnh+Xi6PXxwHWSJ+i+PnlinqrTw+Y/1+QqGP8j4DcaqJ5D4mWO99eHmldLnH60FX318hxzyAISnrjYYmPhyGfMashTcGpL+ur1KKBR7iLFEGIh08v8cq+eQmYI13nxTdfLy0CvNw1zk+qX+sux/S+OBNzvdARdoeRlr6rGi+5RQFFDetRvn4kBr7UmlJLhSDl9p6z1Wb30ikaEilbCkSnVuXype1mgVdfT8L42hKV19cHcfXkGJkyOkXGhTbZ8L06HRDxwifWotK/AEr5GndrJyPjz3abtSOfrt8u6V09UB8DPQ75dM5/lk/7xTbZx27oVb0KmtQfFKKgVQqhuj8IUX9rqHqnbCumS0gjzIzuwbA+uRZIr7O3a9z941HGF8B4GZ3vxbAzflrsYvZyhxcQgghhBDiIuE+3b8ZpepF2M9jlKT5KjN7LUYXTj9xAet9DoCb8vJNAJ57AcsSQgghhBBCiJmhqh3xXWb2YQBPxehm7Evd/cGK63AAf2mje+j/zd1vBHDY3Y/lyz5mZpeeR9uFEEIIIYQQOwUvPK4qklS1IxqAbwbwle7+NgAdM3tKxXU8zd2fnM//A2b2tVUbZ2YvMbNbzOyW7vEzm88gxA6G47l3QvEsZheO5f6J1YvdHCEuCI7nwUnFsxBiOlR9HPF3AXw1gBfmr1cA/NcqM7r7F/L/9wN4M4CnALjPzI4AQP7//sS8N7r79e5+fXPfQlkVIWYGjufGXsWzmF04lut7O5vPIMQOhuO5tkfxLISYDlUvwv6pu/8AgDUAyE2GzcmzAGa2aGbLG2UAzwRwO4C3Arghr3YDgLecY7uFEEIIIYQQOw2JOSpRVVHfM7Magh3xEGKpborDAN6c2+zrAP6nu7/DzD4I4A1m9mIA9wD4js0WVDPHUmM9UhoXSWnYa5H6mxTPkRK92gOsvI4OuV+XaiSLLOqAc4Z0zbs+CJv+ZL89LvfJsdor+F33ZOERtk7WpXqbq+RZab8+LN/t/cJyTvfDdXY3MQ/Dul3W8af0/Z166AMAtMhTywr/QULjy2p+3m7x+sIyl2phfa1aUY+/ueK4Vinkq1Ezx1JzPakTLxLHdrl2vH4e8bwBp1vgMgAcaIXHc071QnCv9oMSeY3Kp9ZD3AwpDvqklV9o0rHTivu9ViuPNd4+Pdr3PP42E2r4fqIdReoJvf6A00uQZp7rsyKd9dIMLweI45ZhjTVrjBdouUuNEM8c51VNo1XSQFTBzNHO9eIpXXdqXdy3WqHdUWwnlM2ptCQdiq89zTA2P9QKsX1/Zyla1tqZELeDNYpBUtdnpIAfksIa7dC+QZvSNayz/51WVhjSWC3P7w0WSA1PmnhQDGbrpNZP6cGzwvb3RLtYu79I6S8WSRlPum1PpFywhMIdiBXdbdpPi80QzxznxXQi4zaxMn4LEwVl5ujkbUmmVkjEedQ+Kx9LmFTqi+I8HTrW23vDtnmkGfbL8Wa4g9fthPHYKD6ytUJKDk5pwPuePsOwVr5xiuelBUWK+vI0CQDQX6QyPQzCy2qs8HQ63linT4cnDxtcHhZuE/SWqbwnkXKB45l3GW0bT6RiqLfjzxSdhbDPeDyqV0gnkkrLIOaPqhdhv43Ro4SXmtkvAXgegJ/dbCZ3/wyAf1Iy/SEAzziHdoo5p5X1N68khBBCCCF2NrN7c2qqbHoRZmYZgM9ipKR/Bkbf0z3X3e/c5rYJIYQQQgghxNyx6UWYuw/N7Nfd/asBfHwKbRJCCCGEEELMIDbDv9OaJlXFHH9pZt+eq+qFEEIIIYQQQpwnVX8T9qMAFgH0zWwNo0cS3d33bFvLhBBCCCGEELOF7oRVotJFmLsvb15LCCGEEEIIIcRmVLoIM7Mnl0w+AeBud5+K1i6zIZbraxNVtA3Wz0fq7s3148wkFXlKB59qF+vghxN0tBuwAnxt0Ije43UcbJwel5eyoGBmzfOAHMVsH0z1odi+5fpa6XtDWm5Kfc/1ud0N0g03C0ZEbmMtUreWq4h5Hbxu3vcLpPJvU0qBc1W4A0BtC3U/mTmWGt2kYrte0DKz7riRUDanVP68LQeJ/cjtKK67l5WnAhgmNOr80PJ6t15aZsU8q92BWDPPRKkHUF6Ht02DlhPHb3lsArFCnvvK02uJtACRor5WroyfNH7x+lipzyruTiLNAo9xVXXdWxXPmXmk0N7AEqkUWDmeUs+XraNsfj6OOWUFxzBv/1P14K0u6sGH/c2fzrcuxc6A9dmkvG6HdQ/qtMz+BB35kLTXtFucNfis0iZ1+nCBlOCDcsW2FVXyvA5qojUotluhH602pUNo8HmWtnOt2r7k43KR4qZDWnreZ8XxaINJKT0uBDNHK0+5kNLMp9InpNJDpFJRFPvAqVlSYwDH/+lu+LwQxfOg2i9HOLOOZ7TuTmhXf5H17DTm8yHPoTnh41akvm/TOvgwoVzZnLohGtYSu57V9cNmYdu2+Bil/Uoxn5Fy3oppHTam81hEKQIWW/EYuNQKG7dd3zyFSCoNjZhvqj6O+LsAngzgtvz1lwP4CICDZvZ97v6X29E4sXuQol4IIYQQYsZxVMskLCqLOe4C8CR3/0p3/0oA1wG4HcA3APiV7WmaEEIIIYQQQswfVe+EPd7d79h44e4fM7MnuftnJEwUQgghhBBCGFyK+opUvQj7hJn9HoA/yV9/J4BPmlkLQC89mxBCCCGEEEIIpupF2IsAfD+Al2Gkp38vgJdjdAH2ddvRMCGEEEIIIcSMoTthlaiqqD9jZr8L4G3u/onC26e2vllCCCGEEEIIMZ9UVdQ/G8CvAmgCuNrMrgPwH9392dvYtoiGDXBZ62SkXQdiJTvD6tdapIgurz9MKGSL8PpZJ8vTWQHPOmyezjr9A82gm2dL4EqffK4AVnrtcblTC0+BLjdO0vxheqpPA7DaPWjwi9uW+8es0Tz1YejHgpM+m/rRSKQIKKrWue1tC/PzPuP0AQOwLj2UG1a+7pS+v8gg4auZlLrgXKlnQxyk/Q7E/SwqamuRyr68Hqvoe8PN4zlS1E9Y9xlr0DwhJrqDMHywtn1Pe610+ukzIZ7PrIdlFte3t31mXGa177mS0v+n0hwAaZU0K+c79RDnC3QccsqFVqI8CU6zkNKtdyjlQkpjnEqFcVZMnUeahjJqNsRyc/2s6azVjrb/eaRlYHh7ctxWGcNZ722TNNCkqsYSjRu1sI9YV2890sTzWXWB+kSL9IIOP2pJol388+tIn031jbKPeFFLT9RIP7/YCfuu06QxmJTxrJJP6bar7gs+ThbrYd2NCvE4SJyTtjJ9SM183N9U3KZim0mlppiUIoPpJ1KIROvgdB4cEol0CMNWvJz+oFw5X1snXf0CpTghdf0wZHtARvHPR/FZQxHFLYeIN0hX307Ef0K776Sez5ZoPG7F426jPigtc0oU3p6TzhMb8HGxpxGPgU06Z6TGvKpj9UyiO2GVqGpH/HkATwFwHADc/VYAR7elRWJXwhdgQgghhBBCzDNVL8L67n5iW1sihBBCCCGEELuAqmKO283sXwOomdm1AH4YwN9uX7OEEEIIIYQQM4WSNVem6p2wHwLwZQDWAbwOwEmMTIlCCCGEEEIIIc6BqnbEVQA/k/8JIYQQQgghxFkoWXM1Jl6EmdmfA2nt0DTtiEIIIYQQQggxD2x2J+zX8v/fBuAyAK/JX78QwF1VV2JmNQC3APi8u3+rmR0A8HqMDIt3AXi+uz8ysaE2xCX1lYnrqaL+TNEjrzBrcIuqXdbw9kgp3aP1scp8f2OV6gd/8LoHRfcCaaf310P9B7rL0bof7nXG5VP94Ie9okm6+1pI2zas8LTp6WHQhrNCH4j7vjIIenzu39560InvrYUy2w6bVq7oLmp3F7OgeE2lEuB1s5J9UGF/p9TzQNzXLqcYoHkmae3PlZoNsaexhjop9FM6/Umk9P+8LznuYq0/xfmEbbOnHpTzw4VQ70Fq40ovxBG3/cBCiGfe3yfPhHgqHqt7SHe+r3mmtF4qfUJUJ7G/usNa6XQA2EPK7P2UQoC3QYdczo3E/utk66XTJ5HaN9Xqb56OYtJYdiFk5ljK9cyRcp6Wz+kokrrts1JWnFt6gir7/kArxOPq3kb03jEqr6+Rf5t10aR2H5yh0+dqiCkjNXytFcbBGmnvB704BvlLY9bPpzTzrKt3Vt/zeSihoQeAA52wHfZzSghKucDlJYr5Vq18v/BYNimdR5xW49xiMDWWbSWZOTq5np9jmOMzlT6B4fqs3+exq1cYi/iY5DQVPA8fPwfbYT+e2LMwLq9QbGYUm54VFPXLoY3D9bA962co/QIdloM29TsM4Rj2Evr4bMLdkDq91yivx7GNGsXNQojB5aUwNu+jWG4V0puk0ow0s/LxOUqNk1DGNyek4kmlgEnVSaVfmFl0J6wSE0cxd3+Pu78HwJPc/Tvd/c/zv38N4J+dw3peCuBOev0KADe7+7UAbs5fi10MX4AJIYQQQggxz1T9KumQmT1m44WZXQ3gUJUZzexKAN8C4A9o8nMA3JSXbwLw3IrtEEIIIYQQQoiZpqqi/kcAvNvMPpO/PgrgJRXn/U0APwGAn6077O7HAMDdj5nZpRWXJYQQQgghhNiRuB5HrEhVO+I78vxgj88nfdzdN31+zMy+FcD97v4hM3v6uTbOzF6C/GJv/+WtTWoLsbPheF4+0tmkthA7F47lzmVLF7k1QlwYHM8LhxXPQojpMPFxRDN78kbZ3dfd/SP533pZnRKeBuDZZnYXgD8B8PVm9hoA95nZkXz+IwDuL5vZ3W909+vd/fql/c2yKkLMDBzPnf36UkHMLhzLrX3tzWcQYgcTx/PC5jMIIdI4RnfCpvk3o2z2m7D/bmb7zexA6g/Aq1Mzu/tPufuV7n4UwAsA/B93/y4AbwVwQ17tBgBv2YK+CCGEEEIIIcSOZ7PHEfcC+BAw0Z35wHms95UA3mBmLwZwD4Dv2GyGug1woH76LBV5FQ07w5px1nhnCQXpJG17jfSrkRKcynszVqYG5e/qMNzZq5FyltXu68N495waNKkeKVpJDX9p/eS4zApfVtFzHxq1oFUt9nVt2Ch9r0HrPlALGu9DtO429YNpIK1hryXU1axurVInms77G7XS6QCiCG94uQaZ0xhcKHUb4NLGyeT7RaVtSnOeUtw2nFTaWbm6O3UsnBnGd5051q5sh0wSi6Rz//zqvnG5PwzLbZMmmKezmnmxGXTBAHCgFWLqcCukpehTG9cHm+8L3h6sez49SN9Vv6QZUjxc0To+Lu+tBRV0rKUP/WONcSORlmESrPVO6efjeJwQzxvzThi6tyrlQt2G2N9cnVgnGuNIFV2vqO/nbZMaq1lRz9uGx9F9lDKksRyvm5f7hRN7x+XBgMbLephnLRr/aTvXQltbpKhvN0O/e414rB0kVPSeSFXA0/v98v24tBiOzyuWT0TvHemEsWexFurxsc7lDtVJjcF8XGxV+gOgkG6ANhvvYz5XXSgb6UOAdD9YOX+uqRSY5oR5h/Re38vTahyilDQ4EIqfpJg98wA9+l6IM28MS98a0FjtrI9nRT3FuTcTY0mtECt0jGWUsiGjZQ2p7ZyiodEO2+PgcjhHXL4UYpnHoeK25fhcoPQLqXQK55pKoTieptKMnOv4NbNs3RAw10z8JJPfwdoS3P3dAN6dlx8C8IytWraYfVIndiGEEEIIIeaNrftqXwghhBBCCLGrsRn+ndY02Z6U80IIIYQQQgghStFFmBBCCCGEEEJMkUoXYTbiu8zs5/LXjzKzp2xv04QQQgghhBAzhRT1lah6J+x3AXw1gBfmr1cA/NdtaZEQQgghhBBCzDFVxRz/1N2fbGb/AADu/oiZTTV7cg1D7KudnqhVZl19lxSfkeKWrKFRnYT6uQiL11vGSumw4FP9kLyUl7Vka+Pyl7TuG5evqD9C9cMuuQ1XRutmPfXeelCxsj57HynxWcm6YqFNpz3sOt6eax5rfvk9XkcnC7rig/WgyF20oBrPEn7S5gQldRUVfZV5I1i7y0p2Kyii6b0m6cVjJXg1nXYV6hjiAG07AMjOwxDJCvJBBZX5OqmcWb9c87RPNk5PELbBZU1SA1M8srKZ572nHhzKrK7f0wjxBACHSBN/oB5UxNze9UT6BIa11af7IUUDt29P/Uw0zyWNsG6OeU650KY4T6UOSFFFdTwJ3sc8Zg0ivXG1YX2wRU+j12yIPfXR2JZSyXO/Oc4bFfXePB7wsZ5KucCpMBjeZjxWAkBzT2jLJe0Qd5zeYI1SI3zx1PK4fIJ09bUs9HWBFPVNSgfSqsf9ZuU8q6r5C15O69Dr0zFJ2vzFVojNQx1Kt9A5Hq3vYCP0b6kezktx+gVKJ5LarzQ9FdvFce1cj4EqqRha2dYp6jPzcfqNaHtkHMObb48UcfqJ+LyVGsuGXj6mMkcWQhqC5uHQ7mPLIU7P9OLttNYNr1dPhTGy3whxbqSxr5EmPsvKz1dG28MKdfjYMOo6xzlPbzfD8bOvHY7XyzohdQmnFdlDsZxKkzOJKqkVeCyaVJ/HJq43rJAaZCtTLlwUHMBwdu9OTZOqZ+GemdUw2rQws0NQFgCxhSQvooQQQgghhJgzqt4J+20AbwZwqZn9EoDnAfjZbWuVEEIIIYQQYsaY7d9pTZNKF2Hu/loz+xBGCZYNwHPd/c5tbZkQQgghhBBCzCETL8LM7AC9vB/A6/g9d394uxomhBBCCCGEmDF0J6wSm90J+xBGvwMzAI8C8Ehe3gfgHgBXb2fjhBBCCCGEEGLemCjmcPer3f0xAN4J4F+6+yXufhDAtwJ40zQaKIQQQgghhBDzRFUxx1e5+/dtvHD3vzCzX9ymNpWSmaNtPRQleillbcPKtbasKM5Iy53SOp+l1wVr6cOy2qQ1H9aCJphVo4/0F8flg7WgVb28FtSrTfKznvb7S9sEAMs1UrFaULGydr9DitZIXe/lWujiNuDlMvuyoO5ezLqldVKK+QvR0J8PXYTtwesrqoQjpTvNw6rrlPb6fKjZEMvZWqyJnyAcjbXG5Sr6lKY80khn5TE/oLgrand7w1BvmLBY7ifl9eE6qZJJ8cwxe6C5d1zuFGJoL2njWxTDrIvmedJa57BtVmshLQMv55L6SjTPcnQshnrtKP1CuXo9xfnEOSuwo9Qb4LQalJ7ASM9OqRSGE75nG/jWGElrNoxSZozbESnOy7fB+cQ/00ukIknZVnk8Lq6vVQ+xxqprrndqEDTerJx/oLm5Dpt15JzqAYg154Nh6Afrvhk+py02QmxeRSr6w62QQoKPPSBOM5JKsxDtP9pPKXU9c6HjWqodHLMc25NSbJwrmTmWamWK+lBOpRNJpVxIpVIojl187KZivkF95e1Ro1hZqIW0N3ubYUw7M4jV5ye7IXXNfa2gsj+9HsbLWCsf1jGkOM2oDh8XRZqUToGXy8fGAh2Hl3dCDHM8d+gzFp8LJqUMqRKTWeJ4Y1LzFvclj8nR50bbPOa3KHvIxUWPI1ai6kXYg2b2swBeg9Hjid8F4KFta5UQQgghhBBCzClVL8JeCODnMdLUA8Df5NOEEEIIIYQQQsmaz4GqivqHAbx0m9sihBBCCCGEEHNPpYswM/tr4OyHoN3967e8RUIIIYQQQogZxIEt/J3mPFP1ccSXU7kN4NsBlP8CVQghhBBCCCFEkqqPI36oMOl9ZvaebWhPkgyO5WwdK8MWFslUdtLbY4vfmjfGRrOe18b1Tnobi7kNamW4MDaddX1hPG/P6+PpPZp+2ltok6GtN6iNX58etiLrzYbJbXXYHBt71oYNdHLT0tCzyNCzMgxmohO5ge5QbYC9WTAYHa0Hy9Fn1i+NLFdsUFsk2xVb3dg61MntTqcHDRzMRst5aNgel784WMLl9WBtPNlt42hjlI/7rt4BXFY/mfepjn15O3qeYTk3Qq0M61jMLZFrXkM7b8ea18brLtbZmL46DOWN1zw/lxv5Nux5liyPLUcezJWr3hjbkwZeH9cfvc6i2OGY2rAiDpBVsuFVoW4DHKqfxEODpbFtcmUY4vT0MI6708NWiOHBQhSDG3F72lvUhzoaeb97HmKW47c3qEcx24j6H75j6aE+juFTg/Z4nt6wHrWRDXgcg5fltsS7u5fgce1jAIBj3b24ojWK7Ud6i7ikEeKu5zUcaYzee3iwhEO5wfD4oIN9tdVx+bL6cQDA53oHcVXjobPKd3cP4TGtkWH09jNX4rH5uj+7fimuzqevDBZwRSPknD85XMBltVF7Hx4u4UA2ateqN7GcHyenhy3syy2KK8MWlsdjS1zeMJKuDNvj8YDHhuLr1SGvo4l2flxxne5gAXvyOseHC+jk+36FxsHT3orGu46t531oRZbH4th2IWzsowf6y9H+2ujP6rA1Nk+uDBawtxZiPtRJb5uzjo3xuF0b1+HY5HheR2M8fW3YGI+hq4NmZFnjsflEvxPVW8ptiacGLVzVHsXLvWf24+rFUaw9cGYRB9ujPj201sHe5qj+iW57PP2BM4vY3xptg1O9Jg60g7Xu+Hobj1oaxfxnTx7EY/c+AAD41MlLcO2eB8flJ+77IgDgI49cjifuz+P51EF82d7R9JO9Nr50cTT94f4ivqR9HwDgvt7e8TEFACcGnWg/HcjH/Yf7SziUj/PHB4s4mE8/PuhE0/fVRjbUlcFCtF83pp8cLpSOaxv7b+P1yeEC9uTHyfHB4tjauDpsjcevgTejMa493setaMxq5TGx7rEB8Hw40hyNAQ/2lsZj04nBAvaO+9oex8fKoD22gxaP53DuaYbYHLSi8ZjP6Sf6C+NYO9HvRMva6Gvfa1gYx3xrXGfdG9H0DcvsSr+NS5qjPnzuzP5xGcjP8YujeL7v9DKu3jsq337/ZTi4NNqXx1cXcKAz6t8jZxZw6dJo/i+e3IP9i6PpJ860x3VOrbdw5Z7R9rv35F48Zl8YX5tkj7z71H48fu9oHP7Y8cPjeP7HlUvwhDyej/c6ePzSaPojvUVc0x7VP9bbhyubo+U+3F8aW25PFGL5AH2eOT7o4EDt1LjexhjE5ZXhApbH43Z5eXXYiuI0tS/j8a8ZnbP5XM7LatGyNsrrwwuPZ7FzMa+gkTSzA/QyA/CVAH7b3R+3yXxtjCQeLYwu+P6Xu/98vrzXAzgK4C4Az3f3R1LLAYAv/YqW/9HbjkQXFUCsZE/pmFlXzBrRLim6eXqk7i4sk+ux7pjr8UGzllDfHyI19le17xqXv6QRlnN3P77Z+MG1R4/LfAF2BZ1c+QKVP0jzBU7q95JZweq8RvpU7gcvt22smd18uVynuD4mtayUerpKnV5C7T5aH+m+Wf3NOmHSgz/j6k9+yN2v37QxCR73FW3/vbc+upA+gduQdtRG8ZnQ0qeOiyrxX9Tmpo4fvuhiNfDlFI+Xka7++LAzLn9q/XDpcgDgknpQEdcSymC+mEjB83YTGvs9lLoBwPii5uxlsaK7ipa+PJXFhc7Dccp94thkUvFVXMe3POaO847no09c9p9903UT183bL6X3nsQwcUwOErGdKieV0IgvwrheltCzf7G7b1z+x1OXjMt9UnfXScPN04sa7z0NSiGSiK8WfYDlY7JTCxfSVzTDsccfQLOCVpu/JEmpu/n4adjmD77wvLyPU2ktiqTGIybe3+XjIAB8z2Pff97xfNUT9/iP/ulTK9cvpvTYIJUyYVIMRmPtcPPtweMB69X5eOPPKUVFPbfl/vWgqD+2Gsq8LxsUt+sDSoNC69vXChcizUIqBr4IW6C45W3IcX5ZK5w/DtRCGpTUeDLpS9LiMbAVTE4Bkh57y+qkxjIAeNmX3nxBnzemzd7WYf+aI/96qut8x92/OVPbaIOqjyN+CKPfhBlGjyF+FsCLK8y3DuDr3f2UmTUAvNfM/gLAtwG42d1faWavAPAKAD95zq0XQgghhBBCiBmj6kXYl7p7lO3RzFqpyhv46DbbxtdxjfzPATwHwNPz6TcBeDd0ESaEEEIIIcTsIkV9Zarm5f7bkml/V2VGM6uZ2a0A7gfwLnd/P4DD7n4MAPL/lybmfYmZ3WJmtxx/OJ0JXYhZIIrnhxTPYnbhWF55ZGt+VybExYLj+fTDimchxHSYeCfMzC4DcAWABTN7EjB+sHUPgE5yRsLdBwCuM7N9AN5sZk+s2jh3vxHAjcDoN2FV5xNiJ8Lx/LivaCuexczCsXz0icuKZTHTcDxf9cQ9imchLpQKvgmx+eOI/wLAiwBcCeBVNH0FwE+fy4rc/biZvRvANwG4z8yOuPsxMzuC0V0yIYQQQgghhJh7Jl6EuftNAG4ys2939zee68LN7BCAXn4BtgDgGwD8MoC3ArgBwCvz/2/ZdFlw1DBEzWKLTKTSZtNMZCCiMluhEladNnpjo1ADwJo3S9sUmXgi6x8vK7SPDVPcvi8OlqkMPDk3Ah2qAaeHYVlszLmvF4SVbLpq53ru0fqoHZHpKjb1pJxBG2r2Ik2UL5cFQIPElyC1wgOwqXq8LG5f6vlZfvw4ZUfk7dHAMIoXGJW5TYl9fKEYRtuXbWuTTEuR3Y+6lyXmyRJ2LK4fm9CG4biyQWwhZcMntZethq2E6pyV4xz/hzon8eEzR8evH+yHY2BDeQ0Ah2rBIrpMJsNlUl5zP9JWtTgm2KzG7Vqm9nL/eH6eXjQ7lq/v3B89HYBNfWQGtHIbWtPDOjhWGjaI4orblW3RN5WZDcf7uXYeBrIq9sjIAsqnLepCZCCjIu+jokWStwcfi40obsP0jRQJAMaafAC4by3E7/1rS6FOLcRWvTbEUiP8tPoQqcI30jUAcbqRlGEysrbS/t5L7QMwVswD8Xg+TPQ7Fc/nY5ZLtTcZj4nzcbQNomOP2mTV4qgKGXysDa9i9UyZO6Pz4QQbL49FGY8zGZ2zE2lZ2aCY2qdsNGSFOhCP24v1EHdrZD482Q0//+/UQ/0DLRqPKa6PkNEQwFjff1a7Eql0eHtwPO+L7Ig0NhfMmPxeKm4nnWs3o7g+3reREdOy0nl43ZF5ko7PZgUb6Y5Hd8IqsdnjiN/l7q8BcNTMfrT4vru/qmQ25ghGF3E1jD4/v8Hd32ZmfwfgDWb2YgD3APiOqg3eqoF2EmuRYr78Amy7eDINYHwBtl1czJzmyQuwKdCboAnejcQnkmpK6QshdQE2DVIXYPNI6gPvTmEa43l63dPdHqkLsO0idQE2DVIXUdvFxYyjcyV1wTGP8AXYNKhyAbaVpC7AhKjKZiPAYv5/qeS9TT9Cu/tHATypZPpDAJ6xaeuEEEIIIYQQYs7Y7HHE/5YX/8rd38fvmdnTtq1VQgghhBBCiBnD9ThiRareP/1/K04TQgghhBBCCDGBzX4T9tUAvgbAocJvwvYAKP8FvBBCCCGEEGL34QCm4DSoipldBeCPAFyGkQrhRnf/LXr/5QB+FcAhd38wn/ZTAF6MkVnrh939ndvRts1+E9bE6PdgdQD86/mTAJ63HQ0SQgghhBBCiC2gD+DH3P3DZrYM4ENm9i53/1h+gfaNGEkCAQBm9gQALwDwZQAuB/BXZvbYPO/xlrLZb8LeA+A9ZvY/3P3urV75uWAY6WlrBcvTIKEljogeTWVbWDls2Fm09Uj5zKrRSA9ONqhFK1cMs5K1TWrY08OggP1cn28w1nCA6j268eC4/AAZ5T6xdqS0Hwdbnw/rBiueWfMbbzR+PrVNOuwatZ2tho3ERq9VEI9NqjOgNg7P8dFiVuhzfMQ66mHBAsi643JdPW+DC8XgUWqBjRYyHHc1TisQtYlU5rz3PEw/ez1nz8tG0GIaiJaV6+dZEc2xzfOuDoNd9LJasLV9efveaFnvX71mXP702uHwRjsUL28fH5f3WFCId6OUFaxALo+hSVa8tm3+7V21Z7hp3YV3eP2pdkUqbk4DEem6aZsn1PWjdZQfA1slB6zBx+kDaontl1SUTzBxRuY4PiYTum5ed5fm5ejtFGyYHKupeI7Sl1DcPYHikcfzvz8ZYvm+M2Gc3t8Myzne6+C65fE5H1c0gqKetwm3g7XVg2gbZqV1OtTW0Xvl40CjwrhWJRVDylDYKMQEx2M8xlHKhURss45/YNtjo6vbcJyKIEvEc6QiT8Qzx3nUbsSfCZJmWiryscDbnFMgxONKettwn5azYOy8vHN8XD5QDzr421auGJcfXu+My51W+Jzz+MVj4/LheqyoZ/g44RQPvK04JjhmO/S5KjXOFOM0Naam6kTTozGrfL8UYz7aT4l9OYjiYo7tmDvoN2HufgzAsby8YmZ3ArgCwMcA/AaAn0CcKus5AP7E3dcBfNbMPg3gKQD+bqvbVjUCVs3sVzG6Khx/LHL3r9/qBu00uom8Q9PgQCL30naxmwSr09CwXwgXM+6mrXvmC7B5Z9pa9OQFmLioWnO+AJsGqQswcXGZdgymLirnkWmPd3N9QTUbXGJmt9DrG939xmIlMzuKkbX9/Wb2bACfd/ePWJw/9woAf0+v782nbTlVo+a1AF4P4FsBfB9GCZYf2I4GCSGEEEIIIWaU6d8Je9Ddr59UwcyWALwRwMsweqTiZwA8s6xqybRt6VDVr2EOuvurAfTc/T3u/r0AnrodDRJCCCGEEEKIrcDMGhhdgL3W3d8E4BoAVwP4iJndBeBKAB82s8swuvN1Fc1+JYAvbEe7ql6EbTwXd8zMvsXMnpQ3SgghhBBCCCF2HDZ61vDVAO5091cBgLvf5u6XuvtRdz+K0YXXk939iwDeCuAFZtYys6sBXAvgA9vRtqqPI/4nM9sL4Mcwyg+2B6PbeUIIIYQQQggBwM/dqLa9PA3AdwO4zcxuzaf9tLu/vayyu99hZm/ASNzRB/AD22FGBCpehLn72/LiCQBfBwBm9rLtaJAQQgghhBBCXCju/l5s4gPO74bx618C8Evb2CwA1e+ElfGjAH5zi9pRiQ31cmz8Kn+iknWkaRU96b2jZbKSeIg1Z303q6fJNMTqaFou673ZULVcO0PrCHXWyLDzhUEdl1G9axrHx+UHBg+Nyx88dfW4/JnuoXH52uZ94/JhanfPg9Z2rfDjSd6aTbLFtFkHzLpVT6u4N2Mw4XeOrMHnNmU0T6+C/Silpa2Zoxfpvrkea9+3x1KYwdG2Prq0/F4ihgAUtPQc27wNWNtefmgPEhp71lQ3MIiUvFmkSx/SdErLkAV9cKyrZ3V6WPVjGivj8uebD0dt/MyZS8blzyLE8+OaQYN8RSP09TT1o8eKaI4hWndnCw1hg2i/lNfhdhTjMZoncTgUUyuUTY/nLU+3kCGO+a0isyH25KprtnpyDKWU0sVDOFLG09jCuudITU6LZTtZLdoGKJ3eLqRe4DhnlXaDtO8c2wez1XH5soXPjMuc7uHm9S8dl08PQrqG9x3/EjzzwB3j14dqJ0M9SlkyjPY96brpWG9mYTqnJ4iV+/H2T42LVRgkxoYG+DjMCvNQHFIb27RteZ6Bx6P+GNoGWXTu2ToTXobh+PycUo7zNk/F5jCRnqaYliGpsk/M30uYcxtU5nYU4zwa62n7H6yFMfmqTvh8wUbF9z38JeNydxi2TScL8XgZKer5s1MRbhePD43kJ7YAf5YqpuRIkTIiR2kWEts8SqvB8U8xWEz1MKTPT91obCo/RuNUBTPuqnbAffeYOC+EC9nTF8V3PG21+KRBZLvhC7BpMOOH/TmxHR9G54Wt/EAjdg47PeYvpjJ+2vAF2Lyzm/bruTKc8rapkhNOCDE9LuRO2I564FMIIYQQQghxkdlZvwnbsUy8CDOzFZRfbBmAhW1pkRBCCCGEEELMMRMvwtx9eVoNEUIIIYQQQsw400/WPJPoYW0hhBBCCCGEmCK6CBNCCCGEEEKIKXIhYo6LQmYeady3ypbIqt0maVuLRGrUSGXOet1yrXAzoTpetG7p9OPDBhZpnhapWK+oPzIuf6G1b1x+sBeeIL1t/cpxeTm7a1xu0yZbi5Ss8e3jJsoZVLjN3E3UaRvrjYuqXiKhnR0kdN+s+u5F6QbK46Nhw+i9pK4+0q1v/XcWqfg9W2Ue2rGWsNylzFeR/tfLtbtFBok0EKn459juUDy3szCdf6fLLeVYBoBHLwQ98v3dPePybetXjcsHa58M66D+rZK+uUEa6baxSrvamMHxyKkcUmkceHoqlgeFsOZ6jVTqAS+fHscpLSg6Rnj8GUQpEbZq7DRwLITlp1IbcLtZaZ8V1NQ8D6dciPpNG72ZyKU5SIzNzcLxklLRR+kXEvps3nePa4VUCseW943L95w5MC7/1cNPwFHSgF9OxwCru1e9WTp9mZTgzXNODgJ0E8d+u0L6hlpibI9iNlKOx8uMVPTg809i3bQ6rtOjmEiN8+eDIajDWe7OqUF47GtkrOanj1SJ8bhbUMxXUdlHaUk4DUdiP3L7eF6O5dH6UjEf4ovj+cHl8Pni7tUQz3ethbQiVzVCXBfXx7ZpjmdeX5xeqHy8Yy39KqWEKOrqm3y8cmqdKM1CeQzG42N52pVJMZhKbhPHfJgepUM4j2N6R+EODGe8D1NiW++EmdlVZvbXZnanmd1hZi/Npx8ws3eZ2afy//urLpMvwOadxQkXg/PGtA/XrTxpzxvFPDZiPuhuU847ce7wBZjYvcx8PqgdTLNCvjEhLjbbPQL0AfyYu38pgKcC+AEzewKAVwC42d2vBXBz/loIIYQQQggxy7hP929G2daLMHc/5u4fzssrAO4EcAWA5wC4Ka92E4Dnbmc7hBBCCCGEEGKnMLVn+8zsKIAnAXg/gMPufgwYXaiZ2aWJeV4C4CUAcOQKPUojZhvFs5gXOJYPX757HhEX8wnH86WKZyEuGNdvwioxlQeSzWwJwBsBvMzdT1adz91vdPfr3f36/Qf07LSYbRTPYl7gWN53UF8oiNmG43nvAV2ECSGmw7Z/EjSzBkYXYK919zflk+8zsyP5+0cA3L/d7RBCCCGEEEKIncC2fuVjZgbg1QDudPdX0VtvBXADgFfm/9+y6bIwUpQuWq+gCC3X3dbIt8fa0mFCX95LbArWbY/qlX/rW1SKbxArYMtvz7LFh+sMYJH6mOe+vH5qXP7GxY+Ny3d0Lx+Xjw864/Jdvb1hfaSiZV3uo+ononZlrHRN/PCR25RUdFeYDgCDxDZMzZ/W11KlKnpvIFIqD6MUA6T63eIff2bmaCPER1V7XZvEySnLYyoeU5reAVu6LNaDM42C8ry0fRTzbePYLm/TVfWV6PWzlm4blz+6fsW4/PBgaVz+x95BakdYH48BRy1ovxep3zWLt1kqtlPfUFWJ7VQs1wq7K9b2p/Tzm+vqWVM+oPqsAz/LFrZFEkyDj1MRsCY+FV8cW6k0H8X5s8T25JQXvO9Z95wl9hgrsoFJ8RzqdbJQ5m3O++toI4yj377vlnH5g62ro+Xesx5i+O5eUHy3aH3cj2ubXyxdd+q4mkT3AoYyHl+H57Gc6DwYxX/YfzyGDIz3K+u9Q51sCy2DZj5ef6yML19HlAKEjrFh4gDL+LPGhGMwlfYG0VhGqTf4s0aiHcU0JhzbqXH7GlLOX7r3Q+PyB5shnu/tBl39A/2QVmQli4+xNdLJt5sPhH5QILQTxyHDMdhLpJHpohb1KSKRxoPHnCGdj1Njc42231mpB/jzaHSO4vG5RnXCZ02O+dlktmUZ02S777s/DcB3A7jNzG7Np/00RhdfbzCzFwO4B8B3VF3gblKLn8/JdVapcgEmpkPqAmweqZLzToithi/AhBBbT/ICTIgdxLZehLn7e5H+rucZ27luIYQQQgghxBRxnN9t8l3IrN/zFEIIIYQQQoiZQhogIYQQQgghxNbgu+dnDReC7oQJIYQQQgghxBTRnTAhhBBCCCHEBeMAXL8Jq8TMXIQ5ys2IkS60qGAeQ7dFSf3J2uNGQUU/ntPjdbJxh99jDT7r8VmZmlLRcx9ifamlVdXE4VpY7t72PePyw6SDZb44CArZ08NWeKMgE2JlM2vE92ZBq7pOt5xZ/b2XtvOQ9fHUv2heGLoVTHUN2gQZ1e+ldMCRZj+93JTiO6qzDdZAjhvWe0+ia2H7cwxHav2sWzo9SaFKVsHMWYs02ZvHc3zbPb38y+thWQdqd4/LDwzK4/nz/X3j8irF811Uh5XN1zSOR/NzPK/SPmhTDKfimekl4pfjrqioP9d4joypiU0YW1W3/5EQg49jgfuXgXXim7ejVmhrFunIWbld4QEOGs9TivoizSidQvlYfa5cVQ/LObD4yfDGInB3f3n8ko8TVnyve4j5L/T3j8sPD0O6hmsbQfV9IAv7fpXiqW3F8xifo1LxDKpD6m6ankX7m86BhWOhm9hnKV198ryeGMq20oRnCDETp4zZPO5YGZ8yORc18ay+H6bSMkRa+s3H2lQqkeK643aVL5d5TCMcV4dqd47L/9jbV9o+TpMDAGsI8fxFGrcfshDPj22EtLEHamG/rgxpPKBjel+2ecoWYPR5agwr7jl9RqSfL9fKp+KgGIO9REoDPoslU8mIXYMeR9zB7KYDtMoFmJgOVS7A5gW+ABNiWvAF2LyTugAT6dxjQojdwczcCRNCCCGEEELsYNwl5qiIvoYRQgghhBBCiCmiO2FCCCGEEEKILUFijmroTpgQQgghhBBCTBHdCRNCCCGEEEJsDfpNWCV0J0zsCJoJLbwQQgghhBDzhvmMqMHN7AEApwE8eLHbcpG4BLuz7zu1349290PnO/Muj+eduk+3m53c7/OO5zyW78bO7t92on7vPBTP54/6vfO4oM8b08bM3oHR9pwmD7r7N015nRfMzFyEAYCZ3eLu11/sdlwMdmvf57nf89y3Sajf88m89y+F+j2fzHv/UqjfQkwPPY4ohBBCCCGEEFNEF2FCCCGEEEIIMUVm7SLsxovdgIvIbu37PPd7nvs2CfV7Ppn3/qVQv+eTee9fCvVbiCkxU78JE0IIIYQQQohZZ9buhAkhhBBCCCHETKOLMCGEEEIIIYSYIjvqIszMrjKzvzazO83sDjN7aT79gJm9y8w+lf/fT/P8lJl92sw+YWb/4uK1/vyZ0O9fMLPPm9mt+d+zaJ556HfbzD5gZh/J+/0f8ukzv78Vy7srlgHF86z2bRK7NZ7nOZYBxbPieb7iWcww7r5j/gAcAfDkvLwM4JMAngDgVwC8Ip/+CgC/nJefAOAjAFoArgbwjwBqF7sfW9jvXwDw8pL689JvA7CUlxsA3g/gqfOwvxXLuyuW874onmewb+fZ77mO53mO5U3261z07zz6rXiew/2tv53/t6PuhLn7MXf/cF5eAXAngCsAPAfATXm1mwA8Ny8/B8CfuPu6u38WwKcBPGWqjd4CJvQ7xbz02939VP6ykf855mB/K5Z3VywDimfMaN8msVvjeZ5jGVA852XF85zvb7Hz2VEXYYyZHQXwJIy+sTjs7seA0SAC4NK82hUAPkez3YvJA8qOp9BvAPhBM/uomf0h3Sqfm36bWc3MbgVwP4B3ufvc7W/F8u6IZUDxjBnv2yR2WzzvhlgGFM9QPO+K/S12JjvyIszMlgC8EcDL3P3kpKol02bWuV/S798DcA2A6wAcA/DrG1VLZp/Jfrv7wN2vA3AlgKeY2RMnVJ+5fiuWd08sA4pnrloybUf3bRK7MZ7nPZYBxTMUzynmpt9iZ7PjLsLMrIHR4PBad39TPvk+MzuSv38Eo28ygNG3E1fR7FcC+MK02rqVlPXb3e/LB44hgN9HuB0+N/3ewN2PA3g3gG/CnOxvxfLujGVA8YwZ69skdns8z2MsA4pnKJ7nKp7FbLKjLsLMzAC8GsCd7v4qeuutAG7IyzcAeAtNf4GZtczsagDXAvjAtNq7VaT6vTE45PwrALfn5Xnp9yEz25eXFwB8A4CPYw72t2J5d8UyoHjGjPZtErs1nuc5lgHFMxTPcxXPYobxHWAH2fgD8M8wuuX7UQC35n/PAnAQwM0APpX/P0Dz/AxG5ppPAPjmi92HLe73HwO4LZ/+VgBH5qzfXwHgH/L+3Q7g5/LpM7+/Fcu7K5bzfiieZ7Bv59nvuY7neY7lTfbrXPTvPPqteJ7D/a2/nf9n7nrMVQghhBBCCCGmxY56HFEIIYQQQggh5h1dhAkhhBBCCCHEFNFFmBBCCCGEEEJMEV2ECSGEEEIIIcQU0UWYEEIIIYQQQkwRXYQJIYQQQgghxBTZlRdhZnZqm5f/djPbl/99/3nM/3Qze9s51j9hZm9PvP8/zOx559qOWSTfFl9Dr3/EzO4xs9+5mO3aThTP84vieVuWr3i+SCiet2X5iueLxG6MZ7G17MqLsO3G3Z/l7scB7ANwzoPiefJ/3f1Z27kCM6tv5/K3iKcDGA+K7v4bAH7uorVmDlA8X1SeDsXzlqJ4vqg8HYrnLUXxfFF5OhTP4gLQRViOmV1nZn9vZh81szeb2f58+rvN7JfN7ANm9kkz+3/y6R0ze0Ne//Vm9n4zuz5/7y4zuwTAKwFcY2a3mtmvFr9xMrPfMbMX5eVvMrOPm9l7AXwb1Vk0sz80sw+a2T+Y2XMq9MXyZX/MzP43gEvpva80s/eY2YfM7J1mdiSf/lV5X/4ub+vt+fQXmdmfmtmfA/jLVHvMrJbP98F8Of82n37EzP4m3wa3b2y/RLufma//w/k6l/LpP5cv93Yzu9HMLJ/+w3kfP2pmf2JmRwF8H4AfydeXXNe8o3hWPM8TimfF8zyheFY8CwEAcPdd9wfgVMm0jwL453n5PwL4zbz8bgC/npefBeCv8vLLAfy3vPxEAH0A1+ev7wJwCYCjAG6ndTwdwNvo9e8AeBGANoDPAbgWgAF4w0Y9AP8ZwHfl5X0APglgsdD24nK/DcC7ANQAXA7gOIDnAWgA+FsAh/J63wngD/Py7QC+Ji+/cqPdefvuBXBgUnsAvATAz+bTWwBuAXA1gB8D8DP59BqA5cQ+uQTA32z0DcBPAvi5vHyA6v0xgH+Zl78AoLXRlvz/LwB4eWHZLwLwOxc77hTPimfFs+JZ8ax4VjwrnvWnP3fHLNzu3XbMbC9GB9V78kk3AfhTqvKm/P+HMBroAOCfAfgtAHD3283soxfQhMcD+Ky7fypvz2swGmQA4JkAnm1mL89ftwE8CsCdE5b3tQBe5+4DAF8ws/+TT38cRgP4u/Ivd2oAjpnZPowGq7/N6/1PAN9Ky3uXuz+8SXueCeArLDwLvhejQf6DAP7QzBoA/szdb020+akAngDgfXnbmgD+Ln/v68zsJwB0ABwAcAeAP8foRPZaM/szAH82YXvsKhTPiud5QvGseJ4nFM+KZyE20EVYNdbz/wOEbWbnsZw+4kdA21T2xDwG4Nvd/RPnuK6y5RmAO9z9q6OJ+aMQEzi9WXvyW/Y/5O7vPGulZl8L4FsA/LGZ/aq7/1Gibe9y9xcW5m0D+F2MvvX7nJn9AsJ2+xaMTgDPBvDvzezLNumHGKF43qQ9iueZQvG8SXsUzzOF4nmT9iiexbyg34QBcPcTAB6hZ3q/G8B7JswCAO8F8HwAMLMnAPjykjorAJbp9d0AnmBmrfzbsGfk0z8O4GozuyZ/zQPDOwH8ED2X/KQKXfobAC+w0XPTRwB8XT79EwAOmdlX58tqmNmXufsjAFbM7Kl5vRdMWHaqPe8E8O/yb6BgZo+10fPcjwZwv7v/PoBXA3hyYrl/D+BpZvYl+fwdM3sswgD4oI2e2X5e/n4G4Cp3/2sAP4HRowpLOHub7zoUz4rneULxrHieJxTPimchNtitd8I6ZnYvvX4VgBsA/H9m1gHwGQD/ZpNl/C6Am2z0WMA/YHSr+gRXcPeHzOx9NvrR6V+4+4+b2Rvyup/K54O7r5nZSwD8bzN7EKMB94n5Yn4RwG8C+Gg+EN2F+NZ9GW8G8PUAbsPoGer35Ovp2uj2/W/ng3I9X/YdAF4M4PfN7DRGz6WfOHuxE9vzBxg9OvHhfPoDAJ6L0fPjP25mPQCnAHxP2ULd/QEb/Wj4dWbWyif/rLt/0sx+P+/LXRg9bgCMHm14Td4PA/Ab7n7cRj/o/V82+gHvD7n7/91kW80DimfF8zyheFY8zxOKZ8WzEKWYe+qutJiEmdUANPIB7RoANwN4rLt3L0Jbno7Rj0M3GywnLWPJ3U/l5VcAOOLuL92aFl5c8sH2enf/wYvdlp2K4nl2UDxvjuJ5aeYAcAAAAL1JREFUdlA8b47ieXZQPItzYbfeCdsKOgD+2ka3ww3Av7sYA2JOF8ATzeztfv65O77FzH4Ko5i4GyPDz8xjZj+CkUb2jRe7LTscxfMMoHiujOJ5BlA8V0bxPAMonsW5ojthYuqY2fsx0soy3+3ut12M9ghxISiexTyheBbzhOJZ7GR0ESaEEEIIIYQQU0R2RCGEEEIIIYSYIroIE0IIIYQQQogpooswIYQQQgghhJgiuggTQgghhBBCiCny/wO9hUV32/biNAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let's plot over month, each facet will represent the mean air temperature in a given month.\n", - "da.plot(col=\"month\", col_wrap=4,) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use `from_xarray` method from the contiguity classes like `Rook` and `Queen`, and also from `KNN`.\n", - "\n", - "This uses a util function in `raster.py` file called `da2W`, which can also be called directly to build `W` object, similarly `da2WSP` for building `WSP` object.\n", - "\n", - "**Weight builders (`from_xarray`, `da2W`, `da2WSP`) can recognise dimensions belonging to this list `[band, time, lat, y, lon, x]`, if any of the dimension in the `DataArray` does not belong to the mentioned list then we need to pass a dictionary (specifying that dimension's name) to the weight builder.** \n", - "\n", - "e.g. `dims` dictionary:\n", - "```python\n", - ">>> da.dims # none of the dimension belong to the default dimension list\n", - "('year', 'height', 'width')\n", - ">>> coords_labels = { # dimension values should be properly aligned with the following keys\n", - " \"z_label\": \"year\",\n", - " \"y_label\": \"height\",\n", - " \"x_label\": \"width\"\n", - " }\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "coords_labels = {}\n", - "coords_labels[\"z_label\"] = \"month\" # since month does not belong to the default list we need to pass it using a dictionary\n", - "w_queen = Queen.from_xarray(\n", - " da, z_value=12, coords_labels=coords_labels, sparse=False) # We'll use data from 12th layer (in our case layer=month)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`index` is a newly added attribute to the weights object, this holds the multi-indices of the non-missing values belonging to `pandas.Series` created from the passed `DataArray`, this series can be easily obtained using `DataArray.to_series()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MultiIndex([(12, 75.0, 200.0),\n", - " (12, 75.0, 202.5),\n", - " (12, 75.0, 205.0),\n", - " (12, 75.0, 207.5),\n", - " (12, 75.0, 210.0)],\n", - " names=['month', 'lat', 'lon'])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w_queen.index[:5] # indices are aligned to the ids of the weight object" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then obtain raster data by converting the `DataArray` to `Series` and then using indices from `index` attribute to get non-missing values by subsetting the `Series`. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "data = da.to_series()[w_queen.index]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have the required data for further analysis (we can now use methods such as ESDA/spatial regression), for this example let's compute a local Moran statistic for the extracted data." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Quickly computing and loading a LISA\n", - "np.random.seed(12345)\n", - "lisa = Moran_Local(np.array(data, dtype=np.float64), w_queen)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After getting our calculated results it's time to store them back to the `DataArray`, we can use `w2da` function directly to convert the `W` object back to `DataArray`. \n", - "\n", - "*Your use case might differ but the steps for using the interface will be similar to this example.* " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "array([[[0.018, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n", - " [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n", - " [0.003, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n", - " ...,\n", - " [0.002, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n", - " [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n", - " [0.002, 0.001, 0.001, ..., 0.001, 0.002, 0.006]]])\n", - "Coordinates:\n", - " * month (month) int64 12\n", - " * lat (lat) float64 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float64 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n" - ] - } - ], - "source": [ - "# Converting obtained data back to DataArray\n", - "moran_da = raster.w2da(lisa.p_sim, w_queen) # w2da accepts list/1d array/pd.Series and a weight object aligned to passed data\n", - "print(moran_da)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "moran_da.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using local `NetCDF` dataset\n", - "\n", - "In the earlier example we used an example dataset from xarray for building weights object. Additonally, we had to pass the custom layer name to the builder. \n", - "\n", - "In this small example we'll build `KNN` distance weight object using a local `NetCDF` dataset with different dimensions names which doesn't belong to the default list of dimensions.\n", - "\n", - "We'll also see how to speed up the reverse journey (from weights object to `DataArray`) by passing prebuilt `coords` and `attrs` to `w2da` method. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dimensions: (latitude: 73, longitude: 144, time: 62)\n", - "Coordinates:\n", - " * longitude (longitude) float32 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5\n", - " * latitude (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n", - " * time (time) datetime64[ns] 2002-07-01T12:00:00 ... 2002-07-31T18:00:00\n", - "Data variables:\n", - " tcw (time, latitude, longitude) float32 ...\n", - " tcwv (time, latitude, longitude) float32 ...\n", - " lsp (time, latitude, longitude) float32 ...\n", - " cp (time, latitude, longitude) float32 ...\n", - " msl (time, latitude, longitude) float32 ...\n", - " blh (time, latitude, longitude) float32 ...\n", - " tcc (time, latitude, longitude) float32 ...\n", - " p10u (time, latitude, longitude) float32 ...\n", - " p10v (time, latitude, longitude) float32 ...\n", - " p2t (time, latitude, longitude) float32 ...\n", - " p2d (time, latitude, longitude) float32 ...\n", - " e (time, latitude, longitude) float32 ...\n", - " lcc (time, latitude, longitude) float32 ...\n", - " mcc (time, latitude, longitude) float32 ...\n", - " hcc (time, latitude, longitude) float32 ...\n", - " tco3 (time, latitude, longitude) float32 ...\n", - " tp (time, latitude, longitude) float32 ...\n", - "Attributes:\n", - " Conventions: CF-1.0\n", - " history: 2004-09-15 17:04:29 GMT by mars2netcdf-0.92\n" - ] - } - ], - "source": [ - "# Lets load a netCDF Surface dataset\n", - "ds = xr.open_dataset('ECMWF_ERA-40_subset.nc') # After loading netCDF dataset we obtained a xarray.Dataset object\n", - "print(ds) # This Dataset object containes several data variables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Out of 17 data variables we'll use `p2t` for our analysis. This will give us our desired `DataArray` object `da`, we will further group `da` by day, taking average over the `time` dimension." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('day', 'latitude', 'longitude')\n" - ] - } - ], - "source": [ - "da = ds[\"p2t\"] # this will give us the required DataArray with p2t (2 metre temperature) data variable\n", - "da = da.groupby('time.day').mean()\n", - "print(da.dims)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**We can see that the none of dimensions of `da` matches with the default dimensions (`[band, time, lat, y, lon, x]`)**\n", - "\n", - "This means we have to create a dictionary mentioning the dimensions and ship it to weight builder, similar to our last example. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "coords_labels = {}\n", - "coords_labels[\"y_label\"] = \"latitude\"\n", - "coords_labels[\"x_label\"] = \"longitude\"\n", - "coords_labels[\"z_label\"] = \"day\"\n", - "w_rook = Rook.from_xarray(da, z_value=13, coords_labels=coords_labels, sparse=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "data = da.to_series()[w_rook.index] # we derived the data from DataArray similar to our last example " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the last example we only passed the `data` values and weight object to `w2da` method, which then created the necessary `coords` to build our required `DataArray`. This process can be speed up by passing `coords` from the existing `DataArray` `da` which we used earlier.\n", - "\n", - "Along with `coords` we can also pass `attrs` of the same `DataArray` this will help `w2da` to retain all the properties of original `DataArray`.\n", - "\n", - "Let's compare the `DataArray` returned by `w2da` and original `DataArray`. For this we'll ship the derived data straight to `w2da` without any statistical analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da1 = raster.wsp2da(data, w_rook, attrs=da.attrs, coords=da[12:13].coords)\n", - "xr.DataArray.equals(da[12:13], da1) # method to compare 2 DataArray, if true then w2da was successfull" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using local `GeoTIFF` dataset\n", - "\n", - "Up until now we've only played with `netCDF` datasets but in this example we'll use a `raster.tif` file to see how interface interacts with it. We'll also see how these methods handle missing data. \n", - "\n", - "Unlike earlier we'll use weight builder methods from `raster.py`, which we can call directly. Just a reminder that `from_xarray` uses methods from `raster.py` and therefore only difference exists in the API. \n", - "\n", - "To access GDAL Raster Formats `xarray` offers `open_rasterio` method which uses `rasterio` as backend. It loads metadata, coordinate values from the raster file and assign them to the `DataArray`. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[827200 values with dtype=float32]\n", - "Coordinates:\n", - " * band (band) int64 1\n", - " * y (y) float64 50.18 50.18 50.18 50.18 ... 49.45 49.45 49.45 49.45\n", - " * x (x) float64 5.745 5.746 5.747 5.747 ... 6.525 6.526 6.527 6.527\n", - "Attributes:\n", - " transform: (0.0008333333297872345, 0.0, 5.744583325, 0.0, -0.0008333...\n", - " crs: +init=epsg:4326\n", - " res: (0.0008333333297872345, 0.0008333333295454553)\n", - " is_tiled: 0\n", - " nodatavals: (-99999.0,)\n", - " scales: (1.0,)\n", - " offsets: (0.0,)\n", - " AREA_OR_POINT: Area\n" - ] - } - ], - "source": [ - "# Loading raster data with missing values\n", - "da = xr.open_rasterio('lux_ppp_2019.tif')\n", - "print(da)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEWCAYAAAB2X2wCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAACJeUlEQVR4nO39e7xtWVXfi35bH2O+1mu/965dLwrkYYwJxpRGQ2IwpIqC+BGTIxEVwxENN+cQj3lgRI/CzcOE3NzPDXhyEkNQglEjYCQaIxQVcpFrVGJVRAFBUF5VtXft93rP5+jt/tH7GLPPMcd8rTXnWmuvPX6fz/qs+RiPPsacs/XWW/u1XxNVpUSJEiVK3P4whz2AEiVKlCgxH5QGvUSJEiWOCUqDXqJEiRLHBKVBL1GiRIljgtKglyhRosQxQWnQS5QoUeKYoDToJQ4FIvIFEflLx+1cJUocJkqDXqLEHiAif01EfkNEdkXkw4c9nhIlAOLDHkCJErcpbgJvBb4c+IuHO5QSJRxKD73EYeJrROT3ReSWiLxTROoAInJKRH5FRK75935FRO5NdxKRD4vIPxKR/y4iWyLyQRE5G7z/XSLyRRG5ISL/5yIGrqr/VVXfA1xaxPFLlNgLSoNe4jDxncBLgS8Dng/8iH/dAO8EngXcDzSBf5nb9zuA7wbOA1XgDQAi8hXAvwa+C7gbOAPcywiIyBtFZH3U31yuskSJA0Jp0EscJv6lqj6pqjeBHwO+HUBVb6jqf1TVXVXd8u/9hdy+71TVz6hqE3gP8FX+9W8FfkVVP6KqbeBHATtqAKr6FlU9OepvrldbosSCUcbQSxwmngwefxHnUSMiS8C/AB4BTvn3V0UkUtXEP38m2HcXWPGP7w6Pq6o7InJjAWMvUeLIofTQSxwm7gse308/Hv33gBcAf0ZV14Bv8K/LFMe8HB7XTw5nRm0sIj8sItuj/ma5mBIlDhulQS9xmHi9iNwrIqeBHwbe7V9fxcXN1/17b57hmL8AfJOI/DkRqQL/kDHfc1X9J6q6Mupv1H4iEvkkbgwYEamLSGWGcZYoMXeUBr3EYeLngA8Cn/N//9i//lagAVwHfgv4wLQHVNVPAq/3x74M3AKemtuI+/gu3KTzr4E/7x//2wWcp0SJqSFlg4sSJUqUOB4oPfQSJUqUOCYoDXqJEiVKHBOUBr1EiRIljglKg16iRIkSxwTHqrDo7Nmz+sADDxz2MO4IfOaJzwEg4qjhCjz/q599iCMqUWJ6PPHEE9dV9dx+jvHSb1zWGzeTyRsCT/xe+1FVfWQ/55sGx8qgP/DAAzz++OOHPYxjhYfMKwtf/zPijbcEi7zfgceSdxduX6LEUYKIfHG/x7hxM+F/PHr/VNtGFz97dvJW+8exMugl9oZRRntaiBHUKuhIyZQSJY4dFLCjZYIOBaVBv0OxXyMeQm2/luEh80oes+/d1/GmGdt+z1GixH6hKF2dLuRyUCgNeonZIAZTibPHttNZ2HkyFHj+odEvjXuJw0LpoZc4dMzknYtxBtUbWFOtQmQgsWiSZOEWMQJEqNV9Gdts3/ScRWGcnLEvjXuJw4CiJEes0r406Hc6xIyPgQevSRShSQJJ4v4PbKZD24M30IEBnilpOiImL1EEagdCPSVKHAYsR+s7WBr0OwwD3rk35u6h97CTETFBtWhvD8tLb8zFCIjhoejb9p081V535HvziOGXKDENFEhKg17iyEAtEAVPF/DlDMI1qPWrgRGhlBIlbjOUHnqJI4XUiJtqFXpdZ2zdG6N3GhXbHn0S1PpkqhgE+snU0rCXuE2hQLeMoZc4LBQmQ70HrUmCaTQAsK32xPCKRNGARz+Ri64W2+0NHQP1laZW+zH6fRj5MtxS4qCgaBlyKXEEoRZNQLu9sfHpge2tcV69Woj916jdZiwtNzTUYty5xCBRhKlG/mVBk6QsVCpx9KGQHC17Xhr0OwWF3nkYOlGLbbemPl5qeKVaQTtdf4gpv91pTN0nZSWKkEoMVd/BbXsHtb2ZQzuld17iIOEqRY8WSoN+J2OvHrCYzChruz2SsjjNedUaSMMsfoUw8/E80kmrNOwlDgZCMlXf8oNDadCPOWYu8c8VEoWPszg5DBQV7Ss0koZ7EjJvXW1v8n5jUBr2EgcBlxQtDXqJBWNfOi1BCAboh0VqNcSYLM6uVkdz1vd4TmfYk+y8YzFBGqBEiUXD8dBLg15iQdi34FbOiIoRJK5gVpYhMmi7A7Nqt8gIGuSk+PiIkv90TBgBq35yKXntJQ4HtvTQS8wbc1FO9GwTiTzbxCcoZW0V4ghUHX98Z3d0mCWtPA2MeBYPzxvwPRpgtQoBE2fU8ctwS4lFo/TQS8wN85S/zRKcPtwhRpATa84L7iWw20SbLbTTGcsTT415+t929sEpL/Lgg9BMOO709dKIlzhIKEJyxLp4lgb9NsNcDXkKn/jMPPTIYG/ecjFzH2KZhhvuvOXEFwsF8fVZK0uDMc0clilR4gAxz5CLiJwE3gF8JW4B8FrgD4B3Aw8AXwD+mqreGnWM0qDfBhgy4kUJw/0aNy++NcBeSY87rUHOe9DpWPc6tpK2WOIIQxE6Gk3ecHq8DfiAqn6riFSBJeCHgQ+p6ltE5I3AG4EfHHWAha4XROQLIvJxEfmYiDzuXzstIo+JyGf9/1Mj9v0pEbkqIp9Y5BiPOoqMuRghWl4iWlnGNOrzPaHaflglz3jZ4/FKlDiOcIVFZqq/SRCRNeAbgJ8EUNWOqq4DrwDe5Td7F/At445zEAGgb1TVr1LVB/3zN+JmnOcBH/LPi/DvgIV3yT7KGBVeCSsy0yrNEg6ld17iIJH44qJJf8BZEXk8+Htd7lDPAa4B7xSR3xGRd4jIMnBBVS8D+P/nx43nMEIurwBe7B+/C/gwBUsIVf2IiDxwYKO6jSBGsM3m/op6Ai30A9dNmRCGkSjKBMPKsEuJowpVIdGpfeLrgVNbhBj4auD7VPWjIvI2Rju7I7FoD12BD4rIE8GMNNOMMwki8rp01rt27do+h3v0kTWkiCJMJXac7L0cJ4qQuOL+h1TDBcKJcFUx1Wp27iKkyovpdiVKHFVYZKq/KfAU8JSqftQ//wWcgb8iIhcB/P+r4w6yaA/9Rap6SUTOA4+JyKfnfQJVfTvwdoAHH3zwiGmf7Q3jmCyaJM57tb29edV5wy0GREEXG7rJGDRemVFIk6e5atN05WAERJzOy5QovfISBwmXFJ2PCVXVZ0TkSRF5gar+AfAS4Pf932uAt/j/vzTuOAs16Kp6yf+/KiLvA74WP+Oo6uVpZpwSBZhDkjJltOz7eFOfVt0k1O0NsmiKIMYbc1smVUscWaRJ0Tni+4Cf9QyXzwHfjYuivEdEvgf4EjCWt7wwg+4D+kZVt/zjh4F/CPwyM8w4JRaIgzSWocJiwug4eirW1Zxusim98hKHiWSOPHRV/RhQFGd/ybTHWGTg9ALw6yLyu8D/AP6Lqn4AZ8gfEpHPAg/554jI3SLyq+nOIvIfgN8EXiAiT/kZ6thjIYVDs0BMP87tk5MLia9PKhgqPfMSRxxppeg0fweFhXnoqvo54IUFr9+gYMbx4ZmXB8+/fVFjuyNR5BGPEM5Sq1nytej9hYylCKVRL3HEYadnuRwIykrROwCZp81wz07Xx5NcSCSZ2FN0uhObYs30UG8923QO2uolShwgnNBFadBLBDiIEEumseKehG8MG+55GNTQWItBIgp7jWZSuICdUZb3MfteHjKvLHnnJQ4NitCdb+n/vlEa9ENAoTbLIj3TUcdOBbm8d+y89WRPBT2F5/PXVeh5i3HMl06nH96Z4ZyHnmsoccdDlVkKiw4EpUE/QCzUCO1nUogizErdGfRuz0nlFmmOuycD58wmgzBkEnjoI7sapQqPZailxG2LqYuGDgylQT8gFBrzQDs8H8eeCqERn2bffBI06+eZIN0eUq1gGg3krnOw28RubEIcu69sYvvdgehrn5t6DUTQXs93M4qy67LdCb1B1e6721AZailxWFCOnod+tEZzTDHSMw86+gywSiZBTL90Ph+vjisuLh3SDcPHOQ974LAVF8/WrW2SWxsu/NLpIsYgXmYgz35J9dLxeulZ6GZc4VA4phmNeWnASxwl3DG0xRJTwnvJA3FnGBv3zvYrMJqpnvnAMUYdK1fsY7e3B+PeACTYZrPgdQ8fd5dGg6jhWtQRmfEl+2lvUL+vG0ruuCPGXMbOSxwVKFL2FC0xAeM81iDmPNCAIrdvEaNkmvMWaqtkx0tym6cNm2NkZRm80FZUr4ExLlzTYTiUlOq5nzkNqmi3h9Sq2PXNgWRs2fi5xFGHAt05abnMC0drNMcUKcVuJGYwXKk416z7zYxpwiFisM0WNFuFK4xRyU6JIpKb66A2E+0yJ1bR3SbJzm5pyEvcJsi0zo8MSoN+FBHGtgs88GkrPveFSccq4rADRR5+fj/b7QVsmAS7tbVnpkvJQy9xWFCOXqXo0RpNib3hsLRP9qrxErJsrGbc9yHJgRlQxtZLHAZm6Fh0ICgN+gFgZmOTGuiDNtKeeTKq8cTQtvNA2MOUguToDCiNeomDhKpg1Uz1d1AoDfpRwKIUDWcehvQ95EnjGTfhzHo9wSSyVw89RWnUSxwUXFI0murvoFDG0A8bB5HgnBIjqzpnQU5OIKw4HbU99D3z/XjoZRy9xMFipp6iB4LSoC8YEz3GI2DI5w0xksXER2xQLBlQosRtBJcUPVosl6M1vRwz3JHLf7XYTmcqga+J4ZUZQzd35P0ucag4apWipUFfEI6UcZnGKB5kDD9lt3iGyzSVrNPiSN33EscaaaXoNH8HhTLksgDM1ajkWCdTS9uGSoa51/sHK+C4HxSmvAZTiQfDN1PsF97/Mq5eYpGYc5PofaM06HPCSCM+yoBOi5EFPNPvPzSkQENlWqSTykLj3RIoOQJSrWJWV7Bb29Dp7CthWqLEvKEKXVsa9GODvBHPVAan8XzD0vpFNLjItFnyp5U9nWvfhjy9xgnXmk4cpl6DOAbfrFp9Vem0KD3zEouGC7kcLYN+tEZzG2HAmAdytqmK4FQ87hCL4KIXHHPPXu4E0bCp4/TjjhMUGdlmE4xAkmBb7cnqkzmUsfQSB4GjVim6UA9dRL4AbOFcq56qPigip4F3Aw8AXwD+mqreKtj3EeBtQAS8Q1Xfssix7gcZTW8vTSrY4z57Pe4Iz33u50mRGvEZ4v5SrSKNOlKvQ6/nTzFihTDmuGUsvcQicafSFr9RVb9KVR/0z98IfEhVnwd8yD8fgIhEwP8NvAz4CuDbReQrDmCse0KWqCy51MOYQrEx/J+tILo97I1bJDduzqfgqUSJuWO+pf8i8gUR+biIfExEHvevnRaRx0Tks/7/qXHHOIyQyyuAd/nH7wK+pWCbrwX+UFU/p6od4Of9fiWmgEQRplbHNBqupVwYApoUGvHhI1OrHwyVMVSQ9H+22STZ3nF89hnYLaNQeuclFgXr+4pO+psBMzvAIRb9i1XggyLyhIi8zr92QVUvA/j/5wv2uwd4Mnj+lH9tCCLyOhF5XEQev3bt2hyHPh5H2Uik/G6pVpFqtf/GNMlXH8fOOh8dlsZMQY4h1U6fZUxH+XMqcXvDsVyiqf72gWkc4AyLZrm8SFUvich54DER+fSU+xVNaYXZPFV9O/B2gAcffPBAeW0TG1ccIjRJXEs5mN3L9XF2xzhJXI/SsF3dIZTqm2oVqcS+h2kydUejMo5eYlGYsQXd2TSM4vF2b7sGD+kcYAX+jX9/wAH2tnQkFmrQVfWS/39VRN6HC6VcEZGLfnAXgasFuz4F3Bc8vxe4tMix7hUTjfoiKImTsJ9WdDmYivuK2G4wVy6Shz7i2NrrZrRHiUBtbzFjKFFiBswQTrkehFFGYa8OcIaFradFZFlEVtPHwMPAJ4BfBl7jN3sN8EsFu/828DwRebaIVIFX+f2OJB6z7x3t/U3J7jhySGPaYrLwy4C87ryQarB72mdGgcwVZKlVbLeH9rrjw0Fj7uVD5pVHdkVV4vZDynKZV+l/6AADAw4wwBgHOMMiLckF4NdF5HeB/wH8F1X9APAW4CER+SzwkH+OiNwtIr8KoKo94G8BjwKfAt6jqp9c4Fjngj0v6WfheB+g8dckwbZbWel91px6zmNIK1cH2EIhayigPobdjdJ9w79pkBr20riX2C/mxXLZpwOcYWEhF1X9HPDCgtdvAC8peP0S8PLg+a8Cv7qo8d0WSHnZqeeaGrRx8eNAjxz68e59U/8WGDYaqZk+Tis+XT2UcgAlDgmqQm9+laIXgPeJCDi7/HOq+gER+W3gPSLyPcCXgLFeSFn6Pyc8ZF6ZGdN5UO3S/dWagXhx2FFoKPyRGvNKjHZ7EBlotw+Mxz2k95KPiaeCYeF4ppDYHTmB5Y49zfFKlJgn5lVYNKsDPAqlQd8DhpbqaRw4MLDOcM3hZKlh9MdMmSeAY58YARHEGNRapF6DxEK3h7bbcxjADENNkkEjHoRMilYNUx1z2m1LQ17igHEUK0VLgz4DCmOuQTx50BOOhj3UvSJjd/i+m7WaM5CnT0KlgtZiEMF0E+gl2EvPZGOZW0hiWg94FEslSZxq5Azx90L530NASX0sMQqlQT9uGGPAjC/qyZJ4k5KfI9+SLF5slpeQagXOnCJZqbu0tiX7H126HuxTgf1ozAxc0JwkfGc4zlCoaNwEuaCQS34Sf8i8sjTqJYCZeegHgtKgB8j/eMMfbhojn8kgWcU06pilBvbWOrbbS98o2tj9D/ttpuyNRt29dfokRJHTOVmqYlodpNUFa9GlmitdA5DUsw2YI6MQhIqOXG/P/P2eqqXd3nMYj6x9ty9cgkdbP8tD0bcdTh1BidsGM5b1Lxx3vEEfR10bem/GH7YmiYtjq04fOkjVEFOGS73mPHJV9NoNtNN1yc7LV1C1qBikEiNLDRd+2dmFXm/6zkbMMSwzL+yFFhncN3cIl0wN8xpFyeH0M37MvtfJ9Pp9H668aigHkPLhS5QA5z/1ygYXRwMHxUFWq6jX885CJ9N2IBID1mI3tjJj4op7Yq+DYKDXw7bavqCgiXZnM+b9Ux2NePVEhK31xqx0UmbMLInpD3Z+DoCHq98x9l6UYZcSKcqQy1FD3hsMWCVziTur5EIAU1oYtdhOB3wIwA3VMWe02Qw2c8dOdnYHzzvLGJlRJiBk9PhK0qkrYqcI/2SFRgX9UKeuVJ3xs8tCavSLlcLPLu/dFzKdgMeSd8903hK3L8oY+lFEyiDJ4sjzjZnuiwOeG8e+aZC5tnemEvcLj2aJO3t+fCoFMPW4xrWgS6mNVS8B0G47/n2OBpmFVabpYDTr55if3Iq6Sk3YNwzhlDj+0NKgH01khveASusHZGC9QTWV2CVOF9nByBtyAOIYY3xjiSRx7d6mPb83rjNPWCOOnXrmEkVgJrS0m+X+5Hu35vfPJ4UJWEW93HY+jo5Usu2yRPes4ypxLHDUkqJHK6J/kAhFoPZIq9vXuYF8IdJCjXkKH8qx3R622cI2m+6v1T6Y848cljrhrXYb/CQxrTZL7kD9v/R5/r288FcyyAZK9eTzxx2in/rJUaLIyfvGlew7VerEHH+ozlecax64cz30Q/amUlqhq/b0pfozdLWfBzKPsz+oBZ9wTKjEh3EQdQZ9XLhjkZz6HLOlMPmaeuuRARUkrdw10o+7H0UFzRJzhpCULJfDR+Y9HZb+R8DESHnPBzqGNOySesBJ0o9XL3Is4+LeqZpizxYmWbMwSF5eYB5Ij5cLv2R5heB8EkWY5SX3eKmBttuOxdTrDYRsbgvGUIl9o4yhHzJCES0Xr/UfyCzx43lh3kaJfhhnUmxbkwSsIpW4MI4803lnvY5piplyx3WGfEFGMp90LUKQRNZu1xl1ESfDUHeFX/R6rl6g2epP1CWOLUotl0NGPq6pSQI9i9RqEEWIVcQsODG5KMxCP0zDG2klae4Yeznv1PATz9iq1DCJuciQ0CyTkVpst+e873abpDMo+ZtOUFKtQrUC3bKj0rGHkhVnHxXcUQY9g1/aA24Z32z135pnGPuolo3PMx497fmKkpSTMI7mOO5c055n1MpgzP79FczgF0UTb9S7PbC2v/IrcaxRslwOCSNZByErIs+QmBXTdqYvarNWtNmMHe4njS39M/Uaplp1zIxxTJIpxjgVUsM867GCQp+Z9t3PJDXu8x/1XcmzYxKLdns8XP2OvY+jxJGH+qToNH8HhTvTQ18gBmLQk0IK0x5rHsnKIIyhQUhpbMy8iGmy1zHM6GWHicWZRMPmUbG6F+RCXhJFaK/Lw5VXoVbLCtJjijLkcsA4UD5wvoBlXsebM/YcNw853PMY2xjDOrJydR7GeI9JXKlWs8lw1CTjqIsWqdSyuHuJ44uS5XInYJLB2K9kbVF1Y9r+bZKuyl6M4V5j4GOOl9IQM972PD3w3HnmKQvs7nMEvW5hviUVUEuVG0scX6iWBn3hmKtHvsCkpsSufDyjt02bqEy3y4x6UL2YFsPMo6FF6InP8R5kOYY4+Oq12wxo6Exxvql6t2ZsnnSnPSaD/XHEV5ROYug4vZlgbGWR0bHFUaMtLvybJiKRiPyOiPyKf/5CEflNEfm4iPxnEVkbsd/3i8gnROSTIvK3pznXZ5743BQDMkhccX+TSssX9WNMy++LjPkU+zqWThfb7WXGxbV466K97vA+YRJ22uTkAsM91vc7ddIDrT0V4aRho8zTH3VN4YQ0bdK76Fj+M5taWXLOE2GJownV6f4OCgfhOnw/8Kng+TuAN6rqnwDeB/xAfgcR+UrgbwBfi+uE/U0i8rx9jUKGGzlnr4/DvOPi+eOmw0gnl2mTekF15aRjFxq1g8AYI5sZxlFGdprJR3MNJ45w7uKh6NvmfswShwtFsNZM9XdQWOiZRORe4C/jjHiKFwAf8Y8fA/6Xgl3/GPBbqrqrqj3g14C/sq/B5IzaVH0+YWAiyFrCzZNOGI5vlm1n5WbPEj6adfuiffcTrvL321SrmHqtL3pVhBGTwth9ph3DtPTTSfBjKwW7jh90yr+DwqKnjrcCfx/XxjjFJ4Bv9o9fCdxXsN8ngG8QkTMisgS8fMR2iMjrRORxEXm8y+RE1ICy3ixL76DCcS8dgYoH0zdEczvmqPPAdMYn7/nPyh/fL5ffH0OTxIWkkoRUd33qcaSaL1NslxntvRj/MqxyZ8MnRaf5mwYF4enTIvKYiHzW/z816RgLM+gi8k3AVVV9IvfWa4HXi8gTwCowJHqhqp8C/hnOg/8A8LtAIf9LVd+uqg+q6oN//E//sYU0Fkj1w4+s4NIkYzSKaiimX1wUGrV88vCwjJan/YW5gmn3A0ZPAmmBVbWKVKuukXe9NmTY9/15585dNr04hpivi54PT78R+JCqPg/4kH8+FotkubwI+GYReTlQB9ZE5GdU9dXAwwAi8nxcSGYIqvqTwE/67f4J8NS0Jw5/OPtZ5ooRL28rSMR0Xt9BIxWN8mMrZH6MiteDa3JRrbrNkgTtdA5m4pqWcbLHxHR6jRJBETXSdVpKIME14R53/rEnmhBaCt4Pv4ulcT8emBdtMQhP/xjwd/3LrwBe7B+/C/gw8IPjjrMwg66qPwT8EICIvBh4g6q+WkTOq+pVETHAjwA/UbR/sN39wF8Fvn4v48j/cKY28HkjIsLCZVH3ULSTGibTaKDWIr2ezw8UGJpQGCvdt9MF36QBq54OuOBimFwl6MjNpqEmjtgPcEqSgHZ7fkLub+P44r66My+0FZ53EvbI+S8bTd/+UMDaqQ36WRF5PHj+dlV9e/D8rbjw9Grw2gVVvQygqpdF5PykkxwGD/3bReT1/vEvAu8EEJG7gXeo6sv9e/9RRM4AXeD1qnprHidPf0QTDXvKYfZe3IGEHfZwjrR1nSaJM0SxM87aag97j7njp4qLqB1YFTpju0BtdB0jU9sfhEtAxyZjs8ySZ8gKrwKZg3wxVl5oa6z08DhPPNWcCRhU00xEpVG/zaHA9B76dVV9sOiNMDztnd8940AMuqp+GLdcQFXfBrytYJtLuORn+vzPL3JMUxl2tYN9JfeCKdgeWZXnXioa0/Lybm826mOwXWZcA3ExCY99GDH0lJKo3Zm99DQnoKncbTIYDx+grqbGuBJDUKAV8vkzjz3/WQbsJwBpNFz4xip2a8vduwmff9lU+vbGnDjmheFp4IqIXPTe+UXg6qQDHbtK0VlwIDSyKYxQWi6+3/NMPfmMCe30GyGbw0sCB+PbS97Cdjr9sE6we5oklTTEBKi1mBNrrntTkjgp5bZjS6ln2BTeh3Dyq1aQeg09exo2t8FaZGkJ2drqTwT+ekYhbbwClEJetxPm8BMZE57+58BrgLf4/7806Vh3rEFfqDGflYOdD0HsYf+pEcSvnUzAoFCXbbcGt52XlEDBOEbF+PvDKfCKZzi22r5mjEQu+SnGe+TLS9h7zmEu34BqBV1qINdvuq5DPl8ixoWyTLUKkXGxdg1UIH0ISJMEmi1Mkrjfd2Kx29sDE3V6r4dWYaGXH0VItdqX3Q0Kp0ojfxQxPSVxj3gL8B4R+R7gSzia91gca4NeZLQfs+9dvGc+L+M3T2XDFKGhm2bb/Yacxhw7wx5olyFTZ6QXrxaI3LXGMebsaeyZNXon6qgIveWIykqVaLeHJAmcP42sbyOqaKuVJYpts+kMff7YuXHZze2hcQ6svsTnLAYuzbWxM42Ga4ohgrTaYGSgjV0pw3tEMedFbC48fQN4ySz7H1uDPspoL8yYz1PZLyzTXwQ0J1p1mCgw5vnJZijkkca9Pd2SPNUyKIpSq5iG286eWqNzZglbM6iAGugtxYhV1MTEGyCry7DbhHsuQLMDzRZs72Sfa/7zHXyeZOfNxpGGriRsIJ1bdfR6btIAzPlzsLWNhtz7gkKpTEog9x0pY/EHCAWdnuVyIDi2Bn2kJ16Q2Nqv4UyTcKZRd13gA8Gs0TuZwZDHqPDDNGPbiye/n2ue9p5Ns13g6WbyCo2Ge8tXiiLp/Rzc1SwteU+2SrK51eeWDwzB/+CiCHP1JtXYIK0OGIOtVRBrkS9c8glRxXa7iDFIu4Nd3xz4HIcm66JVRm4V0f8uDE9UEKwuvCefPHVpxErE/R/QhCmrVI8ASoN+uMj/CPYQf04fhzFO1IKqM0bdLtqeLEOQxmkzNkQ4nimLVaa6hhytbt+riDH0vbQRhDNm1idZ3T2yzWZm5AopiClPvtPBrK4g589CpwOtNrrbzCbLzMD2ehAZ7PZ2/7oCj9806pjlJfdekmBv3ESvXsN645mxYSIDnr8PfhXtPebsuOOue8T9GZiAxlA11U9a+fcHVnzz1g4qMR8cseLxY2fQZw2pSFzJPLtpDZ37gfc96/SHmGxvz/TDcz9kkzFLpuI/71VzBEYalLkjSaBWwyw1oBI7g97tIdb6xGKgE6M2S1y6oSpCgt3YQnab2D/+HJJ6TLzdQT7/NDRbWXs3bbUhMtlniIibSMVgVpa99+7oh3Znd2A1lKcyjppcsgl8mpXGJIxbSRV8xgPfx9xKZhTzpuS2HzBKg36EkBnLyFVadnt9jfIRP9BpmipMhSyOPYYalx4v/SGnhv8oasrkVi/abqOdjgtfnDyB3diExE8sAcMm9ZJDBgpR5Kh/9RrmD74Ez72P5sUlarVnET91Hd3YwtRW3fGMYDe3nQG3CstLIOIaNasdMuQHcR+GzjVlZWyGvEdexP4Zs0p6uPIqPtj9+alO9ciJ17oJN4og8udJLNrtop1udu/KZGwBZissOhAcO4P+mH2vizNOEYZIl93OK2TyD3/eBmEazzkwlAdqmPaC1LNMEkQFaTScNsyNmy7Jlzal8BNTqpWjSeJCHt6wkyTIyRP07jsLIsRXN5ELDXbvqmHO3YNYpXa9jVglurKBdLrY7R23MkhjztOESdIK0igaTlTudVVT5OkX0VKnGZuY7P7kq1zH1hLEFV7a+C4ebf77kYd/KPo2l69IKaxGMi5+eh6pxJkD8VD0baVRL0DZJHrBCAs0xiL9kXmDaVvt/UnY7iUxOeUP+8BCJdMiP+4gbJRCE7CbW4W7h6El58WnUgOJ0940gt64Sdxu03vevbTvO0VnxYBA42mXm4h+/wtgnWSBrK6QpOcqaso84TNZqOhakcHNjHNx0jirPK3V3KolijIKo/GPs+bT4WehNgtHjevGFSZW0+NowtC9y9colOGcApQslwPADEZVe110jLwsOPncwkRe+n695nbvdEb+SPc7zpAxcSgIJskBg+QeOL76NBNUwWQ7IJSVgKiPqVerRLd24dQSSIXGlS4qUP3s5f4EDOhW8cSxL+THOcLz3gvS3MnIsF6qy9Ppem66QZYbSL0OXZcTMPRDL2EyOPW2H2397NBxH668auB6pqkxGJA9gNJTz0GOmId+rFLnU/UUTZFqX09hhNKYb5G+dpEuyMjns8Ivuffk/c8D/v6kJe6mXnP/l5bcX8oDz3mJY483AmFuIu2Paq/fRG7cwux0iFpK7RNfJPqNT5Bcveb6e47qoToPpNcy6pomXeek78KkVYN3ILK/Zgt712nsfReQkyfAi7AVeeJF8fPUmI9dhRaMO7+9GBmcGO5kTKuFfoBG//h56NN6TtMwR9I4as8WGw6f2NRW2x8mldidzzxZqN63vwMOvzbmXqUUREQwJ0/A6jJqDFy5Bj5hNjbZN+OElte0SQ2ZfPESq1frWVz8UFcqkzDhu7TnY1hFugmd88tE9XOYMycwT13B7u66wqggPBKGVAaaaO+HqZMmav1xSk8dQMqk6KGjyNudByc79GbTL/+4opSpDh3QI/eLwNPGiCtp9yJWheMKxaeWGujZk0i7C1euods7k69rgjGfhvWReqdSiT0LIwmYPvsvCEvHWaRtUzi+Cfz78F7ueQIuqpyNIhdyuXSNWscpa7Kz66pLve5Maljy5x0IZ+3XKQjyORJFpVGHA/W+p8GxMujP/9PPgd9hmEOcw0Jof+GXvRJ7TvSM0q+zFBcF+4zV8c4284JTqXG0Ful1+/HR3LmyexfHcGLNGfNegqyuoDtNQh7+yOvII5zoQoMcbD9wv8QV/Ig0HJ1AFbOyjG02nSrinOAUE30islrp67akFapTTMap4ZSay6fQbs8+6YzIL9Drohtd91lv76RvDGwz8TstBjGDRICx++Q/m9x3Mv2u3/GJ0iO2UDxWBh0GudoDBu4g4tCpUfdsg6z7zxQUSlOtgtqpNLSLzpmdbxJjI+Qbpz/yUT/sKMJu72BUHaMkkJWdyhiMey+/QgqNWd5YqUW6LsRjN7fn/hmmevIhBgzeNKEK8ZotkyqEx322+ftAv5+tY6MMx7+ndU6y70Xq7MSVrGhdfIOUVD9+4Ds4QO3Uws/vjjXqJQ998RiZJDuomKs3sJOYDIW77oNnPsmQp+Eb9WXy6QRgO0P15v3EZKsNakk2Noa3ITV6c5ooR+U01KI7TffbSVcU8/wsRzJN9nPIMWOcMo7d11r3OYw4diJe4cQzTR6IYaMvRnyf3KjP2+90xmoLFX2/shqOIFE6bUHTLHi4+h1Z8ncct/4wcNRYLsfKoH/mic/xZ+TZgy8eFkMkf85xCUnN6ZDnMSNDYtRYBnpqjpWcnXweU4nduHyce97Iio9qNaTimjjrToLr6jyn+Pk8EGrHBM26Zx5jQTWoWi/rG7y254kiOI9adYlUErfyCipCZznW0EQRRTxy4rVor+cqd4H3X/nX040rh0zCIw0P+Wbej5x4LQAf2PipPR137rjdDLqI/C3gZ+fV03PhCPnSaSVckhyp4pw+r52pwjFhwu2olP2P9BTnDG23XRx9aYno1ImsInTfrfH2OymEUgfp82rVhW6yxhczHG/E5D+XblajzmcNmvT2vjLMEsDpeBMXckq7Qd11loe/9h8SbezCzi66tQ3GOP0dMWivh3Y6PLrz09khi7SYMnJA2/ZrRkoUYhoP/S7gt0XkfwI/BTyqetQKXh2e/6efw2OPv5uHzCv7gk1h0+QUI348c9MzDyF9BUJg2KMdwzIZGBMHnA8YhwM4b0qJlGrVeXvVqiuqCSe0RVXnTjVA3ygkijJvVKoVaHfQnd3px1E0lqFwx/6GWnTssE1f4Rj2iijCnDoJSw3sUhUSJTmxBKeXkc4psGDaXbi5ju46eeSHq9/Rd1byv4eBfIqvJoYhLrzEFVBbWFAVYkB+eA44aiGXidOdqv4I8DzgJ4H/FfisiPwTEfmyaU4gIpGI/I6I/Ip//kIR+U0R+biI/GcRWRux398RkU+KyCdE5D+ISH3ai3rMvjeLA/uDZdWcA+fwHryp1zCN+thy6b0i866TBET63ks6cYyiDGac9mC7UeGQoEgqFbva56D7Mdy4spD7MhbBtUmj7pg2vR7a67kEntWsyGkmhOynsIhmH/crZQ5JrQpLS30aYVi4NircNurzPyikv5E9jCH7nklfE0niCubMaVheQisx0fUtoi89g9lpE12+idluI50e3LjlPstGHXPxgvvtpQ4YjGdJ+b+BoiurWX7lkROv5ZFT31u4+8OVV833u6y40v9p/g4IU8XQVVVF5BngGaAHnAJ+QUQeU9W/P2H37wc+BaSG+x24Jqi/JiKvBX4A+NFwBxG5B/g/gK9Q1aaIvAd4FfDvprusfoxYmy1MJe6HCPL0vIyNspgflhtH4paKI8YwvNOY5Xp+3/QH5T2Uflx7ivMUIP3CR+fPudPtNp3eSvDjWbgRSg1vFCFx7EMYXms+jrMEoXZ7mHrNSS5Ms+oJWCST9FSmRVr8RBSh65v9PqPjdMwPe4U1DSaMMd9aT6oVtwptttD1DXdfUtXLm+tus0qMWVlGW20XZ7/7LrRRdfr53R72ytXh7lOzDLkSuzGsrvCyB/4O9BLe/9SPAy6xmq1055pUn9+h5oFpYuj/B67j9HWcMf4BVe2KiAE+C4w06CJyL/CXgR8D/q5/+QXAR/zjx4BHyRn0YGwNEekCS8ClaS5oCGr7krhDbymadFwThUVjwT9eTRLXzMFadNczfcYt60cdx/+Ykms3soIWc+ok2mpBq42kTIt5X08RF35lue+dt9qIMaCaKQKK77uZLtXDhszptQx5oOFkmfPQpw25DXQisiZrH5exRCYlllNDPymOP6KeYiGTap6bPsP3RYzFNluI16LvH1L6n4E/ZnKrl90n+9Rl93q14j5X8cwbktkmWv850O25HEvK1Y8ML13+69g03LqA3+BRC7lM46GfBf6qqn4xfFFVrYh804R934oz+KvBa58Avhn4JVwX6/vyO6nq0yLy/8Z1um4CH1TVDxadQEReB7wO4P7775/icsITzRZ7TZeWAwbtCDAu0h+VNpue5ub45QPbTBuH9cYmayKxmSA7zcVL9xZ4z3ZzG+NbwwEuD2Ktj68P9tlMwxwDBVGdHIV1wmeVeZ1SGR2KSPnmAUMpTQqqLVB6TM+bXmPR8zHIqnsjN5lljaPn/b3LrwrHVRCnhjqQYujL8Nr+89x3rl+16idcTzHOs6T25KGrHVyVikHCsGuAx+x7EZlTGOSIGfRpYuhvyhvz4L1PjdrPG/urqvpE7q3XAq8XkSdwhn7IPRaRU8ArgGcDdwPLIvLqEWN4u6o+qKoPnjt3Lnt9roUOqTGv1ZyhGBjAcAjkoCFGMCsrmLNnkBNrLiZZrWbGfey4wtcDQ2Pbrcy7CoWw5uEdpvHWoVh9yo2m74Xq7i5Sq7pYurX9pJv4MBODnqDtuhWEbbaGf8xFIZAC3rf4Yp6xse9xrxXFz/Ne+yTPPFcBLMYgcewldfdojEbF84swYeLLb5OFmoL/RRWs6iUnwvez71YqtrbX3EJwXsDlq8JJXsz8C6DuIHGuFwHfLCIvB+rAmoj8jKq+GngYQESejwvJ5PGXgM+r6jW/3S8Cfxb4mVkGMM2HN1XLupTRAO6HFXZuz5fe54xGZqD2o7U+DXzS1d5az9q8jVw+5yl3BGMMMSpcMCtyRiQrmAFkeQl6CXZrK7unUqu5ylkfJ0fVTaSV2C2rr10f1JIZOt2E0EmBUU+bOGu4+poiHJKeD3CTUiUe1CufdJzcMbNJLY4R0w/n2GZz8FrDxHHaG3WSkuKsKPLSJz3PvxfQbkfWK4ST3hzyC07bxrpGKUZ4tDX/Yidw4ZbbMeSyJ6jqDwE/BCAiL8YlQl8tIudV9aqPwf8I8BMFu38J+DoRWcKFXF4CPL6IcaZGf6JhVx8nHGMsstiqh6nXyPpcwmzL5BmKidQqttVG7PpAs+Nx+2WVrItGmOBMDV+1iqyuQK3mqIhxjGnUXUItvVe9nvNGV1ey51jFNlsDk1XR5zHUbDn0+rONhg2T0znJfbYTEqwSRc6jTxLMvRfdW09dHo6nTzJUYb0BznFIVyEpm2NohSFpqGcKEbdwHEVGdtRKc7+hnSwklQuPpY9rNRdK6vb2nMgfPqX2V9SLZlgfsQYXh8HS/3YR+QzwaVyi850AInK3iPwqgKp+FPgF4H8CH/fjfPsiBzXVUqyI5pWjUIXLRe10nGFKqVKzeGpRhKnEjlKZhk5GhQBwBto2W4NsnlHnK1r+zzLRTDsRpNdRrzmOtvc8iWPs+oZjNbTaTr1xe8eVoy81snZ1ycYmybUbbtuNTefFh0ybos+j8HK1/yNPP4s8bXEUjbFodVNADZVaDVpt7IklzLkzZGJo4bYTvNmQhmebTRc26nUHrzH8rMLPborPUOIKpl4jWl7CNBqklN2MJRVQX+fGTy+4l2rV1Rc0Gm7iC9oTDn23wrDcNJTcYJKzzRba7fHSxnctTMM99dIn/U08jkhdRP6HiPyup2v/A//6aRF5TEQ+6/+fGnecAyn9V9UPAx/2j98GvK1gm0vAy4PnbwbefBDjS/GYfe+gpx5m/mcxeGr7ib1ZGTSp95SGASDrYJN5keMwIpFV+N4Eb7FQ3IzUw8p5vSO82EzXxsNUYmy7ne1nW+3MoGiTfvwat8LRfGn6KE9yiqX/kCc7KrSSn7SGJvDgaTqsbo9obRX5wiVnSML48SxGUcNOQlNmsiexmdKJdWXZMYci4+mEtwYE1/ISwPtG/ljBOLXbyxK803yHpg7FhCu1Xj8pupBqW2CO8fE28BdVdVtEKsCvi8j7gb8KfEhV3yIibwTeCPzgqIMchod+pJF56uFydtKXqMgLmUbjZIS3kSWLfJIv7eAzdLyQ+pb+5eL3aWHQUDHNuHGMYAeIEUyj7roVNRoFsfjCixn4SxNiAz+8tDuPZz1ItdL33Nrt0YZ3Fszizc7g9YbjsrfWHRMnZFAEoZmFhLim+AwyB6Hddlr43Z5bFXkWUMhWGZz8+ucYWsVMg1EOhNrs+5xf1Y491l4+f7/fIkTDmNI7n8ZDV4dt/7Ti/xRHDnmXf/1dwLeMO05p0AuQGfUZf9AzIV1Khi3msrdksgFIE2j5tnhBrDRrNuyLPoZ++MHyWuLKwLI73GbIaFRiZHWF6PTJyZPFOBQYTu10nEduC5gSgVGRKOq3wEvfO2jkz5lYUHXx/jBpmUoY1GuD93i/Y/aT+SSjmDkIqUywMdlEmc8rpKyTodDOfscZnGNhGDPxTEV+2AumZ7mcFZHHg7/XDQ3fVdV/DLgKPOZDzxdU9TKA/39+3HCOldriPPFY8u79h18mwR/HJdD6y0LTaGRLYGfURi+DR45Hnba6qcT9RGNwLQMGp1bDrK6Atdid3azBQxiPzwSYOt0sQSm1qlvCN1tZcdZedUfyrJV0UssfL1N59F2XTKOejXfmStZpE8+TwhBico3CNRtr9nhtFZadNIDeuOUKYKYJoY2D2umah2erRkiuXutPBEVJ4txx5tKcfJFG/DDOE0CmP+V1VX1w3AaqmgBfJSIngfeJyFfOOp7SoI/BUEx9HAqoV4U9Qf37plp1Rgmc/kXYgafqmg/YGzdHn29KY5AVQYVGKWfItNN1lZjViout5qr7BrZNElBBt7ZgKzhWevxZvbEiyhr+fkm/YGXg/CS+M5BvG5dew5QFKenKSJMEqVb6Mfr8Pcrft1FQ20/sBYZSbT8hrq02khr93d1hg7oX5EMjk6AjmDzjdikK8wXHmyvmGb+/jaGq6yLyYeAR4IqIXFTVyyJyEee9j0QZcpmAMPwyVso08LZNteqYBEtLWbx5IDSRxhC7PSSO0Y1NV/683AAj6PYO9sbNfrhh1i95PrYejG/osf+B2u1tetdvuiTuhKX2gChSYPgzNkLI8JiEEZNMur9pNDDVqmP8BAlTej03AUHGu58K6XmMuCRhpYJUK/1jj0uIjrwEze7bUCFNeq86HfTmOvaZa3vTxBnFcNpLbDn/+c8S/tnrOWcd1yjMGqYKQnTzVloEZgm5jB+myDnvmSMiDVwtzqeBX8ZJr+D//9K445Qe+h5Q2OotSKKKWtRaFwoQ48u2B3tTqlUkIuNVpx56ymke+MFPm+FPkd9uzP5Fyc8hYzOG8YGEBSMBt31GDzd/HrXqCkNSymNikXoNu7nl2BFiMm1tQ9152VMyitIKU8d/7vYnhLx3PglFq4oR1+VyAwGrY9Lx85/ZIgzo7egRzzBxh6J1U3H1Zx7LdAnPKXEReJeIRDhH+z2q+isi8pvAe0Tke3D1OWNDBqVBnwJh6CXkM+c1PVKkPSqdN59Ar+BHPBQyGQ5xqFXnOUZRJkg1SjVyJIKEZr8RQX8M0+w/FgPjNfR/NHvQfskZdjGSNUPg5Aqouli0tS6EsbriJstuDza3+k2vJ5wjpZJKMAGMLTqaZswTtgmbM089WUyLKWL82TEHvg9HrMxxHgjIAEBA+e3yWPLu+Z9vTrdQVX8P+FMFr9/AFVZOhdKgT4nH7Hvdkm2a2HWQhJpmu4GXcgJJttvDiMk0ZEwU+cTbFF576qX4vpSEkgBTGowBxo3a8eGCwGDM3JYuMDTZDzL9v7qCNmrOb1lbRnbbyPIy1Kuw20Q3t4ZL48ch/HyCyWNu/VHHnDev9JgvOttTO78pvgMOUT+Ml98v3E7GVJAeBKRfyTrT9zX9vqd5Cz/Bf7Dzc4sb6xGbE0uDPgOGmC+LhjeQmUe5B3lTF0MUxBioVoYZL1MiTejR7SJeXqDwhxYyYiaNy28farsASKPuNMbBdShqO9leAFaW0JUGKoLZ2Mk6GPVlV2f8haVhEzFIFIx/0YYsW9nJ7GOegKHcxTSGMfjcUqXOAc2ifQ8qp3k0Dn4spuLO7xQcB4kGY3Vh/HgXwj0PT8VMLJcDQWnQZ8RMzJd5IAxphBVzo5Jk+ZeSBG0WFAnlfvTjfmSaJNBselZIddDLmzDmkeGAXGhJ/LGJYxdiadRdAUyz5ZQWk8Rpv/QSpNVBdnZg1YVhbGtvk1Q4loGVUZDgzucC5t0Qe6gadz9x7SI6IjAU/hqVU8nyO2Z4bPuBmCzpPMQmGgXN9TGYFPpLVzz+PGGf0oXhCIpzlSyXPWDuEpwzwvgiFVOJMwbIUIER9BN9BayErNhkgnHOtEW6PbetZ3NMzWKZAbbVxm7vOM0W36TAnDnlVBZrNbAJdDrYS5ezZtEsNYjOnx1hyPaHgcrJZA/e/0Ejl6QdQsBmGVtduiAWy746XvkxjavAljTXNOdJd/y4pvw7IJQGfY+YaNRlQrn3XqoE02VrGmuu1QYohNN4Pf1DSf9HPc1+4Q/K6uBKIaCGDV3zlAnATOIglDvodNHdJlhF1zewz1zFPuNpuEniKjOTxHWT3y+KJj7NcfJnwZiqxYljGHG8aauHTb2WTfiDb/c/84VNTiPGZwOe/qIQ6uEvhKJYeNIp/w4IZchlEQgTfEVxyHFhkzwKPGu6vo1Xuz178iqfENzHD8wZiP5XaF8VhUPX6ah+0usiu7uukEiMK0tPufPXbrhwTNbIYAovfdZr3sv1pCEG6WuUzyXhKiGLKP96enj17fqG8y0D38O9OBR+v2ziTicHX7w2Nlm+SBR9pnu9vllPfcQWbaVBnwMkMyiSxQgnqgNCFibJysa7vX5XoKGTBGyM/LKz6AczC/87uIbsrTHe1ICnl280Ma8f70A8N3Gdi1IdlCChaLd3nFaNbyxR1PosGPjg/3mNteBea5IgtdiX/1sIFCb3jBGslCJDWjiBjHo8A8SIi1OLuF6z2zvOoB808oVoIVXR3+dUMnehydHSoB8vZIqGjbrzWJqtvmEZ8wPOBLFSnjk4PjUU7zuOCpnjGYfbz3QtlTj7caYSuYXGIh9PXqQHFni36X3NEEUILjQj1Qqm0XByvNGICWaBdMSiMWvYEGUWSLEnHWqGh3RSgeH2gNPy0meEWoVOx60Qd3bBWiefsBf21F7Hkb/+9LeXqnPKgqmKKbRkuRwrPGbf67yAMK4dRdgJ+s6I1+BIeyimEJNVtiE63FUo5HnvdXlb9CNKvf84diyWbteFc45gEjCTX0jpmEtL0Gy5H3clRpLESSpME36ZBvn7Ne7+h9uGn3N/8NOdK4+C/dLCNqmaTH9nauxnYlML+ErptG/rmLh/4flmzbGMOnYUYc6dARNBuw0ry7CxBc0mL61/p9sujnl0+13jj7UfHLGfSGnQ94mUcqettmv/lteR9l/YgVZ3Rd62j49mlW0MqvU5A1EJduhvN0speWaMisIEo7ysKSmSc8eIH37GTe50+7HcSoycO4NYdYZmewfd3R1Oas467jxzRHM8+7AIBnd/M0G0cYZu2sl4xGtpxXARJXXRyMKKKa1w1HWOMOQDfXb3mMfJVmpWYWfbfQeWGsjZ03zgs/8vAB6ufge02+4/LMRrL2PoxwyzlhOH7JgBPnueb+7/Dyg2Bl1l0g5GIxNleYwL2aQhAtsb3iesNk2SrGI1a0QdriL2mEAc98PvL+eD8I8fr0Rk4RZttRER7GoNExmk20VOn0Sv3UB7vUwfp4hvPhFTGFqpxM64eEpnYUGOv65sos7yBIHoW8B7H+v9FsTTR03UC8EcYvC0Z1hZ5JwKTZK+IqmIE2sLuiDBYgz4EI6YQS9pi4eIkdTHPE0w6OySdfdJDVQYssljFtpcSNvLeZcpv1dqNczKslOPrLjwjMSVwbj2qPPPEE5IYeq10VS99P74zjdiDPaLT2HWd+DaTeytdexTvh2c75I00Ahiv0YvxyyxrTba62Z8/SGD75khphJjTqxlnZ8kdkqPEkWY1VXM2kq/MUlR45DcPc0ULuMKplEf/VksAntgkqS1DGmbvpkm1fB3MRDKUsyZ08hd5zB338XLHvg7M49rT5iWsljSFu8czFx5OkvcfI/JpoEwj/qKPTFIksDurms/5yeTST/KVE53wDuehn0jhmRnd+z1ZAwbn3xEDMmXns6qBfNe8L7iteH5Z2APFR4rjpFTdfe0lzhVzijCrm+4lUSe9z4qsRtOKr0uJAGL56h46QVGfxZef2GDDfH9BCLjjPnpU+jairuX1roitAOAUIZcShRgIL4Oe/Jm5wXn7Rnv+Q6eO+Wu22bTvzRdMZPaIHxQ5K3vJbmbDdgMTBqoazYhldhps2TVngpEfe9ulkTduG3ph8Ukdo1JMJKtCvqH0EwrBmtdmKBeg+1d7MYWUq9l3Y7SyWkqzZO0YQWwJ4XLUQjj3ZMmxRETSNZiMQsniUvgFjkCBcn//Dmy+1ytunHFMbKyjJ5YRrabWQvA9z851IN+YbjjDLrX930ceFpVv0lEXgj8BLACfAH4TlXdzO3zAiAMTj8HeJOqvnXR4z0sPBR92+Ql7Lz50wWwkzjF+aTgBAzoUI9jPez1mlKjpv1QgxhBGnWk4pPI3oDa9Y0htcN0+0F64IgxFpx7IPThVwa2VcwQEiMuBxHHoAo7TdKG0uonSXd+15Vp6pDGgr4PGTUwExAr7gEwrngui5cDGIPt7BbnfQpyAv3jJK5QK46zXgSyvOQmxChC1redtw4HaszduA/2dJNwEB769wOfAtb883cAb1DVXxOR1wI/APxouIOq/gHwVZBNCE8D7zuAsR44pi1R7vfYTAYN6iw/5uAHGGKv1aZjDU4B3W9uoaHCQ3jVxdRT32miURuzuoK99zzSTTAn1rBPXXJ8/3zCt1qBbg+bccf74834ztUKrilGrz+RhNcQRW7yIDBy+etLrOu/Wq1CpzNclDODB7xopNTagfOHnnIUjXYA/MRvm013f1O20agVUsG+WY/dSow0Gq5/ba0Ky3XYbcPWNtr2SVARPnDz387r0qfHnWTQReRe4C8DPwb8Xf/yC4CP+MePAY+SM+g5vAT4I1X94qLGeViYRW8i9ZCccQljw7N57VkvU697sW+M8rrzRn8Uk2UvksAjh9KvvNVuz8WmV5aRVg/pdJ0xHeKFe4+PimPDFF0buGpUYwqrdNMVgm6P15RxjCCFSsUZ82Yrqw7uh6L2QbGcJ4Lvl5hU4jhy9y8yLgntVS7DgqdwX/fQSREMvJ6uakZVuuYretPJVFzhniSJW+HUa9Du8IFb75j/9U+DI6i2uGgP/a3A3wdWg9c+AXwzrjfeK4H7JhzjVcB/GPWmiLwOeB3A/fffv4+hHiz2Kh6kVrOKzr2IRqXGY6w0Qd6rnsSbDuPik+LPfvuMyeHj37bV3rcnmiXQ4hiz1MBubaPXbyJnTkG7g712vR9nt0Hc2SrQGtQkyaPXG+2M5b31gnFJrZYVPw3smiZ2p/0sZ7lHgdE0q6uOl58/36QwXhZiczRBoigLZQ3RKqddgaW0zOA+h9+H8Bql0XCdqXwlKOAmZiO8/wv/YuItWDjuFIMuIt8EXFXVJ0TkxcFbrwV+XETehGuAOrIRpIhUccb/h0Zto6pvB94O8OCDDx6x2zsaA3HbnEczatssTisCVrOElZ2yl2bf8MwwEWRGoaCPanDcMIk2LnmWxWWjCERc4wyrWTu4iV2eRp0fF/8XI85wtT3V8sQq9uQy5qYz9K45R7irIsY6jn0WhvENwYNuQgMTwV4nHWOQRsN5lo06stQg2m2S3Npg5GcyTQJ5DNQq8d13DcTo0+MO1jiMPQhpnYIkiZNXmECPLOy7O3B6GQhjZTUOiXXfcd8AXMS4frv1mushay3v/9JbJ132geFOKv1/EfDNIvJyoA6sicjPqOqrgYcBROT5uJDMKLwM+J+qemWB4zxwPFx5Vd9L9dSr1DssQubhLTXAeI2QRuS8xlRXY14a0HmPK+dJjcNYOd6golKtIrFnPGzv9D38fdIK0wlOrSIk/dZ7iY+vV4bDKtmk6BUshzz07PnepV/Fe7Xa6UDTwIbjAMhSo9+ZaRRyseupOgj5e5neb3vtuj9U4BGHBhWg0x1f05Af1oSOQePez/rkWoXEYtZWvL69xZxcc4ZdxBnvp3586jEdBu6YkIuq/hDes/Ye+htU9dUicl5Vr4qIAX4Ex3gZhW9nTLjldsUHuz/Pyy6+HpaXXCz76nVvlCH01EJvVpMEvfc8KkJ0c8tVQHqa20IrBPfgEY46TlrdKUaySlOHfTQ+8AhZEVKteH0Ti+w2Ma22mzT9/RraJx1iSK2EoVj5XuGahm/56w5WUyH9c0o4Q1wZnniGN8xWHwOdrvx7qTMhxvgm5P1iq0JjnL1HPwaenwDVjrxPmRCdVVfZ2+k6JtJd56DTRc6fdkb8xga02lCrHnljftBFQ9PgMHjo3y4ir/ePfxF4J4CI3A28Q1Vf7p8vAQ8B/49DGOPCoedOkazW0dhQAewz1xgl/ZoyIcwfPYVZW3E6JZ0Cb2qRtMZJS/8JceSB7byXOWSQZg0vBJ65VGLM0lLGebbbO86It9qZ0Fi+ucMQFS89337v34hCpKwvZiDbMHXTh4H7m2tblztmKiU8cHwJjG6aVO+BpvtXK0hUc4ZdzERvXYxglpfcITsdsDoY+gsZQun4fUhMqlV41t1oFJEsVegtV1AD9Wd24MQqbG4ffWOe4k406Kr6YeDD/vHbgCGyqKpeAl4ePN8FzhzE+A4D0k2In76B3tpAg0RZyPpw3nc/RGC3tmB7x5fde42TnDEYJ3u7t4HmGhpk0gP+/VHnCEIs2RI8H8PfQ+l4flwAZmXZJT4rMbZWwbS7mOVl2NmBkyfg2g3Y3R0cb1i8Mu8JcFxycCAhPX2IY+y5guR1amQligZ7reZCaSEtUIz0heXSiXaaexMZxIdHBoTdwlVOUCgV5o20GtO8exmNBNNTaleaSKeHbVR49PdvD2NeVoqWyPD+T/1TXvbA30F7rhHyxCU0/QSeev5yahjyCda+Nzos+p9uMxXCxJlapOaMBbu7SFR1LwcNOUb1NS0MCe3XmKciT1aRM6ewaw1svUK03oRbG44aeOYUGhvkxCpSrWA3tpjLZLKPcc9y78dum6spkLjiG4I3M8PcLwoqLopKq0yHVlbTjFGMWylCdr6+6FhOAz51CqoVFz5Ui3QTKls91AimZ9GKQSPh0Sf+weRzHyHIEZOYLg36ISKlXaXynpMQNma2QRI19XozjewwnpxKlfrlcJpYm4X/7SovG+5xJUZWV92k4sM+Yen9QPelcMIBspXDiHOkx8r48ZOMWurp7jad6FWzi1Yilzhe33CND6ox9vQq0mogvR60jEsAHmQj4RSzrASm+Ez8A3fv/cSqSf/9lPVTGOveC4LPI1ylmXqtWIMmuBZNgLZfRZw7Azstqt0Eu1xDI+GDv/WmvY3pMDHHGLqI3Af8NHAXYIG3q+rbROQ0rmr+AVxl/V9T1VujjlMa9COAUOYz46eP8GZTudoixoNazRgEZmUZGk4Aim4PvPCT1GoutmwtkulqjOaZZy3gvFDWkM5IOrY4duqIPo4tvmjGxbL7q4Z8B580/i1+rLrTHNxmlFEXl8yTWg3WVtxriXWJtZVl7P3nnffX6mJubLpuNt0e6GAoamRF5zwxz4R1ELowayuOPdNquy5CXiulb7wT8nmCcOU21sCLGfqsTCUe6FaVTioDoZ0CpDo2UvWrut1dpGng3Gke/e037+0+HBHMMeTSA/6eqv5PEVkFnhCRx4D/FfiQqr5FRN4IvBH4wVEHKQ36EUNeX/0h88rMy5JaLWtX5xoo94aqPTVJHHNmRzAirsQc5zGnFLXMex7gXY8x6oEAlJjYGexAO8W9bhAx6OoydrlGdH0Trt/A1Gv+B58MaKdn19RoILVqv3oyMgh9kauRFa3qqj6104FLV9x9uec80knQmvPWWxdXqHYturaMXL6e6aKn5++PZW9Vt2MRsEDc8xHFOLMeM2DmSK0Gy0tO12R9w92PdhvtjGANheyUyCAZ82c4JNOPe/fj/kPftRnorOnKjSjiA9ffPtt1H2XMyaCr6mXgsn+8JSKfAu4BXgG82G/2LlwusjTotytCzfSX3ff9EEeuKKXZct7Y1WsDXlNmNNttbNhBSa37sZM+TQ20dZQyn7gc9yPNqg5PnYTVZfdis4W9ct0Vm3R7SLuNiWNsGpKZZLySBKpV7PmTSLPrjlGruiKStMR7cxu036UnDD2JMb7JBX6ycwa7e7KOxoJYi9zcHEzapR5zmBhdAByjo+GkBapVF9/2tL9xDZ5HHCwLbeGT1Npuu36izZZbCbkDjj1mKl2QxbRxnnf2nl9JhZ59OuHtC16g69DK9BeEGTz0syLyePD87b4ocviYIg8Afwr4KHDBG3tU9bKInB93ktKg3yZ42T3fB0sNqMQu0RfHQM/FtptNsuV1yioZ1YEoxIBWSdV1gGkNM2fC5bdaxV6/gWxuuUMGMrHas8V0t3AVAANeJlGEbu9gGnXsUo3kKx/AtBJMpwdfvIR2+5PCgJRrkriORd2e8+RVkWu3XJjJuIIiNdA9USeqncfsnkI+9+RQCGIotjyqyCk0/GOYPdnDNJ8Rxy4Ukt7jNGEZ+4KeNOQ17rj+nokRZGU5C7PoTtOFW4Iw0tgJIihS0iRBejKYsxjIyfSvaej+7AGHkrM4CExv0K+r6oOTNhKRFeA/An9bVTfT7860KA36bQLd2s4EimS3ib12o6/1DYHRnD3xlYUB4niQBgdZ0+oBbe+EIcGlUZ69ySiZzntOJ4XMC2w23fkuX8GcOYVtnMZ0esi1W47XHK4a8nkFcR3fpVbF3n2WpF6ht1IhavaItzvYiqFys4l56gqqtr9imHRvRhnzKe7pgDY7uHxF2+nUDEx2OXnYdN9RBtlN1upoqz4pjVoXAksleLs9mEHWYSCEMmkSyMY6O9QqtNtIFPHSlde4WLq//ts6/KLMtfRfRCo4Y/6zqvqL/uUrInLRe+cXgavjjlEa9NsE2unA1namFmhOn8LeWkeiCjZPeyz44Q3odgTGN0ueJUBa/g4DIkmDhTgppqiilMEmDUXnRaW/bl3fJK5UsMt1xA7z68NYb0pblEqMveccWolIlmJ6DYPpOgpc9dou5tLVrCHH0MSUl9wdR62cssgpXR2Z5SVnaH0iOg35mCVH/bS7u74FXezCS+JWGdpqYzud/pgC3nyq6pik0gFeXz0r3hGBDsV67rkx7gmB8wBBQjuOXTivKGQXFlWJIqrY7R2yAqPbGPPkoYtzxX8S+JSq/n+Ct34ZeA3wFv//l8YdpzTotwnUKrTamOUlJ7vabGJOnoAkIapWPZ+92TeigYct9ZoTwYoiZzS2d9B2e8BDy+Lj9ZpLtp064QzExmbWGCLPepjG0y2sHg1ZO6k4lrqyeNncwvQSJ2QVxyg4hkStBu32gMeuSYJubiOfadH9My+gst4m3jaoEaRn2X7uGo21GvHvfQ6sRVMRKD+GNOHnKnSnFDibgOx6IDPiEsf9LjsAIphGwyVprbokc7XqtNTVDhrzcJIJWEepBEAaO0/L6UcmXefJtAnH5QvNiOOxekTpd6Hf6LxyPMIwOjeay4uA7wI+LiIf86/9MM6Qv0dEvgf4Ek6hdiRKg36bQIw4A16vIWur6PWb2Os3svf73lzQ6UaMYzOsLEO9hi7VkK2mUxz0S+D0xynVCub0KddAoBJjl5z3JHHkmkQ8+bR7HoXnimYz8OmYcrHYLJxge9j1DaTdcTHwlJUSrBpSgxAKcGm3R/Wjf4CcPcPOV17AVoT69Q5Rx1L50nU0jUOK9ENI3iMXI6PVKsNxTmkQTSV21E118XO7u4t2OpiVFYhcMQ4wNJlqbgwDK5LwngUeb7iPNnNyyAGbaF/MmlFIJ5deF9Fo8vbp2HyoTJOEDzZ/bvI+Rxzz8tBV9ddxTn8RXjLtcUqDfrsgirC31l1RRmKR1RW02x1kkgwp3SXQtk5tz7c9y+uBh2wRqhXsSgOztYvZ2nEiSXEEqyuYixecjKlnV9DpOo8sLCiZNsZasF2/HF7Qnd1BLzVJCjv7aEK2KgHQK1eRLz+P1gy9RkRSNS4k0ai70Ae4iaLbRRJLsrkFRJmRj06fIrl5ayhs0Kc4RpmBDO9fPpwlvV42IZlGw3W5980viibA1HBHa65tgHa7br8kcffa3+OQ9pkfX2G4bdQKaV4oSr6PC1MFIZ8Pdn9+gQM7IJTiXCVmwSMnXuuWdJ6Drru76Oa2+x/EVweaNYxCQGFMGRPm7Bl0a9tzlztIs4VJEqdUuLGZ6V7LbhMqriEvcezCH80m0kuyUM9w44hBDIVrCl4PZQamKkfPGw9rqP9/P445e5qNP3s/OxcM3aW7qG5bqus9TM8S39qFRJH1TeJqBXv3edoXlqj9+u87rn5K4UzHlGsmEo5LjGRjlWrVSRwnLlxEZDAry2i7janVsNs7wwyg0Iv2nHqp+fBYow7rG24F4pPDo2Lg8zDaY3Xsp9k/5a17YbBxwmvHwph73El66CWmxEvr3+mMto+jpuqAGdKwSOQ00ENmRBYLzjYdrasSSqbK6oorSqnErjfj9g72xs3B7VNj2247DXavXW5WlmG5AatVpNVGW22kavzYBo1CKEVQaLwDDMgWTEJO7Am1mXiYvbVOZfdeGjeF5hlB1GDjCmu/fxN55gbJ+jrmxJrzrp+8TD05T++rn4/pWidPvN3CbLewV6/149KjhlGtuvth1YW1dnZdJWSjge56eVwK6JwD9yhI9p5wXrp95prTyk+NY57FNOM9GrqGoUlh77OCGHGTvG/xJ7Wa0ycqCK/lC+dud5QGvcQQbLeHWNfkImxWMWD8Uk8wUGYc13ghTZ65J6mRTCAxYAS9cdMZ6FAnPP8jz5KPZMeRyCe20iYE9RrmrnOutH5nN2N36OYWttnCVFwhTCYBXAkMdsqHNnbQMI9iY4RGPCwuOnsGvbXuPFx/f+KdHtsXY9RAbSNh+ZNXYXsHVleQZtMZn1oNzp52HruF3koF6SmmHaEnltELJ4ieuk7v0jO5YQyW3lOvOXmFtHlGFGXx7TBROES/FN/sIc11pIlrYzCnT7quPbtNF25ptd3KycviTuVJ55KoUvPVmmGFcXCcTPdnVk9dDCkXfogKeYyNuQu5HK2YS2nQjwJSY23EFefkvPO+jjYusdeow+7uxLZhWWx5IPaa9M/l6Y5u8+m+mI473oIbt5Azp9DlumvxttOG02uocT9uqVSIul3srXX3POd5mxNrIILd2s4MjbbbWRNioL8aCYS7ZKmBnjuFtLrYlQZac63sOLuGabaRdo/uXWtc/ro6GsOZTyY0nt6h9Zyz7F64yOknrmMuXkCrFXonG5h2D+lZoq0W0S0LsWHzy0+y8qVdoisboEp87gxJunpJKzaXfRf6bg977YYz7OnqKjchj4wlQ9YpKa34lKUl5+nvNtGb6/39fQJ7VOhqHLLK2l7P5VIoDoEN5EJmmTRC9ko63nRFmV9JHTOU8rklijEugZXR1gSb6npPeUyg8Mc5iy55qCUjSw1Hr/MeunQTos0d6CXOEBvfVq/dzjxWs7oCS0uws+Mol0nitFuSBLO26ihvG5sudLG6gn3mapY7MCc9fTJJoJdg7z3vFBUbNf7o70UsNTrsfO4EGivRziqVbVADZz+eoJGw/mURG88+wdmPd1i60qV9zxrSU6pXtog2m6T879a9J6hdc/d29Q83MRu70O3CUgO9ftNNQN2eC4vVa+69ZstVbHa6QzIHefnY9D5mieuh5GigOd/zHrn2Rc0GmoLP4j3nEpfiJ/d8EnUgCT1rHL2owjR8L67NdrzbCaVBL5HiITOWUjqEmeKcOc9oZEOKoEgkfX+kB2iMM2aAvXodbtx0Rj7lrCfWhR6iyCkePn3ZhRFWV0jOrqJyEulZTKeHjQ3S7iK3tmB1GanX0GoFbVSQpfuQ3bZbCSwvoY0q0nUGXTo9tp6zStyyLC9tsbNb41u/8Te5WN0A4P/62IupfrrBxnMi7n33F1h+6izSTeicbmA66QQmaGxoX1zFRoJWhOqtDlqJaJ+tY2NB7AqNp3cwX3yGzYe+HBSqWwn13/i0k0hoNp2GzIBevIsjZ2Gk3D12D1P54sH3ZbmB1L065s4O5sJZdHPbr8T20aIvN5nnJ3IxnsoZmb7Y25RFVJn+OQxPNuHKMrH9VdcxQtngosTBIBB/Mo062usFdLuctx7wmx19TxiQW42DAhARdGPLhRV8rFSbLaTddomwTtcZJl/ABECnC70e0UYTFUGrfsnfsyRrDfTUErYSoZHTXhF18e/IGPTkMrZeIWlE9JYibCTUbnWprfewNcOFf1qltxrz4Ytfzz968zt4unuab/2K3+E/fe7PknShd985xFo0NpiepXrVUQd3nnuKXmON3nJE3LJ0ViOe+ZolVr8EzfMgvoD1XHeJeO1Z1NZ71H7rD5wn7sWxTKNB0nEa5K5i0sW/Zdk3mthpBvz2fjgim1jDiVStmwh2my4PUK0iDQbCN3v9HmT/Rxhpta7YylRi7CTJ4v5O/XBKBNrrYuq1QuXGVKv9g83jw2zJoFo2uCjhMKt3PoBxJdzemJvVVadauLyEtNpYL686RG0MfvSaJJjlJUwcOzpiwHPXhKwoJu/Fa+KW8hJFvgy84/TTE4ucOuk8d3Dl/L5a0zZqxNe3YWcX1lbonl0mvrGLbO3AypKbCBpVok4PW12mstXj0ovq2GpE+64eX/bcy3z29++BquXsR+FNb/5e6rcSSJT7mi1MJ0GsJdpoYpdrVG420WqMrceIwuYDFXbugd6KsPbcW/Q+c4rmOUEFNIbuKohVol//OLERWFrCrNUyMTBV61rfpSsbI26VUq0i3S4sg4lMMUsmjS+rdVW8K8voxmZf+rjTdQnc9P6OMrShamPBOSDUMC/4zvjJXJOEZGd3eP8xkg7p/rKyDM1+jgNRl5gWyUJuj7Z+tvg4xwFHy56XBv0wsC9jDhO9J4icXka77Tyn3WZxU+ngWGmi1PqiHvdWnls+2ltMGTeJT+RlWh1e7tcuVdHYgAXT8bFca10Yp9NFLGijAmYZub7uJoQogrsvULnZpHeiTnUTbAXkjzcxopz/shucauzywq99mvf8+tdx/6OwfV+FqBsTtZXapqXz7GUa17tEOz20Yog221z9UzHVTXjW+3dJahG1z25xcbnnQjw9b1S3tr0sry+p397JYtwhG8k19WiAqpNIsOoMfSVGGg1swIIwK8tIo+7yCJ0OUq07o9dqDzCNCkNe4zxsd8Pdv3SCgaxxiFh1dQhpgVZYfDZldW8+iZo2rdCdpmtc3um6vq7VCnrZ60dFER/Y+KnJx7+NUYZcSjiEP5L9aG8UeOuZtGynQ/L0M9n7k6RVwx6TU7eoK9JlSY3LUsMZZd9JSBLF3PICY+sbroGzD2HET113zTjiCCoV17TBWri5DmuryEqN2oaSfNsN/tLFP6JtY1iDX3v3n2btFW3+xJ/8Ajd+7QGWr/SobCV012I6q4blp1pIojz1l5Z51i/fQiPhWf/qUy5EtL1DlOYFdsDeuIlUq9id3eyabbPlL9MxkAYaZvesE/5Kxb887NbW0L1xN0ihWnGqmeubzsimoatAu2VWvvlAsw6vcy5rq644KTLopSue6STFIZzwu1ZgvIfO5bdTq0hKQ40MbGzBidVjp3k+EkomlndUsHCDLiIR8DjwtKp+k4i8EPgJYAXXI+87VXWzYL+TwDuAr8Tduteq6m8uerwHAZOqzMWx01VJBY5yvN1C9kn4w0u1TaJoJENmZmbEOMbCqO1zY848/GYL2h1kZRkDzqC1O66ytNly70fGacisLJGs1NFqRNzpoidX4Mln4L67SFbrYBXTVewvnOHX7RnUQP2WZa1m+f1fej5xC+o15ZE3/f/46M0H+Nyn7ufLfr7D1R9os3FlldXPQOfMErVPfgmWllwlbLOVUSFJEleUlBrzoBgn1GE3J0/461bsZvC5ZbdjWJEya9W2s+tCTAFSBku+peBEemLATsm6WamrZSCK0M0t2NmdrvFFwWtFBV4SRZjlJRdGiyPodLDPXMVubrukaqPB+//wn48e83HE0bLnB+Khfz/wKWDNP38H8AZV/TUReS3wA8CPFuz3NuADqvqtIlIFlg5grAtF2gzaNOqukXFQMg8M09tSFHHFU+562OYsQCZ/65sHHxhSdoNaR10U43RURNzzOHbL9HNnnBAYOOOw28REAm2DLtUgUXjW3Whs0NjQOVnB9KC2YTEJNE8btu+JSCpQvwVJFUxPefe//0b++d/8Sb7mubd45uVCVw2v+dh307p2kvXn17hw6QT2S087lcpn3QvtjjNM2zuuWMnEmXbKgFFLhby2dxznPElcOKaZ9JPKvklIpr0yQkp2IIa+tORlGWxfmSkZ0Sik8Ha7UJf6lQRq0a3u+DzLKKSsl3SCMAa7szt8L+pVt+pK3DWnnv/7r/3E9Oc6JrijQi4ici/wl4EfA/6uf/kFwEf848eAR8kZdBFZA74B1yAVVe0A89E3PQQ8ZF6Z/YilEiOnTzoe99XrLoYacI1D2leKkQY5x13PjES1OiDHmumdz4LcMjw858A24evpMjyUIkgcP106HR+f9zTK9Q1nxOo1p0q4vQNXrmPWVrAbm07DxFrEGCqNOhWRrIpST67Q/NozxE0lagtJDTSCrXsN/8/X/QzPq9ykQsRdkeUNTz3Ea573W/zaqefz8Y8/i927znPys+c4+Xs34epN7MZW/5rS1m5FZfJiMl68vXAKrcZET10n8vFzKhXPv++AtSS3Nkg99OGOP/3VmPR6mTRw2C1qYrgrLBwrSnaPqCAeiTDUIooYk3WLCqUbSBL0qcsQKFhqr8ejOz89/bmOEe40lstbgb8PrAavfQL4ZpxQ+yuB+wr2ew5wDXinD9E8AXy/qu7kNxSR1wGvA7j//vvnOfZ9I5/87HOW1XGrz5/1Hp1Fr9/wnl2BAQ/DGvnY+hDHGAg7/UyKf48ory9s+DAKYfl4ECpQ60MJnW6WLE2vLDOavR7WOmaE9rpOxMq6Rg/OuFpsy3HMswrNTodzH9p1RUzAzp+44JQVVfkXP/wd/NN/9m/4jn/wN/kLf/ujdGzEf7/5XC5tr3Hm2be4eWKFnc06p3bbTmvFCFgnHyxLDZcMLbp2ta64Z3ML0+thL55zRs0Y103qhG8pZwza7mST61jDrBbbamOqlX6ycka9eX/TpzvfhGOFTJhs9ZgePxUQC9QtAT7Yuf0lcPeMO0ltUUS+Cbiqqk+IyIuDt14L/LiIvAnXjaPI846Brwa+T1U/KiJvA95IQWjGN1p9O8CDDz54xG6vR1iCb5Xk0hXMzZrTEomMM2KehVLY2MC9URxTDxF4yaHHlhmocEgZGyJgRYTiWREBd3362OtQOXkwlkzHfIBF4/tapi3tPA/bxYLNIDtHrUtCrS1Dt4u9fjOLaUcdS1IRUPjhN7yOmloev34flSjh6vYKGzdXiK9UOPVHwvlf+FT2O5Q4dgnEVjvz0KniWSHD12tbbbftzXXXBMSrJNor1wZXSH6CGrzpwyE1MYLd3B4MaeyhgGjPfPVwTOlzzTXk9oqTcLyUEvcLV1h0tEzOIj30FwHfLCIvB+rAmoj8jKq+GngYQESejwvJ5PEU8JSqftQ//wWcQb89kS2j+4JZttlEOh0XU853miky3PmY6CjveeBcvuFCNRoM56Q/Yt8+jEqMpJNOWgyjmvGiJ2LKkExYdDLQdSivsmjViXwlyYADZFZXnWBVJYZuNxMCEwvVWx2ny9LuOiqkKp//7/fQfrabKJ7zLqWyvoF55ubgJFGJnYE+dYLuxRNEOx3MjU2nzyL97k/ZGAMvuHf95sC4NfGFXEmCGB9ii9NeokFrv8jpz0i95nMdXtMGt6qZemU04p7nE6xDyFWuhuMyqytor+cSnTiqJapOP6jEMPZQvLtILKweV1V/SFXvVdUHgFcB/01VXy0i5wHEWZAfwTFe8vs+AzwpIi/wL70E+P1FjXVReMy+l8fse/sv6KBORtrAYGzbsLxxD1+bQvAoNT7GGw+pVjGNuvsBB80ptNNxHYKiCO10sSE3elrkr6EgH1A4bs+LVusacGTeZrXi+ewVolR90BhY33AhgSTBnj1JvNUlvraFuXQduXbL/W01uf8DO1z81Qr3vi+mcyL28gFeDTCNyXe6roF0JSapRSTLTqvFLC/5JGkQP07vZy6/kf6h1vd3VWRt1RUM5YS61KtqaqeL7jQz2lumR75fpBzzSbUK+Umg0XDGO4qyyT1DZNCdJtpq89KV1+x/jMcIojrV30HhMHjo3y4ir/ePfxF4J4CI3A28Q1Vf7t/7PuBnPcPlc8B3H/hI54QBox5gIMY+wjhnZeUwKNA0xX6Ak3g9fxatREjPIjduoe1O39PsdMnobkXHmUbdb2Kh0+Dx0vFlKpBFoRvf7IEocnowqtg0Du/vQXTmFGogavsiJWvR0ycRVSclUDGc+OQtUKV7doVkpU58swLNVtY4Qq3FRBFYS/13Po+qhZVlJ2mQNncOCn5cGIohg5giOuVpjcYglYq7zl6Y6AzCTz1Lsr7eP0xKP91DyGVm5L432nQGO9WUT8dgt7b6jKnE7j20cxxxJ8XQQ6jqh4EP+8dvw1ES89tcAl4ePP8Y8OBBjO+oIi1icRQyQeJecfMLGIrPSuqFN+qOXpYKJDXq6MbWwL6pxnZGgfQl/ES+4tRM2cqswPMeei/gzw8h5H+r9dRAR5HLGCBx7Pt2NrB3nUUSxRqnmKgXztA91cBW3Ll6S4Zot4K5tU31yZtoo4qeOQFnT8Lla6CKObHmujZ96enMW9Zmyz0u6DU6aXJzErgN1xxjTFK6UH8+neDyCoj5e7kX5MNi+TCgimO3RBWyDzvj4idZgjvTVC8BlFouJTweir6t0LsOk1MSRa7RwYlV7FIVc30DNrYwtarzsiOTVWGS6pundLK01Ht7J0vUpTFxzXmcrrhmCalVHVUtVUxMl4riVbMmxGQz5b6MgpmTZM3FpMP903EM9OpcbvS95Gavv6KoxI4e+JnPoyIYVVdNurNLpXsSu1zD1iNEI2w1wmxsOerklosLs9SARt11bfKt9WRjM6Mwamdwsilsp1aQ71CrWeXoSLZKWHhVFOfWoBo1jhHPSR8bE899DgPnHJWPyTGTst3S2oF0LDku/kgJiTsVd1BStEQBHq68ajAJKK4PY7isB2fQzMk1l5Ts9pCmwJIzcMkpnwxs9TCtjjPqqmBvOW9btc8aST2xoDx90Ovz3nKzhbbbTmuk14N2G6lWiU6sOQ32sGAoj5RTHTvq3qjwTbod+IbINl0hOCMhyw2MGGcUez1ILMm1G4E36ZQNs+NbBXUTlF3fdEleazFPtvrJoWoFvecCWonQSkRvqYKoEjV7ROu7JCeXMM0ushP7Rhq+2US+SUX+OgqQNZIAHPe/eBUyabWTJYqTxMkTRzVn2NOcxzhu+uCARp8kNxGB6yaVOefVKo/u/DQPV15Fyp0fKjC606GULejuVLy08V0ubFKruSSJT/6liUqp1xzTIbGZcp/d2sacPU1yaoVkuUL1mU3YbRJ5bWmNnK63rtRdwm/pLqdo+KXLfU+cAqOUS8w6z8uFF8QnQsWIi6l63rEzVAkhzXEgbKA2kIsdPo87eGBgrHWeNo7+lXo6mRGMY9c8IunHnFOWSV/VsT1wLu10XFLX00MBR4P8/FNZV6DaxfMkJxqY3Q567QayXKN9fpmaKrK5FYQ9NPNQJ4VZspZ0Xis+YwqNC0FNgp9AtdkapBTOipDuOoqBBP3vIWSrvEfOvq6kKU5C6aHfWUgNOZGj0rmYcOIZFh1XRn7fXdhqjHnSOrqYSEYj09UltGIwncR5k7UKctk1L3ZxbgPnT7sk3IZr9hxKto40RuGPvKA4KexFme3i9ULAJy3z9LhpCmE8T942Wxiz5Mrnod9gOIpcC7ZWe3T5exCP73usvh9mWviSn7R8VapWIjf5gTP6iVJ/ch195trgymJEsdT48bhCo2xCCCpC94SBugIYW/0Zfp7p9efPnb6eN+ZZuMu6RG6t6kXVEh459b13jtjWXjAney4iPwWktTtf6V87DbwbeACne/XXVPXWuOOUBn2BeOnyXwcjmLXVrA+m1mLssvPkpJ04A6nq+NNrq64lmwhaiSESpNUl/qNLLumXxi/PnUHSkEqzhTQ7zphYHWAiTBUuGGVsAmMikTPmsuYLfns97Ppm1plnVlmBlIoITrBK0rJ5qyQbm4MhlnBCSZsYJwmm6il2vZ6jWAZ89rSRdbafGDC487S6yKVrbvtOB9PuYutVzMXz8NTlAapmFv7KF/0Ued5i0HbbtaoDt7LRCXzwaVBklNPXizCJ0jrqdSNIqi+Uhuf8SuiRs68D4APX3z7j4I8/xM4t5vLvgH8JhBoKbwQ+pKpvEZE3+uc/OO4gpUFfAB459b3I6gr6J5+HGnHl69UIW1vBRkLnRIxGIKk3bKC6mbD0+XWX9DMGabYdU8KHZ8AZKanE8MxVZ1zrNScsdekZshZwMJx0TJGvPIUgnEJ2jvw2muDi6M0WZnkp855n4k3nmRvtdhYyCUMned2T0KiqlwgQUezubiYMNXiJSqqYlB2r10XiCnZr28nWpjH/SoxevuoLeiw64sdZ1B8UyJXbO6aI03Ch//qc2CmmWh2gFKrVgRXMQE4mfZx+rj4WP9BsI/iOpFILg6f1fVyTxH0nofTW81DmVlikqh8RkQdyL78CeLF//C4cU7A06AeFP/8t/5zazQ7meffTvtAg3nG9M21FMD2ledZ1y7GREHUUjSBqq2trBq7BwvZu31iRcpOD5gedDirGJf8aDcebpp9YHGnMYXDJHXh+abJrrAiYD8GkBT1Ta6UH5xnUeUkTca4v5ZDMb54nnYp++XCVK87pDLyfniN8nj7OcgSZ4ctJ3w5VtRYb44yTjSvWUp+07u+zGBaI+4zi4ok0nZSjCJMWMyXWhU4qFeh0smYdtEd/P4YSnp5CaU6fzKpvS/QhzFQ0dFZEHg+ev93LlozDBVW9DKCql9OizHEoDfoe8dCf+zHaZ6okFcky3bZmsJUIW4mo3uoQbbXonlmieq2FWd+i/qUadqUOPYu5ehOWl2Bnl96zLhB/8YrjQ6fMD1/koV4eQILiHzGuvZfd3cWcPtXnjG9s+krIMSGXEUvyIQM9YmmeJg1NNeWSR8PGOHe+TNgp530DmWZI/vWi1zJPOMcI6p9K+hrxIySIw2sbZKEM6s2MDE2lFMulJeTkCaTZxG5ueYmEGVQtCzRUhhgs4b2bwHBJE8HaanuKatetSMCNLY5d2G5AcMv2pQhIJyvP6U/bBYJLYPsQWYkcpjfo11V14XU1pUGfES/9mn+AdBOkGhPvJtjVmO6yAXEUpqidYGOhtxzTXVmhdqPlGhWvLsGTz2QsAlUXdiCxxJ+75DyqM6eQ7V1XDBTH6LpbvqtXT0zpjVJ3ol5Sr6NrK0i749q4RZHrR+mRhVKCmHQekxJ+mWEMQzTVikueJYmjGBbvmO075AnntERSOmLGW88dJ0ReeGwg1ODbxWXPo2hA4Cvtbj+gN54zoBPvBfSN4/UbWSeglNqXT8iOg6n0f36jKjCnDdukKxhtt/vX26i7783SkhOAC5uoaNoU3DqBspVl9PRakNgVR5tc33bGfHtI6LQELHrVckVELnrv/CJwddIOpUGfEo+88EfpnF9GT9ep3Gpj6xHxVodop0fDCLZqqFzfQbZb6LIrWU+WfNik03MsirTaMV+YEhn0vovYpQqSnCSpx3ROVqhuniHe6mCevOIaL7faLoa8upL9yKTZzhg02ulmzJOsn+TqCvQS14S40ylsvBA2ych3bR82oNadJxOZMv33AyOeGdN2u8+G6R+0/zAhM/qzcJwzzncUYU6ddLK16xve2/S6876Nne1tQlxzSVFAeyMmoVGrk9x1mXNnXMXt5av9z5TpjW8YIpFqFTl7Gr1xy/Uy3d3tHyccz6QkKOFn5btY7e5iTp7IGn2H26RKmuKbSHNyDa1Vsr6vu/cuUdlJqNSrRDe3Jl/TnYg5xtBH4JeB1wBv8f9/adIOpUGfAg9/3T+EpSrVS1vY5RrRxi7RFzcGquaiyJA8/34iEccDt5bIU7+sj2n2OdXBt8AbMXP1Jr3nXYSKIdrtUQWaF2rUIyGWu5yMmgW6CfqZz7uwgxHXxzEbhIFeGv9xjBe6PVeQ5MW2ChEkCY2nrw0bAbLHYmzWvs2sLLuq1FR7RdV5sJBpmw+cJ4+CApdpILHrzSmnT7o4sbXI8pJbAXkPk0oMnS7m7Bk3rijCXrtBX054RHxkVOioEmNWV9yk0eog6X3t7S0Bqt2eC5e12q4NXq+H8aG1MFYuRjCNFdTarMdpdq5cmKofUvFFWM2WKxQbusTBnIRcvQGdDqZew959nsYzLaRnIZI7r63cDJgXy0VE/gMuAXpWRJ4C3owz5O8Rke8BvoTrHzEWpUGfArYaIYkizTbR1RvuxdjHG5PEeYetdhY6yQps8lrehE/7lDyJIpL7zmE6FluLsI2YVBa2fbJCdznCVgyV7YTGp5+BtVVXhFSvOcU+6PfmjGP0xk2013Peo7XorfXiBgoBpBI741ytui9pp5st08OmxZmnmhb5VGJYXnIt5nw4w968NTAhFF3/fpB6l3RwBvXGLXd9Rtw1+ASevXbDhbIadWcwjWBOrGLXNwZiyYXjy3vF6njyyc11p4We57lPHvRw+Eptxh4Kw2MDlEsvp6DW9pUi8yi4t6nI10BfVILkt090S4SLvbfb7hytNubWJuapJvaBizz6m2+afG13LHRuIRdV/fYRb71kluOUBn0K/NeP/J889KJ/7LjFcQS9hPdf/r8Htnnpymv6SajUWPhQCEVhjtQwxjGcPIGtRmw+u0FSFeK2svJkm7VPXIftXddy7f4TdNYi6mvLSGKd8TaCrCxjr99057EW4sh1QlrfxO7uDnt0IyBR5BpugPufJO74UcX3vOwNJBrNqqdN+iQanY4rCNradoYnP3nkGTa51/IqjOG+A5NIsL9UK/2EpL+X2mpjmxuD17vrEnxy8bxbrTRbLn8RxuwLim2gry0TMmUmTk4FyVjHva9mCex0VeOSwgUyAUEIbGhlNc3kmEoMpBTOAjpq+rrEBjmxhrTaLuRz7QZmaYlHf/vNk89zJ0M5csyf0qBPicf++4+Mff/R7Xfx0vp3Dnh8eZGn1HgNFMyows11Ks0WZz6vdJ53F9F2F9PqoLHB3nuW9tk6SVVY+4MNF1Jp1NFajLS60EsyI2jXNzDtjtP3brUxYTIsM4gaGIv+l9G22kTNFnr+DNLtuRVCHGcTwlCBT+yMkt30IZ9UGRFc4+N2GxnVLNkdcPB/eosKvN3Ue5UlX1marj58SCvtWpQ15WgOHxdwlNAbN7MJdigclKdzTkPNLLiGAepkOIF5TzjrjBSwWEaeY78rmyLaaNHqo9t1xrzTQeLKHdnweU9YbAx9ZpQGfd6YJUGmrk8l1QrS6dD7srvZfFYN06tl3cQlUarblsp2Qvd0g8q1bdfgYWfXMWU8xMetOXUC2dh0xUy7uy4kYqTvGaZFPElC2LJcjLhxbO24Sabnmm+YRt2FT0zcVwBcXnLD39l1HnrPKSGmfUDTsUi16mL4KeUuR/3Lx+bDsQwlSKMIOXPKMYQuPUNaYWuWl6BaBVWSK9f6+/vVRtpARHd2YWe3f9+LYvuh51q0upiAbCXhWUB2py9qplYhZdYkCeqPOZU0w7QYtU8u4Z2uPsLuSUC2sny0+e+nP+cdjjupBd0dg0dOvDZbek1M7KUeUxRhzp91iUsj9O4+Q/NCndqmpb1mSGruR7b8TEJls0u02cbc2HDnWWo4Slmj4TxlgN0mLLt2YayuIM1WsKTHJ1F7jqGRVmq22lCJXTeaJMHeuIk0Gk7Pe9cZP+vFoaSx7DxvI45W2Ww5Wpxv8pwZjQTY3XWStCvL6JXrQEBPDD39AspkSEM09VomuIUIbG55DroPYzWbTu53cztjyUij0b/VYcel0HhOKoYiMM456uXQRB0yYDyt1KwsQ6WCAV9G71cqSXCOcQVIBRNe0dj6FzoivJXb11RiNyFXXELZ7uz2q3SXl9znuWt5uPod2TEfS949epwlypDLsUQUDSr/wVhGR0a5a7awF8/RPd1wLBZvyyo7SmdNMF3YPR/TPhHRuFnBPmsF01WWPnkZWVtzxrgWI7tt8OwLrbmE6gA3OoVV91evOVbIUsNVp3oD7RKNHR/O8FWC9Ri11nm8ccz2n7yArRiijqV+pUn0R13XkzMwImrVhW12mpgTTpvG3lrPmBeZjrtal5RL9wkNlVo36Z1Yg6UGemIFVUWaHYwRV8bedfRJ9fdUuz2X0FXNKJrpscd+LsFnEz43nvapO7suf0LkQ1YyZEDFl+Ubv4qw5086xkqn46UFgolgQvHWwJjz4ZzIUTLN0pJrML617a7bTDbqAyqU1notfTd2u73j9HpqNaerz2CYrUQBVPtyG0cEpUGfB3KNI0iLZ4qWvrkfqNlpUbu+TnLvWXbuqrJ0pUt31Vm56rYStZS1T91Eepbu+VUnE9BqO156p4O5/x60UXUUs+1dp5veqMOFc+jTlxEiFwJYXeknMUXoPes8veUK1Ru7mKeuoru2z11vNNx27TbJc+6GROHmFlQrrPzW592XuObi5drpZJ6p7XgOtJG+9kuj7lhAafEQYE6fQleXkW7P6dCoIiRZTDy7Xd2uWy2cPUFvtYpGgvSW4N6TmHaCdC1mqwmXrvhenrbP4x6F/Ocyxqii6qiP993lOrzvtrHPXHXj9eO0aVtA3/HHXr/h+nO2wtBWmvSNsolL4srIRGXROLP9Uj2XUL0z3WVcRWmQJNVmwKIJ5IbDFY3xx31p47vKEMw4lB768cMHNn7KJUQLMBBmCPnCSeLib1vbznB1E9Y+32TnnjqtU4Z4F5audJ0BF4Gb68SXrjhKXtVzsM+ccoe9etN14en1XIFIJSJZqSNnVoiubGBPr9A6t4QaiHcTomYXFCrrbYwvGjF3nYdaDa1XUFXsUpXmxSVnJIwQn6r57VYx7YRop+NWBimzx4gzAn6pLlHkpAt6vcGKVTHYGzddl6FuN/PU1SrYPi1PvPofSYLGxhnwxCI9S7JU6cd9PctmUqf7QW8zyj6bUduKEWR5CV1bJlmuYbqJu89hX80hg+s1b5pNFyNPVxyRgR7esNsshBOydtKxZ9+XnBdvqlGWC9FO13nRxiBnT7sJdXvX00U7U7NghhLT4mmMs1Iy72SUBv144tHWzwK+tVyuYGXgR5r+eJKE5NaGez2OMU9ewT73HhpX2jSuCdF2l+jzl9z+atHE9hkSVW/Qdnbh1Ak4ecJ9seo1136tFhM/fcN5tnedRHqWymaH+NoWnXtOsnPvEkuXW3RP1th+4B5EXfLVxkJ3WZyYWFdZutJj6XefxN59FvPUVVfAE8fY0yvYRgWqMaYWY1q+0fSNW06SoNP1cgW4661GmLUV6HRdEljE67Z3svuTYqCU/+wZUEu00SRZq2N22kgnwXzxGVcpu7LsNj19Em6uDykGZseMon58PS3VB8eWCSmBaUjMT0B2cws2t4iqVZcPaLackU7CXQZbtDlj6FUYu37F1kuvMeWE02+xd+Y0qGKfvpwlqtOxyqmTaKMGkcCTz7iagrSsv1qFWhW9ej1TUUwnolFVuUXPU4Odxtfp9SAKvH6i0ksfBaUfwjoiWLhBF5EIeBx4WlW/SUReCPwEsIITbf9OVd0s2O8LwBbuV9A7CGGbeSBNIj0UfdvIGORAoi7pL3ujT30Ref59SDtBnrzsKht3djOvUKoVxBgXMz13huT0smtDt77lCooqEeaZm3DhFL17z7L5nAaNq13i3R67F+vUlivEO13WPnaT3vkTxDtd6k+u0zuzwvrzG9hYOPnZFre+vI5FaJ+KqD5wAdPqohfOOAmDSOicbtBdieg1hPqNGlHbYjoJ0VIN/ewX/YTjjVtknKd79qSjGDY7sLHlNGAClUUJvFkxLmlMzZfqx6afF1CF0ycde2Zzy5X2+3vUD2v0BieGtDPSuTNOZ/7yVTc5JokLfXj2SRZzT41jWpq/1IBuF7nrPFy74cSvut2seGqgAYX/P16+2OURJIrcpLy8hDlzGvXiamZt1U1WImijgq1XkOfd7ya0Jy+h3R7J+vpwWCbBxcEhGFtBiKnIg4+i/vcsigYNVRlLH4HZq4MXjYPw0L8f+BSw5p+/A3iDqv6aiLwW+AHgR0fs+42qev0Axjh3pIb94cqr+nHOIM4+8IP34luIQT7xR87bBdRrnPeFpHxxi7WItURfeCZjtWAMdrmGMSdp3bXMzl0xaoTNZ1cxHWcY2yeqrH4JkJNoJPSWYjqna6jgBMUacOMr66BQbapLyl6sI1on6iimbbEVQWNBrLL6+RbRTtvx4bd3nAcOWaNhZ/h66M4u0uu5MnS8sfFt9KRa6TeFaNShEpOcaKDGEK/vul6gsUE6PZKVOqYau/ZxcQ2Wa0jPOr77TjMLZZhKPGi4/L21T1/OlBohNeDDxjf97F5a/85+orVRR1NGkZdyyCOdRPbStu1ld/3vTnStY9CzJ0lOLCGJJanH2KrBVA3Rxi7Uaq6hh1XHlAmZL/jVhfE8+k6XIXmDIJRjUh0XyJQ8M/qpj/OXnvkYKHdWUlRE7gX+MvBjwN/1L78A+Ih//BjwKKMN+m2PD3Z/3nV86XRdp3mffEqb/vaNuzMupGHkMFmXGifrk2DViit3b7edwfGiVNHNJajXMBeWidvQqyvSATVOjx1Rtu6rAlWWr/TYvhijMag/VdRxNElJlF7dUNvoUf/cDVjf7OuB3HcXWnE0QvP0Nde5KJigxEgmx2q9YUgNoDTqaNvHeHu27xW2287Qnl7DNmpOZqHXw9Yqjqq5tQ3WEq8sw4lVWvedwMbumkzXUm21vSa5Ojpes8Vwu7bhMFiIInpeGkY7CLz/mX819NpL//SbqX7+MnrmFETiJB1SNlKKXKLdNluut+kUhkb9CkWqFRf2yTUSH6n9U6KPOyyG/lbg7wOrwWufAL4Zpxz2SuC+Efsq8EERUeDfjBKDF5HXAa8DuP/+++cz6jkjbN31yOm/MfhmmijLo6ByMfWWXnbub6I9ZwQHwjrVCjTqxNtdqnVDdRPinYTOyZhew6A9SGouVr59T4z0yCrdkhpoBNsXI6pbytrnm671Xc8pAKZiYHLpKnj9GJvnZafeok/cZRWT3Z6jVDa7fUMjpt+JyXPapd3DWJBegsYRsrnteNypAFKni0ZC7fIWrXvWnOfaTkjOnSTq9tDNrYztMq3Q11HmWT/6xD/gkVPfi/7RFwBvZEdoz6d5Gk0SkrSAahzScEyv2w855XIZs4il3bG4Uwy6iKQNT58QkRcHb70W+HEReRNOHnKUG/AiVb3ku3Q8JiKfVtWP5Dfyhv7tAA8++ODRursF+MDNfzv2/Ycrr8oef7Dzc4XbzFKW/Zf+wj+hdqNDXaF1toqNDb01yJrbCsRN96cCpz7bJt7oOAbOyrILpYgAXgWwuz14giK6XSV2qn3bO35VURlMWEpf3jcV1aLTRW/eQk6dxJ5Y8j+UFSf6tbIEWzsukfr5pzCNBnWge3YZ07Gu+cfmFllzkGNkiD5w6x1ZPmZcRylgsHBpSrjm0J6qWJGS1TIT9M4x6MCLgG8WkZcDdWBNRH5GVV8NPAwgIs/HhWSGoKqX/P+rIvI+4Gvph2qOLfYSfx2H//prPwzAw1//j4h3LVHTsnQFqptdOmsV2qciWqcMtQ1l6UqX2tOb0O2hN26RPPc+R/Db2u4b7pz3O9TLMjKO8x7HmOVlp1HuQyEDvUO7PaAFz7kPuglcuebCUiKoMZhWh97ZZTizRLTVwegSEpksoWqu3CD28XQSiw067Exj/GYxekcBM01S+XqHIkmA7POKMLXYqy1qlocoMQWU/urxiGBhBl1Vfwj4IQDvob9BVV8tIue9kTbAj+AYLwMQkWXAqOqWf/ww8A8XNdY7AR/8zcE0xUN/7seobLsippN/2KHxyUvuy9npuj6lqkSXrrsqyWSEEQfX9QYcBU8MnD6JXa2jxhBtNeGe80i75xKSYaLSa8dgLb0zS1S2luBkhd65VbprVSSpIarOA+/00Gs3kErFlfqrYnd2nTEHUl2XiRSywKAd5VBLHkXMqZGce2+oU/GvgR6kgZFPk56pxo/Uauju7sD7JabAHeShj8K3i8jr/eNfBN4JICJ3A+9Q1ZcDF4D3ieMLx8DPqeoHDmGsxxaP/bqTBF77gw2SlRr27EnM5Wv9xJiqM55hYw7fDWiglRnemFcqWUNiaUcuVNJL0JSJ4ql+JD4ZJ45NYXqW+MYu3WefR43QXYuxcb9Pa9RMsHevEq/WiDaamDhGU4VHVbQSIUnIWgHE8FhSHK66nfFY8u6BkNxIpJXKkUGimpukw88wDdH4+HlK3cwaYI/rEVsiwNEr/Rc9YjPMfvDggw/q448/PnnDEhkeOfFa1yjDa6MM6beH1Yt55DXKY9/RZ2U5042n2XLSrNs7fdVFyDjemiSOO336JPR6JOdP0brYcMtZgcbTO5CoY7vEsWvW0em4uL7/MZmTay55u7Xlh6wj8w/HCQ+ZEQ1sRoWT8qGYnLRuum907gyI8P6nfnyOoz1aEJEn9lvbciI+p19/8q9Mte2jN/7tvs83DW6vQGKJ+UPVaaanZfgT4qd5oauU5SJZc4wqdq3hhZ/EqyK2ho/jG0xrt4f6pgrsNF0D7p4iidJ4ahtzfQPT7qKn11zVpK/yFNOPC9ut7b48r5g7wpgDPGbfW/yG2kHvOv88fS2HjMIo0lfxLDEeqeDdpL8DQln6f4cjE2ZKcu3NCppgQL8sfvBF42iKjXpW0dm9a414vYnsmCHRrfS4AwnVes0rRgoYoXatibm24aonb65nx0hb6WWywOCSqUds6XtQGGXUHzKvLA6ZjBCMy/TaE+MapZw7s4DRHkMcsQhH6aHf4dBULRD6nlzABBEjmEqMaTQwjYZv3hC7pFq10t8PvA5IhHQT4hu7yM1NdGt7rGaLGGfAtZPK8ArStSSNCiw3nFJjp4Pd2XUefS4clDdQZWWjw2P2vaM9+ACpzo2pVt1nXK+5yl1VXvbA3zmAkd7GSNs+TvN3QCg99BIORUtwIyAV17ChUceeO4HZbEK74+iI4JUfq85zP7lG87lnqV/edgJdm1tDTSb6p+sXwjjtER8Xt5bGF9fRazewrfawBxRMNvOmeB5HpEZ9ZLwdvEyDb+3X7SGdjstrBNK8JUbgiHnopUEvARR4z2myrBK7UEi7jXQTl+A8c8KVl+82EVXsXachUVSE3pKhd6JO5coNVx2aM+YjE6w+1m4ueemeTtd5/CljI9BcuZ0oh0cFj9n3Dhj1gedqnbRDpjNkEGYrYLszoUeOs18a9DscaQLRyf4OQ7s92Nx0HPOnrjhdmJ1mpu0ip06ilYj2+TrdlYilyy2kk6C7zcIydTFS6FmnevLa9uGZQPmwpM/NB/kQzDQhmRJjcCfK55a4PZCpQ1a/AwhobGrR7R1sGiJJRbgqcb8DUatH1PJ0xF5CtL7jGlingl4BVW4UAyUVwnrk9N/IpG0LW8iVKHGUcMScjdKglwD6HnpYiZgPjQwIcSW+ZdzODhIZzEqNeLPl2tl5YzygwMh06oWp1k3msZfGvMQRhXL0vp+lQS8xgKIY9zhD/LIL/xuAq9a09HW1fTI0FX+alRueP+eokFCJEocGvTMbXJS4DbDXROP7r/zr7PFL/9Sb3QPVvixwQWPsgxxfiRKLxFFLih6r0n8RuQZ8cU6HOwschW5J5TgGUY5jEEdlHHB0xjLNOJ6lquf2cxIR+YA/1zS4rqqP7Od80+BYGfR5QkQePwp9TMtxlOO4HcYBR2csR2Uch4GyUrREiRIljglKg16iRIkSxwSlQR+Nwh6mh4ByHIMoxzGIozIOODpjOSrjOHCUMfQSJUqUOCYoPfQSJUqUOCYoDXqJEiVKHBPc0QZdRL4gIh8XkY+JyFDvOhE5ISL/WUR+V0Q+KSLffYhjOSUi7xOR3xOR/yEiX7mgcZwUkV8QkU+LyKdE5Otz74uI/LiI/KEfy1cf0ji+XER+U0TaIvKGRYxhynF8p78PvycivyEiLzykcbzCj+FjIvK4iPy5wxhHsN3XiEgiIt+6iHFMMxYRebGIbPh78jERedOixnJkoKp37B/wBeDsmPd/GPhn/vE54CZQPaSx/HPgzf7xlwMfWtA43gV8r39cBU7m3n858H5AgK8DPnpI4zgPfA3wY8AbFvgdmTSOPwuc8o9fdoj3Y4V+TuxPAp8+jHH41yPgvwG/CnzrIX42LwZ+ZVHnP4p/d7SHPgUUWBURwf1gbgK98bssDF8BfAhAVT8NPCAiF+Z5AhFZA74B+El/no6qruc2ewXw0+rwW8BJEbl40ONQ1auq+ttAd57n3sM4fkNVb/mnvwXce0jj2FZvxYBl3Hf3wMfh8X3AfwSuznsMexjLHYU73aAr8EEReUJEXlfw/r8E/hhwCfg48P2qC1PjmTSW3wX+KoCIfC3wLOZvPJ4DXAPeKSK/IyLvEJHl3Db3AE8Gz5/yrx30OA4Cs47je3Crl0MZh4j8FRH5NPBfgNcexjhE5B7grwCL7o4x7Wfz9T5k+n4R+eMLHtOh40436C9S1a/GLZVfLyLfkHv/pcDHgLuBrwL+pfcMDmMsbwFOicjHcB7Q7zD/1UIMfDXwr1X1TwE7wBtz2xS0G5q7NzjNOA4CU49DRL4RZ9B/8LDGoarvU9UvB74F+EeHNI63Aj+oqotWrZpmLP8Tp9nyQuD/Av7Tgsd06LijDbqqXvL/rwLvA742t8l3A7/owwt/CHweF78+8LGo6qaqfreqfhXw13Ex/c/PeRhPAU+p6kf981/A/Wjy29wXPL8Xt4I56HEcBKYah4j8SeAdwCtU9cZhjSOFqn4E+DIRmVY4ap7jeBD4eRH5AvCtwL8SkW+Z8zimGov/zWz7x78KVBZwT44U7liDLiLLIrKaPgYeBj6R2+xLwEv8NheAFwCfO4yx+Ix+2rX3e4GPqOrmPMehqs8AT4rIC/xLLwF+P7fZLwN/3bNdvg7YUNXLhzCOhWOacYjI/cAvAt+lqp85xHE81+d68MyjKjDXyWWacajqs1X1AVV9AGdk/3dV/U/zHMe0YxGRu4J78rU4e7eICffI4E7WQ78AvM9/3jHwc6r6ARH5mwCq+hO4Zeu/E5GP40INP6iqi5AHnWYsfwz4aRFJcF/c71nAOMCFc37WTx6fA747N45fxTFd/hDYxa1iDnwcInIX8DiwBlgR+dvAV8x7kps0DuBNwBmcJwrQ08Uo/U0ax/+Cm2i7QBP4tiBJepDjOEhMGsu3Av+biPRw9+RVC7onRwZl6X+JEiVKHBPcsSGXEiVKlDhuKA16iRIlShwTlAa9RIkSJY4JSoNeokSJEscEpUEvUaJEiWOC0qCXKFGixDFBadBLlChR4pigNOgljiW8HvfviUjdV+J+UhakIV+ixFFBWVhU4thCRP4xUAcaON2Pf3rIQypRYqEoDXqJYwtfEv7bQAv4swegAFiixKGiDLmUOM44jWtMsorz1EuUONYoPfQSxxYi8svAzwPPBi6q6t865CGVKLFQ3MlqiyWOMUTkr+OUD39ORCLgN0TkL6rqfzvssZUosSiUHnqJEiVKHBOUMfQSJUqUOCYoDXqJEiVKHBOUBr1EiRIljglKg16iRIkSxwSlQS9RokSJY4LSoJcoUaLEMUFp0EuUKFHimOD/D8x5G2eVRAGfAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "da.where(da.values>da.attrs[\"nodatavals\"][0]).plot() # we can see that the DataArray contains missing values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll look at how weight builders handle missing values. Firstly we'll slice the `DataArray` to reduce overall size for easier visualization.\n", - "\n", - "This time we'll create `WSP` object using `da2WSP` method inside `raster.py`. Since our DataArray is single banded and all of its dimensions belong to the default list, we only have to ship the DataArray and the type of contiguity we need." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Slicing the dataarray\n", - "da_s = da[:, 330:340, 129:139]\n", - "w_queen = raster.da2WSP(da_s) # default contiguity is queen\n", - "w_rook = raster.da2WSP(da_s, \"rook\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After plotting both contiguities and sliced `DataArray`, we can see that the missing values are ignored by the `da2WSP` method and only indices of non missing values are stored in `index` attribute of `WSP` object. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "f,ax = plt.subplots(1,3,figsize=(4*4,4), subplot_kw=dict(aspect='equal'))\n", - "da_s.where(da_s.values>da_s.attrs[\"nodatavals\"][0]).plot(ax=ax[0])\n", - "ax[0].set_title(\"Sliced raster\")\n", - "splot.plot_spatial_weights(w_rook, da=da_s, ax=ax[1])\n", - "ax[1].set_title(\"Rook contiguity\")\n", - "splot.plot_spatial_weights(w_queen, da=da_s, ax=ax[2])\n", - "ax[2].set_title(\"Queen contiguity\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `higher_order` neighbors\n", - "\n", - "In some cases `Rook` and `Queen` contiguities don't provide sufficient neighbors when performing spatial analysis on a raster data, this is because `Rook` contiguity provides max 4 neighbors and `Queen` provides max 8.\n", - "\n", - "Therefore we've added `higher_order` functionality inside the builder method. We can now pass `k` value to the weight builder to obtain upto kth order neighbors. Since this can be computionally expensive we can take advantage of parallel processing using `n_jobs` argument. Now lets take a look at this functionality." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Building a test DataArray \n", - "da_s = raster.testDataArray((1,5,10), rand=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below we can see that builder selected all the neighbors of order less than equal to 2, with `rook` contiguity" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/magito/anaconda3/lib/python3.8/site-packages/scipy/sparse/_index.py:124: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", - " self._set_arrayXarray(i, j, x)\n" - ] - }, - { - "data": { - "text/plain": [ - "(
, )" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1)\n", - "splot.plot_spatial_weights(w_rook2, da=da_s)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Few times we require the kth order neighbors for our analysis even if lower order neighbors are absent, hence we can use `include_nas` argument to do the same.\n", - "\n", - "We can also look in both the examples we used `n_jobs` parameter, and assigned -1 which equats to all the cores present in the computer for multithreading" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
, )" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1, include_nadata=True)\n", - "splot.plot_spatial_weights(w_rook2, da=da_s)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Additional resources\n", - "\n", - "1. [Reading and writing files using Xarray](http://xarray.pydata.org/en/stable/io.html)\n", - "2. [Xarray Data Structures](http://xarray.pydata.org/en/stable/data-structures.html)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/docs/_sources/notebooks/examples.ipynb.txt b/docs/_sources/notebooks/examples.ipynb.txt deleted file mode 100644 index 2c7203878..000000000 --- a/docs/_sources/notebooks/examples.ipynb.txt +++ /dev/null @@ -1,1092 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datasets for use with libpysal\n", - "As of version 4.2, libpysal has refactored the `examples` package to:\n", - "\n", - "- reduce the size of the source installation\n", - "- allow the use of remote datasets from the [Center for Spatial Data Science at the Unversity of Chicago](https://spatial.uchicago.edu/), and other remotes\n", - "\n", - "This notebook highlights the new functionality" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Backwards compatibility is maintained" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work. \n", - "\n", - "For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from libpysal.examples import get_path \n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/jovyan/libpysal/examples/mexico/mexicojoin.dbf'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_path(\"mexicojoin.dbf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a `builtin` dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import libpysal\n", - "dbf = libpysal.io.open(get_path(\"mexicojoin.dbf\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['POLY_ID',\n", - " 'AREA',\n", - " 'CODE',\n", - " 'NAME',\n", - " 'PERIMETER',\n", - " 'ACRES',\n", - " 'HECTARES',\n", - " 'PCGDP1940',\n", - " 'PCGDP1950',\n", - " 'PCGDP1960',\n", - " 'PCGDP1970',\n", - " 'PCGDP1980',\n", - " 'PCGDP1990',\n", - " 'PCGDP2000',\n", - " 'HANSON03',\n", - " 'HANSON98',\n", - " 'ESQUIVEL99',\n", - " 'INEGI',\n", - " 'INEGI2',\n", - " 'MAXP',\n", - " 'GR4000',\n", - " 'GR5000',\n", - " 'GR6000',\n", - " 'GR7000',\n", - " 'GR8000',\n", - " 'GR9000',\n", - " 'LPCGDP40',\n", - " 'LPCGDP50',\n", - " 'LPCGDP60',\n", - " 'LPCGDP70',\n", - " 'LPCGDP80',\n", - " 'LPCGDP90',\n", - " 'LPCGDP00',\n", - " 'TEST']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dbf.header" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## `available` is updated" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The function `available` is also available but has been updated to be more informative:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Name Description Installed\n", - "0 10740 Albuquerque, New Mexico, Census 2000 Tract Data True\n", - "1 AirBnB Airbnb rentals, socioeconomics, and crime in C... True\n", - "2 Atlanta Atlanta, GA region homicide counts and rates True\n", - "3 Baltimore Baltimore house sales prices and hedonics True\n", - "4 Bostonhsg Boston housing and neighborhood data True\n", - "5 Buenosaires Electoral Data for 1999 Argentinean Elections True\n", - "6 Charleston1 2000 Census Tract Data for Charleston, SC MSA... True\n", - "7 Charleston2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "8 Chicago Health Chicago Health + Socio-Economics True\n", - "9 Chile Labor Labor Markets in Chile (1982-2002) True\n", - "10 Chile Migration Internal Migration in Chile (1977-2002) True\n", - "11 Cincinnati 2008 Cincinnati Crime + Socio-Demographics True\n", - "12 Cleveland 2015 sales prices of homes in Cleveland, OH. True\n", - "13 Columbus Columbus neighborhood crime True\n", - "14 Denver Demographics and housing in Denver neighborho... True\n", - "15 Elections 2012 and 2016 Presidential Elections True\n", - "16 Grid100 Grid with simulated variables True\n", - "17 Groceries 2015 Chicago supermarkets True\n", - "18 Guerry Moral statistics of France (Guerry, 1833) True\n", - "19 Health Indicators Chicago Health Indicators (2005-11) True\n", - "20 Health+ 2000 Health, Income + Diversity True\n", - "21 Hickory1 2000 Census Tract Data for Hickory, NC MSA an... True\n", - "22 Hickory2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "23 Home Sales 2014-15 Home Sales in King County, WA True\n", - "24 Houston Houston, TX region homicide counts and rates True\n", - "25 Juvenile Cardiff juvenile delinquent residences True\n", - "26 Lansing1 2000 Census Tract Data for Lansing, MI MSA an... True\n", - "27 Lansing2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "28 Laozone Ozone measures at monitoring stations in Los ... True\n", - "29 LasRosas Corn yield, fertilizer and field data for pre... True\n", - "30 Line Line Shapefile True\n", - "31 Liquor Stores 2015 Chicago Liquor Stores True\n", - "32 Malaria Malaria incidence and population (1973, 95, 9... True\n", - "33 Milwaukee1 2000 Census Tract Data for Milwaukee, WI MSA True\n", - "34 Milwaukee2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "35 NCOVR US county homicides 1960-1990 True\n", - "36 NDVI Normalized Difference Vegetation Index grid True\n", - "37 NYC Demographic and housing data for New York Cit... True\n", - "38 NYC Earnings Block-level Earnings in NYC (2002-14) True\n", - "39 NYC Education NYC Education (2000) True\n", - "40 NYC Neighborhoods Demographics for New York City neighborhoods True\n", - "41 NYC Socio-Demographics NYC Education + Socio-Demographics True\n", - "42 Natregimes NCOVR with regimes (book/PySAL) True\n", - "43 Nepal Health, poverty and education indicators for ... True\n", - "44 Ohiolung Ohio lung cancer data, 1968, 1978, 1988 True\n", - "45 Orlando1 2000 Census Tract Data for Orlando, FL MSA an... True\n", - "46 Orlando2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "47 Oz9799 Monthly ozone data, 1997-99 True\n", - "48 Phoenix ACS Phoenix American Community Survey Data (2010,... True\n", - "49 Pittsburgh Pittsburgh homicide locations True\n", - "50 Point Point Shapefile True\n", - "51 Police Police expenditures Mississippi counties True\n", - "52 Polygon Polygon Shapefile True\n", - "53 Polygon_Holes Example to test treatment of holes True\n", - "54 Rio Grande do Sul Cities of the Brazilian State of Rio Grande do... True\n", - "55 SIDS North Carolina county SIDS death counts True\n", - "56 SIDS2 North Carolina county SIDS death counts and r... True\n", - "57 Sacramento1 2000 Census Tract Data for Sacramento MSA True\n", - "58 Sacramento2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "59 SanFran Crime July-Dec 2012 crime incidents in San Francisc... True\n", - "60 Savannah1 2000 Census Tract Data for Savannah, GA MSA a... True\n", - "61 Savannah2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "62 Scotlip Male lip cancer in Scotland, 1975-80 True\n", - "63 Seattle1 2000 Census Tract Data for Seattle, WA MSA an... True\n", - "64 Seattle2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "65 South US Southern county homicides 1960-1990 True\n", - "66 StLouis St Louis region county homicide counts and rates True\n", - "67 Tampa1 2000 Census Tract Data for Tampa, FL MSA and ... True\n", - "68 arcgis arcgis testing files True\n", - "69 baltim Baltimore house sales prices and hedonics 1978 True\n", - "70 berlin Prenzlauer Berg neighborhood AirBnB data from ... True\n", - "71 book Synthetic data to illustrate spatial weights True\n", - "72 burkitt Burkitt's lymphoma in the Western Nile distric... True\n", - "73 calemp Employment density for California counties True\n", - "74 chicago Chicago neighborhoods True\n", - "75 clearwater mgwr testing dataset True\n", - "76 columbus Columbus neighborhood crime data 1980 True\n", - "77 desmith Small dataset to illustrate Moran's I statistic True\n", - "78 geodanet Datasets from geodanet for network analysis True\n", - "79 georgia Various socio-economic variables for counties ... True\n", - "80 juvenile Residences of juvenile offenders in Cardiff, UK True\n", - "81 mexico Decennial per capita incomes of Mexican states... True\n", - "82 networks Datasets used for network testing True\n", - "83 newHaven Network testing dataset True\n", - "84 nyc_bikes New York City Bike Trips True\n", - "85 sids2 North Carolina county SIDS death counts and rates True\n", - "86 snow_maps Public water pumps and Cholera deaths in Londo... True\n", - "87 stl Homicides and selected socio-economic characte... True\n", - "88 street_net_pts Street network points True\n", - "89 taz Traffic Analysis Zones in So. California True\n", - "90 tokyo Tokyo Mortality data True\n", - "91 us_income Per-capita income for the lower 48 US states 1... True\n", - "92 virginia Virginia counties shapefile True\n", - "93 wmat Datasets used for spatial weights testing True\n" - ] - } - ], - "source": [ - "from libpysal.examples import available\n", - "available()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the new column `Installed` is added to the tablular output of `available`. This tells the user whether the dataset has aready been installed on the local machine. All the builtin datasets are by defninition installed. The interesting cases are the remote data sets that are new in this release. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Remote datasets\n", - "\n", - "The listing from `available` above shows that the dataset `Tampa1` is avalable, but has not yet been installed." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from libpysal.examples import explain\n", - "explain('Tampa1')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from libpysal.examples import load_example\n", - "tampa1 = load_example('Tampa1')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Name Description Installed\n", - "0 10740 Albuquerque, New Mexico, Census 2000 Tract Data True\n", - "1 AirBnB Airbnb rentals, socioeconomics, and crime in C... True\n", - "2 Atlanta Atlanta, GA region homicide counts and rates True\n", - "3 Baltimore Baltimore house sales prices and hedonics True\n", - "4 Bostonhsg Boston housing and neighborhood data True\n", - "5 Buenosaires Electoral Data for 1999 Argentinean Elections True\n", - "6 Charleston1 2000 Census Tract Data for Charleston, SC MSA... True\n", - "7 Charleston2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "8 Chicago Health Chicago Health + Socio-Economics True\n", - "9 Chile Labor Labor Markets in Chile (1982-2002) True\n", - "10 Chile Migration Internal Migration in Chile (1977-2002) True\n", - "11 Cincinnati 2008 Cincinnati Crime + Socio-Demographics True\n", - "12 Cleveland 2015 sales prices of homes in Cleveland, OH. True\n", - "13 Columbus Columbus neighborhood crime True\n", - "14 Denver Demographics and housing in Denver neighborho... True\n", - "15 Elections 2012 and 2016 Presidential Elections True\n", - "16 Grid100 Grid with simulated variables True\n", - "17 Groceries 2015 Chicago supermarkets True\n", - "18 Guerry Moral statistics of France (Guerry, 1833) True\n", - "19 Health Indicators Chicago Health Indicators (2005-11) True\n", - "20 Health+ 2000 Health, Income + Diversity True\n", - "21 Hickory1 2000 Census Tract Data for Hickory, NC MSA an... True\n", - "22 Hickory2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "23 Home Sales 2014-15 Home Sales in King County, WA True\n", - "24 Houston Houston, TX region homicide counts and rates True\n", - "25 Juvenile Cardiff juvenile delinquent residences True\n", - "26 Lansing1 2000 Census Tract Data for Lansing, MI MSA an... True\n", - "27 Lansing2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "28 Laozone Ozone measures at monitoring stations in Los ... True\n", - "29 LasRosas Corn yield, fertilizer and field data for pre... True\n", - "30 Line Line Shapefile True\n", - "31 Liquor Stores 2015 Chicago Liquor Stores True\n", - "32 Malaria Malaria incidence and population (1973, 95, 9... True\n", - "33 Milwaukee1 2000 Census Tract Data for Milwaukee, WI MSA True\n", - "34 Milwaukee2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "35 NCOVR US county homicides 1960-1990 True\n", - "36 NDVI Normalized Difference Vegetation Index grid True\n", - "37 NYC Demographic and housing data for New York Cit... True\n", - "38 NYC Earnings Block-level Earnings in NYC (2002-14) True\n", - "39 NYC Education NYC Education (2000) True\n", - "40 NYC Neighborhoods Demographics for New York City neighborhoods True\n", - "41 NYC Socio-Demographics NYC Education + Socio-Demographics True\n", - "42 Natregimes NCOVR with regimes (book/PySAL) True\n", - "43 Nepal Health, poverty and education indicators for ... True\n", - "44 Ohiolung Ohio lung cancer data, 1968, 1978, 1988 True\n", - "45 Orlando1 2000 Census Tract Data for Orlando, FL MSA an... True\n", - "46 Orlando2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "47 Oz9799 Monthly ozone data, 1997-99 True\n", - "48 Phoenix ACS Phoenix American Community Survey Data (2010,... True\n", - "49 Pittsburgh Pittsburgh homicide locations True\n", - "50 Point Point Shapefile True\n", - "51 Police Police expenditures Mississippi counties True\n", - "52 Polygon Polygon Shapefile True\n", - "53 Polygon_Holes Example to test treatment of holes True\n", - "54 Rio Grande do Sul Cities of the Brazilian State of Rio Grande do... True\n", - "55 SIDS North Carolina county SIDS death counts True\n", - "56 SIDS2 North Carolina county SIDS death counts and r... True\n", - "57 Sacramento1 2000 Census Tract Data for Sacramento MSA True\n", - "58 Sacramento2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "59 SanFran Crime July-Dec 2012 crime incidents in San Francisc... True\n", - "60 Savannah1 2000 Census Tract Data for Savannah, GA MSA a... True\n", - "61 Savannah2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "62 Scotlip Male lip cancer in Scotland, 1975-80 True\n", - "63 Seattle1 2000 Census Tract Data for Seattle, WA MSA an... True\n", - "64 Seattle2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "65 South US Southern county homicides 1960-1990 True\n", - "66 StLouis St Louis region county homicide counts and rates True\n", - "67 Tampa1 2000 Census Tract Data for Tampa, FL MSA and ... True\n", - "68 arcgis arcgis testing files True\n", - "69 baltim Baltimore house sales prices and hedonics 1978 True\n", - "70 berlin Prenzlauer Berg neighborhood AirBnB data from ... True\n", - "71 book Synthetic data to illustrate spatial weights True\n", - "72 burkitt Burkitt's lymphoma in the Western Nile distric... True\n", - "73 calemp Employment density for California counties True\n", - "74 chicago Chicago neighborhoods True\n", - "75 clearwater mgwr testing dataset True\n", - "76 columbus Columbus neighborhood crime data 1980 True\n", - "77 desmith Small dataset to illustrate Moran's I statistic True\n", - "78 geodanet Datasets from geodanet for network analysis True\n", - "79 georgia Various socio-economic variables for counties ... True\n", - "80 juvenile Residences of juvenile offenders in Cardiff, UK True\n", - "81 mexico Decennial per capita incomes of Mexican states... True\n", - "82 networks Datasets used for network testing True\n", - "83 newHaven Network testing dataset True\n", - "84 nyc_bikes New York City Bike Trips True\n", - "85 sids2 North Carolina county SIDS death counts and rates True\n", - "86 snow_maps Public water pumps and Cholera deaths in Londo... True\n", - "87 stl Homicides and selected socio-economic characte... True\n", - "88 street_net_pts Street network points True\n", - "89 taz Traffic Analysis Zones in So. California True\n", - "90 tokyo Tokyo Mortality data True\n", - "91 us_income Per-capita income for the lower 48 US states 1... True\n", - "92 virginia Virginia counties shapefile True\n", - "93 wmat Datasets used for spatial weights testing True\n" - ] - } - ], - "source": [ - "available()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tampa1.installed" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/home/jovyan/pysal_data/Tampa1/__MACOSX/._TampaMSA',\n", - " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\n", - " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',\n", - " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',\n", - " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',\n", - " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.geojson',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.gpkg',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mid',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbn',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.xlsx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.mid',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.dbf',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.mif',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.kml',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sqlite',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.dbf',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.prj',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mif',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.geojson',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.shx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sbn',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sbx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.prj',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.shp',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shp',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbx',\n", - " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.kml']" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tampa1.get_file_list()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "tampa_counties_shp = tampa1.load('tampa_counties.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tampa_counties_shp" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "tampa_df = geopandas.read_file(tampa1.get_path('tampa_counties.shp'))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "tampa_df.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Other Remotes\n", - "\n", - "In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rio_Grande_do_Sul\n", - "======================\n", - "\n", - "Cities of the Brazilian State of Rio Grande do Sul\n", - "-------------------------------------------------------\n", - "\n", - "* 43MUE250GC_SIR.dbf: attribute data (k=2)\n", - "* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)\n", - "* 43MUE250GC_SIR.shx: spatial index\n", - "* 43MUE250GC_SIR.cpg: encoding file \n", - "* 43MUE250GC_SIR.prj: projection information \n", - "* map_RS_BR.dbf: attribute data (k=3)\n", - "* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)\n", - "* map_RS_BR.prj: projection information\n", - "* map_RS_BR.shx: spatial index\n", - "\n", - "\n", - "\n", - "Source: Renan Xavier Cortes \n", - "Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495\n", - "\n", - "\n" - ] - } - ], - "source": [ - "explain('Rio Grande do Sul')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the `explain` function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "rio = load_example('Rio Grande do Sul')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Name Description Installed\n", - "0 10740 Albuquerque, New Mexico, Census 2000 Tract Data True\n", - "1 AirBnB Airbnb rentals, socioeconomics, and crime in C... True\n", - "2 Atlanta Atlanta, GA region homicide counts and rates True\n", - "3 Baltimore Baltimore house sales prices and hedonics True\n", - "4 Bostonhsg Boston housing and neighborhood data True\n", - "5 Buenosaires Electoral Data for 1999 Argentinean Elections True\n", - "6 Charleston1 2000 Census Tract Data for Charleston, SC MSA... True\n", - "7 Charleston2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "8 Chicago Health Chicago Health + Socio-Economics True\n", - "9 Chile Labor Labor Markets in Chile (1982-2002) True\n", - "10 Chile Migration Internal Migration in Chile (1977-2002) True\n", - "11 Cincinnati 2008 Cincinnati Crime + Socio-Demographics True\n", - "12 Cleveland 2015 sales prices of homes in Cleveland, OH. True\n", - "13 Columbus Columbus neighborhood crime True\n", - "14 Denver Demographics and housing in Denver neighborho... True\n", - "15 Elections 2012 and 2016 Presidential Elections True\n", - "16 Grid100 Grid with simulated variables True\n", - "17 Groceries 2015 Chicago supermarkets True\n", - "18 Guerry Moral statistics of France (Guerry, 1833) True\n", - "19 Health Indicators Chicago Health Indicators (2005-11) True\n", - "20 Health+ 2000 Health, Income + Diversity True\n", - "21 Hickory1 2000 Census Tract Data for Hickory, NC MSA an... True\n", - "22 Hickory2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "23 Home Sales 2014-15 Home Sales in King County, WA True\n", - "24 Houston Houston, TX region homicide counts and rates True\n", - "25 Juvenile Cardiff juvenile delinquent residences True\n", - "26 Lansing1 2000 Census Tract Data for Lansing, MI MSA an... True\n", - "27 Lansing2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "28 Laozone Ozone measures at monitoring stations in Los ... True\n", - "29 LasRosas Corn yield, fertilizer and field data for pre... True\n", - "30 Line Line Shapefile True\n", - "31 Liquor Stores 2015 Chicago Liquor Stores True\n", - "32 Malaria Malaria incidence and population (1973, 95, 9... True\n", - "33 Milwaukee1 2000 Census Tract Data for Milwaukee, WI MSA True\n", - "34 Milwaukee2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "35 NCOVR US county homicides 1960-1990 True\n", - "36 NDVI Normalized Difference Vegetation Index grid True\n", - "37 NYC Demographic and housing data for New York Cit... True\n", - "38 NYC Earnings Block-level Earnings in NYC (2002-14) True\n", - "39 NYC Education NYC Education (2000) True\n", - "40 NYC Neighborhoods Demographics for New York City neighborhoods True\n", - "41 NYC Socio-Demographics NYC Education + Socio-Demographics True\n", - "42 Natregimes NCOVR with regimes (book/PySAL) True\n", - "43 Nepal Health, poverty and education indicators for ... True\n", - "44 Ohiolung Ohio lung cancer data, 1968, 1978, 1988 True\n", - "45 Orlando1 2000 Census Tract Data for Orlando, FL MSA an... True\n", - "46 Orlando2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "47 Oz9799 Monthly ozone data, 1997-99 True\n", - "48 Phoenix ACS Phoenix American Community Survey Data (2010,... True\n", - "49 Pittsburgh Pittsburgh homicide locations True\n", - "50 Point Point Shapefile True\n", - "51 Police Police expenditures Mississippi counties True\n", - "52 Polygon Polygon Shapefile True\n", - "53 Polygon_Holes Example to test treatment of holes True\n", - "54 Rio Grande do Sul Cities of the Brazilian State of Rio Grande do... True\n", - "55 SIDS North Carolina county SIDS death counts True\n", - "56 SIDS2 North Carolina county SIDS death counts and r... True\n", - "57 Sacramento1 2000 Census Tract Data for Sacramento MSA True\n", - "58 Sacramento2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "59 SanFran Crime July-Dec 2012 crime incidents in San Francisc... True\n", - "60 Savannah1 2000 Census Tract Data for Savannah, GA MSA a... True\n", - "61 Savannah2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "62 Scotlip Male lip cancer in Scotland, 1975-80 True\n", - "63 Seattle1 2000 Census Tract Data for Seattle, WA MSA an... True\n", - "64 Seattle2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "65 South US Southern county homicides 1960-1990 True\n", - "66 StLouis St Louis region county homicide counts and rates True\n", - "67 Tampa1 2000 Census Tract Data for Tampa, FL MSA and ... True\n", - "68 arcgis arcgis testing files True\n", - "69 baltim Baltimore house sales prices and hedonics 1978 True\n", - "70 berlin Prenzlauer Berg neighborhood AirBnB data from ... True\n", - "71 book Synthetic data to illustrate spatial weights True\n", - "72 burkitt Burkitt's lymphoma in the Western Nile distric... True\n", - "73 calemp Employment density for California counties True\n", - "74 chicago Chicago neighborhoods True\n", - "75 clearwater mgwr testing dataset True\n", - "76 columbus Columbus neighborhood crime data 1980 True\n", - "77 desmith Small dataset to illustrate Moran's I statistic True\n", - "78 geodanet Datasets from geodanet for network analysis True\n", - "79 georgia Various socio-economic variables for counties ... True\n", - "80 juvenile Residences of juvenile offenders in Cardiff, UK True\n", - "81 mexico Decennial per capita incomes of Mexican states... True\n", - "82 networks Datasets used for network testing True\n", - "83 newHaven Network testing dataset True\n", - "84 nyc_bikes New York City Bike Trips True\n", - "85 sids2 North Carolina county SIDS death counts and rates True\n", - "86 snow_maps Public water pumps and Cholera deaths in Londo... True\n", - "87 stl Homicides and selected socio-economic characte... True\n", - "88 street_net_pts Street network points True\n", - "89 taz Traffic Analysis Zones in So. California True\n", - "90 tokyo Tokyo Mortality data True\n", - "91 us_income Per-capita income for the lower 48 US states 1... True\n", - "92 virginia Virginia counties shapefile True\n", - "93 wmat Datasets used for spatial weights testing True\n" - ] - } - ], - "source": [ - "available()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grabbing all the remotes\n", - "\n", - "All the remote datasets can be downloaded with:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AirBnB\n", - "Already downloaded\n", - "Atlanta\n", - "Already downloaded\n", - "Baltimore\n", - "Already downloaded\n", - "Bostonhsg\n", - "Already downloaded\n", - "Buenosaires\n", - "Already downloaded\n", - "Charleston1\n", - "Already downloaded\n", - "Charleston2\n", - "Already downloaded\n", - "Chicago Health\n", - "Already downloaded\n", - "Chile Labor\n", - "Already downloaded\n", - "Chile Migration\n", - "Already downloaded\n", - "Cincinnati\n", - "Already downloaded\n", - "Cleveland\n", - "Already downloaded\n", - "Columbus\n", - "Already downloaded\n", - "Denver\n", - "Already downloaded\n", - "Elections\n", - "Already downloaded\n", - "Grid100\n", - "Already downloaded\n", - "Groceries\n", - "Already downloaded\n", - "Guerry\n", - "Already downloaded\n", - "Health Indicators\n", - "Already downloaded\n", - "Health+\n", - "Already downloaded\n", - "Hickory1\n", - "Already downloaded\n", - "Hickory2\n", - "Already downloaded\n", - "Home Sales\n", - "Already downloaded\n", - "Houston\n", - "Already downloaded\n", - "Juvenile\n", - "Already downloaded\n", - "Lansing1\n", - "Already downloaded\n", - "Lansing2\n", - "Already downloaded\n", - "Laozone\n", - "Already downloaded\n", - "LasRosas\n", - "Already downloaded\n", - "Liquor Stores\n", - "Already downloaded\n", - "Malaria\n", - "Already downloaded\n", - "Milwaukee1\n", - "Already downloaded\n", - "Milwaukee2\n", - "Already downloaded\n", - "NCOVR\n", - "Already downloaded\n", - "NDVI\n", - "Already downloaded\n", - "NYC\n", - "Already downloaded\n", - "NYC Earnings\n", - "Already downloaded\n", - "NYC Education\n", - "Already downloaded\n", - "NYC Neighborhoods\n", - "Already downloaded\n", - "NYC Socio-Demographics\n", - "Already downloaded\n", - "Natregimes\n", - "Already downloaded\n", - "Nepal\n", - "Already downloaded\n", - "Ohiolung\n", - "Already downloaded\n", - "Orlando1\n", - "Already downloaded\n", - "Orlando2\n", - "Already downloaded\n", - "Oz9799\n", - "Already downloaded\n", - "Phoenix ACS\n", - "Already downloaded\n", - "Pittsburgh\n", - "Already downloaded\n", - "Police\n", - "Already downloaded\n", - "Rio Grande do Sul\n", - "Already downloaded\n", - "SIDS\n", - "Already downloaded\n", - "SIDS2\n", - "Already downloaded\n", - "Sacramento1\n", - "Already downloaded\n", - "Sacramento2\n", - "Already downloaded\n", - "SanFran Crime\n", - "Already downloaded\n", - "Savannah1\n", - "Already downloaded\n", - "Savannah2\n", - "Already downloaded\n", - "Scotlip\n", - "Already downloaded\n", - "Seattle1\n", - "Already downloaded\n", - "Seattle2\n", - "Already downloaded\n", - "South\n", - "Already downloaded\n", - "StLouis\n", - "Already downloaded\n", - "Tampa1\n", - "Already downloaded\n", - "clearwater\n", - "Already downloaded\n", - "newHaven\n", - "Already downloaded\n", - "nyc_bikes\n", - "Already downloaded\n", - "taz\n", - "Already downloaded\n" - ] - } - ], - "source": [ - "libpysal.examples.fetch_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Name Description Installed\n", - "0 10740 Albuquerque, New Mexico, Census 2000 Tract Data True\n", - "1 AirBnB Airbnb rentals, socioeconomics, and crime in C... True\n", - "2 Atlanta Atlanta, GA region homicide counts and rates True\n", - "3 Baltimore Baltimore house sales prices and hedonics True\n", - "4 Bostonhsg Boston housing and neighborhood data True\n", - "5 Buenosaires Electoral Data for 1999 Argentinean Elections True\n", - "6 Charleston1 2000 Census Tract Data for Charleston, SC MSA... True\n", - "7 Charleston2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "8 Chicago Health Chicago Health + Socio-Economics True\n", - "9 Chile Labor Labor Markets in Chile (1982-2002) True\n", - "10 Chile Migration Internal Migration in Chile (1977-2002) True\n", - "11 Cincinnati 2008 Cincinnati Crime + Socio-Demographics True\n", - "12 Cleveland 2015 sales prices of homes in Cleveland, OH. True\n", - "13 Columbus Columbus neighborhood crime True\n", - "14 Denver Demographics and housing in Denver neighborho... True\n", - "15 Elections 2012 and 2016 Presidential Elections True\n", - "16 Grid100 Grid with simulated variables True\n", - "17 Groceries 2015 Chicago supermarkets True\n", - "18 Guerry Moral statistics of France (Guerry, 1833) True\n", - "19 Health Indicators Chicago Health Indicators (2005-11) True\n", - "20 Health+ 2000 Health, Income + Diversity True\n", - "21 Hickory1 2000 Census Tract Data for Hickory, NC MSA an... True\n", - "22 Hickory2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "23 Home Sales 2014-15 Home Sales in King County, WA True\n", - "24 Houston Houston, TX region homicide counts and rates True\n", - "25 Juvenile Cardiff juvenile delinquent residences True\n", - "26 Lansing1 2000 Census Tract Data for Lansing, MI MSA an... True\n", - "27 Lansing2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "28 Laozone Ozone measures at monitoring stations in Los ... True\n", - "29 LasRosas Corn yield, fertilizer and field data for pre... True\n", - "30 Line Line Shapefile True\n", - "31 Liquor Stores 2015 Chicago Liquor Stores True\n", - "32 Malaria Malaria incidence and population (1973, 95, 9... True\n", - "33 Milwaukee1 2000 Census Tract Data for Milwaukee, WI MSA True\n", - "34 Milwaukee2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "35 NCOVR US county homicides 1960-1990 True\n", - "36 NDVI Normalized Difference Vegetation Index grid True\n", - "37 NYC Demographic and housing data for New York Cit... True\n", - "38 NYC Earnings Block-level Earnings in NYC (2002-14) True\n", - "39 NYC Education NYC Education (2000) True\n", - "40 NYC Neighborhoods Demographics for New York City neighborhoods True\n", - "41 NYC Socio-Demographics NYC Education + Socio-Demographics True\n", - "42 Natregimes NCOVR with regimes (book/PySAL) True\n", - "43 Nepal Health, poverty and education indicators for ... True\n", - "44 Ohiolung Ohio lung cancer data, 1968, 1978, 1988 True\n", - "45 Orlando1 2000 Census Tract Data for Orlando, FL MSA an... True\n", - "46 Orlando2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "47 Oz9799 Monthly ozone data, 1997-99 True\n", - "48 Phoenix ACS Phoenix American Community Survey Data (2010,... True\n", - "49 Pittsburgh Pittsburgh homicide locations True\n", - "50 Point Point Shapefile True\n", - "51 Police Police expenditures Mississippi counties True\n", - "52 Polygon Polygon Shapefile True\n", - "53 Polygon_Holes Example to test treatment of holes True\n", - "54 Rio Grande do Sul Cities of the Brazilian State of Rio Grande do... True\n", - "55 SIDS North Carolina county SIDS death counts True\n", - "56 SIDS2 North Carolina county SIDS death counts and r... True\n", - "57 Sacramento1 2000 Census Tract Data for Sacramento MSA True\n", - "58 Sacramento2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "59 SanFran Crime July-Dec 2012 crime incidents in San Francisc... True\n", - "60 Savannah1 2000 Census Tract Data for Savannah, GA MSA a... True\n", - "61 Savannah2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "62 Scotlip Male lip cancer in Scotland, 1975-80 True\n", - "63 Seattle1 2000 Census Tract Data for Seattle, WA MSA an... True\n", - "64 Seattle2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "65 South US Southern county homicides 1960-1990 True\n", - "66 StLouis St Louis region county homicide counts and rates True\n", - "67 Tampa1 2000 Census Tract Data for Tampa, FL MSA and ... True\n", - "68 arcgis arcgis testing files True\n", - "69 baltim Baltimore house sales prices and hedonics 1978 True\n", - "70 berlin Prenzlauer Berg neighborhood AirBnB data from ... True\n", - "71 book Synthetic data to illustrate spatial weights True\n", - "72 burkitt Burkitt's lymphoma in the Western Nile distric... True\n", - "73 calemp Employment density for California counties True\n", - "74 chicago Chicago neighborhoods True\n", - "75 clearwater mgwr testing dataset True\n", - "76 columbus Columbus neighborhood crime data 1980 True\n", - "77 desmith Small dataset to illustrate Moran's I statistic True\n", - "78 geodanet Datasets from geodanet for network analysis True\n", - "79 georgia Various socio-economic variables for counties ... True\n", - "80 juvenile Residences of juvenile offenders in Cardiff, UK True\n", - "81 mexico Decennial per capita incomes of Mexican states... True\n", - "82 networks Datasets used for network testing True\n", - "83 newHaven Network testing dataset True\n", - "84 nyc_bikes New York City Bike Trips True\n", - "85 sids2 North Carolina county SIDS death counts and rates True\n", - "86 snow_maps Public water pumps and Cholera deaths in Londo... True\n", - "87 stl Homicides and selected socio-economic characte... True\n", - "88 street_net_pts Street network points True\n", - "89 taz Traffic Analysis Zones in So. California True\n", - "90 tokyo Tokyo Mortality data True\n", - "91 us_income Per-capita income for the lower 48 US states 1... True\n", - "92 virginia Virginia counties shapefile True\n", - "93 wmat Datasets used for spatial weights testing True\n" - ] - } - ], - "source": [ - "available()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/_sources/notebooks/voronoi.ipynb.txt b/docs/_sources/notebooks/voronoi.ipynb.txt deleted file mode 100644 index 75d7263d3..000000000 --- a/docs/_sources/notebooks/voronoi.ipynb.txt +++ /dev/null @@ -1,478 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Voronoi Polygons for 2-D Point Sets\n", - "\n", - "Author: Serge Rey (http://github.com/sjsrey)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Basic Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "sys.path.append(os.path.abspath('..'))\n", - "import libpysal" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from libpysal.cg.voronoi import voronoi, voronoi_frames" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "regions, vertices = voronoi(points)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "regions" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 4.21783296, 4.08408578],\n", - " [ 7.51956025, 3.51807539],\n", - " [ 9.4642193 , 19.3994576 ],\n", - " [ 14.98210684, -10.63503022],\n", - " [ -9.22691341, -4.58994414],\n", - " [ 14.98210684, -10.63503022],\n", - " [ 1.78491801, 19.89803294],\n", - " [ 9.4642193 , 19.3994576 ],\n", - " [ 1.78491801, 19.89803294],\n", - " [ -9.22691341, -4.58994414]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vertices" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "region_df, point_df = voronoi_frames(points)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "region_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n", - "point_df.plot(ax=ax, color='red')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Larger Problem" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "n_points = 200\n", - "np.random.seed(12345)\n", - "points = np.random.random((n_points,2))*10 + 10\n", - "results = voronoi(points)\n", - "mins = points.min(axis=0)\n", - "maxs = points.max(axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "regions, vertices = voronoi(points)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "regions_df, points_df = voronoi_frames(points)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAAD4CAYAAADIOotxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAdJUlEQVR4nO2df6hlV3XHP2smM4VHqCZvJio2775SatFa0eQpQm0ak6akoahtaYu8loFGBl9EVLQ2JdBiYUBjUeo/hgFDAu81oBh/tNDWGPzxT015ExI7NtpYOjOJTp0YWyoESpJZ/ePcl9y57/zY55y991n7nP2Bw7v33HfPXWef/V1r7X323kdUlUwmMw0ODG1AJpOJRxZ8JjMhsuAzmQmRBZ/JTIgs+ExmQlwW88eOHDmi6+vrMX8yk5kkp06d+rGqHl3eH1Xw6+vr7O7uxvzJTGaSiMjZsv05pc9kJkQWfCYzIbLgM5kJkQWfyUyIRsGLyNUi8jUReUxEviMi75vvv1JEHhCRx+d/rwhvbiaT6YNLhH8O+KCqvhp4M/AeEXkNcDvwoKr+IvDg/H0mkzFMo+BV9byqPjx//VPgMeCVwNuBe+f/di/wjlBGmmRnB9bX4cCB4u/OztAWZTKNtGrDi8g68AbgIeBlqnoeCqcAXFXxneMisisiu0899VQ/a62wswPHj8PZs6Ba/D1+PIt+KLLzdUdVnTbgcuAU8Lvz9/+z9Pl/Nx3j2muv1VEwm6kWUr90m82Gtmx6bG+rrqxceh1WVor9EwbY1RINOkV4ETkEfB7YUdX757t/JCKvmH/+CuCCV09kmXPn2u3PhOOOO+CZZy7d98wzxf7MPlx66QX4DPCYqn5i4aMvA8fmr48BX/JvnlHW1trtz4QjO99WuET4XwX+GLhBRB6Zb7cAHwVuEpHHgZvm76fBiROwsnLpvpWVYn8mLm2d79Tb+2V5fqitdRt+e7toF4sUfy21yyzbNiWa2vCL12l1VfXw4Um096low9sVfO6MybhS5XzL6pCPztYEnH16gp9yT3gCFSoJqurQ8ibifsytreL/jQeiKsHbHUs/1c6YKdzjj9WOdq0rrp2tOztw113FdVkkpbsCZV4g1JYjvANjP++YTTWXCN/mt+uO1yZLiADJRXhrPeG33QaXXQYixd/bbgvzO2PPbGLeNy+rQ4cOwepqcR1nMzh5EjY33Y5Xdw0SuSVrV/Cbm8XFmM26XRyf3HYbfPrT8Pzzxfvnny/ehxD92O/xh3BoVU2Esjr0rnfB5Zd3+52qayCSzi3ZsrAfakt2aO3Bg+Vp3MGD/n9r7HcnfDdZ2pRX37It+75I0ZFnDJLrpbdEXRswBGPupfft0No4EB/OJpFrkwXfh74RPpFK8gKh7fV5/OVbZHWdaG3+N3HSE7yvSuHjOFtb5RXFJZVLLUVPzd4c4UtJS/C+Kp3Pyru19WKkP3jQvd2W2m221Owdug1v1BmmJXhflc5C5U0tjUzNXtV2UbdPhLZQnxxJS/C+Kp2FyptQJVHVuPYmkh6/gIX65EiV4G3eh/d1L9rCPe2qAUS33GJzmmasAU8pDiG2UJ/6UuYFQm1Jt+H7sBzJtrZs2FVFjMibWuajaqc+OUBSKb2qrV5636RY2X2TUHp8CRbrUwlVgpfiszhsbGxofnosRRpfVu4icPFifHuGYH29SOOXmc3gzJnY1rzIzk4xrv/cuSJVP3FimOHcPRGRU6q6sbzfZhveAiGncI6hLdgXa5OjoF+/QipLZ5WF/VBbMiPtQrfVEmoL7sNnSmstPe7a1DJ4PUmuDT8kMdrY1iq7CxYqdshy69qvYLBPJgu+Dal2KIVm6Iod2uF0Pb+Q9aWjg8uCb8PQFdsqQzvC0Nelq0MJZVcPB5cF3wYLqatFhnaEMRxOl4gaqr70KO8s+Lak2MYOzdCOcGiHU0eI+tLDwWXBT5mx9KwP7XBikyP8hPA50nBMIplS5pXb8BPBp0gtp8GZZjz30ueRdntYGinlcynnsuGrdfv7YKkMx8LmZjHU+OLF4m/PYb6XeTEqdfaGVO6JbG9IJQwzjtrnUs4HD764vPbyfp9YK8NMKXnyDNibyOHTHpHqz3xee2tlOHE6T54RkbtF5IKInF7Y93oR+db8WfG7IvIm3wZHxdrTXnxOLJnN2u3virUytISlpk5Zw35xA64DrgFOL+z7CvBb89e3AF9vOo527bQb62IMTee19zm8uHhml/OP1UufOwfLGeguCX166YH1JcH/E/CH89fvBP7W5TitBR+rsHz+jouDcv29tnZV/XYMp5nS7b+Yt/YGcoS+Bf9q4BzwBPADYOZynNaCT21BRddK73pebc7fguBSuEceu5wGmn/gW/CfAn5v/voPgK/WfPc4sAvsrq2ttbN66MkabXEVqOt5tTn/nFK7sboat5yMRfiu9+GPAffPX38OqOy0U9WTqrqhqhtHjx5t9yuprQzj2nHlel5tzj93mjWzswNPP13+WVM5de14s7ayT5kXWN7YH+EfA66fv74ROOVyHNNteB+pqKs3D9GGzxG+maoyaionH0+sidzUoWtKD9wHnAeeBZ4EbgXeApwCHgUeAq5tOo52Ebxq+MLy3WHX5rFHLufV5v+GbsNbp6qJBPXllKAz7Sx4n5vJsfS+L+bQs8msd5oNSdW1Xl2t/15qfUk6dcHXCaHO62fRjAtrK9oEZLqCb7rIde26nBaPD0sr2gRkuoJv8s5lFzMhT54UKTc5ErO9SvDjnzzj8pSXvaeNVE0ZndITYUKxPJsOittTJ0/m2XQBmO6TZ1zuZe/NOa6aUGL1vn9K+Jzjn+nM+AXfZuBD20ESlmZBWSfEwKBc/u0py/NDbSZ76bv+b2odOUO3QUPc/rRa/kOXtU650y4UdRXYwAW/BAvi8G1DXwcS6hr1OU+PNmXB+6bu/v3Q4lrGyn1k1zUAXLKrqrJ3GQwT0gF2LWvPNk1X8KE8edWF3Vuswqe4+p5DCiPF+swvaFvOIR2gkQdSjlvwdQs/hPLkVcfuE3na/E6bc7AS4evou0ZAm3IJ6QC7lrVnm9IUfN/VY0JX9DL7fP+mj+NZaMM30XeNAHA/n5D1wsjw3fQE72P1mCFSWRe726Tovs7BWkfiMiFWAaoitAM0MHw3PcH7WD1mqFS27oK3vbAppOM+CLFGQNPv+XSAPo436V5618jWdHtsuXIcPlxMh0zlfnQK6bgvfK8REAuD1yg9wbsKo6mwFyvH6qrqoUNxL8xy5axqf9al6Nvbl67Ftro6fCUfE30diMEsLD3Bt/Garhcs9oUpO4eqzKXOBoMRZDT4KNuQfUUdnVF6gu9xspXE7sSrcjDLdjRVMIMRpBPWUnFVP2Ub6vr0cEZpCt43sYXTtJqOa8WP6agsDjkNiY+yDXVuPeprFrxq/Erny8HEclR9y6fOWfQZchoyK/BVtiHs7OGMsuD3iJlW1gmojR2xHFWfyt9kY5fKG+O8rWYeqjnCJ0mZsLtUshiOqk9621Q5u1ReX4NsXEZrWuhbWLZjayu34UeB1U64PnY1OYsuTq5v+9py9F6mytatrQn10o8Vq7PX+gjExVk0RdLlz/s+B86qYy1j8mPpx0yX0XYx+x26/JaPDr+yUZF9BkpZdaxl5NlyI6btoKKU0tKujqnKCa6u+j9mShG+6ak4FaQreCsdKr6xOjpwKEJE49Sc5XI2s5fldLA3TcFvbxcn7KEAksXgsM0ghBytZuUcm/D47Po0BV9VAB3TnCQxOGwzCNbsGQKPzr1K8LbXpX/66Xb7U6VuffW2a+U3HW8Paw+G2NwsnkIzmxVP+pnNpvdUGpeHpvSlzAuE2lpH+DJvt7fFInRK6BLZQozKS6kHeyp4zHLomtIDdwMXgNNL+98LfA/4DnBn03E0xZQ+RprpO2V3Pd5UOgNTw1OA6SP464BrFgUPvBX4KvAz8/dXNR1HXQRfNrRwuefy0KF47boYovAdaV2PF8qZpdRJNmI6C774LutLgv8s8Bsu313cagXveWihF2KkvUNFeFX/4swdb2bwLfhHgI8ADwHfAN5Y893jwC6wu7a2Vm2hxRQzxq0i38tuDSk6i9dwovgW/GngU4AAbwL+E4pnzddttRG+TTSNlTaGEE/VEFKfC2vGTqv3fq+qg7XtYhK5SdAb34L/R+D6hff/ARxtOk6t4F2jQ+wI5rsCji0Kll2PrueWmwTe8C34dwN/NX/9KuCJ3hHe9WKnLpix3Q6ri+xtBTvktR1ZZtGnl/4+4DzwLPAkcCtwGNiep/YPAzc0HUebBK/qVuh168RZuVh155G6w1qmad2+NtdiKGc4wsyiV4T3tXmZLdcUUYa+WE2VZ2yVy6cDG8oZjs0J65gE79JmHPJi+VgIIiV8OrChnOHYmlk6JsGrXiqYuvR+CEZYeRrx6cCGcIY5whsX/CLWLpYle8aUSYRkbM0sHbPgrV0sK/ZYsSMVRuYcxyt4VXsXy4I9ljKNTHSqBC/FZ3HY2NjQ3d3daL83aQ4cKCS+jAhcvBjfnsx+dnaK9QfOnSvmvJ844W3+v4icUtWN5f22F8DIdKfPYgouC2hk+rGzA8ePw9mzhWM+e7Z4H7isxyn4XGG7rZQDg1XEyTHUikNleX6oLcoy1W2XgB66rR2SLueX2/7hiHg7mVF32i1idRJOKlgYRzBGRxx5wNh0BO9aYXMkK2fochmrI+47JLylE5yO4F0rrIVIZpGhBTe0w9nDd5bRZ9JXh2syHcGPfZptjHR3yJTagiPu6vRCzZLs8N3pCF7VrcIOHcm6kKLNbamr3LEcURdxhpwl2cEJTkvwqu6iT6lzKNWspA1Vwrjxxv0VP5Sz65JlhJwlmSN8A2ONhBbS3RgsC2Nrq/rcQzi7Lo419DMAcxu+hrFGQgvp7hD4WiDTlS4BI3Sdy730NYw1ElZVxK2tcWY0ezQto9WXMjGVZRl1gjOWVU5L8GON8KrllXPM56tafX4i/QXlIlRXMRvKsqYleGPeNjh9MhpDlbSSsuspUkTdvrg4ywQd6rQEr2qjIlu+jbRnXyqOMVRZujjLBJuI0xP80MQUU9ff8hW5LDjXruQInwXvhdiVpIvofESulLKEMny24Q2RBR+bFNLAtk5prB2GIQZpDZz1ZMHHJgUhtF07oOx/y87RmmOLjcsw28DOIAs+Nqmkga6Vr8qBHTxo37HFpmmAVIR6kQU/BCl3Zi1TN/glBccWk7rmXKTMr0rw41zTzgqbm3DmTLFK7Jkz3VYktbI+X9Xil7MZnDxZ/BV58b2n1VeTpG4B0XPnyj+r2u+bMi8QaptchO9L3VDakJlD1VDTFCK5hayqrqwGjvDjFryFi9+WRZur2sd9p4nWlUtdZY1dnl16xq04pSrbcxs+EJYuvitlNrturhGiqVzqOudilp3FGWu+sNxLD9wNXABOl3z2IUCBI03H0diCT+XiL1I3DbRpc70N1lQuTZ1zsURvbU56YlQJ3qXT7h7g5uWdInI1cBMQqbehJUN3jnTBxTaR8v0uT5Sp+429/XXHifGghGV7XPdDv6ftTIRGwavqN4GflHz0SeDDFBHeHile/CrbDh58sQf83e/u9kSZpt/Y21/2xJpFYjnMLtev69N2pkRZ2F/egHUWUnrgbcDfzF+foSalB44Du8Du2tpavJxmLG143/OuXceODz2gpuv1S7GjNgD06bRbFDywAjwEvEQdBL+45V56B2LY7Dp2fGiHGWv8uuv3EqpPPgX/KxSdeGfm23MU7fiXNx0n34dPgMVKvbpabAlU8F4ZgWtWNbQDbIE3wZd8ZjfCZ9qRWKW+hK53ZVy/F/quj+fsobPggfuA88CzwJPArUufZ8H3xUqqaP1WZl05db0l5/o9Y8tQN9ErwvvasuBLsBRVLd/H7jpgKIUIH+DYWfCh6BudLUVVS7Ys02RbmUNwWejSQhs+gKMdh+CtpL6L9vStBJaiqqVsYxmXcip7Qo3PW3mh6l+O8CVYrIw+LpS1qGrNqe7hUk7WytKV3IYvweLFzItAxsOlnCxlS22x0kvvc+sl+KbHDQ0RkXw5IatR1RpN5RQ7KBi+bukLvupixnqEcBk5Otsi5vUwfu3TF3xVL+zQab5hL+9E6vYvE+t8LDYxF0hf8Kr7L2ZVip9Cm80CxqOUaYz3F1QJ3u4ilmWLNy4vCjmblX/3wIHhF32MQd8FLu+4o5jjvkjMOe9lWFm0s4mu06+HPr8yLxBqc47wfQZDLG9jjVhjGwOgmlbG0cXWiOdHUil9m/bRYpo/9BzumIxxDIA1e5po218Q8fyqBC/FZ3HY2NjQ3d3d5n88cKAoimVEinTe9/dSxMe57uzA8eOXpvUrK8OtKz/26xfx/ETklKpu7DPB66/4omv7KOSyVkO3vZbxca6bm7YeIpHismRtsHB+ZWE/1Oa9De/re6HsCYlFm/oyxnNaJLfhawi9XFEbura9Qt8THts9dNVxntMikc6vSvA22/DW6NL2stY+zkyKtNrw1ujS9vJ1j9ta30HGHYvXrizsh9qSXQCjS9srz6SbNgNfO5Jrw1tjiHuuVvsOMtXslX3VQLBIYwqy4H3TJKqhRsLlrGA4XEZ+RhrFmAXvkzZDf9tG2r4jB1MbrdYXS9lMXWTPET5h2oqqzZppfecGWBsfHxJr2UzdIi25DZ8wbUTVplJWOZKDB02O1x4Ey3MnmtruER1RFrxP2oiqzf/mnv16XDKgIbMZQ2WfBe+TNhe2jYjzGnn1uLSRh85mjJR9FrxvFi9s3UMX2071NRIhTNLURs7l9QJZ8KFoEmlbERuJEOZsUe3Xx2HtXAKTBR8KlwieYmWzmG1Ym0VpmCz4UIz1NljM3v42DrGL8xz7nYsSqgSfZ8v1ZX0dzp7dv382KxbaTJVYq7PEmFU49pV0Ssiz5brgMtvpxImigi6yslLsT5lYq7PEWDnXwkozVigL+4sbcDdwATi9sO/jwHeBbwNfAF7adBxNLaVv0+7r00a32r4PuXpQ7GcL5Da8exseuA64ZknwvwlcNn/9MeBjTcfR1AQfo93XtyKmtqJO2fnGenpQ33Ox6pgr6Cz44rusLwp+6bPfAXZcjpOU4GN0xvVxKlajVp0wqs7Xx/MBQwhycarrkM8w7EBIwf8d8Ec13z0O7AK7a2tr0U64NzEifB+nEtK+PusJ1jmhUE8ADuH8XIbxGu7lDyJ44I55G15cjpNUhI8RQfuINlQG0ue8m84nhJPa3g4zicZlGK/hW6/eBQ8cA/4ZWHE5hqYmeNU4beRQ4upKSCfk24k2ReE+gnQZxjuVCA/cDPwbcNTl+3tbcoKPQaj0uSsumUOVzbFHHTZFYde+kDbn4rOsAwaUPr309wHngWeBJ4Fbge8DTwCPzLe7mo6jWfD+CVFhmkRb52hidyTWRWHXIbeHDl36vUOHqs9l7/dC3bHwWFa9IryvLQu+AQu3fpoqootDiHUOdZNpXH53dbX8+6ur4c8lcKdwFnwXYlZeF48fy56637E0d6BvlKxL2UMTot/hksNnwbcjdnraJ5WOSd/IFGIwT9fjDSX47e12A446nGMWfFtiz7BqipxWZnz1cTxWnNYeTSm9TxZFW3UbUWR/WXQssyz4tsROXZsEbS2Vboo4Zf9jxWkt2rgsPtf2f9vfcVmLryyz6FhmWfBtiV05+3aWWaLqXNq0W2P0V2xvqx4+fKkthw/3H6Hn6uhcrmVHR58F35YhJrbUfcdaOlxHXe951f7Fc451rr6daFtH53J+OcJHxNqgGAu37Vxouj/eVPGr2ta+sxnfzaS+jq6M3IY3RJ8RZ2Om7vxdOq/apP6h7OxCG0cXOFvMgvdNnee11ME2BK5RyWW8ekiH6TsTc3V0EbKzLHjf1F3cqUd4VbcKXlVOq6vx+it8CtFQP0tagk+hrVoXxQ1deNM0jcu3XgfKMGJ3OoJPRSyWxpSnTC6nIKQj+FTS4VQcU6adUxmJA0pH8Cl1eI2kcoyaNo55RE48HcGnEuEzfgjtNNvUpxHVvSrB23sQxVgf7JDZz95TZ86eLaR19mzxvuyBH105d859f5v/TRR7gt/cLB4zNJsVjwKazfw+dmhKuDw5Z0isPXVmCk+oKQv7obZR3Ye3Tgrt0Rj9NbkNb7wNP1Zid/Cl0B6NZWPupc+Cj8oQkSOFux0jiqjWqBK8vTb8GInRVl3GUnu0qi8h99fEp8wLhNomG+GHiLZWoqcVOyYGOcIPyBDR1kr0HCK7GZq+d0dC3l0p8wKhtslGeN9RLqWOpRT6EnziY6UkD3WF5DvtUqrkZfiyP7UUOYW7BT7pe76eyittwadWyUPiU0CxFoqc0rXrm9F4yojSFnzoKJFS9uArRS4Tos9npy3/Virl25e6de1czjtHeA3bDkwtAvlyfk1LJ1suA8uU1ac2ZZrb8Bo2wqfWxvTloBJ//rlptrerF+h0KVMPGVHagg8ZhVPsRfaRIrs8HMFyGVhn4HpVJfg07sPX3VPue8/S0og0VzY34cwZuHix+Nvl3vqJE3DoUP3/WC4D61itV2VeYHED7gYuAKcX9l0JPAA8Pv97RdNxtE+Er8JH5E+tDe+Tqgc+TKkMQtF2lp7nTk26pvTAdcA1S4K/E7h9/vp24GNNx9EQgvfV/p5SL/Iide34qZRBSFzqVaCAUyV4KT6rR0TWgb9X1dfO338PuF5Vz4vIK4Cvq+ovNR1nY2NDd3d3XZOPZg4cKIpov8FFupupZ329WGVmmdmsaCpkwhPoGojIKVXdWN7ftQ3/MlU9DzD/e1XNDx8XkV0R2X3qqac6/lwFVttJqZCXExueyMtqBe+0U9WTqrqhqhtHjx71e/BcYfthZYLNlIkctLoK/kfzVJ753wv+TGpBrrD98dHjn+lO5KDVVfBfBo7NXx8DvuTHnA7kCptJmchBq7HTTkTuA64HjgA/Av4S+CLwWWANOAf8vqr+pOnHvHfaZTKZUqo67S5r+qKqvrPioxt7W5XJZKKSxki7TCbjhSz4TGZCZMFnMhMiCz6TmRBOQ2u9/ZjIU0DJOMJ9HAF+HNicPli2z7JtkO3ri6t9M1XdN9ItquBdEZHdslsKVrBsn2XbINvXl7725ZQ+k5kQWfCZzISwKviTQxvQgGX7LNsG2b6+9LLPZBs+k8mEwWqEz2QyAciCz2QmxOCCF5G7ReSCiJxe2HeliDwgIo/P/15hyLaPi8h3ReTbIvIFEXnpELZV2bfw2YdEREXkyBC2zW0otU9E3isi3xOR74jInZbsE5HXi8i3ROSR+UpNbxrItqtF5Gsi8ti8nN43399LG4MLHrgHuHlp3+3Ag6r6i8CD8/dDcA/7bXsAeK2qvg74d+DPYxu1wD3stw8RuRq4iWLq8pDcw5J9IvJW4O3A61T1l4G/HsCuPe5hf/ndCXxEVV8P/MX8/RA8B3xQVV8NvBl4j4i8hp7aGFzwqvpNYHku/duBe+ev7wXeEdWoOWW2qepXVPW5+dtvAT8X3bAXbSkrO4BPAh8GBu2RrbBvC/ioqv7f/H+GWS2JSvsU+Nn565cAP4xq1J4RqudV9eH5658CjwGvpKc2Bhd8Bc6LZA7MnwD/MLQRi4jI24AfqOqjQ9tSwauAXxORh0TkGyLyxqENWuL9wMdF5AmK7GPIDA54YdXoNwAP0VMbVgVvHhG5gyLtavmom3CIyApwB0UqapXLgCso0tQ/BT4rIjKsSZewBXxAVa8GPgB8ZkhjRORy4PPA+1X1f/sez6rgbSySWYGIHAN+G9hUWwMZfgH4eeBRETlD0dx4WERePqhVl/IkcP/8eQn/AlykmBBihWPA/fPXnwMG6bQDEJFDFGLfUdU9m3ppw6rg7SySuYSI3Az8GfA2VX1maHsWUdV/VdWrVHVdVdcpxHWNqv7XwKYt8kXgBgAReRVwGFuz034I/Pr89Q0Uj1OLzjzr+QzwmKp+YuGjftooexxNzA24DzgPPEtRQW8FVil6IB+f/73SkG3fB54AHplvd1kqu6XPzwBHLNlHIfBt4DTwMHCDMfveApwCHqVoM187kG1voehA/PZCXbulrzby0NpMZkJYTekzmUwAsuAzmQmRBZ/JTIgs+ExmQmTBZzITIgs+k5kQWfCZzIT4f25kFN7jUerHAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "points_df.plot(ax=ax, color='red')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "regions_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n", - "points_df.plot(ax=ax, color='red')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Trimming" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "points = np.array(points)\n", - "maxs = points.max(axis=0)\n", - "mins = points.min(axis=0)\n", - "xr = maxs[0] - mins[0]\n", - "yr = maxs[1] - mins[1]\n", - "buff = 0.05\n", - "r = max(yr, xr) * buff\n", - "minx = mins[0] - r\n", - "miny = mins[1] - r\n", - "maxx = maxs[0] + r\n", - "maxy = maxs[1] + r" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "regions_df.plot(ax=ax, edgecolor='black', facecolor='blue', alpha=0.2 )\n", - "points_df.plot(ax=ax, color='red')\n", - "plt.xlim(minx, maxx)\n", - "plt.ylim(miny, maxy)\n", - "plt.title(\"buffer: %f, n: %d\"%(r,n_points))\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Voronoi Weights" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from libpysal.weights.contiguity import Voronoi as Vornoi_weights" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "w = Vornoi_weights(points)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "200" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w.n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.685" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w.pct_nonzero" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(1, 1),\n", - " (2, 6),\n", - " (3, 17),\n", - " (4, 34),\n", - " (5, 41),\n", - " (6, 63),\n", - " (7, 24),\n", - " (8, 7),\n", - " (9, 5),\n", - " (10, 1),\n", - " (11, 0),\n", - " (12, 1)]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w.histogram" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [i for i in range(w.n) if w.cardinalities[i]==12]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[16.50851787, 13.12932895]])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "points[idx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/_sources/notebooks/weights.ipynb.txt b/docs/_sources/notebooks/weights.ipynb.txt deleted file mode 100644 index 3f867ad7b..000000000 --- a/docs/_sources/notebooks/weights.ipynb.txt +++ /dev/null @@ -1,1313 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "sys.path.append(os.path.abspath('..'))\n", - "import libpysal" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Name Description Installed\n", - "0 10740 Albuquerque, New Mexico, Census 2000 Tract Data True\n", - "1 AirBnB Airbnb rentals, socioeconomics, and crime in C... True\n", - "2 Atlanta Atlanta, GA region homicide counts and rates True\n", - "3 Baltimore Baltimore house sales prices and hedonics True\n", - "4 Bostonhsg Boston housing and neighborhood data True\n", - "5 Buenosaires Electoral Data for 1999 Argentinean Elections True\n", - "6 Charleston1 2000 Census Tract Data for Charleston, SC MSA... True\n", - "7 Charleston2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "8 Chicago Health Chicago Health + Socio-Economics True\n", - "9 Chile Labor Labor Markets in Chile (1982-2002) True\n", - "10 Chile Migration Internal Migration in Chile (1977-2002) True\n", - "11 Cincinnati 2008 Cincinnati Crime + Socio-Demographics True\n", - "12 Cleveland 2015 sales prices of homes in Cleveland, OH. True\n", - "13 Columbus Columbus neighborhood crime True\n", - "14 Denver Demographics and housing in Denver neighborho... True\n", - "15 Elections 2012 and 2016 Presidential Elections True\n", - "16 Grid100 Grid with simulated variables True\n", - "17 Groceries 2015 Chicago supermarkets True\n", - "18 Guerry Moral statistics of France (Guerry, 1833) True\n", - "19 Health Indicators Chicago Health Indicators (2005-11) True\n", - "20 Health+ 2000 Health, Income + Diversity True\n", - "21 Hickory1 2000 Census Tract Data for Hickory, NC MSA an... True\n", - "22 Hickory2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "23 Home Sales 2014-15 Home Sales in King County, WA True\n", - "24 Houston Houston, TX region homicide counts and rates True\n", - "25 Juvenile Cardiff juvenile delinquent residences True\n", - "26 Lansing1 2000 Census Tract Data for Lansing, MI MSA an... True\n", - "27 Lansing2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "28 Laozone Ozone measures at monitoring stations in Los ... True\n", - "29 LasRosas Corn yield, fertilizer and field data for pre... True\n", - "30 Line Line Shapefile True\n", - "31 Liquor Stores 2015 Chicago Liquor Stores True\n", - "32 Malaria Malaria incidence and population (1973, 95, 9... True\n", - "33 Milwaukee1 2000 Census Tract Data for Milwaukee, WI MSA True\n", - "34 Milwaukee2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "35 NCOVR US county homicides 1960-1990 True\n", - "36 NDVI Normalized Difference Vegetation Index grid True\n", - "37 NYC Demographic and housing data for New York Cit... True\n", - "38 NYC Earnings Block-level Earnings in NYC (2002-14) True\n", - "39 NYC Education NYC Education (2000) True\n", - "40 NYC Neighborhoods Demographics for New York City neighborhoods True\n", - "41 NYC Socio-Demographics NYC Education + Socio-Demographics True\n", - "42 Natregimes NCOVR with regimes (book/PySAL) True\n", - "43 Nepal Health, poverty and education indicators for ... True\n", - "44 Ohiolung Ohio lung cancer data, 1968, 1978, 1988 True\n", - "45 Orlando1 2000 Census Tract Data for Orlando, FL MSA an... True\n", - "46 Orlando2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "47 Oz9799 Monthly ozone data, 1997-99 True\n", - "48 Phoenix ACS Phoenix American Community Survey Data (2010,... True\n", - "49 Pittsburgh Pittsburgh homicide locations True\n", - "50 Point Point Shapefile True\n", - "51 Police Police expenditures Mississippi counties True\n", - "52 Polygon Polygon Shapefile True\n", - "53 Polygon_Holes Example to test treatment of holes True\n", - "54 Rio Grande do Sul Cities of the Brazilian State of Rio Grande do... True\n", - "55 SIDS North Carolina county SIDS death counts True\n", - "56 SIDS2 North Carolina county SIDS death counts and r... True\n", - "57 Sacramento1 2000 Census Tract Data for Sacramento MSA True\n", - "58 Sacramento2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "59 SanFran Crime July-Dec 2012 crime incidents in San Francisc... True\n", - "60 Savannah1 2000 Census Tract Data for Savannah, GA MSA a... True\n", - "61 Savannah2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "62 Scotlip Male lip cancer in Scotland, 1975-80 True\n", - "63 Seattle1 2000 Census Tract Data for Seattle, WA MSA an... True\n", - "64 Seattle2 1998 and 2001 Zip Code Business Patterns (Cen... True\n", - "65 South US Southern county homicides 1960-1990 True\n", - "66 StLouis St Louis region county homicide counts and rates True\n", - "67 Tampa1 2000 Census Tract Data for Tampa, FL MSA and ... True\n", - "68 arcgis arcgis testing files True\n", - "69 baltim Baltimore house sales prices and hedonics 1978 True\n", - "70 berlin Prenzlauer Berg neighborhood AirBnB data from ... True\n", - "71 book Synthetic data to illustrate spatial weights True\n", - "72 burkitt Burkitt's lymphoma in the Western Nile distric... True\n", - "73 calemp Employment density for California counties True\n", - "74 chicago Chicago neighborhoods True\n", - "75 clearwater mgwr testing dataset True\n", - "76 columbus Columbus neighborhood crime data 1980 True\n", - "77 desmith Small dataset to illustrate Moran's I statistic True\n", - "78 geodanet Datasets from geodanet for network analysis True\n", - "79 georgia Various socio-economic variables for counties ... True\n", - "80 juvenile Residences of juvenile offenders in Cardiff, UK True\n", - "81 mexico Decennial per capita incomes of Mexican states... True\n", - "82 networks Datasets used for network testing True\n", - "83 newHaven Network testing dataset True\n", - "84 nyc_bikes New York City Bike Trips True\n", - "85 sids2 North Carolina county SIDS death counts and rates True\n", - "86 snow_maps Public water pumps and Cholera deaths in Londo... True\n", - "87 stl Homicides and selected socio-economic characte... True\n", - "88 street_net_pts Street network points True\n", - "89 taz Traffic Analysis Zones in So. California True\n", - "90 tokyo Tokyo Mortality data True\n", - "91 us_income Per-capita income for the lower 48 US states 1... True\n", - "92 virginia Virginia counties shapefile True\n", - "93 wmat Datasets used for spatial weights testing True\n" - ] - } - ], - "source": [ - "libpysal.examples.available()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mexico\n", - "======\n", - "\n", - "Decennial per capita incomes of Mexican states 1940-2000\n", - "--------------------------------------------------------\n", - "\n", - "* mexico.csv: attribute data. (n=32, k=13)\n", - "* mexico.gal: spatial weights in GAL format.\n", - "* mexicojoin.shp: Polygon shapefile. (n=32)\n", - "\n", - "Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) \"Interregional inequality dynamics in Mexico.\" Spatial Economic Analysis, 5: 277-298.\n", - "\n" - ] - } - ], - "source": [ - "libpysal.examples.explain('mexico')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Weights from GeoDataFrames" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n", - "gdf = geopandas.read_file(pth)\n", - "\n", - "from libpysal.weights import Queen, Rook, KNN" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = gdf.plot()\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Contiguity Weights\n", - "\n", - "The first set of spatial weights we illustrate use notions of contiguity to define neighboring observations. **Rook** neighbors are those states that share an edge on their respective borders:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "w_rook = Rook.from_dataframe(gdf)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "32" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w_rook.n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "12.6953125" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w_rook.pct_nonzero" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = gdf.plot(edgecolor='grey', facecolor='w')\n", - "f,ax = w_rook.plot(gdf, ax=ax, \n", - " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", - " node_kws=dict(marker=''))\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
POLY_IDAREACODENAMEPERIMETERACRESHECTARESPCGDP1940PCGDP1950PCGDP1960...GR9000LPCGDP40LPCGDP50LPCGDP60LPCGDP70LPCGDP80LPCGDP90LPCGDP00TESTgeometry
017.252751e+10MX02Baja California Norte2040312.3851.792187e+077252751.37622361.020977.017865.0...0.054.354.324.254.404.474.434.481.0MULTIPOLYGON (((-113.13972 29.01778, -113.2405...
127.225988e+10MX03Baja California Sur2912880.7721.785573e+077225987.7699573.016013.016707.0...0.003.984.204.224.394.464.414.422.0MULTIPOLYGON (((-111.20612 25.80278, -111.2302...
232.731957e+10MX18Nayarit1034770.3416.750785e+062731956.8594836.07515.07621.0...-0.053.683.883.884.044.134.114.063.0MULTIPOLYGON (((-106.62108 21.56531, -106.6475...
347.961008e+10MX14Jalisco2324727.4361.967200e+077961008.2855309.08232.09953.0...0.033.733.924.004.214.324.304.334.0POLYGON ((-101.52490 21.85664, -101.58830 21.7...
455.467030e+09MX01Aguascalientes313895.5301.350927e+06546702.98510384.06234.08714.0...0.134.023.793.944.214.324.324.445.0POLYGON ((-101.84620 22.01176, -101.96530 21.8...
\n", - "

5 rows × 35 columns

\n", - "
" - ], - "text/plain": [ - " POLY_ID AREA CODE NAME PERIMETER \\\n", - "0 1 7.252751e+10 MX02 Baja California Norte 2040312.385 \n", - "1 2 7.225988e+10 MX03 Baja California Sur 2912880.772 \n", - "2 3 2.731957e+10 MX18 Nayarit 1034770.341 \n", - "3 4 7.961008e+10 MX14 Jalisco 2324727.436 \n", - "4 5 5.467030e+09 MX01 Aguascalientes 313895.530 \n", - "\n", - " ACRES HECTARES PCGDP1940 PCGDP1950 PCGDP1960 ... GR9000 \\\n", - "0 1.792187e+07 7252751.376 22361.0 20977.0 17865.0 ... 0.05 \n", - "1 1.785573e+07 7225987.769 9573.0 16013.0 16707.0 ... 0.00 \n", - "2 6.750785e+06 2731956.859 4836.0 7515.0 7621.0 ... -0.05 \n", - "3 1.967200e+07 7961008.285 5309.0 8232.0 9953.0 ... 0.03 \n", - "4 1.350927e+06 546702.985 10384.0 6234.0 8714.0 ... 0.13 \n", - "\n", - " LPCGDP40 LPCGDP50 LPCGDP60 LPCGDP70 LPCGDP80 LPCGDP90 LPCGDP00 TEST \\\n", - "0 4.35 4.32 4.25 4.40 4.47 4.43 4.48 1.0 \n", - "1 3.98 4.20 4.22 4.39 4.46 4.41 4.42 2.0 \n", - "2 3.68 3.88 3.88 4.04 4.13 4.11 4.06 3.0 \n", - "3 3.73 3.92 4.00 4.21 4.32 4.30 4.33 4.0 \n", - "4 4.02 3.79 3.94 4.21 4.32 4.32 4.44 5.0 \n", - "\n", - " geometry \n", - "0 MULTIPOLYGON (((-113.13972 29.01778, -113.2405... \n", - "1 MULTIPOLYGON (((-111.20612 25.80278, -111.2302... \n", - "2 MULTIPOLYGON (((-106.62108 21.56531, -106.6475... \n", - "3 POLYGON ((-101.52490 21.85664, -101.58830 21.7... \n", - "4 POLYGON ((-101.84620 22.01176, -101.96530 21.8... \n", - "\n", - "[5 rows x 35 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 22]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w_rook.neighbors[0] # the first location has two neighbors at locations 1 and 22" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Baja California Norte\n", - "1 Baja California Sur\n", - "22 Sonora\n", - "Name: NAME, dtype: object" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf['NAME'][[0, 1,22]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, Baja California Norte has 2 rook neighbors: Baja California Sur and Sonora." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Queen** neighbors are based on a more inclusive condition that requires only a shared vertex between two states:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "w_queen = Queen.from_dataframe(gdf)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w_queen.n == w_rook.n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(w_queen.pct_nonzero > w_rook.pct_nonzero) == (w_queen.n == w_rook.n)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = gdf.plot(edgecolor='grey', facecolor='w')\n", - "f,ax = w_queen.plot(gdf, ax=ax, \n", - " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", - " node_kws=dict(marker=''))\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(1, 1), (2, 6), (3, 6), (4, 6), (5, 5), (6, 2), (7, 3), (8, 2), (9, 1)]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w_queen.histogram" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(1, 1), (2, 6), (3, 7), (4, 7), (5, 3), (6, 4), (7, 3), (8, 1)]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w_rook.histogram" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "c9 = [idx for idx,c in w_queen.cardinalities.items() if c==9]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "28 San Luis Potosi\n", - "Name: NAME, dtype: object" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf['NAME'][c9]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[5, 6, 7, 27, 29, 30, 31]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w_rook.neighbors[28]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[3, 5, 6, 7, 24, 27, 29, 30, 31]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w_queen.neighbors[28]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-105., -95., 21., 26.])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "f,ax = plt.subplots(1,2,figsize=(10, 6), subplot_kw=dict(aspect='equal'))\n", - "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[0])\n", - "w_rook.plot(gdf, ax=ax[0], \n", - " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", - " node_kws=dict(marker=''))\n", - "ax[0].set_title('Rook')\n", - "ax[0].axis(np.asarray([-105.0, -95.0, 21, 26]))\n", - "\n", - "ax[0].axis('off')\n", - "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[1])\n", - "w_queen.plot(gdf, ax=ax[1], \n", - " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", - " node_kws=dict(marker=''))\n", - "ax[1].set_title('Queen')\n", - "ax[1].axis('off')\n", - "ax[1].axis(np.asarray([-105.0, -95.0, 21, 26]))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "w_knn = KNN.from_dataframe(gdf, k=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(4, 32)]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w_knn.histogram" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = gdf.plot(edgecolor='grey', facecolor='w')\n", - "f,ax = w_knn.plot(gdf, ax=ax, \n", - " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", - " node_kws=dict(marker=''))\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Weights from shapefiles (without geopandas)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n", - "from libpysal.weights import Queen, Rook, KNN" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "w_queen = Queen.from_shapefile(pth)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "w_rook = Rook.from_shapefile(pth)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/libpysal/weights/weights.py:167: UserWarning: The weights matrix is not fully connected: \n", - " There are 2 disconnected components.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "w_knn1 = KNN.from_shapefile(pth)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The warning alerts us to the fact that using a first nearest neighbor criterion to define the neighbors results in a connectivity graph that has more than a single component. In this particular case there are 2 components which can be seen in the following plot:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = gdf.plot(edgecolor='grey', facecolor='w')\n", - "f,ax = w_knn1.plot(gdf, ax=ax, \n", - " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", - " node_kws=dict(marker=''))\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The two components are separated in the southern part of the country, with the smaller component to the east and the larger component running through the rest of the country to the west. For certain types of spatial analytical methods, it is necessary to have a adjacency structure that consists of a single component. To ensure this for the case of Mexican states, we can increase the number of nearest neighbors to three:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "w_knn3 = KNN.from_shapefile(pth,k=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = gdf.plot(edgecolor='grey', facecolor='w')\n", - "f,ax = w_knn3.plot(gdf, ax=ax, \n", - " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", - " node_kws=dict(marker=''))\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lattice Weights" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from libpysal.weights import lat2W" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "w = lat2W(4,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "12" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w.n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "23.61111111111111" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w.pct_nonzero" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: [3, 1],\n", - " 3: [0, 6, 4],\n", - " 1: [0, 4, 2],\n", - " 4: [1, 3, 7, 5],\n", - " 2: [1, 5],\n", - " 5: [2, 4, 8],\n", - " 6: [3, 9, 7],\n", - " 7: [4, 6, 10, 8],\n", - " 8: [5, 7, 11],\n", - " 9: [6, 10],\n", - " 10: [7, 9, 11],\n", - " 11: [8, 10]}" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w.neighbors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Handling nonplanar geometries" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "rs = libpysal.examples.get_path('map_RS_BR.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas as gpd" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/libpysal/weights/weights.py:167: UserWarning: The weights matrix is not fully connected: \n", - " There are 30 disconnected components.\n", - " There are 29 islands with ids: 0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "rs_df = gpd.read_file(rs)\n", - "wq = libpysal.weights.Queen.from_dataframe(rs_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "29" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(wq.islands)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{}" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wq[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "wf = libpysal.weights.fuzzy_contiguity(rs_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wf.islands" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0}" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wf[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9sAAAM9CAYAAACMlGBDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd3hUVd4H8O+5905PhTRCGgkhIYUWkkCAUAVEelMEBBQRlVWUVVSU2QuyYl9YAQsKC1iowspKlSSUUAJBCITQQkJLD2mQmczMve8fJ4EAAfHd+Lru+/s8jw9m5s7MuXfC8/C953d+h6mqCkIIIYQQQgghhDQe4fceACGEEEIIIYQQ8t+GwjYhhBBCCCGEENLIKGwTQgghhBBCCCGNjMI2IYQQQgghhBDSyChsE0IIIYQQQgghjYzCNiGEEEIIIYQQ0sgobBNCCCGEEEIIIY2MwjYhhBBCCCGEENLIKGwTQgghhBBCCCGNjMI2IYQQQgghhBDSyChsE0IIIYQQQgghjYzCNiGEEEIIIYQQ0sgobBNCCCGEEEIIIY2MwjYhhBBCCCGEENLIKGwTQgghhBBCCCGNjMI2IYQQQgghhBDSyChsE0IIIYQQQgghjYzCNiGEEEIIIYQQ0sgobBNCCCGEEEIIIY2MwjYhhBBCCCGEENLIKGwTQgghhBBCCCGNjMI2IYQQQgghhBDSyChsE0IIIYQQQgghjYzCNiGEEEIIIYQQ0sgobBNCCCGEEEIIIY2MwjYhhBBCCCGEENLIKGwTQgghhBBCCCGNjMI2IYQQQgghhBDSyChsE0IIIYQQQgghjYzCNiGEEEIIIYQQ0sgobBNCCCGEEEIIIY2MwjYhhBBCCCGEENLIKGwTQgghhBBCCCGNjMI2IYQQQgghhBDSyChsE0IIIYQQQgghjYzCNiGEEEIIIYQQ0sgobBNCCCGEEEIIIY2MwjYhhBBCCCGEENLIKGwTQgghhBBCCCGNjMI2IYQQQgghhBDSyChsE0IIIYQQQgghjYzCNiGEEEL+8GRZ1smyzH7vcRBCCCF1mKqqv/cYCCGEEPIHIcuyDoDDbDbbf++xzJkzZ7hOp3u7pqYmWFVVjUajyaupqRliNpuP/N5jI4QQQihsE0IIIeSBvPPOO2/b7faZgiCU2+32Tmaz+dzvNZZ58+bNMRqNLz/yyCOmgIAA6HQ6pKenY+fOnYdnzpwZ+3uNixBCCKlDZeSEEEII+UVz5859ymAwvPTyyy9LvXr1ctfpdCt+r7HMmTPnUb1eP+Ppp582tWrVCnq9HowxaDQaABB/r3ERQggh9VHYJoQQQsh9ybLsLQjCwscff9xoMpkQFxcnaDSaNrIsd693jPh/sWZalmUmSdIno0ePNjo5Od18/Pjx4+rmzZuvWyyWqb/1GAghhJAHIf3eAyCEEEL+m8iy7A4gCkCm2Wwu+b3H00hGhoWFwcvLCwAgiiIGDBhg+v777ze/9957R1VVbQ6ghUajKX/77bdnOxyOT8xm82+1Tk202+3u3t7eNx84fPiwsn379mKbzdbHbDZn/EafSwghhPwqtGabEEIIaSSyLMdKkrTL3d1duXbtGux2e5TZbL70e4/rTrIsCwD6AYgEcALAdrPZrNzjWEmn050YOnRoWHh4+G3PFRcXo7i4GCaTCb6+vigpKcGaNWuuV1VVLXvttdf+9FuNf/78+TsTExN7JiQkCDU1NXj//fettdf6XO2YDQDczWbz1d9qDIQQQsgvoTJyQgghpJHo9fo3HnroIafnnnvOpWPHjgaNRvPK7z2mhkiS9Jqbm9va2NjYvzZt2nStTqfbJcuypv4xsixLsiwHabXa73x8fPzDwsLueh8PDw+Eh4fD398foijCy8sLTz31lElV1cmyLAf82nHJsvzQO++8s3zu3LlP3O84q9X6+sGDB68DQGFhISRJulQvaDfTaDTnJUnKnjdv3ku/dgyEEEJIY6EyckIIIeQB1a5J7gZACyDFbDbb6j+vqmpMQADPmHFxcZrDhw8/Kcvym2azuaKB9zLodLoljLFQi8UyzWw2H/2/OAcA0Gg0j/Tv398UFhYGRVE0K1eujL18+bIZwJuyLAfrdLoPBUF4RKvV2sLDw4WHH35Yz9iDLcfW6XR1//urSudkWe5vMBjWdevWzbR3795RsiznmM3m3fc4/ERlZaVRVVU4OzvDbrf7yLIsAmA6nW5tbGysZ4cOHaTFixfPk2V5Nc1wE0II+T1Q2CaEEEIekFarXaDT6Z7U6/VKWVmZdc6cOcNnz569BwBkWQ6UJMnL1dUVAODu7o5WrVoJZ86cmQVgZv33kWXZV6vV/hgcHNyqZcuWhi1btiTJsuxnNpurGvrc2pAfBcAfQKrZbC77d85DUZSrFRU8/wuCgKFDhxo//fTTGe++++4IjUYT1LlzZ018fLyo1+s1v/BWd8nIyABjLPPXls9LktQ/ISHB1LlzZzDGjMnJyc8BuFfY9hdF0QFAdHV1haenJwoLC5eJohjq6+vbpmfPnpIgCGjfvr149OjRlwC8UnsN3QFc+w3XkxNCCCE3UdgmhBBCHoAsy4wx9uzzzz8vubi44OzZs85r1qz5UZblCLPZfEmn0y3q1KmTZDAYbr6mf//+hrNnz744Z86cHFVVPzObzYosyz00Gs33CQkJTomJiRJjDBkZGUJOTs4gAN/e8ZkaxtgknU73hiRJHm5ubo7CwkJVluWBZrN57wOMWQDAzGazo/7jVqt1RWpq6sNRUVEmg8EAV1dXvPjii/r8/PzwZs2a1Z+dvkVVAYcDkO79TwdVVfHTTz9dt1qtM+4xniAAWrPZfObO50RRdNNqtQCAkJAQJCUl9ar3OmcA/bVa7cOiKMZKktSyf//+Yt1s+5gxY5zS09PHOjk5Ce3atYMg8FVyfn5+2hMnTkQDgE6n22C32x8RRbFAluV+ZrM58z7XLQxAKwB7/t0bG4QQQv7/ogZphBBCyAOaP3/+rj59+vTo2LEjA4Ddu3fb9+3bly8IQq7BYGg/depUY11grJObm4vNmzffKC8vtyiKonU4HE4xMTEYOHDgzWNSU1ORkpKy8vXXX7+5VlmWZT+tVrvTx8fHr3v37qYWLVqAMYbz589j9erV1202W+d7dd6WZbmJVqt92+FwTFRVVdJoNCusVus0s9lsqX2eabXav5tMpkkNjfku1dVAmzbA1avAnj1ASAhQO4N/57l+++23F61Wa1D92WNZlpvodLqVAHrWPpRktVqfMZvNl+uOee+99w4PGzYsJjQ0FIqiYP78+VabzTZckqQEAC/7+vraw8PDnZs1awZvb2/Uv6nRkLy8PKxatepGdXX1RFVVzxqNxn0vvfSSMT09XU1KSjoyc+bM2AauW1+dTvcxYyzI09PTnp+fb7HZbCH3qjgghBBC7odmtgkhhJAHZLVatxYWFnYBX7ONbt26Sb6+vn4Wi8WvVatWaCi0BgYG4rnnnjNeunTJuH//fjUrKwtZWVnKwIEDbzYpjYqKQlJS0khZlp8FoDLGngKw0MfHR504cSKrv146JCQE3bt3N+7Zs2cmgHG15dFjAfgB2McYi9ZoNPOioqIMiYmJOo1Gg40bN47Nzc31rZ0RV8xmsyrL8p8YYxGZmZk927Vrd++Trq4GbDagVy+gXz9g717g/feBsWOBvn2B2nNWVRV79+6tttlsi+qCtizLzoIgTNJoNHJ0dLShX79+OlVVsWfPnr779+/Pmjt37kuKoiwF4CGKYpS/vz8AXto+YsQI3fbt279r1qyZ2Lt3b4O7u/sDf08lJSVYvnx5tc1mmzR79uy1c+fONbdp00YrSRJatGjBdu3a1fzO17zzzjsfm0ymKQMGDDCGh4dDEAR8/PHHgs1mCwXwf7aenhBCyH8PCtuEEELIHWoDbFcAdS24swBk63S6p0NDQ28masYYWrZs+YvvxxhDQEAA/P392dy5cxERESFUVFTAxcUFAODi4oKWLVuq2dnZ5xwOh3tgYKDNbrfj4sWLbO/evY5u3bqJ9d/P29ubCYLQqvbH3k5OTp9GREToDh06JKmqiqioKAwaNOjm8aNHj9YvW7ase1FR0ReyLE8xm80Os9mszpkzJ2/Tpk3IzMxUgoKChNDQUHh6et76oBs3gLg44NVXgc8+A954A5g1C+jTB7h4EejZE3jmGeCJJ7Bv3z77xYsXcxRF+bssy931ev1sURS7tGjRwt6zZ0+Tr6/vzbft1auXFBERIW3YsOHjioqK1xRFCTYajYper795TFhYGMLCwpwf7Bu73alTp6AoyrLZs2evAQCtVtvO29tbAoCqqioIglBad6wsy6IkSbOMRuOUqVOnGutmzCsqKnDjxg0BfGs0Qggh5FejsE0IIYTcQafTfa7T6cYEBgYyAGpeXp6joqJCGxISooSEhPxb762qKtLS0pCfn68+8cQTTJIkYPt2jPLwMBZ27250c3PD1atXdStXrgQAlJeX3/UeZWVlUFU1u3asT3bu3NmYkJDA/P391fPnz7PMzEwVABs4cCAEQYAkSRg/frzx22+/fSw/P7/fvHnz1tvt9iKNRhPftGlT6HQ64dixY8rOnTsFURQxcuRIhHl6AqIIzJwJjBnDP3jVKmDkSKBDByAiAvDxAZKTgQkTcEmSFIev7xCm0QzW6XRf9e3b1xgeHg6DwdDAAnDAx8cHU6dONSUnJwfv2bPn37qmd2KMgTEm1vvZpNHwXm+KoqCmpiZg7ty572q12giNRtPVy8tLM2rUKGP90vTq6mpIklQya9YsmyzL7fV6/VuMMQ+bzaba7fZvAGQCyDSbzSWNOnhCCCH/NWjNNiGEEFKPLMtNJEm6OmPGDF39mdbGkp2djaNHj+LECT5hOqZPH7Tq2RMIDgYyMwGjEd+PH68ERUcz17/9jX03ebLaIjOTBXTsiC4vvggA2LJ1qy0tLe0tVVU3ajSaoy+88ILByckJAFBTU4MPPvgANpsNd64NV1UVFy9exKVLl2CxWBQ3NzehQ4cONxuK2e12/Pjjj+rPR46wN1atgjRlCg/bt94AuHMLMFVF6bp1KJ85E02Lih7f9Oijn/eSZafmze+q1G7QjRs38P777wMAzGbzg19IVQXsdkBzd8P0wsJCLF26tNJms4WZzea89957LyU2NjYxISEBWq0Wp06dQnFxMVxcXODv74+mTZve9R7V1dX429/+ZhVF8YjD4Wjfq1cvA2MMW7Zsgbe3t01RlBvXrl0ziKJ40mq1jrtfw7X/K7V7pQcAuGw2m62/93gIIeT/O/Evf/nL7z0GQggh5D9GSkpKpJub29guXbo0OCP773J3d0dwcDD27dsHAPDz94dfQgJvQOblhbMDByKpqIgNeeghVpKVpXqNHcuCPvsMTa1WuPXuDbi7o6pfP5h++qnTsLVrp7dt2lTTtH9/hvPnAXd3iBoNOnbsCBcXFxw5ckRp06YNq1tLzhiDm5sbAgICEBwczHx9fVF/PbggCAjTaFjxyZOOjLAwZn34YVRdv85uhtHXXgNWrgSGDbv5mtJr1/DdoUPYHxqKUi+vwd2zs7V+er0ArRbw9v7F66HRaGC1WnH58mUkJCRAFMW7D6qq4qHabAYKC3mjtoQEQJaB48eB69eBAwd4APfwgMnVFaqqClevXp2wd+/ejlar9ZHc3Fy4ubnB19cXnp6eCAwMhI+PD4xG4z3H1aZNG6l58+b+/fr107Ro0QLNmzdHSkoKgoODxSeeeELfpUsXUVXVZvn5+a26du268kF/B34Lsiz31Gg0B/R6/Z8APLtr165FPXr0sP+eYyKEkP/vqIycEEIIud01i8XSQOJrPHq9Hm5ubqioqIC9sBDqkiVgaWmojo1FGWMIfuklVd+xIwvftInZ7XasmjlTuXr1qtDp2DH0On8e7by8xMDSUhf3H34A27yZB/UVK4CpU4HsbBjS0xH97beoMJuFHeXlGDZ7Nt+y685Z6TupKjB4MIYMHChu7NgRybt3K9evX2cTJ06Eh4cHjH36QM3PR0FeHjw9PSFJEs6cOYOioiIIgoAzLVtqLoSEYFrr1nCZORNo0QL4+GOgoa3E6gkNDcWBAwdwOScHwc7OPFi/9Rbw2GNASgpvyJafD1y6BISG8qD9/fdAUhKwaROg1wNFRfxmQGQkIElI9PDQRNhs3vnBwY8eKCrClcpKtGrV6r7juJOrqytc7+i6Hhsbaz98+LAEQBk+fLggSZKiqmph3fOyLIvg25tV/6oP+1+o7S3wsCRJ/XU63eRRo0YZPD09sXDhQhGABoDltx4DIYSQe6OwTQghhNyupbOzs+OXD/v3CIKgKorCUtPSECWKcGUMXwwerCQ6ObHH3N0Z7HZAkiBJEiZMmCCsWrUK6enpSq8zZwT24YdocvYsMGkSXzvdogUP2lOmAOfOAQcOwKDXo6PdjvQDB7B1xAj0y8oC+/xzYMcOwGoF5swBDh3i669dXIB9+4C8PGDPHkju7hhZO8ykpCQsX74cAOCk1cLv7FmcyckBYwxhYWFqZmYmAwBPT08UFBRA5+wMoUMHYN064OhRoGtXYMQI3kitfkfxq1f5nwUFCJ43D94hIdD07g14eAC7dvEScVdX4KWXeIjWaoFly269fscO/n6HDgETJ/JzWreOn8ulS0BpKTy++w4eqakwWiyoSE+HNS0NzhMnAgEBQFAQ4On5yzcg7vDwww9LTk5OSEpKEoYPH4709PTrVqv1CwCQZTlWkqTtiqI4zZ8/f85rr702t+51b7/99iSNRvO6oiiba2pq/mw2m5Vf+qy5c+cO02g0461W66tms/ncnc+LoviSs7PznOjoaH1MTIzo6uqKb7755jpj7GOz2Vz5q06MEEJIo6OwTQghhNSSZZnpdLpZMTExLr/1Zz377LNs9+7dOPzTTxD690dNTQ2uqargP24chLFj+Yztp58C4OXfww0GbD12TPikdWu19WOPqT0VRRBMJmD+fCA7GzhyhAfOF14AWrVCeXk5vuzfH1VVfIvo6kGDHMPi40VcvszD6OXLPMQOGQKUlQFLl/JGaCNH3jbOnj17IjY2FqdPnwazWtH2nXfA8vNx/MwZbNq0ien1esyYMQMLFy50ABA7derkcHJy4pUBXbsCP/0EfPgh8MknwM6dgMPBm6pFRADPPsu3EAsPR2BoqLp84kT21tzafPrFF/e/gOfP863IGAO++gpYvRpYswbw9QUGDeJhukMHAMDl5GQc9faGa34+RmZlwSkjg8+2//OffKZ87FjAaOQ3LWq3H7uX7OxsJCUlIS4uDgBQVlZmAHC0du/y1YMGDXILCAjA3//+9zdkWV5gNpsrZFmONxgMn4wcOdK4bdu2KUVFRacA3HaCsiyLGo1mtiRJQ1RVza6pqTknSdK0Nm3a6DMyMtxxa4/ymyRJiu3cubOpbizV1dXIzs6WHA7H+3e8d2sApWazueD+F5UQQkhjorBNCCHkv4Isyz46nW6DzWaLkSRpzuuvvz7vV7y2syAID+v1+v5OTk6RHWpD2m9JkiS0adMGx374AaU7d6rer7/ORFHE5bw8NP3hB+DKFeDdd4HJk4EmTWBKSUGC3Y7PbTZ20tMTqfPmITg4WB3Tpg0TDhwAFi0Cfv4ZGDECZ199FeuSkqDyLqjMx8cHmYWFYuCpUzDFxiJs3Dg+iAMH+J+vvgqEhwOdOgEZGUB09G1jdXJyQkxMDP/h0CFAp8OB2tcOHz4ckiRh+vTpYnJyMnbu3Cl26NABNzt7u7jwtdUvvwzs3s3D7JEj/PxMJn7M228jIjeXHcrJQUFBAbwfYK03xo0D6sYkisDjjwOpqXwmvFkzfg615euhYWFIPXBALTMY2Kl+/RAbG8tL5p95hs/oV1YCx47xbc2WLQPus51bXVl569atAQCBgYHWixcvvq8oyiWj0egVGRkJxhj8/PxsOTk5AwB8ByAyODhYDQ4ORr9+/Uxr1qx5RZblpfX2I9dptdq1Xl5evXv27GksKytrW15ejsjISDg7O+PIkSOdZVn2NZvNV+uPxWq1fpOWlvZIXFycMwAUFxdDq9XmvPrqqxV1x8yZM2ekVqtdoSiKKstyX7PZvO9e5ybLcjudTjdTUZQcm822BkCEXq9/TFXVEqvV+jWAnx5kRp4QQghHYZsQQsgfXm0H8RNxcXGu7du3l5YsWfKmLMurzGZz7i+8jmk0mn9JkvRwfHy8w9nZWYyOjoYoilixYoXjwoULYkJCgvrQQw/9ulrjB7R582aHRa8XTwYGsqNbt8LZ2VkNDg5m0Ot5KE1KAmbP5oF70SK4WywYevo0rly5oqalpbFz586x64MHw7lJEyAnB0hMROGmTbg4axb6TJsGbXAw27hxI0aMGIGlS5fihx9+gMlkUsLCwoSbg6iq4qF30yZevv3XvwIzZgAdOzY4ZmXNGhyvqkKByYTu3bsjNDQUAPDll1/aCwsLpfj4eIder7+15t1sBk6fBr79ls/CX7jAZ9Yffhh47jm+LhtAYGAgAODrr7/G9OnTb3ZIBwCLxYKioiJ4e3ujoqICGRkZ6DJ/PrT/+Mftg0tI4Gu8ly4FPviAn0ttMzrGGHvqqadwc79vxoCmTYHBg2+9Pjz8vmXlqqpixYoV0Ol0qp+fHwOA4cOHm3bs2DHe4XCo3bt3N9Y1nPP09NTl5OTUbS5ukCRJqDtPV1fX5uXl5ZfefffdNMaYqNFougQHB+tHjBhhlKS7/2mmKIoOwJU5c+Y4JEmqkCTpXE1NTQoAW1VV1c127E2aNIHdbg+UZbmj2Ww+zE+TtY+Li9NJkiTs2bNnJIAGw7Ysy0ZJknYnJiY6l5aWWs6dO/d806ZNERUV5VxdXa0ePnx4RHV19R4AA+7xegMAS90NBEIIIRS2CSGE/HcYHRwcbOjVq5cEAP7+/jXZ2dkdAdw3bDPGxtlstocZYzh9+jSKi4uxd+9eSJLkKCsrEwEgNTWVFRQUqOPGjWvUwG2xWJCbmys6W62IOnECy44dw5AhQ+Ds7MwP0GqBp5/ms8CtWwOVldDn5aFt27YIDAwUTpw4AYvFgqysLHSIjITy9dfYevmyeszDg4XExzu6v/uuKMyeja16vbp06VL25z//GWfOnMHatWuF9957T5kwYYLg7e0N/OlPQJ8+fK306NE85Ccn8/B+R0k5AFz4+WeU2mxoMWqU2q5du5vXJD8/X1IUBVeuXGHZ2dkI9vAAs1qBzp2Bdu14iBVF/lm7dwMffcTL13v3Bp56CnjsMQwfPhwbNmxAbm4uvL29ceLECRQXF+P48eOq1WplkiRBURR4NGmi+BQWCtvXrVO6du8u3Jx1B3iztOefrx3sBSAtDQYnJ1gsFmzcuBHTpk2795dy4QKfHX/33QafZoxBFEWIooi6UOzk5IRhw4YZ7jy2vLy8BsC12qUJY1u0aGEAAFEUMXXqVGNeXp6xsLCwuSAI8Pb2fqDZ/Oeff14URdG9sLAwNi8vr6Pdbkd4ePjN78BkMmHEiBH69evXJ8+ZM2fE7NmztymKsuv06dPTfH19NQ6H48x93t4EQNe5c2cwxvQA9ABQVFSE4uJiFhwcrM/IyLhryl+WZZ1Op/uGMTZUq9XulmW5j9ls/s17HhBCyB8BhW1CCCF/eHq9fkDr1q2NAOBwOFBQUCACuHq/18iybNRoNAvGjh2L8+fPq5IkCZcvX1YuX74sxMXFCe3atUNubi7Wr1+P3NxcpijKbbOt/66MjAwAQJ/u3SF9+y0AICIi4lagP3WKl0kXFfGfP/oI6oIFWPv++8rprCxBBfDII48gKSlJ2W6xCJO/+Qb5RqP62BNPsJYtW4rYtw+4eBHPu7iwDwsKcP78eYSEhMDLy0stLCwUvvzyS7VneDjrlJkJVrs2HAAPxy4uwCuv8JnfHj1uzvbW1NTg2/bt0S8+Hk/07XvbzYfmzZs7PDw8BADC119/jRfXroVrdDTwzTe3DhJFYPp0/v4tWgAAzl+8COmrr2DLzsb53FxFFxwsrF69WrXZbKxJkyaKwWBQg4OD0bdvX7G8vJzPgOfkCNczMuAXGIjNmzdj//79jmnTpt2aTWcMmDaNl8T//e+IjI7GYUFAy/uUhwPgNwV+/PG+h5SVlQHAL9546dixo9OFCxcWAphuMBhaRkZG1hseg6+v761Z9l9Q+52x1NRUDBo0CG5ubmjVqlWDYwgLC8O4ceNMy5Yt2zpnzpz3AcilpaVavV6vACitO06W5SAAJXWN1Mxmc9H8+fMLLl265B8QEACbzYa1a9dez8nJqZEk6bjNZkuz2+0L7vw8rVb7uZ+f38OjR48WFixYEAcgCsCxBzoxQgj5L0dhmxBCyB+ew+Ho4ufnBwAoLS2F1Wq1Azh1r+NlWWaiKM4NCAjQBgYGIjAwsC643PZnZGQkmjRpgvXr16sLFixQR40aJXh5eeHChQuorKxEhw4d/tcBvKKCL6v1iY6G6dNPYTh9Wv3kk0/Y+PHj4enpCTz5JC+L/vBD/oKXX4b6/POwfvYZXp03D4tffBHt27dHTEyMkJaWhksaDSZfuSKwukDZpQvQogU006ahR0kJXJ55BjqdDs8++yyrqqrC4UOHmGn6dHzcvz+cV6xQw8PDmYuLC9q2bcu3z1q1is/wHjwI/PnPgCRh8eLFSpvyctbxyScZLl++7XyefPJJHnbnz8eN3Fz1q6FD2ePPPYe75mtfeglYtAj2p5/Gki++cFy7dk10fuQRBGZlqd0OHxbiEhKgc3FhrkOGoK70uo6bmxv/n7NnYaqpwYgRI4SCggIUFRWJN27cuHvP7OhoYMUKNDt6FBMmTcK/SkpwISwMLWqD/l0SEnjDOEUBGvhe8/PzAaDhvcDvEBoaiqeeesrp008/baMoilpZWQn3+t3YH9C1a9dQWFjIAKBr164P9Bpvb2+Iogg3N7c/VVZWTnA4HHqTyVQDoBkAzJ8/f6FWq31aURSrLMtdzWbzCQCw2Wzvb9u27Z3Jkyebvv/+++rc3NyfbDbb6DfeeMPa0OfIstzFZDKNHDVqlMFiscBqtQoA7jd7Tggh/6+If/nLX37vMRBCCCH/ln379v0pOjraxdnZGQaDAcXFxUpJSclrqampU1NTU5kB080AACAASURBVAft3bt33K5du/6WnJzcLDU1dZhGo1nk7u7ebdSoUSbdffaAZozB2dkZERERzGKxYMuWLSw1NRWnTp1CVlYWIiMjYapr8vUAFEWB1WpFcnIyDh48iJCQELVTs2ZMO2sW2n78MUtOTkZaWhr83d1hePJJaIYMgc3huBnumCShoqZGSVMU4aqXFxJXrQKrrkbzvn3hGx0N9uOPQGwsUFeK7uyMK5GROHL6NKKXL4dhwADAaIRWq0XAvn0oSE3F8TZtEN66NY4ePcqysrKQnp7u6NixI+903qkTbxim1eKfR444cq5eFbuPHs08IyL4LHB9DgefUV64EDc8PHDSZGKRUVG3AnKtyspKiI8/DqVTJ+w4dUoAgFdffRURgwcz05QpcBZFGD/8EMLJk/wz7gzQ/IsBWrUC/P0RFRWFy5cvK/v372eRkZHQarW3H6vTQfD3BwQB6tGj6vEDBxiCg1UfH5+7Z4ZFka/zjo+/fZsyAMePH1dXrVrFBEHA+PHj7zqvO23atMnx448/CrxHHdjBgwfh7+//qwO3wWAAYww5OTno27cv2H3WlNvtdpw/fx7fffedKgiC+uKLL2p8fHxMx48fR4cOHcRLly65JScnu2o0mj9Pnz7d4OnpqTt37tzju3btKklJSclSVfVATU3NJG9v7yYpKSmC3W7/BEDi/v37JyUlJQUlJyf/3KNHj5sl4gcOHFiYmJgYFRQUxHbv3m3Ly8tb9tZbb22UZbllSkrK2H379o1LSkpqnpKScqpHjx72X3XihBDyX4DCNiGEkD80WZZ1giC8HhsbazCZTGCMISIiQtOlSxcxLCzMOSAgIKhZs2bB586dMzDGOrm5uXUcNWqUa69evbR6vf6BPkOn0yEkJIS5ubmhrKxMLS8vZwAv435Q586dwxdffKHu3r2bVVVVqbGxsaxbt25Mr6rAvn3Qjh2LhIQEKOXlasTAgWylxYKjV64oP/74I0tNTUXTpk1hNBqxbt06Ifaxx9CvXz8Yf/4ZaNOG70mdlgb4+ACFhXyNdy03Ly/8kJ4Ot/Jy+O3fDyQm8pnbEyfwnV4Pq8GAPn36sEceeQQxMTE4d+4cUlNTVZPJxLz8/YFhw2BdvhyVq1cLLUeNQrsePYDMTB7o6wKnqvLmYgYDMH8+0iwWpaCgQCgoKFB8fX3ZzXXoALZt26asadaMnSwtVRVVZS1btkRUVBQPkYzxcxg5kp9D3758Zn3AAKB+47CPPuJblyUmQqPRwNfXlx07dgyZmZlqTEwMY4xBURTs3r0bq1evVnft2sXSampQ6ObG+mzbhopdu5j7mDGoqKjA8ePHIUkSnJyc+Hvn5vLtwDw8bn5ceXk5li9fzry8vJQZM2awXwralZWV2Lx5szBw4EAMHjwY3bt3x9mzZ9W9e/cyT09PeHl5PfDvDQCsWLECAODl5XXf127ZssWxfft2ISoqio0dO5aJoghBEHDw4EHk5eWpwcHBTZ2dnXuMGjXK5OLiAm9vb+bn56evrKzsXVlZOV1RlH85HI4qk8nUvVOnTqKiKN1DQkL6tm7duqPdbu9RXV0d17Vr128A3pSQMfbpsGHDJI1Gg82bN9+4cePGtoMHD84RRfHtqKiovpGRkV1UVe1948aNsbt27fqiflAnhJD/DyhsE0II+UPbt2/fX4KCgrp16tRJU/9xQRDg5OQET09P+Pn5oWPHjhAEAadPn8bx48eRlZVl79ix4wPXgDPGUFNTg927d9+cWhRFERUVFfD09ARjDHl5eRBFEWfOnMHZs2dx7tw5bN26FSdPnsShQ4fUrl27Yty4cSw+Pp4FBQVBr9cDFgvfBisxEaIoIiQkhGkiI6F/6CHYbDY2fvx4HD9+3HHixAlhz549cDgcKC0tdcTExAjaAQP42uevvwY+/5zvW/2Pf/CQWi+cZmZlqWpCgtJ6zBgBPXoAhw+DFRcj8I03cOTIEQQFBcHHxwcajQaRkZGstLRU2bVrl5CSkoKjP//s2M0YC6yqYnFBQRAMBmDmTB6s4+KArVsBPz8eUIcNAyQJ4eHhQqdOnbBjxw6m1+vV4OBgdvXqVRw7dgwXLlyAp4cHGzdjBstp1gzZdju8vb156fytCwu4ugJPPMED+Lp1wLZtfG9uFxe+p3bbtvxGA3hjsKioKBw+fBh79+7FiRMn2I4dO1BcXKwOGjSIDRs2DAkJCRCcnLDDyQmCt7eqf+UVti03F+evX1cOHTrE8vPzHe7u7oJzcTFw9CgvKa/1/fffKyUlJWzq1KnsrpnzWtnZ2Vi1apVy6NAh9cSJEzAYDOqgQYOYRqMBYwwdOnRgKSkpuHHjBtrdWRXwC9LT0x1BQUFCYmLifZctqKoqnD17Vh07dizTaPhfh8rKShw/fhwWi4UNGDBAk5iYqK1fbu/u7o62bdtq3d3ddefOnRsnimLXuLg4Q2RkJIuKitK2bNlS9PPzQ1RUlObgwYN+O3fu/FePHj0Kdu/e/VSrVq16t2vXTgMAGo1GtFgssZ07dw4bNmyYJjIyUgoICECbNm206enpWovFsrVHjx737aNACCH/bShsE0II+cOSZbm5IAjfjhs3zvhLs9RarRbBwcHw8fFBRkYGqqqqhP3796uxsbGsoe2WGuLk5ASdTofExESUlJQoGRkZLDMzE8eOHVN3797NDh48iNTUVFy4cAGlpaXKpUuXmJeXl9qkSRM1KipKiI+PZ3eFpcuXeUB+7DEeXF96CZg3D15eXggLC4NGo4EoioIoinB2dkZsbCxqamrwww8/sOLiYiUiIoIhLg6YOBFYswaYN49ve/XGG0CvXkBSEs56e6vDp0wRhSFDgPJyYMUKwGpF2WOPoduECdjvcCiRXl6MzZoFccQItCooEEKbN4fo46O2b99eCGrRgrV//nloTp7koX7+fL622WoFunXj68PvCPiHDh3C+fPn4eTkpHp6erLPPvsMFy9ehNFoVOLi4oSUoiJc9fODotHg5MmTaNGixd2l2VotXz/evj1w+DAP+J9/zh8bPPi2EnOdToe2bdsyh8MBh8OhDB06VOjTpw/z8PC42UX89OnTSlF5uTL5L38RJJ0OnXftQsLMmSwsPh5nz55lycnJzHrhghKSk8MwbBgAvmZ6x44dbPz48fedVf7555+V8+fPCwkJCYwxxgYPHszu/J1MSUlBWVkZEhMT71sOfqfr168LR48eRWho6M19vhvStGlTnDlzRs3Ly2NhYWEA+I2Ibt264ezZs2pxcTGLiopq8LXe3t4sPDxc16FDB11Da9oFQUB5ebman58fkZycXKzRaD5++OGH3eq+M19fX6F9+/Y6X1/f29a0V1dXY/fu3aqiKHN69Ohx/V5jl2XZKSUlZXRKSorQo0eP/Ae7MoQQ8p+NGqQRQgj5w9JoNK+1b99evF8AuVN4eDhmzZqFQ4cOYceOHayiogIPWk4uiiISamc8CwsLma+vL/r3749Vq1Yxi8WCVq1aobi42NG/f38xNDS0LlUz3K97tZcXMGpU7ZGswf2tY2NjERsbCwCwWq3Yvn274FlQAKfKSj6rPGUK7/Kt0fAO5qdOAdXVwMKFgJMTOiuK8El+Pgb5+CDks8+AkhIgPh7+/v6wvv02CgsKhORt29CjuhoCAMyfD9+WLeH70UcMOh2Qnc27iq9ezUP8+PHAmTM8AF++DDSw7r1NmzbIy8tDWVmZsGzZMoiiiOnTp8PJyUkEgMiVKyG88grw0kuQFy/Gd999h/HjxzfcodvdnZ/j+vXAiRO8jPzrr/me2i+8wLdICwuDweFAr169GIAGO5hdunRJjY6OFgHA7emn+ZZj/fvDOygI4xYuZBfy8vDd558LPoGBqNq/H8XFxY6TJ0+KoaGhalBQ0H3TcZcuXYTLly879u7dy1599dUGp58nT56MjRs3qu+88w4zmUzKCy+8IDxIg71evXrhwIED+OqrrzBr1izc6+YQYww6nQ5ZWVkYXG//8JqaGhQUFLChQ4fe93Nuqy5ogE6n0yqK0tPX1zcmJibGOSgo6NaTdvvtpf61srKyIIpi6qxZswpkWXYVBGGaIAhedrs9GcAms9msyLKcqNVqv/X19XW5ePGiVpZlH7PZfO2+gyGEkD+AxtvDhBBCCPk/JMsyAzA+Li6u4bre+5AkCTExMdDpdFiyZAksFsuven1FRQWsViuLiYlB8+bNMWnSJDDGcOnSJQQHB4sbNmxQ7fYH7AdVVAQsXsyDbJMmwJw5DR9ntQKHD0M7aRJ6enlh4ObN8PvpJ0Ft3ZrPjE+aBDzzDA/AlZVAfj7w8MOA0YgWISFoER+Pr7/9FmlpaSqWLOHdzgHonn8egyZMwCFRxDcDB/LP+te/gAULeEfuCxd4oI+KAvr1A2pq+Hvb7Tz4RkXxcu7sbB56k5KA8+fhlJuLEcOGwd3d3V53fevf1BBEETh0CDhxAl27doXFYsFPP/2EK1eu1F1kYN8+4J13+PXp3Zt/hsnEZ9E/+ogfd/YsYLMBmzcDdXtVP/ssn30HeFf16mrk5eUhLy9PjI+Pv3VNBQH45z95Q7SlS9GisBDBbdsqPitW4MyePY6SkhI2bNgwjBo16henobVaLaKiokStVqve65jmzZvj6aefZhEREUp5ebnwzjvv4Msvv7y1jrmqCjh+HDh5kv/Mm6tBEAQMHz4cAG/Udr9xtGjRQqiursaNGzduPmaxWMAYQ9OmTX/pNO4rNzdXAYDx48e7dOjQgaGggN+E+cc/+Pr92bPveo27uzusVmuvuXPnvq/RaLIiIiLe6t69+wteXl4rADjmz5+fbjAYtg4bNsy3a9euTowxO4Caf2ughBDyH4LCNiGEkD8qX0EQtP/bAKHT6TCstlR49erV9w0w9WVlZWHRokVwcXFBdHQ0AN64KiEhAdXV1Thx4gQsFgur29rrF9lsfBb6gw940KpTWMhLpq9fB1q2BB5/HFAUXHJ2Vg5mZ2P55Mk4OG0avvvxR0Vt1+7mXtgAeIn1q6/ytdVeXkBmJoYMGYJhw4Zh27ZtrMJi4Q3IagNZSEgIEhIScOHCBaxZs+bWtWAM8Pfnoa95c96ZfPlyfmNgwABekt6pE59xNhr5+nFV5c8PGwbk5qL/yy9L444eVZsUFKBs7lzg4kV+TqoK7N0LdOwIgTForFY4du7Eltmzca5lS1T4+WH7N9+gukkT4KGHgKwsfvNg4ULg++/5DD4AbNkCdOgADB/OxwPw6oCICN4Ibto0oLoabP58PPXZZ3BxcQFkmQd5ReHjeOopoFkz4Mkn8WhsrODZtSsm9OkjTpw4Uagrx34Q58+edTQ3GkUoCr/pcOYMv1nx3nvApUvA4sXQTpuGYb17C69s2oSY1FQ03bxZVH18+Pr0bt34WvTaKgbExgK1s9ER8fHwKChA9l//yvJ8fVFeXs5f8+ijwFW+FLq6uhppaWlgjN12Y8PZ2RmSJOHyHdu1/VouLi7Q37iBgi+/BF5/HRg3jlc2tGkDTJ3KKyYOHLjtNatWrQIAdOnSZcbIkSN9RowYoevatSumTp3qFBkZqUqS1G769OmGwsJC2+rVq4sURRliNpvvWW5OCCF/JFRGTggh5I/KXa/X2wAY/rdvEBYWhoEDB2Lr1q3sk08+USZPnizcr6S8pqYGGzZsQFRUlNK5c2eh/trUli1bYt++fQCg9u3blzVp0uTBBuHnBzz/PA+LDgcPUF27Ateu8cA2ZAiffW3VCg7GsGzLFgEANJKE/Px82O12oaSkBE5OThBFERqNhs9KG408QFZW8tCWlobo6Gjs2rXL8fOFCyxxzBgBpaWA0Yiamhqkp6ejefPmOH36NFu3bp0ycuRIfkM+PZ3PLrdtC2zcyAN1QQEwaxYPuceP8/BsMPASc4CvFX/jDUBRYF+yBJl796pO5eVwOXOG4fx5XhJ+6BAP0L6+6NGlC9p7eUFwccGNnj1RkpCA9YqCi1ev4sT16+okDw/mnpvLZ+63beMdyxtSV8b81FO3HisrQ0VFBbK7dMFVRbGPBCRkZPCAmJnJtxez2/lscr9+wFdf8ceXLgXGjuVbjH33HZ9NP3+e31hYvJh/X+3a8cefew6YNw99V60S9Vu34mudTh0jy0x44gl+oyAtjd+ccHXlJfGZmTAOHYqe332H7cHBuNK3L/xcXfk1GTaMh26AB1dH7cT3v/6F0S1aIG3TJqQqCoKzs9FeEIC1a3mTvY0bUVhYiPLycgQFBSn169MZY/Dy8lLWr1/PXnrppQdfLF7Hbge+/hojDxwQMux2XNq7F65TpsDtr3/lN2QUBXBy4hUPX3zBf0dqqww8PT3ViIgIlpiYeNvnMsYQHh7Orly5YtdqtVJhYaFNUZTVqqru+tXjI4SQ/1AUtgkhhPxRlVksFs0vH3Z/MTExiIiIwNKlS9lXX32lTJ06tcF1tKWlpVi2bBm8vb2VQYMGCXc2uPL09IQgCLBYLCw9PV3p3LnzL1ePORx8ZnLbNr7m+soVIDCQb88VHs5npoGbwSW/rsS61qxZszB//ny1oKCALVmyBAAwY8YMGENCgPff543EBgzg4dhkgnXCBHhFR4snT55UEqdOBfbtgzp6NDZs2KBWVFSwyZMnIzMzU92yZYvQzmJBy9WrecO28eOBQYNuzZ4fPcqbrH3zDfDpp7xb+PPP32peVkcQsOzMGZRptQLz8sLfAgLUFzp3ZvqtW4HSUmDnTkAQwNzd4bZmDcAYXAD4AIgE35f822+/xaovvlCfGzSIiTt2AKGhSE5OVoODg1lAQAAAQFVVLFmyRDUajXBxcUHv3r1Z3Tp+VVWxatUqpaioSHAJDubfybp1t8ZYV4Hg48MbspWU8PM7epQH2U8+Af78Z+DFF/ks9Q8/8BL2o0f5GvmHHuLl6CYTatq0QdX58ziXk8MuuLmpIcHBDG5u/Pv78EN+g+Ldd/ma+uXLoenYETmnTiFLUZRXXnlFwNChwJtv3prZlqRbNxASE2HPy0NaSQn08fEY3qYNn5UvKQH0euDNN+F/+DCaDR+uqLWbe9fXv39/4fPPP0dBQQG868rt76ekBDh9mjftS07mv0djxsA7IAAb/vEPVPv6qg/V/iVYv3ixMuyFF4Qlf/+7PcThYF6PP872Dx6sKIKAqqoqsYHhAAAKCwuh0WhY7fiMxcXFk8rKyuJkWZ4FoFqn000TBKGtoijpVqt1ntlsPvXLAyeEkP8cFLYJIYT8UfURRbFRlkMZDAaMGTOGLVq0iJ08efJmebjFYkFVVRXKy8uxdu1alTHGOnXqdFfQLi0txaeffgpvb2/HE088IWq12vuP68ABPju6YgXfysrZmQesXbt4w697NKpq3rw5pk2bhk8++QQmk0kFwHQ6nZKamio6Ozsr169fF9avX68AUMedOyeyum2qnJxQnJ+PG7t3Q/H3V/sMHizcyMmBbu5cHAsNRXZ2NqZNmwYnJyfEubgwl0uXkHfwoNry8ccZunfnW3HV5+wM1G9kNnIkX7NbVQXlq6+gTpiAU6dOobS0FGVlZQCA4OBg1W63q5s3b2YuLi4wFBWhW0wMsHs3XzPdQHduQRCQmJjISgYNAlJTgY0bkZGRgZSUFJaSkgKNRgN/f3+1pKSElZeXM41Gg9zcXGRkZCAsLEx1cXHBpUuX1IqKCjaT37i4/XvJysK1Vatw0dcXoQsWwDh8OL/Z0aYN39Zs8WJejr56NS/Nt1h4eb+LC18j7ubGZ6rNZqBtW6SnpjpsgYGim5sbTB07Mnh78/OqquLVCXPmAMeO3TrX/v1Rysu+hYJLl+BdVsabvk2dCvz8813Xo7i4GAD/vSxaswZeksTHuHMnsGIFLBs2oOLsWaFFTg7s48bd1kitWbNmiI6Odmzfvp2NHz/+7t9PReF7jBcVAR9/zMc8bhy/DjNn8rXyALRlZRAEAbrapngVFRU4UVIiXJ4/H906d5ZYfDy8/vEPDL14USgaPx6MMTTU3fzUqVM4cOAAHn30UZH/ijphypQppvT09NhDhw6tt9vtrEOHDqaAgAAhNzc3bM+ePcPnzJkzcvbs2T/e9WaEEPIfisI2IYSQ/3iyLAsAugCIAWDQaDRBAKbcuHEDNpsNly9fxrVr1+Di4oKrV68q0dHRAsCbMz0oDw8P+Pj4KDk5OYJGo8GWLVvUioqKmwnwoYceQufOnRvcskkQBKiqioKCAnHBggXqK6+80nCp7owZvMTa3Z3PSioK367rz3/ma4/PnOGhrEcPPpvcwGc5OzsDAJo0aaIAEJs1ayacPn0aHh4egt1uR05OjqAoCv4WHq66iaJy/eOPBTtjuH79OuwTJ7JO8fFq84QEtrlXL5R27YrCjRvRKiKCNbFaeUn4qFG4FhGhnhoyRO02blzD5+HszBuj1denD46sXAlPsxl79u/HuebNAcZgNBrVbt26sfDwcPbPf/5TycrKgpvdjlFffIGkRx9Ves6ZI8DdnYfYBvaw/mH5cnj17o12b72FgoIC/PDDDxgxYgTWr18Pm82G7OxsBgDu7u7quHHj2Lp165SqqiohNzcXer0eYWFhwqRJk6DVavn1PncOts8/hxQWhvyVK9UrFgs78sgj6tahQ9mrixaBtWrFz23uXF4dYLPx2fovvuABdMYMPrAOHW4NcuRIAEDgY4+J3wEQKyuRNmiQOqhfP379li/nywEuXuRr4Ov93kydOhWffvopMj74AN6bNvHlAyEhDV726OhoGI1GrFq1CiUFBfCaMoWXpoeGAv7+YFOm4KEVKxD0+ef457Jl6vBJk1j9DuExMTHi6tWrlZvr1bt25TcWNm3iNxTefJOvzV+5kt/8qbe1GgDs2LEDqampYIzB1dWVXb16FStXrlQTjUale0GBKNRdkxYtgEWL0PzSJV4RcQe73Y4NGzZg8ODBCKl3roIgoGnTpkyv17P8/HxtUlKS6u3tXRkdHe3crl07w/Hjx8cDoLBNCPnDoLBNCCHkN1XbNdztXlv5yLKsBzAcwGGz2XymodfrdLqNer2+Z8uWLTU6nU4yGAxCZmYm8vPzsXjxYrWsrIwBQGRkpHry5EkhKSkJADBu3Ljb/jH/S9q3by/s2LEDmZmZaN++vRoXF8fc3NygKAoEQbjnWlc3NzcMGDBA3b59O7NYLGzDhg3qyJEj+fHZ2cBbb/EAU17Ow1tCAv+vTlERbzRms/FZxJ9/Brp3B95+m5eU16PRaGrfNlu8cOECRo8ezY4cOYKYmBhkZGTgzJkzGDRoEHbs2MG6Tpokprdvj5/790ePHj3QpEkTJCUlqbaRIxX/ESOEEZ99BtVohOTlxWcxH30UOHkS+xcuhM5uF9LT09Ghfqisc+4cX8s9ceLNhzZv3uw4kp0tRsoylF27MGrtWlx4803HI2PG3JwW12q1aOLkpHrabOreTp2EE66uQnejEfZp05D0ww/wCw+HTqdDenq6Wl1dzapOn1bHLl7MStavR7VOh1Vffqm2a9eORUVFobS0FP7+/jh27JhqsVjUxx57TACAKVOm1N9yjZdDl5Whevp0OLZuxa5p0+B0+DDO2WwoGzBAnTRpEut44ABbdPSo4+qrr4rNPT15IzgfH762PD6eN6jbtYuH5fJyXlY9ZEgDl+WcAkBwOBwoLy9XUH8LstdeA/Ly7nqNTqeD4fp1dPrySygzZ0Lw9b21TrsBLVq0gEtlJdxXruTd300mIDsb6unT+HzrVtViscDjww/VHq1aCQgM5Hugv/wyIAgIeOgh1LzyimBZuBB6Z2e+NvyRR3gZelgYr6rIzuZr1tu0uSts13YjF0aPHg2TyYTly5ejS5cuauKNGyLj/Qq4pk15yF67lt84iIi4+VR5eTl2794NSZLU6Ojo2/5OORwOfPPNNza73f4MgJ8AXM/Ly+tTUlIyRBTF6JqamqX3vDCEEPIfiN1rHQ0hhBDy75Jl2Vun022tqalpq9Vqd1qt1vFms7ngjmOed3V1/fDGjRsORVFeevPNNz+/4/nWBoPh8IwZM4z1G5IlJSVh//79anR0NEtPT4eLiwvi4+OxY8eO28bwzDPPwOdeDbXuYLfb8cEHH6gBAQHq6NGjhXvtZ3wvn3zyiVpSUsJ8fHzUsH/9S71uMqHbyy8LLrLMZ7DvCC83paTwRmlHj/IO4ePH8y28PvyQN+EaM4aXN9f67LPP1Pz8fDZw4EDE1HXlvsOCBQsU8exZ4ZFnnoFfePjNkH4bLy+grIxv3ZSQcPMzTp48ieTkZEiS5HjmmWfu3rP67FkgOxv23r2xZ88enDx5UikpKRE8PDwwbtw4rPzqK2Vkaqrg89xzPKjWzeaqKjBhAkpKStQlnTszo9Go+Pj4sPJDhxizWFAaEADGGBwOBxw2G0Z27oyIvDyoEyZgxYoVSk1NjTplypQG99C+SVWBjAw+Sy8IcCxYgD3h4eplRWEVzZvjetOmalxcHGvatCnCw8Mh2WxAYCDOh4SoVarKIkePhmQw8O3D6nTtym+GfPABcPAgD9onTvDy+nrVE3l5eVi+fDl69eqF27YYA/h4Fi3ia9zvqFjY8s9/4nRSEqa+8w70gsDL1Csq7prpv3z5MpKSkiBs24auGg0Cly3jT/Tpg9JWrfB3b28MGTIE7dq144+/9hpfn/7ppzx0HzqEZefPO5r7+Yl9+/a99cYnT/IO7YMG8b4BHTrwUvKPPuJd6wFcvXoVS5cuhaqq0Ol0KmMMdrud2e12mM3mhr+L1av5OvfnnuPnBF4+vmbNGvj6+jpUVUVoaKjYs2fP2q9OxZIl/8PemUdFcaV//3uruputgWZp9h3ZN2UXBNod3PfEGKNGo8ZJjJPdyUJ6sqhJJjGaRE1iJtG4xESjcY0K2IjsCgoIyCI7qOw73V1V7x8XUFwymfnNe97fvNOfczxHurqqblXd4vC9z/N8nx29XV1duTzPl4AumAhqtfoagJ/v/92hQ4cOHf/bYd95PFCIhAAAIABJREFU553/12PQoUOHDh3/n6BUKl1VKpWXSqVqUalUHmKxOC0iIsJtyZIl7MDAgPPt27cfS05O3qNQKPqG9lGpVNF+fn6T5syZY1BcXDwxJSXFMjk5+axCoRjaPsXOzm56cHCw3r3ncnV1RUxMDPHy8oKbmxsqKyu5pqYmISgoCIGBgaSsrAwA0NXVxdfV1fHl5eW8h4fH79ZSMwyD4OBgcvz4cdLf3y94eHj8U87NbmZmxPbdd3HV2ppENTaiXyRizovFfPjWrQQPE7tDHDsGnD1Lo9gvvEDdtF1cgPnzqah76y36s5kZYGAACwsLcvXqVXR2dvKhoaEPHaO/vz+5UFgIx02bkNTUJBTW1wsuLi5kqNYWAI1i1tbSNOjQ0GERaGVlhebmZr63t5f4+vqSgoICnDhxgj9//jwuXbqEtkOH0Hv8OPbcuYPW1lYEBASQhIQEolAowHEcki9cIE5r18Jaq6X9vH19ASen4f7chm+/TcYqFCgrK0NzczPmlJcT95QUTP/pJ4wbNw7R0dGw/egjQX3hAmw/+4wkJSXxFRUVWLt2LcveXz8OUIF97RptC0YIbffFcdCsXo3dPC/UWFtj0tq1ZOqiRYiOjiYuLi6wksvBPP00MGYMsHw5bl65Qs6EhCBUo4Fk6tS7PbsBmqY9fTp13HZwoJFiiYRek6XlcEq5vr4+Kioq0NTUJISEhIx8LlZWtM57/PiRIrqlBbbjxuHyhAmImTSJCvjnnqMLM/eJ8uPHj+NmaSmmnToF259/hmho8ebxx5FrZoa2tjZh1qxZZLjUwcAAGDsWmDGDmrm5uKCoqIipr68XwsPD6Zc4jtbDe3nR63jnHZo+D9CMi/Z2wMkJly9fRlVVFQICAvjZs2czQUFBJCcnB66urgjasYMu2CxadP8kpO7qSiUtleA4DHAc8vLy0NvbyxgbG5PCwkKSnp4ueHt7EyMjIwQFBYnNzMycHRwcQlxdXUNcXV1DDAwMxre2tm5ITU3tT0lJyVIoFLpIkQ4dOv4j0KWR69ChQ4eOfwubN2/+TCKRrJZKper29nZDlmX5+Ph4cXBwMAGAKVOmiDiOs7169eqPACbfs+utrq4utaWlpd6aNWsMv/322zUdHR1NAD4c3C6VSqW/G810cnLCunXrhr/T39+PiooKAEBpaSkzeJDh6PfvRaxv3boFiUSCuLi4Py60Dx0CmpthsWABhIEBwUgQiNn335Nj330njP8jruRubtQoLDSUOpMfPEhFKiE0wrp4MfD99+AOHUKWmRl/QSplAGDJkiWPPLZUKsXs2bNhcewYCqqrSQXLkpqaGjg6OsLQ0JBGuteto/24XV1p3+qEBLS++CIuX77M5+TkMGKxGB999BGkUqlga2srxMTEMDKZDOx334EdGMCcOXPg7e09XMdeU1ODvLw8Tk9Pjz1+/LgQ8Je/EGzeTIXjqlU0/bqoCNDTgxg0EqzRaMgPLi7wTkjgXBiGBeiih6efH9kHCAV796K2tpZZtWoVrbsGjYCS1lYqsp9/nhp7/eUvtJbczQ24fBk8z+PQgQO8mmWFDevXsyMc5gUBGBig/alv3ADefhsOO3fC4Px5rvjECdZv3bqR/eRiYmiKv5UVrakfO5Y6kJeUUBG7di207e0ofO011NfXY9WqVSPmjlqtxvHjx7kgtZpVr1/PF0ydKujp6SEgIIDlqqpQGxmJ5S+8cHeH556j92twwWkIjuN475ISxm78eOjf21+eZRE+bx5qX38dI66zoYGK5XswMDDgeJ6/+z4dPgzs20cXfDgOCAigfd+ffJJmV/z1r0BjI8SDhnh2dnawsrICALi6uvLNzc3A6tUMeh7RGvuFF2jqvZ4eYG8PhxdewOsbNoAnBAYmJqS7uxvbt28nSUlJmDZtGkxMTBAYGHj/UQxaW1tx6NAhZXt7+0ylUjklMTFR/fAT6tChQ8f/HnSRbR06dOjQ8W8hLS1t16JFiyzi4+P1oqOjmZiYGNbOzm6E6HBzc2NzcnKsz507l6VQKG4CgEql4vr7+9dER0dLxGIxPD09xZcvXx6XnJz8i0KhuKNSqUy1Wu2iiIgIvYef+UFEIhH8/f3h7e0NjuNQW1sLtVqNyspKyOXy4dZHHMfh/jZfR44c4T08PARfX9/fF9v9/bQtVlgYcPIk0NICMmcOjP70JxI5cSIMDQ1RWFgoNDU1kStXrggNDQ1ELpejubkZzc3N0Gq1IIRQ0evoSIX1UJ3sW28BLIt2R0fo6emBGBig298f+y9eBNvQICwuLiYdBgZC9WCv4kcN0draGuyTTyL5yhWIjI1RUFCA7OxspKamQi8vj3Pcu5fBmjXQarWoMDPDuZ4evuDIEcK1t/MTH3uMiYyMxMSJExETE0P8/f0ZS0tLSKVSGNnZwSA8HPKAgBGGcXl5eXxeXh4bExODKVOmECMjIyrkN2ygCwhaLRX3HAewLAoLC4XQ0FA8sXw58XvuOQYxMVQIT50Kbvt2SDw8SHZ2NiZPngxPDw8U//QTn/rZZ6Rx2zbIt2/HFbVaMHR3J+LXXgMbGUlF8WBa95kzZ7iKigry7LPPsiNS6Pv7acR19GgabS0tBYKDIU1IQLhczrQfPizs02jIqFGj0NfXh9raWlhaWtKIs7c3FcCE0H9GRoC+PgR7exxNTUVpXR3iTp/Gz21tuFFZKejr6xNLS0ukpqbiypUrjNjAAM7p6aRl8mSmu7ub5KWlCRbffEOy4+MxcfI960+nT9NsADe3+6ZcP+m4dAmyp56CbNSouxsYBg2FhbjMMCTy3uNkZtJxDkbeBUHATz/9xDg6OsLHx4cuIPz1r7QdmYUFXUDIz6fPzMKCOs6PGQN0d6Ni0yZUOzrCx9eXt7OzYwaPR2pqarhId3cGLi7DjuUAgGXLgHPn6LEnT6YLFK+8AjzxBEQ//QTxrFnASy9B0teHWx0dKC0tRXZ2Nvr7+x/qs2BgYIAxY8ZIioqKLHt6eq4qFIoH/B106NCh438bOrGtQ4cOHTr+LaSkpNwqKyub5uvrKzYyMnqka7epqamkvLx8XHJy8i6FQsGpVKoWABvc3d2NjI2Noa+vD0IIU1tbm5CSkjJeJBJtdHNzM87MzER1dTUxMzODVCr9Q2NiGAaurq5oaWkRmpubyahRo5CTk4Pa2louKysLJ0+eJB4eHtBoNOA4DhqNBr/99huZN28eMTAw+P2Dr15N+0pbWlIzrZkz6f/vuW59fX1kZWWRrq4ucufOHWRkZCAvLw8FBQXIyclBeno6rl69KnT39ED+3nukfepUSC0t0eXujpKGBuHQsWMkr6QEnZ2dfFZWlqC1sBAef/99RuzqCpfLl0nPiRMko7dX8AkNJQ+7362trQDPY/zjj2PsX/6CbpkMMpkMzc3N8G9vZwzb23He0JD75ZdfmPLeXt4xIICZl5uLwNpaxvK552BsbPzwLICffgLS0oBJk0Z8LBaLSU5ODqZPnw5LCwvao5rjaAp8bCy9Z6NG0RRtMzOcb2oiigkTiMzMjEb2w8JoLXh5OT64eRPFxcWwra6GxZ49uNHSwvu++y7TwXHIDQ1Fur8/yggh2f39uHjtGmJiYoYXTrKzs4WMjAzyzDPPMEPu7QBov3ETEyro582jQrC2li6aACDbtsEqIoLUOzjwSUlJJDc3FyUlJUJhYaEgEomIZXQ02Lw8KkA3bABYFv39/Th77Rqfr9WSQEdHOCQloTIkRHAuLibJlZW4kJaGxsZGxMbGYvKTT8KEYeA1ZQoCIyNJZEcHYfbsgWT9+pHtsSZPBmxtaTR4kI6ODpz+5BPEZGbC5cMPQe5Lp9cGBODmmTMImjXr7rt36hRNex80KCOEIC8vD93d3VxEeDiDlSuBJ56gQniIa9cAc3O6HwBYWoI3NUXHV18h1tkZXkuXMkNz/Pr168KNGzdY3xdegIFGAzJhwt3jmJoCQUHUB4AQKuwDA2m6emgoTTmXyQB7e3AmJuDd3bnxMTFMakYGP3bs2IfO56amJmRmZmp5nn9HoVC0P/AFHTp06Phfhk5s69ChQ4eOB3j33XcXZWVlHUhLS1uYlJR0RaFQ3P5H+8TFxRVeuHChMzc3V8GyLOPg4PBAP2oAkMvlqK2t1evu7nZNTk7+NTExUUhPT5/h5OTkPJSe6uDgwDAMI7t586aPmZmZpLq6mnR2dpJbt24hNzcXgYGB+IdieBBCCHx9fUlcXBwCAwPR1tYGjuMYPT09NDc3k5KSEly6dAkZGRnIyMiAXC4XYmNjfz+q3dND20Dp61OzrKoq6no9dy5NS3ZxAXp7YeXpSS5evAiJRIKEhASYm5tj6dKliIqKQmxsLNra2uDq6koKiop4vepqcvrOHZJXUiKoiouJ6/HjCG5qIllyObq6ugBAWLp0KSuWSABnZ4gTEiDr6oLj+++TfJ6HU2DgsDjjeR7p6ek4ePAgMrOy4LNxI0zGjYOXjw+8vLxQUVGBW6WlSPbyQn1DAyMSiTB58mQhJiaGMPPnU1fyTz4Btm8fbms1gtpaGgUNCxvxsbGxMXp6eri0H34gFpaWxFypBKZMARISqPlbVxcVWosXo9nODpLPP4f/hx+CefFFakL2449UmD39NEz/+ld4lZaCCAJkcjkyxGKiioxEk68v/+Jbb5Go2Fj4+vpCT08PtbW1uHLlilBeXk6uXr2K/Px8snjxYmJ3by/w/n76XEJDqcBkWdrGy90dmDiRfufAAWDpUvjFxZGwsDCEhoZCoVCQnp4ekp6ezldUVAijExIIvLzQ6eiIg/v3C+YWFuTMmTPEwcEBUkdH/pRcTiT6+mTpL78gOi4OPnPnIn7mTLi4utJz/vwzPZeHB2BggK2EoKW9HT4+PtDX10dDQwMMXn4ZzMGD9DkM0tXVhbpff4VNQIBgNWPGA/Oz/exZhH7wAeoWL4aZuTn9MCODXt89EXI3Nzfk5OSQKImEkKNHaRbFvQsqqal0DsfFDX8kGBpi761bcLe3h8UHH9D7ZWQEBwcHIpVKcUAmQ55MxvM8T5wuXKBR7S1bRpj6ISyMppMvWwaVgwOOpKRwmdnZwuXISL5MX58EZ2TA//33SVZMDK79+iv0bWzIvRko+fn5+Pnnn/s4jntSEIQslUoVqVKpfFQqVaNCodA+7DXVoUOHjv/X6MS2Dh06dOgYgVKpNGYYJmP+/Pl2dnZ2LlVVVcuSk5OTFApF/T/aNzY2Njs5OflAXV3dQltbWxPzoT/674EQAg8PD3FxcbGnRqPxS05ONiCEPDt9+nTRUBSVEAIbGxty48YNfjB9lFRXV8PGxgZSqRRBQUEYYfT1T+Dl5QV/f3+MGjWKZGdnCx4eHliwYAFpampCZ2cnWJYlY++N9D2M06fpv337qEDT1wfWrqV1rmo10NwM7NgBJCUhiGFQX1mJ8s5OfvGyZYRlWYhEIrAsC19fX7i6uiIyMpJxqKkhY4KD0SGXk4SEBHitWEHM6+rQ6uoqMPr6/KpVq0amQ7MsJGPHon7SJPTs2AGHy5dBDA1xqb4eBw4eFMrKyoYFWVhcHKTLlwMLF4IRiaC9dg3R27bhclSUwPE84TgOUqmU9/T0ZAYfABVghNAobmPjsJs0AFojLRaPaOk09Nw8GxuZoI0byWFBwKjvvoO+tzfdePIkrdeeNQswNcUPR46gyNwczmvXwszRkWYFnDsHZGWBLFwIEcvicni4UO7kRNi4OIyLjwdhGGHp0qWMRCIBy7IwNjaGu7s7ioqKOAMDA6a7uxuNjY0wNzfHpEmT7kZ4Dx6konPZMlqbPkR8PKo9PNDW2wszQmhUd/ZsQCSCSCSCnp7ecHaEq6sruXjxIomJjYV21CiUP/aY4Lt/P/lOJIJYLMaKFStgbW1NWJblK6uqSEZwMLq9vYWQzz4jZOtWWoMNULf5hgZ6rueeQ+DXXyM9PR05OTkoKSlBWloaigUB6uBgwWnIxAzA/q+/5qZ/9x1DduwgMkvLB6ak1s4OO9VqDIhE8B16LhUVVNQPLmIBgKGhIcpzcwXDzZshO3qUMPc+V4Au2Fy4MKI/9u3bt5F5+TKc4+IEO42G4MYNwMYGrLk57O3tEbNtG+rkcmSXl5OoBQvA2tggpbUVWq0WFvfWluvpAceOIU0mg9zBgRk3bhzj6uXFuPv4EJsFC4jxhg0IDAoiIY89Rq60t/NeUVFE292NL/7+977S0tJ8tVo9RxAEtUQiUUml0jUymWyhRqN5PDk5eYfivvp2HTp06PjfgE5s69ChQ4eOEahUKn+ZTPbktGnT9Ozt7YmVlZWktLR0fnJy8g8KhaLrD+wvY1l2hZ+f30PFNkBrqgMDA8X9/f2eIpFoakJCgpHVPYJg6DthYWFkzJgxxNzcHFlZWRCLxbxWqxXS0tJIZGQkHupM/QcRiUSIiYkhvr6+xMjICGPGjIFKpYKenh5kMhkKCwt5V1fXh0e4f/iBRve8vWlP4qYmYMIEWm8dGEhrgufNA2JioFdeDnFSEoSODuJ15gwVqtbWtL713vFfvAhReztGLVsGY2NjKmZdXeG5di05L5cz4bGxD71eM2tr7K2rQ4+xMW4eP85bHjpEgidPJgPW1kJzczPR09MTpiQkELJrFxAfD5iYwMHREQbR0YhZsYI0NDSgtbUVjY2NTGdnJ+fl5UUFt7097TG9ezc1aHvppbsp8qdOUcF4bw/wH38E1qwBNm7EzagoZN6+jaysLISHh9O69MhIICwMagMDfP7551xzczMTGxeHwAkTwNy8SZ2va2uh9fTENkKQptGgTaMhDMMIM2fOJM7OzvDx8SH3p7Xv2bNH29DQIJo7dy7i4+Mhl8uRm5sLd3d3mJqa0oh2QgK6Q0Jwy9oaBgYGqK+vR25uLs79+CNv+Pnn5EhPD26dO6ftyM4m+27fJsXFxdoxY8YMZ2ao1Wrs37+fd3Bw4O3t7ZmcnBxB1dpKbjs6YsJTT2HBwoXQ09ODkZER7ty5I1RUVBBBEHD7zh0S+7e/UQfy0lK60PDaa7Sf9YYNQEICDEaNQmxsLMzNzZGbmwue5zExNhYtx46RXK2WH+pFrfnqK6ZVq4XZ0qUwuV8gg9Y06331lWB/5QqRL14MAOA/+ggIDQWxtwdAa7Y/+OADhB47Ru6YmpLOkBCMiP4DVBAXFND5PEhVVRWKi4vh5e1N7BYtAniejt/JCXBxAbN3L3wXLyZhf/oTdurr84WGhrh58yauXr1KLly4gKqqKtqOTF8fWLwYPZ98AjtBgM+8ebC0tIRcLqelIQYGkBgaYqehIW8UEsL4HDkC8uKLSPL3F/wzMw+2ubrOF4lEf509e7Z8xowZktDQUL0LFy6YC4LwsUKh0CiVSlOVSjVTpVJ5q1SqeoVCMfDQ9/cPolQqRQqFgv+fHEOHDh3/3ejEtg4dOnToGIFKpRptbm6+ICQkRA8ALC0twfO8uLGxcVJSUtJuhULBK5VKkpaW9mxGRsaetLS019PT05empqZ+cPHixZdYln1p3LhxxqNHj2YflkY+hEgkgoeHh2j06NF6jxLlQ+jp6SEuLg7u7u4kNzeXqNVqREREDLtT/7tobm7mGxsbSVlZmVBZWcmwLMs7OzuPvIjKSupIvXEj/Tk0lNa8rltHBcrQmAgB9PVBIiNx1tCQ63R0hNekSUSr0UC8axcV7Hp6tPbZ0JAadtnYUJE7hEwGpqIC9Z2dvKGfH3lUpoCTkxMuNzby3kuWML5hYbC4fh2SI0dQLpeTAYYhvn5+kD7/PBWeRkbA8uU0RdnMDD4+PrC1tUVRURGampqY0NDQkfc1JARYsYK6Wq9fTwWjkRFNTba1pX2nS0rod729gfBwWLi6wtHREdeuXUNNTY3g7u5O9KuqoJkxA1sHBni5XI4lS5YwXl5eEFVWUifsoCDgzTfRHxkJ/V27oCEEet7e3PPPPz+y7vo+SktL0dbWRjo6Orienh4SHBxMWltb+XO//UYs//QnnL11C5dmzOCTy8tJQUEBVCoVCgoK0NXVJUR7eZExZ88S9rnnhKDiYtZm1CgSsGoVioqKSH5+Ph8aGsoAwHfffcfxPM8sWLCAaWhowMmTJ4lGIoG+pyc/LTGRoL6eCmrQ2uri4mICABKJBEYmJrD196eGYx0dNK1+0yZqXvbmm8PXYWVlhbi4OPT09Ag1x46R2WfO4EJICAoKCngfb2/m+nffIS84GLd6exEYGAie56FWq4dr6tVqNbIOHyaCRgO98eOxY8cOuB08iH0ch9yqKsHJyYlIpVJICwtR29wMq+eeQ/i9iyVDGBjQjI0JE4bLEqysrJCRkQF7e3s4OTlRQz8HB6C1lWYrvPceiJMTxFZWQFQUGhoa8PTTT5MJEybA3NwcmZmZuHz5Mpeeno5Lly4Jtvn5xLa7GyYLFz70maZnZSFwzBhit2wZyNq1cDY3Z0NfeSXae2DAO+6zz8S29vYghECj0SAtLY0HoFSpVGNEItFVJyenOaampnN6enpeSElJOaBQKDoeNXeUSqVIpVKZKhSK/vs+98zIyMjiOO6T9PT051UqVVtsbOyVR05CHTp06HgEutZfOnTo0KFjBBKJZIWXl9cIB7LY2FhReXm5R0NDwxoAXxBC5hkZGX08Z84cQ0NDQ9TW1jqcPHkSixYtgpeX1wMO3/8utFot+vpoi+4/apL2z7BgwYKhgZPU1FSkpKQwgYGBNEI6xOHDNHp9L/r6NMJdVESjwffB8zy53dHBfHzxIsRiMf5y4ABNNb99GzhyhLZH4nkq5PPzR6Zsf/opAqZMYdr37eORmPjQG+vi4oK1a9fSbaNHAwoFqktL8af9+3H7r3+FtVQK1NRQc6rqaiAnZ7iPtEgkGn5mPM9j27ZtiI6O5uPi4u6ey8KCXlt1NXUUP3KELib4+tLWZLNm0f7M99DZ2QkAYFlW+PLLLzFjyhTCmZsLTo6OxNbOjvniiy9g1tqKKVVVvO3EiYxpXBwwcSIGLl+GyVNPYYZSiT3m5uymDz7AmrVrqSP4Q1i0aBFTWVmJkpIS9vz58wgKCsLc2bOZsMBAmKWm4qatLQ9TU+Gp5cthZGQErVY7JFDJ4ENHrCAQvPce8O23gJ0dVqxYQb744gv2gw8+QEBAgMBxHHF1dYVUKkVZWRnHMAzr5+eHoKAgBr6+gI8PfX4MA39/fyKXy7Fz50709/fj2rVr3JgxY1hIJLRWfPly+uzb2oDiYrrvPUyfPp1sKSgQ6rOzyWpra7Jnzx5yYP16RDc1wfjVV5Fy4QI2bdoEjuMGH40FHxgYyNTV1XHd8fHsnWvXUHLgANRqNcRz5iBkxgxUNDfjm2++QYifH+f9449M9ahRQujEiQ9/SQmhfgQ1NYCfH/r7+/G3v/0NWq0W7e3twvB9mzCB1navX0/Nzr77DuSZZzAWGFGGERQUBHNzc2i1WpZhGFRXVyOltxdsdDQcNm2ikf77fl9IpVLhxIkTRCqVwsvLC86jRwNr1sDq1i3gu++AP/0JIATNzc3Q09Orfe2117gtW7Z8OmnSJJOQwTKB77//vquqqioMQM3QcTdt2rSZZdlwjuOqGIaJYBjGgxCCzZs3pwwMDMxOTEzsVyqVRCKRnJ44caJrWFgYaWpqsti7d+9WpVKZn5iYmKNUKgP09fX3CYJgqFarX3777bePPvQ+6tChQwd0YluHDh06/utQKpUGiYmJfY/YttDY2Hh6eHj4iL9+CSGIiYkxOnr06AqlUvl3iUSybebMmYZDDsqtra0AaCrrv1toa7VafPbZZ7xGoyH29va8o6Mj29bWxgH413PI/wDjxo1DSkoKGhsb74ptnqftkMaNG/llQoBLl8CXlKB/+3YUhIcLtbW1HABiYGDA1NXVMdbW1jzDMExfX5/Q09NDjCwtaZ2yRAIUFtJocV4e8N57tLbYz2/48PrTpkG6bRvDvfnmH0udNzBAhqcn0V+2DDFHj1LTq2efBa5epSIvP59G0wdhGAaPP/44Dh48CI1GgwsXLjC1tbXCzOnTiam+PnWSrqujqeIHD9J2UQxDI/G5uQ+IJQAoKioCACxfvpzJyckRjp46hWBzc9KYl4ea2lrBR18fkziOZBgZMckikbDOxoYMrF2Lbdu2AQCkzz+P6UeOCO0SCTrnziWPEtsANf1iGAY5OTkQ+vpAAgLguGsXkJSEGcCIwT3grh4VBURH0/s9mFJtaGiIl156Cfn5+Th16hThOI4AEDZv3iwMDAywtra2w7XccHenPbstLYGkJGDMGFhbW2Pq1Kn8b7/9xvSWlZGcV19FSHMzGHt7ajj397/T77q7077qU6eOGJLMzIw3iYxk9bOz8fTTTzNl/f1wmzwZ7OD4LC0tMXPmTMjlcpw9e5YpKiripFIp093dzT3544/steho/rHduxmzF1+EPDoa4YaG5M6dOzj15pusYGiIYnd3VGzdyr/88ssPf1lDQoDsbPA+Pti9ezev1WoZuVyOoqIiMn78eBgOzR1PT3o9v/0G7NxJFxOuXHlgwcnR0REANe77/vvvERISAi9vb1p2EB9PfQHuYdWqVcwnn3wi/PLLLwgLCyPju7vBfPop3ZieTo0I9+wBIQSCIEiUSiUxMDBgh3wctFot6uvrxQCyh46pVCptxGLxhhkzZuh1dXXBysoK9vb2YFkWhw4diqmqqvocwCoAkfr6+lZhYWGEEHJvzbkGAPT19b+KiYkJsLa2xo8//rhfqVT6JyYmVj70PurQoeO/Hp3Y1qFDh47/IjZv3vwpgA2bNm2qVKvV0xITE0uHtimVSn2xWLxz4cKFhvr6+g/s6+zsDIZh/BiGafPx8eHu7YXr6ekJANi3bx/eeOONf9t4eZ5HUlISuru7GQsLC6G2tpbVaDQAwJ46dUqYNm3a77uG/w9gGAZ+fn44cuQIAgICuOnTp7NMZiZte7VkyUP3Sfv+e8H3669J2muvCaM8PUVarRaNjY1CQkI4wBmkAAAgAElEQVQCUlJS0NXVBT09PfLpp5/CFhASmpuJTWYmmI8/pvWvZWVU/H31FRX08+cDDAPn9evxXW0tPyMxkbF6770/NH5jY2NBXyYj+OILKlCSk6mo//VXKrzffZeKPqkUGDsWHm+9hTeeeQZlBw9Cfvgwdjz7LDFavZqKoRdeoCnPH35IDb6MjYHOTiqSNm+mx8zMpOng4eGArS26u7uHo6BhYWFELBbD/OBBITQhgVjPmUOQmAiMH4+bbW2cm4sLe/LcOcHt0iXE/fnPRCQSoaSkhD82dSoz79o1wa22lkb7B0Xbw7Czs4MTIOz89lvy0htvgAymdf9D3nmHRpo7O0csGjAMg+DgYFhZWeHIkSMAAB8fH0EkEpHe3l6uvr6e+eSTT0hQUBCCgoJgtXcvTYXneUAQEOngwISXlOB2WhpzZsIE9EVFIWrlyrtiPz6e1qkvW0bv2z0CddGiRWzlV19ByM/nQw0MGN9t26DatYvLPnqUAUBcXFxga2sLAJg2bRpwd+GJxezZmGBlxUBfnzrkD7r2y1ta8Hh+PtL+/GdE29qSCxcukK1bt/JGRkZgGIYwDENYlgXDMLBraYFpSQly1Wo0NzczLMvCx8cH6enpSE9Px6Shlm8DAzST4vZt2uKtoICmyZeW0kUdDw9AJMK+ffu4zs5O0tHRwQiCAH19fVg6OND5XltLSxHuE+gLFy4kt27dwslff0XURx+hYd8+uA/6IODYMeD0adhMmQKRSCQbGBgI6+vr256ZmRno7+9vnJOTwzMMk5mYmFirVCpdAWgBNPM8z3Ech6CgIEgkkuGFwblz5xp8/vnnT7z77rsFhBArLy8vyVAJTFtbG9RqtQDg1uDQbFxdXWFpaQlBEBgAPX9sounQoeO/EV3Ntg4dOnT8F6FSqQ6vX79eIpFIZE1NTe7jxo3bf8+26TY2NgvGjx//UJtvkUiEyMhIUUREBBsQECC+tx6bYRhUVlZyWq2Wkclk6OvrG5l6/U+SmpqKc+fO4cKFC3x1dTVxc3PjVq5cycTGxqKxsVFoaWkho0ePJvb31jf/X8Db2xuurq44deoUY2pqCtuCAgy4uKDW0hI1NTW4fPkyb2dnRyQSCaqqqnC0qIgI69YJy6ysGO/wcPiGhSE4OJjY2toiKytLGBgYIC4ikfCMjQ1xOnSIlKjV/C/x8RgzZw4RGxnRGthVq2h69o8/UtHi5gZibIwblZXw/PvfYRAVRTAotB7FxYsXhZKSElJWVkYNytzcqIj/5BPqUF1fT03P9u+naeFjxwJ1dSBjx8I8JgYH79xBh5kZvL75BsZr1tCWWatXA66ud3skE0JN3KZNo1FhDw+6OGBmBhACt+efJzlBQQixtITE2Bg2jo4wHT+eSEeNotH7GTOARYtw+vRppqGhAb29vZj01VckIzpa8PDyIjY2NqSyrg413t58eGsrgw8+oO2ozMweuF6e51FWWIiYFSuIOiBAcHv1VXJ/H+pHYm5OFzd8famx3X2YmJggIiICISEhxNvdnXjq6cFv7FjGub2doKqKy6yuZow//1xwXr6c4Kef6L28eBG4fh3c3LnYJpejzdISVd3dqKio4ENCQu6+OEZGNCXaygpYupQazhkZQaPR4HhtLSwcHXnnzEwGdnbIksl4qVTKzps3D2PuiwSPgBD6fKZOBXbtotFjjgOOHYMoLAxuS5fCxcUF1dXV0Gg0CAwMZMzNzYmpqSmkUikMDQ0FCSBIm5oE9ZgxvEajYZYuXYqAgABUVlaivr5eiIiIoNfw0Uc09X4oQt3YCKSkACYm6IqMFCoPH8bPra1CS3k5GzlpEiktpWt7+vr6CAgIoGP99lvafuy550ZchqmpKezs7BAbG4vM2FjhXEGBEB0dTcAw9FxVVSCvvgohLo6tbm+3FAShQqPRzBo3bpx47969GrVaPevixYuLxGLxUYZh1jMM08Jx3Gfl5eXjUlNTzYuKirRDGTwikQje3t7impqacQAiJ0+ebDDkEyCVSiEIAlNfX78iOTkZhJBJISEhBuXl5aiqqsp84403tgOAUqkUq1QqR5VK1aVQKIQ/Nvl06NDx/zu6yLYOHTp0/IehVCpNAfgDyEtMTOz9Z/blOE7fyMgIo0ePJhcvXoxTKpVMYmIiDwBisTje29v7dwuhGYZ5ZH/ruro6VhAE/PTTTyCE4O233/5nhgaApn8mJycjIyMDADB16lQSFhYG9p7caR8fH1JTUyOMEC3/F1Cr1di+fTvf3d3NAMDZn36C/PBh/PDUU2AOHRJYlhXUajWTm5sLf39/lJSUIDw8HAkJCQRxcdR9e8uW4eOtWrSISVu5EtKuLiJ68kmIvv8ecW5uzLXt27mUlBQyJTqaEZ0/D7z+Ou1P/Le/0frUDz8Epk2Df2ws+bmyEstrayH29aV14g9BEASkpKQQgIoaQ0NDGoH+8ksqrj/9lNbj1tcDX399d8dBsy4GQOC6dag9eRLso3qZGxrSvsmZmbS+99ln6eeDqeNXT5zg2lxcWFYiEXoXLoTUx4dg/37q0N7cTFOO585Ff3//cK24g5MT6bp0CT1ZWfzRo0dZQRDg7OwMDw8P9qeqKoSvWwfn48dpJP7pp4eHUlxcjJtvvSUUeXqS4K+/5mMWLnxof/cRcByNqDY0UHO33btpZLWoiEaaT5ygCwLXrtEUfKmUps57etJU6aoqWP/wA6Z0d7N35s3jvL79lkVd3d1n0tEBxMdDtG4dXjx8GOW//oqykhI4JiQ8mLZtYEDT9Lu7gYYGaE1N8eWXXwrLTp6EDcuyYFngzTfBnzxJWltbkZKSgilTpuB+9/5hTEyomV1DAxAcTD/78Uf6rHbvHv5aa2srzM3N+ejo6PtXJQhaWgiOHUPA7Nl3HegBeHh4IDMzk7S3t0NmYkJF7+zZAAD1t9+ioroatUlJ/JSpU5mDr77KC2o1O6m/n7jv3Anmgw9QfukSV97VxRoaGt6t/X7xRSrWr14FAgJGliR0doJxdkbz5s2CVqsdee9mzAAqKhBsbMz2mJpOazA2nhQeHq6v1WohCIJILBaf4jjOOSIiAsHBwdi1a9cWAH/TaDTvANjb0tIi+vDDD3kjIyNeIpEw4eHhzOrVqx/6+y8uLk7k5ORkWVxc/J5cLhdZWVnhhx9+6Onv738TAJRKZaRYLD5OCJEyDHMWwOyHPxwdOnT8t6GLbOvQoUPHfxBKpdJRLBYXmJiYrNBqtetTUlJ+/D233fvJzMxc4erqKrOyssKVK1fUAwMDFxUKRY1SqbRmWfarKVOm6BsZGf1LYzMzMxNKSkqG/zLPycnhHR0dyR+JcNfU1OCbb77hL126hKqqKmJnZ8ePGTOGj4mJYe6vAS8uLha6urqE0NBQUltb+z+KoD8KrVaLTz/9VJDJZGTFihWEZVlE19RAZGSEgBdfxPTp00lUVBQJCwsDz/MoKSmBRqPBkiVLaJrwkiXA5MlU4NjYAN9+C/6ll1BgbY2SgACc02iQXVAAkUgkKBQK5vz588KFtDQirq8X6tzdSU1NDapra3nnuXMJDA2BzExY3biBDAsLXrp1K7FsbAQzceIjx69SqWDa1obYs2dhu3IlSGcnjWw7O9OU78mTqZgfO5aan92HpaUl8vPzkZ2djbq6Os7Ozo4xvKfGGxkZwJ//TF2rN2wAFi4cUQOeXVJCMvX1iZ+fH1G5uSFq61aCvj7qwt3dDWzdSlPbXV1x+84dNDc34/bt24h8/nnEfPYZEzl5MsrLy/mqqipSVlaG3t5eFHd28lEzZhC8+y41FbOyAlgW33/1lTB7714S+vLL8Jk3j7YGE4S7Ttnt7TTtuKCARrB37KBi+7XXaH3xpEnA2bM0Jfqxx2ikPzqaCuu4OOq6vXw5dRv38aEu9AYGdOHg8cdhZ2/P7NNo4KBUCibvvUfw9ttUwD/1FLBwIcReXtA/fRrIy8Ol3l74L16MpMpKPi89HVxyMmH9/XHk119xxtycv3zjBh8waxbT7OaGsI8/JoxWS03I5s9HX1+f0NDQgPr6elJSUsJHRUU9fEWBEDrexkZ6naNH0/m4axcgkw1/7dy5c5gwYQJjPWiUNwJDQ7rYMGcOcM/7paenh8zMTLS2tnIB+/czKClB76xZ2L17N38uJ4eoz55FyPbtxPTFF1FQWAhzKysSvXIlyPPPA/r6CJw5k+EkEiFPoyGGRkacnYMDfblFInrPJRK6iDOESIQ7RkY42dxMli1b9uC7HhkJcWkp3PfsEY1evlxP7u9PPvvsM66vr4/leV5GCEF1dTWmTJmCwMBAcUdHR+jAwMC08PBwEc/zYFmWaW5uZjQaDSksLERkZOSDNf2DmJmZwcPDg7W3tyctLS3IycnpePPNN59TKpWmIpHo8oIFC8wjIiJEV69eNRg3btzWhx5Ehw4d/3XoxLYOHTp0/AeRkZHxRWhoaOiSJUsMWlpamNu3bzfExcVl/tH9VSqVo6GhYYSrqytjYGAgrqiomJGSkhIhFos/HDdunNTX1/dfNh2ztrYmsbGx8PT0hK2tLUxNTXH8+HHS1dUFLy+v4e/xPI/09HTIZDIMGRrV1dUhPz+fLFq0iEREREChUBBXV9eHmjdZWFgQlUpFcnJy+OzsbFJUVMSFhob+42jmP8HHH38s9PX1kfXr1xMjIyO4u7vD/M4dmC5aBNN7atVFIhHc3d0xduxY5OXlCYQQ4uTkRPtnHzhAjZz27gVMTCD6+mvcdnZGF8D39/cTAJgwYQKxtbVFVFQUCQwKgvW775Jzjo5cQXEx093dTczNzXGT43CDEBjU1kJRVER+9fKCnZsbTBwcaO30fZDaWpSeOgWNRIKo3FyYbNhA61yHhIpKRdO/16+ngnDGjAeOw7IsoqKiIJVKkZGRwZSVld1NHQaosdeGDVSULV5MBeucOTQCDEAQBFJUVITJkycjLy+PxMXF0TZSTz9N24jJZMC8eci8cEEoVquJIJGAB+BdUQGzp58GY2KC0aNHk9TUVACARqOBo6MjAsePJ1i6lNbNf/458MknqAdQbWdH6jMzwaSk8KY//kiY9naa0n7lCjX7qqqi9d5D44yOptHUJ54ARo2CsGAB2sLDoe/kBBIbC0EsxtWGBiGjtlZwdnYmYnPzkT3R76Hh5k1UZmXBorGRWG/YAGJoSM8TEUFTuV1cwMXE4O9tbegXi1Hv4gL5+PHErLQUDvv3k508j8WbNsHDwQGOkyYxfH8//F9+mRhkZFDX+MceA1xcYG9vz4SHh5OoqCioVCqSn5+vzcnJ4aurq4mfn9/Iyb9zJ10MkcmA48eBDz54wPX80qVLmDJlyvA7+AA8T+eMgwMA4MyZM8KpU6cIQFO8A2/cIJg3DwczMri+vj6yatUqUqfRCEJNDckxNuZvlJUx3t7ecHFxoc+eEOD552EcE0Os9uzBqPfeY36wtBSC/fwIxGLg8cepT0FzM51HajXU69ZhK8tilKenEBMT8/AX3MsLkMsBtRqorobG3p5paGgQOI4jgiAgICCA8/HxYfT19eHr6yseO3asnqurK2NnZzc8v7RaLeRyORcSEsL8EQNCjuOQmZnJpqSk6Ekkkk8DAwMto6KiROXl5aioqFCNGzfu4D88iA4dOv4r0KWR69ChQ8d/CEql0lUsFi+IiYkRA4Cjo6P+jRs3wv7Rfvei1WpP3bhx45kJEyaYBAUFEalUavXDDz8skMvlsBt0Ov6fwDAM7O3tYW9vj3PnzhGWZXHlyhU4OTkhKCgIAJCeno6kpCScP38eMpmMW758OZuens5ZWFgwo0aNIv9INJuYmODxxx9Ha2sr4+fnh927dzPHjx9HeHg4uru74eHhgba2Npw9e1ZYuHAh6e3tBc/zkEqlyM/PR05ODtfe3s5wHEckEgm/dOnS4egez/M4dOiQ0N/fT5YvX343ylVWRs3ETpx4+HVnZcG+spI0NTbS2tO//pWKWoAaiolEwLlz8C8sRCbAaAcFTkZGBj/UbkwmkwHGxnh22TJ26/ffc3fu3GGPHDnCC4IAkUgEFcsyTmIxEs6dg42JCY1enjhBhTMA9PTQGuA//xnRpaU4vHAhepOSHnQKX7CARnm9vWm9bHw8TZV+SIZAcHAwzpw5g+Dg4AcfSlAQcO4cjTCvW0cjvt9+CwA4fPjw8P0EgNu3b8Oqu5u2TXvlFbS2tiJtyxbuWm4uu/76dZgMtU2Ljx8W/szNm9iwdCl27tyJqefOQfzDDwSvvEJT4A8cGBa/8/39SW1NDV8mkzHnRCIGJiZYu2gRyMqVd8f6O1kA3d3dOJSSwmszMphFR45gz+uvC90aDRGJREJfXx/j5uY2PHfvpzE1FdbTp0O7ciVOzJuHm8ePC/PmzSPQ0wPefpve1/BwGBkZwdDQkLe2tiaL33yTiKm4JNznn+Pl/n7oR0TA0t6eoL0dyM6m/dCfeYYK3uXLgW++oU7pISGQDAxg1VNP4U5Hh+jIkSNoaWl5cGBLl9LI/pkztB/6Q2rRAVpy8EhkMhodH6SqqgoajQbOzs7CExoNgzVrgDFjcCcnh4mPjycymQxzV6wgGe7uCHrjDeZGVBT87z+voSHkLi4wO3wYB7ZsgUwsFiCXExw7RuvVz5+n11tVBdy8CWRkQJg/H5MnT/79XwoJCcCFC8Cbb8L77beRolaT0aNH8+Hh4Yytre0fWkCcM2cOO6K//O9gbGyMp556Sr+srOxNKysr4jfYOaCgoKC7v79/uBWYUqkUA9AmJiYKSqXSGIACgBmAMgDZiYmJ3B86oQ4dOv5j0YltHTp06PgPgWXZp4KCgpihmunBVkgjnJKUSqWbvr7+3wHYaLXaO1qtdh+AA4mJie2DX7nW0tIyHMpyc3PDuHHj+GvXrpF9+/aRV155BSPShf8FeJ7HN998w3V2djIrVqwg2dnZwtmzZ4mxsTHc3NxQXl4OT09PwczMDFlZWeyOHTsEAwMDZs2aNf9QaA/h7u4Od3d38DwPExMTkp+fj8LCQjAMA0KI4O7uTkpKSsi77747vI9EIoGenp7g4eGBOXPmkP7+fpw6dYr57rvvhNdee43wPI/du3cLAwMDeO6552Bubn73hJWVNFopEtFaWEGgzt7vvUeF30svYbSxMU6EhQEff0y3VVRQh+uhOt7kZFgeOYKI+Hi4v/8+coODEfn00yOV8ObNgJ4eVq9ezdbW1sLLy2t4+/vvv4+ojRvhIBKBfPQRPXZNDY0yCwJgY4OuPXuwPSQEmsE+4A9NsWcYKmQAamy2bx/tmXzhwgMR7vT0dGg0GlRWVmLc/e3O7nWPXrGCivW33gL3zjswMTGBqamp4OnpSSQSiXD79m1iVVEBHD+Ogvh4HDlyBI6OjuSZ556DiVxOr+PkSRp19vQEioshTJ6MpokTeQNHR3gODDBqOzu6PTWVLmBcuAA8+yyY2Fg4BwUx9kVFGHPpEi66u2P/p58KT2zcSMjQQsR95OTk8HV1dYK1tTWbmZkJU1NTPP3FF2iWyTBJoSCsiQm8vLyYLVu2CFZWVg+flAUFsAwMRPVf/oIxUVECIQRXrlzhMeQMvngxvaYnngAOHAAhhNTV1ZHq6mq4ubnh+vXr8PHxgaGRETB9+t3jlpRQwe3sDHR1Abdu0VTy3Fw6D5OSYDN6NCx//RVPtrfj+qxZAjZsIHBxoSnYLS10IaS8nB4vMZGmZ9+HRqPB+fPn+fnz5z+8/ZeVFW33NoihoSGJioqCYuxYwvj4AMePQxAE6OnpCT09PcP3aGxsLCAW47WEBNoO7SGIRCI0isX8gthYBlevAra29H41N9OUf60WsLODpLAQrnv28CqViixcuPB3fzkcaGzk+qKjid333yOgr4/pcncXbH/HSFAqlcLa2prv6upCUFAQY2Nj83uHfwBHR0c4OjoOj6m9vR21tbUA8CMAvPfeewsZhvlBJBLd+fDDDytFIlGojY2NxtjYmG1qauJ7enq6lErl9MTExPx/6sQ6dOj4j0IntnXo0KHjPwSxWPyEn5/f8F/N9vb20Gg07kql0jQxMbFDqVQSPT29XyMiInwsLCyYX375xdPQ0HDswMDAp1u2bEnv7+8/SQgxNTQ0HI6mEELQ09MjdHZ2MgzDoLGxERcvXoSXlxfGjh37L42zs7MTt27dYtesWQMrKysoFApy7do17N27FzKZTOjr60NERAQZP348ZDKZIJFIiJ+f36PTWX+HHTt2CM3NzQQAVq1aBXNzc2zdupVcv34dAODi4oLx48fDwMAAgiBALpcTQggLAP39/ejs7MRQenR+fj4aGhrI+vXrYWZmRgUsz9O662XLaOpxZyd1rX79derCvXw5dcdOT8edS5cEgytXeFRWsmhqAn7+maa2+vsDN25g982b2rqAABFTXY3W4GDoSaWw+uQTKsx37aIp2R9/DPj6wtDN7W7qvSAAdXUwB4SWoiJCtFrgjTeAV16hKbQWFkBTE1BYCI2xMcTl5Rhsj4bc3FwkJCSMvGmOjrSWdwh/f2D7dmDmTFrPbWIyvGloweHmzZtDhlp39/v007tRc4ahgv2bb6BNTUVrayvCwsIIADg4OOD69euc/6JFLGbPhuVgtHREvXBBAa3ldnenbcYANF26hINffcVAEPDFihXo3bYNYrFY2PjVV4TY2tJ7f+0aja4/8QREDAOL/HxMPn4caZcuka4FCyB2dITB448Do0ejVa3GhQsXUFBQAACMq6urcOfOHd7FxYWZNWsWIxKJYPPRR7B56il6LV5esLS05I8cOcI8/fTTZIQx4PXrwIQJEGdkYNTGjbBoayM7duzA3LlzR0ZRJ08Gtm3Duc8+Q29vLxEEARcvXhSOHj1Kenp6EBAQwM+dO3dkCcSXX9I6659/ptkRjY3UJG+IQddu0bp1aDt+HC0NDTxGjWLBcVSMX7lC5+3339PvP2LxLCwsDLdv3350aNvOjkbnV60aPIwhMjMzUXHiBHqeeALdx44Bx46BEMJw3H0B2qE2cyUlNIPiPsrLy8HzPOPi4nJ3Dv3979R938xseNFHe+gQqsvLGb9HROaHuHLlCm7evMkuWLkSXEEBnN54Aw3Ll/9uRFsqlWL16tXM5cuXkZqaiszMTKxZswYPrWH/Aww+QxaAI4BSiUSyOiEhQWJqamrf09Nj7+joCKlUOjyJrl69anzq1KnjSqXSKTExUederkPH/6foxLYOHTp0/AegVCrNWJZ1dbynz7BYLIabm5v65s2bXyqVyg9EItE6mUzm4ufnx3z55Zfw9fUV5s+fz6jVar3S0tLx9fX10QMDA/y4ceOGbaz7+vqQl5fHAjQiferUKa61tZWtrq5GREQE7jcn+0dotVrk5+eDZVm0trbCysoKZmZmiIyM5DIzM9mOjg4SGhrKRUVFsQAQGRn5Pyq0XrBgAdm1axdef/11DKWAzp8/H5mZmfzcuXOZRzmn//zzz3xJSQlDCEF+WprQfPiwUCqXM0vy83mzr79mMH48rbf+29+o+Lt1i5qCGRjQyOFQXedgBBkAqm/e5BJOnxbB3Z26kA+JnLg4oK8PxsbGDADwIhGKBvdznTkT/jU1NP07JIS6WM+eTU28du4ETp8G6uqg9fNDYHAw8XN2psLc15c6Nx89SgU9ADg5wZwQvPzyy9i0aRM0Gg2am5vvuj4PYWVF073/8pe7keyoKJoGvmwZ3TbYXsvb2xvPPvssduzYge3bt2PlypVwGKzhRVQU7an80Uf0Z3Nz6p7+zjswk0hQVlaGyMhIxMfHk6+2bWM5Y2PcOH0ah5KSYGRkJJiYmBB0ddFU+MOHgZdfpj26B3u2W1lZwd/fH4WFhdAMDGDJ/v3ImDOHF8rLWTKU3s+yNLugrIzWoI8ZA87dHTkMg6sdHZC3tGDyxo0YaGtDoZsbCMvC3MEB8zduhJ2dHXng3gC0tdngYsWKFSvYnTt38ufOneNnzZrFQhCoudvKlWhPSUGnRIKsn37iWltbWZFIxPv4+DAAfa+6u7sxMDCAO5s2webZZ+Hi5YWb7u6oqakhAODj44OCggKmoKAAISEhMDUxQUBbG2T79tFab3d36iI/Zw6NWN+fyi6XA4GB6O7tpfXOQ8yZQxeFfviBOs7b2gKzZtH978n00NPTAyHk0SLP15fOA40GEIsxb948tJaWwiwhAeqzZyF2cwPDMNi+fbvWxMTkwb8nd+4EiouBpKQHNqWlpQn+/v48o1azeP99unDyyy90fM3NtDRCq0V3YSGcq6sx9bPPcIvjUHb6NCeprSX8pk1MpL09YG+PL3bs4Do7O9n4+Hh4enrS+RMfD48tW6jg37hxhKP6vaSkpGjT0tJELMvCyMiIH3pH/xVMTU1hZmamf+fOnRKlUrlILBYHW1tbP9I1PigoCCdOnJADsADQ/K+eV4cOHf+70Rmk6dChQ8d/ACqVarK9vf3MkJCQEf2e3N3dJe3t7W59fX0rPTw8QubOnWvI8zxycnKgVqvh7e1NWJaFqakp/P39WR8fH9G9buNisRgBAQG4fv06BEFAT08PAwCjRo1CamoqL5PJiOUjUkHv5fbt2zh06JDw22+/kc7OTl6hUBA/P79hsS6Xy5nMTOrjNmvWLOZ3HcQ1GuDgQeDVV4Enn6TC8+pVKjw2bqRGT62twKFDaHZ2Rsfp04Kou5tY+/sDOTkw8/BAgLc3Eff3U3EMUKdpADhxAh2HDyOpooKs3bcPMXFxkJWUIPzkScb//ffh1NlJytRqnOnvx1WGwW1LS9zy9hZuJCQQl5kzQSorgU8+QXdEBG5UV8PKygptbW1IPXcOjlu2MD3BwaiaPRtVTU0ghCA7OxvNHh449fPP3M3mZtbR0ZFfvnw5aWtr41paWpjQiAiYjR1LhXlcHK1ZnTePiqOpU2kU09QUGbGxSDcwQNzq1WBWrqSCOTaWCvOXXgL27KFiddkylN64gWvXriE8PBxTp04lD8ic16gAACAASURBVNShEkJFkI8PcG8U7/+w997hUZVr1/h69p6ZtEkhhVRSSA+BNEIIJPQSCAjSexcRVIoHj+LhYFAUPCgoKggoIKJ0FIQgoYZAeiCkk0ISQiqppE7Z+/vjTiVBPed7z+/z/V2zrosrmszs2fPsZ+ba617rXreTE6394cMUqNZqf9fT04NarUZhYSHu3bsHOzs7Uv6nTCGy3cmRkPH4Me5euoRRd++K/T76iOlSrzJyc3PVj0SRu9XcDJ7n4ebmxvrX1YHbsAEZRkbicUtLltLQIKiqq0WViQkz6t8fHMfBwsICCXfvQs/AQBymVDL/t97iWGfywhidq58fnQfPQ0tLC4MGDYKbnx/4vn1xWhSR4ewMUSZDr+pqBCUmwuTBA3A1NeBkMiKUnUOxAgLILbBtG9j48TA3N2cRERGcr68vGpOTIXv7bSS5uuLojRtITU1FRUUF19LSAsYYu3XrFiIjIxEdHY379+8jPT1dKCktVQuCwAXcuIFkb284OjlhzJgxGDFiBAYNGoS7d++iurgY/Xftwr26OpSuXIle7u6QSqX0+ZFIiLjOnNnto9Lc3Ix79+6xoKCgDjY5ejRZzletokLIokVUCBkwgIo506cDS5fi0aNHqKurE729vXsmmBxHLRN2doCRERhj0BVFcH37QjpiRFvLBuLj4wUbG5vuNuyxY+mcnz2j/V1fT+q1pyf41atZn5MnuVv29mrrb77htEJC6PHr11OLwNOnwPbt0Pb2hvm0aUjw8VGHZ2RwRpWVzNXAgLtcVQXv+fOhqq9HfmoqN+LGDURJJIK/iQmFrRkY0P7+9FMqVJiYvIhwcw8ePEDoxImYPnYsk5aUALGx1DO/fTt9rqqqaE/8+iv10f8Obt++Lerr6zMTE5PxXl5euv369Xuhui6KIm7dusVEUQwbMWKE6ncPrIEGGvyvhUbZ1kADDTT4XwCZTDbFzc3N4Pnf6+rqYvr06V1mdcnlcsyePRvHjx9nX3zxBQBKl/5H6xzl52FiYoJly5YhOzsbBQUFSE9PR0FBAZRKJXf79m2oVCq0BQD1hObmZhw4cAAcx7Fly5bBwsKi2827VCqFg4MDHj16hNjYWLz00ktdH6BWk/315k3qJ46NBaqr6W9lZUQ4BKHdMoz8fCg++wxHSkow79o1VpOYSKnaY8eSknbtGqnLVVV0493UBERFATEx0CkpQXOfPihZtAguM2ei/5tvMgDQAwBPT/yyc6fQ9OgRt/HiRcRxnJA2ZAi4mBj27NIlGJ48CVy8iKPffCOYpaZyiaGhePLkCRYdPy6m2tmxZDMz0SArS1QoFOz27dvMyspKHfT119xEtZrTvXoVxsbGXEtLCx4+fMgDQEtLC9nE33uPVMi0NCowyGTtyd4AYGlpCUEQxC+++ALLly9nBm1W74wMIumzZxOZaG6GbUUFAAq0et5CLooiSkpKoBMQgF5tinhnLFhAo7I++gjYvLn9HEaPHo2WlhbEx8ejqakJrS9ANuHWmdePHj3CyZMnMW79etg2NzN8+y3Z7QEs6dWLx65deNnGBndu3YLwr3+JV3R0mGhvLyY0NzMTExNYWVlxYkQEYs+ehdWMGThz5gyy09Lw9ief4Mflyxk7dKjLmrTDxYUCwfr0ofMGzRfX1taGubk5/Pz8IJPJUFFRgX379iHB3x8WJSWwuXAB41taIFm/HrC2JqLXSiwhl5Ol/t13YWtrC1tTU6HE15e7EhqKykWLIM/NVc+ZM4d3cXFBVlYWTp8+DUXreg4ePBijR49uC9fjAHDFxcWI6NNHeCM5mTN+7732UDtdXV1seeMNICMDQl4enrz0EqLu38etxEQAwLp162AweTKYQkHOiudaO3r16tURclZaSn3aJ07QuDlBINLd3EyFG4Cul78/wBhc9uxBfdsc7hdBpaK+bTs7eu6CBfTZ7ASe59vbFrpAKiUr++rVNGZtxgzg88+BiRNh/vHHiLt/H9UNDfxnM2ZgvqEhnAwNaU9Nngx88QVZ4l99FRYZGbCYMYMf8dJLUKlUTFdXF68rlbjk7a1OuXeP72VkhKZevdDMGJFhuZzcB2vX0n7YupXex44d9DMtjcj3hQvoW1qKpb6+4KZNg8LCArKZM4GKChrz5uzc3k4AxshJolQC779Px+whtdzKyopTKpXCwoULu31XPw9BENqunUVYWFi+xkqugQb//wT73SRKDTTQQAMN/hLYsWNH8vTp0wc4OTn96efU1dWB4zgcOHBAsLe3x8svv/yHFkmlUolPPvkElpaWoouLC+7evcuampqwefNmKBQKlJaWIioqCoaGhsjKyhJaWlo4URTh4OCgnj17Nv+iGbUAkJeXh6NHj8LLy0ucOnUqyUzV1XRjfOkSqbja2hREpq8P3L9PtuLnUFxcjAsXLojl5eWsf//+6uTkZB4A3nrrLejq6qKyshJmZmb04LIyCtR67TUis1paEEUR+/btE8vLyxkArFmzBp3V+8cLFyKa48Rca2s279VXYWdnhy/37BH9z51j9yZMUNWJIicrLeWW/PILMgIDRe/0dKbz9df45NYtODo5Yfr06V1PuKqKbsw7qfkpKSk4e/YsAKB3Q4MYkp7OHA4fpkCpV1+l4LI33+xymLKyMuzbtw+zZ8+GW1sfrL09Kf9tpDoiApgzBxf27EFSdjaGDRumjouL401MTARzc3OxrKyMlZeXc/3v3oVKWxs6K1aoQ0JCurIGQSBCYWgIrFnTrnCHhYUBANauXUvJ6V98AVy/jpYTJxAbGyvevn2bqdVqbN68GayhgcjO8OGkrPr50TWYN4/+394eSePG4UJ0NNqunVwuB379FWciItSpxsZ8/+RkuG7dCvfGRhxJS1M/fvyYnz59es+Fn7t3aUxWa793S0sLnjx5gr59+3Z52JUrV8SCggKmp6eH7OxsAMCEwYPh19QE/s4dUlS9vEjVnTyZiFm/fhCkUtQtXgzJ8ePQNTFpd2zU1NRgz549kEgkUKlUEAQB7T3/z0OtphCwkJD2AgVycqgnet06sn+DSFh9fT127drV/tRxDQ2wSE4We585wziex4MHDyAIAvLz88VHjx6x4cOHI/DLL8HV19OorzakptLork7ztdtQOXAgIqdOVb88ZgyPJ09I8X4eO3YQeV2zhloWIiKAr77q8pD9+/cLnp6e3JAhQ6iopaVFKvqmTWSFnzWLSO/8+d2PD2D37t2qcePGSTw8PEj5Tk2lglvv3kTuAwNfOHZNFEV8+umnUCgUWLlyJX2ORZEKRj/8QAW41hFxMDKifvj0dHLJAKiTSJAhleJuYiIcBwzoXgScM4eKE2+9RTPi8/Opj/7BA/pcP6fmHzhwAMXFxRg0aJBqwoQJfyhoRUZGKu7cuSOIolirVConbNmy5d4fPUcDDTT43wWNjVwDDTTQ4C+OsLAwX4lEsnHixInSf6eHWktLC0ePHlVXVVXxpaWlrG/fvjh//rw6Ly9PbOstfR6MMdy+fRtLly5lTU1NyMjIYFKpVFSr1ezHH39sv8mvr68XgoODWWhoKBs6dCh8fX25Pzq3Xr16wcHBAVGnT7NeKSkwO3OGenWdnICwMLJFe3p29DqPHUsEpFNgV3V1NQ4dOiRaWlpizpw5zMfHh7OxsUF9fb1w48YN8c6dO4iJiWFpaWlq58OHOX7TJvD79pGyBgCt1ld/f39mYGAgPHz4kCUkJAAA7Pv0AVpaYLhvHyxef51FFxcjICAAcrkcllZW7Fl1tRAUH8/3XrGCBYWGwnj5cvQ5coRJ8/Kg1tXFTY7DvHnzuge9SSREGmbMaA+DMjc3h5eXF/yTkuB98iT7ZuxY6JqZwdramsKxRBFwcGh/fFRUFH7++WcEBgaq/f39OwK11q0jBa4Njo7AmjWoy8vDwF27EKGry8mNjUVzc3OupaWFMzExYQsWLEBfQYBpaSnO19dzVlZWMDExAQAUFBRATy4HP2oUcPIkUFSECmtr7PzsMzDGIJVKERMTg8rKSqHaxYWVjRyJCxcuiEVFRaKjoyMrLy+HKIqQGxtDz9eXih2iSCp5aioR7cOHgVmzEJeWpq6qqmKbNm1i7WsWHg4PpZIbvHo1PN95B71Xr8ZjAwPU1NRwVVVVoqurK+uxB7b12uHcOeRbWuLw4cNiUlISs7W17UJ8o6OjBQsLC27atGlITEwUFQoFyykqQopCAdsVK6A/fTr1QufmUgDdqVPAhx+C9esH7WHDIDt/Hmz4cCKFEgla1GqU3biBeokEXh4esHdwgHu/fugxVZ/jSGEuKSEyWVlJBNzcnEh4Kxhj0NLSgoeHBxITExEcHIwbT55Ar7CQ/fbgAW4kJ6OyslL99OlTsampSTRrbITk+HGm2LQJvd96q6td+o03aP/179/tdGKcnFAmCKJPSgqHy5eJWIaHd91PUilw4wb1b+fkUBvHcyg+d45pGxvD9vRpOsbbbxNBHzuWVGFvb1rHadO6rwmAmJgYwdnZmTNNSaFixzvvkP18wgRyePj5kSW+B8LNGIO5uTnS09PF0aNHM47j6P3r6JD1WxDoO0ShIPfI5MmkVru7A+7uOBEbKySkpTE1z2Px4sXoNmM7JYVaOiZMoMT45GRy33AcYGtL310eHu0P9/HxQWxsLAoLC7mhQ4d2P95zsLOz44ODgyUmJiby7Ozs+devX48aMWJE4e8+SQMNNPhfBY2NXAMNNNDgL4SwsLBAiUQyg+d5I4VCkSOKYq5UKv0iJCRE+/dU4xfBxMQERUVFkEgkOHToEADwpqamPVqaVCoV4uLioKWlBX19fZw7d45JpVLIZDLcunULhoaG4qpVq5g2KZ3/XpBQYyNQUAD5mjWYkZsLk48/poCtgIDuc6ABurF+7TUiagBqa2tx4sQJ9dOnT3lnZ2dh2rRpfNuNrJOTExwcHLiff/4Zqamp8PTwEKp/+40/ZGAATJuGGUVF6BMRQf2rGRlobGxEdnY2PDw8OF9fX0RHRyMhMlI9/O9/55N8fcVrEyaILTExnIuLi9rCwoIHWsf8rF7NYeBAmDo7U2ryypWknvE8iuPj4fPjj6j7+GMYfPRRV8IjlQIFBUQkOvVI96qsJCJ04QJGFBcLMTEx4qBBg3jY2BC5aGpCSUAAzp49q25sbORnzZoFJyenrnfvvr6U/NxZuZTLYeToCF5fH7xSiSGursx37Niu6xscDMvoaAwePBinTp3C9OnToVarcfr0aVhYWAgTJ07k+uzejaqpUxF17RpYv34QOQ5KpRITJ05ETk4O9+j6dWHyZ59xhQcPYurUqVx0dDSysrLEqKgoVldXJ0yZMoXGOk2dSqRp/HhSO5uboaythXjoEG8/cSK45GRSG+fPJ9tzeDi0N24EyssBxnDs449FhULBPD09mUcnYvM8FBIJlDt24KeGBgweOpQxxnDmzBnxjTfeYM+ePUNmZqaYm5vLe3p6Ijw8HIIgsA0bNkAQBOzbt0/cv38/27x5MzgXFzqP116jMVSlpbRH794loqxQ0HWvrIShoyPm3riBk4MHw+3oUdiUlIB79Ij29bJlRFwPHiR19dAhKgiMGUOktHdvSnRfurTH9yORSMDzPEaNGoVRo0ZBHR6O4AMH0LxlC4xMTDr2wccf49GjR2KdRMIEUQTXee+FhFC/dg9oLwhs2kQ/KypIhc7PJ4XayopUcZ6n8WFyORWNAODOHSAxEVi1CuN27ULKpk0CVq/m2pLSsWhRxwv5+wMHDpCLpQfFXxRFOpO33wY2bKDPziuvUO/5/Plk6S4vp3PpAX379oWZmZn4r3/9C0qlko0ePRpDhw6lufPW1qRCf/QRFfQcHakw04pZs2ZxX331FRoaGvDTTz+pFy9eTOt66xYp2eHhdD6enuTSCAoi18f771NBplcv+h6QyYAvvwTHcQgICEBkZCRqa2vxZ/IuAMDDwwNaWlry48ePXwoLCxuwZcuWRwAQFhZmKZPJtgKYBOAHhULxtsZuroEG/7ugUbY10EADDf4i2LZt2xqZTHZ0yJAhwc7Ozr7GxsbDeJ6fMGrUKOMBAwb8R6ndbm5u3KBBgzB69Gg8efIEVVVVmDlzJjM0NOyivqlUKuzatUvMyspiL7/8MszMzNCnTx8oFArhyZMnHAC0tLSw6Oho3Lx5E42NjaKzs/Pvn5MokoX7zh2ynqamomX6dHxrbo5SY2N4hYa+MCUY9ILUpzpwIPbt26c2NTVlkydPZoGBgd1UdI7joK+vj3v37sHh/Hk2+to13B02DM0yGe7fv4+clhZB39+f1VtZ4cCBA8jJyRFjY2MZANG0ro4lZ2VxPGPiFWtrNiYkhE2cOBF+fn5dRzLp6REBuHiR/r39NtlRjY2h368fGnJywB8/jow+fWDLcV1nDL/6Ktmx28778mW6Sf/0U8DWFpaWluzatWvcgAEDoKOjAxgbIys5GUcSEuDi4iLOnz+f61HRjYigfu1OhRi1Wo2vDh7EAzc3yFpa8NJbb4EfM6YrWWEMuHABTm+9herqanVUVBTLy8sTvb29mY6ODrt27RoeZmeLV3V12YyHD6FobESpqSkWL1mCfv36oX///hjg78+0WlrgvmYNY4zB1tYWwcHBLC8vD0ZGRnBycmIwMKD+1qoqIquvvw7U16PexgY2W7fC7/33wV2/TrPKlyyhfzU11Fbw0ktAbi5chg1jTU1NyMnJEevq6gRnZ2eOMYaKigo8ePAAlZWVSE5OFs9ER7PsoCD1cm9vzm3sWNjb2+PBgwdCXFwci4yMZNXV1WKfPn2Yubk5bty4gfnz56N3797Q1taGtbU1S0lKgvnFi4KZoyPDxo10jW/cIHL8889kKZ8yhVobliyhUXAeHsgdMwaRhYUoHTtWiPDxYal5eUJNcLBgMWMGJ+vblwjfgAEUFGZoSMfbu5cU0Tt3aLTco0dE8Kqrad9oa6OmthYPHjxon2/OOTlB8tNP0A4IIKKek0O2+e3bcU5PT4yLj2eRkZGoqanpaDPgOFJnBw7stnXy8/NRXV0t+vj4cO37+913yVkybBipuLNmUcHhvfeAESNIcR44ELh9m67nnDk46+SEZhsbwdXLi2ufKd8ZMhld0+Zmcms8h5iYGLF/eTln+OabqPPxwYkTJ0TL/HxwHh5M4u4OFhJC3x+DB79Q3U5KShJramo4AOB5Xuyfmsrg40OFjLbX3LSJXAvPpbonJCTA3NxcLCgo4IKCgqhFIDOTiipjxtCDvL0p1V0mox70MWNI2WaMihD29lTU2L4dqqAgpKSmIjMzE35+fn+obrfB2NgYarVaUlJSYhwcHPxzWFhYH4lEkubn5+c/fvx4w4KCAm+VSuUWFBR07k8dUAMNNPhLQKNsa6CBBhr8BRAWFmYlkUh2rly5UruT7VXa+u//Crqttuy5c+fi888/Vx85coQHgEWLFsHBwQGCIOCrr75SNzY28p1HOzk6OqJv375cSkoK9PT0YGdnh2PHjon5+fksMTGRFRQUqAcOHMibmZnB3t6+64umpFCfY3Q0Ecr33gMcHdELwFATE0RHR4uNjY1M9wUzgAGQMnXoEJ5MmoSGhgY+NDSU+npfgCcFBfCLj4fR3/+OA1euQM0YdHV1hbVr13JRUVFc3IkTIjZtYoOXL8eoUaNYRkYGLp86hdWffw73kBDcGjAAaoUCtra26JKWXl1NCldbv3F6Oqm0gwcDR48CL78MTi6H75YtiJswAU/27xexfj3DxYtEqBgjpTo/n0h2ZiYRjz17iDQB+Pnnn9t7ydeuXct0J0yA7L33MHbhQnHIlCk9360LAqUlP2dbj27tgwaARrkcR155RXzF15fhn/+kPnBTUyJ9ffsCjY2YMmUKP2XKFKDTGKxhw4YhIiKCjRk7FibvvQcnX19Y2dmJ9nZ2HdUHHR0aFdbcjM4kq7a2VrCysuLa1chjxyiY6vPPiaQBuPHzz+rMjRu5dzw8GDw8yA6vUpFVt7GR5o2npaFh40ak+vkJuioV8/f3Z1EJCbyjoyOcnZ1x7Ngx1NbWAgBMTU2FWbNm8U46OjyCguh1bGywcuVK/vTp0yIA9ZtvvskDQFZWFvT09NTHjh3jBw8eDB8HBzjk5GBMWRn07tzhMG0azbXOyCDl0seH+tMPHqSihrc3sG0b9QODlFUjIyNRT09PnLt2LfLy8rj4+Hh14vHj8Pf3F4MmTiQ3SOee5Vdf7VC+y8vpX0oKXc/LlwHGoM3zGJ6SQmqqiwsRxW+/pd7qq1dp3JlCAUilWLZ8OQcA4eHhQnV1NdfpYtBIrVWrum2fHq3ubb9LSKAwsO3bgf37ScmvqyNVWC4nkt/aHy/T0kJLS8vvq622thSsNmpUl18LggBZaSnP79yJn1atEpiTE8vPz2fHjI3ReO8eZvXvDxdnZ5o5PmAAFWCeQ11dHZRKJZNKpVi0aBGsjIwYHBzounWe8R0Z2TEmrxUSiQQrVqzAzZs3xYKCAlb59CnM166l6xsS0vFAX19aE0dHypKIi6PPj7Y2Ke8AhTJeuQLDNWsgr62FSkdH3LVrF1uxYkV7m8YfQRAEURCEktZzW+3n56c3fvx4CQDMnTtXd8+ePdMBLPxTB9NAAw3+EtCQbQ000ECDvwZ6aWtrq3oMVvofAkc24PY77O+//x4LFizAhQsX1Hp6emzNmjV43qrOGMOA1pnQERER6vz8/PaZ3OXl5fylS5cAAA4ODhgzZgystLSAf/6TCGlAAPUpt7SQhbM1HGr48OEoKCgQv/jiC4wcOZIFBAT0fMJjxgBubgg/f14YOHAg5HL571rXB5WXg+Xl4XhuLlpayR9jjJPJZBg1ahQUZ86wmooK9G694Xf/7TeUGBuzQ6tWoUwmA69WUyp5W9r2uHEUDGViQir23LmUWP355/T/VVU0F7hPHyA9HeLp08hevlzQ691bxLVrPPT06GZ/714iJo2NtC6rV5OtdciQ9nMfOXIkXF1dcfXqVXb37l2MGTMGuZaWYv+oKIb163t+w2lptMaNjV1+XVNTIwDgXnnlFVRUVEAikTAIAqm0Pj6kzrbZorOziTw+B2NjY8yePbvjpTZsEDx27+bg49OlvxiDB9Pc707hWu7u7lxxWpqAqVM5pKWR8v7kCfVtX7sGjB6NiooKTiqVdswAFwQaXfb114BKBWVkJCJ0dJAwbRrsra05rwMHYHr9OnJDQ1G1ebPwmYMD19hphN2aNWs6ChJHj7bb6jmOw6xZsxiA9r+7urrC1dWVT7p2DbHXrgnOR49yhp6eMP7Xv3BNELBg5kxIOxNDY2OyNT98SAR72zb679OngREjwJuaIiQkhP3000+8gYEBfH194evry2dkZCAiIkJMTExk69evb58DD0Egdbgt7LB3b/rn6dmxtpWVqI6NhSIvT8TJkwymppTOrVIR6bWwIBvzc2nzDx8+7DpWz8+P1l0Uf99F8jwiIug12tLK+/YlW/XTp1RcUShI5QVlQzQ2NqKoqAimpqbQ7kndHjaMCm5r17ZnMjQ3N+OXX36BurERD6ZNg8XQoVxsbKwIAG/+8gtODxxIM6oZo+LJs2e0dhwHURRRVVWFa9euCRkZGRwAxqlUaAkNFXHjBkNrIn8X1NXR+ygt7VIcunTpkpCRkcExxvDLoUPquTk5vP7zLpLz5zvs53p6VEB0d++YMQ+Q62X0aJz/8kv1uj17eD4piR2IicHVq1eF2bNn/6m2m+bmZrVarTYKCwubIZVKX/X09Gz/Qr53755aIpGc/zPH0UADDf460JBtDTTQQIO/BjIaGhp0VCpVN8L7Pwm5XM61j24C8MMPP8DNzY17+eWX2R+9roODA5+VlSVWVlYye3t70dLSEmZmZuz8+fN4ev8+ks+eFWUVFcxgxQrIPvuMbkjHjSOLZUwMjRCaOBHce+9h8eLFXFxcnHD16lUWGxur9vDw4IcMGYIuSrdMBmH2bBj068f6TZ78YqbQ0gK8+Sa47dtRHB6OnO+/h1wuF9RqNZs+fXr782RffYXeABGP2lpg7170+/xzlMjlGPvdd/h1+HAEx8eLugcOsGcZGah2ckJhURG0ra3hnZ8PJgjgQ0PpRj04mA768CH9NDZGY3MzcnJyuPX79hERmTSJHiuKZMXNzaURVW+80YVoA0Dv3r0RExMjNjc3s+joaMTHx0Pt48OGenjQuvXUr+rqSgncz8HQ0FAEgG+//RabN2/u+MPt23QuHh6kcHt6Uj9rD2T7edi4unJXQkPheeMG9aDPmEF/yMvrNo7L4O5dlPftKyInhyzUAP1cu5aSsh0cUFxczACwH3/8UXRycmI+zc1oOHsWXxgZwbi6GssOHkTpzp3CrDlzOBcXFyR6e4tHrlxhpsXF0MnO5qylUkyWSqFydkZUazGoHYGBpBgfP95jmj2qq4GSEvi+/jp8163jPlqwANYuLuJihYLleHsjQkdHDLG2Zl3Ykbs7FYxGjKDrKopUiPngAyA2Fnfv3hWMjIwYx3Gs4ynucHd35z755BMhLi6ODRkyhAK8CgqosNCpQKFSqbBt2zbI5XJMnDgR7u7uaOjTB8ljxojDW0fTISuL7Njjx1NPdUMD7Skfn/b2BMaYWFVVxQ4dOgSe59HU1IS5776LQ0uWgHNwEGUymdA6ckpsbGzkmpub+f3796v19fW5uXPnMiQmEkHfuZMKQz/9RORaS4uKOqdO0Z62s6O9vGkTtCUSZGdnS7Kzs2FnZycYGRmJmZmZ/Lhx4+DbStZFmQyYORNs/35g3Trk5+fj+PHjsL9/HzNv3cLFrVuFFaNGcUFBQUylUoEbOhQFV692OEz09SEOHYqyGTMQ5+2tfvjwIadQKJi+vr7o7u6OjIwMcBIJxIYGlhobiwHPFSEAUNHkvfe6OTFkMhkHAJ7370ME+N/efVecYWDQ9fsmIoLWpa1g8s03QFMTHqek4LuzZyGXy9X6+vpMEARWVlnJP8vOxFSR3wAAIABJREFUhpG9PWa++iridXW5m+bm6hEjRvyhnzwwMFBWW1s7X6FQzB00aJCBTafPfXx8fHNLS8snf3QMDTTQ4K8FzegvDTTQQIO/CD788MPGt956S0dHR+e/cnyFQoGPP/64/f+HDx+O3r17o2/fvj2rUT1AFEUIgtDeh9hcUoK7K1dC1dICma4uYtzdodLTg4dMJr506hSTdCaDokg3radPk6o3ezYazcxw48YN9ePHj1l1dTVzcHBgAwYMgIGBAaysrJDx6qt4yPPilK+/Zi9MO09OJpvs9evYe/iwWF5ezlatWgXzTmFk7fDzAwoLydrcGnKFX38Fhg9H2UcfYf/lyxB4vjN5gUQiEZVKJTM1NcXKkSMh3bGD5gf3sDZbt26FTCbDu+++S6Tqk0/IiuvgQK/l50d21E4QBAFHjx5Vl5eX8+PHj0dUVBQqWpW5cZGRop6Bgci2buU8PT27Wn9zcigZecWKbueye/du1NXV4Z///Gf3NbhyhfpPIyMp6X3hH7tS7927h8uXL4vvLlzI8NFHZJ2dORPYt4+UvrZjNDej2dgY3y9ejJV793Y/0LFjaD53Dp+6uWFAQADq6+vVVUVFnDw3lxWamcHdywtpaWnob2IiTFu9mmu7Dmq1Gt99951QXFzMtS42QoqKhABDQw7BwbSnNmwg4skYjXry86NQq9Y1Tk5KgvuVK9D+5hvg7l0omppwOT0dmZmZ8KiuFkJPneIur1mDpMZG+Pn5wdbWFhzHQa1Wo6SkBAGnT0P/1Vfbx0YBACoqIG7ZgrTkZLFo7VoWMmtWt7f86NEjnD59WlAqldzcuXPhYG4O3LtHPd+dEBYWBgsLC5SWlmLdunXIzs5GXFycsPq11ziUlRHh//lnGsf1z3/S+3NxAZYvB9avR9mzZzhz9qyor6/P+vTpA5VKBbVaLfqHhbHS115DjaMjOI5r/9fS0oLm5mZIpVLcvXoV76xdS0Wd7GxS2t99l/ZqYCBZwMeN60gjLywkhfjaNSiPHcPxCRMENWNcoa4uxB4UdB0dHegVFGBcSgrSNmxAcnIyIIrwSUqC0sICU77+ukuR8f6CBUJ4nz7cxg8+QHJyMh4+fKhuuX6db5ZIoB8UJPr4+DA3Nzfqr25uxjNzcxQfOoRyMzMEBARAIpGgsbERgiDgyZMnKCsrw9OnTyFmZYny7GwhzdOTKZVKTq1WQ61WAwBmnDyJ2v79hSHHj3f/ounViwobxsZQKBSQSCRIW7cO8gsXkLJ7t+Ds4sJVVlaivr5edHR0ZM5tie5nziCpthax8fGYbmAA048/xr8zUaIzTpw40Zibm3tOqVQu3bJlizIsLKyfRCJZwRjjlErl6S1bttz+jw6sgQYa/FehIdsaaKCBBn8RfPzxx5WrV6827mID/R/G/fv38csvvwAANm3aBKn0P2wJVyqpvzQiAurhw3FDT09wHz2aO3jwIAAg+NYtKKRSjLpwocM+2wZRpH7lAweIrLm5QTQ2Rm5uLh48eKDOzs7mm5ubAQDyZ88wW19ftNmypfsdfFMTqYx79xLpAJCamoozZ87A09MTL7/8Mt3Y1teTAuvpSaqsIFDgko8P2Y3d3QGQrfXLL78UjY2NRalUymbPns1kMhmKioqQnp6O6OhoWD1+jBUJCWD373c7naqqKuzZswcBAQEICQmh9/nNN2RrDgujoLBZs6gXVColdRDAuXPnxMLCQixfvpx17klXqVQoiIxE1ZUruKWvL9q7uopTpkzh2q/ZyZNkY31O3VYqlTh79iwKCgrEtWvXsm6jyNpgakoK/yuvkNV4+3Yau6SvT1bfTqTgxx9/VCuVSrZ48WIO2dk0vmvQIOrJNzCg8KlNm5A3bRqOXrgAOwcHccmSJd2umaKlBZcWLoS1TCb4Hz3KgTFgwwY0Jyej4Ouv4ejoiEePHuHujh1YFBsLlpLS/tyoqCjh1q1bXK9evdQ1NTU8QHsY1dWkwAYGUiCWlxftq337yGbv44OiESNQ++wZbo0cCR0rK7FOLmcKhUIwlskwuLSUs1uyBHcuXRKzpVL06dOHlZaWqpuamkQArKmpiVMoFMyouhrzz53Dxfffh0QmU8vlcnh6evL2vXrhztSpGBgWBt3q6heOuNq7d6/o7u7OgquqwLKywN55B6WlpaiqqmqfX+/t7Y2UlJR2Aji0uFgY89tvHB4+RPGDB9CWy2F85w5lB0ilUCuVKMrJgfG2bWiMiMDh9evFSZMnsy6zyFNTaS/2MP4LAJRvvIGaEydgVl5OarmrK/Wnh4RQgJuzM/3s37/D+t4ZGRmAmRnEoUORb2yMO6NGqYcMH87XmpnhwYMH4HkeZmZmyEpPF/r9+iuX4umJWmNjjLx2De4TJ8LsnXe6HbJFRweZP/4I00GDcOTIEfTr1w+enp5wOHQInLMzZQUAQGUlipqacO+115Dk6wuZtrYoCAJTq9UQRRGMMcjlctHQ0FAwNjaGY24u7/rZZyiNjoahoSGkUinK8/JgevQoypcvxw/Hj8PGxgYTJ06EpaVl+/lERkYiKSlJrVAoWHNzMwcABlKpuKKqism3betQvHtAYWEhHvztb6JzcjLL271bmODhwbV99v8dtLS04MSJE41PnjypZIzdE0VxbEBAgEwikXBRUVHNSqVy4JYtW9L/7QNroIEG/1VobOQaaKCBBn8diIIgAAAeP36MmpoalJSUwMzMDCYmJtDR0YGZmdn/1Qv89ttvIgC2YsWK/4xoq9V0c71iBVmpv/wSvKkpxrSOAhswYIAqOztbwgkCsvv1w7gerOmCKKJ24UKUBgbC7sYN6O7bh4bQUBgNGwYfHx++qqpKKCkp4bS0tEQXc3PYfPklw+bNXUeECQIRRTMzdL5xNTc3J9UpJQV6Bw/CaNMmDP7hByAlBZVnz6LX4sXgliwhK/UXXxDhLy4GcnMR++QJGhoa2IwZM1jnwDcbGxvY2NjAxcUFp44cEffo6rLqsDBoa2sjNDQUbdb/iIgIyGQyMWjAAIZPPqFQtbVriXB7elIP8MOHZK9PSiK1EEBOTo44ffp07vnwN4lEAsdRo+B48iQ8jI3ZvoIC8dixY+LMmTOZnp4eEfdOSmpNTQ0iIyPFhw8fsubmZqjVapadnQ1PT8+uFyA/nwKefvqJUr9Xr6Ye3IICIlvvvEO91Y8fo2HECDyZNQtFhYW8YXU1sgYNgqu7O/VhHz9OvbgTJ9K++O035OjpwcrGRly0aFGPtv/frlxBmrMzQp8+5XDwIIWGvfkmtKVSuLZazh0dHXHC3BzqoUMhUSqpMAEgPT2dqVQqzJ07l29ubsb+/ftRWFgIW1tbeg+iSI6D334jR8Gnn1JBZskS8JMnw2HbNoRPnAinmzfZtIQEZISHcwGzZ4MVFaGlqgqJdnZs6Zw5bSSri+VXEATk5uaiJikJZrGxYom/P69Wq/HDDz9g5MiRiBw3Dg23bmHC1atkqx8ypJu93sjICLdv34YyMhLGVVVIMjVFWVkZ5HK5SiaTsd69e/MjR45EQEAA0ubPR4uWFp6NGMEdGzIEeTt2QBAE+BcWYqiODppCQ2Fubo6Cx4/x45kz0OrXT7AwM8PfJ03iMHMmKeCtBSiEh1MAW+f+YoCI8/vvQ/n3v+OwoSE2FhXRaLqvvyYV+5dfqIVh6FDg5ZfpemtpdRmdBaC9WMUyM1F5+TJsf/iB9T19Gjh5Ej4qFRXETEwwfvx4DhIJhpuaonb4cMiPHoVWJyt9G4qKivDtO+9gfv/+KC0thYGBgXpKW0igoSF9/wDUPmJjA8uoKFyYOBEoL4exsTEWLFgAbW3tdgWZkR2Eni+KwKpVsOtUTHLIygJu3oT+9u0IDQ0VL168yPbv3w8AmD9/Pu7du4eQZctQffAg5+TpyZycnFBfXw9DQ0MmaW6mz8Avv3QZ69cZtra2UO/YwcLDw6F//z6Nwisr6zqu709AS0sLCxcu1C0oKNCtrKzs4+bmBr3W3ILs7GxFUVGRDQAN2dZAg78YNKO/NNBAAw3+IoiMjHy7srJS98qVK+r4+HguPT0d1dXVKCgoEBMSElhSUhJ69+79p2e3Po/GxkZERkYynueRnJwMLy+vP20fhyiSpXTfPrqx/OILCnN6Lk3c3d2dq8rKEk3u3GGZXl6QyGSiRCJh5eXliImJEW/fvo0rV66whIQE5BUVCVEKBUsXBDy+fx8mH36I+w8figaDB3OTJk1CSEgIcx04kMHFhQh1G9luaCBV1deXgso6EfrG48fBR0TgsaUlZp46hd8Ywz1vbyHR25ulHj8OSWWlaPn22wy6umSFnjOHyJy7Owzd3ZFRUgJbPT30dnXttgRGRkYIkEhY/y+/xNMpU2BnZ4fIyEhkZ2cjNTUV8ooKrFWpmHZdHRHabdsoQKwtbKmmhgjQ5cukJp89C+U//oHrxsZs/PjxLy5+GBpCduYMPLduZYmJicLNmze5Z8+eqfucPMlJDx2C8NJLuHnzpnDy5EnW0NDAPDw8UFRUhGFUvOg+emjyZCLXr79OhYbz5+lazplDfa0vvwysWoUz586Jz2JjWa6eniB/8oTNOHUK9bNmwWTDBuoffuklSpxft44I2O7d6K2ri8SYGAiiiD4cxyCV0vWqqcGdpCQk3byJgYMHq52nTOGQkUHPl8mA1jnggiDgzJkzKK+qwqjXXyfLe6siq1AoWEVJiaDIyGBWDQ1QZmaKzYcOiWl37oiWW7cyrU8+oX1x8SLtiexs6s9duBD6Gzfifn4+6lxdxUnvvsvky5bBZsAAsLVrgfx8XFerxQqOY3lFRcK95GQhMzNTVCgUsLKyYgC1E5iYmMA4KAjO+fnMd906uHt4IDo6Gvn5+RAEASU8j+F799LaTppEWQVtdmIA/fv3Z8OHD4fjlClQhIYiOydHsLW1FZYtWybx9/fn/P39oXX2LOSurrB/+BA5KhUeGBuj0tgYurq6kEqlMEtNxQMAEfn5aGpqUhsaGnIFBQXqtzZu5L1CQlj7uLkhQ2hOeFMTqft6elRgSkig6/bKK9TaEBoKhbY27sbHI3DJEjxsaIDpa6+RFf/772l/TJxIx1y4kNoPnhud1Q7GUPD0KZIYEwO+/ZZBT48s79eukfshLg7o3x/cd99B9/FjSPbupZTy57D388/FN3buZMYffID7ycmCVCrl2wtGQ4eS1f3gQSK5y5ZBYWeHhIQEtSAITEdHRwwMDGQcx4Ex1j1xnTEqgF25Qu/r1ClqNXjzTYDjYGVlxUaMGIGGhgYUFxcjPT0d5eXlCJTLMWDLFta7tZinq6tLZF4mo5FtlpYUvtYDUlJScPz4cTQ1NaFfYKDQ9/PPORgakrPG0PCFjoOel5jByMgIVlZWXRxDjx49EqqqqmQ3bty4PmLEiJY/fUANNNDgvw6Nsq2BBhpo8BcBx3F1Dx8+NLG3t+fmz58PpVKJ1v5tBgB37twRTp8+zQ0aNAjjxo373WPl5uYiPj4eQUFBsLGxQXJyMi5evCja29ujpqYGNTU1bPfu3fD09FRPnz7994N7btwgi/HIkUQeP/jgd5ONhcJCocHUlFeIIqKionD16lXwPA9LS0vW0tIiuri4YBpZbTmFQoGGhgYYGhqCi4+H461bDL/8QkSgzcYZEUE9rhs3kiWc50nRbQspUyrphvnSJYgFBTB5+hS8VIrdGzbQ38vKOC8vL7VBVhYvGzSItbkHOI5rDx5LCg9H+MWLGJaRAbfTp4ks/fQTEc9OPfQSc3PIR47EvHnzAABDhw5FyvHjgtbBg5z5pEmQ6OiQ/bbN5toZvXoRSTUwoPVzcUG9sTGYKEKZn09kqCcEBwNlZTCIjcXq1av5kpIS/Prrr+xcdjZctLWF9B9+QHl5OcaMGQM/Pz/k5+cjLi4OZWVlaplM1nFtd+8mVTA8vEN1HTGCFODWlOc2xKWmIjUzkw0+fhxB1tbc+fPnxU9dXdmWwEDqjxcEUkafPiVyuWkTMGwY9OfNw3RLS5ZUUIDapCRUz54Nu/v30RQZiaT58/HGjz9COy2Nx6hRwIcfkjthyRIihhER2DFvHuZ+/z08Q0IE/PYbhzfeoNT18nL4PHiAQltbrl90NKp79xZf2raNPe3Vi6XJ5eL3PI+g6dPhNXIk9RN7elIvs5cXqYh5eSh56SWgokLUtrJi7fuX44CFC3EvIoJNvnABFhIJl/7VV0hMTERpaak48Pn51H37AiUlQFwcZAEB+Mc//oGamhokJCTgzp07qFUqYThgABWjBAH4xz9ovToH3E2YAPuVK7Fq1aqOBS8poZ9vvw24uID77DOw8HD0KS9XL1y4kOc4DjFXrsD+s8+wf+VKAIC3tzdfVVUFjuM6+gE5jooookjXp6WFeqvXrqVrNX8+kVRRbB+jxS5dgqy2Fp8vWQKlXI53AHIqbNxIe7YN9+71vD87oVevXqiuruZqmppgZGREhTlRpALI/v3Al18S+U5JoT3zHE6fPi0oW1q4IgcHVMTGCk+ePEH/58no0qUUuMgYEByM5qVLUTV0KD8SQGBg4B9Hrk+dSmvR1ERrdeFCR0GsFQ0NDWpdXV2+sbERWjIZUkaPFgM5rudvvaNHKQMiPh7w9+/2596txx45ciS8vb35OlGEAUCvPWoUFbsqK+l9/YcICQnRzcjImA8gDsCe//hAGmigwf84NGRbAw000OAvAp7nn8yYMcPB3d2dcRzXLZW8rq6OqdVq/N5s6rS0NERERAjNzc2cjY2NcOTIEU5LS0tobGzkgoODxZEjR7bf4O/atUtMTU3l3dzchH79+nVP7ampIeVm6VJK7505k+bM/g7UajXsfv6Zf9TaMxoYGMiuX7+O6dOnw41m3na5X5XJZB0KTUAA3ayePUukYPt2sqja2pLCplBQKNRbb1Gq8PvvU2LyyZOAszMU1dX4ycRErJkwgUGthr+/v5Cdnc3V1NRgTEAAz2dmYteIETi3bRvs7e3F2bNns4yMDHh5eeHmnTvqYWPH8sFbtxLRqK+nPmuZjJK0nzyhBG65vCMgKyYGhiUlCDp+nLvn5SX+oq2NNX/724uD3ABSWz/5hEiHtzfKNm6E/UcfQcvXF1HnzwtB48a9+MlffgmMHQtLS0u88sorXM3YsQiPiIBKpcLq1au5tn1x4sQJAEB+fj7/7Nkz6OvqErlIT+94D23o25fIwqVL7XOj6dd9oaOjI6SkpIjW1ta8kZFRx3UbPbrj+ffu0fovXkz927duwczCAvzVqzg3dizKy8sFxcCBnODriyVLlkBnTyceIJeT2vnKK0BFBa5bW0OpUMDg9ddhP3gwh8uXqZBy5AhiNm8W0pRKrtTeHpn9+oHjOLY5JAS9Q0LQG2DFx46pcysqeK/MTODQIbI8t7kTNm4Eiosx7uJFfPrpp9ylS5cQGhracR4rV+KtsDDEhoQg6/59tBw+LFrW1jLvbdu6cyueJ0W0tLT9V0ZGRpDJZAIA7uzZs6qlS5dKMHw4Ed2LF4lU7d/fQegWLOgYKyaK5Bb55z9pjxUWtheyamtrIZPJ2u3QgxsbgblzYWFtjZKSEuzfv79NYe2+ZxgjMltWRj3kxcXkMJDLKTyuDaIInddfR8CwYYh0dsa7b79Nv//pJ/p85eR0Pa69Pb2XFxT72kLLuuQ0MEZ7a9IkOp+qKiK6zzk5VCoV0tPTuenTpsFoxAici4riVCoVZrQl37et1/LlHePueB45VVUAAKtjx1CZkwOLffvIodEWIHj5ckcgoq8vnfvevbRfCwu7zakHgJEjR/IHDhxAcHAwEu7excB169jWigqEhoaiWwEGILv9d99RIeE5mJubw8jISH3z5k3++vXr4Hke7733Htjy5fSA1FTg8WMqOuXl/eF3bE/Izc0Fz/MP1Wr1oX/7yRpooMF/FRqyrYEGGmjwF0BYWBgnkUgGWFlZ9ZhW29TUhLi4OMYYw+DBg3s8xoEDB1RVVVWSwMBAFhgYCKlUytXW1iInJ4dzdHSEkZFRlwObmJigrq4OKpWq+8ESE+km/cQJSuEtLKQb1z+4EeRVKrgLAhx37sSD/fuRn58PAFAoFGJLS8uLw7rawHFECKdNo5v6b78l4i2RkPq3cydZnkeOJNLXOrYqae1a8dKxY0xfX1/YvHkz39DQAH19fe7Zs2eoqKiA3vnzwI4dWDx6NCIiIoSnT59yu3fvFpuamlh6ejqePXvGe7eNwOJ5CgnLyqL/372bCgBTpxIxfPSIZm/v2UOFiBs34PTsGYs9dkzYvn07k0gkgpGREbS1tTmO4+Dt7d3RNy2TofMMYDc3N9wODlZ/aWbGq27c4IKOHKH3/Ly9f+pUsgKXlLQr/kbLlmGulRWHH37o8tAJEyYgOzsbOTk5aGlshP60aVQwOHy45zWvrwd27SIS2br3TE1NYWpqympqakQAMDAwAID29oN2HD5MxQcDA+oJ9vaGZOdOCogDIAgCt3//fpSVlaFL8F99PV3n5cuRnZ2NkydPQqVWY8To0Wrjn37iERdHJD4/H/jsMzg8fcoNfP11HNqwQagxMmJq0Ni6OXPmMIlEgt48zxw2b0bS9OnwjY/v0lqA774DVCpoKxQYMGCAkJCQwFVXVwuTJk3ijIyMgHv3cOzMGTH/1i02aNAgYVBpKbPMzaUgrp4QEkJzsn192/uX23pnbWxsOl5YS4v2q1JJzzE3p/VSKknpnjGDbMSffgrMm9ctZCs3Nxfz58/v6jpZuBCzHByQnZ2NS5cuobGxEYwxThCEju8NUSRVfdgwavM4dYqU4Of7o/fsIQdAVhbu79snDvH07CgUBQfTPnwe//pXz+PUWlFTUwNBEKBUKrv/URSp4HDlChXRPviA9uWyZQCAY8eOCUZGRsypvp5pLV0K248+EgoLC7mKigoYGRlBsnYtkdHwcCLcdXXAnDng338fSEzEsfnz4ePjg5d0dakIZGVFx28Li9u/n4LlBIHGtvXt272Y0AozMzNMmDBBvHTpEjPQ11cn7tzJs5oaXLx4Efb29t1beTZupILTC9TttWvX8gCl+p8/fx4pKSkY0Dayrk3h/+gjKiK+8Qat8x99V3aCnp4eBEFwlclklz/55BOVIAgOSqXyjc2bN2vmcmugwf9jaHq2NdBAAw3+Arhz585mIyOj4GHDhvUYa9vc3IyUlBQwxsT2eb2dEBkZidzcXO7NN9+Eo6Mja+vT1dbWhpWVVY+92VpaWiwtLQ12dnawsbEhOe3qVeDVV8n6OmYM3VhzHN3Y/u1v1Lf5e8FqKSmQWFujvn9/xMXFYerUqbh//z4yMzNZVFQUBg4c2D2dvCcwRoQmOJiSkN94A/j8c1KdXV0pSTshASgsRE1pKRIPHGDjBg7E+F69OK6yElpSKVBUBK3mZvQyNgb79luw0FAYSKUwy8xkBTo6gsvFi5xMqURNeTkWJieLev7+THL4MKWGz59PVvZnz+jnkSNkSQ4LI7I8axY9rpV4amlpwc/Pj3l5ecHNzY21tLSIMplM1NHREa9du8bc3d0hl8up93zyZFL2Wm+mf/31V06ppYX1K1dCduQI2Xs5rus6cxyppOfOAW2q7Esv0X8/d22tra1x4cIFmBUV4VF4OFzmzcMpCwvx2q1bLC4uTigoKGAKhQKGhoZobGwEZ28P3tAQsLBo78EPDw/Ho0eP2OzZszkdHR307t0bmZmZSExMxIgRIzpeLCyM7MpeXkRkxo4F1q8npXzCBDDGcPfuXXVzczOXk5MjDBo0iPbZhg3Ahx/ijKkpbt26BSsrK3GVlRVz8vHh0KcPEUVrayLNrq6QW1ujeOZMRGZlsRXffMMMnzxh8aamzMXFBQbFxTCXy1l9drZ4xsKCBQwd2tUVwvO0f21t4fr++2zAsGGIi4sTb968yaysrGBw9Cjca2pYLM+jb9++woAVKzi2ZAmR4PffJyW6s4GY56lYkJwMBAUBAAoKClBZWSnMmzePA0ilvXnzJuzs7MAkEiK6jBFRfPdd2j8WFtSmYGPT7RpmZmYiMzMTkyZNor5jhYIS42fPhraZGaytraGtrY3m5mbU1dUhMjISiYmJQmV8PLN77TUwXV1wr71G59nS0jEXvTO2bgX69MFDPT2kpKRgzpw59L2iUpH9/ZVX6L12hrs7fQ4HD+4aWNiK6upqJCYmorm5uc3J0oEFC6h4t3EjEX1zcyA2Fo2jR+MMpeezZcuWMX1bW2DYMDiPHMkqKirUSUlJLPLSJRa8bBmtt7U1raWWFnD9Om5nZQmCvT2GBgWxmJgY2NraopeLC52fg0NHu8nSpVQgiYmh13Z2pu+QYcN6bIuxtLRk/v7+qC0v5/S/+gqWK1agrKxMbGpqEs3NzVmXEY2MUVFyzhwKD2wtvnSGIAg4cOAAACAwMJCKT4JAe8HMjIoRFy7QsZ49o4Len8znqKiowIMHD+Di4mI7ZMgQezs7O8PCwkKnoUOHHvhTB9BAAw3+a9CQbQ000ECD/8f44IMPVunp6W1dsmSJ3ouUXy0tLSQlJakbGhq4yMhINDY2om2Wa1VVFc6ePYvZs2f/W2nljY2NuH//PpydnZl5ZSX49HTqHTQzI+W4NR0aACXnOjiQMvV7c8DDwwGZDNm6unj8+LEwYcIElpqaqm5qauIAwMPDo10l/UPcvk2kbe5cusGfOpUsuV99RaTO1BRgDDIjIzzLyxPy8/JgkZjItEtKALUayZs3i8rDh6GXlsa4o0eBAQOAe/dgcPAg/A4eZBbff4+ylhZUGxtDnpfHfqmogLmLC+KLi8USCwvBdupUDuPG0Q36nDkUbtbYCEyYQOQoObnLzGXGGLS1tWFgYAAnJyfm6urKXFxcGAD1hQsXmJubG6WIT5pEffCtI6LawpgCR42CrLUfF9bWFHLVeUSQuzupvdOmEZHYv58UeCurbktXXVqqDt2xg3tqaopfdHXxtKaG6enpwdbWFiqVSoiOjuaio6PSwfhfAAAgAElEQVQRGxuLmJgYNCYlCfbff8+EWbMQERGBpKQkLFy4sH1WOWMMoiiKOTk5zNfXF+37dNIksv+3kRXGAMagMjXF6bg4WGpr405yMvfKK6/g9u3brLa2FrW1tUjs1QtNEyfi9r17EAQBMplMDNizhzEtLWDmTFTr6uLmzZtwfPddsAkToDQwwL7vvoOxsbHwyM1NbTR+PDfX1ha9WlPRZRMmoNfGjexWZCQMDAxg3XnvAlREGDsW8PCAjo4O/P39WU5OjjohIYFT3b4Na3Nz3NPVFYyMjODi4kIs0saGyJCnJ9mfOyvzgwbR+gcHA3p60NPTY5GRkVx2djYSEhJw7do1PHr0CAEBARR8J5NRO8QPP5Ca6uNDxZT+/bu7GAAcPXpUHRQUxGxtbWlhCwqo6LRoUftjbGxs4Ovri5qaGujcvYvxp0+zuw4OqGppwTm5HF5jx0Lr11/JUt15Nvj69aT87t0LeHnh+vXrajMzM87DwwPl5eWQ5eaC37GDHvc8GKNzGDWKigUgNbu0tBQtLS04duyYKJFIWGlpKVQqFRwcHDqe6+lJBTQbGyLe27cDCxYg39dX1M3OxsC//53ZtM35vnkT0mHD4OnpyQVeusQ8vvkGsu3bwXVOQmcMmDIFt+7exbLvvmOOb70FbVNT8fz588zKygrGxsbdzz81lSzl69cDU6ZQZsDOnVRYfK4I2D7b/eFDNvHSJdjs2gVXV1eWmJgoREZGcmlpaaKfnx9rD2GzsKD3aG3d43dkcnIysrKyMH/+fDgUFdF32bx55BgaPJhyJ6ZPp0KHlxc5TWJi6Dx/JyOjrKwMh1tdKzo6OsL48eNZcXExcnNz7wQFBZ1+4RM10ECD/0+gsZFroIEGGvw/RFhYmI9UKv1s0aJFOn9EQmtqavhZs2ahpqZGuH79OmsNUBOzsrJELy8vODg4/H7Q2XPQ1taGLs+LERcuMNuDB5Hn6AjHM2dg0XoT3Q11dWSVPP8CZ6Io0s3skiXIfvhQbWdnxwPAokWL+OvXryM5ORnffvvtH8/3rqggde2334jkWFgQ8V60iKyfs2cTyfvwQyAvD9yPP8Jr4kTuwIED+D/svXdYVNcaNb72OTMMDDAMvRfpIF0QpAgRNTaMLZYYY2xRk6iJqcZ7rxdTNTdNTW5ijIkxGss1xlix0JugiChFmqiAghRB6sCc8/vjpQomufm+7/n9cWc9D48CM+ec2Wfvw17vu9715jQ2YtOmTQCAE7dvs+7ubjg7O0Opra3mbG35yNBQyHv6+srj4tBy6BAabtyA1aFDeLB7N441NKDJzY3ZFhdDLyAAHoaGkDU2kpHY558T6e/qovr18nLKtDk7/+5mOCoqim9qahKOHDkirlq1isPOnX1Z6//85z+ora0FABQWFiIoKIiyYufPExH79FPKfNnYENl7/nkiKRs3UmDD2Jjq2AfegzfeQIAg8F+vXg2VTIbQoCBx3LhxvWqHvjZIjY2NkEgk6OjowNmjR1F1/jx+3bJFaOd5buHChbAZaOoFICkpifX0LaYf3LpF5x4giweA7uhovP/++4g+exbqt99G98sv96kZcnNzMfr6dXg0NwsnJ01ixkql+MK773KnX3gBH8+dK3IdHeh4990+E7uHnp5o+O03GNnZQSqVCi+++CIHgIMgkPy4sZFOeuQIhOBgAMDNmzfFvgz6QAQFUbZWrQa3ezfGjh3LHzhwAM3Llql3XLnCM0FgEwfWzNvZkUv1Dz8Ab71Fdbm981YqpePt3w+88gp0dHSgVCrF6urqvvOam5v3+ytkZ5PXwMcfkzxbpSIStW8f1fArFINMujo6Oni3gY74339P8/5RHDyISW1tiGcMJa6uWPfPf0KlViN361akvPceJn/2GbgB2en7NTW4UVyM5u5usennn1l5eTm6u7t5xhiuX78OURSh1dmJ4K+/RtRAafpAlJf33ctTp04Nkow7ODhg5syZaG5uxr59+1BdXS0sGD+e4yIjiTj2fqY7d3oOVY704GC2IDgYWkolzd+8PJK+v/IKUFQEtmED9j18iPZ//UuUSCQiz/OiSqXiLC0tmba2NtqMjSGdPRtobUWwjw8TBEH46aefOIVCodbV1WWRkZGcm5sbBcouXKC13FsO89RTQFERran16ykw0YMDBw6o29ra2KuxseDefRcAIJfLsWrVKl4QBLz//vssLy8Pvr6+/a7nUVEUFPvoIwoODoCPjw9kK1YItysr4bR6NcdGjKBgzldfUcmKiwu559fX0zMgPZ18FpYto3KFDRsGBWa6urpw+vRpdX19PYceL4zq6mpOEASo1Wqo1Wr32NjY1wGc0vTf1kCD//+gyWxroIEGGvz/hNjYWImWllby1KlTzQZlgIZBTU0NsrOz4e3tDR8fH1ZZWSnk5+dzlZWVTBRFbuHChdyQFk9/AN2mJoxes4Y1chwKVq2C0YIFcHV1HXaDLQgCdp06JVqcPs1SR4xQV9+7x2lra0NXV7d/o1leDnz2GRpefBFxcXHc9OnToa+vD5lMhrKyMlRXV0NLSwtjx44d2pIH6K81dXWlzNA//kFS6StXqNXP5MlkavT++yQvlsuJiLu4QOrsDJ3nnkPZ/ftibW0ts7Ozw+XLl8Xu7m7W2NiIaj09Tp6YCNe1a6G1fj3A82CMwc3NDampqZgyZQoaGxthbGyMpqYmsb6+nrtx4wZ8cnIgcXSEJCam3xiM58lteudOCjwcOUJZ0wHjVlRUhNu3b8PIyAgdHR2Qy+UsIyODSSQSxGVmCuKGDThx86ZQXl/PdXZ2QhRFBAQECCYmJjQwVlZ0vA0biIj5+RGh5ziS4S5fTrLY3rpPgAywGhqAffugXrUKWbdvE3nS0mJ99egDoKOjA5lMBl1dXXgHBLBSPT3B5epVzNyyhRkOdKHuQV5enhAQEMAce1sctbdTACAsbNDrDhw4gKamJni9+iqO6OhAWVeH0ceOQTVxohAUFMRCTUxgqq/PxujpsdFr1jDe2hpG8+axSzk5TCKRMCsrK9Hd3R1RUVHM/eRJ5Bsb41ZdHQCwgoICtXlBAWdw9ChlA7/6isiJszO4l15CvUqFO4zhVk2N6OXlxa5cuYITJ04gPj5eDA4OZpyxMe4ZGCCnuRlxcXEYO3asevLDh3zw1q3IGjOGXbt2TR0UFDR4Afj5UaCnpobIcnQ03QsLC6op9/SEVC5HSEgI8/Lygre3NxobG9VGRkach4cHyf+vXCHy9cQT9F6JhEoyZs4kwr1kCQVVmppwubQURUVFCA0NpfIPUaTzPv98vzt4XBygUODB8uW4UFODfH9/+L/4IiytrSGRSHA1OxtzP/wQqcbGGNFTY929YAHufPIJTkyYgLu6uqy1tRX+/v6Cu7s7Ky8vx9KlSxEVGYmQefOQJJNBy86uT9kwCK2tEIyNcUihEN38/dmSJUvg7e0Nb29vREZGMplMBoVCAR8fH2RlZSE7K0sItLHhWI/7OQC0bNwI1dat2N3aKo5++mnYTZ/OEB1NveffeYfm9/ffU3Dv7bfhGRYGFxcX5ubmxqysrLj8/HwGAI2NjeL4CRNgvWgRQ1kZMG4cbNevZw5eXrC2tuYkEgk7e/YsLl68KDR/9ZX4MDMTP9vaChkZGWJpaano7evLsbFjieRu3gwEBEA0MkJOTg4yMzO5V199lUlra+mZ9PrrfdfPGINcLsf58+fFlpYWwdnZubepN3kq6Oj0t327dg2YNw/s2WehLCtjqQ8fslpHR8F5zRrW98yoqCCC/dRT/TXfUik9B0aOpDp3S0saHw8PgDGoVCocOXKEEwSBiaIIqVQqOjg4CD4+PlxPoMdcqVRG1dfXr0hLS9MNDw8f6t6mgQYa/D+HhmxroIEGGvwfIDY21iotLe31zMzM7SkpKeMvXLiQEhUV1fLIawyTkpJWJyUlhSclJZVERUW1AUBycvIb5ubmUydPnqw1LPkcAD09PaSkpECpVMLR0RHe3t5cREQEtLS0xLCwMDasZPJxKCmhmuTZs8EHByPXwUFdXlHBKRQKuLu7D0uEr127hrziYpiGhzN27Rp3taNDyMrKQmFhIdzd3ZmWlhbVE3t7Y29RkWBlZYWQkJC+A+nq6qK2thaNjY1oa2uDlZUV2tvb0dLSQtm/sjLaZI4fTwRz/Pj+k+fkkOz6X/+ijK+TE5EOGxuSbfbAdNEiOL33HrROn2b7W1vh6uoqeHt7c01NTWJ7eztrMDKCUWSkYBUaytDdjS5RRHZ2NsrKyjB69Gj4+fnB3d0d1dXV7P79+zBqbYX/F1/g544O+C1ZgpqaGvA8D1EU8bNcLtbV1LBSExPIT51Cu60tdC0sIEgk+PHHH9WZmZlcUVEReqXahYWFUKvVuHnzJpycnMTQtDTOLCqKGz1/Pi5dugRtbW00NTWhsrJS6JMxM0YkzN+f6kpLSiijHhFBztW9NfXW1kSyo6OJxH36KXSsrTF27FjcvHlTqKioYEZGRsMTpx4wxmBlZsbM//Uvxp55Zlhpc1lZmZiTk8MKCgrUjY2NnF1jI3gHh0G9kg8fPoybN29CKpWKra2tcPfzEyybmkTn27c5l40bmYVKBc7enrL2S5aQN8CoUai5fx/Xr1/H22+/DT8/P+bs7MyMjIygu349Aj/+GApHR3ja28Ooro4zeecdiIsXQzZvHl3nlClEukeNgqy2VpSVlzNWUQG3qVPZrj170NzcjK6uLlZSUiIm3bwp5paXs5APP4TV+vVCaEQEDxsb8KNGwXfWLJw/f56rqqpCfn4+LCws+ozPoKdHjt6ff07SX4mE5uD3PebPPfXJcrkcBgYG4HmeJSUlsZFHj0I3JYVabw1jnAWep6DBokVAayvE8eNxNS8PEStXwro3AHf7Nvr6wt+5A1y6BHHdOnxbVobzwcEIXLECc+bMgWWPcR5jDB6enviXRIIaURSbGxsZ19yMlLIyodzNja3ZvBlPPPEEwsPD4eTkxNra2lBUVITJkydDRyqFlqUl0hUKtZ6+PjdcEHD/f/4jFAgCMx0/Xpg+YwYnkUigq6s72AAPVPoScPw4q87J4XLGjYOXlxcYY0hNTUVySgpuMQbjiAg2ceJEek5Mn07juHkz1cp/9RXwwguAtjZkMhkMDAxgaGiIgoIC3L59Gx0dHZg7dy7z9PSk91tbU1mFry+UAMydnODk5ISAgAB46egw919+Yerdu5mrpyenp6fHXblyhcvMzER6erp4T18fnm5uDJcv41Z1NY5evIgnn3wS9r1lHC0tg59JIG+E+/fvo7KyEkFBQf0PTU9Pysy/9BIFpHx96fk2cSLueHgg6dYthIWFsT6TtaoqKg9ZtozKRh59lhsZkc/DnTsUaDQ3B+RylNbUID8/H1OmTMHTTz+N8PBw5u3tzdG04mFtbQ1XV1fexsZGmp+fb3bhwoWdaWlp/0xJSQlMSEjIjIqKEqGBBhr8PwcTRc1a00ADDTT4K9i8efNUnucP+fj48CNHjpSVl5d3Z2Vl3e/q6grYtGnTPQCIjY1lWlpaBc7OziMkEgkKCgpEiUSSLAiCto6OTuCiRYvkxsbGwx5fFEU0NDRgx44diImJwalTpxAVFYXwHlOm/xr37pHLuK4ucOIEud/2yHuPHTuG3NxcrFy5clgZ+e3bt/H999/jeVNT2P/6K3D2LLq7u/Hll1+qHzx4wAcHB4vjS0pYq58fPo+Px8aNG4e0LgOA7du3qxsaGvpS8Lb37mGplhbw4YeU4Vu+fKgku6WFrlNLi+pMKytp0zncmF2+jPLcXMgCAmA1bhy2rFwJlY5Ob80x7OzsxKfi4pjy9m2ceucdoaCggIWHh7OQkJC+jL7Q3Axh1ixUrVyJ1NZWlPVkiAfC0tJSfOatt1jymDEonjhR9P7tN2ZdVYXTc+ZAamkpPvXUU+zgwYNiSEgIG3i/BrlGd3aiE8DevXsFQRCYrq4uSktL2RNPPIGxY8cO/mCZmfT5OY76Tu/fTxL2pUspa2pvTz20Z8wYYlyVmpqK+Ph4zJ8/HzKZrJ9ADIdLlyiT7uo65Fd3795FcXGxyPM8Ll++jEnffw+3p55iiI0FQOqLXbt2YcGCBdDX18dXX32F6dOnw7+3Xjg9na6ZMWpFBpDJlVyO7777TpBIJGzx4sWDb35zM83XGzco4xkWhriRI9UXL13iRVGEvr6+sH79eg6lpcCHH6JywwYc27YNT6lUsDE0xG2ZDN8DkOvqwt7eXpDL5aKzgwPvumEDuO+/7zeg2r4dwsqVePfDD/tObWZmhtWrVw8do8xMClZlZZEE+dAhIuED5q3Y1ISbMTFitqMjYt59l8kH1hr/DoouXsTJQ4ewIi0NCm1t6jG/ezdJ9Ssr6fvCQoAx/HvXLtTV1eHvf//74IMcOAC8+iqab9zAyZMn1SM//JA3aGrC8TfeEJcsWcJ6AwgqlQq//fabUFJSwsaNG8eCg4OBb78FgoKw5+pVCIIgRkVFMUNDQyiVSgiCgN27d6tbWlq4eZMnM8vDh6m++Pfw5ptosLbG9gcPAACrVq3C4cOH1ZaWlry/lhbMQkOh9+jzz9KSnlWFhaRuGTeOMr7Z2UBkJI5euIBrPZJ3Jycn4dlnnx084Y8eJUl4Tg4FRBobyQ3+0KFBHgiiKKKmpgY1NTX49ddfsXjxYjjcuYM7f/+7WBoYyJ7417/ohZ2dQG4ueRMMQFNTE7788ktxxowZzNPTk2Tq27ZRMCYwkObuf/4zqKXewYMHUVRUhDVr1vTXlL/3Hq1hGxtaI9nZjx9PlYrI+bRp6Fq6FB+0tODpp5+Gp6fnY9/S3t6Ozz77rANAtqWlZZBKpRIaGxurAWiJoijp6upa+I9//CPx8SfVQAMN/k+gyWxroIEGGvxFZGRknJ83b55JSEiIxNDQEI6Ojlx3d7fO3bt3p8THx++LiorqTEpKmqitrb0qLCxMJpPJuKioKGlXV5eTn5+f/bRp06T6+vpDjtvS0oKPPvoISUlJyO7ZeJWWlkIQBEyZMuV3+2wPC1GkDMuyZUQQNmwAnnxykNOwKIrIz8/H5cuXcf/+fdHOzo7JZDKIooiSkhKcO3dO7Orqwrhly5jUzg4wNwenrY2QkBDO2toaWfHxosFPP7G9jEFuYCCEh4cPm6oPDg7mbG1tUZ6aCr2HD7HUxwd8WRkZAwUGDl/7vHQpkJREGUwnJyJqpqZECh8Bs7KCUUAAFKamYHZ2sHrqKfi+9hqs79wBP306ykpL2VWFAvkKBe49eIAlq1YxNze3/mx+XR1YWxu4S5egfO01+AQGwtjYGIWFhX3n2LBhA0aPHs20li2Dy4oVCBkzhiEsDHcKCzHS01O0UCrFX5KSmLGxsXrmzJmDiEDfeSoqAAcHSDZuREBgIBs1ahR78OCBUFFRwbX19BAeZPJlY0Mk5L33qFY7IoJIiFxO2e4JE0hmP8z42dnZobKyUp2cnMzdvn1bDA4OfryMote46bnnhpB2fX19ODg4MDs7O6ZSqcT47m4uaO1a8D013IcOHRIcHR3FkJAQpquri+LiYnV3dzfc3d3pfEZG1L/ZzIzu6dtvA2++ibpnnoHD/PnsgZYW3EaPZti1q38uODmRoZiFBWXDly2Ds4sLFxoaCgsLC+Tl5TFnZ2foWFtDiI6GIioKDaGh6hO6ulxuczO0MzIwLiEBkrAw9fSlS3lXNzfOxMwM7JlnSJp76xbV3I8fD7ZoEXzGjkVYWBiMjY2Rm5s72Hm9F1ZWFPgIDSVSmJ1NY9UboGhpAVu2DAbjxrF4CwvW0Nk51Jl7GFTdvo2DR45gYkAAbC0swBYupODT11+TpHjLFqodl8kAnkdSUpIwYcIEWFlZDb6f7u7AmDGQWVjAW63mDJYvR8vMmYieOpXJZDI8fPgQgiAgPT1dKCwsxOrVqzmn3hrm558H/PzgHBODrKwsISsri8vMzERaWhoSExPR3d3NrV69mhnp6FD9+6pVw3cn6JVlv/cedMaMQVBQENLT09HR0YGmpiYYGhqKYxYtYlrR0dS7uxcPH9KXjg7w4ouU1be1BTo60Pj667hYVwfPb7/FE+fOoXXBAvXYb77h9E1MGGQyug9WVjRPRo2iLHd1NfkeTJ1K9dQD0FOnLp48eZIplUpx4sSJjDk4oLCpCXxlJbOtqyNVSVER9ebu7UHeg2vXruHGjRvM+dIl8d6OHdD28WE6n3xCr+3qIvn4d98NWpPOzs7IyMiAjo4OHBwcKJDo6UkmkNOn05j+nsqJ56HS1UWyoaGgbWXFXD77DDJRhGFU1GPfJ5VK4eDgIDE1NbWfOHGiJDAwUGpnZ2cUEBBg4Orqql9UVDQ3ISGhLDIyMv/xJ9ZAAw3+KjQGaRpooIEGfxGiKEra2togimIfiYqKipK0tLS45OXl3dqyZUsSx3FPdnR0aJ87dw4PHjxgMpkMgiCgtrZW8Pf372MzarUa169fx8WLF1FbWwsdHR1hwYIFXE5ODlpbW9XTp0/nOzo68Lgs+GPR2UmmYnPmUMbrMW23PDw8sHHjRtTX1+O7775jN2/ehI2NTXdlZaUEgGhjY4MlS5aQXDwhgYyGtmwBALi4uMBh3Diu/PhxcdLs2SwgIGAYVyWC0NWFn/buxYs//giThQvBVq6kVmO/h2efJXINEGG7e5dcnd96a9iXNzQ04OjRo7h37x64O3egN2UKdHheXO7lxYRly1B19SryCgsx+e23GadSkZkR0L+p/vXXQT2p1Wp13/91dXXRVxtvakrkxNUVju+8A8cDB4A9e1jd3/7GDObPF5e9887j/8Y6ONBnGJAx7+2DXltbi9OnT5NZ2qPYsYPan735JtVK799PhlLDBG0GYuHChfzWrVvFhoYGVlFRQRv94TB6NMle6+pIrjoMWlpakJSQwL124AAaXngBhw8fVuvr63N3797lYmJi+l7n6+vLJycn0zdnzpAsuLS0n8Tv3InkxEQkfPklgr28EPL88wy3b5N6Ye1acoG/d48yybGxg65HKpXCxcUFcrm8r50SACyZPx/j9fT4UkB8wHHs2ty5aJdKheiDB3ncvk2yZA8PevG5c+QR8OSTlIVkDL0CXqVS+fjB5DgigqWlFADasYMywtOmUS31+vXAmTNIq6gQH8THs3v37mHbtm3ddkolmzFrFo/6eiJhGzeSaVpXF7B5Myzc3BDwzDOiq40Nw+HDVCM8axbV/vr7kzP/qlUAaI50dHRwQ0j83/5GdfRvvEHS9awsaGdkYODd/vTTT/s+yZNPPtnfHaCzkxz2GYMeY1izZg0vCAJqampQXFyM3Nxc0draWpDL5TzkcgpUPA7XrpGXQY9Zoa6uLkaNGoWCggKR4zju6tWrmNHUNJQgpqfTmjh7lvrZf/ghBaaUSuxZulTd1NTEp02bhiUTJmCMjQ2vlZNDz7TsbDKa272bAkV6enRvNm2isZszh4h7UxMFI3oCBE1NTVAoFGJraytLTExEZGQkugIC2N3WVhGZmQwqFQWGMjMHX2dNDXw2b0a+vb3YffcuOhoaWIuxMQxPnybifOAAEefW1kFrU1tbG76+vmJiYiILCwuD5N136blz7BjNqVmzhsjVB6Krqws7d+5EfX09lwzANzJSPe3OHR7JyXTfh/FmAABbW1vYDlBXDFz/S5Yske/du/f7999/36u7u/sfmzZt0kheNdDg/yI0mW0NNNBAg7+IhISEvLKysuiKigqJu7u7VCKR9JpuSdzc3LTNzMzcQ0NDJW1tbWJlZSUzMjKCo6MjIiMjkZ6ezmpqalBeXi5IpVKWkZEhZGRkQCaT4cknn2QzZsxgCoUCiYmJahcXF97Jyem/z2gD1Cv49GkidmfPAj/+SIRyGHAcBx0dHSQmJsLS0hK3bt3iFAqFet26dZyPj09f724olUByMkk7e8Bv3QqTpUuZ5aPy54G4dw/M3x+2EybggLe3WOLmxiwsLPrdrQdApVIhPT0dgiDAsLiYNsy99bMjRxIhmzUL4HmUlJRgx44dcHFxQXFxMY4dO4aGhgZIpVJ0dnaiy8AAbXp6LGLmTLCQEBiMGgXXOXPAwsJImvzwIRmd+fsDY8bQ1wCYmZkhNTUVhoaGmD17NpRKZX+GurGRHJadnel7Pz/IoqPReuIEWq5eZRbjxj1+PFQqImc9G2Rzc3N248YNsaWlhQHAzZs31QMDMgNuFN3HkhIyTHr9dXJv/wODvFGjRrG0tDR0d3f/ruwUTk5EUh5xU+7Fhx9+CGl7O9yKi7FHW1t0cnJiOjo6gqurq+Dp6dl3vUqlEikpKRgdFATpP/9Jdck95+3u7kZycrKYmJTEPDw8EPP++9Czt6e624gIIk5btgB791Imd/ZsGusB9ay1tbW4cuWKOGLECObg4CDevXuX5TKGqpISTDp5ElFffcUiIiPhHBjI+MWLSeZ7+jQFi5RKyhpPmUKqib/9jbKMPfdi+/btAICsrCzx7t27rLS0tK8euw9GRkR+nZ2JcFdUkHR57Vpg/35ojxnD7PfvF4Lj43ErLIyfs2IFx5mYkBphz57+Fm6enoCfH25Mnoy4mhqW3tyMZFdXjAoMhCwigup1LSyAX36hNTxhAs7Hx+Pu3buY+Ohazs+nAEJWFpH5ZcsGKRTa29uRm5sLhUIBtVqNmJiY/r73zz9P4zNgXTPGkJiYKGZkZLCOjg42d+5cru85dPEiZZHffHMwaU5IoJ+vX9937kuXLonp6emss7OT8TwvBgUFqZ1u3ODw+ef9feM7O+mefPAB3R9DQ/Jk8PEBbt6Ec1AQl5OTA7mBgVAnimJ6djZL43moR4yAzcSJ4FevpoDMnDmk9AgNpc+jq0tjnJZGvcrHj6fM9/nzSGtoYCHHjrFuXV2x4coV1nXhglAkiqyrtVXwf/VVDrdukTljWhrdh3ffJbO6uXMhuXgRfn//O7Nevpz9dPcuIiMjIbt2jUwclyyh8TQx6V+KjTUAACAASURBVHdg74GbmxtLTk6GoaEhLGxtaR6YmJD83cuLgqOPQW+vewCYM2cOxi5dynEzZlC3hnXr6NkuldKa+ZPQ09ODj4+PtLS0NLCrq2ttSkrKOykpKevi4+NPRkVF1f3pA2mggQbDQkO2NdBAAw3+IiIjI8vj4+O3t7a2Ol66dMlVT09PamxsDI7joKenB0tLSxgYGMDT05NxHIeoqCgEBgbim2++gSAIaG1tFSsrK7m8vDw0NDRg6dKlLDIykpkNaAGUmJiI4ODgYd2h/xQuXSIDpunTqY1MXR0Zabm40IbT0rKvNzKA3npidV5eHieKIjo6OrikpCRcuXJFCA4Opp6yVlZEXDo76f+CQEZRy5ZRRulR5OeTNHnhQsDaGicY66598IBvampCVVWVGBgYOET/WFpaiuPHj+Pq1asI/+ADlLm743p9PVpbW2FgYQEJz0O9axfY5MnYsWMHACAnJwe3b98W/fz82OLFixEREQEbGxtcvXoVWlpaYnhEBOvbyE6ZQqZjKSlErk+epI3+5s2UiRIEyngHBYGlp8P+4UNk1tej8ehRNImi6ODoyNDSQmPb1kY1nT2EmzM3R0VZmeC0YweXoK8P4xEjIAgCGhsb0d3dTQ7TAHD8OI3b0qV9n3vkyJGsvLwcLS0tMDMzY97e3oPHpraW6u3/9jciOStWEMGLjKR7/DtGeRKJBDU1NUJhYSHr7OwUnJ2dh9edKhREtidOHPZ45ubmsNTWRnN4OEImT2YhISHM3d2dc3BwGBQY0NLSQsGJE2LAhg1McuRIX6/nu3fvYtu2baioqGAAMG3aNBgaGgIPHlA28+hRyjQ/8wwFQ9asISfwmBioLS3RYmOD48ePi2fPnmVaWlpswoQJCAwMZCkpKQCAJoUC0xhjMm/vvl7QACijHRlJwZW8PMqCWlqS/H7CBAri9NxDOzs7+Pv7w97enlVWVqoLCgo4xhjcHB2JZDY2Uhb4o49oLW3eDKSmkolVeDjw1VfQk8thFhDA5DY2zHHqVOxQKGD77LMw8PEhIqajQ9fk6gowBlMrK5iamqKoqAgBAQGCl5dXfw9nAwO6RgsLdE+dissqlWgXFib2SfQBCkw8+yxllU+cICfvAQGY+vp6bN++HXp6euqlS5dy48aN6yfaQL8JW4/RGgA0Nzfjt99+Y8888wxmzpzZbxgH0NofNao/0NSLadNo/YSGAgBSUlKExMRENm/ePGZhYYEbN26wyspKzpHn8eDiRSjmz4coirj55pvQcXSEZM4cNDQ0oKurC2onJ9xNToZucDC2cRy6OQ7m5uZiVVUV6+joYLNmzcLZs2dRUlKCkSNHUktBuZzIq64uEdeODuqrHRpK2WNTU2qjNnEifMLC0FBWJpQwxnm0tYn2mZlcoauretG//sVLrlwhlcennxKBDgigsfHxobkcEwPo66OlpQUZGRkYz3FgLS1k7gbQeR0dqQTkEbS2tqJr0ya03bsn1gQEMFNTU7Dp0/tbkj0GJSUlqK2tFY2MjMTg4GCm09vP29eXMvoFBUT07ez+8FgDIZPJMGrUKKm7u7tuSEiITmtrq3ZNTc2dqKio9D99EA000GBYaMi2BhpooMH/AaKiooSIiIhj586du1hRUeGRkZFhZGBgIDE3N+/bBDPG4ODg0LdRTUpKAs/zWLduHVMqlaiqqhImTZrUXzfZA0EQkJCQwMaOHQuO44Y1HPtDaGuTsY9SSVmT3vZVokgZpfffJ8K2bh2QmAhYWMDB2ZlzdnaGl5cXpFKpurq6muvu7mYRERH9Gd0zZ8gZPCaGWn61tREZGIjKStr8NTRQFnbqVMDXF8dPnOB6DcdeffVVVlRUhD179iA3Nxe2trbQ0dHByZMnRTMzM9bV1SVkOjiwMkDd9PAhCgsLhYSEBC7r3j04792LH5qa0M7zmDdvHmbMmIGxY8cyJycnMMZQWVmJvXv3QltbW5gxYwbX5/4L0HjI5bQR/u47urZly4hI+ftT8OHoUfr57t1QFhQg29QUC/fsgdLdnelVVNDnXbaMNt9791LtdI/cXcfZmcvw9ITtgQOo/OknHKypwdW8PKSnp6Ompy0VAgKIaItiX7BDKpVixIgRyMrKQkBAALMb4PQNgBQK27eT9F4ioQyWrS0RNj8/ul5Dw2ENzgCA53mWn5+PyspKNmw9Mr2IghEVFcAwbtSmpqaw2bsXNleuwHDp0uHbuAGAIKB1xw5Rb9QoptfTezkxMVE4duwYc3d3x8qVK3Hp0iXxUnY2s7e2hkFEBB5WVWGrkRH05s6FlrY2dL75Bvj3v8FeeAE/d3aKZwsLmcPatahsamL3TUzQ1dWFvLw8XLx4EY6OjkJDQwMTGUP71Klw+e03MtkKCRn82Tw8iJzwPAUt1q8ngmhlRWPX0QFDtRrKjg6Y/fILfIKDOb1t2xCwcyfqx42DbNky3Lx7F7dlMpgrlWAuLkTkSkpIIeHpSQTUxIQIV1ERZMbGCHjtNSi//Racjw9l7u/coa+8PMDQEEJ1Nb4/eBBObm6YOXMmGzKuPI9uMzP8WlIihBQUsJAxYzjm7EzZY0Gg+RgXR1n2554bItFuampCdnY2dHV1MWbMmMHHLyggx+wnnxxw+wRs2bIFoihi1qxZQ+8zYzR3v/mG1AiCQHNmwwYgLAyiKOLs2bNCVlYWW7x4MbO3t4euri6ysrIAALnNzci1tERyYiIunz8P9yNHsNvTE1nXriEjIwPp6enIzMzEtaoqZI8eDZVUisDsbNjNns2io6OZj49PXynB9evXxZSUFKanpycMqmGXy2kOREYOvnZ9fcDQEMzAAL/V1Ij1WloYs24ds9u4EYEREZzk7bdJDq6vT+Z0hYVUKuDh0V+G0IPy8nLcunVLDP3iCwZ7eyrFAOjel5TQXHsELi4uMDl8GFd0dcX4qirWVFYmuo0Zw9jf/vbY2uvdu3cLubm57Mknn2QxMTH9RLsXMhmRe09Puu6XXqL1OwzZHw6MMejq6kJbWxstLS38nTt32sLDww/GxsbaJCUluSclJbVERUV1/qmDaaCBBn3QuJFroIEGGvxfRGxs7GipVHp00qRJlgEBAcPumlQqFSQSybD9rAeitLQU+/btg0QigVQqxapVq6Cjo0PZmz+L5ctp8/fCC8P/XhDI3dbWljZlH39MpO3AAfS6TO/fv1+oqqrCmjVruL6sbF0dkWw/P6rNrKkBXnmFfqdW03EnTSIzrJ9/HnTKiooK7N+/H11dXfDw8BAKCws5juPAcRwEQYBCoRA7OjrEZcuWcUYPHgDPPgtuQM2kIAgoLy+HQ10dGuLicD0iAlFRUUPGMzMzU4yLi2OzZ8+G14AWYQDomleupM9x+zaRrby8YYeopqYGpaWlSEhIgFqthoGBAdatWwd29ixlYGtr6XinT5PM1McH6tmzkZOTg7qCAnXg99/zKdHR6mJB4FWMYfLkyf312NOnEzHudT4GcPPmTfz4448YNWoUpvU6GZeUAF98QUTqcRBFyrT6+RERb24m+fXgMUFcXBzmzJmDkSNHPv5YNTUkIz9+vN+xeyCuXKF/e53GH0V7OzBjBn50chL1xo9nSqVSzMjIYCYmJoKjoyM3oScw011ejpqxY5EREYGbtrZo6wlI6evrq1tbW3nnigro1daia8kSNWMMeXl5vHduLhyUSjjFxOCnzEyx1cSEtbe3Y9WqVTA3N0dWVhbOnj0LH8YQ+vPPMLp8GdzjZLWCQPfso49o/IqLKcMdFUVZ9Z9+ApYsQZdEgm927UK9iQm0dXSgVCqF+vp67pV9+yAPC6Oe6wCRskfIWC+++fprQaujgxvt64uRXV1EBAsK0Jmbi87Jk9H+4ovQbm6G3pYt4FNSSHnR2kqy4GnTUJaSgrNFRYLU0RHPPv00p/3ii1SK8PPPROxyc6m8oPdaBKG/vOC773DeykqoPnSIcykuhvybb+D75puU1V+wgBQAPE+f2d2diJuhIX4uK0PZvXt4e8OG4YN9aWkUqLt0iQJO69cDtbUQRBG//faburi4mFu2bBkb6DUhiiKOHj0qXLt2jVv/8ceoXLoUWhIJ6tRqUWfZMhw9epRZWVmJMTExrLi4WAgPD+eOHTumvnv+PP/s3r1I/PZbccrs2ezR6/nyyy9RV1cHhUIhhoWFiaNHj+agVtMz8IsvhhgqqtVqNDY2Qi6X49y5c6iurlavXr26XwqgUtHckMlI4bFixbBmcAkJCeg4eVKY/I9/cIPOcfgweSocPTp03FJSKLjj5ITGxkbs/uYbMUQUxbANGx77R2Hbtm2inp4eWzpACfNYCAIZ6xUXU0DUx6ff9+JPIDk5WUxJSbnN87xaFEUrPT09VXNzsxbHcTtUKtVbmzZtEv70wTTQ4H8cGoM0DTTQQIP/ErGxsXYAxgNQAsgCcG3Tpk1NALBp06as2NjY1VeuXPkxICDAYLj3az3GpOxR9BoX2dnZiWq1Wti+fTvf3d0Nb29v9axZs/ju7m6cOXNGzRjDlClT+GEzjOPGDepFPQQcR0QboEw0QAQrKwuiKKJ9yhToW1tzbba2KCkpgbe3N73GxISyZ/PnkwT75Zfp562tVK+5YQNlv4fZnDo4OMDc3FyorKzkCgsLOYAI9Ouvv466ujqUlJSwsWPH0ma6pmaQrJUumYOzszNgaAizL77AuMWLhzhnA4AoigwAjhw5AolEQo7Qly5R+7MVK/qztra2lPEvKurrl1xSUoIzZ84IRkZGXGlpKXiex9y5c3Hv3j0knT8P1tZGNbGLFpFzdloabcq7uoCHD3Hmu+/U7RcvMvXEifyR+fOFCfHx/JT2dki+/x6SgeT15ZeHkNnegMbly5fh6ekJR0dHyq6VlT3+PgKUEduwgf6/bRu1Opo2jYIiPQQwJCQECQkJYnt7++83djc3J6lza+vwZPvAAZJ3Dwe1msbY2hrdAQHCtWvXeIVCIcyZM4d3c3OjG3XnDhAbC8nWrbB+5x3MWbkS3aKI2tpaKBQK6Onp8U1NTci9cgUPbtxAfn4+Lwi0v7/m54eoNWtQPG+eMD8nh0v44gsxNCqK9fYRDwoKgra2No4ePYqrM2fijagoaO/aRST6UXAcSebb2igrbGRESozeedtTvy8FsOzjj6FSqahuOz6eS7h9W7jw2mtczPPP02ubm2kuXL8+rCLAydmZpaWloTo9HU6vvYaSkhIkdnaqGy0seDEnB1i+HO9s2AC+rY16cuvrk2FYaSkqL11C19atiOF5zmrCBHDPPQfMnElBHisrmh95eaRIcHenuezvD7i5of7LL2G4cSOuL17MLZk8GfpSKThf3z5zvz6zQZWKgj/NzSTn/+knhN68CVelEjzHkXQ+LIwyp6amNFZhYbSm2ttpLUybBrUg4NChQ+qqqiq2atUqpniE5DLGMHPmTC40NBSyOXPgoVQCX38NpxdeYLC1hbe3N3rT7hYWFhwAzJgxg28ZPx73XnkFJV9/zcr//ndonzkDuwH1zS+99BKam5uRkJDAzp8/z4KCgsB4ntZFRQURzgHIycnBqVOnYGlpibq6OnR1dfFtbW3kjSGKZAJZV0fGfl9/Te3nhnmWqjMyMPbHH7m+tdeLiROHn3OCQMHPDz4AnJxgaGiIxcHB7MwPP7DRXV3DBlO7urrg5eXFsrKyRAC/v3YBFBQVwX7xYlJTvfYaeQtkZVHA4Q+8HQBgzJgxTKlU2isUCtjb24Mxpv3w4UPs27fvxfv375cD+PcfHkQDDTQAoCHbGmiggQZ9iI2NNQDQsmnTJvVwv9+8efNCqVT6mZaWlr6jo6NaV1dXeuvWrY7GxkbtLVu2lHd0dBwC8C7HcR4KheK/SD/3QxRFtLe3QyaTwczMDK+99hq0tbWZRCLhT506Jebk5LAbN27wO3bsEOvr6xnHcbxUKsXkyZMhiuLQbHlBARGA/wKt48Yh18hIzPr8c4zu7ATv4IA3PDyYfNIkyoLX1FDGetEi2oAKAm34J0ygbM7OnUTyfydzX1dXx3l6esLY2Bjp6elQq9V48OABLCwsBrnmwtyczNCGg7ExZV972mg9it4azsrKShw7dkx0sLNj2unpJJdfuZJqMAHajAcEADduoNnKCocOHVLX1tby3t7enCAI6tmzZ/O9mfHijAys2LkT99RqWGRn03tTU/sdh7dswb1799C1di0/Mzsb/K5dQGMjh+XLSW7bS8R6+/1OnEhmWR4eVMMLwNLSEtbW1qiqqoJNXR0wdy5tlE+f/vM3ce1a+rp0iQhYSgrg5ITk9HQAYL9rkNaLGTMo89nb47sXokjkY+3aoe8RRSLhLi7A7t1YCvA9vcVph9/SQi7eV64QKZfJqI4WtCGxsrLqO5SBgQHGBgcD0dG4/tZbEGQycBwHKysrtSiK/KnwcE4aFATfn39mep98QkEiGxswxuDj44Pk5GTU19dDe9Ysyvj2uOcPixUrSII/nOdAD3R0dNAn3d26FXK1munExPTPc4WC5uJjMojh4eHM29sb33zzDXbs2IH29nZER0fzDg4OsOwJKDHGaKxHjaI3uboiOzsb586dQ9iXX4p2QUGMk0ppHerqUpCgV/WRl0c11z2KFGRno/bBA/z7q6+Al18Gx3EwWLCAMtkA/fvGG0Siv/iC7l1XF0npAWDZMuyJjYW9nh5GTZ1K9+ziRSLyNTX0bDE3J6J95Ah5MuzYgabvv0drRgYf/vTTUJSUkLSZ5+k5YWAAMAbGGCwsLGg+bNhAaoyedT9c0JAxBn19fejq6oKJIrolEpw6c0Zc9eKLg16sUCgQExOD4uJiYf/+/fD19eVGjh4NdubMILLd0dGB3raLNTU1UCgUor6+PiQSCR1v3z4KKB0/Tmv85Emq9ffyQkdHB3bt2iUGBwezoJEjcU8ux/GXXhLnGRkNvnK5nII1tbWDDcs6O6n/9oA1qExIQGhKChoaGmA+TBeAoqIipKamDmsm+Siam5tx+PBhmJmZiatXr2b45BMKpvz4IwWUPvtsiGnbo5BKpfB5JDihr68PZ2dnWV1d3Z9rGq+BBhoA0JBtDTTQQAPExsaO09LS2sPzvIUoivjoo48SOjs7X9q0aVNJ72vee++9Z+Vy+Tfz58+XW1tbD9wQanV3d+PAgQPu5eXl/wCwQUtLS/3EE09o/5VrOX/+vJCens4BQHBwsPjEE0/0ySUnT57M7ty5IzQ3N7P6+noWGBgIiUSCzMxMfPzxx/D19RUCAwM5Y2NjMMagUqmgdeHCY12lB0IQBJSWliIrK0tdUVHBK5VKISwsjA9ct44IfEsLufIClM2aOZPqvYOCKNvT1UUkUlv7d1vXALTRValUmDlzJrq7u5GTkyO2traynTt3Ijo6GmFhYQB6Nt0ffUQZ9wMHhj/Y5MlUj9lDtAZCoVAgMDAQAQEBqPD2ZlW//CI6XbzIhiOJgp0divbuxa/5+XBxccH8+fN7N7ZEEru7Ifz971BZWCApMhJFEgmeLS+Hk5MTOWgPcIU+ffq0oDtnDuMPHmRobqbrysigGsovvyT38DNn+jPGGzfSa3rr6QEolUqxqqqKHb50CQsiI/+w5OCxCAwkKam+PmBvD2HMGGHsK69wf8rZ3tyciNfly4Md2tXqoQSiF9XVRATff7/vRxzHEZHrLS0wNKT7tWTJH17CDwcOiLWvv85EHR1AEODr64vp06fze/fuFXmeZ10yGfLGjEGUri5d16uvUi9yXV2MGDEC9fX1qFm0CObV1WQe9uOPwweBGhqITP0RPviApNmnT6MrLU1MTUyEq6srM+o1kjMxoWzmnj1Dglza2tqQyWRQKBRiU1MTGzFiBHx8fCCXy4clmCqVCt9++y0ePHiA2bNnY5AZmoMDGco1N1N7LCcnylK3tQHz5qGurg53797FsWPH+ohqnyoFoPuxaRPVaIeEEHH38yN/h2++6XuZn7+/eOXKFdZsZQWFu3s/URdFOndSEnkVSKXkQv7wIYx++gkzlEqc2r8fnlevQvHyy6T8OHOGMvW+vhQUeO45MhMrKKBnlCD8boAOoLmk7ewsHJ43j3tOqWSwtaUs/ABJOcdxWLFiBXf69GmcOnVKyL55k4toaIBZczN0dXWRlpYmpKamchKJRNDV1WWMMdbR0YF169bR+Obl0fzcs6e/fvrAgb5g2OXLl1FfX88uXLgA0/XrxactLNgn/v64evUq/Aa23pJKAR8f1BQW4lBKihAdHc15enjQvNi5c1BtdsPLL+OAtjbe+Z12e6Io4uHDhyw2NhZz586FxzDlCoIg4OeffxZlMhnq6upYZWUlbGxsKDi6Zg0FyebOJXO/v4CbN2+2qNXq/NjYWKZpEaaBBn8OGoM0DTTQ4H8amzdvniiVSn97+umnjWfMmMGFhIRwUqnU8c6dO4vi4+O/jYqKageAjIyMd8ePH+/l5uY2ZGPcY14m5OfnM1tbW2716tXSP5OBGA7FxcWijo4OCw4OxsWLF8WioiLR1taWxcfHC3fu3GHFxcVsxowZbM6cOXB1dYWVlRWuX78uODk5sZycHHb58mUkJiYiOTkZKSkpyFcokFxbi061unvEiBFDdrINDQ1ITU0Vjhw5woqLiwVzc3N+wYIFiIiI4AYFFbS0KFsJ0GZtxAiSJqamkkz53XepPvhPmLhdvHhRrKurE0JDQ7nGxkakp6czgMi1VCpFZmammJycDAMDA2bq5UVEb0DGcxBkMpLQ1tUNXz/83XeAhQUOVlSgxMdHHD1u3LASzO9Pnuwe9d13nMennyI0PJwbJPWvr8fDzEzUb92KHEdHwXXRIna/rk68dOkSq6qqErUKCwWT1FQOMTEoLy/HxYsXsXTpUsZLJBR8eO45GrsnnyTp7YYNlCm7f58I0iuvDHEOdhYEFrliBVJGjxbTlErcv3+fubq6Pt6M7PegrQ1wHO64uiKttpZFV1VB+8gRClT80fFmzADi4/szrQA5qL/yCpnDDcT27SS5/fXXPmICgEjhxImUQd2yZej7HoP09HT1rVu3OPe0NNjL5eLsDRtYj8QYxsbGrLy8XO3l5cW1d3aK5xsbWWhgIPjDh4kI6uhA394ely9fxvX8fHTo6kLv4EHcl8uRUF6OIev4k0+ILM6b9/gLUqtJWuzmBnh7w9bWlrW0tIhnzpwRfX19qQc9Y9TLOSioP0M8AIwxhISEsIKCAtHc3Jz98ssvyM7OxpUrV9SCIHA2AwJGR48exa1bt7BgwQK4Pmp419lJGWylkoI4dnYU9Nm3D3j6afz755/FsrIy0c3NjXl6eop37txh9+7dE5qampiRkRHk//43lSUsX96vyli0iIJoPeNSWVmJCxcuwM/PTxg5ciQ3aLxUKpoDoaGUtT5zhrLk8fEQvvsOn06bhkYTE5RHRopBK1cyzJhBQSalkkonZs2iOR8RQWR56lQKxBQU/O68FAQBcXFxTBRFOEdEwMzDg+ZmQ8OgOaetrQ1vb2+EhYUxUSbDg6Qk4WBVFbt06ZJYXV2N2bNns5iYGBYaGsry8/MFmUyGwMBAhuxsCtjs2kUEtQf3vvgCTStWiId1dFBYWMiMjY3F5xYuZJmXL7PqKVNEj1GjWFxcHAICAgaVCXU4OWH/kSOioFBweXl54ig3Nya9e5fWwIDP+CA6WtTW0REdp00b9oPv2bMHWlpaUKtJdFVQUAAdHR3IZDLo0DXh5MmTQlJSEh4+fIjnn3+eFRYWCkZGRsza2poO0txM/ciff56e3/9Fe7BecBwnvXXr1tTu7u73MjIyno2Pj9+jMU3TQIPfhyazrYEGGvzP4v33339dJpNtnj9/vo59j6xXJpMhLCyM1dXV6RQUFOyJjY2dtWnTpi6VSpXT0NAwFb2ZzkfQ1dXFSSQShIWFDTHu+W8glUqZWq3G6NGjYWBgwB04cAA7d+6EjY0Nd+/ePZiamooeHh59GzK5XI5XX32VA4DJkyejtLQUubm5Qnh4OFdZWYng8eOxfcMGJCcnS0RRRFBQEGQyGQoLC5GZmSnU1dVxpqam4vTp0+Hh4fH4tFJFBUmgz50jcr18OUmvGxtpc7xmDTksD1ffOwDt7e04f/48mz59Og+Qs/Vbb72Fzs5ObNu2DTdu3AB6ahIvXLggeuroMISH//6gbdxIJOGJJ4bKyXftQm1DAxrt7MTg4GBu+/bt3SqVinV2dvIymUzN8zysrKy4uw8eSHRmzIDxlSuDie++fcAbb+D8e++pG995h1u9dCkHAKNHj2bHjx9HXl4eM+3o4Nx7Mk+nT58W/Pz8BpP1Xln866+TjLajg/olFxdTJrGmhsgHfXagqQlaXl7Ad9/huQkT2Ndff43c3FxER0f/KRnpcOjo6MD+/Hxx7IIFULa3M8TFEUH54QciF4/LKMrllNULCOjP1Lq6DiXM7e20kd+0qf9nDx5Qe7LNm0livHjxYBL+O1Cr1Th37hwPAKE1NXAcPZoN7HFtY2ODtWvX8jt27FDX19eTX4GpKdWpq1SAry8so6Ph9cQTwvXr17m07Gykx8Qg6MABiDIZEhQKcByHiIgISCQSVLz0EizMzHDrxg1YWlqiq6sL8fHxmDlzJhmDLVpE9+3w4b5rYIxh0qRJXHNzs3Dw4EH1smXL6NnwzjtkjAUM2zO5vb0d9+/fZ01NTX3ft7e383FxcTA0NERcXJzY2NjI6O2OgrOz8+CbIwgkB25u7pfG8zxw4gQEjkPprVvQvnuXrd66lfUoIlh4eDhu3LjBpaakCAVbt3Jt3t7iyNdeYza9RLv3Xk+bBuzdi+vV1Th27Bj8/f0xefLkwX4Q1dU0FunppNi4dq2fOM6ZgxP5+WpDfX1u/gcfsCurVw8mj4wNNioLDCQH95deQucPP6CzpASKL7+kzPInn5BKYMDc5DgONjY2QnV1NRefnCx6rV3LcP48rZ/GxmHrkQMmTAD+/W/O5amnUMtxzN3dfdAz2tvbm8XFaUlgCgAAIABJREFUxbH2oiLoHDlCgcMBPhGXL19GdlkZxnl4MGdnZ1hYWKhHmJryxuHhCN+3Dzvj4tiEnkBkRUUFvLy8+tqBmcTGil4mJojYuhWb//lP1vzKK5Bv2zZkvd0yMBDtIyKGXYTd3d2QSCRiZ2cnAwATExN0dHQImZmZ4unTp3me5yEIAkxMTFhQUBAbNWoUGhsb0drayvXVf58+Tdn0o0dJUaJUknHafwl/f3/e399fR61WY+vWrTYATAA0/9cH0kCD/yFoyLYGGmjwP4l33313pZ6eXuzSpUt1Bm7iezFlyhTt5ubmcZWVlRdiY2OnAbjX1NTUCWBY/a2VlRW6u7vRS9r/KqRSKevu7gZA7WEWL14MhUIBIyMjpKSk4Nq1a481yNHW1oaXlxe8vLw4AHCwsQE8PLDqtdeQmZWFtLQ0pKSkQCqVQltbW+3t7c0vWbIEWlpawzvm5OQQuXZ2Jrfs8HByGzYzoxrXSZPI6GrjRqrlnDeP5MMDWy09Ao7jwBhDL9HovW5tbW2sW7cO6enp4sWLF9nIkSNFqVRKZM3EpM+47DEHpbrkL76gesTubsrynTgBZGRAt6UFOt9+i9TUVACQuLq6iuPHj0dTUxPf3NyMlJQU0cDAQJQGBTFcv07HTE6m7POqVUBSEm4eP85FR0f3jbtEIsHMmTOhVquF0oQETnj4EAaZmWhtbcWTA9onDUKv1DwxkUhnbi7w1FM0XpMmUeb0wQO69rIyYM4cyLu60NHRAQB/iWhnZWUhNzdXkMvlsLa2RkhICAfGqKb+xg3KUsfE0Lnd3IaSbqmUyOPANdLcPLjN2/nzVL+dm0sKCJWKasXb2ujzSKU0jn8SvS78vbj//vsYNWnSsK/18vLiU1NTwRjDli1bIAgC7OzshGczMzlpTQ1mf/45N8PICKULF8LJxQWXvvwSge+9h10+PlBxHFJSUiCKIp44fx63ASQ8UgJRdPUqwtzcYOTvj7KODoy9fx+mA2qyGWMwNDREc/MjfOPrr0lq3+vQPwA6OjpYtGgRCgsLhdDQUE4qleLy5cvqxMRE/sCBA9DR0WEABaLmzJkzlIDt3Utk9D//GfRjYexY5M2cKeoWFWFxczPj3nuPVA2gdefh7g6P/HzuXleXuEdXl3WWlQEGBrCysiKpP2OAVIqcuDicuXkTDg4OuHr1KnN1dSVDQgA4dYoCbcnJdM99fYdkoUv09LiosWOZkVSK6FmzqHSis5OeHQNw8OBB4fbt2xwAPJ2QgEstLbjh4wPfkSMFU29vVJw9K04dN44vGDVKTBs/HgJjEASBdXZ2cmq1GpGRkXTi8eMpEKhW05p/6qmhE0Uuh1lZGcxmzhzyK2NjY9FGS0ssWbiQU02ZgoDwcAwc9JSUFLXMx4e3NzSE69ixAGM8KiqA6dNhFhQE5cWL4pkzZ5ipqakgCAL3ySefiO3t7czIyEg0mz+f+Ts7gzGGcFNTUbpjB/v0hx8ERxcXbuLEiZDL5Thy5IhYExnJFj9y3cnJyUhNTYVMJkNXVxdzdHQUysvLubq6OmzcuJGTSCRobm5GQ0MD7OzswHFc340wNDSERCJBXl6e6Ofnx1hCQn95T2QkUF8/dIz+JL766iuxvr6eAWCbNm0q/8sH0kCD/xFoyLYGGmjwP4fY2FiFRCL55Nlnn5UPR7QBMohZuHCh/MSJE6Pz8/OvA5C6ubk9ttDV1NQUPM+jlyj/VUgkEqjVagEAx3EcHB7J1KpUqj+vI+7oADZuhFxPD+PGjUNNTQ2Ki4t7e3zz/KNZIJWK2mAdOkRZVxMTysoGBFDNZS8Rq6+nutzGRvpijAjj5s2UOampGX7DC1IO2Nvbd6vV6iF/fxQKBSZNmsQuXryI/Px8BgBX58+HaWUlVoni70uo584l+fLWrUTQt23rkz7r6enh1VdfZd9++y2qq6vh5OQEU1PTPtLU16KtqoqkxNXVwC+/ECF2cEBBYSFUKhXchjEVmjNnDqdSKnF961bx+NmzLCoqivvD+uqoKPpqaqKAhkpF17x3L0k8i4qIcIPmQ3R0tHDhwgXu888/F1555ZU/Xbx9//59nDt3DhzHcXfv3sXKlSsHj6GbWz9JcXammuZ//nNodjAsjIIdmZmU8du4kcbZ1pbcynNyqLZeS4sCHStWUAuszMw/rN9/FG1tbTh//jwAwMjISDQ2NmbjKyupfvzGDbTLZNDS0kJnZycaGxsREBCApKQkAMDy5ctRV1eHX3/9lduyfTt8fHzU/pGRvE1KCtxEEcjORsi6dcCKFdiwdi1uPvMMbgJISUmBWXAwtGQyvPHGG/j/2Pvu6Kqq7eu5zy3pvVfSENKBEBIglNBb6CAgICAgRUDxqWALUURBfSgWQKRKEUQQUAg1hATSICTEhADpJCEhvbd7z/7+WKnkBvC93xjf8707x2Bocs89de+TPdeaay7WbOBVU1MDcexYVJ88iQMzZ4Ixhvs//shtbW3F2bNnS6RSaUvvamHevHkdLyQ8nDLQDQ0q5bpOTk5wcnJqfZZDhgyR3Lt3jz969IiJoojg9gqB9khKIqXBiROdPsrIyECeXI5Ra9cyWUAAPdtu3SjjzznVduvrw+DECVbfrJa4fft2S2tBUSKRwPu114Toq1fx0iuvwNHREXFxcTh27BgmOzpyt8uXGT7/nNQeY8fSXFHRw9nOzk48FxoqyXJ15VOsrBg4b6sv//lnYMkS5OXnIzU1VQAAFxcX6F+7hkmZmahycsLhkyd5iZcXMzc3l9z66CPIi4rYi3fvwuD+feR/8AGONL+LHj9+LHLOSd5ub09S9uXLyVH9yXfFokV071Sgu62t4GRhgXAjI0RIpYjctk00MTFhQUFBLCkpCdXV1ZJX1qyBRu/e9J6LiiK5e3O7PkNDQyiVSt6vXz/h9OnTkMlkzMfHRxw+fLggT00ln4AxYxA4cCB7FBsLj/R0PHz4UPz8888FExMTZX1+vuTNzZvB3nmnw3mlpKTwpqYm1qtXL9HFxUVwdnYWPvnkE/j4+LS2YdPX18eTbu8ABTD79OmD2JgYVjZ9Oow/+6ytDGjSJFIitbjo/0UUFRUxuVwOznnOv7QDNdT4H4OabKuhhhr/c5DL5Vt79OghmD2j76ggCAgKCtJwdXW145yje8ti5SloaU8EkHT35s2bUCqVSE9P53369GEdDHTagXPeWo8niqJK45m6ujooFApWWlqKVkOmpyEzk/oyT58OAHjxxRcRGhqK27dv4+DBg3zo0KGsm5ERGZDt3EnZyLfeohrO9etVO9Z+8QWR7rAw6v3cXk48cCCRxI8+IpOhDz5QeVoSiYQ9LSgxf/58aGhoQBuAjpsbtrz5Jk6ePAlPT09YWVmpzvAaGRFRzMmhDPu5c2SUlZREZGPDBgSam8NhxQpsEQTm+vHH0KupoXpkCwvKvpmaEok8fpyuDUT+Tp8+jREjRrDWHuNPQG5jg+7jxjFUVCA/P1+JLkoNOsHAgLKfjLW5NZ8+TZnk48fJWXvgQAQYGgpFDg5iVknJcxNtsbn9kpOTE3r27Mlyc3O5paWl6vOSSCi4UltL9bQpKZR9byEsWlqU/U5MJLK9bx+R75b+5MePUw3o6NEkm/7qK5Kp/oX68qrKStxOSID0m2+gMDeHhVKJV3fvZiw7u9XRmY8bh72ennhl3z5sXbsW+goFbwAY09MD5xwNDQ3w9vaGp6cnsrKycOPGDeFwXh638ffnL2VkCGzNGpJf29kBVlZw/OknOO7di2HDhtG1aGiQlBoAkpOhmZcHnDoFU2NjfNhcFpCens4uXbokHDlyRJw3b55w6dIlpY2NDbO3t+/4bBgj0mdg0CYpfwZmzZrFtm7dioaGBmzZskVcsWKF0GGsR0ZST+uIiA5yfM457t+/j+PHj8Nr/nwuGzSI4cABGsM3bxI5bFGpHDsGTVNT+Pj4iLdu3RJee+011NXVobGxUThy5AjyfvoJ644ehXTjRgCAb9++MFMq8WDjRkgaGrh5dTUz0tIisqaCaAPAhAkTJHl5ebhw4QK+++475Zw5cyRJSUnQPnIEbu+9h9u2tsi4fp1DLmcABZSMbW2B5cth7OuL1z78sPM4bWwEjh3Do6IirP76a0T1748oURQCAwPb+n+3dEvIzAQ+/7xjFwNzc5prT0IUgW+/Re3du4gYNAivvvoq0tLShIyMDPHrr79mADB48GBRT09PwA8/0LskPJzaG7Z7bmfPnsXZs2fRvXt3Pnv2bAY0J8cZo2x7UBCEV16BTUoKbJydBQAoLy9HamqqJCcrC7s//JAvlkhaJ0xtbS2KioqYg4ODOG7cOAEAGhoayFG+iwDxkxg7dixyfvsNtTExMLS1bcvWGxpSkDQjQ2WZgypwzpGWloaYmBgRgNCrVy8kJCQIISEhtsHBwbnPtRM11PgfhZpsq6GGGv9TCAkJ8dPQ0Jg9bty45yoeZYw9F8l+EpWVlThy5AgKCgpgYmLCnZyccOrUKTQ1NcHX1xcA8OOPP4qFhYWCUqkEb87cCoIAe3t7lSxl2LBhSE5O5j/88AObOHEintm+ydy8g+OzIAgYN24cLBQKlBw8yBKuXOHGDx4wvf79ydzsvfc6mAKpRFwc1d8C9J1DhzosPOHgQAvds2epl+x33wEyGR4/foz6+noolUqUlJRIjFSYR7XAsaVHcVkZMGkSRo4ejfDwcJ6UlMQAwMPDgwcFBbFO/cpffpmISGUlmXU1NtLvb94ETE3hIpMB8fHwffQIP9XVIWj0aNgBlBF3dSXSOWgQMGkSeHQ0kvLycKuykhsbG4t9+/btmkBnZ0MvJgarvvsO33//vaSgoAAWFhbPNjNraqLgxbp11P7qu+8o0KGr25Yxrq2FMjQUumVlwpw7d4jYfvQRZQidnCjTVlhIta9NTSgSRfwSHo4KOm82ZcoUQVNTE71Vmce1h0xGxPDjj6kk4P59Crzs3k0EY+tWOk8TE9rm0CHaZtYsMkVbvJjGxbhxKo3BWpGWRvfZ2JjGz6+/Qvnhh2j66SeErVmDl+Pi8GjAAIzesgWsV6+2fseXL4MFBcH0rbd4tY4OU2ho4JWvvmJgDF+98Qbe3LMHGv36AebmEB4+hJOrK5ycnFhWVhb279/PvtLX51NPn2bdGhpI+rxjB431devIYfzNNylgsGULnWdICBHa/fs71Gy4uLggPz+fhYWFsc2bN3OFQiGZpELFoVAokLZ0Ka7Hx/PRubnMtgti2h76+vp4+eWXsX//ftTV1QkNDQ1tgaWsLDrnAwdaAwI5OTkIDw8XMzIyBKlUCltbW0VQUJAUSUnAP/9JZLuggAJinJNPgEyGC+fPK3JycgQNDQ1uYmLSenlLly5FU0MDpO2z6jNmwKGwELonT7K9u3bxycOGITEgAJbvvMN7dlHOoq2tje7du8PR0ZHt3buXfffddzA3NxeVSiWu/OMfAr99G6u+/polTZmCeF9f5aBBg2huffkldT1QKDobLcrlwNy5cMjJwZHZs6HR0IBlSUmQBgWRa7ixMX2HMSLVWVl0zS1z0NmZfAWamtp6qAOk2ImPh97Bg7Ddt4+fOXOGL1myRAgICBDKysrQ2NgICwsL4qnJyaQ+2bSpQ023VCqFn58fUlJSuI+PT8d74uZGah8dHTJda/fOMjQ0hL+/P/rW1SH08GHW1NSE2tpaHDp0SBQEQRBFEYMHD+4QxFEqlSgoKOhqCHVEeDhG29pizyuv4IP2gULGSNLf1PRcuzlx4oQyKSmp5f0njBs3Dn379oVcLreLjo5O+/TTTw81NjYGq0m3Gmqohppsq6GGGv9VCAkJ8dPU1NzAGBPr6uqOAzgUHBzc2PxZX5lMdmHy5MlaXWUp/x0wxrBv3z6FXC4XCgsLBalUihEjRigHDhwoaWhoQHx8PM6ePYuamhoMHToUZWVlwpQpU+Dk5AS5XN6+vZPKDKZUKsW0adPY77//zn/99Vd2584d5axZs7omgRkZVPsLANnZtNiuq4PP0aMQAwLwi54e+8bGBoFBQbxXr15M62nmVdevkzT7559psdbYSA7Czf2nO8DEhLLLlZWoHjUKp+fOVT7IzZXo6OiIjDFwztEpE6gKTU3A2rXo5+2Nfv36MVEUERsbi/PnzzOJRMJHjhzJdHR0iOSZmVFG9eJFcvluHwBoX0fv6YmRnp7IzsnBnqtX8Y++faHTvq3Vp5+iuls3ZCxezC1v3WKV69aJryxZ8vRMtVQKGBvD2NgYNjY2ip07d0oBYPXq1VAZVBBFUhMYGhKBWriQggNz5lDGeP16Clg0m1cp587FjU8/hfD667BwdUVUUhKc/PxgYWpKJmeJiYChIereeQeyhw9hOnIk5mRnM/1+/ZiwfTttM2UKBR2sran2vraWyOaTpMbUlJ5rWVkbad6+nTLbublEHBgjkt2rF0nPW4zf5s+n7XmzMGPDBqrVjomhQE5yMrWOGjCAjL1MTABRRPSIEbimrY0hQ4bAITgYDi3n0r7Ou5k4Td+yhWWvXAmTP/7gCb//jsjz55lGQwPE2loaA+PHU8Dl7Flg1y44mJjgg2+/xe8bN7LoO3eU3WbPluDLL+nad+6krO/Zs6Qm0NMjMpWaChw5otJsCwD8/PxgYmICXV1ddvr0aZ6Xl8da+rDn5uYiNjZWmZSUJJFKpXC/d49VvfEGxy+/PDPNX1ZWhri4uFZPBhMTE/ogPZ0yyRs3IhnA8ZCQ1neFjY2NsHTpUlhYWEAQBHqYnp5ENnNyqJTkt99I9i2TQaFQ4NatW1JfX99OQcRWpc/p0/S8xo2jZ+jkBFMtLSydNImlnjqFCBsbDC0qYj2f5qMAel8tXrxYEEUREolE4JwjLCwMERER2L5+PWa9+CL6b94swdGj9H554QXg/ffpGWzdqnKf9vb2WL59O7744gseqqeH+draDCUlwODB9AznzKEA1LlzVNceHEyt67S06L/37rW9s/74g8ofwsIAiQR5eXlMW1ubZWVlwcHBoW3uKhRE1A8coADYhg00b7W0gBdfhPLwYfxRUMBdzM3RXansSPIlEsq4W1vTWFN1n5KS4JSRgTNnziAlJQVGRkbM0tKSOzg48PYdJORyOXR1dZGcnAxPT084OzujSzNOzoHXX4di/HgImppofgZtn+fnU2/0779/6jMEgBaiPW7cOPTu3bv1mMOHD5cPGDAAV65ceSkxMXHOZ5999ltDQ8PC4ODg+mfuVA01/oegJttqqKHGfw1CQkJcpFLplWHDhmnL5XLEx8cPKSgo2Lxx48azAAxkMtmoqVOnaj9rkfivgnMOf39/aWlpqdic7RA9PT0lANUqv/fee9i0aRPq6upw4cIFNDY2ory8HH+F+Nvb22PZsmUsJiYGFy5ckDx+/BjmqrLRnNNi6rffqJbxH/+gLNfw4cCqVRAYwwxRRHR0NKKjo8WLFy9Kpk2bBldXV9U9nW/fJtLasoi8do3IVlfZW7kcuRMnIjQlBdM3bZLUfPstbMaO/WvNog8epHO/cAEAZeb9/f1hYmKCs2fPit99951k7dq1kM6dS8QVIEfyIUNIEtzcr1sVHj9+jG7dukHjiXpaxZIl+GPgQJQGBfGFhw+zNUePSjB9OpHMroIR9vYkoQawcOFCaWVlJbZv345du3ZhzZo1nY6BTZsoy5WVRUGQFpSUUMYpI4Pu9+DBAGiRbW5uzqNu32aRN2+2bm6uUKBXr17oP306ysvL8f2sWRg4cCCfOWAAQ34+1WI/ekQZaJmM9nn+PBHtAweIAJSW0lhZtYrGi60tEdacHCIqSiU5cBcXU4upwkIiYW+/TYR0wQIiHgsWUKBjxgwi5TExRNrGjqXa+bfeopOOi2u73qNHwTlHXHo6tCwsMHToUJW3tyYlBRqzZ2NrSAgsLCzEzMxMAQC7GBlJGUMAaZGR1Efax4fk1rdvU7nD2bMQKisR9PHHiBk8WFIXFwet8HB6ngUF5Hadn0/Z81mz6HkYGHRJtAGay+7u7gCAoUOHsj/++IM7OzszS0tL7N69G926dRNmzpxJfb63b0fukSNs27ZtfOLEiexJD4YWVFZWIj4+HikpKQwA/FtMBouLgePHoZg+HfDywvHm/uUTJkyAnZ0dTLty/+cc8PKi3uF1dcA77wCennhw8CCW/vOfkH/yCfTNzOhaKyrI86C4mMoqpk6l7PmLL7YR048+gkFSEvyio1F4+jS/c+cOH9SFe3Z7MMZaSV58fDwiIiIAALUKBf588AB2X35JZQvZ2dRmrqWM4ylISkpCTU0Ny6ypQdSoUWJ/V1cBBw/Su2jmTBrfx48D/fpRAIgxCjq0BGI8PCjrf+AAKTSayWP37t1x//59nPzlF7wREEDEvKmJPCwmTqTvJSZSoCYri4IXpaUo/eADyAMD2QgAzN+f5nH37qSg2LKF7mv7gN6TWL0ap2pqoJGVhdGjR4u+vr4t97XDy5UxhqVLl2Lfvn38xIkTTBRF6OnpKRcvXizR1m5nJ6JUAj/8APH8eRzZuRODAgK4pJ1EHQD9HeiCaFdUVCA1NRXm5uYoLS0FACxcuBD29vadttXS0sL48eM1hg8fjh9//DGooaEhEMC5pz0/NdT4X4OabKuhhhr/NZBKpct8fX1lLTJtb29vnby8PJ2zZ8++XFZWxhcvXsyeq9b5OZGfn4+wsDBwzsE5hyiK8PLygqamZstiqcNilHMOc3Nz8c6dO4K+vr6oUCiEa9euwdbWVuVCpisIggBfX19ERkaKO3bsEN544w3oaWsTkamoIOJQVERkIieHSJSXVydiLAgCBgwYgAEDBkgOHDigOH78uFRTU5N7enqyluwd6utpAbtjR8de1/v3UyaqCxQUFOCngwfhNmqU0nD+fInhl19Sy5+nEOBOmDWro+t1M7p37w57e3tBa8cO8CNHiFi1QCqlVlaJiZRB7SIYEBAQgIiICOzdu1epqamJbt26CSYmJrx8xgxWq6+P5cuX07ObOpUIa1MTtToaPrzzzrKzqY5z4kQAJAd+5513sHnzZr5//37MnTuXaWtrE3keOpRq2d98s/N+zM2J/IaH0z7HjiVSqKmJ6dOns8jISNHKykqwtbXFlStXkJmZiQsXLqBHjx7YsWMHNzAwQEBAAINE0pbNd3Jqu+fta1hnziRCVlhI5NjCgozwqqtJ6n35MtW/7ttHslxPT/pOYSF9v7KSgi2nTpF839yc7tGOHa0EuMNzeYoZU0VFBYyMjFozumVlZThz5gwyMzMhlUrBGEPTO+9AizLmgo2NDZ8/fz779NNPYW1tjfz8fCK/DQ0kn37vPcrKM0b/OEdTXBxu7t+Psrt3MZYOSvfW0JCylXfukGT+SVfxZ8DDwwOVlZU4duwY9PT0oKOjIy5YsKB13tu88QYsFi9G/b597NChQxg4cCA3NTVl7u7uyMnJQVRUFC8vL+eFhYWCXC6HjY0NnzFjBtPV1UV8ZCTwwQfcxMeH7auvJ6d/UNszbW3trok2QNd95QqNt5oaGsfnzsElKgpRixbxhKwsBAUEsG4nTtBLats2UlwApGKwtSXyC9B9ra5u9WCws7NjWVlZnXwloqOj4eXlhTt37qBv374dsq7NnRA4AGZiYqKUSqXw9/eXQF+fMtE1NTS3hg6lGvcFC0hW3pLdbwcjIyOYmJgoS0pKJPn5+TTBW9rS7d1LAaXERPIfCA6mcb9xIz3v27fJvG/VKirbsLend9yjR9A+eRKT792DdPZszteuZayl+8LkyaSaAEgNIZdTRhxAblYW9i9dinfeeYeCpu++S9ulpLTJ4Xv16qiueRJDh8LLwACBR45AW1v7qQEMPT09rFq1inHOERMTg/Pnz0sSExPRvz2Z//FH4MgRFAUFQRTFNgPI9rC2pkBEcjIFnJpx6tQpZUJCggQgci+VSmFkZKS0t7d/pg9FVVWVACANAD766KO5MplsZGNj45rg4ODyZ31XDTX+m8E47/S+VEMNNdT4W2LLli23pk6d2qe1TU0zmhclcHV1RVNTE3r16gU3NzfU19ejrKwMoijCptn9+a/g3LlzSE1Nhbu7O2eMQUNDA4MGDWLPrNVtRkhICADA0NCQa2lpMRsbGz5+/Pjn+zLnwL17OB0czAPHjGF6H3xAi7q33qLMRt++RG5LS9uyvs9wyQ4JCcHw4cP55cuXGQD4+PjwCTY2DO+9R5LM9tm+ggLK9Kow6zl48KCYmZkp9OvXj48ePZqu5/FjqusNCKBzfJ579NVXRPJUENy7KSm4dOgQN8vIYFbLlolDhgzpeHErV1KWu9kcThWysrKQnZ2NjIwMMS8vTzA1NRUDz55llzw8MOvNN5lJ+4V+fDyRkKtX21x9W3DhAmVxv/22w6/Lysqw44svMP3oUXT/808iP336AJaWXV9zZiZl5U1Nidx+/TURAhX368svv0R1dTUACpysW7cOsvb1qP8XaGqiwIqXFxGU2Fiqf506lRbrqsoI/gIaGhrw2WefwdHRUTF//nxpQ0MDvvnmGzQ1NcHY2BheXl7QqayE52efgYWFtX5PoVBg7969vLCwkCmVSujr6yuXbdsm0Zo7F/XvvYfKAQOQZWMjXh4wgGlpaaGiooIBwNtvvw1NTU2qpxdFylA+eEBSX21tmi+bN1NrtGf1d2+HgoIC7N69GzY2NljwZGDh4EHgrbeQdOECoqOjxfz8/Nb+x3p6erxHjx4s6gmJMRNFeGRkwPrBA5wfMwZgDD169GjpQw8AMDAw4K+//vqzJ1JJCb0P/viD5uuePYgcMkQMj4gQFApFx6wl51A0NkIqCJS5jYyEdNEiIq/Nyo4rV64gOztbuXDhwtYXwp49e8SHDx+2zkFdXV2xmagJ+fn5vLGxkclkMjQ2NmLlypVtgQJRpLEdHk4O69u2UbuyhgZysr94kVQZd+/S3Pv+e5qLL7+Mqw0Nyrr589nYH34Q8PPPRBzv36fgYUMzAAAgAElEQVRShcRECpJ160Y16zIZlWps2EDvSamU3pODB1N7Ng8PZA4ciKqKClyur+f1cjlbt25dZ9+FujraZ1xcK4EOCQmBo6OjOG/ePEHluz83l+6diQlEUURMTAwYY9DS0kJdXR2kP/+MOIUCy7755tk+D+2wf/9+ZGVltRF9gO6NkRGgo4N6fX189913oqWlJebMmdP53DZtor8Rr70GgFz5r1y5AisrKyxduhSiKKpWOalAamoqTp8+Hf3222/3/+ijj4K0tbV/NjU1lebk5Gz48MMPP33ui1JDjf9CSDZs2PD/+xzUUEMNNf5thISESDjnW0eOHCl90jhLV1cXWlpaqKmpUVZWVrKYmBgWGxsrRkREsMTERNy8eRNyuRx2dnZ/6ZglJSUoKSkRX3zxRcHZ2Zl169btuYk2AHh7e8PHxwcRERGsuroaTU1N3N3dnakkTKJIztr5+WSqtXIlYGKC3Lt3YRUYyDQ/+4za2zg4kNFTi3RZS4syvAkJJKN8CsLDwzF37lxWUVGh1NTUFOy2bmV5nMP2hx86EvWcHKB3b8ogqrje06dPMxcXF0yePLntQx0dWjzfvUvS9kGDOtcLP4mNG4l0tmStWhAVBbPRo+F35gwTPDxw9uxZZmxsDDMzs7bFqpERZeIWL+6S2BsaGsLBwQG9e/dmUqkUaWlpbMKpUyzL3R2PGGMdWn1ZWbXVEA8cSIv0Fvl+aSllx/z82rYvLYVWeDiKLSxgduUK/rSyEu0nTWKsuQ67S6xdS1LXyZMps5+YSORh3rxO96u+vl7Z0qeYc47IyEgMGTLk6ft/XmRmknxWFCnDOHIk1dP26NEmM167FvD1JXKk3WVXvC7BOcfx48d5bW0tVqxYIUlISOC7d+9mBgYGytdff13w9fWFnZ0dLHR1wS5epPvQDEEQ4OPjw5KTk5U1NTWCVCoV4q2tkWxmJl6+cQMSAwNuNHSoUKmvj6KiImZqasrnzZvHDh8+LFZUVEBTU5Pp37lDgZyNG8mhvOXZhIVRQIExCjA8JWADAKWlpTh27JioUCjQkpXuAE9PYOlSWNjbw8fHh/Xp0wcODg4ICgqCv78/c3Z2hrm5OZqamuDn54fAoUPBd+2Cc2oqfp80Cd0cHFBRUYE5c+Zg5MiRGDBgAGpqapCdnc0SEhJ4Wloa9/Dw6Prdo6lJHgvjxtF42rIF9rNns0EzZiA+Pp5raGgwR0dHpKWl4dratTBdvhzfADwmMpI/PnGC5dvZiYK/PzM0NIQoivjtt9+UHh4ezMTEhB0/fhyhoaFiZWWlMHPmTLi7uyMgIACZmZm8qalJ0NbWVvr4+AiBgYGt799RFRUMUVFEenV06P2kVFKruKlTKUhWVUXEe8kSknv36EHvgp49gZ49oczIQJOTkxCRnc0G3rpFhPrXX0k+PnculZJYW9O74803aV/Ll9PcKimh+aylRUGVZcuA2bNh1LcvLAIC0H/oUBYWFoYBAwagU3tEmYzM2AYObA0+SqVSxMfHM3d3d+i0qDraIzER2LULGD0ae/fuFdPS0lBSUsIzMzN5UVERl0qlYr8FCwSTpykVVCA5ORlNTU0YNGgQ/aKykt7xbm5As7LAy8uLnT9/ntna2nbwj6iurkb03btiWn6+eDopiYWFhbH09HQ4OTnxBQsWsJb2d8+LZi8A/StXroRqamrumDhxorW7u7skOTl54PXr16dcvny5Jjw8PHno0KHqDJ8a/3NQy8jVUEONvz1CQkI0GGPrlUqlZnZ2Nrp3794hw2doaIjBVP8qAYDs7GyUlJQIpqamsLe3R2ZmJo4ePYrS0lJMmDDhuY9rYmKCmpqa51+RPIGWxc+4ceOUZ8+elZSVlQlffvklPli9msixnR1lYzQ1idDt2kUL1H/8g6TStra4WV8Pz759VWaYW7F9O5GH54QgCNCXSuFZW4vDVVVI2bNHsWjRora/F9XVtKAVBCQlJeHy5cviiy++KFhZWeHu3btgjKkmfWZmtLDdto0k12fPPrU2Fr/91pmQKxREavfsAWQyuLm5wcHBgZ88eZKJoghvb29aJA4YQFLPn3/uQNK6gp6eHmpra1GZlAQeGipyzjufmI5OWy2rREKkbNAgMhMrKqJtGhuJOPz8M7BzJ6YkJiKnXz9E/vSTcPXjj7Fq1aqnt20LDqb72wJfX7repiYKeLQb18OGDZMEBATgxx9/5EVFRR0z8f8qLl8mJcRXX1EAYdEiyjrGxVEWOz6eyMt331EA4KuvyLn7vfeoRvZpz/MJHD9+XLx//76wcuVKFBQU4I8//mC9e/fGiBEjJB0yahYWJAFWgV69ekkunDuHl7/5BgenTIFjnz5svJsbs7S0ZOzCBXiWlLCrgwcjLi6O7d69GxKJRMg8dgz6d+7A8vJlSK5coaBJWhoZ0y1bRvPF05MMrVrOoyXgMW5cp3PIzMxEfX091qxZo7o9HGOUQZ80CUhPV9kb2c3NrbW7gBgTA+f0dJyYOhUAoKOjg/fff7+V+EmlUowfPx6CIEBTU5PduHGDhYaGYpyKcwNA1/DOOzQ2p00jqXVdHdimTRg4YgS7efOmsq6uThIXFwdLXV1UvfoqZk+axCzmzmX8iy9wrqyMHzp0CPr6+kpbW1tJTU2NpKqqCv/85z9hbGzMfX19MXjw4A5Z0FdffZV+2LuXTrquDtNfflmy+a23UBsfD+2W8Xz3LgUIgbae7Pb2NMdsbOjZ3LtHY8DBoXXbTbW1EJvnSeO1a5DLZNQWbc0a2keLAkAUiVwbGVHZy4wZFCgbM0ZliQoANDY2gnOOo0ePirW1tWzevHmspSa6vr4eMS4u0Bg7Vnzg48PL7exQWloq6devn2hmZqY6DVxdDaSm4u7duygoKBCWL18OIyMj+rtRWUnn1uJp8Jxo7jPPbWxsWOt1ZmVR2UdzGRVAgWYTExPFqVOnJK+//nprQCY5ORkRBQXC2u3bYXD6NIqqq+Ht7Q1ra+t/6e+ZpaUlJk6cqP/rr7/erq+vh4GBAaysrPDmm29qPnjwwOfq1as7Kyoq/rFp06YYxhhrbGzcEBwc/OhfOZYaavzdoM5sq6GGGn87hISEsIiIiFUxMTHfhYeHb+GchxgZGfVzcXGRx8TEiFevXmWmpqYwNzeHQqHoJIUzNDSElZVVa79SIyMjuLi44OLFi0hOToaHh0fXLq/tIJPJcP36ddZM5P86RBG4exc2xcWCh4EBzHfuhG5lJZz374dw4wYtPisqaFE4ZQqR0x9+oDpcT09g+XJEx8Zyz969mY6ODrKzs1FbW4u6ujrI5fK2rIylJS3EfH2BV1/t2PqmHcLDwzF48GDU79rFzWJjBbsrV9Ddzw9hYWFCdXU1ampqUFBQAFl8POqGDEFobCyuX78ObW1tFhkZifT0dDE2NpYFBQV13S5NKiUi7OpK5GP4cMoUqYKxMZH6FmJSXU2Effp02kczPDw8WGxsLE9OTmZGRkawbJFp19SQCmDCBApYPAWnT59GVVUVhr/0Em4YGwsDJk5U7STOGBFsQ0N6Jo8eUZaLMap97N2bpKzBwZRJA2BgYICePXsiIyNDGR4eLkgkEpibm6uWfBsa0sLb2ZkIhkxGxOCHH0juunBhB5WBRCKBmZkZS0xMbHUn/suku7aWzM4MDCib3rcv1Z7OnNlGnh8/pmDDsGHA6tVEOs3M6Dl4egIPHxJZtbMjsvQcyM7O5hKJhPn5+WHPnj3c0dGRTZ06tfN9SU+nLKWKvu12dnZIT05W2hQVsTH79zMnFxemp6dHAZeffwYiImC/di0ePnzIx4wcyQa7usIpPx+1f/4J6xUrwFpaOIWF0TwbN46I3uLFJBOeNo0+j4yka01OpizqggXIzc1FWFiYGB0dzTw9PeHq6to1UTEzo/E7YMDTAxIXL4KtX49zS5fCyskJ5eXlKCwsRGBgYIfNBEFAjx494OzsjPDwcOTn5yM8PLz1nlpYWHTY/vHjx9DQ1sYlV1ecf/gQvfbvB37/Hb8ZGqKioUHIzc3F1KlTMQ6A8auvwrCiAtJHjyBbsQKubm6Cv78/Hj16hKSkJMY5R1lZGTw9PTF37lzm6OhIFG7XLno3nThBpHbVKuDDD6mV3fDhuCYIyASgNWYM7BcvphNrKXFpj+PHyYBx8mTK1Do4QOzbF0wQWtUjWVlZKC8vR0BAgLJ79+5da50ZazM2NDWlcggzM3qeVVX0Hur0FYaKigrU19ez3NxcFhgYiMuXL+PEiRP82rVrLDMzE+PS05mVj4+g7+8vVFRU8PT0dOHmzZvitWvX4Obm1rGzQ/fuECdPxqGjR0V3d3fRy8ur7Xw1NGiuPcffmxacOHFCeeLECaGuro6NGjWK5vv771M9+bvvdlLyuLm5CWFhYYxzjtzcXH737l3ExsYyv4ED8UJGBqwDA9F92DDoPUt18wyYm5vDy8sLsbGxCAgIgKamJgRBgJmZGfr06SM3Nja2tLa27iuVSnuXl5frDBo06I9/64BqqPE3gZpsq6GGGn87REREzDcwMPgqKCioW2BgoObIkSMFf39/uaurKwYOHMg0NDQQFhbGjY2N2fbt2xEXF4fk5GTR1taW6ejoqJTH6erqwt3dHX/++Sd/9OgRc1WxCHsSMpkM165dQ//+/TvLDVXhwQPKDJ4/T72KzczIXKqgAA+9vPi9ggJmPH06NN96CzrLloGZm1O20MaGSJeZGS1gAwPpvw4OcJ41Czwuju15/JgnxcXhTkoKv337NiIiIlhGRoaypqaG29vbCzA2pszWkCFdyqqvXbuGwYMHQ/nll6zMyorZTJwIDQ0N2NjYIC4uTszOzuY5OTncedMmdrWkBFUWFsqZM2cKw4cPR1RUFCorK5mfnx8GtCPCKsEYkac+fUgmOnBgmwFRCzhva1klCFQrqaNDdbQ+Ph02FQQBAQEBLC0tTZGZmcm6d+9Oi11zc5IG19URCewCKSkpiI+Px6hRo5AXGwuTefPQ62nuwXRQ6iPu70+EYPt2CgIsXEhk7Yl7rKOjAz8/P6GwsJDHxcWxW7duobS0VMzLyxO7desmtLREE0URVTt24NKff/Jz2dkoLy/nxcXFTLdfP2jq6hI5YKwDYdPX18e1a9fw+PFjJCUlITExEf369Xu2DPTePZKLf/MNtUqaP5+Ivptbx/O/e5dqtVuyhnPmEHFpIcUWFiTz1dIiU7WDB4mAP2Px3tTUxBITE0W5XI60tDQsWLCAqZxHuroUeFJF4ouL0ePWLWGHtTXT1dfnVlZWbXLqwYOBKVPACgvhOWAAM3rxRWhevAj++efYX1aGJoVCdHJyou1XrqR/lpYU6Pj4Y5I0t6C5fVN9WRlSk5LE/ZmZzPuVV1Amk3G/l19m/fv3f/rNZoyI4mef0XmpejapqRRU2bIF3mPGwM3NDc7OzoiPj4etrW2XiohevXpBX18flZWVyM/PZ3fv3kV4eDjCw8NRVFSEK1euiBERESwiIgK5hYWora2FYWwsLvj7wz4lBSMNDRG4bh3sJBIKgBUUkEHfli2t5ymRSODm5sYCAgIQOHAgAsrK0GPSJLD16+mcZ86k8R8QQPO6Vy/yNpgzB/D1RUZWFn6/cwd6+vpIT0/neXl5cHFxYSqDmhcu0NhpDmJG376NX0QRtbq6otPlywymppDb2CAlJQU5OTmCSrl3V1i3jgj+5MlUD56bS67hHR4VQ8+ePVFcXAzOuTInJ0e8deuW0NTUxIKCgjBq1CgYr1gBwz59YCuVot+IEczHxwdmZmasuLgYUVFRPDU1lcXFxSnT09OF+tpaWLzwApKGDsXMefOEDsHfP/6gVmeqjBe7wPXr11FVVcUA4N69e3yQiwuDmRkFgZ5QTAD0d6q8vJw/ePBALCsr4/fu3RM0NDT4Sy+9xNCnD5UmtS+Z+TegoaGB27dvizKZDPb29q2DXBAEmJubw87ODllZWU0FBQVhgwcPvvx/clA11PgPh9ogTQ011Pjb4ZNPPvm8X79+b4wcOVLlCkuhUOCTTz5Bz549UVRUJI4YMUK4c+cOT0tLY0qlElOnTuXu7u4qF8fh4eE8IiKCmZiYKJYvX/7MdMNnn33GlyxZ8nwS3oEDyfX4xAnKtPbp0+rcfObMGWV8fHzr9VhbW2Pq1KkoLy+Hs7MzEUZLS6qftbJqrb/+dtMmsbG8XBhiY4M+774LVlICNDbiUU0Nzp07h6qqKnHNmjVtq7v336f9NJvitMc3a9Zg5aNHuDB3rlhaXS3MmTNH9XWEhxPJbNfSSqFQICkpCWfPnsXixYvxZGatS1RVUdus+fMp695CQkpKSMbbIuu3saEs2auvdrkrURSxY8cOsba2VnjzzTeJbD56ROT30KFWwl1QUIAbN27wKVOmsOzsbBw5coSPGjUKpaWlvOzAAaFu0CDMX7Lk+WsWnZ2JtIaG0jHq6ynD/RSEhoaiuLgYBQUFolKpFMzMzJTV1dWSsrIyyKVSPsbFhQk9eyIuLk5ZVVXFGGPstddeY9LVq2kMRUZ22F9NTQ20tbVx7949nDx5Eo2NjXB0dORz585lHRb3nAOXLlHAwsODstRvvfX0bOvbb9P1/fIL/fzpp+QfcPhw523r68nE6sgRynT27KlSVaBUKpGbm4t9+/YBIBn1jBkzVB+/ro4I/JIlnT/75Rdg0yakHjmCEydOYO7cuR2d/bdvp7KL/fspY29tDVEqxaFDh3hubi6mTZvGXnB2pox9WBgFUZqaiGxbW3fo9V1dXY0DBw7w+vp6GBsbs5H378Nm1Soyx9u+nUo/ngaFAnBxIaM8J6eOn929S0Two486kb/t27fj8ePHAIDXX3+9VZGjCrm5uTh+/Djq6uogCAIEQUBtbS1ee+01aGpqQlNTEzU1NUhISODu7u7MuG9fMKWSyHXLfHnzTTLPcnCgAN3du3ROs2ZRC6sZMyggk5ND11xTQ875XaC+vh7V1dX48ccfuUwmY6NGjcLNmzfFwsJCJggC19DQ4IMGDZL06dOHvhAeTsEcd3c8ePAAP//8MwICAhB1/Trm3LyJ+qwsfnzCBKZhYIDa2lq8/fbb0OqqLd+TiI4mxcwLL9D5f/EFZbsXLepkIJmTk4ODBw+iqakJ3t7eSExMBEDEccmSJbBcupTGe3NrQgBoamrCjRs3RACCRCJBdna2mJeXJ4zJyhKdN2wQdJ5s03j4MNWRt+8U8AxUV1cjOjoasbGx6O/kxAM3bGA4doyu4znw+PFjGBoaQi6XU4u+b78lwv9/AIVCga1bt6K2thZvvPFGp3IJAPjyyy9rqqurXw4ODv71/+SgaqjxHw412VZDDTX+dggJCXlBJpMlvPrqq1pdkdyjR48qS0pKMGTIEIl7u9YmsbGxuHz5MhwcHBSNjY2straW+fv78969e0sAIgGpqak4d+6c+I9//OOZVqxffPGF2L9/f+HRo0fK8vJyBAYGSpy7qo++epXa9/z0E7WGGTasw8fp6ekwMTFBbm6ueOrUKUGhUAAAgoODadGrrU0L9u3bqc5v1qyO+8/KAhwcwB0c0DBlCjJefhlXQkP5a+vWtbHGL76gdjoLF3Y6vRPTpmGyjg4uvvSSWFRUJMydO7fzNfzyCzmTHzig8hJPnz6tzMvLw/Lly5+/eLekhBygW0zItLSAo0eBzz+neyaTkSrAze2ZjuoKhQKffvopvLy8lKNGjZJoaWnRNXt7t9ZoxsbG4ty5c7CxsRELCwuFwMBA7u3tzbZt24Y3vv4a2+fNA6ytsXr16mdnzO7fpyzcgweUiVu/niSw16/TIrrFvKgLiKKIrKwsXL9+HXl5eXjllVdgIpdDeOEFynQaGUEURXz99deihYUFC+zXj1kVFhIRtLbuEPBoQWNjIxISEnDu3DnY2dmJixYtElBeTuMjIoLqOo8cISO9Z7mXl5VRqyO5vG3bW7doDDxNGdfURAGdhw9pMe/oCJFzXLp0SZmamsoqKysFmUzG9fT0RF1dXcnEiRNhqEpSDFD20cMDKH+ig1B5OWXymsfE1q1blSYmJkJQUBAzMjIikn72LM2TVauoLVg7XLx4ESkpKXzFggVMtn8/BR7aH/Of/6Q67d69oVQqsXHjRgDA+vXr0cGE8fFjCgxNmkRZ/l27SI4uCKrHa3l5R/l0fj6RLnt7yhA/gcrKSmzbtg1KpRLLli17/kBWMzjnXQeOcnLo2c6fT8/V1JTG3YgRFNi7dYvq8QsKSKbs6tqacX5e7N27Fzk5OZDL5dzAwABLlixhgiAgPT0dnHNUVlbi4sWLsLKygra2Ntz37YPdnDkwmDcPZ86cQXx8PBYuXIiIiAhlWlqahIkiXjp4EJVmZjg9ZgwcHB1FT09PwdrauqX9Wtcn8/vvFDhatYp+Li2lwIKtLSkbnpgPxcXFePDgAfr3748tW7bwuro6JpVKMXz4cPh7e9P8ex7H7gsXKCj3F7wzVKGhoQF79+5FYWEhmCjiw0WLKHij4n3+XGhqopKXjz/+S34LqpCbm4tDhw7x+vp61qdPH+X48eMlqtzMm99NuevXr/9rjqRqqPE3hVpGroYaavztMHTo0JLw8PCye/fuBfbp00emihB5eHgIvr6+gvkTmQQbGxsIgoBHjx4JeXl5QnV1NbOyshIbGhqEI0eOiGlpaaKzs7Nw+/ZtFhMTw01MTJiqfrbnzp3DsWPHIIoiS0tLg7GxMTMzMxPOnTsHU1PTjs7YLXBwIAn4Bx9QS6eRI9scrQEYGxtDU1MT5ubmTCaTIT09HVKplBcXFzO7bt0g9/GhTGFqKvVSfbLFUPMC/oyFBU4XF0N5+jTm7NjB9llY8D+vXOHRf/4pxslk4s2qKi55/338UlGhjImJEaNv3BD1V61iCe7urO/33yM7OxspKSnMzMwMZk/KuyMiiPB3YS5kZmYmXLt2TfhLdeza2iRvDw2lPs1TptCidNw4MqYKDSXi9hyZZkEQ4OLigoiICB4WFiZIpVKuN2oUExYsQKKWFq7fv6+MjY0VAgICkJyczHR0dODv78/OnDkjampqcr+lS1mJmRmycnLQo0ePp9cxZmZSBrt/fzJsmz6dSMorr1DGvlcvIjHFxZTdVUFsGWMwMjJCTk4OtLW1lX5+fgLT1KTrd3EBNDTAGIODgwMLDw9nGbm5vN+0aQxz51JtqwrzN4lEAhsbG2hpaaEyLk706t1baJWLbthAteSmps+3uJ4zhwIeU6a0/c7amoILPXoQUVMFiYRapdnbU/Bh3z6kco6rd+6wESNGCGPGjMHw4cOZr6+v4O3tDZXGYi3Q16es75MYOZKy9M1u4VpaWkJiYiKMjY2Z1YMHJBV+7TVgxQq6jifGj6OjI2JiYnhVerrocu6c0OFe6utTHe2OHcCoUUi+fx8ZGRl83bp1nbsF6OjQvARoLAQEkMP5smVE4Csq2rL7nJP6oWdPun+VlbSttTWVlKiAhoYG+vTpg5s3b4IxBmtr6+dv8dbUBFZZSXXvlZU0fyMjycTv++8pm//oEQXRSkooS11RQf3Uu3envtfBwXTv+vZ9er/oJ3Dv3j3s2bOHV1dXQ6lUMlEU2euvv85kMhkEQYCJiQlMTU1hY2OD7t27o66ujtfV1YnSGzeEiKYmpFZUoLS0VKyurmaenp4YMmSIYGhoKKbev8/ynZzg6uQEVFaKVcXFQkJuLpKSknhkZCSLjIxEREQEiouLuZaWFusQxAkNJfl4i2pHS4ue1/HjQEYGBXXajWltbW3Y2dmhtrYWUVFRLW3mMH78eMi0tMgBfe7cpypuANDnZWX0/m8Pb296Vwwc+Mz7mZSUhMOHD6OsrAx2dnbwO3YMOlFR0Pr03+isJZGQMqOy8t9u47dnzx5lY2OjsGjRIvTt21d1GzRQvX1aWlr11atXER4eXjp06NCSf+vAaqjxHw61G7kaaqjxt4RSqdxeV1c3Izk5eWjvZ0h2n8Tdu3fFvLw8QRAEuLm5ibdu3ZLU19fD2tpaSE9Px9SpU6GhoYHa2lpWW1vb4btxcXG4deuWWFNTI3h4eIje3t5CRUUFvL29GQAoFAr+66+/suaseueD6+uT43NoKP08aRJliZ+QhpaWlooABIVCwZKSkpCSkoJXfvgBxzIylIKGBvquXCnpf/myylq/Jrkc9p6e4uh33xVuzZ2LnsbGzC8oiOW9/DJKpk6FtLQUPTIzodGrl8CNjaHx8CFslEpMXrECUqkUL7zwAktJScHly5eVrq6uHRlZjx6dSX4zOOeoqKiAXC4XATxfg9YWyOWUXQkLI5Lk7U335Jdfnp19fQI2NjZYu3atJCUlBb/88gsLDw9Hbycn0fH775li9Wph2bJlMDU1hYuLCy5duoS9e/fC2NiYL1++XIKZMzHh8GEUlZfzkydPYsWKFapbKjU2EmlKSaH/f7KeWE+PDLEYI5m8pia5q+fnkyS+HRQKBdLS0njv3r3b7pmLC2UWm3uxW1lZwd7evrWuWzhxgghRWBgZ37VvN6VQAA8eQD8iAmN37pRg+HCSnj/Zkup58NZbnSXPAMnyLSzIVf5pGDSISg5+/BGmeXkYEBrK3JcuhVSFvLRLFBcDo0aR30F7nD9P/Zib0atXL9yKioL20aO0/aZNbSQ4JIQyylu3tm6fm5uLxsZGhvJyjoqKzscdPpwy89u345apKYyNjZ9tnNjSmzskhLLiABHpkyeJrAoCZSJ79qRxExpKwaZnkDVdXV0EBAQg+vJl5F2/zl9ZtYohIYGIsrs7BTT09akOv6CAnsuHH9IY+eQTICYGSnd3xIeGwrp7dxRLJOhhbw9NGxuSH7u6UgZbIqGgnYcHBVRiYkgqLooUPHFwoPO1tqZ3wVOyugqFAnV1dQwA7O3tuaurqyiVSlVGeCwtLWFpackASBRnz0JvwgRk1deLlpaWbOTIkXB0dAQA6OjoCABQamCAU1pa4rSHDwXHkyfJuE5Li5WVle5hALoAACAASURBVKG2thbFxcVISEjAwYMH0bNnT25lZcVeeOEFmC1bRuS4PbS1ScXwwQfkX7B4MQWjmiGKIrZv3y42NjYKAFpN6xwcHCgY0dKJ4Gk4caLNqK09du4kRc8zUF5ejhMnTsDDwwNTpkyBoqEBMXv34pynp/Kl5i4b/zK8vSmg9qRS6jlx69YtXLlyRaytrZUsWrQINk+83zofzhuMMevHjx9vSk5O3vjZZ58lNTQ0zAkODk7/l05ADTX+w6Em22qoocbfFTqiKNo9j2v4k+jfv79w4cIFZWVlpSQlJUWYMWMGbG1tsXXrVvTv3x+ampoIDAzkYWFh3MXFRbh27RoYY6iqqkJiYiIUCoXAOYeOjg5zaGlb04ygoCBWVVXFr169ygYPHtw5u21gQBmUyEhafBsaUtshGxuqpW7G+PHjheLiYkVjY6N0xIgROHDgAB4bGaGyslIiSiR49PixEseOSRAT08nRl3MOXV1drq+vjwEtZCg/Hw6MwWHXLqoLLSqC+9WrlN0aMQK4dQv2OTlAcTG6iSKmFBUhVF+fY/NmIo2LF5PENSGBFu/Hj5Mc/eWXgdBQFJWW8rTyciatqUE/Hx8iKpqaJG+8epUWtJxTjeOwYUTWampovzduUB1lSQkt6gcMIKIgk1EvZxVS6eeBm5sbBg4cqLx+/brkvq8v9+zVS5hhYQFp80La3t4eCxYsQFhYGAIDA0nyGB4OiCLmzZvHNm/ejKNHj/IJEyZ07p08axad15EjRGjakb5WtDz78+dJrpmYSMSzqookzs2Ec9++fUo9PT3Wr1+/NuaiVFK5wYYNrfvx8vLCb7/9xj7++GMsXrwYMg0NmKxfj1p3d0ROny6aSKWsj5cXk86Zg+r8fJx+8UWM/PVXGLcQwL+KhQvJjVuV4d22bSS9fR7IZMDy5TAsKIDkl1+QO2IEZBs3wmbMmOeT4MrlVGPbAs7JgG3XLsokt/t930OHuGFjI/tMEDB35UoI+fkkT3d0BKurg4YoQhAExMfHIzQ0FP7+/nyoubmkS9XEtGkoXrMGGnV1sFqwgHHOkZ+fD319fWRmZiI1NVU5efJkifzJDL8gtJ1zTg6N73ffpXnz4AHVOFtZ0bj59ltqrdbQQOMkLY1KJnbvprn34ovAwYMY3NQEd1HE49xcVu3oCJ0TJ8BazOl0dCj44+lJhNnJiea1oSGNn+XLUV9Tg7MtwcOcHAAAKyyEY1mZaFhSglEzZggab7wBnDpFc3LyZFKcNDXRvC8pofkbH9/mmN+zJ73TKiooa+voCJiaIurPPxEdHS0CECwtLZGTk8MsLCyeKwAnLShA7wED0NvAoNP23bt3x+rVq5GTk4Nbt24JB2pq8M6NG9D88UcgPh5G27fDyMgINjY28Pb2Zvn5+bh06RKau1QIy318oLt6NX4NCVFWVFSwV199lTKwEgm91z79lOr7p09vzeKLooj6+npBIpFAqVQCQJvjv6kpkfe33qKyl65w/jyZEO7Z0/H3aWn0d6ALcM4RFxeHZsWUctq0aRKkp0MYNw6RU6fijXXr/j2iDZARoKYmzau/0FsbQKvM38DAQJgyZcoziTYAaGlpwY9c5TXGjh2LmJgY32vXroWFhIR4BwcHl/1rF6GGGv+5UMvI1VBDjb8dQkJCXORyeWRTU5Nzamoq8vPzubu7u+oMpAqYm5ujf//+QkJCgrKhoUGYMmUKNDQ0kJubq0xNTRUkEgn38vJi4eHhLDo6GvX19WJxcbEoiqKora3Np02bJgQFBcHJyUnlAd3d3VlsbKz48OFDrlAomKWlZUfSzRgtoEtLKdMmikTCXF0po9mMxMRE0d7eXujTpw9KSkqUJteuCY4rV/IHWVlM08FBUPTuDQ0LCxTX1uLq1av8zz//ZE1NTSgqKoJcLuc9evRoW6zKZCSL9fMjWa1USovlmzeJYC5YQKZZMhlJTw8dQpq9veitrS1AS4sylA0NVKPs5UXy2GHDADc31Do44OjDh6zH7NmwHDECLhMnMmnfvrRQd3WlbFifPkQEevQgEvLCC3Qubm5EOtzdSbZqZ0cO7D/9RERh5kzKHpub/6X2OC1wcnIS/Pz8UFFVhazwcCbbvRsp7u4wMTWFXC4HYwxOTk5tz+f11wFNTQgSCSQSCeLi4lhBQQFvUS60okcPkrkbGVHG9IcfupQBA6DFvKUl7V9Li0hKWRkuNTUh7d494eWFC1kHybqVFWU7a2paJcgWFhYYMmQIcnNzlVevXhVuJyQg0sEBaRIJd/v9d+b01VfscmoqLvv44JKHB2wdHeHq5dV1LfTTwDn1Y542rUOpQyvu3aPezc/Rw7z1FujqQjcoCGcYE+02bGClu3bxnyoqWHpODl544YWus8ZyOWWq20vNi4rIqKvlO8eOAUuX4pdhw1ichwcaBAG3b99GfHw8bty4gRvl5bj1+DFw5Agvc3Bgp0+fxrRp09CvXz/G7t6l6xk6tPOxNTQQlZrKdZKTmY2/P64lJvILFy6wqKgopKWlobS0VEhISODXr19n5ubmqh3DtbVpzo8YQc9UIqGsd4u5mLU1ydUfPiRSXl5OgQwzM5pDfn4kmV+wANKXXsK+mhpElJYi3MYGei+9BOt+/YiwubjQ96ytaYxpaXUgT5WVlUhISMD69ethY2OD9PR0GBsbQ0dHh+mcOcOqamq4zdSpDG5utI+GBuqnPngwkWgXFxq3AwfSdUyeTL+ztCSJdFwckceTJyHZuhWWDx+yoAED4O/sjEeJiXiQn88i4+J4r969WafgRAsUCvrn798l8dPS0kJFRQVu3LgBABgwbBhkbm6kGPLxocx+c0BNT08P3t7erH///iw9PV2MuHmT6RYXo6x/f1ZQUMDs7OzaZOYtbf3u3CH1gYUFYG4OQRAwePBgDB48GDk5OcrKykohOTmZ+/n50d+cpiYy4XvpJdXZa4Bc+jMzO/Zpr6lp83noIuh0/vx5fvXqVTZ69GhMnTqVNoqKQqOGBq5pa+PBgweiu7t759KGv4KdO6m0xNi4tbXa80Iul6O5dANjx47t1GbzWRAEAXZ2dqyqqkqrpKQkICAgYN9f2oEaavwNoDZIU0MNNf4WCAkJYQB85HL5alEUZwwfPlzOGOMpKSmSiooKzjnnEyZMEJydnaFQKNDlYq4damtrUVtbi/Y12d9//73Szs5OkpWVxcvLy1nfvn3FsWPH/rUVBIBHjx7h+vXruH//Pndzc+Pjx48XOiyIcnNJaiyVAjo6KLp4EYcvXYJHQgJu9uuHJg0NcM7h5eWFSZMm0XcGDUL9sWPY/MMPkMlk4LW1eO2bb3B4zhxwT09RJpOx/Px8ZmBgABcXF+WECRNasx6cc3DO2xZDSiVJRc3Nqf77ib7Y8fHxiIqKUqxcubKNAV25Qt95911yqG7O3CUmJuLy5cvKtWvX/ntZlpgYIiRFRR1rXHv1oqzZp58+vyGRKnCO1E8+4VFFRSzf3Bx9+/bF6NGj2z4XRQo2NGevADLd8vDwkIxsqVEvL6cFfVRUGwltaKDM1bMk1c0oKSnB1bNnkZOVJbomJgojr16FpLiYjt/+2pYvpyz4wYOdd6JUApWV4B9+CPbbb4CFBeomTsRRe3tkZ2fDxMQEurq6yM/Px4oVK/464T5zpq2fuCpkZFDda3T0X9tvM0qLi/Ho4EE0/PYbKjnHg9GjseTdd1VvXFhIMv2GBuoJHhxMhnqCQFLsxETgxAk0DRiATc1Sc0EQMHfu3FbzOaVSCbfcXD78t9/YN81O/OPGjRN9fX2F2uvXIb93D9JFizoduri4GDt37kSvGzegWV+PzBkzlJOmTZMYGBhAKpVCoVAgMTER9+/fR1paGpYvX44nfSI6oKCAzLK2biV5/J499C54800Kvm3ZQve9i9ZznHNERUXxixcvtjLR4ODg57rnoihiy5YtmDhxItzc3Dp8VjJ7Nq42NMBv2zbYtigWKipoztXUUOb2Ofunx9+8icvHjiFAT0/s7+YmoK4OyuhoVB88iJSePdF79GhoGhpSEGXgQCL2pqb0PLOzqQNBUtJTj3Hjxg1cvHgRAKCtrS2+9dZbNHEuXaI6/aNHO7m6NzY2Ii0tDT0vX4awbBl2HzgAFxcX1eU+v/5K7e6mTaPgXztwzrFp0ybY2tqKgYGBQqv7fUEBvRNUvZ9Ekch2O4O0xsZGSJqDeqqQl5eH/fv3Y8qUKWhtRfnGG1SqMG0amkt9UFVVhTFjxqDVzf15ERxMRo7z5pE53Pvvq/ZGeAZ2796tzMvLk7z77rvPLrPoAo2Njdi8ebPigw8++DeiBmqo8Z8JdWZbDTXU+I9HSEiIjoaGxjlNTc33/f39e0+ZMkXu5OTEbG1thd69e6N3795MoVDg/PnzLCYmRvzzzz+V/4+9946K6ly/x/f7nhkYekfpCoKKNAuIDRGNLXYTYzS2JBo1Xk3UtJv2JfeaqKk39psYozExGhJN7AUEFGk2pIgKAlKkShcY5pzz++Nh6KAm9/e5n89as9dimQzDzJnT5t3P3s9+/P39H8nIlEolDA0N2zymVqt5fHw8LCwsJE9PT15UVCT5+fnxe/fu4erVqzA0NERXs7pbw8TEBJ6envDx8WHh4eFyeHg4u3HjhpyYmCgnJCTICenpsvzJJ2jcuBHfazTyjeJiiSsU7NmMDDasb18MW7sW9+7dE62trbmrtmfW1hbc0xM3b99GdXU19IyM0GhoiOA5cxAyfz4rKCiQqqqqYGxszG7fvs1lWZYKCgqgVqvxxx9/yKdPn2YDBgxA1vr1MktPZ4Y//khWya1bSaVqhYKCAuTm5srN1ub790nJfuMNso9/9RVqR4xATGIioqKi4OXlxdzd3Z/Mg6hFRgYpe3PnUl+ogwNtT0YGKXvLl5Mt/Z//JEXtb397YrsjAIAxWHPOBp46BdVzz8mRFy8yURTRvH8liQjGxImt/oTxyMhIMMZkZ2dndj8zEw+LiiBPmQJJkiDLMi2WfX2p57WL7aqvr8f58+cRFhYmXbp0idU0NGDGnDlsyMsvQ5g5k/aptTUVM7T2Y3d3Urhbz8CtrKSfIUOA2lqwV1+l4seyZSjq0QNG778Pj1dewYxnn4Wfnx9ycnKk+Ph4KBQKZmxsDP3HseRXVZHKu2hR12TbwoJ6xQWheXzdk8DA0BC2gYGwmzkTQmwsiouL4VVYSIWV9uTDwIAImJ0dWZi3baPzQBSpOJOSAuzYAaFvX4wePRqurq64du0a0tLS4OPjg2eeeYaupaFD4bVrF4s5exaSIODOnTtMo9Egc/NmlMbGYk9eHpydnWFhYYGCggLs2rULMTExNHd5/nwE3b+PwcOGcSMfHwiCAMZYcxidj48P7t+/L0VFRTE9Pb2W0UqtIctELC0t6Xz/4APa17m5wM8/07nz2WekqDJG19uyZVRQaCrUqdVq7Nu3jwHUx61Wq+Hg4IDHGT1YW1uLmJgYTJkypQMpUhkbI83cHOcTEuDi4kLjxVQq2r9Dh1LuwP37HQO+2uHbb78VL1+5whv19DD7jTeY0scH8PEBnzwZvzk5ySkqFVNbW8Nt0CAqriUl0c9331G/9LFjFObm7t7Sb98JIiMjpfLyciYIAoKCglgz4XV1pW3lnIjpU081t6EIgkDBlS+9BEydipv374NzLnl4eHS8aD09qRjw738TgW56/crKSvz4449STU0NjIyM+KVLl9CnTx8KUnR2JgdAZ0FjDQ2AuzsuDhsmxcTGsri4OPne5s2scdMm/FhTI9+8eVOura1lnHOYmpqivr4eYWFhsr6+fkuxt6aGci3WrAHMzWFjY4OAgADIsowzZ86goqICaWlpqK6uhomJSdfX+h9/EMn+6SdyTyxdSsdgxw7KGuhqDnwnUKvVOHfuHBdFEWPGjHn8cYntUF1djcuXL9cGBQX9hbQ3HXT43wldz7YOOujwvx56enrbXV1dR8yePVu/MxVAX18fwcHBzMrKSs7KysKNGzcUGo3mT1XZR4wYgRGUDCtcu3YN6enprKysDD/++CPMzc3l2NhY1tjYiJdeeqlFAeoGZmZmWLNmDS8vL0d+fj4D0Lwa4Z6eyNi6VXZ0cODuHh4wNzeH4u23gfp6KIcORa+gIIhOraajLFsGnpiI559/Hjt37pTfeusteq1164CGBphaWTFRFFlxcTEcHBwQHR3NAUClUqG+vp4BwJYtWzArOpqVm5tL1oxx+PsTmS0ubmMXbgriarE+9exJKqaxMS24f/sNKZ99JsVbWXEPDw9MmjSpzSpLo9Hg3r17EEUR7u1U8zaQJFKxzp4l0ujtTY/PnduipDFGC9+PPiKlKTeXel5//72N7f6xMHIkcPgwHDUapqenh4bWvdayTHb3VggMDMTly5cRGRnJajdtAmcMqSEhUs0XX3BBEKBUKjF16lR4GhgQKeokVTs/Px/ff/89bGxsxBEjRghZWVnIzc2FkZERuELRQqbPnKHP//rrZO+/cIH6PG/epKLI999T6FdcHCl3fn5t3udsdLQ0taKCW7e6RubOncvPnj2LqKgo8dixYwJjDOvWret+PJJSSbbq9kn07bFhAymwr7/e/fOaoNFoIMtyc5J2Q0MDfjtxQr7t6soGm5kRaX73XWppaK28ajQUJObgQG0Jly+TxT0vj1L9WymYjLHmz9bY2IgTJ07A3NwcixYtop1SW4u/f/opiqKjEZOfL+fm5jJbzlHRVFT4oZN5w3379tWMDApSwMuLzksHh04J1fPPP89jYmJw4cIFKSkpCS+//HJLIvOmTdT77utLZEnb21pZSQRbO+f58GF6vLCQbMlKJRU1vL2BPXvAtm0D12ggKRSoqakBAPz0009Yvnw5bG1tUVdXh9LSUhgYGDQXBrUoKCiAUqmUVSpVW0Yky+Dvv49nT59G5I0bOHDgAN566y1UVVUhPT0dNjY2ODFjBsbIsuS5fj0ve/pp/DsuDrIsw9nZWWxyErHa2loUFBQIAPD22293IHvTpk9nn2dk4Gpjo/xUcDBrY9uPiaGiyty5pPCfPk1Fp7Fjqc9draZ2kldfBQDMmTOHb968GaIodjyX3d1JjZdlysbw82sbQrZtG2Bri6FDh+LQoUN8yJAhnY9TGz2aroGPPgJqa1EzfDh2794tW1lZ4a233mIKhQKfffYZjh49Ki9btowhI4MKKZ1BpQK+/x7nIyK40tAQjo6OrE9AAMyMjTF69GiWm5vLkpKSxAsXLgiNjY1oKozJCxcupPM2OZns3vHxbZRzxhhGjx4NpVKJs2fPwszMDMnJyTh58iSCgoIwpnVxpK6O7OwzZpArJTaWzq/Tp6m4VltLLpoXX3xsF8O+ffsktVrNBw0aJDLG/rSz6cKFC2rO+cE/+/c66PC/GTqyrYMOOvzXERoaOpYxNkaW5d8//PDDxHa/G66vr//s1KlTOyXareHt7c28vb1ZUlISMjMz0be1IvgnUF9fD4VCgSNHjoj6+vqCi4sL6uvrJVNTU2ZjY/PYJXyFQoFOx2gB+PHqVTz9yScwT0hoSd02MADeew88MhIGmZlEbgWBbOdNSqosyy3v7+wMnDqFoF27OGNMFEWRjR49mms0GuTn5+Po0aOiKIrCmFu3oExNxW/PPAPGGFd+8gm8vLzEqceOCTh/nvrIm9DB3qgdbwUi4lvc3DDrs8/48l9/hXlTn59arcaxY8eQl5enqampUSgUCjQ0NCAkJERbwGiLv/0NuHaNFsStR0sBpKa1cx2Acxq1JYq0WLSxoQThYcPa9kI+Ch9+CKuBA8GeeQYTW6nYqKwkG6U2RboJq1atwt27d8HS0+EwZAgmrlvHKyoqIIoi7t69iyNHjkDx9tuyR6tjcuvWLcTHx8sPHz6UKioq+LBhw+SQkBABAEaOHImdO3dKN2/eZE0JzARtUNK77xLBfviQnAQffkik47PPaKFsb98hnCwvLw8FZWXc+Pp1WlBPnQr8+isUenqYNGkSJk2aJHz99deSRqPhXSle9+/fR3ZUlDx42TKWfvo0zOvq4Nzdovu11zoNrxNFEffv30d1dTWUSiVMTExw//59HD16FJIkwdPTUxYEAcnJyQwA8/X1xfjJk+n8Ly6mhP7+/SkgjHMK5fr8c0qczs6mcL3duymQql3BAQAsLCzg5OSE3NzcZsK9cuVK+qWREdjNm+jp5obZTYWvKhMTnE9MlPr06cPy8vKgVCpZTU0N1q9fr3W+0FrJ0pLOj61bye7dSaL6iBEjMHToUP7pp5/KhYWFsLOzo4LSrl1UTNC6SbQIDqZRfj/+SHZl7T2rZ08qPgFkZ66uBoqKoPfFF3g/NRXFH32E9MREKDZuxNmzZ7Fz585OD5GFhYVsaWkpz5o1i2dnZ0tWVlYd71sVFVREs7SERqNBfX09du/eLd2/f5+bmJiIdXV13NzeXv6lsJDP4Fy2nzuXeb3zjuw9dSq7mpQkSJKEhw8foqioqHn/d3aOHT9+XALAR40a1bINDQ1EAp9+Gjh+nJT0wEAK4MvPpznpffqQCnvqFLVW9OgB/dBQvDR6NJI3bICis2vfyIiIY3Ex3Uv++U8qXgA04aCxEX3GjYOXl5e8Z88etmDBgs7DvTw9gc8+w8MFC3DGw0O2DAyUFi1aJGg0GuzatQsPHz7EkiVL6PNYWhJR7dGD7PftIJeXo2d1NSa8/DJdV/X1gL4+ejMGf39/ABA0Gg2+//57KBQKzJs3jze7I3btou+FLlpohg8fDltbW9jb20OhUCAqKkqKjo7mxcXF8rPPPsv4u+8CX31FgZQPHrR1rOTm0j3FxITcRCUllG7/GOMbLSwseEVFBVq3LD0JEhISNLGxsXUPHz4sUavVT+5h10GH/wPQkW0ddNDhv4rQ0NAegiAcCwgIUF2/fv31Tz755IharV7x4YcfVoWGhvYGEDN16lSo1WrU19fDwsLika85YsQI6ejRo3B3d+dPGtjSGt7e3jh37hyXJAkqlQqJiYnM0tKSLV++/E/3prVHvZkZJD09Sgdu1c+HOXOQ/fCh/PTGjRAjIyEcOEALODMzKIhstzx39WogLQ3s7FkEjR/fvOjR09ND7969sXr1amHzpk2Sp5sbTwVkAEylUqGuro5URlNT6tsOCelczayvpwVkk926rq4O5ZWVUH/5JczXrYPm/HmcOHkS6enpkpWVFYKCghQODg6wsbHBF198IQuC0HaBf/YsLUzXrOk8xRugJGxvb7KWtocgECEFiCBnZJD1ND//sRaIMDWF3syZGHLrFjIzM1uUdzMzGs/VCVx/+40IXlMBQNsDbWVlBRsbG4hPPcXKJ02ChZ8f0tLScOTIEQwcOFA2NzcXbG1tO4Tpubi48JycHBGdje2xtqa+XVEke3ldHRGQ+npaCNvbt3m6JEk4duyY7OHhAZVKxZpV0+JiUlAZQ1ZWFsrLy/kLL7zQ4dzV2lDj4uJgo9HIlZMmIf7MGQYQcfLx8ZGCg4M7Xkh9+lC40+7dbR7+6aefpLt373JjY2MRAKupqeEKhQKTJ0+Gqakpjh07JjPG4ObmxiZPntw2VKxHDxpndvEiqZyjRhFBKCuja2TePOqnvXix0+MkyzKuXr2K3Nzc5sfs7e3bjqJzc6NixOLFwOzZMM3IwPShQzlmzQIA/PzzzwAgdjqmKiSEyO/WrfTZO7HNKhQKODo6St9u3y68c+gQFLt3E/l7+eXO51RzTq6G6GhKWG8PPT06DwBS8wEk5OWBGxtj8sCB8A8Jwe9vvinnVlUxZWMjfOfORU5ODkaMGIGMjAyWkJDAzp8/L9+7d4/Z2tp23ODs7GYiGhQUBO1c7NmzZ8Pc3FwAgFu3brGff/4ZiR4e8tCTJ9nUb75huHIFvTZtwgOVCgcPHoQoihg9erTcdI9u8z61tbVIT0/nAFpm1+/fT/eAoiI6VwWBFO7586nwtHYtpf2bmFDbwCuvkFodHg5YWyPnp5/glZwMydiYer9dXWm6wo4dRHqtrcmtc/Ik/V1oKN1XSkpoBB+AadOmMVmWcfDgQXnFihXMoF3AmUajwa8xMeK9oCBhwenTzHbUKAGiiOrqahQWFgJAWwv/hAltpkq0we7dcLGyQvM4ST8/SgL/+OPmpygUCrz88stt/+7LL8k90jT+rCv0aeXyCQkJ4XV1dZLP3/7GS95+Gz0++YTupVoHRWuYmVGIpRabN5Pq3cU11hpPP/00Nm3ahIqKisf6bm6NiooKnDlzRhRFcQWAkx9++OGDJ3oBHXT4PwJdz7YOOujwX0V0dPSiPn36jJsxY4aev7+/sqKiwr2srOy1qKiocs75Gs65R2VlpXTmzBkWHx8PKysr2Nradtsb5uLiwlJSUuRz587hwYMHrLCwEA0NDW2C0B4HSqUSVVVVKCkpQRPhlh0dHSULCwtuYmLyp/vTWuN6UhLTzJ0Ll2vXiIC2UhyKiork45aWvLZ/f7ifPk2kZvx4SBYWuHjxYkuwD2OUoPvqqy1px61x4ABcN2xg6u3b4blwIQsODkZERARkWUZ9fT3TqFS4l58vpxYXi0n37klpaWnS3bt3WVVVFVepVJJ9fj7Hiy8CTb2Eu3fvFkVR5EZ9+sDKyQmp//iHdKtnT4wePZpPmjSJ2dnZwcjICLIsIzIykvXu3but5X7VKiLJzzzTedI1QOrm+PFENLrDpEmkXB09Skr56tW0mH7ETGkWFITq336TCg0MWG/tnPbycvp7rQKmRVYW7ddXX+10e8zNzXHnyBFZNW4cM7W3R1hYmDh48GA2btw47uTkBEtLyw7nSkJCAhQKhezt7d15NaixkSzz9fVEFmbOJPXzjTfocx4/TnZZxpCRkYHr169j8eLFTBAECt2bN49I4ZIlwAsvwMDICKmpqVJCQgILCAhotnLLsoz09HScPn0aPaursaKykvXZs4cFBwdDrVajuroaqampbNSoUR2ThpVKIpArV7ZRM8YuugAAIABJREFUuKurq1lxcbG0fv16Yfjw4SwoKAjDhg2Dk5MTrKysMGzYMBYYGMh8fHzQntw0v66bGym9Bw9S0cHbmz7Xs88CL73U5XGtrq7Gvn37AABLlizB9OnT0a9fv44Xak4OuSQcHMim3Ldvc0hgTk4OkpOT+cWLF3H58mXIsiwbGhqy5nyHwEAiSB4eXY4/83Zw4Jdu3IB5cbFsm5jI2MGDLS0SnWHGDOov3rePSNgj7i0H0tJQ4OiIkcHBUI4bB8/nnmPDEhMx5Phx9Nq8GT7bt8PCxwduw4fD1NQUFy5cYKIoyosXL2YdjuPx41SoCg6GIAjo168fPD09mapVS4S1tTWGDx8Of39/1sPOjq678nLg/n2c27ABGSYmcHVzk6ZPn87buDWaIAgC0tLS8PDhQwT8+98wT02lc1PrUBEEcrkcPEhFFhsbIq6jR1Prh9bmzRj9t4kJjiUlSZljx2L0U08xFhzckqS9YQMdoy+/pMT8996jaycxkXr+N26kY910j/Dw8EBSUpKUk5MjDxgwoNn6L8sy9u3bJ5WVlWH56tXcYulSsE2bgIcPYeDnh2vJyVJDQwOLiopCQ0OD2KdPHw4vL3Kk/P57h5YU9uKLSFKpxOzsbObr68swezY5GzppPWlGSgp9hldfffxshIoK8Hnz0NfQkJVVVyNXXx9On3xC11Nn59X69ZSJoC20jh9Px+bOnZYiTxe4ePEicnJyEBIS0mXQW1dQq9VITk5WKxSKKZIkvRkTE6M3atSo80/0Ijro8H8Af17y0UEHHXT4D0BfX/9v/v7+RgApsTNmzFC99NJLJj169Pi8T58+IQsXLkReXh7XqnG//fYbGhsbu31NzjmWLVvG586dy8rLy8XU1FT50KFDUKvVT7RtSUlJuHr1KkxNTcUZM2Zg4sSJ7ObNm8Lu3buRn5//Jz9xR8iyTH1zn3/e5vEJEyYIPoMHi42SRP2K168DW7dCKC8Hb/9Zxo0jwtlk5WxGYyPg7IzrgYGS1EoNd3R01Li6uoqCIIjx8fFSxaBBfOTGjQqVRqNQKpUKS0tLZmxszM6eOSM8nDIFNVFRAICIiAiIosgDAgLkixcv4t+ZmTB+8IAt8fJiAwcObEMqGWOYPn06oqOj8eUXX4jw9aUZtidPkq2zO/z4I+2Tx8WiRaTQFRcTcbp0idSsrqBSwcLVlVt+913Lk+rqKGW7NXJyiAjk5nZJ4CVJAmtoYHUFBdixY4dYXl4u+Pn5dcmWIiMjkZGRgfLycl5SUtL5k44cofddsaKlf/m112j7SkupKPD770BaGmozM6FSqcQOgVwBAUTcGhuhUijw6quvcisrK3Hbtm3NT0lPT8ehQ4fQq1cv+ZWBAykEq+kYjh8/HtO6S1jX06PtaZXeDgB+fn5oaGjgWnWZc/5Y0wE6wMWFCkxXrtA2nTtHn3nGDCJMMTE0wz07u/lP7ty5I9Om6cG0E5t3M955hwoEx46Rs6OV4txkuwdAfd/nzp1jO3bsaLnmFQoi6EuX0rFoj5oacCcnvD1tGqyzs1mWNnG7O3BOdvMNGx5LURzeNPv8xo0b1Auurw+EhoKnpdF5n5tLx+Wnn+C3cCHeffddvDF/Pld05vTx8moTCtgV2hxDQaBCi68vRpSXo+/Nm+ghSV3+rSCKmBYRIRtVVyPF2JjInaVl2/nphoZUTNHCxgbYvBn1334Lqbi4w2u6uLjI5eXlkCSJXBbe3jQ6MCaG+tzXr6d7qiiSdXzKFGrJcXAgYl5bC4DOz0WLFgnZ2dn80qVLzR8iKysLxcXF7NVXXxUMDQ3puB84QEWsf/0Li2bM4FpXRkJCQgvTjImh57XH119j0nffCXl5ebTN27d3PSqMNoAU+NjYR5JeAHTvO3aM/o2MBNLTcWjQIIRPnNg1oa+vpwyEzlquAgLo9bqBcdM9UaPRPHr72sHU1BRr1641fOONN4xef/11PcbY+tDQUN9H/6UOOvzfgs5GroMOOvzHERoa6gLAGMDNDz/8sMMKLDQ01ByAHwA7Q0ND597t7HE9e/bEsmXLmht2X331VRgbG+PEiRN48OCBVF5ezhUKBVQqFZRKZacLecYYevfujd69ewsAsGXLFjkiIoJNfIxFpRZqtRpWVlbiqlWrmhdSlpaW2LNnT7ONuLa2FmfPnhUNDQ1ZUFAQV3WnUnQCxhiR7e+/pyCorCyyC0oSsH8/Bn77Lc93cyMV7uRJICAAwnffYeXWrZAWLwbPziarrZ4eqeJDh1KwlpMT2XHnzQPu3kXq0KEY0GoxvGTJko73/6goTLGzIzUJNBrt4sWL2Pfii3IPgFlGReH69evw9/eXx44dywVBQJ8+feA4cybDsWO0OGvXqzlgwACYREVhf1mZ0KygPA7S02kh+CTgnIKQtHO5g4KIiGzahPz8fBQVFaG8vFyuq6uTGhsbUdWrFzfMz2d+GRm0WLezo4Vta7zyCpGCn37q9C2rqqpw/PhxeFdWIu7IEbjMno0FCxY0L0Lbo7KyEtHR0XB1dUVxcTE7duyYtGTJkrYM6McficQtXEgL99YESavs3b1LpOrll9Hr/Hn5yptvcqSktA3uMjWl3tfPPwd++w2K6Gi8+OKLwqeffgpJksA5R1VVFQBg0dSpDGVlHVT93377TQwMDOQdWgG0eP992qavv25+yNjYGMOHD5f27t3LDQwMpHnz5nG71gFVTwoLC7LAuroCX3xBir+ZGSV5R0YS+WmaS62vry+PT01ld8zN8c0nn+CNf/6zJQuhPfbsIUXXzq5NYYZzjoCAAAQ0jXx68OABtmzZgm+//Rbr1q2jY2tiQts0f35LUUiSaF+vWwckJoKvW4eSzZvx+82bGBAWBkEQpHHjxvE289Rbw9iY+tIrK7vtmb1161bzjOlO53ozRmo1QNeB9rMNHUrW+TVryAb//vtEmt97jyzWfwJqNzdsGz0ajvfuIXDrVgYLi7Yj8NRqICoK6oEDYZSSwvTs7VGjbQ9oj3feoX3XCln9+yMyLAwjhw9H9KJFUpm+Pq+rqwMA9OnTR6irq0NWVhb09fXRs2fPtt8FPXu2WLpjYmg/vP8+PVZYSPv6X/8C3n4bht7emD17NgsLC2ODBg2CgYEBJEmCUqmUOrQTbN2KinXrUPLOO2h0coK5oyPWrFnT8vulS+mnoqJtb7SfH1SSBHVVFctMS4P7N99QHkNXWLCA3A7jx3d7DFBTQ/f/zz+nlPvJk4H799GoUED98cdw7i7IMz+fUvHbq9KMEdE3NKSiRBequlKpBOf8L7dVGRkZYeTIkaqLFy+uA7DwL72YDjr8L4PORq6DDjr8R/HJJ598LQjC9yqV6kUAqyMiIk4EBwc3y3ehoaGBCoXimo2NzXNGRkZTp0yZYvyosTWGhoZQKBSQZRmXL19mCQkJSEhIwKVLl3D79m1IkiQ5Ojp267ssKSmRb9y4gZEjRz6R9zs+Pp4XFhaKAwYM4AAtvG/cuAEXFxc8fPgQe/bsgSiKyM/PR3x8PNMqTo+LpKQkmJmZobe7O6m+y5cTyfz6a6ChASmDBslZXl7Ma+VKCmfy8QEbNQo7i4sxaOxYKJYsoXRqe3tKkA4IIJVOlokk+vgA3t6IjY2V+/fvz7qdtzxhApGZiRMBzqFUKuH2zjswsLVl58vKkJOTA1EUMXHiRHbt2jU5MjKSZWZmyu7BwczwyhXqndaGfGlRXw/jyZOR2bs38ry85L6+vo+3/ydPJqL8Z2BiApkxyP37o8bBAae+/BLKjRsRa2mpqVeruSiKnDHGBQMDNs3CAopjxyigKTWV3lO74H/wgIoVs2d3XIwC+Oabb8SIiAiuVqth4OYmDX3hBTZ49GjenYp79epVFBcXi8uXL+cpKSlS3759uUvrPt7qaiJwa9eSK8HevvMRSIzRz/TpOGlnh/rr15nfunWMjR9Pi2NT0xaS7uVFhNPDA4JSibiEBLlXr17MzMwMx44dg0ajwciffiLV+Pnn27xNWlqanJubyxwdHZmZmVnH7bC0pH2jteI3oVevXszd3R0ajQaXLl2SmkfIPSm+/prC4aZOpYKItohRWUmBfuPHE4EcNw7o1Qu2AwYwZU4OTC9dgmFtLcSvvoJJdDTqzp+Hvla1LysjsjxlCimpqanU89sFoTAwMMCgQYMQFxeH2NhY+Pn5QaVS0XWm0VCC+Jgx9LovvQTMmUMW5kGD0HPpUqSnp0tZWVmsuLiYpaSkwMXFBV0SbqUSOHcO0qJF0KxcCaGTQoGxsTFiYmIAAKmpqejVqxc6PTYAnQc+PvTfr71Gluw7d8gZsHIl2YVPnqT9eOUK3UP09UntLCqie1FuLp1LRUXk/lAqKWtBECAolRBlGak1Neg7cyYzV6koiEs7m/rf/wY++ACJo0bhJyMj1Bsaory8HPX19VJsbKwUGRnJIiIimIu9Pcw2bCDy2aT2SpKE7du3o9LMDEY1NfC0tmaDZ85EQFAQevTogRs3bsiyLLMbN27g+vXruHLlijxw4ECm7Ky4Eh5O58s//kHFhTVrSDnPyaH3274dVnV1SFepxCtXrzIPDw+mUChw5coV1uE7gzHE6enhfnQ0Bicno+/8+bJ1u0wGJCeTWvzmmy33Djs7MM5RpKcnZeflwfeHH1hXgWeIiKDzesaMR4/h6t2blPsxY8gJs349oKeHxsZGxMTEwMzMTB44cGDnL5KYSFkII0d2/J1SSYW/iRPpc3QCxhgSExMxevTojm0mT4i7d+9Kubm5V0eNGvVHaGioYVxc3LGoqKit58+fPxMcHHz/L724Djr8F6FTtnXQQYf/GD766KPJRkZGL61cuVLfwMBA/+jRo4ZJSUkvAlgPAKGhoYKent5P06ZNMx4wYMATv76npyfi4uLk3NxcNmrUKJSUlODOnTuIjo7mQ7X9el1g/Pjx/MqVK7h79y6SkpIwcOBA9GodCtMJHBwcMH/+fPz888/Nq4hevXph3LhxCAsLk2VZZkOGDIGtrS0/deqU7OHh0XngVTfgnKO8vFwGwLB0KfVsZmXReBobG1SdPg1Ra1X19wcOHQJ694a6Rw9s3rkT837+GX2cncnut2ED9TwOH05EMSmpdcq3LHVj8wRAZKO6GvjtNyIMAGBriwHPPIMBAwfihx9+kO7evcu/oRAnNmvWLPzxxx9s2/bt+HDZMlIKb96kJOl792gBHx8P4cEDTC0txfbt25larZZnz579aML9zDNk9/zXv55kd2oLMvKpU6eYLMtgjGGApaXk3qcP91u1SoHPP2/b/9jYSD3kFRX0flqinZ1NJCUjo8u+ckNDQ+7k5CQtXLiQs+ef5+jfHxg8uNvtO3fuHMaMGSMAQENDgxwbG4usrCxx/vz5QnlmJqy//JJUOGtrIiwU1tUpRFFEaWkpJkydyj5NT0fJ5cuwdXam88TZGfjhB/p85uaktL30EnhWFnouWCBfvnxZysrKEoqKijBu7FgKTurEdr9w4ULh559/xsmTJ6Vly5ZxURRRXFyMS5cuISUlBbNnzZK97t1jEMU2BQnGGOzt7WFoaMiuX78u3Lp168mnA9TWUlhTa2Vv5kxSJd97jwpT27eTvbcVqbTYtAm1eXmQbt/GLzExMKivh0NeHuY2NtK1dfgwhfStWEHE88cf6TWWLKH99NVXRJZOn6bi0wcfwDQwEMueeQb3lixB5fTpMC8tpecsWkTq45491F+bkUGvs2gRFQAYw/LlyzlAVtuPP/4YN2/elOzt7btkJveHDsXeJUtg+dprqLSxwUM9Pbi7u4tz5swRJElCbm4uAgMDERcXB41Ggz179uDD7hRSLTgnIj14MGU8AGRPViopVMzHh9R5CwuaxaydC56VRUr0xo2koK5dS+FmogjY2iKwuBhVJiaw3rGDrhVtL3tgIJHG1asRyBgqqqvl+Ph4Zm1tjZKSEt6jRw/4+fnhzp078vEdO5jXK6+Ixjk5Qv2tW6irq8OFCxcAgNwEH3xABYzt24EvvoCdnR38/f1ZRUUFSkpKUFpaiuTkZPz666/SggULWvatWk1uldOnSdXWhiGGhdHjO3bQ/xcVAZcu4aXnnxeSdu6U95WVYdTUqW2DKAEUFRWhsLAQtQ8f4uqQIVA+eCBP3LaNwcWl7RhCb28KbWxdeKupAaZOhbRli+wVF8ebE9bbIyyMxo1dvNg10c7LozFyr7xC52tGBpHzVrZ0bSK8k5NT1/fb+vrux3w9/zw5ImS5023Rtlbk5+fDpbPwv8eEJEm4dOmSprGxcVNoaKiJnp7eSTc3t8E9evRQXbp0aTWAxX/6xXXQ4b8MHdnWQQcd/mPQ19dfERISYqgNPcrMzHwoimLrpq9nLC0tbTxbz9B9QowaNYqlpKQgODgYnHPEx8fjzJkzj/w7rfXw559/Ru/evcW9e/cKpqam4gsvvCB0NpJLC2NjY2g0Gnby5ElMmDABnHMEBgYiICCAAUSWN27cKAcHB7P8/Hx8+umncr9+/aQpU6YIDx48QHp6OoYOHdqlzc7GxgY5OTlEthkju3J1NZEtAIwx1rzg69evmSSuWbMGO3bskMvLyxn69KFU21mzSKnRkvMTJ4gA/PwzlIMG0es+Cu++S327skx27DVrmheoCxYs4Ldu3UJBQQEsLS3h7e0Ne3t7bN26lRblLi5E8KZPp3CgmTMBY2NUVVdj+/btAACFQvF4yvYrr5D62A2Kiopw584d9O7dG1euXEFhYaFUVlbGBUGQ58yZwxwcHNDUbkCL7/Jy2r+DB9MC08mJehmXL6dtPXSIyIIk0e/Cw7sOcAPw7LPPss2bN7OSkhLY2tg8UoGqr6+HLMvIycnBqFGjMGrUKOH+/ftISEgQwsLC0GfzZtT07Sv1NDbmqqa05NYpwXV1dUhJSYG7uzvMzc1x/Phx8dq1a4Ktra0EgJvb2dE2JCTQwj48nEjf7dtEtL78EkhJwciaGn7g4kXIggADAwOM2LGDyMHbHSfvcM4xadIkbNmyhX/88cfNeQnaMKQbycmy1++/M0ycSOSqHczNzTFlyhQ5LCyMDRkyBBMmTOh2HzVDlqnocOdOx77Wnj3JLpuZSYr6pk1tRr/p6+vDzc0N9vb2UKlUiI6Oxq3+/ek4A3SdSBIptpmZRG5u36b3lCT69+FDsqkDZLUtL0cJ5+hRWoqelpbkJklMpN75W7do/JyhIRF3Y2NKLW+n9JWXl0OWZRQUFKC+vh6dtZxoA948hw2TJm/fzusLC3HihReQl5fHdu3aJVdVVTHGmGxubi6HhITwiIgI/JnCJQDKeCgtpc8eHU3KqPZe+NRTHZ+/aFHH/1arkZeUhNQjR+D/1lswsrKiFoeLF0k55xxgDFVVVbhy5QqbMmUKBrcrSHl7e7PsyEiU37jBL1pYaPT09JhKpRK0LTbx8fGasWPHKrBmDRXDLl2iVhelEubm5jA3N4e7uzsyMzORmZnJt23bJq1YsYJzWSZV1tqaipCtr88ePdpavJcsAZYsAU9Lw0CViuVXVYmVa9YIfceMYZ999plUW1vLlUolZFmGqamp9ODBA25qagqD4GAJKpWAF16gAkRrwt2zJxUwDh+mwD8rK+DYMfTX1+fxUVHwnDIFHc6AvDwqsh040OloOezfTw6EJUvo+6Cmhs611r3uTWCMwdTUVI6Pj2eBgYGdt7b88AMVG7sC5/SZBg6k6+b999v8+sKFC+LQoUMFx+6s6o8JpVIpaTSa55VK5UJPT0/7qVOnqrKysiDL8qjQ0NA5SqVyBOfcWq1W7/3ggw8e/aWvgw7/S6Aj2zrooMN/DIyx5qRbWZbR0NCgYIy19oi7u7m5Gf6VFG93d/fmUU35+fmIioqShw4dKuERirKZmRlefvllGBsbw8zMTGhoaMAff/zB9uzZI/Xr149bW1ujuLgYISEhbYKVrK2tsWDBAhw+fFiuqalhzzYtalpb5iRJYlevXpXr6uqYn58fi42NFSZPnozDhw9L+fn5PCYmRlq5ciVvv9jJz89HcnIy+vfv3/KgkxMR1Q0bgMmTwTlvUVc+/bSZhKtUKujr68uiKLLs7GxS6SWJFnj795OqzTmpTE5OEJVKOIeEkGL85ptkO1y+vO2CEyDr4yuv0N9++CHZCzdsaPXrvm0USgMDA/jcuEEEb+JEWgR+9hmRlaZZs60D7bTHLjc3F05OTl0fsF69uuy1TUxMxKVLl+SKigpmbGwsh4eHMycnJ8nc3BwlJSVYv34979RKamFBIXMALVANDEjB8/EhonToEPWynjtHKm27kVbtoaenB0dHR/ncuXPyvLlzeXdpwWq1GpmZmQCAIU1Wez8/P/j5+SEnJ0cy+uEHnjJ4MMpcXVG9aRNeHDwYbPlyOHKO0tJSHDt2DDk5OQCoQBMcHIyUlBThb3/7Gw4dOsQBtBR0OKeF+rRpRAStrKgYsnw58OabcBg3DrMrKhD23HNYv349JWt3Q4ItLCywYsUK3LhxA66urnBxcQHnHNHR0bh9+7aMgwfpvO0Cvr6+zMzMDHv37sXAgQPx4MEDnDlzRhwxYoTQnng1Y/9+Ot+vXOn89woFnau7d9PYOlEkJbkVMTcwMMCYMWMQHR0Nj9ZBXNp95OJCPykpVHzR0yPiDVDxResK2b0bkiTh3FdfSYH79/Nezs60L5cvJ+fA//t/lBD/j3/Qft+yhbavHWxsbLBw4UIcPHiQ5eTktLmO6urqEBcXh5iYGNjb22umTZumwIQJMH74EHPu3kX9tGn8/PnzcHZ2xoABAxgAJooiMjMzpfz8fKjV6m7bFzqgtpZSrg8fpnvGjBlE3j7+mNLAHxM1ajWOXbgga/T02OGrV7Fq1Soq6n3+ObljBgwA/v53nFQqodFoMLBdu4EWvfr3R69589jAwMDmHVdQUIBffvlFvnjxomLQoEE0WurLL0nBvXiR7k+tvkuGDBmC6upqubioiEfOmyfZP3jAS2fNwi2VCs/X1aE5TR6g893fn1wSrUd1eXoC+/ZhYk2NkLdoEcwOH4bvqlXcztkZpYGBsLC0hLGxMde6ZqD93jl0iPbhN9+0uFsEgVoVWhVV8rZtk4q8vVm9hYWst2hR2y/C+nraZwsXthSGtNi9mxLgFQpyHt2+TXkFXeURNGH58uVs586d+OGHH6QVK1Z0dFM0NrYNqesKX3zRqWtHX1+fK5XKJ04ib4+mkDqD+Pj4dxwdHfX8/PyYNnfFz8/Poays7FtnZ2djQRBYeHj4s6GhoarO8mB00OF/I3Q92zrooMN/DBERET6WlpbDXV1dGWMMLi4uitu3b0+Jjo4eHB4efkxPT+/Fvn37+vwnquAAEBUVBbVajZkzZ/LH+bI3NTVtVpMUCgX69evH9PT0UFhYKKWlpXHGmBQZGQkHBwfWemaoubk5ysrKpIqKCnSWMm1lZYXGxkY2ffp0dvr0aWns2LFwcnJisiyzrKwsKBQKlpqaioyMDLi4uDTb+/bu3Sva2tqyCRMmcO1jYIwWfyoV4OaGu3fvssrKSvj5+ZEas2RJs+Jx+fJlKSMjg12+fJmlp6eLQzZv5rh6lWayDh9OlnR7e2DVKsTFx8tWH33ErWfNorTaHTtIvXrxRVK4pk4lW6WjY4ud/J13SO1qvYh//XVadB06BMyahYYVK2CwYQOsevak5xobU29kfT1ZNwcNglqtxrVr1+Dq6oqYmBjk5uYiIiICiYmJcmFhIbtw4YJYUFDAioqKWFxcHBobG8EXLwbi41E3cSJUKhUkSUJOTg5+/PFHMSkpiXt4eGDs2LGYMmUKGzZsGPz9/ZmHhweLiopCXFwcsrOz8eDBA7QP32vGggX0mcPCaJF84gSpkl98QcRx1Cg0z6vuBs7OzuzkyZNsUEoK9MvLW8YPtcMvv/yCCxcuwN7evoO6O0StZn1OnMDALVvgGxzMEhMTYRYWJt3NyGD2Tz+NkydPynfv3mW2traYPHkybty4Id+4cYNNnToVvXv3xs2bN2ULCwvJz8+v42LayIjOqeXLSZ3auROKU6eQ8Y9/wKOiAk5pabQfWgerdQIjIyO4urrCwsKiOXG+vr4eiYmJ3Pv+fRhs3twtSYuKihLLy8uZUqlkZ86cgYeHB28qmkiiKLI2FtSHD6nYMnVqt84CAHSMpkyhAs/WrURW2hGQ1NRUaDQaafDgwZ1X+Rij87qb5PVTp06hsrIS06ZNY833moMHaRTbjh1ky96zh1T2boouFhYWiImJQWBgIDM2NoYkSdi5cyfOnDmD/Px8uLu7iwsWLCDCqVCQDXr4cCgCAuA+fjxsW+0Pzjnc3d1ZREQEi4mJaRkF+Cg8eEAZBadOtViIBYHyFmxtyWremph2g5qaGly4cIF5e3tjzJgxMFMq6Rh8/DHdN6ytcU1PT0q8fp3pm5rKnWZm1NRQSNff/96GPJuYmMDa2polJycjICCgZUzchAmU1m9l1Wb0mrW1NfPv3ZsFWlnB5Pvv2QkfH6QIAqqqqxEXF4dbt27B19e3pVA6bx4VG9qP+gNwv6QEv9XUyCnu7syupERy3ruXmU+eDL3jxwFPT7D2hQ0zMypkzp1LbTTa83nsWAqq1Ggg29ig5K23WDbApv36K1OWlUHROrCztJT2/6JFLfuhspLaHT7/nBR9Q0NyH73xRqc5Eu2hVCpRWFgoZ2dnc1mW27ZO1dbSd8FTTz1eX3hWFrlXli5tvsYiIiJkV1dX5tydFf0xYWRkhL59+yrs7OyY9h7DGIO7u7vCx8dH38XFhUVGRtY+fPjw1/fee+/Xv/yGOujwPwSdsq2DDjr8R9DUjz3fzc2tecHv5OSE1atXG/7xxx+Tb9++fU+hUBj+actjJ6itrUVRURH75JNP8P777z9xQIsgCBg6dCgbOnSodtXCExIS5AMHDiAkJASBTbbY+vp63L59mzcpSh3g6ekJT09PFBQUoK6ujnt5eUGj0SA3N1dmjLHa2lrY29v6pQpMAAAgAElEQVRLjY2NbP/+/dLKlSuF+vp6VFRUCPPnz29W0u/cuYPLly+LM2fOFFSrVkEyMWkOQjpx4oQ46exZgWnnzYKcBHp6eqxXr17SzZs3hYaxY6E/Zgz90sCAFOw5c4Bhw2BtbS2fOHtWNnzmGebs50cWY4BsgSUlZKldvJhGcjU2kgXUwIAs5c89R/9GRxMhz88nguPpCUmScOiFF/CeNo137Vr6Ny6OegknToTQ2AhZlhEUFITi4mI5MzOTAYCtra1cVFQEQ0NDnpubKyclJTFJkpCeng7ruXPl8ooKJn71VUtie9NhW7JkCZydnZuPhbZQoVAosGrVKhQVFSEjI0OKjY3lVVVV8vTp0zseN85pgTxvHrkFjI2JeO/dS5+vm9aC1rC0tIRSqYTay6tlFnA7aDQa3Lp1C3369MH89gv77Gzg3j3wf/8bcHCAIYC///3vwOuv850aDb788ksAYEuXLoV90/iofv36saKiImgTvuvq6mBnZ9f9atnYmH7efBNsxAgM9/EhpR+g4/o46lY7uLu7Q6lU4mBSEhYqFGhNzyRJQlRUFDIzM/HgwQOpvr5eaOqnx/z589GrVy+Iooht27bxyMhIBLVO3l6+nLa1qfXgkWCMgrguX6Z+/JAQIlCMoaioCBUVFejVq1fXNwcjIyIydXVdjmIqKCiQfX19WRv1mHO6Dvbvp4LVqVOPJECiKEKj0bCIiAgYGxsjJydHLisrY5MnT8bgwYPBOW/7AmZmdC3Z2lKLSLu+WCMjI/j7+yMxMRHh4eEYM2bMo++Dr79O6mn7c3zECHLEBAaSavoYxSatWycgIACOjo5UYLOwaCk4PPccjn7wAV/31Vc4PWtW5y8SHU0Fxk4IX69evWBgYCCdPXsWzz77LOec0zH617+ol/+558iJA1BexPLlUM2di57Xr2Ml0Jy6n5qairCwMGzcuBHjxo2je/v333c5EkutVqOyspItWLAAR48elS/OmyfPrqlhzidPgqWmkm3b0ZFIqBZWVjQJ4umnaSzac8/ROfL118CECWBeXrjw7ruinkIhRM+ahZkzZrT87a5dlNORlNSyH86dI+dRUhIF7125QgWJ7saFtUNdXR1u3brFjIyMEBUVBXNzcyrcAmT3T0/v0O7QJTw86DuiaZ+lp6ejrq6OD2o3T/z/T1RWVooNDQ3f/I+9oQ46/AegI9s66KDDX0ZoaKiJQqH4u6WlpUX70DE9PT3Mnj1bVVRUpDI1NW1r5fuLmDVrFr777jsUFRXh119/FadNmyY0NDQgNTUVubm54s2bN4XVq1ejtUr9KAQEBDBra2scPHgQhYWF8vTp09mRI0dEhULBQkJCuiU0kZGRcHR0lO/du8fCwsJga2sru7q6Sunp6cLw4cN509gnWZIkXLhwAZIkQV9fHzU1Nbh58ybOnTsHAPzMmTPiNDs7gf/yC8ycnGRfX18WHx/Pfe/cwY2yMsnBxYXr6elBkiReVVUFz5oaLNuxA8qcHEBPD2lpaZAkCV5eXmQ/fOUVLDhwQHE2IgL79+/HoEGDxHHjxgkKhYIs61pkZFC/6tGjpDbV1JBSN20aqRoNDbTw18LGBnJlJTptCwgMpACppCQYzZoFx5AQODo64rXXXmOyLEOWZfC2rKDti2zZwhpsbFD2zDOorKyEgYEBrKysoFQqO+11BUgFsbKygpWVFTw9PXlAQAB2797NnJyc0OWCMDmZ1MOcHApJA2jxP3t2d4e6Gffv34csyzDjnBb7nYzp0dq/J7fqKwZASdavvEL9xq3Dw/LzgZwczPv+e4SFhclWVlawt7dv3j+cc7QepdWjRw+WkZHRceNqa0ktc3EhhW3bNiJRf/87qZuzZ5NC2Nms6McA5xzm5ubifXt74XB9PYy//14cNnmyYGtrix07dkilpaXcysoKvr6+vLi4WH7uueeYUqlsPl8EQcCcOXOwd+9ehIeHY+zYsfTCgYHU9/8kYIxswWZm1IPq7Ax4eqKxsREajQaZmZnYsGGDrFKp4O7uLk2bNk0AgIqKCly/fh2BV65A1c34Is45GhoaJAB0zi5cSPsxKIi29ZdfHkm0tYSPc47bt283b/lbb73V5TkNgI7fgQNUPMvI6DBeb/LkyTAxMREjIiKEixcvwsLCQtTX14dGo4EkSZg4caLg7u5O1/batUQU25+LWvj6Uvgc0OnIp127dknl5eVMFEUmCAJkWYaRkREctMS8sZEcNU2oqalB3wEDsG/hQhgPGkTE0bfdKGVb207zAgAqoHl7e/OEhATU19e3fH8oFNRvvGsXEBVF96yQEGpdaTXFQHuLGTBgAAYMGICIiAhERUXJAQEBjJuZUWFn9OgOKfy9e/fGgAEDxCNHjrDXXntNiIiIwIHISNl43Di8tHgxM/j0U7q3xsXRiCxt+rpKRWPXli2ja2zZspbCZl0d/A8e5MqsLDgPGACuHYPY0ED74Ntv6TyWZXJJbN4MBAdTK89rr1EIZnR0ty0fWjS1ceH69evQaDRYu3YtPvvsM/z++++IiorC8OHD4ZeVBWWr4ytJEk6fPg1/f39YN7UrtQHndG589BESr1yRTgwaxN3c3GSVSvXn+8KeECNHjjQ5derUvtDQUJ8PP/yw8n/qfXXQ4a9AZyPXQQcd/jRCQ0PHxMXFhcuyvMHR0XHwnDlzjDtbNDLGYGxsjE77aP8CtHOeq6urkZeXh5ycHKSnp+Py5custLSU29jYyCNHjmRP2iNuYWGB/v37IzIyEhcuXGDl5eVs6dKl3OARioKDgwMiIiJYenq6rNFo2Pz581lgYCAfOXIkLC0tUVxcjOzsbOn27dtITk5mAGBvb49Lly6J8fHxfOzYsVCpVEhKSuK2M2fCpndvFJWVSbWCwDnnUuDGjfy8vz/LzMoSk5OTWXV1NXN2dsbo/v1Z2p07cFm0CL/88oscGxvLUlJSMHLkSHB7e0oGP3UKbkuXol+/fjh//jwSEhLg5OTETNuH8KSlUZ9lRgaNBJo8mVTvn38mpWb4cFrs9+0LCALqyErcVpnUoskSrw4IQPS9exi2Zw/Qty9Yjx6dE/TWOH0aCkmCyaRJsLGxgbm5OfT19Z9onquxsTGsrKxw/PhxWFlZoTkIr6iIgt98fWlxfv068MIL9Lh2Zrmvb6d9t+1x584d5OXlySMFgeHOHepbb4eSkhKkpKRg/PjxLZ9blqn38qmnyHraen+cOQPU1UF/yhQMHDiQ9evXr9udZWFhgZtHjjB/b2+whw/pMyxfTgTiwAFSo2JjicSOG0e9uoyRKmdvT4R//Xpa1D/hNTp48GAeFBQEp82bUVhezmOqq6Xw8HBWXV3N1q1bh5EjR6JPnz7w9fVlgiB0OO7GxsYwNzfH2bNn0d/DA0bTptGx6cr+/yhYWVGLwOHDwJYt0Hh4oMrAAKWlpZAkiUmSxAoKCvj169cRHx8vRUVFsZycHNgcPIie69dT4FknSE1NZSqVSnZ3d6d55F9/Tdb7F1+kffuIgl5jYyN27doFABg1ahQWL16MK1euiGPGjOGPmooAgALsnnqKCI9K1UGNdHFx4aNHj4aXlxdMTEy4ra0td3Bw4EZGRjw8PFzW19dnJd99B010NKTVq6HqalQYAPj5kXK8eTM5PzhHTU0Ndu3aJZWUlPDJkyczNzc33Lx5E6IoNmdhiHl5qF28GOd795bziovZ1atXxVOnTvHa2lo5cPp0NnnsWAY/PyKmrY/vqlUtoYXtUFBQgKNHj+Kpp56SXbXjtdRqIrBeXkS233+fgvQ++eSRDg1HR0dERUUxfX19yoxITKQ5602FmfT0dNjY2IAxhr59+/Lo6Ghma2uLgIAAODs7s9jYWFZRWSl5vvoqw8qVpA7/7W9UZFCp6PzT06P75q+/UnFrxAg6p0NDYfnZZ+yORiMbHT/OMidORE8LC7qnzp9P+0U7CeLmTTqn9u+ndgrt+RUWRgXSzsLOmtDY2Ijff/9dPHz4MM/MzISNjY0mMDCQjxo1Cm5uboiNjcWdO3cgnTolNwQEMLWDAw4ePChGRkYiOzubXbt2DWq1Gq6urp2/wa1bSMrMRJ85c9i0adOe+Pv1r8DS0pLFxsaqRFH8MSoqqi44OLjx0X+lgw7/XeiUbR100OFP4R//+MdsfX39H2bOnGng5uYGhULxn2XSjwlzc3M8++yzuHjxIg/XKggA/P39xYkTJwp/dvantbU1VqxYwfbu3SvLsiybmpo+ckVhaWmJd955BwDYli1bxJycHKFnz57NBNHJyQmVlZWK8vJyODg4yIWFhayyslJyc3MT0tPTERgYiNDQUAYAf5w6BVVsrNSjtFRwCQuDjY2NUDx5MlaSlVjIzs7Gvn37MObrr2WLrVtZ+uzZUtzmzUwQBPbUU0/h/PnzkkKhoA//0kvUj3z0KGymTsXq1av5Dz/8gD179iAwMFAaM2ZMS8/7rFkUGLZlCxHCAQOIjE6ZQiFUa9eSfXL3bmDTJrDjx9EnPZ1SdJv6J4uLi3Hnzh2IoojGxkbU1dVJ1fr6HA4OZDNVKCjhtjsy+8EHf+q4tceAAQNw/PffkXzjBjy3biW1/uOPqSedc7KNr1hBhHTaNFJ558yhoKRjx6j/shvY2NigtraWaWbMgKKL8KfDhw+DMdbW3vvNNzTXun1KMkCzq7Wj1zrD0aNEvI4dA3buxKkFC/DcoUNg1takPA0cSGF5v7Zqa9y5s+PrVFZS/39DA43CWrq0rcL+mOCcw/LLL2F85w5Ks7I4ACxYsKDz9ONO4OXlhcjISPl2eDiz1dOj5Oa/itdeQ46pKa5s2wabigpUzpiByocPJX9/f65QKBAeHg4bGxtmaWkJOzs7Oby+ntl+8QXsuni5vLw8uWfPnvzKzp0YqFCAnzpFRYrdu7stUGjt9NHR0eCcY82aNc1tI7W1tUL140wI0MLbm4oldnak6rYDYwzW1tZtVElZlmFiYoI7u3bBs6BAvvD883L2vn3MyMhI6tWrlxASEgIAyMrKQmFhIdLS0qTZs2dzx7VrieBVVEBtYoIvvvgCsizzFStWQKVSadsbMHfu3OY+8qO//CJyV1fhWmoqc3R0hJmZmTBo0CCMHj26xX6fkUFTBnbuJNVXoyEFXasMt0N+bi5MKivh7OTEMGsW3YfMzKhgdP48ZVg0NNDjpqZE4s+ebZsK3g76+vrN0ykQGkqEubERaWlpOHLkCFxcXKSxY8fy+vp6qNXq5hnme/bsAQCYmppST4tKRX3uFy5QRsXMmdQDff483ds2biQ3yerV1GdtagqFnh4C8vLYx6amUJw6BTuNBrbBwWTZ/+EHOp969qRiTv/+be8NQ4cCV6/SOLXnn+/Udn/06FHN1atXFebm5li7di309PSgp6fXfKN1cnLCG2+8AYVCgbvPPisn3LrFsr79Fs7OzmzatGnc2dkZ5eXl2Lt3L2pqalBdXS2XlpZKQ4YMEUaNGkUvsmwZZFtbZvnGGyj+9Vf0+Asjv54U8fHxkizLxwA4Abj5z3/+c9l7772ns5Xr8L8aOrKtgw46PDE++uij55RK5Z5FixYZtLaz/jeRnJws2tnZCRUVFRg5ciQGDhz4p4m2FgYGBnj++efZ9u3bWXJyMry9vTs+SZLaqEyccxw8eFCura0V2isDFhYWmDdvHsrLy3Hy5Ek2fvx4nDlzhnPO4evrKwNgpqamYlVVldDQ0ICzPj6Y5+cHExsboKEB9sOGkd0Z1Mv4wRtvAJcvM7i64uWAAJ6fnw8bGxt8++23speXFwfI5tyjRw/8cf++xvOnn3gfLy9+LDlZzsvLYwBw+fJlHhMTg/cXLwavrSVbpLs79a/27k0EVLuoEwRSugCyz06cCI2JCQIvXqQwtTVrgH37kBoYKCfk5jIbGxsIggCFQsEHDx1KvYwAqb8NDbQo7Qrz5hHZP3Dgzxw6WvRWVgJlZVi9cSP+eO01CWPH8uZQpUOH6Hm1taQs+fuTGmpjQ5bOdeuAsrJOrbSt8eDBA8iyDHbtGlmy9+/v8Bx7e3uUlpbSeDeArOvu7pSs3Bkh/de/KAG+upq2aeFC6rOtriar6ZIl5Dro1w+YNAn6+vrY8eqrWLBgAVwVCrKgPg6USiJu+vpk762spGMTFtatctYpLC0x5I8/EOHtjVmzZnWtinUB85oayfrqVQGnTz/Z+3aChoYGhIeHy9fu32ezVq1C//37EaJSAVOncq16OnLkSKDpeIiiyM4YGclxhw5hiiAwpfY8bUJpaSk0Gg27dOkSpv32Gx66uMB461ayC3fR11xVVYWYmBgkJCQAINfLyy+/3OY5QUFBuHTpEoqLi6V79+7xtWvXNucPdImDB6kgVFraPJmgOzDGMNzLiw2/fBnYtYsNDAxkWVlZKC8vF5KTk8WvvvpKaHIeScbGxrJC8f+x991hUV3r12ufGYZeBKUjHemIShMLFrBrFEti7N1EU+6nuUm8iVfT1JjEmKgxxt41sYdYEVAYFQFBRJFeBKT3NjPnfH+89KYmuff3e75v1vPwKDCcOWefffbs9b7rXa9YdOTIEcHFxUWYtGoVp/D1xV1tbaHX5MmYP38+09HRAc/zkEgkUCgUrfJ3QYD3zp1c/hdfYOL06d3XjffqRc/VF1+QAiMvj2TtzQELhaLVnHHpUlj98gtcjh7FSRcXfoG7O8ecnUmh0VzmYWxMcv4lS2gtXrKE1q3Vq+lYHer+L1++rJDJZKKUlBTeyMiIs7e3h2TIEODLL3EuKwvNa9axY8cEkUjE9+/fnzMxMWGPHj0CQEEkGxub9vUCKir0df06Xc/ly+Sk//33tI5dvUqBhY8/Btatg0p8PD7t1w/3HRz48lu3OMPjx4lYe3lRGcq773a/5ixbRtfeu3enkpWsrCwkJCSIFy9eDHNz825rGjQ0NABBgKO/P+e4aBEEAwMwxlpumImJCRYuXIjdu3ejd+/esLOzE8XHx8uHDh3awhkmjB+Pkg8+wLHvv0fwmjWIiYnhe/XqxZmamr7ys/8q4DgOjDEfFRWVMQ4ODkhPT58MQEm2lfhfDaWMXAkllHglfPHFF++rqal9P3/+fI3/LUQbAKqqqtjTp0+ZIAhISUlBv379oKuri1u3bvFRUVG8m5vbn2Leampq4DgOoaGhvL+/f/tUQkUFbRjnzWvJ0sbExCAuLo4tW7asy7q3pkwawsLCMH36dDDGEBgYiEGDBjEA8PDw4Orq6mBvb4/ghQuZ2vHjVJM4fDiZ2TTXsp45Q5nNnTsBLa3m+lns3btXqKysZOrq6rhy5YpCKpVyhYWFwuOKCpEAMNW7d1Fmb89kCgW/atUqpq6ujszMTLjv3g127hyOi0TQ/eAD6Lm4EMkODCSjMyMjyri2vxjU1NXhIGMY+u231Jf4xg3kWFpi1I4dbKggoP/atXC3sIBDsykPQBvQYcMoi3PiBGXrOta7WlkRCW4yBXsplJaSa6+PD/1tWhqwcCEaJ03CxbQ0pufnB+O2vaB376Z79/PPRLJv36ZxNjamjW9aGmWQZs7s1pSovr4eSUlJGO7kRC2kOtQaFxUV4dq1a+A4jg0ZMoSCAG+9RdlnP7/OBzxxgurkjYxIEhscTLW6HEcbcjs7+t7MDDA0RL61Na5duwYAGDhwYEsW7qVw7BjNYSKeJM89f75LR+8Xguche/ddSH19MXHSpFcuGTHavZtVxsWxnwoL4eDgAO0X9FjvDs+fP8eOHTtQWloqjBs3jrn6+JAMt76e6l41NEi10AYcx8HIyIip/fADUm/dYvd0deHc5jXHjx9XmJubY6WREYswMxO0qqthsHQp6855PiYmBocOHcKzZ8/g6OgorFy5knXV4szKygqenp54+PAhysvLmY6OTosJXrfQ0Ggt4/Dz61J63Q5VVSTT3rGjpVa6V69eMDExQf/+/TlPT08MGjQIAQEBbMCAAZyXlxf09fVZREQEGzp0KI5lZCgMdHUR/N57XLP0nDEGf39/PHnyRLh9+zYbMmQIuIwM8Hv3spO2toKC51mP0ngNDboXIhHN76Iiams1axZJ15cupSDbqFHQnDsX1728kPTkCbNeuBB6HVzzs6KiUPvHHwgzNxds7e0ZFxBAz4pEQmNjZESKgIULAVVVREdHC2ZmZlxtbS2fmJiIyMhIhjlzhDxLS1ZWXo6SkhLMnTuXjR49mg0ePJhrLuHIzMxEamoq0tPTBSsrK9bt/NTWJmXQhAkUyPLyomdpyRIq1XBwALZvBxs6FOJr15gkIQGqV69C5O9PBH3ChPbdHzqC4+j4u3ZRPXfTunT37l3+/PnzrLmU4IUoLydlxocfdlnSo6WlBV9fX/j4+LDKykrk5eUJ3t7erZ+hIhE0Vq9GYUqKkLlrF0vgeZaRkYGEhAQMHz78xWVCfxIWFhbM3Nxcx9/fX2JoaIiEhAT1GzdufB8QECC8+K+VUOJ/Bn8t7aOEEkr8f4MNGzZYfvHFFz+pqal9vnTpUg3jtr1J/xdg5MiRbOjQoS0ZlXv37il++eUXhIaGcrm5uX+6CSjP88jJyRG6NFnT1SVH2xkzKAMKIDExUbC1tRX0OxLTNmCMQSQSQSaTYcSIEe3MrjQ0NDB58mSMHDmSrmX8eKpHVFGhbE8zoqOJQLRBY2MjCgsLmYeHh0JFRUUxduxY0WuvvYaCggJeW1tbUTNyJIofPxY0Dx7km+SF8HZ1xRxtbWGXry92jh+PCf/8J6L27+erq6tbD3zlCnD3brfj0wJHR+DAATSYmCB69mwiqnFxVGMYEtL6xXEkFX7tNWrzVFxMRLcttLRe3nX33XeJMMfHU1aprIz+f+AAoKkJjQEDEBAQIFy5cqX1ZMvKqE700KFWZUJ+fvta2OHD6d6Wl9N5dnHNly9fFjQ0NATY2VHgpQNqmuaFmZkZSWabCXyzOVIzGhtJHvrwIc2r5GSSw8bH0++DgjrVg8vlcuzZsweCIGD8+PH8y7Tfqa+vx4EDB3D8+HFkJSejoS0p0tGhvuOPH9OGXqF44fGacTYyEjuXLsXcSZNe3QTx8WMYbd3KnG7fhpWVlXDo0CGhqKjo1Y4BcvoOCQlR9O3bl1+zZg3n0WzExRgFX7ZupaBYs0qgDXR0dGATGsquBwVR7/Am8DyPoqIizktHh8OKFRhz9ixLVFeH0IWzdnl5OcLCwoRLly6B4zjMnj0bs2bNYj0pbLS0tLBw4UJmZ2eHJ0+evNyAa2tTiYCbG5VF9IRVq6i2uam/fedDaUNPT6/l+6ZaZchkMjQ2NiJLQ0Pk/N57HHNzozWnCRzHITg4mAFAQkICkJIC7du3Mfm111hUVBRq2zwvncDz9MwvWkSy8jt36Bn78UcgMpKe/dOn6bxVVfEgPl4ICgpCV/P717NnEe7oiNjYWHbhwoXWB3P0aAp8icXA66+jUVUVmUOGCKZHjnAeHh5YtmyZaO3atdz06dMRm5XFW86dC0VFBe/k5CR0FbDy9vbG+vXrYWtry3799VehTXeErmFhQQG8hw/JIf/GDVpboqMp2Lh8OQxlMmhyHHZ7e2Ofn59C/rKfqc7ORLT//W9UV1bixx9/FG7evMmCg4ObFRsvRmwsSfd7IMWqqqrgOA7W1taoqKgQ/fbbb8jLy2v3mkkNDWxcdja0tLR4AFiwYMF/jGgDND+tra1hYGAACwsLqKio6ANwfuEfKqHE/yCUmW0llFCiEzZs2KAbHh4+ISoq6h2pVLohPDz8a5FI9N6AAQM8p02bptbJVOt/AeRyOU6dOsWPGzeOVVRUyPPy8kRlZWVMU1MTffv2VTTLql8VSUlJiI6Oxrx587guHYMNDWnjvm0bMGMGevfuzW7cuMFEIhEMDQ27NfSKjIxEfX09HF7UcsnCgt7jxg2qp167lur/1q3r5EpbVlaG+Ph4YeHChZyzszNnaGgIjuNw79495uvry2Xn5PCVNjbQfvSImxwczESWlsCRI9D79ltm8913KHr2TGGqpsYeuLggLCKCicViMhGaN4+yqtnZ1IqmDaqqqhAfHw9fX19kZGTg+vXriqdPn3ISCwu4v/Yanf9bb5G08+hRkm8HBZEhkK8vZVcePaKa6cGDW1sArV5NxLmDSzB4nohvWBhtqBcsoM26szOR0UWLaLPegeBYWFgwqVTKeJ6HZbMR2kcfta9RTk6mLG8zWWSMvt+5k4jarFlIzcjAjz/+iNjYWN7FxYVdv36dBQcHM4PycsrUd6i11tPTQ3h4OBQKBfyuXydZ+Jdfdr7PkydT+6n166nP7rRpVDJw9Chw7x5lMTvMpS+//BI8z2PlypVwcHDocYcbHR2NX375BZGRkaioqEBJSQmEzEzcKimBND0d169fh4mJCQwMDIjMFReTg/kLUF9fjxs3biAmJgZzpVJYyuUUpHhZyOU0H3r1gsjPD25ubiwtLU1ISEgQvLy8XnrXnp2dje3bt6OiooKbNWsW67Je3NiYsp2xsaRqCApqF9ApLy+H7+zZ8Jg6lWk1uddHR0ej5MkTjPDxYZyrK1RMTXFBTw/3799n169fh0gk4k1NTdnvv/+Oc+fOITc3l40fPx6zZs3q2tG5C1RXV+Py5csoKSnhAgICXu6CLS1JhTB7NmWCu1pnNm0CRowg87+X6MncjJqaGkilUsTGxvI8z7PRkyaBmZlRprY5sw4KDJaWlgrS8HDm8v330Jw1C3369UN2drbi+vXrbNCgQazd+icIpMZZtYqO5eREGWdDQ1oDxo/vshVXeHg4mzBhAjoaVJaXl6Pw+HEMqa+HxvTpuH//PouOjgbP89DW1oaamhoEFRXc1dTEyZMnodbYyPsvWsT10dentWPZMhj07g3fwYM57du34ft//g9zeYGhpp2dHUJDQ5mxsTE9K11BoSCzxdJSeoZLSuja1dQoQPLHH0BFBdiPP0L1p59g5+WFe/fuMZlM1rMioAk8z6PaxgaSQ4dQIBIhrqQEay9ojXYAACAASURBVNasYYYv6knfFhkZ5MfRTRCmLTQ1NSEWi+UZGRmQSqXMzc0NUqkU5eXluFBUpAjt25cbo66OqatWMYOXbJn4d4AxhszMzIbS0tKYgICAhP/aGyuhxCtCWbOthBJKAAA2bNggBjBBTU1tuUgkGmlmZtbg4OCgY2RkhD59+kBHR+c/GrH+qzh79qygp6cHNzc3uLu7i5uzfoWFhRg2bNifzmw3NjZCJpOxhoaG7l+0dCnVAa9cCfMdOzBx4kSEhYUpwsPDRbNmzYJtF4ZP8+fPx4EDB+Dk5PTiGreqKjIUiooCEhIo+7hhQ6eXPX78WGjKMLRcb7OTd3h4OBwdHYUJEyaI5M7OUDlyhIx9PvoIWLAAZoxhTkWFCBs3YpWlJUtLS8Pp06cRFRXF8zyP2VFRnHlmJpmltUFzG6+ffvpJqKqqgo2NDTd8+HB4tG3v07yB/vxz+re8nLLKCgWNXXQ0XZtUSiT855+JZDbPt/p6qu8eO5bk03PmULZo61bKxm7e3PP4gTJxrq6ueHj1Kj/0nXc47N/fvs+wXE6mbKtXd/7j998H1NQgf/YMF86d4x0cHASFQsG+//57ALQBR3FxtyRz2rRpuLZvH4oA9GlypG4aPJK9JycToe/bF/jsM6CggL5/802S8d+8SRLgjRuJkDRJWNXV1VFdXd2uzvfBgwcICwvjR4wYwclkMjg7O+Ps2bNo2xpswIABsLOzg2FBAU4JAsrKygAAx44dA0COzboeHjBctgyeFy8icv9+WFlZwcLCAn/88QeSk5NhbGwsyOVyVlBQ0HJck08+oVr8V0F9PWU0mzJyIpEIU6ZM4X744QecO3cOr7XtRdwBgiCguLgYWlpaCA0NFYyMjDBmzBhm1E2/cwBEFtevJwLk70/z7733AMbAGEPo2LEInjy55eW5ubkIPnoU4pMngfnzIfroI6yqrmY7duwQeJ5nN27caDFm1NHR4d9///1XCupFRkY2t/zD0C6UET1ixgyat4JAX23X5/Pnaa1YuvTVSwIAuLm5CTzPcykpKUJDQwNTmzWLatQXLKD2U001xdOmTWMpFha4c/8+7h85AhMTE8HR0ZErLi7Gnj17BB0dHcx/802GDz+kgOHOnRQoDAig8718mZ7rkBAKWL73XrvAwe7duwWFQsHq26h4FAoFqqurceDAAdjU1cHIwwNmI0dCoVAgKysL0dHRfFhYGGdubs6rqKggKyuLCwwMRP/33hNJJBIKGo4ZQwG5adNIvn7qFBHQ7lBfD5SXQ9zYiEHFxYr4Tz8Vmc6cCa2SEuDpU5r3YjGtR/v20To1axatdTxPLuUBAa2tEwcPppKQY8dgMGcOXFxchIiICObu7o6uVFHFxcUoLS1Ffn6+IJVKmUKhgIaXl/D6hx8y+3/9ixeLxa/2GZeY+OIShDbw8/MT+/n5YcOGDfj+++/BGIOKigo8PT3Z5MmTYTJwIIOREV3zfxHm5uba6enp/QF0NstQQon/JWAvlMIooYQS/89jw4YNKhKJ5Kquru4gb29vLVdX1577vv6HIZfLwXEcpFIpvLy8IOmphg3A7du3ERERgfnz5yMnJwdJSUmCm5sbc3Fxwfbt24WgoCAMGDDgT0cKvvvuOyEoKIi5uLh0/6L6enLItbNrIWwRERGIjIyEh4cHP2bMmFbH7yaEhIQoUlNT2VtvvcX12NJKEMh8Z/t2IpgODq0ba7mc6oBNTXH2H/8Q9M3N2fC33qIM+L/+Bdy7B8X+/Sj7+WcYvP462NChtGG2s6MMcFYWbRDr6igLHR/fYvrU2NiIZ8+eISoqCuXPnyveXr1a1HHjXlhYiF27dkFdXV1455132CvPm+pqItvu7lRr7eFB/XL37iVitHUrkfCdO4k8FBVRYOMVze+ys7Nx8ocfsGLfPmj/9httfNu/gLKEHeXsbVAZEIDU2lo4hoVBXV0dKSkpSEtLU4wdO1bESkqA6dMp494GgiDgl3/8A5OOH4d2WBg0HR3pFwUFtBHftInadDXXkt++TZvzjq3Umk2jBIEysqNHY+M330AQBKxduxYaGhqIjo5GSEhIt+f/1ltvQV9fHy3zcOpU4OuvIdjaQiaT4auvvmr3enFjI2zS0vDU0RFinoe8zfzt16+fkJyczAAKHFlZWZHi4F//Ar79tttzaIfUVLrOJ09oDrbB3bt3hcuXLzMAWLlyJXR0dDqtSQ8ePMD58+cBAKqqqsJbb73VuZVdT8jJoTICPT1g6lRUamlhx9df4w2RCGd1dXme5zkhPx8z9fTQt6SEAiNt5l1jYyOioqIQERGBoKAg+Lb1A3gJ1NfXY+vWrRg+fDgcHBzQY5CgJ0ycSMqRH36g76OjSQ0xZkyPrtwvg82bN2PAgAEIDAykefn116S86NOnNVs+YwYe+Poi0ciIb2hoQGFhIWtsbGTGxcUYFBkJj/nzIeY4Op+OSp5Jk6j85LPPiIT37UtKmKZ1ZkNTUPHjjz9GTU0N9u3bB57nW8oz+icmwi04GDavv97usA8fPsSZM2egqanJDxo0iEtISFCUl5eL3nvvPbSbIxs3Up10cjIF8fbtozVQW5vWwfv3qZxj2DAyd+vTB8KdO7hRVwfvJUugIxa3dlfQ0iJfC23trtenjRvpNcOGkYt5aCgZnsXGQqitxVf79mHq1Klw6tABged5fPbZZ9DS0lJoaWnB399f5OzsjCdPnkA3KgpmFy9ScOVV8PHHtF41KTheFg8fPsSFCxcwf/58mJmZtQbgm41Cc3Loc+S/hMuXL8vv3bsXIQjCO+vXr3/0X3tjJZR4BSjJthJKKIENGza8aWxsvHvJkiWaHQnhfxvPnz/H/v37hcbGRiYIAuzs7IQ333yzW6LM8zy2bdvG+/v7cz4+Pti4cSP69++PpKQkGBoaClVVVczMzEwxffr0P31hmzdvhlgs5j09Pblhw4Z13+u5WUqso0MZJVBGYs+ePcKkSZNYR+OahoYGHD16lC8tLcWaNWt6Zo/r1pH8eMAA2kzr6lLdZm4utZaprkaKoyPK9PTwdPFi/o29e7nUTz9FTXo6et2+jasjRmCxkRHENjYkHzx9mjZGa9fS8YuKaJPYhtDIZDKEhITwjx494mbPng0rmYyygZmZgIYGKisrsX//fgVjjAsODmZm3TgzvxLkcnLDfvNNusZDh2gz/BdVFU/v3MGZs2dhV1LCv/bTT52DGxkZVC86Z063x4i9cgXFx44JQRs2MHSUe5aXE2F+8qTdj5/ExCBu61ao19XhtXPn6If5+RRQWLWKMvRtsX07Zb0GDeryHPhHj8CdPw/U1OD3p0+FGCcn5jFwIGpqahQpKSkiALCxsREkEonAcRyXlJSEvn37CmPGjGEmJibt1SmBgSRrb5JcFxYWonfv3iguLm5pI1VXV4f6t98Gf/Uqzqxfj9deew2qqqrQ1dVFXV1de2mvIBDpCwtDp/HpCo8f02tXruzy1zk5Odi3b1/L9x4eHvD29oaRkREEQcD27dsFNzc3Zm9vDx0dnS4zgi+EIJAp1+HD4MPCcPq33/gp777Lfb1mDWyqqvD67t0QTZtGZnJ/Iy5fvozo6GhoaWlh1apVr2wo1w4JCcCzZxSEKS+n0ot33211//+TEAQBn332Gby8vDBu3LjWXyxfTpncAwcowOLkRAS1ucTk0CHU7dyJPwYPhnZGhhB4+DB7KXf7xkYKVHp5UemKRII//vgDT58+FYYMGYIrV66wXr168Y6Ojpy5uTl0dHRQvXIl7hsYCJaLFzNzc3PExsYiJydHUVxcLAJIKaGqqgofHx9ERETgjTfeaK80EgRS2Njb07o2bRqpR2xtyZxRRYWuq4Nnx6ZNmwQTExM2Z84cvNRnZn09ra8SCUnYV66kjL4gkIO5hwd+mzFD0J46lQV1cBlXKBT48ssv8f7773dup8fz1L6wqqrbHvGdwPMU1Lx37+V9Mdrg6tWriI6OxqpVq9obMl64QPetrOwvr9cvi9zcXOzduxdisbiBMXZYJpNtWr9+fdp/5c2VUOIloZSRK6GEEgCgrq2tzf4niTbP8zh37pzw+PFj5uvryw8dOlRUUVGBX375pdu2W9nZ2Th06BB69+6N/k1u1xoaGnx+fj4LDAxkRUVFQl1dHT948OC/dGErVqxAcnIyFx0dzd+/f5+tWLGi6wyanh7V7C5YAAwaBLmbG+Li4iAIQpcmSaqqqpg5cyb3zTffoLq6uue+xAMHkhRxxQoioIsXU4ZETY3qlAGcX7JEcHNzY2l37nCbJk2C+OFDwcDAAOKxY1lBVhYSfXzQ390dAMD37g0WHU29j3ieyN3p0+363T5+/BjJyclYsWJFK5HZtAlpeXm4eu2aorS0VGRra4vg4GD2l8hCWyQmEvlZs4YySx2NxP4MFAo4LF+OmQMG4LCVFTdRLu8cMElNpexWD4jJzoZ6QACPceNEWLmy/eZWR4cyn23lvIIA/S++gEF1NaT+/sj+8kv+zfv3uSvq6nD//HO4NrcvaovTpym714Zs8zyP4uJiyGQy7DtzBmZmZhCnpvIGNTXc/IMHEZGRgQxra9GUadNga2sLiUTCVFVVGUDZUzU1tc47X5mMyEQbM7Pmms+2tZ8aGhrQ2LEDYUeOQFcQFIYSiQhNG+yONbRgjMoBuqtlbYu9e8ksrqv69SZYWFjA0tJSyMrKYowxFBQU8Hv27Gl5kLS1tYXAwMC/tqtnjBQp8+aBe/99zHRw4C4cOACt7Gy86edHtbd7/npnofr6emzbtk2QyWSs2WDvjTfeeLFnQ3eQyymzXFNDNc+2tlQHHRREa8S4cXSP/+RzWVhYiGvXrvESiYR1GuNPPiHX/OZWgSEhRFi//JKyvHI51Netw1Bvb/z8yy/MsawMFt2tbdu2Ub32nj1ERHftIjLP88CCBRgzZgwePnwoXLp0ifPx8cHYsWPbLaR6o0cjV0tLiImJEUJDQzlLS0uFpaUl3N3dERoainfffRfa2tq4efMmzxjj2hHtnBzK7q5eTa3+CgqADz+kjPMLauetra1ZWloaIiIi+BEjRvQcKH30iIwi792je7V5c2tQk7GWDHr6gQNs3kcfUd/vFSta/jwvLw8qKiqClpZW57nOceSt8OmntHa+jBN5bi51Q/gTRBsAgoKCEBMTI9TV1bF2ZHvyZApYNM/Nv9h680WIi4vDhQsXAAArV65UvXv37rwHDx7M2bRpU3hDQ8NJAEnr16/v2tlTCSX+i1AapCmhhBIIDw/PqKqqWsHzvIaurm7nTfR/AceOHVMUFBSwBQsWMFdXV04kEkFTUxN6enq4ePEiBEGApaVlu785ePCgwtXVFbNmzeKayV5mZibLzs5mHh4e8PHxYTzPc4Ig/LmsVxPU1NRgZmYGLy8vVlZWxl+6dInr6nwAUDZ23Dgo1q3D8bt3+Ry5HJMnT2aOzfLhDpBIJCguLubDwsJ4a2trTkNDAwcPHhSkUinv5ubWmoG1tKTs9sCBtMFtrhFet47qXVVUkJKSwvM8z1VXV/ODBg1i8+bNYwMGDGD9+/eHQqEQbt68yUpKSoSMjAzcPX2aCQ8fIsbQUIGGBo7T0wObNAniNpvz+Ph4oa6ujmtoaMC9e/eECxcusASRSNF73TpO4uTE1Pv2ZUFBQVyPQYKXBc9Tj+otW6iXsKkpff1VSWJjI4TMTJS6uiLM1FQoLStjAwYM6DzHExJQVVyMcwUFcHZ27tKf4NKlS5g4eTLXa+ZMkotWV7fWfTNGm83Fi1sJTm4uNG/cgM3x4ygMCwMrKmIa8fG4P2gQqnV04Orq2uKeX1painv37sHq7bdJedB033meR0xMDI4fP4709HTU1dWhtrYWpRzH8szNkWZjgyGRkQgSBFgNHgxVS8t297BbFUZNDQUXRox48RiqqKBBSwvm773H9G/eZHjzze5fu3079fmeOrXnY/78M5naddESqy0ePHiAiooKtnDhQowYMYKZm5tDLpejoaFBWLZsGfeiEpOXhrY2MHIk2LlzcMzPh9/mzRQ4OH+e/rWzI0VJWho9i9u3E0kqKyPZ/IgR1AP90SMiPHPmkAolKQlVy5fjp5ISPuDMGc6ouBgiT08s3rMHxrNng4WEELFasoTUDqmpJKu2saEx3LaNpMbvvEPlE3l59Fz07UtBvY8/Bv79b6p1/vBDcr+OjaWSjIAAev316/T7YcMoeHXxImVyV65sDZA0Kyo++gh8ejr2nzkDt+hoYdSUKZx2QQG1H7S3p7prDQ0yGPTwIKIdHU3GX5GRwNy5lB3u1w+aWlpQUVHhz58/z5qDCp3uV3ExEbPmNniMkbT855+BoiIwNzfckkqhUCjYokWLOj2X4unTYbVtG/MeNowNHToU7u7unIODA2dpaYk7d+4IHMcJ1tbW7MyZM8zd3R329vakBFi+nGre1dUpwPjwIdXyP3tGLuYzZ3Yyg2yLvLw8ZGdnIysri9nZ2aHH8oXAQApOXLxIa8SCBZ2UH3ItLYTeuoVhQ4ZA1dCQWvIlJkJhY4Ndu3bBxsZGcHV17TqwpKtL53r5Mt3DFwXNc3PpHrb11XgF8DyP8PBwNmzYsM594dXUKFD49Gmn7gl/N/T19XG7qeyntLRUmDZtmnjw4MFihUJhqaamNqaiomLuzZs3NyvbginxPw1lZlsJJZTA+vXryzZs2DBCKpWuu3379gSJRMIZGRkJgwcP1rL7izV/L4v8/Hw2efJk1qeDm6mbmxvU1dXx66+/QkNDA4OaMn6///47FAoFN2rUqHZZ49zcXB4Ad+fOHcXTp09Fjx49glwux6effvqXDd4YY5g0aZLIzc0NJ06cQF5eHj9z5kyuU9ba0BDXXFyECbt3cxoJCVB7QaZv8uTJ3PHjx/m9e/fCzs5OKC8vh1gs5k6cOMEvWLCAw+PHtCEsL6eNVHPWIyeHNsEVFcD587Crq+PSOQ4ffPBBp5TCqFGjmL29PS5fvixwHMcN9PKCmlyOgoICZrp5sxDm5ycUffcdFxgYyHt7e3MKhQKpqalCRUVFswkWmzt3Lk6fPi2SNDaiJDGRlYjFOHbsmGL16tV/TRJRW0uGb9XVJGvW0SG3aE1N2jz+FbzxBsoLCvBjUBDEYjFjjKGwsLBT8IXPyMC11FThiSCwXbt2Kfz8/ET9+/fHzZs3+cLCQtZc829iYkIb9OxskoPu2dNqjGZuTtlEgEjJhx8C9+6BnTiBoCNHhONTprDzTS2jMjMzERISAjc3N9y4cYN/9uwZBwBDJ04El5OD5xUViIuLQ0JCglBXV8cAcl82MDBASUkJjI2NBX9/f9anTx+ULlkC7fx8qjcNCaHgy4gRPUs5Cwt7rE/vCFtbW2yeMYOt+cc/oHrvHpHPrgJYI0aQHLgnHDhAY/MCqbkgCMjKymIAYGpqii+//BKKpnZkvXv3Rm1tbc9qkJeFTEakedcucqdft46yczo6RBrmzCHSumcPPWvDhpFhoasrzYWQEKrHTUkhEsMYUFGB0sJC/PHbbwrn4mKRh4cHnGUylEokiCktxSNLS8FHX59h0KCWkhN8+CER6d69KfBkYUEke+5c+v2DB/RMqKmRLJkxku2HhhJptbSkczx9mn6enk5BrPp6ItampnTeYjEFCubPp2soKqLgi7o6oKWF2IQEXk0s5rwMDGhlk0rpPQIDiZAOGkRzXhDI8yEwkAInO3Z0Glo/Pz/u7t27wk8//cQAYMaMGe16mGPUqM6kjzF6/letAqqrIeF5gVdR6awOqqqiIEk3veXnz5/P9u/fz+zt7VFdXQ1fLS1yAq+qotpxTU3wixeDa/awAGjckpPJSTwtjRQDXcDGxgZSqRR2dnY4ePAgli5dik5u4DxPgaezZ2msjh8nbwoLC1IHLFsGgMolrl69ymtqakJzxQoOHEf3cds2CDEx0GpogFgs7vnDa/hwqi3/5ZduyzJa8OABKTb+JLKyssDzfPdeKkePdjtufyfKy8sBUFvFESNGMIDKBkaMGKHS2NiosnnzZjkANQA99KFTQon/PJQ120oooUQ7bNiwQQTAGoC/RCL5wt7eXn/KlCnqf5tMuBt8/vnnePvtt9FlP2sAt27dQmxsLL969WqO4zh89dVXmDt3LszNzdu9Ti6XIzU1Fenp6UhOTuaHDBnCXb16Fe+++26njXlDQwMePHgAb2/vVybi5eXlOHTokKCrq8vPnz+/hWzm5+cjKysLYWFhwigbG3jt3Mlw/ny3G8JmKBQKbNmyBTKZDMuXL0dlZSV+/fVX4aOPPmKoqqINb4davnYIDkaBRILf3d0Viz09RS/MKiQmEtmaN48yaXFxCE9MxO3btyGXywGQ6VRz7TxALtXPnz+Hm5sbCpKSeGtAKLGyEtXX18vz8/NFAGBoaCjMnj276zZpXaG4mDI948e3d07+5BPAyIg23H8GPE8ZPk1N/HLtGl9aX89NnDix/Ua/5aU84iZNQrqZGSZs24YbN24oYmNjRcOHD4dUKoWDg4Pi6dOnosbGRqxevbqVqEdFEYGpq6O61a+/Jkl/r16UqfTxIQfnb7+FXCLB5sOHW8ZWRUUFMpms5V8A0BCLMe/kSRQcPIjzTWqOtlBTU8Ps2bOhr68PxljnftaCQM7lMTGUqVu5sjVj2BEREUSOTp586SH9+uuv+VmzZnF9g4Mp0NDBUK0F+/ZR3XBXCpmSEsqCRkW9kGxXV1fjm2++afezd955B4WFhbh8+TI0NDQEU1NTNm7cOPTUy7rLcwgJodrg9esp8/vZZ+SWv2kTkW1jY/qZlhZlQWNjW1pevSy+/PJL2Nra8tOmTeParp+1tbX4+uuvMXDgQAQEBEBLSws5OTkwMzN78XU0lymYmtL4T5hAxOn8eZJCq6pSVvjIEQrEveK6VlFRgR9++KHFTK9HxMRQ3b2TE43Zrl1EVjugqqoKZWVl+PXXX4WAgAA2oK0p16lTJONv4wReXV2Nc+fO8c/z84Uhly6JaiQSeOzfD4MOaz2Kiqjmf8aMbk/xxx9/FKz09Njj5GS8e/EiSvr1w53XX0dJSYnQ0NDAN6amilQbGmAwcCA/a/Hi1sH/9FPg3DnKgncAz/PYvHmzMHjwYAwdOpQdP35cqKmpwZIlS9oHBE6fJnfu1FSaV59/TjLyhAQi3E2fdVu3boWKigoWLlzYLkOenZUFo6wsiIODsev11/HW99/3PD/y8ojcT5nSs5x81Spam5qDOK+IXbt2KSQSCVvcdrw6IiuLVCvp6Z3MD/8uHDp0iM/IyOAGDhwIR0dHtE0MVFZW4ocffmhUKBRLBEE4sn79eiXZUeJ/DEoZuRJKKNEOAQEBQkBAQGlAQEB8aGjoTxUVFf1TU1P7urq6qvwna7qlUqng5ubWdX9cUCbrwYMHiIiIELKzs1FYWMgmTJjQafPBcRx69+6N/Px8pKamsoymTZyRkVFLNqw5Ip+ZmYmzZ89CX1//lZ2A1dTUYG5uzqRSKfz9/RkAxMTE4PTp0ygvL+fd3d0F/4kTOaaiQrWMs2b1uPHlOA4DBw6Et7c3DAwMoK2tjfDwcGZmZgaDrVu7Jy/NmDULfFAQMo4c4Zy3bAG3ahXJO01Muq6dS02lnrfu7iRD1dKClZUV+vbtCx0dHeTk5AAA43keenp6fH19PausrIS2tjacnZ1hsXkzs42LYyl+fiwjI4NbsmQJ8/DwYE+fPkVmZiZcXFxevMtPTCSp7BdfUO1k2/klElH2ydj4hYfpEl9/Tde1bh2SMzP5+vp6TJo0qdM57d27V3758mVOr6ZG8Fq6lOnb2KBXr17cw4cPhczMTDZ58mQEBARw/v7+sLe3h4GBQeucs7AgwrFwIUlff/kF6N+fggdz5tAY29gAkyaB09NDUVGRorCwkKM/teDlcjlbs2YNBg4cCE9PT0jq6uSPq6q4+5WV0NHRQV1dXbtz1dTURH19vaCnp8e67OHcnOn09aVz++YbIiOurp0lsYWFlJl+BSlpfHw8r6Ojw5mtX0/ZzN27iTB3JP3vvEMy546qGEGgrOG6dTQvXwCJRIIhQ4bg1q1bAMj13MTEBL1794azszOuXbvG8vLycPfuXcHV1bVrJ3y5vNVQcNYsIkCGhmR45uwMVFZS4EBVlSTWdXXkF+DsTM+GuTmN69ChRGKMjbsklF0hMjJSmD59Oqfd1KqtGSoqKrC1tUVISAikUimePHmC27dv48GDB7xYLO7eaPC330hqvmYNqRfGjKFgwKZNJA2ePp1eN3Agkal58yiI9QplQSdPnlTo6OjAz8+v5+e3qoqO/dlnNMfz8mgsnz+n8Wmz1jUb6qWlpQnPnj0TPD09WU1NDfLy8pApCGgYMQI69vaora3F4cOHhevXrzMDAwN+1OjRoj4zZ8IqIwN6MTFkztg26BsdTUGGSZO6PMX6ujpEnz7NXv/uOzzX0EDIqFHIdHLi1dTUeEtLS2ZiYsICzp2Dp6kp+72hgQ0ZMqT12Q4IoLUpJYWCfk3XU19fj6NHj/Icx7Fp06YxkUgEGxsbFhYWhvT0dEFFRYXJ5XLU/vYbGn18oD5xImXfv/qKzCg5jgJdxsaAjg7S0tIQExPT4rjfjOvXr+PCxYu4k5uLRAsLVOjrw331asiMjaHaRcAQAL1PQQFl74cO7br3OkCqm7lz8VKmdR2wc+dORU1NjWjx4sU9+3To6ZF6wsvrld/jZeHg4MD09fWRnJwsREVFsfT0dLmnpycH0Jyzs7MTpaenBwmCEHjjxo3fAgICXiC5UUKJ/wyUZFsJJZToFgEBAbLQ0NBTDQ0N/eLi4uzNzMxUdF+Qof0zCA0NRX5+Phs1alS3kXuJRAIvLy/Wp08flp2dzRsaGgru7u5dvvjZs2c4c+YMRCIRJBIJGhoaYGtri19//VWIiIhgd+/eFRISEoTY2FgmEonw5MkTiMVihYWFBVdTUwORSPRSmbLGxkZER0czHx8fhKH2QwAAIABJREFUlJeX48SJE5g6dSrGjx/PbGxsOMYYZfH09UkmPWlSj/V0KioqLTVwIpEIJSUlwtP79+G5bx/D++93v3lqgqqqKiKePRNyZ81izq6urRsuOzsiHW0zczk5lNHbtInIRhOp1dPTg7W1Nfz8/CCRSGBlZQUvLy8WHx8PADAxMVHExcVxyQ4OiHZ2ZvKGBoVILIaFhQXLysoSZDIZKyws5H18fDoNYHPLHgnHUf3i2rWU+e2weaytrUXV7t2oLiuDho/Pq8v/w8PJIGruXMSmpgpSqZRTKBRsWId2Wo8ePcKdO3e4qVOnwu/QIaa9aBGgq4vS0lLcv3+fCYKACRMmQCKRgDEGHR2dzvPCxobusURC2fTKSiJ0jx6RS/CQIS0b9fT0dIHneW769OkICAhggwcPBsdxUFVVhaamJqwyMjiXK1fAz5nDJyUltVz0/Pnzoa+vj7KyMj4tLY2Li4uDm5tb58x2MziONrrBwUQe168nabe6emuNeWwsqQpeYTOcnZ3NVVZWKpxdXGgQli2jDGvHLJq7O93Tjuf3889UY9zU1/plwHEcwsPDAQAODg5oLjNRVVWFr68vXFxckJiYyCIjI1FSUiL0MzVlrK6OlBGpqTTPdu0i6XefPpQF7d+f5r+vL92jGTOIqBYVtZoPhoVRH/qJE6mUQRAoG+/nR87bZ84Q+esGcrkct2/fZl5eXl16YOjq6sLMzAxGRkaCXC7nx48fz+no6ODq1avMzMysfVbZ25uMpxYtouvo27e1/V1cHJ3TjBntOgmgTx8ixDt3Ug31S453RkaGkJOTw9nZ2XUb+ARALbEKCiioxHE0z62sqG3XnTs0bh3es1evXiw8PJxFREQgKioKDx8+ROnz57zWwYPs4LNnuHPnDhoaGphYLBZWrFjB6evrQ0tHB6qjRtHzdOoUyc6b18EnT+h568rMLCQE4ilTEOPjo9BfupTz+/BDDA8IgJeXF3NxceEsLS1ZX3NzpvXsGZO/9RakMTFISkrik5KSWGJiIhIfPUJmURFsli8Hq6kBGzoUjY2N2LJlC6qqqtjixYtZ8/MnkUjg4+PDkpOTkZCQwD+IjcWod99laaGhyJg+XTCsrWWizz9vDRQEB1O5grMz6urqEBsbC0tLSzQH0Hiex5EjRzB06FCMHDkSpv37w93TE9kPH/LSnBzWGB8PM1VVmgMd4eJCQVRNza6DQjIZ1Y/PmPHCz5OOOHfunJCens4tX7685xr1Zri7U2Dt5MluAyJ/BSoqKjAxMYGtrS27e/cuampquLZrvLa2NgYNGiQpKSkxLisrmxAaGno0ICBA9refiBJKvADKmm0llFCiR6xfv54HMG/jxo0hhw8f/tHMzEwtMDBQ09TU9G97j3v37gkzZsxgPfaaBm28+/Xrh379+vWYYjczM8Ps2bORm5uL5ORkRUNDgyg0NJQ3NTUVVq9eLUpJSWEVFRXM2NgYqqqq2L17NwAgISEBZ8+ehYaGBtTU1KCuri4sXryYdUf2DAwMYGVlpfj22285hULBHB0dFc7Ozu3PjTHaIF67RiZE//jHS298VVRU+KLyclFFQgJ0X0KWnZKSgpKSEjZ69Ggi9VlZ5BL8738TicjOpppTXV2qCx0yhOTOTU7ubSGRSODfRCa+/fZbAQALDg6Gq6urKDExERcuXEDv7Gws2r9ftPmf/8SVK1dQWVnJALQQhcjISCgUCgwbNgy1tbXYvn27ICorYzMbGmBZXEyGSmIx6urqEBUVxdfW1grDhw8Xbd++HYNTUoT8/Hzmm57e0qpHLpcjMzMTVlZW3dcM3r5NWZs7dwBTUzy6epUB5CjfEZcvXxYAMDUVFbDERMDUFPn5+cjJycG6desgEolejugPG0bqhU8/pbrZU6doTDv8rUQi4bSaFARdwt0dWLsWurq6TCKRICAgQPD29mYikQhWVlYYNmwY19zqZvfu3VizZk3PPehFIjJ6mjKFAgAHD5Kkc+ZMImKvWBpibW2NyMjI1ouKjSUSumQJXX9zzWpNDUlVT5xo/eOGBgqADB78yi7FK1euxK5duzq1x1JTU4OJigrWammhwN4e+Rs2sLq5cxF36BCGyOU0nitWkHwXoGt/8IDqjx89ovZyly61nrMgUDBAV5cyw59+2vpmjLX2sd63j2T6PE/P9NtvdxrLsLAwqKmpCb169ep2Atnb28Pe3p4BEAGAqqoqE4lEMDAwgJCQAMWwYXh45Qo8t2yh+aSh0Z7gV1WRxP1f/yLy1hErVhBRDwykOfkSLvGvvfaaKDQ0lN+7dy+Cg4NZv66OC9C93bKl/c90dGidy8mhex0cjIa5c3E/JgbV1dVCXFwc8/f3h7+/Pw4cOICKigph+fLlHPvqK7j//jvkCgU0NTWxdetWdvLkSWHq1KlMIpEQKXznHQrWLFlCNf8iEWXRPT3bn8OBAxQEGDUK2LwZamVliKut5R3E4s6TbtYsYOlSaBkbY8mSJcjMzOQaGxshk8mE5ORklp2djczp01FbXQ3DTz5RNJiZiczNzfl58+ZxHVVeEokEs2fPZsjKEkEQwPfujXIjI6SfPi24HTnCJBkZFJAD2rUHbFZj9O3bt+VnKSkpAABXV9d2deD2p09z1gUFyJ45E9mHDgmxGzeyoMBAaHRUuWzZQs/ZkSMk8W+Ligoi4S9b5tMGPM8zAPjxxx9haWnJz549+8UGhf7+FPD7D0JPTw9GRka8ra1tp3ssEokwZcoUNZlM5pKWlvYdgGX/0ZNRQokuoMxsK6GEEi+F4cOHJ4aGhn5fWVlZkpCQEGBmZibprr76VXDmzBm+urqaBQUFddke68/CwMAA1tbWGDRoEDds2DD4+/szd3d3rrl/sJmZWUuPUKlUCltbW+7333+HpaUliouLoaWlJRQUFLCcnBze3d29E+GuqKiARCKBvb09Z2RkxBITE+Hr68uZdCWP5TjadNy7R9JHX9+XugYHBwdu4LRp+CM/n7cMCGA9bWzkcjn27duH0aNHw6NZGswYvffIkZStq6wkMthsnvXxx0QUupIkt4GnpyeTSqXIyMiAv78/+vTpA2NjY7iMHAnNCRPgPHYsoqKiwBhDr169YGJiwrm4uODUqVP806dP2YABA7Br1y6+b00N5xkRAQMTE2j9/HNLlv/y5cuKxMRELjs7m+vfvz+io6PRNzMTgqkpnCZMYOrq6oiKilKcOnWKi42NRWRkJKKjo+Hr69su01x7+TIqzcxQN3MmyjQ0UF9fj2vXrgEAxowZ09nJWCxmqamp0AdgNXAgKl1dsWfPHqSkpMDa2rpb/4B2EATqm71rF0mkRSKqnR01isieSEQGcFSXy0pKSuDZkSA0IyQEKCuD0dixbOjQobCwsOj0TOjo6CA8PBw8z6OgoEBwdXXtNhjU5kLpXNzdyYRu61Yi3r16UVbwJUm3trY2wsLC2stteR747ju63uaseV0d1Y42G38BJGnOySGi/wpQKBTYv38/6uvr4T9wILTu3yf56/z5pMoYNgw4dgxa/v6QTZuGg2Zm0Le2hu3bb1MGmDEKOlVXk+y6tpbKFoKD20vrv/oKuHu31VzKzIzIbP/+nTOAnp6ULS4sJPIXHEy1qZqaLeqRixcvKgYPHsx19JToCVFRUcKwHTuY/NIl/qBYjDw1NXa7ogJ2o0dDp6P5FkDzzsmp+6whYyTXLywk6bKPzwvvNWMMNjY2TFtbG+fPn2dxcXGK9PR0LicnByYmJhTcefCA5NsLF3YOnIhENK/MzaG4cgXHb9xAWna2UC0IqKioYJaWlnBwcIBEIsGjR4+Yf0AAxB9/DBWJBKqqqi0B1cTEROHKlStMKpUKiYmJglgiYcbjx5PZ2+efkxLhzBkK8Dg70/XxPHD4MK1xU6cCTk6wsbHhrly5wow0NdFbEGj9ffyY5uzFi2Qs5u4OHR0d9O3bF9bW1rC1tWWGhoZ48uQJ//6nn7JBOjpwf+cd7p6NDabOm8c6lgW0Q1AQsHEj2OnTsOzXD/oGBuxySQnKjYwUVlZWNFhjx5KBmIUFeJ5HUylSSzBJJpMhJiYGWlpaCktLS67t862lpQXdGTNQEBTEFMeOCSbLlzO2aBFEbVUIYjHNiy1bgNdea6+mun6dfB2aSw5eAf369YOVlRUGDBiA6Oho4fnz57yTkxPX4/pjb09fEybQdXenxvmLiImJEUpLS1lMTAyfl5fXLkjEGIOhoaH47t27/cPCwr4MCAjg/yMnoYQS3UBJtpVQQomXRkBAgGL48OHRoaGh99LS0qb7+fmp9PRBW1lZifz8fJSUlEBPT68d2amvr8eJEyeEJ0+ecDNnzmQGL9Ob9z8AiUSC5ORkPj4+nqmpqQmrVq1iAU1yQwMDA0RFRbHw8HDcunULYWFhiIqKQnx8PG7evImIiAhERkYiKSkJjDH0798fXdbS0hvRpuOrr2gj1F1dZhswAEXa2rgCsPSMDH7QoEFdDvbPP/8shISEMG1tbUVwcHCniMXz589xLSICBhYW0HzrLcpsuLpSneX06ZTxamjo1gBKLBajvLwcubm5GD58OBhjMDAwgK6uLsRWVtB87TVo+PpixqpVuH37Nurr61FbW4vs7GymUChw//59eJSWCgMiIpDv7c3Shg8Xrl27xtva2nIaGhqIjIwUHBwcuJKSEl4qlTKJRCLYJCczr0mTWK+mwMRvv/3GHB0dmba2NiorK9HQ0IC+ffuC53nk5+ejLjcXkrFjcb6iAqFFRbh//z5SU1Mhl8vRvME/duyY/OHDh4KTkxN3+fJlSKVS8DyPKc7OUI+MRJyJCZ4+fQoAGD58OK5fv66IjIwUzM3NOU1Nzc4DI5WSXLm4mO7p5s1EBDQ0iBD2708kfNEiYM0aqL/1FkpFIr7f6NGsS3XD9u1EBkeP7nZOHDx4EOXl5Rg+fDgSExNZZWUlHOzsiFiFhdG56OsT2QgMJFK9fDl9OTkREVq7luZhbi4FXo4codrbuXOJ8Ovrd7kplkgkkEqlQr9+/VolxhxHpEsioWNNnkxyYj8/2vSrq1NAIiaGiGkP7ZTaQhAEPAkNRejNm/DYuxfmeXlwUVGh7OaAAUR4Fy4kCf/06YC1NVR69cItqRTz58+ntSYvD4iPh+KNN3CnsBCnBg/mo4yMhF76+u27HpSXkyLiX/9qvW6Oo0y2nV339eVaWiSJ19MjT4Xbt4GJE1Gdn4/wu3e5UaNGvZxbenk5BEtL/Kary0o0NZFiYwM7Pz826p13UFNTg4iICKGwsJCXyWRcnz596NouXiRvgG+/7VkpwBhlOaurqZxgzJges5r5+fkoKipCYWEhVFRUMGDAAK6hoUGRk5PDbt26BX19fdanthbw98czkQjx8fGQSCTQaApuxcfHIyEhAVmM4Uh5OQY8eCDMvHePeW3dyp7m5CgeP37M9evXD0ePHoVYLMaIESPIm2H4cKqNBnkTeHp6Mk9PTzg5ObGIiAiWkpKC4QEBtG4JAqkOxo8nbwKZjGTrAwYQoVNXJ2XLuXNQKy3FoI0bobZvHyQODuDu3CFSXltLQSGJhAj7hg0UjGh6zisrKxEdHc0CAgIgNjODeNQo+EydCi1V1dYMdUc8fEhB1dGj6TxTU6G7cCHy3n4bkVIp5+3tTYT63j1SFfXpA4lEgry8PMXVq1eZo6MjA4A7d+6gsrIST58+5VxcXNBx7ZFIJDA0NEROr14slDE8SE3lB/3znwz+/q0BL0tLCrL88Uf79n7nz9M68IKWe11PJQqm6urqwtXVlV25coWpqqrCzMys52gfY2Q2N3LkSz//rwobGxtWWlrKp6amcgUFBRg8eDDaqg+uXLlSV1xcfEUQhGMBL+ihroQSfzeUMnIllFDiz0BaV1cnkcvlnaSdbbF///6W9hzq6urCBx980PKh/M0330BVVZUtW7YMXWaD/4swNTXlnj9/Dn19feTm5rY4nLu5ucHa2hqxsbG4ffs2tLS0oK6ujufPn2PEiBGwsbFBVVUVLl68KEyaNKnbXtotMDQkk6PFi4H33++x5hMAsj/4QLhaU8OM3N35gQMHdruj5nlecHR0ZFOnTu0kr8/JycHhw4cFVVVVJjp0CAH5+dC5c4eyYbq6JD/96SfKghQV0SZ2zJhO5zZu3DgkJiYiMzMT1h1qATl3d/j4+QESCYKDg/H06VM+JSVF8PX1ZY42NlzZTz8JDhkZ3IO33xakqakQPX7MZDKZKDw8HFOnTkV5eTnn7e2NwMBATi6XQywWsz/y8xXnnjzhJhQWMkNDQ2hqagpJSUlMLpfDxsaGf/LkCXfixAkwxtC3tFShkMuZ/KOP+AUffCAGgIMHD8oLCwvFcrkcGRkZOHr0KPLy8sRGRkbCli1boFAoYGJiwufn53MRN2/yw9TVOQcHB1y5cgUaGhr89u3bOQAiVVVVnDp1Sli4cGFLjSZu3CDzsbffpnH096fNs50dEdevviIiCFAWdeNGQC4HLxZDXFPDsGsXEfO4OMrIOThQdm7Tps5mVrW1VCs8ejRw4AAsr1yBeOJEPmDlSq7X++/zafv2cfLgYIgbGuicHBxa5b0KBd3LZtl6VlbbidFa86mmRrXXgkBy3ffeo9rPDz6gLNjjxzR3LS2hoaGhKCgoEHcyFNTUpPdu9nT45BMi+0uXUjZ7xw56j+4gCNRuaf9+wMkJ8t9/h9GJEyiYOxfGWlqwXLqUZN9vv93tITiOg0KhQHFuLgy//x44cwZPjx7FpalTYejoqAh0dxdJpVKEhoYKTk5OzY5XlP3btKmzzHrIEDqvFyAqKgqREybwpkZGbMiOHcz4o48gfPABDF8UQFy3DnxSEs7OmSOoenkxpq0tjHnvPdanT5+WkprJkydDKpWyZ8+eif744w8+PT0d4wIDOYlIRMGZl6m7be5dHR1NsvnmZ78D0tPTcfjwYYjFYojFYkEmk3H19fX8ggULRIIgIDIyEiGnTglmJ0+yxH//WxF+86aoV69eQlhYGBOJRC2u+hKJRNDQ0BC8vLw4/08+YSwjA4qTJzErPFz0s51dS9nOG2+8QW/8+utdSty1tbVRVFQExhjebr7vHEfz6eFDum+qqtSKzMyMyOWvv1KwKDCQzP9cXHB+7Vo8razEP19/HSI1NSoZWLeOylgYI1VCeTkFHBctAry8UNMUVGzBoEE09x49oqBWR1y4QAGuuLhWcpuRAbi5QU1TE7q6uoK6ujodcNu2diUmEyZMEG3btg07duyAioqKIJPJmJ2dnWBpaQkDA4MuiaxMJkNYWBhgYgLW2MjJhg6FSnN7MW9vypzPm0djFB/faoRobExBib8IHR0d+Pj4sOjoaN7b27tnss1x9Ln38CGtTR988JffvyP09fUxbtw47sGDBxgxYkSn8hobGxtJenr6SJlM9nDDhg3j169fn/23n4QSSnQDJdlWQon/h7FhwwZDANNUVFQ8FApFEc/ztwDcWr9+ff1fPPQUCwuLOhUVlW71dDExMaitrcXq1auRkJCA8PDwdh/ITk5OyM3NFfT19V/R/ervx/jx45Genq4oKCgQ7d27F25ubopp06aJAJLtDRs2DG2NVx4/fox+/fq1SGlbNu4vA11dyjDNmkUZjh4yXxqXLqHf6NEYumJFt0Q7KSkJpaWlnIeHB+rr69ttMq6EhPCxMTHc+zt3MrWdO/FrUZGQYmDABgIkt2zG2rVEsuiAlJGNiABmz6Zes4mJkJiYQBAEZGRk8NbW1u3P57vvqJ3NlSuwHTMGLbVzeXnAiRMwqalh2L8ftjIZMzh+XCgqKmIAkJiYiMTERKipqaE5UNFMMsaWlIgiNTSEY8eOCU2t2ZhMJhMUCgVTaeq5u3LlShjo6wOOjiKsWAG8/37LeVlYWLDi4mLBysqKZWZmoqioCADQt29f4fnz5wwAFixYwNXU1CDln/9kcdnZSPvtNxgZGfErVqzgoqKicO3aNZibmwsFBQVCWloac6uspM30o0dkDjZ2LEk0i4uJrGlqEjHuCJEIEImQtW4dsh884LFkiQju7pRl3r2bslDNbbpcXIgUmZoSIfD2psxtaSkqkpLA5eVh6NChHJYuhcfEidzFnBw8dnSE1dGjguO//80GNmesLl5sff/mnreMEZGOjycS3QwPj9bNeGoq/ZufT1JpTU0KwGhpAT/+iNnbtomTVFV56OpyLS3P6MZRFj0xkbKMP/9MpOfxYyJBHV3lKyspaOHhQZ4CV68S+UlIQG7v3sJhXV3WuHo1BgwahMFff91zbToACAKy4+KENw8fZmoPH+KSjw8ezp4NhIcjaOpUYeDAgSIAuH37Nm9hYdE6f2NiyOSuq9IOa2si/z1kASsrKxERESH069ePU1FR4c+UlEC2ejWbOWsWmJEROdRPndr6BwoFBbguXYJizBgcy85GZlISC96yBeMdHTuVDXAc1+KdkJOTw+3btw/OmzcL9osWMSxZ0vOYtAVjpF748UdyM//mm04tmfLy8gCQv4GBgQFXX1+PrVu3cllZWbC0tIS/vz+MCwpYxrVruJmSIgoKCoK3tzeTy+UICQmBiYkJZDIZBg8ezEDCHIKtLa4bGeH/svflYVWU7/v3O3MW9lVFZJNVUXbBNVBRKnFJ3DXLXFvN1MzK6nRcSstyqSy3XDI1s9xyQQVBwxVE3BAEUZBFQET2s838/ng47CD2qc/39+nivi4u5XDOnJl33pl57+e5n/uxzMiAmYUFuhoaYujXX9eWIkye3KxjuomJCXieJ0MunY5MHyMjaf4AFDD6/nvKcFtY1PSurnY2x6PkZFGlUrFRY8aIBgYGtE+7dhG51hNeFxd6DaD5KJXC6dYtTNixA8cdHPCsPnD25Zc0X/S+F3pkZFBWfOrUWrPHykqa9z/+iKTVq3UhISF8DXk3N6ea96FDq381x6hRo2BkZIQdO3YwjuMwYsSIFuXqMpkMoaGhiI6OhsjzKPvgAyp7+egjqptfvZpUGlu2UJBg505yKz9/npQOfwNyc3NrFA2tavV48ybdd/4Bsg3QmAwfPhwnT54UQkJC6l1Ifn5+vK+vr/GZM2e6xsXFnVEqlUEKhSL/H9mRNrShAdr6bLehDf9SKJXKbhKJ5IKHhwdvZ2dnWFFRoUtLSysvLCyUyWSyuKqqqihRFG8ByAGgA6AB8AhAnkKhaLFFxooVKy4OHTo0yKvahVitVuPYsWPo0aMH7OzscPfuXWzbtg0+Pj6IiIioyZj07dsXYWFhAMhxdf369Tq1Ws0NHjyYde/e/R8dj9YgNzcXGzZsQEREhNCc0/nfhpwcyjr88UetsVRdVFVh35Ejws3kZK5bt25CZmamaGxsjAkTJvCxsbFiUlISk8vlusrKSh4ATE1NxbKyMhbg5oZKnQ42O3cKXqdPc0VJSXC+dAnasDAsW7sWdnZ2wowZM558bGVlUO3bh60VFbrBX33FV8rlODh8ON6IjobFwYMkV6yb+Xn1VcoY7dhBv587R4s+Cwtg9uya98bExIj6wIuxsbEwZMgQzsrKqrG6YfhwCK+9hh9zc7UVFRXM1taWHzx4MCoqKrB161ZotVqYPXqEAYaGov/ChayhPPGnn37SWVhY8ABw+fJlSCQSCIIAQagt11u4cCEtEr/6CjcKCrDX0BBBQUEIDw+vPgUUvPh97lydf/fuvOuWLRSU0GfkACKXkyYRCX9Ca7zExETExcVp33rrrcaB7oICykB7ehI5GzaMMuV1JKSbN28WraysEBERUTPwGo0Gn332Wc17FApFi/uAvDwgNZVqnZ8WpaW4M28e4rp00b1UXMxj924i5/37k1JjwADKnkdF0ZgsXEiZ9QkTqJ7d0ZHIkExGcvMNG+g9lpaU2bS3R0lJCVatWgUnJydMmjTpySQbAE6fhu7DD7G9WzfY63QIXrkSX3zzDURRxPz58+tJuSMjI4X4+Hhu0aJFRJyWLCGJa1O4dYvmdbUbuh537tyBiYkJLCwssGHDBp2dnR2aUpUgMZECHQoFsG8fHf/p00Tapk7F1Zwc7Nu3D0Arzls1LsXEwPLVV+EaGwv2V9riiSLwwQd0/oODiYBVIzY2FjExMZgyZUqNid8vv/yCO3fu4LXXXsPBgwdF31Wr2MVhw5ArlWLEiBHN+w80wPnz5xEZGYnX+vaFzaefknnb5Ml0X+jRg7LVCxfW/5AgAByHi4MHi26CwKyio+kzixdTIESlos/Z21PgMj4eAPlX/Pzzz7h79y4MDQ1RWVmJnj17imFhYaQYSEyk427Ymg70XLpx4waidu9GjzNnUPX222LYypUML7wAvP8+jZ+zM+3DlCnU5cHIiIh2dcYeAJ3j48eBEyewbNkyjB07Fh4eHvS3e/co+NRE2Y5KparpftASRFHE4sWLIZfLMWnSpHoGaxBFCtTt3UvBri1bKKv89dekkDlx4qmdyJvClStXcODAAQDAnDlzYNFaifgvv5CB3j/Qf1sQBKxZs0b09/dnzcnFY2JiNGfPni3RaDR9FQpF6t++E21oQwO0Zbbb0IZ/KeRy+ad9+/Y1DAkJ0S8C+cGDB5tVVFQgLS1tUG5ubsiDBw8qS0pKRFEUIQgCq6qqkqhUKoMVK1ZcV6lUi0VR/F2hUNSLyCmVym5yudzLs47L6b1795CYmIikpCQwxqDT6eDg4CAOGTKEAYCLiwvCwsLEEydOsLKyMkRERIDjOLz66qv877//jmPHjomt6sv8D+LatWv4/fffERISovPx8fnnGorr0akTGZRNnUpZyIZ1lxERcHr0iLs6ZAiuXr3KPffcc8jIyBBWr14NjuPY2LFjIQgCX1hYiIBu3WCYmsquy+Xo0qsX4mbNEvhZs9jjDz4gybezM6DVgud5jBw5snVBBBMTRFlbQ1dayhmfPo0d69ejj6cnLO7cIdmxvo/0hQuUWVq8mGoui4upTvDaNVrQP/98vc3279+fGRoa4tixYwgNDeX2B67GAAAgAElEQVSaDbK88go4b2/MGDq03nPK0tISs2fPxuHDh8UeX3wBK3v7RkQbADw9PbkjR47A2tpaGD16NLO2tmZbtmyBpaWlqNPpMGPGjNq+zO7u6B4WBk8vr3qGawZZWcCvv8Lv9GlO5+1NmdeG5yk9nUyR9ETbw4PGYsKEem/Ly8tDdHQ0TE1Nm57nHEfuylVV9D3TpwNr1tST81dVVQmOjo715qZUKsXcuXOxatWqpsexGspPPsGbW7eicO1amPXsib/US8DUFMZLlyJr82ZeXLIEbOlSWtg//zwFCs6epUBEVhZJaS9fpqDL0aN0TD/+SOMTGEj1tnUzvtW4fv06TE1NhZdffplr0TBRFElGfekS8PrriPP2RmaHDihv31534bvveMYYRFHE5s2bhTlz5tRsqHv37tz58+eRmpoKw+nTkT1wIDplZsLe3r5xWzcPD1IclJbWkNIffvhBm5+fL9EHb3Q6HT9p0qSm99Hfn85n375U/21uDrzzDkonTcLqdesgVCc7eJ6HIAhPbjeYk4OgNWuwfOJEjC4thftfINs6QYC4dCkkn3xC9fkrVtQEdIKCghATE4Nt27bh5ZdfhrOzMwoKCkS1Ws3Wrl2LTvn5zKyiAlXW1ujh7t5qog0AgYGBiIyMxLGcHEw5fJjUIIGBRAAPHKjNFKvVlBHu0IHGPDUVD3v1ErUSCesLUDAvLIxUEx99RIGjjh2Bb76BUFiI0kWLsMndXSwrL2cA8N577yEjIwO//fabmJqaisHt27OuS5aAv3FDbzKIO3fuiNevXxeLioo4gAJYvKEh+sXEgOM4BhMTMpbbto3m3OrVVJN9/z7t5+TJFDjVQxBIjj5+PABAJpMJSUlJzMPDg679Awfo/tBEkFXejG9GQ1y4cEEAwGk0mvpEG6AAxtKl1N7r1i0as+efpzZwnTv/50RbrQZUKvh16gTzZ57BiT17YJSaSt+bn08BPWtrel9eHgXanJ1pDNPT6Xlhb0/PCGPjVnfnaA04joO3tzfLyMhAc2S7f//+0mvXrhkWFRV1B9BGttvwj6ONbLehDf9CLFmyZIyBgcGwoKCgRqTRyMgIPj4+8PHxkQJoVHCt0+lw+/Ztn8jIyG2VlZVjAEys+3e5XP5Jnz59ZBqNBn/++SdiY2OhV8gsWrQIOTk5kMvlaN++fb0naN++fZmdnR22bt2Kdu3aITg4GBzHwd7eHgUFBSLqyg7/D6BWq/WGPf880dZj5EjK/I4ZQzK/ulI8pRJl167pLMvL+alTp8LU1BS9e/fmMjMzYWxsTPLpnTvRZfBgkoTu3QuvW7eA/HwMMDdvtGqXSCSwsbHRXbx4kddnbluCIAhITU0V+vbty3Xs2BFmZmbiueRkNvCnn6hOf+1ayhDl5VFdcmYm8OgRZXfGjqW6Zb18uQ4YYwgKCsKxY8dQ2VJLmFOnKCDRcCEJwAzAxHv3GGJj62Xm6iIwMJAFBgYCQM1Y9OrVC6mpqcJbb71V/xwfOgRMn15LdiorySzs7l3g999x0tRUCPTz4xsR7ZQUqjNeubL2tRUrSFLaAKdPn0ZZWRlmz57d9PyaP5+ktN9/T626XF2JTMTHU61yu3aQy+V8U9eKmZkZJk2ahJ07d+Lbb7/VAUDv3r356uPHrVu3wACk29rieEIChKQkdO3aFeOrycDTwMbGBqIooqSkhNz8GaOMZGkpmZVZWtIiW69UMDKiDKJUSkZWhw4BP/9MgZrDh0kmn5VFGcLXXwc3fz6CrKw4rk8feo+3NxFWvcFgaSmRB19fIDubCHvfvribni4iI4NZW1tj/PjxuHLlinDx4kXOzc2t3kmzt7cHz/O4PW8eHvfqBa5nT130jh28RqOBVCqFTCYTZDKZYGJiwltZWbHeW7Yg7fZt3HV2xp07d8BxnGTu3LnIz89HYWEh4uPjxXPnzolDhw5tzJR//JEyjJmZdBzbtgEADGbOxMjycqSOGYPh8+dD1pK7dV38+itgZQXe2Fh8+PAhc3d3BwAUFRUhNTVVvHv3rjBy5Ei+KUlvZWUlIiMjxaSkJAYATm5uwsT0dE4eFUXZTiMjGBkZYd68efj6668RFRWFl19+GQ8fPmRhYWE4ceIEXhg0CB2mTcPbf9FcC6gmkxYW9PPee1R+8ttvJAvfurW2N/zJkxSocXLCRZmM8/DwQI34edcuMnwzMSHSO2AAYGiI6A0b4PnHHyifPp2NHzUK9tWZa2dnZ8ybN487deoU0n75Rch1ceFyduzQ3b9/n+c4TrSwsBC6dOnCd+rUCVqtFjqdDvv27cPvv/+uGzNmDF8TMFRXC770BmOlpSRfT0urb5q2eTPN0WpFxPDhw7lffvkFmzZtwrRp08DWrgXr169pRVMrYWVlxQHUZaFJcBwFeyoqqDTFz4/uMb17EwHXammc796lfed5us6Skug51K4dHd+FC0TOO3em6/foUbqWBw9GTl4e1JcvY5CxsShzc2MwMqLnAc9T1loup0CIrS2Nk5ERBWB376aAxMmT5OWgUJA/wt+AhIQE8cKFC6zZcQFQXFyMx48fMwCHAGDJkiVDZTLZLJVKtf6TTz458rfsSBvaUAdtZLsNbfiXQalUWkml0s2TJk0yNGymDq4l8DyPrl27wtXV1Xjt2rUjlErlQIVCcap62x0kEskLPXv25FeuXAmdTgcLCwsYGRnBxcWlhjw3BycnJwwZMgRHjx5FbGwsnJ2dBZlMxnXo0OH/tJ4lOjoaV65cga2trQ7VPW//awgJIVnfmjVUO81x9HtBAUKmT+cbin0d8/LIoGf+fDKhat+eMqn6XsJNGB/p8eyzz/I7duyAgYEBfHx86junV1XRYiotDdDpEH3woOCVnMy6+fkB06djWno6O9ypE/J79RLtunRh4DhajM2cSRmqwkJayK1fTwTR2ZmyjkFBjfaD4zgwxtBsD1+ASFtzssTt24Fjx6j29CmyIsXFxaJKpWpMii5fJmltWhoFEcrKqE90nz6AXA6xOovZCAoFLSLrondvyt7UMQO7evVqTZ1/s7LosWPrBye8vEh6vGIFdKNG4cH8+XiQm4vw8PAmD9jFxQWjRo2CRqPhs7OzcfjwYaSlpWHUqFG4u3q1MLqqijPavRvhRUXC/fv3uStXruDKlSs4e/asyPM8pkyZwp5UdykIAiIjI6HValGRnQ3zL76gQEt4OC3eT56kjN7hwyRR3ryZCEXdc1RdRgJRJDdtUaSSigcPAAcH5NnaghMEpMXHwy0/nxbov/9OMvs5c2iczM3pO774AoIg4GxcnHD//n2O53kUFhayH374AYaGhpg5c2a9PsV6eADofv06On79NQw8PHj9sZWVleHRo0dccXEx9+jRI7GgoECXYWbGFd26xWRduuj8/f35sLAwGBgYwNTUFK6urjAxMWGHDh1iAwYMqHWNPnyY6niHD6f5L5XSXHFxAUxMwJ89i0KFQjA5e5bLjI4W3WbOZBgxosVrF8ePE2F/4w1UfPYZ03sQpKenYweVbjBDQ0Pu119/FcaNG8fVzZDu3r1bSElJ4QCwTp06oUePHvjjjz+45TIZxqxZA+dffkHBJ59Aw/NITEwUAHBDhgzB48ePAVDPcKlaDes1a2rLRJ4CDx48wA8//AAAGKx32n/8mM79oEHAvHn0/5Ur6VzrTTcDAmq6A/j5+dEcCwggsq1XxOzejYKVK3Fv1SrEDR2KuFmzsGDGDBh5eBBxrCbcHMdhUL9+wNat3K0VK6DOzOQHDx6MTp061fQ51+P69euQSqXIuXuXx6VLVGtsb0/S8Ph4+nfECCoB6tWrsTu5pWWtBwaArl27om/fvjh79iyWLFkC2fTpmNSuHZyeeiTrQZRKpSwjIwM9e/Zs/l1GRkRok5PpOJKTiQg7OdE9//ZtGtN27YhU29oSsba2pt+feYb+ZmJCJHzJEsDQEBUVFdj45ZeQde+OhQsXshYd8euitJSeX3FxdC/Qaim73b8/lc4sWPCXB0QQBBw7doyNHDkSLZWlWVhYoH379sKDBw+WKJXKa1KpdH1wcLDJn3/+GfbZZ599qdFoPm2o6GtDG/4TtJHtNrThXwapVPqRl5eXzK4VraWesB0MHDjQ6MSJE+8D0LspBdjY2Khyc3MNdDodAODtt99+Yn1ZXQQFBcHExAQajQanTp1ipaWlCAoK+mfro5+AhIQEoaKiguvXrx+XmJgonDhxgtNoNJg+fTo6/pW6yKcBx5EB1TffUDZ40SLK6ubmUt0rQMZAL79MNYPJybRQee89ctFtDlot8PAhbf/2bSA7G05eXpj64IGYvWoVzkmlzOHuXXQYMQKddu+m+sc1a4ggOTmhKi+PPePiwkyMjIDQUJiPHg3h9m3Edu/OJs2dW781lL7Pr15SbGdH3z10KBmx9epFC7zqBVlubi5EUcTDhw+bb5VWbdZUD4JApGX2bKr9fUo5ZMeOHZGdnS2g7uJaEIjkLl5MSgNPTyKMdcgKx3GNyXZODklgGxKk7dspc3v1as1Lzs7OsLe3R2pqKtavXy8MGDCAqxdoyMggwl9tmFSza6KIxLAwXHz4UAxYtoyNdnAQOhUXc025evM8D29vbwCAv78/unbtitOnTwurV69GQH4+V5abCztLSzg7O3NeXl419ZY9evRAQkICS0tLg96DoSnkpqRg+/79YsSuXeg3fDgzs7Mj0qNUUm2ofj4sWkRZv88/p7KC5ctpkb99e/0NMlY7dhYWQLduEEURN4KCoNVqYdOzJ9z0CoHRo+lfUaQ62aws+mxJCfadOIHr169zY8eORbt27XDv3j3O09MTJiYmTd9TdDqMdnLCxueeg1l8PCZV19FyHAczMzOYmZnByckJIPUAGdlt3w6MG9dkEM7T0xMnT57UrV69mp81dSram5gQ2TY3J0Kjx/ffE/E2MQHH8+i/eDG3ZMkSwMqK2ZqawrhPH6pjVSga17Kq1TTnV6+uIaIlJSXYtGmTkJ2dzTk6OoovvvgiEwSBrVy5ki1fvhwAMGzYMERFRaGyspILCQnBgwcPMKG6vCEgIABJSUn4TRDgf/kyhAULcM3HBzqplBs2bBhsbW3BcVxNLbmQnAxOrW4cXGoFfvzxRwDAtIkT0W7bNgqsdOpUa0538iRdS03cCwoLC8HzPDw9PemeNmgQKRz0GD0autu38cjODiO6dhVdhgxhRubmVNLg5kZeAq+8QkqIPXsAjQZdvbzQte5cF0W6/508CaSk4FZSkjgiIYGZlpVR4M3YmIzPli6lgMfFi0QYT5+m3vV1cf48BRKmT6/3cnBwMO7cuQNjY2MMevddRFVVwUmpfOqxBIC0tDT89ttvTKPR1AYvWkJcHBHql14iUttg354WgiDgULUB49SpU59c/qBHVRWN34kTFJCYPZvUHwCpeK5do/ry27fpvNVtz9cKMMag1Wpx9erVFsk2YwxjxowxPn78+Gy1Wq3r27evibu7O7y9vQ23bt36bklJSRWAz/XvVyqVcgCeAHIVCsWDp9qpNrQBbWS7DW3414HjuPFBQUGtsAZ9Mrp164YjR470VyqVZgqFogSArLCw0GTXrl3o2LEjnnvuuaci2gA96LpVL6wLCwvx559/Pp2b9z+A2bNnc/v27RNPnTrF1Go1s7CwgEQiEdavX8+98847JJf9JyGXUw/iiRNpAadU0qJ6504ykzlwgBbc5eWUOXVzoyzsuXO0aLl9m7KpERG0WC8ooFrTkyfp/dnZRHQ7d0Yne3vWqWdPVHp74+dvvkGvZ55Bp/nzaUHJccCIEcjKysKVbdtYyNtvI+7aNegcHHDhwgWhoqKCk0qlKCgvR/sm+jBj61YiCqNHU2ZE/72DB5NE+vBhAED79u1hZ2eH+Pj45rPbVVUUZKiLK1co27tgQfO9bltAQEAAi4mJ4TNOnoTzmTM0zra2FBhYuxb5AQG49+ABAmWyejptjuOYruG+zJpFWZ/336//+nvvNTJ6MjU1xbPPPouYmBhdVlYWf+TIEaFLly61K9Tjx2nx/vHHNS8lJyfjwIEDoiAIzLZbN9F94UJmefQohxMnSMr5xhvNOjgzxuDu7g5XV1cu/7PPcHrMGCHu9m1OSE5Gnz59IJfL8f7770On08HIyIjduXNHvHfvHmtEtr/6Cnd9fFAYGwufL79Er8hIwb1jR565uRGZ1Ad79AZkd+9S5uvcudp9Cwxsvk91A5SWlkKr1YIxhl5NSPERG0slE0ZGFJB64QUYvPoqALpXAWgyk10P69aBP3cOD9zd8eD27SfvlIUFkcJqs66G4DgOc+bM4ff99pvI+/gw1YwZkK9b1/R2mrhmzhcV4XxRESxffFEYyxhn+9ZbRGhfeIHGjuPoWI8eJWIKwMDAQExLS2MymYxbsGABjIyMGAA8fPgQ+nkqlUrFP/74g1laWgqjRo3i3JowA/P19YWrqyskjIFbsADDMzLA7djRpFqE+/BDCpw8DXQ6IDcXL4kidNu3w6FXL6qxHjKkfo2zKFLAqby8niEgAJw4cYKy2mlpFNxp4E1wZtYsxCUkYLq/P9qvX09GZnRwtN3bt8lHoriYSPqyZaTCiYujAJeNDd1zO3WiMhgfH7Tr149Fm5tjilJJteENMXcuzfWmTO3Wrm2y9EWr1cLU1FS8ffs2c3V2xjMNAmtPg+joaEGtVnMAsHHjRvH9999v/vn5xx9ErlNTKZDo7U2GgHXH/wk4f/48YmJixEGDBrEjR+qrrE+fPi2OGzeudc/vx48pWObuToHSjz+uva7CwugnPZ2CGbm55Gmib0PYCjDG0KFDByE/P58rKytrsce9tbU1Jk6cWG+ymZqaYvLkyUbffvutUqlUfq1QKFQAIJfLY2UymVdlZaVk+fLlUSqVal71R/TR7oEA7raZrbWhObSR7Ta04V8GxliZvt/pfwoDAwPY2dmpMzMzhwHYyRh7TqvV8qNHj0Zdg7S/gh07duhycnLYlClTmJPTfyio+w9hYGCAiRMnsuvXryMtLQ2enp4wNDTkduzY0WrDmv8IFRW0+FiwgGSy+fm0sI2LI+L86qv02u3blDFs144ysSkplHHx8Kh1sv7+e1pAduhAC5WGqJZ1GwIo7tpVLJbLWcO658LCQpiZmQkJCQncuXPnYGVlJQwcOJD5+/tj+/bt+OWXX8Tp06ezRmUKSUlE/vWZSL0cNDKSFk+RkcBrr0Fy+TJKS0vFGsOgpjBiRP12UZ99RouxpKQnun7Xw4MHNG7PPAPDgACMHDdOiDt8WHT+808eo0dTlmX3bsDDA4dOnBDu37/PHTlyBGFhYbq+ffvyAJHtepltUSTCMG1a4+9jjCTUO3bUy2w6ODjgpZde4k+cOCFcuHCBO3ToEDp27IigoCAyWHrlFRQUFEAQBFy7dg1nz57F888/z6olosTyXnqJxvHnn0nmPmMGSd2bAadSoeO332LcmTPcrYAAtK+TKao7r4ODg1nU/v3i0PBwhq1bKQN49Chw8CCuXr8uXHFw4NSHD+sGDBjQ8sCvXk3nv+68GDyY5vDmzU/MqOnvW/b29k17OEyYQPM7IoIM2D74ADJBgFNCQqsNxtCxI7BkCQZmZyMuLg6iKLYcMLSxoSBYaWnTMm+VCpg1CxELF7Ltr7wiSLy8MFEUuUbbNDZupJZwcnLCvXv34OvrC2NjY2y+cAHz1q6Fkb63+aefUtDqyhUKYFTDzc1NzM7OFmfNmsXVlf7rZd9Dhw5FYGCgfgdaHJQaQrJ2LZ1zhYKyjHWDWTdu0DlswoOhEUpKyHjN35+u4YAAWL32Gnalp2OYry9sGhgmAqBr5tw5KmOZObPm5by8PABA//796Tr9889GHz2TlCSOPnkS7V9+mcHWlgi03lSQMSKbAGWgr1+n+u5Jk+je2q0bqXJGj6bzDJAPSVQUDOztYd4U0f7uOwpkTpvWOChRWAhh9WpcvXMHD6OiwBiDkZERzp07pysrK+N5nmfDhw+H11tvQWZp+eSxbAbPPfccd+zYMeTl5UGlUjU9eePi6Jzu3EmBgVu36PeSEiLeT0G2KyoqoFKpaoi2ra0tRo4cCSMjI/A83zqiXV5OpU8bN9Lvzs50j0xIqF9q5OpK13hpKV2ru3fTebKyIiPKJwT3XV1duXPnziEtLY2CNE8JS0tLeHh4aDMyMi4olcqVAHYaGBhUhoWFGXt6euL48eODbty4kcAYEysrKw0ACBYWFprS0tIKpVLZWaFQVDS3baVS2RnAMwCiFQpFE9KtNvxb0Ua229CGfxm0Wu2v8fHx8xwdHZ++YLsJhISEmO7evXvD559/Pozn+ZETJkyAa2sWXU9AUVERFxYWxvQtZv5/gJeXF7y8vCCKIlatWiX26tVLNDAw+Hsk7qJIRklxcSS1nj+fIvrjxlF93+zZtHA3MyPpYlISLeqcnSkzaGBAi/WXXqrdZlNuq01IjJvDgAEDEBkZCUtLS3Tr1q2GrFy9elXo3LmzaGxsDAsLC+G1On2+X3zxRWzcuFHcvn27OHDgQM7d3b2WrKxe3fQXyWREPO3sgC++AMzNMX7NGpY/ZUqzLagqV66E4OkJY71Rz08/Ud/nJxHtykr6vp49gVOnIH77LYSjR5G7fz90s2bhkYMDl37jBh4FBsJy4kQiEsXFwMmTYE5OorW1NUpLS8UTJ07wycnJwsCBAznGWH2yPWQIBUOa80Rwd2+cla/GoEGDuMrKSly+fBkA8GdUlDBn+XJOffMm1ukXovQ+sWfPno1Xlra2VKuemkoZdHt7GveG45KfT3OumrB0bWpnoqKAwkJcKS8X5i5bxmHoUDJS0hvXxcaiePt2ziAvD30HD25+4EWR6m5ffZV6SDdEfDzV4z6BbKtUKgCAWq2uL/XXIze3/mL7xRcRGBuLgIMHcWndOvR6663mNy4IVJffty8EZ2ec/fVXqNVqVFVV4YneFidP0rkeN67+62lpRALy8wFzcwxftIj7/vvvsW/fPlhaWsLPz496HwPU7qnOHNq3bx/u3bsHABhJxIe7fv26+OWaNWzEiBHw19coz5hBhHvXLiIqc+ZgdFoah7w8uk/8/jvKPTyw9cwZFD5+DDAGn4bS5tZAIqH70pkzpJrYtKn2bzk5RICaK9t49Iik9lIp3bdKSigrHxUF2NvDCEDF1avipYQENmzYsKa3MWgQjXMdsp2QkCCYmJgwi6wsqmkfO7beR3JycqDRaFg7jYbmxrp15Px+5EhjCfLcuTRHFy8mRUozPcqjo6NhaGhYI7evhz/+IOXK1KlNk74FC3Dl5k0xcuRIZmNjoxNFEeXl5VyfPn04Hx8fGOmVDTIZBVD0PbmfAqIoYuvWrZDJZLCzsxM6dOhQv6c5QPdLjYbmJsfR3PX3r1UaAc0qNZpCSEhITanV3Llzqef50+LPP4n0173WrlwhRc9PPzV+v6kpqWQAMhZ87z0KKMfHU9CtmedAVVWVYGJiwnX7C2Orx9ixYw2vX7/ue/r06fWPHj3qrVKp8h8+fAiJRILw8HB5eHi4HKDgoE6ng4GBgWzPnj3szp07kUql8kWFQpHZcJtKpdKa5/mrTk5OLDMzkymVyn4KhSLpSfuiVCptAPRRKBTN9Cds8jMSAEMBnFQoFOVPceht+IfQRrbb0IZ/GbRa7crk5OR5JSUlf+2h2ACurq545ZVXjDMzMyd6eHjA2tr6b9hLwM7OTsjIyOADAgL+lu39XSgoKEBUVJSoUqlYZmYm8vPznyxN1aOigurOXFxI/p2YSIRo1CgilV27AtHRtBB95hkiTqGhRPgkEiItP/9M2Zk//6SsxNtv0+f+RpSVlUGr1SIwMJDJ5XLxwIEDbP/+/bCystJ16dKFv3v3LhccHAy5XI6oqKh6KzKZTIapU6dya9euFXft2gUAePPNN9FOFCkr8ehR84RYIkHViBHIzshASrdusLGzo3G6dIlMh6oXsCkpKWh35w5++vFH+OTlCQ6dO3MOly/DoClSlJtLxHfNGsqichzJsv38sHvLFiFFIuEkERGQ7dolyGQywTY5mXvh3j1O+tVXtQu/4GBUvf8+inv14gU7OyEkJIQ7efIk7t+/zx0/flwsKyurDQo9eECL2ZbOycaNNBeaAMdxeP755+Hq6oqqqiqc3LOHO92jB9KPHoWdnZ0QGhrKOTo6QiKRtJzC8fCgzNDhwzS3li2jekw9CdDX9+szooJAJFqrpYV3airJk/PykOPszGVcugQ3fSunOi2devXqhd27d6O0tBSmzblmR0VRhsrJqekF/JAh9KNSNdlbWI9OnTqh2vmaT09Prx/UCwmh4NPEes0RYNS7N76ZNg1OJSXoNWsWZR71ioq6yM0lkjN9OrRaLVQqFcaMGfNkog1Q5i0xsT7ZjowkRcLNm5QRBmAJYNy4cTh27Jh448YNxhirbT/0yiv1iMadagl+YGBgTRZ/9uzZbN++fUJKSgrn7+JCxPWTT2p7OH/3HQVyLlwgqW1VFbB3L26VlYm9jYzQ/dYtJvv6a3Cvv07ne/lymh99+lBQxtCwZZ8DqZTqkvWEe+1aKl/5+uv6PaR1OpIEb9tG96h168ijYMIEOkcNiGhJSQmKiopYnxZUGBg9mqTkVVXQSiQ4ffo04uPjOXNzcxHjxhHRXry43kf03QyMT52ic8txJFNeupTuB3UxciSNoyBQlvuHH4iY18Hp06chiiIcHR0FR0fH+hM5O5uCpVeuNNmf+/HjxzgjCEJqv37c66+/DgsLi+aDU1lZTdantwbVc0qMiYlhQUFBnK+vb/03vPceBQVu3qwfjB05kgIas2dToCEvrzbr/wRIJBLMnz+f+/zzz5Gfn//06wp9L/JTp+q/PnYs3Q9EseWM9ZQp5Fly8ybNt9xc8oXw929kovn48WNWVlaGjIyMlg04WwDHcfDx8YGTk5NRdHT0tIqKCp3eD6MupFIpdeYAMHr0aKMzZ870jouLS2uJopUAACAASURBVF2+fHmxTqczkUgk93U6nbUoiu/LZLIe3t7eBsOGDZPGxMSIcXFxH3722WcPeZ4fotPp/tBoNFcBnFIoFGn67SuVSgeZTHZKq9U6K5XKIQqF4ngrD8ELwH6pVHpfqVSGKBSKjKcdg+o6dV8A1xQKRbNtQ5RKpRmAMoVC0YSDaBv0aCPbbWjDvwwKhaL4888/3xkfHz8lNDT0b7nG7ezs8J8arjWESqX624j734W0tDTs2rUL9vb2GDNmDA4ePMj27NkjhoeHM1tbW1qY603HEhNJ2jh/Phlbde1Ki5stW2iBLJHQwtjRkeqMXVyIhOpdaquNmerh0iUymOrShX48PYE33yTpeO/ejeoZ/yrWr18vlJWVccOHD0dAQADz9vZGeXk5jh8/jjt37uhcXV05GxsbZmhoCJ1Oh8rKynqkxMjICO+//z4TBAFLly5FRkYGrLt1A1u3rsXMc2ZmJnbu3AmJRCJ0nDRJDJ88mcehQ0ReVSpaJH/4IU6cOCG49urFVRgZwTE+nrvGGHZ+8QXM1WrY5eYKKS4u3PQNGyAdNw7Zzz2Hjr/8goSwMITv3VtrLrV5M1IXL+ZGjBih7wfMAeDwzTdEmOvKOBlD1auvov/atXCKjOTatWuHbt26oaysDD/++CMDyFF8YP/+YLt2UQauJefumTOJzDYhewUoYNG9e3eIooh7q1bhZteuKMrNRXBwMOdSXZfbKhgbEwEMDKR5cvcuBXEcHEi2nZFBc+eDD8ikzs2NFqtjxxKJWrqUegp/9RVKm9h8VlYWYmNjIZVKm69/3LWLxuLYsSZrkmuQl0dk/MGD5l3mQTJeALUZYT1Gj27S2f7GjRsoNzVF5169KEsWH0/jUZdw371LZD8+HpBIsHfnTgEAZ9vKWnJMnkxBMIDcsrdsoXZVN240yqC6ubnhrbfeYlu2bEF8fDwkEonumX79eLz8MjB5MnJzc7Fp0yYIgoDBgweL/fr1q2EZHMchIyODG2FiQsf64YfAwYO147Z7N9WsP/cc/QC49dlnOL5vH5s7dy4MVCoinTodjfejRzQmlZU0Hw8fphKBuXPJMOu11yj4FxpK14OxMf088wwFED7+mMhz7950Xe3bR+qUt9+me11wMNVQ9+hB860ZyOVyeHt7644fP86bmpo2TYJ4Hrpr15C+bRt+LSyEpaWlIJfLucrycnbiq6+EsOefbxTF0Wq1AADJ2bMU0CwuJkL988+18wAgbwEfH/q7REJzydcXUKlQolLhp59+0qnVar68vBx+fn6Ci4tLfeYninSsJSVE/OoQQ61Wi4sXL0L7ySdiu+7dmd+8ebBoYX4DoODFxx/Xeh08JQwMDBhAtdQ1ZPvcOVJYzJ1Lrv0NMXBg7Xi8/fZT97auLnsQf/75Z/bhhx/WkMxWYd06MlE8fbr+62ZmdA2tWkWqg5bAGNVuHz5MWftJk6gsICqKnjk2NkhPT8edO3eYmZnZXybadWFubo6IiIhWKQR5nseAAQMkffr0kZSWltrIZDI8evSoy8OHDxEZGfmZIAi5nTp1kgJAQUFBpVarjXB2dmYDBw6UJCcnzyopKdGmpKRolUqlp0KhyFEqle0AZGo0Gnh7e2uSk5PDARwHgGXLlo3heX6ySqV6S6FQ3K+7H0uWLJkik8m+FARB5+/v3ykpKWkvgKfq1bd48eL+Uqn0d47jTLRa7RdKpXI1gEcNCbVSqRwC4IhUKj2/fPlyC51OZ8dxXJFarR6nUCguPs13/tvRRrbb0IZ/IdRq9XeXL1+eMHDgQMnTGpj9N6DRaJCdnc3/JbnjP4jy8nKYmZnppk6ezOPePcw1NcWuigqhbORIvtjaWjRcuJDhnXdoMSOKJOPkeTLa8vEhCffUqbSxui6xdd1zW8LevSQb16NvX5KdKxRkajNzZmP3278AtVrNhYaG4tixYygpKUHv3r1hbGyMiIiIRkxZJpMJhYWFnEMTLsQcx6Fbt266I0eO8Enr12Pae+81WyAqiiIOHz4sOjs7s/Hjx9e+bfhw+snNpQX+xImY4e7OFV69ik737uHnyZMx7/ff0f+tt5C9d6/YJTaWK//qK2QZGAin8vK4sosX4bZmDdKvXgU4DlKpFH369AHHcXB3d9edO3eO9/f3pwz6Tz9RNmf27Eb7Zz5qFIy+/BK6vDygXTtYWlrq5fWiqakpu3DhArK++050/PlnVretT5P44gvK5jwBWq0WIceOQfziC2x/9EiIiYnhCgsLMVpf895auLjQ2G3cSAvpY8dIKTBsGJnWffABBQhMTWnRumJFzUcvXrwIqVSq8/f3r3fu09PTsWvXLtjZ2ekmTJjAN3kfKS2l+t4dO1om2gDVXyYmtki0Acqkx8TEYN26dbC1tRX8/f25gOJiYPz4+jX8APLz83Ho0CEYGhqKQYMGMYSGUjbf15eIoX7BvXMn1dhWB6uysrKYm5tbY0LfHIyMiPT27Vtbky+TtdgnOSIiAvv370dUVBTv3rkzOhgY4ODBg7hy5QoAYPr06bC3t68ZVK1Wi5MnT8IsP1/0aN+eYe1aMosKD6fv3LyZyF5YGGWcPT1RVVWFo0eP6kJCQjgDAwNWEwDSdwcA6rvAf/kl/btgAW2ztJTOSceOFDw4fpxM6KZOJQLOGM2tOXNoPywtSV6+ezdJelsJuVyOUaNG8YmJieJvv/3G5s2bh7q15ikpKThz5oxoLIrs8eXLGPr66/Dz8+NUqamQ+PpixTvvcEk3bwpTp07l6gZok5OTRQCs0tcXpocO4eHDh7C0tEQJAMny5TD59Vc6ho4d6ZrQZ/UVCkAQIFpa4tD48WKhnR0PUBBs+PDhXKPa/7g4ypT369co4Ll3716kpKRgyt27zGnBArAWWl/W4NdfKUj7F8h2Tk4Obty4AQC13QNEkTL1hoaoMYirC5WKykP0ygIfH7o+7txp+v1NQBAETJgwgX3zzTfC+fPnWXBwcOsWFoJAcyc0tOm/BwTQvjwNpFIaw5ISup9/+SWwahUSr17VAeCnNeWl8V+CXC6v8cMwMzODo6MjOI7rkJmZaa73ucnIyACARVqt9iMHBwczBwcHGQDZ8ePHVQkJCfFLly7dASAdoOemjY2NNCUlZRyAd5RKZTupVPqdh4dHu1u3bvVfsWJFslar3abVajcB8OJ5fuNLL70ktbOzw6VLl0TGWNWT9lmpVJrKZLJ4xhivUqle5zhurouLi7GHh4fs0KFDHwH4iOd5hVKpPCKTyX5kjMWrVKoZAHI4jtPI5fKgsrIy/fPDFID67x3V/320ke02tOHfiSsajeZxTk6O8d+dkf4rEEURqampiIuL0/E8j5ycHN7c3Fzn7e393+1pXRePH5P0tVs3WoxfugTLGTPw0pIlPFJSAF9fcNHReHHpUj61tBSn79/HC8HByI+MRG5eHhwdHWH7xhu0raZMf54WOh1lH3v3rv+6VErmYPv2UZYqOpoknn/BjRugRb1Go0HPnj0hl8vFuLg4nDlzhllZWekmTpzIW9UxBSouLoZGo+FaIiWjRo3ib9y4gf4xMVAJAgz1rVyq8fjxY0RFRemysrK48vJyNr25ul1bWzofANQvvgjb7GxYFxejS9euYpWvL9rb2DCrVasYVq2CDIDlu+9yhrdvo7S0VAwICGB37tzB0aNHBa1WyxISEvTGQTxjDMlJSfB0cCCSVAe5ubnYv3+/8Mwzz3BOTk5I7tIFw2/epB7X1Rg7diwDgBvXrol7MzKY35tvCtbXrsHX17f5gkdzc5JzfvVVi3WRPGP4cdo0vBYRgbnm5tySJUtw/fp1eHt7w9XVFfzTGMEBFIzZuZP+P3Agkc2UlNp9agBRFBEVFYUxY8bU+yJBEHDgwAEhODgY/fv3b3on0tLou5KSWk8aPD1JyrphA3IMDZGdnY2ioiJcvXoVI0eOhJubG0JCQuDi4oJHjx4hOTkZhw8fRsAff1Bm+tNP623uwIEDAID33nuPFv6M0fX8xhtErBITKftlYUFZ3GoYGBiIRUVFjBUXUzbQ0pJk2YxR8CwhgYjJ+vWUIX7vPSJcetl4KwKYFhYWNTXZW378EW7h4bhb7X7+7rvv1vbkrsamTZtEw3Pn2IvnzjHutdeoJOP992sdr/Wt0nr2BGJjcSorC6epRIBv0rm9AQRBAKe/Z9Stm9ZnOyMiiBipVHSOrlwh6bROR5niZ56h1/LySJ4eEkIkqrCQrpdWyPH9/PzYwYMHcfbsWfTu3Rt3795FVFQUioqKEBQUJDwzZw5v8sYb4KqNreTu7sDRo4iwscGePXu4rKysemqo8PBwlpSUhK+//Rad09Mh0emQ5uEBCAL81WrkvvYail1cxInXrjHHpUtRXFyMW7duQafToXPnzjj04ot4YGPDxnXoAOuICOw7cED48ssv2eDBg1mPHpQM1OzaBX7yZFx8/XVwNjY1BPfWrVtwcXFBamoq+kilcIyOBmvChbxJtNSysQVoNBps2rQJoigiPDyczBVHj6bAx7ZtzX/wxAkKMGbUUROfOkVzvQWyrdVqcfr0aaSkpAj5+fkcYwxSqZSLjo6GtbU1WlUXvWoVXYNbtjT99zFjSBmTmdmki3uLMDOjDPfgwYAoYuTLL/OOtrZgs2a13LP+vwjGGPz8/ODn51dTP2NkZCSq1eoxPM/XeziEhYXJPT09bVNSUuZlZWVV9unTB126dIEoirh8+bL58uXLb8lkMklAQIDls88+y2VmZloUFRX1OXbsmB/HcS8JgtBPEATR3t4e0dHRqvPnz1dqNJqZjfeqEV7s3Lmzvbe3t9GJEyd+l8vlQufOnZm7uzt69OihNTU1lZw7d84JwAGJRGJrYWHh+vDhw2fVavVmAInu7u49bt26pdZoNMVarXaoQqG4UnfjSqWSq5sVVyqVARzHnRYEYZJCoTj4Hw7x/wSYKLb1bW9DG/6NWL58+dbQ0NAp1W7G/2fQ6XTYt2+fLj09nfPx8WEcx6FLly74x43RRJFq4wwNqQb62DGSZQ4eTCTkhReox+eiRfSgLynBQVtbXWV2Nhs/Zw7XcEG9cuVKwcLCgsvOzka7du10jx8/5m1sbHSurq6cl5cXa7ZfdGsRFUWkOiqq+ffk5VGGxdiYFll/wRH+/v372Lx5Mz755JMaYzO1Wo09e/bo7t27xwcFBaF79+6wsbHB6tWrBXd3d/GFF15okfUVFBRg48aNYt++fdmAOnWClZWV2Llzp6BSqViPHj2Yv78/ZE8KEty9CyxciFK1Gn907ap7aGfHjR07ltlUuwU/CYIgID09HQYGBjAwMEDi5MkIvnEDhvfvY9u2bUJxcTHz8fFBp06d2L59+yCVSqFSqaDRaNC1okIYz/Mcli5tRJIvvfKKziY6mt9SHSwYP348ujZXty0IlBFMSCBzq+agVOLW4cNi8dq1rHd1kOXHH38UsrKyOAsLC8yePbv1PWznzaPM9YcfAocO0SL2CdBoNPj888/x7rvv1po3AYiNjUVcXBwWLFjQvFx02DAifp980rr9q4Z25kzEeHnpLpSV8TKZTKisrOTqrkPqzsvExEQcOXIEixYtalTXmZ2djU2bNsHZ2Rkvv/xy4y+6c4cI4qNHwIYNRC7UaqBfP9xZu1bM7NiR9VepwHieaoG3biW5dr9+lFHu2xcoKqJrrXt3IqCvv06ZXX19/BOQmZmJLVu2QFZZifF79sClGZJVXFyM3959Fxbm5hg9YgRtOyWFMspHjzYi93t/+EEc9NFHrHj9enQaNgxSqZRqhs+cQVFREUJDQ+Ho6Ijbt2/j5s2bNdn09u3b6954442mr+Xr14mwde1K5Q/379N+ODmR0ZaNDY2flxdJ6SUS2q8dOygIsXkz/b5pE7T796Pczw9yHx8YWFnV7L8gCPjss89q2pPp+9e/8sor1Ntcp6N7859/UnlCWhrVqQOIiYkRY2NjmZGRkTBo0CDOw8MDBQUF2F6duR916hQ8nJ1x74MPIJFI0O7WLYjffIM7ixbBYeRI7Jk6VSw0NWXW1taCKIp4/Pgxp9VqAZ0OH69ZA+7wYYj9+uHQoUNITk4WFyxYwB7m5iL2gw9EI0FA+QsvCPn5+ayoqIgTBAEymQyMMVFVVcUW7N4No8WLGxvoNQd7e1JgNOeB0ARKSkqwYcMGlJeT39WiCRMgadeOnmv+/i2branV9OxoSGZFkRQ41de4Wq3G9evX8fjxY1y6dElUq9WsXbt2gouLC3r27MmJooiUlBQkJydj6NChrfMy+fxzKl9pYG5XD/PnU8Dro4+evL1mcPDgQd218+f5jlVV4islJYy/d4/mc8eOrTaC+2/h8ePHSE5Ohre3d6OgW3PQJyw0Gg26d+9er4NCYmIiDh48CJ7nNcOGDZP6+flh5cqVZeXl5QoAIoA9AOwA9AOwCeQR0QFANgAtY+zb4ODgmQMHDmwyinjr1i3s37//pkqlGsVxXNKrr74qr6qqQkJCQqW1tbU8MDCQk8vl+O233zTJyckLFQpFTX8+pVI5EsA+iUQyd9GiRauVSmUvnueP63Q6MwBDFArFsb80iP9jaMtst6EN/wIolUoOgBuAXjzPe0ulUgdBEAa3yvznH0RZWRk2btwoarVa9vrrr7O/w7CtHvSmT5mZJJMdN46yWlot/fvuuxTRl0hoQSGVAt9+SwsTa2vgxRfrba5qzx5IHR0bEW0AcHZ2Zjdu3ICvr684cuRIvqSkBPHx8XxKSoru7NmzvImJieDr68tcXFxYSUkJkpKSdKIoIjQ0lG9VbWhcHGXvWkLHjkTIz54lkjNwIB3nU0Aul0MikdR7WMtkMkyePJm/ceOGePHiRSE+Pp4HIFpZWWH48OFPTK+2Zwxzf/qJrRJFODg4wMzMDHFxcbobN27wFhYW4syZM7knkmyA6kpXrgReew2mAwZgQnAw/6O3N354+LDJbGBTqJaP0xy4dg13BwwQ240bx8zT03H37l0uICAAaWlpQkJCgujs7CyOHz+eFwQBSUlJsLez4zB3LpGtBtL/oAkT+NQBA4B79+Dg4CBmZGSwX375BU2683IcBQ0aQBAE5OTkwM7ODowxCJ07I9nZmQXVkZ6+8sor3K1bt/Drr79iyZIl8Pf3FyQSCdejRw80GXCoJgxwd6d6WrmcFtf5+S3KnIHaVlE3b95EYHWG8/Hjxzh//rw4cOBA1iTRVqvJJfi77546E5WXl4cNDg5wz8nBq6NHo13PnjWr4I0bNyInJweLFy/GO++8A41Gg5MnT2L0zp0ofvAAmo8/rte6LCsrCwAwatSoxl8kCHR9OzjUtkobNw5wd4fg6Yn96emsz4gRYHWd8AcObHnn58+nAIOjI8m4y8qIkLeg+nB0dETHjh3Fonv32OMWMm2lR44g7PhxWGzfXkvijYwogNLgXlRYWIgbDx4wg+BgaM+c0R29coUVSqUcYwydO3fWWVlZcT/99BNzd3dHcnIyAMDJyUkcMGAA27lzJ69UKtGzZ08MGTKE5s7ixSTF/eILmj9DhtQaOH76KUl1168n0n35MoTYWAiTJyM+Ph4eHh6w0t9De/XCkbVrkbZqFXpeuIDi48fRrrAQARoNuHXrgG+/Befnh74uLtrU2FhJpZMTSjQafPzxx7UBJZ6vbVXl6lqv7r5fv37M3t4e165dY4cOHQIAmJmZ1ZjLGW7aBImzMzz0ahAXF+DkSfhLJCi/dw+hWVnMxcUFMpmM0+l0WLp0KRhj4GQyZCYmorOLC9icOQhftAiJiYls2dKlePvLL/G8pSWM791jjDEeoGxveXk5DA0NER0dLVy+dInPXrAA7i2RyYaYPLmR54MoitBqtc0GtziOqyHaEp4HFxpKioum6rMbYv58Ci43vF6fe46CKNVu4Nu3bxeys7M5IyMjBAUFITAwEKampvWYau/evdG7ofqqORw7RkqtJ11bM2dSMOBJRmktwNHRkUtMTESHPn0YP2QIlX3k5FD7uY8+eqpWZ/80zM3NWz+G1WCMNVuH7uvri8jIyEqVSvX2kSNH1rq4uBi6urqyrKysTy0tLQ0yMzMnA+jq4uLCpaWlfSwIgqWxsXGZSqXiOI4rNDc3t/Ly8mp24D08PMBxXGfG2GUPDw+dlZUVJBIJ6na8qTal0wK4WO2GbiiRSCYB+KH6LR8rlcq1AMIkEomRRCKpUqlUD55qEP6H0Ua229CG/0EolUoGapVsxRgbJZVKFVKpVG5nZyfa29ubGBsbw9raGv/N/tUPHjxAZGSkrrCwkJNIJKKRkZGYn5/P29nZCVOmTPnP5OLZ2ZQlDAighXNcHEl0Bw+mh2lgIBniDBlCBjbt29ND/tq1xttqICWui6KiIq4507aIiAgWEhKC9u3bM4DqsUJDQxEaGsprtVrEx8dzSUlJurNnz3IymUxwdnbmNRqNuGXLFshkMtHS0lKobhXCjI2NRQcHB87W1pY5OTnBWC6ngEBTfbEbgjHKvtnZUX3luHFkQNPKzLq1tTUYY8jKykLDOuzu3buz7t278yqVClevXmVubm6sVZlVlQqGwcHo7uWFvXv3QqvVolOnTmzGjBmwsbFp3bkvKKh1Eq5enLHwcAzPzsYPjGHNmjUIDAwUwsLCGvcwbgorVgDbtoFfuFA8+fAhNLt3s+eee06/yKl3UBzH6U3UKCO8bBllOvX44gtALofb7NnAkiXIzs5merJ3+PBhMTw8nJk3JFNTpxJhqs7MAZR9SUpK4l1dXXWBfn582eXL4rVu3VhEHbJdXQcPKysrsaioiCUlJXGMMVy6dAmOjo7iSy+9xCR1HaX1Lvd15fuLFtF3P2GB2a5dO4SHhwvHjx/noqKiRMaYqFKpOH9/f6F3795Nn7etW+la+/jjp14Yx8TE6KytrfkJhw7xTN+erRpWVlbIycmp/oqtqKqqgrm5OZJ799Zd1Gi4u99/zxhjsLOzE8aOHcvdu3cP5ubmgomJSf0JKghEgn19KSglCLToP3sWKCpChYcHSk1NYfY05owqFUmnx46l2lM90Vm2jGpHmyjVEQQBP/zwg1BQUMAZ6nRgzagIy3//HcdOnQKefx7T65L/efNIItsAV69eRadOnXT+M2bwDy5e5Ie+/TbYtm2QUE0sDwA2NjbCsWPHOE9PT3HkyJFMJpPVnCipWg2X7GxyDvfyooDMxo10bx0wgIzGXn+d5lWvXhTI+eMPkgPrdLgycyZyc3NxuU8fREZGwtzcXJg2bRq36eBBodTamps6dSo4joOYlYWjJ0/Ca8YMlFRWosOAAYBajf7GxpKgmzdxLiMDnR48AJeQQGqdw4dpPqSmErnfu5fu7fr9lkrh5uYGNzc35u/vj23btqGkpIRxHAdDQ0Ph559/5uZ+9x2KV6+Go37cli0DzM1hPGAAuq5cSWU4Pj64cPGijmm1vCiR4O2336ZgmVYLnDsHycOHePPNN8GlpUGWkgLDJUtY3XkukUigv9aff/553uv118WKhQvZU10L+gBwHRw/flx3/vx5vn///sKAAQMa3XRNTEzgYGcnBK5dy7l9/z24OXNanxnPzW06u7tqVb3nhpOTE1dUVISwsDBYW1uz/zhY//77RPSfhK5dKWisULRKMdIUfH192R9//IGqqioa21GjiLzrWzSOHEmqiWXLWnbk/x8Ex3EwNjbWqFSqKzzPx9y+fXtIRESEMUCqmW+++cZ/yJAhQmBgIP/rr78a5Ofni2+++aZJUVERiouLHZ2dndHSM5XjOAwePFguiiJ69OjR5HNBq9VCrVZLDA0Nv6qsrOwFAKIoVtnZ2eny8/OZIAiWcrn8olardQ8JCZFERUUxAIZKpdISQLlCofhX13n/u2ZcG9rwL4VSqewtkUhGSqXSZ0RR7MjzvK0oijKJRKJ1dnbWBQcH/9dqs7VaLQ4cOKDLzc1loiiy9u3bC6WlpcjPz+c9PDy4oUOHsoqKClZaWorg4GB06dLlP6/L7tuXnGQPHCBZ5wsvUKbl2rVa9+kpU+jfJtqytBYFBQWsOdk9x3H1Mmt1IZFI9BF//bHq/2VarRapqamssLCQl8lkkMlkePjwIbKyssSrV6/qysvL+W75+UL3s2dZ6bPPCoGBga0br86dKXNnbEw145s3k/HNExZ9giBAFEVIWlhwyOVyqgdsLYyNgXXrMJxkocLt27e5hw8ftq6tEkBy0alTKcNS5/xp330XuYsXI6JjRxx59Eg8d+4cFxISUs9cqRHS08ns6f33gXnzMFUu53bv3i1qtdrWZRPCwyl4UbeGMCYGeOMNcByHsWPHoqKiAidPnhRVKhVLTU1lFhYWaN++PQICAmqzdBMm1MteqdVq3Lx5kx80aBCysrK45NWrdSGRkfyLMTFN7sbs2bMZQBK+X375Bc7OzsjIyGDLli3D1KlTYXv3LqR61/GG5nXp6a2WTgYGBnKenp4oKytj58+fZzdv3sSzzz7btCHaTz+RZHX8+Kf2DNDpdEhPT+enTZsG9sYbNE/rZLJCQ0NhZWWF4uJi6HQ6WFtboz/Pg+vTh4evL7RaLRITExEbG4uvv/4aPM/DwsKi9iBFkVQfAwdSoKVPH6onfvFFUr307Al8+inK9++H6+3bNVnCVqGggLJkdcfkiy+ofnvdOsr2v/suZQmrsYR6BHMAIFOpYK7RNN5uYiKkS5aABQZi8Icf1s4dUaR7W/V8PXjwoC4jI0M0NjbmCwoKEB4eztvZ2cEuIoLq0Z2dKZhQ3cqqV69eXHUdN+2wSgWUlGBaVBSM4uPBHz1KAYKwsFriIYokuS8oIF+Iw4fpdYmE7isHD+JK585QiSI6FBRgxvTpKCktxe7du7l169aJUqmUW7hwYc21KZPJcPz4cazYsAEABScrKyuhuXIFGDwY9vb2ol/37gw5OZTRvnGDSKE+s/3llxR8ZKxRMKNz587/j73vjovq2r5f584wQxk6SC/SRVBUwK4IKjaMsddoNagyxgAAIABJREFU1MSSGM1L1fiNzxdTNCbPxBhjS0xiYsXeEKSKBRAQkCIKiIAgTUHqzNz7+2PTm2g08f0+rM+HDzAMM3fuPefcs/Zee+36FnGYOHEievfuzV25cgVRKSkoLSpSWtavv9nZFCzQ1KT6/ZAQoLoarsuWifoVFcF/7lxBbcoUhgED6ByOGwfU1sLAxobaqqWlNTesbInqalRqabFrPC84tOx13REsLGjM1J37xMRExMbGilxcXBAZGclVV1fD2tq6PqNI/1NZCUc7O0FSW4taxqDeWaJdW0trRFvKip49SaVhbw+MHQsfHx8IgoCgoCBeLpez7t278zNnzny2+3dODgW5Oln+g4ED6Ro9I9kWBAFKpbK5+omxRqNSVVXqv37uHB3XBx88lcnfyw6JRCJSUVE5Wl1dbXHr1q3qfv36qQLkHbF27doGZUZZWRnU1NQEAExPTw9NPVo6Qt++fTscBzo6Opg3b56KXC7vn5KSwsfFxXFeXl7iIUOGiADad+zatcspPz9f49atW+A4rozjuGmCICwDUL1+/XqbdevWlfylk/ASo4tsd6ELLzHWr1+vKZVKT8lkMnc3NzdVMzMzkaamJnR0dKCurg7G2N8+h+Pi4oSMjAzm4+PD1dbWoqioSGRqaopXXnkF3bp1ezHW51u3UnbF1RUYPZo2vunpnb+RdwI1NTUQBAFudeY8zwtisbg9IxkGQFRbW4u0Q4e4hMpKFFy9yvXr16/DKHMziESUiR07liL3ffqQZK4DuX5hYSEAoNNtjzqDESOAAQPAbd+OV199lautrcW+ffuwdetWTJ06teM2LMXFdOx//NFAtHmex9GjR4X09HT0uXOHOe7fj55btmC4l1fHRBug9jIXL9JmSk0NHABNTU2+srKyc5tGqZSyLMHB1Bv5zBkqPahryVV/LXv16sVKS0uRkJCAy5cvAwAiIiKwYsUKCmR4eZGhFMjl/ptvvoGGhgY/aNAgjuM4hkmTRLh3D/pNe0m3gXpvgxEjRqCkpIR/9OgR98svv2Dxzp2QTJoE9U2bWsvrv/iC5L+bNnXqI2toaEBDQwOvvPIKbt26xd+4cYN5eHg0H4RZWaQ6OHHimcyHMjMzIZFIeBMTE2IPK1eSWqVJq68RLeWm9bXYv/0GsVgMDw8PeHh4cHFxcSguLhYiIyPZsWPH8MqgQeBEIpKO+vk1OvarqhLZAmit2L4dhteuoe+2bcj95BP4z5unHPnKK6JWqoSW0NGhgEtLODvTfNu4Ebh6lc753Ln4fd8+JQBR7969cePGDVSpqeGqqyua0bZjxwBBQMWOHcg9dw5qamooKyujLGtqKpHNujmamprK3N3dRWKxGF5eXrBrGlAcMYKe//rrRKqGDKHHeZ4Ie14eqYDGjIHh6tXYFBQEnRs34OnpiX71RFsup/G6Zg2VqrQM3HIcynbtwglPTwpyWllh5dtvw0RTE+9s2YI7d+4wW1vbZnOzW7du6N69u5Cfn89ef/11nD17Vqmvry+6fv06Vq1aBW1t7cbxdeMG9ekOCKBzvWsXuWf/+SfV/BYU0GeYOrWhvMPJyQkXL15EaWkpAGDgwIEILSlBrb+/aNu2bULfXr1Ydzs7GOfn0+vMnNkQkC3u1Qu//fwzBrm7MxVLS5KrP3hA108iodIkTc2OiTYAxZ49uDpzJiqeVvpcVtZAtHNzc3Hq1CkMGjQIXl5eSExMxIkTJxAVFYUJEyagb9++5B1gZ4eBAQGibxcvRk1ICHoVF/N+fn5PjqgdOkTlALdvt/33pCSaI2PHguM4jB49GiNHjuSCgoJw5coVUVZW1tP7qwgCrelr1lCGuTP47DPyVpDLm7ft6ySSkpIExhhrt468Xz/6KiiguZqTQ2Nr+nTyY/gfx9y5czXy8/M1unXrBnV19WY3yab7iYKCAvTt2/eFFLHXjxM7Oztu2LBh0NbWbtifchyH119/XaO4uBgGBgaIjIxUr62tfaukpESUlpYmBWABoItsd6ELXfh7sX79eiaVSo85OTkN8PPzkz61M/ELQHZ2NoKDg5m3tzfr27fv3/fGY8eSvLCggNrVSKXAkiWUsTl1iojarFkd9nh+EsRiMf4Jw0gJx8H1P/+B1enT2HLgABMEofNkux4aGhS137mTarlffbXdDEFcXBy0tbUbah2fC8LDaYNVB4lEgoULF3JXrlwR/P392ZIlS9ruqZ6QQNctJKShvrguAs5XV1ezuXPnMlNjY4ji4tBdS4t1KJs8doyyjBcuNLZfqwPHcU93bV1c6FyWlhIpPHCggWw3/YxGRkYYNWoUjI2NkZCQgNu3byM+Pp7qn+/fBz90KHZv3crfz8/nAGDFihWNbYW8vdt36G0CVVVVaGhoCBkZGVi1ahUXP2gQ0iws8OuyZZArlcDmzfDx8YGHhwfEYjE4jgOzsaFjfwY4OTlxERERQjNlQ2oq9YCPiHhiHXh7yM/Pb2729tZbzcZMm2jatqoJ6iT/TEdHB7E7d0KxZAkkQUHAtWvNn6itTVmsJhl0rn9/sF9+QfHRo7z3e++JYgMCMKKuh7YgCCgvL4empmbDHOR5HggJAbd9O80xkMmguro6ZYXU1IjM3LwJbNyI2tu3kVtTI4KaGiorK/k33niDyw8Jge3hw43HlZMDrFmDsu++Q26dMd2OHTsgCAK0tbWF4VFRzERXF8Z+fkhJSYFCoWBGRkbo2bNn2+fJyYmCkSoqRPz9/IDFi4lwHzhApm7W1uAEAVYPHggFBQXs7NmzUFNTg7OdHT1vyBBaY7dsaVXbWzlxIg5ER4NTKvHaokVUnnTjBpCeDl2FAu69erWpdJgzZw6rV9HMnz9ftHnzZgBU36mtrU1rlYYGEbPhw+n4P/iAvsrLyeX+3XcBpRLKkBBUqKiANzCA9PRpHB82jOc4jgsLC4Obmxt0dHQwSFUV3NGj2OXhIZi+9hrL1dPDubff5mdHRnISXV2wuuBNRUUFIBbDxNaWsrsAfR8xgtQKOTlAWhqU69ejaOlSPHz4EDKZDA8ePED37t0RHR3N3wwP5xZt2YL8pUsxzs+vYS2tra2FXC6HmpoaGGOt13K5nNrz1ZV95ObmCgDg5eXFAMDV1RWurq74+uuv+diYGK72q68E3XXrmOPZsziVlydUVFQwALh161bbY6ElvL0pQNIedu6k7HdtLSCRgOd5bNmyhReLxWzw4MHPFkCvraVA2dPUSXMcZZ1NTZuVD3QW0dHR0NXVbfCeaBdGRiQll8upHO3oUaCyEqioIOL9kpmpdRYaGhqwfULgFgD69u2LmJgY9O3bt3MGd88AxlibveYlEklDkN/Ly0sKAMXFxRCLxcrU1NSwDRs2vL527dqn7AP3v4Eust2FLry8GCqVSl8aog0ABw4cgLOz89NJjJ8HVFQoy+PvT7WiAP0sCCQ9XLmSbuxBQWSM1EFddnuIjo7mVVVVGcdxf29j8ps3gW7dkPboEZ6JaNdDRYUIzNWrdI4eP6YNdJOsXXV1NWJjYzF9+vTn9xlLSiiD8dNPrf40YMAAlp2drfzxxx9F7733XjPHa9y+Tdft6NFmRPvXX3/lq6qq2OLFi5msvqXUo0fARx8RkW4r61FTQ5JAL682ZfQcxz1d5w17expb8fGUyW2P5NTB1dUVjo6O2LVrlxASEsJiY2Pxyiuv4Ld168A9fsxNmDAB9vb2jU7sNTUUMGojY8TzPLKzsxERESHMmTOHAUBVVRXr6eQECAJ69esH00GDMGPWLGRlZeHMmTMIDg7GxSYu9r2dnZGdkYHyzz/HiBEjhEGDBnXqegcHByMxMRGTJk1qfL4gkKHTvHnPTLQB8grgOK7xIjg4UO33smXA9u2t/+Httym41pSkNkVmJvr98AMCjY2xf/ZszG+v/7yeHmXQmzjHS3V1kSaRcIUzZmDwo0eCsGYNY3Pn4rfoaL7ORE8YOXIkk0gk+Pnnn3nVhARujrc3qisrcenSJVyhllvw9PTkfX19KYDSsyewZw/ygoKwcP58XOvfHyPee4+TyWQw7dWrsb/4/v1AejpyDh/G3uPHG5y53dzcBEdHR5aRkSFUFRQIp8rLOT1/f6SmpkIqlTJTU9P2T65SSXNk+/YGsyscOkQZ6ibzgTGG2bNnMwC4ePEigoOCBOf58xmmTaPM75w5lP1tMYdSc3MxOiAA4cOHN2ave/emr2nTSK4fEtLqsFret9TU1HhXExPO7KOPKHMvFpOk2tER+OILyOVypCUlkePyqFHIevttFPj4QC6X4/KwYVBWVMA8N1foc+8eisrL2cotW3DZzQ2KefMAjoPExwcoLcUyxrjqYcOQn5yM7ORk7lddXfQoLAS7dAmCICA4OBhisbhZ8EIQBDz+9Vew995D3MGDMJDJID11SojJzGTZLi5KhULB1bUThJaWFl595RVUTp0K+enTOHv2LExNTREQENBAgiV1xNXKyko5efJkUcPap1RCuHQJ0VFRuHTpklBeXs6AutZsTYiegYGBUB4bC8fAQJa3dCkee3rifnw8xGIxfHx8YGZm1jlWeOLEk1tTuruTYmzzZmRnZ6O8vJxrZlz3NOB5KvPasePpiev773fae6QpBEFATk4OG/40EnQVFbqnABSQ27aNuntERpKiqem96v8jjBkzBvn5+UJISAg/Y8aMf3xjqa+vDxsbG9HNmze1VVRUXo5+bS8AXWS7C114ScEYG+rs7PzSEO179+6hpqaGXGz/CcyYQRmbpi7LjFEkvLiYfj95ksjlgAGUPdiyhUhNJ3D9+nUMHDjw7yXaAJCWhiPjxyvvBAeLRo0aJbBnZtt1GDCAslw//EDfX3uN6uEAhIeHw9DQkHdwcHh+4fvcXMpytQHGGGbMmCH64Ycf+Js3b3INQZrgYJLfHj1KstU6BAUFobCwkHvjjTcga9q7edQo6oP88CGZ3zXF9u1kgpOVRTL6do6D5/k2/9YAnqcARWQkja/SUnLrTU5+0hkAQJvrZcuWsa+++kooKChgP/30E0ZeuACPZcsgqevZ24A7dyhr20R2GxUVhcjISL6srIyra4nEvv/+e0GhUAgmJiYw8PLisHgxuK1bUU95ra2t8dZbbwGgus+rV6/i/v37KA4MxMI//sA3H36IwMBA1q9fP0ilUgiCgNu3b6OsrAz9Wh4TgNjYWH7SpElcQ9lDTQ2dX3//jrNjnUC3bt1QXV3dfDGTSqk9X1t4800i2y0hCCSNDgsD4ziMnzoVx86cwdatW/klS5a0dr0/dqxVpraeuD4yNka2jw/L/vVXjJkwAZW+vtzY6dNxNTGR37Rpk0hbW1vgeR7acjl/ID6ey/j2W2hpafHjxo3jDAwMcPDgQSaRSODt7U1BMhUVXC4tRfH06RinqirINmxgWLyYxtOaNZSt/fJLyH/6CftOn4arqyvU1dXBcRx8fHwYADh268Zh1SoYnDiBmNhYhUKhENva2vLnz58XcnNzRa+//nqjSqRO2YCjR8kUr55UzZtH62IH6OPkhOtBQaxi+XJojBpF2dzAQApOtEDfvn2RO38+xLdvIz09vbkr/r59dD0iI6l8xdW17Tc8fRpDL1/mgkxN0SMlRbhy6BAmffQRk9atz1lZWcjIyMCVK1egra2tSH/jDTzW02PCrVucWCxmNjY2GD16NAwMDBgAPDh/Hn+++iqGjxkDg2vXKDiTkAAsWgRcvgzV3Fz079kT/QFcCAjgeyxcyB0bOxZ5df4GLbN65w8cEOLS0pjWrFngk5OFmpoawWj0aGG2hYVIPGGCCBYWUCgUSEhIgIuFBSfp3Ru4fh1jxozB6dOnsXXrVgAkZ/f09ERhYSHkcjmCg4O506dPK6dPny4CgNziYlxYvZp/EBLCevTogYcPHwru7u7NjSivX8e0tWtF3y9YgDsXLiAlNZU/8s03HAAmkUiEAQMGdP4esXEjBZo6ksQfO9YwXupreKurq5sHRzuL7Gy6Hzcxfew03N2J8MbFdejy3xKMMWhqaqKysvLp3xOgeTNmDN3LQkJI4TFlCpU1Pc9yq5cEPXr0YLGxsX//XqcN3Lx5Uzh//vwjAIPXrFnTuZvt/yC6yHYXuvDy4u/PsraBqqoqnDx5Unn79m1R//79OzTWeqEQi8kB+K232s921TsyZ2dThlcsJiJmZkYb0CYoLi5GYWEhxGIxUlJSUFZWxnq1lx17UVAqIezdi6IePbjh06bhqTZRHUEqJRfY8+epbvPiReD995GVlQWZTPZ8x5SrK1CX6WsP3bt3F65du6bs16+fiAsPJ/K2e3czor1//35lZmamaObMmdBta6P11luUqQ8PJ0KgVFL/9NmzyfyqYzdVymzX1tJGTiQiMn3qFGXz3n6bPAAuXCByOWECBRCMjWnzfuRIa5LfBCUlJYiIiEBVVRUUCgV7++23sXPnTkEkFkPc1hx+7z2SrH74IRITE3H69GmB4zg2dOhQ1rt3b4jFYty8eVOZkpICD8ZE3ceNIxl7nQFWW6iXnwIgieSiRVjXvz++/fZb/uTJk6xv377szJkzDTWubZFtkUgkPHz4sLHEYN06Oi8rVrT7vp2Fnp4eamtrm2fwXF0pIHTjBmVJ63HjBnD3btty0oUL6RrGxQELFsBZoUBCaqpw584dLjQ0FKNHj27+/MpKMv1qEixQVVVF3759ERsbi5vJydDy8cHXrq4YceUKPH/6CZ5r1oge29oiPDycDRkyhGl8+SVqdHRQsmABzM3NG1iRl5cXCwoKQlRUFBQKBYA62bmhISpeeYUCRBMmUK37t99SVjs2FlmZmaipqYGZmVlr2evVq4CDAxycnODg5CSOjo5GUFAQxxgTzM3NhRPHj/MLBw0SYd8+MvAaNaqxPzZAmeZOqDjUxo2Dt4oK1D7/nAKZ+vptqlPqobl4MWynT8f5ixcxpL4uHKC1pnt3Iv23bhFhr4dcTrXWkycDN26g8s4dVDg5IXjFCnb37l1cuHABNjY2EAQBx44dg5aWltLJyQlTpkyhm8yiRWSS1gaBr66uVt43MxPFy+W846xZHMaOJRlyaCitDU3Wg9G+vhxWrYLFpUsotLGBnp4eP2/evEZ2q1TCa/16xjk6Cr4nTjDQ+KcX2LqVzBZ//RVisZhqqO/fp8dMTNDHyAg3btxQ5ubmisRiccP4q5fRqqiosIMHD4r27t0rqKio8PnJyaK3fviB4+/fb01meZ6UTlZWEC1dih4uLrgQGAi5XM5paGgIw4YNY25ubk+3fmdmPvk5trbkfj9pEo7cvi0YGxtDVVX16e8TgkBBl8jIZyvpUlMjV/yEhKc2SjMyMlI+fvz4r2UmzMxob6FU0l7j8GG6t1VV/SXj1ZcNtra2CAgI4CoqKjrd5/tFISgo6LFcLl8JIO0fPZAXDNG///3vf/oYutCFLrSB0NBQlfLy8qmenp6Sv5rsfFbExMQIe/fuZdXV1VixYgVzaiLF/Edgbg4cPEjy244i59rawNKljXVgqqokDfbxAebOBc9x2EySOWVqaqqgVCqZr68vM3+WaPxfwePHyElOxiUtLebr6/v8b3x2dnSuYmKAkychsrVFSmHh8yP1AGXNHzzoULpvbW3NRUVFsYo9e2AeHs5En35K0tE6nD17VkhKSuKWLVuGdl31VVWJiGhq0v9+9x3Vdn74YWMWj+eJgGRnA/n5qN2yBRcvXOAt9uzh+v/5J+DjA27lSjC5nM6NUkmbu1mzqH7U2pp+7tmTpJDvv0/Z8t9/J0m5p2eb0sijR48iOTkZhoaGSm9vb87S0hJDhgxh5q+/zpiTU+uNp4MDMHIkzoaE4OLFi7C3t2fz589H9+7dmUQigVgshomJCdfL1ZXTHzsWIjMzqsPvbKBLJCKzLBsbGDk4sIiICCQlJcHCwoLV1NQIgwYNYi1Nj1JTU3H79m0uJSWFOTg4QPPUKcr2vPZaq57AbUGhUIAxBkEQUFNT0yood//+fdy8eVMYOnQoa/EHqpFfsaLxfX75heZ5vUEaQG2KoqOpHv/ddxtqhDmOg0wmYwkJCcjJyYEgCOjeNIs3ZQrVabbwmDA0NERUVBR4nseUKVNQ8ugRxn/9NTg9PeDuXUj27IH9jBmQGhiAMzeHirs7tFqcM3Nzc7i5ueHKlSvk8s/z0C8qQo1UCqM7dwTre/cY/viD1CbZ2bQmWVtD18ICBgYGOHnyJFxcXJqTrtxcGoN1AScdHR1ERUXBwsyMjc3NZcbbtnF6zs5Izs5W7jY05Kxnz4Zmk9Y9grExCktK8N+rV1FcXCzY2Ng0bxX38CGCduwQLqqrs7tDhigHmJtzcHUl6WwHxlQ1ggDJxo3IsrRE8I0bSEhIEIyNjZmGhgYFT8aPp8DXN9+QciUxkUjyBx8APj5QTp+OX+7fx9ixYzF+/HjU1NTwt27dUmZmZvJZWVm8pqamsHz5clHPnj0bJ9iBAzQnW/S8B8hbIDY2VtDX12c9qqoYtmyh+bxhA7B6Nc2VDRuoZpkx3LO0RMX+/dAYMEDILCjgMjIyBKlUyjQ1NaHCGCJTUvjrTk5cXw+P5r2uPTwoSHf/Pq0Z1dVkoPjvfwMSCRhjCAkJQU1NDVu5ciWkLZRU+vr6cHNzQ3BwMDQ0NIQZM2dymrq6UPHyan2Sf/qJvEjWrIGKlxdsbGwQHh4OKysrLF++nJmZmbWS5neIHTtIKl3fqaMjHDoEmJoi9MEDXktLi3NxcYEgCCgsLISGhkbnypvOnKF66Pp777Ng9GgiuH36QC6X4/r16w0dCqRSaZufvy5Yw2lra+O5BMw5jlRNb7xB43jJEvIW0NV9JnPIlw3q6uq4fv26UktLi3uuRqnPgMTEREV5efl0FRWVecHBwUe8vLzK/9EDekHoymx3oQsvL4Krqqpiz58/P2Ds2LH/COHOzMwU7O3t2ezZs18O1xAVFSJFx441tMV5Ij7/nL7n5FB2UCoF99prGFFWJqivXSt6oqHKi8S//43imhpAUxOBgYFKPz8/kWYLEzCe5/H48WPI5fK2TcaeBH194JNPoIiLg/68eejfowfVSj8vfPFFu5LVwsJCJCYmCjU1NXC/dQvKwkJ2yMWFn2tr22w8lZaWCmpqauyJbUh+/bWxzvvTT0n2feIEZTnPnSPp/PTpwODBiB8+HEUxMci0t+fSBg1CzfDhqDh3Dhg7FkZGRpjSsycM29rwPn5Mm/vgYMp0eHjQ53vnHcq+2tu3arU1bdo0fP311xg2bJio2ebl9GkiMUVFjY+dP09GW99/3yCVT01NxcCBA2FZL3cWBKplXrWKMlNP2WYLAHD8ODBkCGx8fLB69eqGxePy5cssKipKGDZsGKuurkZhYSHy8/P5gIAATk1NjZfJZFzgf/4Dv/PnBdGpU0y7A1M6hUKB3377DffakIK7uLgIeXl5bOrUqTAwMMDRo0eFNg18TExIXt2U4KxeTV8ASVJFIgq0zJxJgYoWsLGxwZIlS7Bjxw6Eh4cjLy+vod4dN2+2eexNnbPNzMywcOFC+mXUKMpk5eWR8aKpKZWnvPYakS2FotErYudOyP7zH/gdOwZOTQ25q1cLY6ZMYTsWLEC3Hj0Yrl4lBYeJCf2PigqwaRM4pRIuY8bguELRWvr6zTfkzAwAgoDi+Hj4REbC8s4d3B01CjHDh0PTywvhpaWstqAAP9epeSwtLWFkZITkuXNRK5HA1tYWCQkJLD09Ha6urvzgwYM5dXV1CNOmwSo/n6UvX453xo0TYdQomjtPaNMXFBzMF/v5cQveegu3Kytx+fJl9ssvv8DFxUVwcXFhxcXFwsCBAxnLy6NAmKYmOV1/+CGgrQ1RRQU4jkOPHj0AAL6+vpyvr2/H95X9+6nMoM68qyU0CwsF4c4driIoCA+XLYPZypWNGe2sLJoDK1ciu6oKdzIyoFZVBZ/sbOaxZAlCQkIQEBDAW0dFcd6RkQhbvJizsbbmf/rpJ+Ff//pXI6PjOCLsixcT4WKMykCaBEYZYxg+fDhart310NLSqp+DIigUrRUbFy7QNfj6a1Jg1RHVzLqs9DN3yHBz67yz9549wMOHKP/mG1FZWRmOHTvGFxQUsOLiYjZhwoQ2lTDNIAh0T96//y8ZlUJVFcKOHYgzMEBgcjJUVFR4xphQUVEhEgQBJiYmfGlpKbdixYqGOcwYg7q6+pM7VTwtZDJSIIWG0hgcNoyCPwcP0nz5HzVTAwA7OzvR1atXeUNDQ05FRaV5acjfiIULF2pUVFTgxIkTJhkZGVMBfPePHMgLRldmuwtdeEnh5eWFixcvnigqKpopkUi0msoX/w5UVFTgwoULzMHBoXmm6J+GoSFtfnV1n64uTEursQ1JSQmypVK+7OFDzm7aNMqq/RM3zn/9CwYbN0Lf0RFRUVGcnp4eiouLoaWlBbFYjPz8fGzbtg2XLl1CfHw8hj6D8RsAgDH8988/+ZtGRmycqyuTfP89MHgwbSb+CsrLgZSUxnZDTSAIAnbt2iUUFhbCMiAAunfu4IqDA8vX1m5lZOPs7MwiIiLg6uradm9unqf64ehoIqEJCbT5jY4m+eyQIbSBdXEhifaUKciSyxGiUOCxpiaq1NXRw8NDcHRyYnfv3kVFRQWio6MRFxfHu7q6smZ1vgoFjTFf38bHtLRIahsTQ9kyM7Nm5mYikQhRUVF8TEwMs7KyanRiNTOjTE3TbH1gIJCfD4wfDwsLCxgbG+PmzZuIj4/HvXv3hOqqKqatpQXpnj2kGnjWWumlS1s5qNMhmSEiIkKIj49HWFgYi4+PR3p6OpsxYwYmTpzIBqmrw8HSEhcsLYULaWnM2dkZampqePToES5fvoyCggIEBQVBU1MT27ZtQ1lZWcNr1zu06+joCMXFxSgtLWXXr19HREQEBEHA4sWL27Yk4Dg61r59KZC2dCl9lZQQYTA3p/NeR9Tagkwmg4ODA2JjY1FSUsK86gMp778PJCSAHzCgmQH9AWGYAAAgAElEQVRhREQEsrOzAQCenp7NM5IqKiTZt7Mj4njsGNWPz5tHRmeGhvT19dc4a26OuwUFcH/jDfSaOJGx//s/uPv5wdDfnyElhWqp16+nTOvcuUTmLS1R/tVXsA0JgZWDA1htLZUsyOVk5jZvHuDvD8VnnyE8NBS1cjmqVq+GyvDhSHn0CFeuXEFFRQWTyWTCihUrmFgsRnFxMe7evSv0un9fmOzjw9wnT8awYcPA8zxSU1P52NOnuXt79uC8szPie/XC1ClToCMIFDwaNuyJw0lVVZUVnjoF7a1b0XPDBnh6eiItLU3IyMhgSUlJyLhzh+WfPQuniAhwGhqkBrG1pTmalgacOwfj06chu34dUomE5rNE8kSS3+DDUOc7AYDM4M6fh7W/PytTVRXO+fnxVx8/5vT19dGtWzdUVVWhWKmE7KOPUHXvHkTu7jinrg6d0aMF56FDmZajI3q7ubFBgwYxI7EY2QYGcJwyBd7e3iw0NJTT09NrXtMtk5FxV2kpke3PP292v6iurmZXrlzBoEGDnmwqlpFByplPP63/Z8rk3r9P6oAma1F4eLjw4MEDlpmZ2VoR0hlkZFCg4Am+JYIgoKioCOq9eqFaqYR48GDcvn2baWpq8r6+vly9AVyHwdAjRygTvHbtUx9mU+QXFyPq7l0+Oj+fDR83DlOnTmUDBw7khgwZgl69eqGkpIQVFhYKMpmsmWmgvr4+IiMjhcGDB/9l25NWqB+nb7xB69Hvv9P1c3enYPb/IOkuKCjAzZs3WUpKihATE8Pavfe+YHAcB6lUCp7nxZmZmXrBwcHlYWFhWV5eXvK//WBeILoy213owkuMdevWla5fv35CcHBwrLu7u+hFmqUpFApkZmaisrIS9+7dE+Lj45mBgYHCy8vr5VonxGIiQunpnc9ut8SiRVCNjhbdDA5W4r33RBCL6bV8fUkeCHRYA/xckJwMfP01xC4uUE1Lg0KhQGhoKMrLyzF16lT07NkToaGhPM/znFQqxaxZs576LXJzc3Hp0iVkZGRAJBJxq778EhKxmDI+3t4UoXdxefbPGhVF5LdFixee57F7926huroaH2poMG7wYGDMGDiYmrZZ8y8WiyGVSpWRkZEimUwmWFtbM0tLS3BlZURuPD2pp/Hu3ZQBkslow7pxY7uHlpeXBwBYu3ZtvfSQ5ebmIjw8vOE5ZWVl3IEDB7Bw4ULaJB85Ql8HDrT9otOmEfktKSEp84YNDRmuOXPmcDt37sSlS5cEKysrOqEaGpRZ1dcn4gGQ3H7JEgBoyPT93//9H/bu3QtFdTVzGDsWJ/z8MPfcuc5cgfaxciW1s9m9u9nDHMdh4cKFXHh4OCZOnAhzc3PwPE/XpaoKmDsX6h98gKkffMDt3btXuHTpkmBvb88dOnSo2evcvXsXAPDOO+9AV1e3pZsyqzu/CAgIgK+vL2QyGWuXiDBGDvq9e1OJgJkZnevPP6fgRCfLV8rLSYHYTF1gZYWMR4/g/803fGVlJaeqqirY29uzvLw8AQDT1NRsNyMJAwNSNBw+3Gg+lpfXGKSKjUXl4cPIsLeHcvBgyu4dOUKb8Q8/JOLh60ty6tRU+my+voCbG+5t2oSAPXuwAgB39CiZOVZUAIWFUPbujeujRws31dRYtqMj3lqxAgZ1Ls02NjbYtWsXb2hoiFGjRnEymQze3t7w9vam8z59OkN0NDB+PDiOw7BhwzBs2DBRzquvoiopCem9esHKwkLAunWoVirZo//+Fx3ltARBQHV1NcRiMTJtbPBqXh4FvzgOixYtYtu3b+d79ejBmR8/jkeXL6P6+HHILC0paGVnR3NpwABAoUDkxo28tyBwWllZVN8dH0++CwsWEOk3M6P51dSROiKCygDq8fPPdH7XrIH+/v0YYWzMRgCi7777Tunv7y+6deuWMjExUcTR8WHX/v0Y4OmJca+9BiexmGHFCqpP9vMDfH2hv3499L/8suHlZTKZcOnSJcHJyYkTi8V0fHI5BV+MjCjD+dFHFMT59FNgzRoMtrFB4tGjqKqqam7s2Bbs7Oj1AAqq5OSQKdeCBa2eampqisTERPA83+4CnZubi7y8PNjb20MulyMrKwt2dna4ER+P/hMmYN/ChdDz8uKnTJnCVVVV4d69e7C3t28mC09NTcWhQ4egMXMmKmQyuOnqChMnTmQ6OjoijuNQU1ODgwcPYtmyZW37aQDk3v3xxx1/9k5g//79MBCLubcDAyH+4ouGxzmOg56eHvz8/GBkZMSCg4Ph6ura0OHBwcEBtbW1rKSkpGGuPHeoqNBcdnGhnt1qajSn33uP7oP/Qxg2bBg8PT2RlZXFDh8+jNra2n/0eHr27ImQkBC32traAxzHbQLw0T96QM8ZL9cmugtd6EIrrFu3LuWLL76oKi8vV22rd+HzQkpKCo4ePQqZTKaUyWTc3LlzYW1t/XKuEb6+DZvWdt1vn4Di4mKe09Ul12OAjHC0tMiAy8+PnK+rq19cC5DDh4mMjRvXUKtdXl4OxhhycnJQXV2NtLS0BnZy+fJlpZWV1ROjLYIgQBAE+Pv788nJyQ3/v3z58sbWU5MnE3mYMYM2Du+++2xtnXx8KCPTAjU1NcjPz2d9r19HbFGRoHz3Xdbf2hodiaGHDh0qioqKUmqoqHDRZ85gaI8eGLB8OdWD+/vTRlwspkzx6dNExD75pM3Xevz4MRITE+Hq6tqsxs/MzAyLFi3Cnj17AAB+fn44deoUPvvsM3AchzGlpejdo0eHxwlPT6CsjNrMbdkCzJmDxwYG2L9/vxKAqJWj/Y8/0ibN1pakrb6+9L0J8eQ4DgsHDQIcHZGRl4fc55EpGTeOyHMbMDAwwOR6lUfd+6OqihQjR440kNshQ4awP/74g8XFxcHExEQ5Y8YMkXZdzeKRI0cwdOjQhs13W0RaS0sL06ZN69zxLlwIfPklSY/XrqXjMDFBh73VW8CqTgVQUFCAH374QSkIAqt99IihpoaNmjCBc3R0RFpaGktMTFTW1taKAMqItZkJ++ILInU7dwK7djX2bm9BpoYMGYLk5GREHzrEu2prcxaHDxMZGzyYnnDiBJHwoqJmruhmZmYo19ZGTv/+6D5jBtVqW1sDYjEe6+ribnU16+fkhCmzZkGrCXnQ1tbG+++/3/4AaREUQVYWsH07zA8cQGlFBUyOHROKioqEBzk57KyTE7hjx4SlS5c2nICioiLs3r0bMplM0NPTE+7evcs1bMRVVSF9/33g8mVgyBCIxWKsmDOHQ0gIyu7fx5Hx49FnyxYqk0hNpfNQn5kWi8FMTYUMKytYU2CASHt2Nl3zkhLKxL7xBq1Fr75KpNvAgIJTQ4bQ+06bRuUiLdrxrVy5UnT79m1ERUWJVFRUIJfLcfLkSQEcx/S/+AIOlpZE6FetImVJWRmt901f59df8eaIEezCoUNChYEBFLduQX/DBgr4jR5N6gNdXWDsWLo3pKUBFRWQb9uGefv2IW3BAvSbOZPuKZMn0+ffsYNKQSorSUFw/z6NpfXraZx3QM4dHBxYQEAAfHx8Wv0tKSkJKSkpSK7rliASiSAIQmPXBUFA3JtvokxbG4Vpadzu3bv5vLw8jjEGFRUV6OjoCJWVlczHxweXL1+Guro6KgDMPHIEjpMns6au9O7u7khKSuJDQkKEyZMnt74HBQXRtfuLRorl5eUoKyvDhNdfhzgwkIwN2zCm9PT0RHR0tPLnn39mAwYM4Hr16tUwhxMSEuoDTy8OjNF4ACiYVFpK41YioTVMS+vFvv9zgqqqKiIiIuDm5sYbGRn9o+l5iUSCt956S+Lv76/Mysr6/84C/uXcSHehC11owPr167U4jpM9UxuOp4BpXdZRLBYLLi4urQyUXiqIRCQj/uUXcvl9SgiCgBs3bnBTpkxpfLC+LZW9Pbl3cxwR+enTqYaysPD5tQERBMrg1MmveZ6HSCSCg4ODsmfPnqLIyEj+5s2bgoeHB5ebmytwHMdlZGSIrl+/Dhsbm/azCwACAwP5K1eucIwxDgA+/fTTtsmEujoRga1byYHb05M2kU+DWbNIvt0i666mqoo1amqo8fDA3YED2YmoKMGD59vPbALw1NaG59tvi6rd3ZFZVQXbDRso6KGl1XrzUl1NGad2cPLkSQBAeXl5o7N2HczNzfHpp5+isrISYrEYHMfh9u3bgvrPP7MoGxucBTAuOrrjXvJaWuS+nJEB5ZQpuGplJZhOn86mTp3aOnMfHd34s6Ym9XNtWUdZU0Pn/8wZyJYtg3znzlZ9d58agwd3um0ZAJJtK5U03utgY2MDkUiEqVOnwsnJqdkme+rUqc9+bE3B82RCJJfTRrW8nFQMBw48teJCVVUVs2fPxp9//glHR0eRqqoqeu/bB1lSEri6Omg3Nze4ubmJAODgwYNIT0/H119/LaioqAgKhQJud+9yalpayDI0hMvHH8Pl4kWIOiijMTE0xJIBAyBftIi74eYGi9OnG/+4YgUFpCZNIuf7nTuB778HQMFNjuPIEJAxksr7+wO9euHckSOCaloaeubkMNHBg0BYGMnP58whJ/6OxsVnnxGB3bWLfr94kYivRAJdqRSLZs1i8PVl/B9/4MJvv2Gql1fDSeZ5Hvv37xc0NDSYsbExq66uZjNmzIClpSViYmJoHbl2DQgIoLXrzh2qMTczQ8DMmdApKeH5uXM5buVKmnTLllGZiZcXkJkJTU1NoV59AIA+h7V1837zq1YBt2+TguD+ffI4OH+ezLe8vIicDhlChHn5cso0//Yb4O4Ou23bEBUdzU89c4Y7MHcueoeGokf//tDp3Rv4z3+AS5do7g0cSIQ9IoKux9GjtJ5s3Qr1Vasw6b33uMsFBfy9oCBuxv79FIzU0aH/37CBjtPYGAgIQE5ODvZoa8Nk40Z+cZ8+HH77jer8GaNgqooKBW3S0ui4LSxonpmaNnbPaAcaGhrttrQKDw/nCwsLGwZCfb/2uXPnQkNDA/xvv+FOUBCCR42CXC5Hfn4+N2bMGLi7uyM0NBQPHjxgMpkMoaGhytraWjZjxgzO0tKSMu0VFa3eb/To0dzevXuRn58P4yZdJADQWGvaTeAZkZycDIlEItg7OzMcPkyu5i1UU/VYtmyZ6NSpUwgKCuJDQ0NZXzJAZOnp6fDy8vpra+fToN7zxdGRVEQ//kiBQhcXYO9eKgvR1W2zpOefRk5ODh48eIApU6a8FDp4qVSKrKwsplAo/tk0+wtAF9nuQhdeYqxfv14qkUiOOjs7KyUSSSedTp4N+vr6ePPNN5GdnS0OCAiAVCpt3ZbmZcKMGbQJi4jo0Am7LaSkpACAYGNj03o3z3GNxPvGDZJDXrxImYrHj2lT5uLS6f7dbeLKFcpA1UkHLS0tsZZq3UQA0MyRt44sbtu2TXn69GmRt7c3XF1doa2tXV+PCXt7+4Za7zt37qAug4fp06d37CLLcSQ3vn6dWqQ9ekSb+s4asTk6tjILgyAAf/wBcWoqxF99BSd9fRyJjGQFBQVo5Xz68CFtqrW0SKWQkYFfZsyAlqmpsodUKmq33dbUqVTfeOdOozy7CWbOnIkvv/wSWVlZrKamppVDMGOsQU3g5uYGNxcXJnzwAXbX1VafPXsWffr0eWKbuxyJBGdHjeKda2ow7do1TuTl1aydGQDgzz8pKBQYSPW/TTO9PE9uzevW0SZXSwvdAIjFYiErK4vZ/JUNWmIiBUIKC5/83GPH6Djs7JoRXI7jMHDgQPj7+0NVVVWo62fLa2lpQalUCtXV1ayiooLV1tYyR0dHvn///iLDDlqkNUAupw1pUBDJMcPCgJEjiXzs309Z9n376PFVq2i+dRJ37twBAHh7e5OqwcGBxnUbqKqqglKphLOzM9QlEujo63NGa9fioUTCVyxfzsKSk5GdlMTsBw5Em0L2+Hjg3/9GupkZHz9zJjf8tdd4AI1zV6FoNNLS1YVQVYXgixd5CwsL7sKFC3jllVdIbZKTQwGrc+cAKyuwzZtxo7KS3QCgzXHCqvffZzh3js7Z+fNE2tzcgF69WitS3N2pFVdWFgVQ/P2phVY9YmMBIyMklpRAqVTi4sWLgpqaGrOysoJSqURJSQmbMmUKXFqc8wH1JTtWVkSGQ0NpjA0cCMybh/QvvoBMJuPOL1iAMhsbYdg77zAzMzNaI776ClBXh6ZMxhU2NQtsAZ7nUVlZiXINDVSYmsJ64ECIS0spYJGdTQGutLRGgzrGGtUC48cDJiawcHDgHoeFwdDQED0MDZmOQkHj7dw5mnuxsTQeHj0i1cyMGVRSA5AnA4CHDx8ipHt3zq6iQsC+fQwKBWWl1dXp84SFNSgXDAwMwHEcDAwMBMZY89ryX3+l79818Xw6dozG9Hvv0TX/7LN220oJgoDy8vJWninR0dGwsrLiCgsLMX36dCgUClhbW0MmkzWu9zo6MPXygvuHH+LRo0c4deoUf+7cOS4kJETo3bs3Zs6cWT/Rm2eqd+2iTHxdqUA9TE1N4eTkxO/YsYObO3cuGswO79yhTgHPooyqQ1lZGfLy8hASEgKlUknHVV5OgZyJE9sMLnEch1deeQU8z3ORkZFITU3lNTU1hZKSEm7z5s3C8uXL2RMl/X8VPE+B34QEKk04fpySANevN3aO+OQTCtJv3UplCFeuUAnFn39SkGfvXlJX9O1L6oenUPL8VWRkZKBbt25KPT29F1ef2AKlpaXQ0NCApB3TT0dHRyEpKWnRhg0bRGvXrn397zquF40ug7QudOElxPr160VhYWF+EonkqLW1tcukSZPU/o5IrYaGBkxNTWFiYoKTJ09CVVUVSUlJsHsZe0wyRiRtxw4iX0+BgwcP8v369eOeaPwmkVA7Ijs7upmqqBApKCqiTe35852uJ22Gn34iktgJY6J6eHp6cuXl5bh8+TKuXbuG6upqZWBgIBcTE4NLly7h+vXruHz5MgAIr732GktISFBqaWnB2tqaKZVKVFVVtXuDg6kpbWpiY4nwaGg8ORIvlxNRauoOzfOUKbp9mzJA+vr4448/lOXl5czb27uxBdGFC5TNmjaNXL+XLEHZ0qXY4+/PP6ysZLNnz+aeaNbyyy9EJlo4qycnJ+P48eOQy+Xo0aOH0LNnz44Nc3geuHwZbPNm9B4/Hi4uLsjMzBQCAwNZVlaWws3Nrd2Jt2vXLqWtuzvnvWQJxyUmUvZs8uTmpk+CgAZPgPnzySSrfmNaUUFS0vHjmzm6V1RUsLCwMAwcOLBzLXfagqkpZUKfJGk8epRq3998s822NjY2NnB3d4exsTGzt7dnampqnFKp5NTV1blu3bpxDg4OzMnJiaWlpSE8PJyJRCLewsKi+UHX1hIxCwsjx+Vbt4jYDB9O7aJmz6ax5OxM88LZmQJe+fm0UfXzo5+HD+8w2/3o0SP4+/vDxsam0cG5sJAysZ6ezZ6bmZmJ8PBwiEQiLF68mHUfOZKZWFhA87vvYDhzJrO1tWX9ZTJmsX079ujpISYmRpmens4qKyuZrooKJF9/DWhpocbMDL8rFGz8nDlwdXVtPLikJKrJrW9b5eCA3MpKnIiPZ0lJSTA3N1eOGzeOxlZxMakQpk4F4uJgP28es3V3h5qaGu5kZLB8VVXeeeVKxjw8KLBoaUlqlE2b6PwcP06ExNyczp+9PZGg1FSaY/Xn7PRpmpubNqFbt26wsLDA7du3WUJCAvr37w+xWIyioiIhOjpacHV1ZS2DVABobRgwgPwa5syhFnmgYO2tW7eUvoGBnMTIiJ3Ky0NoaCisrK2h7eUFbN8O09WrWaCDgxAXF6cMDAxkISEhLDIyEpGRkQgLC0NoaCiuXbuGxMREpF25AufVq6G+dCmRUyMjWi/79KHAkK4uERctLfp8mzYBY8fiXmEhIjQ08FChQLm7u7LnG29wUFGh8W1kRAGKTz4h8i4WU7a8iby/pqYGP/74I3QePMCwn39m5cOGAStWQLV+HpmY0HiuM+sTi8V4+PAhEhISuD59+nTsiP399xSkVSjI5Tovj8YJQHLpFutzTU0NLl++jFu3bqF///7gOA6VlZX49ddfkZeXB7FYjBEjRsDKygpSqbT5WlG3pquoqEAmk6Ffv37M09MTWlpaLCwsjA0dOrT9tcXGhghgk/aMANCjRw929epVwdzcnBkbG6OkpASiOXOgfPQI4rY6O3QC9+/fx7Zt25CSkgJ1dXXluHHjOCMjIzrPGhp07+3AUZ0xBisrK/Tr148NHDiQDRo0CA8ePOBPnz7NOTk5Pf92mrm5RJT79aPSpvv3aU6KxVTX7+5Oao533qFzOHcuZefFYmDCBDqnjx8TsR4+nMaiRELlElZWVE6zeTN9zZlD9zdd3UZlhZXVc/OTKS4uRkZGBvr37//CW93I5XJs27ZNERYWxkkkEshkMly8eJE3MTFpts7Y2tpyjDFkZWW5hYWFBXl5ebVudfE/CCYIwj99DF3oQhfqsH79emdVVdXPlUrlCB0dHW748OGazs7Oz77h/guIi4sTTp48yQDA1NSUr62tFdzd3UX9+/f/24+lXSgUJN3q0wfo5HHxPI+NGzdi3rx5eOa+2oJAdYlLl9Kmbe9euuF2JighCERoZ8x4ajfwkpIS7N69m6+qquL09fVRXFyMPn36KEtKSpCXlyfy9vZuyEBlZmZi//79ePPNN3HkyBG+oKCA8/X1Ffr3798x+QwLI/O527fJtKq9zcqBAyTjLCmh33mepLJ//EHXREsLCoUCmzdvhoeHB3zs7Kiur08f2jBERwN2dsjLz8fZs2f5vLw8zsbGRjlx4kSRVmdq3nieCEWTHrwKhQJfffUVLCws+NGjR3euh+hvv1H95K1bDS1rBEFAdHQ0zp8/j169evGTJk1qRbjrSf0HH3zQ2Jf38WPKwo0dC6xZ07h5Tk+nzZJMRkT84UParJ0712YrK57n8dVXX+H1119vrQZ4GsyfT9ewvXEZE0PXVyxus5fx0yI3Nxd79+7FggULYFZTQ2Ope3fKrFpYUMlHbS1tSNtTDXz0EbVyu3Ch8bHLl4mMlpTQMX/yCWW7WxhGbtmyRXj06BFbsmRJo9T1/HkKlDWR1JeVleG///0voFTinXv3oLt9O72nh0fz4/r2W+DhQ1R89BHu3buHrKws/kFMDPoeOsRVaWsLCRMn8m7e3qLTp09jzpw5zYOSxsYQNm5E9fTpUFNTQ21VFeDoiC1z56JKKkWfPn14Nzc3TnT9OkyNjcHqM6KJiUS+68hLeno6/P39MWDAAHi1JDQKBa0/585RADAqir7HxFC2vOnYEQQKOPzrX83KPniex7fffssPHz6ceXh4MJ7ncfz4cWVGRgZbtWoV10zdIZfT2LW1pXFblwluCZ7ncfjwYaSmpqIhS/74MTJ+/RXHqqqE0aNHs9y8PMTExECpVGLFihVQU1ODRCIhNUJJCcI//1xpnpUlsjl8mAIJGzdSbexXX7V+w/rM9dCh4FesQHVEBH5atAgrMzMh+uijtgOitbWUOZXJGmqNBUHA/v37eWlsrDDR01OUFRsrHNbSYnUGgvz06dM5G0tLujZHjwLdukGhUGDTpk2Cn58fc32Sf8iYMUSmTp+muu0RI+jxOXOIdF+9SsHduvW5uLgYP/zwAwBg/vz5KC4uxum6MgVzc3N+3rx5XJsBVJ6ncoPExFbKI57n8eWXX0KhUAAAXFxcoK+vD2tra1haWpL8urS0oc97Sxw9ehSJiYmQSCRQKhSwS07GbTs7mNva8lOmTOHaNRtsB4cPH+aTk5O51atXtw4G//ILKdeeILdvCz///DOfk5PDGRkZ8fPmzeOeuQxPLqfs9cKFjYZ2+/aR2q2oiAh300RIQACpmMzNKUj0NFAq6bXi4mheT5pEwbJly2icjh5N7zl5Mj33zBki7N99R4HS06dpLD9+TCqMJyRoamtr8e233wr29vb8pEmTXpgBr1KpREhICCIjIwEA/fr1Q2xsLOr5p5mZWXVBQYHUxcWFf/DgATw9PUXHjx8HgNp169b9BQnhy4MuGXkXuvCS4LPPPpuvoqKyfciQIdIePXpwT+w5/ILRp08fZmNjg/T0dKG8vJxFRkZySUlJyv79+/9tkqMnQiwm2e6pU7SR7ERQIiQkhBeJRMzU1PTZIxiMUTayvr/wnj30mFhMMrz63t5tISaGjre+r+9TQE9PDx9++CG3detWZXFxsUhTU5OfOHFim9fD0NCwPpoMExMTwcfHB+Hh4UJ8fLwwY8YMrt267+HDSaK6ezeZvqxf3zYRmz69cbOoVFKmLSaGov51G/SE+Hj0iooSPBYsYPj0U8oKnDlDG3bGkJqaCn9/f7i4uLDhw4fD3t6+82OL4yir8Oab5OYLMkbjeR7z58/vnAzk8WPq3TxqVDPixhhDnz59cO7cOSQmJnLdu3dHjx49IBaLkZycjLCwMOWjR49EY8aM4VVUVBrfSyZr6KGNw4eJUDs50dgcMIA2Zp99Rhm2hQvbVQ9wHAcrKys+KChImDdv3rPPt/v3ibi1Rbbz8+n6fv45man9VZSVway6GmOuXhW4c+dYgzS3Tx8aFx34DDTDp5/StRWExvk8aBB9VVeTlFoqpXNX76cglQKMoXv37iw+Pr55P/oxYxqdxOsgkUjAlEq88frr0J00iTa2TeW/9aiuBlasgIaGBpzs7OC0cSOH5GQoLl1C3sOHzCw5mV2oCwqoq6s3ay2muHUL3+3cicebNsHV1VWZnp4ucu7dGzzPY9GiRTh+/LiQmprKDz12jNNUVYXy6FEUFxfDNj4e7PjxBrJtb28PfX19/sGDB63HtFhM5+Gtt+j34mKSJ9dnBptixw4amy1k/hzHwdfXlztz5gx69eoFqVQKCwsLLjExkRUXFzf2342Lo83+zZu04W8rWFJdDd7ODkc2b8b9/HxeT0+POTg4MAAolcvxe1ERZuzfz+wvX0bB8uVQKg16CPUAACAASURBVJVkzFVR0dBWSkhJARYsgNLPDwlDh8KmnjD0709y5bagotLQu5rbtw8Hdu8WVPPzGScIJBXfu5fIeGwscOUKalxdcejkSd7YyAgDv/ySu9W9Ox6IxXxycjLrHh+Pifn5ItHMmbCfPp2tAZHw33//nf3+++/o06ePMNHNjeH2baBbN8TFxUEulzP7JwWrDh6ksSuT0TrQ9Pr88QfN1a++otKl7dsBExPUmxEOHz4cCoUC6enpAIBly5ahW7du7a9xPE+EsGWJD+h6L1u2DEqlEhcuXODT0tI4XV1dPiwsjDM3N+cXLVrEQUeH1qsjR1q11xw9ejQcHBygrq6O7kuWIHvWLHhPmIB9+/axsLAwfsKECU8lwXN2duaSk5MRGRmJEfX3k3oMHUr306ZrQScxb948rrCwEKdOnWJ79+5VLl++vHPrqEJB88TDgwI8Bw4QwTYzowDTxIkUZAbaPL+4d4+e19SboLOovwf17UtfAN1H6vHwIX3fvp0y4zxPQUddXQrW/Pwzke2BAymD/ssvQK9eeOjvj8KoKGhGRyN+2jRYFxaCt7BAVmUlPD09WUREhEhVVVUYP378c8/q8DyPPXv24D4ZqboBcI6Li9vBcdxPIpGIr62t/Sg3N3cTgJqEhAQZz/Orjx8/DqlUerampub/Gxl5F9nuQhdeAmzYsGGZqqrq5gULFqh1qubxb4K2tjbc3d1ZUlISxGKxMGvWrJeHaNdjwgSq/czMbEZeBEFAaWkpdHV1wRhDcnIyf+XKFSE/P180efLk52ugcukSfb9wgaSNAEW1p0xpXU/OGGW1/4Ja4Y033hBt3rwZTfuMtoREIoGGhgavVCrZ7NmzRTKZDJ6enty3336LuLg4wdvbu/0D0NamTXtmJsng/PyolVHTc7ZhAzlr6+nR5uTKFdrUisVAYiLKIyNxPi8P71+6xCQFBWQO1eQc3LhxA8ePH8egQYP4kSNHcp1Vb/A8j9raWpJrOjvThofnkXrrFgIDA4Vu3boJaFo32xH8/IgIt9FCTCQSwcXFBUlJSQgICMClS5f4yspKVFZWcgYGBtw777wDmUzW+n309SlAsWsXXefjxymjHxZGkldbW5L3tuOkXo9XX32V++abb1BdXd2xNLUjXLhAgZCWKCujbPv27c/ePo/nqVaxsJA2hKmpwMcfw7J3b3b04UM81NCAmZmZcpar69NlTDQ0SE7t4UFZ7qZQVaXxCFBmKT2dggVRUSh4/32kXLsGSKWNSgOAsnR9+lANc/3L3LyJjzZuxNc8j7U3brR9HDk5VJOtrU21t5mZRNp/+AFiDQ1YamnB0tKSGzBgAL777jvs2rULs2fPhr29PeDiAm7nTlhYWvJ37txhRUVFMDMzU7r26ydyNjaGubk53n77bREeP8Z3UqnywsOHonrjtPl+fuArKqBbt3bV1taiuLiY65Rhpa4uZUcDA5s/fuoUBc9ee63Nf3N1dUVoaKgyIiKCjRw5kisvL2disVgwMjJiKCkhMrh4Mc3v+hKI+nZVmZmN64JCgZR+/XA7IwNeXl6sd+/eDT3s69oP4tHHH0M0bBjK6owDq6ursXfvXnh4eKCfSISwwEBBbGzM7mpqikzrW2QBFAQsLaXASBPZd1vgxGK+UKkUxbz1Ftzd3cF69qR1WKkE/9pruGZuDhVXVxinprID8+fzIz78kMuaPp1Nzstj1vPnM5iY0NpSB8YYDA0NWWZmJuLi4pjMyor3DgjgMGhQgzHZ9u3b8e6777Z9QKmpZLLm60u/1xtwNu1JbWJCa8KPP1IwY/NmlCxfDsYYwsLCAJBkfeTIkYKhoWHHi+XFi23P+zrUBzbmzJlTv35x586dQ1RUFFdbW0sZZje3NrsZyGQyUipUVQEiEawmTwZ0dODj48OOHz/OpFKpYtSoUZ3iFkVFRfD394dUKhUGDx7c+jPZ2VEA4soVCrY9BVRUVGBqaopx48ax3377TaRQKNr24KhX937zDa1lCxdSmcLu3dTebe1auqdt2dK5Nw4MpBZoo0fTHuBFZIvrvEUAkOEmQGqx8ePp53rDSZ5HrLe3cPH4cdYzJ4fvnpjIHgwfznt89JHoVu/eAu/joxz86afigk2b+N5hYQznzzcapvr60jwThA5l/E9CaGio8v79+yIAs9atW3cDwA0A+5s8pWW/uDXr169X+fjjj7v6bHehC114fvjss8/mSSSSbxYtWqT2T2ez20Jubi5OnTqFkSNHsry8PNjY2PxtTp8lJSXYs2cPJBKJUFtby9TV1XkAGDduHNe9e3eUlJSgtLQUcSUl6L1woWAfEsIUSiUqKiqQk5ODI0eO4P+x991hVZ1Z9+s951649N5UUBBQQEDBigUsWGM39ha7RpN8lkniJEMwxYxmYmI0OoklxhJjsHdEULEDIgIqTZpKlV5vOef3x+bSQVAz3/ebsJ7nPuKtp7znPe/ae+219fX1RUtLSzx58oRzc3PDxIkT8acd5+HDa1qCPH9OC56bN0mOGhBABGX5ciJgrwGZTKY2VGvyRGhoaDRoERQVFQVRFOHk5NQyZmtrS47lBw9SBvmTT2qcg+/epah6SAhJdH/9lQj4smXAmTNQnTsHjBoFIS0NKZmZqIyLQ5eqGsCMjAycPXsWw4cPR79+/Vo8mARBwP79+5GSkgINDQ3RyckJnuvWMZPff8eZZ88EW1tb5uvr2/LBuXZtk2ST4zhMnDgRw4YNw8GDB5GTk8NxHIe+ffvC19e3WWd1AJQ1HjuWFi7XrgGpqSTNT0ysU59dH/n5+UhOToabmxskEolYVFTEXplsz5xJi6Xfaq1tRJEIa/v2tG2tgUpFGbqQEDKVOnSI6mjXrqXv09GB2ejRmFZQgJycHJw4cYL7448/VNOnT2/dinP8+Ebrx+vA3p4eI0YAwcHILS/Hsu3b8cTVlTK9hoa0SNbTI2m/KFJw4MwZYP16XPnHP6BSKpGVlVWTva2NkhIa33fv0n5u2tTAcbm4uBhbq0jykCFDyMhKEICJE8F5eEDrxQvI5XI2bNgw3s7Ojo5TbXLfqRPe3byZ36+vj7KyMsHCwgL7Tp/mpv/2G/6IiEBGhw7Q1taGIAjooW6h1RyOHydH99oZt6dP6TgdP95sG0MfHx/+2LFj1VJPAOyfGzeKb5eUMLvgYJq31It5gAzCPvsM2bm50NbWpv7SSiWuDRgAd2dn0cvLq3qOKSkpQUhICDw8PNCnSkUxwdsb3WfPhs4HH+Ds2bN4FhAAl4sXoTFzpqi9ahUKbtxgdYI0jJGk+MkTCgQ2g7lz5/Jbt24VAwMDWUREhDhz5kx2vqREVfzrr7zJv/6FhPh4Ye2QIRz39ddwff99hh9+QOfPP2cYOJBquhsJeGtqasLGxkY1btw4fv8//8l1vXAB2u+9h759+yI0NBQA6igbqqFQ0FiOjqYxCQD29lDevo3IsDCEhoaiuLgY06dPp/nxgw+ArCyoXrxAxEcfiV06dGClLi5If/YM5ubmQv/+/V8+v508SRnaqnr6lmDQoEG4e/cuNm7cCHd3d0zYsYOMvprC7t2UPa/aJ3d3d1y5ckWMioqS+Pr6tug3U1NTIZVKxY8++qjp+1F8PAUqW0m21cjPzxeUSiWXnJyMavXBo0c0lxUWUknFsWNU8tOxI2WE4+Je6beQlVVTKuXtTcHAV/F0eROoIshnOnZk48aNg9p7xAngMXcu+gEMCoUEXl6Y0bcvh5MnawKSP/1E83lcHKme7txp4HnREoiiiNDQUPVFfKeln/Pz8/uvItpAG9luQxv+1+Dv7y/R0NDYqqWlNW/u3Ln/J4k2AGRkZIgKhYJduXJFKCsr4zp37ixOnTqVNWm29YqIi4uDvr4+ZDIZKisrYWFhAZlMhqpsIpNIJDAwMOCKi4tx+vRpcfjw4ez333+nDzs4oNf160wVHY1zKSliZGQkA8hp2sTEhMXExAjTpk1j/1GjN3W/25s3a8iVmxvJBaOiKNtRXEwkwMqKIuBKJS0CjI3p5i+K9DAwoNd5njIiWlo1/28FoqKiIJfLX+qyXQempmT2cuIELbYPHqSWaAEB9PeDB5QFYoze4+sLfPwx+JUrofj2W/yzVtZYV1dXcHR05GJiYqCtrS2mpaWxwMBAeHl5Qb1AKykpQXl5OQoKCqCpqYmEhAT069cP2dnZOH78uEoURW7OnDlMKpWyY8eOiRk3buCdH35Axdq13JgxY1qWBS4sJKldaGizzuscx8HAwAB6enqqTp068WFhYbh9+zZcXV2bVRVUIzOTFnP//Ce19+ralVQAn35KBNDdnTK5ta6lwMBAPH78GOfPn4dSqWSljbThaTHWrCHyVxshIaRSaElGWy6nhVZ5ORFOLS0aD0OGUF16vZZvahgaGsLQ0BAzZ85ku3bt4vft24cZM2Y0bdBXH3PmkIt2YeHLSTfHAcOGwSovDzuXLYNRQQE8tmyh7N7PP9PxXbeOzOgSEoCbN3E/Kgp3lEpI5XLIiovpOuQ4IkSMEYFYvJjIac+eVIfZCHbu3Fn9t6urK11XO3cCH38MyGS4d+8eBwAXLlwQVqxYweGzz+qej4gISKytMfTpUxw+fJibMmUKJk2ahMrISLjKZMiVSlFZWQmVSoWrV69i4sSJqKofbrgxas+EpUtrnhNFqg3u25fk9s3g2rVrAAAjIyPMmjULBps2IWP3bnZy3TpI5s0TK7dvZzzPi8uWLSOjQw0NYM4cPPf0xOmxYyHV1RVHnDqFKQkJbNfq1Wzo0KHV12JeXh5KS0uRlJSkQpX7NXf0KOx69ABUKszT1ET5nDkonzgR48aM4YKCggAApaWldVv3zZvXpLO8IAgoKSlBUVERTp8+jcLCQmZvb4/k5GS2ZcsWaGtrcxoaGqJQWirOMzDg4OpKga8ZM+icTZtGypN6RDspKQkXLlxAUVGRaGxszJmYmGD88uV4kJEhxnz1FRMtLYXKykpOLpfj2LFjwoQJE7g6QYJ160ghceVKzXPDhiHq+nXh8uXLzMzMjBUXF+P48eMYPnw43N3dwVtYIHDaNDx/8kRccOAAY9nZKNq2DeD5lgUSt29vtXJKR0cHq1evRkREBK5evYpxo0eD8/YmpUT9WvSEBDLweqeuylcmkwkFBQX8tWvXhEGDBjW7rXK5HLdu3RItLCwE1HdEr42336b7p0LxShnW7Oxs6OrqCg7l5RxmzCBp9eTJFBT39yei7elZ08LrdfD4MZUlAdQnvqjo9b/zNdGpUydVcHAw7+LiUlfxA9DxrHLVx6RJNc9HRtJc+NVXFDB+BYd0QRBQVHf//+vaebUGbWS7DW34X4C/v7+lpqbmSUtLy27Tpk3Tfqnz8v8iPD09mYWFBTIyMriOHTvi2LFj4rfffgtra2smk8lQVFQklpWVCR4eHny/xuoeW4CUlBQcPnwYjDHwPA/GGBhj8PT0FDiO41auXFndW1qpVGLPnj3C77//zmtrawvu7u6cra0tTr94gUGzZ+P+5MnMyckJ8fHxSExMFO7fv8+ZmJiIRUVFdXoXJyQk4LfffoOjo2PrM2+tgZcXRcwXL6Ya0Lw8kt0OGULZzsxMImGCQFmopCRa3Ghr06Lm3j2Kkstk9P7Ll4moWVrSYvHyZapdNTenm3tQEBFeGxuq8bp3D+jVC+X6+rC+cgX9cnOhJ4okuVcoSJbZrh0tMnmeMvJGRlT3KYpERLS1KVu2ezdJe0tLiYRNn04EPCCgxtilCnp6evjkk0+QkJCA8PBwMSkpiZWUlHD37t1Dz549ER4ezgwNDQUAXFRUFHr37o1Tp04JKSkpXG1CoVQqcePGDfA8Dw8PDzZs2DCmXjS8//77DACK16zBArW0vCUoLKR68xa2OBs9ejS/bds2aGlpiWPHjmXNEu2HD0n22KEDmaR9/DGdr7Vr6RhradFCODGRsqwXLlAm4YcfgB49YNO1K8rLy0WH0aNhYGzMbF4imW0WnTpRr1o1tm2jAElQUNMGZampRMgtLEjN4OxMWd2lSxsaiL0E7du3R4cOHZCSkoKff/4ZU6dORYvLZDZtouuhntt8UzA2NoZCVxeZ2toQPv0U3MyZNG5HjKDryNycghsWFsj96CPMSkhAp+Ji8JWVdD7MzCiL3b17jYR25cpm69mtra1VUqmUT01NFb///numJYp4f8sWXNfXF28kJlazHbUZFVJSqObz2jW6/qOjAY6Dubk5KisroZbwav36K/o9fox+vXtDFEX88ccfQkxMDBdT5Vxtbm4u+Pr6cp06dUJhYSHVqCcl0XbXDqJkZBDZU9fPNwO5XC56eXnB18iIISkJmDQJFd264cWTJ0AN4WUbN26EIAjQ0dER7Tp1Yj0LCuBhZCTajhrFJJMmQWJsDIMjR1QHDx5kAwYM4OLi4gSuatLNy8vjnzx5gg4dOkCjd2/KWjo5gbewgO7589Ct6s5gWJUtVSgU7NSpUygvL0dFRQUqKyqEKX5+3PGlS5UF+vpMpVIxlUrFBEFgSqUSPM9DKpVCUSU/T0xMxMcffYSMlBQUrVghPm/fnmmVlzPh1i18n5AAQx8fUaNbN+Zhbw+n9HQa8xs3AmFhyN65Ew8fPsT169fB8zycnZ1Zenq6AIDZ2trCtmNHNsTYGDE+Ptzp06cBADExMdyIESNQ3XJKECAuWoRHqakI2b5daN++PdLS0phZWZngGRzMLwwNhZmZGQRBwK+//qo6e/Ys//DhQ9HCwoKFhYVh+vTpHFu2DEhPh/7y5TQvf/tt3W4H9VFRQfeG58+bVTI0Bj09PXh7e+POnTtISE5Gl9zcxn9LW5vmuHrmmUuXLuW//fZbMSQkhOvTp0+Dlou1kZubi8LCQjZ//vzm770GBsCpU3QNt3AuQGUlBTg8PeG1ZAknsbamuUtfnwKIsbE1wYg32d40N7fG30Qup/H9ChnhN4nZs2fzn3/+ORITE+FU5aDfLESRMv/Tp1Nt+qNH1c77rcH27dsr8/LyNCUSSZxSqRzg5+fXdM+/vwDayHYb2vAfxoYNG2ZJpdIfe/fure3j4yP5T0myXxWMMVhbW8O6Spq4fPlyLjw8XHz69KlQUVEh2tjYcFpaWnxwcDCUSiViYmKEMWPGcGqSEBQUhMjISGHatGlcU8QhOjpalEqlbMiQIejevTs4jsPVq1dx8+ZNzs7OTmVkZFR9Q5ZIJFiyZAl/+/ZtuLm5VbuMWnz+OXT8/eHYqxc0R43CrVu3BIVCAVEUcfXqVf706dO4dOmS2L9/fyYIAq5evQoLCwtkZ2f/eS0ZYmMps71nD2UZt2+nbMDMmVRPO23aq9/sRZGy4JWVRNSrarSwejURIlGkG/5bbwEyGS6fPYvn7dujQE8P0NUVXGxsOFRU0HeoSapcTsGAoiL6u6KCFjkqFZFtxigLr5YpVlQQcZ0/nyR09eS4PM+ja9eu6NKlC8vLy4OOjg5UKhV0dHQwevRoPH36lNu/fz/Gjh2L7777DhzHcXPnzoWVlRUkEgk4jsPhw4eF+Ph47sMPP4REImn0YtETBOh5eJA5zcsCV+fO0fvUtW4tgL6+PqysrITCwkIYGxs3TBnl5lJdr9pUx9iYAhNhYXT8pk4lwyv1Am/DhprPfv45navBg4HKSnSvqIDl778zMw0N6Obmkpx540YKbvTq1bgpT1N49IjG24sXNUGb0aPrLpKLiugcbt9OJHvRIgrAODhQpuY1W+csXLgQiYmJOHLkCH788Uf06tVLGDlyJPfSee/s2ZabqlVBqMoacxxHQYLAQMrAJydT9urLLwFjYxSFhKgOJSXxDi4uwvTp0xvfkLS0OrLxgoIChISEqPT09Dhvb2/G8zySkpJ4lUqFefPmsRs3bqCkoABfr14NJCQwDQ0NccSIEczOzq6mtt/RkSSmurq0mK0iIzKZDAYGBqpjx46xkSNHcobPn5MU/u5dMMYwZswYbuDAgcjNzYWenh5iYmKqg5MqlQqdOnUS5kREcKxPn5oxFhlJ2ap7914aIKnKQjGPHj1IejxgAPDPf8LW0xODb96Enp4ezp49i4ULF+LChQvgeV40NzdHZmam6sbXX2NIly68hbk51bWOGoVFixbxx48fF8+ePasqLy/ntbW1hW7durGYmBi2f/9+zL50Sew8dizDsmVE3GQyGt8rV0K+Zg2izpyBtqEhRHNzVFRUKHV0dJiZmRkvk8k4uLtjuLGxRDF2LGQyWfVDU1MT6ozyL2vXIl8Q4BwTA4WJCQ6+/z6mKZUM7doJBV5enGrLFiwxMsLz589ZdHS0eG3bNmb1++/4af16wcfUlCs1MFBd276dN1GpMGriRJWnpycfFxeHpKSkmmt/8GBo7NkDj9WrceHCBVGhUDBHR0dRR0eH3iOKqOzZE/fGjhWvyGRwd3fn8vPzqd5ZpeLbZWVB19S0erzOnTuX//bbb5Gens4yMjLQsWNHwdHRkcZNp07UMm/fPjIbjImh4Fdj15BCQVL0V3TfLikpQUVFRXXAA127UhBK3SEiPJzGVWJig8+ePHkSxcXFjOf5Zok2AFhZWcHc3Fzctm0bpk6dyuyaazU5bBhJyZvfcCpjmjSJVDFHjgAJCbg/cyZuKxTwGTSoVa02Xwn37tWUWlha1g10/i9AEATcunULPM836NXeKPLzSbEQH08lBG+/TSqoVkIulyMvL08TwFqlUvmdn59f0wYCfxG0ke02tOE/iC+//HKNrq7uhmnTpmm3r21y8f8ZevbsyXr27FmHdOTn56tCQkJ4Nzc3tn//fshkMpVEImHl5eVcZWUl98cffwhr1qzhAODGjRsqiUTC+vTpwwGAo6MjHjx4AB0dnerspK+vL4YOHQqO4xqNfPetJ4PVNzIC3n4bfFVNpJeXFwdQzfnDhw/FiRMnsoKCAvZHlbunKIowMDAQFQrFm89qh4dTxrKwkLJN167VSOAWLCASk59PMmY3twb9VVsEtft5/YV01QKuGlV10u7m5pCHhSEmJgaPS0s5Z2/vV28pl5JCtWmiSO6ns2YRyR86lBY0FhbUY7QqA8wYq+sQDZJnHj58GAMHDkSXLl3Qu3dvMTw8nP3yyy+ws7MTZsyYwVXVTXMcxzUvfW/XDvDza9YUqBqnT7+893Q9SKVSuLu748KFC9z169cxefJkeuHf/6Zz++67VBrg40MksTY2bybC19SxVgcHliyh/wIIzM8XNKRSvDNpEodbtyigsn8/kcd27YjEf/wxBXMsLYlAc1zD3/DyoiBIXByR6H//m86LuowhJoYWVXPnUhDll19ocf2GWw3a29tj/fr1+O233xAWFsY9fvxYWL16dfNs28iI1BdXrpB3QBMICwtDTk4O5HJSKbq4uIhISGDYv5+CTwsWEHH+7Tc6fj16wLV/f/7h06eo7nPdGEaNomNUhYcPH+Lx48e8UqlUEztRqVQyqVQKa2trzPTxgaJTJ2xeuxYKTU3I5XJ29uxZLF++vGbsamhQcG3PnroBFwALFizgt23bhu+//x7/+PRTsHHjKKAllUJHRwc6OjrVbeA6derEDRo0qHqfd2/ezGUFBOCsiwsMAgIwYcwYSHge+OYb3EtOBpKT4aF2N64HQRDww3ffqRbv28cbWVrSnFRF4iQSCbp164YffvgBMpkMVlZWeIekw+oBQnOnszOR9MREoG9faGhoYNq0aQwAj6IiQKnkkJSEie+8g8//538Qa2LCNCwsYG1gQPOIRELS/V69oGFhgWFffSUaPX7MDsyejQk5ORKNefOofh4ADh+G8ZUrDZ38AwKIFC5fjjmHDuFEv36I79IFac7OGDBihND5yy+5zvXkyg4ODnBwcGAKDw+U2tpi7Nix3OXLlwVp377s44wMaBw8CHzxBQ9QwE0ul9dcGEOGkEIkLw9jxoxhMTExYnx8PPvpp5/QrVs30dXBgT2SSnFfT0+cNXVqw0Dzd9/RvnfuDADIyclBaWkpdHV1MWzYMMHNza3u2LS2Js+MqChSRnXrRkE9F5e635uW1mR5R0tw4sQJlZmZGSwsLOhYjRpFBF4NHR0yE6t3z0pLS8P9+/cBAPPnz3/p7zDGMGvWLLZ582acOnVK/OCDD5qedKZMocBubXO81FRSJn35JRAcTI8HD0givnFjtellXt++kMbF1S1H+DOgVFKAVW165+FBKoT/JYSFheHq1asCADaWAlPN739YGKkBNDXJgT0igoJgapl5K1BLrv4Nz/MTvvrqq/T169fPbPUX/RehjWy3oQ3/IWzYsGGkpqbm5wsXLtQyeFkd4v+HGD16ND9ixAhIJBLm6+uL2NhYLiUlRRg+fDi+//57AGBZWVlgjCEoKIgHAAsLC5w4cUK0sbEBY6xB3+tWZ/379iVC0bNntXyrffv2WLFiBQMomu7t7a0qKChgjDEuMjKSVROnN4GsLMpg6+hQNnPAgOrsVTWcnUk+/P339L5+/ShjWWW09GfB2toaVlZWiI6Ofv0vO32ayJtUSkRvzhw65h06UNY4Lo7qvQwMKNPg4ECLyiq57PHjx8XExEQ2cuRI9KzK7I8aNYp5eXnhzp07uHXrFqeuLX9ZhqQaCxdSLfTmzU33Fw0KotdfIVubnJwMJgjwtbIiRcKoUZQxHjyY2hI1ZuCTm1vTL7UVGDduHLd7925kjh4Ny+XL6cnjx+nfwkJaFHXuTAZFublEKj//nBQTRkZUPjBvHi2SJ02iMTlhAr0/LY0k6wMGkOmVkdErZ8Fag5iYGCQnJ2Pq1Kk4cuQI5+/vj6FDh2LAgAEN3qtUKpGUlASVuzvu7d0rcF27Mg0NDZafny907NiRy8rKEp8/fw6lUskkEglsbGyU5eXlDKLI6+3axeRdu0Jj4EDa58pKWvgKAh2TW7dwNyJCNDU1hb6+ftML0OhoWrSPGgUAcHFxwdWrV9VKGfA8jw4dOqCwsFDFcRwPCwvk//ILOoGctzmOgyiKqFO/0GNwgQAAIABJREFUW1FBJMvAgIJTtRAbGwtBEDB9+nQwjiPZ+8WLpExpBLV70a8oKsLJsWPxLDMTTzMyYP/NNzBwdsZJNzexMCaG6ejoiEFBQWLnzp1Fb29vXi6XQ6VSoX379nj8/fcoycvjLd97D9zkydXXTpV8XRUXF8cbGhqKxcXFLD09vVrhVAf379clXzk5Nb2ke/SgDNkXX0Bx7Bhw4wYiPTwgGhnBGiD5/qpVFLCqMoHrePcue5qaCsWWLcjNyUG72Fjgs89oPP/rX0RorK1p/lm0iFy81eZOHTui7PFjxH73HWbMmKE2xWr2RiLNz4dhjx4wdHKCk5NTzXtXriQ59q5dMFu/HnK5HNWu1ozR9ly/Dvdx4+Du7s6io6Nx584dMfz4caYbGIigKVMwd9o0rtFjpqdXh2xbWFjg3XffxdGjR3H37l3RvZ4ZXzXc3al0KDWVShLmzyenc3WAbM0aCrDSPbdZlJeXIyEhAc7OztU+Genp6fzatWtr3rR5M/3eiBFUv719OwX+6sHGxgaWlpZCVlYWd/369RaVZqmDvba2ti8nwmfPUrZ68mQKMIwZQ/8fP56uUR0dUi3VgiiKiIiIQLdu3f5cog0Q0V+woCZ4ampKgdGiolYHd18XZ86cQWRkJLy8vDB48ODmzTxFkQJsX39N5qaLF9PzwcEkI2/hGkzd/UVPT69ObbhKpRoAoJleqH8NtJHtNrThPwB/f/8uUqn08LRp0/4riTZAN051BkdHRwe9e/dmvXv35gFgyZIliIyMFHft2sUAwMzMTFVRUYF9+/bxFhYWLDc3V5gzZw5rsvdzS8HzdPO9cYMIbCMZOm9vbx6gmrHo6GikpaXBzs4O2q9DOOLjKZP9yy900/L2bkiy1bCyokxOfDwtLvfsIQJ1+zZlKhwdX307XoLaN12lUtnQMKUlKC8nUrtoEe3HN99Qz8//+R8ivKtXU4uklStJypqQAERFQfzhB5TFxOBqt25imb4+kxoaIjQ0FE+ePIGxsTGGDRsGAwMDFBcXw9HRUaWtrd06xYFMRvL2xYsbd4AtKiKSfO1aw2xQc5DLAbkcA379lRtx6xZk06YRwZ4wocZduCmsX0/SxlYa2ZmYmIDnefHZs2fM0tKy7osGBjS+AGoTpIa6NvD6dZLkZmaSCdCzZ1TK0KsXtT8qKyPCoqX1HyPaWVlZOHXqFMaPHw8nJycsXLgQu3fvxuXLl3H58mUAUDvTQ6lU4uuvv4ZKpULP2bMFz/h47qGmpooxBkdHR/7+/fuijY2NMHPmTN7Q0BDa2trgeV6C9HSUTZ+Oq/r62Kuri6XqIFpGBpGFTz+lEglBgMbBg0KWRMIfPXpUNW7cOL72dZCRkYEnT57A2cMDRtu2VT8vkUjQvn17JCcnw8fHB4aGhuzs2bOim5sbh5ISYMkSmO/fj5k8j5CQENy7d09cs2ZNzQRUWUmGbatWUab+4cM67aWCgoLQp0+fasd+5OVRXWwTZLsamZnQS0/H7F9/BYyNcefmTUErMJC7ZGEBXV1dDB8+HA4ODiwlJYUdOXIEDx8+BEDZJ83iYizdsgXjd+wQuXnzGkyWjx494ufNm4dOnTqxPXv2IDo6WrC2tm64+tbQIGXGzz/TIp3nadzPn0+lDFVEfG9iosAY4yQSCdyreo7L5XIgLg55t28jp7JSbYiJsrIyFJuaInzIEIwbN45UI5cu1agy+vUjr4P+/cmH4LffiGylpEDPzg5ubm7ioUOHmJGRkcre3p4fMGBAnQBFHRw4QMdb3ZpLDVNTInmBgZD84x/Q0NAQCwoKmKlaPfTWW3WubVdXV7i6urLLK1YImpWVnJLUU43/5po1DeYPU1NT+Pj44MSJE81PGIyRtPzaNVJIeXvTtqxbR4ZfLVD4PH/+HAEBAWJBQQE7fvw4pFIpBEGAvb29SkNDo+b3y8spYJeSQvNIM/fo6dOncydPnkRcXBxfWVnZdKBUEADGoJWdjSHl5ULw/fvc4OBg6I8YQfvStSsFBT/4gMZTdDQFeMvKKKM/ezbNay8pj1B7wLyW70VLkZ5erSIDQOPC05MCsv8hsi0IAq5du4b79+9j8eLFsLS0bJ4p5+VRUNzUlK4ldbIjK4uCuN27N/nRsrIyvHjxojr4du/ePZw5c6bOexhjU0RRPP3JJ5/8pc3RgDay3YY2/Onw9/fXlkqll0eOHKnfol6p/4WwsrKClZUVZ2pqKsbFxbG0tDR+1apVOHPmDMaMGQMDA4M3V7g+eTLJmQcObLYe2tTUFG5ubqqwsDA+LCwM7u7u4oQJE1oXAY+IoOzTixdkIhIW1jJiNXgwkdSDB2vMR1asoOi4umf3nwCO4/Duu+9i+/btuHPnTqNZxZdCS4syp7WhrU2LYKWSiLaPD8mce/Sozlal+vri0g8/YLBMxkYaGKDy2TPkX7ggZru7i3cNDLh9jx9j3sqVSEtLE/r37996aT/HEbHPymr4mihSVjE+vmWmaEolLehzcymIsGoVdBYswD5HR9glJmLcsmUv/47ERDKtag2xR3W7O1EulzOFQtFy+WOVxBhvv02P9HQi5h4eZDS2di2N08JCCoJs20bZoAkTaFsFgZQhBgZEBtUmekZGry0rP336tGhoaMhcqo5Fhw4d4Ofnh7S0NISHhyM6OhqBgYHo06cPJBIJGGPw8vISfb28OCxbBqfHj3l1f2dvb2+SJ6tRWkpO6Tt2QHvvXsRcvIiy8nIUFhYS0TE1rWkVxRjw3Xd4+8ABfs+nnyImJobv378/1AGNyspK7N27FwYGBkJQUBC36tQpVH73HSz698f58+eF1NRUTktLC927d1f3G6YD8+ABBTd4HtHR0bh+/Tq6dOlS47KsLrfQ1SXjt6QkUoPs3Al4eKCkpARKpRLe6iAKQO/Pzn75wX3wgOYOY2MgIwN91q/ncOECupCDcPWJc3BwwLp168DzPPjkZIjjx+PFkSNQrV6NbiYmDU6wmqiYmpoiISEBmZmZaNBWr7ycMqgffkjBQp6n/8+bR3MhY3Uy3ra2tmJW1fV58ODB6h7V0sGDoZmVJUquXhU1NTUhk8lEmUwGxhifm1vlq2RhQWR4+nQi2OqMub09ZdF5nqT5Q4cCXl4YsmwZyxk3Du3u3uX1jhzBuRUrML1DB7oe1AZmanz6aV2pdG2MGUOPa9ewbPNmljtpEqrJtp0dZVVjYmqukfBwDF68mPu8aryqpf4NcPUqBfLWr6/z9K1bt1SCIPCNthGrD0NDenz9NQUitm+nvwMDKZATEUGZcHWQtVaw9cCBA1AqleyTTz6p73Bfd+7V06M549kzmh+a6iVeVgYDpRKdOnWC+aFDUGRkQDMzkxRbhw4RYe/cmTLlEgnw5AnSduxA5/37ueClSyF/9owCotraRKZVKjov69fT9ZOYSJnsVataNR+5uLiIt27dQq9evf7c7HZ2drVKoRqZmRRUa0m99Gvi1KlT1V1YZs6ciQZB2vq4fZvucXp6FCCvrUy5f5+y8o0EVhQKRbUHBwD4+fkhKysL8fHxADmOvwdgJwCIoljo5+f3lyfaQBvZbkMb/nRIpdK/d+7c2djDw+PPlzL9H0fv3r2Zra0tfvrpJzx//hwuLi5NR/5fBxs20I36/Plm3zZ69Gi+b9++qKiowP79+xljDGPHjn25fD0tjRYBNja08Bs6tMZkrCUYPpwWRamp5FQOUJayspJIeEICLcTfcO0sANy5cweMMYSGhsLBwaHxHsPNYflyitTXaukFgCSdUik5xhYV0UJPWxtwcoIoijh97pxYamnJLFeuBKerCy2FAlpPnrB2x46xTllZeHz4MKIDAuDJcZzrwIG0kG+tS78gELk9dapurdk335CC4NGjpj8riqSIsLIiObZKRQvYo0cBOzsYCAIKY2MRGRmJPn36vPy4xcZS1qCVkvXo6GiUlZWxtWvXotpsqTUoKgK2bKF9vXaNggtr11K2e+dOWrRaWZEyQRTJWCgjo+aRk0Mk4OBBMhabMYOe19Qk4mprS+oLlYq+p0OHRqWGKSkpiI+PR8+ePeHh4cGCg4MbBA5sbGxgY2OD4cOHY/v27fj6669hYGAAURTh7OzMoKNDi9Uq4tIA9+/TdWNgANy6hYS0NIAxEQDLz8+nueXuXarnHTuWPrNmDbBqFVwCA5GemorKysrqr0tPTwcAvPvuu9y///1vMbeoiN3Ytw/ZN29CpVJxS5YsaXjeBYGOS3AwAOpyYG9vr5o6dSqRFpWK5qNhwyiwIZFQBuyTT2iMP3+OS3eoBW0dpYlMRlL/iIiaQEp9lJfXrf8ODaWMZxOtejQkEspOdu0KNmkSTNUt+5qARCJBcXExzp07p+rXrx+rzmofPEgBqXHjqFf7woVE9KysKPgRF0ek2NmZ5N9VGdwRI0bwJSUlYmxsLFu1ahX09PSI5KWmAk5ODMXFrHawcu/evSpjY2MegkD7eP48bT/P09hljIKqAwfSB8LD6V+FAs+/+04sePSIDTQxgW56OvoNG0bHc9Eiuia2bqXvO3SIFEnTp9fU2zaGnj1x39dXpVtUxCMujs6hjQ2RlISEGkXS4sXgZsyATceOSEtLw44dO7B+/fq65QQAfb6qxrk2CgoK0Lt3b5GpmbZSSQHEzEwaS0lJdDyPHqXXBg2iGm51F4mcHMrUf/45kePMTJqrDx4kwuzkBEybBm1NTXHpV18xbt06cNu2Efn6/Xea++bModKYFSuofvfwYQqgDB5MpTijR1OARSaj6yo7mxRlHIcQLy+8Gx4OMTmZrkt1dnvZsppAZ3ExoKODWB8f8Z6+Pps8fjxM/fxqDsLGjfSvOqgxfTqV6axYQVLtHTtafL91cXFhDx48aNF7Xwt799Ixrg0bGzpetfvT/wnIy8tDTEwMpkyZAmtr66YVHGr8+isFZBYvJvVJbahUdG3Uv78DKCwsxO7du8tLS0uVAPQAwN/fv/ZbNPz8/P7t7+9/HICLn59fyOvs138T2sh2G9rwJ8Lf319XKpWuHDp06P/d3l7/YchkMiiVSpw8eRIAEBgYKAiCgLlz53JWTS0qW4t+/SjjoG6T1QR4nq9uRTRx4kQcOXIE3t7e1U6shYWF0NLSqukPrG7XtHcvSXEHDGgdyVZDIqEMQWhoDdkGaGEycCAtXkpK6Mb3MqlyK5GZmQlnZ2chISGBO3PmjODo6Mi5ubm1POgxZEjzJFjdKumLL8g9Ny4Oz58/R15eHgOo13f//v2JmHfpAnz8MQwB2KWlIfTQIZVXdjantX07g6UlLaoNDSk40bXry43keJ6kpGozJYAW5TNnNpSIqhEWRov09u1pkacO0qj73ldF9zmOw5QpUxAQEIDMzMzmyXZODpnl/Pxz89vbCNTSy1b1Qlfjzh2Se5aUAHv2QNTSwtUrV0TX6dOZybp19JqxMRG/jRtJWq6nRw9HR9TJps2s8rMpK6PxWFhIcs6iIvqeS5fo+hozhhb9Zmb0vh49kGlujmvnz4vl2trs9o0bEDkOjDH24MEDuLi4NCAeurq6WLp0Ka5fvy7I5XJ4e3tz1QaSycn0GxERNR/IyqLr8OJFqvWtyggfOnQIAJiXl1dNH/SMDDoeVRAEAQLHwWn+fJS6ueGMuTlkMplQ5aDLOTo6CgC4xYsXs6tmZpjo4IDT9++LHh4erNFzfvgw9aGvysBmZGQIjDH+6dOn5EGxZg2N23Hj6o7fceMo+LFxI/pv3YrY2FgkJyfXuAZLJLQQrrXtDZCURMfd0ZFIo64uzU1NYedOyiwmJLy077YoUpMGhUIBQRCgp1Ry4siRYCdPUiaX5+naSE2lbfzwQ+DECco0u7vT9XbuHJGw4cPp4eEBLS0tJooisrOzq9s5omNHCpDVQklJCZ49e8ZP8Pam8hBNTXoPz1Otc58+dJ01BqkUsr59mZiWJnT196+JBFH2jaTXFhZE/hISKDAWEEBke8AACpL17k2BFHWZhbY2YgcOhE9CAgUX8vNpm378sYZsp6URuTI2Ru+HD5GWlgZBELB7925xyZIldaMa3bvT/J+ZSb+fmAjY2WF6Whr/6NIlCN9+C04U6T526lSN2V9ODgU7R42i7KO9Pam59PXpuDx4QD2e6eTRv5s2kZEYQOfI2BjmgYHihaFD2ViJhBRg6vtMu3b0XZWVRM4BOo+CQMEdgFpTGhjQb+/ZQ89dugQAWFdWhi0SCcaZmsLV1bVmPA4fXrPvOjqQy+W4e/cuA0AO7c3B3p6ucYmEAjmtUAyp1YRlZWWvVyrWHJKTaZzUz2D7+jY9Rt8QysrKsGfPHsHd3R3Ozs6sWTVEXh7NR2PG0P25MaVlUBDNI42ck9jYWBQXF2sBMAHwNoAwxtg3oigOrnrLRQDw8/PLBtACWc5fB21kuw1t+BMhlUo3Ojo6Sk3rO0T/haGnp4ePP/4YUqkUZWVlOHnyJJeQkIDExES8MbKtbv1z4gSRwxZkiLt06QI9PT0xKiqKeXt7IykpCQcPHgTP8+hcWKjqmpfHGyYnI9fODt3DwyF5lXrn2rC3J6MZdYZGjT596KGWBCYlvZpbeRMoKioSHBwcuMLCQtXTp0/5p0+foqioCFFRUTA1NRVmzJjB6TWRGave7mZquarxySeUUf3tN5hv3QrnRYuQUlCAsrKyRt9ubmODyR99VFcenJRE2Z916yiD6upasxCo3eaoNnr3pgX+xo20IBs6lLJZavIIEAHbtYuyQt9+Sxm5xYspS9PId1ZWViI0NFS8ceMG09DQENu1a9f8gHr6lBaHrVQN5OXl4dKlS5g8eXLLjeGAmiz8jh20H5s30/Nk5sWuAhi9aJHQ64MPOOzbR4Z1pqa0yHdwADQ0cO3aNTEkJIQBwLJlyyCKIkkRtbVrFmW1TZumTKF/FQoi2XI5cP06inNycOXyZbgXFTG3igoo9fUhGhsjVi4XpXPmsEgnJ9Fp4UKmI5WiTEcHzNYWmkZGMDQ0xFtvvdUwRe7qSudbpaLr+uhRarHTrh0Fc2oFfnieh0qlgq+vb83nvb2BAQMgiiJiYmJw6tQpqFQqmKxcKVh268ZpZGSIpqamnIWFBezs7GBubs4BFFwZnJAAHD6MOUeONH2+Z8yg4EUVxowZwx04cAC7d+3CyqdPoTNsGDB8eOM94JcsASorYV5YiH69e+PQoUMYN26c6OrqSr/39tu0r+revbUhivT5X3+lYMc//kF1l41dEzt2kKv7gQM1XgtNQBRFJCcn4/z58yIAtN+xg419/Jj7LS8PnUtKYFRWVp15lMvlCAkJES3KypiFszMszcyqpQsFoohYGxvkv/++oHvtGvS3bUOHrCzOzsEB4UZGyMrKqqlPB4js7d9fnWlTKpVgcjmMZs2irOa//lXz3k6dqPWVStVk2Y5CoYBSqeQuXrwojhgxou5BMTIi8vf553Rs3n2Xxpko0nixtqZjtXo1BZc2bQJ69YJCoRCZuzsFVnieyO/bb1Nwrls3mnsiIwHG4OLiAicnJ2zdulWV8fQpf/3ECbGvhweTfPopBWc+/ZSyixkZNeqIbt1gOWAAghQK8TcTE8xatYrByIik0y1BZCSNFzXZrg31/arqmA8bNYr7ITERvpWVkNVWB3zzTc1n1q2jOfHkSfpOtS9E7axnPU8BbW1tGBgYqG7evMknJCSobG1teXd39zpqsYMHD4rZ2dnV5yQtLa3puupNmyijrQ6e3bxJwTa5vEX3RXXZQkZGBjrXl3m/KaSkUPa9PoyMSHFQ/x7/BrF7925Vhw4d2KhRo7hmiXZ8PN3r3N0bBv5qIyCADAcbgYODAy5RUGW5n5/fl/7+/ma1iDYA/Lkp/P+P0Ua229CGPwkbNmyYIZPJFo4ePbotq10P6kyxjo4O0tLSYGVlhV69er3ZHxkyhLKW0dHUXusl4DgOAwcOxLlz56CpqYnr168Lvp07w/PSJS5LELg4Q0MhaupUsUwQuCvffy8aGRlxVlZWGK3O5LYW3bpR1jAlpfGarnffJaKYlkbZs+Ykji3E7du3UVpayvXo0QN9+/blHz58iMzMTCE8PJxTqVTIyMjgTp06Jc6aNavxu3Z8PMmzS0tb9oMyGTBxIqRpaRg+ahSuvPsuIhQK0dfX9+UrDx0dOm9ubtSaqqiIyPfjx9TmS1OTZI2dOtHi2NOTFjSMUfY2O5skk7160VgAaMF++zYZ76SnU2arSmHRHMrKynDjxg0GAM7OzkythmgUFRW08HqFHqv5+fmQSqUvz/TURno6HY+BAylAU8vRXxRFcByHPn36IPDWLa5HdjYkQUFEMAA6Zn36IPXDDxESEsKMjY2Rl5eHnTt3AgDGjh2LnJwcQVNTk3N3d0ejBoZSKdC+PZ49e4bTpaV4UV4OmzFjRLfZsxljDFJBAF68QI+KClZ+6xauHjzILv76KzxlMuSFh8O4oAAFenrIsraGVXY2pBYWovWKFexZairsvbzAtWsH5d//DsmxY5R1cXQko7tG3Jp5nhctLCyYet/z8/OR8be/CRBFmGzdyh07dgwAIJPJhNyKCm52584wmDOHITu7cen15MkN6zBr49gxkiDXysqqM+r9r19HfGkpQszNoYiLqw4E8DyPXr16wdjYGKGhoaKrqysb4u8PVyMjJPbtizNnzjAnJydSNmRmUneFadMa/nZQEGWEO3WibQgLa1hjWVBA142pKc0lLQgQRkRE4PKRI1i5axfTfPQI/OXLsHd1Ze0rKnCua1fMqvqN8vJy7N27V8zJyWFjT57Eyd694RkejoyMDKhlu9ra2qKZmRlT+viwFyoVHkdFqfTu3+ffPXwYt6Ki8FRLCx3U8/6tW2RGVkW2hZMn4XvhAp5+/jk61FelcByRwNTUBi3ARFFEXFwcfv/9d+jr6+PBgwcNyTZAn/3jD/IxMDCouUeoM8CLFtUQqAcPABMT6Kam8g4+PjQPXb1KaouNG4kAGhuTSZuZGcmkk5LAbdqEdj4+4oyNG1Gqo8PuffcdeuvoUObY2JhKblavpjnL0hLgOERHRyPl4UPm6OiogolJ67wrnj9vsRmXcZVyJzo6uuG9Vy6n+VNfn7LeHEfS4rKyugqTJvD222/zR44cUT169IiPjo6Gra1ttVqsoqICiYmJTENDA1paWuLUqVNZk0RbXRIzb17d53v0AD76iIIWzSA7Oxv79++HVCptWZ/pV0VaWsOWmwCdY0Eg5UdzAexXRHh4OEpLS/lFixY1X/q2fTutIfbta9gyrzYePqR7otpHpgpKpRKRkZG1kyFfAPjSz88vx9/f/28ANgGoAKAPIP919um/FW1kuw1t+BPg7+/fTSqV7po3b57WnyZd+i+BoaGh4OTkxDWa+XkdcBxJjw8coEh8CyLL6szezSNHMD4zE53j4zluyxZY9+rFrKv6VCqVSoSGhgp5eXliWFgYS0lJUc2aNYs3MDBAVlYWBEFoWYaeMcoy7NtHdY2NoWtXChb8/juRb339Vjtbq6FUKnH58mW89dZbUGeuu1OGmhs2bBiioqJw5swZvPXWW00fKEdHIgCtidJrawMffwzt+Hj43L2LF6amLOPpU1jVa/P2Uujr1xiuzZhBkriHDykztHVrDYnu0IEyMs+eUdbpb3+jesEJE2jhM2kSlRm0on+oeiHTVKuqOjh5kr77FdQsJSUl1bLdFrW9O32asugeHhSQqDU25HI5zp8/L0ilUjZo0CAWHx+vCl64EMOTknjk5QHGxhDv3MG+XbsE6wULuLmAaHv5Mjt//jzu3r1b9fWnIZVKOYVCgaSkJCxcuLDJTYmIiEBWVhYGDx6MgQMH1sgZOY4ICAAta2tYdemC4OBgVZZMhq4zZ/I9Bg+GdX4+bDIzkR8Whrg//mDR+/fDMDcXWX/8AcdHjyAtK4NxWRktuufPb9LkTi6XM8aYmJiYyAICAiAIAnrKZODNzLhz+/dj/PjxOHnyJJYsWcLt3LkT5VZWMAgLo4WwIDSsPbe1pYXqpEmNt8CxsCCFRS3k5+Whf1AQek+fDnHsWFhXKQSeP3+O1NRUPH/+HImJiYJCoWDFxcXs1q1buO/sDPeICOhlZQmZosg9fPgQbm5uFEwyNW182x49ogDczp2UjWqst/GwYUQi1VLfpqBQEBGfPx/uz57hbP/+KJg/H+319asVIQMTEhAQECBu2LCByWQyyGQysbKyEksnT4bWv/+Nq+3aiVeuXBFVKhVzcHBggwYNgpWVVd2JYvJkHgD2fPONsvPlyxKLgAC6Xnx8KHu7ahXta1oajLduRe7IkeKtx4/hpqHBSkpKYGpqioqKCpSVlanc/f25Ej098erUqWJlZSWrrKxkCoWCKZXKagl8WVkZlEolFxERIXp6etK2PHlC5/PGDQrcbdrUePcCoMZI7cABAEDeZ5+xuIUL0U1Pj1QE1tYkt/7iCzIczMqigMeaNVROY2qKqW+/LUns2xcHjx/H2/b2JDuPjKRzM2cOXbP29sCsWYC/P4qKimBiYiJU1/y3Brq6VKfdQri4uIh37tyBp6dn3fZQM2dS9v7KFTo3AMnrW2jeaWFhgVWrVvE//vijmJOTw9REOycnpzqQZ2hoKCxfvrz5SS4zk+a3+ve8R48oQPIS7N+/HwqFAjzPt76NaGvw4AGdv8aQkUH3ptpqmzeE4OBgsUePHkyrqbKu4mLA35+UMQcO1C1ZawxRUaRaqxeUCwwMFMLCwjgAAqiNXnWk08/PbzOAza+1I38BtJHtNrThDcPf37+TVCoNHDNmjFarzaf+gujcubMYHByM58+fi9OmTXuzWqsJEyiL11T2uB5mduuGvGPHBPu8PE575EgOBw40WORKJBIMHjyYBwA3NzccOnSIj4+Px/Xr11VFRUW8np6esHr16pbd2RcsoMzBhx82XQft6kqEOzKSpLvXrlF9cSuxZcsWQVdXl2uqf6u6bUez7cA++YQW71Ontvr3pY6O0E/C6L0cAAAgAElEQVRLQ9aGDTDt3Zt6Pb9Oj3NjY1oAAkSqc3Np0ZOWRlm+r76ioMCmTfQ7kye/cpurGzduiADY7du3BQcHB67J61qlogXL5tavPc6fPy/eu3ePDRo0SOBetjIURQrQJCXROWmELFy5ckWIiYnhZsyYAZlMhhkzZvA7duyAQUiIaBcayu4vXChERkZylZWV3JB582BTWMhQWopRJiYY5ecHQRDw6NEjuLi44IsvvoAgCM06o1dUVIgAmKenZ7Muyu7u7nB3d6+zeuaMjGBkZAQjJyfYzJyJwsJCaGpq4l/ffIN8PT304XkRsbEMeXlUL7x1K2Ueq3pgA9THGwCePXvGDlYZFZmYmKiG9e7Nc15eeB4TowwKCuI8PDyYkZER09TUVGVnZ/OWbm5E8h48oGxlbfA8ydaXLm14jBMS6JqtLbkURXDHjsEkLw8xHTuiT/v2UPteq83g1Lus/qOwsBAZGRlw+NvfwA8axKn8/cG5utKLMhm5+r/3Hkk/1Xj4kIzffH2p/nLixBppuLq2d8IEkimrvQfqQxConlRDg8ooEhKADz7AnagoESkpzOiLL+rMSQ4ODnj//fdZSkoKLl68KPbo0YP1798f3IMHwNmz+J8ePRhe5pwvCACAcomEPezTB31Xr6ba+5s3yXBSTVZ79ADCwuCQlMSeXLwoJCcnC+np6bympqZoaWnJZDIZ/+D996FpaMhcjYygp6dX/TAwMIBEIkF4eDhiY2ORkpKCx48fM09PT1KdmJuTVFxLi+qunZ2bba8mVG0zABh36CDmd+nCUFlJAT9r65oxKJHQIyOjJotZFYixNzKC1oUL4h9//ME+XLsWsh07KDOvJpF37pCM/rff0KN7d4S8eMG9Uo1xXFzzSox66Nu3L9u9ezd++eUXLFiwgIiZnR0FEupnYletouOlVL605ZYahoaGYk5OTvWYOHnyJARBwIcffgiJRNL8HHfqFAVJq0wL68DAAFi8GIK5OZ688w7s7OwakGmVSlU9D2lqaqpJ4ptHaSlljTdtavz1MWNIRfGGkZSUBLlczvrUC/ZVo7iY1CzTplG/+5dJ7ktKKGi3fXu9p0sQFhbG8Tx/V6VSeQOo9PPzE9/MXvx10Ea229CGVsDf398cwEiO47pKJBJzjuO0GWOGAGwVCoWNKIq8RCIRhwwZInV3d//Lu4+3BL6+vryNjQ2OHj2KFy9ewKQlrZlaCsaoBm3ZMjJTagqxscDOnehgYYEOw4dzGDu2RU7Y1tbWMDY2Fi9dusRMTU25t956C0eOHOEeP36Mrl27oqKiAjt27FCZmZlh5syZfAMOpaNDWdjLl1/eT9fdnWqgjY1pEV3bcOYlOHXqlFhWVsYtWrSoSSJkamqK3NxcbN68GUuWLGk8O19URIutVwSTSmHv5qa6vWABP9DRkaLtHh51+g2/MkxNa+TiAAVX9u6lxedr1uq1a9eOmZqairm5udzOnTsxePBgDBo0qOEbz58nWWpTBKcJVFRU4N69e2zOnDmwsbFpflEYG0vj+YsvKIPWhGw0JiaGc3V1hV2VbNDExATvvfceIt3cWNDx42LRhQvMc/Ro9OrVq8a99tgxIpaZmeB4HupWXTY2NkhOTmYnT56Eo6MjBEFAQEAAAKBHjx7i4MGD2aNHjxhApSGvA4lEAhMTEwiVlfAJDoaFm5to8fe/M8TE0Hm0sqLgSkkJZSjbtwf8/KBZtZh0dXWFpaUlgoOD8eLFC14VGQnO1hZz5syps97R1NRkd+7cEd3c3BjWrqWx3RgiIxvvLfzTT5SxCg2teW7HDphlZeHcwoVCyt27nIWTU7VBU1MwMDCoMSfcuRN8RgYRDDUx79evoUlaRgYR7UWLqE3S8uX0fHo6HR+epyxWY+Pw5k36zgkTaJ+vXKEAkbU1FJaWuHzyJOvXr1+jRE9bWxvOHTvCuWtXhv79SWkQGEgZ/gcPSFXyxRd0PQ8aROT2rbdIhXLzJs2pv/yCJdbWPNatw7P589Fp3z4iAn/7G53XoiKaDzkODg4OcHBw4GizbyImJkacP38+TWCpqZTND2nc8Lh79+44e/YsACImx/z8hLf++U/u4DffCCpXV2DPHjhfvoz2ERGcrFevOoaHUVFROHHiRN0vFEU4xMczh9BQkmsfPkwZYJmMsq9Ll9I2NRHUnT9/PtuxYwfuHj2qGsTzfJ35vuqeJ9++HREmJoK2tzfjeb716wd1WUELoTYhtNTVJXVDRgYFlRobN0VFtM8LF1JpTgtQXl7OOnTogOvXr4sJCQnis2fPOBsbG0Emk708mOjrSz4UTSm5fHwQ+uiReOXgQaajoyOsXLmSk0gkKCsrg76+PhhjKC4uBgAsWLCgRUS7rKwMR48ehampKVxcXFrWn/vBA/LJaGo7PTwoCPKGERQUBEdHR8HQ0LDuvokimSVu2gRcuNB0J4P6uHSJznu9lmGBgYEAAJVKNcnPz6/ijWz8XxBtZLsNbWgB/P39NSQSyccSieRDW1tbVfv27XVkMhmTSCTQ1NSEnp4eTE1Nq11237gk+r8c9vb2sLW1FQ8ePCi89957r6aTbgrdutEi5Ny5GqdsNZ4+pb6w0dFEer28WtVuSiaTYfHixSwpKQl2dnZMJpPB19cXp06dEpKTkzlzc3MUFRXxAMTt27cLc+fO5Rq4fnfuTNnql5FtjqNF9fPnFKn+4w/a3pcgJiYG0dHRbOHChY3X3IIIzsKFC/Htt99CoVDgp59+gqWlpTB69GjO2tqa3iQIFLh4DbO2O3fuiA8fPuSTdHWRGhUlenz3HbR4XrS9do1DawzBWoIFC+jx6FGDGrTW4tGjR6rc3FzezMxMVKlUYvfu3Rsu3qqkq5g7t1Uye6VSiR9//FGwsrJqnmgLAsk4t26lbGftnsz18PTpUxQXF2NI7eADAH19fXiPGwcoFAy//kqEqLaSYdIkyrhlZZF8NDQUsLDA0KFD8csvvyAqKgr367UrioyMZJGRkQCAqa+geGgURUXI/uwzSBmD2bp1DFZWJJF1cyPDPHUtbb9+wKFDKAoJgcbf/oaxkyah+8iR4LS10bdKTsuFhTUwGIuLi0Nubm7Nse7Ykc6fvT1ldmrXCG/YQNfeli01z4kiqRdqZT0RFkZZ8EOHMEyp5Hbt2oWQkBDhnXfeaXlGzcuLsuuzZxOxadeOAiq1M+55eUTsLl6kDN/48fT8ixd0fE6fJif82oiNJUluz54kX374kOqL1XNdlWnW3bt3RQBsmNrwLTmZ1BN9+tD8tHAh1e1u2UIy7CFD6HX1+Th+nI6htjYFKQDaDzXKywGOw5VLlxC1cSPWdOxI3/Xzz2T0lplJsuz33qN5W+3kDvL5UCqVNRlKMzMK/DUmsQeVQKgxnOOgHDyYe9ihAxycnKrfXKSlhQgzMxhcvIjZs2cjLi4ODx48QGpqqiiRSJiTk5M4wcmJcbt2kTw7OZmO1eTJFBj44AM6Ju3bU6nP7Nlk8la/jzcAc3NzyBgT7T/8kP916VJxliCw2q78586dQ+SoUfD09BTfP3CA4w8epPmrNfj668aNuppAaWkpmCBg6IoV1G2gSuLdKDp3Jqm0+n7QApiYmCAqKgpPnz6tVj107tz55dfDF18QQa2S79eHIAiI8/BAQmQkm6ihgRs6Ojh69ChSUlKgVCrRvXt3qDO+rZGQv3jxAk+ePEFeXh7u3r2LQYMGYfDgwc1/KCcHcHVFWVkZQkJC0LFjx7qeG1padC3Mnt2ibWgJQkJCkJmZCR8fn7o7lpdHwa+LF+nxsl7baogi8N13RNLrwcLCQoyOjmYAnr+BTf/Loo1st6ENL4G/v7+OhoZGaLt27bqMGzdOqynC0oZXB8/zGD16NPfDDz8gOjqaWoa8KXAcSeCOHiXJH2MkRa3dV/Trr1+5Flomk1VnAAHA1tYW58+f59R1r507d1ZNmjSJP3LkCPbt2yeuWLGC1Wnr1LcvbceLF03WotZBu3ZkVCaKlN3etq2mx2sjuHLlimrgwIFchw4dmmWAMpkM06dPR1xcHDIyMpCens4dOHAA69atI7OmoCBayOXlvXwbm4CDgwMLCQmBo6OjKjo6mk8ePx4yDQ1x3Qcf0OIqKKjx2thXRVkZSVJjY18ruz1ixAg+Pj4eubm5DAALDAwUp0yZUvd4RkSQu/Ht29VPyeVy3LlzB46OjtWZM6VSieDgYHh6eiIgIEAoLS3leJ5nM2fObPr8yOW0qC8qAo4cwcsCE0VVWdom24dNmkQBpoyMmgyqGlIpSW1nzKg2kGs/Zgz+/ve/Qy6XY+PGjTAwMBDfeecdZmBggKioKEgkEgQEBFT3yG7WFfdlSE+H+PnnCMvOht6nn8JEXWeop0cZtejo6vIBlZUV4seOxYnjx+Exb55yxPDhEowYARgagvv5Z/rMzJlEVmtl6xp1xGeMghj1a/IHDqxu61WNuXPp+lOTgSNH6HH2LCCToT2AlStXYtu2bZxcLq9pHdgSeHvTsU9PJzKtUJBz9ZgxJN/9/Xca08OHUyApL48ya3v30rFReyFUVpJx3oYNRGjVstLS0kaDQUl79oiP795lXg4O4Ly8KAi5axfVNV+7RqTe25sCE8uW0YfeeYeuMXWms3bGUx3gqk08q67tPn36IOXsWTF8/Hjm1qkTNAcPrhvM+PZbOhehoUQYXFygoaEBlUpV8x5tbSL3RUV12iOmpKTgxIkTQnFxMTdlyhQYPnmCdnPngq1ZU+O4DQAVFRCXLUPs1KlITk7Gxo0boampKXa2sxPGDBvG62zcCI1t29jeUaPQu7wcrr17E8EGiOTPm0fnSS0j19Gh62XPniaNu8YoFMhs1w7JjLHw8HCxT58+jDalAmFhYZBIJHjw4AFvLQiCi50dJyoUYC3telFeTmO8Ba0iCwoKkP/iBfIXLYKsVy9Ig4NfbiIqlVLpwldfUcCvBZgwYQKzt7fH8ePHwfM8LC0t6zrQNwUHh0YDpEVFRQgICBAyMjI4mUwmjk5JETsXFnKxCxaI8VVt3aRSKbKzs7Fr1y7wPI9PPvkET548wZ07dyAIAnx8fJqU51tZWUEmk4kFBQUMQHVbUEEQUFBQUG0qVwfx8Xikq4sz27cLBgYGuH//PqepqQkHdYDPzg4IDm4yKPQq6Nq1K65du1anxAHPnlF3Ag8PCly1BiEhNDeoHd9rwczMTD1Z6ABopg9hG5oD/1lTxjxtaEMbAAC3bt3abW9v7z1jxgytJo0o2vDakMlkEEVRGRgYyFlZWb1ZObmZGRnxPHtG7cCiomhR6u9PC8Q3SPAqKiqQlpYm9O3bV3Bzc+Oc/h977xlW1bV9D4+19jmHzqH3jiggClhQkYA1xl5SLIkl0WhuYkwxuUlubmJMctNMNUaNSUwz15ZobIgaC6AoTUABC1URkSadA4ez9/5/mNLkUHO/vL+X8Tw+Kpyyy1prrzHnmGP6+XEbGxt4eXmxq1evytHR0bKJiQmzsbGhiLtCQVLO6mq9Dst6oVLR++LiKNNiZNRpPfKpU6dYaGgo60mQyNLSEj4+Prhz5w4KCwshiiLMzc3JYdnenjJ+fagXb4aRkRHCwsLg5+fHhw8fDhcXF6SmpbGwV18Fd3KiDcnvv/dYptgtlEqS3tvZ/a32K4aGhkhKSoKJiQk0Gg1KS0uZra0tGhoaWn4v/fQTaoOC8NGJEzh//rzs4eHBvv76a+Tl5SEpKQl5eXm4cuUKjh49ivz8fKSkpKC6uppZWVnJ06ZN69zhPCaGSPzDD1N9dg+Im42NDaKjo6HRaPRvbhmjbOWYMaT2uH8TyTmZc0kSkQl3d8DfH4IgYNy4cRg9ejRrVu84ODhAoVAgISEBFy9eRHR0NOzt7dGlY3tnSEsDDh2C5OaGSCcnqeD2bWZiYoKjR49Kt27dkga98ALH4cN03ABu376NHTt2YExoKCY/9RSHqyvVKFpaUsbpyScpiBMR0S4g5ejoiPPnz8vDhw9nPm2z3sOGEaGYN48k2ozRuZeWtq/Z9vamuermRpndV16hjGYbomNoaIiYmBiMGzeu9+ZMI0eSp8HevXQ+Zmb0/TodEeDHH6f7Ym1NGcmjR+l11tbAb79R0CokhDKvc+ZQcKCZKDY1EUlVKEiuPXcumhYvRuP8+cyUMbg9/TSqOYfhpEng8+ejbNYsGBsbg4WGtpxfU1MTtFot+TvcvNmS3WuL/Px8HDhwQDYxMWGSJEGSJNTW1qKmoAB1r74Ky+pqlsc5HN57Dyb3152amhK5T0ykse/jgwoTE2RlZUmjR49uvZjz5lHgsY351L59++SSkhK+6MEH4fX557B87TWwf/6z47y5dQssJgaDPvoIHh4emOTvj/HGxszvyy+5nYkJLAYPhtmLL6Jy0CAcq6/H6ClTKHgly1RPfOUKOYm3DdKOHUt1zRYWHWueq6pgt38/kz//HBnU6pI1l6L88ssvuoaGBubm5saamppw0cSEFQiCaDt9Or926BCM58zpXi1XXU33Pzi4y5c1NTXh848+wtXERISdPAnzxYvh1lPzrm+/pev93HM9ez2AmJgYub6+HkqlUqqsrORNTU2iRqPhUVFRYnR0NBNFkZWWlsLKyoqu77ff0lhq006vGVFRUbh+/Tpbvnw5pkyZwmwfe4wJS5bA1sKC19bXi/X19Vyr1cLa2loaPHgwKykpQUxMDDIyMqBQKMSMjAxeUlJC5oOg7hyxsbGyp6cnA4CNGzdKZmZmmDVrFmtqahJjY2N5RkaGmJ2dzaKiopilpSUUCgWioqKImBsYAKtW4WcbG9Qzxl544QVWWVkp5efns1u3biExMRHuAwbAIC2N5u99ppmVlZXQ6XS9C8aB5taVK1cwfvx4GBsZUdBv505g9Wqa673FoUO05ugpgYiNjW0oLi5WCILgEx4evrf3H94PoD+z3Y9+dIn169erBUFYMHPmTOXfytb0o0eIiIhQaDQaad++fczMzEzy9PTkU6dO/fsXnnPacD/xBLV/Wrq0V3Lx3sDa2hqrVq3qsLs2NzeHu7s7T05Oxp9//omoqCjZ39+fTZ06FYrnngPmzUPx5Mm4dPkyxo8f33lWsu05bd1KG3B3d2qFoqcfqyAIcl1dXa+uYduNXYvsfffuXjl4dwczMzMYGBhAqVRCYWdHpCAhgbIm8+dTLeTfrP8FQJ/z4INEkJpbXvUSjDGsXbsWjDFoNBp88cUXLTXLSqVSNi8qYmPOn8fhGTMAxqDVallzzaeXl5dsbGyM9PR0du/1WL16NfLz83H48GEEBwczvW1pGhqoHvCll4h49aBkoO3xDhgwQL548SJ74IEHWtrutIORERHKEydI+qsPCgVJZzknyfC4cdTP+T5YWVnhpZdeQkxMjJScnMxv3rwJv95K90+coJrxmTMhTJuGlwG+e/duMTo6Gmq1WkhNTcXEiAgY799PxxEcDCcnJyiVSlRVVbWat5mYtJqJabW0AdXpcHXTJii//14++fTTrE6rlXQ6HU9ISMDYsWNba9YByvA9+GBrAE6no0zljBlErH7+mQJOkyZRacrbb5Oa4b75qtVqwRiD0EfFDNavpzZUubmUlf7pJ8p0x8XRWiZJ1Ee+sJCk2g89RAHFkhL6Y2BA7bT27iVS+v77dJzHj9Mx/+c/RMw++QRnzp+X45YvZwCgiIqCsYmJ2Lh9u2BqaipVVFTwgIAAafbs2bypqQn79+8Xs7OzBVmW4enpKT1eU8NZdjawaBFyc3Nx+vRpqbKyktdSnTnLz88HAKhEET7Z2QiNjcXF0aOR5+cnVwsCm95VUHX+fJK+p6TAecMGGN3vg/LZZzS/22DcuHHszz//lP764w829dw5VhgTI4+JiOj49C4tBd57D9Z1dbBevpyURVFRRO5HjYJOFJGUlNTibF5XV0fr4rZtdE+2b+/YSs3amsocoqNJGdAWW7cCnp5wGjgQ1tbWkpWVlQygeXBwrVbLSkpKZMaYDICrVCrOfv0VmampcuZ778F/6lQ2fOLEzlUjeXlUZnJ/mywANTU1MDMzg06ng/LKFbz+1Vf44plnsO2ZZyDk5yN92zZ50aJFzFSP/L0ddu6k7+mivzkAlJWV4dKlS2hsbMT169fZypUrYWNjIxQWFuKvv/5ieXl5Og8PD4WXlxfS09PF6upqdv36dXnhggUCNm6E9PHH2P7996KRkRF79NFHuUqlgiRJGDhwIC5duoS4uDjxkUceoQPYvRsOq1djQWmp0NDQgLq6OlhbW3MACA8PR21tLczMzHD69Gnk5uaipKRE+uqrr6SBAwcqrl+/LldWVrIvvvgCgiDAyckJS5cu5YwxODo6CteuXQPnnN+6dYuNHDkSBw4cAOccgiCgqKgID7q6wmHgQJh4ekpidTW/Vycu5+bmwt7eXlKr1di8eTN7ys6O2RUUtATs4uPjkZGRgYKCAlhaWmJNJ0oISZLQ0NDQLhOfmZmJffv2YdSoUaI1YwIiI2mOf/11927j+nD7Nsncn35a769LS0t1AJaLovh77z+8H83oJ9v96EfXsFKpVE2GhoY91HL14+9i0qRJ3MbGBkVFRUJKSgoMDAw61J72CbNmUV/hr76iLFdIiN7auv81rl27hlOnTolVVVVcpVJh5syZzMHBAX/++ad89epVlpKSAm8vL9l3wAB25d135Rx7e5adnY05c+b0rIVYc4bbwIA2dOPGtcvCGRoays0Z2J4iIiIC8fHxsiRJLDo6Wvbx8WHYto0IWmdtcvoAtVoNxlhrq6uQEMoYXbtGsrZDhyjb+Hfx9tsd6nZ7i+ZNrpGREZYuXYpr165h5MiR2LRpExuWnIzbDg4AY3jllVdQVlaGhIQE0c3NTQgLC2OWlpYICgqCh4dHC/mytrZGSUmJlJmZyUJCQtrvoHNzqX+vWk0BiD4Qtscee4x99tln2LJlC1577TX92dV//pMku3v2dO4w3/y+116jzExaGmVT7zOIMzc3x4wZM3hycjIuXLiAKff3Ru4KP/1EJmBPPNEuoDN//nwBoAz2999/jxqNBsYfftiSieecw83NTdJoNB0ZyPnzlO27cQNgDLkffSTbKpVsoL8/Alev5mVLl0I3c2Z7ok0nQlLZN94gWeXzz9O8kiS6DydP0hzw8KA2Tvv26XVn1mg0fSfaAJHId9+lLL6nZ6tplJERlcG89RaVR6xY0UrEP/+c3jN6NElKn3ySggNhYZQNX7CAzi8zs/V7fHyQ/913DACWL18OGxsbGBoaChkZGSguLuZDhw7F999/z2JiYmRRFFFUVMSef/55CIKAzz77jH+j0cDL0lIs+uEHduvWLR4cHMwHDhwIWZapTZ4ooume/NjA2Rm4dg1zjI3x66+/smoiP3BvQxLu3r2L27dvQxAEaDQaaDQa1BsbyzaCAJdr13g7c0hbWyIZ773X8n6vqiq8/NVXHNeuIWPZMpz4/Xc2wN8fdnZ2JK1XKCg48cwzpHjZsIGun7c33V9PT8iyjPPnzyMmJga2traSm5sbU6vVDEVFNBYOH+68Z/nataQ0KCmhzweoFt3JCRg/HjqdDlqtVi4sLBQaGhpgaGiICRMm8J9//hlPPvkks7S0bB7LDACWTJzI7vr5IScmBic//FCaOHEi10u4q6s7mCXeuHEDFy5ckK5evcoNGxsxJCUF9UuWSA/98gt/ZdYs6HQ66qt+8iT75ptv5Jdeeol1mWWNjKQgzdGjFATRg+LiYvzwww9wcHAQBUHArFmzuI2NDQPIkG3p0qUcbZzBw8PDhZs3b2LXrl1UsnDpEuITElB17RpXKBTyl19+KdnY2ODu3buQZZm5u7tLfn5+rRfg4YdbJOf3WtK1/EqpVLb4lHh7ewt1dXWio6OjYGBgwOPj4yXOORYuXMh27dqFpqYmTJ8+veXampiYwNTUVFar1UwURWnq1Kl83LhxUKlUyMvLw/Hjx6WMI0eQBfC7d+/yl156CYIgoKqqigHA/PnzuYWFBbZs2SKfy8rCNI0GiWfPIi8vDzdv3sTo0aNhYmKCnJwcubKysqU9WkFBARITEzFnzhxs3bpVLC0tFQwMDKDT6SAIgiyKIjM1NcU4tVooe/JJNAwaBKf9+5Galib9tXs3f+CBB2QrKysWFxcn2trawt3dXQgICOg8SBMdTWuZnuSDLMu4ffu2KYB969at68RBsh89Qb+MvB/96ALR0dHVsiy/NXLkSEWXLZH68T8D5xxOTk4YNGgQjI2N8ddff6GhoQEDOsu+9fyDSTr+6KNkHnL4MG2AeurW2QNIkoTz589DlmVcv34df/zxh5iWlsaHDx/OJk2axCZNmsTs7e1hYmKC4cOHs4CAAJSVlUkNjY3MXKuFIiuLqR98EHfu3MHNmzclc3NzFhkZiaqqKuTl5XXubGxhQRm9DRuA7GzajN57uFZXV/OMjAxpxIgRPc5uM8YQFhbGDA0NkZmZycaOHQv29NMdpKJ/F4IgICYmBqGhoa3EhDEidUFB9H3/+he5G/egr2qncHOjjNNrr1H7tL8Jc3NzeHp6Un1gerrcWFMjF48ciTmLFjF7e3tYWFhg8ODB3NfXlyS4jMHKyqoD4dVqtSwxMZF5eHiQgkCWqd4uN5cI1iuv9LnEQRAEBAQEIC4uDg0NDfDRF2xgjGTFr79OpltdfdeAATRftmwhA66nntIrzY++Z+Y1dOhQdFt2I8skG01LIzJIfd874MSJEyguLsb06dOpXCI0FFi+HI0AoqKi2NixY5mVlVV7crtgAdVL3jNKcwwKYrtqauSykhLmaWMjec+bx2zj4ymj0ywZb3suaWk0DocOpYxeXR2tFfPnU53yP/5BcuJO2sBVVFTg8uXL3fdl7wqMURb77l0ighs2kEz000/pOvj7k6GbuXmrOdpjj1EwYO5cynwvX06k1Nm5tZ77Pri6uuLKlSuIj49HQ0MDBg4cCDs7O3h6esLY2Bj29vbs1EsV2EwAACAASURBVKlTcn5+Pn/44YeZk5MTVCoVBgwYAMeKCvhu3szvzJjBXF1dMXPmTLi7u8Pd3R3s/HmwbdugSE+HYs0aGmP3nqMmJAuXU1NTmbe3N2pra2FkZIRPP/0UV65cwa1bt3SFhYVSRUUFGnU6XjdsGPMJDGS233xDa1xEBN2HZcvI3JIxIrguLoiOj5d+v31bzsjIYL6SJI/U6RgbNIiu0/DhdN3q6ym4FxoK7cCBqKmrI2JfX4+ysjIcOHAArq6u8rRp07iXlxfTxMUBK1Yg5csvkX9vPc7NzUV2djaysrJw/fp1XL16FVfz8nBt926wL7+Uf66rk86ePYvGr75CQXIy+6WyEjExMWhsbOT19fU4f/48Ro4ciT179ojOzs58xIgRekmR0T/+gbKQENR88AErv3pVkgYMYMXFxfjxxx9RXl4uDho0iCMpCRUqFX5KTpaPHj3KYmNjkZKSAlNTUwwfNoxNbmhAwJkzOO7ujujCQubi4gJbW1u4uroiNDQUp0+fZrW1tfDtKpgqiuQlMHUqjSlQFjsyMhJNTU0wNDTE999/L48aNUqaN2+eEBQUxO3s7Lp97jQ0NODa6dMY9dxzqJ49Gxezs1FeXs4GDBggubu7CzqdTnJzc8MTTzzBO3wm56Q+eewx8mfohFRaWlrC19eXu7i4wMHBASNGjGDNNfMJCQkYPXp0i7wcoGegTqeTkpOTuZmZmTxy5EimVCrBOYe1tTVCQkKYb2Ul8w4PR+jixS0k38jICHfv3pVHjRrFFAoFzMzMUHvxolz9xx/sOOcwMzPDtGnTMGLECAQEBKCqqkr666+/5FGjRvHIyEj89ddf8u3bt5mnpyfi4+P55MmT0djYKE2cOJGNHDmS3S0vh9uRI7iTkSFdMTTEMXt7VlNTI1+8eJE3NDQgOzubpaenw9LSkomiyBMSEuTTp0+zu3fvyiqVirWrO29qoufh9u0dFCLN1yAxMVEriuJzERERH3d3H/vROVizRKYf/eiHfnz88cc5TzzxhJfz36hV7UffERkZKWVlZfG5c+f2rBVHV6ipoUxpXBz1Ns3JIWOZNWv63IO5GTqdDj/99JN0584dLooilEolJk2ahICAgJ71Sy0uhuajj2Dw3nv44ttv5fr6eibLMiwsLFBRUQEAWLFiBbodh7JMG20vL+SvWYODBw9KOp2Ov/zyy70+px9++AG3bt3CmBs35PDUVFby559wdXX9ewZYbXAvoyH985//1M/yGhuJhE2a1FqL21dkZZE0txOH277g5s2bSH/mGfiYmEieO3fybqX/90EURXzzzTewt7fH/MmTiSwdP05Zy/+RguDs2bNSbGwse+mll1indZ+ZmSQx7qn7sSxT3e+rr5IKoc15R0ZGyomJiQwA1qxZ06kDPhobSXHg4kJGU/fVMzajvr4en376KRQKBf71r3/RD5ctA158EX/m54vp6elCs3GWgYGBLAiCPLGhgQ1btYrB1rbd5ru8vByHDh3CjRs38Morr8CktpaydUuWEBndsIHGmVJJ79Nq6bsMDMgbYft2qmU3MaFr1dZ1+D5kZ2fjwIED8tq1a3s2WWpr6XuysijDN28eZdVnzCCDtmY34zVryOPByIjqt//4g7LfISEUELCy6lOJjCzL2LRpk6zVatnatWs7/L657rrDGC8sJNVQcnLrz65doyy7nx+piJqNKfXg999/R319vZiXl9cSKVGr1fKLL76o/w3V1US216whGfmoUTQe4+LIU6K8HBs++UR+9Icf2M4FCxAaF4eRnMP4zBly2W8bHDlwAEhOxvbqahRYWkKlUslt1jbGOZcByEySsPDbb/mJiAjc9PSEg4ODpFQqZc45OOeyIAjs3t/gnKO6rEzx8MGDqHz/fSjValj9+99o+ugjGDg6QqlUgjGGxsZG/PLLL5KDgwP38vLCkSNHJK1Wy8ePHy+OHTtWrySi/qmncLGyUoobPRqcc9nJyUnIzc3F/Pnzcee11+RSgBWOH4958+ahuLgYhoaG8D90iMZIQgIgy5BkGVu2bEFZWRmefvpp8uNA61q/aNGiDoE5rVaLqKgo6MrKMOvkSSimTAGWLIEkSdiwYYPs6uoq5+fnc0EQZC8vL/nRRx/tVYSwuLgYWzdvhp8oSllGRtzExERUKpW8trZWFkWRy7KMVatWwaaTNQJaLc2X3bt7XXokSRJOnTolJSYmciMjIzEiIkIIblP3XlVVBZVKpT9wuHAh+Wi0MUjVi1u30Dh+PLauXi1PmjSJtTVUFUURGzdulBUKBe7evct8fX0hSZJ0/fp1bmZmhjVr1rTOuaoqnFy8WFbfvs1yH3sM+YCsVqvFxsZGoaKigjk5OUnDhg3jqamp0vLlyzlA62dubi7+/PNPWFpayjY2NlJhYaEwZMgQcWxWlqCoqoLqjTc6PfSkpKTmFnq8v79239Gf2e5HP7pBXFzck76+vg7/KxdyURSxe/duqaCgQLKzs+OCIPw9ueH/cVRXV8uXLl1iGRkZeKCtm2xfYGBAG0ETE3L39fEhuem5c0S2/0ZAZd++fcjJyWEvv/wyxo4di9DQUHh4eKDHighTUyj/+18we3uYDx0qW1paytOmTWOhoaGQZRkFBQXIysoS7e3teVdjsbqmBk0ODjiflYXziYnw9fGR5zz+eNfywE4wcOBA2NjYIDU9nZXJMo4WF6OmpkYaNGjQ/4RtGxgYIDo6mo0aNUp/jbpCQRmLESNICnvhAtV29wXW1rQh276dzK3+BwGDbVu3SuPKyuD9wQec98EQjHMOe3t75P34Iwb9+CMUERFkutTTli09gIuLCzt9+jSTZRnenTmyc07O1X5+PZsDjFHW2NGRCOenn1IGmXPs2bOHNbvkRkRE6B//d+9SRlanIxMvPaqFxsZG7NmzR4yMjOSCIGDOnDktpmtiaChuf/gh/tLp+OrVqzF58mSMGDECVlZWzK+6mjm9/z7bDkhVOh3z9PREY2Mjtm/fLkVHR7PnnnsOFy9elDnnzN3fn+qWOSen6qFDKXO/aBEdV3k5uXx/+imNQU9PchZ+551u28ndvn0b5RcvysPc3RkEgcpXnJ3J8O7VV2mjPmoUScHVavr88HBS3DS3fdqzh3733XeUUXRxoWDA5MmUWXRyIsJ4/DgFK/7zHyLi339P8nFLS6r5j4ykf+/eTWQ1M5PWQXNzCjg2NYGZmCDq2DGmbWrC2bNnERYW1i6oxhjTX4pgbk7BiYAAyhZ/9RV994IFdKx+fl3ONRcXF5w5cwY6nY5xzmFhYSEvW7as88CQgQGNO2trUoFs2EDft24dkJQE7NoFqzVrmOHp0/LIN95g8XZ2cu3s2eSLcH/ZkK8vUF0Ned8+uIwdi8dffJGFhYU1/8HYsWPZWH9/NnbdOmZ+4AAsxo1DWloajIyMsHLlSh4UFMQDAwP50KFD+ZAhQ/jgwYO5v78/Dx4xAoYPPgiL5cthlp4OxbRpMAgLgyAILddUoVC0dCsoLS2VZs6cKZSVlSEvL4+NHj1a7wVTzp4Nt/nz2djPP2ehSiUfsnYtNBoNjh49CkVVFZv47LMYN38+zM3N4VhTA9uaGgoSjR1LY5cxMMYQEhKC6OhoXL58GVqtFjY2NqioqNDdvn2bFxYWtrTNAiho9P3338ucc7mishIBO3Yw1c2bwJNPIjMzE7m5uVi5ciUfPHgwDA0NWUREBOutCtD43DkEv/suql96iS1YuBBjx47lISEhLCwsjAUHByMxMVH28/NjHdpmNkMQaM7Gx/e6bpkxBi8vLzZq1CjcuHGDJyUlQafTobGxsbmkQv8aVl5Owdv33uv+WWJqCkVxMUY//zyzu691Guccrq6urKmpCZxzediwYXJERAQfPHgwJk2a1DrnLlwAXngBdkuXsl/NzVEqSdDpdEwQBLzwwgtcrVZj6tSpzNnZGcOGDWs5IKVSCTs7OyQnJ0sVFRVcq9XC2dmZZWZkMOHPP1mUoSGCp0zp1MQxJiYGFRUVl95+++2+GZ70A0B/zXY/+tETCL12k72HxsZGVFVVoaamBgkJCbK5ubmclJTUUrOUmJgIgHoZPvPMM/0ObHpgamrKFQoFAgICWk2Q/g42bKCM0ZQptGFbtw64eBH45BOSF+oxgOoJlEol/Pz8dKampn1fV1esADZsQMChQ7xtr86wsDDodDpJqVTynTt3Yv78+R1k9WVlZdixY4dYVVUlAIBPSIi05MQJbvLRRxwLF/bpcExNTTFs2DD41dcjz8AAKZGRuHjxIq+qqpIXLlzI/m6QqLCwEJzz9i1MOsOJE5RF+/VXIgy9cMRtgShS1njwYCI6fwORkZEYdPYstx84sO/14E1N8Pjvf1GpVkvHTEwwe+ZMXlxcDH5PbtitA3EP0Lx2XbhwAZM7cx22tqZNY0MDEeCeZOgNDIgwFhW1ElRHR6xduxYbNmyAJEm4c+cOOpi/5eZSq73QUKoZv3d8ubm5uHDhghgUFCQ4OjqipqYG+fn5/LnnnsOWLVvg0CYAkV5QAKtjx/DY11+3GPiZmZkh2McHsLZG+b59CFer+bFjx1rKOnBvzS0pKYGJvuxX83h4/XXqNS6KlJX97DNy916zhuqg166lkgRXVyLFVVUkRX/oIeDNN8l4a80aaA8dwoyff+a4coVM/w4fJj8Fa2sioLIMfPwxfY6nJwUgGKOg0rp1dCwLFpASIyiISMSSJURohw1rdRff28YguLlEQqMhMmBsTGS4tpZKTZqN8lJS6L6ZmlIgoa4OmDAB/zx5EucMDOB54wZYUhIFHA4eJFIfFERmSsOHU6bd1JSujyjSMZqY0Bo6cyadr7Ex3WtZpkx7WRkdu0JBf6vVQEoKzAUB/xwxgp/64QeZ1day8AceYML339N1iYqiMTlhAmX7q6roOqnVNIcvXaJ7AVCAwc0NGDQIfjodcPgwg5UV7G7eZImJiRg5ciTM7ncIB5ATEIArrq4YvXcv+US0DUhJEt3/2bMBLy94oqWlG2tqatJPwiSJHNqVSjrn1FQqOTh6lK6juztdG5UKISEhTKVSITk5mf/666+wt7eXHnvsse43G//4B7mdV1RgyvjxMDExge+hQ7Buux4//zwFd7Zv1/sRb7zxBk6cOIH4+Hj5/PnzDPf4wP3z9ejRo2JQUJAwdepUlp6ejqiLF2WPyZPZMFGEJEktAQRra+s+B8O5tTUsXnkF4/V4s/z444+ik5MTd+2uv7coUgD95Mk+dbNQqVQwNDQUm5qahJiYGDg7O0sDBgzoXK105QqtCT0J2jbvH2/c0FuK5eLignutOVs+zK653l+WaWzX1wPPPw/Thx7CstGjkZ2dDR8fH/z6668cAIK7caFfvHgxl2UZtra2dDCXLrHbly7htJ0dfv/9d3nmzJlM37ro4eEhXbt2bej69evV69atq+r+ZPuhD/1kux/96AayLJv1JSt45coV7Nmzp+2PGADm5OSE0aNHw8XFBRcvXsTZs2dRXFzMms1S+tEeJSUlkk6n46mpqSw7O1ucNm2a0GVdWXcwNiZCERlJG0OANq9ff01ZpwkTyASni97V+mBkZNRijtJnjBlDZiW5uZQ5bPPZU6dO5QBQU1Mj/v7774Ktra00btw47u3tjerqamzevBmBgYFswoQJUKlUMDAw4Fi0iDane/bQpu+DD/p0WEazZsF/5048+OCDiI2NRU5ODouLi4OzszOuXLkCANLUqVN5b4NSN2/ehLm5uWxsbNz9dbOyoj9Xr1IN7bJldB97U8stCETYRVGvoVBvkJaaKj9z5Qozevfdvn1AfT3VsDo6YtCLL/JjO3ci94svpLq6upYyhNdff733baM6QZcBDUmiMffMMxQ4WLuWSGFzyzc7u3abysrKSvz2228SAF5eXg7T559H7Xff4Z9ffYVT8+dDcnKCra1tS0udFsTHk9w3NJTuXxvk5OTIWVlZQnFxsajRaITmGtC9e/eKTk5OTKPR8M8++0y+5x7OTsydKz91vzv1c88BsgzrX36BNQB/f3+kpaVBkiR4e3tj06ZNAEga2mk5BuetNf6ffUak9Ouvqb6xuJhes24dZQuLi4nYGhqS4sLfnzLIDzwAze3bOPjvf4tPP/00MaCYmNbvaDaBGzdO/zFoNNSze8cOUjloNLQuPfQQKQKeeopk/J2NfSOj1vrstkZ2zWaD06a1/mziRDQ1NaGiogIV8+YhfvduXPX1xYo5c2Do6UnEhTGaLzk5lFXeuBGorKSs+Sef0H2NjqZxUlxMJm1OTiRfDgykY09Pp/f4+ra2VLtyhQihJGFkaCg7Gxsr7790iRmamsoTx4xhRvPmEYl3dSWZvKEhBXmUSiL7ixdTZv+rr2jdNDOjub1+PX3X/PmYEhsLi7w8lLq742BCgmwWFCTNWrJEAEg6v2PHDmDgQEybNYsUB5s20bFLEo2nceNaAxsgY0NBEHD37l3YarVgGRlgnp70vqIiCkbExZG03sSEggu1tUQCNRoKbNy5A8ydC2H7dgx3dsaAJUtY4uHDUpmZGVc6OdH48/SkZ5U+Qt/cFmv8eMDaGmG//05lGW5u9EybPJmeb10EQ1UqFaZPn47p06ezlJQUHDx4EEOHDpWmTZvWbsEZPHiwkJCQgKlTpyIgIAAet28zec0abL9wAUVOThg8eLCEVlf13uPQodZSLj3w8PAQcnNz5XPnzsHMzAxDhw7VX8YkCHTP/8Z6OXfuXMHNzQ1HjhxBYWEh/89//oO1a9dCr0t7QYHevtSdwsaGgkW9gSQBzz5L5/bmmy3f5+rqCldXV0iSBFmWkZ2d3a2nTQcJflISnKZORbCFhZyWlsY+/fRTPPPMM7BvU2bR2NiI9PT0BgDGAOzWr18vApDWrVtX37sT6Ud/zXY/+tENPvzww9JVq1bZWN3fi7YbrF+/HgDwwgsvoKCgAM7OzqitrW1Xd1xUVIQffvgBAEUmp0+f/r878P9DaHb2jY2Nlc+dO8fUarVcWVnJrKyspNmzZ/Ne13KnpVHm5/5IuiwDsbGUcXvvPdqoGhj06CP37t2L6urqllqpPuPHH2kzeh8ZaUZFRQV++uknVFdXt5QfiKIIKysrefXq1UzvRiQ6mjbIO3fSZrS3NZ2NjbTp4xxarRbbtm2TKyoq2P0Ebvjw4WCMwdXVtZ3ZTGfIz8/Hrl275Ndff733QYrdu8lALCWl03rfTjFlCvXdPXSo11+bnJyMw4cPwyMvD4M9POQR77zTu2OXZSIhb71FRMHHB2AMtbW1uHbtGlxdXWFkZISNGzfC0tJSfPbZZ/92jUl+fj5+/vlnvPHGG9TTtbaW+uWqVNQqaNkyCmR88AERGC8v6hes0aDpyy9RIAi45usLA6USDbKM27a2qFKrUWdiAvlenaokSXDLy4Nu6FD5MYCpQ0NbzMkAUGb3yBGS8uvJsFdVVWHjxo0wNzcXBUGQtVotf/zxx/m2bdvw8MMP49ixY7IgCGhoaJC1Wi0f7+ODsW+/Tfff2JgyoEeO0JzWU2Zx4sQJxMXFAaDyhbVr10IpSZStvXqVgh87d9JYnzCBZNZqNZGXp54iIn2vnVtPcOrUKdy8eVNctmxZ7+5fbi6tAZJEZHbOnNbOCc2BnehoOu8lSzr2SNeDpqYmNNcTNzQ0IDExUS4tLZXUarVQV1eHzMxMNDY2AiAS1tTUhClTprSTE3eALANffEEBwk8+IYL5n/9QSc6333boa33z5k0kJCSgub7eyckJsixDluWWenBJkuRbt26hoKCAvfnmm12XV8XEkJ9DdjZluA0MqAyg7fFVV0OXloYT27ZJckUFd6islMtMTdn4q1fBw8NxdsAAsTgjQ9A4O0tLP/6Y4/x5OoedO+n6fvMNqRcUCuD0aSAzE/WDByP7lVfQpFKByTK4JMF340YYXr1KUvpRowDOIcsyCgsLUVNTA4BIeku2UpJonGVnt5C2rB07cDUvD+OtrWFaX0917uvXU0DxzTdJ+h8QQGTd0pLmVlERrX2FhZTB3rqVWpNNn95rz4c7d+7g22+/BQAMGzZM9vHxYQcPHpSbnf4nTZoEc3NzBGzdCnbtGjSvvw4WFvb3EwRPPEGBhTaO8m0hSRL27Nkj19bWorCwkLm6uspPPfVU52tuUBBl/let6vMhaTQafPLJJ1AoFHjjjTf0Bzw//phM+tqucV3hp59IQbdxY89en5ZGSreNGymA00nC5/PPP0dISEjvjBhra6l8Jj0dMDBAeXk5Nm3aBE9PT3nJkiUt1/by5cvYt29fu7dyzv966623etiYvR/N6M9s96Mf3YAxJvZI5toGCQkJAIA5c+bAwsKipc/t/YQ9KysL9vb2YmBgoHD06FG4ubnJQ4YM6ZeT34dmc5IJEyawmpoaXUVFBZ86dSrbuXMnP336tLx06dLeXTNfX5Jr/vZb+ww2Y0Sw4+LIxfeXX+ih2oMIdlhYGLZv385LS0tbakv7hFGjiARKkt4ovaWlJRYvXoyffvoJdXV1mDRpEoKCgmBiYtL5NYiIoD8//USbmsuXe24It3s31bV/+SUA2oyvXr2aSZKE0tJSWFpaYvPmzaiqqkLyPZOkpKQkODs7w8rKqkszNRsbG+h0ur6N9/nzqR5RqyW57bff6s307dmzR8rNzWXNDrKBgYF0X/vowXD69GnJ2MiIz9NoYBAc3Ltjr6oCfv+dNjn797cbe6amphg+fHjL/5944gns2LFDiIyMxIMPPth93/UuYGBgAOPaWtStWwfVkiUkh9ZqSR79+edUr5yXR8fV5n7V19fjK41G9jA2hoeZGdPm5sq2BQVsYkMDVEolKurqYFlSAmZpCcybBzk8HMzJieGdd+hchw0jovLdd5QZXrGCNo56oFarMW/ePLm+vl4QBAGRkZG4ffs2hgwZIv3xxx/cwsJCeu655wTOeesBxsYCt25R/fKIEVRPre++yjKaiovhlp8PH5UKfiNHQjl9Os2Dt9+mwIK7O2X0BwwgAsMYmR9t305BiPLyXl3zxsZGqFSq3mUzjh2jGm43NyLS06fT/GtsbB8gCw+n2uyFC0lq3ckcq6urw48//iiXl5czgBzAtVotDAwMZCsrKyErKwsNDQ0ICgrCxIkTcfz4cRQXF0sNDQ08KioKxcXFmNXcs7wtNBpaEw4eJIn1++8DO3agNCYGtdevw/jWLVjs3YtL4eGIS0iQ6uvrmVarZQYGBggICBBTU1OFnJwc2Nvby4yAe2ZjrLlNYXx8PBwcHODVRuHTAkmiuf7xx63mdaGhNLeaSSZjgFoNRXg4poaHNy+k7IvPP0d6QADsSkqgu3VLWODrC8PGRo6JE0kNMGYMXX9XV2qdNns2GU6ePg24ucF49mwUzpihuySKCgNHR7muro7dqauDytsb0GjAY2IwatQo6HQ6nHzrLbiVlEixo0ZxlUqFiRMntpxCy7poZwcmijhibo4Zb74J07atDhcsIGWSRkOE1MyMxnxGBgVZfv6ZgkXx8TSf7ewoMNRZ14pOIMsy4uLiRBMTEzZv3jz+66+/sszMTBgaGjJRFGFtbS3Hx8ejpqaGxZqayqFjxrAghUKvg3WvcP06rQ1dfA7nHDNmzGA7duwQAQhqtbrrNfettzq0JewtmgNCSqVSzsnJYUVFRQgNDW1dg0WRAk2rV/f8Q4OD6T51B1mmfumffEIlHmPGdPFSGTU1NSguLsaOHTukJ554omeB/i++oBKRe4kEa2trrFy5Etu2bWNnzpxBWFgYFAoFPDw88MgjjyArK0uXlpamAABJklb06Dv60Q79ZLsf/egC69evV3DOrfTVeumDVqtFU1MTcnNzAQCBgYFdvv5expuFhISgrq4O+/btY05OTrC2tv7bx/5/EYwxzJkzRwHQRhIAHnrood6TNQMDMt7KytIvF+ecNtlHjlA2KSSEMn73SfqysrIQGRkp6nQ6ZmtryxQKBdu8eTMsLCywdOnSliBLr+DvT3LG2bM7zU7Y2NhgypQp2LdvH2pra/XXoOrDkiW0odRoaBP/5JM9e58eYt5s7gUAL774Im7fvg1TU1NwzrF161Zp06ZN3MXFBUuXLu2SKEqShIMHD+rf1HeHoCAyONJqcW7HDuSUlIh8wADB2toadXV1KC0tlerq6vjcuXNRVFSEI0eOoLa2FmPHjqUNzYoVlDG6L6gRHR2N4uLilvpJnU6HqqoqWFtbQ6fTsanBwTCT5dYyhJ6gpoayVQ88QPLkbuSO7u7umDt3Lk6ePCl/8cUXbMaMGfDrxpSrHRobAUFA48qV0ObmwtrbGwYlJUR6T51qHcsREURehg4l8t1mc/ftt9/C0NCQzVqxonmMtZtrVg0NlF2Ljwdqa8Gys6m2/tgxGjOTJpGxz+DBJMU+doxk6XfuEEmePZuyPR4egFqNwQMHMiiVyMrKgiAIcmBgIAsODuZzyBSvY5rzjTcoWLBqFY1td3fK/FVVkYy2uJhkypGRmJKXhzQPD5TJsrz/wgU26bnn4DF9uv76dFlurTH+xz8ow3wv69fzy9+IHpcf6XREnFJTqfZ9zBjKUHp7U6DP05PIdTMYoyBBYiLVmK9bBxgbQ5ZlXLt2DadOnUJlZSWampoAgK1YsQKiKOL27dtwd3eHo6Njy+DTarUtxzlv3jwA4OfPn5eOHz/OU1JSOs7LlBQK1nzzDd1rpRLIy4N2+3bkennBVKPBoS1b8NivvyLjyhWMeOgh5h4WxnJycuDn5wc7OzvBwsJCjo2NZcuXL9e7dsfGxuLEiRMA0KrGaIYk0Rx66y26VgCVg6xeTcS0C6Snp8vVNTXMNyQEzc9cw2ZvDEmiLPOBA6SSKCqiufLhhxQgevbZe7dKhwQDA0VISAgmTZrE9u/fj+Lm8gKQiuTKlStoamqS3auq2Hi1mgevWYM9e/aISUlJMgC0VZQ2NTUxjUYjNGf520EQWlpsoVm9FRhIxPriRRrjSB8yoAAAIABJREFUFy9S5vuvv6jM4Pnn6TyOHyeH+Jkzu1UyVVRU4OrVq8KaNWtgamqK559/HhYWFm0zuuzeseL25s1M/vJL1MfGksN7XyGKVCP/xRfdtmPcunWrVFdXJwQFBdHa3RUefpiCqebmfTbTbA6EaDQatnfvXpiamsrx8fHy+PHj+YgRIyjr3Fzi0FN4elIAR0+7xBY0NpKcvqiIAkfdqOoYYxg5cqQuMTFRAYDHxsZ2Xzff2Ei14ytXtvuxo6MjrK2txbNnzwoXLlxAeHg4hgwZAlmWcfXq1eZF0njdunWanp1wP9qin2z3ox9dgDH2pJ2dXaOBgUGP7DU//PDDln9bWFhIuGfK0xnu3r0r+vv7C0CrWVqnjpv9aIfi4mIYGRlJ9vb2fZNtv/giZY4iIjq61AL0QJwxgyLS77xD5Gj4cGDIEGzcuFEWBEGuqqriY8eO5XZ2diwvL08CIAmCwLOzs1lKSoo8fvz4vmVtH3mEJIH3ssn6MHDgQAwbNkxOSEhgaWlp0ty5c7neXsptwTkRoLQ02qxPm9Zpn+AWTJ9OWeRu4NQm+79mzRq+d+9eOTs7m124cEEOCwvTex1MTU3x5JNP4rfffkNTU5P88MMP9/56mZvj94UL5Tt5eVi5dauQ+/DDcnJoqGRkZITAwEBh6NChMDMzw6BBg+Dp6YnffvsNKSkpooeDgzB08mTcjo/H3YoKDB8+HGq1GpmZmYiOjoYsy/yrr74SJUli1dXVHECzXJqpP/tMxuLFrEf1gaJIioIjR6g/dS8Is7+/P1xcXNiZM2fw+++/Q61Wi2vWrOlcW5ueToGUvDwiYb/9hjwrKzlep2MFbm4wvley0gGc0wbvvhr22tpaBAcHdx7MMTSkTeT9JmjvvQfk59M5x8bSZ5uYkMR4xgySY586RWNr1izKxg0aBKxdi7rffoPRa6/hH6WljD/7LBGrNWuING/ZAmzeTIoUUaQa8++/JyWKuzvJbnNyqM5ZFClINmwYMG0aBDs7DAMgSRL7448/8OulS3jQwUEaNWpUx5u4dCllso8cIcMzOzs6vl5Aq9XCyMio+/FcUkLSZW9vIpB2dpTxq6uj7JZGQ4T7/lIZxlA7aBAyzp2Dw5w5csaLL7LmZ4iBgQEUCgV8fHwQFhYGR0dHANDbOlFfQGDYsGH8+PHjACgYxjknMrp7N13nF1+ka3sPadXViH3iCUxcsAB+bm7w+e47NF69imVqNTBwIMPkyXD55psWkhEYGMhOnjyJ8vJyvYHloqIiALQ+fPPNN1i1ahXq6+thbm6Oprt3YTJsWMfs5apVtJ598oleE6q9e/eKmZmZgq+vL+bOndvxvHfvpkz9f/9La2JcHJ3njBl0L86cAVQqKJRKmJmZid7e3oJSqcRjjz3W7mMSEhJw4cIFyc/Pj4csXQqo1bAAsHLlSr3ztlmqu3Tp0s5bW+p0FLQKC6P1Y948UhPs20cy47AwMmXz8wNee408Aa5coXOprGxtebZ8ud467urqaqhUKsnU1JQDHRV4zVAqlXCfOxdZqanSkYYGPvHu3U5f2y04J1VGd22zABgbG8t1dXWwsbGRbWxsup9TCQkUpOgj2TYxMcGbb76JW7duwcrKCsbGxmz//v3syJEj8Pf3h/GdO73Pnpub03O3rKw1gNIWly7R2hYRQc//Hgbqpk2bppg2bRpOnDiB69evd0+2Y2KodEnPc3/16tVCfX09du3ahTNnzuDEiRMQBAGiKEKhUGS8+eab/US7j+gn2/3oRydYv359uEql+mrOnDk9KnD9/PPPAQDe3t4YPXo0PDw8utyJV1dXo7q6WvDw8EB9fT00Gg0mT57crk6tbdahH+1hamoKrVbb9/popZKy2v/9b4cobzs4O1OW6cgRMuJ54AFUV1YyUZYZAAwePJjZ2NjAz8+v5VhEUYQgCH0vB1i4kKTkXZh4GRgYYObMmWzChAn45ptv+OnTp2UfH5+efWdgIEW3c3KofjkyUr9UXpZp85+S0iuyoVKp8Pjjj7PLly/jwIEDbNCgQZ1K611dXfH0009j69atLC8vr6N7NSgQFRsbKzo6OvKZM2eytoY1R48elbOzs9ny5cthsHIl/HQ65vfWWwJGjaIsRxu4u7vjxRdfRGpqqnDjxg3xkK8vC1u7llU+8oi8LTmZS5IEY2NjhIeHyx4eHqyiokKoqalBQEAAkpOTZWtra+ZjYwOzvXsZ7ttk68XNm7SJioqijVQfWnqZm5tj1qxZGDBgAPbu3SvU19e379uekkIk9IMPiLwuWkRy6HPnAEdHeI8YwXZ/8AHMzMz0u/lXVhIhTUnp8KshQ4ZIaWlpfMyYMb1T20gSmfJdu0akl3OSu/71F9Ujv/9+62sLC1v+qX30UXz6wQcwHj8e9sbG8hIjI4b33qNaVZ2OSIWpKWV8NBr6WbOB2MKFwNixkMzNceTYMQBAQUGBNKKighdcuoSAgAAMGjQInHM8+uijuDc2eWNjoxgeHk6LbkMDScvnzGndTOflUUCglzJ+rVaLbiWv8fFUThAQQAEHQaA59/bbVDeuVtM1XL++gxJCkiRkXruG4xMmwK20lPm//z5Gvv46wsaNg/nfMP4DaG15/PHHsWfPHrm+vp7m28cf0/3bsYMk2/eg0+lw5MgReemFC8zZxQVYtQqqAwegCgig63j5MmWc33mHSNDhwy3j9/Tp03hET1ZzwoQJMDIygrGxMc6ePYs///wTWVlZGBkfD8eiInidOQP1/WobxqgEZ+dOvWTb3d2dZWZmIjs7G3/88YdsYWHBAgMD4WRlRXPAxoaIH2MUXLx7l54Rp04BOTnIfPddWP/3vzi3eLFcq1QKnbW3CqGsOd2sLVvo+RIb2+m11mq1UCqVsLGx6VgbvGcPEeiiIhrfpaWUrbawoAxlWhopoR55hHwgtmyh9mdTp9K1aPYY2LaNjsHHhxQmq1dTEOveOVhbW6OxsZG3BFa6gosLBpSU8KaCApzevBkP//vfXb9eH8rL6Rl0+XKPXv7MM88IH3/8sVxZWdmzjiSbNtE8Ki3VT2x7gGYZdTOmT5+OzMxMnD59GtPz83ttoAqA1sXr19sfkyxTYGTRIlI8TZnSq4+UZRk3btxAamoq2nYw6RRXr7YLlN0PY2NjPPXUUwCAnJwc3Lp1S46Li7us1Wp7b/Hejxb099nuRz/0YP369aGCIJyYP3++UU/Mt8rLy3H27FkoFAqsXr0aVlZWXT60JEnCjh07JDs7OykkJIQLgoBbt25J6enpiI2NZWfOnMGZM2dw9uxZcM5Fd3f3/40l8f8hKJVKxMTEwNLSsl1boF4hIIA2Vd30ggVj9HANCgKKixH+11/wX7gQ2ZWVUkJCAvPz82tHgP62g7RCQRus6upuSa5KpUJ+fr5YVlbGhw8f3vPaXsZo01ZQQNH0vDz9G5OHHqLr1Ie+1Pb29sjJyZETExOZLMtwdnbWW8NtbGwMpVIpR0ZGom0WXJIk7N+/X05MTGQTJkzg5eXlUlRUFE9PTxe1Wi0/ffq0nJWVxZ566imStBsaknz5xg3KILi5EUlpI8dTKpVwdXXFkCFDeEhICHPYvZsNWbSIeYSHY8SIEXjooYfg4eHBLCws4OjoCHd3dxgZGcHb25s5OjrCYONGkvl3ty4kJBBpsrUloqJPPdEL2NraIjs7W8o7d44F+PsTAUxPp/O9c4fu4RtvkHLB2JjqOwFcu3YNmZmZmDdvHuvgSAuQVLa2ljbo98HX15fduHFDio+PR3FxsVRdXc3Nzc1h0JW8UasluXh8PEmvN2+mLOGDD1JQy96eHK4bGzu0X9OJIpKSk6ERBFRyzi4kJsrGw4Yxx8GDiQyNHUv3ePRoIgsmJrRxjIoiObmxMTKvXsXJkydRVFSExsZGdv36dVhaWopxcXG8oqKipUe8vb09Ll++LFtaWjIvLy8ac7NnUwbxnXdaSycOH6bv0UPgukJycjJsbGxY2w17O3z7LbWDmj+fvrd5zYiLoyz3/Pmt5SxpaR024VevXsWBAwdgbGaGZ99+G84XLsBHpYJBZw7nvYSpqSmKioqktO++477vvAPle+9R9rhNTXxDQwM2bdokWVtbIywignGlkmrnFyygwEFSEq2tZmZUCy+KgKcn+Btv4JKlJUytrDBYT2bT2NgYgwYNgpeXF2pqauSMjAwGAAOys1Ho7Azb8eNRXl6O1NRUyd3dvdUUcswYoKEBTbKMLbt3y+fOnYO9vT2ztLSEs7Mzi4iIgJ+fH5KTk+WsrCxWWVmJwDffpMDm/bW+zY7q3t748eBBXaJWy+0nTQL38MCijz9mVmo1WJugQycXkVQfXWRvU1NTcffuXbi7u6OoqAiFp09D++9/I9rYGJZvvonLOTk4Ym4uXggPl+IuXJDOJSfj1MmTzGLTJtne2Jjh1VfpWhsYUJBtyRLIRUWo5xwKhYLW2+HDKRvePP/t7Ei98fPPwKxZUFVWIiUvT7548aLk4+PDjbqSnTMGtnEjTAQBWU1NOJyXJ126dImZmpr23KukqoqeJw891KOXM8aQkJAgBQYGCj1+1r/wApVX9KVFpB4olUqYmpri3LlzGBUVhcaHH4aqO1XY/dDp6LkUFET/v3uXyhQOHaI/3ZjaNe8bOefM3t4e9fX12Lx5s5ySkoLAwEA2Vc8a3g63bpHqaN26Hrm2K5VK7Ny5k4mi+A2AuHHjxok9PNN+3Id+st2PftyHd999dw5jLFKSJNXly5dxzygEdnZ2yMvLg1qtBmMMDQ0N4Jzj559/lk+cOMEAYNGiRd3KqiRJwvbt28X6+nq+YMECrlKpwBhDYGAgCw0NZW5ubhgzZgxCQkLg4+ODY8eOMVtbW/0b5f8fQxAEGBsb4/Dhw7h8+TIYY5238+kM5uYku3N37548AZRp8vcHjI1h8scfGO3ry86UlsLJ2bldy4z/CXQ6cqvtQc9QtVrNMzIycObMGeTm5uqCg4N7xvabZeV1dfQ9QUHt+8wePUqbgx5I/TqDn58fy8jIkNPS0tiYMWP0BgPOnTuHuLg4uaGhgaekpIhxcXGIjY2VY2JimCRJ8qJFi9jAgQMxZMgQHhwcDFEUWVJSEszNzeVHHnmEdbj2ISFExj78kDZdzz/febBg8WLA1BQWZWUw76Z9Cu7cIfK4bFnn9Xp1dZSJ1GjIF6AvtehtodWSvFWjwaBDh9iAjRtxLjAQzm5uSAoIgNOsWWBTpxI5vO8cjx07Jh8/fpwBwOTJkzuS5Pp6kra//nqn18fPz4/V1tay2tpanpKSIqenp2P06NH6X9zQQPWjaWlkfMQ5Zdh1OiLKzQEHBwf6/4ULlIW6t0lUKBQICQmBh4cHZs6ciaamJnb58mVxxIgRnY9nZ2eq+eYcsLSEnZ0dqqur0dDQIL/66qssJCQEwcHBXK1WIzY2lsXFxckpKSlSQUEBLyoqYlOmTGGmJSWURXzmGSKUbTeiH35IxLfZSbqHSEpKkhwcHFiHYG1TE8nibW2J8LR10C4ro/Vo5szW1l1KJc3Le1LwZtjY2CAxMREGBgYYNXYszeOaGqpXHT++T8GxthA4h01xMZf27cPtgQPhsWxZu+x+dnY2tmzZAisrKyxfvpwLXl5Uw/3AA0T8rlyhOnpzczpHS0sKrlRXA999hxvOzqIf59w+MLDLFlWDBg1iFfn5mLhpE/6aPBnFbm5IS0tDeno68vPzmUqlkl1dXdmNGzeQd/MmirduRemPPyLFxYUZGRnJNjY2rPm5wBiDiYkJsrKypOBdu3j4yJEwfOMNSIsXQ9PYiJqaGlRUVKCkpATVKhVuKhTYvn8/7t69y52cnOQqMzOxRKNBtosLrhcWonLDBqY+dgwGkybpN/kyMKCAZhdmjN7e3jh//rzs/K9/sRt5eXJhQ4M84MwZZIeFSaUzZshs9Gju7OLCvb29ua+vLx86dCgz//NP8KYm5vzll/QdNTXAyy8Dy5dDqqyE7OSELXV1uHDlijxw4EDWEgg2MKCMsqsrcocNwxWtViq7eVNyfPVVPsLbm0kaDWIOHYLnmDGsS8I9YgSUixdj4LhxsPTyYklJScjIyEBGRoZkYmLCOOft1TdtkZlJJL+X3CM6Opo5OzszKyurnqn9wsMpuNeJAqEvcHR0hJSXJyr++INvMTSEm5sbLHtjtFlcTN0GQkMpGBsXR6qKzz/XW59dXV2N3bt3i6IoMgAsIyNDTkpK4tevX0dJSYl85MgRZmpqKr/wwgvdl5AB9Fx68MFOTSrvh06nQ2pqalNTU9MkAP8aN25cH/tc9qNfRt6PfgBYv369PYBQlUr1hKmp6UOPPvqoYufOndBoNKiurpb37dvHmlsg2NraorS0tO3bmZOTE8LDw+HdlqjogSRJ+O6770SdTsdXrFjB7n8gcc7bSZdsbW0xZswYtn//fixZsgRmZmaor69HfX09SkpK4OvrC3Nz8/9ZL97/ryEkJASenp5ISkpCVFQU8vLyUF5eLg4fPlzosm1NMxgjslVd3fIjrVaL6urqjn0p275n1izggQdw64kn8EhODvKGDu1Rq6teYdQoikA//HC3G313d3cEBQWJt27dEm7evKk4evQopkyZgtLS0p4FAWxsSO5saUmmYf/+NxGYM2coC3GfHLs3MDQ0xLhx49iuXbuQn5+P+3ukS5KEs2fPyqNHj+acc5iamgo6nQ5OTk4wNDSElZUVb5sNNzMzQ0REBIuIiAC6kxR+/DGR7agoki///LN+F/Zmc6WoqK5PJiurlSjpw6VLJNfU6UgW2ENjxQ6QZSJNhw8DGzZQTe+bb8Lkww/xx/jxUvrlyzzZ2Bj1Fy8i6uJF+Pr6wsjICLm5uWJjY6NgYGAgOzs7s8zMTAYAERER+qXF586RN8ALL3R6KCqVClPuZVULCgrY9u3bkZOT03Gtq6sDfv2Vrk0b7wq8/DKdT1s0ByDS00laKctU1750KVQqVctnW1tbIzExkRcUFMDV1VX/ATJGWfSLFymzD6CkpETknHPGWMum39/fH5WVlbKDgwO7ceMGy8jIkGfNmgUHBweG2bOJ3K5c2Z6k5ucTOe6JPPM+6HQ6uYPUuKyMCPbs2bTu3E9ocnKoLVrbzfD69WRGdh80Gg1cXFzknJwcOmBTUwpgbNpE0uc2ztd9wlNPwebyZXw3fTpkzhFx36937doFc3NzrFixgnHOiWzu3Alp0SJwd3dSWtjZEQE/eJDayc2cSWvMiRO4/fnnwtQNGyig+OGHXdaozh00CJqBAxExZw5CRo1CVVUVLly4gNTUVNjZ2TEAOHDggKzT6WSj4GDZ381N8LayQqOxMY4dO4YTJ060mI8pNBo0KBTCII0Gx44eRdaNG9DpdBAEAYIgyIIgyAqFQnYoKkLol19yxbJlsLCwYGq1mhkbGytcXFxgGhwMMzMzHNm2DYOb+2avXElKlrZj5dtv6X7cuKH/xE6dAl+zBnWPPsqqLCwgmZnJ5qNHyw7vvcfnd9a7etcumNTXY//Qof+Pve8Oi+rcvl7vOUPvvQsodiwgKFgRgxqx9xpbLKkmUa8pNxqTmMTEJGpMNMbEHlvsIipWlCJKEVFEQGJBaTJ0Bpg57/fHZqgDgpr73d/V9Tw8KAwzp75nr73XXlvlra9PrxGEqmdYbGoqwj7+WJr/0UfCtm3bWEREhDR06FCh8phxa2trxhjDsWPH0MLFRUhLS0PakiVS3v37mJqXJ3TcvRu3fXzgnZBA16imROuGDYBKBdm9e3A/dQodOnRAcnIy4uLi8NdffwEAvLy8EBAQUJ8YnzlD62Qz0a1bNxYWFiadOXNGGDJkiNStW7fGgx4DA/qszz+nUXnPAYwx+Dk4iOXvvAMfd3fV9u3bxYEDB8K3EcfwWnB1JSf506fpupg9m+5vDcjPz8fq1avBGBOzsrIktUP6xIkTUVBQgKSkJObl5QU/Pz+hSfFfTg4R/ZotPE+AgYEBxo8fr7VlyxYwxjKa/IcvUQ8v52y/xAuL5cuXm+vo6HzLOR8qSZKpg4NDmZubm1H37t1Z3QdEWFgYIiIieKdOnVhkZCREUcSAAQPQpk0baGlpNblH7o8//pAUCgWbNWsWa+p8SkmSsGnTpirHU5lMxmUyGVepVKy8vJwBgJOTk+Tk5MQqKiq4i4uL4ODg8Mx9e//XsH//flV5eTnMzc3F2NjYps9uliSq6h45goeMYceOHby0tJSZmJioVCoVUygUAmOM6+jocG9vb8HOzg5OTk44efIkbiYkwKOiAp3++AM2P/wA2ejRTZJnNRmffEJSxiZK4SRJwv79+7maZAGAo6MjJk2a1HCloSaUSuoNnDyZzIaaOGO8KQgLC8P58+fx/vvv19qW8+fPIz4+nr/zzjuaZ4Q/D2Rl0Uzudeuo4lY3EaNSUXVN/V0TioqAceOo51FTBfy336j3fcmSps9eVaO4mAIhlYqSHXPnUhUxP5/IfY1tqqioQGxsLHJyciRTU1NB7doMAL6+voiIiKj6v3r+9cSJE9G2bjuCSkUktxm9yHK5HGvXrkXXrl2lESNGVF/o5eVU7W/RgnrHa94DISEkK25sdFZGBlU/ExJoeyrVQZUzdqFQKDCjgbnzACiAjY4Gpk3D/QcPsHnzZujp6aFSaikpFAru5eUl1ptFe/YsybQ3btSchDl8mNor3nvvyQenDtatW6fs1auXzMPDg35w6hTdz7//DnTuDIVCUXtG8aVLpMCIjq4+fmVlJLt/9KgWMVepVPjqq68gSRKcnZ2lGTNmVB/w4mK6zg0Nn05CGxFBbQADBwJdu2L91q2SXC4XFi1aVEWcDh8+jGvXruH999+HelLHb7/9pmJRUWKWtTXeXrIExkZGpO7Q06OExZUr9P2XX4D4eOzp3Bm9Bg2C46uvUpUzIYGSP3XXgBUr6H6qkzzYs2cPbt26BZlMBl1dXV5cXMwmTJhA1/nq1UB8PJQbNyIvLw+CIIAxBqZSwbhNGyjWrEGapyfCwsKQn58vLVy4sD5hKSigHuf79xtMBHz++ed45ZVXeM9OnRhWryZimpxM68Po0fX3pbi4uiVh/Hhyut+6FT/r6EhFxcWCIAioqKjAxx9/rPnchIYCV6+ioGdP/HjyJMaMGUN9uqWlwIoVODdggCoiIkIcNmwYOn3+OW6mpXHFxo3MwcEBBw8elEpLS5lMJlOVlZUJrVq14qNGjRJXrlyJ8vJyCIIApVIJPV1dac7QoYLZ5s1EDh88oPdfupSq9IJA1/Djx9UqnhpQKpWIiorikZGRrLCwENbW1qrZs2eL2tra5GGhq0sJuWas9QqFAklJSQgJCZGKi4uFV155RdWrV68nz6/PyqoeofW8sGcP3VuBgQgNDZXOnTsnLFmypGnzxh8/pjVy8mRqW2hETadQKHDx4kXExMSgf//+6N5In3WTsGMHrSezZzfrz/Ly8rBu3bpSlUplt2zZsvxn24gXFy/J9ku8kFi+fHl7LS2ti126dDH09vbWsbKyanQe8PPAnj17+MOHDzF37lzW5FFNTUBBQQGOHTvGlUolkyQJmZmZvLy8nHXr1g0eHh5VTrQvCm7fvo1du3ZBR0eHC4LAe/bsKdQLsuvio4/wWKXCr6am6NWrl9SxY0chMzMT+vr6kMlkUKlUyM7O5jExMbygoICVlJQwS0tLaejQoUKLFi2w/dtvpe7btgk2b7wBsyFDqIrzPKCuhB092iwSL5fLkZmZiVu3biEtLQ36+vqqefPmPTk4UUOSqLKdkUEGToGBVPl9WufZSnz//ffSkCFDhJojrL777jtp8ODBQqdm9sQ+FVJSSGK7c2d9N9mEBArqc3M1B9exsVQZr+sQn51NZGz8eCKMGgzeNCIri95v1CgKvrS0iKxHR1NVsBFpbU3k5ubC0NBQXZkDQIEaYwxr165FeXk5fH190blz59pqjY8/JjJ540aTPufGjRs4dOgQOnbsqPLx8anunczPp0Dcz69277EaRUVQRkcjpKQE+fn5qJzPrqrMrPB67uotW+J+v37SyV69MGrUKGHjxo3o1asX+tY5X1lZWUhISICfnx8pewIDUbhoEbYnJiI7Oxv9+vVDTk6OysbGRlAqlezixYvQ0tKCKIrS8OHDhXZubtR77+hIva6a8Oef9PunmNu7Zs0a5YABA2TubdsSma8c0ZRjbIzDhw9LDx48EFq1asWdnZ3Rq2dPhiVLwPr2BavZdpCTQ9dJhw613luSJKxYsQJt2rTBuHHj6iub7t6l6+rQoaa1xwCUeCksJFI7Z06VaeTjx4+xbt069OzZEwMGDMCFCxcQGhqKcePGoUOHDti6davy0aNHYkVFBZutVCL7+HFYHjoEh5ISup80JVlycrDvrbcw2tERYosWtL61b09y/VWriMxZWBA59/Iix/k67TS//fab6uHDhyIADB8+HCYmJnBxcaFjkZdHpGL3brqvlEq6R1eurHbtBhAUFISYmBhYWVlJkydPFuolqOPjiRw3oFjbv38/EhISsHTp0ur4Yc0aqqiq3cLv36fPvXCBElIFBZRU6NoVMDREZmYmfvvtN/j4+PDU1FQ+fPhwQeMzOzGRDNBGjgT8/XH16lWcPHkStra2KittbXj9/LOwdcQIzJ49m1lbW6MgOBhbTp1C3+nTcfz4cTg5OUmTJk0S6rbxVN6P2LlzJy8pKWFLlixBLUVGQgLdB5Mnk8LpzTdJQXH9OsmiJ0/WeI1JkoT8/Hzs27dPys/Px9ChQ4X2c+eSGeOaNRqPZ0PYtm0b0tLSYGhoiDfffBONStzroryciGal6dczY+FCSkh7eYFzji+//BKDBw+GdxPavbBuHVXaDQxITp+S8kQviPDwcB4dHS298847TX9+14VSSUmHESOa3RLGOcdXX32lVCqVA5ctW3buqbfhBcdLGflLvHBYvnx5Sy0trUuBgYFmXbp0+WcZdiWCgoL43bt32dxmk91DAAAgAElEQVS5c5s+E7mJMDY2xuTJk2vuBwsLC8OVK1ekK1euCB4eHpKFhYVw48YN7u/vz9ye1Jf6fxyVs2VRVlbGjI2N2ZkzZxAWFsbd3d1Zv379EBQUhMDAQNR0tMaHH+LuwoVwattW1a9fPxFAPRm5i4sL8/b2ZgDUVamqCHfKokXCXy4uKuM//xR7HT8O43/9iwL0Z61yW1pSsBkRQQFOE2FmZgYzMzO0a9cO6enp+OOPP5r3oBYE6hEtLiZZ9JAhVI2Ij6c+W7Xr7ccfUxA8ciS5AI8f32ilVCaT8fv371fNiy4oKEBJSYnQ8nklJ54ENzfaBx0d6uv++uvqipm7O1UfNRFtlYqqhGp3XzVSU2kGc2AgfTVWJS4vp+P6xhv0fcIE6tt79VU6xmoVgb9/s3ZJk0eErq4uCgoKYG5ujgcPHuDixYu4dOkSPvzwQxw8eBCDBg2C6aefAlOmNPlzoqOj0aFDBz5y5Mjqa4lzqvYPGULXgIaEZbm2NkI3b+aJLVowVWUbjKenp1haWoqUlJT6HxQXh0t79/IWe/eKRV98gfJZs6DuRywqKsKxY8eQlJRU9fKEhATJ19dX8PTwQNyKFTy7Tx/Wt29fyc/PT0ANKW6vXr1QWFiIv//+myUtXQqnmBgYJCU1nNTgnM73t982+RjVhCRJTFuSSIrbogWwZg1yFAps2rQJrq6ubM6cOdi9eze/e/euwL/8Evk6Oki2t8fwlBQYGRkhIyMDZrt2cafMTMY2b6713oIgwMTEhBsbGzONElJnZ5LWT5lCxGDUqMY3trSU+tU5p/u7xjExNjaGg4MDDw8PZ+Hh4RBFEVpaWjh69Cg/cuQIKioqZIGBgWjdujUMo6IgP3IERUVFKOYc7O23oSwoqK+ysrREYseOUH34IZ0gLy+Smh84QORu+/bqCn9cHJFupbLW/aVUKqv+bWdnV9sk09SUWmEWL6Y2jMJC6o/Nz68i2qmpqbh69SoAIDMzU8jMzKy/nQcOUDWwZltEDbRv3x4JCQm1fzh9Ou3Pjh2UZMnPp+q+kxOpPpRKUtZUEtrr16/D3Nyc9+nTh73yyiua45HMTGpx6dOnan1wc3NDUFAQRFEUi5VKnmNry6ZOnQrrypajRHNzSca5YDxmDAL/+gtdGvDxEAQBNjY2+OCDD9jatWtVhw4dwtixY8Wq5IG7O203QLOfS0poH+/fp7XT1paSCBre18zMDDNmzBAuXbqEA3/9hQ9++w16zs4ad7ExKBQKCYAgk8n4lStXeJ8+fYQmF0dyc0ltNH78M5tUQqGga/ObbwBA7bXDL1y4wL29vRt/2MfHU7uSev2/f58M4tavJxVJA5VxzjlXKxifGvHxpJx6Cu+ViooKKJVKmSiKJ5cvX67FGEtdunTp/3YQ+Q/gpUHaS7xQWL58ua6WllZsQECAlaen53+k0Tk4OBjXr19nM2fObN74nGdAixYt4OPjwwwNDXHt2jWemZkpcc6Fy5cvw8jICDY2NvWqIWqVyz9d4X8eyM3Nxfr163Hu3Dnk5+erbGxsBLWMy9raGl5eXjAzM0NSUhLs7Oz44MGDWWhoKC5duoScnBxERETgwYMHKCsrg52dHfIUCpStWQMLCwvBtn//J35+3eqAIAhwd3cXCtq04ScfPkQXuZyJBw6Qu6ip6bPtbGkpjU3q0uWp/vz69es8IyOD9+zZs2knds0aCiqWLCEyPXAgkSj1DGBTUyKtHTpQRcrOjqrg/v4k1X7nHfq7N96gvzUwoNd8+CFMJkwQIvbvh7ubG3TMzKClpYWIiAiIoshdXFz+Mxeenh4FuiUltD+PHlFvtTogHjSIvtd0kT5wgII2NTlVqUj6+vXXVB3u1UtzYuXGDRptdfYsyUr79aNgu39/Ol7jx9M2NHOsVFNw+fJlHh8fz0xMTHhZWRkDgOjoaJ6RkcFsduyA3b17tebQKhQKKBSKKqlwcnIytm/fzuVyOTt+/LgqIyNDsLa2Rvv27dn58+dxYs8elcfnnwvCl19qlsyCnNB//fVXjNu6lXm8+y46Dx0KLS0t7u/vzxwcHBATE8P79OlT+w91dGBmbS0kKxSqh4Bg5OmJXqtWIdHZGZt27MDjykqpt7c3pkyZgoqKCly9ehVhCgXTKihgBU5OmDRpUj0SqjZUtOecmefm4pSeHh4aGjbss/H33yR5/vDDpzr+iQcP8t6rVwuyhQuBefPAtbWxYcMGuLm58XHjxjEjIyP4+vqynu3awX7lSrTZuxd35XIpIiICV65cYYmJiShNTWV/a2ujzfDh9dblkJAQJpfL0bMhR2xRpAkKS5YQ4W6oJzoujva1vJxk7nXk9KIowtPTk7m4uODatWvgnMPIyAivvfYa8/b2Zj179oSzszN0dHTAWrbElZgYfi45md07cwbXFQqcun4denp6kMvlePToEUpLS5Gfn49r166hZ8+ekOnoUDIiIIC+OnQgyflvv5HR3qJF5CmwaBG1VDg6AoIAhYkJG/fhhyysd2/4BAVBf/duug4HD6bXGBqS2d2KFbQWzZpF92hlslu9DUCV0zSvcqVXw9ycEhAN+HFYmJnh2uHDMM3MhNWvv5JSJTubkqO2tpRoOX+eqr85OaQUMjKidVFHB7h5E7bh4bgC8LDgYCYvK1O2adOm9oVbUkL7YGQEvP467t69i02bNvHU1FSpuLhYmDBhAnw9PJjNF1/ApMYYrmvXrkmlSqXgW1wMp3feaZJSplOnTsKxY8eE8+fPw9TUtDqBwTklDQIDyfBu+XJaQz/8EPDwqO89UOf6cXF2RseJExFvYoIWTXi+1sWdO3dUpaWl8PDwEEJDQ5m1tXXTnc8NDYF//YvW2GeNbaKjSXFRIyGqUCjYnTt3Gn++FhXRVJGxY6sl9CYmdF22bk1JlIsX6fqtgYqKCuzZs4f5+/sze02jOZuKr7+mbX4Ksi2KIvz8/KCW7d+9e9f80qVLxefOnYvwe06TD14EvKxsv8QLBVEU33RycjJ7YhbyOSE4OBjXrl3DrFmzqjLO/0l4eXmhppPvgQMHcOrUKR4VFYW5c+cyURSRkJCA27dvq5KTk8XKvmSmUChQXl4u3b9/H0ZGRnzChAnif5MJmyiKKCwshImJCX/06BFbt24d2rdvr+ratavo5OQEQ0NDeHt7w8DAAIcOHWJubm5YvHgxcnNzYWBggNDQUNW9e/cQHBwsBgcHQxRFuHXtynvY2z/T07hbt24sKSlJ2qVQ8NesrAQcOEAB2/TpT/+g796dZJ1jxmjuK30CSkpK6nkQNIroaAqk6j5I1ee/ZctqmXxNSbXaZG7VKiKmADkku7lRoHn+PNqvXg3h1CnOzp9nuHIFCnNztJsyhXfX1mYYNYok6ydPkolS9+6N91A/K9SmYIGB9BmHD9M58vKqnSDhnKS8n35K/09OJrM1e3syuqlbDYuJIQOhb76hoHr2bKpo+PmRc3YTnWCfFgqFAsHBwYiPj2eTJ09G69atmVKpREpKCkRRZAkJCVLOlStC3X78TZs2qR4/fizq6upyNzc3lpSUBHt7exYbGwtPT0/WsWNH2NjYsP379/PkmzfZ2G3bxEOdOyP57Fluc+MGnzlzZtUCoVQqcenSJURGRoJzDr3s7CqJe0BAAAPIeE1XVxcrV67kpqam0vTp00V1wszR0RHjFy4Uo6OjEXr4sJT94IFw9PRpPkomY9d0dXGnqAhXrlyBt7c3+vfvz/r374/cx4/BhwxBqZMTCgsLNbsEf/89sGEDrG/cQL/cXGzduhV5eXl8/Pjx9W/OiAgiqs2FJAERERi2fbuYt2MHbCuVEyXFxSgtLcXIkSNZzdfKfvgBsnPnAGdnTK4x4jEvLw9JEycizM4OR44cwYgRI2p9jLe3NyIjIxvfli5dKIjv3p28Bvr0qf37Bw8o4fP551QFbgQuLi6YPXs2du7cie7duzf4PAu8f58FDhtGhnddumC7q6sUFhbGZDIZl8vlQqUjPjc2NpZkMhldFNnZVNlOSCDi5upKhPvBAxrntWwZJT4A2h8LC9zavFn1cPhwmbm5OYx69aLqNUDE2NKSKoXa2lQVT0sjlcLBg/TvTp3gumABlkyeDO2vv8Yhf3+eHhoqoXVrETUrr507U1Lt1VeJZJWU0Fpw6hRw5gyEtm3hd/kyN+ecISCAiKezc/VaGR9PVd+AACKr6pFPsbF0nYSGQpGUBMO+ffnrP/+M4mvXRGhrUxJzwQJaj/bvp2Tl+++jsLAQu3btgqenJ9PS0hK7detGhLioqN46JAgC07e1hf7Ro+RL0L//E1tc9PX18cEHHyAsLAxBQUEICgrCNF1dtPjxR5KNr1xJa5qWFsnB168nJcSlS42+L6uoQHlgoHSupETQunwZTTIvrYFx48ZVcZW4uDjV3r17xUWLFjVdJahS0TPl1Knm+2nUxP379fw6zM3NUekWrhnZ2fQ8ycmp//xW///IEUrE7NpFz5Z9+4DKqTcAqtbOp8Lt29SC0Ezpfv23uY3Q0FAAgEql+g7AOgCKZ3rTFwgvyfZLvFCQyWTv+/n5NZ+xPAVOnTqFa9euYebMmf9fiLYmjB49GpIksRUrVuDLL7+Es7MzT09PZy1bthRGjhypnskryWQygTEmuLi4ICYmht+/fx/OTyH/+qegq6uLdu3aSQ8ePMCcOXOE3NxcBAUFsf3790tlZWWChYWFJAgCysrKWEVFBcvNzYWlpWWVNHzQoEEiQJljxhhVqjln8PCgsRzP4Czu7+8v/Pbbb7gxejQ66ugQ8Zo3j4jpU5BlWFpSNSY0tMlzSWuidevWVXLJRtGlC5H6bduav401YWBQPRbrX/+q/nlcHAAg9qOPEKFQSNbHjwulY8bwwR9/zPRzciiYBahf09GR5OlaWsCtW0R8Nm+mKtGKFUSIBw6kinH//s8m1w8KomD1o4+o13XnTuqn27mTKtlhYRTgz5tHUrwRI0iiOmkSBcMZGbSdU6ZQINe2LREGxogs/AeVIkqlEps3b0ZWVhZMTEyk1q1bCwApMdq1a4eMjAzknDolJHt6IuDdd2v9rUKhYJMnTwbnnJ09e1by9vbmAQEB6ihPAMjMLismhi06eRLCX38hLStLVRYeLt67d6/WTq5ZswZFRUXo168funXrBvHjj+naX7as6jWMMbz11lssNzcXJ06cYHv27FFNnz69KqosKipCYmKiVCCKwi8jRmDIq6+i87vvovOQIcifNw/ZWVm1qlvmFhbA229jWocO9UctSRLJcPv3p+qRtjZsbW0xb948bN68GZs2bVLNmjWrdkIxLQ14ktdDXeTlkXQ4LAxb33mHT3N3rzouDx8+hI6OjlTLiWvbNpIIa3BaNzU2Ro+7d3FnyBDExcXB1dWVd+rUqcpEsG3btoiMjERCQgKZZDUEbW0aLbRpE5FBQ0NqD1mwgMj3pUtNHmvm6OgISZJ4SEgIa7Ci/sEHRDjXrgX09DDN3Fy9v+zu3bv466+/eFFRERs9ZIgoxMfTMQgOJqny669TZZsxIrVHj9I9//bblMwbMgRwdaXrsKhIltO1K8pzc5HesiWqWlHUsn/OaS2fOpWI++uv03EAIH32GYSuXaELoDgxEbdatGDjgoPF3EOHYH7tGpnS7dhBa29YGDBzJpEsuZzUSrm5VJX08sJ5c3Opf//+oo0m5VFFBSmTzp4lopqYSIk99ZSCYcOwNz1dlZmZKR77/Xc+etAghocPq4lz//5E8N56C9zbG3Jtbd7TzY339fAQYGtL95RKRef4nXdqfbQoipAkif6zZQu9bs6cJ55jfX19BAwYgL7ffosUpRJpEyZILXbuFGBmRolJNRQKkuY/Sa2nUAALFsDu55+FgLg46cKFC/D29m6ag7YGvPfee+JXX32FkpKSppNtUaQk7rOajGVl1euxNjQ0hEKhYH///XetSTJVGDaMWhfqtILUgp0dfbVoQWoobW0UjhqFo56eklIQhJKSkqff5v376Vp9xufQ3r17AQCMse6c87+XLVv2kmg3Ay/J9ku8UFCpVGbNMtd4SkRGRiI6OhozZ858/vOXnxGCIGD+/Pm4cOECJEliw4cPR6dOnRhAM01RZ5xSbm4u37ZtG7O0tJRsbGy4v7+/aNoMaXRpaSnS09ORnZ0NHR0dlJSUSPfu3ZOcnZ3F7OxsKTMzk/n6+rLOnTs36WmQkZGBjRs3wsTEhL/++usiQNnladOmCQAF6QkJCQJA1bO4uDi+ZcsWvPfee6yu/LuWEQxjJPc7dOiZyLatrS0CAwNx4sQJVceFC0V8/TXJIb29yTSngd7WRvHJJ0RUnoJs29vbg3POGgzKIyOJvH7+Ocmc/2EMHz6c/fDDDyw9PR0zv/iCGdjbU8V30yZ6QeVDHQBVgwwMSOKq7tFMTaWgJD8fGDqUvi9eTD2qkZF0fOfNo0B7zRoy7yoooMC0Ibm2oSHJTA8eJPL8449UkZgyhSpmgYFEsNPTKekRFkZVuI0bqQqRnEx9saNGEXGZNOmfPYgN4PTp06q8vDxh5syZrEWLFrWi2ePHj0tXrlwR3jhwACZ1eiwzMzNRVlYmuLq6QiaToa6UNTg4mN+8eVNiWVni7LQ0yJYsAbp0QQAghoeHA6BKrKmpKSRJglKp5KNHj2ZVpndt22rsSdSuJL0DBgwQdu3aJdX83bZt21QAhPnz58PKygqCIDBUOq2bfPYZTLZuJUJcEwMGkFTz0qXaCZgVK2iM2vnztSSvpqammDdvHtu2bRtbt26dav78+eSarFQSUWqO7PLWLTLkYgw4dAjKlStrrS/JycmSekwVAArczcwoyaOpcqVUAps3Y0L37ggPD8fBgwfZ4cOHMWfOHNja2sKksvKlniHdKPz9KeD39SVS6+RECYixY+vNiw8JCYGrqysa8vZQk6ScnBzN4xFtbUnVcfUqeRLUgHOLFlg4YACL+OYbLj9xgt10coL72LGkOKh7DFQqIseffUYVv5Urqa98xQpwzqFUKtGtWzc4OTnBVVPFNjOT1rOpUynR8u23wLvvQu7qinU3b0J2+zbv168fS//gA1TcvImo995DSkoKPpUkCNu20ZoYGUnn6fhxWsNbtqwnmS4rKxMafB6qfRpmzyZlS1pavcrorFmzxKioKFy4cIGFx8dL/fr1E7BiBSVEAgIoYffWW8jKzkZeaip63r8v4K23aK0JCqLfL1pEygUnJ/IK0dKCIAisimyHhlLyITGxqmddI+LiaO3ctg3iuHE4n5wMZ0dHzc+FCRMoWRMdTceooYRNTAy9RkcHPXr0EEJCQrBy5Uru6enJ1CMFm4Pc3NyqJHmz4OtL+zZ1an2FR1Px669E2jVA7RVTC8XF9PqmKsssLChGKC5GZlYWN05PF/ra2MDnaWeFFxRQsqOhxFgTceXKlap/L1269EojL32JBvDfowt9iZf4D0AQhM9+++230tu3b/9jn3Hr1i2cPXsWEydOrG3a8l8EKysrjB07FuPHj8eTXKAnTpwoLFiwAF27dhWysrKELVu28IyMDFRUVODkyZPShQsXeExMDE6fPs2/+eYbrFu3Tjpw4IAqKChICg0N5UeOHOE7d+5EXFycKjQ0VFXZNy6LjY2VCgsLmSiKQlRUlNToRtTArl27ePv27fmCBQtEExOTer83NDSEj48PfHx84OnpiVmzZjHOOU9MTHzym3/wAQXLcnlTN0cjbGxsUFxcLAYFBeHnjRtVf1taUpXmxAkKIGvPaX8y3Nwo852c3Oxt0dbWhq+vLz9x4gQvKiqq/UvOqVJ78CB9f9b+8iYgJCSEM8YwcOBA6Yl9aEZGFKy6u1ePLPnjD+qFNDWlqpG2NvWIq8l6+/YU+KWnU3UaoCSKOmBs2ZLI2LVr1cY+YWEU3C9ZQqTp+HF6vwsXKEADqOKgUlFVa8UKCpyXLaNg18iIXvf/UcFy9epVfvnyZXH8+PGshQZ34NjYWAEANsyfD5VaEl+JsLAwWFtbS3WTUQD1bkdFRbFAFxfx3StXYLJ4MZmiVWL69OkAqJq9atUqrF+/HgqFgnWo6aA9cyZVeBqAvb09lEqlUNMwraioiA0fPpxp8pfAZ5/ROcvJITn/o0f0czs7Okdq06r8fLo2Zs4kyb+GRKu+vj5mz54tmJubs7Vr10oFBQXkEnz+PFU2mwK15LxzZyg/+QSHjxxBeXk506kh1U9JSWEdOnSoZgirVhERadNG83v++SfwzTcQBAG9e/fGRx99BEmSsHXrViiVSuTl5YFzjiZXvWQySvgNH05JqjfeIMfxxYuBO3eQ/9VXuLhuHW4eO4ZTq1fjSnh4/fnoAN5//32mq6uLn3/+GRqn2VhaUsJSX786wZKdTeqQESOAxYvhO2cOw+LF0v6OHfFFUhIUmoiKnR2dPzWWLKH7bcMG9IuNhbmuLmeMoXPnzpqJ199/0/oAAIMHI6NHD1zeuBHHP/tMsrOzk9zd3XlISAhu3rwJPz8/7urqCisrK0kQRfJtOHCAEnX37tFaMXkycPMmcnJycPjwYRw5cgTnzp2DQqFgDT3nC42MkNW2LVWYW7QgNU6NZGJ5eTlWr17Nz5w5o+4Zr77QL1wghcCJE0Dbtrg5f750dNQotnP6dOnhr7+SasjXl8zWbGzo/a9fp7aAVq0gA5jR3buUVAGoB74h88WlS6nyb2hI++nqityRI/HYyAg9e/bUzBO0talS/vXXtI0aoJTLcTM9HQc//hh30tIgSRIWL14MJycnduvWLZXmjWkYcrkcv/zyCxhjmhM9TwJj1e0GzcWtW+TjUedcm5mZwd7eXkqu+2zeu5fOibFxoz3tmlAqCNg5cCAThg9Hn8JC5EyZwr9duhSZV5rJcyMi6L5/hji0oKAAx48fB4ApAP47A9r/A3hpkPYSLxT69OkTfvbs2di0tLSRPXv2bEYja9OQnJyM/fv3Y+jQobxdu3b//U5jTYSOjg6cnJzg6enJ7ty5w0JCQhAWFga5XM7lcjnu3LkjFRcXw8/Pj9nZ2bF79+6xhw8fIiUlRcjOzmbe3t6YPHmy4OPjI/Ts2ZO1bdsW3bt3F7p06cJsbGwQHh4u9O7d+4nZaqVSidOnT7P58+c3ayZzdHS05OjoqHmkSk0IQnW1oCmjPBqAnp4eFAqFKjs7m9vZ2QmhoaG819ChDIGB9AD897+p0mFu3rQqtyhS4JmXRzLGZsLBwYElJCQgMTGRde3aFUyloqDb3596kf8DY7fKy8uxd+9enpKSwubOnYvWrVs/v/vD0LCa6L7yChEwK6tqaeXkyUS4GKPgp3dvqnxdukTVvTfeoN42f3+SsUZEEBFZupT6NFu1oqDWwoKq3HPnUrVWX//5zlV/SpSUlGDfvn0YMGAAayh51qpVKwx66y0o27RRxRQUoFOnTkypVOKbb77hjx49Yh06dJDc3Nzq7YyZmRkS//oLvnI59EeNqjfv2NTUFMbGxrh9+zYqKipQUlICV1dXVM2XBijwfOWVBnugK2f8SuHh4VKPHj0ESZJw9uxZNmDAgNrqk5owMiKTKV1dOm9LllA1+vXXidy5uFALx+XLdO4baeEQRRHu7u4sJydHOnHiBOscFcV0unTRWBHKysrCzz//DGNjYxhpaSF8+nTJJCyMXR8/Hkdzc/nZs2eRkZHB+vTpgzY1iHRcXBy3tLRkTk5OlBy4cYMqkg1Vva5epWu4cgqBKIqws7NDUlISj4yMZCqVChkZGbCysoKjo2OD+4byckoObdpE1254OBHiK1cAZ2dInTph4/790D5zBun5+fArKOC9IyJYUHExPMaOhfLcOcisrGjNMjBA1Pffc6urV5n7xImwv3mT1mwDAyLmaom0vz8pTK5coUReUBCtX1OnUiW/ZUs4tm/P8vPz8ejRI0RGRqKgoIC3adOm9prg60vnVy3RtrMDevUCO3YMjqLIrkRH4/Ldu7h+/TqPiIhQnThxQjh//jxCQ0NRuHs3sjMzcUah4CEhIYgqLGRaOTkYmJHBvIYPZ+1feYX16dMHffv2hYuLC2OMISoqiv2dnCy5//03Ey5dIoLapw/1WM+bB9W+ffgzMhKCjY2qpKQEiYmJzMDAAJpGS+bm5uLIihXc9MgR9uvjx1Bxzl2nTmXFLVpApqcHpq2N4uJiREZGMkmSoKWlJQ0cOJBctktKqJI/dy49hwQBLhMnMmdnZxQVF0snrl4VDK2tYTd/PvDaa7QWtW5N63hMDHDzJvLc3NBr6VKIycm05mlrk9JHFGkdzMykBKGdHbXReHjQtebjA4giDAwMEBcXJ8lkMjg7O2teq42NAXt7SP7+gIUFwsPD+fbt21lZWRkUCgUyp0zhxvv3sxBHR9y6dYtfvHiRqVQqlJWVIScnh/n4+Gh209cASZJw5swZPHz4EPPmzas9TaSpGDqU1CSZmfVbTZ6EmBhSDqj77mvg/Pnz6NChA6tSmahUlCAePpzu4WZCS0sLjx8/VsbFxQmJLVpIke3bC3YpKej2ySeQTZxI99uT+rg5p1aOwEDgGVoA169fryorKxMAdBAEIbVfv35NqFq8RF28lJG/xIsIc0tLy+c+YD45ORn79u3DkCFDeFMl0f/XIAgCpkyZgpKSEty4cQPdunXT2HtVOSKLZWVlISUlpWHHXNDYlsrxEg0H1pVITEyEoaGhShCEZjmGVFRUPPG9qzB9OvXuStJTEyktLS0MGTJEBChBEBcXx8rLy8npedEiInhLl1KQ9PrrVJl4EtzdyZBm6NBmm4Zpa2tj/PjxbN26dQg6fBjDRo6k4Kuk5B9xwq6J2NhY6Ojo4MKFCypRFNncuXOZpnFV/zgEgQIQf38KVrKz6ZiqVNSLPWQImaRZWVElQl1Va92aiNvcuf/R/usngXOOAwcOqAoLC3H37l1REATWmPGQg7098OmncO7eXYw4dAg7duyQ7OzsBJlMxqZNmwZHR0eNF9X9ffvgd+kSjOUTutEAACAASURBVH74oR7RVsPT0xOenp5IS0uDjo4O6ikWRo584qzqnj17CtHR0di2bRt3dnZmnHN89913GDZsGDw9PTX/kShWJ1QKC4lYpqWRS7KpKcmPm+jELAgChg8fLpqYmEgxX30Flw8+YC0kCTk5Obh+/TrKysqgUqkQExMDADj7+++4m5ICy5wcYb2/P5TJyXBwcMDUqVOZg4NDrcRhcXExcnJyhNatW1O19bXXquftNgRzc6oE10Dbtm2xaNEi9sUXX0Ct1GlQwqyuJn/2GZGqTZvoPadMIZXNTz8BZmYoO3AA5c7OKFm1CgM9PGBsbMwAoGdsLN/ToYOU8+CBqBUWxtuam3PLoiKhi6cnS05I4OdOnGBe169T8q9lS1Kc/PvflOA4f562YcwYqsC2b1/dp1wDAQEBuH37NlQqFa5du8aGDBlSW8UwdSqtkTVhYgJ8/z2sDh6E95EjYAYGvGj4cGZoZCQzMDCAiYkJioqKoHX5Mk46OMDZ3p77+/sL5ubmWLNmDUZ6eUErNhYAINYgyU5OTpgxeTJyRo0Silq2hMnevbRmtG9P60HLlkjQ08OgTz6B86BBonLgQKxYsQJvvfWWxsN/4MABVQcHB9bOwoIZGhry/Px8bExJUTkdPiy2S0rCpaVLJWMTE0EQBHz44Yf46aefhNjYWHTr1o2UAUZGlPg7fhxwdQVjDM7OznB2dhZFUeTXr19nVfeFkVF1i9HWrYAkoTQ4GCGBgVxHkqTW77wjOKamMjEoiKr2np6UANHTo7WtgeeznZ2dEB8fz93c3KBOVHPOkZubC319fSQ7OsL2p5+QvnMnjtBkAwYAERER0BZFrj94MH979mz2KVVWWWRkJCIiIiSFQsHKy8uZXC5vkqt4TEwMjh49CgAIDAzktra2T78QjxtHyVi16qmpuH+/wQqxra2tdO/ePdHb25vaP6yt6VnytHJ1AGPGjJENHDgQp06d4qIoqpR+fuIfLVuq3rS3F9GiBak83nij4Te4coWq8c+wDQBgbW1dLgiCjlwuby+TyQYA2P9Mb/iC4iXZfokXCsuXL3fU0tJa5+fn94wDF2sjKSkJ+/fvx5AhQ3jXrl3/eyLyfwj6+vrwbkLl19ra+onmcIIgQCaTITMzs9EKDeccFy9elDp16tRsa05fX18xKCgI9vb2Tx6/1rEjzVTW1qZq3DNCJpPB0NCQJyUlVVcdXVyoarJqFTmRWlvXC6zrwd2dqq+3bzfed9cAVCoVtBQKvFoph9w2cqRSceaMMM7cXNDo3PwckJ+fj+DgYAAAY0x89913n/uc+XooK6Pvt27Rsf30Uwowvb1JxjluHJGNe/foSxSp8ufrS1WeESPIpG3QIDI2ys+nKuTAgVQZqimP/v+IixcvSqmpqczV1VUAgKlTpzb+B4sXA59+itZGRhivrY29e/cKqampsLS0hJMGgy4AKDtzBgn798P41VclccCAJ2aeNPbOAlSB/ukn2oYGrjUdHR3MmjULISEhUnx8POvWrRuPjo4Wg4OD0bVr1/pScoD666OjqXpTXk6y8rg4+vL2piRWMxIkjDH4de8u3JXJsDM0FLL4eJSqpcg1XtP+0SO8WlGBqxYW3GH9ejbfwABGRkbQ0tLS+GE3btzg+vr6kpWVlYiICCKnvr6Nb8z335N3RB0yIggCpk+fjsOHD/OKigrWpq4MPTOT+tOTk+m4nztX28Tq99/pntiyBfjhB0Q4OWHMgQMw9/GBrpNTlTmXh4cH8/DwECVJQnp6OktOTsalmzelw48fC2JgINPX1+fshx8Y7t0jQ8M//iBF0Pz5RPL19amilpYGnDlDa2odx2x9fX34+fnhypUryoKCAtk333wDKysrrq2tzbS1tWGipQXj0FCU5uejR48eteZgy0aMQOSlS5jq4sJMtmwhOXPNUVWOjpj75ZeAlVXVhWNiYiJFm5oKPh070tprZFRdNX/wAPZr1uBSmzaIHTQIAx49gqORESVH/fyAkhKExcWpfBYtEpxv3WJ3bt/m2traiI2NZb6+vqg78UEulzP7vn2FsMxMLgiCNGjQIHHVqlXi9C1bUPbddzDW1+cZGRnSlClTBB0dHbzyyis4ceIE79iyJdNdsYIUCAAR4m3b6LiCpg3ExcWxV199VeNlU1hYCLlcDm5mhnsDB7KsrCwxrE0bWDCmetvYWARjlIgZOJCSt8XFDV2BGD16NFavXo2NGzdCV1cXjDEwxmq1Lsy/fh2ti4rg5uaGMWPGVD3PhSFDGPz9WU2CWtne1ewMtppof/DBBzAyMnq2+OrUqaoZ581CdDTw5psaf2VraytGRUVxcM4gk5EpWXPNFTXAyMgIY8aMEQHyovnxxx/FcsagffYsxSeffELPsG3b6q9zGzfS758RkyZN0vv++++VgiDkl5eXf/TMb/iC4qWM/CVeKISGhr7u7u4+0MfH57klml40ov1P4MaNGyoArN6c0xpISkrCtWvXMGXKlCZLz9RwcnJCdna2dPLkSWZjYwNzc/MnG6wkJDw3w7CioiKekJDAu3XrVv2hMhlV+ySJZLapqdS/2Vh/l709uZoOHdq8DZAkGBw8CNHHh59nDFGlpSw3N1fQ1dVFQkKCVHM83PPCnTt3sHXrVt6pUyepsoUA+k/jxq4JKhURrd9/J/nje+8Rge7cmcbbtG1LxOvQIZKQa2tThr9HDyJ8Dg7kTKvuI1ZX3srLqQ9RkuhcnDlDRK5lS6pCbt5M7+Xq+v9VPl5QUID9+/ez4cOHMz8/P/j5+WkedaVGXh7t9/z5YDo6sLKyQlRUFFQqFSZOnIia3gcFBQXYsGGDKm3zZiZs3sxKvb2lTgsXCs+cJFmwgBIYjSTf9PT04O7uLvTo0YO1adNG8OjYEelBQTh6+TJUq1Zx3fh4ZuTiQkTV2prOz6pVRESuXKGE1Ouvk8Hdhg3kj8BYw33RVYcnD6dOncLhw4f51YMHmWFaGlLatoWOjg48PDzg4+ODoUOHwsTQUDLbuZMZyOVot2ABXN59l5mYmEBPT6/R8TzBwcFS69atxVbl5cDChWSq19j6I0mUNBo0SOPrTE1N0apVKxYVFYVOnTrRfVVaSvOPjxyh63PSJJKx1r3nXF3Jkd3HB3B1hezbbxH1+ee8U1ERw6+/EkE3N6+qujPGYGJiAldXV9a9e3fm6uQEYdMm5AkCa3/mDHTffJOq19bW9HmtWpEXQm4unZ+uXYk49upFP7ewoNdUQi6XIyUlhb/55puCiYkJTExMmIGBAWQymWR78aLUct8+dsDMjN26dUvy8fGpOhiMMVyIj5cMe/RgDoaGRDjMzIjg5+dTwmXo0FrHLy8vD3fv3kXXUaMY2renKrC+PiU0BgwARo5E6ahRvLikhJ86dYr55OdDtmIF4O4Oyd0dp7t3FwbPnMl0bG1RvnYtyrKz2U0tLSQlJUkXLlyQLly4wKKjo6WYmBheVFQkZoeGwjk1lQ9ZvVqUyWQIDw9HqSRJpkOGsO4LFghec+Ywk8rEqY2NDbt9+zY32LiRWc+cWd3GFBFB5m6VZPvx48eIi4vjY8eOrdqx8vJy7N69Wzp79izOnz/PkpKSpKSkJMYYk7S1tVl5eTm0dHXh27kzQ4cO5LQ+aBC1WKiNBmNiSMVT4/kjCAIUCgV78OABnJyceJ8+fViLFi0wbtw4iKKIgQMHwnbuXGiPG4fOvXpBZmAAURTp2fr4MV1nz5jILSgoQGRkJCZOnFhfMfM0EEWS6P/739WeHU+CXE7r54oVGu9HExMTREREMKfFiyXz0FCGhQufuwpKEASEhoaS0ayjIyxat6b7tKiIrvkPP6RnnK4u+RWkplJiuTljP+uAc46TJ08iLS1N4Jw7Llu2LO/57dGLhZeV7Zd4oaCtrd3Z3t6+vi3uU0JNtAMDA3mXLl1eEu2nRJs2bcTY2Fju7OxczwWXc474+HgEBQUhICCgnqN4U5Gfny+Vl5cLu3btAgB88sknaPC9ZsyoMg6qmin9lJDL5bhx44ZQWlqquXXB25tIwrlzJJucN6/hKre3NwVH+fkkp2wq7t4FPvgAvrdvM8X48dKjR4+ksWPHinl5eWzv3r3PnTVW9hDz3r17o1evXmKznWMBclI9dozk3Tt2UGD4xx8UYCxZQsdi716SxfbtS0GioyNVbdTGVBMm0PeJE5/8eXl5ZJA3ciQ5/f72G8nsAQqcxoyh4H36dJr5e+DAMwUyTwvOOXbv3i05OTmhQ4cOTTt3KhWQlFTrR5IkgXNer6odGRkJp+vXxd4tW8Jw7Fh0Dgh4PteH2rSsLnJyKNFx8iSpNubNo6Bx/nyYMIbpR49CceYMcu7fx42EBG7n5MTw++/kFq2nV129+eqr6vdUV6PVCRk3N8DeHgWcQxCEqn7P4OBgfv36dV5RUSEoye2e9U9OhkXnztKyZcuEutvZ/dQp4bahIU/w92fqXurGUFpainPnzqkyMzPFUaNGUWVsxownB+IXLlCw3MjrsitNFiOOHFENMzQU8e9/07F4773Gjd3MzEjmnZ4OBATg/LBhPPDLLxni4+l+CQ0l6X3fviRTVbdf/PEHcOECnDdtgtm1a8ixtMSW9u2lBZmZAhij1pjDh+n+SE4mF+RvvgEePqS/lcmoQnzvHpGdv//GzcmTEX3nDoqLiwVdXV2SUFdDQK9ewDvvYAJjOHDgAJMkqZbCoaysTOjQuTPJoPv3pyp0UBCRPA1j90pycyUDXV2xaob3+PGkXnJ1pevP3h5eAIuKioK5ublKu1cvEba2SDI2xqUZMzB40CCura3Ncq2tYbN2LRsaEYH8R4/wc1GRIIoi5s2bh/T0dLG0tBRubm4I+eEHXvzoEX799VdV3759xddeew0hISG4du0aH9+qFW/18GHVzjDG0K9DB0G+eTNXLl/O0u/exePHj5GuUkmOf/whdFapIIpilTP/3r170a5dO7i7u+PixYs8JSVF6NevH9zd3WFpaSkolUrIZDIBAJQVFRAmTBCgo0PXB0DPtZYtaQrDgwe03nXrRoTSyIjOlaEhzMzMYGZmppw2bVqth2U/dSL6wgVKsqxbR8aVAPDuu/T8bEAx01TcvHkT8fHxsLGxkepOSHgmDB3avEkD16832kZkYWGBt99+G/uysgSLkSPxT9iMymQyTJo0Cfv27UNSUhJat26N3r17o8W//kX38r175Ap/8iQ9A52cGm9TeQJyc3Nx4MABZXp6ukwmk32gUqk2LF++fPWyZcvCn+NuvTB4SbZf4oUCY8xJV8MImqfB/xzRvnmTKiPu7tTTlZ9PFZbsbArebGxoUX/4kKqJRUUkx7p7l35nY0MSxsxMqiwqFLTYJyZSRdbGhgJruRwqGxsoy8uhY24O3L6N/mZm0FKpWPQXX8DcxwfmLi7gAK4kJUnp4eEC19Hh4wcPZm55eUTAZDL6bBMT2p5K2SCKimiby8spEDcxoc8sKYEyJkac5OUFPX19lD9+DDEqioKKoiIiWi1aELnS1qaqjEJBZj4//kjb3wwkJiYiJSVFdefOHaGoqIg5ODhIgYGBDQcLenpEKm1siHQvXEgjQOpWuU1MqNoeHd2ws2xNXL9Olb6wMCA7Gww0B1z9a0mSUFZWxoqKip7OcEYDbt26hX379qF169ZSo0S7ooIC+86dyQxq82Yiz23aUBXsrbcoKO/Shc6NSkXn/PZtOk6CQNUZgIJmNWo4QDcZFRV0/OfOpUCdMc3BlY4OuURHRFDlcNQoqpz/B6vc5eXlyMjIED7++OOm/UFYGElGCwtrbaeHhwePjIxkGzZsUM6fP78qFtCLjkab2FhuPWcOQyM94M3GyJF0X4WG0nnz86NzP3s2kbSbNym5ZWhIyZT+/QFbW7B586AHQPfbb1nYL7+g4tw59OzZEyYNKUDWrqV76OBBMkabOZP8EQ4dwpbRo7mcMdatWzdwznHt2jU2ePBgFhsbKw0dOlSwtLSE1uTJwLvv1j6hly8TmRg2DBdtbSW3du2e2MpSWlqK9evXcx0dHbz++uuwCA6mxFBTVCnh4bT+NIIOZmboe+eO1Co6WkTnzlSZbOo9fPgwrZcBAXAeP57tVyoxpUcP6IeF0fXv4QHEx1M/9uPHwJ499Axwdwe0tZF04ADuHj8OCwMDXuvaHzGC1ntLS7pvBw+mdo4BA0h58v77AICYvXuhOHoUUX/9hQl79yK8Z0+WJ5fTrPSaMDYGxoxBmwMHYG5uztevXy+99dZbIgAcP36cS5JUfYOqW3P++ovIn5ro9ehBiYCuXTFk1izx8KJFHJGRDKmpdBxkMnpO1Vjjy8rK4OvrKwrBwYCFBeIzM6Enl+PuN9+woM6dIYoijIyM+BtDhjDzlSsxoKyMn27ZkhkYGKBLjXnbowMCWPHt24x37Yrg4GD+r3/9i82aNUs4evQoIpycpFaGhsAXX5CsH0CrO3dw0cUFK3fsgFKphJGRkcrW1la0HD0aFwYMkKwWLBA456ioqMDdu3eltLQ0dvr0aVZYWMgCAgJqeaPUTCTLystpDVWT4ZoQRaqMfvklPf9DQkiRcPMmkJICs969odLkFq+Gjw8Zs6nNwfLzifB98YXGlysUCtSMwS5fvoyLFy9KkiQxbW1tlSRJTKVSiRUVFVXjtObMmSM8VcK2IXTsSOd8xYqmSa1zchpvH4qJgcX48TBeulR1PDtbmFxnfOrzQps2bdCvXz/pzJkzgkKhkDZv3ix07NgRY8eOpbgoJ4eSKYJASZCnQHp6On7//Xf1pAEZAJVSqfwBAARBsAXQuPnGS2jES7L9Ei8Eli9fLgMwQ1dX17dej9tT4H+OaANEbI4coey2SkVEQ/2A45x+VlFBQWB5OX2pVPRw1dcn4pqfT9Uka2uSQZaXU8aVMUBLC/zhQ8ivX0fYw4cwMjTkfsOHM1y/DsHBAX1btULsrVtI3L0bHgEBuJWaKqUXFwv+enowsbdnEASqjj1+TAGCqSkFdVev0nb16EGZ3cJC+rK2pgAqLg7IyoJTaSms9PRgpq9PMqtHjyjISEigAHHwYCLenNN7GhnRA6xjR6r0tG5NiYQzZ4ic1yR4dRASEiLp6+uLPXr0QPfu3aHRRU4TunUjSfMff1DP15Ej1UGMGq6u5KL9JLItl1MAWulmqwnJyckQBKFev+Gz4MaNGzA2NsbYMWNEdvMmSTQzM6kH9ZdfSNrGOc0sff11VElXHRwoUXLkCBEBQ0MauQPUrkI8yVG+ubh/nwjG5s1EJgC6xo2N6Xqqa0AlCJQMsLOjSriZGZ2zZ1RANBW8sjrbZIVHr15EeOpcA15eXszIyAghISGylStXcmNjY3ROS2OGSUmIHj2au/To8XzWNaWS5hUfO0bnNyGByI+nJ0mMHz2i9aEmsdcwp9zS0hJ+fn48MTGRR0VFCS4uLtKECROEesnTTp3qz/T+/HNg7Fh0+vVXZhIRgVRbW1ViZqbIOYeXlxeq2ijkctqmGjJn/P47XSMzZ6K8d29k//CDGNBQb3olsrOzsWXLFlhYWEizZs0SkZlJBmU//dS0xMzo0Q2bJhYWAitXoiA9HQWFhcKj2bN5ixEjmneutmyp+mffvn2RlZWFP5VKzAwMhHj4MK0xrq5AcDCdu/79a5nMqefuyuVyMTs7m0yuBg6kc6gmWWFhlMDy8KBES3IyrqWm4k5mJhKSkjD4xx/xVufOKOvRA6XBwbg/fDj0DQyg++eflFTU0qoawyhkZmLatGnCqlWr8P3330v6+vo8Ly9PnDZmDAxjYighHBdHrQSTJtHan5ZG19vChZTUcXPD8W3bkJ2VJaGSsOPQIbrHv/uORsO98gqKiorQpk0bdvnyZZVPeLiILl1QZGcntXrwQDApL+d+GzcyMzMzrF69WorKzBR6Ll/OfIODWZeUFOjVue50ZDLo2Nmhd+/eSEhI4Nu3b+fTpk0T7ty5o+rbty/1T+/aRcmlggKwCxfg++WXDPfu4dy5c5g0aZJoZ2eHirw83KqoEM6cOaMqLS0Ve/fujX79+glFRUUICgqqGnepEX/+Sb3ZoaFPVlSIIj0H1c/CoCBY/forvLS1BchkNPe77jQMHR1SbOzaRQm069dpvdHwWStXroRCoYCxsbEkk8m4JElQKBRiYGCgYGpqioKCAlleXh4ePXrEe/Towezt7fHgwYPnIx+vi+xsSsw0hWwnJjb+vG3dGnjjDQwKDBQ3bNiA3Nxc/FMmoL179xZ69+4NuVwurPv+ezy6cYMjNZVh7VpyzZ8+ndQzNdewJiInJweb1CM0AdjY2KgyMzPVN36eJElDns9evHh4SbZf4n8Cy5cvF0AzAJ0AmAFwkclkHbW0tNpzzlsJguBgY2NTNmzYML1nrWz/TxJtgEhPaSmRyPbt6QHj4vLs71sp5U1LS8PJjAwpz9WVmXl6soqKCu43aRKrGVh7jBuH1atXq07n54uOvXtjzJgxMHnS7GdN2fqamD4dCoUCkStXsgGffNJ09+3CQgoyZ84kE5kVKygYCQ+nav748RQU7thBgem33wLbtiHh9GleJJcLM2bMqGXo02To6wNvv03Hbdgwqp6++Wa1m2+/fjQ7+J13Gu6H27uXevweP6aKXAOQyWScMcaemmzL5ZRwUSiA1auBRYsw7OJFZB86hHVlZfztL79kslWrqk2IKiqogmJiQpWntLTq91IHM08x2uypkZ1NEvPvv68m2gAFkOHhjc8db9mSgvU//iAitWABnZt/2LFc42zjhnDsGPUuHztW71cWFhbw9fWFiYkJbt26xbJDQuAQHIzIGTOQpFQKw5vj4N8Qxo2javapU5Tg2rGDrpVBg6jS1gwwxtCvXz/Wt29fdu3aNZw6dUrYvXs3nzFjRu0Dbm5OaoO66NwZTnPmQH71Koa0by/66+oiu27l/sYN2j5tbSJq779PFbA5cwBXV+zZvl2lpaXFHB0dG2TMnHP89ddfkqurKxs7diwFqhcvEglt6oi9OXPIjK9mwP74MV1vK1cCX32F866uiHvwAD66uiwlJQWtWrV6sg9FTTg4VCVXx44di+U3bmA753ht2jQImzbR7zdsUO9UrT/18vLiwcHBTJIkGKnXpSVLwK2soFIqce/ePRhqa+PvDRtwyt4eWlpavNvKlbBNS2M3x47FuIkT0a7yPteaPBljRo3C7o0b+YMLFzDo8WMm8/amPvCpU+nYKZUw0NPDu6++ityMDEFv716URkbCzsGBZOPdutE5f/NNSoJNmULrjEJRKylaUFIiKZVKITMzEzZ37tA6e+sWrU/btkHp54dffvlFbYonlvz0E/QtLZG9ahX6bt6MpKQk6fj69cIrAQFMT09PLCwslGBnx4Thw2G4eDG1+Lz2WnVCxcEB8PcHYwxTpkwRtm/fLn399ddQKpViUlIS2o0YAb3r1ykJqa8PWFtDy8UFRnI5ZDIZysrKIEkStGbPRkBaGgLatRPXrl3L1XMvDQ0NMUHdKgOaulFeXg5JkqBSqSCpVNBOTkbFzJlQZGSAMQZtbW1kZ2dXXStaWloQBAGiKFZJ9Lds2QKVSgXOOXi/fnAoLkYvxkjhMXcuJU/efru6jSY1lci2erxfaqrGCQCtWrXCjRs3UFBQUHX/vPHGG5pMVKsu5BbNXCeaDG9vIqd5eY2v85xTIvbttzX/zt+f+r8XLoQqKwsAmncfNheFhcClS0jNy8M7v/wC448+YvD3J28Ra2syCnxKWFhY4M0334SJiQnKy8vBGBN3795d+uDBAz0Ag5ctW1b0/HbkxcJLg7SX+D+N5cuXW0VERHzPGNuppaX1vqmp6WQLC4uxLi4ur3bs2LFXly5dWvbo0cN04MCBYvfu3bWNNIwfaQ6Sk5OxZ88eDBs27H+LaANEQgcMoKrKjh1UDZg5kwhSE/qb5HI5wsPD+Y0bN6ScnBwhPz8f9+7dQ0ZGBuLi4vjp06dZ27Zt2bRp01i7du1w6dIl5OfncwcHh1pkz8vLS2jZsiX8/f3Z85L8p6WlITExEX5+fk3/I86JQM2aRUGFlxf93M+Pgg2AyESXLiSvDwuDNHo0lH37Mp/ycpjNmkVJi8mTqbqfkNC85IWBASUSzpwh2bhMRgRVEIjQhYTQttTeUapkzJhBn/uERIWpqSkLCwuDiYkJNM4glySqcJSVkdmUvT1JL2fOpN5aZ2eSrnXpQlWCV16B2LYtDF99FSpnZ7bNzg7hZWWw9/aG+axZVHG0s2uSaU5eXh7WrFkDW1vbf6ZKEBZGCY3Dh2sTbYAkr4LQtPPl4UH7n5hIlUtPz+b10zcTN2/eRGpqKu/Tp8+T15/Hj+ncNWD0xxiD9f9j77vDorq6r9e5dxjaUAQLIE1REQSxgYhKxIItlmgsUWONxhqNpppCMK/GaGISoybGXqOxRaNYsKGiWBAFBEUUkF6k12HuPd8fm6EIKCYmv3zv63qeedCpt5yyy9prN20K5zt30PHWLezq1w9JjEGhUODGjRto3rw5TExMns94zM+ntaNTJ8qQTZ5MLAVjYxpPCxZQcMLL60+xFBhjsLCwwL179+Ti4mLBw8Oj5hs6d6ZHtcyOWq1GSEgI1AAuGhlJFmZmgs1336GxQkFCeVrn6ORJundaVfo33qD5X6EInpqaipSUFNatW7d6RRoTExNx48YNvPXWW+QTnTxJ9Ob58xumgsw5MVemTSOHJTGRxuj69fTar78C7dvDslUrxMfHS8nJyfzGjRtCly5dno+h0qwZZaIr6PgmJia4lpYGa1dXmO/YQffP1JSUlfv0oXroClhaWrLU1FTk5OSglb09TCZNAps1CyeiozV79uwRoqOjkV5WhpKCAjgOH8779+/PmvTty0zu3IH3jBmwdHauEZTS0dGBm4cHO5Gejit373IvPz8Ga2sKKk6aRKUB+vrQ//VXmJmYQGrXDgEGBvB47z2aw9270z2q2N9lAEUdOkAYPBhFnTuDcdgpXAAAIABJREFUmZlBFEXY2NiwvLw8+dyRI0LYo0eyzZdfMiMbGwqWFhejSJIQcv8+rK2t5by8PPbKlCnI7d4d1x4+ZLdu3cK4jz8WSqytWVBqKsrKyuDp6cmaNGlCjnLv3hQYOHWK6t0FgZg5+/cDw4ZBV1cX7u7uzNXVFQkJCUhISEDr1q2pddtbb9F9PXYMUCigo6ODCgYHs7S0ROPAQAoKfvABdHR0+MWLF3n37t1rTEpJkrB06VJcvnwZ169fR/ilS3B6+2385uTErwC4ffs2rl69ym7evIk7d+4gPj5eioyMZOHh4SwiIoJHRUXx8PBwfuvWLTDG8O677zJvb2/4+Pigs68vg4cHrXOdO9Ne9uuvlC3X0aGgtJcXBdGesu+0bdsWrq6u6NWrF3x8fPDw4UM5PDwcHh4e/ze2VEoKBUTmz6+//Oj2bQoYvv563a9HRNBaYWSECxcuyIwx1u1ZnQaeF0VF5PCr1dB8+CGEgAAEd+iAkBYteNfPP2ewsnqq8GRDwRiDYYXInVKpRGlpKU6dOqXDGPsMwO+9evXS/PWT+d/Ey8z2S/x/iyVLlryqo6Ozy83NTc/T01NZYZA/Rcr5ryMuLg6yLFf2Iv2vg5kZOR7R0WTANG5MDlZsLBnRFX09Oec1DPCIiAj88ccfaNKkCYyNjcXU1FS5pKREViqVkGWZ6enpCZMnT66kgxkbG2PatGls37598rZt2+TZs2dXWq4KhQL2LyKjXgG1Wo3ff/9d7kGtOBpeXKunR85KaWn9CuFaMTcPD+DXX5GdlYVtc+Zg8UcfEX22Tx8yPNatI0pmcDAFLj74gOoKv/ySnNj6enrr6hIF9vRpEoCaPp2Muv79icIsSTUzCJcuUVZ7zpwGZQ4NDQ2hr1Ti4rlz6HjjBglTpaVRrfrJk0RlNzcnw+rkScpcuLtXGd7p6VVG86FD9NfeHgKA+1u2yKampqxZs2bs3LlzkoODw3O1bHvw4IG2zY3UqlWr52739lQEBxM9eOfOWvX4JSUl0OzcCSNr64ar0Ts5EZUwP5+cEysrCsS84AyHRqNBQEAAunTp8uwvvn2bxvDnnz/9fZGRwDffQDx4EPOaN8fDhw9RUFCA33//HVu3bkXbtm3lYcOG1aZrPwltFv2PPygL1LFjbSXstm3JER89mtaay5fr7fH7LOjp6QmJiYl4UjQLly5RGUIFIiIicOTIEW0NLC8sLBQ11tY0p/LyyIGYN4+CR9u3V2VUP/uMnIhq97B///7CzZs3sWbNGr5gwYI670F8fDw3NzfngiAwFBcTW2jRoqd3GaiO6Gi6dnl5xEopLycWxYoVNQxqY2NjvP3226Isy/jyyy+xZ88evPXWWw2/gEOHUiDvtdcAAB07dsSVK1fk33JyhKmenrBcsYKOe8gQoHFjPHr0CHfv3uXJyclyVlaWKEkS55yzPevXY1FxMRQqFe7fvy/2798fFy9elN3d3ATn7GzAx4euU5Mm1Nt4xAjK8Pv71zgcQRDg2a4du7tjB8vgHE1DQojS360bZVTt7clxNTaGwtUVmbGx+H7tWt66dWt50KBBIlCVUTx48KAUFRUlDisuxt2vv0bZgAHSxIkTRXNzcwwfPlzM+vZbpObmCpsZQ9OQELlfv36CfVkZDA4ehF7jxtzc3JxZN28uRZ06JZ44cwbWdnaSra2tGJyWJqfZ2zOUljJ9fX1uZWVVNQaMjUlQbtYsWguHDqV7l51d+RbGGBo1agQuy7KrqSns1q4VwDkFb7/7jkoq7O3RuHFjvPvuu2zt2rVyUVGRgPHjKVsPwM3NTfjjjz+wc+dOecKECZUDXxRF9O3bVwoNDRXUajWf7eUlGCQnY9by5ayedaj6msrQkDpjxmi+uLlREC8oiHROBg0iVoipKTEFntyXqt3jxtXE+xwcHISrV68+82f/NlhZUYD6aQy0hASqS38SmzbRuX77beVTjLGnCrjGx8fDyMjo2e1Htdi2DTh+HJmzZ4P/9BO/0qsXv+XiIhh4eqI4Ph6vjhz5t9qh6enpAADOeYCfn1/JM97+Ek/BS2f7Jf6/gL+/vwqAHYBCAEkA+iqVyt8mTJigX1+P2L8Dvr6+CA8Ply9dusRUKhVFtf/bsHo1RVHv3aNaNgDSRx+h7MQJ7HnnHSktJUUsr1BGFQQBgiCAc86HDBnCXF1dtYu/gGc4tk2bNsXbb78tLlu2DOUvgrJaD+Li4lBcXCz8qbqvMWOIttUAQ1mSJOzatYvb29vzigsDrF1LL376KT0AuqaurpQN14qY9OlD2b8//iABpR9/JMOlsJCy2X37UhZqxw5ysidOJEf41i3KNCxbRlmGEyfqp9UfO0ZOiFJJRvTatcDHH2PmqVM49sMP1EKpSRNyHEePpozFtWtV9a/nz1d915OZ4CcQHByMlJQUYf78+SgoKMDmzZuf21nW9myOiYkRpYrx9kKwaRPRZ9etq10PDyAsLAyB7dph1KhReK6O2goFOZkJCeTgnj1L1/gFHXdsbCx27doFxhh8fHye/YH16+lYjh2r+3XOKdiwdSs5vRXH2bKi9tzNzQ2FhYXYtGkTX7NmDWbNmlWrR3pRURGKFi1C0xEjKDDXsSO9MG9e3b/ZtCmtL4cPk7M9dSqwZw85Vc+J3NxcmJiY1HS0r1+nObJ6deVT1YW0Fi5cyCpUmulFPT0KEEREkGObm0t/9+ypM1PEGMOoUaOwe/dudvHiRfTs2bPG62VlZbh+/Trc3d3poNauJfq8dh1oCA4dotZgW7bQOOrRo1av7ep4+PAhAFTSjnUbKhAYEUFBuQpnGwBmz54tHD16FL+EhmJmq1ZotmkTMG0aZEdHFPn64v7bb7PWrVuLHh4eaNeuHVszZw6sdHSwa+JEOeOHHwRJktC+fXsEBwcjJDUVjrKMWqN/9WqiyGdk0DWOjqa1xcEB7lOmwNLCAnuNjDBm4UI09fKi7PaxY8DAgRR0UCig+uYbfBgYiFPvv8/k9evFVefOwapzZ+mNN94QARLhcnFxgdvnn6N8yRIk3LwpqMeOhSiKSEpIQKCLC7qNG4f3u3ZFYGCgsHv3bkwePRpWZ85gwsyZbNOuXehkYyNYjxsHRUmJPGnSJDqNjh0FBAQg1ccHW7ZsYTt37uQjRoxglawgbXvAzz4jivLo0eR0a6FWI3flSgz/+WchePRoGebm5ETb2VFQVatvUOHcKRQKEgkTBCoLGjsWYq9ecHd3l69fvy74+/tj+PDhEEUR5eXlUCqVYpcuXcDfe49duHGDD9iz5+9zxiSJ5svOnTROx4yhYOOECSRyWcEWgKcnBWhNTWsErrKzs3H58mUMq6/zxj8FS0tirR04QPfhSWRn1/28vn6NDL4kSQgPD2d9+/at8bbCwkJcuHAB9+7dk/Lz80WVSoUFCxYgLCwMaWlpcr9+/QSlUkmBoqIiYsEtXEjdL8rKUN6lC7ZduQLTuXOZk5MTm92mDSIiIpCVlSV37tz5b1XmLCoqgiAIybIsR/2dv/O/gJfO9kv8q+Hv799IV1d3myiK/Q0MDMo0Go1YVlam1NXVLR81atQ/6mhrMXLkSOHMmTPyhg0bmKGhIXdwcJDt7e3FVq1a4UXRnv8vwfX0IJWVgX/wAXI2bEB4eLgc2qiRIE6YIHsWFIiTV6yAnJGBUo0GZWVlKCsrg5mZ2Z+ifCsUCiiVSp6Xl8caP61dzV+Ao6MjfHx8sG/fPkybNg3N6hMeqgsZGVVG4VPAOceJEydkWZb5uHHjnu5dDarQGLGxIXo4QFnv/Hz6tyAQHXLHDqrPTE4mmtrAgWRwzZ9PEfXz58mYadasqp9uWBhF6xcupNYrAQHk3Gdnk0E0ahTR/Dp1Iud+3Toc+/13XlRWxrRiZBqNBrEDB4LHx8PR0fE5qABVSE9PR+PGjWWVSiUYGBhAEAQeGhqKGn3Gn4FHjx7BxMREysvLE1NTU2FdLVv5p8A5CQWVlpLBX4ejDQA5kZHSu999J65RKPD48WNuY2PDmjdv3vBgkJ0d0Ybv3KHrvHTp8/dFfwLp6enYtWsXWrZsyX18fJ7d/k6WKZhQX30356SofuAAtTCrJyCgUqkwf/588YcffpBXr14tODg4SJ6enmJeejos9u7FxkaNMPjyZZQ4OMD2vffA6hNpqo7CQhrHCQkUXFKryWCfMOHZn62G3Nzc2gyYtDSihoLKWrZu3SqXlpYKixYtqnxLrWvXowc9rKyohv/YsTrnuyzLCAoKkktKSgQAOHv2LG7dusVHjRrFLCwsAJASP2NM9vb2FivV9jdtatgJSRL99pdfUtYwP5+Cac8ouWjVqhUmTJiAnTt3YvPmzXzWrFkNm2Pdu1deq+p49dVXERERgf1KJUYaGyP5nXd4So8e3C0vT5gzeXKNdkI9Hj3iphER7HCLFsJrr70GW1tbplQqMW/ePGHVypWc//QTg79/zRZ51tbkWDZrRsGODz6gMWBhAeHhQxgUFCB77Vr8FBiIzzw9KZji5ERrXEAAfceWLRByc5G5erXknpMjumVkIHrTJjFowwbc6NoVhbq64qiKWu02Ojqw2LePfW1uDrG0FLPXroXNsmVSu549RQAYPHgwioqKpE2//ir2tbDg7l99xZp26cKLjh+H0f37NYUwk5KAefNgmZWFIUOG4OTJk0hMTKxZgiOKdH4LFtD4Tk6ma/3xx4CeHs4plfLj/v0Fg06dhEc9e1bVJDMG/PILBT0rtCwUCgVXq9X0uixXKtQPGjRIaNeuHbZu3YozZ85wURS5Nvitzs8XhqekIMTXV8KLtPE5J8V7Y2Oq21apKLg4eDCtb4sWERvD3p7YV6amJDS6aRPtb5JEn23UCPDxQUhMDDQaDS5cuMAPHDjA7O3t5cLCQnh5eQnl5eUwMzOr1Qb0b4EgEO2/vrXy999rZK9RXEy0/82bawgxPnr0CCUlJejUqRNkWcahQ4fkpKQkXlBQIFpbW0u9e/cWAfATJ06wFStWoOK+Cg8CA6FWKjEsMBA2KSkou3QJpmPHEm3d0BAXz56VcfMmpk6dWqmz2pv0TV64ox0TEwNJknD69Ony7Oxs7YbXHIAXgLMv+vf+l8CeS2zlJV7iH8by5cv3tm3b9rUBAwboaJ05tVoNHR2dv1eEogHQaDQICwtDTEwMMjIypKKiItHKykpyc3MTW7ZsiUYNqEv9pyHLMjIzM5GSkoJHjx5Jjx8/RmlpKVOr1ay8vJyVl5dDo9FAlGXYJSai3MCAq11c5F69eomOjo604V+4QLXCb7xBSp7PyHI+C6tWrZLc3d3FHj16/K33dO3atbKzs7PQoKygFuvXE2X7GbT2oKAg+erVq2zq1Kl/T9Bgwwai39rb0/W+d69K+VUUieY/eTJRx7//nu7LwoVk8BQVkQNYx7XVUlCbNWuGpk2bIj4+XiopKRGVSiUvLi6urNl7Hjx8+BB79+7lw4cPZ05OTgCAw4cPIyoqCh999NEz77EsywgICJBCQ0NFIyMj3qFDBxYSEoJx48b9tfKCxYvJUFq8uN7giVqtxtolSzCqqAhZkybh3LlzUn5+vti8eXPu5OSEJ+skn4nISBKsGT6c7tefYMLIsozt27dztVotz5gxo2Fp8m7dyID8+uvar3FOxuOZM0T9bkDmPT4+HlFRUci6d0+SHzwQRCMjaeDGjYrNU6bAukMHPHr0iFtaWnIbGxtBrVYjJSWFZ2VlsU6dOsHBwaGSpVAJSaK1REcHCA+HNHw4bi1fjsZdu8LCwgKCICAuLg4ajQZOTk51jplt27bJycnJgqGhoaSnpyd26dIFnV1dKx27EydOQEtR/fjjj+uvZ05KokCUqys5t5mZVU5dNRw9elQKCwsTraysYGtrC319fWRmZiI6OhpvvfUWmjZtiiNHjkjFxcXi2FGjyAH5+muat8/CgwdEGX34kAJiMTGU4QwJAfz8KNCmpZPXg8ePH2PNmjUYM2ZMpfjYM/HZZ6S58NNPNZ7ev38/7ty5AwDon5gIw+JilL32Grq0a1fVAikrCyGxsTh58iSMjY3ld999t4bx/+2338qTz5wRzFevriq3Aeg8ysuppl6tplKdhIQa2cPAwECEhITg/fffpwB2SQmpnR87VoPym5iYiM2bN+P9998Hv3IFioMHUf7GGzCYNg3Cm29SvbeJSVVXDFtbCkLVUVqRmZmJnWvX8vHr1rHf5syRx44fL5QnJGBXWJj83nvv1enY7Nixgz98+JC5uLhIIzt3FmFmRi3Tvv2WsqVLltC5fvghZXgHDcLuffukjIwMcM5RUlIiOjs7S6+++qpYGQTSaICkJBw4d06KfPRI7Nmzp9a5qr/cSIuLF5G2dCmOvf46n/bWW399M5Uk2u+Liyko9+ABsa5yciiAIIqUwR47ls4vLIyYCgCts1phTM6rAte//QZkZyNfqURhTAwkzpHcoQNKmzdHpqEhHqanc6VSKefn54ve3t6Sj4/Piy0hqg+BgTS2qwdhk5Np7oWHVz0XH0/MsjNnamgwpKamYsOGDXjllVcQFxeH3Nxc3r17d+bk5FTZVrOkpARbt26FIjwcr7dvD0WzZhAWLMDjDz5AYEKCnG1gIKiVSnTq1EkeMGCAwBjDyZMn+c2bN/Hxxx//rQZvRkYGfnpiHQCwFEA+gB/8/PzK/s7f/2/Hy8z2S/xr4e/vz3R1dbu5u7vrVM+avsg2RX8FCoUC7u7ucHd3BwAxPz8fV65cEYOCguSjR48KkydPhl1d9KN/EJxzpKWlISIiQo6NjUV2drago6PDDQwMpKZNmypatmwJQ0NDGBoaQqVSwdDQEEZGRnSNL1wAli5l+OorsbJ/qyCQ08A50atNTWkjsrFpkOhVXejTp48YEBDAjY2NWfUepS8apqamQn5+vozniQiHhBDd9SlOXnl5Oa5evSoMHjwYf1d2HtOnV/07M5P+jhxJzvb06ZT1sbIiAyc7u6bx/BQHr0JsiqelpTGVSiUNHDhQtLKygomJCTt+/Lj84MED5qOtuXwGNBoNNm3aJD1+/Fjs2bMnb9u2beXnhgwZgsjISCQmJtarLltYWAhBEHD06FE5OjpaBICCggLm7e0NhUKBXbt2oW/fvtzDw4M9V1BGkogN0KMHsQqeUp939uxZrpuby6yXL4e1SoUOHTqIiYmJuHDhAjt9+jTc3d2fb/1xcYG0fTuYnx+E77+n2vcGquuWl5cjJiYGUVFRckpKCps7d27Djc7166vU66tDloki/fgx/W0gxd3eyAj2ffoAW7aIKCwEjh9XxL/9NiwuXMCrr74KpVLJjh49yuLj45GTkyM3atRIUKvVCA4Oxu3btzF06FCkpaVJDx48gIWFBbO3thYcPD1x9L33ZNnVFffGjhVaHjqEkg0b+M5evZhSqYQkSRwAoqOj5REjRoiMMWg0Gty7dw9t27bFxIkThYSEBKSmporp6elyQECA4DJuHHTnzUPO+PGQJIkzxtjgwYPrv2eyTLXDzZpRZn3+fHLopk+nLCNjKCoqwuXLlxEaGiq++eablTR7LZRKJd+4cSN74403EBUVJQ4bNoz0DfT1nx2ILCoiI3/zZqLhLllC66m3N+k12NmRw7hiBWUI4+OJ8u/pWcvpMjc3R6tWreS9e/cKH374YYNYVupOnZCdnAyLJ56Pi4uTAQj9+vWDp5cXBfvCw+lYzp6lkpbWrdH555+R4uqKiIgIITc3l8S+KuDk5CQEh4aiR2oqzLTOdkEBlRr88AMForKzKbDg5kashAphwZ49eyIpKYmvWrWKmZiYyKNHjxaanD5NZTe+vpBlGYWFhbCxsYFKpZKioqLELn36AH36QBcADh3CzTNnUDp9utzy9m0hYuhQqd+uXSK8vetU5t++fbuUnZ2NfMbEI0OHYoKJiWC6ezeydXVrz5H586mG/OxZjL91i2VeuYKr+fkUfB46lNabu3eJ1fLRR+R8urrSfFywAONWrhS1WgYVIpCira0tOlXooUChAEaMgLVSKZovX17V0mvPHuCdd8hhrQ8HDqDE1vava0WcPk2/N3Uq8MUXRHX/7js6B+13FxZSJjsyku7n5s2079y8SayMu3fpfW5u9JlmzehRochvLMswTkkBoqJgExJCwRAdHSAigqFNG/FikyaIOHJE9OnRo2HCgn8V771HooTvvFP1XEwMKdxr8c03FES7cKHWxxs3boyOHTtKERERgiiKfMKECUINeyAxEdmLF0M0MEDf8+dhVFAAxZYtwJAhMNTRwbQKuyQuLg6//vori4qKgp6eHrKyslizZs00+Jv9tbrWSB0dnSaLFy/+9O/83f8VvHS2X+JfC0EQphkbG5vVqZL8L4SxsTH69+8PKysr4eDBgw1qWcE5hyRJKCsrg1qtrmzboVQqKx/a1hwNBecc6enpiIiIkMPDw5larUbTpk3RqVMnwdnZWSvu9uy57+1NNOe0tJqZCYA2T63RolXr3bWrwcdYHW5ubkhOTmY3b96U3Nzc/pYotizLSE9P53W0GHk6bG0pq1INGo0Gx48fl1JTU6FSqXhSUpLCwMBAateu3T8Tgb95k0TVtmyh6+7uTkbAzZukXPz99yTC5ulZ2V+2PjDGMGPGDFbRu7nG8Xfr1k34+eefce7cuWdmtwsLC7FlyxbZwMCALViwAAYGBjUGrCAIYIzV6wCEhobyY8eOMQBo3LgxnzdvXmXrGYVCAW9vb1hbW+O3335DUVGR3Lt374ZNCEmi+mFra8ouP8UBkSQJV69eZe9rHaUKvQIbGxuMHz8e/v7+CAoKQr9+/Z75s2q1GocOHZKUSiV79OgRy9fVZY6+vvIrH30kGFlb497AgVBZW6NFixa1aM2SJCEiIgJBQUFcrVZzQ0NDPnv2bKHBbeTmz6e6/SezyZwTZVebyWyIYJdGQw97ewoUVHPQ7e3tazANXq9S6xUAmnOXL1/GmTNn8OuvvwKA6OnpidjYWOnOnTtS++nTBbWNjZCTmck9+/ZFb3t7YNQoZuvri1RJgruHBysuLsaaNWuEr776ClZWVjw9PZ2VlpbCx8cH3t7e1Y9B0NPT0xyKjVW0trNDyZ07uHHjBgOAo0ePwt7evrYo0c2btHadOkVMB3190ijw9SWRte+/hzx/PrZs2cJzcnKYt7d3LUcbAAYPHszMzMywa9cu6OjocEeViuHMGSoDedq6ffYsaSyYmpKjojXMhwyhTOjMmTTPV6wgvYePP6Ya2WHDiHbfqhU5P9XWtPHjxwtr1qyR9uzZI5ibm7Pi4mIMGTKkUmwwISEB+fn5PDExkdna2vLCwkJIDx4w2+3b5VyNRlapVBg9erSic+fOwsWLF9FRW4M/fTrV9s+dSzWujAFRUdCxtIR5UBAHwFJSUmo4276+vojYvZtfWruWOVlYwCo7G4ZublWZekGgzL+nJ9HZjYxIkHDlSuh5eMDb25vt3LkT2dnZwoYNGzDMxwetRo1CyqFD2B8aKhcXFwsAYGBgwE6ePAkDAwM4V2TdT6ekICQvDx3nzRO2XroEW8Y4ioooGxkSQjTtCxeA9eshl5QgISFB7NWrF7Kzs9F79GgY+fsTTbplSxrvskxikHv2UD29ri6wbBmEhASIenpwCAwUb7z5ptzl008FWFhUza3Ro4nZ4uBAwYrUVDrvBQuA2bORULG31OqSEhyMUytXYlGzZlVr5qBB9QdvNBoS+PviCyTFx5OT+DwoKKB9fuVKOm8LC3KKnZ2rdEWexLvvkgjjtWtU+z9xIgV8lywhscgWLejcP/us7m4mgkDrsrU1zTmAGAiJiUBgIDyKioDQUPBp08CSkyl73qED7Xn1MLX+EirKqMB51XcnJdG10OLoUTqvOpgjOjo6GDJkiHYPZeCc9p+FC+navvsumiqVaOvhge1WVvjwww+hqCOI0KJFC3z00UfsyJEjiI+Pl8eOHSs4Ojr+7b6adgwKgnBRluWPAHxXXl6+7e/+3f8VvHS2X+JfCX9/fzuFQvH9yJEjDZ/H0fw3QE9PD3p6eqRqrNEgJyen8pGRkSFlZ2ezgoICoby8HJIkAYBWbIxXOCVclmUmyzKTJKlSbZcxBsYYBEGAUqnkurq6XFdXl+vp6cHAwAD6+vqMMcaio6NRVlaGJk2aoG/fvszV1RWCIPy5nWnOHNrgDx6sf6M/dYqMkfXrKfqtjWg/B7y9vfH999+LxcXFMHhSwfgF4Pbt29BoNOy5acC+vrUM5tu3byM6Opp5eXkJ+fn5aN++PVxcXP4ZRxsgWiLntIFnZZHDMGUKbeoA1cvl5pIh+9lnVJ986xZlXVq3rvV12nH1JExNTTF+/Hhs27YNpaWl6NOnT43od2FhIU6cOIGEhASpuLhYtLGxwZgxYwT9epw4xhjftm0bGzhwIHdxcan8waysLBw9epT17NkTvXr1quX0a9GyZUtMmTKFbd68GdevX0fr1q2lPn36iCb1tdnKziYnYcoUakHVwP7qGYGBsK+DkdK5c2dcvnwZDg4OdTpdWnDOsWrVKpSXl4vm5uZys2bN+LBhw1hYWJiwVZLQ4+xZPE5Lk8uUSuy1txfMzMxkFxcXeHh4CPn5+fjtt994aWkp79Chg9C7d+96W0zVi3v3ahuiGg2J6Lm4kAPQEEd71y5y3NPTyTl5TtaGIAjw8vKCgYEBHB0dsWrVKnh6esLX17fq/s6eDcyYwSoF0m7cgO3KlbCNjQW6doVKpcLs2bNZQEAAcnJy2LBhwxASEoJz587BxcUFurq6uHbtGlQqFbo4Oiou5eXh6M2blUJugwYNwqlTpxAcHAxjY2OUlZVJvr6+IsvIIEf33XfJWeWcauzt7Wm+f/AB8PPPeLB0KS/R0+OffPLJU+9Dt27dEB8fL+no6IjC559TNu8JMblKZGYCy5eTYzJoUO32fQkJVf+eN48cs927Kbtoakr3A6C62dBQety6VdkpYvLkyeLevXv5zZs3AVCpGodgAAAgAElEQVQdOQAYGRlxc3NznpiYKKhUKlhYWPCUlBRM3rGDHX30SEh0cxMEQcDXX3/NK8oLWU5ODirn8+TJNCbc3GgNyssDJAkdOnRg0dHRPCIigmmdXYCYXx379mV6R47wE3v3ym8tWybuHDcO6NVLGivLosgYmNaJbdSI7oGLC92D69dhWsHIkWUZw4cPx/6DB2EyZQryzp+HRfPmbMKECQgJCUHz5s2Fs2fPIrua6nfz5s0hyzIM4+LkwWfPMterVxUwN6esZJs2tD42bgxwDqFpU/QaPFgW7OwwLDpawODBJEhpbo4SS0sYHDjA1E2aoNzMDIU+Pmjq5gZmakpK9W++CVM7O8TcvIkzZ84IJhoNmqrVMNHXJ6p1ZGSVMn779rSPlpRQcOHGDTgYG8MtLg5HjhzBvHnzqtZXfX0IZWXQdXND8E8/8SIbGzRq1Ajud+4wKBS1nb3gYOD+fcDCAsnBwZWCgE9FUhLRw7/+uqqkxN2dgpLPKnd58ID2e20gundv0n8AaD9avbpKgf/KFXI6G1JOoVRSYMLBAZfOnEGkQsF7zp7NcOMGXctbt2ieXr5MyQB3dwo6NW9e0yn+sxgxgv4ePEh/4+NpjsbFUVChukhoXZAkCqhERdEasmwZBSxefRXw9ISia1ecW7IEKpWK6+np1XuPBEHAcGov+kKM38JCao999epVfunSJQYAY8eOhaOjIwAKDn9V0Z9bluXzfn5+lwF0fRG//RKEl872S/wroauru8XLy0vvucSs/iVwcHCAoaGhtGrVKrHCMZZ1dXVllUqlMDMzEzt27AgLCwsYGxtDpVJVd2DYE38BkLFRXFwMreNdWlqKwsJCVlhYyIqKilBcXIzi4mLk5eVBo9HAx8cHbm5uf97Brg6lkrIZ5849nRIpCGQMWlvThjNxIinwPqPPsxYqlQomJibSqVOnMGzYMPFF124XFxejpKTk+R35mBhy2qr1zTQyMoIgCKhoJfbP4do1chxv3yaj98gRepw4UdM4EkVq17ViBRn1Dx+SMbZ7NxkCbdtS5rMB7UdsbGwwatQoXLhwQf7mm28EX19fdOnSBenp6diyZQu3tLTkXl5egru7OxQKxVMNgzlz5rDvvvsOwcHBzKXaWDI2Noa9vb0cHBwsyLKMJ9Vcq6NZs2ZYtGgRS0tLw8mTJ7FmzRptT1Du4OAg9+zZUzQzMyOq5ddfk0E8eHCDsiCyLKNZair033yT+uU+gVdffRX37t3jp0+fxowZM+r8woKCAsTGxqKsrAwVtZaV18Te3h7qwYNRMH8+zGNiBPz4Iwp0dRHetq1w8/Zt6UIFNdHZ2RnDhw8X/lSQ8d49EiN68nxnzSLnYuzYp2b3AZBx2b49CewFBdF4aoCjXasFF8ho1NJjGWOIjIyEtbV1FesnN5cMWq2zzRgJ+C1eTE4YtRLE2LFjK7+zbdu2WLNmDX755Re0qVDmBYAWcXEYfu4c4nv1khQKBevfv79Q0dNYunv3LpMkianVatHl/n00X7OG7rF23pw9S9dOSxk1N0fBoEFgY8aw0d9/36CAR0FBATMIC8M5c3MU29jAKiwMrq6uVcwFWSYV9k2biLKuFTV8EiNGUF2xhQU57KtWEb3V1JTGsvZYNm4kpzchgboZXLoEiCJU1taYNm0a8/f3h4uLCzp06ABJknibNm0YKnR6KtZX+qIpU+CeloaWjx8jJSVFio+PFzMzM6FSqVCLVWZiQiJdZWVUijBzJkwSE1H25pvoPX8+bty/L3d54w0Bfn6UfTc2htODB8zpxx9FecYM9NZosH37diExMRHbt28HjIzQJCYGWf7+sLC0lFmHDijavVsY88MPSGzSBOLQoRg+fDhcXFxgaWmJrORktOrfH5oLF5iepSVeq1BSVygUOHHiBMzMzNCmTRuEhYVxI1lmHbp0ER6vXo17d+/C8e5dciQZozGuFapMSUHT+/eFkI0b0S0sDIIoUtA4KgqWenoY2bw5u+/szB+6uvL7enrC6z/9BNtqjI68x49RVlYGzjl2794NJycnafTo0SLy8oix8KQ6vL4+1S0DUK1ejeHR0fjS2hpBb78N5yVLUFBUhPz8fGiUSuz69lswpZLJ4eEIKS6G+6lT2klQ9X07dlCp07lzgCiitLRUtrCwqDv4GxVF+0W7dlS3Pm4cBdRWraJ72xD9lQsXaBzm5ND4LCujNUXb7cPDg9gZR47QGD9wgBzP9evrVvWuB/Hx8byoqIhBR4f23+q9qwsKyOFOSaE6cX9/0i1ZuJCuhZ0d4OiIyvK3hsLPr4opolZTZ4IPP6S/hw9TqceT0GjomnbrRuUVDg5039evp2tRbS1mjKFJkya8wa2/XgA0Gg2+rRJ4qzyYPXv2wM/PDwBw6dIlNQAlY+wY53zVP3Zw/0N46Wy/xL8OS5YsGa9Sqbp6eXn9c9nCFwhBEPBEfeUz22A96/tUz7tpvEi88w4JvuzdW/dmo4VKRZvw48cUCdbXpyi0k9PT6ZQVmDRpkrh27Vrepk0bVM+QvAiEhYVJFfXgzzemTE2plgyUgVUoFLh9+zbX09P7Z5UlJYkyMuPGVWVoBw+mrJixMf37tddIJbU6BIEi/1pBoOBgai104QIZ68OHkyDTU4Iijo6OcHR0FO7du4eDBw8iMjJS1tPTE+zs7PjYsWOFhgZGjI2NYWtri0ePHuH48eO8T58+7OHDh3BwcMCwYcOEH374AflaRfanQKlUwtbWFtOnTxc1Gg0yMzORk5PDrl69yn766Se4GxtLvU6fFpUzZxJdtYGIiIiAIMsweIrqubOzM7t27Rqio6NlJyenWoP6119/5ampqaxly5Z1BmOUSiXRmbt1A9q1g1FYGLp/+im6/+c/YrydHQRBgK2t7Z+LNJWVUdbx9m0yNAFyxmbMoHvct29NVejqSE0l9dsjR6gut1MnCsY0wCh8+PAhDhw4gOLiYjRt2pR37NiReXh41HK8ra2t5cDAQMHIyEhauHAhzcPdu4mhkZlZ5fhaW1O/6/nzKfu2d28tVsLs2bPx+++/Vzra/fv3h1KphGrDBizQ0akxx19//fXK/4euWIFLV6/C6z//QTMTE1RejYgIyhBXONuccxyKiJBU06axETt3CnBxeWatfV9PT0H85Rd+q3t3FKvV8o0jR8SAgAB88sknlAWePZuc602bqHa1PuTm1jxfIyMy3AcMICeiukOko0O01owM+syAAQBjkA8ehFlWFhwdHeHg4ABUM7JrzdfkZFhNmgSrs2fh6uoqlpaWYseOHeCcyxkZGUKTJk0glJRQj2sTE1JnvnWLhCO3bQNatsRrOTks8+FDXMrJEZo/fAjLvDz67u3biT69eDGEbdtgBcDQ0FDevn27qFAo0NvHB51eew0358zBbVkWMjIyIIoitrz1FmSNBoOuXYPp/v0oDQlB48aNSRNjzhwonqijtrGxAWMMR44cAeccr54+jdGpqVB88QXubNnCb5w4ITuGhIhhaWlIiorC4MGDq8ankREcO3XChY4dpdVt2ggziouZwfTpwKJFEBYsQLOPPkKz3FzWzs6O+fv7Q37idgUEBCAjIwMWFhZSdna2WNkhZeNGWlefptD/zjvAO+/Afd063nH9enaKMciNGkklVlZo06aNOGbMGAjbtkHz1VdY+s47FFCpjrIyqqnesqWyvEOSJKbRaKreExdHDnnr1kSDt7EhlsLFi/WvB/UhLo4yysnJVWM0PZ0CK9W/S1+fsttDhpC2iI0NtdibMKFuSnkdsLS0ZElJSXW3BTUyovGnxbRptE/fv0809KAgyib37UtBFX19mr8dOjzdFnFzI82GkBD63IQJlPF/990q5hhAgbNdu6oU4nfsoOTC5ctVzno93S7Ky8thaWn5j6n7Ptky85133kFJSUnl82lpabh48aISADjnE/z8/HL/qWP7X8JLZ/sl/lVYsmTJMKVSuWH8+PH6z2xv8xL/HNzdiVL++uvPFlUyN6dNByBnZ+FCokI+A8bGxloWwAsNsuTn5+Px48di/+qbc0PRsiUQFYWcnBysXbsWgiDAyMhIfmZ7rxeJqCjAy4uMiE8+qXre358i+xs3kmHREOGt7t3pIctUe5mbSwEUHR3gP/8hA6pduzozwY6Ojpg1axZ++OEHQRAETJ06tcGOthaTJk3CyZMn+bVr19jDhw+RlZUFxhi0XTGio6NRWFjY4OCSQqGApaUlLC0t4ezsLOQHBeHumjXCYaUSvl5eqIdgXgsFBQU4evQomnXsqDGaNavehWfgwIGIjIzEtWvXoFVarw5XV1eWmpqKHj16PFtIzdiYalT9/YGLF2F/6xbVXf5Z6OqS06qt/5RlcvDatKFyiLrW08OHydFcsIDuu1IJjBmDjIwMHNuyBYIgwNnZGZ07d8b58+cRHBwMfX19lJWVQRRFLkkSY4yhvLwcLVq0AGOMnzx5kp05cwavvfZajaBZWlqaIIoiqgvnAaAAUvPmZLBWh79/lUK3rW2NjLwgCGjdunWlsy0IAjqtW0fG9Ny5dV+fNWvQ+Y8/kPXWW3z3gwcoW76ctWnThnt5eTHbJ9an6OhopKSkCAsXLmQIDKQARGDgU0sRWsoyMG0as5s1CwDEK1eu4MyxY7jzn/+g9eHDOOjszMu6deNvNmny9NDj2To67DRqROU6PXuS09umTc3Xtc7IyZOAWo3TK1fyGb/8wsSPPyaBrtat6z/2Zs0oUKrRAAoF9PT08Oqrr+KXX34Rfv75Z7Rzdsbrv/1GTsq2bXQf+vcnEa3Zs4GlS2HbqxeKli1D/rffIq1xY1j+8Qd99++/02d69KA2WIcPY/qaNSKAqlrkI0eQHxeHrNhY9OvXD66urhBFEZcvX0apnR2uHD2K7klJsAoKIqfq00+pFnju3EqxwzNnzkBXVxfjEhOh16YNTH7/nbEyEk92d3dnYadOiXcNDHA8KAj6+vp88+bNvHnz5kJqaiovKyvjjo6Ogo+Pj5g0dy7XrF9PAYKJEylL+vXXxDRISICuri50dXWh0WigVqtRXl6O8vJytG/fnvfr10+7J9Df8nJy9hqAAbNnM8yahXEAMGCAiPBwojI/fgxMmYLTAEzS08kR/OIL6ieflETBqsjIGmUhSkGAWFxMc/r0aTr28nKqwX7jjQYdT53QfsfZs5Sx1WLpUhp/a9ZUPffqq1WBajs7en96OjGqzp9/qkhl5TUZMAA3b97E9evX4eXl9fQ3M0a/Uz1zvmABBfuzsijr/ccfFFAbMoSo3Xp6FLhq1armfldSQseamUlr4rRpFBy1tCRhvG+/pb3y11/Jvpk797nWbQsLC/6IAvj/iMP95B69evXqp709E8A/oEb3v4eX3sxL/GuwZMmSMTo6OlsmTpyo//8jffy/Gt7etLns3k0bZkMRG0sb8dy5lMHavx8A0boLCwthZmYGhUKB+/fvo3nz5igrKxNfpKJ3fHw89u3bxy0sLNCoUSMGkADV+fPneWhoKOvcuTNEUeRxcXFyz549xVatWqGkpARXr17lXbt2ZUJJCXSSkvD48WMAwOjRo+Ho6PjPOdrp6cQM2Lu3trq0o2OVcTFrFhnjd+40LHMgCFUZF19fMiJKS0ncpkkTquu1s6vleJuammLq1KkwMjKqIYbUUAiCgIEDBzJJkhAWFgZRFDFt2jRcu3YNsbGxcmFhoZCamorWddSWPxPnzsF4xw6Y9e3LArOyYBUZCQBIT0+X1Wo1LywshFKp5EqlkjHGuCRJaNKkieDl5SXcu3cPnHPM+PRTBQwMyLmqB87OzvzGjRvCDz/8wGfPns10dHQQHx+PuLg4XLp0CW3atKnd6upp6N2b6IdffEH/Dgysv963PiQlkUNz7x79Pz+fsldvv00Gf/UAGeekLj1gABnDmZnkbP3yS+Vbtm3bJpeWlgraczt16hT09fU5ACaKIgYOHMg1Gg07fvw4AMDDwwMDiUUg3L59G7///jv27dsHHx8f5OXl4e7du7y8vJy1bNkSnTt3rulrHjpUN53a1JSy21OmUKDp1ClAECBJEsrLy3Gwoq7S1taWd+rUiaF1a6JwPgmNhsopBg4ERo5Ef0tL1h+0NuzYsYPdu3cP7377LTLWr0erN95AXFwcAgIC4OHhwZRKJbFG9PSINbJlS90lCbdu0Zi5cqXyqW6MwSQtTS65dUv4rk8fKC0smKKoCMuXL+dz5sxhdWoNREeTYZ9bR3LJxISybEOG0BipT+xRqcR1gLEDB9DPyormd/fuJH6Vl0dZxifejyNHqH63wiG3tLSEhYUFb/7HH8xl506k7t+PZi4uyM3LQ9rDh2jZsiX0LCyodGjdOuDBA1xr0QKcc9Q6L5WKaMRvvQWYmkJPVxc4dQqF3bvj7t27uBEYKHn99ps4fPduVC8v6dOnDzjnWJKQgHZnzsBq2TK6xoJA1OSWLaksAkDvzp1xIDtbigsNFZX5+VKnmTNF7XqpVCoxvl8/FMTEYNq0aTAwMGD79++Xs7KypJYtWwolJSXC1VOnEC9JXNfbm3dp25bh7Fmql4+JIRrx/PnAo0eY9dVX+CknB2UGBhAEoTI73u7JNTc9na6ntjyiIdCOq5MnaS0+fZrm8O3b4AYGmLB5M4ebG6vs+/3553S/9PWJVh0eDmRmYsDKlSzC2ZnKEaZMoZIQb++GH0ddSEqi8ZacXKkYX4np02szowSB9qLt2ykYDNC4dXWlbPE339QOGD2B9PR0SJL05zu6CEKl8jl8fOg4i4tpjczOJoHEL76g+WZrS/NCpaKSjBEjaLwOHkznPGIEldJMm0Yt3Zo0qbM9YENgZmbGoqKioFar/5bOOrm5uUhISEC7du0qS1jeffddfPfdd7Xeyxi7zzl/E0AigAOMMemFH9BLAHjZZ/sl/iVYsmTJAF1d3QOTJk0ysHgRQhcv8eKRnk6R8TVrqnqtNhTR0ci5fRsZDg7InTsXJwYMAAShcjOQZRkKhYIzxpiRkRHXaDTylClTRJVKVYuOmpSUBCsrqxrPP1kvKkkSAgMDce3aNTg6OsrDhg0TtKJ1P//8M5ckiRsbGwupqanQ1dXlZWVlDABmzZqF69evSzdu3BABwKCwEE4PHiDUzQ26uroQBIH7+vqyDs9jRP1ZpKeTWFBsbN2UtJAQMhC0r40ZQ4ZQNafpuSHLZIhs2kQG+KefUhZl+PCGCdw8AwkJCUhOTkZqaioiIyOxaNGiGlns58lq18DvvxOVtk0bZDg74+jRo5AkSa4YF0LTpk3RqFEjlJWVVdZXiqKIW7duoby8HIwx9O7dGz0cHCjr8gxnNysrC2vXrgUAfPLJJ/juu+9QXFwMd3d3+Pr61lIYbzASEijTO3o0OcoNaN8EgJyzdeuo1rmggDJ/JiZUd62dF7JMdZ2vvEJG6HffkcP9BHbu3MkfPHjAfH194enpiYiICERGRsp9+vQRqgdBJUlCdHQ0SktL0aFDh8pzLisrQ2xsLGJiYuTw8HABAHR1dVFWVgaFQgFDQ0NpwYIFNQNWS5eSw7pvX+1z02iIKVNYCHTtip3Hj/MHDx4wgJzCGTNm0Pvu3KF16UlneOVKmiubN9dwFB49eoTt27fD3Nwc1gEBMJkzB7KenlYEj48aNaqqVluSSAMhL48c9+qQZVJ3NzamQOTjxxSUjI0F9/XF/uJiuaSkhL355pssODiYnzlzho0bN67ugFJxMZV41HFfKnH/Pjm5hw9Ttq0OfPXVV+jcuTN8fX0puKJWU1Z08mSaJwkJlO3Wnt/8+VR3qg3WFBQg7fx5pH/7Le64uuJ+tQCooaGhrFQqWc+ePZluQgIeBwXJXTgXlI0aYZ2JCXfr1Il5V3fuHj2i471wge5NbCzK27fHyvnzoWtuzr08POA+ZQpThIbWWc6ybt06GYBgZmYGt5Yt4eTrS9/VvDll/AWBsvPLlqF0/Hh8//33fPz48cymelAhMpI0L6ZOrfX9sizjTufOvKVSyQwr+rLj0SP6jQkTAAB5eXk4+NtvUvNDh8QrHh7oefkykl95RSpo1KhSxLRC1BScc7QJC2M2sbE4+dpr4JxDlmVoNBoqdVCp5Nzc3MrNSldXV+acs+qfB6iUwaCwECX6+ljwzTdI9fWVW2/bJiAri+593760NrdvT86gkxOwZQvuRUTIe65cEbT1uC8EHTsSu6pizatEaSmN+braCBYXE01+/fqar508SS3Rrl59Ko1948aNUk5Ojvj++++/uPOoC48fk+BbdjYFte/eJUZDQAAlCm7fJsfcx6eqLv0voLCwEPv27ZMzMzPZwoUL2YticGZlZeGXX35BeXl5jecXL16M+Ph47N69W/uUB2Ns/eeff97pWd/p7+9voVQq1+jo6BSVlJSkybK83s/P7+ELOeD/Mbx0tl/iX4Gvv/46bMiQIR1edK3uS7xgBAWhUrCkARTi0NBQdOzYEYIgYPny5bxJXBzzDQjA3nfewaIxY5BvYgJRFGFoaIjk5GQUFRUhPT0dly5dAuccSqWSd+nShUVHR8sWFhbCgwcPtLWhmlkVdN8ff/xRys7OFkVRxKRJk2BjY4Mvv/wSskzVdXPmzMGVK1dw//59XlJSwlQqlTR//vwalsGOHTukhw8fitp2ax06dEBYWJhsUFSE6WvWCIUxMYiJicHdu3cRHx+PPn36oE2bNjhw4IDk6Ogo9u7d+8Ve5zNnaHNPS6t/gx8wgByziRPp/2VllOnQaGqL8vwZcE409W3byEDX06MSgXHj6Jj+hIjd9u3b5bi4OEEQBFhZWUnTpk376yyB/fsp6zlzZqUic0Nx/vx5BAUFoW/fvuiuVJLxWr2v6lNw/fp1BAQEwNvbG8XFxbhx4wY6deqEIUOG/JmzqEJ2NqnIe3mRAf2sc8rMJGN33jy6T++8Q876a6/RPVKrKdN9+jQpBMfF1WvkHj16lIeGhjKVSlVTHflPYuvWrbKTkxO6du0qcM5x6NAhKSUl5UlNC8oI379fNZbrwqxZKAkNxY+vv46SkhJYW1vz9u3bM3d3d8q8OTuTM6wdl3l55Fx+8QWxQCoCF2q1GpGRkfjjjz/QqVMnebCLi7D/l1/kaD09AQCMjIzkhQsX1mZ6FxQQu6e8nEpqtPj5Z8q8L11Krx87RvWeQ4bUYqOcPn0awcHBWvG8Wj+hiY+H8OABhD59nn5hz5whB3/9+jrr6pctWwY7OzuMHz++5gslJXQdmjencTJjBgULlEp6XqkkJ6lnTwrKrFqFzMxMBAQEIDExEZ6enujdu3elYnnvI0eYWXY2Do8di/E5OUi7dAnHR46Eja0tHj9+LPXs2VPs0qUL3V9BgOzujrCwMBw7ehQjR45EO19f6tE8ciSN4zoYbRERETw5OZknJycLkiTJM2xsBLzyCp1D06YkkJWZCdjZQaPRYOXKlXzq1KmsBjtuwwYaXytW1Pzyb74BDAywy9CQP4iLY84uLpKLi4toFBMDsbgY5d27QxAEJCQkICgoiPfo0YOVlpSg64IFSJ45EyXt2kEwNYVCVxcKhQKiKEIURejGxEChUECuyC6KooiSkhIkJSVBT08PSqUSgYGBkqWlpejh4QE9PT3o6upCqVRWdofQ0n8FQQAePoQiORns8GGqhVYo6NjDwylj26FDjcCcv78/Pvvss+dqGVonNBrag4yNiX3ypGMYEUGlZdogzZMICCDa+JPXPS2N5sjevXV2yIiLi8OOHTuwYMECNLjl4YuAVsRQu07a2xNbZsIEYlW8IOFWzjl+/PFH3rhxY4wdO/b5O07UgQsXLvBz585VP8ASAPoAdUu4cuUKRFF8JElSSwDOAFQAIvz8/Arr+84lS5YsF0XxAy8vL5aYmCjHx8dLCoUiV5ZlA4VCcausrGwRgFA/Pz9Nfd/xEoSXzvZL/J/D39+/ta6u7u33339f/0kxh5f4l4FzcgJnzqyk8FVHfn4+fvzxR9jZ2aGwsJCnp6czBwcHCIKA+/fvY/HixSR2IklkiAYFUT34E9iwYQNPSUlhrVq1kh8/fgxZloUmTZpIhoaG4u3bt+Hk5MRHjx7NTp06hdDQUMyYMQNr1qyBs7MzoqKiAFA9r0ajgY6ODkRR5M7OzszW1hYODg51Zk9lWcbJkyeRn5+PYcOGUU2hRkN06sREQBBqZDQZY3BxceGRkZFs/PjxWiGiv46SEspYnT//dBri2bNE865uVI4fTwbzli0v5liqIyyMKL+entTSZOjQKgphA42QlJQUbNiwobJP8l8C5+RwyDLVkD4n9VyWZXz55ZfQ1dXFW2+9hcaHD1OGXFtv2oDPr1+/HhkZGZXPzZgxo7aC85/Fvn3kTH32GQW36nN8DxwgR+/YMTr+oiIyFiWJjOOOHSmTvXUr3aen3KtVq1bJ7du3F56mCP9X8J///AdTp06FVV0BpLg4CghMn17nZ5OTkrB31Sp0un0bOb16ScM++USsNFLVaspIadkXJSVEYb1zhzL41faVXbt2ITY2FnZ2dvKkSZME5u8PHD6MH6dO5cXFxRg1ahSrt7Xbo0dE3504kSj/ZWU0R3ftogx6hw5VNaFPICMjA5s2bYJGo8GiRYtqdUa4ffs2Ej/7DB4hIbiwbp38+uuvC8nJyYiMjET37t2hp6eHs2fPokWLFpQVP3CAnJlvvqEMbzUsW7aMi6LI5s6dW9kGrQYkiYIGP/9M9aeZmeR8l5eT0nXv3jWYLAcPHpQjIiKEMWPGoG3btpAkCXl5efhp3ToYGRlJmowMsUgU4RwdDdfwcOwbOxZyhZPJGOOjMzOZQ1oazs+YgaCgIJiYmGDixIkwu3WL1pNZs6rGr6kpcOMGqV27u9NzeXm4kZXFG6emwn7kSIZVq8hhAyjr2L49IIq4ceMGLl68yBcsWMBq1Kp++inRlrXBnIpALJYtA0xM8GvjxjwmJoa1bt2ap6eny71/+40JGg07OWYM55yjvLxcMPDchjsAACAASURBVDY2lufOnVvTKxowgMbWsWM1r6+HBzEpnqLuvX79eql9+/ZiN63KtlpNgeyff6YgmygS/V7LWElLA77/nkQxmzUjUcM65jLnHEuWLHkxzrafH7E07t+v+/WcHNrH68vOastboqJql4qcPEmZ8l27agWlVq1aJTk7OwsDBgz4x4TEwDnNhYAAGi8//UT7ys6dFLj78ktaVyZMaJDg67OQmZmJdevWQaVSYf78+X+eDVUN5eXl+Oabb7harWYAXAEUCoLwvizLT4sgC35+fjUcQX9/fwZgDoAfBwwYUN61a1cd7THn5uaiUaNGuH//Pj99+jRkWWaMsc855wf9/Pzu/OWT+C/FS2f7Jf7P4e/vv7Bjx47Lhg4d+gLScS/xt+PKFaJ8Tp9ea5Pdv38/7tyh9dbIyAj9+/fHiRMnUFhYWNvBys+niPnw4WScLF5c+VJxcTG2b98u5+TkCIMHD5bbt28vAKScuX79egBAjx49kJKSgiZNmsgDBgwQDhw4oImNjVWoVCqMHj0ajRs3rrN/9HNj/XraYKsZrZIkQa1WQ19fHwEBAdL169fFiiwg9/b2Zt27d/9zv7VyJdWKPc2A0WLyZHIkqhvaYWFEKfbx+XO/3xBwTvc/IYEM4exs4OOPyVDs3LleZy43Nxdbt27lVlZWfPTo0X/NWuGcjMDAQHI061F+rQ8ajQZLly4FgKoA0J86DI6ffvoJ2dnZGD9+/PPVajcE6emUabl0iSjh2hpELcrL6XqnpJBh3Ls30TqTk8nQv3yZ6gvNzBpkIPr7+6NNmzZ4468IKWlRUkLOaFYWGd0mJti3ejW8u3VDM616tq0tndvly0RJjYmhjPyBA0BUFEpatMClS5e4paUlO3z4MCRJwuhbt3hbgFX2wgXIYBcEKnN58IACgRs2kLpwtfGodRqHDh3KO3bsWPmCRq3GsuXLMX/+/No1x08iKorYHuPHk0PVvj05vCtXknFez7w9fvw4v3btGmvcuDFXKBTMysoKurq6cnh4uGBpaYnY2Fh069YNt27dQklJCQRBqGTnVBcRVCqVfOzYsayFvT2JNZWWkghlhTOjHdsGBga8uLiYmZmZ8ZkzZ7J6x3hRETmfJiZ0rdasIef3CaxatQpOTk545ZVXsHHjRjknJ0cAgM/GjwdcXLB9xQqkFBTAPjISPUNCYHrxIvIZw9GjRyHm5WGItTV2JifLEufC/PnzoVudfTNoENHYvb3puqamUv1s377k6DVujM0XL8pdu3QR2nl7k1MUE0NaB++/TwGC119HyuzZ2LhxI1QqFWeMYcSIEczOzo4CTZ07V82frl3Jyf/hBwA07kVRxKeffkqvFxZSoLWC1r58+XLeu3dv5lFdGAwgBzkvj2jqfn4UPM7LA/r1o9KFupIHN28C5eXYe/48733yJGvyxRdUsrN2LbEnBgygTP/QoaQYPmhQ1Rq7cyfNqYEDKeu6eXOt39A623+ZRp6YSJ0BcnNrBXMq0b8/aQIsWlT/99y5Q4GCutgacXF0rjt3VgaowsLCcOTIEbz++uu1a+H/Lpw4QQyYzz6jDLZWtVwUqYPHrl3kbGdlEYsoIYHuz19kkK1YsQIlJSWYN28ezMzM/vJpbNmyBRXia9P9/Pw2+vv7ewMIeuJtAwCcA6AB0B1AMIA2jLGxOjo6b8iyrANAt1GjRibDhw83rDMwWoHt27fzuLg41qJFC3VcXJxSqVTGqtXqkX5+fuF/+WT+y/BSIO0l/g3Q8JdRn/9/0K0b1b8tXlxJD5NlGUlJSZWO9ueff17p6LZr165uMRAtPezNN2lTv3ePakpnzoSBgQFmzpwprF27Frdu3eLtK7Is1VuaXL16FaIocktLSw4AI0eO/HvWs/Pnyaio5myLogj9CgXYQYMGiQMGDEBOTg6OHTvGrl27JqekpAhDhgypUtxtCGSZjMKuXZ8taMM5UfB+/LHm8x07UmZ20yYSc/k7wBj9TseOwLBh5HgLAhm9ANXjGRsDnTujqIJezTnnwcHB7P+x991hUV3d1+vcO8NQHHrviIAioNIEFUSxRTSKmtg1do1Rk2hM/KUYYkzMG32NJiYxMYnRaCzR2Cs2iqJioSOo9CrSYSgz935/bIYiYEnytnyu5+FBYbhz59xT9tp77b0dHR3/PNFWKiknGaBIzzMUartx4wYSEhKQn58PAJg6dWoL0baxobF7hnz8Bw8e4MGDBwAAq2ck/E8FMzNyaunokGQ0PJzk4mpCt2QJOb9WrKDCPcXFtD4vX6ZiWh1UTO8MDQ0NAICBAwe2/UVjI0WwlEq6fkUFkYKaGiJo58+3RCFTUshIz8sjRYiXF60fAMoJEyAvKIC0ooKUG2Zm1Ev6/feJYMyeTeRx8WJyxPn6QnnwIPJ27WLK4mKYjR+vmjt3Lg+AQaEgY/ett0jyfP06FYry8qLq5a+80uFzLC8vh6urq9CnT5+WOThqFPDBBxBFEV999RVWrVr1+Iigqys5s0JDaQyKiohAPWGtu7i4sFu3bqGkpIQB5DhEU0vIu3fvwtLSEsEJCRhUVoakiROhVCrRs2dPaGlpNef9iqKItWvXsoiICHLsvPkmiufPR/HixYgfNAgyHR2kpaWJZmZmmDdvHrtw4QKio6PZY52OFRUkx/74Y1pPEglF79PSKMqqrw+lUonq6mrwPI/PP/8cJiYmrH///oiOjkahtjYsU1PRq6wMD8LDBdvFi7nT+vqYNWYM5Hv2wNHRUYiOjuYK1q+Hk7U1N2zfvrZEWxRJxbFtG9UpkMmApp7sR44cgUQigVZ+vliqUnE+VlakWKiooD1ArWY5cQKQyWB57hxW7tqFjN9/Z78fOiSWl5dTga2EBJqfp0/TOtmypU2BLiMjI6Fv374tD33uXIqCN/XilsvlYmlpKfBoBWkNDXJm2djQnGCMSNuSJeQEWbuWyGh+PkVLL14kx4itLeDtzaotLWFiZUXjvXYtjX14eMv1J02icyE/nz6zKJJT5aWX6DOEh7dtgQXgLzGl7tyh9VNS0jnRBogoP6nLR2Mj1TkICGivznFwICfFzp3k4LG1RX1TFfm/TCn2ONTX094RGUl1C7y8yHHQ2EjnSlwcdQAZOrQlf3vtWhqX8HBSHlhZPX1tjUcwePBgMTIyUjA0NPxLJJ3e3t5idnY24zjuHQDbAGS2/r1EIln67rvvng4LC+MBWNKPJFcYY7179erFTE1NpZWVlTA3N0ePHj2eqIyYNm0a+/TTTzFq1CgNqVSKtLS0rseOHYsLCwuzWL16deFf8Zn+LnhOtp/jvwEX7t69+zzn438JL79MBvKHHwLa2tiyZYuqtLSU9/DwEMaMGdOuJdRjcz/Hj6fvP/xA+bcLF5I3XV8fvr6+iIyM5ERRBGMMVlZWmDp1KjQ0NMBxHIqLi1nv3r3/tbkHurp0+D4GHMdR/2RAVVlZyScnJyM5OfnpvfNz5hDZSHpKFZYgUGGXjqo437tH3vh/FdluDTXxBsiQLSkhqfnvvwNz5kB57hzSlUrkWVszb29vhISE/DmirVBQND0zk3IXn7Fq97FjxwAAhoaGcHJyUnbr1q3lDHznHYqQPSUSEhJw48YNEU0GeH5+Puzt7Z/pfp4KjFG0LzeXIqiLF1M7In19MlCLi4F//pPIkqUlfQbGqAJ1a6hUFLErLiajvaSEjPurVwGJBJyZGQIuXoTBrVtERGpq6D02bqRrjR1LxfoUCnoPHR2KmHfvTgZnt26U56urSz/X0KD7aHLCcIKAIo4Tvs3L49xv3BBGHz3KIS6OnuPgwUSWQ0KIDMlkQFUV5KKIgPPn0XDrFvqGhvLw9KQIsjqn+ORJci5s3EgR8mnTSP7ZSYExlUrFtevlrq8PiakpXn75Zezbt+/Jz0OlImKYkUFOprlzaT9ctYoISqs2TK1RU1MDQRAwZ84cWFtbQ6FQQKlUQt5aQrt9O3iOw6MFGBljzX1x+/bti6tXr+Ljjz+Gjo6OqsrKin/x8GHYdOkixru4iKGhoZyLiwsYY+jWrRuuX78ubt++XVS36muzNzc2UmR+yxZynr79NjlP1DnclZVA//6QrF6Nnq6uqitXrvC9evXC6NGjWVFREaKjo3Ho0CHh1Vdf5fpMm4Y+06ZxP6WnC5r9+4NbsIDDsmUY8t57XImLi4rz8+NfNDVtQ0yE+/fB+vQBy8lp6avu5oaG5GQcOHAA+vr6CA4ORmxsLCsvLwfbvRsPRBEmpqbkbElJoQupZf8jRkBTpcLN2Fhh9rZtnOnQofTzzExyKLz0Eq2f1jn3TWgT+dfUbFNMT19fn928eZMZGxuD53l07doVehzX4mh5+JBI9/LlFC2XyVpymYODSfr966+0PpucT6rdu5E6darg4OjY+Z54+DCt8dzcttWvAwNpLSsUpO5Rf85W+Oabb1Qcx8Hb25v38vLq9C3aIS+Pahzk5j5+fy0uprXwpGrhvXuTU7aigsboUYwZQ063+fOB9euRlpYGfX39Z3NU/xEIAo2bpSWppEaNoiDCqFH0zAYPpmf54AHtL9u3U6pKeDj13Q4NJefgmTPUUkwq7fg8fgyys7MFTU3NPyW/UygUiI6ORmpqqvLhw4cSxlgDY0xdVU6H47jjgiB8xxirVyqV0WFhYUGgyDYkEonS3t5eOWHCBA3ZH4jScxwHExMT4fLlyxg1ahTXp08f7tixY2CMRYaFhTk/Kk///xnPyfZz/DcgWaFQSOvq6v71G+xz/DWwsKA8teXLUbpmDUpLS/kmKdQfJ1Nz5tBXVhYZ+yUl8PLywrlz53D//n04Ojo2G5BqWFtb/xWf5vEwNSVi8hQ5wZMmTeJTU1Nx+vRp1NbWIioqCo2Nje2M53aor3+24iuxsVR9PDOz/e9ef50MtPz8v6R66lODMTKm5s+naOzDhxBv34b3sWOwz8iASVISEbOuXf9YoZmKCiKZPE8S4WfIm6utrcUXX3wBAPD39xeGDh3KMcZazr/UVIpAPeX+09DQoG49xUJCQtpU4/6XwdqaSHVGBklglUqKMC9cSGNRUkJRZXVrm4wMKnYVHU3PxdublCN5eUQQa2upuFZhIeDkhBptbeRZWSHNywvuoaHgpFIi2x9//JcUBuI4DjN//53LqKkRj/TsycUtWYJehoZEsNV45x26TxcXgDGk372L3Vpa1LLrzh34TZpEpP6bbyjfNi+PooTFxbQfff45RacegbpbgZeXF06dOoWkpCRygtXXk5NPJoNNdTXkcjn27t0rTp48uf0Hzs+nKLyREe1T69YRwbezI8KqpUXR0Bs3yGm2bx8RiSblxdWrV0WVSsXUe5ZWR6T8lVceO4Y5OTm4fv069PT0UFNTg759+/JaWlro+uab0J0+nQUMGsRaO4zs7e2xfPly9tlnn7E1a9bAwMAAs2fPppoVP/1EkdubN2kMAVpbBQUtxbcA+re+PsZv28aPy8oCW7ECEEVYWlpi6NCh4tmzZ2khLlwI9O0LxdmzsLCw4Kq6doXuli3AO+/A0dKSpQcHw6O2luasXA5BpcKXR4+qTEJC+HubNmHChAno0aMHCj77DD/v2AEtTU1xzpw5rEuXLrC3t8fZdesErcOHuUOjRmHeG2+QA+pRmJgge/BgZP/8M2fwwgvgrKyIKB07RulASUmUavPwIRp1dLBr926xr78/BEFAm3oxK1fSHBRF4O5dvGhqyiKuXBFq1q+HXWwsl62ri+5BQZCKIq21F1+kvbaykhwxu3fTHn3gQMs1H9mLpVIp6uvrxczMTKSkpAhKpVIMCQmhOgRKJSlWhgwhkvfoXufuTqoyXV1SIyQmNkeNOY7D+PHjUVtby8fFxeHGjRsqLy+vp3NIiyKln/z4I5HJx+HQIZJUd5By0A4BAXS9yMiO95K+fYHZs9EQGYmqpCSUm5igrKys2Umv3lv/CCFsB1EkB19uLu0jrq60D4SEkPPmH/8gsq1uhRcWRl9vvUWqGbWS5rXXyDbYupXW0LJl9LfDhz/1fjlo0CB+8+bNuHjxIoKCgv7Qx8nKykJ0dDTQxOdEUdSQSqWBAH4HUMAYK+J5/j3GmJVSqbRkjIlSqVR47bXXuC5dukjanINPQENDA3788UdlQ0MDGGPQ0NBgcrmcj4+Px6hRo8BxHLS0tKBQKLoBkAGo+0Mf6m+I52T7Of4bIIqi+NfIn57j3wcLC6CgANU7dgCgllx/Rd4R7OyIKMjl4AYNQkC/fuK+ffvYggUL/prrPyt4nojMU0BDQwMeHh7w8PBAVFSUKi8vD8eOHeOdnZ3bFUQCQF70IUMoZ+1ZYGTUYRubZqxbR5Lo2Nhnu+5fBcYAY2MUz5iBw1IphtrYoEd+PhkrY8bQ5161inLTn4Y0FxVRBIExMnyesUDNt99+KzQ2NnK9evUSm4h22xds3Ei5oefPP9X17t27B5lMJr7zzjv/mgI+dXWkUOjZkwhkt25k5PXuTdEXZ+eWQm4PH1J0muep9sGhQ+SIUSop+rV7N0VcDAza1EV4FHoANKuqcDw9HdfOnhWnT5/O/hLnZ0oKRZx37QJeegkRycmsXEMD4Twvnt+4UZgyZQrfXDn67FmSloJSRtRRaG1tbcGvXz8O/frR65YtoyiluoihSkU5lWfPori4GA0NDbh69aqYkZEh1tfXc+pCiRzHiY2Njez06dNEttevJ9J59y4aGhogkUhQV1fX9plmZpLDYeFCeg5Ll9Ln8fcnUnbyJH22V1+l6OXdu6TM2b4dKChAIWPIOXgQJYMHM9/+/ZuVEB3Cw4OcPuvXd/jro0ePijzPs9dff739L3/7jcizmVkbVYOGhgYWLFiA+Ph4pKSkCF988QWnq6kpvvLpp+xmdrbo9/bbrM1TnjGD5Mtff02qI+qfDnTvDnb8OJGUkBBgzRp4DBnCzp49i507dwrTp0/nEBGBQWfPst99fHD16lVMnjwZzp9/DoM33oDk999pXiuVuD9wIAw9PeEwejQ/cscOfPvtt+KRI0dYWlqa6nZhIT8oLg4B9+8z9vbbAIAu9+4hND+fy926FeWXL4uYPZth584Oxyg9PR0WNjYq2fvvE8HctYtyq3/5BeK776I6KAg3PT1RZG6OiYcPsx0zZ2JQdDRz+v57Ik29e5OE/s03iRzyPOTjxyPEz4+DpiYeTJ6MgzduiLqzZzO71kqWr7+mub50KTlNDh6kefrFFx2Sr5qaGmRlZfEpKSmitbU1l5GRgaFDh1LA4Ztv6O/u3qX58CgYo/lXWUn3+OOPdB40kVN1v/KGhgakpqY+3WZZWkr7RFpaxxHoRzF7NrUcexq4u9M6vXu3U6d1Y2gozi9fLgZHReFKcLC4Z88ernXxSblcLk6aNIk9Lof4iSgqorP87Fk6I3v2pHztpUtbVGU1NfR93TpykCxeTKRbIqF5/9JLtNf88AM5G86doz3r++/JJurdm173ySdPvB1tbW2YmJiIUVFRzN7e/g8po7p3747Vq1dD3anl4MGDaGho6AYAEolkrVKpnO3i4gJXV1dYWlrCyMiIdZpXUlNDc1ZPj6L2rVBXV4dvvvlG0NLS4gcNGsRUKhVqampw+/ZtAU3pMAAwcODAxlOnTkk1NDTOA+j3zB/obwr+ww8//E/fw3P8f45Lly4NMzAwGN+/f//nBdL+l8BxwIABkBYV4UZhIVw9PWHyNIf000Atrbx7F7avvsqKT58WypOTxa6Bgf++6qRqKJVkwD5jlWlbW1vO1dWVS0xMFM+fP88aGxtha2tLeVCiSF/V1eRFf9ZxKyqiv+nscPb1JWPsr6qM/QcgiiIOHDggyOVy9uLs2ZAMHEhtVNTF2377jXLODQ2JMHbWTiwzk/IzpVKKLD5jlDUzMxPXrl1j5ubm4rRp0zq2M0aPpsI4T4mLFy+qGGPcM8kzW6O+ngx7KyuKjMTGUosvKysi1cePU/R0xQpyxMjl1ApMV5ci3IsWUbRu7VoyequryXkxaBBFspcvJyMyPJxIoI0NGeKmpvQ+r79OeZLR0ZTLGhEBGBqiZ58+cHZ2xoULF1i3bt2g/wz58O3w4YdkkKpboY0YAZWHB45ER0NDQ0N0dHREQUEBV1NTg+aWj2+/TWvN1hYXL14Uw8PDmZaWlmBjY8Pc3d3bPji5nKSgM2dSzvjw4TgQHq6ymDePO5udjVoTE3Hw4MFcr169MGLECPj6+sLDw4O5u7vDx8eHnF+enmQ86+vj1KlTyM7OxsSJE0narS4AGBdHYxcSQo4xDQ1SQixcSOTEyaklHzU5mSLrcjkRrgEDcOHsWaFLWhrjhw1TjVm8mENlJZH2tLT2c37kSJIcdyLfvXPnDquoqEBAQED7X/I8kZ85c+j6reS9Ojo66Nq1K/p6e7Ne778Pmbs7u79wIaLz8lhUVBQePnwoODo6tvT85TiaW+npLRJlLS1yBhgbk4zZ1hbc0qVwOXoUkXZ2LMDbG8jIgMmNGyzgq69QWFiIhw8fqoy6duXS5XKx8eJFpsjNFQ916SJG37nDHhoaom9YGAyMjaGpqclSU1NRV1cnzpw5k+s5ZgyYiws5laqqKCr67rsotbbGvago5n31KrglSzoco7y8PBQWFsI7JYXh229bKsdv3w7GGDYyhsAPPoDn1Kng33oL7sOGQT5kCLqMG0fr48EDUicsXQp89BEpdfz86LO7uqLBzAyR168zl+7dYazuPx4RQXNxxgy6BseRNHrTJmDwYKQUFCAzMxMFBQV48OABSktLkZ6ejq5du6rmz5/P9erVCxcvXkT177+Lqg0boL9xI+OXLOm4wJoanp60P0yfTs7CpKR2Rcju37+PiooK1ked6vM4jB5Ne8rjnLit4eVFe9LTqMsYIwfrb7+h2WH2CHbv3q3KlskwfNgwTquqihXk54v9xo5lHMepvL29OY7jEB4eznr27AlNTU3k5OSA47jmdmlPREoKOZEsLMiBaWrarFyAnx9J9r/4Apg1i16/aBER56Ag+tmYMUS8hw8nlYtcTioANzdy0t69S3vX+PEthdV++YXO4k5SSyQSCXx8fFhGRoZw6dIlJopiuyKbBQUFkMlkeFKnHkNDQ5iZmeHSpUsAkDVw4MCdFy5c8JDJZEELFizgzM3Noa2t/fix2rOHHD2VldTic8AAcpRYW+P48eNQKBTi/PnzOXNzc5ibm8PW1ha3b98WbG1txR49enAAYG1tzcfFxQk1NTW2ly5dOn7p0qWioKCg/+8jac8j28/xH4dMJnunX79+8ie/8jn+06itrUVjYyPKysrw888/w8zMTPTZtQvDdXTQ/ZNP/noi/PHHAAC/rCyuJjpaxHvvUXTk35luEBtLXu1n7OEMkKRv8eLFLDs7G3v27MGVK1cwcOBABH78MRGrP9qia+9eIgOPFrNSQ1ubCPmMGVRQ6D+A3NxcFBYWcsuXL29baKVHD/qaPJkMIMbIsCktbWnl5e9PBktiIkUh+vZ9fA/mDpCSkoLDhw+L9fX1TCaTYebMmR3PT0Egg+jiRaqA/ARkZ2fj7t27/Mim4kkdQqkkiXFAAElJk5IoIu/iQgV3HByIWJWXk3EPkIG+fj2RN1/fln7f33zTct1hw0iK368fRWWWL6d7vnChpZft77/T91Wr6AuguaBSUeRCTVIKCkiaD1B+6S+/AFVVMFu0CNpvvSWqli4FBgxgWLmSiPOyZS3VdzvLTSwpIXKyfDk5Vnr2JONWXa1YpYKurq5oa2vLysrKRDs7O3HkyJEtz6W8vPmerK2tmaamprhy5cqOI3OiSOPw5psk6dTWRvr+/Zzx8OHiy3PmMO3vvuNYUhJJ75sMzHYt/7ZsaY7OmZmZiWlpaZA3NjJkZLSoTh6NTH73HUWOW/e39vKiCPfcufSsWzkpDIcN405LpXhvzhweQ4a0FGV67z2KhO/eTeP0yiv0HDuKZDbBwMBALCGVTcdzuUsXqpS+fDmRgdbpK/X1AMdBz9ERfuPGATY2CB4yBE17E1u3bh3c3NzE8vJy0dzcXAj57TcJqqtboqat0XTd+JUrcXvbNsz09aXaDWvW0JgePYqePXvi4MGDfEJ8PAY4OopOW7dC7/332dx//pM1OjigPi4O8nXrgJAQWJqYYPTBg6LHjRs8Dh6kubl4MTmJGKM9wMUFtoIAXUtL1T8mTeLHp6XBuanIWV5eHg4ePKiSyWRobGjgDaVSEaWl5HSIiaGc+pUrAYUCXVxchJKSEs62STqvAbS06Dp7lgjGRx+RjL6urh1RSktLA4CW9nV37pDTcOnStvnLRkbAxYtIfPFFsbSigiVNniyIogiVSgWVSsXq6+tZ64r4L40bB8nJk6wqNhZ19fXQeFJ/aYmESKC6IGZJCVVDb3UmKJVKkef5J5/LhYVENp8Wokjr28Xl6f9GXx8Nhw8j0cwMrhMmNKcMVlZWQqlUIi8vj/f09ITGsGHouW8feh44wKCnh76TJ6tZJtu+fbvy66+/lnAc16xSsbe3V82YMaNzJlpbSxLwl16i56RuNSoIVNvh3XdJvXHmTNsz3tqa7AyplBxMp07RmVVSQqlQGRkthR5XryaFwa5dROLffZdSXG7fpj3t3XcpRaaT/Pbp06dzBw4cQEREBLy8vKCrqwulUomNGzeKtbW1rMk5yfz9/WFjY9Pu7zMzMxEZGdlcQJbjuOtr1679J2PscGNjY8P58+f54ODgx88DQSBb55dfSClVUdGcBpM9ZQq8oqPR+9Iljlepmp1A6enpePjwIT/1kZQOFxcX7urVqwBwnef5u2FhYUsBnAUgBaBavXp1w2Pv5W+I52T7Of6jCAsLc5fJZH09WvX0/P8dgiCgXSGb/xJs2LChuR2NpqYmnJ2dWfXrrwt+NTUcKyoib/6/AFkrV+L69etwPniQDFqqDPvvgb4+Raz+BGxtbbFy5Uqk3LqFk7//LgauW8f+1FjNnk3G8+NgY0MFdP5DsLa2hpmZXPu9qAAAIABJREFUmbhhwwa2ePHilihQa6irZV+4QEbM+fNUmTglhQ796mqKKE6cSBJ0IyOK7j5hbQiCgH379kFHR4fNmDEDBgYGndeDUCiI4D6GaKtbvUkkEuzatQt9fXzQq7iYDLkrVyhi88035BQYOpQI55gxFBmsqaHPAVAEytOT1klVFf1M3W4IoGhCZ9i7l8YhP58It50dXSMoiAxJNam4d48MxNZQyyBlshbiu3Jly+/Vcs2aGqRt2oTGjAzWxceHDGpRJEn0woVUnXrTJjLOBw4kQvr66/RvMzOKlpuYkDHaQfu7I0eOqOrr6zlPT084ODi0f4g//9z8bI2MjCAIQucPWhDISB4xgvJ3jY0x/eOP2c8//4x+vr5gjY2kHoiMbMnxfrQ12759FNVycoKPmxu7u3cvGgYPJgJ/61bHvc03bqScz0fxwgtAQgIao6KQdeUKrvXsiZycHDDGoFKpUFdXB021se3s3FIY8uhRIpeHD9O8mTCBxtLfnwizoSE5b+ztUVVUxOrr6h4fJbK3J+fI2rXkbOnVi+Z4t24kdd2+vfmlHMfB3t4eK1asYN9//z2SkpKYpqYmy83N5W7fvg0nAwPhpX/8g2NeXnSdR+Dq6Ymo3r3x/Y0bGL1pk+Dp5cVh+nQgPBzumppw2LIF5devw/izz/jE48dh0dAAZmEBDYkEGnV1RFT9/SHq6sKkqIiedWZmSy0KDQ2am02kjuM4vLJxI58xfz52798Pd3d30dHRkR09elTs3bs3p6WlBbslS0QLMzMeZ8/SHjlxIjkC8vLI2VBZqXZYNE0jocUZ+OGHaHzxReQ5O8N60SJIevcGtmyBIAioqqqCIAior6+Hnp6eqFAoWH5SEmTR0RD8/FCmpQXV1asQBEFNqCEIAu65uiI0JgaBU6ZwaN9Wjt54xQq4RkQA164hTKGAV0RE28J5HcDKygrdAgNp/hgYEPlbupScbV27oqGhAQqF4vFpCwDlmL/7LjnknoCDBw+iuroaBpWV8Jw2DVZNlcqrq6tRU1OD5nSQDpBZWIgLjo4Qbt3CqexscebMmWz37t1CbW0tJ5FIIIpiS9u9l1+m/TgiguZ+kzPklVdekQiCgMLCQpiamrK6ujps2LCBr6+v7zifOy2Nzm6FguZA6/EvKaGzxNeXHKM1NW3z1D09W5yL331H+ytAxDs3l76r25cyRmqPHj3oLAsIoLX27bdEtjU06Fzr2pX2oEeUBhzHwcbGBsnJyQgPD0dOTo6qvLyc79KlC1u4cCHKy8vZiRMn8OOPP0JXV1cYPnw419DQgLKyMnTp0gXnzp0TLSwsUFVVxQBApVL9X9N1XxEEISQqKuq8mZlZc3pBh9iyhT7T4MFARQXEJUsQu2IFLh49KmjY2nJTgoJgYmhIr/npJyAgACX370NfX1/Q0dFp4xAdMWIEAgMDIZFIcPDgQZs7d+6cYIw1iqIolUgkpWvXrn3r3Xff/bHzm/n74Xmf7ef4jyEsLIzJZLILgYGBAf369ftzVYr/Jnj48CG2bt0KlUqFPn36qEJCQvj/FtKtVCrx6aefYuXKlVAqlVAqlS2H465ddMjv3/+XFFN6FLW1tdiwYQNWrFgBrawsIkeBgWTcqyvR/quQkkKy0ScVjHkKNI4di5z4eOT9/HPHUtCnxapVRGZGjXr868rLKdL2R/t+/wEIgoCIiAixR48eLDU1FRcvXnz6quxKJcl2dXTIcLG2poJZY8cSEVepyBA3MaGfHT5MZHDePCKZtraAIEDQ1sYXp07Bs0cPIWjMGA6mpp3PS4WCIhiP/v7UKXrv/HzcmzFD3DVuHJv6669QaWuLzjExDMbGROSKiogIb9tG/7ezayk49VehpIQIR0ICEfzGRhqfyEj6fUAA5aXu3Eky4vBwiso+AwRBwOHDh8WUlBQ2evRosZ1su+WFFIU/fpyInZUVfV65nAy2MWM6Heu9e/eqCgoK2Ny5c7l2UWaAoszOzsC6dcjNzcXu3buFDiPb4eEUpTp0iN6rrq65ZdSGDRtUw4cP55sNy5ISko3Onk3pCEOGUM51a+zcCXz2GXaOHAkFgPlqlcCjyMqiSKepaSejCFQdPYq62bOxdcECqKRSWFpaqqZOncp3WLOhNZRKcpwEBxMxVqnoOYsizXPGkK6lBWVWlthDR4c1RxbNzMj5YW5Oc0IqJaKSlEQkws+PJMLnzpEE9jH1Dq5fv44TJ07Azc1NbGhoYGlpabDW0xOHubszGy0toF8/lJaWNjuekpOTxaioKGagrw8+Ph6TLS0hV9c+KC0FfvsN4tdf4+YLL+B0ZSVCQkKgr68PO1vbllzwkydRVFyM7du3i2+//XbzxBFFEek3b6L+9m2YjR4N06YxF8+eRU2vXki4dw/nqcaCKIoie83NDfrGxkR+bGyIgO7YQeoY9fMsLsbdV18VDvj4MKlUKiqVSrG+vp6XyWTCOBMT7kxpqepBdTUPAJoKBUKnTcOeffsAiQQSiQSMMVGlUjGe58FUKjH46FEm6umJ1158UeQ4DowxcBwnNkVewXEceJ5HcHAwbzV3LhEZdYtEgJwsRUVEAquqAA8PHDp0CKWlpe2c7a1t9YqKCujq6qrmzJ7NY8ECUp307EmEUFMTYrduWPPPfzb3Zx8zZgx6deAsQXEx7aXFxTSPHoPMzEzs2rULvr6+ou2WLWjMzGRJq1aJw4cPZ1u3bhXr6+uZhYWFEBQUxFlZWUFbWxs5OTk4ePCgyt7eHj4+PvyOzZvx9oEDuLRmjZBSViaWlpby06ZNg6amJuLj44Vr165x3t7eGDp0KDlA9u6lqP2PP3YoV4+IiMC1a9ewYsWK9jf8ww/kFDt4kJyGrbFnD41VU9HM5nz3b79tec2sWbRPBAfTOhw4kAi0+hz7+GO6r/v327/3zZvksJs9m5xn6rX/0UfkRD5/nhRNrUh3WVkZfvvtN/HBgwfM29sbxsbGsLOzU3c5gSiKaGhowIkTJ4SkpCRO1aQS4nlerKmp4aZPnw5ra2v88MMPytzc3OZAKs/zuSqVajOAf4waNQqenp7tAzlKJeDhgepff0VCbS1Kiovh8u67iBoxQvSaPp25ubm1yNizs2mv37AB4vffY+1rr+EtJyfIxozpVC4viiJqa2tRXl4OhUKBvXv3NnActxOAwHFcf6VSWa1UKkesXr26rMML/A3wPLL9HP9JvKytre3dpr/l3wSCICA7OxsqlQrm5ubQecoWRQkJCaKRkZH48ssvc99//z3T09MTAgIC/uPjU1FR0VzNWSaTtfciT5xIErx/UXRbnWukUCig1b07RSwcHcnQP3WKvNCPMYD/FIqLqW/xnyHb5eXAnTuQ7tiBe+fOIenGDVVAQEAb6VtDQwMaGxufbq6kplIO4ZNw+zZFPYuKOjWys7KyUFpaCltb2+aDvSMUFhYiLy8PNjY2zYZvRygrK8OlS5dYU+4YtLS0hJ49ez5+DtfXE7nYtImiCDdvUgR26tQWg0SdU61UUo4uY0QuiorI2/7gAV3j6lXUlZbCqqIC3a9cYfjySzKYCgvJUIqOJpKo7hG+cSNds6iIpKtJSfSauXOBr74CXF3RCKBP795ocHaGi48Pg1TaIsEGWvJa/4wDpSMIApHnLVsorx0gR5M61/CNN+gzq6Owc+ZQ5HvcOPr9M6yJO3fuID4+ns2aNQu2trade8zU80hdRfznn0kCvWoV9QuOjaXc+g4iTWPHjuUPHz4sfPXVV+Lrr7/evgDba681Kwx4nodCoeAaGhratw6srqZnqTYaCwpofkREwMzMjL969arg5uZGN2psTFXsBYGiswUF1Ebp229p3Pz9gUWLoNq+HUUXL0IqlaoAdCxLXbSIIulLl3Y6PPLRo1EeG4u+CxZAplDgwuDB/NatW1UeHh7cY6WcEgmpDczNKdrWGvPnAwCOf/GF2HPmTNbD25vmq0JBz7++nqLECgU5F+7epbxjpZJSE9RV5199le6/f3/aQ7y92xjI6shkcHAw09fXR0FBAX777Td2fcsWGEVFIe/SJew9cACMMWpbpq0tjm5sZC4nTyIaEM9WVrKRmzdD09iYWgFWVoJpa8Nr6FBg61aBmzmT2x4ainfffReSgwdp/PfsgXZ8PJFkqB9nAfbv3y/W1dWhW2oq0pOTxXEbNnA4cABHTp7E7cuXIZVKoaurq1qyZAn//fffi9w77zDMnNmSOgGQ4651W6zaWtgnJ3ND33oLiXfuoLKyklu8eDH2/t//cWZr1qB60SLeuU8f0d3dnR04cAA5y5ZhZk4OdC5fVitzmp9fyqZNrMzGRuy3Zw/zlUie7GFet45SBsrKWnpXv/46RVSbWhICwNixY594qQsXLiAjI6Ol9eKlS7Qe/PyAV14BEwTYDxmiysjI4DmOw6FDhyCKYtuuGCUl5CS7d4/O0Q6Qm5uLw4cPqzw8PPjq6mro6emJQ4cOZejWDWUPH+L85cv46quv0KdPH8HX15e/desW2717NwBAX18fVVVV6NmzJ0tKSuJu374NYxsbsPHj4SqRcJeKiyGVSiGRSGBmZoahQ4dyDg4OOHz4sBgTE8PGjRsH94kTKRK8eTMR10daM2ZlZYlN1f1bxj8tjdb7okXAkSMdS7fPnSMVEkB7ybfftj8fx45tKeYmldLe1Po177xD+fINDe0VMJ6eZA+tXEnP9rPP6HN88EHbfutr19I5bmMDAwMDzJs3r9N5xBiDTCZDaGgoN2zYMDSRbYZHlAtz5syRbN26VXzw4AELDg7GmTNnrDmOCxEE4b1Tp04tSEpKMvHx8dF0cXFpVnSo9u5F5Lhx4uXjx5mmpqaop6cnWmzbxmatWMHY66+3rR2gdiR//DHYO+9A78svVTWvvcY/vHcPSicnWJaWQrJgQRuHK2MMOjo6zbbNggULNNLT0+cAQElJCW7evAkAwwDs7ezz/6/jOdl+jv8IwsLCDKRS6behoaE6Tyr88L+IEydOqBITEzmpVCooFAre2NhYCA0N5R4nswKAnJwcwc7OjjcwMMDkyZO5HTt2wNzcHE5P0XbqX4HLly+jtrYWiYmJKgMDA37ChAkdv1AioUhTUBAZWR1Jhp8RRUVFuHjxIsrKygQTExOOMYaKigqqSC6TteQir1lD0aoPPqAN/hkrVT8R5uYdEodnwpo1FGW6ehX2bm64HB/Pr127Fn5+fnB0dIQgCDhw4ABqa2uhq6srTpkyhZmamqK2thb19fWoq6tDUlISDAwM4OXlBbZ589MVPwsKoshOU4QjMzMTNTU1cHFxgSiKOH/+vCo2NpaXy+Wq8vJy/oMPPmjn9a6rq8P3338vVlVVMV1dXdXJkyd5fX19US6Xi4GBgZy6oEtubi7i4uIQFxcHa2tr5fjx4yWlpaWQy+WdPxB1lNTZmQjb++8TkVy5kohkRwW6JJKWgnKt55maoIwbh6gzZ5B65QqKAwLYkiVL6H0qK+naw4dTFElDg6SlDx6QVLexkQyopjQJ5OYCAFJTU8XfJkxg8/38Hutk+JdATaLUhjFA+YHqcVm1qn3FeUtLcu4IAuWfbt3aEo15DBwdHaGvr686e/YsmzVrFsc9zTp68IByHdWF7jZvpujPmDEtkvJWkMlk8PPz41JTU5vTUdrA0BCK3FzUl5fju+++Q4f38H//R8S6NSkxNQW0tXHv7l3cu3cP3Tvql85xNKcAykm8dInmwKuvQhw4EOs++QRKpRLOzs5cTEwMSktLUVVVhdGjR0NbWxupqako0NISVPb23JM0A8dOnIDU2hqT8/PhvXQprty8yUdFReHWrVuCp6cn17Nnz44lty4u5Lx4lGwDKC4uRkVFBfMPCiKHRGunZkfKleRkIgP9+tE88vMj0q1QkJNp+XKS1//jHxSxj46G+bJl6NqlC5Tp6UBhISyGDMGsV17BESMjfN29O0w//RRBkydjgK8vhIsXwe3fz0EuBz76CAPd3dlnn38uGiQksEEDB1IxqnXryFDX1ITLoEHczYQEMaBfP1ESEMBh61a6bx0daGzZAp1Bg8Si6Gh2LDUVubm58PT0ZCEhIVCFhiImI4NlZ2fD7PBhGN+9i/lr1sDCwgIQBB5jx8JDRwcXPv1UGBMa2jJhlEpaC+ocXQCwt4ckORme8fGosbJidxoaRB2JhE157z1kjxiBF6ytcfv2beHAgQM8AGjNmwebujpwj55nv/wC0717cXniRKGfRPJ0BkyvXrQO3dxovxs0iBRhf8D+EUWxZW3MmUPXnD4dZUolro0ZI1gkJXG6TVJ5QRDg4eGBEydOoLi4GMOGDaN1a2zcknfcAZRKJbZv345evXpxly9fRn19PcaMGUP7yty5MDh4EHNdXVl1dTVMTU15ABg6dCizs7PDnj17UF5ejq5du6pCQ0P5oUOHYt++faK2traAefN4kxkzMPW992Dj5NTGcd+tWze8+eab7KOPPkJRURHc3d1pLRQUUL50VFSb4oF+fn7s119/RVRUlNi3b18mLS6miLOLCzkhHh3bhgYizZ980rI3LVxI58KuXW1fe+gQ7Q/qfXfkSLIzoqJICSWREJF3daWUkDVr2v69VEqO3MhIOs+GD6eghFxOCofLl2mOenmRk/abb57azniSQ37BggUMoHliaWmJiIgI7/v37w9UKpXIzs7+LTMzc4STk5PGxIkTNYSqKtxfv14sHj1anD17NjM3N28h8BMmkBpoy5aO36hLFzh7evJfLlkCNDbCY/duiLdu4QwgTDh6lOsSFgapt3e7PzM2Nm5OK8vJyVGT7T1hYWGRq1evzn+qQfgfw3Oy/Rz/EWhoaGxyc3PT6qjYw98BycnJ/Msvv4yuXbvySqUSx44dYz/88ANmzpwJqw68yA8fPsT169eF7OxsfmBTgRMbGxuMGDEC+/fvR2hoKHqo81v/hbh58yaSk5MRHBwMMzMznD17FgDQtWtXTJo0CdJHc0FbQy4n437vXooQ/gmcOHEC169fR8+ePQUXFxcuMzNTEEWR6zDvl3pMkvF4/jxFS/5KyOV//JrZ2eRdX7++mcQ5OTlh1apVyMrKwu7duxEVFQXGGPz8/BAUFIQ9e/aI3377bYcebk1NTbGmpkYYOGsWj1OnyGh7EgoKgAED0JCQgB07djRLC3meR5cuXTBz5kwUFhbyUVFRYutS3Xl5eSgpKUF0dLTAcZz4zjvv8BzH8aWlpbh9+zZLTU1lO3bsgK2trVhYWMhEUURjYyMAYNasWRKO4x5fzTohgYyMzEwyPNTrQkODDJtXXqGo46ZNTzfWTVAqlYhpyq8bpi42xXEtBNXXl57nZ5+ROuKHH+i91K1eHul1/ODBA1FDQwOmpqb/vnyOxkaKOu7aRWOjxjffUFGtiAj6/6BBZFyGhrYlaOqK92opflHRE2WiGhoaWLx4Mb9+/XoxOTn58fl9anz9Nckr1S35tLQo6uvjQxHFr7+mZ9jKgSOTycAYQ2JiInyb7jkuLk68fv264HvqFKcZH89+nToVenp6qtdff72ttVxbS+kq8+a1vQ8dHWDvXkT98osIgL388sudewoaGmhNh4RQtMnYGIwxDB48WMzOzhbLyspw+vRp1tQvFmlpaZDL5YLn4cMc7+PDRdy6hfi7d8WxY8eyrh2ksGRnZ6O4uBgmnp6izrZtDLNnYzBjsFy+HHn5+bh8+TIiIyNhZGSEwYMHo1u3bi2Re5mMZKZr17aT4p85c0bkeZ6lpaXB83HFGkWR9hulkhyfBga0xjZupMJmalVIXBx937aN9ghRRF6XLhB4Hhn79wvG4eEc3N3R5YUXMMXUFMoffwTfrx/YmTPAK6+A09Ehx0eTwoajZysqFArWXM180iSSSF+5gkI7O1wYMIBZ37uHMmdnCBoakIeFQSMrC6X790P7gw84+UsvASNHoueIERjdVMWeO3wYaT/8IETu2MF18fSE4Okp9Lew4JCfD0EuR6qGhnjD3Jy9NGBA2wFLSSFHY0dqnRdegN3s2YgzM1NhwQKJZlUVnJt6Yh88eJAHKBp39s4dOI8aBeN588iJBNC+VVWF9AULwHXkMGqNggJy2jk4EKmbMoXWy3ff0bOZPJmkzCNH0l74lClY6rouAFAviigODUXmq6+K57t1Y+bm5lyJXC6+uG4dP2TnTvyUkiJWVlZi1qxZbOfOnSguLhamvv02x95+G1i6VO1MVTHGEBoayltYWIDjOGRmZoLjOIwePZoNHDgQWVlZcHNzY3jwoLkatzbHtWlpyRiDi4sLxo4di+zsbDEhIYG/ceMGvLy8MHv2bIYmxQiTStHt6tWWwnSPgDFGRFuNMWPIWTFxIp0HTbJwJycnTJgwAYcOHoTVe+/BuLQU+Xv3wsnFBWlpadi7dy/c3d1VY8eOpf7l0dGkXGrdQnTBgo7VePX15KBVQ1+f9o3YWLJz1Ni7l85hteP4UQQE0BpZt47WmqcnzQH1uCUk0Pp85x0qyrd7d5u9Wt0Wl+M4VFVV4ebNm/D19YVWJ5JtNVQqFX788UcxPz+fDRkyRMvIyEh1/fp1XqVSTQCAe/fuZUVHR9vUREaCc3Lixn3wQUs3AjVefZVSWI4coV7yHaB79+6IiYnBirfeQmVlJVTV1bCPi+MeZGWJNz74gPXx94fR7t007h2MT2pqaiOocBokEsknn3zyiVFjY+Mrq1evfvjYD/g/huetv57j346wsDA/DQ2NdVOmTNFqt7j/JkhMTBQaGxuZk5MTOI5D9+7dWVFREe7fv69yd3dvs+PcuHFD3LNnDxMEQRw7diyzayV7srS0hL6+Pg5TpVBRX1+fqY3VvxrXrl3D8ePHUVZWhtu3byM7OxtlZWVYtmwZ+vbtyz2VAiE4mIw4ieRPyckjIiLg4OAgjhs3jnNwcECfPn1YYGBgx0VQ1Bg4kCIl2tpkSE+aRPfxZyEIdKBOn/7sf3voEBnPs2e3OWh4noeRkRG6d+8ODw8PJCUlwdLSEubm5vD19WWBgYHo3bs33NzcYG1tDX19fWhpaaGkpITJ5XKxh5ERhzFjnu7zGRsD+fmQDBqEe1lZqt69e3Mvvvgi+vfvj8DAQE5PTw95eXlISkpiMplMtLGxYQDw5ZdfIjk5GS4uLmzq1KnNkU4tLS04ODjAy8sLubm5yMzMZCqVCosWLYK7uzs0NTXRrVu3zu9n+XLKdVuwgKJtPXqQhFQQiOi+/joRx7lzSfL6B+b6pUuXEBoaCldX17ZrpbKSIhBlZTQ/79yh/zNGRlRmJkUvmv5GpVLh4sWLokKhQL9+/f49ZFsQ6LlmZpKR2Vqi+PnnZJy9+Sb9XyKh8VQXtmkNjiMDSSajugZGRu0K8zyKplY67OTJkzAzM3tsWgFSU8nonjSpfa6ejQ0ZxosXE+lzdARkMjQ2NuLnn38W6+rqmJeXV3N049ixY2JhYSGfrqvL7nTvDgNLS3HMmDGcXuuCRllZ5GTYsqVFhtsagwfDvL6e3TAwgJ+fHzo8Wyoq6Pm6uRFRfPiQci49PWFjY8Pc3NyYt7c3CwoKQv/+/REUFIT6+nqllqYm6719OysaNAi9Q0KQkJDA4uPjoaGh0aY68P379/Hzzz8DAFasWEG+qx49wCoqYOLri65ubiwgMBAGBga4f/++mJKSwq5evSr06tWLaWhokEH/4ou0hz1y/7///jsTRRH37t2Dn59fx62AVCoyajdtonltZkbzgDGKYL/wAkXF//lPIgE3bxIRMDMDNDRwWSYTkxUKpuPry1z++U8wAwNak6NHg5PLwcrLKVK3YAHtsXZ2be4zJyeHJSUlob6+XrDv359x6elEJI8cgeGxYzCcPBnX4uNZrLExbqSkID8rC65eXpB6eMB+yRKcDA1VVZqZsck7dzKJpyc5vxob0SskhClkMiHkl19Yv19/ZXxDA9C9O86kpQm3AwIwYd481k51cu4c3VtHqR3LlqHYzg4piYmi/wsvcJg6tbl4VnJyMjQ0NESJRMIaGhowoF8/yNavp/2/oABYtQr7a2tVsaLIGRkZiR4eHhxEkeoIVFTQPJ06lRwbb7xBZ8DcubRegoOpq8KdO9QVYPlyUlj4+lIxxeBgIuBHjtC87EihASAjIwNJSUnctWvXxMjISPawsVHlWlnJDfv4Y/Tr1w8efn5M5uoKDRMT6FtZsRuJiWzYsGHo6eiI8OPHWZKnp+jz9tsMjOHrr79WyeVyTqVSsaioKHbz5k0BADt16pQYFBQk2tjYMJlMBjMzM9pLq6qIhD2mI4i5uTlcXFyYtrY2jh07hvr6+rZngo8POb26dm23vzPGkJGRIdy6dUv08fFp8f/q69PXokV0njbNf5O4OHS7d4+ldukiRgUHC7dTUnD16lXcunWLyeVyobS0lOXm5orO6emM19Sk4oHqtfP117Q/ddTVw96eSHRrh/Ho0fQcWz8XMzM6TywsKM+7o8J2mpq079TVUUG7hARqqSWT0RxV798yGa3PceOoboO9PbZu3SqeOHGC1dbWiocOHWIZGRmIjo6GQqGAnZ1d8z5QX1+Pe/fuoa6uDrGxscK1a9eEzMxMDgDu37/P8vPzOWtra7G2tpYJggBRFPUfZmXVDdq5U+NK797oq04Lag2epzm9YkVLXv8XX1DU/sABNLz3Hn7Jy8P8H3+EPC4OcqUSusuWwdHfH0Z1dcwkPh4JBQWwvXULbODA9gUqAfA8z6tUqjpTU9PGuro6r9raWhcAa4KCgv5WFcufk+3n+LcjJiZm95AhQ7rZ/tVFhP6LoKmpya5du6by9/dvZlimpqYIDw/nfH192xiCly5dEqytrbnJkyezjiKBZmZmsLCwQGRkpBAVFcWlp6cL7u7urLWxJQgCamtrwfN8x9LLTpCTk4PvvvtOiImJEZKTkzmZTIb58+fj2rVrEARBDAkJgZ2d3dOTDJ4n7+zmzWRw/EGnwKlTp8Tu3bs/23tLJHTgFReTQThpEkV0UqY+AAAgAElEQVS67ez+nLRcQ4Pyujw9n/46iYlkTK1Z89i+pV26dIGenh4cHR1x9uxZREZGIi8vD7169YKWlhZ0m/IYjxw5gpqaGowfPx4eDg6ckJKCIicnJCUlobS0FDk5OTAyMuqYYHAcGTc//IA4ysfiXF1d2+TBWllZISEhAYmJiczW1hZSqRTR0dHQ09MTZs6c2WFvasYYrl27pqyqquJ0dXUREBAAQ0NDOD5ajAYgI2PECMqTMzGhqG23bm0P3/v3iUgtXUrz6O23KZLZicHZGfLz83Hz5k34+PhQyoEaERGU369QkLGYlkaGljqn09+f/h0aSgZVUBCysrIQERHBFi1axJ4USXhWFBUVYdu2bbh8+bIYFxdHskt7e6pS36sXRcJaE22lkoyyzZtb1hXP0zy3tydi0hEYI8LUrx9FV9RGbiewsLBASUkJLl26hAcPHqiMjY25drJFUaQou4lJp31zoaVFBGXvXoooWVkBxsa4ePEi43leHDlyZPMeZmFhwVJTU0XT+/fZkPPnURAYyAYMGNB2L1u9mir9dlaxPTAQNd7eiE1NxfXr18XCwkJWUVGBI0eOQFdXF8YcR/OpspKktxxHToFPP6Xn3kkBM0dHR66HSsVKx4/HiYwM8cUXX2TBwcHQ0dERT58+zXR1dUnSDFLklJaWQiqVIjAwkC5gZERz6+WXgaNHwV56Cebm5vDz82P9+vVDcnIy8vPzWXOv8SlTaHwfcYxER0c35z5GREQgJiZGuH37Nqqrq5mDgwOtsTfeQE1sLHa98AKOXLiA69evC/X19czB1ZVaimlqEql87z3K1V21iox7PT1ATw/Ozs6ssLBQmZSUxBUUFFCRPA0NIhBaWkQG7OzIiTJpEkXhpk1rno9xcXEoKSlBWVmZyLZuFc1/+IHd9veHxUsvgdXXwyw3F9YjRmDwsGFQKBRCikLBBixbhob6eiSeP4/uH37IjYiIYJLCQnKINCkyOIkE3RYsYJoBAeB37KA9YeFC7MvPZ1OnTmWmpqYoLS1FWloaEhMTkZaWJuh8/jku+/kJtzIyEB8fr4qLixNu3rwpxsbGitdv3ID56tUYvX07xw0ZQvvj5s2Ajw8i9+yBn50dM+vZEy5OTnB0d6e1eO4cpZ0YGiIrIYENGzOG+eXlcdzEieR8GD+eSEhAAI1Xv36098+ZQz8fPJjWr3rede9OZHvkSCLoW7aQSsXbm1IJ7t0jibKbG/3s4UOa/+7uUCgUyMrKUo0fP54LDAyEX0gIZ3jxIjS6dm2Jijo5Afv3w3D7dsTY2gr5BQXQmDuX+cTG4s6IEfD28WF79+4Vi4uLuYULFzI/Pz8WEBBAEf2zZ5mFhYU4evRort3eP28enW9PUYDR0tIS6enpyMjIEAe0Vh7o6hJpNjWlc+ARODs7s3PnzjGZTNa21ZWTExHewYOJINfVAZMnQz51KhxXrGBe/v6cv78/09LSYv7+/njhhReYm5sbCz99Gn5r1jBJUFDblJrNm2l9dpC2gc8+I4VR68JyKlWLo6k1qdbRoWt0VISuNRwcaC8oKSHSb2zcUryN58nxZWJC80VXF/jiC1TEx7NsExM8ePCAaWlpoaGhATKZTMzOzmZRUVEICgrCnTt38Msvv4gJCQlISEhAQUEBKisreU1NTUEURSaRSMBxHMrLy9nw4cNx9+5dAIBVdjan8/AhF+flhfz8fBgZGbWvgv/11+T8iYkhZ8KmTbQHuLvjSmEh0nV0MCwsDGzSJAp2vPYajcOLL0JWUQHdkydRY2QE7U2bOrQH9fX14erqKjEwMJBeuXJFKYqi1+rVq3MeP5D/e3hOtp/j34qwsDBvDQ2N/wsNDZU+Cyn8X0NVVRVSUlLE1lXWtbW1cevWLZVUKmXW1tbNu055eTnLzMwUvLy8Oh0QQ0ND9O3blxswYACioqKEoqIisUePHlxZWRmOHz+uOnToEBcTE4PY2Fh4enp2TLo6QHh4ODQ1NcVBgwbxffv2xZAhQ6Crq4uBAwfC39+fmZmZPTtb7tWLNuPCwg4riD4JSUlJSExMZFOmTGF/aI7o6ZFhC9DB1r07eaj/qBqAMTIux4/vvL/wo8jPJ4IxYcJTva9cLkf//v2hra0tXrt2jXl4eDTLxORyOXR0dJCdnS0kJSWxrGPH4LB9O7Zra4t3795lKSkpyM3NFXNycgQPD4+OB6yxERg3Din9+oldTE2b86xbo6qqCjk5OYiPj0d1dTWKi4sxZcoUpte+XU0zrly5IioUCm7+/PntexgDFDlbv54MyshIkj17ebUne7dvE/nduLEl6nDkCBmlT1PFHCR7j4mJwZkzZ8Tu3bujT58+TCqVEnFZu5YK1UybRpGjK1fIyOlIrSCXA6amUFlYIDY+HnV1dWJgYOCfimqnp6ejsrISV65cgYODA6qrq7F9+3ZUVVVBKpUyLS0tlnP5MnMPCICGr2+b6HozNm6kXGV1bQI13n+fIg+vvdZ5zp+ODv3Njz+SEderV7PjqK6uDvn5+fjll1+EM2fOsOjoaBQXF0OlUqFLly7s/PnzLD09XYiNjRWLi4tZYmKiSpqby/HHjoH/4APwj0stYYyIbFUVsHMnOJ6H3/TpuBITw6Kjo8V79+5BX1+fxcTECOXl5czZwEAwz8riIkxNERcXJxobGzMjIyNSHUyfTtHyDiK6Dx8+RPSZM8BPPyHT1haBgYHIyckR4+LiWG1tLTJv3YLX229DYmxMqgD1viKTkZPnzh1SWXSGiRMht7JCuo6OmJ6ezjw8PGBlZcUuXbqEtLQ0uLm5QVtbGxEREVAoFHjppZfat7obNYryPwsKyKCm6tYoLy9HQUGBqKOjw4qKimDaRHyhJt9NuHDhAiwtLcVXX32V2dvbQ0dHh2lpaSEmJobV5OXBWaFAfXw8NhgaQqKpiZCQEGhra7OrV6+id+/eSMjJQfyXXwoW69czjVmzaA3OmEH3FBAAJCTgoZ8foi9f5urr6yEIAktLS0N8fHxzLYbiiAjklZfjvrY2Hg4fjsYXXoBueDgaX34ZR01NkZGdLSqVSrZgwQIWr6WFg+bmLC09HTJNTcFm+HCGnTthGB0N2fjxSE5JEYuKiphKpRLT79/HTX19jBk/nkFbm8ilvz8556KjKSr/5ZeUp792Le3FXbsiMzNTFRERwUVGRuLmzZvIz89XNjY2cqywkJkkJbFLdnZcWUUFrK2teV1dXc7AwICz5DjOzNGRuezZw2QlJWCDBxPhmzkTWLYM0rVr4Xn5Mrp+8gms3d3JQaFQUM7t/v2AkxO6nDnDdFxdoT1oEEWjHR1p/Q0cSM/O0xPQ1ERlZSWys7ORkZGBo0ePCpcvXxavXLmC3AsXoLN2LdsOiDEZGbja2CgWMSZGNDSIDhMnsivGxuLR3r3FaykpYo6ZGaIqKlC1bx80d+5kO6RSwX/WLFbPGNd75EjoXL1K919YSLnYrQif4OaG2J9+EnPNzNiD3FymPWMGErp2FRukUuTk5LD79++zadOmwaBJKdLUgor5+PjAx8en4/NXU5McN60dmY+BTCZDamoqa9N9g7GWiHEHqVBSqRTx8fGilpYWc279e5WK9q/kZCL92tpUWbzVGcEYg4WFRXMKE2toQNm33zKLPXug7e/fcq2cHHKsdlbUMi2Nzp/W9XJkMnI62dm1KegHgNbTxo3kyF248HEDQvfbvTtVSj9/njqrqG02xkgV4eAAlJbCMD0duXV1cE5NxbT169E/IAD9+/dniYmJqK+vh7GxMfbt2wcvLy+mq6vLysvLRYVCwTk4OKi8vLz44OBgDBs2DFVVVWJhYSHLzc3F7NmzYW9mBvdNm7j9Y8ZAxXGwsrISzp49y7Kzs1UeHh4crl+nVAd7e0qF2L6dnFLHj9OeLpPBePNmXPH0xIAJE8B0dVtUNACwbh1UH32EG97esNmxAxpPUDpu27atob6+/oPVq1cfeOwL/0fx99TwPsd/JcLCwrylUump4cOH/23l42ooFApIJJJ2ffUGDBjAnz59Gnl5eSo3NzfeyckJdXV1zbmuTwLHcZg1axa/ZcsWcdu2bUJRURFnZWXF5s+fj+rqauzatQsVFRWd9xQGUFlZiQMHDggNDQ1iXV0d7+DgAJemPqZq/CmZOsdRtGTlSupj+5SHMgDExMTg3LlzGDVqlCiRSFhdXR2uX78uGBoa4okVrR8FY2TYchxFVS0sqD/kH0FjI0nFnlT4LS6OiH58PBkBzwgfHx928uRJJCYmtkTG6Ofw8vLiUlJS4GBhAe033sAqZ2fW0NAAQRCgVCrZhg0b+J9++kmYMWNGe8m/XE7G2LZtHRemAjBkyBAMGTIEUVFRuHjxIgAgPDxcNXv27E7zBzw8PPjU1FSVoaFh29ccOEAGYEkJySMBylfrDB98QAZr66JXX37Z+es7QFZWFq5cuQIAbOzYsSSvu3WLIuUffkhGkHouvvoqRZVaV+dVIyQE9fX1KLOzQxdHR/ht3vyHFkNlZSVUKhVkMhnUFXoBIDMzU2VqaspXN/XeHjt2LBwdHaHQ0UEKz8Nr/fqOLxgcTBHJR9fm2LEkOa/5f+y9d1hV19Y1Ptbe53DovYOKIKIgSBHFgtgb9t5jTGxRozHGm+71ptybGFPUxESJsRs1tqhIUEEULIAoBJAiCAKCSK+Hw9l7/f6Ygiig5t683/e995fxPD4klH32XnuVOcccc87atiWMLdE0/ydMgKxQ4J++vtBqtQAAQRCEefPmQU9PD5aWlk3yRJaTk4OMjAyhpqYG+fn5XFtfL3p8/z2OBgRA7+RJzJo16/mDMWkSRW2++AKKu3ehr9HApFMnZm5uLh04cEBsbGwU5s+fj84dOoh86lQMKSpCQkICDhw4AL3aWry+dStu79iBWxqNbGtry0aPHs0Akm2HhYVJlZWVgnN9PXpduMDUHh44d+4cW7NmDTMyMsLdS5dw+cIFfqJHD9ajTx+0om7WriVHe/78tuXpnOO6n598qbBQMLaweCKvceHChYiLi5O+//570cLCAlVVVXzUqFGszQJtJiZkVAcHk8H+yy8AADc3NxYdHc1OnDgBtVqNG/r6GHzvHuwmTGgmTase5Y66ubkxxhg6duyIR8ow5mVnB/XcuYjv0oWf798fxvr67KWXXoKhoSHc3d1RVFQkb9myRZAkCR5jxwqH9PUx6uRJ2M+YQRE0U1Miu+7cQf7YsRiiVuP+229z0cKi+Rx49JXZJSdDbWnJb2RmyrW1tYJGo2FSRQV6dO2K0tJSeVZ4OLMICYG+uTmmzpzJgkeMQOnkydgTGSlkZ2dL9W5u6JSUxAI+/VQYumKFkJycjKKiIsYYg5+fH43T4MH0taGB1AyvvEKqiLo6ipxRMSUAwJw5c8SQkBAUFhaib9++8rBhwxSCICD80095go0N0zU0lPv27ct9fX1FcE7qEENDukZqKnD+PKSQENwZPhzn3nlHwvbtvHrcOIXxlCnoCtD+JUnk+Bgbk0Nub49UPz8ekJDAkJJCKpgbN8hpUyrJeTQyQnJyMo4ePQojIyNJlmXY2NgIAQEBTKlUgvXvD9szZzBiyBDGH60zPmwYXACUOzrCub6eeaSkMKG8HHcWLkTKlSvwf/dd3OccfWRZKFi1CtlFRfQMr79OxMSxY1SsMCIC2LUL0sKFuKBQSHfGjRNW/e1vDJyjdscOMHd3Fvrbb9DEx2Pu7NlwlGVSg+nq0v7xSD3RJiorqUDY81pOtoC7uzvOnDmDzMzMJwu9jh9PJKy9fZvpLXZ2dsjPz5eRmirgwQMiMgwMSKE0Zw7lEs+Z89xUqjsLF/Jev//OzJ+uDzR7Nr2zTz9t+w9tbel9Po2AAJJ5X7vWei+eO/fFlVg+PjRXdu+max492pqEnjMHxrNmQV62DG5paRDi46GTnY2tBQW8vKGBAcAvv/wCPz8/lJaWypmZmcKECRMEV1dX6OvrP3EmBwcHMycnJ/zyyy84fPiw3O3cOUF2dZWHjB2LPn36COnp6ezevXs8LzdXQHk5kZsrV5I6w9b28Vl5/Dh9vXsX4pw51JLM2Rm6CxbQOX7sGJEODg44vnixNCgpSTRsS+3WAqGhoZqqqiodAL+82OD978N/t8fzF/6vYMOGDU66urpfy7LcT5ZlPcaYVpIkfaVSiYkTJ+q4P8XY/zeiqQjV0/Dz84OhoSGSkpLEY8eOcTzqdT9//vwXLklqbGyMJUuWsGvXrrGJEyfC0tJSAID9+/dL/fr1E59X8Tw9PR337t0TDAwMYGRkJP+P9Dj39X3ccuoFne2CggKcP38e06dPR8eOHVlcXBwPDQ1loigKCoXixfo0P40mZn7bNnJGbt+mvNedO//YdVaufL6EXJLosJw379+uXv6oUJocGRkpuLm5PVGxWBAEGoM9eygyu23bE1LwuXPn4tChQ+zSpUvy4MGDW99sXR2mrlkjXn3Osw8YMABOTk44dOgQd3V1fea8lCQJNTU1QklJCUXyIiJI4rd5MzlYq1dTFORZuHKF5KhPqwZ69CDjtmVf2megV69euHjxIg8MDOSiVivg3Dl67+vXk6PahPx8+sxnROx1dHRwbvlylJeUwOrwYcmvZ0/xj+b/79mzh5eWljIdHR1uYmLCKh+1Cnv48KH48OFDAJT/3iU1FbC2xrHPP+f5NTXQv32btSqGWF392MB7GkOHUrTh2DEiEV4AVZ9/jv1bt8KoqAgzfHxgPGsWdHV12yTZnJyc4OTk1PS/DKGhgK8v7s+bh6hLl1BWVvakXL89uLhQvt/WrVhQWoqjRUXotHSp2L9/f8THx/OOHTsyFBWBBQYisKgIAwYMYLExMbh4+jS+W7YM1XfuoEuXLoiNjWWxsbHNl3V1dRX79OkDHx8fSB98gEUPH+LOnTu0NtRqdF68GHYvv8w+c3eH3t278GhZeAmgtbp2LRm9q1c/8SNJknBv2DCeY2MjWHh48Ly8PNbsFIKKWHbo0EHs2LEjP3PmDAPA2lKNPIGTJ4n4+uEHIDgYjo6OWLVqFUxMTJCYmIjz+/ZBZ+9efAJAR1eXGxsbQ6lUMgDwerrd3+3b6JiVhdJVq3CwqIihoQHLli17QmUyZcoUYdu2bTAzM+MTJ05kZ3V0eNVbb7GyH3/EufHj5ZdfflkwNTUFvLxQ/OabwPbt8DEyYrbHjpHx3DI9Yc8eYM4c5jdyZPO+cObMGSQZG+OlIUME++PHySm+cQPo3h16AQFw9PDAFGtrZGVliU5OTig1MeFZX38N+9JSGHXsyBsbG/n06dNb911XqSiVhB6Cqts7OdH+HREBnD0LgXMsXrwYxcXF2LNnD8rKyqTp06eLNjdvolIQ0NDQwGxsbARs306y4KwscixNTIDMTNzduBH3NBrkfPopGry8xCFDhuDXX3/F8ePHZaVSybvfuoWAs2dF6ZtvYBkcDMTGAkFBSGCMV7u6wkhPjxlnZED33j245+ZCiI0Frl8Hc3GBoa4u3CVJnjZlioiePYnsbbm+Nm+GO2OtlTtNRcMyMohwNDaGwa+/wmvu3GbJcYIgAGFhko6Li4jx44lw69WLWtrV1aGqqgopd+9yoVcvLFi4kAmffAL07QsjjQZ1hYUI0NND5aVLcNTXJwJZraZ5aWJCZEdjI6k97Ozo/gwMSEJ/9iyN4ahR5JyLIpE1lpa0f7dx7gmCAC8vL+ns2bNodZ4MGEB7/1POdvX9++j19ttMcf48w8aNtF4GDyZio2kvys1t9VmtEBeH3LFjebKrK+t79Sp3cXFhlZWV6Nq1K4Rt2x5fqy2kp5PE/Gm5vLMzpeJkZbWWwFtZUV2EgQNpzq5a9ez7MzCgPXvcOFLtzJ5N/1rsp1VVVSi0t8f5t97iiz09GT75BKOrq9lZb29YeHpyr/79WWFhIW7cuCEsWbIEts+IIHt4eMDBwQG3YmLQpaiIOy5fLjSpDy9cuMDtFQphXGQkvf+kpMdpTK+/TmdMyzXauTP42rUw//FHOXz9emH8hAk0P2bOJEXAzz/D5cIFMbaqio/W1283fBMfH89v3br1EEDP/7aiaC3xl7P9F/5UbNiwwUmhUCT17dtX393dXdTT04NWq4Wuri50dHT+Rwp7/b+CsrIynDt3TqqpqUFlZaXQXo6nm5sb3NzcoNFo2L179+Ds7PyH5dLm5uYYM2bME98zNTVlxcXF7feIfQR/f3+EhoZCFEUsWbLkf07L/8orFM3atavtnKin0NDQAEmScOLECTQ2NkKlUnFjY2N4enqy5ORkjqf6Sf4hNBnAkZEkCQQouvGsqr4tkZlJB3N7DG12NhkMd+9S9PE/wMiRI4UHDx7IBw4cYEuWLGH6TzuhstxawgZq3TRq1Ch26tQpFhQU1Dp339gYMYsWSVxP77nEjqOjI958881njndVVRUKCwtRV1PDdv3rX+jTvTsf8OabjGVmUtGfF0FmJjmR8fFPHuQAOSNt9UhtBwUFBWhsbGT9e/RgmDqVDKXjx58kSUpKyCi9erXttmKPUFhYiGxZhtLaGi/t2EHj9dlnz/x8WZZx+/ZtuLq6Ij09HSqViimVSsyYMYN17twZWq0WSqUSpaWlqKurQ1ZWlqyrqwssXChgzRrMXLqUXbx4EYcPH8bgwYOfUDYgKYlkj20pVgSBDKGwsBdytquqqvDVzz8DlpZ4RxCg89NPz6wr8AQqKqi41vHjCFAqkXr7trxlyxZh5cqVL+Zwq1TAm2+iOiQEzgcPwurkSZi99x6GDx9Oc83WlmTCIOKpz/bt8Lt/H6Gvv84DAgKYtbW1kJWVhX379sHc3BzOzs7yqFGjmpUcQlAQ7GfPhv2yZTRmRUXA8eNQdu0KfPwxsrKy2r6vwEDqtVtd/YQ64NSvv0q+ubniqE8+gUm/fkyW5TZrYjRVqp8+ffrz28MpFBSpvXCBlBfff98sefX29oa3tzfq+/WDbWEht3FwAOecp6enMycnJ9nY2Pjxh5eUUErE6tWwWLoUjj/+iKKiIuzevRvl5eUAADMzM15bW8sYY7y4uJh9/PHHmD59Oqv85huId+6gY0EB27dvH5YuXYrMzEzcys2V6wIDhS62tuQ8xcYSYTp6NMnNKypaOUbDhg1DRUUF3//rr/yt8HC6v759qf3S9u1Afj7cnJ0fq6f692dJtraI2rULwzjHUVNT4dtvv8W0adPg5OTUenxra4lkKiggx18UyalpbCSnZO9eWPfrhxVjxwpbT5+Ww86elUanpYmdfvkF1S+9hNQrV3jj+++zjrt3QwCaSTZtWRmQnY2HY8diqkKBhgULYG5hgS5duqDiwQOBJybCYv9+VKtUyNfTg6WJCaUgrFkDPz8/oaCgAJWVlcgpKwMMDXGhsJA32tpCCg5mA+3tce/iRZhZWHCEhhJZO3cu3bOTEykoYmJo3+vUiSLuajV9ZYyIW40GeOUVaLOzwRjj+PlnhqIiQBTRrUcPGO7dK2bExvKuixczvPUWRblnzQLu30dFRQUihg1j77zzjiiUl9PY6eoCjGHP99/LD42NBavXXtN6Ll362Af4/HP63Pp6IkwqK+l9V1eTfF6jeawq2L6dctLT0uhz3d1pbd+6RVL2SZMAY2Noq6tRlZOD7t27iyW//w7p7FmIskwRXT09ygGOjKTzIiiIzgIjI0RPny676+mxDubmDN9993gutHSOXVyIjHjKDmpGYSEwYwZGHzggdA8KwokTJ+SYmBhBkiS2MCaG286Zw9qrhg6AFCht7beM0X575Uqb+eYASEbesmL589ChA0XsN2wgojooqFnhkZ2d/ehxCtnvubnwDA2Fi0aD195/H8Knn7K6y5cRevAg5r788jMd7SaYmppiUHGxAI2mOc2vtqYGHcLChD4rVkB34kTKS28Z1Tcze6KqvyzLCA0NRWJiIrRarWDt7k4/T0uj8+jIEcDSEt4bN+Ly9OlITEx8ssf7I+Tk5CA8PLymsbFx8H+zow385Wz/hT8ZKpXq7/7+/noDBw78j5pnS5KEjIwMWFtbw8LCArIsIzU1FYwxdOvWrbkCoyzLiIuL4zdv3uScc9jY2PABAwaI/6f74dbW1mLHjh1wcHAQXF1dmaGhIby8vJ65vnR0dJ5dtfkPYurUqcKWLVv4uXPn5MGDBwvtSfVrampgbm7eJHP/n2M/FAqKGEVHv5Cz3bFjR4wcORIWFhYwNjaGjY2NIMsyNm/eLD+Kav/n9zp4MP2rrCRJYlNe1vPg4tK+RLewkJz5PyiZfxamTJkibNq0CV9++SXef9p5DwoiQ6ANOD46PLdu3YpJkyZBoVBAFEXExMRIpqamLLdHD4zatInGoA2H/Y8gKiICmenpmHz0KMwqKvDjokXMNTUVtk/1Vm4XajVFRKKj287tb2h44YJ09+7dw6H9+/m44mIurFkj4M03aZyeJvf09UnO/ozoI+ccsbGxkiiKIgeQvns3evfqRVHyqVNb9Y5uwrfffiuVlZWJurq6XK1WMysrK/nVV18VmvaiprZ5FhYWsLCwQIeQEAFjxpCxCjqMhw0bhoSEBEQ+Km4VHBwsdOvWjZ6jsrL9mgG7dhFZUV/fujL4U2hqiyYIAnQ++IByvk+eJKl9QsKzawxs2kRjYGQEXQCvvfaasGHDBkRGRmLKlCnP/NyWyO7WDXlBQdJAHR0Ra9aQkWlsTI5UcTEZ+0ol8N57UBgaYryDQ/NNubi4wNraWuvn56fo3bv3kxNkyRIi+GSZ0jmWLAFGjGhmH6urq5t/lXMOjUaD2tpa1DIGlVqNyp07UeTri6qqKqm6uprX//abIvXNNzHqURG49kjRS5cucW9vb969e/dnTlhZllFWVoaCggJYf/017IyMSL6+bt0TbZD0oqKwpGdP9iitounZH1/71ClyQI8ebSYAvby8kJ+f3yw5f9QeiBkbG8PX15cBwBdffIHDhw9j/fr1QNeu8B47lu0LCsJXX33FDQ0NWadu+88AACAASURBVF1dnbBq1Spy/nv1Iudw8WKSvG/eTJFOIyN6R5WVgIEBVHl5MMrOhsvduwwHD1KO6/Tp5KSPHUtVunfufKKlntewYbDU10fl0qXw9PVFeWCgtHfvXnHAgAEY2lKFAlB0fdMmIubWr6cWXE3qhKQkMvJDQqD7+ecYExEhVC9YAKbRwNTVFcZz5rDbpaX8cHi4rNFoBOnCBejq6sqiKEKj0QiN8+dj6ODBskFoqGCQmgoEBsKIcxitW0dr6Ysv8Gt0tOxka0tjf/o0YGuLIS0iuJcuXcLFixcxdOhQplQq0djYiOjoaLnnokVCv379HttBpaUUDa2tpWeKjSUHNi2NSITYWCoY5uX1uDWboSEUtraosLXluHeP4dIlQKGA3sqVsBg+HN/v28eW9ukDi5gYUgFs3gxcvIiOkydDqVTy/Px81nHVKiJ3tm5FRkYGqqqqBBcXF8yePbu1gSCKtI8YGgJ37tB79/Ghr8eO0XtfsYKiu1evklKlyc7gnPbs4mLUV1biQEgIN2IM+qWlrOHGDQT6+UG8c4fmQ7dutE8VFZHjzjkpjt56C7CyQs3Nm/zAnDnsDZUK7SbE/eMf7XdXaGig8+WXXwBfXzgBaGojuGPHDkmj0QjtEudN+O03uq/2FHWbNlGEv61aJbNn01i5uRGp8rz0M4DG8aOP6Pd/+onm+6RJ8PLywr1795CYmIioqCikpqaiX79+6PD555BXrsTxf/2Lv3TkCLPy9SUy4nldY9RqmoNN67G2Fvd37ULPpCQcP30aS59O31Kr6Sx9NOfLysqwd+9eiXMuTJ48mR05cgSdrKzobFq+nOa4kxNw5gxYnz4YPG0aO336NM/Pz+fBwcHNBfcqKyvx888/q7Va7fz169dnPn+A/nfjL2f7L/xp2LBhA1MqleN69uzZal5pNJqmqpQQRRF+fn7PjACEhoZKKSkpAuecrV27FteuXeMxMTFQKBT8+PHjgkql4lqtlsmyDF1dXd67d29BR0cHycnJOHLkiLx8+fIXjtg+fPgQ0dHRkp2dnejo6AhbW9tnFhgrKSlBRkYGGhoaYGZmBgsLC4SHh8sWFhZ87ty5/xHJ8J/gUc9kdvjwYTkpKYnPmDGDObZwYvbu3Svl5eWJTfnhAQEB/7PONkARs2++IQfn1Vef+asKhQIBTxUpiouLQ2VlpdCtWzdUVFRAqVS2n0/2R2BiQoy9QkG5SIsWPbs3+MOHJI3093/y++XlZHj8/jsV0PmTYGBggNGjRyM0NBTJyclP9jx+6y3K8Vq7ttXfqdVqKBQKlJeXY+fOnVCpVJxzzqysrFhubi6vra0V9aurSSL39LP8ETQ2YszChcDEifKZceMEy06d+IdLlrRZtbxdLFpEh/6xY23/fNkyyqtevBh1dXVoFeFvgbPffINOBQXMW5IYvvyybQLl/Hly6pp6VLeBwsJClJWVITExsXkdx6ekyL0HDhRw8CBqy8qgXbECxcXFyMrKQsKjSI9WqwXnXPTw8EBKSgrz8vKSJk2a9Oy9ICOD2p716fPEt1977TWo1Wp8++23wqFDh7B+7VoydkJCSFbfFgoKaF68/TaNWzvIysrC1atXYWBg8GSudd++pEQB2ld8pKcT0TB16hPfdnJyQnJyMprz5F8AxcXFksLNTcSkSaQ+WLgQeO89Mp6//ppkmFOnAnFxj/uvt4BGo2l7PgQHkxw8LAy4fv2JVIFXX30VISEh+Oc//8k1Gg1TKBSQZRlKpZKLosgdGOM9jh4V7ujqQtfYWDQyNETwlSsQm6rVt4O6ujrk5OQwLy8vFhUVhezsbK6vr8/S0tLg7u4uOzg4CLIso7CwUE5NTRUAitoLgoCRI0fK/qamAsLCyKhvWj9mZuSstj149J5DQp5Q2vj7+8PAwABlZWWIjIzE6NGjW/3pyJEjcezYMWRlZcHFxQWKb7/FFCMjXE5KQty9ewCA7du3Y9y4cejevTsRHj/9RBHXDz6gzxRFcgjUauC996C9dg16mZnMWleXDO2m9+XoSPMqLo4iuLNnk2P12muAIMC+Xz+Y7NrFsv75T+5fVibmg2qdPIE7d6h6fGAgOR91dQCokv/JkyclzjlGjx4tdlyzBlsVCm35L78o3rl5k8iW4mIIK1diBMBGAOzWrVs4efIk1Gq1sGDBAiS88w5Gx8RAd/16AY2NtD+o1eTkjh9PeadqNaqsrZsVebK1NapdXRH2wQcQTE2b1XpNQYAmQs3FxUWorq5GQUEB7O3t6e8fPqS2bAcPUpHIa9fQaGGB1EGD4CIIMFy7tk3SsTApCbeiouTAlSsFREWhbOFCbNm3D/3794ebm5t09swZNnf8eAHHjlEl8thYgHNYWVkhLS1Ndly2TMhWqXAnLEy+fv26AAAODg5tn/0NDUTc2dtDOnsWWTducFMPD2a9ejWRAH36AIMG0Xz44Qcimpuk/oxRJLhjRyTHxeG+pSUzMDCATpcu0owZM0SrJpJy5crHn7drF82xigp6t/b2QKdOmOriIu7bt08KCQnBihUr2t5U3N3bzqkGiCCqrob6wAEotNon7DnH/HzhyqhRcsc+fZ69WckydSFpC/b2JBFvaGjb2QbIwZ48+ZnpSm2if3/afw8fBj78EIrhw9Gpc2ckJiZCkiSelpbG0tLSsH79etSYmOCOtTWbeuECmKnp4x7emze3f/1Dh2iOr1hBhMLq1XA9cQIlkyfjwY4d0Gg0EAQBhw8flnV1dYUhXbuChYYiYeBAFBcX86ysLObl5YWRI0eyyspKKOvrYfTNNxyzZzNUVVF3h23biBDcsgWexsa4e/cuu3HjBhs2bBh0dXXR2NiIvXv31kmS9MmHH3544o8N0P9O/FWN/C/8aYiKinJSKpVvDB8+XME5R15eHiIjI3Hq1Ck5KiqK5eXlcaVSKdfW1vLIyEihtraWu7q6tmmdp6amckEQ0NjYKF+5ckXIzc3FzJkzWXBwMOvZsyecnJyaKmZi2LBhrFOnTnB0dES3bt0QERHB+vbt+0KGX0lJCQ4cOMDVarVQWFgo3bhxAxcvXmQpKSlyeXk5NzU1bZbx1tXV4dy5c9Lp06eFqqoquaamhmVkZEiJiYlQqVT85ZdfFv9vV1g3MjJCQECAUFNTw8LDw9GzZ0+oVCpEREQgMTFR8PX1xaxZszB06FC0N/Z/OpRKcgyXLHk+6/oU7O3tkZqaKiUkJCA2NpZdvnwZ3bt3b7vy9R9F07vq0IHyjKOjKefoKecHAEUfa2ufbHPUxNC/8sofkju/EDiHHWMo3b0bZXp66L5vHxlpTk7E6Ccm0sFqYNAsDwSA48ePS05OTsKiRYsQGBiIgQMHssDAQPj6+rKAgABh4MCBUC1dSiSBldUfr9B+8iTll73+OoRBg+C2bBlzdHXFtdhYVlZWJnfo0IG1zCNvF5KECh0daGbMgKo91n/lSsDPD2q1Gp9//jnu3r3LFQoFU6lUKCwsxO+//w6BMRhFRUFv+3YY+vigQ0hI+8ZNaipF5VrKs1sgMzMTu3btQuqjSLOzszN69eoFtVrNLkRESJddXBDT0MDs1q5FZGEh7mu1mDlzJoKCgmBqaipbWlryoUOHsiFDhsDd3b39jWD6dJKEb9qEtmSMOjo60NfXh729PZKTk5F1+7ZkqdUKeuvWQWyPaCospEhTUBAZxe3gwYMHSElJQWNjI7y8vJrly82ta27cIGP6zTdbFx769luSeT/V6qtLly64evUqSkpKuIeHx3MnVH19PaKjo5mtrS1z7daNxsDJiUi50lKKXFpaUsTy6SinLAPV1bh14QL3kWXBqLiYUjcuXyYD7+BBkpZqNJTHWlpKEXPGYGRkBB0dHeTl5TFJkmBkZCT97W9/EwYMGMD69+/PPEeOFGxOn2Y+o0Yxz/Hj0dXFBSpfXyiDg5+5TrRaLa5cuYLCwkLcv3+fV1RUsMbGRq6rq8uUSiWys7Plhw8fcn19fdHBwQELFy7EgAED4OjoiF9//ZU5vPoqzAcPpkiyvz8Z8n360P7UUjbLOUn4f/yRCmG1seeEhYVJSUlJgqOjo+zt7d3qpm1sbJCRkYHY2FgEBgaCde4M1Z49cD1zhgV9+y1s7O2RmJiIlJQU2NjYoNlBEgR6F5mZVFBKqyV1yJtvQggORpWnJ07X1eG2lZV8paBANhk1SrAcM4ZyPu/do8h2VhY5Qa++ipJz5wALCxi4uECPc6b8/HPkOzggq7ISGRkZckJCArt06ZJsf/ky435+UPXuDeboCEyciLz0dOw+eJC7urqirKxMLCwsRG5uLs/LzxfXrFkD1dixREp9/jntjV26QJZlXL9+nZeXl7OZM2fC2dkZUbduyZ1HjmSGvXsTYfrWW+TgTJ5MEf3Vq4G+fREfH89tbW2Zo6MjToeFQbx2DaxfP1lraCg3NjbKdXV1ck1NjaBSqVBcXIyEhAR+7NgxlpSUxOOuX2f90tMhbt+Ogvh4FFVX4+H8+aj9/XfELFwoH/fxQUVqKnP57jtklZRIBoMGCU/vn7m5uSgqKuLGxsZCalkZj1KrWY2REfLy8uDu7i7obt3KnH75BcKaNXhYU4OGN9/E4dxcKbe+XghMTeVlhYXs8P37kCSJ+/j4ME9PT0RFRSErK4vX19czWZZhWlpK79XVlXLihw1D0cGD4IWF7EdnZ2TY2sqVUVHMrqwMiqb3OmcOkYUrVhD52+JMvnTpEtfX15dfeeUV4cqVK0wUxda1DG7dosKOM2YQ8WpgQHuBuTmYry/c3NyEy5cvCwkJCVKfPn1a76eDBpFt8ajCeFVVFcLCwnBmzx5k1dfjvIsLIq9exfXr17m/v39zYUOHWbNYjY0N7zR27LONNTs7er72auBUVxOx2R4Byhjlex86RGd2U4eUF4FSSUEAe3tIycmwOnECAX/7GxoAlpeXB0EQkJiYKAcEBLBr166hXl9f69qrl4BBg0gxcPky7aO9ez9JBnBOQY/33wf27ycZ/PDhqOvRA6np6Tw7O5u5uLhg586dMgAmCIIcdfGiUAcg38xMUiqVfMKECYKfn58giiL0lUoMGDcO9wwMWOHatbA1NQXr1YtIspQUYOpUnA0LQ0pKChYsWABzc3NwznH06NH6oqKi0MbGxlWDBg168XH5X4y/nO2/8KchKipqPGNsSnp6Oj937hxSUlKYjo6ONHz4cDE4OBgDBgxgnp6eQs+ePQVXV1eEhoay3r17gzGGtLQ0PKpMCgsLCzg7Owvnz59n8+bNE+zs7DBhwgTW1EpFV1cXZmZmzS2RWkKpVOLWrVtSRkYGzM3NmbGxcas88YqKCly5coX/+uuvuHr1KnNwcOAvvfQS6927t9C/f3/m5+cHURRZZmYmj4qKYpmZmXJubi4/ffo0a2ho4PPmzROCgoKYj48PAgICmv5G+L/taLeEi4sL7ty5I928eRPu7u6soKAA9ymXS87MzOS9evV6YlA0Gg1qamqgUqn+/Lx6e3tqFXH8OBmUfwCMMTS9l8DAQFy6dAnl5eWtCwX9J3hUPRZhYXRAjBtHOdgtKxMrFOTENVULbWggA2PQoFYtel4YWi19nlJJTuzmzRTB8fAAMjLAJAm6Bw6gw5w5MDE2ps8JCgLeeIMcKnd3Ml7//ncyVsLCkNbYyHPu3WOpqano3Llz63xvgA5cf3+Korxo0bmFC6kgzYgR5AB4edHfMwZTU1N06dIF0dHR8qVLl4SYmJhmia65uXnr+VRRAbi740djYx6ZkcEKCgpgZGTU7PjV1NTgq6++4tYrVyIuP1+WHByE1NRUWFhY8Fu3brHo6GhkZmZynpPDjd57j+UKAmK7dIHjrFmwb6/X9KlTZHi3kLK2RJMSYPDgwbKurq5cUlIilJeXIzs7G2VlZQgODhb8evViQYMGwSo+Hr2nTkW/efNgamYGlUoFBwcH1qVLF6ajo9N+n/umKvDl5WQEPactnrm5Oezt7eH34YdCYpcuPKq2tv32gM7ORPzExlKKQDtr2MrKCpxz5ObmQhRFuUuXLk+qEeztiRiTZZJIjhlDRnB6Ojnzq1e3Isx0dHRw+/Zt5OTkMFdX1+be8C3R2NiItLQ0hIeHy2fPnmWiKMozpk0TRM7JsblzhwoK7dlDBQZDQ2ld3rhBxNLWrRTNvX0biIyEMipKcMzKgkJXl96rJD12UmtraW326EEkWmQkYGGBqpAQnElMhJGtLZgoyg4ODrxVhwNvbyIaFiwgZ/1RP9lnISkpCenp6fD09MTixYtZUFAQAgICWEBAAHr27Mn69Okj+Pv7Cx4eHs0pUKIowsLCAsXFxbhw4QIGDB4MoaGBpJh2dhRhnTqVcj8FgZ6vKSq6aVPbVdNBJLVGo8HSpUvbPYy6d++OuLg4XlpaKnfr1k1A376AWg1mawurLl1gZ2eH5ORkpKSkICMjA56enkRcM0Z7pCSBm5mBiSLQoweq33sPd+zsoKNS4d69e0ytVgtlZWWyn58fQ0EB7ZteXkSerFmDI6dOocOyZUg5dw4HcnKQWVLC79rbsx5JSRAdHeHarx8zMTGBaUkJcPw4O2xigs6PosaFgYHQ7tiB8nHjMGzYMCEyMhKNjY28oqICL7/8MjNtivb36UM1Gd57D3EdO+LA8eNyXV0dmzdvHmvq2xx7+TJ3c3dnRo9aS0kJCTjr5ibrhYSwtKIi/quODr9x44ZcWloq5uTk8OvXr/P8/HzW76OP4MoYiy0sRHFxMUpLSxVarRb379/nRUVFUlZWlthdqcRiW1vm8eWXyKqt5el9+uA4Y+yumZmM8HDut2sXK3J1ZUOXLWNBGg1USUk4NWkSN1m0SJCOHOH68+c3r8vs7GykpqYKycnJqKup4fN37mTuW7fiZmIicnJyYFZbi+pevXCjogKhoaFwKC/nlqamQoa+PnwuXWJZpaUIWL0aI0eOZJ07d4axsTGqqqpYRloa0z11CgXh4XDLy4NQVUWy/5s3gZwc/Ozjw6N79GCz58yBvr4+qzp5EnoWFjBrUnooFOTYnTxJEWA/P4Ax5OTk4PLly2zu3LmCkZERHBwc2NmzZ9G1a9fHJHllJUVAPT0fk58KBTmuXl7AO+9Aoa8PK39/xMfHC1FRUa1I9pO2tvxQXh67kZAgR0dH85iYGMZTUrBk3z5027cPPkOHYsiQIcjLy5MjIyNZQUEBe1BUhAtWVrL+kCFwdXV9tsF25AjteUOGtP1za2siv2bNenbqTlMnk3/HqbSzw43GRly9fBlmGzbgvr4+ChUKcACiKKJ///7M3t4e58+fF7p37w79jh3BPDyovkJsLCmF3niD5OxWVqRMGTSIitKVlwOvvopya2t89dVXuHfvHuvZsycuXLjAe/bsyadPny707NlT6CWKcLt6FX7/+pfQo0cPobkP982bwNdfg/Xrh7ylS7WR164J5RERuOvrC5vaWqiGDMG+ixeljIwMYcGCBc1n840bN3h8fHyeRqMZvn79+hdrw/NfANZWxeS/8Bf+HWzYsOEfAD4YMGAA9/DwYDY2Ns903Hbt2iUVFxcLnHPW2NgISZIgCAIYY3BycpJyc3PFVatW/eEoplqtxokTJ3hubi60Wi2zsbGRXFxcBH19fXb79m2poKBAtLS0lHx9fUUfH59nSsbVajUiIyNRUVGBgQMHwqENSeP/q5BlGT/99JNUWFgoAsCYMWPg5eWFTz/9FEuXLsWDBw8QHR0tV1RUCE3tf/r16ycPHTr0z2cNKirImJ49m5wBlYrYcZWKmNcXJCpCQkJQWlqK5cuX/znR7bZw7RpFcaqqHjsWp08DFy9Sz+gvvySHwNz8+ZF6rZaibJmZdI033iAHpqnN1bvvUgTmwQPK3fvkE5Kkd+8OqFQICQmRdXV1MXfu3LYHqKltS20tMGkSGlasQGRCAhoKC+H80UfwbI+UyMigA/kZLeJQXk5qhN27ge++I0fk6UjjU4iNjUVCQgK0Wq22oqJCoVKpEBAQIAUGBj4eqMJCqLduxRd6ehg5ciQKCwvl5ORkYdq0aejUqRPUajW++uorvHbzJqK7d5dTDQ0FzjnWrVsHURShaWiAXlgYcOsWbtfW4oihIbggYPbs2U+2lmmJ/v0pEvNUAbFHbdNw7NgxpKen4/3334coisjLy8O+ffug0WgAAO+9996T+0RDAzlzO3e236f1aQwfTg72H2k/J0mAkREKJ07Eod69paa8wzYRFET93Y8cabul2SPIsozTp0/j5s2bAIC1a9e2Ts2oqSHDd98+yl1dt44ifi2LEdXUkCFpaIi6Q4ewOTkZ3fX0EAxAsX49pNWrUWxlhVgLCx6wYQM7NW8ed46PZ97JyTAtKoJgY0P7wauv0thcv04Vpz/4gMivL78kYsfCgtaZpSVgYwONJOGzzz7D22+/3SzbbUZiIpEDTbUwOAeqqyElJ6N482act7WV5508KcDamlrznTtH6R9NUWLOqRCetzdFhjZvblPG3hIXLlyQoqOjxTFjxsD/D6Zm1NXVYePGjQCACRMmcO8OHRi8vMipjo2lyJlCQSqPqqrnthOMjIyUExIS8Oabbz5zQy0pKcG3336LV155heo8cE7O0sKFwIoV4Jzj+++/R3FxMczMzPD666/THx4+DH7mDP7h7Aw9PT241tfLA7ZuFX5YuhS2FRUosrKCoFBg2rRpUCqVKP7iC/Bbt3jY8OFMFEXo6enJNTU1goGBAd584w1otm6F8h//QFFSEuyjo2l/vXSJnJfdu1GUk4MfAKhUKt7Q0MCMGhqgVSpRLwgQBAGyLIMxBmtra1kQBIz65ht2u18/+Y63N2RZ5jXV1eKsn35iOiNHwm7zZrAW50zE+PFyYGKioMzNBbZvR22XLrjyxRfo+PLLqHV2hsw5JElCdHQ0jI2Nub+/PwPAvY2NWYOPD3Z99BEPGjGCWVtbw8zMDEyWgf37ceunn1AJwMHXF/YrV+LniAi5sbGRDxw4UERlJYy3b4ftiRMQL16kfV6pBDQayJGRuK+jg3NHj/LAqVNZl+xsYOFCnDp1iickJLBFixaRw/LSS8CWLdDo6qLhk0+QoFbLV83MmFarZePHj+degsAacnJwJD0dSl1dWcfenk+aNEmMiIhAbGwsV1ZVscG3b8vVEyei35YtwmkXF6R5e+PtYcPAbG3JIZs4EV9dvCj7+PiwQYMGMc458jp3htHRozBrUY0fAM2dsjJgwACUbt2KH2/cQMeOHaWZM2c271Xh4eHyzZs3mVKp5EaGhvwlhULU0Wja7kfNORFednbA4sW4V1GBn3btgr+//xNFYaOCglBsbQ33v/8dJiYmUMgyzOvroZOURJ1QHqG2thYREREoLi6WRn74oZg3d67s/8kn7da0aUZYGOWRPyv9LSOD6gY8lVrTJhYupN9rr6BbO2hoaMC//vUv6NbWYkZYGPS9vKB57z04tuhYsWfPHik/P1/09vaWxowZ8/iMqKkBPv6Y9k5jY9pbbW3JxtHTa0pXQm1tbXP3nClTpjyZtnb8ONkAJ1qovZsk/4GBVDdCRwc8KgoNCxbg2EsvyQN//FH4acECQEcHy5YtQ1OgLCcnBwcOHKhubGzstX79+ow/NBD/y/GXs/0X/jRs2LChr6mp6W+rVq16TrNXQlVVFTIyMiDLMiorK7mOjg5LSUnBw4cP4efnB39/fzyvjdXzUFJSgqSkJNy9e1fWaDS8Q4cO4pAhQ56Z//nfhpqaGgiC0PzM27dv15aWliqUSqXk5+cnuru7w8rKCr/99htycnJ4UFAQMzEx+fOJBQcHinKMG0cGcXY2RRyHDaOflZSQlHv8eNrI6+qA5GSSiT3arLU5OTiZlgbZ2BjTZs4kKVdT0R4dHYq+mJiQsaZSvbAT3wp1deSIurnRQaPVUr7h3r1kiG/c+GRLq5ISMhIHDaKfFRZShHDkSHKwLSzIsN+yhZ6xS5cXiioXFRUhJCQEa9aseaE5W1tTg6iZM9GvrAymp05R8aW//73t/Oxhwyha+HRrk9hYiiBOnEgEyaFDT1QifVGUlZXhyJEjKC4uxoABA9CzZ0+Y792LvFu3cMjDQ3ZwcOCzZs0SASAiIkIbHx+vaGxsbO77/NK8eXDq3Bky503V6YlceOUVcvI2bgTs7BATE4MbN27wxsZG3qaTkZxMkV89vVYR3+3bt2sLCwsVoiiid+/e0ogRI5oNlYaGhuYCc23iu+/IcDIze3ZeXlUVzZ+8PIoutBd9bwtpaYCZGbKrqnD46FH+9ttvt89enjpF4zJs2AsVv/vss8+4sbExW7p0afukaHHx45ZF4eE0z195hZ7Jz4/Ig717AQ8PXH7jDfl+fLzQPzERsR9/LFlu3SqW2dgg1dkZveLicCcoiBuJImYMGsR0AgJoTNoyeFNTaX3ExJC09/r1J0it9PR0nD59mrdZLX/GDFqf77wDgJzZ8PBw6fbt26JCoZD79u0rDPDxIZWGJJFDP2AARb/LysjR2LmT1sAHHzy3rkFjYyM2bdqE8ePHw9bWFnFxcSgtLUVubi7v2rUrnzJlynM3oLKyMvzwww/QaDRYtWoVTKOjyQnbv5/2H5WK7nfJkmeTYyBJ+8aNG+Hl5cWDg4PbnStarRaffPIJXFxcMHfuXPpmSgqRFWPGNFfqLy4uxrZt26BUKvm6deuYQq1G3ZYt+EqrxZx583D37l3U19fD3s4OHsOH4+Hq1bjYqZOUm58vKhQKbnj3LtOtr0eBi0szkS5JEkRRRJcuXeRp06YJqKigtWlhQQqGiAh673PnAgkJqKmvR11dHSwtLZGQkMDZu+/idycn9sDFBb1794ZCoYAgCBDUakBfH4IgQKFQQKlUIjQ0FGN9fdHjt9+ILPL3b25n9MXGjfK0KVOETra2gJsbGoYMQdbNm3C/ceOJXOBt27bJvXr1EloSKZe2bUPkgwcYOmwYdAoLYXbjBhz27sUlT094vPYaYtVqvI7XNQAAIABJREFUOSUtTdDR0Wkm7ARBwLxdu3gDwMLGjEE3QZD9o6OFsvXrcfr2bT7/n/9k4cOHI8PTEz63bsHnxg0cXLFC1jQ0CL38/TGi6bz57js6V5ydIfXsiYO9e8N54UL4+PhAT0+Pai5s3kzqCH19YOdOhIaGouTnn+FjZ4fuVlZQJCWRbNzREWdDQ5EcFYXXvv8ewo4d0Js+HdevX0dYWBjmz5+Pzp07oyw5GQXz5sEzPr5dgln65hscS0mRRW9vNnTOHGbSYk+UJAk3b96EWq1GTUgI75ySwuxCQ2H8vGKajwqBZm/ZgoMHD8La2hq9e/eGh4cHUkaM4DmmpmxCU82PSZPIVli3rtVl6urqcPXKFVl/wwbB78gR6LxA1W5cvEj7b1uEQBOSk2ldRkU9t993sz3wVA2IxsZGhIeHc29vb9aezZWdnY29e/dCkCTMA+CUlEQqoxZFQEtKSvDDDz+gZ8+eso+Pj9B8repqIvM9PIj4X7EC+Oc/UZGejh0RETAwMJCcnZ3F69evo83WYXl5FCxpUvj89hspj8zNqc5G03M3NAD5+SgPD0fy2bM8um9f6OvryzU1NeKYMWO4i4sL27ZtW31DQ8OkDz/88LdnD9Z/H/5ytv/Cn4INGzYE6ujoHOzbt6/loEGD/r0mwwDi4+Nx/vx5zhiDp6cnOnTowGJiYlBdXS2PGjVKcHFx+f+Vo/x/ElVVVdi3b5+k0Wh4fX29Yvjw4a3k5v8WZJmiY0VFlOf3tAPMOR0CDQ1k8DZJS9Vq2uhVKjpQ1GogNRUxiYnyg7o6QanRYKC+PnTMzCBbW8NAEMho1tGhg6GujhhcMzPKM9XRoQizSkX5zjo65DwwRkatri59BucU9VWpKLI3ezZFpbdtI+NcFIn13rSJjLfhwylyun07OdO3btE1Z86k5/oP5+vu3bvlBw8eYN26dc813JuiwiNGjIBf164UKRo5kqKdN26QIVtfT+TEBx+QRO5RixFcuEDj8vXXJBt+ThQtJSUFqamp6Nu3LxzbkUTLsoyQkBD5wYMHgo6ODtYUFOBMfj6cPvzwsTy1Baqrq5GZmYnOnTvD1NUV7FGUBQCRFe++S2Pc9P4AnD59WiooKBDLy8tbO6NlZRSBDgt7Ipf5zp07SE1NRWpqKjQaDTw8PKRJkyb98ZoLNTUUFT16tH2ZYHAwzanQ0D92bYCubWWF6shIfPPNN3j33Xfbl6knJ5MyoqSExqoFfvvtN6m2thaTJk0SmxzrL774gtfW1rKlS5e2JjXv36dc6IULqfiaIFDbo7VrKQe3e/c2perJyck4deoUd3Jygo+PDzt8+DCGDx+O+Ph4WFhYYObMme3fP0Drq1cvkpY+eEDR5ffeo+d55GyEh4ejqKhImj9/fmurPzaWyAxHR5SVlWH37t1cX1+fjx49WujYsWP7n/vgARnXnp5EVhQW0vd37CCyrGkvsrOjqBnnwMmTKN6xA8aLF0MWRexYtAgrN2/G5cBAuDY2wjwhAaFr1kgTv/9eFDp0IIm8nx+tvb17qcDSoEGAjQ34wYM4O3my7HT+vOBubU0G/MyZ5PQtXvzsgkdP4eLFizwuLo6/9dZb7Q50k7M9atQo3qdPn8cv8vZtItguXKBnBVUN3rt3L+ecY/ny5ax+0iTs7tgRw19//UklSVPbqqaUk4sXIa9bh5pOnaDzyisoKioCQNX4ExIScPHiRVgrlVojhUIw6NxZCE5IgM7ixeTsZ2QQ6dFWPuzcuSgdMACHOecGBgZ8/vz5wqML0zjNmdP8q1988YU8depUwcnJiYjHmhoiD5VKHF+wQB6iUgkmixYBly+jyscHOyIj8eaGDU983HfffSf37t1b6NWUAiXLKPn6a4iff44HLi68wdgYDwWB3fD0hFpPD3369OEjR45kkiShsLAQenp6JN1OSYHlnDlQb9gA7ejRuHzpEvR375azjYwEiwcPYD5/Pmzc3KDT0ABDZ2doNBrIcXEwmzEDhTdvwqVbN/r8OXPIsTQ3R4G9PfadPIm/NRUpA+jMmTKFSFZTU+C333DT0hI2a9Yg2d0dgSdPorkt6Z07wLRp0O7cia9PnkQtYxBFEZIkoWPHjvLMmTMFPT09PDhxAjF79uBOQIA8YMAAod+jug2yLOOrr76Sa2pqBENDQ0l+8EBcvW8flD/8QKT600hLg3rXLhxUKHi+SsUCAgIw/FkFCCsrKa+7tBRXDx/m4XZ2zNjYmNfV1TFZlmFubs6XL1/O0NhI59maNSTvboGUlBQcP34cvZKTZc9VqwSHRznez8XBg2Q/fPPNs3/v2DFSfL1IIbTqalof+/c3n18fffQR5KYUIwC+vr4IDg5utU8mJibyEydOMABwbmjgc1NSGJs8mc7wR6kR+fn5OHXqlFRZWiq6u7lpx0dEKHDuHNk9CQl0HvbvD+nsWWjnzMGJn37iM+zsmNbXF1xPr7VSCKBaHffukeKntJRsHjs7ygtvcY98/Hj87ucnG+zbJ9zetAljxo6FIAi4fPkyYmJieENDAxNF8Z/vv//+u88fqP8+/FWN/C/8x/j4448X6urqbhk/frx+9xbSln8HPXr0QE5OjpySkiLGxcUhLi4OAGBoaCgce8RgqlQqDgAjRoxgvi/aJ/kvPBcKhQKNjY2QZZmpVCpERUWh1x/MsW4Tb7xBG3NTj86nwRg5tipV64jc0zLo4GD0B4SEhAScOnUKTVe0trbGsmXLqNp0Ezgnx7K29rGcu7KS5NEqFTn0JSVkWGu1dBDm55Oh5+pKTrVGQ4ZvUhIZ3N9+S7mxO3c+bnuSmEj5Z8uWkZHJOTklc+eSo+XiQvcVFERGdkYGOe6XLpEB2KsXReRefZUcyaQkKr62eTPw4YcY07WrsKukhJyOtWspAnX3LknZf/6Z/t7aGrh0CbojRsC6vp4rs7IY/Pwo99TWliKQERF02Lq7k7RswQKKsl+7RgfonDlEGDxlbLaHY8eOQZZlZGRkoEuXLnzo0KGsoaEBNjY2zZJrQRCwePFioa6mBgmjRuH3Dz9EcmwsJrYjczYyMkLzmg4LIxKktJSinD/8QNVrn1IEJCcniw0NDRg9enRr78/IiIyhp+bRmTNnJFmWRT09PW5jY8OnTJny73URMDSk+/T0pPy+loZcVhbNrSNH/n2FxauvAl27ora2FpIkIT8/H+06jU1kU7duNAcZA+cc+/btk7Ozs8VHkUV5woQJgiAI6Nq1K27evPmkPD4ri+bnN98QiXTrFlWSLi0lJ+Xrr2kethMJ79GjB3r06MEAICQkROacC+Hh4QAogrtp0yasXr26baMOoLloYUHOto0NFfLJzycHNy0NsLPD/fv3YW1tLbTseZ2Zmckrysrg/9FHDEeP4u7duzhw4AC6deuG50aX6+rIufzsM1rX69ZR5P7cOZo3mZm0zs3MaK3b21NU/LXXcCoxUctmz1YM7dMHS2fOhODigqDhw4HqahT8/DMULi7sQt++XM/YmLuIomAXEECEo60tzZVRo4CcHLDOneETFCSUhYXhXlERbNRqqBgjAjIigvaVmzfJgXlG6opGo0F0dDSbPHnyM0nSpnceERHB+rQsCNm9Ozn3Dx7Qe9DRgYmJCebOncu+++47fPzxx5hqZwc7Y2OcOnVKWrNmjdjiovR1yxZSLeXlQfjuOxgPHgwsXw6nadPIIVKpMHDGDHS5dg0m8+crtJKELcHBGHXwIPXzfv992jvfeYf2pKcihuVbtmDP9u1oZIxVVFQ8/sHNm61UIxqNRjBrym//6Sdaq999B4wfD1GrBS8thTxhAoQOHVDk44MG1VNxAo0G1jk5MG5qxZWeDpw4AUtvb6CsDGYDBjDs2QOYmGDYpk0oj4hA6Zo12PO3v8mzamqEDv7+tD/n58Ny7Fhg40boPpI4jx4zBhgzRvC8fRslkyfzuh9/ROeAAKY4fvwxYTt2LCKXL5diT54UVh86xFR/+xtFNBkDJk2C8W+/NUfOm8E5reMlS2iPP3MGaZ6eUsQbb4g1NTUYJIp0lh0+TE7izJlQeHtDPneOL5o7l5mZmYFzDn19/eZ1Y5Obi3FTpuCn2lrh3LlzSEtLQ1lZmdzY2Mh0dXXZq6++iocPH4q3bt3iu8aNY7OdnGBw6tSTDndZGfDFF9CdNAkvBweznJwc7N+/H3379m0/JczEBOjZE9i/Hz2vXmXhEyZg8cKFTGIMyunToWNpydCtG82Va9cAQUBVVRUiIiK4sbExe/DggZSZmSm6dOqEUZs3C3jvvbY/py0MGIDntgcDKJiwcCERrs+DKJLNUV3drBZzdHSU79271zzWCQkJqKqq4hMnTmT19fXNEmxPT0924pGUO1ulYve3boXDmTNEqA8ZAowfD0dLSyybOVOU3dxwMjBQUf3xxzD6/HOaL35+wL590DCGM4Ig5370EX99xgwR3btDsXAh2SJXr5ICpCWsrOicO3IE+PVXqg/z9tutzoAHhYW89uJF5jRmDFzGj2/+fmBgIPT09HDmzBlIkvTJ8wfpvxN/Odt/4T/Chg0bDBQKxeaXX35Z/8/oba2rq4upU6eKxcXFePjwIZycnOT58+cLpaWliIuL44/6brPdu3fj1KlTSEpKwsyZM6Grqwu1Wg1dXV20NML+wosjLS0NtbW14ujRo1FUVCS7ubkJ6enpiI+Pl7y9vYUXqTTcCg0N5NgVF/+p9+rr64sePXrgwIEDyM3NRXFb12eMosr/bmS5pITkrNnZFOmrqaGCI5s2EXFgaEjRYlkmGWRcHDHM9fWPC6l9+CFJ5o2MiHTo1IkY7aaexH5+5NgrFGQo6ujQ9SSJfp6bC4WNDVQNDai9cAEGy5aR4xkTQ872Z59RHrKXF/DaaygNDUVAaCjrcOwYVT/18qKccDs7Mr6KisiB3b6dHIqmiEhT/9r168nACAmhSN6uXVQAZtEiqA0NceeDD3jkkCHompoK38ZG5vaPf8A2NBSnb9/m+69eZY4lJcjo0gXdGOPOHh6s1toa6ZcuSZr798WpaWnYHhODISNHymjZM7g9pKaS4bh+PY3TmTNt/tqSJUuwefNmhIWFoXfv3oiLi0N4eDjGGBnBZ/NmICMDWq0W4eHhqK+vR21tLSorK8Vp06ahe/fuDP9p+zt/f3JGx48nKXdT+7ovv6SoTDv3/Vx8+ik5mh98APHhQwAkFWzP2a7r1AkZu3ejIT4eFmfPwmX0aOTk5CA7O1sYPHgwvL29sX37duzfv1+2t7dnWVlZMgCKdFdW0uclJZF6YN8+St1Yvpwivd27EzHj4ED5lF9++dzbb2hogKWlJXR1dbmbmxvz8fHB5s2b+caNG9nMmTNhZWUFIyMjaLVayLIMHaWSHICn4ehIhjoA3qMHmL8/4pycWGJiIjp06CDb2NiwK1euMIPqanS7dAnnzpzh5eXlTBRFPmXKlLbfbVkZGb6jRpEx+dFHtCb8/Ojf/fv0NT+fnO8ffqDxaEna2NigLD5emLZ+PTo1VQ1vym0G4NC7N+w5F9L690d0dDQy4+Px8pgxtJ6cnSm61YR162AHIKKmBnczM+GamsrHL1vGVGPHQvjyS3KeNm6key4ooOJxbRRuUigUsLOzk48ePSoYGxu3qzgBqPhkm8rG1auJcNuyhSqfAzA1NcW7776La9eu8WvbtzP/X39F8vTp4qeffgoAvKmApSAIRJh6e9OeX19PstPGRnrubt0Ae3uwqCg4dO0KxMaCcw7h009RExcHvSZZ8YwZtKauXqViTt9+CwgCOOc4fPCgvPL994VTH38saWifFTFxIlV8brE21Go1tFotmgs6iSKRn19/DYwfj9szZrD6W7dg2bUrqsaNkzPDwoSudXUcu3YxlJUR0fr99/C2sGCYPJlIyqaIsZ4ezZnFi+m/MzKADh1gVlkJvVmzWF5eHnt44gQcRJFIo6b5ERdHY7N3L5Gzurow79QJxrdusR8++UR237uXNVdD19EBBAGDNm4UC3fulBvfeYep5s2jdVhUBJw5g/OxsdyvoUEGIGLCBCpwNno0KRQUCiA5GWpbW3SIiRFnJCdD6NOHyIaPPqLzrLQUuHIFePttiEolsx0yBML/x953x0V1bd+vc+8wMPSR3rsCCqiIgkbsYm9YYheNGmtM8mLK0/BMVxPTjUZjNGpUlFiwoUSUJoINLEiTKr03YZi59/fHpgoqeS+/l/e+z/X5zAc+lJl7zz333LPXXnvt48fpOH/+mci74cOB2FiobdiARebmeGRujjSZDP29vTkjR0fo6etDQ0MDFhYWcHZ2Zluys+k6zplDz5i5c+mZdukS3ctN0nBbW1vwPI/q6urn+q+c0ddHwqxZWMQYtLy9ScHy6ac0Rjdv0mc17flKS0uRkJDAeJ6Hm5sb7+XlhUHu7nQvd6VTRjNiY2kdPHny2X83bBjdm42NT29F1gxNTRrT+Hjgxx+RM28esrOzOYlEgnfffRdbtmwRGxoaWFpaGvv8888BAIGBgQBaiGs8fPgQYWFhOPDrr6RouHsXijNnoHr9dciqqoCCAnBhYUg5d050YYw567RWdYorV+JEYaGYqVKxJUuWkKlvcjI9Z4ODaT8wdSolA8aNo3mrVJLq77vviKTZv7/Dad27dEm86uODuT17MlnT9W1GXl4eLly48BiAd2BgYO1zx/3/KF4E2y/wr2KooaGh6s8ItNti5cqVUCgUUFNT4xhjMDQ0bJe5CgwMRFZWFvbu3YuvvvoKoii29E5VKpXgOA5vv/02utSG6AUAAO7u7oiNjVVdvHiRX7VqFaelpYWDBw8qi4qKJCdOnICjoyPVzXYVzfXWBQVdY4j/IKRSKZycnJCVlYUJEybg1q1b6Nmz579+zd9/n0zKjh+noFNfn+qTmnHzJmW5XFzau5A2udpCJiNWHCB5eTOWLqWvhoat9U+vvdb6+5076euUKa3S6X37kHfvHspLShC0ejUCLC1bA2SAsjmguvzoXbuE6ydPclZr1wo95syhnUd1devfNjTQ17AweoBqa1O20sWFAnY/P9qsWVtTRqKkhDao/fujQS7HsaNHhQFaWsx/xgym/uOP0JNKITEzA8LD8fLq1ZxQWQnuyy9R9O67kMyaxUqPHMHtCRPEWTt38jd790aqqyve3roVeOMNTnRyAvP0pEBm0SLarDx4QJubuDgat+vXaXP7wQfPdLGXy+UYOXIkwsLCAAC3b98WtLW1udtJSZBPn46CuDiEh4eLOjo6UKlULdmwoKAgTJw4EX+KOsbAgDKgGhqU5XB0pADhX8Hp0xSs79yJe/fuiQCYqpmEeQL379/H0aNHMf/gQYhKJfLNzVEsl6N5Xba2toauri7Wrl3L/fDDD6qcnBzOw8ODH+rqCq3AQNrA795NWeu26pLCQgpGnZ3peiQk0JwKCAB27CCFyFPg6enJIiIihPnz53PNDuXvvvsuO3jwoLB//34OAExMTITCwkIOAGYePQojHR1RPzKSdTAv4nkIgoD4Hj2EAmNjziYzE2ZjxqBUpeJyc3NFDw8P0bNHD5YycSIK798XKyoqmEKhYFVVVa3u6A0NRCA1q0b27aMNu49Px/O4e5cCqUePKGCoq6NN9+TJLX+SmZmJxsZG7lm+IowxuLi4QE1NjR08eBBZv/wCm7b9zdugoqICfXr3xuTLl5F05w7b4ueHWZ9+KjqvWsWwfz/VhfI8rRnh4XT/njhB5mlNkm+O4xAQEMDt3btXuHr1KjejjVHUkxCbvBA6xfLllIWurSWysAne3t7Mu0cPiCNHwnzpUqg4DnFxcbh+/bqYmJgoDB8+nLezs6NSrx076NgWLqS1xNm5dY308Gh5z9u3b4PjODx69AgymQzas2YRCbJmDX3+w4d4XFSEtJQUXE9PF6pqa4GYGEzt04eHRNKi4ngyq33x4kUYGBiIHMe1J1zWrQPmzMEbdnZMUleHlF69xIbYWOaZlQUDLS3W0ufY2hp4802E791Lpm7FxWIfS0vOq5m85Xm6P01NUWdiAkVNDaJLSnBHTU10sLER5BERPKRSIqymTKG5tGoVBSx5eXQfvfkmEBEBLjkZkw4c4PLGjoUtz9N68t57wMiRYPb28EpJ4YJmzhSnxsUx+VtvERkbEAD9kBBxQGwsL374IdiYMUQGyWTUIzw5GcL332Pfrl1ClYsLN+jiRfrM5ufM66/THFq2DA06Oqi/eBFs2TIiBzw8iDScOZOIOMaAkhJIS0uB2FixzMKCOcbHQ8FxSO/bF8bq6tCfNQs1kZHwyM8XOYDmrFxO89Xbm2TZp051mINPS4zk5ORAXV0dx48fV9XU1HBLlixhJgYGrcRFSAiRzh9/3O75YGdnh0WLFuH48eNiSkqKOH/uXE7d3R3VQUHQeZqzeGfQ0upaq1IDAyKmQkI6ZoWfhrQ04No1XHd2FgEwd3d3cByHESNGsLNtyo102gTKAGBmZga5XI6wsDDo6upSr/RevXA9NBQDMjJwdd06eAoCpO7ukF2+LOTk5DBnZ+eWAc5/8ABJu3YxJyen9qaYjJF52/TpdD/t20f7lKoqUpowRqq6FSs6nEpRURHKP/yQzS4vh6yigtaOJtTV1eHAgQOPlUrl3MDAwDtdG5z/m3hRs/0C/zQ2bdqkKZVK706YMMHO7TntUf5/ITIyEpcuXYKtrS0GDBiAyspKKBQKXLp0CWPHjkX//v3/kuP6b0VCQgJCQkKwfPlyGBkZ4fLly2J6ejoqKiqE8ePH887NdWPPg1JJD6qrVzv05f2zsamN7FlXVxevv/76H38TUaQN1uHDxPZXVlKG4FkYP56Y8uPH//jn/UFs3rwZ3t7eGDJkSIfflZeXY+fOndDR0VFNnz6d77Kp4OPHrWSBKFLGZ8oUql0NC2vJvAmCgD179qCmpgYrVqz4Y4QLAIwciTpbW3zfvbugqKzklGpqeMnaWhwxdiyDjg4ZYPXuTVLZL76gjYunJz3oJ016rnt3cyurffv2QU1NDTKZDHYxMXBxdMSpbt3Euro6NmHCBPTu3Rv3798Xo6KiMGPGDLZz504olUq8+eab/7qzvSBQNikoiJQcRka0EX+OodVTUVoKFBcDdnbILyvD8ePHUVZWhrlz56JDv1oAFy9eFGNiYtgaIyNoNDTg4P37yGvKaspkMnH9+vUtAUdISAgKTp3CXJUKms7ORCTNn98x4ExJoQ1ns9FOWVlrKcSFC1TH9/nnTz3H2tpafPPNNzA1NRUDAgLaBTyCIODBgwd49OgRLCwsYGFhgbLUVEQEBamKjIx4Pz8/uLm5tRi3KRQKfPfdd4IgCNzcuXNhPGUK+JdeovnSjA0baBN7+DDq6urwzTffAKKIpR4eMOjXjzbkL71ExyyTPbV9FgAKZurqSJECEPm2ciXVhE6dCkEQsG3bNqFfv34YOnRol2RU+/ftg9q5c5i5fz+4JzJgSqUSX3zxhSgpLWVDIyIQO26cCAMDBHz6KdNMTCTCqVev9s7IycmkbFmxgrKV7u4UQAHYsWOHWFFRgWcZ6m3btg3V1dUtmbMOEARSbXz6aXszSICyYH37kiQerY7JampqaGxshJWGhuh96hTTLSkR45cuFQxyczHg0CFe/dEjQCJBTU0NMjIy0K1bN+zevbvlbXmeh3d8PFyXLoX5hAkAyEvkxHvvYdr+/bi6ZQt85s6FdlkZKRE++ogIx969O8hat2zZIkydOpVzcnKiLG59PT2P3n6bSnisrWkNPHaMypzWr+/UvDAjI6O5hzpycnLEgQMHMkEQYBkcDE4uR+XUqQgODm5Xd2thYaEaNmQI77B+PWWGMzIoW5+YSAaDbQLM4vx8HNq3T3C5cYN5Ll3KutXUUL2+lhbN2e3boVq/Hr+pqcF76FBYffwxnc+9e/j54kVVdnY2r6+vL06ZMoXZ2NgQSVJXByxbhoPBwaoaAwM2f/58TlNTE2hoQGlSEh6XlsJEVxdqqanAnDnIzs7G4cOHsX79eiKarl8nRY6aGuDqipNubsrc3FzOJyICYR4enJePDyIjImCorQ0HmUxVe+cOVyCRMLeEBJg0NIjd33qL4fBhmkOhoXQ8e/fSmr59O62V3t6IVleHy5Yt6DZrFoSQENzcsEGwjIvj9q1bJwRs28bd9fBAna+vatyOHTwXGUkkWWwsrUFOTrT2lJS0I4SaIYoiDh48KOakpzPXGzdgtH49BjaT4F1BQQGNxZMGop0hPJzUAr///tQSmyeRnp6OpLVrkWFnh1GrV8PZ2RmCICA1NRWHm/xSxowZg7ZlHvX19di8eTMAKqf09/dnISEhqprKSl63shJ6lZVwsbGB9z/+gfz8fPz444/o1q2bOHnyZGZtbQ0sWYJcZ2f8bmws5OXlMXt7e9bY2AhjY2N4eXlB/uSaeOhQa7vM/Pz2SQfQfb99+3bRXV0dIyoqGPr0adk3NY1/XXZ29u733nvvNfyP40Wf7Rf4p7Bp0ya5VCo94eTk5DJ06FC1P703cxdhbW0Nc3Nz+Pr6wsTEpEU2d/v2baSlpeFpfV9foHPs27dPHDlyJLO2tkZjYyNu3ryJuro6ged56Ovrt2Sontk2QxSJfTY3f37A+ifA1tYWDx8+hEKhwKhRo57ea7kzXLxImWx/f8p6DRhAWeentZBqCz8/6peclUVZrz/Y9uePwNzcHKdOnUJiYqLg6enJGGMoKChoNoGCXC4XVqxYwf+hoNHCgoLC5o2qjw8FIFVVtDH19gYmTcL52lpklpdjzpw5HR/Gz8ODB8CiRVCbPh0+gwYxZzc3lJSWolSlEvsOGcJaTOoSEmhjduIEjesHH9BD3t+/U5fX4OBg1e+//84cHR3ZqVOnVL///junrq4OHR0dGBoaqgZevMiM3d1ZilwuDhgwQPT29mYcx8HExIR5eXkxTU1NDBo0CNHR0YiLixM9PDzYHyYRABqnb78lWbWaGo3n+PF0LqJIWaI1VhkwAAAgAElEQVRONoLPgiAIqF63DpKVK3HUyUkVevEip62tjcbGRtHW1pa1JVOUSiXS0tJw5coV1tjYCLfRo2GQnQ2X48dhs2YNCmtrBX9/f05PT4+O58IF2H/1FbI4DqWamoLdBx8weHl17qTr708B9tChVC9eWoo0URQzGhtZRo8eqKmpAf+PfyDJ2BiPKipQWFiIuLg4HD16FLGxsYiKioK+vr5q4sSJ3JMZGsYYjIyM4ODgACMjI2hkZ0N+4QJ6BwZyampqCAsLE27fvg0jIyN26NAhITQ0lJmamoqvvvoq09XVBdfcLuxvf6MsjL8/yX49PQEHB6hlZcG+ogLyyEhR84cf2A2ZDLbffgu2cCEF0M/qiwtQMLRkCWUCe/em69hcf7pmDS5JJCiqqsKsWbO4rj77YnbuxKSwMGi99VaH3xUXF6Pmxx/Z3OhoWN+8if5Dh7L+/fsztTfeoGMdPJhknG3bmhkaUqbd3Jx8KGpqSLr9zjtIEEX20pQpzOwZa6EgCHj48CEGDBjQeQ19c2eH+/eJpGh7nidPUna2KaMokUgwePBg+Pr6kjv40aOsRFMTGjU1TDFxIpcpl3MXevRA8YkTgmLzZvycn8+SkpKQlJQkMMbw1ltvMV9fX/jY2yMhJUW4WFHB4uPj4ePjg6ioKKGA58VBr73GHPz8IP3ySyqF2bSJyAcnJ6rpb6ptBYg0romKwrD0dMYGDKDMv4YGnUfPnkRMODpSBnfoUAq2q6qoVjo/n96z6XwzMzNVKSkpYk5ODtfQ0MAqKytV6enpKCktFY3OncMZmUw0MzMTVq1axZWVlYHjOMFBX583XLgQeQsWiAbLlzP27rtEhMyYAYwdi1uPHuGnn35CTU0NtHR0cPPOHTZwzRpmNWAAlSb1708u2B9+CBgY4KJcrrqrUrHxcXGMbd1K2UNra/Tx8uJ8fX0RGxvLUlJSyOwuNBRYsgR1mzZB/fJlbti337IWU9m330b6Dz+Ix3R0WMnRozDatUs8Z2vLrl27Jhrm56O3lhbDK6/Q87uuDujVC8LbbyM4OJgb5OnJen3xBasbNAi9R4/G3Xv3RG25HOPmzuWshg5lAyZMADdqFLTmz2farq5Eog4eTP4ECQl0/xw6RNnTl14CnJxwuqICbuPGQezeHXtu3VJVGxkxl8WLmePIkcx42DA4LlyI7iNGcMzDg57Lzs7k5+DuTmSwjw/VGneCuLg4VXx8PDfhwgVkDB0qykxMmMMfUdjduEEeDl3JVtvakm+KsXGXCdaQkBDRMziYeQ0bBusmJVtiYiKOHTvW8jc+Pj5IT08Xjx8/Djs7O5aeno6UlBS4uLigoKCA3blzBwqFghtz7hx6z5kDbz092O3dC7ZyJXR0dODi4oL8/Hzx0qVLrG/fvlC/fx+6ffvCfdo0pqWlxaqqqlR6enosMzNTvHHjhuDt7d3KAgkCEZMffEBrrZcXfd/k6C+KIo4dO6ZS1NeLs06c4JCaSiqDJsTGxqoSExOzGxsbpw0dOrRzSdb/EF7IyF/gD2HTpk1eUqn0NYlEMtXV1ZWbMGGCxl8VaAO0aeveLE1rgo2NDd566y1s3boV6enp/1W9sf9qmJqaqs6fPy8JDaXODHp6esL8+fP5X3/9VXXhwgWEhYVBKpVi3bp1nWc4RZFeP//8dHfmPxm2traYP38+tm/fDven9ZV+EuvXU02SsTFJ6YB2D4ouwdCQXgcPUkZzxQrK6D+vbuufgL29PSwtLZGbm8tlZmbi8uXLQnFxMcfzvGBnZ8emT5/+x00KYmM77yE8ZAi9amsBDQ2UPX6MCY8ewTw0lOTDXUVSEtWz3bgBdOsGDoCJiQlGjhyJn3bu5Gq++QbaU6dSdsrcnGSnbZ1pTUwoCyWTUZ0kgKysLMhkMiQlJfFWVlbCjh07mEQi4V566SVcv35dGDJkCOdWWcnj/HlATw/LGXvquPA8j7///e/YvXs3jhw5gqXNMv/nobGRApDffiPp4Ouv0zn06EEb9IoKqk2USKiW8LPPnimDfxKRkZFCVn0959a7t1hQUoIVK1bg119/FczMzODk5NSy2FZWVuLnn38WKysrGQC88sorsNDRAaZMgaa9PbqrVOi+ciUHlYqyVMnJQGIi1F57DdoA7qans+HPKrn44QfA0hKpqakIDQ1FaWkpGGNs8fr1SJo2Ddfc3UVHS0vofPMNHgwerKrS12eiKIqenp6ShoYGJCYmYsWKFV1zeE9IINn8m2+if//+6NevHxcaGopff/1VVCqVnIGBAebNm8e1uNczRq+5c8lToaKC/t/RkUpWRoyA+ZQpMN+7l91NTUXE8eOojI1VTZkypetGeK6uLS2wABCRoq8P2Nri8aFDEO3s2HfffSfIZDJMmzaNM3hOezzt2lpUGhjgt127RJlMxiorK4Xq6momiiL46mo2mjGB/+knrp2Z3tChFKCtWkXzaPz41uC/GYy13pdVVWjMzIRQXCw6z5zJMHw4BekSSQdJrFWTg3FsbKwwbNiwzi/Syy9TVn/4cDIXa17zvb2pBrcNcnNzoVQqUZuRAau6OlFvwwZWvWoVrt67B5mWliDR0GCuffpw5jdvYuK4cbCxs4OBoWH7z/3xR8yIi+NyDh3Cnj17sGXLFtTX13MAUDJjBgzr60lJ5OtLx8UYESISCZF2jY3A/PkodnQUvCUSxm7dovPOy2slvZydKeD74gsKpBYuJL8KXV0izX75hWrqw8KAJUuQmJiI0tJS3tDQEEZGRhg3bhzN6cpKYO1a9HjzTdYcmHfr1g3V6ekYfv48Ct5+G0eqqsSwwEC2wMEB5y9eFEYkJnJnTp5U5WZm8tqGhrh161aLCWxwcDAWeXjAPCyMrvsXXwDjxiErKwuxsbH8su7dwWJjiRAeNowy2H5+qKmpQVVVFUaMGCHgzBkeVlaAszOK1q1D+PHjsG5sxP79+1Ucx7Gh5eVcjJcXW7FiBTQ1NXH50iVW/8svcC4vZ7YZGTinVKJk+nRVn7AwrtcrrzC8/DIqysrA8zx8hg0DCgowrq4O+O47rFy5kn355Zf4okldYmZmhqGurrAOCqJjq6mh65SYSGv7kCFEaHTrRmTByJEolctxITISGRkZorW1NZs+fTonlUohB1rl4kArae/o2Eo2bdvWqjzpBDk5OcxaTw+9CwqgGDgQF2Ji4OXlBf229/SzYG7epfacAGgeJicTCdUs0+8EycnJKC8vx927d1FUVMSkly/DwNKSCNs1a1Dc5M3RjP1UH91sOCk2raUsKSkJADBw4EDExMTAsb4eBg4ORDouWkQZ9pEjYWJigrlz53Jbt24VSktLOd3AQKCpzNLT0xOenp48AFRWVrJvv/0Wt27dQp8+fejDN24k8nLsWLqHqqro/EaPBvr0wbWEBDErK4tbO3kyw3vvtVMZ5eTkIDw8vK6xsXF0YGBgQ9cG8f82XgTbL9AlbNq0yVNDQ+MbTU3N3j4+Phru7u7cf3LGODw8XAWAd+pKhvIFWrBw4UJJVVUVtLW1m2upeABYunQpX1paCiMjI2zbtk0oKSnhOiUxAgJIAhsS8m89biMjI+jq6qpOnDjBpkyZwnWaqSkspFq2zz6jjF1JCW1qnvFw7BLmzqXXhQvEsj98+C+3++oMzffb4cOH4eDgwBYsWACpVPrPOQEuWUIbnmYjt86gpQUEB6Nx717UZ2WJ0NNjqK4mI60PPng2qdDYSDLN06fbB/TJybCIjYVxcTGqdu0CeveG9hN1fO3g6goIAgRBwLVr18QLFy4wADAzM1MuXLhQAgCCILCmejcONTWUlQoJ6VKAyxiDnp4ekpKSkJ2d/XSnb4Dmy549RB4MH07H9s47HbOkiYlkBlVWRhuU4mIKzJ+TIamvr8fu3bshuX+fm5ucDJ1Ll5iHpiZ/4MABgeM4NmfOHNZ2Xh8+fFjQ19dn69ata/9Gx46REV9KCtXAa2hQRmnpUpJBAzBLSMC9Bw+efjAbNwLa2jjl4qK6desWL5fL8fLLL0NLSwuJd++K41xcGF55hbJg586h74EDErzzTosXQWlpKe7cuQOlUtk1D4UZM+jVBI7jMHbsWAwdOpTt2rULpaWlOHbsmGr06NF8u6C2b1+SVwcGkjzVwYFknxkZLVLdXu7uuHT5spiQkMCPHz/+6U7oIOl7Y2MjNDU10bB8OTQyM5GVloaCggKUlJSoysrKUGFhwUkBtvL4cRSuXcudbWxUbd++Herq6hgwYIBqyJAhnQb0j2UynB80CHqamjAwMBBsbW05Ozs7aOXmQnfRInChoRyenH+vvtrq72BpSYHmtGkUCHaSnRd1dPCTTCY0mJhAeuIEQ34+zYfNm2mds7KiIILjYG1tDZ7nERERwQ1rbv/XGXr2pOD+9m0K9ABaN7dta6npbmhowN69e8EYQ5/0dEHbyIiz8/SESkcH3IwZkEgk3JEjR2A1Ywa0AwLQd8cOqle+fLm9U/8HHwBVVbCSy7Fx40ZkZWXh7NmzKC0tpTaBcjndf3V1rSqchARSJ927B9y5A2H0aNyrqGC91q5laO4Z3Paal5WRWZaREd0b+fkkmx4+HI1r1uDGwIGQ3boFq+BglNfUwCIhgRdNTMRadXXx1q1bnLOzMxwdHSnrb29PaoIm8mOwlhYcduzg0j//HI7+/lgPcGHffSdGJiSw5ORkLi0tDYu//57PMzfHBX9/KJVKAICxsbFYk5XFHnzzDdjixTCztaVgFdQqrV9WFpLDw2Fy4QKVIKxc2VKnn5GRAQAYMGAAj2nTiEgAoDQwgO+FCwhNT4dq+HBuZHg4khwcVFIHB6anp8c9TE6G8dtvY+SdO0h+7z3Iv/8ejxITxXEffMBftrXFTYVCNDp3jqlUKkilUhWa9gK4dw/46Sdov/kmXnvtNeTn50M9IwOxhw+L98+dY2YqFXQ4rrX8p7kka/p0WjN37qTn4y+/wFZLCxb5+YJTQADr7eXFddnY9sQJmn+amp2S+iUlJUhNTeVG2NgAaWnozxjLKC5WffPNN7woiti4cePzTXQ5jkiarsLfn3pYN3sItEFNTQ0SExPFixcvMi0tLbG2tpYtW7YMZmZmNH8++wyYNQtt17Zly5ahoaEBp0+fFocMGcLc3NxYVVUV9u3bh7KyMshkMtTV1QlSUeQMmt3rm/HqqzQ+Ta7gmpqaYnh4uGB3+zaHK1fonmkDPT09WFlZ4cqVK0KfPn047N1LteiLFrWSdLq6VLIhilDa2KC+Rw8288cfIXv8mI5/3jwA9Bw7cuRInVKpnBcYGJjZ9QH8v40XwfYLPBObNm2SSqXSL9XV1QNGjhwp6927d4feuP+JyMrK4rt16yaamZn9dWn3/1J0RqJIpVKYmZlBEAQ0NjZyycnJYnBwMGbMmMHMmh76EEUydWnaJPy7ERAQwO/YsUOMjo7G0LYP4MhIkpj26kUGLR9+SGZQfzZGjqRaMjU12vxv2PCnZbmbW2wBwLp166Ctrf2vzevS0i7LmydNmoRvs7JY3/ffB0tJoQD6/fcpm+/j03nAvnQpZfkPHKB5ERREks3PPwfKy2E2fz52mZoC4eEYJ5PB62kS/IULUX/vHsI/+URMVFODn58fMjMzMW3atJZnV4dNU1TUHzLkmzRpEktKSsKpU6eE1atXt3+z2loKWn/+mbIFW7eSaVDbrMuTGDy41X1/6lTKeLz1FgXry5Z1+i/l5eUIDw9HVVUV5pmYQOfmTRQ8eIC9YWGilpYWCwgIaBdoC4KAgoIC7u+dtbIJDiZi6cAB8kv47jsofv0Vp0JChPJdu+Do6IgePXpw9fX17PLly+A4TnRzc2O///670L17d+7Bgwcqu/h4Lktbm92rr+cHDhyIIUOGQCqVIjs7G/f79xfH6ekxHD3aIovF48dUt1hTgwI7O+zduxe2trZKqVT6/D1GdjYFlKWlHeTsMpkMa9euRVVVFU6dOsV27NgBT09PlZ+fH7monz9PLWn69qUsTHY2BWFPbHY9PT1ZWFgYPvnkE0yZMgWOjo7IzMxEcXGxUF9fz8lkMmRlZYk5OTlMaCJ3dCoq8OrOnQjeuFHsZmgo6uvr83Z2djAzM4O1tTVks2fDtrYWK+/d45Vvvonw6GhER0fznfkqiKKInnfvQmVjI/rOndvqgF9VRef9+uvoEGgDVFLR5MQOgO61desoG9uJc3tNTQ0KCwu5adOmQeLm1hqou7pSffXAgUR+HTgAVFVh9OjROHfuHH766Sdh8eLFnUviOY68LD7/nNbSv/2NxtfammqgR46Euro6vL29cff6dXH8jRscFxUF8Dz4119Hr1690NDQAI7jWr0RpkyhEozGRnppa9Ma7eVFihvQfW1iYoKSkhIEBARArqtL1/fiRaq5NjWl8bt6lUgwfX2A4xDt4wNlXFzH3vEAyktLwS5cQM7Onai8eRMSiQTyH39E96IisPp6PCooQGhoKExNTVUxs2ZBFAS8lJfHvZSXxzQWL2anv/pKbHj8uHWQqqvJKd7EBIiOhtrRo4gcPRpaHCc4NnVcGGllxcqdndFj9mwcOnQIpxYtwogFC7CkWzdcvnxZcHFx4QzlcpY9YwY0nZ3xU2oqxhgaQj5zJkquXYO+TAafs2dxcfZscM3Elbs78n/6CYetrVX19fV8r169oFZRQSSZuzvKysoQFBSE2T17iuMGD2Zac+cybNkC208+4XNyc5E0aZKgyMnhUj08xN5BQczF3h7Iz4fJjh1M9e23cNDSQnFxMQoLC1Xp6em8gYFB6zl7edGadvs2dMPCoLtoETB7Nmx9fNgWV1ehWC6HSXS0KJfLeQ2FArZKJURBgHFEBM0lhYLWxalT0bB0KXplZHAZwcFizK5dYrK7uzh5zhzOsE1JQKfIzSVjz076sOfl5eHnn3+Gq4mJsv+aNRIMGwaYmWHy5Ml8YmIiQkNDcfPmzee3NW0y5+syrK2JwD57Fo+HD0ddXR3y8vIQFRUllpaWMolEgtGjR0MqlbKoqCiVmZkZbaSNjck8r7QUPXNykNu3L3R0dNC8r1q9enXL2EdGRqKsaT3w9fVFSUkJN/HECVGMj2esueuFpiaRcXI5rR3dumH+/Pn8V199BeH998F1Yor46NEjZGZmYtmyZRyuXqWgetWqTnuH1z1+jANz5sDFwwN2V66Q+szDA1i1CqIo4syZM/WNjY1H33///Wew6P97eBFsv8BTsWnTJgepVHrC2traYdq0aTLZ8+rc/oPQJMdhL9qA/bkoKSmBUqlEfHw86uvrWV1dHf0iMpIMYLKzO6///DdAX18fQ4YMYZGRkRRsBwcT27x7N23mRowg+e//L3AcyaBLSigAWLCAMoz/4njU1NTg4MGDUCqVcHFx+dfNvLKyKEP2nPIPhUIBiUSCyspKaGhokFlVjx4k3wSoLVBODqkZ7t6l8QUouB4/njJoP/5IDufNmccmkmMygMlTpuCzzz4TL1y4wPr06dOpD0Bubi4Kly5Fz+JiDLh2jXXr1g3eza21nsS1a1TDmpPzh4ZDQ0MDvXr1wt27d7mioiIYGxnRBvrYMTovX18iUr76qmv9shmjWlI/P8pw9+hBY3X/PhmRff99i+N3Tk4OSktLcfLkSTDGMHPmTFhXVQFvvIGs+/fR0NDAFi9e3MGVFqBgpKqqCt2eMK2BoyNlI5ycgHffRXV9PQQLC+Rt3AhLKysuNjZWvHHjBkRRxM2bN4Xq6mouPDwcALjc3FylDWO8bNEi5tCjB0Y7OLQj3wRBoHmgpUWEy/TpdL7TplGwvWcPrmtoCC4jR3KTJ0/u2sQ3NSXp7jPuE11dXcybN48rLi7G9u3beRc9PRh/9hk0fH3BFi8m9/olSyiA++YbmuM//NDS6sfHxwdhYWFgjOHs2bOiQqFonvyctra2oK2tzSQSCZs6dSq6d++O6upqyOVyCNOn421vbwae73iz9OtH8vWvv4bk4UMM+/RTxMbGoqGhodMSmxJDQzBnZ6ZUKnHx4kUxITaWzTh6VBRmzmTh5ubCuNxcrkOrrg0bKPi8caP1Z9OmUWZ3wwYqX2gDHR0dWFpa4rfffkPPnj1bn33N0tA7dyiTGxcHvPUW+v/wA7RlMpy/d48TBOHZRPrw4VQq89prRCLOnt1uDfHx8YF861aWOXky7PX1qXThlVcAMzM09OwJtYYGnD17FsOHD4eGqSmRBh9/TMZZKSlUd+7o2C5jr1FbC8eSEkFj40YOJSW0nufn09r66adEbv7yC2Xwms716tWrwtixY9sRB6Io4vr160j9+mtMOH0aF957T9DW02N1dXWoqqpiC/btQ769vRg3YQL09fWF5cuXtw7EqlX0NTwcfa9cQd3gwbTWTJhA2eWzZ2nNCQnB9VGj8KigAMvamuZduQK5oyPk3btj/vz52L9/P9RPnIBJRARmnTvHAUDFzp2oMDSE186dyL54Ubh6/jwbc/o0u1peLg4LCWG7Fi6EZdsSKUGA9q+/omrlSl4ikSA1NRWXf/gBmrW1ODdmDHieR79+/VR2773HIyICCA2FEByMuwsW4L6FBQwaGjinTz7BDFdXMqm0tSXSZ/Nm8AcPojcAeHkxXLvGJ6qpCYbHj3M4fpxa2335JRkIZme3KgTi4wGex/SHD7n09HSUlZWhqKhIVM/IEOqKi7nLu3axwMBAcg8fMwaorITIGPKtrbHHxUWlWVfHxsTEcLZXr7KTDx/CadQoNGpoQCQAAExMTFiTsz+Ra0OH0nO2jQHrvXv3cKpJKeU9apQErq4tCgANDQ30798f9fX1Ynx8vNCvX79nZ41sbVsMB7uMujqIe/diT2amWFJSwjQ0NERHR0c2a9YsdOvWjQHkJ1BdXc2Hh4ejnaJk925Ig4Iwse29/gTGjx+PgQMHIioqSggNDeUAIHPECHaloQFj0tPRUpNua0u9x69eBcLDIZFIwPM8UtPS0CMigsro2qBZYRF95Ihqek4Oj5kzWzsHtIEgCDhy5IjAOTqKg/38eGzYQGtRE+l7+/ZtMSUlJV+hUKz+YwP3fx8vgu0X6IBNmzapSaXS79TU1BYMGTJEzcfHh/8r67L/GUilUigUCnz44YeYN28ebGxsnm3q9QJdgrGxMRYsWAALCwsWHBysCg4O5tetWweppydl/f6iMU5OTsaZM2dUjysreYfGRhENDSRxdXcnA6V/JwwNKbBSKkn6+cMPxOT/E6itrcUXX3wBdXV1ccaMGczV1fVfPz53d9qgtmlj9CSqqqrw7bffQiKRiKIows7Ojnq5tkVzzeahQ/Twzs6mgGvRIqqBPHOGNmGLFpFRWicYP348++2331BTU9Oulk4URfz8889CXl4e57J2LTxnzmTPIwdgYAB8/fU/pSYwNjYWZHV13IkNG7Csqopcrb/+mjInzcqNPwI7u/YttIyMSAVw7hwF7WvW4GF5Ofbv3w81NTXR2NhYXLJkCSe9c4c2kfv3Y4C/PzIzM8Xz588LCxYsaDf2HMfBwsJCde3aNX7skyaECxbQ5xgYQNWnD4L27BFtBwzA6jVrOG7QINyxthZ/c3VlUjU1vPHGG1xwcDBKS0vFXr16sYEDB0owbx4F6y+/3OG0BEGASqVCobc3TE6dos1zs8PvsGGAhgZ0v/sOJgkJz5xf7fDjjxS4dQFGPA/v7GyVxpQp/Ek/P8h79BBGDxzIMQAqNTU0Ojvj9tixKpuyMt4sLY0IH3191NfXAwDmzZsHW1tbJggC7t69C1tbW+jr63dgUJqNALmkJMpuzZ/f+QHp61OG+dEjSIYOhV3fvkJWVhb3pI8IYwx9CgpwuLYWtz7+GFKJhAX4+iI/MpJdkEqhLC7mrl271rEv9rffdiR4eJ7GduPGVkKxDby9vXHs2DEUFha2ZMfa/a+lJb38/AAANh99hKkPH6LG3x96KSl0LTozeerbl6733LnkocDzNJeb2hvqVlbCqLxczBs0iNkDVKdrYABUVUE3PR1vf/YZPtiwAfJt20Sf/v0ZPvmEJOp791Kwevs2rdVRUfQ5pqbgjhyBN2NcopeXauRnn/Ht7sWqKlpX160jQ7EPPmiZc4aGhlAqlcjLy0NZWRmuXr0qFhUWMstBg6C7bRveNDZuN6i1M2ZArKpiAxobYWZl1XkQNmwYjr/xhujTvTvDl19S2dC0aXTMcjnw3nu4f/06+vbtK8rl8tbFSkenxc29vr4eEokE3UaNar2uoaGoDgrCo+HDRV5dnU2YMIGDry9QVYV11dXskZsb6jU1kZaWhvT0dGhra8PAxQWFBw+Cu3IFfby8BENDQ+amUrGsHj2Ae/fQr18/+Pn50Xm8/DJQUgLlwoVorK3FpEuXIMnJgdTbmwijzZvpud3QAGH0aCQUFQmPGUMPFxfOwNERafHxYlWvXjAfPJjmTUEBkaZ9+7Z2HGnKMNvb28O+VenEsHcvX1Zaiuva2tSmatAgurYcB7FJQfLGG2/QcW7YANTUYMSiRdBetQqRy5ejXl+fKZvI5Tt37qhOnz7Ne+noqHy3bOFz/vY32B89isfr16OyshIxMTGq1NRUbuzYsSwuIkJUGzWKoakevi3c3d1ZZGQkr1Aonl3iUlxM7eea6qM7QzMR0NLVaeJExD58KDYUFuLdDRsglUo7PLSsra3Rq1cvISIigtPQ0ICPjw/94u236Tl67Rpl7Z9SpimXyzFx4kTOz88PQT/9BIvwcEQMG4YrV66gnQFcYCCpQJRKaGpqwsjICGePHIFjUBD4J4JtGxsb/G3VKiRPmMDH9OuH6zduoDwsDLNnz27niXTlyhWhtLAQrxcU8PjoI7qWGzcCZmaoqKjAuXPn6hsbGycFBgb+NfLG/2C8iD5eoB02bdrESaXSI+bm5mNmzJihofn/ofb034EVK1bg0aNHiIuLEw8cONCy4I0ePbp1cXuBfwqampo4cOCAUFRUxEGphMTZmTYcc+f+Zcd0+ehRsRMyq6kAACAASURBVKahgQ9IT4fZrVu0kSsv/8uOBwBtYA4fJonVvn20semq4UoTSkpKAADr169nf5pCIyen1RTuCeTn5+PcuXNCYWEhs7S0FI2MjMT4+Hg+OTmZ3759u9CvXz+uQzu92bNpQ1dXRxt4pZKy++PHk2T/GdkyNzc3/Pbbb3jw4AFcXFygoaEBlUqFgwcPCiUlJeydd94hkmzLFpKvR0R0/ka7dlF93dPaGD0NggDEx2NwTAznnp6OGKVSxLp1DEOGdLmFS6fYuJGk3G0hlQKffoqw779XeXp68jFDhsBrxgyMGzeuVVZsbk4Zs6Y+xKNGjWLbt2/nCwsLO8hifX19+SNHjmDYsGHQaBsc8Txt3IyNkWtigtz6emb12muU5dy6FW5GRpxVUhJ0FiwA3n0X/uPHAxoarSe7bdtTT0tPTw/q6ups//79wt/8/DisWkV1t03BaaWrK1JNTLge8fGkDGiqH30qRJFIulGjKDB7GhQKICYG2L4dfnZ2vJCbiyElJdizZw8Xe+MGTExMBHV1dU7m7Q3e0JAPNTVFwM6dsL5xA/jtN9xKTQXHcbC0tATHceA4Dr179372sQHUvq3hOf4+EgkpWJYtw4ATJ7iEkydV3d96q/2kr6+HdVUVFq9bh+SHD9E7KAha+/fD6OJFmBQWYteuXbCxsen43oWFlEl+UjKur0/1919/TcROm17xDQ0NIgBWUVHRMdhui6ZAg4WE4JfNm7E0Lw96Bw9SRuv8earPbmq/1e5cHR2prOLjjylTXldH0tXgYOQMHCjoXr7Mo7iYSnba3EOsrg749FMYDxnCWsowliyhcwgMpDKNnj1J+u7rS4Zac+bg0t69Kmtra9aB9Dp0iBzJ8/Iow7l5MxqtrPD48WPuwIEDYkNDA1NXVxdkMpmgr6/PDb15k2nv2dNpr2AtV1fYr1oFe54nZcRTwPE8hJoaCvDV1CjYLymhspH338e47Gwc9fOD161b0B83joKmkpIWOW5KSgrs7e1V2v368XBzo0yxiws01q5F4bVrLPrYMWHQ9Okc4uNpPH7+GRYDBiBQKkVYWJhw9OhR1tDQwHiex9y9ezHd319wHjuWY2fPoqG6Gkfu3YOFhYVq+PDhPBNFkhHX1gKNjZC+/DKy3N2F9MhITvfSJYxpqr8VV67EzU8/FR7m5+N+UBAn09NjjDEWFxsr2tvbC+kqFW/Xv39reZS+Pl0zV1dAFJEaH4/GffugPWwYjI2Ncfr0aVRUVIgmJibMNyQEF42M2ipJiOx96SUITYqBqqqqVvWMtjZsjx0D0tIwNTOTSJ1XXqG9hZoa/+jRI8ScOMFdHDdOdaOigsfMmeC//BJSqVTQ19fHmjVrmLa2NvSrqlg5Y5AyhidDVn19fRgaGgqRkZHciGY1VicIvXJF5VFRwe9s01q0KxgVGsom29o+NZCXy+WYPHkyl5CQgAsXLrTfjzJG5PTCheQH8gxIpVLM8fBAw+bNiJFIoFI9YfitoUFqM3t74Px5jBgxAgfy8xF96BB8AVy/fl3MysoS6urqoFIqmdvp01y+oSFum5hA1bR3Mm32PACQFRcHk7VruZVubuDXrSNypEnuLwgCjh07VieK4ubAwMC7f2jA/kfwIth+gXZQU1P70tDQ0G/OnDmyZxnJ/KdDX18f+vr66NmzJ2tsbMT9+/fx6NEjMSIiAm5ubuxfluL+jyI+Ph5NTuWcj48Phvj6gtPTI3favwBCXR3CoqIw/5NP2NXp00WrAweenwH9d6K5dvzECapz++ijrkmRm2BgYACe5/HRRx/BwMBAtWrVqn/NMGHoUMpktOndCZCz9a5du4SGhgauV69eoru7O9enTx/G8zw8PDyQl5eHuro67sKFCzAyMmrf67mqirJT588Tk66nRwFAv360SRo7ljLFL7/cqfKhT58+YmhoKAsNDYWamhpEUQTP8+zVV19lLWoUf/9nm7mlppL5U1eRl0dS43feIYflzZuhvXgx7v/yC0u+fVs1zd6ef6ZZ2vMQHU1Ztrb1tgBS09IQXVrKq6ZPF8eamTGDtoGcIBBxsW1bi+Nut27dIIoi9u/fj5UrV6It+eno6AgrKyvV4cOH2cyZM7mW3zHWQnJY+/hg/oABOHLkCMzMzODWlIXWd3KizBxjtGluzlZu3UoqhL17Oz0tAwMDTJw4ke3fv5/dUCjg+dFHgEqF6upqREVFCYmJiZxN//6Cydq1HI4do8zs6tVPJy4Yo+vwLISFUeBmZkbBlYsLOJD78bvvvovi4mLcunWLu3HjBpTOzph565bo9u677OfQULg5OsLsp59UhSUlvLqJSdfM2trio48omOwKFi5EtEKhGvP++7xQVARu69bW3zU2Ajt3wtDMDIZqaiTD/ewz8Dzfskk+c+ZMxzpSqZQyiZ3Bzo4yq4GBwC+/QKGlhaioKERFRbHx48fDxcWlS4ddWFgIcBy0Ro+mGnxRJIPB8HAKsE6fJvKmOev+0UdkkvXuu61SVScnoKgIWhMmsNrNm8VahYJpLVyI0tJS1NbWwtraGvfT0qClVCJJXR3qSUmw3L6dFB9nz7Z6GYwbR983PZ+VSiVKSkr4CU8G/QDVxy5fTt937w588QXKvLzQ38tLlK9dy1xdXaGrq8uhqXa6wtgYZ+vrMbG6utOyDMyZQ/L6zlBUBOzbB8OaGrH7999Tne2pU0T+paaS4/vWrTBMSoJpTg57+MsvsCkuhkFNDUndMzOByZMx/uOPUV1byz8WBMh27CACieNgFBWFCadPw3bnTg7Tp1PAVVKCilOncPTYMVj6+YlV1dVoaGhgTk5OwvDhwzmZVAo9BwcOjAH79kEyfDhQWIjFixfzXFERjU3PnkT4hoSg3ssL1Tk5XGbPnnCprkZhYSGO/+MfmHTyJCKsrDCgtpbzzMiA+Z49rL6+HomJiSw/P59HaSlqz53D459+Qu2aNdB85RVobthA53TiBG4ePCgODwtjB159FVWCAH19fdHa2lrMy8sTk4qKuEwHB6ZQKPDtt9+KCoWC1dTU4CVDQzE3OVkEY9yXX34JU1NTUU9Pj2lra0NPTw+mpqZwGjmS1vyaGiIfx4yBxdatmJGczLBmDT8gPx/d5s+HmJsLiUTS7qFqW1+PbUuWCLKDB7lXX30VTyoz+/Xrx0VFRalGjBjBK5VKnDp1CoIgwNzcHDzPIyYmRlTW1HCDN2zA35vnGNr7g3RGfqtUKuwsLMTiJwPfNqiursaePXtEAKxXr15C8/xswd27RJgmJhIx8QxwAwdClpmJgeHhQnJysogn1Weammh84w1E/v47rlZXgzEGx9mz8euUKUg3NWUODg68RCKBXlAQ6mtrkeDjg0UBAcjKykJYWBhSU1PhWVcH5aZNuNK9OwaOGQPNTZs6GMCGhYUpSkpKEpRK5R9s6fK/gxd9tl+gBR988MFcTU3NwCVLlmj+Uz1n/0PB8zxMTU3h6OjIHjx4gEuXLrHBgwf/1Yf1X4mQkBCVgYEBV1VVBa9vvhEfHT2KK4MHq24nJooPHz4UKysrOVNT0///dfKCAJw7B1W/fvjVxgb2W7fCc8UK1tIS6D8NL79MLPOiRSTB66IKQCqVwsvLCy4uLoiKiuLKyspEFxeXf+4ERRG4ehWKqVMRc+sWLly4IHIcx8zMzBAeHg6lUimuXr2aubi4cObm5i3XUFdXFxYWFtDW1saNGzcwZMgQyqSKItV+37lDG4TmbEdzT9u1a0lifP06BfgLFtB56+kh6OZNVUZGBrp378569OjBMjIyYGdnJwwbNox5eHhg7NixTKutgVtzXfKhQx2IApw+TRm4JqnmU6FSkbz65EnKhPE8ZbneeAMwMwOnpYU+ffogOztbvHLlCnfv3j1BV1eXPdespzNYWlJmvykbXVpaiuPHj6uio6O5UaNGYWhAANO0s6PA5f59qofNzaXghTEKOppw8+ZN1NTUIDU1VfDy8mp37R0cHLhz586xmJgYcBwHKysrlJeXQ/boEbBjB5hSCbmfH/Lz81VpaWnM09OT/p+x1uApIIDqHvfsIYfo+noKcJOSqAxgzx4ar9xc4No1yGNiII+IAMvOhmmTRLbs4UOcVijYmDFjMGrUKAZ9fTKp+/lnykq7unZ+X/r4UOatM7Lu4UOUzJmDq7duCfEymRBqZCRGp6QgOjoaMTExYnR0tJidnS1YW1tz58+fh6ampqhUqdjwsDBm1b8/LEaNAnr0QM3Vq8znwAF219kZPm1by3UFSiXNvaVLn2soWFdXh9CwMO6lr7+GhrY2zTVLS5KDhoZSOYmGBrn2njjRku3U09NDTEwMxowZ07FNpaZmu3Z7ubm5UKlUUCgUePz4MR48foxSQUDNF1/gp+xslJaXqyZOnMh5NCkjugKFQoHr16/DyMiIMuGMkUeBnx8Rg3FxVPM9axbVSw8aRGPxwQeU2Xr0iIzCzp6FWU4OS16xQjxRXY3U1FQWHxSE8t9+Q+LFi2L3f/yDvRQZibs6OijPyQHn5YVu331HhoM9e9L64O1NAUZ1NTB4MG7cuIHCwkJx5MiRHSfPpUvt2gUmZWQgpLISo4yMWA8jI6g3m8MBwI8/QoMxJNjaqkJDQzlHR8eOAbe1NZ3v6dNkticIZGx4+DAReSEhSDEwECtXrOBs//EPIkIMDIgY09Sk4CggAC4+PjjFGIq6dxdc//Y3hsJCIrIGDQIfEIBjurqqLB0d0eW99zikpBCZs2IF9jAmFs2ezbS0tCD/8EPg4EEkv/oqPD79FLIrV9hNS0vWzdhYHD16NGdubg4NZ2dae83NAQcHCH5+iIqOxqC4OPB6ejTv3nwTkXv2CHlFRSj+/ntmEREBcfJkpKSkoCQoCEaVlbi5YIGw+s03OauKCsgzMiDx94eGhgZsLC1h+/AhDHftAlMqcdrfXxnX2IgrERGsuKZGTHN1FZJLS3G3oYHze+01DBw9GkMmTMCAAQOYi4sL62dnxywfPMBL27bB2dkZ9fX1YmZmJhsxYgSs/P2ZSUUF84yPR8OgQbCysmI8z6uqq6uFvLw8MSYmhvP29obEyIjW0EmTSKn2xhtEBL35JrSsrcFVVIB7sowmPh6YORNO27ezyxERcHV1hdYT965cLsfvv//OXblyBdeuXUNjY6NYV1eHlJQUVlJSorKzs2OL/P2ZdNMmcAEBLWoYxljL60k0NDTg1KlTyKyrQz8zM2g0NBAh9gQqKysRHR3N3nrrLXh4eHR8I44jgq1XLyKenrXuODoCLi6Q9ezJrl69ynx8fDoo4L6MjhYsb97E5OpqNurzz4HiYjR6emL2qlVwd3eHzrVrUNy/Lw7avZt5+vnB0NAQ1paWGJqeDvOQEAi2toh69EgoHzBAHP7OO+zJMq2EhATxypUrxQqFYsgL+fjT8SKz/QLYtGkTx3HcqxKJ5PM5c+b8Vxmh/REwxtCvXz928uRJPLde5wU6hY2NDR8XFwdtbW3BZcMG7l5xMfT09CQSiQTV1dWIiYkRbt++jeXLl3fubPtnYPBg6pW6cyeCAwPh2bOnyqF37/98i3yApJdpadSaKCamS0G3TCaDhYUFLC0txbKyMhw7dkx0cnJieXl5SEpKEgYOHMg91TSsDYTLl3FixAjc2bkT+vr6op2dHTt79ixiY2NV5eXl/PDhw7nODJIyMzMRFRWlys/P521sbAQ9PT0OSUmUxU5IoDYjT6tpVVMD/P0Rb22NrJAQDDUwQLfKSmjt28f3un+fAmdNTZiamuL69eucurq6csyYMZ0/lzIyqNZ87drWnykUFAg1t7vqDOnpVE++ahVtot9+m77vpERGJpNh7ty5fFVVFWJjY3H8+HEwxqChoSGIosjU1NTEsWPHcvbPyrIDFMhHRFBme+hQBAUFCQ0NDfyyZctgZGREf2NqSlmvu3dpHixbRhn3J4jOxsZGEQArLi7m6urq2mW3tbW1sWDBAvzyyy+4efMmYmNjhfr6eq5vaqow9u5dju/VCwBgbm7OZ2RkCGiWq7eFri4dp5sbZRRffZVMjDiOAq+qKsou6egAjY14mJiIFCMjOBob088EASa3b0Pq5gbLPXvI4MrFhTbJP/xARlZpaVSP+OSau3Fja9/cZlRWkmz60CEkeXujoHt3+Pj68v2bNrk8z0MURQYAwcHB4oEDB+Du7i5MmDCBdplvvw1UV8OpWzc4OTkBL73E0ufNg9NHH+Hu3LlCrwMHuC4TchIJBXRdIJ+vXr0KY2Njla6dHQ8bG1JNzJxJ5pENDTQm6enU+uiJDatSqYRb2+CwLaysUPPhhzippqZKS0vjGWNQU1MDY0xUKBRMR0tLHKihgQVZWcxs164/vA6amJhAR0cHV69eFfv27dt+YOztiUQTRbpnqquJLDxyhK7t779TAH7+PLBuHVhGBkbq6XFO8fGCZmIi05o0CRo9eiDX0ZEVeHuLJnPmsHEKBQ4ePIjraWmYl5MDOzu7VnKWMSLwHj8GYmMRd/Wq4EJzuOMFq6lpcf3Pz89HUFAQZBYW4hmFAgF79jBkZ7dKxi9dAgYPxrxVq/idO3cKSUlJzNzcvPU9a2sp6P31VzJy/PhjIplsbEgN5OsL+Pqicu9eNPszt5ivMkZk1caNZGbn7d0sixbBGN1Lly7R/a2piclLl/JfffUVho0ZA3lZGXDjBgq+/ho9b95ktxQKpKamws/PD/23b4eHry9UGRkIf/99jDlzRnS1s2MtPadFkVouFRQAOTmQODigT26uWHnvHjOcMQNYvhzV1dXIzc7mHPLzUT11qjisWzfWe/ZsFBYUQGfwYKROnozEsjIafH//Vpf469eBb79Fdb9+uDVqlGrB55/z/ZrihfrPP0epILC7ffrw8fHxMDIyAt/0TGa7dxNxCFC5Es8DjMHU1BTjx4/nUlNTRblczhwcHGhNePwY9q3eEDxA2eGPPvwQRb/+Cmt/f1oLNm+mv9+zh8ZbV5eC0Ca/gHbw9ATi42FoZgYtLS2hrKyMMzY2bvcnTes8lEolDA0NIZfL2aVLl4SkpCRh9erVdA81NHSpfSQAJCcni8eOHWNqamrC8OHDOf3792nd6KSlnrGxMTQ0NFBUVATbp3W1MDUl80WJhJ5dT1NZvf460L8/TPX0oK+vL165coW1lcZXVlaivr6e812+HKypPEL373+HjyDQ+p6SAusHDxBlZQUzhQKOHEcqjZ49aX8yZgwitbSEOFdXvL5oUYcMSlFREc6cOfO4qZ928ZO/f4FWvAi2/8exadMmY3V19d/09PR6+/v7y55clP6voWfPnoiMjBS3bNnCfH194evr+1cf0n8Vxo4diz6lpTD+6iuOi4+HG2Nou0UUBIHbvHmzmJGRARsbmz+vTVxREW1WMzJo8+foCIVSiQf19Vjk5vbfEWgDreZER49SVrPZ0bcLm/9Jkyax3bt349GjR7h3717zj7mkpCTR29v72W8gimgcNw6Ny5aJ89auZfb29owxBh8fHyQkJLCkpCSxe/fuHd5DEAQEBQXBxsaGHzx4MPp7eXEIDKRs14cf0gb8Ode4pqYG586dg1wux/dmZughCMh0c8P/Y++7w6q6tu3H2vtw6F1AEBDpWEBERBQUReyxYNdEjUZNYu8mN9GQoolXE40lJlGxi713EUERFRRFwQJIV0GalMOBc87evz8mh46a++77vfteHN/HRyKwz9prrb32HHOOOafIGA79+afw+cqVnJ2fH5LatcPzCxck6N+/6QsNGkRfxcWUNwhQBd3ExNrItxoKBRGCvDyK/nTpQs6BpnrDNwEDAwP07duX69mzJ9avXw9zc3POw8MD9+7dE8PCwqCnpyfOmDGDvVEBdOMGRdMDAlBSUsLJ5XIYNmylYmBADoeICIrMBwVRZK3erSiYj4+PmJubK27evBlubm7cgAEDagiKra0tOnbsqHr58iXXt29fztnZGRvXr2fbDQ1hyPOqvI0buYKCAqalpcWadDLOmgVERtI85eRQ9PUNjsizsbGwk0rRceNGcg7s2AFu4UK0lctV8k2b+D0Khejq4cE6FxZSXt9XX5GD5OhR4IMPaiM1MTEkI1YX4FEoqOr2l19ShPPiReQePw49DQ2xOefGnDlz2NatW0kKrYa6an5EBEWHANj7+OCUoyPcjx3jkJZGxuu7FnN0d6exNrMvo6OjkZ6ejpSUFADgc3JyKEK9ejXt1aFDaZ1jY0l67Opa7+9VKhVEUURsbCx0dXWhq6sLU1PTmiJfD+fPFy4nJ3Nmzs5s8eLFdZ0trOb7J59QO699+0gO/RfRtWtXJCQkiGiK1AI0p+oij5WV5KiysaEzrLSU5nLVKqBjR7BOnWA3YQKHX36pIQmt6YsBgIaWFj799FNs375d3LNnD9PS0sLEiRNr88urc4ELnZ3RvWVLzu3zzxsNp7S0FAo/P1yWyyHftUuVnZ3Nu7m5Cfn5+Szz1SuGn3+mQlNbt9LztX07jVmphF55OeMfPhQRGcnQty9FnePjKfXj7l1SM7RpQ1Lxrl3JoVQNnuehVCqxZs0aoaKighsxYgTatm1L5OjDD4E7d6B0c0NGRoY4dOjQ2oMxNBSYPh2CIODQoUNKURQlLDeXrm1ighRTU3SLi0P/0FDsP3NGvHDhAmv3z39i87x50DAxUYnm5tyxvn2Zi6Ul+KdPqf3a0qWUX/78OTkd+/SB/qJF4oGOHdlwbW1oFxVBpVLB2MVFVGRmMrmbG8tp1w42np6w8PICjhxBVVER3I8cUSEnh0dJCUWQlUpyUkyejFQ9PcgfPaq3J7QePkSrbt3Qql8/pKWlqTp27MhDKqX0F6m0ts/00aONUn/Ky8tZTR5wv370deoUPVsSCfDbb+CSk6FpaoqW8+bROgwcSGkKBQW01yZPpjNy6FBg7lwIw4dDJpNBKpVCmpFBZ2hKCuRyOcrLyzmbZtKLHOs4+RISEsTY2Fju4zoqEkgktIfegvj4ePHkyZPMxcUFY8eOpUO5Y0ci/SUlTRY6s7W1FS5dusSmTZvW/HtbU5POzoMHqVp/4w+mugaGhkhMTERDp4JMJsPevXsFKysrMD8/Dl5eVP/h/n1SZOzYAZw4Ac7LC3ZaWix90SLRZsoUphkVRYq07duRkZGB6L17uUmTJjUqMKxSqXDw4MFylUo1932e9tvxnmz/jRESEqInlUqjPD09HYKCgiR/hxZZGhoa+OSTT9jZs2fFiIgIdufOHXHatGnvc7j/Alr6+ZGktAmCyHEc3N3dxQMHDjBBEGBnZ6fq2bMn36jK7rvi66/Jo71/P7Uc0dOrKQbEVberaO5l+h+NUaOoeNTdu2Q0PHhQU2SqOZiZmeGLL74AQNJPQRAQGhoqWFlZ1XtwZTIZ4uPjoaOjAw8PD9y5cwf37t0TXoeEcAsWLaonMzMzM0OfPn24Pn36NPmZSqUSFRUV0JRKha6FhRz8/GhNZs0iovQOUCgU4DgOM2bMwL1793Du3DkEDRkiuC1dyt359Vdu3ezZ8GzTRuXz5AnrGhZGRbfOn6d1rtPWBQAZWVOmUOGo+HjKB1f3QhVFkmSnpJDcMDiYfnf27H+537mmpiasra2F1NRUbty4cWjbti2nVCrx888/4+7du6Kvr2/zxtK+fSQtPHRIVVlZyXfp0qVpNQ3PUzT52DGS79aButXWrVu32LJly9iTJ09w/fp1cf369cLUqVN5AwMD8DwPtWGfn5+P33//XVWpVPIzjh6F0sSEj16zRvzoo49w4sQJcdeuXcInn3xCJOC338gQXL4c0NDAixcvAF9f8dbKlWzgyJFNjzUsDF0OHxYlISGM/fADGYSffw44O2OwrS0v79MHTgkJ7MX69QKqqmojzUuXEuFJSCBprrExGXuFheR4unaNiCjHUbG76qq6NVV+mwHP87C3txeriS5BIqFc8TqODcYYAqdORaiRET7buBHmGRn0+U3l7jbEhQu0Ns2TbaFVq1YsMDCQVVVV1W/FZmREz8qQIUTIGhBtACguLgYAPH78WFVVVcUqKytZaWkp09LSEquqqphVfj6b0KcPLD/4oPkXtKEhpals3kxz1zDV4i2wtLREdHT0uxkAmpqkyDA0JIfWwIH07EmlFM1cu5aiY9VVp9HE2c8Yw9SpU1lqairCwsLwxx9/YObMmVCnbAiiiN/Gj8cnY8ZAc/VqIvrVjpPc3Fxs3boVvuHh6JqcjLj16/l58+ZBR0eH+/7779G9e3cRrVszcBzttTlzKAK6eTPg4wM/c3MolyzhUFBA97BnD5EPnqc6BleuEAkcM4bu59atmncdz/NISkpCeXk5N2jQIPHo0aPM0dGRZMb+/uDOn8ezZcsgdXKqrQwdFATY2+NFcjL2nTypKisrkwBAXlISjKq7S4QXFUF69Ci6fPUVJnXqxOIGDULZli2obsfG8zwvlJWVsWg3N/To2pUUEvfv01lcWUmdH8aOhYlczvKPHcOff/5ZM9cWL18yN4kEd+/eRWJiorCkRQsOoaHAo0eI9fLCsMOHeXTuTM6uoiJyEGVnAwC4nBzBrl07roZAP3pEDpbqNZXJZDVrhmHDKAJ9/Dj1ZJfLazsVgN4DKpWqptI/qqros4YNo/9PTQWMjMCsrKCQy7EpJEQY37YtZ2FhQU7SIUNIFaKvTxXr9fSA0FDs2rVLzMjIYDzPw6aiQunp5cW/CA+vIfVFRUWNZOR1IYoiwsPD0aNHj/oFBTmOitsplc065qKiohAREcF8fX3Rt24ak74+PQPu7vQuagAvLy9u3759CAkJgaWlpTh9+vSm3yPffkt7uKiI9mpdG331akAQULV7N44dO4YPPvigpv6DTCbDpk2bhBYtWtQ6ALS0yJk5bx7N5S+/0P316gW/lSuRnJuLnU+fih/u2MF0dHQgk8lw8OBB+Pr6Nk5vAXD27Fl5WVnZDUEQtjc7ue9Rg/c5239ThISEMKlUutfZ2bnL4MGD4egSiwAAIABJREFUpX8Hoq2GhoYG3NzcWHFxMTIzM1lMTAz1ZX6PN6Oykoy44OA3tvVxdnZm/v7+cHV1xfPnz7krV64gJydH9eLFC6ZUKpmpqWmTOU81EEWKRPbpQ0aQkxMZqR4e9Qh+eno6kpOT4Vfnhf6/CoyRAW5qSvP6++/kEX+HZ5HneUgkEpSVlbHo6GjY2trWGDF79+4V4+PjWU5OjhgZGclysrLEj3/4geuyaBGkzUmt3/A5Ji9fovDQIeYSEwO2fn1tzuY7QkNDAzdv3hQtLS2Zu7s7/Pz80Lp1a6atrY327dujc/fucPf352z79WPc/PkUpd6wgQw+HR0yKLt0IWLo6EjefGtrItC+vmSI7dhBhO3IESJymzZRHreZ2Vsj72+Dm5sbu379OoqLi+Hi4gKO41BYWMiSkpLQuXNndu/ePWhoaEAqldavVXDvHlQdOuCkszPHGIMgCEJNznRDREXRHpgypd4/nz9/HllZWQgICBAdHByYhYUFOnXqxPLz83HmzBn2+PFjobKykllYWODChQvimTNnmKurqzh+/HhOizFo6ujAccECpqWlBScnJ3b9+nWW/PChqqO7O4ft28lg7d0b0NZG2O7dKrcbN7jLjo6IunYNSUlJwtOnT8FxHCt/8QLGHAfExKDw1Ssxydpa7ODvT/fy22+AuTmYvT00NDRw7do1VVmbNlzHlSsZRJEUEKNG0XodO0akzMKCIrDu7tSjVRTp/qdPr6dSSEpKgkQiEV1cXJp9KM6dOyd26dKFq2cQtmlDhcMGDKg5MywsLJCRkSHk2tgwN46jfaNUNttipwY+PlQRuZkzKz09Hbq6ukLfvn25Nm3aoF5h0ZQUqmHQpg2dmS4uJH+uUz0+JycHaWlp4qxZs7guXbowX19f1rZtW3Ts2JElJSWJ/S5eZFp37+KegwPKyspq0xAaokULmtvr1+nM/AsOZF1dXVy9ehX+/v6Nz+ayMrr3PXtIajx3LhW18/Kis0oup2etXz9yHmZmkvR661ZSTLx8SXvExoacZDo6NfdvYmKCHj16ICMjQ7x06RLT19eHlZUV9YC/f18Vc+8eZxcRIRq8esVY374QBAH79u0TbG1tMWDaNGYUFAS3AQOgUe0siouLQ0BAADPS1wfLzCQ1wcOH5HDz9gYWLcJ2xkTe3l50nDaNwcKC5onnKZXj7l0iMenpEOPikP7HHzA4cADcd98BQ4ciKSWF5eTkMGdnZ6Ffv37cjRs3EBUVhaioKMTFxQn2o0ax/D17RJP27eGodsTxPPKDg3H32jURnTuzKVOmMMaYkHztmtC+fXuOkWJG1cbBgbN0dQVCQ2ExcybySktRYmcnuLm7q0aNGsXfj48XLaqqmN2RI+R42LuXVD2iSBFuMzNYWFiwVq1a4fHjx9DX1xclEokoyGTMpLQUvvPnIykpifnPmEF7cu1aZHEcrrm5id0++4zB3p7OHx8fmrMPPsDD6Gix58GDTEOt0pk2jeT2vXsDjOHq1atcjx49UJN6aGBAkdjRo2kfUqcDpKenIyI8XGUcHc0Vt2gBg0mTIGzahBJ3d+gcOEBn9vffA+7uKOnQATExMejQoYNw9uxZLi0tTTA7dYoptbSgPW0afc6BA3TtBQtwSUcHoydMYL26dkWrmze5pIEDhaLiYvHBgwdMEATWq1evZlMGy8vLsX37dkEmk7Hg4GBWL3rLGPWrdnRs8p2clJSEM2fOwMnJSRg2bFjjw8HcnIrINSF1f/z4sfjs2TNG24NvXpnGGD0r7dpRVL9nz9qfjRxJthjH4fr163j+/Llw//599uTJE/Hq1avM0dFRGDduHF9zHqkrnSclkXNXKiXntakpsG4djKdOZQ+ePhUiIyM5GxsbnDlzRpBKpcKIESMa3fyzZ89w5cqV11VVVd1XrFjxlnYN7wG8j2z/nTFIW1t7wAcffKD1v62H9r8DjDH069cP9+/fh6mpafPyufeoDx+f5nNjG8Dc3BwjRoxAVlYW4uPj+WpjSPT19RUDAgIav70uXyYZ5Pbt9FLhuNpq3k0gNTUV5ubmKjSswPm/CRxHL8DXryn/zsuL8sTe8Zn09/dHTEwMIiIiYG9vD7lcjhcvXsDMzEz89NNP2YsXL2DeogXTUKn+crQLr18DN2+iQ2goSqytxZXu7kz7wgVhsIYG5+Li8s6XkUgk0NbWxpMnT1TOzs58XYOmUfExNTHes4e+p6eTAcdxFOX29CTpeocOJFEdMYII94QJZHzMnfuXqr2/6/gDAgIQHh6OIUOG4P79+3jw4IGoUCjY999/X+93HR0dVS1btuTMzMyYqYEBUgIDRUcHB+bRsSMOHjzIRUVFNZ26MnMmkZoGPZOzsrJU+vr6vK2tbc2G4HkeQ4YM4by8vPD06VMWGRmJq1evQiqV4uOPP4alpSVN4qBBFHVWqQCeh46ODiaMG8e02rWT5GVkwPz332s+R6lUoiA/n+cvX8bH+vo4dOiQaGZmhsTERJaSkoJZv/6KjN69UfzVV7hjYICX6em1k2xhUS+K7OjoyC5dusRCQ0OF4OBgzjAmhn5w6RKRzeJicioplZQLO3EijbWBJL+srAz5+fmNe0/XgUqlQmFhYeP9qKND6ojERNor1cjOzuZGjBhBRH/rVsqrPHHizekFEgk5+rZsob3WAP7+/mzPnj28t7d3/QhQaSlFNb/5htYBoCjXxYtEZqrx+vVraGlp1XsHqeWg3t7ewlVDQ3AcB2ViIsvPz+faVkdDm0RgIHU9WLMGWLnyjekAdSGVSqGuu2FoaEiS3Xv36Ho2NpS6M2BAbcX/f/yDvu/fT1Hn+fOpXZGtLeU737tHZ1hwMK25oSFF+SdNIqIeFkakbORIwM0N48eOZZu3bMGdO3fg5eUFjuMwf/58vqSkBKGGhoKmhgYb7+XFPQsKQrGxMZs0aRLjXr6spwaqrKyElpaWuHvHDrbs4kVoGhgQKWvZksh2NSwsLDiZTNa4XHRgIKlpGAOWLMFmQ0Mh/+xZTq+qSpzWrh0zqKzEqOfP2WZjY1GdDjJ79mykpaXBwcEBFy5cYLtPnRJ7DhjALLZupRShasfRIx8fVKSmshEjRkBXVxfdu3fnzv/2G249eCDa9O7NXFxc+Hv37qk6TZnCw88PvJ8fHPPy4PjddxwcHTkMHoxPbt9m52bOFFBRwcHKihxXR46QE2vCBDozJRI4OTlhxIgROH36tLBo0SIeKSnA1KkQXV0hCAIUixZBMnIkVLNmoedvvyEpKYnSS5Yvp1z5X38FYmOhyMjAbUdHrufvv9OapaXReXz/PtC7N1SVlTD29IThunUkt+/alZw827YRaRfFmjZh+RkZQrmWFj/q1Cmc8PAQn/XtK8iKi5nswgVuYV4enVGvXwNGRnjy5AlatGghDBo0iO/WrRuiDh4U844cwfXAQEyrqoKWpSUEPT08DA+HY1QURFdXJpPJYBgfD8OTJ2G9atVb7YHi4mLcvn1bSExMZDo6Oli4cCFrKJOuXmBywKjTluqgsLrTRHJyMpeVldVYYdetG515sbHk6KkDd3d3FhMTg/Lycmhra7/9hRUVRWdURQXVxHj4kM7Nu3fBA7Czs1OlpqbyPM/D0tJSHDJkCKt5DwAUwT5wgOrdfPMNKRTmzKHuE9V7mQPw8ccf85cvX0ZoaCgAcDo6OsL69euVEokEGhoaTCqV8lKpFBkZGVVKpfLDFStWlLx17O8B4D3Z/ttCS0vrsx49euj+nYuEpaWlgeM4fPTRR++J9tswbRpFFzdv/st/amNjU/Mi+u2330SVSlV/vr/4gmRNhoa1JHPNmiavVVxcjMePH0NXVxdPnjxBQUEBf+zYMVWfPn34Jtu5VKO0tBRFRUUwMjJCcXExjI2Nm27/8j8FQ0MyZgAyTleseKfiaS9fvoRKpUJJSYkQEhLC8TwPbW1tNmPGDHAcR8b/778TmXvXHFWApIB79wIjRoDt3YvupqasXXExzp07xw4fPgwDAwPV4MGD+TZNVFsNCwsTkpOTOQMDA5WZmRn/4sULQRAEztXV9a87RezsyPhTqSh6lpVFUeuHDylK5uNDxt9fubd/AT4+PggPD4cgCCguLoZCoWAA9QkfPHgwlEolUlNTkZWVxWdlZSExMVEpl8s5QxMTLsDQEC5ubvD29hZTUlJYk2R7+3YyVBugU6dO/JkzZ5CQkCC0adOmnlFmZmaGY8eOCYIg8N26dRN9fHxY3eJpeP2acuuzsshBtmQJtD/9FGGjR+OlRIJeUVHQ1dWFVCrF2bNnRZeUFJj27cuQmoqZM2cyVFSw4X/+Cfnixcjr3x97L12C7pUrqtatW/MD6lYALi2t1x6rbdu2XFVVFdLS0vDnn38KixYtonGr2ztVVlIuokxG45s2jQxTb28i7s+eAbNn4+HcuXDU04P1ggUcTpygmg2GhkQujI0BxsBxHKysrIQDBw6I06ZNq91fjFFLpga9zi0sLFQ5OTm8q6srRavbtqW9FBf3RrUOhg5tVnJua2sLT09PcefOnUwikaCyshKzPTxgZGRExnbdAnBr1lBBscxMSh0ZNAja2tqo14e4Dnr27Mn31NQEvv8epfv3Y8OGDc2PUY1PPyUp965ddI/vCA0NDYEtXMjhhx9IJr5rF8n+09JqyYa6+JUaqakkI1+7lhwnp08TMRs7liLdRUVEqPv2rY12qlRUG6BNGyIP06ZB4/592H7yiWh65Ai7nJ2t6rNiBR8RFYX09HTMnDmTP3PmDG67uqpkaWk8xxi7efMmAo4dIyJ07RoAwE5XF0sTE1lMdraY/8MPrJV6rA2qVWdnZ4uCIPCXL19G7969SY0ybRopaKytgcOHkSWTQSgv5+aEheHsypXChufP+d7nz4u+u3ez4CVL2K5r15i/vz/09fXRvlrePmTIELZv3z7V5YwMPsDYGEfmzkWGp6cgCIKoMDLihpaWQlcqZQCgo6ODQZaWSFQosG3bNtja2iI/P5/2L2M0fwsXUpTdxgYICcG9zEyxKD+f3g1qHD9O5MnSkhwiCxcCALS0tFBWVsYLggDOwACQycAYgzXPC8rERO7k1KmCe3w8lxIeDsYYFAoFpB06kMNBWxuYNg35K1bAxMqKagS4udHXkiVU/XzKFCQfPQrlw4coLCyEVnQ0dOPiwE6fJofSoUOkeJg8GUhOhoEois/NzMTXT56wcZaWDABf5e6Ou0ZG5Ex9/Zr2Q2wsYmJiVOqOG8bGxhian8+rFi/GyZQU/PnnnxAEQeV57x6P1FQxavt2Mbh9e87ByYnOz6ZaxDWB/fv3C4IgcD4+PujatWujCt41MDUlp0ET8PPzQ4cOHbBu3TokJibWku3iYnpmPD1pf54/T06RV6/oetbW0NXVRXl5OQA0ruPRFKyt6dywt6ezQyqt131j/Pjx/Nq1awUfHx/Wo3NnDpGR5HCcNInGs3YtBTM8POhM2rKF5PtNFEPu3LkzoqOjAQAymYwTBIGzt7eHpaUlFAoFoqKiIJFIHi1fvvz82wf+Hmq8J9t/X8grKysb9/j7G8HKygqCIODhw4fo3lRVy/eoRXV7kf8qFAqFeO/ePc7H3h56O3ZQ39ysLDK+Ro5s5AGui8LCQmzYsAEmJiaCKIqCUqlknTp14l+9esU2bdqESZMm1c+5qsazZ88QFhYGiUQiVFVVcZqamkJlZSUXEBCg8vPz+8+JiqsdDatX04v60iWa82aKQyUkJOD48ePQ1dUVZDIZ98EHH0BfXx8mJia1hemUSjLO+vZ9t/zUR4+oQNX48ZRnW12NlYEMn3HjxrHc3Fw8evSI27NnD4KCgtClS5ca+XROTg6ePHnCeXl5iWZmZnxWVpaqR48eXKdOnd6tWJ5SSQa4OmJTWEgRm6VLSZL67BkZxQUFRMSlUiIUo0dTFPFfzM1+E9R9rgGqSdCzZ0/4+/tDEISaojFSqRQdOnSoW1GafhAQQOs4cCBcXFxYfHw8tmzZopw+fXptjYzvviMHgtrZUgcRERGCh4cHN3DgwEbn9Pnz51FQUMAvWbIE2trajcmajw8RJn19IsSXL0N/yhR0+uQTlJeXIyIiAhoaGlAoFADAUi0sUPXrr5ACRGC6dgVvawvddu1gY2YGFbXX4vX19WueM1EUUfziBSQvX0IfVJ02NDQUKpUKCoWCa9mypRIN3zE5ORTtefaM5sbKij6vqooM0tRUyFu0QKmeHvy7doWWri7DkSNE4uLiiACuXg3MnAnm4oIRY8ZwKZs2oVhLC0aiSGT+gw8o13bSJIrGVZNFHx8f7sSJE/D29qaK0d26EQGcM4eeueaq/s6bR9dpBgMGDGBBQUE4ffo0Hty5A4O5c2ldGzpW1PL11aspMjloEBxIHs5UKlXTz4ixMWBtDcYYlEolVq9eLXAcJ44ePbrpXvAcRz2WFy8m58SoUc2OGwA9X1evQtPdXeDu3uWQmkoFuBYtop83EdUDQPMcF0cEzMSE8ks3b6Yz/fhxciA5OlLu9rVrtBaWluQMcHKiOQXobHr5EsMZY4U5Obhz8yZ/s3t32OXmIjUwEA/v3EHvoUMh3b6dT0pMRI9Bg/Dk7l0BFy7QvpLJKIo/diyUrVsjwtqajbK1pfNUFGkv5OUB2tooKCiAXC5nPj4+ePLkiXD//n1x4ZQpvHjzJq7ExeH+zp3CJ5s3c7t0dGDVvr3IDx7MJowbx8c/eoSTJ08yn9u3YaVQYOb06XhRUQH9devqTDtX08ngxYgRaDd+PJ55eXEGQ4ZAEAQ4XrlSWwgRgGbHjvD08WGR4eHIyclBu3btUFBQABMTE7Dhw6myuZER7UsdHciLi1lh3UJZSiVFLO3tiXDv2wfs24fC/v2xY8cOeHl5CRzHcTAwIMcHAGtTU2yZPBklz55xRYMGodP69VD27YuY4cNRZmKCCj09uBQXw8zMDHnW1vCOjKxRxgCgyOgffwCCgAxnZ7zW08Nuc3NVSUkJP+vzz2G6dCm1TFMoSKkyZw4gCHC2tOR9S0txackSTPzjD6C4GM9++gm3Hj8WugIcDA2B3btxOSEBKpWKCwgIoPPs+nWgogL88OHg1q5FRUUF+vXrxzt6eUF37VqGOXMYTp4kdcpbnlE17ty5IxYWFnLz58+v19mhSUyZQvcDkBKjupc51q9H5YYNyB02DIEKBdrPmUNy75gY2u///CedVwkJZN9kZ5NDR6EAQkJQ9sMP8DcxgbeXF/SvXqU1PnaMikV260apF/b2dHar7QJLSzo3zcxoDCEhNLaMDHAWFui/fz/XuriYnIf79hGhnj2biLqLC11fje++ozz/kJB6NQnKysrw+++/g+M4fP3110hPT8eNGzfE9PR0MTk5mbOyshIkEkmZUqkMxHv8JbC3FSB5j/+bCAkJGdiqVav9n3zyyVsS1v7voqSkBL/88guMjIxUn332Gf93jvI3i8uXqZLq3r3/lssJN27g8L59QraGBrfg7FnKH3rHnNrnz5/jzz//xNdff92oj/elS5cQFxeHiRMnolWrVigsLMSFCxdUVVVVyMnJ4QMDA+FTR0atJuBdunQRS0tLBT8/P77ZXMj/KQQH08t2x456svLc3Fzk5eXh1KlTUCgUMDIyEgMDA1m7du0a51uqC9u8Da9f01pv304GxrBhb12XR48e4eDBg9DQ0ICzszOCg4Oxd+9esaKiAs0WfKkLhYKMhtOnyfgOCyOJ7fHjVNm4Xz8qSpWSQpVt3d2JPAQH10quT5+myMHWrUQqiovJaWNl9fZ7bgBRFKFSqfD48WO8evUKUqkUDg4OCA0NRVVVFdzd3TFcXZH5XaFQ1HMAvH79Grt37xZbtGgh1hSumTePKpffvt3oz3ft2gUNDQ3VuHHjGi1GTk4Odu/eLfr6+oo9e/Zs7DQVBIoayWRENm7coMiGtTWqqqqQ+Pgx2rVrh6dPn+LOnTui0dOnooWeHte1d2+KED14QBJcAJGRkeLVq1dr1tTCwkIwMTHhXr16JZjeucMKjYyYe3AwYmNjRWtra/Tq1YslJCSgd8NIqCjStYcMIUJYVgYMGAAhIABbbWxUCoVCZIyhqKhIolQqsXz58qbrOwgCGbFyOcDzuPnrr8JLQ0MMa9GCw+PHVBn6iy+I5Dk4UJT7xQvAygq//PKLaG1tzUbVJaFlZeSYSE0lQ7lhpOvwYbpecnITi1yL1K+/RlpCAvrs399ka7lGOH4cOHMGPzk6ih9//DFrthuISgVwHAoKC1FZWYm7d+8KmZmZ4ueff978Q/rkCY27f39KT1Gjqor2pLU1cPYs7Y2YGGy1shJdXFyYv7//28cNUEG7Awfo3VBaSsT62TO676NHaS4//bQ2J17dWsvIiOTnH3/cZCQyIyMDyY8foyI2FkklJeh38SI05XI87dgR7RITxWxPTzi2a8esHz4kQuftDVy7houBgWLs8+dMqVRi/vz56vZb9I5xdUVxSQlCQ0NFU1NTYeLEiXxJSQk2rVmDJT4+OFBcLL54+RL+3box3WfPYN63b21u/I8/QpwwAWvCwmBpaan68MMP+VthYYiNjsYMQ0NoODjU64Veg+RkclrevEln6dOnNBfqatzLlgEffogSW1scOXIEr1+/Vr0uKuIn7tqFWxMnYvDixdALDQUMDFA+ciS2bduGoqIiLF68GHK5HLqpqSheuxayVavQpk0bus/p01G1bRvWHDmCUaNGUes7QSDJ8c6dyBs2DE80NAS79eu5VsbG4AIDgZ07IY4fjweTJ+OGVKoqLS1loihCXl7O+dy8iX4ff0yRdvUaBgQA2to4OHOmUlNTUzJo0CCsWrUKy5Ytg8bly6QyMjam81oqJfK5dSueZGWJNrdvMx1PT+D6dbycNQv7JBKIOjoiZ2IiuLi48DZffgmrKVNgOmcOfdbatZS60acPvv32W3To0EEcPnw4Q2UlRfJLSylFo6yM7v9N6hQAWVlZ2L59O4YNG4ZGvejVpPrnn4mcimJtGkRiIjl3//wTMDOD+OWXWNm9Oxzv34dPYCDsZsyg89LVtfa8FwQ6hwYOpGejuj3n1fBw1Z1z5/iBgwfDTVubHNy9etH9SKWUyrBrFzmPDx6kiubbt5NDqWNHesZ27SJnWnExcO4ckJCA2I8+Ep46OTFtf38hMDCQf2vEvLAQ2LIF4rJlOHnqlJidnY38/HxW7TRCww4QmZmZann59BUrVvzZ5DXfo1m8j2z/zRASEiIB0BZAF47j/tbyaQMDA/zjH//Azp072dGjRzG2tt/je6ghilTo47+Kc+eAAQPA/for+vI8t6VDBzIG/wKioqJULi4uXFP7NigoCDzPY8eOHXBwcFClp6fz1tbWnLW1NfPy8qqR+qlhb28PBwcHVWpqKiQSCR8WFibOnj37P+t5OHqU5v+LL8hDfvYssrOzERoaCkEQEBAQIPr5+TGO41iThKSqirzgcXFNSpQB0PVTUkju5+1NbVjeMefZzc0Ny5cvR2JiIo4cOYJHjx5BEATm4uLSOI++qgp4/Lgmyotx48jIP3SI7s3BgYzP1avJUDt4kMjRs2ckS5wyhQxGtSFjYECRtZgYMrCGDqX9dOgQEYwHDyiS4O3drLPh1atXCAsLE+VyOTQ0NGqii2poamqKERERTKVSwcvLC4PfUaJYD1lZ1IO8WpZnaGiIMWPGsD/++INFRkZCWVKCNpqasK+WwqqRlJSE+Ph4FBQUoKZVTgO0atUKPXv2ZBcvXmQPHz4Uxo8fz9VU+hVFkjAqFFQAqkUL4IsvoJo8GdF5eYLPxo3cg/nzBcmsWUzv9WuUTZ8uBv/6KyetqiIVQUZGDVnMz8/H1atXWefOnVFaWorCwkJoamqyp0+fgjHGDc7OFhVt2+J0XJyqbdu2fFBQEDiOa0y0ASLHPXvWypv19IDjxyFeugSn33/njUNCoNTRgUqlgnV1NLdJcFy92hFpvr5iSUkJEfiTJ0mmvnQpRf8++YQiXh4ewPXrGNa5M9t35Qpu376NLl261I5j1Chg6lQiSba29SWWH3xA5OFNUKmgFRYmtpw8mb0T0QZojo2MoKWlJeTl5vLNkm0dHeD2bZhWEwRDQ0Nu3bp12LdvH7p169Z0v14XFzLO9+6lfTh0KKWTREVR9GvVKpLuengAQUEwOnyY5efnv3sNjLg4eiYBcgqmpZHiRFeXSMo339DPjx6l9dLRobVQqei/MzJIkTJ0KDnOqoumtW7dGq1bt4bYty8CKyrwfPp07N27F728veEokTBHGxtg3Tq6Ly0tSjOZMQMvd+2CUqkEz/P45ZdfIJVKRZ7nxQlRUZzZxo34/cAB0cTEBMHBwTwNWR8BT5+KJaGh7Nn06ezzmTNhIpfTuVE3hefMGTAHB0yfPh3r1q3jt27dSuk7LVpApq0Nw1u3iBhJJLVEGqAzd/Vqiujv3k3EcNQoSnsByMFpbg4DAwNUt5ziqyoq8PLaNaRVVmLr1q2YN2kSsHgxUioqRLlcDlNTU7ZlyxahtLSUc0lKgkVuLmIPHRIWLVrE7bx1Syj19ORGfPklOgcHixcvXhScnJx4cFxNezBVhw54Aoj+asnzrl3A2bNgJ0/C3doa7tVrv3r1akHkOKR4eMBswwbxcXy8qszBgWvdujXn2bUreFdXKGNi+NZjxyI7OxuamppUGPDoUVIgbd1aWy+ge3ege3ecWr2atXdxQf/MTEAQYN6lC6Z+9hl4QWB5ISF8WUgIWpibC6ZGRvQCCg8nQljdoWHMmDE4ePAgGzhwIDQ1NSlFQU+PnteLF6nA2huQm5uLvXv3wsPDQ/Rwd2d4/ZrG2bs3dRz48UeKQEdF0Xk5fDit3apVdIE6UXN29iyUISEoCAggog3Uqw0BlYocLWvXkrNl374asm1kaspkhobQtbenc0YdBPj559q/Hz2avn/+OUW5DQzIoaOlVdNbHomJ5KDt1Al48gReU6ZwEp7H/aIibvNn+3GRAAAgAElEQVTmzRg9enRtRfymYGKCklmzkBMUJFqUlbHXM2di7NixMDU1bfLXs7OzBalUGldVVbX1jRP9Hk3iPdn+myAkJITxPD9fIpGs0NbW5szNzcXAwMD/oKTV/xlIJBJwHCe+U97M3wlyObU+2bGDDIl/BUolETk7O7rWvXtAWBjO7d8Py6oqAQAnimKNQS2Xy3Hp0iXVo0ePeDs7O9XAgQP5ui3Z5HI5jI2NmyXEvXv3hqenJy5fvsz36tULPj4+byTPY8aM4QHgwYMHCA8P/0u3lpmZiV27dkFbW1uYMmVKLcn5d4MxynW9dQsoKsLrAwdgbm4uTJ48mdPU1Hyzc0BDg4y85oh2UhJJzXR0qKhNE22J3j48hvbt28PQ0BDbt28HRBEd3dx4hIcDV6+SwfHhhySdHTmSDAeAImJqQtMwhaOsjAyJpUupcFZsbNMOgAMHaH+p4eJCJEsuJ7L+xRf0+bq69fo7V1RU4ODBg0JGRgZnYGAgjhw5kispKYG5uTkMDQ2b6mP8r6NlS3JW1VEYVLdbE69evcp8U1IEu23buGgbG8F7+nRu7969Qk5ODsfzPNq1ayfIZDL4+vo2S358fX3h5eWFsLAwtn37dixcuJDu+9IlIkNyOf3iH39AaNcOe8zMhGKpVGxx4wY6cRyn6eEBoagI3Tw9mbJjR0gLC2uJEagmwO7du2FsbIx+/frV7bXKFAoFNm7cCHlxMVxsbTFv9Og3kzS5nAzOkyfrqyZMTVE5YAA0Nm9Gx++/J8P3LxTtlMvleP78Od85I0NEz55kNP/0E9WYGDSo9hczMwEDA9gFBWFcejoiLS3R2cwMnLrugJkZjW3dOlJL7N5d295OU5MqavfvXyPJrYfvvwfS0nBqwQKxjb09c5TLoamp+eauCwDQty/SnZ3hNXMmb7thQ/39XBdJSfWcC7q6uhg6dKh47949HD58WKzJi2+IQYPIAfHLL+Tw+fbb2oi7mihXw9TUFM/UbfTeBrUisi7BCAuj90VUFK3fihV0BsyfT+uhrsDO87XtwtzdKYWCMSIRH35IRAd0tmhra+P69euCg4MDetRNpfj9dyLZW7bQuixciIkTJ7LY2FicPXsWixcvRmJiInv16pXIr1+P7WvWoIWnpzhlyhROvSalL16g1cqV7Pc//oCRsbFgYmLCISencaeFa9cgVFTgRUyMuhMBBg8eLBYVFYl/xMVh/saNnGTbNnIuPH1KBFC97n5+5BQ4fJgcEFZWQHk5EfPz5+vXQFm1ClITE9heuQJ+9WpKKTA2BsaMgd6BA0w0M4NKU1P09/fnPDw8kHfsGHR1dRF15w63atUqmJiYsKIWLXA/JQWtV65kN0ePru35LpEASUngc3KQ6+xc+/ClpVEaQGIiycM5DoIgoKqqips7dy5UKhVUYWHMPCtLcqqsDLGxsagqLVXZbd3KDX/0iMlmzMDjnBzoZGYicdw44dX06Zzn06fQs7Rs5LGxad1aVeniwmPoUEAUIahUKPr6a1Hb3p7Z9+pFaQvBwRx276aorYEB1TjIywPMzWvaXSqVSiLbbm4kh87OrmlV1ux2zcnBk88/F9sEBrJhu3czfPUVnTOXLlGq1MSJ5GDR06MzQI2ZM2v2Y0O4u7urcnJyGJpKxbx+nd51Vla01+PialqIeXh4cCdOnMDz58+hqakJCwuL5geuoVFblPCDD0gF1bkzOZadnelMy8oClEpwt27BUxDg+eQJy9bUxKXsbDGe5yFv21bQMDYWtbW1mY6ODqelpcXUZ9O1a9fQxsEBA4KC0PUN6SalpaW4evVqpUKhmLhixYr3cuh/Ae/J9t8EGhoa3xgYGCwaPXq0TrPe878pZDJZvYjWe4AIj1zeZAGNt6KwkOS+ixeTNPnBA5JCVSM/P180NTXl7t69ixs3boiFhYVMKpVCoVDA1NQUw4cPR3h4ODZu3IgRI0aA4zgwxvD8+XNeUEu9moGxsTFGvS1HsQFat26NiooK3Lp1S+zSpUvTUWIaN8LCwlRVVVVQKBQcAObq6ipu27ZNaNbYfUfk5ORAQ0MD5ubmEAQBr169gqmpKSQSCR6Ul+Pi8+fCuEOHOOcff8SZefM4zQaVm5vEggWU39wQxcVkxLi5kVf8o4/+tfZYKhUZEHv2wKagANOtrSH58UfxQVQUs+/eHVKlkgyVvXvJMHiXiPnBgxRtmDMH2LnzzZXvNTTIkA8Pr1cNG1paZDxNnEgy4kOHaD+npgJTp2LHpUtiRUUFGz9+POzt7bn/1raHOjp0H6Wl9VpM+fj4MB8fHyA9nTvFGO4WFHDhP/4Ic3NzfPTRR2jRogV0dXXfaWASiQR5eXnikMhIER07cpg+nZwMQI3RL/r4IOnMGbH41St8NnUqL1W316orpbx2DcjMhLB0KR6OG4fomBihqKiIc3R0FEaPHt1oLHl5eZDJZGi9dCmDm9vbB/rgATlEmlhTJWO4GhQEvw8/JILy88/vXD0/9/hx2EdHQ/ryJTvl4aFKvXkTr0tK+IGMwXv6dHoOPvqoJveYXboE6ZMnEDZuhDBvHrjkZJKstmlDpGTOHPr+/DnlkKurf6vl+A1RXk5Ryo8+QkDLltzJkyeF2NhYTktLSxw4cCB7Y/VwAAcOHIDQoQP0AwMFj8pKDtnZjetjxMeT0qNOm8P27dszJycnrF69uvGBlZND0eJvvyUD/f59kuNu2tRs0TQLCwvEx8e/28OQlUVzU7dGxtixNBdqcBypGK5eJYfhrl31r8FYTdQTT59S7viePTTHM2YArVtDJpMhOzubW7JkSeMxaGoSOV+/nq49cSI8PT1x9uxZCIIAb6r/wYnZ2RiRnw9jY2Ou7tle1LMnnrdogcr+/dG1fXu6b2NjIu91oFQq8cjPTzTKy2Nm//iHMGHCBE5fX58JgsDS0tKEXbt2CVNmzOAwdChFWT//nCKZ9vZ0Rv3wA5FCT09yBkZHk2OzZ8/6TiVBqJEy6+vrC3l5edyBAwfEwsJC0fPZM65LaanQa9WqmvWxjogABg/G7NmzoaGhAT09PaZQKCAoFMjdvRs+x47hVocOCA4Ops87fx5aeXl0Fqs/z8mJHB6Mkdpo+XIonZwgCAIMDAwoXWvmTGDWLARwnHDGxASD1qzhuTNngO7doW1rC297e5heuADDmBgu7dIlbOreHTNMTNAwPpqTk8M7VhcLfF1Sgh07dgjinDls8uTJ9PkffUQpay4utAf696fosr4+EBICmw0b0MbPDzVvvqAgmtt582rvCaA1OH2anmMPD6B7dwgjRqDlo0fM+9tvqQicsTER4fNvqPElipSjXlTUZFV/b29vPiEhAWfOnBEHDRpUu5AvX5Jq49KlWseltjY5h+r03L5w4QKkUqmooaEh6urqCjNmzJA0+y4SRZKbJySQXZaURM9I+/aUt6+nV3s2ZGbC+s4dTDAyYsX//Cc0L17kc3x9oaysRJaLC/IlErHQ0FClFAQml8v54m7dBM2RI3l4e5OjMrBxOvb58+crAPy2YsWKvyZHfI8avCfbfwOEhIR01NTUXDxp0iTt/6gKzP8h6N69O3fixAn0798fTbZ/+Lth2TLyol648Nf+Tp2f6uhIL5amch8BuLq6shs3biAnJ0do3749N2XKFBQVFcHQ0BB6eno8ADg5OfG//fab8sCBAxItLS0VYww8z3Oenp7/dqm3gYEBRo4cyY4dOybk5OQIaplhQ8TExKCgoIAfP348cnNzRY7jVJ06deLj4+P/S5+v7tPKGINUKhUqKytrCKCDgwMyMzNFhULB7VIooJg1C4OcnUU4OTFERzcv8ZfJyJAICan9N0Eg58ehQ7RG/ftTddR3QUUFveQPHKDIT04OEeOVK8mLb28Py5EjsbegQCzQ0hICZs3ia9b+bbnwokjG0dq1FJX98UdyArwNLVqQsV1WVp9s14W/P30VFqJq2zYUBQfDpk0bFvTpp9Bs0+bf3iasSQwZQgZknVZbAMhR4eODoB9/hEdgIMzMzN6tDUwDxG7ZgvLycs5UKkX58+fQbcJYqnJ1xbHOndlSxpjU05MiWg3uvbi4GHcePBCcw8K4p0VFguOoUVyvXr0gkUiaHJOZmRkYYyi7fBny0lLoODrW9LN98eIFOI6rjdqoUwYOHGh0nXPnziEvLw8czxPh/eYbIiebNzdfsAwgo7q8HK1/+AGyIUNw0MMD1tbWfICXF06cOIHw8HDRe/p0pi7yVwOOg7WbGyRduqguBQTwA1q1ov3Wti1FSvPygFmzaLzffUeRLjs7+ll+fv1rLVxIUavISACAKwBXV1cOAG7cuMGOHj0KPT09NFnIrBq2trZ4Kpejw7x5HFatovzM1NT6vxQWRqSoDtkGqA2cIAj44YcfwBjDIAMDeNy4QQ6rYcOI0JWUUJX3ykqK2nfqRMS7gVPR2toapaWlbM2aNVi4cOEbo/LFUVF4lZuLIz/9hDZt2sDY2BgODg5waNmS1njMGPUAgeXL6YyYMoXmsKnaKM7O5BgoLyeHwLBhwPffQ9PMDDzPIy0tDU21GxSMjXFKV1dlun8/K7hzBxW+vhzP86ioqIBaFcU6dICZuuhjNWQyGSK7dUOfBQsw3dy8Nj9bXYk9JQWQSiGTybB+/XpR1b8/mztvHj41M6t5FjiOw5gxY7hNmzahpqWfmRlFMjU1af9OmEBz36MHEe2BA6lQ1T//SecSQFHZ776juamecwMDA5afn4+cnBzRy8uLw3ffoecXX3BITq5VKkVHA4sWwaROX3qpVApIpTiYmYkBhYWic2kpXdDeHujWDVoffQRh506Ubd8OvRUryGmyfz8q5s/HbW9vOE6Zghx7e3COjpDJZDVziFWr4DpqFBfp4YFnRUVwPHOGrhkUBKmdHVwvXgS+/hqDzMzwYswYNFR6CYKA0tJStK52oISGhqrMzc254OBgpqVWPKidMWfPkjrCyor2j1wOREfDqKgI/c6fB2doSGT5yy/pDOjWDbhzh5QDn31Gv3/+PJHRjRshc3DAhn37IHz8sbi4XTv2lzpXREc32z6vVatWGD16NE6ePMmMjY3RrVs3+sHCheSEqqsc6dePiPaCBWCMoUePHsq8vDz+1atXsLOzE5OTk/nIyEj06tWr6XF88w3ZV1euABs3Ug0GR0eStuvq1rYEA+jMtLWFFIB5r16AIMAwMRGIi4O7nR0wdy7D69cSbNkC+aNHOHr/Pnf84EFx2PDhrF7nhGpkZGQgOTlZplAolr/7xL1HQ7xnFn8DaGpqfufn56f5nmg3xsuXLxEZGQmuWj71HqAoQ90Ixbvg5El6uRQXk6H4Bll1UFAQevXqBY4AAE1WBf3ss8/U59N/e8VwJycnfPrpp9z69esxdOjQRlWBo6OjcffuXXTu3FlwcnLinJycGAC+oqICABGGfv36NSrc9iakpqbiwoULqlevXvETJkyAmZkZ0tPTOScnJ+jo6ODFixeIjIxUtWnTBkOHDuXVEsYWRkYMqalUAfjUKZKKNvxcxsgTrkZFBRnAEglJxt8ke5fJyDt/8iTJRP/4g4zl8+cpR6xfP8ofmzGDrtO/f82fplRUcA5WVuI7z0NsLI0zPJyM0cDAvyQhRlAQRTYOHXrjr72orMQfMhkwYgSmGRpCc9s22qv5+WSEv6vT4V/Bnj1NK0TS0wGlElpOTm8kY29EaSk6L1iAxHnzVLv9/VGRnc13j4oSnZ2dWd1cb57nwRjDJRcXDDp5kgrTXb8OYcQIpKam4saNG0J2djZnZmYmtjl6FCNzcznY2r6xpZpUKkXv3r2FWz/9xBXExCAzOxuurq7CyJEjuW3btkEURUyaNInu7aefyAhuYm3j4+PRvn17ofqZYggKoiJMvr4U2frii/r7Wy6n9Q4NpZ/Fx8NOoQBWr4aXlxc6duyIhIQElZGREQ9j45pex42nP503V+dsqyX3N27QZ+bkkINkzx6KJl26RBL43r0poiuKZOh37UoGfhPo2rUrwsPDawt1NQP1e/nkyZNCr1mzOMP58+na585RdA4gGXITkEgkWLZsGVQnT+JmQoJYnJ3NYGlJBGHZMvqle/eAdu1I8aGWqbdrRxFLdf/v6nHo6emhrKwMq6vnsmfPnpSLWwdxcXEqrR9/5O8HBEBbWxuvXr0Sk5OTWUxMDPo+fgzfkpJask2DpOj1mTMktw8Jaf4Z19UlWfPixcDu3ZBs3Ijhfn7io7VrRZctW7iG59y+fftUJfr6nNeHH7LWJ06ID1+8UAUHB9cvdrl4MaUUqKFSQdWlC2QDB4rm7dpRf2V1VFldC+DxY6B9e9y8eRMqlYpNmTkT+pmZ5KhIS6t5LnR1dTF69Gjs27cPDg4OJNlevpzO0MOHKYd9wQJKoVEq6b26cCGtrzoHODubrllnTmxtbVlubq64YMGC2hveu5fy2yMjaZxjxzbbIUTPxkYonjGDk/I81W64cwdYtw7S06fR1dBQ2F5UJE7duJFPiIlBWloa2mdl4ZW5uZgzdargc/06/9mJE9AcPx41ihV9fbCffkLfkSNR2K8fOQpSUykHXV3lWiJB0tGjkD14IDSUC128eBEAEBERIR4/fpwB4F+/fo2bN28iICCg/uCdnWsj1QMH1vyzMiMD+9etw7AzZ2BnbU1zJor03LZsSc6agACK8lb3VZd37YpdO3aICoWC6enpCXFxcXzHjh0hlUrf/q4uLqZ3XzPtThljcHNzgyiKOHHihJiens4+cHaG/pdfktOwLnx8aB88egS4uaFXr141B2tlZSWfk5MjlJeXN/1Q5OXRe/f33+l+i4pqVT8tW9IzfOsWrXFT4Dh6j6tTPhIS6AzLzobWkycYYmPDKubMQYWnJ7TbtCGHXmoqIJVCpVLhxIkT5QqF4rMVK1b8RaPwPeriPdn+P45vv/12tLa2dmCXLl3+ti2+moJMJsPx48eFtLQ0ztbWVpwxYwb721cjj4ykQ/3o0Xf/G39/irj8+mttNeV3yF/+T1QQKBQKsOrevQ1RXFwMAHBzc6v3Q21tbYwcOVI8duwYe/XqlWrixInv5BiQy+U4cuSI4OzszBsaGgoODg4cY6xehVRLS0uMHTu23vVqjMhly4hAf/YZRbHqEF6UlFCEJSODDOzQUIpof/UVvaTV9yeKZBQ+fEgFZkaOJEUDQLmXWVmUH7Z2LZFRbW2KujWDwsJCMMbg7e39dracnEwy3WXLyDDaufNdpq0GCoUCEokEzMEBYmwsylJSoGtvD47j8OLFCyQmJoqlpaUYMmQIy83Nxf79+6GhoYEvv/ySLjBvHhlrO3eSxDUoiIyMBoX0/i3Q0iJDcP36+v+ekUGG8Juk8s3h+HEy3lJTwZeWYoqGRk39gZiYGOHatWv83LlzoaWlhZycHFRUVGDKlCnYs2cPTM3M0PHmTWD2bGxOTRWVHAdnZ2du1KhR0NHRof22bh0Ro+joNzo/unbtysHDA+jYEQX+/ti5cydbu3atijHGe3l5CUePHsWswYM5yeTJtbmHTaB3795c3foM0NCgCNfu3fQ1ciQRsdWryQA+c4b2fPXz8DIrC1KpVOzQoQMrLi5GWloaP3HiRCJ9M2dSoaM6UDtWa4iwWu7Zpw85YDQ16TOHDCHyvWQJOZ9+/ZVI2ezZ5Ciq0zanIR4/fgxBEN7aR3fw4MFwcnLCgQMHuLS0NHz66afQvnGD2hmpyfbu3US2GspeY2Oh6e4OYeVKyC0tmXTBgpqCUjVgrLEC5uefiUhFRNCz7e6OBw8eoLKyEr1798aDBw8QHR2N6OhozJ07l3qGA6isrMSF48f5iWZmmLB6tTr/mwHA3bt3cVqlgn5gINrLZPWrsWto0H46d47IwYYNb3TkgDFy3o4fD/sHD5ji449Z5bBh0PzmG3IUaGqirKwM2dnZ3LBhw5i1qyugo8Ns4uL4RvfaoQOtpxqvX0O0sUGulhZbuXIlvvrqK3C+vuQQUBepmjABcktLxBgZwcnJSWVlZcWjZUs6rxq8H+zs7NC9e3dh3759mD9/PieRSOjer1yhVJvAQJr/NWsogr1vX030Ef/4BzkDqsmoGs7OzoiIiGBKpbL2XWljQ3O3fDk5OzMzm917KpUKmp07E5FfuZKIvo0NcO8een3zDXd74UJsePRIrLp/n/E8j7KuXdHn6VNmHxrKY/p0eifMnUsKg2HDKMrs4QHWu7dYtGoVOzF+vDD05EkOnTuTRJ0xwMEBuUuWwNzcvKmcXgEA9/r1azZ48GBUVVXh4sWLbw9wzJmDq9bWYl58PBt++DBU338P44kTKap7+zadg6ampJrYsIGIdh1kZmYiNzeXff7550hNTeVu3bolXrx4kXEcBx0dHdW4ceP4ptqFAiBSe/lykz8qLCzEw4cP0aVLF7i6uqKkpISFnz6N10uWIGXmTHgsXVo/kZsxcuA9fYq6KTehoaFCZmYmp6Ghwb18+RIlJSVCQUEB8/LyYp06dYLWH3/QM3/uHP3Bw4d0LiyvE2T+8Ud659dt0fY2mJnRl6cn9ABEOTioqu7eZcMsLTkUFpJdIJUiIiICpaWlOQCa9va9xzuD/+abb/6nx/Ae/w0ICQmRRkVFzZRKpRsnTZqk/b4AWC1KSkqwdu1alJSUsBkzZsDX15f9J5K//+84dYryKt/SPgMvXpBH/fPPKSdqzBgyLv47o4P/H5CcnIxnz55BV1cXBQUFyM/PR0ZGBiIiIlQPHz7kAGDo0KGN5JUtWrRglZWVYkJCApeYmKhszrEVGxuLHTt2IDMzExcuXBDt7OzEESNGcO7u7s3mib8RGhpkjDs7E1EURYq8qInC7duU25WcTITZxoYIzJYtVLCsb18iC8bG5D338qKq5F9+SbLZfv3IIDQweKf+1adPnxYKCgpYcHBw879UWUntUzZsoGjT8uWUy/iOuHHjBg4dOoTw8HDcuHEDV6KiEG9nh5QrVxD17BmKiorEU6dOseLiYjErK4uLiorC3bt3UVVVhY4dO9aXohobUyRk9GjKV165kuR/6uro/64zQaUicj97dn1DvVMnImwNiOAbERZGBH3AACLp7dvXM7AsLCzg5eXFJSYmqmJiYrirV6/iyZMnwsOHD1n36kJ0MTExuP7yJZL69FH1MzXlhhw/zty++QYaajknQE4ZHx/aPw2jNA1x+TJgbAydLl3QqVMnZmpqyvXs2RNubm4sNjYWrVetYgaC0GShxczMTMTHx8Pf37+xA05XlyLHf/5JEezYWLrfpUsp6lynkNWFCxdURkZGzN3dnT18+BDJycmQyWSi22efMXTuDFEqRd1ijAAQGRkJTU1NsV27dvUfPvU4Zs2iljw3b1LKhK0tSZwzM8m4nzWrtuhXEzh//rxYUlLCnJyc8DZVWYsWLeDt7Y2IiAikpqai89SptC/CwigSOn48fZY6olVaSnncwcHAgAGIGzAAkRUVmDx5cmP5d04OEdS657OjI6VehIQQ4R4xAimpqSgtLRVGjBjBvL290aNHD0RFReHWrVvw8PBAZGQkzpw5A+uyMpVfp05cwx7ilpaWuBYdjU7ffgu9qirwDVuISSR0rpw6ReNv3/7taRwcB97SEhGGhqpntrbMbf9+hk2bkGFmhgPHjglt3NxEPz8/ysV2c6PCUadOkXxevY7jx9P51rs3zUVYGDQ3bICDoyNKDxyAYGoKyyFDKEqrJuU9eiDlt9+QoauLj2bPJgLNGHU3mDqVzq46a9q6dWv29OlTMSEhQayX6sRxFJ11cKBo4YMH9BlVVTSudevouTA3x9OnT6GhoQGlUonT/4+96w6L4ly/55vZxrICghQBUSyACIqCCtixa+wlRmOLJWqMNcabxMSQmHJzbxKTG3tNTIxGY41ijYpiQ0WajaKC9N7bzny/P16WIjXt5ncTz/PwAMswOzvzlbec97w//SQrlUq5e/fu1W+Qmxs9r+Rkat1WNWNfBffu3ZP1er3gPHo03YdLl2gd/+gjYOFCnL90CZIkMXd3d5iYmEjJer3Q5OFDHLl1CxpbW9h07EiZ88BAmncpKYCLC8yGDmXYsAHpAHcuLWWYMYPWtZISIDUVP1ta8oLCQsHFxQUGXZGUlBSkp6ez+Ph4AFSu0L59exQXF8s3b95kt27dklFSwgo++QS7b96UbXftYqbLlgGLFqFk/Xpczs1lMW3awOrzzzFs1Cho+/Wj+9akCfDjj7R/5ORQsmDQoGoBCHNzc1y4cAEeHh5o37498/b2Zn369EHnzp0N95mFhoZKOp1OqKFl1LRpRYbcgPz8fHzzzTfSpUuXhOjoaAQFBeHq1atIS0uTFPn5TGVqyq61asWDgoK4Vqutxi6CjQ2tHZ6e4IwhPj4eFy5cYMuXL4ezszN0Oh3S09N5SUkJu3v3LlPn53OHy5cZVq+uLMUyNibafNUAgVpNz9bBgea4h0etY6I+lOj1wo2EBNnH21vA0qWApSUKCgpIT0KWh65evTrhF5/0GarhmbP9F4S/v397pVIZYmdnN+z555/X1qt2+DdEYmIiwsPDsWrVqlrpy387lJZS5uYf/yDjrS688w7VVb34Ii3uXbvSAl+Pwfm/hGbNmiEuLk6Ojo6WY2Ji5NjYWDkxMZFrtVoxLy8PsixDoVDILVu2rOEZ29nZMWdnZ4SGhgqhoaHy/fv3WUJCAhhj0Ol02Lt3L7927RqTZRmtW7eWfXx8hH79+gm/ysmuCoOxyhgZHSkplJ0uKqIMiiwT++CVV+g5JyTQ8xowgIIkM2ZQwMTPj+i2Zma/qo45LS0Nx48fZ46OjjX7lwIUCPj3v8lQNQhWubs32qHlnGP//v382rVrTKvVGlrloKCgABPc3eETEIAgR0eelJSEiRMnshEjRjBfX1+YmJigf//+sLa2xuXLl/Ho0SPJyspKqOb8MEaU5VmzqP776FEKSiQmklHzW8tv1GpytqveV87pOfj711Q/rg1xcU+qzvUAACAASURBVHTcd99RxmXYsHqz8G3atBGMjIwwatQo9OvXj8XExEinTp0S4uPjYWVlxadNm8b69usnNGveHOzmTSpFKCiodDYYIyryiy9SkKYuRXuAnH1HR8DKCgqFApaWltBqtWCM4eyxY6ybqyuMly2r9VmvX7+ed+vWDc7OzjUnAueUkfviC6L1FhXRuuPnVy2DdefOHVy9epVNmjSJGRkZwdbWFq6urggODgbWrGGnQ0NxPCwMV65c4S1atGBmZmZgjMHKygqBgYHMx8enRtlIxT0QRbrPPXpQTbeh/tjOjoIoPXrQuFm8mBzjMWOoPtfFBa1NTJjNp59id14ePCIjoY6LQ7G9Pcp27IDS0ZEclAcPyAgvK0NSaipu376Nbt26Va4xSiUdN24cGdxmZuR8L1lCGa358wF7eyQkJCA6Ohq9evWqycwx1AfXpqo8ejSd+8MP0eyzz3C+dWv06tWL0cdnyMzMRGpqKq5du4bU1FTY2NjIU5o3Fxlj1Xt3l6NVq1Y4HxOD+7m5aNm3L2oIOSoUxEg4d46ydf36AdRXHbt27eJ5eXnc0tKSRUZGwtTUtILCbtGsmXDmyhUW2rYt7pmYIP3iRQwPDmZdnJwEwcmpctx6elL2LyiIng1jtB7260fjeedO4NAhxPn5YdeuXZj8/fcwc3SE0cSJ1bLfX+/fL182NmaLDx+Gxty8UkiQMQr+dOhQjZHCGEO7du3Y+fPnGWOselmIsTEdm51NmVgrK3IMXV2ppMfMDOHh4TiyaxfCT5/GxZAQNE9IkCeOHy8qSkooQ25lRQHB69dp7k+fTutTnz7U/tAQkHnuOeDOHeSbmDCfBQuYqk8f0sPYu5eCrnPngpmbw8nJCZ07d0ZgYKDco0cPcdDQoTBOSUFOVBTg5kZ9u0WR7qGlJV3D6dNgNja4amMj9dy6VdSsWEHO7pYt9NmWLIGkULCoqCgeHBzMnjx5Il+9epWfP3+excXFwcvLC4mJiZjy/PMwDglB+yFDWI+vv4ZjeDjOK5Xw3rSJRTg6MnWfPmi5ciVgbo7vysrwSBAwd/58tG7blubpZ5/RGJo2jRgu/ftTpt/Ojmrkn3uuomznyJEjSE5ORv/+/auVQ6jVarRu3Zp17NgRgiCwU6dOMQsLC5iamlauBadP07q4cCEA2uN+/PFHSJLE8vLymIuLizxv3jzm4uIC67g4YfB//sNaBATAt2dPptFoEBAQwAIDA9GqVStit5iZAf/+N7L1emw8e5Zfv36dqdVq7ufnx0xMTODo6IjOnTszb29vlr9hg+y9bZtQsm8f1OWMIEmSgIULiUbytA4FQIG4oUNpjP5Cu+Ls2bNSy/x8se28ebS2qNU4e/ZsaVpa2vZVq1ZtavgMz9AQnqXz/mLw9/d3USqVF4cOHWr+R4hJ/RWgUCigUqm4LMvsD1Ui/l9BbCxtLIZ+klUhyxRJ3bePsqcGVXFDb8m/EBQKBV588cU6B8SOHTt4QR217Gq1Gvb29nj55Zdx7do1VlZWJqenp/Pbt2+Ler0elpaWfMaMGaxp06YwMTGp+R4lJWTshYdTRN3amqL3y5ZRPXNoKNENZ84k49jLiwyhmBhS5I6MpL8pleSgzJlDBqGLC0X7BwyoUGP+PcE5x/nz5+WUlBTB2toacXFx1Q+QJMoKhoXRZzp5koR1Gong4GB+/fp1rlKpWEpKChs7dizcy2vPhgwZgiFDhpBTdv8+Vi5ezKrWRqtUKoMiMaytreHi4oKAgABx+/btcHNzk0aOHClWC3YIAhnrnp70PN5+mxynzZvJSejV6xcbMRUYP56MVoPS8cKFZPwuXdrw/3JO2YqvvqLa50bA3Ny8UrAHwPTp08WPP/4YAwcOrE7zt7amMoRLl8jxioqqLAMxKDPrdFR7WhdzJSCAsoodOlR7mckyZnz9NUx3764zIKfRaGp3dNPSaJwPHkx106amFGQICCDj+qOPADc3SC1b4siRI/Dz82NVRZmsrKzw6quvMvn4cXgOGYLigQNx5coVedeuXaJWq5WHDh0qGBsbg3Nee0kL56SiHRNDQYijR4nyO3Uqjenr14F588h5MjWl14uLab3s2RNo1gxN8vPh3q4dHnbpwqM++YSVqlQIvHEDc7ZuRUhmptTdzExUvPMO0dZdXMDMzGDaqxd6jRkj4OhRyiju2EEZbDc3muNr1lDg7K23yBkqD9S4u7sjICAA165dQ4+nW+kFBdUfMGIMaSNH4tq9e1Dr9bTWl3d0GDt2LEaPHo2EhAS0IKNfwKBBJCRWCxwcHNDt9ddRMG8eNn3wAdp6e8ujRo2qXsKrVtO9e/FF4NIlFHTpgm3btqGwsJBlZ2fj4sWLkGUZPXv25P3792cAZc179+4tBwYGCk07dpR9lywRLPV6unddutCY+PxzYljNn0/Xt2EDsa/27KG5PXIkMHMmQnv0kJv27Cm4T5umb5qcrABjKCwsxP79++W4uDjB2NgYubm5wuJly2A0YQJRvOPiKsX6jh2jzPK9e9XaJep0OkycOBHff/89WrdujRr0ZB8fclqPH6fnumwZrdf+/rBbvRojjhyBQ3w8ks+dg+OECWLkvXsQu3SB27lzlMU+cYJKT4YOpbXkzh0ac82aVSrmT58OODiAFRSwM3PmyKM9PAR4epIjWlhIgaoxY2A7b17F5ylXMofuhRfQ6exZfjYigrVo0QLtDAE2V1cS6AoMBP7zH1gmJQlnxo6F78qV0HPO4xwdeZc33hC+SE9HmUIBjUbDioqKkJ6eDkdHR2F4374oFkU4rFqFgtJS8MuXaW4/egTFhAmw1WrZgj59cGvAAOQEBMB34kTkFhZCJ8tITk7mEydOrMwQHz5MgYpp0yh4MmsWrT2M0frj5UX7zYwZiE9Lw+3bt9GpU6c6kypmZmbo2bMny8rKkgICAoSioiKmUqnkLl26CH5t2uBO7948Ys8eVlRUhJSUFM45Z1OmTGHlwRQBoLXGSq2m51keBOzSpQu7fv26nJaWJuzatQtdu3aVBwwYIBQOHIicrVuhHDcOq1atAmprL/noETo7OAg/WFryJ//5DzMyMoKpqamUmZkpDrl5E85Dh6LWHjE9epCd0L8/BdUb07EEVFIZExMjDh0xgoKmJibIzc3FrVu3JL1ev7pRJ3mGBvHM2f4LYc2aNbOUSuV/hgwZon7maNcNe3t7KJVKHDt2DMOHD/9FolZ/OaxdSzTasLDqr589S1Hr9etpA5Mkctr+xkhNTcWgQYMqB8ujR+SsFBSQATBuHHTHj6O/RsMwciTDuHEo+de/INy7B+WXXwqYN48cmvbtibJsZETG/KZN9JWSQkbE8OFkTH/6KRnWT56QoQ1Q9tnIiDJ8EyaQUzBnDtEyATLkEhIoYz137h9+T/R6PQIDAwWAjLYKQ59zcoxEEfj+e/o8DbRyqqjDLndob968yY8fP86cnZ1ZcXExXnnllRoqtwDI0HJ3J3rxl1/WeX6dTocJEyYgJycHmzZtYgEBAdKwYcNqL3JTq6k+eM0aoknv3UtOi60t3fdfyohZsKC6w3PlSsPaBrGxZBxHRJCR/Rsy7JIkoaysDMbGxpBlueaa17MnORFGRpQ9nzKFXu/WjbLvYWEk+FRbsIFzWh+eQsbt28hp0gSBjx9jgLMznqZpFhcXo6ysDE2aNKk8aUwM9dv97DOiak+eTEZ1SgoFQZ48oTmyfTtw6BDumZlxc7Wa16DblkM4cgQqUYRKEDB48GBxwIABuHXrFjt8+DAAEo6rdi/CwqiOf9w4KqcYNIi0DhYtItZDnz7kuD16RD9v3UpjvCplumrQct8+jATYKa0WzZo1w0w7O5QuXozre/aw6yUlvPW2bWgdHs7sgoOR8uABSs6epbXXyYkcKnt7Os/s2bQe2NrWKoplZGQEURQRHR1d3dnmnDK7VemsT6GsrAzbjx+HaY8eWOTkxDBlCpUSlWuYCIJgcLQpm2pqWntmrRyurq4A52hubo4dERFCREQE+vXrB41Gg7S0NJ6amirn5eUxh5kz0XPtWuGhRsPlTp3g6+vLQ0JC8Oqrr7Ldu3dL+fn5QmJiIqzKGRM9evQQYmNjZaVSySoc2RYtqKzi6FF6TmVl5IC98gqNkcOHKVBkyHD36gVxzhwhvkUL5Bgbs9y8PJiYmGDfvn1ySkqK4OnpiQcPHnDGGGvSpAmtK02akKO+eXMlbXvBAhrz5ePIAEdHR3h7e8vfffcdlixZItQayOnalYJIokhrvZMTzHv2xH/KadbKU6fA5s7lbdq0wd27d5n0xRfEFqrKJAgKIrE7g+iVtzd9Lw+SZP70k5zt6krBx7g4yoY7OpKzf+8esGMHIiMjubmjo2Bra0v/27YtbI2MmE1UFD9QVISVK1dWp8P37Qu4uUH3r3/xAQcOsLyOHWFz4QJT/OMfLH/7diw2M4ORkRFYWRnk5cshTJ8uYN8+WkuysqiEID1dDtZomE9mJp176NCKt2jXrh1OnDiBvXv3Ijo6GowxcM5ZRelPUBAFF9aurWRpbNhAgQRzc3rGH31EQbIRI1C6Zg0YYxg9enSdYxUgVsLIkSNFzjnu37+Px48fCxERERJPSxMTjI2ZEWOSqampGB8fz5YtWwbdU3Xh2LixsmVgOU6ePCnl5OQIb775JrKysvDNN9/g+vXrECUJ3czN5Z4uLrW3nAwIAF57DbbnzuElKytmKGdLTEwUbS0tERIZCbtevWp3tgEaDxs2NNrRBoALFy5IXZ48gdl774n48UfDayWMsS2rV69OavSJnqFePKOR/wXg7+/PgoKC3tJoNB/OmjVL26ZNm2eOdj0op3mxS5cu8QcPHsgeHh5/T2+bc8pQd+5cacC9805lP+Br18g5HDbsD8mK/ldRUEBZhMREyt7Z2pLokEJBG9PLLxMFdONGcg4HD6aMvqUl0Vfd3HClVy/e69NPmWrzZnIEHByIXpiVRcbX0qW00aWmUib566+hGDYMopERHePnV2mo2tlRBLpdO3p9xQoyFubNo9+VSnI6lEp6PgaDYfDgyv6tAwbQ/1Q1yrdupfPWJfryO0MUReTm5vLk5GS2YsUKtG3blpzErVspE9O3L302w/WVIyUlBWlpaVCr1QgODsaVK1ekgwcPCnfv3uUpKSlQqVRs3759zNTUlM+ePZt5eHjAqL6e7woFOfRP10XXAo1Gg9atW7OjR48KtdJuq39AekajR5MD9N139Fw6d67MKjUGzZtT1sHZmcZi587kzNX23hERFOjq35+o/337/uZSjfIWUdLJkyeFzMxMtK+tL7a9PT27F14gA9ng3Pv6ksHu7l77dZSW0lyoKoCWnY3suXP57sGDGRcEXL16FRcvXkR+fj4vVx3H5s2bJUtLSwwYMIAJMTE0XoqKiD0zfDg5NoastyG73rUr/ezlBfTujetnz/LeQUGCyd271et0DejcmY4vpwELggA7Ozvm4eGB27dvo7SoCH28vMhQ3r6d6L55ecRoWLaM6MeiSPNs5EjKkk6ZQuthhw60Vlhb03pSD9q0aYPmzZtDp9OhSZMm6Ny5M0tJSWHp6ens5s2buHbtGmzt7RHz8CFaeHnB3MqKHAjDc/L2pvewtq5TgfrcuXPIzc1FNVp8fj45hGPG1Hltjx49wp07d/iSJUuY4OhIlGRDf/EJE6q31bt8mfaHOmqFKzBpErSuroiIiZFLSkpYVlaW/OTJEyknJ0e0t7cXnJycWFxcHL+q0XCPn38WfGbNYq79+rGMjAx+5swZPnr0aPHatWvy9evXhaioKN6kSROWm5sLR0dHdvHiRfj6+lbqXKjVJNLZrx+NndJSCrw5OdFaP2wYBZR79QLmzYPV7NkQp09HZHw8CwwMxKNHj9jDhw/Ziy++CC8vL3Tr1o1duHAB3bp1o1ZaZmYUrDC0VzI1pXKryZMrFcyroGXLluzevXs8PDyc10h6FBcTK2L1ahpHBgaHgwN6vPQSevbqBZVKJSuVSj5+/HghNDQUsbGxso+PT/Xz6HRUp/zmmzU0VvLy8nDo0CE2btw4ZmJiQgyd7dtpjxs6FOjaFRkZGQiLjmYjHR2hO3aMHHljY6jat4fdnj3soq0t02g0sLW1xblz5/DNN9/AxMQETe3s0HzoUKYZNAimO3ZAzMlBk++/h/G770K5dCnYv/8NvPQS2Mcf0z42dCit/woF0L8/rqSkwMjIiLWpZQyr1WpER0fLDx8+ZADQu3dv3rJlS7l169YC0tIoiDJoEK0BBsTH01w1lOIwRk59dDRMmzXDrdhYOHt4NKpckDGGZs2aITs7G+Hh4YLtTz/huVu34LFhg9C+fXv07t27ZlmELBOjbPr0irICWZaxe/duYcyYMczGxgbGxsbw9vZm7u7u6Nu/P9qFhTEbw75eFeHhNF/HjKlgKmjLg3SOjo7IPn0ajlu3YrMk4e7du3pjY2PBwsKiukaDIcO/YgXZIpMm1fuZi4uLceDAAWGcVitoWrYEevZEcnIyTpw4UazX68f27du3sMEb9wyNwrPM9v84/P391SqVaquxsfHYGTNmaBtqM/IMBDs7O0yePJlt2LBB3LdvH58wYcLfK0Bx9SoZJmFhRMX76CMSIbp7lyiLEycSLenPREoKbWbNm5NjPGoUZbYOHSIj4733yMifPp2u+cIF+tvmzWTQdOpE4jP+/mSc5ObSBhQQQG0ytm6lLNn48eTgcE4GrkGM5IUXyDmytwe2b4f60SP54erVgptBgMRAqQco8wgQ1deA48crfzZkIKpE8uHj8/veL84pAztr1u973gYQFRXFAODf//wnxp4+za3j4lja5s1wWrIEgkoFWZaRmJiIa9eu8fj4eEiSBEObE85JuNbR0ZFNnz4dt27dYlFRUfLNmzeZIAhYsGBB4+ZlmzZUmxge3iiBmLt376J58+ayKIqNC7QxRo7O+vVkEH3/PdXCTpxIzsfw4fUrwZaW0rG5uaSNsGkTvVYVxcVUAhAURA7S/PmV7Zt+B/j5+Yk5OTlybm5u3Z/ZyYmYESkp5LwePkzOxpIlZMTt3l2ToRAZSc5I1fH89dcwNTZm7VxdpWHDhol5eXkIDAzk6enpFc+TMQYpJ4fJiYkUJLGxIXptVWX9qhg9mupWR46k35s2RUnfvuxEy5aY5eJCDsPCheQgGoIgK1ZQsKAqSkvRJDUVQ5KSYP7DD7TOtW9P1+DmVhnYehpHj1af83360LweN47m9euv13lbn4ZGo8H48eMrft+yZQsuXLgAlUqFK1euwM7ODpqnAxvjxlFQoF+/OgULZVlGRkZGJYW5rKzBgJCpqSn0ej1LSEig1lWGsW7oF/zjj7T2KhQ0Luqr3zdAkiA7O0O5ciV77bXXoNFoBKC6QHPXrl3p93feISfq3j2MmD9f+PTTT5GamopFixaJubm52LdvH9+9ezdjjOH5558HY4w9fvyY6oqrQqUiB1aWySm+fp3G5g8/UOeB2FjKBtvZwQ7A9OnT2eXLl3lpaalsamoKBwcHAaCAjEql4llZWczY4MSNHk1zYfZs0i2xtiZ6+dSpRCmv4vCU/z9iY2OF7OzsCiV3ADSGli0jJxGgbHNICCCKUHp5Aa++Ct85cyruU0FBAcaNG1f7fDUxoc9TxeFPTEzEli1bYGdnJ9vb29OLX3xBawtAz7OsDKr16wFfX65duJBh/34av6tWAT4+aOLtjendu2P3zz/j5MmTFW8X8OOPOPv99xjp4gLnEyco8GgIBuXkULBZqaRruXix1kvu2rUr++mnnzCoDpbcpEmThOzsbDRr1gxKpZIBEJGbS/f8P/+hkoGqCA+vue4yBrz3HsTduzH20CGcaNZMnlK1hVoDsLOzg7u7u9x15kxBU6VDTa36Ktu2EfOpnDnCOcelS5dkURSFqv3PBUGo7Ic+dizZMVVx/TrN7Q0b6hQMvZ2SglbTpvGFCxeyoKAgduzYMQQEBMhjx44VasyFiRPpmTSAkJAQ7pKUJJtNmCCiTx9wznH06NEiSZJWrV69Or3BEzxDo/HM2f4fhr+/fxuVSnXAwcGh3fjx441qRN2eoV5YWVlh7ty52Lx5M6uVWvlXxq5dRNk0NSXncutWMhYb6FdcJwoLaZMVhEphkcuXKZM8fjxlA194gaizL71EVK/XX6ds1bZtlOndvJkcxokT6XyG7NrBg5SpcHOjTMCpU+Rsl5URlY8xyv4pFGQAG+i5P/xAdaZaLR0nCEQLXrOG/n7hQuX1G3pUTpxY+VpVJdKRI4Evv4TUpEmN9iL/b2AQ92rb9r/ydmlpaUhMTERZWhp63LiBHhoNHk6YwE5qNDz2xg0mXbtWcaxKpeI6nU7u0KGDaGJiAmtra+h0Ohw7dgw9evRA27ZtBYAElgAIgYGBCAoKQnp6OmwbyBpWIDiYaJK7djV4KGMMpaWlvy7AptMRdX/aNDLiP/6Yaqn/9S9yVmtzbkxM6PkoFOQ01WZQde9OjuGGDX+YJoIgCCw1NVXGU45PNYgizZsWLSp7Axsb05x79IiymlUNz3btqlPiExOBzp1hPHMmJpmYiEVFRQgNDZXS0tIErVbLATDIMqYOGSKWeXnhSWQkWh871nDbmgcPKEhhcLYBuLi4sP0REbjZvDk8r10j6v9XX1HQbMIEEpYyMqLAxsmT5Bz37UvZvUGD+PnRo9krgwY1XCJz9ChR/z/8sPrr5uYUeDl5kv62fPkvonAaMGfOHOTk5OD8+fOIjIzk//znP9mQIUN49+7dK2+0UknOxc8/E8OlCvbt28cBsK5du1avFY6Opj7b9aBZs2bw9fWVd+7cKbzxxhu0DwoCBVj0enIOGSOHc98+UmRvAMVKJe6NGgUTlUrWaDT1P1jG6B6uXImEn35CaWlpRYtDExMTzJo1S5BlGVu3buWJiYmMc14zEFEVgkCskP79aU0wqOC/+SYJpHEOMAaNRgM/Pz+GKnWzsixj8+bNkiAIovnT/dlHjSInOTa2sk3czp01SitSU1MRGxsrLF++vDrlmHMak1WCLAAqGSQff0zfDx8GzM2RVx40LCkpqf1ztmxJ++itW9Uo5owxvPTSSzS/MzLoPty6Vfl/Dg5Q79uHlPXrWd7x42hiUL7/xz+I2eXpiVaPH+P50aPxeOdO2fvxY8Fo0yagZUvc8fHBjeJi7tylC8Ply6T+vn07Pb8VK+p6IlXe2gF6vR65ubm19p83MjKqyWCaP5/WotrWzIiIWktYABDz4OpVyNHRDElJjWZ7NW/eHGPHjhXw44+03i1fXvuBly7RevTCCxUvpaam4ty5c8KkSZNq1uwbYFC079CBgvmhoZW2SS2fMSoqCoGBgXK7U6dYkY8Pt7CwYCNHjhQlScKJEyeEgIAAacGCBWKN9+CcxsW//00BuqcgyzKCgoKw4NQpEV5eQJ8+ePToEdLT0zM457U3F3+GX41nzvb/IPz9/QVRFJcolcr3+/Tpo/Hx8fntqsZ/UygUCqjVai4Iwt/jBur1lMFet45UO9eupY1y61Yyyjw9ycn86CMyLn/8kTbgyZMrlXft7CiT+9FH1JP77l1y8ObOJTr1xIlExTxzhjbD8ePJQMnKoiyxgW46cCBR8wCqkyynsiGhvMtE1R6XiYmVPxsM4/ffr3zNYACamVVmXqq2efqN8+PChQvIz89XWFlZQZIkBAcH8xs3bnCVSoVhw4YJ9k/RpP8U7NlDgYhfGzBpJAoKCnDjxg1cPH0a3uHh3MvBgbXSamG0eTNcbWzgCjBZlnH37l0oFApkZWXBy8uLKRSKGkb39DraXvXu3dvQZgWvvfZa4/qyT5hAqriNwJ07d2SdTvfbBoVaTQJJO3cSO2TtWqJsrlhBzufTTunq1fQ/W7dWGr8lJXTc/v1Eo/6DO0cYGRnxkpISISYmBrVROat9tm+/pZIIe3sSR5o1i9aKl1+mwJgB9+9TgKFvX/r97bfJWe/dG1lZWThy5AjPzMwUnJ2dWe/evSlzP38+dLt3I2DVKsSqVHyBIDQ8Q8ePJ2pqlUxehw4dsH//fty7dw+enp7EdklJofu5dStl9caMoedx/z4ZtFeuAM2bo0NGBjuXmIjExMSGAzp5eRQsqQ02NjT23n6bRL9+5fwzNTXFqFGjMGLECPb+++/jxIkTrPvTLAKD2FcVSJKEO3fusOHDh8Pr6VrqoqJKJe16IIoi0+v1yMrKgkVVITyFggJKgkBZxSdPatR/h4eH4/Lly3KfPn0ESZJw48YNOT4+XrCxt+fDk5MbF8Fu1gwln38OqWNHjBo50hB0q4AgCOCcs6SkJJSUlODBgwfIy8tDixYt6i8v8fCgGnhJoj1o1y66h2++SdT8KvRiWZbxxRdfyDqdji1atKj28y5cSE7punXkZA4aRGNy927Axgb379/Hnj17YGtrq9fpdNUXrXnzKsVIa8OwYfT9ww+BH39E/KZNYKWltQsIliP38WNofX2Rcf06rDt1QlZWFoyMjOSKguD8fNpXnxozqvbtoVOrJf7GGyLv3BnMxYWcstjYilKZNh9+iDZubgJEkdaD5GQkXLyI/IcPGaKiaK+ZNo2CMvv3UyDIwOCqA5cvX6b3r5IxrhdbttC6U5c45Zdf1qp98dFHH0GlUqHY2hrT8/MZXnqJWDP1aBfUQHw8JSRqQ0kJBVC//bYi+P7w4UPs3bsXtra2emdn57o3LMZovTh4kL7Pn0/j8bnnahyamJiIvXv3wtvbm3nFxDBWhekliiLMzMxgoN3X+j5jx1br610VFy9e5E3S07n2/HmG5s3BOcfp06cLysrK3lm9enVZndf/DL8Kz5zt/yH4+/sLANxVKtVmCwuLDuPGjdNa/I/3Nv6zYWZmhrKyMpaRkYG/xb28coU21eBgMkR27KDN/7PPaFH29KS2LDk5tFiX03zRsyc5FioVGQOGdkazZ9PfqzrDBsXud96pfO3AgcqfDZnHqn13Bwyoo29pcgAAIABJREFU/LmxG/F/EfHx8Vyj0bDjx48jLS0NCoWCq1QqISkpCREREfKNGzd4VlYWnnvuOdGQlfmvw9iYsqZ/MCLDwpC0cSMG6fWyl729ICxaVINaKggCOjylTv1LMWTIENy8eRPFxcU1RWlqg05HRvWnn9adjSiHtbW1EBUVhc8//5y3aNGCjxw5UkhISICNjQ1ycnIQFRXFPTw8WEP9kSvQrBk5cpmZlIH9/HOaI6+8QvWkGg0FmSIjiZ2hUpHDOncu0aabNm18/fdvQO/evQVRFPHtt99iwYIFaHCsWlkRPb99e3K0+/UjOmdWVnUjt6zcNsvJoTHYsycAYP/+/bJer2dz5sxhuvPnqa5+zhwyLjt0gKNCgdsHD2JNOdtElmV4enry5557rqYBaWREzuzcudWySa1atZKLiSZLHoW1Nd339HRyAkxMKGv4lMNhyI4GBwdj1FN1r9UgSZTVnTy57mO0WmI23L5N92jHDqod/RWoyrB6ujc4SkvJwbt/v4IJkJGRAQDo8jTFFqA50YhyoO7du7PAwEAcP34cU6dOffqC6PmWlND35GRk5OTgZkICIu7ckQsLC4V27dqxvXv3wsjICK1bt+YLFy6EWX4+g4cHzYsGsv2cc/xw+LBUMmMGe0mpFHD2LGVkqyAnJ0dOTk4WTExMcPv2bf3NmzdZYWGh6OzsLI0ZM0asEZDLzKS5FxREWeBu3Wg/a9OGAkiurvTa1q2AkRG2f/21JEmSOGPGjGptomrg/fcpiBMaigql79xcwMYGtra2EAQBEydOrGlbv/suBS4awptvAv/4B1x27MD8TZuwrbAQR5o25U5OThg/fjwDqNY2KioKZ86c4SXLlzN+7Bgm6nS4fv061TgbIEmVHRCeQq+lS8XvRJH3O3gQLj17MvTqRXogb78NJCXRXHvKkRU5525nznB89ZUAExMag9bWtHZ9+indy3oCoz179kRoaChSUlLQskrrtFrx9ddI/fpr/vPkybKusBC9e/cWdTodQkNDkZaWJg3o319kI0cCaWkoKy1FUVERkpOTwRiDmZkZLy0tZVOnToW9gwMFWMLDKdnQ2MD4kiV1/+3990kN/sABlJaW4tChQ/Ldu3eFXr16wc/Pr2G/6rXXKOkRE0PK+bVkngESIFUqlRjQpw/D6dPVdTFAYr+XLl2qO0751luU8Hj+eSR+9x1OnTkDExMTZGZmIuXRIzZv61a2p7RUMurTR9RoNMjIyEjlnH/T4PU/wy/GM2f7/wH8/f1bMsbGCILQSpKkGAD3ATwCkAbAAsBAIyOjSaIodtVqtVLXrl01PXr0UPytaM9/EJRKJTQaDS5fvowRI0b82Zfzx8PHhyjeL79MBn/r1hTJtrEhytk//0nR6f37KZq8YAEZLQsWVJ7DQHG1tKysb/6LY+LEiWzdunV48uQJhgwZgg4dOgiffvop2rRpA0mShDt37sDKygoBAQHS1KlTK1pK6fV6pKWlQZZl6HQ6ZGZmIjExESqVCu7u7vXTIX8pYmMreoL+3uCc48GDB8iMjuaWS5cyO3NzbnfkiICnFKZ/TwiCAGNjYykiIkL0NqjtNgRX10Y522PHjsW9e/dQVlbGTp8+zT/66KOK7DnnHDqdDhcuXMCiRYtqpTvWCXNzyph99x0ZrK+/TuJin31G1N+2bSkIFRZGc23WrEa38/o9kJGRgUuXLgEADCrRDaJHD3KiV66kQNnFi6R2//bbRJs31BBzTr3bZ80CTEyQlpaGtLQ0Yaq7O3RRUUR/trEh+mR5j3AXFxesXLmSpaWl4fTp04iJiUFYWBhr3rw5ZaqfxsiR1bKRWVlZSEhIEGpkgAFyACwtac2rZa8sKw8QdK6jRrICu3eT4V3u1NYJQ/Z3wQL62rWr7nZpDWDEiBE4evRohbFdgdatqb42M7Ni7TU426WlpTXXk/v3iZ3QANRqNZo3b87L162axntWFgVR3n0X6zZskCa8/77YwtVVTp4xg0+YMAEajYaFhYXB1dUVSqWSogBmZkRjLy1t0NnOzc1FXFycuOLNNyFkZdEYevyYSo7KMW/ePCE9PR2tqXWgAgCio6Px/fffi5mZmTXU7iFJ9NyqOnWMVTo2t27RPPz2WxTv2AGVm5u4YNWq+h1tgMb68uW0j3p40HMODgZCQqBwcYEsywgODoatrS1sbW1x8uRJbrt9Oy9u1465vfUWaxSZWRAgzJqFbFnmquhoNv7IEXZk0CBZHjuWlZWVYePGjVyWZW5hYYEpCxeyEnt7fB8TgwQHB2HevHnIz8+HUqmEsnt3RL76Km60aCEPGTJEKK+FBkC1yba+vuz27t1os3kzlBERlUJjCQkUGCwPmhngGR3NCq9cYRnFxbCoysIx6FGcP189cF4FpaWlOHbsWPnHa8B2vXgRuTod9nXtylrY24tJSUny559/DnNzc15cXMw558KdsDA+WaViGz/6CIwxCIIArVYrlZSUCE2bNkVBQUHlfJg0idbg996j4GFV0b+6MHkyBUm3b6/+ekkJBXHKu30cP35cjoqKEiZNmgTnqmy6upCWRsmJb76hc23cWOehtra2KCsrQ0FAAIzfew+4caPa301NTSFJUv2kIHt7oG1bnD51Co/j49GhQwepRYsWbNygQYKRvT2cunUTw8LCpMePH4sAFq9evboOXv4z/BY8c7b/ZKxZs2amUqlc7+Liwq2srIwyMzOLU1JSSnJzc4Xi4mK1UqnUt27dGu3bt9e2atUKFYIdz/C7oUWLFnJGRka12q2/LBQK2mzKyshY+/bbypYVhYVkVLu5ETX83j0Sb4mOJhpl587kSKSnkzKujw/Vm/0NtAJUKhWmTJmCbdu24d69e1L37t3Fdu3aSWlpaby4uFgcOnQoa9OmDTZu3CisW7dO1mg0PDc3VygoKGBqtZozxnhZWZmgVCplExMT5OXl4caNG0yj0chlZWXo16+f2K4xwkN1ITOT6KsrV/5+H7oKQk+ehPTWW7DIy2P6L76A3ZAh7LdS8xuDjh07ikFBQdzb27txbzZkCGXzYmPr7ectCAK1KALg7u4uZGZmolmzZggPDwdjDG5ubmz9+vXS+vXrRWtra3n8+PFCo7PcAL0/56RH8NprZLh++SVlPnU6UkkeOZLmUnIyGbfDhhFN19Lyd59TxcXFiImJwf79+yEIAkaPHl3Rr7xRMDWlz1RWRtfp4EAZs88/J4q5kRHdbyuriprq4OPHJUVOjmg2dSoO+/mh55YttbKHBEGAtbU1cnJyJD8/PyE7O5udPn2ae3p61nzmL79MIogg+vS5c+dgZGRU0Y+5Btatq5MxYAiINehcvfhinZmnWjFhAjkcvXpRNvPpGt1aIMsySktLce7cOWg0GqSkpMDW1lZWKpU1vZLZs4mCWm7sFxYWQqPRcI1GU/MexMc3Wsne19eXHThwAMXFxdWd9gMHKGt/+TLAGPR6PY75+8u9vL2FqUFBYN7eQGgotad6Gt9+S/vNiRN1vq8kSTh27Bhv2rSprFKpRFhbU0nG1av0rMsF60xMTKoFvlJSUvDDDz/Ax8dHtrS0rH6f1qyhPau+QJa5OZU+9O2LWyoV8mJiYDRoEAWbN26keVoXo4Yx+mwHDpC41fvvAw4O0Hz1Ffr16ydHRkbi1q1bQlFREaysrDDIwkK4U1Ymb9++nbVp00aaNGlSAwIFBMc5c9iivDzICQloVViI3UuXyhmtWgkFhYXsrbfeqnjeCn9/eDk4IPnWLWys4rw1feklyMbGkqakRNy+fTskSYKlpaU8depUQafTITY2FrmurljbsiUmLF6MVosX0+cfNIhsAIOzzTnw3nswmzEDFy0spDt79gjz589nFc+DMWIirFhBjI5adEN27twpcc6F8ePHsxZPZWirQh8VBfbuu7jm5SULTk585MiR4u3bt4WjR4/CxcWF9evXjwHAhVOnICxbhrkzZmDPnj1yYWGhwBhDly5d2LVr1yDLMrZt24Y33niDTrxsWWUgdMiQevcHAET7fzpLzznRvZcsQTxj2O7vDwCCubm55OzsXP8zLS2l9XLnThrXX3xBc0OS6tSrePLkCQUSTEyqBZ4MMDU1hVqt5j///DPz8/ODXq/H2rVrZb1ez1QqlVxQUCDKsgx1u3bw+PFHuGg0cvfVq0U8ekTr9NWr6KLRQKlUisnJyWElJSU/1X9TnuHX4pmz/SfB39+fKRSKN9Vq9ZszZ87UNKs0CDTlXwb8/+PU/sWQk5ODv127NKWSjIQ2bYgWOn48CYhotRSxHz6cjjNQJ5OTKxVUExOpxvrKFcreWVjQ33r1qmzL0aJFg+1w/tdgZWUFHx8fBAUFiZxzTJ48ucYOuWTJEhYcHMwkSYK1tTXs7OxQXhtsGF8CUNFygzdt2lQURRE//PADFi9e3Di6dG1QqUgYqiHH4ZciPh7y2rVIiYuD3KoV99yzh/3u71EPevfubWgNJvv4+DRM5TEI9ElSTTGrOv9FgGH9reqALliwQLx37x4CDh/Gl598AkcbG8nl/n1YLV0qmh46hCZlZZTh6tKFSjNycsgxy88np9DKipztW7fo9+7dydkuKaHgyN27pBJ86BBd87BhVF+7YAE5AZMnU8bsyy9pfu3dS/N06VKaW3v30s8PHtDzd3KieuanDLfk5GRs2rSp4velS5f+unGmUNC5bW3pGktKKvtMl5aSyNKOHRQoOHYMg955R7R97z38Z9EiQKPhA42M6lxjU1JSkJ2dLXp6euLkyZOSjY1N7danQgH+9tsIVKtxLSYGRUVFqJVybsCaNZQJroUCblqe3Tp06BDmz59f+/8XFxNl/RfWYeubNEHEqlVg27cj9tgxOa5TJ966TRvRyckJdnZ2CAwM5FFRUXJZWRlUKhVycnJEWZYNvYUNpxHS0tJqUv05J/bG7NmAICA8PJybm5vXfg90OsrCNQJOTk6wsrLi69at48uXL6+ca/fuEUOjPDgxbdo08dy5c9h3/Dgf6u3NPN55h+bbyJE0HqoKQ40dS2O9Hvz0009yXFwcmzt3buUzb9uWPqehd3Z5/+iqiI2NhampqTxgwICa60JxcU3V/DqQkpKCn5OT8cLs2WD+/hRc/uEHyjyuXUvZ3k6darIjzM3p8w0fTvNh6lSw/Hz07t1b6N27N0pLSxEbGwtnvZ6xefPQgzHBLScHW7ZsEU6dOlWnIncNNGkC4dtvMay4WCjp0AH37exw1M8Pp0+fxkBDKda8efDYuBEdBw9GvrMztFotFJs20Xrw6qsV91Wv12PLli1848aNKC0thV6vp7do3Rp3IyKgHDECTUJDIbRuDVVcXKXxGRZGbLeFC9HJ11e8df8+iouLq7N+WremNez2bfpZEJCdnY3IyEh+8+ZNnp2dLQqCgCNHjnCtVsubNGnCmzRpAlNTU9HY2Bg6nQ4lKSmIXr8ehfb2yLa1xaypU0VDRrx///7wrdJZoH/nzsA//4kbAwYgJydHePHFF5GVlSWePXsWOp2O5+bmsrKyMuj1+krND19fEkMdP57sn/q0QHQ6esZVkZ5O1Pr+/ZEaGSkBEAGguLhYqBGkMiA3l+Zqbi4JOE6fTgwfJyf6ez0dNHJycmBsbMyNHj5kT5dVABQw9PHxwblz5xAZGSmZmJgIBQUFwpw5c1BYWCh+9913AEhkT6vVcmcnJxrEsbFUQqHRQJZlnDlzpqCkpGT56tWreY03eYbfBc+c7T8B/v7+CpVKtVWn002YPn36s3ZdfzLy8vJYg/VDf0U4OZFB8/33lLmaNq3uY21sKsVFunatVOouK6MMQlAQ0RWfPKGMxPXrVMvVsiUZewkJ9N3Dg97rf5ShYTBQatRSlkOlUqFHI2okNRoNJk+eXHGCsLAw6fDhw4KNjY3k5+enyMzMhCAIaFqL+EuteOcdyoj26tXgoQZjnjGGnJwcSJKEGsq72dnAl18iXadDSGgoQr29sezdd1m9xskfAJVKhUmTJrHdu3czrVYrd+rUqWGHe+ZMYmSUqw7XipwccmDd3Kh22tWVxuqcOZR5+PJL4Nw5uFy5AhcvL6Fg6VKElZaKjrt3Y7ORETrdvo3+Xl5QiiJRqp2dKej04AE5nz//XPlee/aQE7JxI4mkabV03L175IisXk1fQCVVuaSEBIiaNycV5NRUMpxNTSv7xR88SPPw7bepFnHXLpqj33xTKYT46BEU8+djYEmJ5Pv99yL+8Q8yGJ88oXv0wgukuGthUamKXB8YI4e6rIyuTaUiocSYGHK4v/6aHNMDB6A4exZurVrh8AcfQKtU8iNHjmD06NGsNoM0NjYWFhYWslarFRQKBatTFEqlQsonn+BSSAgEY2NuZ2fH6x0TXl7VacQ1/uzFb9y4wepSR0ZyMhnJjRz3jx49wqlTp+TMzEym1WrhOncuH7B5s1B85QqO6HTS/fv3hYKCAmZsbMz69esnarVaFBYWomXLllAqlTA1NUVxcTEOHz6Me/fuYf16EgWeNm1aZasrDw9SrM7PRy6A+Ph4VmcJVGZmpe5GAxBFES+88AJbu3YtTRrOiTr+xhvVehs3bdoUY8eOxaVLl9jJoCA4zpsHU0NrKVNTyta5u9N9t7UlRtSlSzUoyQBQVFSE27dvC4sWLaq51rVrR+va3buUQR47ttqfO3XqhLNnzwq55e3YQkNDYdOsGdr9+9/k1DSSwp+RkQHGGNHTGaO1wNWVqNGckzPdogWdU6ejGm0DzM1JxOvJE2oxuX49CfSB1i4XZ2c6Ztcu4LnnYGpqir59+7Lz58/L3bp1E6q1BWsACo0Givv3cW/9ennk9u2C5c2bxKAwrHEnTkAoKoKJ4VllZtbQP1EoFHj55ZfF69evo6CgAJcuXYIgCHj55ZcR7uuLXd99hyH9+iGiQwfYJiTgRk6O3PvsWWR7eAjiJ5/AuZyaLYpi7Y7lrFkoGzoUMVFR/GdTU56VlSU0bdpU9vT0FLt164aCggKkp6ezzMxMlp2djdzcXDx69AjFxcX6suJiDPrmG9GjTRuWMX8+unXrJjDGsG/fPsnU1BS+vr7VFwVjY2DUKHh5eSEoKAi3bt3CmDFjDCKB7MmTJ5Akqaa45quv0pq6cCGVGbi41H7DFy+mHt4ffEC/x8fTa3v3AkolPD09RU9PT2RkZODrr79mn3zyCRhj8PPzIxvg2jUK+E6fTmNo1CgqrTCAscpx9c03ta4x7dq1w6FDhxjftQusDqEzb29vplQqodfrxYiICG54zm3btsXy5cvxxRdfYNy4cXBZvZpVlPr4+FSIXIaGhqKkpCQKwNnab8Qz/B545mz/l+Hv7++rVqs32NjYtH3hhRe0z9p1/bnQ6/UoLCxkNfoU/l3AGNEDAaJKjhpVvzDI01AqyZioauwZRLqys2mDkiRyIvbsIQcjNJQMc2NjcsC7dCHnwMGB+o7+P1XW1+v1uHXrFh8yZAh+b/V6tVqN6OhoFh0drbh+/TpkWYZer8fSpUsbVzNcUEBtz+pBWVkZ1q9fj+zsbCiVSgiCUNFWZvjw4UhLS+PRkZFMExODURcvcpNBg9idTp1wuVcviKLYOEXwPwCOjo4YOnQoDh8+LJTXhNZ9sCRRjdrYsVRrd+wYBZFataLscseOwJEjpHS/bRvVUx89Ss5ju3Y0/pRKylAb+j1nZ8NYoUDc3r1S5McfC8UJCcxt2zYoDUI7S5dWvn9tQaQ7dygYxTk5qB98QJmiHTsoAzRxIp2jap9qtbpSRbpqAGXbtsqfDa3VymnVAMgxFASae6amgCDgQdOmcomJiWgQt0JhIV3PunXkbA8cSGPnlVeIPnryJNF+z5yhoMHUqTSnDWJSixeTs//pp/TavHmker10KQUJ3nmnIjCnALBs2TIkJycLly9flj/77DNmaWkpDxs2TLCr4rQ8ePBAtrKy4gBgY2MjxMTEyLJcKapcFYWxsRgSEICOV6+yL7/8kgcFBcl9+vQRgMpAUlxcHM6fPy+ZBAWJmsxMyVKtFuLj4/mIESOEquN42LBh7MaNG9i0aRNfsWJFzTltZUVq1o3E48ePkZSUJBho+oIgMAwahCZnzmDWqVMi1qxBsUYDtVpde99eUCDu+eefR3p6OtavXw/OOS5evFi9r/Tu3UBqKrLeeAOyLCMiIoJ7eHjUPKGlZaNbHl25coWfOnWKlbfFgyo6mhy2OrJuPXv2xJUrV+TvvvtOGD58OFqW1+Piyy9JOMzfnzLMN29SYKcWZ/vBgwfQaDS8adOmtd+MDh1ob/D3p4xiFTq/VquFtbW19M033wh5eXlMp9NxzYMHrPnly9DodI02bp2cnKDX62s6Zobg8s2bFOz64QdyrLdsod8HDyZn1s2NPufZszXbojFG7K8qjmmnTp0QFRWFbdu2ScuXL28UnbwCCgXGvfyyEAzIGadPM9vwcCYYKMGHDtH8y84mUcClS2ulwQuCAIMGRnBwMPfz82Occ3Ts2BEdy1kQHhkZkMrK4DRkiGCxdStuTZnCo1NT5ZuhoaIsy+CcY+fOnVhUXoKWl5eHyMhIHhISwqX27YXRO3bAffNmobuvL1QqVcVnNDMzQx0BBgX276e1Y8QItCt/Djdv3uSxsbHCokWLao6PzMyKGuZhw4Zh7969OHHiBIYPHw7GGOrtEOLgQEGguXOpzry2GvKAgOoO8A8/0Bh8av+xsLDA3Llz8fDhQ6SlpsoZH38sSGVlEFetonVz5Mi6g3X9+lE5wsOHtfavP3r0KLextubYtEmoax4KgoCu5QEWV1dXtnbtWuzatQvLly/HzZs3odfrK9X9o6Kow0w5q6e4uBgnT54sKikpmfMsq/3HQnz33Xf/7Gv422DNmjXz1Wr1t4MHD7YfPHiwssFasWf4w8EYQ1hYGM/Ly2P1tsL5O8DYmIykoiIyIn6rKrhGQ9ltGxuqlRozhhyKUaPIwDeUTsgyZfE++IAoVWfPEpXvp5/oWpRKOqa+Fi9/IO7fv4+AgAAcO3YMgiCwsWPH1p15+5Xo0KGDYGlpCWtra3h4eGDs2LG4ffs2kpOTuaurK6u3tZ9eT9TlKlRLvV6PzMxMSJIEURTBGMOmTZt4QUEBevTowXr37o0mTZrA09MTubm5UmhIiFASFYURu3Yx8+RkfD96NLtmZoa0vDypdevWwtSpUxvfruUPQHp6Ou7fv49u3brRdeTnk1GZnk410a6u9H3+fPpucIBfeYWOdXOjr2HDyHnw86s8ZvJkchp1OqrH02rJSSnPiN4MCcHJkyeRkJAgZGVlMUtLS7nOGuGquHuXnNl336Vsu+EZGhnR3Jowgcb27ds0vg0trX5LH3cD1dvYGHByAucc39+/z9qNGAF7JycSCrK0pHk+bx79z6JFZBBaWtIcdXKiAFqrVnTPLl0ip0ehoJKT0aMpGxMQQP87dy4533PmED3zqXpNtVoNCwsLeHh4MGdnZxQVFfHjx48ze3v7imzm6dOn+cCBA0UzMzNYWFggLCyMnz17FomJiWjdujUz7JWccxzctg1uCQkwf+UVWFtbs+PHj7OkpCQpPT0dP//8M44dO8bCwsLQvHlzYeCjR8jLzsbFoiI8efJE6NKlS7WMHGMMXl5eCAwMZBYWFrCuKvpUWkpOSy2qzHXBysoKly9fxoQJEypFoJRKMqTv3wfeew+KGTPAGrF+aLVa9OnTB9euXUN6ejr6GlqrAfSsHjyAydixCAwMRFZWFqv2dwNmzKBx1gile6VSyW7evImVK1dCHRxM6++OHfX2P2/Xrh1LTEzkFy5cYEZGRtzOzo5h6lTKuK5dC0yZQkFWg8PxlFMTEREhJyQksB49etS9xhlE9W7fpvleZXy5u7sLBQUFrGvXrnjO0pLZGBtjZ9u28oWLF1lERIScm5sLU1NTpq0iqvc0YmJiEB4eDjc3t9o1cRijOdm9OwWeNBpSDD96lDKT2dnkuNna0rwbNIgCaGVlFEybO7fa3iWKIlq2bMkuXbokhISEcEmSmJWVVaODmbIso5WPD/vh8WPmXFwM3YoVdJ85p7renTtp3A4c2GDv6/DwcDk0NFQIDAxEnz59KAA0YABQVAThq69gUlAA5ZkzcPDwYB4eHkKPHj3QsmVLhISEwNfXFykpKfjpp5/kc+fOsczMTLlDhw7i6Jkz0dTdnbXctg3ihAmNC55v2EBO4GuvVdyr5ORk7N+/n40fP57Z1NayKyaGWDQzZ8LCwgLNmzfHxYsX5eDgYKhUKmZiYlL/nuXlRbbI0KG0Hj7ddnHpUnJKW7WiwGazZrS/1DIfVCUlsN63D604Z6mXLyPYxgZs0CBYDhxYuyNfFd27U3D4pZeqndtAn5+gUAimmzbVzzwsh0ajgZWVFTQaDW/bti0zNjZGcHAw3O/cgW7fPspqt2tH4xdAYGCgPiEh4fCqVau+bPDkz/CbwHgjKUbP8Nvw3nvvjVapVLvnzJlj9LdoMfU/hPDwcJw8eVJ+7bXXnsm7AyR0Ym7+h/drrhWFhUTJKysjYy8tjYyrixcpC+nlRVFpNzcyhp2daTP8g7Kuhr6pnp6esomJieDq6opm/4UWTQDVEm7cuBG9e/euUzW6sLAQoZs2yW22b2dxO3awpKQk6cGDB6KRkRHS0tKqHatUKrFkyRLUMDyDg0mspWtXwMcH2U5OuHDhAjp06IC2tQjd/Feh16P0/Hls+flnPtDaGk4//sjw88/kCA8YQPTrDz4gKrVWS4awiQll+lWq31zDfvbsWVy/fh3Ozs5Sz549RYOqfKNqnp88oZrT7dtrGlyPH1OmpKoxPHculWCEhNDxvxPDY//+/TwyMpK1bdtWmjJlyu8bJTpyhO79K68Qu8XGplHXfebMGenKlSuiLMtwd3fH3bt38eqrr1ZjcTx58gTnz5+X4uPjBW9vb9anTx8kJydjy5YteGv4cChatQKaNcP+/fulkpISIScnR87Ozha8vLxY586dqdbAs60nAAAgAElEQVT59m3AzAz5zZrh008/xejRo6HVapGdnY3MzExubm7O7OzssGXLFrRt2xZTpkypfqENCO09jZ07d8qCIPBp06bVfp/j4sjx3LyZAjyNwMaNG5GSkoLBgwdDoVDA3d0darWaKPudO+O+Wo09e/Zgzpw5NfuF+/hQUKQRdGXOOT788EPMnj0b1lOmkJNu6DxRDx4+fIiDBw/ywsJCNnv2bFRzjFJSiKVhYUGskpAQygKXBzwkScKaNWuwcuXKhjszXL1KteNbttC6/zR69CA2yttvIy0tDdHR0YiIiJDT09OF6dOn19lLXZZl7Ny5k2dnZ7NJkybBxsamYbVsgALBO3dSNvvbb6l0qrSUxOROnaJ9acOGOpliISEhcnh4OMvJyeHZ2dmCUqnkJiYm8vjx48UayurlSE9Px7p162BqaoqioiK+dOlSplEqqSxlyBCisXt709oXE1MnKwEPHwJPnuCGQgHN3LkI8fCAe8uWvONXXzEhIoKCNGfP0rjJyiI69Lx5kM3MsG3bNp6UlMQEQYCpqanUoUMH0dfXt/rzKyujQOPcuRR8qA+XL1PAwsWlYq6VlJRg3bp13NnZmQ036Mc8jZwcCspX6VogyzKOHj3K79+/z/V6vTBp0iSDen3d2LGD7IuNG6snGKZMIQr4gAHkEC9cSL9XRXIyaaWMGEF76Ouvg3fqhAMHDuDx48eYO3du4/aKl18m5kd5273CwkJs27ZNUiqVbJ65uYCQECrX+YWI/eorHHz4EC+0aAHbpKRqgoFFRUX4/PPPi8vKytqvXr360S8++TP8Ijxztv8L8Pf3b69UKm/MmDFDW9eC/wx/HnJycrBu3TqMGTMG7euoi/lbQZLIIDpwgDZag1r5nwnOqTb83r1KWt5PP5ES8vDhtFk3a0avu7lRX2BjY6JQ/ganJTc3F59//jkmT56M36QW/itx9epVnDlzBs2bN+cTJ05kOp0OxcXFEEUR586dk0NCQgS70lLZKilJuN2qFRdFkVtYWAiZmZlS586dxaSkJACApaUlPD09q6tB37lDAlJTppBzOn58w1H4PxKpqaQfMH06Rd6jo4Hdu1HUoQN+Hj6cD5s/n7Hz54nGLIp1Z9wmTqRsxcyZv/mS3nvvPUycOBEuddX11YaYGKIHRkbWXQcdGUlZ9Kio6oGi/HyqCdywgajcv0NgJyYmBgcPHgTnXF6xYsXv84BLS2me2dqSge/qSsZip041qbS1gHOO/Px8REZG8lOnTjF7e3v5pZdeqvXaHjx4gGPHjnFLS0uu1Wr548ePhaWXLjH4+jbY4g1vvQXY2kKePx979+7lCQkJXBAErlarodPpxNTUVLm4uFiQZRkAsNpQOw9Qbefs2ZV0/gZw8OBBHhUVxebPn496lesvXSKH7F//qhRJqgcpKSnYu3cvFwRB1uv1KC0tFZ2dneVuP/7ImpuastJPP8VH5Yb4zJkz4eDgQP+o15Nw3+uvI6Xc+czIyEBiYqKcl5cnFBUVwdTUlJeUlDATExNZFEUkJiYKb2o0UE6eXKOnL0DOzOXLl5GQkAAjIyNER0dLJSUlYseOHeWQkBChZ8+evG/fvpULbk4O3cPjxykYlpZGc0OlAk6ehH7QIKxzdcXM5cthYmTU8HiPja0U9KyqjREURI6lVltjvT916hRCQ0OxePHiOjOdpaWl+Oqrr+S8vDxBFEWsWrWqwedSAb2enK4VKygra2lJLAadjpzwRqCwsBBJSUkICwuT7969y1555RWWkJCAc+fOScXFxZAkialUKs45FwoKChg4h4+PD/r7+dEJOKe90MWF1vOoKAoE9O8P/OMfFEh1cAAmTkTm1q0QN21CaWgoX//CC2z40aMI79aNi66uMDt3jg04dw7qqCiIjx9TFlStJmbB6dN4fOQI7p/+P/auOyyKq/2eO7NLLwICgiIqKFVUVARBLNhLRGOJscUSjUZNNDHdGNO+JLYkGqNJ1BjFXkBFjYqIKKCCShMQGwIiSO9smfn98bL0qvjF/D7O8+yzumyZuXPn3rec97znRO7dd5mHh0fDAZKsLOqO8fXXlZT8moiPJ5XwTz+tdj2Dg4PFmzdvisuWLat/vTp/nuZ3PUr3Fy5cQHBwMHr16qUcM2YM3yAbLSOD5uXevZX3u1xOa3N0NI2vk1Pl3IqNpYDF6dO0D61eXU0jIDMzE/7+/sKTJ0/YtGnTWMU9CSpxSUhIqBS3ow/QvP7wQ0BLC/v370dBQYE4e/ZspnbpEjEnmpOky8sD9PVR1rUr/ra1xc0+fbBkyZJq+39QUJAyNDT08EcfffRa07+4Fc+K1prtF4w1a9bw6urqh4YOHarZ6mi/nNDX18fYsWPFY8eOsbi4OLi6utYbBf+fgIqGmpdHjoMg0CbzT9ZSM0YZy6rZoBEj6FkuJwG2oiKKxkdH00Z/+jRlG+bMISPMxoYy587OlBltgl6Cnp4e2rdvr9y3bx//2WefNS3j0YJwdXWFrq4uDh8+zI4cOSI6Ojoyf39/SKVSCILAjRs3Dj22b+fw2msYMXBgVdXz+i2Lx4/JMDt9mujUQ4f+99q3qYSUVG19Nm6kOukxY4jifPQoZQmmTaNr3rYttq9apXRwcGCsb19WVaipXjg5kXH4nFCpQzfrmsvllNFavrxhwTEHB3IckpLo/Sro6BBdUBTpPlywgCjbzRBSqgkrKysMGDAAERERLRNZV4ki9upFtPPyFmo4coTE1pqQDWaMQVdXF66urszR0RHq6ur1DnK3bt1gbGzMtm3bxlQaA/joo6aVuZSWAoWF4DgO06ZNq6u9I5eXl4c9e/YgMzMTkZGRYo8ePeg9CQlNXvMKCwsRExPD3n777YYdbYCCEr17k+L85MkUMGjgd0xNTVFes8oDJGjk6+vLJZqa4v233oKaVAoPDw/l5cuX+WpzNT8fuHULhcXF+Ouvv6Curi7K5XJBR0eHd3Z2hiiK0NLSYubm5njw4AFXUFAAjUuXwMXFEVMBVb8qH2fOnMGjR48EnueZtbW1UFhYiMGDB/OOjo74+++/OXV1ddja2tKJZGWR43LpEjnb+/dXBscCAmitFkVItLSg26GDcP/TT7me58/T2jR1KgWipk8nzQAvr8rx6dKF6mAXLCAHu00b+o3XXycHqI5xHD58OGJjY5XR0dF8nb3bQayf2bNnc48ePcLx48eR9OABLDt1IicoKYkYVT4+tIdYWlKGfd06Yn8FB9Pfbt+mDGVKCgnYqa5Br17UuSMoiPYiQaB5eeIEMQ/69oVWRASsrlyB1ejRXPeUFDHx6FH0vnED7RwdeYWbG9Tv3oVWdDQKunZFqY4O8gQBXb75htaRkSNpn46KIo2BR4/oGIYNo+Pbt4/uzYMHkWdhgSubN6PEyEhoa2ODrurqSO7Th03p1Ytpl5SgYNUq+OrpIXPvXgwdOhS2v/8OrkcP4O5dKBQKPAgKErsqlazzkCG0Tm/ZUr8AoZERzfPVq4Eq3RAqkJVFY/vWW9UDJwByc3MFU1PThlk4Ojp11jmrMGTIEDDGcOnSJd7BwQENlgmamNCaEhBAAXoTE9L++OEHepw5Q3MrNJSCKePGEaW8nsBi27ZtMXPmTO78+fPi7t27YWlpKUycOJErKCjAnj17RLlczu7duwd7e3u4uLggMS0NspAQUVi2jHXfvBnZ2dlKa2trpqamxvDRRxQ4aaqz/fAhBV1SU6GemIiOt27hpp8fbt++LQwYMIADKLgUGhoqKysrW9O0L23F86LV2X7B4Hl+hbGxcac6e4a24qWBk5MT09DQwK1bt5R//vknr6enp7SwsOC9vLyevR3Tvx1LltDzp5/SZhMR8c8eT32QSisdlqpO1pw5ZNQlJZEBIpWSgvOuXbS5JyaSk2lvT1kcW1vKTHTqVJHhDQkJwePHj/kOHToIjLF/JO3r4OCAdu3aYceOHfD39wfP8xg1ahQcHBwoU+PvT/WyjaGggATrXn2VMkO7dr24g5bJaL707VuptD1rFrVe+e03ygZlZZFTeewYZfi0tckgBSqcNUEQkJeXxzdZTyE0lMaiZv1dM5Geno6dO3eKbdq0Qdu2bZu2dufk0DyKiakumlYfioooixIWRkwMFaRSclBycigTGBZGCsgODs1mHoiiCIVCASMjI+Tk5Dw/hVwU6Ti6dKFM4mtVkiKmpuT82NqS0FETmQVNWV8NDAwwcOBAXLx4UXznnXfoerz+OrUjaqiE5O23G3WY9fX18fbbb2PNmjXw9fVlPXr0IGfo7NkmHT+AinrbJvdi19QkCv6ePURfnTu3yQGvHj16wN/fH0U6OlRzunkzLl++zEul0mqiUMVPnkD55An2+viIhoaG4rx58zjUE4Tr1KkTcP8+dp89i5RNm2BZ5Zrk5+dj69atgrm5OYYPH845ODigZpowNjZWHDNmDGtnbEzK22VlRCH/7Teqd09MJJGzPXtoDqvGydcXY9LTua1bt+LO6NHKKQAPZ2dagx89ovMrLCTnNiurUtTQ15cYLF98QQ5dcHCF6BNEkQLFWlr0WapH5pPXroXdmDGQjh8Pcfp0ZK1YAY1Hj6C2bh12vPmm+MqGDUwwNoauhwfad+2KtIMHYRYXRzTju3dpvRw1ipywp09pDbOyIjo5QMfSsyc5Y7t30/3QqxcFp/bsoePj+UpthrqC2IzBShAYGAPjOFTrEcEYDMtbwx395Rcx7eOP2RBVZrsmZs+m37W2Js2BIUOADh3wsHt3QUtXlxvbrx/HgoJofD/5BFi1CkJODoqiouC9ZQv8/Pxw4sQJkT9+HHr5+cxs6FBEREQgvH9/0ePddxlkMjqXNm2I3pySUrfjOWcOBVDPn6egrgoyGWW0O3akHuU1UFhYKKpa89WLNm3ovBrA4MGDERMTI9y/fx9WVlYNL54zZ5LOxsiRNM+2bqX1/IsvaO/csoVYR999R4G4RkqUOI7D8OHDWd++fXHixAlxw4YNAAB3d3fR2NgY6enpLDAwEIGBgWCMoeewYdBKSBB//vZbVsLz/LBhw2iOLFjQtODxxo3E9gsIoLKCcufcyckJfn5+uHDhAhcTEyN06dIFOTk5DEDA6tWr4xr/4la0BFqd7ReINWvW9JFKpWsmTJig2aDAUSteCnTr1g3dunXji4qKEBMTwyckJCg3bdrEvfHGG8ysiYqu/y/x8ccUJc/JofrepvYHfRmgrV3ZygVARa9KQSDqWEYG1XUmJ1PWe/9+qiFbtQooLYVebi5csrLEkRMmcCgsbFp7pBcAIyMjvPfee+zy5ctwdXWtpEPm5ZGz6uhY/4cFgYydzZtJIOzKldr9Q58HsbH08PYmKt4bb1DW8YsvyAhu144MTWtrYiCoqIdTp9Jzr151fm1+fj7++OMPwdDQEB06dGial7lgATlhH3/crFMQBAEpKSkICgpSKpVKLi0tDb179xaHDRvGNWntTk4mw+z06eqZ6oago0PBgbrqTwFyUo4dI+ehVy967NjRqPMoiiLKyspQVFSEw4cPC0+ePKkYu6ysLDyzZkh+PhmY77xDRumaOpIi6uoUmNPToznRAgwDFYqKikRzc/NKzYGYGMqiN0TF/usvGq+q9PB6YGFhgeTkZBw5ckSc+PHHjE2eXNn2pxFokMK4kJ6ezjWoglwVpqYU0Hz//UpWRxPtBLlcTn3C58yhdQuVSuwAZa6O7dghdhEEpqmpKb722msN3z+CAMyZg866uuLd4mJmJpMhPDwcBQUFiIyMFG1tbTFu3Lg67wVBEFBaWsr0798nJ1OppDVg/vzKN0kklCWuA6GhoUqJRMK3UfUK//DDyj8WFdHziBE0/wDKQB48SLXJ7u401x4/Juf7yRMK2Bkb07XX1wcWL4ZzSgrU09KEm7/9xgUmJmLiw4e4cvq0IMpkrJulpejk5MRZ7N8PCyMjyNLTsU5XF4PMzWE2cSIFm4HqwZeTJ+l52DB6AMTyCA+nTHtkJAUahg4lJz0qioIFU6Y0qi/SmHheWloaCgoKUKcgngrff08OYVAQHdM33wBFRbjaowdLS0vD5agoMENDiIcOQWfAAPEVuVzMf/qUO3XgANrn54vybt1EpVLJHR49GhAEtPnxR2Tm5cHe3p7U/CUSChYBtPcYGlKAysaG2Aiq4KiGBmW+f/uNGGWGhrSeXblC12/BgjoPv7CwkDV6H928SWM9YUKDb2vbti2XkZGhbPjLymFnR/Pr8WMKkOTm0nkJAq3tb77ZbB0QAwMDzJo1iz9+/Lh48+ZNNmjQII4xBkdHRwwePBglJSVQU1ODVCplWLgQLm3aINTBQezQoQODiu3R0Lrw6afEBBk8mMp6AApilIPjOMyaNQvGxsaIjIzk4uPjxfT0dCaXyz9p1om04rnQ6my/IKxZs4aXSqUnvL29NWv1sG3FSw1tbW3069cP/fr147dt2yZERUX9bzvbOjpEedy7t6J378vanqvJ4LjK3uHl7U4AUHS7tBSK5GQE7NihzMvJ4WyiohjWryeHPSSENt6xY8nI69aNHAwbmxcm0lZ5yBw8PT2rv+jrS3TEw4fr/pCvLxle331HzlE9jm2jkMvJyNi6lQxZKysyHH/9lRz5sDD6/+uv01zp2rVSTEbVkx1oUNm4KnJycrBt2zbRxsZGHDduHN9kKvetW806LT8/PzEpKUlZVlbGKRQKzsTEhCkUCtHOzk4cOnQo3yRHWyYjI+3cuSb1Oa8Ge3tqu/Xuu/UbjYwRSyArixyzR4+q03JBjlZJSQnWrl1b8ZpUKoWRkRFWrVqFoKAgXLlyRdyyZQsbOHAgXF1dwXEczp07pxRFkbVp04a5ubk1rHrfvz/1T75zBwgMrD8Ta2NDTvDIkTQ36xNpagYUCgViY2NRQfEGyKHJyWn4g717k/PXBMyaNQs+Pj6IiYlh97y98c6SJWhOcYUgCM3vUqCuTsJKYWHENlm/vknBGo7jkJeXh6/lcritXw8MGFBNVyA0NFTJFxQwFy8v5jZrVuM3T1QU8J//wMzEhO3x8UFYWBjatGkj6Onpie7u7qx///71Bp04UcQof38YXrxI2U07u9r7Q+fO9dbW5ubmolevXhg+fHj9x6kKkgI0/wEau2++Ibq5mhrVwKsyxXJ55fsfPwYAOFy8yAGAOwCsWQN7QPV7FQcbExODkLAwQcfYmFWba03Fnj00J11cgC+/pPVo/34KPG3dSnTvt9+uzMI/A1JTU6Gvry9wHFf/ZGvXjtZ8Q0Pg778hxsdD7NEDmR9+yD7+7DNIJBIoFApwHIdbt26xaGNj5tCnDz6WycDc3dm1gwdZSdeuCAkJwcJNm5BoZYXzo0ZBQ0NDKC4u5qqJbKrafioUdG4dO9I+mp9PdPp+/ej8w8MpUL95M60LJ0/Wux8IgsDu378vDhgwoP5r0LNnk7oEZGVlKS07dKBgeXo6sS5UHTzu3qX5IpUSQyE0lI4pO5uCX2ZmFLDYvJkC85aWFNxqQN2+Pri4uLD4+Hix6iLLcVx1BfwPPoDeypUYsWIFvSc4uP41LiCA7ot79yiIPWlSvWutqm2gu7s7SkpKFBkZGftWr14d3eyTaMUzo9XZfnHw1NXV1bZXZdRa8a+EXC5HYWHhP30YLwdef50yMJGR5BycO/fcas8vJTQ0sOnkSUFo25bvO2oUTL78kjZdUaRI96NHRE1PSyOBl19/pc3unXeIXmhkRAZzx470bGj44oITJibVqbwqXLtGho6BARmkjfTgrkBJCZ2XsTFRoWfMoFrElSvp9dBQymQMH06qwK6u1Q3hGrWezwKFQoE///xTsLGxESdMmNB078XLixxWVflDE5CcnCzIZDLJ6NGjYWVlBQ0NjebxtM+fJwf7wQMas2fBK680TnvnebrWS5dStvvRI+DYMZQtWoS79+/D398fJeV01lGjRqG0tFQVmOEAolMOHjyY+fr6KkNCQrigoCAmCALU1dU5CwsLdv36dVy+fBm6urrinDlzWDXho9RUCij4+FDt59y5FGhqCF26UHlDhw4tkuG+c+cO5HK56OnpWXkjHTxIAcBz5+r/oJoasT+aAIlEgtmzZyPr++8RfPMmLt++Da9mBFkVCgVrVFG7LnAcBTJSUylgFRDQKINmypQpOHLkCOSiCKf793HPygr3qjgAqampTM/MjOObMu7+/pTNu3IFVnp6eP/99yGKInR0dBq+F+RywM8PirVrkWRjI2osWsScGrJ3OnYkynENxXcnJycuICBAGD16dNPvPT8/cuz19YkhI5XWXzvcBCgUCvzyyy/K0tJSfuDAgaxv377ND5zs2UP1vapWlcHBlc6PgwPVTj9+TKU1c+Y0m32jQmpqqlJbW7vxDWXFCgreALiYlqZMWLaMGzF+PFObNAlYvRpq5fXrffr0QZ85c2hPnzcPePwYbhIJ8M038PziC/xRUACXV1/F/A4d4OPjg7i4OEycOBFWVlbVe8VLJET3B0gU7fFjcrhNTMhmmDePGEC6urR3VNVcUFHrs7MBjsOrr77KnfjkE+xjDNO8vCjAN3gwzVULCwpSfvYZrYuiSOtTTg4Fwzt3pjKNpCTAzw8jjI35QpWganAwsbu6dKHPPHpEAWgDA9q3R40iZzwxkQKGq1ZR4PjmTXr/gQO0Bufl0Tro4ECaC1u3Epujnvs/ISFBjIyMbPyaWVkRQ+yrr+i358ypLS4nl5Pj7+1NwYL9++v9urKyMpw7d05UKpWiRCIRRVFkN2/e5ARBaJzq04oWRauz/eLg2rVr13+mMXArWgyTJk3iduzYgejoaHTv3v2fPpx/HhxHm6edHW2u2dktS0l+CVBaWoqioiJuyZIlaFNVmIox2pQNDKorFL/+OkXKMzIo65+TQ5v6xYvkbB8+TNm1JUvIqOjUierNunZ9/t7hgYFkVKmQkEB0zSVL6PUZM+p39C9dIvVfVf3c1q2kPJ+eTn1FFYrKPsMzZtC/q9Z5N9WBbyb27t0rqqmpsVdeeaWW8S0IAg4cOCB27tyZubq6Vv/jzJlNbqekwoIFC/i1a9fC0NCw8dZDNSGKlL3/8cdqPc6bjffeIwfr3LlKSmp9KBdf2/fZZxiyfTuOJCWB09UVPMeO5VxcXBoVc/P29uYBGse4uDjo6ekxCwsLJCYmIjc3F9evX1euW7dO4ubmpvTy8iJvY/Zsmifa2nTNGztGFZycKIP19tuUOX0OkbeUlBTB2NiYVTu/8eNp/WkIMTEUJJoypcm/ZVRYiK6amrhy757g5eXVZAdQXV1dSElJ4Qya2JO7FiZPJjZA//6UFW2AHmtjY4NPynvlKl55BcW+vhXBFoDq0E3u36d7f9Kk+n8zN5cCIlu3VgRQ6uw1XRNXrlCgMSAABydOVCrNzTnbPn0a/szevXWWu9jb27MTJ06whw8fUu14U3DyJDkh06eTnsFzIjAwEFKplL399tuQSCTNj4zGx9N9PGYM7Q88T86rINC19PKqDAicPUvZ0o8/pqBlPW0d60NmZiYrKyvjwsLCUGsNrIqOHWk9XL0aWd27o22/fqx3r150H5ub0z3ZqRNd9++/r2Q96etTDfaxY5CsWAGn8eNFrFwJ89Wr2cqVK7kLFy7gwIEDcHBwUHh7e9ftQ1ha0iMvjwI53brRvTh/Pq0fb7xBzISLFyn7/8EHVBc9bBjg4IC2f/2F2bt24SctLQj5+eB+/ZWCdj/+SMwpW1taW6ytad/V0qLHgAG0r+ro0Pe/+y7C/v5b1DY0FHpNmtR49OT2bbpekydTEFuhoNcvXaJnR0fa7wG6Tzt0oPd88gmtkx9/TOt4eDgF3ydOBNzdcX3/fjFDV5e5uro2PreGDKF5tHQpOf9nz1YGkr7+msTS7t6lsW1gvRdFEfv27UNSUhIzMzNjenp6uHv3LhhjEa2tvv77aHW2Xxxyy8rK5Ggd43812rVrh7Fjx+LUqVNi9+7d/+Xc6RaCuTllc2NjaTNPSWkSnevfAJlMhi1btgiWlpZimzZtmp7akEhoXFQ1U1V7gy5cSJHynJxKsbb33yfja/JkctC1tKj+0NCQIvNt2zZOSy8spAzPmjVEMb50iWrjZs0CbtwgQ1sU6X3bt9MxBQSQYZWYSGqqU6eS4TNqFP3m6dOVzvmffzZn6FoSzNraWqwrs7R3717h8ePH3J07d/D06VOMGTOGHMyjR2n8mtmeTU1NDYaGhsr9+/dz8+bNY3qNZWxV2LqV6jGTk1umXdrJk5TZbIIj6+fnhztSKe4sXIhOBgaY/d13HLp0IZZBE8FxHByqZD1Vbe369u0rKReH40xzc0VHNTUGPz8yiH/5pVZWslGMG0eG9uPHNA+bWs9cA7m5uYKBgUH1G8LQkLJkN25QPWhdmDKlUsCqKVAogDVroIiORpqvL1dfjbsoikhJSYGGhgZ4noeuri769u3LnTx5Etra2pDJZNDS0kLVlj9Ngq4usQe++YYybQMGNM6K4XlM37oVWxYvRlBQkHLgwIG8XC4XkZ4ugjoU1A1BoLVi0KDqAbuGkJREbI7AQJoLe/fi8bp1bEC3bqy+tloVMDCgcoiqTBhQK6Tyns2N/75cTk7Nd9/RHpST03gpQRNQVFSEtm3bipJnKQWKj6drlZxcPVurqUlreU0BwE6d6OHrSw76jh20b9TTW7smJk6cyF2/fl0IDg7mwsPDlSNGjODrbUvp6gp8/jna/Pwzn5yRQWvVvn30t2nT6Ph27iTHrWqwsUMH0mVISoLr/PksRk0NGXfuwGTIEAwZMgRGRkY4f/48L4piZXY7PZ0cai8vwNOTgp8DB5IS+VtvUYC+tBQoFwrD3bsUMOnXr7KfexUR1vMnTghcXJzILVnCV7CVqrJY9u6lkpUGkiBhYWFIycpii6ZMaXwvV3XIWLqU9pIPPqDsds0SLdUcqapJoAr6LV9eGXjNzqb5GhWF1776istKTYXpN99QJnrLFmIdTJ9eO1V60vQAACAASURBVHOtCn4dPUrBt44dSdR07FgaR5Wz38C+k52djaCgICQlJWHAgAEYMmQISktLsWHDhlK5XD610bFoRYuj1RF8cbiTlpYmB9Ca3f6Xw9bWFidPnmQXLlxAveqf/4twcCB6mJ4eUbpWr/5X08oLCwvx119/CQYGBpg2bdrzKzeroKVVu1fvjBlk7ObkEAU5OZmMtpwcMrp+/pmo64sXU8TfzKwyW6CnRwa4IBCVdts2EgNat45UoP/8k5znKVMoc+XnRy1gevemLErnzvT52NjK41m5ssVO93mRnp4uuLm5VbMkBEHAsWPHxIcPH3KLFy9GWlqaeO7cOezZs0f09vbmdL//HmzaNMomNRG5ubk4ePCgkJ+fz8nlcvbjjz/Czc1NGDp0aOOiaPv3U+bE2vqZzrEW1q+nwEh6eoOU8vz8fNy6dQtOTk7geV5ITk4GoqI4iCIZz05Oz0xPVcHU1BTe3t6scM4c8bEgiGY6Oox9/TU5Wc9SDmFjQ+vDiRPktD/Dd+jq6nLZdWWxIyJoHtfnbPv703t+/71pP7R8ORAcjB63bsHf3x+bN2/GsmXLUFhYCJlMBisrKzx58gQ+Pj6iXC6HKIpMEAQoFAqoqamBMSYcPnyYSaVSobS0lLOwsBCmT5/etNp/Fbp1A/74gwzsQ4com9cApZlzcECJmRnMtbTw8OFDNnDgQGhra7NH+vqsb33jAtC64uREQbfGIIpUW375MolE/f57BSuHMSY2KUi1axe1ZazhbOfm5qJNmzZKAwODxtfcixepVEdXtzJ40wJtC3v27Im9e/fyaWlpaLY+y8SJFLSsKcL311/kmD14UPfnvL2JnfH773Sdv/ySnONG5oqhoSFGjBjBeXp6IiwsjB0+fBiGhoaKhQsX1rbnrayAEyfQ7uhRMUZHRxQEgatgh1y8SHvIjz+S0z9nDu07KSk0vmZmQPv2UAwfjuO6uvC2soJJSQmgqQndkhKoJyUxhUwGac+eFKQ9dowc96gockT79qW5nJhI8/nzz8nZvn6dMsR19HFXQRAEhIaGcrNVuh81cekSUcEbcLQFQUBQUJDo7e3N2jTGqrl3j8Zg7VrKWAMUgFIJ9DUVJiaVQZPduwEAJSUlWP/FF/jE2JgC3jIZ/X3bNhL+O3qUaONpaXQMPXtSIPettygALgjUIs7env7fQC/6vLw8pKWl4ciRI1AqlXBzc6uwWa9evarkOO7k6tWr7zXvpFrREmh1tl8cwjIzMzXlcjmk/2IHpBWU/fL29saZM2eUQ4YMaTkn7P8DrK0pgnv4MBlhz1E3908gNjYW58+fVwLgioqKmLGxMaZMmcI9U4ajueA4yogYGVEf16pYtIicb5mMjJW0NNqcOY6yBjExlKXOyKA6stu3yQAZNYoMGrmcouOGhkTfvXy58rsbUm/+h3Hy5EllcXEx36VKn+bi4mLs378f2dnZ7K233oKhoSEMDQ1Z+/btsWvXLmxcvx7S8ePF18aNYzW7O5cbbbh79y44jhM9PDyYvr4+fHx8lNnZ2bytrS26devGJBIJEhISxPDwcE5TUxMeHh51H+Bnn1GG5uLFlj1xjqMM15dfknFexeA+dOiQkJycDDc3Ny4gIAAWFhbCyJEjuZs3b7JHjx4JFbXiU6eSoXzzJt2TNZyaJuPWLdgeOIDCs2fZlk2bhLcWL2a6Bw+ipsNYLavVGL76ipSH//6bDMdmCkRxHMcUKkpnVaxdS45gfdDWrreOsk6sXUsMAwAfffQRvvrqK8TGxiIoKAgKhQJ9+vRBdHQ07O3t2dixYyto+4IgqP6tChLxpaWl+Omnn7igoCBh0KBBzaM/SKV0Xx86RGO3bFm95TqcRIKOe/dixIUL2PnwIRcbG4uQkBBupoZGhVJ5LQQFEb0/MrJh0UKVcvTKleQIbNhQTemY3iKymJgYwc7Orto5KhQK8DxfOUd++KHW1yuVSgQFBcHZ2ZlPT0+HaUPaBYcP031x5gxRa/PzKZhYj8p5c9CpUyf06tUL+/fvF9955x3WJEFGQSBWkqrfd00sXkzBjHXrKEtcl3PJGN0Xbm6kpD52LPDtt7WDs3VAU1MTgwcP5vr3749Nmzbxfn5+4vjx42vfkGpqsD13jp10c2NXr16Fm5tb5d84joKyX31FTrIokpPH87QeuboidMYMwfXePTh+/TUHY2MIaWkQ587FVAMDUaquzjBpEt1jy5dXtjycNYueRZHmrpkZ0apPnKB53UhbQI7jYGZmJkZERLA6Swt+/ZWCPTt21Pl5QRCwbds2pYaGBtetsf3u4kUKOOzfX/0aGRjQeZw581zspczMTKirq1OUY8SIyj/cuUPP3bpVMpq0tSk4Lwh0PQAKwCYk0Lru40PXa9Uq0ggJCKDsuFIJ8Dxu3rwpBgUFMVtbW+XUqVMrbuyysjKEhITIZTLZp898Iq14LrQ62y8Iq1evLvrPf/6TX1hYaPTMNVyteGlgYWGBoqIivll1Zf8rMDQkx7C0tLJGubH6vZcAgiDA19cXLi4ufFlZmejs7Axzc/N/pJd2LUillVH7ciEbLFhQSQu/d48McaWSstW5uZVORb9+9Pysgl3NRFZWFrS1tZtf8wwyyCUSCU6fPo34+HhlaWkpz/M81q5dK/bt25eZmZnh3LlzAsdxbPbs2axtlah+mzZt8M477zClszMe9u0Ln717oaurqxQEARKJBIIgsIKCAk5HR0fs3r07y8/PF/bv388rFAr06NEDc+fOhba2dsX1dnd3Z5cuXUJISIjg6upad8ClqspxS2P2bDKyaziw8fHxnCAIOHv2LDw9PTF48GAOAO7duydqampWekre3vR88CA5R7GxZLQ1lRqvQmQkcP8+dKRSLD11ivtr+HDIg4LETklJyMnJEfr27ctHRkYK8fHxnK6urqinpyf06NGD69u3LwPIuNTQ0KjeP5sxctKWLSMhqT17mnVIlpaWLDw8HOvWrRN4nhfNzMx4Jycn2FtbEw0zIaHu+T5kSPUe5g3hzh2id/74IwCoqM1iQEAAs7e3F/r06cP5+fkp7ezs+FdeeaXaR+tyzjQ0NGBsbIySkpJnKz/S1CQGzLp1lBELDq6/tCQtDSY7dwLjx+PEiRPi4MGDWZenT+sPasTFUeauIUf7wQNyRA4epHGpp5PByJEjuSNHjiA+Ph55eXnizZs3xeLiYlZYWMh4nkeXLl2ULi4uvKWeHiRDh9L8AvD06VOoq6tDJpMhLCwMoaGh0NXVVVhbW0tGjRpVfUzlcuCjj6D85Rfcvn0btvv2QTptGgUNWiiZMWLECMTExIhxcXHMoSnCclu3Ep09KanubPSyZRB1dZE8ahTu3rkjPgkOVgqCwNTU1ESJRMIGDhzIV5QodO9OTLHHjynzGxjYNCFCAOrq6pg1axbbuXMnCgoKIJPJRFEURQcHB87ExAS6+vooHDkSGunpSElJEVFFfR0AUdj79aN6YG1tat0FAAoF7m3YgBuxsezNBQsYAgIApRK3rl6F/7Rp+OSTT+h76uiTDYD2qq1bae6+8gqN0fDhdL+WZ8gbAmNMrLd+ft++BoNs0dHRKCoq4pYuXdqw0N29e7QmHT1aOxiirk6Bt6dPGxewbABZWVnQ0NAQUBmIqw41tcrypy+/JDvq3Xcp+9+lC12fN9+sLIcRRZrzERHUXnP6dLIRrKzguX8/6+XtjVMffMDk585Beu4c8MMPuHbtmpIx9vfq1avvPPOJtOK50Opsv1g0EHJvxb8Jurq6GDRokNLHx4cfM2YMerZAO5v/d9DQoDonOzuKwvbo0TL1rC8IERER0NLSEocNG8ZQ0wB5WcEYZavMzMjpLisjqttXX1W2vvkvIT8/H4cPHxZSU1M5nudhamoqjho1ipmr6tbrQEBAAHJycpCXlydmZWWJpaWlnFQqFUVRZGPHjuV5nke7du0QFxeH0NBQMMbE9u3bi97e3pxmPcYZ//33sLKxYW/r6CAlJYXneR755T15ra2tYWxsrBoUXqFQoKysDNra2rUsMMYYPDw8cP36dS4kJKR6m7Xly8nY2br12QesMfA8GVY9e5KavJoaIiMjIQgChg0bJvbv37/axe3YsSPCw8NrG3FTphAFOSqKaKpHjzYt+BUeThT0M2fI8V+3Dpru7njz229xPiCA3bp1S1AoFPz9+/cBgBs9ejQ0NDRYdHQ0f+rUKaSmpirbt2/PnTp1iqmpqUFDQwN9+/aFjY0NjFWO8KFDVB+6fTv9RhMZJLa2tli8eDEKCgq4y5cvIyEhATKZTLC3t+ewYAHdC3U520FBVOe5c2fjP5KURMZ3FcyfP5/l5+dXBOHefffdZjGb0tPTG+8X3BA4jtbUV18launatUTPrYkhQ6CxYQNWu7sDUinNk+++q5ty+sYbZKDXpw9QWEiqzuvXU6b19OkGD9HR0RExMTHw8/MTJRKJ6OLiwhkZGUFNTQ0BAQHC3bt3+aSkJLQ3M1POdHLioVTiRmQk/P39IZVK4ebmJjo4ODAdHR2cO3dOEh4eDmNjY7i4uJCQ3/Xr4LZtEx795z8oLi7mog8fxpsyGcz696e51EICnRzHoXPnzlxsbKzSwcGh4eucm0vq2uPGVay5giCgoKAAmZmZKC4uRllioph26RJy9PVFz9BQrvSrryQA6YLk5+cLW7duhYeHh9CnTx9OW1ubrvXs2bSef/wxjf2RI3QNG1nXTUxMMHXqVAQGBio7d+7Ml5aW4u+//644L3OOE2b4+HB/6euznKFDUSsB9MUXteeKRIIjSUlwHTqU6RgbUx3z4sUwXr4c7WbNEnmeb/igtmyhdeznnysDIhoa9Ftz5zYs3Adau2/duqUEUP1afPoprW0nTtT7WW1tbZQHNur/gRMnSBvh2rX62S8REZW072dEZmamqKen17R1IyaGsun79wMbN5J6/48/Vi+POnSInm1tK7VhwsOBsjJwjIFbsQJyfX2E//KL0i0qild8+y1CQkJkZWVlnz3XibTiudDqbL9ACIKgwRhDfn4+5HI55HI52rZti/8KRbUVLY4BAwbwBgYGOH78OCwsLOoUzvmfx+LFlE0bNYpogyo62UuIqKgo0dHR8d/hZFfFe+9RjXdQEBkJqankUBQWNtoy6HmRn5+P8PBwRERECMXFxZyBgQFbvnw5srOzcevWLfz111/iu+++W60FUmlpKZ48eYLbt28jMjISnTt3Vnbs2JFzdXXlunbtitzcXKatrV1NBdnDw4OVU7kZahpbVbF1K9XrduwIQ1A9Y0OQSCQNrr88z8PExERZUFBQtYk1OSaC0NjwPD9MTcnZLiwEDA2RWU6RreloA4CHhwcXFBSEOum3PE+ZyIMHqdRjwgQy2OpTkFdlS2xs6LO7d5Px/8UX4Hgew4cPx/Dhw7n8/HxkZGRAV1e34je7d++OuLg4+Pr6cpGRkUxNTU0cN24ci4iIQEREhPLSpUu8ra2tcsKECTyTSkkwautWyla+9VaTh8bAwACampp48OABhg8fjoq6/nnzqBa8vJdsNTg4NN1YHjy4lgOqo6NTPUPfTMjl8mdifNSClRXRi5cto2taMwvHcZSRPHmSMuEA0VFrrgepqZTVrk9Mz9+fmEmurlRjXVO8qR68Ru0HK4KWBQUF+OWXX2Btbc2pq6sLycnJ3KOUFH6np6dQsHEjyy0uZpMmTYIoiggICBAiIyO5KVOmsKioKLRr104MDAwUnZ2duTNnziiNv/+eM5XLuaeDB4slpaWCYVYWVyIIuJ2aCotLl5BVXIxbiYlKU1NTrmfPnqy+oFxT4O7uju3bt/MhISHQ1dWFTCZDdna2+PTpUyEnJ4eVlZUxDUEQ3/jySy7knXeUmQ4OfG5uLgoKCsSSkhImkUigrq6ulEgkonNEBD8oJoZp+/oybtkydBo9uupPcampqTh48KAYHByMjh07Kl977TVeTU2Nsss//0yBhGnTaE3YuLFBNX+ZTIaioiK4ubnxpaWluHr1qqijoyMOHjyY69mzJziO44TkZNiUloonT54UZ86cWT1At3MnrRcLFlS8pFAoUFJSAmdnZ5oLM2cSU8TcHHbh4bUz5FVx6hQd95df1mZXTJlCQeLGrwV36dIlxMXFwa4qO2XSpEbFIMuFCYW1a9dyQ4YMEfv06VP9WK9dAzZtovuloftz4UL6+7ZtjR5vfUhLSxNMTEwad7bj4khf5ZNPiFo+fz7NgaZ0cZBIKgKXup9+ioGLF3Ppjx9DiIvD7du3ASBq9erVMc98Eq14brR6fS8Ia9asUWOMaUVFRSEwMBA8z0MikYg8z7M333wTjQo2tOKlhIODA+7fv6/ctm0b37t3b3HEiBH/PmftRYPjSMVTU5OMw1dffWFtop4VhYWFyMjIgLu7+z99KM1DTg45QlWNFSsromZGRVHW6sqV528pVo7CwkIEBgaiqKgIjx8/RkFBAfT19cUBAwagvNUUA8gpsbCwYPfu3RNPnjwJe3t7XLlyRVluoHJqamowMjJSent7c3Z2dtUMD5MmqvDWiWPHyElsZsuv+lBYWIgHDx7wlirtgQ8+IPpuaGiLfH+j4DgyfLdtgw9jirtPnkgcHR1rZ3dAgYGePXsKR48exaJFi+qmkLi6UpCgRw/K3F69SqyIqnW30dGURbt9G9i8maisGzdSdqXGPNLT00NdYlh2dnawsbFhqVTvzCwsLOBIbZ744uJibNq0idu3b58wePBgLjU1FeY+PjA3MKDs1+LFTR6egoICKBQK9K2a3X30iBT562qVJQhUC9sYYmOJillU1DC1upkQRRFV9QeeC/37ky6Duztlppctq57xHDAAOH6c/i2Xk/NUpfc2IiNJPDEsrHamND6e9Ai8vSkAoipFeUaUs4aESZMmcQC4/Px8XL58GZ7z53OFQ4dC/P77CiEyBwcHfvfu3eKBAwcgiiKmT5/O1q9fz3x9fZWGISEsbcIE9FuwAJ10dBgAFrVuHVLS04UbZ88Kw0ND+eicHFY6ciT/6NEjZWBgIO/i4iLY29tzOTk5SEhIUN67d49XKBQQBAE8z0MqlYr6+vqitbU1l52djZycHNHOzo6VlJTgxo0bgq6urorFIfI8z3R1dXkjIyO+W7du0NXRQWxkJA68+ioeSSQ8EhLQrl07TJw4kbVv314VWKEJVFpK6vY6OlRr+/hxZbcKAO3bt8fy5ct5mUyG33//HTt37hRmz57NVQRn9PWJlbJ7Nz3n51OdfXmWWBAEPHjwABEREcrExEReQ0NDqVQqeQCinZ0dN2rUqGqBRW7zZgycNIn9oaNTm3E5cmRFZluhUCA0NBS5ubmQSqXwP3IEQ2JiYDxuHMBxeLJ8OfRXrWI4fpzo4eUoLS3F3bt3UXjpEnqePg2N1aurabgoFApwHAdOVXP8+usNZuwLCgrA8zyqMaWSk6m8YeLEBuefmpoa3n//fe7ChQs4deoU6927N2kHiCLVex85QgyexkoQli0jIcHnQFZWFtcoE/LwYRIg3LmzUnTT1pbGr1MnKnFpitaKXI6ckydRcPkybi9ahL5qaggODi4sLS39+rlOohXPjVZn+8WhiyiKXHBwMIYMGQJ3d3dwHMd2794t+Pr6YubMmVyDtSSteCnBGMMrr7zCd+vWDQcOHGDu7u7Plfn4fwuVkVdUVNkC4yUSCoyIiICmpiZsbW3/6UNpOu7do0ydSo23Jrp0oeycVNqkmrjGcOTIEWVMTAwvkUigo6MDhUKBSZMmwcHBoU7aPWMMrq6uyuvXr3OPHj1i1tbWGD16NGdgYAAtmg8tu+AVF5PgVgtCQ0MDenp6uHjxIkpSU8Xhy5Yx7tVXW/Q3GoPP3r3CsNWrOfUBAyRTvvoKNYMTVeHl5cWtW7cOycnJsKhP3bc8Qw2A+q9fvEhlHjxPWd+OHYktoa1NmZRVq8i4b6Y2BcdxdR6DlpYWXn31VbZ//36WmJhY8XpbpVJ4/ZdfuKPx8WKaiQnT1tZW8jzPFRcXgzEGjuNEbW1tsU2bNpylpSVzd3dHRkaGCIBt3LhRXLlyJc3BQYMo8yaKtY33lBQKMDQGe3vKdrXwnqyuri5mZmaiQ4cOLROUlUopa/jDD2SgjxtXmZkbNowYEUlJdO///TdRblX4/ntaP6qOUX4+XWtfX6L2jhnTImOQlJQkWFhYVASA9PT0MHr0aCA6Gjr6+rXKB6ZPn878/PxgZmYGmUwGqVQKtYcP0e/IEc5/zhwB5GgDAJzOnQM+/5zzdHfnoKsL+x49VGUS/MOHD+Hv7y/euHFDUFNTE9u2bcu5ubnh6dOncHNzw507dxASEoInT55whYWF6NChg9LS0pK/efOmoK6uLnp5efE9evQAX59x9uabMImPZ5uHD8ekiRPRYG13VhYFme/epesQHk6B0BpQU1PDokWL+N9++025Z88eYd68eZXdEDQ0qF73/n26L8+cAYyMkGNnhz///FOUy+WipaUlN3/+fJiamvKpqanYtWsXk0gktXUEJBJomprCKTSU27Jli8hxnDB27Fj+6dOn0E9KQmlQEMISEvD48WMAgLa2tmhvbw/d27eFe9HRvM/Ro4JMJmPm5uYsx8mJdVq2DFtCQlCqqQknJycxNjaWGRcVCaZxcdz+tm1Rcv26UHDhAufs7CxGRkaywsJCGBkZYcnbb9NY3L5N87Ee5ObmQkNDQ9DX1688kdOnKUDXiLMNAPfv38e1a9cwfPhwgTHGQRCo1vvePbp3mmKP2NiQWvygQY2/tw6UlxawznWxblTw8aH68J9/rt7dYtQoEmn75RcgKQn5W7bg/v379ZcwlpYiZf58MeP2bXZiyhTMHjsWycnJyM/PLwBw6plOoBUtBiY2pOTZimfGmjVrVgBY7+TkJEyYMKFisZDJZNi8ebOyoKCAHzFihODq6vryFrW2okF8++23mDFjRvN7qf4vol8/evz88z99JACAzZs3K+3t7bkhQ4b8O5gJCgUZqMnJDbZMAUD1luPGkdH9DIGg3NxcXLhwAbGxsXBycsLIkSOh3gLtdVocjo5ESfz88xb9WplMhrT330ebXbvw03vv4fMW/v6GkJWVhc2bN+P1KVNQkJWFnr16gatCr6+Ju3fvYv/+/fjoo4+aXp5UUkJZ6++/J5X72NjK/tdz5pAB/P77z30uMpkMHMfBx8cHjx49grOzs9LNzY338fERO3fuzAwNDYWOxsac5N49aCUk4JGXF9LT00UtLS3RysqKKy4uRlBQEB4+fAgA0NLSEouLi5muri7s7e3FkSNHVt67CxaQVoRKDVmF3FzKKNrbN3ywixaR7kEDbXWeBadPn8adO3fE2bNnN95+qDkQBDrmrCzqN6yqTV28mFoPjR9PGWpVoOHXX6nm29q6sm1gQgJdb09Pqg1toUCDTCbD+vXrMX78eNjXHPeICCp/aaCvd0xMDM4dPIg2mZkok0rRfcqUSgZSbi71MT51ipylH36gDH4d9eexsbE4XN4j2cjISMzJyWFqamro37+/2K9fv8b7gteEKAKhoRC1tXGloEAMCAhgkydPrn2OKpSW0jp89iwFsUpKiFVSDxQKBdatWyd6e3uzuoLAebm5yAgIgP4334jRJiYs5403hEmvvVbLfkxKSsLRo0dFc3NzTJ06tfr+lpaG9HPncEtXV8iVyRAfH88ZGhoK9pcvo+Pjx+zGkiWiuro6GzZsGKso61m0CE/79MGjnj1x5swZqLoC6GdnY/Tp0zg0eTI0DA2F8UOGcNbffIMyT08E29ujqKgIDx8+hFQqVRYXF7OSkhKO4zg4OztjlJER7WMNjEd8fDxOnDghrFy5svo51hVUq4KysjLcvXsX586dE7t164bRo0cziCLw0UcUqN60iQKLTUFZGXULSU9v+meqIDg4WAgODuY++eSTut+wfj0xTTZsqL2vT5xILJbZs6G4cwdrAwNFmULBBg4cCA8Pj+rrfWEhcPYsrvz0E5Sffw5PLy88ePAAfn5+ivz8/Pc+//zzl8Pw+h9Ga2b7BWDNmjWcVCpdMnbsWDg5OVVbKNTU1LBixQp++/btSExMhGsjtSeteHnRvn174cCBA5BKpbC2tmajR49uWsuQ/0Xs3k3GUVgYOYBEMf3HUFZWhvbt2/87HG2A1GqnTWuaY9m/P9FJNTQok9IMqnxycjJ27twJURTRtWtX5ciRI/mX0tEGyGFsbk/cxiAIULt5E5YbN+LG0KEQb97E8ePHYWBggJKSEnTo0AH29vYQBAHXr1+HtbU1CgoKEB4ejqysLKFz585cRkaG6OrqyqytrSuonhzHobCwEN3r6QsbERGB5ORkREdHQ1NTE9a2tmDDhhGNcPv2eg9XqVRCqVTWqYZdL1Qq1998Q9RhlaP9++9ExR45sjkjVgu7d+8W0tLSUFJSwmlra6OoqAhjxowR+vTpwwPA0qVLVfcdHXRODrBhAxznzkW5hgIrKSnBnTt3kJKSAltbWwwdOhTZ2dlMT09PVSte/d7t2bPumvqICKqBbCi7XVZGrfGqUq5bCAMHDsSNGzfYTz/9hPnz56N9+/ZQKpV4blYbx5EDff481eKvWEGO9IoVxFZgjOpNAeDGDXrvpEn0elQUMQHkcsqqWVk9/4lWQUZGBmQyWd1O6N275Hw24GynpKQIk06c4DS7dYP8xx+r972OiwNee60yK2loWK9OxaVLl5RWVla8p6cnLCwsWE5ODvT09FCvwnVDSEqioER0NJieHgxv364on6kXGhp0j5WUUN3t2rWUga+r3AGkJ+Hl5cWOHDmCvn37ipaWlszU1FSl1i5ER0dz2traStOFC7mhdnYwXrSIQ0kJlQ5VcT4tLS3xxhtvsK1btyI8PBx9qoojmpnBNDAQI3r35sS334ZSqYREIuEwfTqQmYmu3bpVH5vERCA2FsZbtsCYMYSHhwvt27fnBg0ahMzMTHSytsanUVHA6tUcfv8dST8dwQAAIABJREFUmDYN6q++iqHVT4338fERNTQ0lK6urvyuXbtgo62NLl98QUGTemBhYQFRFLlTp04RK+LaNZrDSUnV3icIAu7fv48LFy4os7KyOLlcznieh42NjeDp6clDoaAyE2tr2j+b4zSrq1OwrpmOdkZGBo4cOSJkZ2dzU6dOrf0GuZw6RowbR6J4dQX5VqwAzM1RrKWFuFWrxFFaWpD85z84e/asGBwczLS1tZXTpk3jzXR1ISxahGItLZwfMgRdHj/G+vXrhcLCQg7k49XdH60V/1W0OtsvADzPLzU0NDSpz7ACgMmTJ2PTpk1cZmYm2rZwNL0V/x28/vrrXGJiIrKysnD16lUkJiYK7dq14yZPntwqglcTqnqjr76ibIyf3z92KKdPnxYLCwv5xsS0Xirs20c08abC3Z0CG2PGkLHQiCORmJiIgIAAISsri3N2dhYHDx7M6lLsfmnwxRcUVGjpoI2vL2UE09PRY8wYPOY43L9/X0xOTha1tLTE0NDQijHheR5nzpyBuro6zMzMhI4dO7KbN2/CzMxM9PHxYVpaWigu73PM8zyUSiXS0tIwdOhQ5OXlVSgCb9y4UcjPz+cAMjBnzpxJ9YW//too1dHa2hocxyE1NbV+GnldyM0lpokqExwcTI7bunWVWdJmQBAEPH36FEFBQbh//z43bdo06Orq4tKlS0pTU1OoHO064eJCTti+fYC+PlJ698bBgwchCILg4eHBDSzXe2hQkHLhQrp2NbNeqgxaQ5BIqG79BUBLSwtLly7F0aNH8ccff6B///5CSEgIZ25uLnTv3p1zdHR89jIkjqNWSjk5VGsdFkaOs6Mj9TTOzqaMXF4eOTWiSCrXnTuTovmUKS+kW4Senh4YY5DJZKiVPZ46lR4NoOjqVTFz7lz0Wriw9lyMiakuGBURQWtbHUkLURRhYGAgdOzYkQMaF09sEBoaxCTQ00NqaiqOHTuGsWPHNsxqu3+fAlerV9P1KClptCd4jx49cPbsWcTExCA2NlZZUlLCM8ZEExMTvPnmm6gmtLV7N2lW7NhB93KVtdDAwAATJ07EkSNHYG9vryrjISxfDkREgJW3SARA8/+DD2oHpfz86P3l95RMJkOHDh0qhQMXLCBKtpcXBbyqCB5mZWUhLi4OOTk5ePDgAZsyZQrfvn17jBs3DscPHRKXZmUxXhDqnYPa2tqYPn06du7cCUdHR3SwsEDxihW4GxmJtLQ0ZGZmIjc3V1lQUMBLpVLY2tpiwoQJLDIyUoyKihInTZpEYzVnDgUtZ85sWAytPnzwAQUkP2uamHdSUhL27t0Le3t7bt68ebXvAaWSmCQKBfVXr29vjozEk9xc7Dp0SOzXq5fooVBwEkdHODo6srKyMly+fJkd2LwZvSIixCxtbXbHzg5t9fWhr6+v7NWrF5+QkCCPj4//6dNPPy1s/km3oqXR6hG0MNasWeOloaHx7ZQpU7RYA1QXPT092NjYCHv37mVLly5lDb23FS8npFJpRfTe1dUVV69eZYGBgfjuu+/QtWtX5dSpU19eZ+Wfwo4dZPRt2EAbfFNa8rQg7t27h4iICPbmm29WtiN6mbFnD2VH4uKa/1lXVxKHyssjg2/t2nrfGhgYKCiVSm7YsGGii4vLy78YxcdX9iFvCSgU5NwuXUpiYYyB53mMHTsWqKKyrFLozcvLQ/v27Ss+zhjjAGDUqFEAwPn7+yM8PByampoYOXIkunfvjoCAAFy5cgWh5WJr5ubmKCgoEAsKCrilS5fWdga6dqXs/Zdf0n1TB+7duwepVCo2i6WRm0s08R07yNF8/Jgoxe+/37hjWgfCwsIq2gxZWloKM2bM4KzKs6VNXgOlUsrI/vADdo4YAQ1NTdSijzYEVV16ly7V+0FbWlLmryGYmNBYjB/f5J9rDvT09Coc6qioKMydOxe3b9/mwsPDlRcvXuSnTZsGyypCUs3G1KnkdA8fDnz4IdWXpqdTK7R58yjQ2acP1er37EmZ4eb2Xm8G9PT0oKWlJUZHR7PevXvXfoO5OWUp62qJduYMhvz6K39vz57ajrYokkBc1Tr0Dh3qdVb69evH+/v7Y/To0Xgu++r114GxY5G9YAHCz54Vr127xmxtbVHnuamgUFCtdVlZ5dq9YQNdlwZo0P7+/mjbtq2wcOFCDpUaF3W3pnR2psdvv1HG188PpW3b4ujffyusra3h4uIisbS0VB49epTNmDGj8l5yciJGwy+/kAAYQLoNNZXnS0spMF4lOKKlpSXm5uZWvqesDLhyBfKoKJxxcRHjN2wQlUolk0gkYmlpKWdiYqKUSCSYPHky37W8l3T37t2RkJAg+KakMIvffuPsZ8yoN+D09OlTMMawd+9eOIaFIdrZGdqXLgmGhoaiqakpc3Bw4Dt37qwKXPLl38+uXbvGFMXFkMybRyye4cOfXTOmT58mO+m3b9+Gr68vBg4cWLf4akYGdWj59FNytOtJyuTm5qLsp58QYW+P/u+9J3p4eHDswgViqTg7Q11dHV4DBnAuH36IVGdnsf/mzUxaeb/wZWVlOH78uFKhUGx6tpNuRUuj1dluAaxZs4YBcOB5foJEIvlg0qRJWk2Jok6cOJFbt26dsGvXLnHcuHFcayupfy+kUik8PDyYu7s7UlJS8Ndff/GpqanVDPJWlIOxyl6xMhlF/Gu2B3lBuHz5MszMzERzc/OX36EEiGb2DJnGCmhokHprQAAJitVhmKalpSE7O5u5ubkpXVxcXv4AUUYGGYstKWYVHU31cwsWEHWwHkgkEujq6kK3kRZrcrkc7dq1UxnNAAAPDw+YmJjA2NgYSUlJYnBwMDM3N2fjxo2rP+vWuXO9RmJhYSH8/Pzg4uLSvPKVY8coG6quTnTGTz8lA7yu/s2NIDMzE+fOnYOxsTFmzpwJXV3dZ0+Vzp8PXwMDhdf69RL9efOa91nGqBazptP68CG9TkGTuhEYSMq/LxCjR4+GnZ0d7OzsOJWQ3IgRI/grV67Ax8cHY8aMEXv06PHsa5KBAWU6lyyhDNymTSRwt3QpZdGKiqjv9n+JzcNxHDIyMur+44cf1q0lkZkJWFoicPp0waKqKJYKCQkkeFf1WnXvXm8pSWhoqNLDw4N7rkyGKAIGBkhQU8ORrVthYmKCSZMm1SmsmZ6ejpMnT6Lfgwei4/79DB9+SIJeamrUmq5HD9IOOHiQMsE18PDhQ8TGxsLLy6t599CCBeRQ/vknin18UGRvLwlISpIZGBjAxcWFP3z4cG1hpokTSRRR5WxbWNBeUxUREeQMlgfgCgoK8PTpU76XKph1+zZw7RoyZDL4zJkDVyMjvAFw8lmzUFRUxDp27Ah1dfU6F+mJEyfyaZcuIfPoUeHHzExOU1NTaW5uzvfp0wdWVla4ePEibty4IYiiyA0ePFjszPOs3Q8/YMyhQ2BSaYPjY2RkBGVZGYS5c8mhHTny+faK6dPJyW0Acrkcp06dUt6+fZsbN24cq5PVmppKKuiDBwMeHnUGXARBwNmzZ3Hjxg10+/JL5ZChQ3nDtm3pfK9dI/aKnx9pt7z/PnS3bYNt7961xuP69esCx3FnVq9e/egZz7oVLYxWZ/s5sGbNGic1NbVlUqn0VTU1NamNjY3E2dlZvakOFsdxePfdd7mNGzdi+/bteOutt6Ctrf389Vyt+MfAGIOFhQUGDBig3LVrFz9mzBj06NHjnz6slw8DBtBj7VoyDJOSGhQ9aQnExsbi4cOHGDZsmICWVsZuaSiVlKHbsYPonk1EuQCj6OLiwgRBgIeHBzg7OzIWrl6l1jFXrlRzKA8ePKjs0KED5+np+XKPiQqenjQmX375/N9VWkr9TP/4gxyzFsCJEycQGRmJsWPHVjOCNDQ04OTkBAAwMzNjTdLr6NePnItDh0gYqhyiKOLo0aOCvr4+hgwZ0nTj/PFjql08cID+7+9P92Fj2d9yCIKA4uJinDlzBnFxcRAEAb169RJGjhzJNVtwqgays7MRGRMjWd69O/SehcmhqUkORFW2TIcODTva69cTjf05j70xaGlpVVOuLiwshEwmQ79+/WBoaAhfX188ePBA+corr/DPrPthZUV08TFjKLjGGN3rW7YQxfi/yJ6zt7dnd+/erbNlHaZOpXZKVengMhnN72nT8MTKCpZ1jUFEBGWZqyIujrLIdQSKyjOsz34SYWHAtm24PHeucCk4mOvTpw+GDx9e5yAGBgYiODgYkpISiCdPsmwNDehMnUoU4qQkcvrS0+m6lGd4VShvN4gTJ06I9vb2qm4PzYOWFrB4MR7k5QntwsIOuIeFjTv59KlaPgWxa39fv37Ut/3IEWrNyfOU6Z4xozKL6+9fraVicnIyysrKKNB46hSxbiZPht7mzSj44Qd0dXVlbRcuJOe0xjnWBMdxaL9iBdo/eMDZ9u+PBw8e8PHx8cpjx47xpaWlMDIyEkaOHMnZ29tXtJdEVlaT5jCfk4M3jh7Fwb594ejpiZ7Pa08/fUpzMzW13t/fvXu3kJeXxxYvXsz060ochIcTHf3rr6n8qQ6cP38eERERYjl9nlm+9hoPnq9c95csoX8XFFDd/wcfULvCOhAVFVVYVlbWKor2EqHV2X4GrFmzRkNNTW2jurr6bFdXV7Xu3bvzz5qVVlNTg7e3N44ePSpu3LiRGRoaitOmTWOtddz/bnh6evLq6urw9/dHYWHhv6+f838LK1eS8ZWWRkJQq1a9sJ+6c+cOjI2Nhf79+7eIU1lnTWJLgePISWhmTbKvry8KCgpYWFiYWFZWxhISEmBkZITbt2/DVEsL093ckHz/PvTU1FAkijh06JAok8n4jh07Khlj/w5nOyioXlGkZqOwkJzssrJnq+erAYVCgZs3b2LYsGFi7969W8a7yc6m7NXo0RVCPVFRUUhLS8Py5cub55mtXEn04gEDgBMnSChr+/ZGa3eLi4tx5coVhIeHizKZjOnr64teXl7Mzs4OBgYGz134GxkZKfr5+TFTU1OlzqpVPIqKiBq7aVPTRfAMDclBqkrTVSobzkqFhhKNfMCA5z2FJiEjIwPbtm2DUEXMTSKRQCKRIDIyktfT0xOfuUPCzZs0j3/8kVoWAVSH37UrlZEMGkR9tj/6iMZIS+uF1GyXlZUhPj5eNDMzq/s8pk6lIIiPT+VrubkUJJg7F8LWrRW079LSUoSFhSExMVGcXljItDw8qn9XTg61LwNw/fp1PHnyBL169YK5uTmKioo466qtlJoJMSkJqenpYvDly8zOzg7h4eHQ0NAQPD09aw3alStX4H7xIgZFRODyrFnK43p6/KDoaNr3LS2plEciobn27rskRgggJCQE586dg6amJjw9PcX+/ftzzyOymmBpWZySkxPY+/r1DK+LFz1jbW17pffqJaIuh1sqpe4gKvbU3Lk0LwDqXe/vTyVM5bC1tYWmVIr8lSspM/7uu9h+86by6Y8/cqIosiIrK7S9dYsCxG3aUI10Q+jUCXjlFagfPAhbW1vY2tryoiiq9tXK9meCQKUHwcGNOvHIzAT75ht0ePtt8Hp6ygcPH/I9q5aVPAs6dqRyrnro/w8ePEBycjK3YMEC1OloX7xIgYJ33qnX0T558qQYGxvLJk78P/auOyyqa/uuc+8wM/SmooBIERXEXlBRwK4x9hq7JtE0jcbkJca88EjPS56mGI0lGmss0dhLFBURBVGxg4qAgoL0Xmbm3vP7YzP0Lpb3fq7vm49h5s7MLefee9bea689hrVs2ZLG/8CBZbfX1JRq4gMCKPBURVIvISEB6enpAHCu7hv7Ak8KL8h2HeHv799EqVSecHR0dB41apSh4WP2sgWA1q1bY9GiRSw1NRVnz57F6tWrMWjQIN6lS5cXtdz/xejWrRuMjIzw119/oU2bNtUb/Px/hoMDEag9e8g5WBAaPAuj0+lw//79Oh0DWZaLXZ41Gg0SExNhb2+PtLQ0/P7773Jubq7g5+fXoOsJgGr7MjIo81BHxMfHyx07dhRGjhzJYmJiEBISIsXGxgoGBgbsYXY2fmjWjLf497/Z6B078Pv778Nn8GDepk0bZmFh8d9BtN96i2R4pbK89UJ+Pkn5du4kJ+oGgEajwffffw8jIyP07Nmz4QawkxPV3mu1xS9FRETA1NS0bsGe9HSSjbq7U2lBejqdb1VIi3NycnDx4kXcv3+f379/n5mamvJhw4axdu3aoSFvTLIs49ChQ8zKygpvvPEGjUMTEyInoaFk/lUbuLpSb+mkJIAcy6l0oiqyLUlk7vQUsXv3bt6oUSOMHTuWmZqaoqCgACkpKZAkiQUFBXFjY+O67de0NHJY/uknItS2tsCvvwLLltHEXqkkCXlcHGVY9++njNjQoTSeAgKonvXHH2kcXLpU4lpeT+h0OmRnZ7PZs2dX/iWnTpX9/tWrKeATEgIwBs45Ll++jKCgICk7O1ts1KiRnP7okaC7eJHMrkrD3R3pSiV++/57GQCzsrLiv/32m+Ds7AzOeVkn8zqAf/QRAtzdpYs+PsKrs2axJk2aoFu3bli/fr3Qpk0bNGnSpGThtDS8YmmJhxoN5JEj4fPVV2Ly0aPSuXPnmFarFXr37g2FUom8Tp1wX6nkTe/dY/tXr+bMyAj37t1jkyZNQmsKjjx25MPMzMyg0NT0kzVz5zortNq80bt2wfDcObbRwqIwV6fTTZ8+3bi4pVfPnnT+37tH587ly0SyW7cGNm4kD4RSx0nIykLP48dhYWFBgRFrayQePix27NgRQ4YMKVFkduhACqrx42sOYHbsSK0Hi2T5jLGKbSYlicZsTa750dEUTHz9dbAhQ6DctQvR0dFyVFRUcdDlwoULyM7ORp8+fSCKYu1r+cPCKJBFnhxlYGRkBMYY0tPTK4633bsp+PDZZ1RfX4S0tDSsX79e1ul0jDHGRVFks2bNKjuu+vev6KFx6RL4yy/jvk4HRRVlipcuXdLIsrzUz8+voHYb9wJPAy/Idh3g7+9vpVQqz3Xr1s2hf//+ioYkwkqlEs2aNcPYsWPZnTt3sHv3boSGhspeXl6sY8eOLxj3fyEEQdAbI/GoqChWW6IXGxsLURTr5i783w4fH4rWhoXRZO/mzXr1tawKGzdulAsKCoTevXvXOKG5cuUKDh06xLVaLWvSpAlcXFxw/vx5CIJQnJHS6XTCEwuEWVjUK+N04sQJaDQaoU9Rls7JyQlOTk4iAGzdulWOjY0V+vfvz1rOnQvVq6/i/U6dYHD5svC06jgbBArF49edck4y+h49KpoC1ROxsbHYsWMH12q17K233no8Q6bKIAgUlDp8GDdMTBAVFYWJEyfWfpBwTgZg775Lda7ffENS0iL57ZUrV3Ds2DGZMQatVssYY5BlGaamprC1tZUnTpwouri4NHjwNzMzE8uXL4dOpyvboogxkqlGRtJ679pVpZlQGXz2GTmQf/st/W9nR9ncyjB4MGUat2597O2oDRISEvDo0SM2ffr04km1oaFhsSt9mzZtar9zP/6Y1n3qVODsWVJo7NtH7/34I6kgVq0iEpOcTMd/82bKApuaknt7nz40roYMoXPq7FmSuY4bR6ZqL71E5OXjj6nuW5bpUZ0DN1BMYuTKWrEB5Cj+739TjblOR6R/6dJiYte4cWNeWFgoeXt7i61atYKxsbGw9p13oMrLKys9B4C8PJw/c0Zq+dJLwvDhw5koiiw4OFi6desWXnrpJaY3LqwL5NRU5G7cyCNnz2Zz33+/uD+6vb09zMzMdHfu3FGYmZlBrVZDp9Mhes4cNDl2DJoFCySDhQtFmJpi5MiR4tWrVxESEiKfPXtWaNSoEWRvb7QaMIA9MjPjthkZSNdq5ddff10sQ7AeE+bm5ipZlh0YY/O5Wr3nrylTupsmJTkOWrpUmW1unr08Pt7w7cWLhWJDMgsLUs38/TcFZKKjKROfnl7s8C7LMuJ374bR118j2sMDgc7OGHT3LkyTkqBWq5GXlyeLoliyn729ibjPmUO93V95peoV/uEHMr2rDrt20Tiv7p546xYFkkaNKm5dOHz4cPHkyZPyli1bMGnSJAQFBUkPHjwQASAoKAhAiapEqVRypVLJVSoVV6lUUKvV3NDQkKnValGlUqF1cDC0iYnIcnKCSqWCUqks/mthYQFjY2OcOXOmpN0d5xTwcnOjUjk3tzKre+7cOVhaWmLYsGEsPz+fNW/evDhYwTnH6dOnZbdZswTzH36AasSI4s+lHj+OX1evhrB1K9doNMzBwQEzZ84svt/odDpcuXJFlmV5U/U79QWeNl6Q7VrC399fqVKpjnXs2NG+oYl2ebi6umLRokUsKCiI7d+/H5aWlpBlGRYWFsU35hf474FSqcSFCxe4p6dnjYNm//79uHTpEhQKBRYvXly3/rn/C+jUiWSOhoaU6WiAPvTR0dF48OCBMGvWLNhX5oBbDidOnJB69uwpuLu748CBA/LZs2cFS0tLPm/ePHb//n3k5+fj9OnTskqlatiDU1BAMrHVq+tcQ3rz5k2EhIRgxowZlZpteXt7C3fu3EHHjh0pc9C7N8QTJ2hinZBQrSnYc4M7d2jS/zhBGFmmTPHatVSj2EC4ePEi8vPz2ccffwyD+rreVgcDAzJWcnPDpb17ZaVSWez4XSvk5ABduyLL2xt3164F4uPRaMkS6MN5AQEBcps2bYQmTZqgWbNmuHr1quzq6iq4urriSZUXcM5x4cIFmJmZyTNmzBDMKnPIdnIiMhYTU7OEFKC6xtTUkv+zsqgc48GDistu2UL1j08BcXFx2Lx5M+/Zsyd3cnKq+3WDcxr/gwdTAMLKinrzNmpUMXO/ZQuR5WPHSO6rL2GaMYMeAJ1H5uZUv29hQVlDF5cS6e+RIxTc4Jz2P+f0mWPHiNi4u1NG3dOT6v8XLKC6X6USWq22eL5SKUSRZNXZ2RRk3bq1jOnZpEmTyow3zjlcIiIglGorpUcCgAStVvTp0KGYrHh5eYn1LdvSnT6NU3//LV9+7z288cYbQnmXbF9fX8WRI0f4iRMnWNcHD+Q2Dx4INwUBpj17crf580V9MMDAwABdunRBly5dhNjYWJw7d05uP2eO0LagAHjpJQYLC+DWrcc+rwoKCnD16lVkZWUhMjJSTk1NFQCAc+4rSdJ9AI7p1tYI7Ndv5pwVK9zuubi8zbdsMcWcORTc8PQE2ral82PFChoLYWEUsGrWDKcDA3nGqlUwS01l6d278zg7O9a0aVN+8uRJJkkSVCoV9/DwqDieRZGI7/r15K9RVd20JFFg7KWXqr7nffABZdqrCkqEh1O5yZgxZfwZlEolBg8eLNy8eZNv27aNOTs7w9LSkjdq1IhZWFjAxMQE7u7uyMnJQV5eHsvNzWX5+fnIy8tDQUEBsrOzkZKSAo1GI18dPJgLOTm84MgRJkkSZFlmkiSxor+QZRmyLHPOOTGDDRto/jJpEqlNSkGj0SA6Opo7OTkxG70Ch44Z7t+/j5MnT8rJyclIfuklnh0VxWdyLsTFxqJJjx7YMWYMdE2b4oN332WbN2/m9+/fZ7du3So27IuKioIoijc+/vjj6Mp31gs8K7wg27WAv78/UyqV6+3t7dsMHjxY+TSk3QqFAn379kV6erq8Y8cOFBYWCowxeHh46EaMGPFEyf4LNBwCAwOllJQUkXOO1atXS7NnzxYrM23RaDRYuXKlxDkXZs6cyf766y++bds2afLkyY91jhYUFEDdALWoTw0KBfUzvXePapZu3apws6oL9u/fz69du8aGDh3K7e3tazxp7ty5g/z8fNHLywsGBgaYPXu2kJOTA0mSGGOsuEVPXl6e8Pfff/OcnBxW7z65IDlwQEAAHzNmDLMtLARu367X9wQGBko9e/ZkdnZ2lU7kbW1tYWZmJv3www/i/PnzYWhoCPTrR0Q7KYkmK//+d72346lg+HDKktRXuq93Y//yS5KiNyDs7e1x586dJ0O09RgwAHjzTYzt00f4zdCQr1q1Sn799dcrvZ6UQWYmMG4c4n74AaHz5qHnpUu4+N13/PDmzUwURa7Vapksy0Lnzp1hW3SuNW/e/IlH+Q4ePChdvXpVHDp0aOVEG6Ag0KFDJPf+4guaxFaHDh3I7K17d8rgGhkRYS+PgAA6195887G3ozbYsmULt7e3ZwMHDqz9jTsxkZQX3t5U37pqFclzRZHatFUGSSLXYoAkrzpd5cvdvEl/N2+mjPj8+fSds2cTkXZ2LllWn/nXB6dkmRzPu3enrPmePfT5Pn3ATU2x7eWX+cxNm4CRIxnMzMjEbOjQEkmyuzs5Kl+5QlLmGpzg79y5A+PcXBhUYjRqefMmHBITeUBAAJ84caJQU5eAqpCSkoIrV65wh3ffZdZWVuydv/9mld03O3TogA4dOrC87GxcWrRIkCIioG7ZEuy77xiqUK45OjrC0dFRX49E7dgGDarXepZGbGwsNm/eDGNjY25ubi5LkiQAgEKhCNfpdCNKLbpk7i+/bPRv0mSIS3r6AuODB8m9vmtXOs5+fkQKnZzo2BQU0HkEwPKrr9AoO5u5/fEHWIsWbDR9X+kxXPV4Hj6cSHTPnuTFUt7tHCD/iKlTqaynKrJ9/37VZQ1nz9JYHj68SiPEt99+m2VlZaFRo0binTt3sLVoPJuYmEje3t5iLfyRBISEUNDgfuXm3tnZ2fjll18QdOyY7L1jh4CXX6a6/HI13Dk5OVi1ahUXRVG+cuWKKEmS3Lx5c8HY2BgxMTH80qVLzNHRkc2bN4+pPvgAKx894l9++SVExriPry/Pc3YWxMJCGBkZYebMmeybb74pzmrLsoxjx47lFxYWrq5pg17g6eMF2a4FDAwMPjE3Nx9VU+/sJ4ExY8YUT3oiIyOxY8cOxYgRI6r7yAs8JwgPD+fBwcGir68vunXrhi1btrCff/5Zfv3118tEzB8+fIitW7fKZmZmwowZM5hKpcK4cePYxo1+vyYjAAAgAElEQVQbFXFxcfWSk4eEhODixYs8JSWFjRw5Eh07dmzITXuiiI6ORnx8PLyTkugmO2oU9UAuqofS6XRITk7GhQsX0KpVK7i6uiIlJQUWFhZlalhPnTqFiIgINnPmTFTW6qugoABxcXGQZVlfM4fU1FTodLoypKkyMt2pUydER0fzLVu2yHPnzq1XhuLSpUvYv38/GjduzC6+/TZOd+8ujTl1SqyP6VpOTg5r0aJFlQRJEAQsXLhQXLZsmXTr1i2xeDyoVJQJPHmSatKe5wz3pUv175UKULZvyZInQrAuXLggabVakXPe8BLy0rC0hJEkYc6cOWzz5s1s+fLl8htvvCFUG1ALDgbUahw9cEAampEh2B08yOxcXZmTszPy8/OZm5sbDA0N8VjOzXVAZmYm9u7dK8fGxoqvvfZaMcGvFt27U8Y2N7d6ZQNjJFM+d44IjVpNk/nyuHGDMnhPiWzLssyio6Oh1Wqrr7MPDydVT1YWkeyMDAqEOTnR61WRbD1at6ZSgXnzaB+MGUMBtaowdWrJ/gkLo8z20aMU4Pjxx8o/IwjkOA1QNjw0lJ6fP4/M+/eRvHo1s3n5Zaq7P36cMpdRUSSlHTWKAgbOzvR8x45qNycjIwP7f/8dY1q2rLRmV+3qCp/27VmcgYG8YsUKjB8/Hs6lAwXVIDc3F9evX8eFCxfkzPR0oXNCAi/YtIl1aN+eCZVkYuPj47Fv3z54HzrE2yQlsWQvL6kJY+J9T08MKuU0Xy2USvLiGDGC5NOfflq7z5WDRqPBn3/+KXfo0IEPHz5cBCCeOHFCDg0NDdVoNDMZY6M450v9/Py0ACWM1Gq1X/vZs9WCuzsR1KlTKXg5bx71XVer6bF2LQUEhg2D+ZgxbPO9e1BLEmq3V8tBFIHFi+naPWAAjeHysLSkMgJ//4rv9exJY/Kttyq+FxREJSL6PvNVQKlUQk+oXV1d8emnnyI2Nhbbtm0Tam1y2qkTrUMVJmmmpqaYOWYMC/nkE5YmCLAaOrTSe+nt27chyzJftGiR+OjRI/z999/s7Nmzkl4N8sorr4hOTk70AwcPYvjSpcL1wkIMDgxkwrZtrFfRb3/22WfgRWZ2rVq1AkBZ7dzc3Aec87U1b9ALPG28INs1wN/fv7WBgcHiqVOnGj4x5+FawtLSEgqFArIsV9oeLD8/Hzk5OWjcuHGZ1/UTwMLCQuTk5MDS0rLW8mTOec0ThBcog4KCAkRERODAgQNsxIgRxa2/Zs2aJfz111/yzz//zJs3b87S09Ol/Px8ptFohB49esDHx4fpSZ69vT18fHzkTZs2MWdnZ1ZYWCgPGzZMKB+F1el0CA4OLjZjS0pKQnR0NI4fPw53d3eekpLCkpOTn/o+qA/y8vJw+PBhXL9+HQDg4OAAx2bNSmSMGg2Cw8IQGBgISZLQqFEjfvPmTabVaiFJEhhjEAQBCoWCF2XshEmTJsHW1hbXr1/H8ePHJQsLCyE9PV3WarVMq9UKuqLMT7NmzSCKIk9KSoKHh0eNrcEYY+jTp4+wbt066HS6WhMVnU4HjUaDhIQEHD58GAMHDkSvXr2g27IFYXfu4Ouvv0azZs2kOXPm1JrAZ2VlobCwsHhbqkObNm2Ew4cPo2nTpmiqr1fu2JEm2qdOkRFNcPDjkdongXHjyJSnvEFSbRATQxnCI0coc9PAkGUZWVlZ4ty5c58s0QZoon73LlTXrmHmzJnCli1b5G3btskzZ86s/IKekABdair2jRjBB3zzjWi9YUOxHLvSXrBPAadOnUJMTIzwxhtvoLSMslo4OFB/8AULKGP9+edVL3vmDMnmAbpurFpF5Rl6cE4E4ykGzl9//XWsWbOGr127Fm+99VbZH374kIKJn39O69m/P2Xx09KI/NSldeSOHZDs7SECkDp1Qtjgwfz2hg3Ms0cPREdHyy4uLoJ+cl4BgYH0Nza2JCM+ZAiNuVLmTlVCEBCRkADR3JyrvvqKttHFBXj9dXp/2zY6djodHc8bN2r8ykOHDkkeksScmjQRKpUit2kD0cAAM3x9xbNnz2Lbtm3w9PSU+/btW62r9+XLl/mhQ4eYqamp1KFDB7FXfj4Us2cL+P77CpLnHTt26KKjo0UpO5vZyjKCXVxYQn4+2oaHi+K2bZjq5la3cq+AALoWHTxIJnb/+Aedkz4+lKmdNImCFIWFFDj56y8KFNrZUV11jx44vWEDd9VoMKxvXxGPHgEWFrh165ZOo9Fc9fPzuwXg29I/KYriFwUFBT3c3d1JPda+PQU69AqyOXMoSHXyJAVDJk8G5s6Fw/jx0H72GTZt2oR6m4GOHk0qqnbtyCzM27vs+3Z2VN9cGdmeN4+k7uVx+DCVVcyeXVImUUswxuDk5ARDQ0NERETUrjWrSkXHIDa2cqVMfDyafvghOvbqhV81GsxKSytjlqbT6bB582b54cOHgj7wb2Njg2nTpjFUNde4dQvNFQo0P3+eAm5F16ubN2+Ccw61Wo05c+YU33NCQkJyCgsLv/Hz86vCLOEFniVekO0awBgb3q5du6plbk8RNjY2UCgUclRUlKDPxOkRGRmJ7UW9U2fNmgUHBwdwzrFjx468yMhII8YYZ4zpFApFJgCzuXPnKkvXdqalpRUTdb3D+uXLl7F3714OgDk4OGiys7MLGGOws7MzcHZ2NnRzc6voHPn/DDExMbh9+7aclZWFpKQkZGdnC0XBCT527FjmXmpyL4oixo4dK0RFReH69etS8+bNxUaNGsHR0RHGxsYV7tZeXl6Cubk5Ll++LBkaGrK1a9fyoUOHsujoaH7//n1Zq9UynU7HOOcsODgYAIqJ3/jx4+Ho6Chcv34dfZ5SW5vHQVZWFlasWMEbN27Mp0+fLgQGBmLDhg0wMzOTbKZMEbWHD2PCvHl4+MorGPuPf+gz0UyWZdy7dw/NmjWDSqVCYWEh8vLyWHp6Otu+fTt27tzJzczM5PT0dNHX11fMyMiQHR0dmYuLi2BkZARra2vcv38fZ86ckRo1asSGDh0qNG3atFZE18bGBpIkITs7u1ZeCnfv3sXOnTt5YWEhY4yhY8eOkmfbtiIOH4YiOBg9AdE+Lg4bN24UCwoKEBkZCXNzc7Ro0aLKyVxqairWr18vOzk5wd7evsYZ39ChQ1lycjI/evQonz59elmTt/btKfsgiiWS6+cFnTvXuQ1aMYYNo0zSN9807DqBJj579uzharWalQ9yPjH89htw4gTEkBAMGzZM+PXXX3HkyJHiWsPOnTujVatWiI6OhjRlCs8EmGW7dmg8ciTU5VsnPSU8evQIwcHBUlFPYbFPnz61J9qlMWYMSZBluWrDpDNnqM7z8mUiFlOmlM1IrVtHTstxcfXenrqicePGsLa2ZllFbaoAAAsXUubOw4Ok3J9+WpIlBqo9/5KSkmBpaYkHDx7g8uXLUuPGjcVG69fL14yNccPKSnB2dpaSk5MFM2NjZnz6NP8rIQGyLLPz58/jgw8+gFF15/bIkfTgnFzd7ewoGHDiBLn3V4OcnByYmJhUHqwsTWrOnwfu3q32u2JiYnDv3j1xQmIincOV4d49ul75+qJXr15wcXHBpk2bEBsbK0+cOLFCzXVBQQH27dsn3b17V+jfvz88PT1F3L5Nrs+3b1fIvOp0OkRFRSl69eqFnitWQJWQAO3atdCOGgW2Zw8My83DqoQkEZnMzKQx3L49kTcjoxJjO0kC3NyQbG6O6zod7NVqKOLj0UKrhaCvcQ8NxfGrVyXnrVtFe5WKCX36kCmgqyt6h4crzVNT5+Bf/9oJYBmARABfysArvQYM6GTfsSNXfPUVw+TJRFabNSODxL59qTzj+nUqKYqIoKx70fywS5cuiI+Pl/E4bunm5lTzn5pKv1G6/rp3bzJKS01FGSl+QAAR9PKKhjNn6Br44YeV9levLezs7OTo6GixVmQboKBdnz5UQlEaERGk3OjTB44LF6LrsWPYvHkz3njjDZiamiI9PR1nzpzBo0ePGGMMr7zySu2ifPb2FGhxcyvTMcPBwQFmZmZybm6usHPnTrlTp06Cvb094uLiBADbarn5L/CU8YJs1wCVSjXE0dHxuWGUnp6ewsGDB7mLiwvTZ9NiY2Oxa9euXAAXAPgEBwfnCYJglJOTg7t37+YCaMI513DOpcWLF8tffPHF4p9//vkra2vrLCcnJ8PExMT8xMREQaFQJGg0mhbt27fnI0aMUIWHh+cC+EClUjVLTEw01mg0mwEo09LSOt+5c2fCqVOnus+YMcNIpVJVf/P+H4NOp0NkZCTOnDkjp6enC/b29oIsy2jXrh0cHR1hZGSERo0aVXpBZYzB1dUVrq6utSJ0Hh4e8PDwEDnnOHHihBwQEABra2u5X79+orGxcbGLfVxcHERRhJ2dXTExy8nJAeccDx48QJ3MlGoBWZZx7do1KJVKtG7d+rGN3DZs2CA7OTlhwoQJAmMMjo6OSElJQXh4OCssLJSMbGzEhHXrMGr4cBjs3UsTBTMzCIIAp1KRZrVaDbVaDSsrK7z33ntIS0tja9asEe3t7eVevXoJqGTC4ODggMmTJ9dLCm5paSmfO3dOeOmll6pdLjo6Gtu3b4evry86d+4MjUYDMzMzEWvWAN9/X9xSpHnz5nBxcZG+++470dDQkEuSxIoCKLKRkRE6duwotGrVCoaGhti2bZuclJQkuLm58dGjR4u1PQYTJkxgy5Ytw5EjR+RBgwYJxSoZKyvKYu3ZQ5mOBw+ejwz32bMka6wrOQsNJcnx+fMkaW1gXLp0CQcPHkTnzp2Zr6/vk89q6/HZZ0TMCgpgbW0NX19f6fbt2zAzM4OpqSnbs2ePIEkSjDIy4DxhAh+g0TDjBw8Y/vOfp7N+5fDw4UNs2rQJkiSJ7u7u6Nu3L7zLZ7dqC29vmvD26kVmaHo5c2n4+FB2WE/IDx+mDJk+MDx5cv0DN4+BzMxMeUTjxgLat6d6ZYWCiKK7O/1fC2RkZODPP/+UHzx4IPTu3Zunp6fzGzduiAAwMzxcaDdsGAYuWIBTp06Jrq6u6JGcDNy9y/DbbwCAH3/8UQoODmYDBw6s+WLBWEmNfPv2tD85J1O17dup5rccmjVrxsPCwsSsrCxUm6CwsiJi6+xMJN7Rsczbsixj//79vEvHjkxx5UrVZDsvr8y/NjY2WLBggbBp0yb5l19+wYQJE4rvD5IkYePGjXJhYSF7++23mZmZGW2Try8pCWbPrvD1d+/ehUtMDHxsbMDWrwfOnoXBq6/CIDy85rZWANXd//vflK3Oy6Pt6NKFJP63blFWe+1awNYWWVlZ2BkdLSckJAgGzZvjjqWllBkeLmpEERb5+XJubi50zZsLgiCI3cPDodTv34EDAQDmUVHYvnq17oPvvjsDYBoA5e4xY+y0CsWcLDMzdHF3Bx49IlIfEUGBCpWKDPeGD6cxqNXSvfXuXcqu9+4NMzMziKLIa97YGjB+PAU0vL1J5aAvZRJF2ge9e9N1Xo8ff6Tx8cMPJa/t3k33g2++oXH4GLCzsxOvXr0qoQYVWzH++IOUGaVx/nyJIuW99wAAgwYNwsOHD+U1a9YIZmZm8qNHjwRRFLmlpSVTq9WSSqWq3e9NnUpBrtu3ywThTExMsHDhQqGgoABnzpwRzp07J2VmZooKheKgn59fbq2++wWeOl6Q7Wrg7+8/SKVS9WpoovI48PHxwaVLl+TDhw8LRa0tsG3bNlmn0/0N4J+CIIy/fft2blRU1BcKhSKDMfZ5+RNQkqRvAWxJTU1tkZqa2hvAdQCBS5YsyfL39598+fLlLZGRkVyW5YcA1n/00Ufl+/WFAlj5zTffhP/0008dTUxMCt577z31f6tpmyzLSE1NhYWFRaUGR7IsIykpCWZmZoiOjsaxY8d4QUEBnJycMHPmzKdiQMYYQ//+/YX+/fsDldwcHMtNVgAUb0uZbEoDISYmBnv27AEAWFtby6+99lr1daPV4OLFi8jOzhZee+21YsLCGEPjxo0xaNCgspNCzqkGMDub2tdUA7VaDVtbW7Rv317Xtm3bJ+Ko7OPjI+zbt4936tSJVdXTtagmH15eXrpevXop9OuGK1dIYvnaa2WWnzRpkpiRkQEzMzPGGENWVhbi4+OFhw8f4saNG3JQUJAgSRKcnJywYMECmJiY1Gnb1Go1Zs+ezTZv3swjIiK4Wq3mkiTBy8sLnTt3FjBqFJnS6XQ0mains2+DYe5cIlUffVS3z335JU0cV61q8FW6c+cO9u/fDxcXF3nw4MHC06p3BkAk7cQJGjexsRXcl4cNG4a7UVFo8dprUI4eLWDbNnKRfgbX59jYWGzYsAHu7u4YO3Zsw3RXYIyywoWFNEbL73ulkgjU2bM0gb99mwyYVCoyqProI6oRfRrIzKSsXqdO6GluLiTPn482EyfSdey77+r8dbdu3cKDBw8EOzs7pKamonnz5sKNGzcgarWwOHEC5kWGTCNHjqQPdO9e5rhPmjRJXLt2Lbp27Vq3ziZeXvTgnGpXPTyARYvoGnb8eLFyQJZlaLVaJCcnV0+2ASLbr75aaR3vxYsXuUaj4QNsbRlcXauu02/alI5pKSgUCsyaNUsIDg7GH3/8gR49esi+vr5CSEiInJycLHz44YdU9sM5GbxdvFjsB1IeRkZGaHHzJphaTUT5k0+IKNZ0rwsJodr3AQOopKFpUwqqAkS8ZZlk3EOGABER0FhbY+XKldzFxQVjx47VO7mLnHOkp6fjxo0brGnTpqxx48YwMjKqtKzvSkSErsDUdB84LwBwGQBufvHFp5Ik4a233oJ5aeXNihUlz/XGZXPn0vkUHk4Z95Mnge3b4WZrC3blingekLoPHiyiefP6X0tataLAhigSodfPrSdNovO5NPbto2Okx/nz1E/+t99q7rldC9jY2CA3tw7cNCKCjN5OnKD/9+yhYPS779JxLoUJEyYIv//+u+zg4CBMnjwZRkZG7IcffpDat29f+3t1t27kayBJlb6tVqsxYMAA9OvXT/z3v/9dUFhY+G2lC77Ac4EXZLsS+Pv7qw0MDD5TqVTvTJw40fB5y9pOnjxZ/O233xAeHs455wyAoFAoWi9ZsuQGgBsA4O/v/5/FixdXWrtRVNNxv+gRVO7tK4yxizqdzlyn0/Xx8/MrT7SLUVhY6AugkUajOZ+amqquhavjMwPnHPfu3YMkSThz5oyUnp4uFBQUMGtrazktLU2QZVlfhwutVgsTExMkJibKV65cEdRqNddoNNDpdMzQ0FDu3Lmz0Lt3byiVyuc6uqBSqWBqaoro6Gh06tSpQb/7zz//BAAsWrQI69evZ99++y0GDx6MHvVo1aUnKoaVmaeUB2MkD2WMpI5Dh5atx6wEo0ePfmLXOQ8PDwQHB+PUqVN4pYp+ooGBgXBzc5P69u1bsh5aLU1eT56sVApXum2Oubk5zM3N0bZtWwwcOFDQ6XRIT09Ho0aN6t3r28bGBm+++aYQHh4OURTZqVOnsH//fnTW12Z27w4cOEDOzgkJzzbDrT/etcXRo0B8PMmNnwDBTE5OLg40TZgw4ekSbT0GDCBZJq+YcBIEAa4tWlCGd8sWqsWtqg3TE0RaWhq2bNmCnj17YlADuC+XwYQJRGRdXek4t29f9v0LF4hk9+5N7+vJ2p07dM5V1YqoIXD1Krksnz9PQaKsLOCnn9BUrcbWQ4fQZNIktK5n0KFVq1Y4cuQIBg0ahF27dskPHz6Ep6en2G/BAigLCkoInR6CQEqA114DZsyAjY0NDA0NpaioKLFbfSS4jBHJBijgcfs2ETRzc+DcOVwNC0Orli1rr6RasoTaOiUkkCwY5D1z/PhxNmLECCY8eEAErSpotRXIth5eXl4QRRFHjx4VHBwcEB4ezrp3717ir/Hxx9SbucgjpALefx+Kq1dxdNAgdPDyguHAgUSiqyL+nFMddqNG5ERtZkatK8sHK//5T0CtRnx8PO60awe3efNwvlcvbtm1Kx87dmyZazpjDFZWVujTp0+NF7Lw8HCRc34YAPz9/ScC6ATADqByo1qVuSgUJfejlSsBAI1v3YK4dy92hYaKHl99BSMTE6ohjoujLHVRh45aY9w4koi//z6VJbRsScGxsWPJvVyhIHf7pCQKbHBO17F9++h41dNxvjyKyLZYa88VOzs6tgCt/6+/UkC3S5cKixoZGeGtt94qPsljY2ORmZkpKpVKjqoc3GWZtlmppDE9fTplzYvOi6pw9+5dMMZi/fz8Lta8ES/wrPCCbJeDv7+/j1Kp3NKiRQvL4cOHG9a3lcSThI2NDezs7HJiY2O1AFYrlcphGo2mzEy/viYJfn5+NwBU1IdVvmwmgMxvv/1254oVK16zsLDI7d+/v0nbtm2fq+bQnHOsWbNGTktLY6IockdHR6FLly7M3Nwct2/fFvr27QtnZ2eEhYUhLCxMMjQ0FAsLCyWVSsXGjRuH3Nxc1rFjR31W5rnatppgbW2NpKSkqi/w9QRjjANgOTk5mDhxIrt06RICAgKQnZ0NLy+vOpUVRERE8KLautqto34yMnQo9Qh99IhqvZ4B6ZEkCYmJiayyIENkZCSSk5MRGxuLKVOmlMzus7Ioy5aSUjspYjkoFIraTZxqgJGREby8vFBQUICjR4+iwrVO3584Lo4mj19//di/WWf07UuTsHfeqf1nQkNpvUtLEhsQq1evhk6nw7Rp056dcaQgUB36qFE0YS09jnJziWS2bk2BqGdAtAHgjz/+4EZGRryCOqWhYG5OMlxZpnZFpffBwoUlJl+TJlE2yt6eJNtXrzb8usTGUqZrwQIyhHrnHTKaSkgoJrwusgwcOoTbt2+jvOdKbVDkQM2NjIxga2vLFi5cWHJNOXGi6oDY1KlU+1r0HdnZ2aKDg0N9trIs7O3pAZByol07dJ4xg9kVFlKQITmZgg41QZbLtFQKDg6Wzc3Nedu2bUV8+21Jb/DKYGtLqoUKXyljxYoVUkZGhvjyyy9zAwMDlp2dDR8fH1ogL4/q+isL1Op0lI3u0wcnk5Pxcrt2MFy3jtqkVUa0NRqqre3cmZy3v/4aetl+BRw5QgGK+fOx7rPPYGJiAu20aXLXXbuY+uuv6x08BYAmTZrkPHr0SN9Qvkzt7r59+/ISExMFX1/fut9wWreG1T/+gZSvv+bnp0xhvq1bU43ytWuUdf3xR5KEL1lC+61du5qDWXoTQJ2OvqtlS/rs2bNE4KdMISd+zklyv24dtf9rwPm4sbExRFFEbGwsWtZGku7oSMoGPz8ySVu9moz+JKlEwXLxIo0RCwsK+np5ATduIGXZMj5oyRLe5euvBXTrRvOW//yHrl9r1tC4iIqiQIe+pt/IiO59NeD8+fO5BQUFT0mq8wL1xQuyXQR/f38TpVL5s6Gh4cQRI0YYtqmh9+OzRH5+PgYPHmyyiuSR9xcvXvxsbGWL8OGHH77h7+8/Pz09fdqJEyeWtW3b9olFKCRJwqlTp+T8/Hw+dOhQsTJXdj00Gg2Sk5MRERGBlJQU9tFHHzFBEMrczUq31fL09CTDFMITTH08PZiZmcmxsbHCb7/9hjFjxtRNOlgN3n//fXbgwAFs3ryZz58/nw0ZMgSurq7Yt28fv3jxIl599dVaG0ZlZGSgVatWdZ9l6CdKffsS2S7Ktj9NiKKIfv368ZMnT8pNmjQRT548yVNSUqQid3DByspKHjhwIHNwcCjZvqlTaYJ25MhTX9/yuHPnDrZt24aWLVvKEyZMqEiKVCqKtp86Vblk90ljxozK3Wgrw86dlFn6/fcnukqdOnVCeHh4rVsMPTEYGxOxjo8vW794/TqRz/Hjifg9ZRQWFuL06dNySkqKsGjRoier/nnzTcpid+hAE3W9wZKFBU2O9VlvlYr2iZUVkR09SWyI3586lcjbxo0kKY2KKgkIlgrGREZGAkDtJvbloNFosGPHDjk3Nxfz5s0rq6b4/nua9E+YUPmHZ80iMgwK1Nna2kpr1qwRW7duLQ8ePLiC+Wtubi6io6ORnZ0NMzMztGjRomIgrjx69QIAZP38sxywb58w9Pp1tOzcmaS3CgURkaqCPjNnUguqH34A5s/HzZs3Wa9evQRkZFDgrLrz+fJlIvXlPDPCw8NRWFgofPjhhzAwMGBxcXHQ6XQsMTERDoaGZDp17lyxM38ZDB9OZPHAAeDkSd5i8WLgyBFWwYU6PZ2CeuvXE8nasYMCOdUR5kuXkHn9OlYVFMgqlUqYP38+FAqFgM6dqbxhzZqqP1sDHj16ZArgkL+/v+jn51e8Ev7+/ur8/PzOgYGBwbGxsbkjRowwLm2OW1twziFzDt6sGZitbYmreL9+1IYrLg5Yvpwy+i1b0rk2ahRlgysLBA0bRufMsmU0PteupcBLVBQF0F9+meTuR45QJrmBVUqMMajVas5KRzgkiYI3OTkkp/fwoN8OD6dAWo8edB9ctoyCndOnk6v+0aM0nqZPJ9PD11+n9XZwoGu0IEACiHx37kyB0E8+oeeenrRtFhY0ngAa1199VWNdenZ2NmJjY18Yo/0X4AXZBuDv7+9pYGCwu1WrVlbDhg1TP40a3Pri7t272Lx5Mxo3blygUCjydTpd9Y0qnxL8/Pw0n3/+eTMHB4cG05vm5+fj6tWriI+PlyRJQpMmTYSIiAjk5eVBlmWWnJwsF7lHIy0tDZmZmWjatCmuX7/OT58+jZycHKZWq7m5uTkfPXp0ta1A/lcxYMAAwd3dHUFBQXz58uXMyspKmjBhgvi4mVFBEDBixAj89NNP8oEDBzBmzBjRxcUF7777Ltu9e7e8evVqplQq5ZkzZwoKhQLm5uYV6jVlWcbx48eh7wVebxw6RDfI/fspGj5t2mNtW13RqVMndurUKXH16tVo06YN9/X1VVhaWqJp06YwMDAou9FaLZFCfWuiZ4zr169DlmVMnjy56qxKjx40kTh8mMy5zpx5sjJcPXbvpmfjy48AACAASURBVMlJZRPiymBtTVK/J4wbN27IRZnBZ3tBEQTKaAYFUc2psTG1kdqwgTIvfftW7dj9BJCQkICAgAD57t27goGBgTBhwoRKe9Q3OLp2pcAb52Vd9CdPpqDWoEE0XpVKukY8LtE+eJAI7smTJRPzwYOLjaqqQps2bdCiRQvs2LEDc+fOLWm9VwucPn1aTkpKwpw5cyp6Y8TH12wAOGECEBMDoUkTvP7662JWVha2bduGX3/9FfPmzcO5c+d0169fF7KzswVZlmFsbCwZGBiwjIwMQZZJJDdp0qQaM/I9evcWCnQ6ee/Ro3xRSooIMzO6HkdHU0vBI0foeJQfl7JM+3TIEOTm5jILCwuqB//gg+rHsKVlpZntlJQUWFtbywYGBiJAQXVfX19p04YNonfv3uizfn3F60pYGJGtNWvoe2/cQPPISBzo0YONsrREcajgwQPa3+3a0X795psyQZWqIBcW4qiLCy7KMrx79GA9evQokS/37k3kKiysXg7bmZmZerXZik8//bSMqrGoFPCsv7+/UVxc3KKtW7d+9M4771TTrL5yDBs2jO3btw+Ghoa8Z8+eJTcLB4cSo8K+feked/QoOZpfvUrHsGtXUhpxTs/1Y3j6dDoOV6+SCmTTJjqPdu6kMXHhAj1/HKItSRQwtrAgUq9SUQ39H3/AITUVVt9+S2Nt3ToqvenRg4I/f/5JwbSMDMqom5lRACA+ngi0tzc5qC9dSsEkgMzm9CgKQAFAUEoKdyookLFwYclgLjr/OedISkqCIi0N1vpgYWRk5T3Jy+Hy5cuyKIp/LVmyJLv+O+gFngb+X5Ntf3//1kql8p8qlWrMiBEjDN2fQA/WhsaFCxfyGGOfJicn5wAI9vPzS3nW66SHUqmc2a5duwaJVMTHx+OPP/6AUqmUbGxsBKVSyaKiori9vT0bNmwY0+l02L59u7xu3ToMGDAAx48fh1KplAsKCgRRFDFkyBBma2sLGxsbhgaWUP83wdTUFK1bt0br1q1Zbm4u9u3bx1asWAFvb2/07dv3sb/f3Nwc165dE4cMGQIjIyMIgoCxY8cKcXFxuH79Ov/1118hyzKUSiW3srLiLVq0ELp3746MjAxs2bIFxsbGfMKECVWai9UKhob0uHGDMilTp9LrT8kQysTEBIsWLUJGRgZsbW2rnhleu0Y34JSUsi1OngFu3LiBU6dO8ZSUFObg4FA2ul8VunaliTJjRDCeNJHSu/guWFD9cjt3kiTxyhXKsjxByLIMnU7HPD09n59ryttv05j/xz9IPn3rFgVG6pG9qi8yMzOxevVqWFpaonHjxjAxMZHd3NyeHtNfuJDkrNu3kwpDqaQsc3AwjY9evajGsgpfhWqRn0/Xl1atqH7S25tIBVAnFYUgCDA2Nkbjxo1506ZNaz1+OOeIi4tjDg4OrELwIiuL5KjVBb+USiIypTLYZmZmmDNnjrBy5Urp6NGj7MqVK4p+/frBzc0NjDGEhoaKYWFhZda9tp4svXr1EgIDA5EjCDABiEBJEplajhtHdfNRUUS49MTS0JCk5Ckp6GZrK50+fZq11GiEqozLitGqVaVSdSsrK0RFRZXZx3369BFt330XqQcPUu11efzrX6SG+OUXupZ88QW6vf8+CwwJQU5ODpHtgADKSK5bRwSxlufYypUrdc779ys8L11Cp8uXUeH4GxoCu3ZRe7CWLYns1wHHjh3LEwRh2SeflO9LVQI/P798f3//r1NTU/1rXadcCh06dMDRo0fl3Nzc6s9rAwMipS+/TP937UpZ70uXgL//pozwunV0Ts6fT0GLFStonLi7E8FNSqJg6++/Vx5skWUKLIoiEd7z5yngtXQpGa1NmULHqV8/uh6Gh1Pd9/ffUzZ51izAzAwFBQXI7d8fljNm0H5/+LAkcKIPIJT2m/jyS1Jb/eMfFID+/Xcyenv/fboOjxpV5lzknOP48ePIysoSfH19y2zIgwcPcPbsWenhw4dCRkYGA4A5c+agUaNGUAQGgk2cWO1u5pzj/Pnz+YWFhT9Wu+ALPBf4f0m2/f39PVUqlb9SqfTu0aOHgaenp+J5M0GrDFevXkVUVJSWc/67n59f6rNen/LgnAcFBgbanT17Vpeamsr79Olj2rZt2zr34s7KysKGDRvg6emJAQMGlJ5FFN+glEolpk2bJly7dg0HDhzgDg4O8tSpU8Xyy71ACYyNjfHKK68I27Ztw+nTp9G0aVO4ubkBoH1en17yiYmJYtHfYlktYwwODg5wcHAQBwwYAIVCgYcPH7K7d++ymzdvymFhYYJSqeQtWrSQp06dWuuWVTVC71b9+eeUQSnqPf40YGRkVH2dOuc0qQgIKGlB9AwRFBQk5+bmCr1790bz5s1rd740bkyTwc2bKVsRH//kMtySRBPiSgzAykCrJeJTheM+5xySJNV5YlkZCgoKsGrVKigUCtairqZATxJBQZR5iYigSe3ChVW6Kz8JJCYmYtWqVbCyspLmzZsn/v3334iJiXlqv1+MuXOJtGRnUxYrM5OuCf7+pHjYvp3cn8vLgSvDzZuUAd+4kT6fnEwTeS8vIkKfflqvVXzw4IHctWvXOt2frly5wuPi4tj48eMrvjl8OLXE21GDwO3kSVI87N5d5uVRo0aJa4qkyx06dCi+B1y7do17eHiwoUOHgnMO46pMwSqBKIpgjCEiIgLFJmyiSGNUr+j517/o+PzxBz3/6CM6dgsWoE9cnPifIUMgZ2RAKOfyXAEJCSXS21KwtLRERkaGcO3aNXh4eIAxhmvXruF269boU75Oe8kSOqYHDlAg8eZNkvFOmoQkV1ewM2dgOXs2/U5QED3qYAZbZI6l6LlsGczy8oozmhVgZUX9p/38yHW7DoiOjoYkSZtrWk6hUHxqaGhYKIpiLdxIK2Lo0KHC3r17YW9vj1qXWlpb06NjR2qvptXSdl67RrXuO3cSUR49mso+rKzomPToQUoUpZKyzDNnUieSCxfoWM2aRb4trVpRCUn37mTWZmJCZQJffUVZc0vLksD76dMl69WhAx59/z0K3d1r727u7EzLxseTXLxnT9qeefNIUTNpEpWr/PYboFYjX6FASFFg56effoJCoYAgCJBlGZxztGzZUuzevTs8PT2xe/durF69GkqtFjP37MHFIUMwpJqgSExMDLRabRKAsEoXeIHnCv9zZNvf398G5MKYCSDCz88vo+h1UwCj1Wr1QmNj41Z9+vQx7NSpE3tmBjd1hE6nw969eyVZloc8j0QbAAoLC+fGxcVd55ynAIg/duzYvw4fPtytRYsW2n79+pnob+TVSQs559i6davcvHlzDBgwoEYW1q5dO7Rr147hf6TG+mlgxIgROHnypLxr1y7BxMREJ8syy87OFlUqFS8sLGRz5sxBTZnmK1euICAggANgw4cPr1ISqT+/7O3tYW9vDx8fHyEpKQkPHz5kbm5uDUe0S2PuXKq1ysmhqHYlbqFPHR06kNT1rbee9ZoAAMzNzQWdTsf79+9f98DU5MlUc5afT9mdUnK5BoOnJ9UcV+fEGhBAmbKUlApmaBkZGbh8+TIuXrzICwoK2HvvvVc7t/tqsG7dOp6RkcHmzp1b5wDiE4W5OWVs9+6lCev06TQBfAoO8qmpqdi1axcHwObNmycCRHRu3Ljx9AOeajX1hx8/nrJh27dTHejy5SjubV0doqOpRGLaNHLwXr6cJvSTJ9P7+kzdYyA/P7+Cb0h1kCQJAQEBzMHBoXKlwOHDlcqoK8DRkczEyqFZs2aYOnUqwsPDywRbDQwMZDs7O7E+SQhRFCGKIjIyMqpeSN+OLzWVAhoffURZwmHDoBoxAk7ffivn7d4tmNTUHk2WSdJb/K+M0NBQXL9+nRsbG7PdRcGFRvHxsJk5E+Lff6NJhw60MOcU1FOpSArNGGXdv/uOSJyHB5pPmYIejRvzqIkTWYcJE6BVq6HRaCBlZSE9PR06na5WzutGqakw+e47Go/VYf58Kg05e7bW11XOOfLz8w0BuPv7+ycUGddWCkEQWhcWFirra8TWrl07nD17Vo6OjkabNm3qd/M2MKBuCvpAyrRpdAxXr6agliAAEydS0MHbm0h0r15EpJcto+PVvDmRdT307TM9PEpeq4XXh5741hqiSJnu2NiSchQDgxJvDC8vck1PSAAmTYLRrFlYOHIkNh4/zgUjI3ns2LGiTqeDgYEBzMzMyhhsjhs3DuPGjQNCQvAgNRU3IiP53dhYDBo0CG5ubhUOWFhYWJ5Go/nBz8/v8Xugv8ATx/8U2f76668XKRSKL2xsbAoLCgqQkZFh9OWXXxaKopgtiqKVg4ODtkuXLiZubm4N0+/zKaGgoAC7du3KVygUxzUaTWjNn3g28PPz0wD4T6mXAvz9/ZtER0ePvnfv3jJJklQGBgbS4sWLK50FarVa3L59GykpKcLHH3/8dFb6/yGMjIwwbNgwwdvbG3FxcQqNRoOWLVsiOTmZBQUFyefOnRPGjBlT7XekpqYiOzubAUBISEhJy6haoEmTJmjSpMnjbUT1P0A3v40biaw9fPhMegyXwZdfPvt+1UXYsWOHHBMTI4wfP75+O0UQSP65YwdNDh88aPgM9zffVN/yJy6OMtoHD1b62ydOnMC1a9cwYMAAFhoayn/66SdmZ2cneXt719uJWRAE1rp1a7lp06bP7uYhSZTBdnEh5+OmTSmz+eef9J6+57G+h/HgwbTchg0ko7x5k7JC48ZRpqhFC3r9o4+IaOqN1Q4dokmusTFNgJ2d6biXum9KkoSwsDBkZWVh1qxZAEghc/z4cf5SOcOqp4qVK6n29cEDknoGB5MZ09tvkyKjNDin4NwHH1CLtHXrKFiRklJyzahDVrcmGBkZ8UePHgF1UF/l5ORg8ODBFcfcsmVENvWtuKpDhw50LXz0iMZLKTg7O1cw+/Px8RGPHj0KpVJZp2s7AOzfv18HQNGnT5+aF7a2BvQqiJs3iUi5u2PSl18K5/r3l3vV5ItgaFimXvqHH36QtFqt2KpVK6hUKsnV1ZUplUph/7FjGPTSS3DXE22AHLFdXUuIf0wMjRcDA1IunDyJ1ClTcCYxkbU1N+dJISHs+vXrPCsri4miCKVSyQsLC9mCBQuqNZDLzMyEZWIiEbCaYGpKYzAhgZQ9tZinMsbQv39/XUBAwC79S+WX8ff3VwOYzBhTiKL4WDdDIyMjISwsDGlpadL48eNFSZKgVCrrrx5Sq+mxeDE99Pjjj5LnTyhgLggCtFpt3T5kakrXl969K75nYlISnAsMBLKyYPKf/+CNI0fYygkTxIJ9+9B85szq75fJybAbOBAfjBrF/vjjD5w6dUouT7bz8vIQFRUlcM431W3lX+BZ4X+GbPv7+wsAvre1tdUmJSWpBUGQbW1t80VRVHp4eDRr27Yt1Gr1c5SOqD1CQkIQExNzWZKk8f9tUSw/P78kAKv8/f3/BjDG3Nz8MwAGkiSBcw6FQoGCggKsWbOGZ2RkMJVKJbu4uHBBEF5kqp8wTE1NUdqnwMTEBDk5OcLx48cl1KAU6NevH/Lz8/mFCxfYc+vcP306ybru3KHJ9K5dT99Ne+tWqhUtLV97hrhy5QpiYmKEN9988/Gd6SdMoF7nt2+TtPzLLxtmJVesoDq/qkhxZCTQqRORwHLZH51Oh/DwcNy7dw8KhQJeXl7w8PBgoaGhSE9PFzds2ABjY2Pu6urKhw8fXifSbGxsjFu3bgkxMTFwqo0U+XERE0NBBTs7ktr260cT8LNniTArFCVuyNOmUbZl0yaaoGs0RL61WqpFbNmSSIS+FZW3N2WHGKMaR85JnpmSQgZFX39NtZOFhfT89GnKRJmbA5s3Q375ZRzr0YNL2dns3Xv3mNFbbwEzZ+J2Vpbs2rs3OqxfL+DVVyljFRpKrbCOHiUJd4sWFABr3pyIUkMHwho1Irm4uzs9BgygzFdpQrl1a4mE9eFD2u7XXivJkD2h4JyNjY2QnJzMc3NzayXNFkURHh4efPfu3ezu3bsYPnx4SaKA87oZ4L35JpHbDRtqXLRLly7IzMzE/v370aFDB1TX9aM8IiMjFZ07dy5eT61Wi5iYGGRlZaFLly4ok1VNTyc5eWwsHYd336WsJgC727eFR4mJsKnOSK7UPjh+/Dg0Go343nvvQalUMgBifn4+7vfsCdcxY7jj0qX0w9HRROo//5wUOgBlSceMof/ffpuubc2aodGbb6Ln338jJCSEiaKIHj16MM+ijKmJiQn75ZdfpLVr1wqTJk2q1HckLS0Ne/fuRb8xY3TC6NHV33z0JTONG9O5feECBbxqAS8vL4OgoCBZp9Ndq2KRNwAs45zDwMBAs3v3bp21tbVRy5YtYVdHU8kpU6YgPT0dW7duxc8//4y8vDwolUr06tVL7t69e0UDv+cMqampyMjIQLNmzSAIApckiaWlpUEQBFjUpk1it25U+71wYfXLNWlCj5UrISQmosfJk7Lun/8UsoODYTpuHHkoeHlVvNacOwcMHQpBEODl5YWdO3dWOMmvXLnCRVE8uGTJkvS6bPsLPDuI//rXv571OjQIfH19eUhIiEVeXl6YVqsdL0nSyqysrHOZmZlhsbGxnaOjo2Ftba2s1cn0HOHixYtyQECAjnP+tp+fX8SzXp/6wtfXNyMwMDBKq9XO5JybbtmyRb527Zru/v37hSdOnFBotVq+cOFC5uPjw9q1a/ffIzv4H4OZmRlOnz4tuLm5VTsZDA0NxenTp9m4cePgWdvWTM8CokiTuVOnKJtXWPhU5LXFMDIiQvEc7KOcnBxs376dOzo6yl27dm2Yc0yhoIzU77+TcUxDkJR//pMmKZ06VXzv8GGq5aukf3R2djaWLl2KW7duQZZl3rZtW9amTRuo1Wq4uLjAw8MD7u7uyMvLY+Hh4SwwMBB37tyBk5NTrSTmLi4uOHfuHGJiYtC9e/eGU0fpdOSSm5BAk/5584iIvvsukdwpU4C0NMrkT59OZCAjg4i2Xkq5dy+ZpCUkENlkjMa+gQFlMg0MSEbcqhUds969SQZpZ0fyaFNTItkjR1J2Zs4c2v/dutGk0sKCCPPMmbj64AEO5OUhp00bPvadd5jKyQno2BGauDiEJiay/lOnMsPQUKrRvHGD5NwzZlDGJz2dsliTJgFt2pCJ0ezZlFm3taXae0ki+XbnzkQM168n8rxwIZ1PhYXAtm2Ulbx2jQISTZtScMLQkLaVMcpm9+lDtZt6J2EnJ5KUW1tTFt/bm9arVAvIJwkHBwdcvHiRnzlzhrVt27ZW487e3p4ZGxvjwoUL4JzD0dGR5Nc9e5a0X6oNBgwgc6palkCkpKTgzp07dTLSzMvLw5kzZ5CUlMSDgoLYuXPnEBQUhOjoaOnejRvsRmgob3rpEjNbupQCZW5uNJa7dQMUCvAePRA/cSJyFQpcd3bmJitXMqtp06oOKmRlISoqCutCQuTExERMnDiR6R2dJUnCpvXr5ZYXL6LTV1+xYtMxX18K1L36Kl2b//qLjK2Sk0klMHFimcCMi4sLbt68Kbdr144PGDCAKZXKYvlv165dheTkZBw5coR16NABeqIZGxuLM2fO4MSJE7yrSiX7LligqNFZ/aWXaDz//DP9n5NDwaJaIC0tDefOnQPn3M7X17dCQiYwMPAigEgAh3U63c9JSUmX7t+/H3n58uUuDg4OirrMixljMDIyQrdu3QSlUokhQ4bAzs4O586dk48fPy4wxmRHR8fnzjeHc479+/cXHDx4UIqIiIgJDg42z8/PFx89eiQFBgYKoaGh6N27d4XrukajQX5+fonk28yMCLGtLSIyM6FSqZCfn48dO3YgNze3TDvZYpiYwLZdOxbZrZv8R1ISk48c4U1+/ZVxHx+Iy5dTYNnKioKj+tp0AwOo1WoEBgbC29u7OEjFOceuXbtyc3Nz5/v6+t6v+GMv8DyC8ZoMaP4H4O/vb8AYm2ZgYPC1tbW14YABA0yfeY/UWuLw4cNSWFjYyk8//XTes16XhoC/v/9LAA4KgvARADNZlu+JovgPSZJcfH19ZR8fnxdE+xljz549UlJSEpszZ06Vx+LatWvYvXs3mjVrJvfu3VtwdHSs3iDsecDNm0TU7t2rs9trnSHLNLldvbrGXplPC+Hh4Th48CDmz59fLzO8GvHnn9Qr9/Tp+recyssjwlQZaU9NpUnwtWtlst6pqamIjo7mhw4dYvpsQL8aXMkfPXqEPXv28NTUVKbT6TBhwoTitkbV1TPevHkTO3fuhEqlQtu2bVFQUMDHjh3LakW8ZZm24a+/iMgeOECZ6NOnyehq0iSa9EdGErGu7BjpXXj796csrL097atJk8ifoHNnIi8NGFCSZRmRkZEQRRFGRkZYt24dWrVqhVfKOXsvX75csrGxwfjx42ufBpUkOuYmJuQYbGNDQYLjx6lu9vRpykS++SaVLIwcSWR96VJyjf7kEyJJ69cT+Zw/v6QPbmgoSeIvXqT9odHQ/h4xoqRVzzNAbm4uli5dCm9v7zKT6JqwbNky3rZtWzZo0CAihImJJFWtC/S9i2tRSrFmzRrZzMwMEydOrPXJrNVq8dVXX6FD69bw9vaG8NtvUD96BPUrr4DPmoXb/fsjIiMDL3fpgjs+Pgi+eFHKyssTnJ2dZVNTUzE8PJz3OnQIV4YOlbiBAUYGBSns+vUjr4ty+ykmJgbXPvmE28fGMuGXX9C2bVsYlBr3d1evRuLOndzz8GGmUCjItb1dOwrAGBrSebZpE+3HpCQ6d7p3p3N09GgKQBVh+/btXKfTyVOmTKl0bC9fvlyytrYWOnX6P/auOyyqa/uuc+/M0EGQomBBEHvFhl2j2GNPjBqNsSTGZ17K+0Xj0zhv1ERjfIkpLzFRo7HFEmvsYgnYC4KKAiK9S6/DzNx7fn9shl4tURPW9/EBM3fuvXPn3DNn7b322p2ZIAjYs2cPGjduLGVmZgozXnmFWYWHV16DXVBAgZ9jx2gMN21K90S7dhSsqkFA5uTJk4Zr1659u3jx4g9r8jkBgEajUYiieKlXr15d+/fvXyv1QkW4ceMGDh8+jDFjxqBTp06Pta+ngZSUFPz0009per2+mVqtztJoNO4A5jLG8jjnu1Uq1a9Dhw5tX7ZsYuXKldxgMLBp06bB1dUVsiwjavlyhN67J18u4aNga2uLrKwsKBSKovffuXNn2Nrawt/fH6mpqbh//76Uk5MjduvWDSEhIVyKjWV9L13iBgcHbmNlJTQoKICNuzuEEiqxTz/9FO+9916R11F0dDS2b98ep9PpGr9oSte/M/4WZNsIjUajBDBOoVBsfvvtt81q2sriWcLX1xdnz54FAFGtVtfCyeH5hUajMVGr1QUAsHLlyu91Ot07ADB79uxaS5rq8OQRGRmJX3/9lS9atKjSVaBOp4NWq8XRo0fl2NhY5ObmCosXL34irs9PFdeuUf3X99/TAu5peTfo9STtXb++1KLtWSIiIgK//vorJk+e/HRk0ImJZAD073+TadOj1Lq2a0cSzrJuz8uXU9bJ1BQQBGRmZmLPnj2Ij4+H8TusXbt2GDNmTK3HoK+vL86fPw+9Xg8HBwfMq8DETpZl+Pv749atW4iJiYEgCFCpVGCMQalUyu+++65QdFxJIvn0gwcUgJgzh95TRgZleVetoqyaiwtJR9u0qdk4jIkhObnR9XbGDDL9Khnkiot74r3Gr127Bh8fH65UKrlOpxNMTEzkOXPmCGUDNqtWreKvvfYac3V1faLHrzVkmTLkOh21mBo+nFr1LF78bM+rEBs2bJDj4uIEABg8eLDcu3fvGk1CPj4+PDQ0VJ43b55YZAxW27ll7FgKUHTtWu2mX3zxhdy/f3+he3VSZmPJwvffA7a2SE9JAb76Cse8vdE6J0fu3LOngPffp8/FxARr166V8vLyRADcy8uLOTk54fbt28jJyeHdmzRh7efMAQsNpQx8Vha1dHN2LmeCuHXrVjS+epX3l2XG/ve/cqcV/957eHjzptzR11cA56RsGjuWMsjnztFcNXMmEevwcFJ96HSkdGCMPCkKCf7KlSt57969WdeuXSsMKiclJeHgwYNSZmYmKygoEBo3bszfeOMNBlkmxckPP1Qe4OnZk+TrZdvILVpExnZvv13uJbIsF2Vgc3NzsWbNGgCYr1ary1+ISqDRaCwBZAOAubm5dt68eaa1cZ03IiEhAX/88QfCw8MxYcKEanuxPyuEhYVh3759txcsWNChouc1Gs1rDg4OG+bOnWthvLayLGP58uVo06YNj4mJ4SqVimdkZIhNMzJ4T8Z4k08/FRhjUCgUYIwhIyMDFy5ckDMyMnhYWJgIAAqFAjY2NrKVlRXv2LGj6O7uXlTjr9PpEB8fj/j4eOQeOSL1XLtWvNC/P2LGj5f79+8veHh4YPXq1fLrr78uOBcaHP7222/59+7d+88nn3yy+k+5cHV4InjOV8ZPFmq1Wg9g9+rVq+enpKT0fRHIdokFTT0Aac/wVJ4YjES7EL0BQKFQZMXFxVm6uLjUZbafA+j1ehYSEgIA8PDwwK1bt5CdnY3s7GwEBwdLWq1W1Ov1MDU1ZSqVijVo0IA/rvHKn4Ju3ag+8/PPSZr6NLLOGzcSedq588nv+zHQrFkztGrVim/bto2NHDmSe3p6PtnPq0EDWsivW0f121FRtQ9m7NpVvjWOTgds3w79sGH4JTCQx8fHFwWJu3Tpgu7duz+W4V6/fv3g5eWFL7/8Eg8fPsTWrVul7t27ix4eHkVutWvWrJGVSiVr3Lgxb9euHcaNGycIggBdRAR2HzqEI1On8i4JCazRjh2UHRs9mkheXh4dZPt2IgwWFvQeawNJomvq4kIZ8bffpjr5t98uTbQB8idYsICO94Rw+fJl3qtXL9a/f3/jeCn3of76668cAGtkdOh9lhAECkz07k0qgsGDiWANGUItAZ+hBFCXxgAAIABJREFUOWpSUhJSUlLYxIkTERAQgODgYPSuoXGiubk5k4ykNiSESGhtsX8/maTVDOzixYvo1q1bcfbdqM7YuZOyxIcP01i7dIkCmaNHw3buXGD+fLQLC8OpU6d45zIGbvPnzxdv3ryJjh07FnWDadu2LQAwpKfTvGGEtTWVFEyeTKok2g5JSUkIDw9H30GDGCsoKLV/yDKwYgWyX38d59zc5I6DBgkYOpQCXwcOAMuW0by/axft/8wZKs24d49k5Xv2UNnG8OE0dv71LwwcOJCdPHkSiYmJ0quvvlouBezk5IS33npLBMhj548//qAL9vAhKXEqCoqEhVFQcuvWYmfrknj7bWDdOqSlpkKn14NzDlNTU4SFheHo0aNwdHSUmzVrhqCgIEGlUl3U6XTVF+OXgFqtztFoNAoAol6vD09OTnapbRD27t272L9/P1xdXeXp06cLz8X9Xwmio6O5Vqs9U8Umu1NTU386f/681K9fPxFAUaZ65MiR7Msvv2QtWrSQXnrpJZgzxjB6NIMklVIe1KtXDyNHjhQA4PTp03Jubi7r27cvs7W1rXDSUalUcHV1hSv9KyIoCF0tLYEbN7Bjxw64uLjIKpWKR0ZGomHDhsjNzUVISAiTZbkaW/s6PG/4W5FtANBoNMzExMTtqcgonwLat2+PwMBAXXx8/BkAz5825zGxaNGijgCg0Wj6+/n5HWnevLmFnZ3dsz6tvzVcXV3Rr18/aefOnaKJiQkkSYKpqSk3MzPjgiCgT58+op+fnzxq1CiBMcZOnz7Nx40bxx61ncifDnt7IDqa5Hvt2pG515OUvUVF/flGbDXEiBEj2L1792Bvb//0Pqy33qKgRlYWSTW9vGr2OrWapJaFC2oAlGX6+GMgOBh+Z87wlJQUjBo1CsHBwRgyZAieVMBUpVJh+vTpOH78ODjnbP/+/dzZ2ZlPmTJFAAApM1OY5emJ+kOHMrz3Hsl3Bw6EaulSTP34YyGmbVuEZGRgz9atctMVK9jYGTNIWj5mzOOdWHg4tTi7coWy/YMHU83xr78C//d/5be3tydy/gSQmZkJHx8fKSMjQ+xY0sW5DPbu3ctjYmLYrFmznr2yJS+PJOSLFpF8/KefSILerh0RqehoyhY+Ixw/fpw3b94cbdq0gSAI2Lt3rxAbG4uakJTAwEBub28vQKEoDuLUFtevU5CxpNt6JXjrrbfYLytWIHv1alhPnEgS/YAAkvhfuUI19x9+SB4LNjalgosFBQXw8/PjFhYW5Q6iUCiK+2+XxOXLFBBJTy/t1tyoEeR9+5AzcSL8Ro7EDa0WoiiiQ4cOUhNLSxF375beT1QUsGkTTAYMAMvPZ5gyhWTzU6ZQecbMmeTOboS7O9X1G8EY1fRrNETQr1+Hl4cHkpOTERcXV+282aFDB5w4cQK5ubmw4JzaI1Z0refMoWNX1g6sSRPo0tOxe+lSZDVtKgOAXq8XRFHk/fr1YwCEgIAAKYd6l/cCkL1s2bJXOOd7GGNHli5dWpNedY0AROoLyXxycjIcHBxqVNogyzJ+//13PmzYMHTp0uW5T5IkJSXlc87vV/a8Wq2WNRrNa2fPnj3SqlUrODo6IiMjAyqVipubm7MlS5YAJY1jLS3JrHLQoAr3N2jQoJpfk48+KjKPrM8Yhg4dKkRGRiIhIUHw8vKSfX19+fnz55lSqeSCIOxUq9V/icTb3wnP54rw6WK0ubl5vcr6Aj9vuHbtmiEqKkopiuKhZ30uTxm+Wq32Pz/88MOyYcOGmXbp0uUFYW5/TQwYMEDs378/AIruWllZFfWHjYuLg7Ozs3zgwAHBwcGBT5s2rciU5oUBYyRJHj+eauSiouj340CvJ+dmjebZtxqrBCkpKZAkCY/a/qpGEASS6m/eTJnumJiaXY/790svgo19cDmHVqvFhQsX2PDhw+Hp6VnrdkQ1gbOzM2bOnAncuSPoGMMPgYEsYe1aiL17o/edO7A9e5YCAZ06UWZsyBBg7FgwxtAEgIskwTk0VDhx4gT/9ttvpX79+okdO3YsZbhz6dIlyLIMc3NzNGrUCA4ODgCAoKAg+Pn58QEDBhS7+//vf0Sqv/+eJLSck2nSq68S4a4I7dqRsuIx5eQPHjzA7t274eTkxObPn1+pS29eXh7u3LnDxo8fX/RenimuX6eM5Qcf0OI1NpauiVJJWdgVK8iUa8KEZ3J6+fn5UCgUnDHGWrduDQ8PD+zYsQOzZs1CdXMoY4zbS5KA/v2pFv1R0KULsG8fjaWy9+SDB0SkJ04ERoyAjYsLGpmZ8YLbt4GhQxlWr6aaent7ClBWAp1Oh++//x4A+LvvvltzwtG1K+DnV4poy7KMhIQE+Pj4QO/hgbFr16LRgQNyh27dBMaYiHPnqN7ZiPPnyc37wQM0atIEnq1bCxg+nNppzZxJxKissuH48YoNLHv0oJ+lS5G5cSPuvPEG+o0YUe1EFhcXB6VSCTOlksaary+pAIw4eJACCsePV21WJwgwdO+Oer6+mLtgQcmTLjqHgQMHigaDAYGBgThy5AisrKy2ZmVlgXP+U2GHHqZWq6uKvg0AAFEU/Xbv3u3GOTcxNze3mDdvnplSqURycjLS09MRHR0tX7lyhSmVynxra2uDp6enZWJiomBmZoZOnTo9n192JcA5R0REhAjgt6q2U6vVR5cvXx78ww8/tPrkk0+g1WorDyC+9x4FnSoh2zVCSAgR7U2bKMADIDw8HPv37+c5OTnMxMSEe3t7C4MGDUJoaCh27drFAKx79APW4Vnhb0e2VSrVl97e3hZ/Vp9tSZIgCEKNTVCMKCgowKlTpwr8/f2VnPNtBoNB/ZRO8bmAWq3mGo3mPmPs+IULF4Z26dLlOXfb+uvDOGZtStSaGY3RVCqVgnOOtLQ09syzWY+DZctIdtiiBbXzeOONR9/X3bvFPYtL9H59XiDLMo4ePcqbNWsmo5rWbk8EM2ZQNikggOSry5ZVvm1SEskpjQvtQYPIebtQcp0UFQXG2JMz3jES+V27SIptZkbS9/nzgSNHoEpOhuvrr8M/PBwRCgVaLlhgEIYPp4FeSas7URRRSKDYyZMnceLECRw9ehRubm7c29ubJScn4+TJk2jYsKFUUFDAsrKyBGdnZ9na2loICQmBXq9n0dHRaKXXEyl8800iNa6udL7e3mSkptFU/d4++YQW+NevP9KlCQgI4EeOHGHe3t7o3r17lV+U165dg0qlMsqAnx1OngTef5+yiLdu0WOhoaSqKBlYd3KicfaMyHZeXp7coUOHontv0qRJ2LhxI7969SofPnx4ldfaysqKO23ZwrF6NSt6j7WFIFCt90cfkcy+dWu6bgMGkNInKIjUGCtWQNehA+5t2MA6vvYaHNzdi3ZhzIDa29uXM9VKTk7Gli1boNfr+UcffSTU+Lvh2jUa8wcPlnp4z549CA4OBgCMeucdbr9gAbOfM0fAkSOUWWSsdJb//fcpyPL558j8+mu4ffABQ9++FICqzMDzq6/IcK+yuWXZMmS/9BI6rVmDFsnJDD16VGlAeO7cOdnT0xOCKAo4eLA00QaIZNevD65SwaDXQ6vV4ubNmzw+Pl7W6/XM2dlZcHR0hCRJaDloEAYsXkylNJV8pygUCnTp0gVdqBe16c6dO/UhISEHAYAxdhLA0EpPFtgJwGfJkiVxAKDRaJx1Ol1cfn4+rl+/bvDx8ZEVCkWETqdrCQCSJLXVarXuPj4+PxsMhiZvvPEGe1xjtT8Der0eer3eRK1WV1tDIctyX1EUr3711VcNcnJyzArbyJVHkyaVB65qAlmmgLSnZxHRBshbJTc3lwGAqakpB8AEQYCVlRVUKlWCTqe7WvuD1eFZ4wVeJT8aOOeq8PBwfdOmTZWVuSfrdDro9XpkZWXB0tKyyMygtsjPz8eXX35paNCgQcGMGTMsxDIR28oIf0JCAvbs2ZOXk5Pjyzl/D8D9v4ProEqlWq3T6Vr069cPx44dMwwZMkTxIkzkfycolUoolUoMGjQInTp1Km6H8SJDECjCbGNDRGbUKMoA1Qa//04L1sI69ycNnU4HAI91vU+cOIHs7Gy89tprf95NpVKR0++FC1UvSvr3p4ytkUiOHk3tqQphYmICWZZhMBhqL1XOzqbayE2baEF9+TJlunx86DFvbzKz++wzyq5NmoSEhATc2rABcseOePXVV9G6desaH1ShUGDEiBHisGHDEBcXBz8/P/nHH38UDQYDxo4di44dOxbVA/r4+HCtVitNnTpVPLxvn+ycmCgUGagNHVp8vQwGqiGdMqX6E1i2jIhTLWEwGHDy5EnZ399fmDhxIlpVElQoiW7duuHChQs8IiKCuZcgZH8aZJkysi1akFy85Njw9S0/3ubMITXLJ5/QWPsT67ePHz+OvLw8sayqxMnJid2+fZt5e3tXObZbtmwpHh80SO7wz38+Xibxiy/IIOzKFTIHCwggCfjcueQ3YGcHfPcdMtesQf3oaNgeOQL+7ruIO3sWwenpPCItDUlJSYxzDi8vL2nw4MGiMTB78+ZN5ObmYv78+bULwublVdh+LTY2VnZ3dxcsLCx4x44dGRQKulffeQfYsoWCcxYWNA4uXqR5pn174OOPYZWbiz2TJvG5S5dWfr1kmVzwq5lXHXv1wv327Xmf69cZwsJIFVVJfbOpqSm/cuWK6LlpE2w/+ghFtHzVKlLvbNyIrVu3SpErVoiyLEOhUMDOzo67uLiIWVlZ8Pf3l3U6nWAwGGBvb49eDRqgweHDNG6rgVarRUREhAwAoihelSTptaq2L/TPMRLtIQB+B6jDw5kzZ2RZltsuWrQobMWKFX6SJPUBEK9WqyM1Gk03AEkVtrl6DmEcnxqNxlOtVvtXta1arU7RaDTNc3JyegNoIsvyhpSUFNNyJUvNmlEAJyWF1BS1QXAwfd/5+FCbyxIIDAwE5xxDhgyBubl50QR1/fp1rSRJG/4OXOCviL8d2dbr9V4BAQFfpqWlvTxlyhSzsmQuIiIC27dv1zPGdKIopnHOHWbOnGlqYWEBnU4HMzOzcr0xZVlGQEAAkpKS9AMGDFCampoiIiICt27d4gqF4kpycrL+8uXL/Yyuo7du3ZIOHDjAOnfurHv55ZdNy57j/v37szMyMpZwzr/9O91YOp3uJRMTk/3Hjx93NhgMgp2dnVOPHj2e+1qgvxNatWqFgQMHwsfHB1qttshI5IWH0Sfg9m36EvX0rF20euFCkpVV4Bz7qLhw4QL++OMPKBQKWa/XC7Isw9TUVGaMQZZlWFhY8NatW4stWrSAs7NzlX2fk5KSEBAQwIcNG8b+dL+KoUPpZ+tWIrlnzpS/tn/8QSZCAwYQIS5j/qRUKiGKInx9fTFkyJCKj5OVRT+JibQQnzmTFuVRUbQIDw6mIMqsWUS6nJ1psWNEodsrAFy/fh2yLGP69OmP7NwuCAIaN26MKVOmiDqdDlFRUfDw8Ch63traGuPHj6f7JzUVw3/6STBr3JhqtEtenw8/pCzt4cM1O7ClJbVgM/bGriH27dsnBwcHC2PGjKkR0QYAc3NzWFtby2lpaeIzIdurVpHjf3g49ScviR07KjYRs7KiNku9elEA40/ApUuXEBAQgNmzZ6NsCduwYcNw48aNagNJlgcO8DH79wv8o4/wyGz79m0KKgUE0FyVkUG9um1tyUTO1ZUCO1otHBwd0SEnh2esXcvW5+dj9urVsPHyYtrWrbH4m28Qcf06Yt59V8z7/HNY+PhAevVVKDw80MTWltdftYpk5/fuUZCtbVsithXNUYmJdPzvviv1sI+PD/Ly8gQPDw/06NGj+C3PnEmZQGM7OA8Pmlf++U9Sw8yYAdjZQT91Kh6uXcuqvK6HDpHDf0BAlZft8OHDyDA1ZXlbt8L61i1SL/3vf+QkXgZTp04Vz509C/2GDfhx82Y4R0eja4cOaGBvj3wbG2xauxZZWVnirFmzkJSUBBcXFzg5OZW8MIIsy8jIyMD69eu5X9++rPOlSzUi2zExMRBF8TaAHkuWLKlV9xoTE5M+BQUFKkEQTu3YsaObwWAoqh2RJGkMABO1Wq0DAJVKtUCWZb5t27bcxMREwcPDQxg4cKCp7dNuqfmIUCqVaNGiRe79+/f7AaiSbANUvw3ADwA+//zzGfHx8YPLkW3GSCVy4QK53NcUmZnkJzFjRjmiDQAeHh48Pj6e9+zZs2hMZGdn486dO1ySpG9rfqA6PE/425FttVodp9FoZsTFxR1Zs2aNV7du3VS9evUSTUxMwBjD0aNHcyVJmqVWq3cBwKeffjp3/fr1XzLGJMaYTpIkaycnJ92cOXPMGWOQJAlbtmzJTUpKCpEkyTw3N7dVVFRUnl6vjzcYDHslSdoIwPbChQtnu3btam5iYoLTp0/ncc6XBQQEfD5y5MhSi+SYmBikp6cLnPMf/05EG6DPBkB3gNowhIaG/tSjR4/no29SHYrQs2dP3L9/H2fPnhXPnj2LTp06yWPGjPlrBEV+Kyzp6tePsjzUUqVyaLWUrQgKeuJ12j6FRHD06NFC/fr1kZmZCb1eLzDGYG5ujgcPHsghISHy5cuXBb1eDxMTE+7g4MCMQZD27dvDYDBg48aN0sOHD8UOHTpQduhZoX9/ICGBFt1abXFrsHfeIUnr2LFEniogt/Xr18f06dOxbds2NHF2RitJogX6uXMkBd+wgeTdr7xC8ms7O1IqHD5M2wkCZbFriOHDh+PWrVs4c+YMZsyY8dg9aFUqVSmiXQStFnztWly9eBGh3bqh/QcfoGHJcaTTUQCiJhntkjAzI9JUQ6SnpyMqKkpo06YNqjJDqwi2trZibGys1K1btz8v8Hb+PNX3vv8+kayy996dO9RSrV278q9VqSjrfe8eyZcrMut6wrhx4wbv2LEjGjRoUO7+S09PB0BB+4rAOQfnHEnm5szQtCkubNokT5kyRTA1LRenrxqBgcDUqcDevUSsJ0yg8g5joLFkCc2JEwCAXtu3s/j4eHQLDkbW9evo1qwZuskyMHs23Fq2xJUuXaQLBQWsT16eEJWaysOtrVkbNzeGX36hDPqqVeRefuQIjcmdO+m+nz2bzOr+9S/KsBsMlJHWaOjvGzdw4dYtNLWxgWdZGbYg0PmfPEky3LAwOvfmzSmQ8uabQMOGsABlNENDQ9GmTZuKr0nDhrR9FUhKSsLdu3fRpEkT3qBBA4YGDYgkKZUUuFu7tpTbuCAIeKltWyAiAr1u3YL40UechYaylbNnQxRFcM4xc+ZMuLi4VNrqVBAE2NnZYeHChUybnU0dBu7fp8BCFShUQBU8SpvYjz/+eCmApQC1ZwXgCSASAMoacun1eh/O+b/i4uJO6PX6hUFBQXPCwsLemzt3rumfEcx9FIXTSy+9ZBEREfGpRqM5oVar79XkNRqNxlqhUHgqKysbGDMG+PHHmpPtoCD6jtq/n9q9lYEsy7h//z5KEm0AuHLlil4QhB1qtfphzQ5Uh+cNfzuyDQBqtTofwEsajabdlStXFp8/f/4VzrlgbW2dn5eXpwew27jt4sWL16GEIcHKlSvXiaI43fj/hQsXpKSkJP+CgoKBSqVyXmho6GK9Xj9VrVafLnnMVatW7d+/f/+E1q1bm2ZnZ5sDuC0Igv7YsWNMkiQJAMvIyDBERERYqlSq/5Vpj/W3gkajcVAqlRP1er1SlmXcvXsX0dHRei8vL2WdU/mzx6lTpxAREQEAsLOzQ0BAgBAdHS2988474gtdv10S//0vScSCgmghVZmh2NdfU2YtLOyJn0L79u15aGgoJEliDRs2RMOGDUs97+bmJnh7exctxmNiYlh4eDhycnLEo0ePchsbG3bw4EFJpVKxd999FzY2Ns82INKkCS0a16wh068HD4gkqVSU1dqxAzh6tPRrHj4kUtW3Lxp/8gnmBAfjQnKy3MrXV8A//0lmTePGEbFOTS2WEdewnVJlUCgUmDt3Lr777jscPnz46QSTLl8G0tKQHxgIvxYtIDg7o5mbW/HzBw6QWuLBg9q72//rX1QHn5dXea1qIXJycvDNN98AANxKHr+GsLW1RVran2iOa5QMh4VV/t6uXqU2aZUFwMzNqbZ71SryWnjK5Ur5+fnc1dW1wjHk4OAAS0tLKTIyUixLChMTE/Hjjz/CSq+HXWYmb/7vf7Pk8+dZUFCQsUa3ZkhNJZL71Ve0yC8oAOLjSQJbjaO/s7MznEuoPiAIRb4FY1asEH/88Uf59g8/oJtazZL9/DBn1qzinthbthS/LiODxnFmJhFUgGrGu3UjL4LBgwG9HjnHj0O/fDkwZw5e/eYbKP39ab5o0IDKdI4do3tjxw5SwURE0D2ycycFWEpAqVTi1KlTlZPt1FRg0qQq339ISAgkSUJ2djbLz88nZWOnTpTVFAQK9o0cScTdiMGDgWnT4DlhAvDNN0yWZbzXuDGsra3x2WefITAwsEYO9ABgamVFJTWRkdWSbQsLC8iy7FzlRjVA4frzUmXPL1269LhGo1Ho9Xq5MCn08fLlywecOnWqy4QJE57qIiApKQnr1q2Di4tLgZeXl4mdnR0aNmxYpS+S0WeAMaYA0AFAjcg2gIYKhcKsdevWFT/buzeNxYKCqs3uABqjubmkgqukF3lGRgays7NZSXm+TqfDtWvXJJ1Ot7KG51yH5xB/kZXxo0GtVt8BMFmj0cwEYMjKyhoAIKGqjLIoil2aNm1qFhwcDCcnJ/j5+ekNBsP0QsfHbwt/yqGgoGBOZGSkdVhY2ADO+Qi1Wn1eo9F4X79+vSuAApDDZCaA/YsWLcp90u/1RYJKpdql0+kGxsTEYOXKlXpRFIN0Op2Fra2tR88KZFt1+HNhdM1t164dhg8fjmvXruHcuXOiTqd79q1/nhSM2a4pU6jn6vHj5bdJTKQvzg8+eCqnMH78eObr64sjR47Azs4OldXHMcbAGEPTpk3RtGlTyLKMmJgYbNq0Ce7u7njllVcEk+oWAn8mPvyQDNBSUynzPHUqtf0KDKQgx/TpJJns2JEyk4cOUYucl19G7sCBCE9L47hx46mfZv369dG8eXMEBwcLaWlp/M0333wyqgBZJtnrgQOQFy7EsYkTYbh/n3/84YfF+9dqiTh8882jt5EbNIicpf/znyo3K7lIPXv2LNq0aYPaZE1NTEyQmZkpaLXaWr0uMzMT+/bt4/Xq1eNjx44VamQi+o9/kGT82LGqt9PpSCZeFd58k+r1z58nJUstlSl5eXmozPel/OnohLLlZyVRv3598datW7x169alWigaZblvmpjA9tgxhm+/RUREBI+KipK7dOlSswiBwQC8/jpJnpcupcdMTChgceoUtQJ7RJibm+O9994TVq9ezWVZZoaq1BTG929vXyyJvnyZjApfe416dQPYb27Ow+fMYS+//DJUixfT+Jck8sVwdKR5wWCgAIlRDXD4cIU13w0bNkRERAS2bNkiT58+vXyw41//omBpBd1pUlNTce/ePZw9exYAjdfVq1fD3d1d7tu3r9C0aVN6bVoaqaDef7+4bGPLFsp4TpwIBARAAGDUZHPOkZKSUu21LYVu3WjfAwdWOR/Y29vDYDA4aTQapVqt1le0TWFv7VcEQZA++eST3RVtUxOUdTmXZfmDuLi44wCeamo7MDBQL4rihri4uLl79+6FUqlMBWBtYmKidXR0ZI0bN7bUarVSSkpKvl6vl2xsbJTp6elSUlJSlsFgmAvgSC0OlypJUuWBVjMzUk4FBVHpWWW4dYvUF//7X5UmrHZ2dmjWrJm8adMmwc7OTurcubNobm4OQRBuqNXqB7U47zo8Z/iLrIwfD4WZbgA4Vd22nPMz169ft71x40ZqQUFBV5VKFbJ48eLIGh5jdJnH/FBYF1KHYhgMhnWmpqamWq12nsFgeLh48eK4VatWnYmPj3fNz89XqlQq6PX6Wi3s6vDk0KZNG1y7dg13797FnTt38MEHH+Dq1av44osvYGJiwufMmfPitQKrDDt20MJu40b6wjTWgObnk9z5+vXSfaGfMPr16wcA2Lp1K0aOHFkjGbggCJg7dy7jnEN8FP2zVluc6bt/n0ifMSrfoQOwezdlWkSRFpUffUSPGQwUnJg9m2oas7Mpc7hrF2W07ezo8fbtyXX444/pugKU6dq1izJFs2dTxuvf/6aM9cyZlP3u2BEsOhp5W7f+aXLlIUOGYMuWLYiOjmaSJD22nBx//EHX4ttvwceMwebff5cfPnzIhg0bVvy5xsbS4vrixcfr0338eLVZS4CyYR988AGCgoJw8uRJXLx4ES+99FKND9O7d29cuXIFp0+flkeOHFljBcDVq1fluLg4ITo6mrm5uaFNmzaoVK4ZH09Z+ilTiklbZUhJIRf32bOrPwknJ7rG06eTiqCG2LVrFw8ODmZz586Fk5NTtdsLgsDz8/MrvXeHDx+OH3/8kd26dauUjN/oVcDmz6f7gbYV1q9fzy9fvix7eXlVf70TE4nclpVLyzJ5F+zYAfTtW+1uKoMgCHBzc8P58+cBAKdOnZK9vb1rNg6mTi33UIcOHVh4eDjatm1bHLwVRSKaAN0b7doV9/h2dKSggb09GSuC5Lj+/v5ISkqCk5MToqOjhdu3b6N9WUn6tm2l2w0WwmAw4LvCGvJevXrxl156iWVlZeHGjRu4evWqcPPmTd60aVP6PO3sSNKemEhBw8REMlD74osKFRNKpZK3aNGidpGdFi1oDrx8uZRxZFlYWlqiYcOGPDY2VrdixYqrsiyfZIzNA7BelmU1Y6yvUqncrtfrHUVRDEEJFecTgJ2FhcVTL30MCgoqkCRpE0jyrtPr9bkA2uj1el1OTk6PyMjI1rIsFwCIAJAK6iWeAOCcWq3OrM2xlErloqZNmxoAVB6tdncnFVFluH6dfDRWrao+AAhg+vTpQnZ2NgIDAwV/f385NTVVAPBLbc67Ds8f6sh2LbFw4cKiDcmqAAAgAElEQVSFABYCgEajcSkoKMh4xqf0l0NhtLXUl0BBQcGroaGh/42IiHi1Xr16iI+PN/nggw/YozrF1+HRYWpqCi8vL/z+++/o2rWrbGVlJXz44YdYsWIFCgoKnvuem7WGQkELOktLyrDk5dHfEREVZkRqhays4vra8HAi7rdvE+ls2xbYuBH9JkyAQ0YGIj7+mMW9+65hRGysQnZ0hDBmDGVONmwg2ezOnVSXPGsWhO7diUC0b09mLJs3U53YuXNkEvbaaxRhb9KE5J3vvUdy7bt3qT/zxx+TK3vnziSRW7WKjMZOnqQF7urV9NoNG4hsBwRQn/EpU4gsGiWWRvLk5gYYa/lGjyYS/e9/U4ZSpyNzK7Wazk2no0XwmjXAokWUtQoPB9q1g8s770CZmQnu6Qnm70/ZrtRUMpu5eJGuWYlWdY8LBwcH9O7dGydOnMAvv/xCfbgfBdnZdP3Dwuh9du8OcI68vDwmiiLv0KED3Td5eWRstnp1pW7HNUajRnStv/mGjK+qgLW1NSwKa+hlWeZAzT24TE1N0bhxY2Rk1O6r8OHDh/Dw8EBOTg4OHDiAAwcOoEOHDnzcuHHljz1pEi1qN2+ufscnTtC4r4kiQKkknwYTE6ohrmH/eWMZTX5+fjVbEuzt7dmDBw+k1q1bVxitcXJyQtOmTXHx4sVSAbWCggI0Cw6G1aBBMKo57O3t4eTkxO7cuQMvL6+qD/y//1H29ebN8pl7pZLMnR53DgPw6quvstWrV8v5+fnCxYsXhQ4dOlQfhNi6leqsy6jV0tLSYG5uXnngxWCguc7UlAIIY8cSYf7hB+Dll5GZlYVz584hoND0zMLCQhJFUfz9999Lk+2DB2luWb++1O5DQkKwb98+7uTkhJkzZzJj6ydbW1sMHjwYzZo1w7Zt25i3t3fRPQM3Nxo7GzcCv/xC9/CQIfRTAt98841UUFAg2tnZgXNeu5awK1fS/F4F2QaAGTNmWBRm5btqtVpPpVKpuHPnzgeZmZkDFQpFu1deecVcp9Ph8OHDcTU/ePUQBKFv06ZNLZ/Evjjn0Ov1UCqVpa5RdnY28vLyGIAbZerSbxf+fmLtQJYtWzbQ1NT0nbFjx1Yd3WvXjgKoFZkthoRQsPjLL8kktIawsrJCnz59mIeHB1u/fn2uJEl1ZPsFRx3ZfgwUGnrV4U+AWq1OAfDG559/npaamtpMqVR2z83NbVhHtp8NTp48KXfp0kUw9oaNjY0FQG7lf5msdkm8/DL9rF4NLFlCC6uwMFroTZtGEetevYgUfvwxOV8fPEiLsBUrqNZuwAAioaNGAenpxQvdLVuIcE+dSsR36VIi81u3kgTY0xOtRRHuycn4b2CgotGRI0g0N0dwdDR/Oz6emeh0tPC0LFzntG1L56dUkhmLiQlJio01gv/9L5Gv+vWJfKtU9JhRApqaWvy+jS2kSi5IS7Y3M/792WfFj5WU3G/bRr/nzi1+bGVh6ZmDAxATQ6Sc8+LsZU4Onb/RvXjAANp+yRLwHj2AH35ARrNmsAUoMBEdTWT7lVdIeWBtTZm8nBxyXFapgG+/pedXrSISdu4cBRtSU6kmv4rWP15eXrCwsMC+fftqJR0uQk4OmcP16kUZ/EISyBjDvHnz2Jo1a9jGjRshShJ/Wa1mF7y8wGfMwLjaHaVimJlR3Xs1ZBsgEgcUt5mrDaysrOQ7d+6Ip0+fRqtWrSo1fgIo6xgXF4esrCzWoEEDTJo0CQaDAWvXruURERGl2cfSpVQffOJEtbXnRfD3r11pR7NmFAhZupSkzJZV84WDBw9yY1DRtQbXFSAynZCQUCWzat26NQo9XIoUFCYmJkizs4Nu0iSUXPHn5ubKLVu2rJqpZWTQPbZtW+US+SZNaL749NPaOSpXAGdnZ+HBA1K65uTkVE+2d++me7sE2ZZlGXfu3OGenp6swu4KskzBo6goqj8XBJrbevSgz2/RIsR7eiLg3j20a9eOT5gwgQEQt27dysPDw9lXX30lDxkyRGjdujWE9PRyYyopKQl79+5Fnz590LdvX1YRGY6Li4O9vb1csi0TDh2iOXLECBqrq1dTRvOzzygTP3QoYtPSkJWcLEKpxM6dOzF16lQ0b968xtcXbm4UtIuLo4BoJRBFEY6OjnB0dBQACAAQExPD0tPTuw8cOBDNmzfHLerTLlW6k1pAo9G0QyHZbVlJLXJFSExMxNmzZ3Ojo6OZwWBQWFtba3U6nVBQUKAEwAwGg1ivXr0CLy8vs1atWjFra2uYmJjAYDBYCIJwZdmyZauWLl269zHO2xmAQq1WR1f0PGOsr52dnVhtCZanJ43LxMTSgSsfH/pe3rq1QvVETXD79m2DIAjrlyxZUvtJuQ7PFerIdh1eKCxcuPADjUajEgTh5r1795waNGjw13DBfsHAOS+1mNq5cycAIDQ0FBcuXEDvxzSoem7xf/9HX6CFmS107Uok1tqa6iIBInWFDsNYv56ybNbWlHkFiuXSAJFLI4yZwf37ix+LiaHfnTtDNXIknH7+Wdrv7S1aWVnxvKwsFvj11+ju5ERy2P79adsPPyx+vZHYtmtX7MxsJK8AYMyMVUMwnhoEgaSs6emUYTQ3JzlocDA9P3o0PZeXB8ycCcXRo3BMTkaySkVku1BaC4AWoQBl2G8XJjrGjSu+1vn5RHRv3KCF+htvUM1uq1bA9u1Euu/fJ6n33r3Anj0UhOjaFe379kXY11/LP2/YwGbMnMksa3K9MjNp0R0WRoGXCmpKBUGAt7e39CAkhNe3sBClBQtwJzsbJmFhtcouV4pdu4rHUDWIjo7mKpUKI0aMqPVxx44dKzZq1AhXr16Vrly5IjZq1EgaMmSIWLLNVWpqKoKDg3Ht2jWem5vLnJyc+JAhQxhAxkC5ubmsdevWEgARBgMRxPh4MqOqKdFOS6PgRg3Np4owbhyNm4KCcvfC7du3ERISgsTERDkvL0/QarWsR48euHXrlizLsmAwGKCqpk9zbGysXF1P4tTUVIiiWKo7iZCfjyaZmTxszhxWUgCt1WrRvHnzyj+n+Hgiofv3F5mZVYqPPipF3oKCgiCKIm/VqlWtxkHPnj1Rv3593L59G8eOHZPmz59fec1FejoR1DJk9vPPP+eyLLMKHfE5Jxd5X9/Sc2iXLvSYlxeQm4uWFy7AvkULHhwczC5fvgwvLy9MmjSJxcTEYNu2bcJvhR0nPPR62bRHD2GUToddu3ZJSUlJLDc3V7C1tUWfPn0qJNoAXfuUlBSBGQykrDEGDf75TzK+srIq7h6waBGpCnJzYXv2LP6xfz8SJ0xAnqUlv7JlC3dfulRgNfVksLCg44SGVkm2K4KHhwcEQcjr0KGDOUC1wZzzqt3Wao5UALC2tjY0bty4Rm8mPDwcO3fuzDMYDIs45wcB5KelpbkV7isLxE0S09PTJ549e3b6qVOnBrq6uqJ79+7mACDLclcAv2k0muaPUsus0WjqAYhTKpV+APpVtI0sy6uTk5Nfun79ev8qFSSCQNnrkJBisp2URAHmdeuqVSJUBs45AgICCvR6/dZH2kEdnivUke06vFDQaDSiQqGIsbS0NHd3d68j2s8IsiwzmxJy3ZkzZyIlJQUxMTHG/qjw9vZ+hmf4lCAItKgyYsmS4r+XL6ffXbsWPzZoUPHfxgWkUXr4CJgxY4Z48+ZNmJmZsT179uDcuXNwd3d/sdUEc+ZU3H8XAErWDmdnAwBctm+X60VH0wuaNydZ+5gxZFLTqxdl9I0S7JLmT8Y+1U2bFhs0+Re2XOWcMoCOjrSQNZrdXL1KCoCOHTF2+3ZhmYsL0idO5Jbx8Qy3blFG7cMPKdAxcya5qe/bRzWkI0YQgVu/vlhCXwE6d+4sdl62jLLQO3bA5IsvkJeXx3Q6XbUkrlrk5REBuH+/ygX6sWPHDP7+/orxNejlWxm6du2Krl27ilqtFgcPHhR+/vlnNGnSRHZ0dGSmpqY4f/48s7Ozk9u2bSsMGjQIQglWmZycDAAQBEFITEhAg0GDSK2wYUPtTiIqinwFaqs+AIBXXwV69cKDTp2wq1EjbmdnJ1laWuLBgwcKgAiLg4MDWrRowd3c3NjVq1eFVatWQZIkWFpaSm+//bZYkerhzp07ePjwodC3mrro4OBgWalUCrdu3ZI7duwoAAD38cGAgwdZYsmgEqjOPjIysmL3eJ2OFBtz51ZPtAG6zuvXIy81FZeUSmPtNVuwYAGqMnUrC3d3d7i6uuLq1auwtLSs+ru5c2dSAJVQvFy+fBmyLLP333+/WJ5dEvv2kVT85MnS84VRqbJgAfDNNxDS0/GPhQvZtalT+dETJ5inpydUKhXc3d2xZMkSaLVahIWFofmwYcLBN9/kX0REMIPBII4dOxaNGzeGlZUVKsyqAzhz5gyu+vqifVAQBTJ27KASnAEDKHAwezaZ+BmhUtEcAcDipZdg8cMPsL14Ebrr11nsH38wuXVriL16URlNvXpUKlGVtLxlS8rs9+9f+ZxZAXr16qXs1atXkS6/QYMG0Ol0jTQajZVarc6u8Y7KQKPRuKhUqv/qdDq88sorNeITkiTht99+y9fr9aPUavXZEk9V1NZqN4DdGo3G/MGDB2/FxsbOUCgUzgCiDAbDOhS2Jqvk3JoDSFWr1ekVPD0KAARBqDQSqVartcuWLTseFRXV3cvLq+obwcaG2g32709B2tRUUsk4OFT5sqoQFxcHg8GQAeDmI++kDs8N6sh2HV40cEEQstq0aWPdpIb1dXV4sggLC0NBQQELCwuTjQEPBwcHODg4oFWrVtDpdLh3757k7e395/Xd/ZtAEISilj+ffPIJNm3aJH///ffC+PHj0fYpGrU9VXTvTtnk9euJeFcGUYS/vz+/2KiRIE6eDCeA5OHt2xMpnjSJJNMff0wkw9h+qCZ1kYwVy2j79i02jNq1q2iTswcPQnHhAixXrmTYv5+kg/b2wJkzpBo4dYoyXZ9/Tv2bp06lzHZ1SE+nBXshMbKwsEBeXh4iIiJqJcusEObmlFkr0zauJBITE3Hjxg3FrFmzyrWXexSYmppi0qRJLCcnB4cOHWL379/nnHM+fvx4sVWrVhUyhDZt2mD48OEI/e47digsjL/1zTcM/SpMOFWN3bvLtX+qDeSvv8bNL76AwcGBJSUlKZKSkmBqaopp06bB2dnZeO4MAKZPnw5LS0uoVCrs3r2bHTx4kE+ePLnUYNu8ebMcFRUlDB8+HJW2DyrElClThM2bN+P8+fPFmd3Ro7Hvyy/l2N27BUtLSz5nzhxmaWkJg8Eg29nZlb+WnBPh8/CgIFQNwf39EblzJ7/98stwcXHhWVlZ2L9/P58yZUqt5nCjjHzUqFFV33SHDpVrf3T16lW5V69eQoVE+9YtCrzs3VueZHbqRGUDBgMRbxsbICoKnpmZ7CiAwMBAdCvsLiGKIiwsLNCxfXtg2TJMmT2bJSYlQZKkKksfACAtLg55X36JrgYD+ru4kKz7wIHiDXQ6CuitrKJDk1IJ9O+PxGbNEJCTg5T+/WF38yasv/iC2zx4wGSlElampjxn4EB0mDuXmTg6ln59164UUAoJIRPJR4RCoYCrq6s2PDz8VQAbq9teo9EoATC1Wq0r/L+VKIpfKBSKXk5OTrZDhw6t9voZcffuXXDOg8oQ7SqhVqvzAKwt/KkSy5cvHwngPQDegiCkazQaT7VaHVlmM6Wtra0hNzd3sEajEcs6qxvBOb+Tm5urA1A12e7YkYIvubl03/3ww2MRbQC4evVqvsFg+KGq7kh1eHFQR7br8EJBrVbLGo1m7cWLF7+LiIjI5Jyjffv25j179lTWymykDo+MHTt2oH79+mjbtm25xZ4kSQgPD+eurq51RPspQxAEzJo1S/jll1/42bNn0bZt2xf3BvD3J6fdKsh2fn4+fv/9d9apU6dit2yjKU2jRsXSfRsbqlMPDaXFaXAw1dLr9aUz5WURGUnZZc5pwbxsGcnIAwOBAwcwYNQo6IYNQ7SDA6936BBjCxdSyYBSSQQvIIBcg0eOpGPWhPR99RWZWN2/XxQUaNKkCR4+fAjrKrLhtYHMGLRjxyLuv/+FR5k+vZIk4ZdffoGnp+cTIdolYWlpiSlTpjDUUA7fvXNntDlwAA9atKA+xbWFXk/qhHs1baFbHgGiiCRHR/xr+3asnTQJA0aM4L17967w/EvWa48bN05Yv349NmzYAEmSJM45s7GxEeLi4oTJkyejRYsW1R7byckJM2bMwLp169jBgwelEY6OomHMGJh9/bXQsmVL3Lt3j0VERKB9+/ZwdXUVz507x/Pz85mx9R9jDEJeHhrn5yPBywuyvz8EQUDLli3LZaijoqIQHh6OS5cuUVcPd3fOGjbEP8aPZ6omTVh0dDS2bt2KyMjIGtelc85hdOmuMhA+ejT5WJSphc3KyhIqrPM+c4bKRU6dIol2WZiaklz7xx9JUi6KwPHjOPif/8jDTp8WulQUdPDzo+0YQ4PqDOIyM4GQEAhTpqBxo0Zw37QJZhWZF8bFkQ9HDUhWkyZN4OLiImXn5AjdFy5ksbGxrHHnzkiJiEDa5s0wO3iQZWVmwiEzk9Q7/ftTQM/Kinwn1q8n063HQI8ePSxjY2OXajSaXwvJbBE0Go2tQqF4X6lU9uWcN2KMNeecM41GM1CtVp9jjP1bkqRRACDLcp6zs3ONpST+/v7ZWq328U6+Emg0GjNRFLe5u7tb9ejRA7Gxsda+vr6BGo2mqVqtLungGFBQUKCzsbGxSElJmYEKAg4ajUYliuKbzs7O1cs7XF2BK1cowHv7drEx6CMiLy8P9+7dY5Ik/fhYO6rDc4M6sl2HFxF7AOgSEhKiAFikpqZ+fe/ePbtp06ZZPLbssg7VgnOO1157rchQqSTy8/ORnp7OsrOzERMTI40ePVqsrlaxDo+Ou3fvIiMjg4mi+GJHvz/4gEhuVlaFkmtJkvDVV1/B1ta2+vZSJRfXfn6AszMtTMPDadHarRvJzNesoUzZhg1Ejvv1ozruGTPIlT0/n0hf585EhDMzkbZ/v3zl/n0h9/vv0cvCgrY1om1bYPJkynZ/+2317zklhWqFBw8uItoGgwE3btxAo0aNHpn85uXlITY2FtHR0Tw8PFzOiowUX715E79u24alGk2pbY8dOyYBEB6lTvuJoaCAzAfXrMEPixbJvfv3f7TyoEuXgMWLH8t/ICUlBSn29sj19MT/TZ0KkzZtanRd6tevj2nTpiEoKIhbWVmJGRkZuHr1Kvr371+rNk9OTk74xz/+gXXr1omxsoxGPXsiOiYG9evX54MHD2ZGN+1Ro0ax3377jd++fVvinINzjqY3b8Lz2DFh78KFEg8NZQB4VlaWGBcXJ48cOVIEqN740KFDUmhoqGhnZyfr9XrB2dkZnTt3Zu03bYIwcybg44MmTZqgefPm+OWXX2BnZ4dp06ahXr16VZ060tLScP/+fTanKnWKJJEipIKaeoVCwU1NTUtfqzt3SKq9eXPFRNuI3r1LtV+6FxKC+1qt8HJ6OoT4+PLHO3iQSHRV3QVSUihg98knkDp3xrrXXkPf4cNly2bNKh6fq1aRDHzBgsr3WQKzZ88uCkgbP1enBg2Anj3Z6tWr5amTJwvIyyMS//XXFEycNAlISCDfibQ0ajv2iPDw8ICpqamDTqcbAqAoRa/RaPoqFIpDrVu3NnV1dTW1tbVF06ZN4evrCz8/v5EAznHOZwKYCcA6Li4u9caNG+hasnyqEnDOkZCQoABw4ZFPvAqYmJicsrOzM33ttddExhjc3NzEtLQ05b1793ZpNJrxarU6FwDUavXNzz77TD948GCb33777RuNRnNJrVbfLbO7VyRJGl8j/5m0NJrH3njjsYk2APj7+8uiKB5avHhxLRuy1+F5RR3ZrsMLB7VanQygyB5Zo9EciYuLy83NzX38Gsc61Ah37tyRBwwYUG7RYWVlhaVLlyIrKwvbt28XT58+jRkzZiAlJQXJyckwNzevcaakDtXj0KFDvKCgwGgs9WLP52vWAN9/X2w+VwZ6vR5TpkwRFDU1FAJIYgpQhhogqaebG0mrBYFap3XvTs9FlzClPXWKfpfIeglmZpgyZYpw5coVHD9+HK6urnB2di59vMmTidxXB39/agVz924pp3CjE3hNFq5lERAQwE+fPo38/HxmZmYm2dnZCS1bthTbT5yIuEmTUH9vaeNerVaL27dvi6NGjar1sZ4YMjNJTdCwIfKsrZGv1wtRUVFSr169aq+M8fev0ISuNsjKygIYQ9qyZXDcsQOwta2xs7mLiwtcXFyKyGK/fv1gYWFR6yCGvb09Gjo4GJqfPCm027BBGEOZ0lL7EQQBr776KgNA14lz6uv81VeYN2FC0Q1y7do1nDx5UtTr9TwmJgaZmZnMzs6Ovf3227hz546Qm5srT58+ncrnNRoil4WYNGkSgoKCcPz4cf7777/L06ZNq/IzOX78uGxlZcWdnJwq3+78eZJeV3APm5mZ8fj4eFZUh37jBvlgLF9efe3522/TvQTg/v372L17N8ZPmwbl559TK67sbKqLNmLlysrLS7RaMrY6fpzKO9avR0B6OgoOH4azs7OQlZUFU1PT8muNdu0oaPSY0Ol00Ov1gpWNDY3nli1JkRMbC/z0U7FRXGhoscHlI4Axhj59+pj6+Pgs12g0PgDamJiYfKhSqcaMGzfOtFXhNeecY+/evdqgoCBTABkAoFarDQCg0Wh6AqhRr3mAAoEGg4EDqJlrYy3BGLMdOHCgaUmV48iRI81kWe4XHBx8T6PRDFCr1eGfffbZW3q93qZ58+YYMmSI2alTp/ZqNJoOarVaDwAajUZQKpWfvvTSS5KVlVX1c9HRo1SGVFH7r0dAQEBATkFBwQ9PZGd1eC7wYi/O6lAHAlMoFJHbt29vOGPGDIv4+Hj4+fllP3z40GTOnDmqF9o86jmDLMuws7PDH3/8IXTr1q1CIxvGGGRZxsOHDyGKIj7//HPZYDAIZmZmPD8/n5mamsqNGjUS0tLSJBsbGz5lypS6eegRYVxUODo6Kkq2DHohMW8emVRVgLt378LExITb29s/XgZWpaLMkyCQURpQo7ZYJdGjRw9cu3aNX7p0iU+YMIECTgYDLbQ3baq+b3FSEpGH/fsBBwcYDAYEBgbC19cXubm5AIBGtXDT1ul02LZtmxwXFyeMGDECHTt2hEKhKDUQEtRqTNm2jaTxIKL97bffwsbGptLez08diYlEJC5eBH75BbqMDHDOMXbs2NqfjySRGdwjBClKom/fvggKCkJMTIzcystLwJYtj7yvCmuPa4hZ3bop8OmnpJKoDrm55Kr/88/lSGm3bt3g4OCAkydP8rZt2wpubm5o0qSJIAgC8vPzYTAYilts2diQ58HYsUW1yG3btkVAQACLjo6u8jORZRlhYWHCvHnzKt8oNZVMAyMiyIiwDAoKClAkPw8JoYDX3LnkyVAFYmJikHDkCK+/Zw/2hoez/Px8CIJQ3FPbxYV628+bR9JxzikgFhRU+l7NzSV5+9691I7xlVeKTAV9v/qKA2BbCseDmZkZX7BgQfFc5O9P4/kRAslarRabN282ZGVlCXq9XjAYDHBzcyOSl5tLpRH16pEJXHIykex69WpmflcNunTpwuLi4prfuXMnWaFQsJ49eyq7detWZPTHOYevr68UHBycJQiCvyzLRc7fGo3mdQBbAeD333/PUiqV3MXFxczd3V3VvHnzCr+LdDodRFHMW7JkyVNRYmm12pUnT55c5+bmZmE8vlKpxPjx402vXLni7OPjc1uj0XwJYAlAruhdunRhQUFBTeLi4v4DYDEAKBSKf9WvX9++e/fu1c9FwcEkIf/66yfyHnJzc5GRkaHCU8r+1+HZoG6RW4cXHmq1ukCj0bTIzMz8cfPmzbOysrJy9Xr9O4IgdDx79uw/Jk6c+AjWtH9PHDhwgEdGRkoKhQL169cXBwwYwGJiYuDi4gKFQoHNmzdzSZLY8OHDq3SqtbW1xTvvvIPMzEykp6cLzs7OaNSoETMYDDhz5gyysrJkd3d34erVq+zu3bto8ximRn9nzJkzh/n5+fE//viDtWjRonym9UWChQXVRy9cCBS2kjMiNTUVj020jTh2jGTkj0HMxo0bxzZs2MBat25NYzc9neSx1ck6s7PpuOvWASNHIioqCjt27EBhoIQPHz6cNW7cuMhd/vTp01JmZibGjBkjVhZIOXDggJSVlcUWLlxYqbLHfcUKbBQEpGg0eP3113Hz5k1uYmIiz5s3788n2rJMC9N33wXOnUNes2bY9tNPUmZmJgMgPFLAKCSEPtcyrt21hampKQRBQMOGDQW0a0fy5DfeAL77rmoZ85NG586llRaVgXOqEW3VqpzhmBGurq546623SqmQDAYDrl27BicnJxnG7DhApRBKZXGPe1DtvYmJCWRZrtSlm3PiTnq9vvJztbMjslgYhMjIyCCinJCA5ORkGAwGoVGjRmQA9vXX1MmhZDeBCnDlyhXp+PHjorkksa7u7tzZ2RnNmjUr3XrS25tKNd59l+rF+/Sh1lxGoh0WRjJ1hYKCABcvlgtydOrUifn6+uKNN96AIAjYvn07Dh06BMYYRFFEy02bIEoS7p86BUEQin6MzwuCgLZt25bzYbhw4QL8/Py4s7Oz0L9/fyE6OporlUo2MCdHZGPGkMHi0aMUKFi9Ghg4kBQMnToR4X5MCIKAsWPHmhrbtpVVDd29excXL15MkCSpdwX9qC8JgpClUCj8Hz58+DWA3ISEBM/bt29P4Zx79OjRQ9mzZ0+Fqalp0QskSQJj7In0964E27Ozs2eeOXOmt7e3d6nJsEePHqKzs7P5zz//XNRC5NixY5g/fz48PT3NIyMj/63RaPap1eobCoViep8+fSwqG++l8J//0FithTt8VQgLC4NKpTq/YMGCKm6mOrxoqCpNgwIAACAASURBVCPbdfhLQK1Wc41Gsyw7O1uUZfmQWq0+qNFo/O/fvz8/MTGxehOUOiApKQmBgYFs3LhxCoPBgJs3b8o//fQTU6lUkGUZkiTB0dERb775JkzKmNtUBEdHRziWyWAoFAoMGTKk6FtJFEV+8OBBXL58mdnY2MDd3R1ubm5PzBzqrw47OzuMGTOGRUZGSsHBweILTbYB6hfOyyc9mjVrhosXL+LSpUvo2bPn4x3D2vqxHKsBkgwLgoC0tDQKEERGAidOVP2i7GzKwG7fDvTrh8jISGzbtg1t27bFmDFjIAhCuWDC+fPnRYDupT4V9GvNzs5GVFSUOHDgwCpLaExtbPB6mza4tmcPtgGoV68enzx58rPJaIeGUvuiqVOBzp3xx7FjSEhIEIcMGYIWLVo8WinQtm3Ffe4fA9HR0bC0tOTt2rWjz8LammpkT58udqt/2oiKInfjhw+rr/9cupQI4s8/1+oQer0eoigiKSlJjIuLK3aSbtqUjMZ27aKSCADDhw/HF198gbt376Jdu3YV7k8URahUKgQHB1cc8IuIgNSjB9Z/8omUlZ/PdDqdAABWVla8Xr16soODA+vRo4cgpKQAPj7Uvm3ChCrfw927d/mJEydEJycnvPXWWxCGDmXo3JkCBmXBGEmyDx+mOWbyZDI0XLuWDA3t7cl/oRICG/j/7J13WBRn18bvmVl6UYogYEEFpIiACthBY0F9xV5fS4zGFhM1JjEazWZjNDHF94sxMRoNJsYSNYqJGOyoWADBiAhYUAQU6WUp22ae74+HRToLrCbR/V2Xl8rulJ2dGeacc5/73LjBd+nShevQoQPKysrQtWtXQSaTEUIIIwgCSezZE6WtWzOKzEz1z0AIgSAIEAQBRUVFXFpaGqZMmVK5zrt37+L06dMYNmwY/H18WDYnB24//8ygQwdqijZyJP0+jh6lPcHDh1Mzxe++oxL7Dz5o8Pg0Rnl5OVJSUqBQKKCnpweVSgUbGxvoVTnnMjMzoVKpIuoItFEx37pVjR+fArBRIpF0jYqK+vjq1auj+/Tpo9e/f3+RSCSCqakplEqlhUQiYZ6Fy3bFc+DUmJiYu926ddOv6XvRvn17LFq0CGlpaSguLiZmZmb49ttvy/Py8ow5jivkeV4FADzPJxQWFtZ9slclKoom46qO+GwhUVFRJeXl5T80/k4d/yZ0wbaOFwaxWJwBYG6V/ydJJJKv9uzZs2LFihWaDwx9ibh58ybu3r2LkpISkpGRwfTo0UPVvXt3EQD06NGDlcvlMDAwQGZmJvLz8+Hu7s5o0/U9ICCAsbGxQUZGhpCdnY2zZ88yYWFhjEgkEjiOg56eHkaOHMnWdFHWUZ3evXtz4eHhSE1NJdbW1sIrr7zCtUTG+rfRpQs1RLp2rVrluWPHjpg6dSr2V1S8WxRwR0QAM2e2cEcBPT09YmlpyWDHDhogjRnT8AJDhlBzti1bwPM8Dh8+DA8PD4wbN67eRQwMDCCXyxEZGQlXV9dapoQHDx7kraysmB49ejRaVmklEmFI167ovWJF43OQnwWJidTg6fJlalZXcR/p1asXoqOjkZycLPTp06fp+0UITWI0dvw1oHXr1pBKpcy2bdvIrFmzGCMjI1oxv3KFSqufR8Bta0vdphsLtGUyei5v397kTRgZGWHy5MnYt29f7X5buZzOig4KAiwsoK+vDwcHB+H48eOsh4cH6rv/u7u7k4yMjFovKhQK/HHqFDH292e6uLoy7u7urIWFBYyMjFDxy4QmfYqLgfXraZ/8++83+hkuXboEY2NjLFTP6ra1pXLu+kYgrlwJlJTQQNbSkt5fAgNpUNvAvVIQBCgUCsbf3x8sy8LU1BTjx49/mqi6f58mPZKS6uxFB4Ddu3eTBw8e4MSJE0xeXh4KCgr4kvx8tq+TE+n9558sM2kSNXPs3ZtW3au2vj15QhMBn31GZ3afO1dvu40mKBQKXLt2DZGRkeUArhBCChiGMSOEOCmVyvb29vbyCRMmmLZq1Qo3b95UcBzX5BlWYrH4NoApEonE+erVq9/cuHFjwOTJk43t7Oxgbm4uLywsHADgQrM/RMPbzl63bt37J06c+OLVV1+t9sUWFxfjwIEDZcXFxSU8z18RBGEMgP8B2LRmzZp89fuUSuW13NzcsQAMUR8qFZ0mMX06bU/SAlKpFNnZ2RyAw1pZoY5/DLpgW8cLi0QisQGwpn379v9up+YaqFQqSKVSFBUV4eLFiwIhBEFBQez9+/eRlZWFbt26oVOnTvVK/kpKSpCSkoKEhASSnp7OtGvXTjAzM8OsWbOYdu3aVbsnqCvYdnZ2Wh8NBNBKt6enJzw9PSt3Njc3F5mZmay5uTmuX7+OAwcOwMLCgp88eTJXlwO6DtpHfP/+fb64uBjx8fHc7du38e677/7du9U84uKoXLKoiPZYVtC5c2dMmjQJBw4cQMeOHZsnmZfJaMWwhRLMkpISsCwLeXw8NXZrKAFFCJUEf/894OEBnuexefNmwvM8qctkUE3FPHsAgLGxMdm6dSsTEBBAnJycGDs7O0ilUmRmZnILFiyo91qvxty5wLRpMOWfpYqzHtLTafVw3DjqFl7leOXn02fckSNHNi8BkJJC+201NGlqCKlUCkIInjx5wvDq48RxtB982TIamD1rE8716xuXw0dFUTnxhQuazZGvA0IIDAwMiEgkqr4COzvaV13l2ps4cSK7ZcsW8tVXX2HUqFFMXTPD7e3tmaSkJFJSUsKYVjjCX716FVHHjpEBt26h465dsLKyqvs7lsmAQ4do3/jKlY3ue1lZGR4/fswMqToibt06WgFuCBMTel/JzaXJn48+oqZtw4fXu8jOnTt5lUpV/1SN0lJg/vx6A20AKC8vJwDYRw8e8O65uYypszPnsnkz9KZOZZgxY+i12bFjdRM3gF43775LTfoGDqQBvZERNXlsBhkZGdi7d285IeSSTCb7TCwWn6n6ukQiMXv8+LFkx44di1977TWD4uJiPQDNdn0Ti8V3JRLJCIVCMSMkJOS7vn37Grq5uRlHR0ePwDMKtgFAEIQfHz9+vKGmqvHSpUuKwsLC3TzPvykWi5USicRULBaX1Fye47j2VlZW9QfaAL2n+/trxRRPTUxMjIrjuKNr1qxRaW2lOv4R6IJtHS8yOQCm3rt3b8exY8cMBEEgBQUF/NSpU400kUE/b3JyctSGHbV6p7KyshAREYG8vDy+qKiIVSqVDMMw8PDwICzLYuvWrTA0NESHDh34/fv3cyqVCkZGRoKZmRkxNDTkioqKeKVSyfA8z/A8z5iYmBBTU1Myb948xtra+vlXuRrA2tq6soLXsWNHDB8+HIcOHWK2bt2Kt956C61a1VSu6QAAtSw4JCRESEtLY3/99VcyZcqUf9/sbV9f+jBcR++us7MzfHx8+KNHj2LRokVNl0GXlwPvvNPgg3FjXLhwAZGRkXDq0EHwXrCAg4MDMGBA/QssW0Yf6GNjIZVKcfnyZaJQKLBy5coGrzu1SZG9vT3mzp3LXLx4EUlJScLly5dZQgjDcRzat2+vsra21vzDBAfTMUi7dmm8SIv59VdaKc3MrDRoU3PmzBkSExMDNzc3oUEX64bIzKTVzBYqbgRBQEHFrPYpU6bAtOoIsWHDaLvAkSM0YfCsAu7Hj6kkXixu+H1799LrpAWfWS6XQy6XM/fv30fnmsGbnh6VXcfEAJ07w9TUFO+//z5z4sQJHDhwACuoOqLaIj179sTDhw+FH374AdOnT+d+/fVXXiaTcVNtbZn2J0+Cqc+oVKWigXJJCZV1a/CZ9PX1YWlpSc6dO8colUoMHDgQ7N279Lzeu7f+BRmGqgHUieOHDwE3N5qwGTaM9m+rVNVUBY6Ojpz6vKiTw4cbVsrwPDokJjL+Li7wOn2aQ2YmDc5PnQIamkeemgps3AhMnfo0GfDLL9RorhmUlZXh8OHD5eXl5W+LxeLv63qPWCyWAnj7448/fiMiIgKgLvitQZ+lmkWFXHy3RCKJuHr16m65XB4A4H2JRPIXgKNisVjW3HU3sE35+vXrf7pz585bbdu2rTyhnjx5IuN5/rTadbyuQBsA9PX1+zZorFtQQCdZHD7c4vuOGp7nER0drVQoFOu0skId/yh0wbaOF5aKm/yvEokkPi4uboxIJHLleT74zp07Rp6NOJw+T1QqFY4ePUqSk5MZIyMjcubMGcbIyIg3MjJCSUkJ5HI5JwgCPDw8eB8fH05d1SOEgGEYDgCGDh0KlmVhZGTEEUIgk8mQk5PDPn78GOXl5bCwsOAsLS0rjW7s7OwY1Bgn80/FyMgIM2bMYD/++GMkJSWhdwvGnWgDQRAQHh6Ox48fC0qlElKplDEwMICNjQ0zZcqUequMKpUKt27dgoeHR61kijaZNm0au3HjRshkWn+GeX7k5QF9+tCH3xqBTZ8+fbi//voLBQUFsLCwaNp6o6Np8Dd+fJN36f79+zh+/DiRSqXMtGnT0MnBgcOQIQ27j9+5Q93P164FIQQhISEoLi5mZmjQX2xvbw9bW1uhXbt2LMuyCAgIQEBAAAdQB+acnBz06NGjaSdSSAhg2HDBRmvcv0/7099/n843ryGLVigUuHz5MtO/f38EBgY2v3983bpaQXxzuHPnDk6fPg1XV1fB1dW19kVsZkbl3TdvUtfqZ4Gtbb2j7wBQufy8ebRf18mp2ZspKirCsWPHwLJs3QoRY2NqCmdc3Vu0T58+uHHjhrBlyxZ2/vz5EIlEUCqV4HkegiDA0NCQlUqlzPfff4/evXszg/r3hz4htSu2agih/eampjTBoGHgIhKJ8OabbzLJycn49ddfYWZmhp49e9L+5sYIDaVV9Lt36fUA0AkBixbR7fv7Q9W9O6Jefx1F27fzCa6ubLlCwdy9exe1nhtKS4Hdu+v+fFeuUKPHmTPhc+wYkz9jBg2WNbn+UlNpb/bAgdVbF5YsqTMJ2RgKhQL79++XSaXS3wBs02CRlfHx8f9jWfaTtWvXNjvQropYLE6XSCSDGIYZRQj5A8B+AJBIJABgIxaLtbIdNSqV6tydO3fmDBw4sNIAxsnJyTwjI2MlgEMNLcvzfHJRUZFvvW+IjqYtCFocY5qcnAyGYRLrmPet4wVAF2zreOERi8VJAJIAQCKR+B09ejTijz/+0H///fc5jeSXzwCFQoGEhATo6+sjLCxMaN26NebPn89YW1sz+fn5yM7O5oqKimBqaoqOHTvCxMQELMtW+y1btXeuan8uwzAwMjJChw4dno5S+ZfDMAx69OhBIiMj4efnxzzv700QBKSkpCA+Ph53794lcrmc8fT0hImJCVNSUsJYWFjgr7/+wpdffil4e3uzw4YNq7WOzZs3C1KplDU2Nsaz7EHPyaHPLEVFRYxCofh3zp53cKBBhSDUesnCwgLdunUT9uzZQxYvXty0azg7u9kPSAkJCcjLy2MmTJiATidO0Nndd+/Wv0BYGJWH3rsHmJridnIyCgoKsGzZMo3UGdnZ2cjKymIFQeBR1TEa1OinXllrQ7RvD6xYQc2n+vZt+vKaop4FfOkSVRHUkZC4fv06BEGAm5tbvX3AjZKRQQMe9az0FmBZ4SQ/fPjwuk8olqWJGqUSiI+nJl7apLiYHqeHD4E29bTJXrhAA7EW3Ndv3rxJzp8/zzAMQ1atWsXUm/ibOhV47TWaLKlwOjc3N8d7773H7tu3j9+yZQtHCAHHcZVu5DY2NjAxMRFKSkpYFxcXVv+zz6gaICGh7m18/jn9PF9+2Sy1gKurK/r3748TJ06QnqtXMxCJaLKiU6f6F8rMpEmL0tKnvdq2tlTxAgCRkdi9ZQtfFh7OLTh0iOuRlIRWoaEwioysPYZMLgf++OPpFILHj2kfr4sL3Q8TE8DVFeFr18Lc3Jx0AphGQ+3UVFqhd3OrNKmr3O/u3ek2mkhsbCwyMzMLVSrV65oYkxFC0gBAEIRNTd5YA1Rs+xgARiKReAFYDmA2gGfhvH0xKyvLsKIoAYCen4Ig9JJIJPZisbjeA8myrGu9Jq1379JESMV4PG1x+fJlqUwm+1yrK9Xxj0EXbOt4qRCLxdEbNmw4TAiZXDN4fZYoFAocPHhQSE1NZUUiEVEoFIyxsTEBgH79+qFfv36s+heClZUVdLPBazN06FDm/v37ZMuWLcKQIUPY5zUu7LfffuPv3LnDCoLAtGvXTujVqxdeeeUVhmGYag/lgwYNwu3bt9nffvsNPM9jxIgRla+dPXsWKpWKNTMzQ2JiInF2dn5mqoL27dvD29ub/PXXX8ynn34KJycnYdKkSey/LuhetYpW15Yvr/XS8OHD2e+//55s2bKFnzdvXuVc2EYxMgKmTUNBQQF27NiB4OBgdK1nZFJNgoODUVBQgN9++w3dFi5seM5tUhIweDCVj1fIbaOjo/kuXbpwmrZBqGdum5iYaPc+lZNDg9RnxVdfAQcPAlevUmOneigrKwMA/Prrr/zSpUub9xlv3qTJAy0k39RJmwYVIVZWNOCdO5cmEuqYF91sTE1p4FZfoP3LL1QdcOpUs9sgCCEICwtj5HI55s+fX3+gDdAqr1RK5fM1rpFp06ZxKpWq8pjl5+dDX18f5ubmDABmy5YtJDQ0lFn2zjtg6lNxhITQyvn69Q0alDX2eczMzJ7OdC4ro9L3hoLtkBAasObn171dY2MQa2uuXdeugqi0lG0L0MCc52nyz9GRmvy1aUPvTW5uNLG2axc97xmGjhd7/fXKVfbq1QuHDh1ipFIpZs2aVf++PXxI1SB2dsCcOdVfKyig11Njxnk1UKlUuHbtmkKlUh3QVLLNMMxqQgjEYnED+vmWIRaLbwB4teLPs1h//oYNG8qLi4v11ffcrl27wsrKSlZUVPShRCJZVFfiQSKRuADwrTeZuW4dMGKE1uTjAPD48WPk5OSoAGg3gtfxj0EXbOt46eB5fryXl5deQ3NDW4pKpcLXX38tyOVy1sTEhC8tLeXatGmD+fPnQxAExtzcHEZGRuq79b9Czv13Y2hoiNdee425ePEiCQ0Nxc2bNzFu3LhnWrk9ceIE7t27x02dOhWOjo6oGWBXhWEYuLq6YvDgwapTp06J0tLS+Ndff50LDw8nsbGxzNixY8mpU6eqfu/PjDFjxjBeXl44d+4c7t27x6alpcGpBbLTv40vvqAu0zV6Sg0MDLB48WLm8OHD+Oabb8jMmTMZjQzTLlwAWrVCyNmzQllZGRsWFqZxsA3QXt5Ub2+Uu7jAqGrVqSrZ2VT+efw47asFDeDS09O5qVOnarwtR0dHtGrVirRv316758vPPze777NBSkpoED9oUKOj1X788Uc+PT2dAwCpVMrFxcWhR48eTd/mtWt0FrYWUPtE3L59u+FRkQMH0oBNoaABWHPmgtfF8uXU1bouZDJAIqGS62YG2mVlZfj222/B8zwWLlxY24W8Lg4dolXp/PxaM+SrBuo1jStfeeUVJmPlSnIlPBxd9+5laqWOd+2i393q1Y3Ppm+Aixcv4ty5c/D09KTzwpcubfz4BAfT89TdHaGTJiE1NVVYtmxZ5X09OzsbLMsiKyvr6TKrVtG/lUrab+3gQPuoT58GFiygzu3ffUcTcJ/XLk6amZnBwMCg4UA7LY1K0q2t6TprEhNDvQ8aQRAEZGRk4Pbt23B1dUVcXFy5VCqNBaBRL7BEIjEE0BPAJU3e/09GJBLdy8nJ6Vk1wTlnzhzDnTt3ziwrK3ORSCTjxGJxUY1l3nR3dxfMzMxq/66/dIn2ajdgqNdUCCE4fvx4Kc/zq8RisUJrK9bxj0IXbOt46eA47vvr168v9/X1fSYO2wDw1VdfEZ7n2dmzZyM7O5tr3749/mlGZP9GzMzMMHLkSNbHxwehoaFk06ZN8PX1ZV7R4pxLgD6wbN++XSgoKGDnzJnTpDntffr0Ebm4uGDv3r3sJ598AkII079/f9ja2jJlZWWoS2L+LHB0dMSjR48AVG8z+Negp0crUMq6FYZ6enqYPHkyd/HiRWHXrl1MUFBQowGb0skJe6OjUQ6w06ZNw5EjR3Dy5EmNvxNZeTmKWrUCW5/nw8OHgIkJyKVLuMcwuPzTT7yVlRWTnp7OmJqaki5dumh8D2AYBv7+/sz169f5wYMHa6+6TQjt9z1yhPYdaou5c2nvKzVWqhdBEJCens5NnjwZnTt3xr179/D777+TTp06MU3qwVcqaU/822+3bL9rUFhY2Pib+venhloeHlQ23FJkMuDkybp7wR89ouO9btyo1UOtKYQQhIeHk7KyMmb58uWoVyJbF++/D9jbN2nEmJubG9yGDWOuR0cL27dvZziOg0qlwrJly2B88iQ1Kly2jAatLSAhIUHo3r07O27cOHp93L9PExINSXzffBMqqRTH//pLSE5OZuVyOZudnY2MjAxcuHBBKCoqYgHU3eqjpwesWUP/LZHQ72Tu3GpjCuuiXbt2UKlUKC8vh5FRHVNI09PpfPOOHWkwD3qdVBYDBIGapdXhV5KZmYmkpCQ8fPiQFBQUCOXl5RzHcWjTpg0fHR3NqVQqIwCTxWJxboM7+ZQVNf7+18LzfGx2dnbPqolmExMTvPHGG8ZhYWF9EhISUiQSyRixWFw1saDS19dXoObYL56n1/rMmYAWDXbv3r2LnJycPEEQdmptpTr+ceiCbR0vHatXr377s88+63no0CH/mTNnGrRu3RpqSVxLKt2FhYVq4yxeEARu5cqV4DgODi18oNBRGzs7OyxcuJBRP6gnJiYKb7zxhtZ68I8cOQKlUsm89dZbzQpUrayssGTJEkYmk0EkEkEkEuHjjz+Gk5MTwXNUMgwdOhTh4eGIjY3Ff/7zn+e1We0hCLTSc+JEnQ+aDMNg4MCBbNu2bXHo0CHo6+ujW7duda9LpYJiwwY8nD8fC5csgY2NDaZPn47du3ejQ4cOcG1IFg4AxcUw+fBDnBk1CokxMXwvQjhjY2OYmJjA2toajx89gsOgQeDmz8eZXr2EmJgYxsXFhb1z5w4pLy9nmnr8IyIihOjoaLam63OLUTsyu7hoZ307dtAH0R07NAoGWZaFhYUFn5SUBDc3N87d3R2xsbFk7969WLRokeZ+DL//ToMfLSaSjI2NSYXzdOPXaEgIDbaq9v42F4ahrQd1sXYtPa7NDLR5nsemTZtQVlbG9OjRo2mBNgDs3EmrxYRoLp1NTAQmTYLPkiUsrl/H77//DgA4KxaTUYWFDLN4MdBC3wpCCKRSKVttDFnPnlQN0wDFhoY4fOKE0PfKFWbA3r3Ysns3zp8/j8TERHTs2JHx8fGBj48PzM3N6z4R9+2jhnxJSdTUTYO2EJZlYWhoKGRlZbGONT0jHj2iPes+PsCrr0KlUuHUqVOIi4sDy7IwMzPjRYQwEz//nIWVFRIvXEBqairJz88XysrKOACwtbXlHR0d2T59+nD29vbq75gLDQ0VEhIS/lyzZk1mozsJQCKRiAB8AgBisThKk2X+ySgUirgnT56UAah28XAch+DgYENXV1fDQ4cOnV6/fv3XKpXqBAB9kUj0qoeHR+3W+qQkOuYrOFhr+ycIAv78889ShUKxRCwW68Z9vcDogm0dLyVyuXySXC7P2r17Nxk3bhyzcydNKr799tswMzNr0roEQcCOHTv43NxcztramgwdOpRzdnZ+2kem45nAMAycnZ2xdOlSZvPmzeyVK1fQr4WS0tjYWFy8eFEoLS1lZ8+ezTQ10FYoFCgoKICxsTGUSiUsLS1RXFyM06dP8wA4Pz+/59oyoJZ3+vrWb6z6j4ZlgWPHgEYq1i4uLhg7dixCQ0ORk5ODgICAWomzO6dPw1BfH/0DA4mNjQ0D0P72Tp068bdu3eIaDbbj4qAXHY23IyJw/M8/ubNnz/I8z0OhULBEKmXalpcLxWPHsi5eXoi9epVdsGAB2tD+W2bDhg0kKiqK6d69u0ZmYA8fPsT58+fZIUOGwNfXV/s3kk6daEXul1+a3+9MCA2yExLoOLEm3DeHDx/O7d+/H0FBQTA2NkafPn3YvXv3QqFQwFBTt/TTp6mJlxYxMjIi5ubmBDUM6erEwYFW17t1o3Ovm9u/rVIBFhZ0HTUVE998Q53HW+h6XFZWBg8PD4xuzkxgW1sa8Kenaz4ybt48Wu3dvBnq4PXO3r14GBLCXBg8mA/w8WnxOX3r1i3I5XIMGjTo6Q8dHIBZs6jCog6ZfFJSElTLlpFX2rRBO6mUKU9JAcMwYBgGtra2wquvvtrwxVBQAIwa9fT/rVpRebcGiQMTExOSlZWFqsH2w5gY2Pzvfyjo2RMXjYyQs2ULX1JSwpqYmGDGjBmMiYkJUlJSOGHpUtw0MsLlIUPQpk0b3tHRkenZsyfn4OCAVq1aVU4lqUpZWRlu3bql4Hl+aaM795QRAMBx3KdNWOafTGJmZqZKLpfX+WLHjh0xe/Zsw2vXrr394MGDNzmOIz179jSyt7eHQlFF0V1UBNGSJeA3bQKpR2lVH2rzwLqIj48n5eXlyaCmcTpeYHTBto6XErFYnC2RSEYXFhYe2LlzpxEAsCybefbsWduRI0eyeo2YkOTm5iIkJESwtrZms7OzCcdx3NChQ+Hh4cFobNakQyuIRCI4OzuTiIgIJjc3F4GBgTAzM9NIpaBSqRAWFoZ79+7xCoWC5TiOcXBwwJw5c5o8zzsxMVE4fPgwyzAMVCqapG7Xrp0gk8kYuVzOzZkz57m5w585cwaRkZGV/4+Pj8fQoUOfy7a1zoAB9OF91SqgS5d63+bu7g5DQ0McPXqUXLlyhXF3d8fIkSOhr6+P/Px8hJ86hdaBgZjav3+1aNfPz487ePBgw4qD+HhqEhUVBUMA4+noMA6gyTZ+wgToyeXslbVrcfLkSVhZWZE2bdpUru+VV15hIiIiyKFDh4RJkyY1GGgUFxdj7969GDJkSIuTR/ViakorNbm5zQ8SBw+mfEUDqQAAIABJREFUMuotW5q8qFqim56ejq5duyIjI4MAYDT2X5DJqLTZ37/J266P69evIy8vj21SS4qjIx37JJVSw6zmmCaJRNRwq2agffYs8NNPwIwZLeoLV98Hc3M1VRHXwahRdB81gZDarQSpqXA5cgRtXnsNm+/c4dqlpKC8vBw8zzd7FOKjR48EAGytiQtXr9JAu0YF8sSJE4iNjUXw11+jvacnC3NzGJeWoodcLsTExLA+Pj71RkVKpRLyXbtgsmoVmJycpy7hBQXArVvA9OkN7mtxcTE4juPS09N5f39/DgD2f/898dqyhTnWvTvuyGTwNDLi/fz8OAcHB9jZ2VV+b9bW1shxcoLe6NEInDy5zsC6Lq5cuaJiWfaAWCxO0eT9FeRyHPeE5/mfmrDMP5k7BQUF5l/UUDvUEQDrVfzBmTNncObMmWov2mVkwI3ncTYsjJrhtZAqCVeG5/mFmjjE6/h3owu2dby0iMXiYxKJxASAHYBiQRAsEhMTdyUnJ/dfunSpfkMVlsOHD/OmpqZcZmYmhgwZwnh7e/87Ryy9IAQFBbG2trYkKSlJ+L//+z9OX18fbdq0Id7e3kz37t3VM2CrLSOTyfDdd98JRkZGzMCBAzmO49CtWzfo6+s3q9Qnl8sZhmEwc+ZMXLhwAYMGDcLRo0eJSCQic+bM0dwxu4WEh4cjKioKZmZmGDZsGKytrdG6devnsu1nRn4+7YduINgGgM6dO2P58uVMeno6jhw5Qn7++WdhxowZ3A8//ED88vPh16pVrYDu7t27vL6+fsNz5+fNo8ZYX35Z/eeCAPbCBbDbtwOtWqGPvj6srKzw+++/Vwve/f394enpyXz55Zdcbm4urK2tIQgCioqKwLIsTE1NwTAMkpKScO7cOdjZ2Qn9+vV7dh4PLAtcvw5o0p9ck7g4OqLqgw8Ab+9mbp6FlZWVkJycTLp27coJgsDY2NgQlmU1i1b/+ovuexMTYg1x/PhxdOzYsfF2gpq8/Tat/JaWApuaMSlp/nw6i70q0dF0HnNYGK16twCpVAoAkMvlzW9h6d2bJmh27WrQYR4A0KcP/UyvvUb/r5ZJv/kmLAYOhNuBA+SXX36p3I+IiAi89tprTVaUDRkyhL116xYOHjxI/vvf/z79XJMn00RMBSqVCj/99JOQn5/PzpkzB3a//87g6lXqLL99O3z/+IONiYnBgAED6gxic588QeTq1eRGhw6Mw5IlcL5wgVfPu8eUKbSKXoPk5GScP39eaNOmDQkODuaOHDmCzMxMZGZmcvfv34dQUoLJe/cyZmvX4j+TJ0OlUsHMzKzuIDovD23GjqXGXBqiUCgQFRWlUiqVH2m8EACxWHwF9HnoRSFbEARh9erVbHMSOgCoD8CUKcDVq+inRbXisWPH5PHx8SFr1qy5prWV6vjHogu2dbzUVGQU1fMWSyQSyRwAD8vKymBoaAie53HixAmkpqbKX3/9dYOSkhKEh4cjJyeHW7p0KbTeT6mjWejp6cHPz4/x8/PjCCF48uQJkpOTmTNnzuD48eNgWRb6+vqE4zihY8eOHM/zePDgAbGxscHMmTOZxpQMmuDt7c0UFhYKe/fuZRYsWMBYWFhg8eLFz72XIDs7GwAwcuTIpgcO/1QOH6Yjq8rKNOpdbdeuHUxMTIipqSn3+eefw8HBgfTz9WX1a5gTEUJw7949Tj2Gr04KC2mlrq7k286d1LgoMRHQ10diYiJCQ0Ph4OAgAKgWLBsbG8PT05Ns3bqVMTY2JuXl5ZX9yWolhKGhISkvL2dGjx797M0UL12iPYh5eZpXZAmhM7qXLwfeeqtZm7158yZOnTrF8zzPFhYWEgDQ19eHTCbTPBi8c6dRx/OmcPXqVahUKgwcOLB5M7/HjQO2bm1aXzNA35+QQEfSqZHJgNmzaT9wC2ehl5aWYufOncTIyAgTJ05sWQtLVhY1BZs9u+HP+OqrgFpFI5PRoPvVV2myCsDkyZMZdTVaoVBg165d5KeffsKsWbOYpvSTcxwHhmGgrCnrNTAAPvsM2LMH+fn5CAkJEVq1aoXFixdT/42SEjrTfNYsoKwMalM+uVyOirYQpKWlVa7/7v/+Jww9coTtk5SEn3fvJhERERzLssKAAQNYFBYCkycj9fhxyOVyODk54fr16zhx4gR8fHzYpKQkYf369TAyMhImTpzIOjk5ISstDabjxuFCUJAwZtYsttHz7dAh2jLRhGA7ISEBHMddXr169QONF3oBEYvFZMOGDdKysrJWTfYqUPP11y1Wl9QkNTUV8fHx5Uqlsp7xAzpeNJiG+gl06HjZkEgknJ6e3lGlUjnK1ta2RKFQMEVFRYIgCGZ6enpQKpWws7MTvLy8iFoOpuOfiyAI4HkePM+jsLAQWVlZCA0NRfv27YV+/fqxLi4uzXu4rgdCCDZt2oSKIF5r620qO3fuFDIyMtgPP/xQq5/vb6VLF/rQvnZto29NTU3F3r17YWdnR9LS0pgPP/wQzGef0eCyioFabm4uvv32W8yePRu1jIsAICWFVm8fPqw9oujMGRoMlZUBVla4d+8eDhw4gNGjR8OzPrdyACUlJUhLS4ONjU1lT70gCFCpVMjLy8P27dsxffr0ut2QtYkgUGlwv36NB4iCQB84FyyglctmqngKCgrw3Xffwc/PD+bm5nBxcYGFhQVu376N/fv3AwAGDx6MAQMG1L8SQqh8/cIFap6nBT799FPSr18/pl+/fs332igro2PqfvwRqG9Gb02KiqpX5+Vy6lHg41Nr3F1z2LRpE0pLS/HBBx9oZ8ylQkGTXvWZfn71Fa0CtmtHA+25c+mfwYPrXaUgCNi9e7eQlZXFzpw5E23bttX4nhUXF4fTp08L77zzDlv5+VJSgOHDkfj77zh69Cjx8vISgoKCnppnyuXUa8DYGNi7F0Lv3th0+HDlXHs1pkVFCLhwARFTpwpvvfkmq29ggLKyMnzxxRfgOA5vvPEGLMzMUN6rF74YMwYiA4NKefLQoUPh5+cHQRCQnJyMzp07U2WVTAYMHYrwvn3BDB4sDB8+vPEv5f59mhxogopk69at0uzs7Olisfil7wXeuHHjw9mzZ3doykSRSiIjgXv3aNuAlhzIBUHAN998U1pUVDTzww8/PKKVler4x6OrbOvQUQWxWMxLJJKJAHpnZWVZAJACiACgBICKvlvdCK9/CWqHeT09PbRt2xZt27aFl5cXUKPqqC3UlZYKY6y/jREjRrA//PADSktLXxz1RUyMxpJaKysrKJVKpKWlMUFBQfTh/cYNGmxXIS0tDQCwe/dumJqaCra2tkxeXh5vYmLC9uvbl+3q4kKD6pqBdkoKfQCLigJxdMRf16+T8PBwpkePHg0G2gBgamoK9xpV2QrlBdRGPtnZ2c8+2GZZWq2RSICPPqr/fWVlNLhmGCq9bUG7DMdxYFmWKBQKwc/Pj1MHVZ06dUJAQACJjIxkzp49i7i4OH7p0qV1R73R0XRkmZYC7czMTCgUCsbf379lppbGxjTIjI7WPNju0IGa1KnPy2XL6Jx26gnQIjIzMyGVSrFkyRLtBNoA7fMPCgIePKit9BAEOmt66FB6HObMoSPSqhqY1QHLspg9ezYbHh6OHTt2wNDQEHPmzKk1u7suunTpgj/++IMNCwt7av7WuTOi587Fhf37MXLCBHh5eVX/UteupUqUY8eAn38Ge/8+Ro4fj5MnT/ITJkzg7OzsIBKJINy4ASYuDr2WL2fVVU21AsXS0pLs2rULvXv3ZjI7d4aPgwMZOmsW8+DBAzg7O1f2oLMs+/Ral0qB8eNxffRoXOd5zPTwaPxLkcuB//yHmudpSFZWFgoKClQAwjVe6MVGJQhC05fieeps/+qrWh31deHCBVV5efl1QkgD8+l0vGjogm0dOmogFotloAF2JevWrVttamr6Ydu2bQ1VKlWzDF10vPioHXL/bvm2VCqFnp7eixNoAzTgfe01oHt3GpQ0gJmZGVatWgWZTEZ7QQkBRoyoVS10dXWF2tF3z549UCqVpG/fvqK8vDyhaN483JLLifWxY0w1X+OrV6kxVlISYGWFC+fP81euXGGHDBmCnj17tugjOjo6wt/fH2fPnn125mhVKSwErlyp//XSUupcvns3sGdPizdnbm6O+fPnM1u2bOG8vLzQrl07AFRGHhgYyNjb22Pfvn0oLCzk9u3bJ0ybNq12QJKZSSvIWiI6Ohr29vbEwMCg5RKQkBDg3Dng//6v0XMUAJWQq6vEZ89SaX67ds0zWqtALpejtLS0sp0kISEBAQEBzV5fNby8gB9+qNvBXiqlSShBABYupL3TwcEaf5agoCAEBQVh9+7dJCwsjMyePbvRYPTIkSOCqakpevXqxQI0GN61axff79dfuTnvvQcrL6/aG585kxoDAnSMl7k53DkO7u7uNKKOiQFmzwYbH1/vzPjp06czZ86cQWxsLD/yyRPWsU0bhjU0RLUxZFXJzwdefx3yRYvwZ3Iypk+fXnnuN0hqKtC6dZOc/mNiYuSEkO91o6QoDMPI1UmSJnH9Ov19o8VRX5mZmbh8+XK5UqmcqjNFe7nQRQw6dGiAIAifFxQUbPjqq68EQRCE+fPni/7u6qWOfx5Hjx4FgLolyc+R9u3bQ6lU4oVLDPXqpfEYJH19/aemhY8e0R7Y2bOrvcfY2Bhq47q5c+dWfbhnebkccbGxwo4dOziGYWBsbMzrAZgtFnOR48YJWYMGMb6+vkxkZCQ3c+ZMrTrNa60S2RgjRgDDh9M+1pqJmZ9+opLg0NAW9w5XJTU1Febm5sTe3r5WIOTi4oL33nsPGRkZOHToEJufnw/LqqoCQmgwO3eu1vanX79++Pbbbxme57UzrtHCgo7sWrCgei92TebMoUZi7dtTOf/y5fRYN9EorCpZWVnYtm1bpZy5Q4cOpGfPntrtIwkMpH9OnHi6r1IpTRokJNDzxsYGGDmyWX2uo0aNYrZs2cKEh4cLw4YNYxu6FrKystjevXvDzs6usj+7devWTJe5c6Ffx+gvAFSZoa505udT87fbt+n/FQraojB9OnWJr4H6uJqYmGDChAkAwMHZueF7UnY28P77wNSpOKJQkHbt2gmOjo6aHZiCAuCI5kpjpVKJ+Ph4olKptmm80ItPgUwma9oSUilV+4jFLUp8VYXneRw9erSU5/l3xGLxI62sVMe/Bp0cVocOzRD09PQ+53l+uUql2nPy5MnSxhfR8TJRVlYGU1NTWFlZNUOzpl0MDQ2hp6eHGzdu/N27ol0WL6aV1ri4pi13/z7g5KT5+2fMAOflBd+PPuJWr16NBQsWYISrKzfIzIw7t2WLENWpE1tUVMQcPnwYHTp0ELQZaJeXlxM9Pb3nV/VYsAAYO7b6z9QBQmqqVgPttLQ0FBQUoLi4mFFXXmtiZGQEZ2dneHh4CNu2bSPVqlLqUUuNSPWbgrW1NfT09PDokZaef729geRkWgFuaNyWVEqD1bQ0KokPCaHndgvIysoCIQSrVq3CihUrMGfOHEbr6hZTUxpMVz1epqa0Mr9vH/0s773XbOmtpaUlxowZg5iYGPbzzz/H48eP631vz549yc2bN/m4uDhs27aNuLu7kzlz5rD6/fvTftu6OHQIWLeO/rtjRxpcK5XAtWu0TUJPD1izps5Fo6OjAdCRdZVIpfWrPrKygA0bgIEDIUyYgNTUVAwePFjzDMTKlcDNmxq/PS0tDSKR6K5YLH6o8UIvPjnl5eVNW+LhQzpW0NdXKztACMHRo0dlBQUFlwRB2KGVler4V6ELtnXo0ACxWExWr169cs2aNZv19fUHmJiYmGzcuFG2fv16RXh4OF/LEVXHS8elS5dQUFCAKVOm/O33VZZl0atXL+HYsWNITk7+u3dHu6xd2/QRS2ZmtDdZE0pKaE9qRZDCMAysrKzQ9csv4R4djdEzZ7Lvv/8+LC0tCQCtft+hoaFCfHw807Fjx+eXsBGLgW+/pf/OzqbqAZ6nwZSW2yH27dtHrly5AqOGKr4VjBw5klUoFMzWrVv5yp7LkBDaz6xF07+8vDwolUrqVK0t9PRotVod1NXk0SPgwAGaAPrvf4E//gB69GjxZtWJia+++gpbt24lzepV1YQjR2gQKJfT6yUwkCZmbt0Cpk6trZJoIl5eXvjggw9gZmaGw4cP15t46t69O5OXl8cdO3YMlpaWGDFiBDVCMzCoVwKOFSvo8QZo9Xr9euDoUXreR0TQ764e4uLiSKtWreBQ1SCO4+puxcjKojPou3YFXn0V0dHR0NfXr75sQxBCK+yBgZq9HzTZolKpLmm8wEsAz/M5TapsP3xIJy7UHMfXAuLi4sjt27cfKRSK8WKx+G9Pxut4/vztD4U6dPzbEARB78aNG5DJZG+oVKr3oqKiuKSkpL97t3T8zfj4+AAAvvvuO0TU96D3HFH3Maanp79Yv9wPHKCmUk2ZpHH6tGbV8IwM+t5Ll56aopWUAPv3A6GhIFu3Ij8/H3fv3kVqaiozZswY1Jzb3RwIITh58iRu3LjBTpo0CVOmTHl+kw7ataNVyT17aJDi7w+0aVN3X24LYVmWuLu78++99x4acwcWiUSYP38+CgoKuDj1d5eUpNVKOwDs37+fd3Z2FixrmuC1lJAQYNUqej7VxNeXBmL37tHq5dtva2WTzs7OGDlyJKZPnw5CCLNu3Tp8+umnROu/nxiGBqkhIbR6r1DQc+j//o9Wh7XAX3/9heLiYuLk5FTn/UupVCIyMpIHgI4dO0IQhKc3hK5dabCkUNRe8MQJYOnSp/9/5x3aX/7NN9QPooH7io+PD1NUVFTdLX34cOCTT6q/MTubutJbWACLFuHBgwc4e/YsRo4cyWg8HWLXLqC8vE45e33k5+fLlUrlC5ZdbRlKpfJJkyrbe/bQXm0tmaLl5ubixIkT5QqFYrRYLNYpIl9SdMG2Dh1NRKVSTeY4biOAXwwMDOYC0DxbreOFxdraGu+88w4A+vD3dxMeTs1o27RpwyYlJeHhwxdEWciy9AF5+HDNl2nVSjPpcUgIHV9Ulc2baRBhbIyI8+f57777DkePHsW4ceMadR7XBEIIzp8/L1y5cgWDBw+u5VT+TCkro39/8AE1jiouplXuZ9TnHxgYyCYkJHA5OTkavd/Ozg4jR44UwsLCkJuUBLi4UNmvljh+/DgpKSnhJk6c2Pi846ZiZFRpjIWa197t23S++YIFwKhRWqvUm5mZwdfXFx07dsS7776LESNGkE6dOuHAgQPYuHEjTp06pZXtAABOnaLnzKVLVAmxerXWHOLLysoQHh5O3NzcMHz48FqJp/Lycvzwww8kIyMDy5cvx4gRI5Cdnc2uW7cOPM/TavPnnwMXL9Zeub5+dSf1oUPpvcTaGti7F3BzoyZp587RZEgVUlNTBTMzM+hVrX5zHPWCUAf22dk0UOa4yiTK4cOH+UGDBpEmGWeeOdPk86K0tFQJIK9JC73gEELyy8rK6si61EFk5NPfL1pApVJh//79pTzPrxCLxbqKzEvMC+Sco0PH80EsFl8FcBUAJBLJDI7jvrp48WKAt7e3XmpqqlBSUqL09vY20MhtVMcLhdpg6Z9gnhcQEIC7d+/i6NGjYBgGhBCsXbsW+fn5ePToEezt7WFtbf3vnMM9ezYNDDUlIgJ45ZWG35OeToPO1auf/mzHDmpc9fbbKC4pwdWrV7mJEyfCxcVFKyZm+fn5iI2NxeXLl9nRo0ejhxakxA2iHmezZAnw2WfAzz/TnuHRo2nQ9NprtPL3jAzafH19cfz4cezcuRPvv/++Rsv4+PiwYWFhSNm/H5Y8D7YBmW993L59GydPnuQFQQAhhKhUKtbFxYW9efMmM3fuXK2oE+rE3R0ID6fzlUtLARMTOkrI3x8YOJAavT2j649hGPj5+TF+fn5ITEzE5cuXyeXLl5nLly9DT08Po0aNUo9BrBuVil5jHEcVHyxLP8OjR7RyfPIkbUEoLKTVbS1VtAHqqK5UKpnHjx8Lt27dQrdu3SoPklQqRUhICDEyMiILFy7kWJaFubk5PD09kZ6eTliWpe/t3ZsGvjUZNAioOjXgrbeqO8f7+dGRbLNmUSn4Dz/QZNvy5fDx9mYjzp+HSqV6GnAbGNARZyUl9PrauZMeq5UrK1cpk8m4JiXmFAoaqDf0/dRBRXVf50JenYKKJETDFzkhtO1jwYJmGfvVXh1BWFiYvKSkJFIQBJ1h3UuOLtjWoaMFiMXieIlEsiA5OfnP27dvMyqV6g+VSpV748aN1T179jQMCAgQadKfqOPF4PDhw+A47h8xcsvBwQFOTk68hYUFN3jwYGzcuBE///wzsrOzIZPJoK+vT3ieZ9q0acM7Ojoybm5urK2tbWXgcfv2bVy4cIG0b9+eMAzDGhkZoV+/fsjOzsaTJ0/g4eHx7IKUxvD0pPOMv/kGePPNht9LCO0nbUgmLAj0Afybb6gDN0ANk9atA8aPx/3CQhw6dAht27YVXF1dtRaJXr58WYiNjWUtLS35Hj16aF86rlLRKvW6dcCTJ1S6vGMHdYpeufKpEdS2bbRP3cICGDyY9oq+/vozCQRFIhHkcjnOnj1LeJ5n+vTp0+D1ovbDEE6fxp9ubsJQhYJt6nkXFRUFmUzGBQUFQSQSobi4GLGxsUJwcDDTtm3bZ5tt6tqVzmQHqIEYywIbN9KkhhaN9QDQc728nJ7P2dlAURHQpg3cHz6Eu60tk2lmhpSwMNxjWUiPH0epiwtMpk+nFV1BANq2peZuQ4dSgzxbW2rwdegQNUVr145W5M3M6DVlakr7tvfsAd54QytBCgBYWFjgzTffxL59+9hz586Rbt26AQAKCwvx448/EmtrazJjxoxqTuWenp5ISEhgPv74Y3Tq1IkfFhzMWd65UzvCOngQ+PJLQG0e6e1Nz3d18kc9vuu33+jf6ek0SVJUhJ7r18P+5k38bGoqzPXxYeHvT6+R3r2Bv/4CLlyg73333crNZWVlAYBGPgWVnDxJk2H1mbzpaApSuVzON/quixdpP//QoVrZ6NWrV/nExMRHCoVikm7Mlw5dsK1DRwsRi8X3AXSt+rNPPvmkbVRU1Fuurq5/+xgoHc8HlUqFu3fvwqkprtfPkNzcXKSlpXF+fn4wrJBNqqXkK1asgKmpKZOXl4dbt25xiYmJiI2NBSEEXl5eePjwoZCTk8MaGRmBEMJmZmYCAM6dOweABkxxcXFk5MiRjJ2dHVQqFWJjYyGTyYhMJiOFhYWkX79+XE5ODry9vZ9N9TwpiVYNGwu2y8vpe1q3rv89hFDpqFr+v3cvMGAAcOcOYGCA0F27YGVlBU1m/2rKkydP4ODgwMbGxkIQBO2Vkk+dokFQly50HFNWFtCtGx0xxTC1pLEA6LGcOJHOSVbL5j08aFLD3FxruwYA7777Lr7++mtcvHiRAWj7hdrvoC4MDQ3x4VtvQYiIwBddurCPdu3CvHnzmqQsUKlUaNOmjeDp6Vm5kL+///Nro/vhByA+nvZvL1tGR651rfIrg+epqzXD0Mqxnh6VoGdnU+n8mTPUib1/fypvtrMDEhNp4DtpEg3ec3NpNfbyZRpAXr9Opeyvv04DRzMz2AUGws7bG/19fHDxl1+EKxzHDu7cGezs2bQybWtL96V1a+r8r75uJ0+u/ZlSU+m5sXQprdKHhFAFSatWWjlklpaWYFlWMDU1ZQB6PwsJCUHHjh3J5MmTa313zs7OeOedd5Cbm4uDBw8yRy5fxoT4eGIze3b1m8+QIdXl7lu3Npz0aN++0pCR3bEDt778khjl5QHz5gEzZlATwZ076XGbOZOqRqoQGRmJLl268FxT5srZ2zerj9/AwIAFoEWnvxcCM0NDw4aPfUkJ8L//0T5/LfyuSklJwblz56RKpXKQWCyWtniFOv716IJtHTq0jEQiEQF4q0+fPrpA+yXi+PHjBABTMX/1b6WoqAj79u0jTk5OcHZ2ZgBg6tSp2L9/PxiGqawkWllZYeDAgRg4cCAAICEhATExMXBycmLnzZsHfX39yiePwsJCREdHw8vLC4aGhti3bx/Zvn074+HhIaSlpTFSqZSxt7cHAJKfn8+mpKSAEILc3FwhICCgydXIRpk9+6mcvKGAUD37d+rUul8/dgxYtIjKqQEqlX3nHeDwYfqgDWDYsGE4duzYU4lqC4mNjcWxY8cq/9+lS5fmr5cQ6gy9ZAmtrG3ZQuXLn35KA2tLS2DcuIbX4er61OSpe3dq7vTOO/ThMyys4URFE9HX18e7776LLVu28Hl5eVxKSgrv7OzMNVTdZi5fBjdsGGbNno1ffvkFv//+O4KDgzUOuDt06ICUlBRtfYSmY2pKlRjLl9PK58yZtGqZmgqMGUPPP0tLGtSFhtLzztiYnotmZrTn28CAfg8uLjQppJYYOznR5JC5OVUm1BUsjBhR60esvj5z6fRpJIWF8W+++WbTStJSKW1BUJuA/fILbb945RXq7K0lD5MhQ4awv/76K65du4YzZ87Azc2NBAcH1/ulGxsbo0OHDlixYgUb/scfSM7Kgg0hlcfk0qVLfPLZs2ygSoWOQ4cyIpGISuS7d9dsh2xsEGthgdGjR7NYs4YqR8LCgNhYGmxHRNDkx9SpwOjREFgW9+7dIxMmTGja8f3uO3r91kFeXh7S0tJQXFwMAwMDtGvXDuqWNUNDQz0A2s2O/fuxNDExabj3JD2dur736dPijUmlUhw8eLBcqVSOFYvFaS1eoY4XAl2wrUOHlhGLxaoNGzYsi4qK2iAIAhsUFGTY+FI6/u04ODgw8fHxyMjIgKOjI0TPyGSqMS5dusSfO3eOc3R0JBMmTKh8MO3atSs4jgPP87h16xY86jCa6tatG9SSzZq0bt0aw4YNq/z/woUL2eTkZFy/fp319vaGj48PLCwsGACVD5ZZWVnYs2cPiYuLw/jx4+Hs7KzNj/rU8fnJk/p8pX9pAAAgAElEQVTfk539tGJdF76+wNdf0wfys2efys4rkgOCIODhw4cCIURr5XljY+PKf7u7u2PkyJGaL/z4Ma1eTpwIdO5Mq5mLF9OAWRBosKNGU6M+IyNaeR0//qkL75df0gBxxw4a+C1apFVZ+cKFC7mEhAQcPXqUS0lJQXBwMNzUEt6aSKVA376wt7fHtGnT8PPPP+PmzZvo3bs3XnnllUaDbkIIVCrV8zcnePSIypM/+ogqJwBale7QgSZEfHxoNXnOnKfHVi05r0rV8U919fE2w5isX79+TF5eHq5fv84JgtA0D4K9e2mFvnPnp/sUFgYMG0YVEQcP0gpyC88XZ2dnuLm5ISwsDB4eHggODtZ4hb59+yJz1Spmx7x5MB06lAQHBzPnzp3jhiqVsN6yBf8zNcWQIUPgPW8emGvXqisN6iE8PBwKhYLJy6vwIBOJ6HfTvTut8BsYUDWIvj5w6BBkixfDfskSdLlwgSoRgoMb3/FHj2iLwbbqbb6CIODkyZOK2NhYuUgkOqVQKO6IRKI2AP7j6uraasyYMYYmJiZ6HMc1bPP/ksGyrLWxsXH9md70dGDsWHqvayGCIODAgQNlPM9vEovF51u8Qh0vDNxHH330d++DDh0vHAMGDIiKiIi4nJubO7N///66pNZLgJ2dHXJycvhz586x165dEzw9PRkDLY0P0ZScnBwcOHCAHTVqFIYPH15rzIybmxtiYmKQmpqKfv36tXh71tbW8PT0RKdOnersSTQ1NUWfPn3Y/Px8REREwNPTs1LSrhW6daNBZ0P92Hfu0J7Kiip1NV5/nVak1H3a//0vrRAGBAAAzp8/zx85coTJycnB+PHjWSstmUCZmZnh0qVL0NPTI1OnTmUa7OdUjyIaPpwGZ5GRtJ9z0SIgKIg+KJqZUZOzlgQ3kyfTilzVY+ngQMeAbdxIAwojo+pOzi2AZVm0bdsWubm5yMzMRGJiIgIqjns1FAo6W33uXMDEBK1atYK/vz9YlsW1a9cQGRlJHj9+LHTr1q3eaDE0NBSFhYVMYBNmFreIdevoiDpfX/p9DRtGq9lt29Ie6IAAmiCJiKBu5IaGz8worSEUCgWSkpIQEBCgeavH/fu0+j59Oj3vqjJ+PE36rF8PKJU0gG3hPfDx48fIz88n8+bNa9IBMjY2huHVq2CUSkQBTExMDAwMDITJq1YxhitWQN/QEOfOnROiBg+GmaMj08bGpsFjcPv2bZw4cQL+/v7Ey8uLMTExoVL/b76h7uNWVrTKHxgIdO2KJD09/GZoiP+uXMno791L70MODvQ69vSk7S05OfT6qkppKb22ayg9oqKiyJUrV5KUSqX3Bx988FNAQMCZAQMG/H727NltBQUFg/Ly8qzbtWunf//+/dJ+/fr90pRj9SJz5cqV6c7Ozj71GtYePgx06kSv0RYSERGhunPnzl9KpXJWYGCgrk9bRyW60V86dDwjRCLR+B49evxNDlI6njcMw2DixInckiVLUFZWxqanpz/X7Z86dYrfvn07nJ2d+fpcrdUu6Y3NONY2wcHBIIRA3futNfT0aL/d2LH1vycyklaD68LICHB0pHLrb76hMuwKF+GHDx8iMjKSGzBgAPP222+z2qzKFxUVAQBWr17NtK4p0RYE4NdfaRJgxQpaNWQY2oMNUOl8fDz9d9eu2hvT9eTJ021UxdmZVvxv3KBJiZs3tbO9CiZOnAgnJyeiV5/LeHw8DdxsbCp/ZGBggMDAQPV4KyY1NbXBZxlfX1+t7nOdpKTQ3uWoKBp49elDj2dICJVce3nRzyEINJC6eJH2C69ZQ03pHj9u2uz4etixYwckEgm2bdsmfPLJJ/jiiy9w7do1IpPJQGqsX612uXPnjuYbuHaNuqnb2dV+zcSEJm26d6dJhY8/rtsjoAnk5+cL9vb2zcpEmH/yCXq/9RaCgoLQvn17vPbaayxu3QICA+Hr64u3J0xg54aEMMf//FP44osvIJPJ6lzP/fv3cfDgQQQGBvJBQUFM5bSJ/fuB8+epoVmNkX3Xrl0jjr17C8bm5tQH4bvvqPmaRPL0vBg+nBrMjRpF+/LLy2kC8I8/au3D+fPnZQqF4lWxWJxf9edisViqUCjWZmRk8FZWViCENGG+2IsPy7K2VZVE1bh6lSpOPvigxdt58OABrly5UiKXy8eKxeLGDdl0vFTogm0dOp4RKpUq9ObNm2UKhWYjHnW8GMTFxRETExNSl0z7WZGRkYGYmBhu4cKFmDZtWr39gTKZDK1atcL9+/ef276psbe3JwcOHIDWkxDqh/76AhU3t9rSW5UK+PBDOgarf39qcHT+fLXANTQ0lPj5+cHX11crY76qoq7u5+dXeW7euJHuj0LxtId86VLg+HH6+tattJL/rBgxgrpO1wXDUKnzokV01NOmTVoJDNWIRCKmdevWQp0vHj9ebYxSVViWRZcuXcDzPPPjjz8Ke/bsIZs2beJ//PFH/Pjjj9ixY4eQnZ2NpKQkoVOnTtqtNBFCA+c336TjpFq3pi7v7u60aj1jxtP3Ll4MfP89de1WB7YMQ42wNm+mSZW7d6kT8qVLLdqtvn37AgC6d+/Ojh49GgEBATh//rywceNGXKwxd1pPTw/q0VkakZgIbN9et2FaVbZvp60OT57Q7+706SZ/jkuXLgmbN28md+7cYdPS0oja1btJZGYCn34Kf39/zJw5E5aWljQhUOGlwjIMWnl5YdmyZWx5eXm9qzl58iTv5eXFBwQEPL23PnxIk3xhYTRYLimpfEmlUiEjIwM9evSofuMwMKDL2NlRdUpaGj2HevWiTu4bN9L7UI3kYcUECSMA9R2EzhYWFoyxsTF4nteOQ92Lg029yqGPPqJKhBaqSkpKSnDgwIFylUo1SSwWN9DTpONlRRds69Dx7DinVCpDd+/eXZaVlQWVSjf+8mVAqVQypaWlzPnzz69lq7i4GCKRSGhI5hwWFoaNGzeiqKgI/v7+zz3zPmfOHMbW1hY//vgjPv30Uxw7doyvWWlrFjY2VAqo7omtycaNtXtab96kVSmlkvZ8r11LZb8VFBcXo6SkhHmlsdnczaRVWhpa5+Xh7LZtVIork1EpsbEx/Ts/n0obO3Sg45aeBwMG0OCvIcaOpXL8yEhamWsgQNGU3Nxc3Lt3D9bW1rWfRwSBypYbcPg3MzPDokWLYGNjg6KiIvTt25dLT0+HQqEgJSUl7Pbt2/HkyRPW2NhYOzptmYwGzF270t7asWPpOWZlRfuza0qr162jPcz9+1PFQs32DY4D/vMfevxffZUmW15/nfbeN+P6cHNzg76+Puzs7ODl5QU/Pz+sWLGCCw4ORmRkJFJTUyvfm5KSAqVSCZsqqoF6EQRaoZ8/v/FWAoZ52pf++DE1UvvlF1rF1QBCCE6fPs1yHMcsX74cbm5uwg8//IDff/+97oRMfXTvTr+PqsexfXs6Hxygsv5t23Dr1i2YmJhA3fZTMecbAK1YZmVlcb6+vtWTmNOn0wSY+ljMmkWvW9CpDa1btyYOmhjFGRnRandgIE1oSSS12mIePHgAAwODX8Ri8aN61iItLy8XTE1NoVKpTCUSSdOH0b+g8DzfybKuNqNTp2iSa+LEFq2fEIKDBw+W8Ty/+cMPP2x6VknHS4Eu2Nah4xkhFouJXC6fnZ2dHfr9999j8+bNcq0EFzr+0QQGBiIoKAgXLlzA//73P1JWVvbMt/nnn38Sb2/vBu/nCQkJBAA+/PBDBAUFaX+mswYsXLgQ8+fP/3/2zjssimt/4++ZAXbpXQQRsaJiQewNsfcaS6yJmpgYYxKNxlQ3m3sTy83vmqtGY4lRo0ZjN3ZAEbAhKIIoVRClSZW2y+7OnN8fB1CkLU1R9/M8Pio7O3N2Z3aZb3tfTJo0CcHBwXxiYkX3jtUkPp7drD4f/GVns2Dm2Vbt+Hh2kx0ZCVy/DvHcOdyLjsazd/EpKSkwNDQU6rSiffMmuyEHoJw+HW4hIeBatWLzrxIJq2IvWlR3x6sus2eX3xr8PM7OLFHRtSsLHJ+rllaX06dPU41Gg07lKULHx7POhCoSDpaWlhgzZgz30UcfkV69euHbb7/Fhx9+SKysrKggCHB0dMT48eNrvsj8fHZtvfsus9VycmJWQSNGMAXuHj0qfm5h4VP/6XnzWBt2eXAcq4YPGMBmvY8eZTPdXl7VWiohBB07dhTPnz9fKjDt0qUL3Nzc6K5du+Dn54cHDx6UdFhs37696h2fPs1s5KZM0X4xX3/Nro/8fJZoWL2aVYS1eA2jRo2i2dnZyMzMxPjx4/m3334bt2/f5kSxGvG2oSFLWt29+/RnMTElmgxYuxbo2xetW7eGKIrijz/+iF9++YWuXbsWq1atwp49ewQfHx/wPF969EahYGMnxUE7wJJCOTkAgPDwcKFXr17V+/IICWGt9+VYfiUnJysKCwsruHDYKy0oKDDgOA6GhoZKAC8oQ9ewkcvlxhqNxqLMqE5BAXNtMDRkn7ta4Ofnp0lJSYlQq9Xf1mpHOl5rdMG2Dh31iEwmE1Qq1f/p6ek9sbe3183xvAFIpVL07NkTkyZNQk5ODomLi6vX42VkZCAvL48MGjSo0u06duxIAOCXX36BILy8S9He3h4uLi7o0qWLuHv37pL55VrRogXzIX6+XVCpZDf4z7QJ0vffh7B0KVTffougzEysfustHP7nH+zatUtMSkrC6dOnhWPHjtVOTT4v72lQ1aoVqwJLpSzoAJB+9ix8Bw9GW1dXFqy9BHGsMqxdy/5og4EBa82Xy1nl8s8/2Yx5DZg6dSrhOA779+9Hmc+Kr2+NqrvFlsbNmjWjRf+veCa8IjQaVsGOiWHX14ULbL76yhV2LkePrvq8+fqycYW5c9n/bW2rFgzjOFY93rmTqbB/8QULxK5d03rp3bt355KTk8vc340aNYq0bNlS4+/vj507d+L3338HABQWFlYewWo0bP76yy+rf63q6bEkU+vWrGK7dy+bca76NZD27dsLFy9eFAGgZcuWkEqlpSzztCI3F7h69en/mzdnCvsA8OGHwF9/wcTEBMuWLePmz58PT09PsmLFCixduhSpqakkMTER04oFFAHgyRP2WqytS+slBAQAzs5QKpXIy8vj27RpU711+vqyTptySEpKUgG4Vcmzj2RnZ0vz8/NhZmamAeBcvYO/tnSxsrIqKJM0jYlhdoj9+tVq5w8ePMDly5cLVCrVOJlMpmtd1FEhOpVkHTrqn07W1tbSt99+W6K14quOV57ilu6W5YlO1QERERHw9vYWs7KyOGNjYxEVJE9zcnJw/vz5kuAxp6j68rIZN24cFxkZKZ47d45MnTq19h8MpZIJD4WHA+ZFY4uBgcC5c0jr0QPp6enIS03FtSFDqDotjbzz228IXbxYGD1xIt+uXTscOnSIbNu2DWZmZqRbt25wcnKqXvVfEJia+YYNrGpy6BBby6ZNrPJpYQEcPgwAJcrtlXlLv3A+/5y1CleHsWNZkuO991gFc8uWagdjGo0GxdXKMp0/v//OZp1riKmpKQcA+vr6RGt7q5AQ1k6/eDE7f2FhrCuhut7RlLIW1T17WAUcYCMLzZtrv49x41hQf/8+CwxHj2Z+6paWlT6tuB1aqVSWUf+fNWtWyX3fgQMHEBUVha5du1b+xvz+O7B8uVb2WOXy1ltMqbxbN6bEnZPDBOUWLHha9S8HOzs7Pjo6WgRYtbtx48a4desWevbsCTs7O+2OvWBB6USQKLKRk9GjmZhdUYsxx3Gwt7eHfVF3h4GBATp16sTFxsaKrVu3fvr+KBSs+vy8rd6AAcDAgQgaMgSWlpaisbGx9sWs6Ghmlfbpp2UeopQiKyvLEMCdSvZAAHbera2t9VJSUpy1PvbrjbuTk1NpkdrERJb8qm7S5jmUSiUOHDig0Gg0b1fS3q9DBwBdZVuHjhdBVHp6unL16tWapIpUkXW8dly9elU0NDSkdWp1VcSDBw9w5MgRtG/fnluwYAGWLVvGlVeJ3b59O123bh3Cw8Nx+/ZtAMCnn35aUvl72UyZMoW7d+8eyS+q+NYKc3PWhv3sayssRJy+Pt22bRsuHjokdBo5EiMiIshnnp7QhIdj3vff8507d4aBgQFmzJhBVqxYgU8//ZQbNGgQWlUyJwyAqXMD7AZ51Ch23OxsICODVXzDw9njw4aVamMPDAykGzduBMCs0xoMOTk1uwG1tGQtwl98weaOtahaPouJiQn69+8PAKWvy8hI1qZeC1E4d3d3jBs3jsbExGDDhg0Vb5iczGakHzxggfHNmyzADAtjyYPqBtoAC7ZTUp4G2gC7VqorgMbzrJLq5wd88AETshs3jlWbK8DLy0uwtbXVVPXdM3LkSIiiCIlEgpSKvOqTk4Fbt1igXBsIAf76iwW5gwax5NiSJUB6eplNKaVIS0uDQqGAiYlJSQZm4sSJ0NfXx86dO7Vvd0hPZ4mTYgoKnnZw/PknG+OoALVaDQMDg6fHOn2aKVeX0+otfvcdIrp1Q0BAAO3Tp0/1Mk4ffMDem3ISVampqSCEFADIqmQPhXp6enGXL19WW1hYGAKowQX7+mFgYNDX2tq6dCtJSAhL/GgzMlMJXl5ehYIgHFy5cuWZWu1IxxuBLtjWoaOekclkVwRBsFKr1f/9/fffRd3c9ptBfn4+USqVpDxhPFEUQSnFo0ePcOXKFWzatEk8cuSIGB4ejtzc3Ar3KYoi/P396d69e9G9e3c6aNCgSis8qampBGAB9sqVK7Fy5UqUmV97iTg7O8PQ0JD6+PiIAJCVlYXt27fTUird1eGLL1h1tahSWhgfD1+lksyaNQsfffMNLzlxAq2jo8EVFsKuHPszqVRaefVz/XoWWKans2p1Rgaz4pLL2eNnzzKf4Uq4f/8+BYAlS5agQkual0FUFBOaqwl6eqxd/ptvgH37WLCqZVv5jRs34O/vD47jYPqsuNiNG6xToZbdQF26dCEAkJ2dXfoBQWCt2lFRrNX7v/9ls9iPHrEA2cSk5seOi2MJluedKD78kIl21QQDAyYG6O3Nkkq//cZmvMtxFkhMTOTc3d2r7Fw0MzND9+7dcf36dWHHjh0IDQ0t+8vJ358lPapTka+INm1Ytf9f/2KJjK5d2TXzzBz7/fv3sW3bNmzatAkBAQFo3LhxSQbGxMQEkyZNAsdx2v8S7dyZfV6Lf+/a2LCAG2CiiP/5T4VPdXFxwcOHD/mSJPndu+WKCJ4+fZpu9Pam1729MXDgQLi5uWl/4RQUMPX2lSvLfTgiIgKiKB6TyWQVvmaZTKbSaDR9rly5oiiyVyy/H/0NQi6X6wuCML7UuQgOZp+bWlp9JSYmIiwsTKlSqT6r7Tp1vBno2sh16HgByGQyUS6X37awsFA/efJEkpycDCcnJxgbG7/spemoJ4YPH042bdqEH3/8EU5OTqKLiwtnamoKhUKBixcvlni6mpiYiO7u7tzDhw/p2bNnhYKCAt7AwADNmjUTJBIJbG1tOTc3N1JQUIDdu3eDUkpnzJhBnJ2dq7yhmzhxIg4ePIgLFy5g0qRJ9f6aa4KxsbFoZWXFU0px584dJCYmkp07d8LBwUF49OgR5+LigqZNm6JDhw6kyjlqQlhAPGgQ0KUL8i5fhrGTk8bp4EE9HDvGrJX8/bULotLT2fxsXBxTnE5IYIJdgsBu2JVKtp9KFODLY/To0VxkZCRCQkLg4eFRrefWKzNmANOn124fI0eyQHXSJFZ5PXKEeaFXwK+//iqkp6fzBgYGmDZtGkqpBisUTCisDnBwcKBJSUlkz5494lh7e8786lVWTfzPf1ib/9atT6+JuvAtb9qUJR2eT6YkJz8N9GqKiQnzZ/bwYPvKy2NB94oVrEUbgEKhINr+bhk1ahQA8AcOHMDZs2dJKaG66Gj2OnburN2an4UQ1v0QF8dsriZOBLZsgeraNYT17o3Tp0/D2NiYLlq0iJTX+ZGZmQl9fX3tg20zM9a+n5DAWr8FgQXMqamsE0MmYy3c5WBvbw+e51kC7tixp9Zuz+Dr64vQ0FCyKDMTJmFhIL/8on2gTSnQpw8TRqugkyY7O1shCEJwVbuSyWQpP/7449b79+8vA6BroQNmWVhYCKUSmj/9xEZ9aoEgCDhy5Ei+RqNZLJPJKus20KGjBF2wrUPHiyM5KytLsnnzZpUgCKnNmze3mjlzpi7afk05deoUBUDef/99XLt2jQsJCREUCgV4nic9evTgTE1N0aRJE9jb2xeXUgkAXhRFhIaG4sGDB7xarUZISIjg6+vLE0Lg6uoqTpgwQeuOpLCwMAqAOFRl6fQScXNz4y9duoSgoCCam5tLJk2ahKioKEoI4QcMGIC7d+/i3r174oULF/D++++T4uqnRqMpK2LGcawVuEhoKKVTJ+SamnLo2JGpOufnVx5o37zJ2onlcqY6vXAh8NlnzBqMUlb9LKYGVU+NRoN9+/ZRiUSCtm3bNiwBh9jYp9X62kAIm1e/eJGJ03XtyoKUcsjKyuK7du2KYcOGwcDgmdHKJ09Y0uTmzdqtpYh3p04l2UuWwCs1lbseE4PeEglMv/76aat/XRIQwOa0y5s1P3SIVeurK55VHoaGrJOjoIAFgIcOAZGREFu2hFqtRnU/8/369UNMTAwCAgLEfv36caCUdWqMHl1azb8uMDSEplUrJL3/vkgMDbnwt94SuaNHObv9+2mflSvJ4GHDKvxsZGRkiHl5eXxBQYH2nSFXr7LPb7Nm7DuiXz/W/dK/f9nZ62dISEiAIAhobGPDZuV37y4VbIeEhODKlSuYM2cOTGti0VdYyEYCKgj2i9agBhCoze40Gs1vAJYBqH8LjAaOnp7eQgcHh6dzFKdOsS6k0aNrtd+TJ08q8vPzr1BK99R2jTreHHTBtg4dLw5fnue/VKvVMZTSizExMRn37t2DhYUFjh07lt+qVSu9IUOG6ETUXhMePHhAnJ2d4eDgUFxV1mpQmuM4uLm5wc3NrfhHvEajAcdx4KrpRTV48GASEREBRZEllkqlgr6+PhrSNda3b1/Y2dkhOzubuLm5QU9Pr0Q5HQC6s+om98svv4hbt24lnTp1woMHD8TExESudevWwsiRI3nL5wWjHByAjRvRfv16qFq25PLPn4fxyZOllaBFEXj8mNmAtWvHgqPCQiaaBLAqdnEwv2BBnbzW6OhopKSkkBUrVpQRrnrpNGnCqtt1Ac+zAILj2Fxu27as2vtMlTsyMhL6+voYMWJE2aTJsWPMyq26CuLPc/IksHs39HfuhG1KCgZ//DF+u3EDtuPGoUtd2ro9S1ZWxUJzx4+zSmtdYmTELLYA4D//AV2+HGbjx8MyI6NaXRdNmjTBzJkzsXv3bk4URdFDKuVw/DhrW68HQkJDcXbECM7O0FActGMH4T/7DM5qNcGqVWw+vDxvZAC9evXibt68iXXr1mHx4sUw0+b9nDjxqf85IUxVnVJm5VaJgGV4eDhcXFwEREbymtBQ5BKC9OhoiKKIlJQU+Pr6wsLCggX99++zmfx//tHuDVAqmUr7tm1sRKAC8vLyJAC0srSQyWSxcjbS8kZbf8nlciKRSGy7devGPuTFVm1ffVWpIF9VxMbGIjw8XKlWqydV1tavQ8fz6IJtHTpeEEVfzmuK/y+Xy+efPXt2g1KpJCqV6ouMjIyf2rRpI2lWSaZdx6vBtSKbntmzZ9fJ/mpqQ2VjYwOJRAI/Pz+kpqbSyMhI4uDgQN977z3SkALuKsXIAHz44YdccHAwwsPDRRsbG2706NE4ePAgt379enh6eoLjOLi7u7PRjGPHWOXP2RnODx5A4Hl2Q6tSsRvtn35iN8YBAay6+emnTITKwYEJmgF10078HMWWa5mZmdWuPNY7Uilrs1cq2b/rgkGDWHVaLgciIoBffimZeU1MTISVlZWgp6dX9u43Lo51FdSE3FwWxLz9NgvW27YFJBLQU6fw98aN1NXVlXTp0qU2r6piwsKAXr2YSnt5TJ7MVL3HjKmf4y9fjlv9+1O7zZsJN3MmsyobMEDryrSzszOmT5+Ovw8c4HqdOAGDrVtLHqOUws/PT0hKSkJmZiYniiLt168f7dKlS7WjF0opgoODxfaurtykSZM45OcD9+4B33/PZqwHDWKdDeWMWdja2uKjjz7CoUOHsG7dOnz33XdVq8xbWrJ9Hz/O/j9yJBAayubn8/Iq9Fq2t7dH3B9/8AUff4wNixZByXEwMDCgPM9DrVYTW1tbKooiduzYgeZSqTg4L483UqlKd2lURGgoa22vJFkgiiLUarUEQHVFLOpm/uIVhRDykYmJia1jcbdBaCj7Pin2V68BBQUFOHjwoEKtVk+UyWR5dbRUHW8IOoE0HTpeHjtzcnLmqFSq8QD+EgTBvEyFTscribe3N9q3b6+d1VA9M3ToUABAcnIyPDw8kJSURA4ePEgvXbpUMjf+KiCVStG3b18sWLCAmzRpEuzt7TFv3jxia2sr3rp1i166dAk///wzfH19Kfr2hfDjj8hNSqIH5s8XTXr2BD76iFU1zp9n85rr17PgCGDCVS8g+O3QoQPc3d0127ZtK0nINCiWLmVBcR2QlJSES5cusfdcJmOBU2Rkifpz48aNkZmZWTZQe/KEzTY3bar9wShl/s0dO7KkSsuWbN570iTmD83zUCgUyMzMJL17966T11cun39erlJ1CR07Mq/teoJSijNeXoQOHy7i/HmgZ0+gSxcW4GspzNm6dWt4xsbSYDc3kT4zvx0SEkL9/Px4QRD4Zs2aEX19fS4oKIj89ttvdPfu3UJBNWbRU1JSkJqayo0qHi+QydioRocObAzk1Ck2srFlS1mRObCAu1jBXiuaNmWCe8UdB3//zar+N29WmlTr1b07lC4uOD5njjhp1ix89913WL58ObG0tKSNGjUSPvroI/LRRx+Rrl27Etq4Mb9j7Fjhf//7Hx4/flz5ehQK5ruVOXkAACAASURBVFxw9Wql4yiEEOjr6xcC0NLnDAATR6vdYPIrjFwu70Yp3fjWW28ZcxzHvku++qpEz6CmnDx5UkEp3SmTyS7V0VJ1vEHoKts6dLwkZDKZCOAwAMjlck5PTy/n4MGD+kZGRpyNjQ03ePBg/YYQrOmoPm3btkVCQkKF3tcvkq5du6Jr165AkRerg4MDDhw4QO7duwdfX1+89dZbaNasGUxNTUEpBaW0QSQJtMHExAQfffRRyWLj4uLw119/kbt374pTLl/mbNLSyKyFCwl35w4L+ngeuFOZXW39M3bsWL2YmBhaUFBQ6i77zJkzSExMREZGBh06dChxd3d/8Ys7eLDKeWKNRoM7d+7g0aNHEAQBAwcORHZ2Nry8vGBlZYU+ffogPDwcAQEBJddS//792cxrYCDU336Lf86fF+/Y2HB9BwzQ4Pn7kIQEFiw/q0xe/kJYINa/PzBtGms7//ZbFmz//HOZzYu9zfft20eXL19e920dlALnzlWuwj57NtCiRZ0fupjCwkKIoghXV1euxIv72jWW5Ci2xfv3v5/60JdHVha6KxRkp7U1ko4eLdGIOHHiBHF0dMSsWbMAAP/6179KxloKCwu5//znP/jyyy9LPL4rIywsTLSysoJUKn36RSORAGvWsEBUo2FWWCtXskTYunVl1nzmzBlYWFiIWo3W2NgA+/ezFn9raxbMm5iwmfQKVMABgPvqK8xPTwd27uQAlsw4fPiwkJ+fTz755BMeYGM/AwcOZE+wsOBPLViAEydO0NmzZ5MK34t//xsIDHzaRVMBhBAYGhqq1Wq1DSoQPZPL5bYAlDKZLFcul3M8z2cIgtBYLpfzMplMO0uA1wie5z8v0kJhP7h/n2lG1OJzFxYWhtjY2DSVSrWsjpap4w2D//7771/2GnToeOPx9PSkFy9e9M7JyYnIzs6OSUhI6JOdnU3NzMyIVjNpOhoMGRkZOHXqFIyNjWnPnj0bTq92ETY2NhgwYEBJMPTo0SMhICCAe/z4MU6dOgU/Pz9kZGQIzZs3L9e7uyFjaWmJzp07Q6FQEO82bcQ2v/9OLJycWADZuvXLXl4JN2/epI0aNSLNi+yUsrKycOTIETg6OgqWlpbc5cuX8eTJE9jZ2ZUEiS+EjRtZpa+CG9M//vgDJ0+eRGRkJJKTk5GSkoJr164hJCQEOTk5SE1NRVBQEBISEjBjxgx06tQJZ86cQVBQkBgfH08uxcQIVxo14hqp1Xg3OJi0njPnaVBYzNq1zGaqvFZvSpkg2M6drBV46VLWIjxzJltzhw4VVgoJIUhJSUFKSgpp3bq1drO+2kIpsw1r1YrN/1fEvHks8HtO0bquuHr1Kh4+fIjevXs/fX0mJkwErGtXVslt3Ji9x25u7LHnOXECfOvWcJ47l3h5eaFRo0YkKysL4eHh+PTTT0v0HoKCgkRzc3O6YMECEhoaKjo5OXGdO3euUg8iIyMDR44cIUZGRqRHjx6lH2zbliVaevcG0tKYRRilwLJlrEpvY4OUlBTs3btXzMrKIosXLyb62s71f/89u7ZdXdl5aN8eOHyY/bsioqNZy39Rl4WXl5cYGRlJFi5cyJXbKj5kCBqPGIGbYWFiQEAA17x589J2dgAb0+jene1Xi2vw6tWrKpVK9benp+fD8h738/PbBOCAn5/fYgMDg+GNGjVqwfO8SqlU+nl6eiZUeYDXCLlc3lJPT++/06ZNMzAxMXnaPv7HHzW28cvOzsa+ffsUKpVquEwme6PeTx11x6t1J6VDx2uMTCYLAhAEAHK5/FRoaOgnMTExw5YvX97AlJR0VMahQ4dEJycnbvr06Q26PPzFF18AAAwMDHgfHx8aFhYmjhw5khdFEefOneMkEgkdPnw4eVWq3MWYmZlh6NChGDp0aINduJOTE/X390d2drY4cuRIbs+ePUKnTp3IxIkTeZVKBWNjY4SFheHu3bt0+fLlVVue1RUxMRUKRl27dg0JCQnw8PBA3759QSkFz/OIiIhA27ZtS3QFBEGAWq0uEYBbtGgR7t27Rx48eCB06tSJc3JyQlNHR8Jv3sxuhqOjmZUVwFTkjx1jgdGzJCayILVlS2D+fGDuXNaWbmhYLVE3d3d3REZGIigoCE2aNKn221MhxZZzI0ZUvt0nn7CZ5HoiNzdXbNy4MRwdHcte+40bM3X41FS2Xi8vJgg4dOhTRe64OOD0aWDtWlhbW6NFixb0wIEDxMnJSWzWrFmpIvLnn39e8h8zMzPO0tKSViUEQSnFrl27qLm5OebNm1f+toaGgK8vsH07MH48UwBv2xaYORM5Mhl23rkDRycnzJ07t3o+9QMHPhUiS0xkf7/3XsXbv/8+cyJwdQUAXLt2jQYHB5P33nuPVHjcJ09gxnFYtGgRf/78eezYsQM2NjbitGnTuJIRsfffZ9fyunVaLdvZ2VkvLCysG4Ar5T1OKZ0HIIlS+pVKpRrI83yBmZmZUV5e3ngA/lod5DVALpcbGRgYnBs0aJCRnV1R1/2mTayjpoaBtiiKOHjwYL4oiqtkMlndWCPoeCPRVbZ16GiAeHp6xl26dMmB47jhDg4OnG6W+9WAUoozZ86QTp06oXUDqqSWB8/z4IuUWVu0aEF69erF2dnZoXHjxmjSpAk5d+4c8fX1haOjY2n/Yx21xsXFhWvRogUuXbpEfH19oVAouOHDhxNLS0vwPI82bdqgd+/eiI2Npd7e3nB1dSUvpMI9cSKr+D1TLRRFEbt27UJwMLP6nTVrFvT19aGnpweO49CoUaNSYwccx5US9JNKpXB0dCQdOnTgmjVrRiwsLNj2PXuy1uGvvmJzlf36AUFBrDrcpw8LCrOyWLD19tvA4sUs+Jo8mVWzy/Fgroq4uDhER0dj9uzZ0LoiWhUqFVvTsmUsUKyMY8eYNkDjxnVz7Oc4d+4czcrK4tq2bYsKfbZNTFhSwNWV+Ytv3syEo0SR+V5bWTGBMgDNmzcn165dQ3Z2NunRoweaVjBHf+fOHQJAbNeuXaUJrujoaNy+fZssXbq04hZrgFV8BwwA+vZlazU1BX75BdkffwzTvDw6ZskSzry4TVhbDA1Z+/jgwSxpc+MG84FnThGlycxkVfXPPgOkUly9epX6+vpi+vTppNIkzSefAOnpwJAhaNmyJXr16oWYmBjq7e1NTE1NxcaNGxM8fMg6MbT8TqWU6t2/f9+yX79+v5f3uKenJ/X09PTx9PSUX7p0aUtOTk7UkydPJoii2MfT01Ou1UFeceRyOTEwMNjRqlWrXkOHDtUnhDBVeFtb5kFfw4RxYGCgGB4efletVr/j6empUx/XUWMabOZfhw4d2FhYWPjnyZMnFS97ITq0gxACMzOzhil+VQ2cnZ2xsEgN+vLly2JoaGiJfZiOuqFp06b45JNPsGLFCixbtgzFLeXF8DyPd999l7OxsaHnzp2jXl5eCAkJqd9FLVlSxuYsPDwcDx48wKRJk/Dpp5/WWBm/XJo3Z/7QI0awP8ePs0q3Ws2CwT//ZMriiYks4GrfvsY3ziqVCv7+/pBIJLRaFdGqePyYibpps89//gHu3q27Yz/HmDFjOJVKBV9f36o31tNjwbafHwtCu3Zl89GEsLlzSmFkZITFixdj/vz5qExYztbWFtHR0bxQ2bw6gDt37ohSqVTU6hoihF0fV66wpMt//4uCxYuhTk8n4QsX0hJxQ20xMWHe75Sy5M348cDUqWW3UyjYeQoNhWhmhkuXLom+vr6YOXMmcXZ2rvwYJ08Cq1aV/NfAwAAzZ87khg4dirNnz3JYsoQlEbRwXyimVatWUKlUXeVyeZUXmEwmS8EbVM0GWKBNCFnOcdyUsWPHSgkhLAH2f//HRjtqaPWVm5sLHx+fQpVKNfNNnH3XUbfogm0dOhooMplMpaenp87Pz5dkZWW97OXo0JL27duLNjY2FRjtvjpYW1tj+fLlkEgk5NSpU/TChQu6G446RiqVQiqVVliFJIRg5MiRXGRkJLly5Qpu3LhRv9fVjBlMbOwZjhw5AoAlByy0tJCqFubmzIJp7Fh2g3zgALNjeviQ+Uebm5f2R68hWVlZyMnJgYmJSd1VqHJzWSXzwgXtkgBnzwJTptTZ4Z/H2dkZ7733Hu7evYv09HTtnqSnB7zzDpCfzxIGmzczxf7OnYHt22Hh5wfHK1dYy3kFDBs2DIIgIDOzcoeq+Ph4zsnJqXr3naamrMrcuzeab96M9vPmIUIQiPo//2GCdFqqrMPBgYmhKZXAuHEskO/Tp+x2GzaAbt+O8Lt38csvv9AbN25g1qxZxMnJqepj7NkDTJ9e5sfW1tYQcnOhOXas2mr0BgYG4HleAFBBq8JT5HK5FEAMAOjr639YrQO9gsjl8k4GBgZ/Gxsby6ZNmyYpHl1BQAATS/T0rNF+KaU4dOhQAYB1MpnsXp0tWMcbiy7Y1qGjAaPRaFZqNJq/Ll68+MqWFZVKJai2N0SvAe3atePS09Nfi+9WIyMjvP3226Rjx44kNDSUf5Wswl4XHBwcsLJIMbnevdEdHcsEL8V+4MnJyfV3XEKYSrSREas+WlpW3ZJdTezs7NCzZ09kZGRwt2/frpud7tgBvPWW9tuPGMFUseuRxkUt6n5+ftp96aaksMpxcDCQlAT4+zOhvKFDmYpzYiILwB89AlxcmJJ2WBjg7c2qwEWIoojCSgJytVqN3NxcWFtb1+yFjRwJnDqF/Js34Rwfjyw3N1aRX7+eJQq0YdMm4MwZllyYMoVV9p+BpqYidsgQbB8zhp46fVrs0aMHWbZsGVdR+3wZmjYtdya/RYsW6FVYKP6zerWAGmgFmJubqwBUorzHkMlkSgCdADT9+uuvt1T7QA0cuVyu/8MPP8xfs2bNoTVr1sQYGhpedXV1Hffhhx8alXQdpKY+rWrXkGvXrokpKSmxarVaVjcr1/Gmo5vZ1qGjAePp6Znv6+ubkZeXN6dPnz569X6zXUuUSiUKCgogkUig0Whw+PBhzbFjx7imTZu+MXO/sbGxePjwodC7d+/XIuAGWJAXEhKCuLg46ubm9sqJpr3qEELg7u4OX19fcv/+fdHU1JTExcWhcePGdRuAX7jAPI8/ZEWxrKwsPHjwAJmZmRg6dGiJ6Fm90KoV8O67QKNGpWbG65JmzZrB398fUqkUbdu2rd3OKAV69WJqx9q2qj54wJ7j6Fi7Y1cCx3HQ19fHtWvXSMeOHasWERs4kAXZb7/Nkh6GhkwwLT2dnYsuXYBPPwUsLJgoXevWwL17wC+/sLb+OXOAoCAEAnBWKGDj6lpulT8iIgJ3795Fhw4dYGhoqJXKPqUUCoUClFKoVCoUiiJsx4zBhaAg6D95giaiyAL+O3fYWqsK5OPj2Wz6558zK7RBg0qutbS0NDzp358mXr0KoylTyKxZs0izYuE4bXF2ZnPmz38m/f3R9N//JscaN+aaNGtW7d+F2dnZfHJycrKHh8fFqrb19PR87OnpmVOtAzRw5HJ5h4CAgI/19PQOOTg4jOrbt69bjx49rEaOHKnftm1bvpQyfFQUYGxctVhhBaSkpODo0aMFarV6gEwmy6ijl6DjDYe8SRUnHTpeReRyubmBgcE1Kysrp7lz5xqVaznykgkPD0dgYGBuSkqKoUql0mvbti0ePnxYqFQqMwRBcACA2bNno0U9esw2FP766y9ERUVBJnt9kuIFBQU4dOgQjYuLI6NGjRK7d++ui7ZfMKIoIisrCxs3biwRtps2bVrdCvEpFGwGuSjI2Lp1K01OTiYA8Pnnn8OkPKuouiAkhFVR27Vjx96xo54OE4Ljx4/jk08+Qa1FJ8eOZa3JW6pRQPT3Z+3L9RhsA6yKvGbNGlhaWmLRokXlbyQIzAqsUSNWBSwvafPzz8Cvv7JgtrxRB0pZgiY7Gzfu30ejDRtg9f77MFWrmcjYTz8BmZk4e++eEBQczAuCAAsLCyE3N5fv0qULRowYUXItl901xc8//1yiFVHs2d6qVSsaFRVF2jo5YZpSCVy/zt5PlYqJ7I0bV/Ebk5QE+PgAERHsmtuypeRcHPjrL8Hk0iV++M8/Q6+m1/mDB8yC7tnZdUrZuEFyMs7FxyM4OBijRo2ibm5uWmfJEhISsHfv3kSVSuUkk8le+RGl6iCXy50BxLm5uSl79eolLVEaL487d9gcfmgoG4+oJrm5ufjtt98UCoVi7sqVKw/UdM06dDyPzvpLh44GjkwmeyKXyzumpaWlJSQkGLWqhrjKiyA+Ph5HjhxRiqL4HYBMAOGxsbG/qNXq/QA2AyAAdhw5cmTmsmXLXvvvnH79+iEqKgrR0dENXpFcW4yMjDBnzhyya9cuXL9+Hd27d3/ZS3pjUCqV+PnnnyEIQkmV0tTUVKSUiiqVqtTnKSQkRFAqlVzPnj2rcmEqH7WaVTivXgUAaDQaAMCECRPqL9AGWHXxm2+A0aPrZD77WU6ePClkZ2eTXr16ccePH0f//v1rH2gDwA8/VP85a9cy5fJ33qn98StBX18fH374IbZu3YqgoCB069at7EbffstmnoODK7ZGWraMdRscOsRatrduZRXuYghh6t4AugPYbWdHRUGgc5ydOc7Hh3lljxkDW2NjvrOrK/pZWsJywQI+LjMTuw8dQlBQEL755hvo6ekhNTUV165dEzIyMkjz5s25wMBAKJVKLFmyBAqFAgkJCXB0dMS5c+eIi4sLJk6axKy8Jk1i9k4KBVP4Dg9n6y6vO6KwkInuqVSsLb642JSXh7HLlvG75s0TRQODmicSmzZlHQHPsn07e99u3MBwFxc4OzvjyJEjRCKRoF1lnuyldtsUxsbG5iqVagiA8zVe3yuEXC5353l+JoClzZs3zx8/fnzlM+saDetY+OSTGgXaKpUKu3fvLlCpVKt1gbaOuua1v/HVoeN1QCaTaVatWuWdmZk5+WWv5VmuXLmi8vLyMpBIJHu//PLL/z3zkMcz/6ZyufxCfn5+/d5hNhDCw8MBAPbVtaZp4Gg0GiQlJVGJRMLdvXsXjx49Eps3b86Vl1Aobv+sU9XnNwRBEHD06FGxb9++nKmpKS5evCgKgsA5OjqK7du358zMzKCvr88dOnSI5OTk4P79+0hLS0NgYCDNysriKaVwdXWFqalp9Q9uZMSUtUUR4DjY2dmRtLS0+r2WFQo2x/z998D9+ywA/OCDOtm1RqNBcHAwDwAPHz6kenp6pEePHrXf8eLFzO/bza16z1u2jFU+XwBWVlYwNTWlSUlJZSPpGzeAFSuA5cur9iC2sWEJgqAgdm0EB5cE2M8zefJksmnTJrpNpRIde/dGHysrzvLuXdzasAFmsbHUklICpRLNPT0x19oapwYNAr9+PUJbtsQ/YWFwaNKEk0qlCAsLEzw8PPiePXuC4ziYmZmhuKL57rvvlj6onR0TJrtwgQmptWvH2t5lMvbYszg7A0OGAEuXlg7I0tJgOHkyDFu2xN69e+ncuXNrNpvBcaxaPn36U+9yJye2liJcXFzg7u4OPz8/0cXFhdNmJIcQAg8PD5MzZ86sk8vlXWQymapG63sFkMvlphKJZLuhoeEYNzc3/S5dusDKyqpKcTikpLDvr/nzq31MSimOHj2qyMnJOaHRaP5dk3Xr0FEZumBbh45XALlc7qGvrz+q2jNk9Uh6ejq8vLwMAPxZWFj4bRWbXwaAgwcPKiZPnmzY0GfPa0NISAjc3d3rtxL4khBFkeTm5uLMmTOimZkZFxwcjIULF8LY2Bg8z0OhUCAyMhK3b99GQkICrKysMHv27PpRsX7NEEURd+7cQUBAAE1LS+Pu378PpVIJGxsb+vHHH8Pa2rrkrnzz5s2iWq3mvL29YWhoKEgkEjRr1ox3cnJCZGRkzQJtgAUg58+zKpGBAezt7XHnzh3Y1MDTWmu8vYETJ1igFB4O/PZbnQXbMTExAJhA1ezZs+vmS4dS1oZcE0uhK1dYxVVbwa1aUGTXR4cMGVL6dQcGMturW7e09/s2NgY2bGDn57PPWHv4mDFlAnUjIyN8/PHHnL+/PyIiIhAaGoqpU6fCyMYGEdnZBEVCf3j0CCQ4GIb799OwgwdJWMuW+PjePZhzHMHp08CxYzwcHLS3eZNImICaiwurcnfoAMydyzzc+/d/uh0hQGws0wd4/BgoKGDn5Px5kP/9D2MzMrhNmzYhMTERlfppV8aRI0CPHizY3rSJdQLMmFFqkwEDBuDGjRvc6tWr8eWXX8LLy0sIDg7mOY6DKIqqDh06EHt7e31jY2PY2dlBIpGgY8eOuHTpUhtBEG7K5fJOr2M7uVwutzQwMDjTsmVLt/Hjx0u0Hpn7+Wf2uTp6tEbHvXnzJr1//36ySqWaJ5PJdLO1OuocXbCtQ8crgFQq/deQIUOMKp1XesGYmprCwsJCXVBQIP3qq69SKttWJpPFyuVy87t37z7JyMio35v3l4RSqYS/vz+llGL06NGvXTZBT08P33zzDTIyMkoCv7///lvcsGEDJ4oi2rdvL2ZnZ3OpqanF9kokMzMToihCo9FApVIhNzcXdnZ2ePz4MbZu3YpJkyahffv2L/mVvXwKCgpw9uxZMSoqirRr1w7m5uaCQqHgmjdvTgYPHlwmqps+fToXHh6OLl26wMjIqORxpVKJ27dvIzs7u+YJDk9P4I8/kNa2LXx8fEr2W29dCn37lrStY8IE9qcOEEURJ0+ehIODA6bUpd1WcDDg5VWz554/zyrF5VlO1TEajQZSqbS0p/i9e0Dbtuz9LifQ1mg0pXzUb9++jZCQEMpxHHieJ2lpaUKPtWv53m5uzJf711+B5/y3pVIphg4disGDB+Po0aPYs2cPAJT2Z5dI0LRPH4x1cSGXL1/GqP79Yc5xbOa2oIAlYNLSWNdDSAjwv/+xBIebG5uTr4gWLYDTp1nC5vZtYN06llRYvPhpYsDamnm4F4kA4uhR1lYOZtHVsWNH8eDBg9y0adNq1tERGPj030ePlltplUql+OCDD7Bp0yb8+uuvNDc3l1er1QBwAMC627dvD7t7924rQkhbURRbCoJgKIqihOf5fAC3ALx2AWFRoH29c+fOzUaMGGFQLRHO69eB2bNrdNzU1FScO3dOoVarR8tkslfW9UVHw0YnkKZDRwPnhx9+GGtoaHjgs88+M9SvJ5XemhIQEAAfHx9BJpNplbhbtWrVRhsbm7mTJk0yqrEFTANCEARERUUhPT0dFy5cgFQqpdOmTSMlNiRvAAUFBYiNjcXRo0dhaGhIp0+fThwdHXHo0CExPDy8zB3T0KFDERoaSlNTUwnAPHrDw8PpvHnz3jiV87S0NBw+fFhMT0/nBEHAlClTapV8uH37No4fP47JkyejXbt2NVMq/+svYOBAhKSk4Pjx4+jdu7c4bNiw+jkxt28Dw4Yxe6niYKxZMybcVYvvh8TERJw5c0ZMTEzkvvrqK9SZqOTDh6x6mpzM/L+foXh0gud5qNVqGBkZgRBS+hwUFLAKXD1/jysUChw/flxQq9VcSUVfoQBatmQV6ufsykRRxMGDB8WIiAjO1tZWmDdvHi+VSrF69WraunVrGBsb0xs3bnDdu3fHzZs34eDgAI+ICDh36QLO1pYlDypQqt+8eTN9/PgxWblyZfWvx8ePAV9f1rY+aRLz3H7/feDgQRZAW1gwgbfyZvBjYoC//2aVZkdH4PBh1o2QmAjs28fOo5UVE6yzty+poj958gQ+Pj7CvXv3eD09PWpqaiqq1WpwHEeNjY31jIyMMGHChIqV+efPZ9fu2LEsqK9EeTw+Ph67du2Cvr7+ZbVaPV0mkz0sbzu5XE4AGMpksoLqvYEvn6K1G8tksrxKtrHiOO6Oq6urzcSJE/W1vk7u32dWdFu31nhOe9OmTQW5ubkLv/vuu93V3oEOHVqiq2zr0NHAIYSMdHNzkza0QBsAWrVqBR8fH14ul3eUyWRhVW2vUqk+e/z4cdbGjRu/lUgkahMTE6WHh4dJp06dXrlKcF5eHg4fPoz4+HgAQPv27TFlypRX7nXUFiMjI3Ts2BHt2rWDnp5eyeufPHky16xZM9ja2uL+/fvIysqCIAjwYlVB0qhRIzx+/Bjnz58HABIWFgZXV9fSFbDXCEopCgsLkZ+fD41GAzs7O2RnZyM1NZWbNWsWJBIJHGupUn3s2DEAwMGDBzFt2rSa2VsZGgKPH8PMzAwAUK+e8WZmzELq2XM+bVrVc8RVsGPHDnAcx73zzjt1EmhTSlm3QJMmQGYm1BwHqFRISUlBUlIS0tPThYiICE6hUBCA2W8RQmBnZyfOmjWLkxSLvk2cyILGOmqTL4/Y2Fjs3bsXlFLeyspKBEDyEhNhlJeH5L//hl2vXtADcObMGdy+fZuamZnRvLw8YmRkRD788EN4eXmR3377TZg7dy4PAN26dSPNmjUj4eHhorW1NTdnzhzcuXOHHk5LI6319TF85UqkFxbi8W+/wcXFBbGxsXB2dsbp06dpamqq2LVrV/7ChQsIDAxEjx49cObMGZiamqL/s+3dFdGoEVOXBoBLl9jfSUksCDczY3PnAQGsFXzNGjYr7e7OHmvVCvjySzYz7ePDqt6XLwN5ecDKlSxQ19NjgdozLePm5uaYNGkSL4oiEhISSHJyMm9sbAxRFJGdnY3Y2Fhx8+bNWLRoEVfutTVmDEumzJgBHDtWYbCtVCpx4sSJAj09vR+//vrrnyp7G4pam1+5QLuIkQBOrVmz5pxSqTwKIBjAQwBpAGw5jpuur6//vb29vfG4ceOqZ2966RJLItXgd0ZRgkmhUChO6AJtHfWNrrKtQ0cDZ/Xq1ReHDRvm6e7u/rKXUi7r168XsrKyCAA9beedfvjhh3copWoATXieX/Xtt9/WYADy5aBQKLBlyxY8efIEADB8+HD06tXrJa/q1eH8+fO4efMmnTNnDrGwsEBhYSHWr18PAGjTpg2mTp1aoR3Qq4BKpUJQUBDNzMzEvXv3hyAT+wAAIABJREFUyLBhw6DRaHDlyhWamZlJANZS26hRIzEtLY1r166dMHHixDp5wTExMfDx8aEpKSlk9OjRuHPnjjhhwgSuWi3l48ezdl25HJGRkfj777/x9ttv172yfn4+m//dtKl0pTcpiQX8NVQMz8jIwMaNG9GjRw+MHDmy1suklOLYsWMi2bmT8wgMxLn//leMioriOI6DVCoVTUxMqIWFBd++fXu4urpCo9EgMzMTiYmJ8PX1FZVKJSeRSMBxHO119Sq6ffwxkQ4cWOt1FaNSqXD9+nW0bdsWUVFR9OLFi2TgwIHw9vYGAAwcOBCO776LfFNTnJo+HaIowsjISFCr1fy4ceOQmZkJa2trtG7dGoQQCIKA9evX07y8PNKpUydh3LhxPCEEf/zxBxwdHenQoUMJwK61v/76CzzHoaWxMW3z558k18wMwWPG0Jy8PNKqVStRKpVyiYmJQocOHXh/f3/wPA+e5yGVSqlUKhU/+OADvtbdLBoNkJkJYedOPDQygnNUFHDyJGvZ37CBdU506sQ0AQ4cYGJq27axiqiZGRNNqwaCIGDv3r1iVlYWXbRoEV8mOajRAAkJ7Pru2LHC/Rw9elR57969v9Vq9buv85ywXC4fYG5ufnrAgAFGsbGxBcnJyZq8vDwDlUol1dfXL2zRooXGw8PD2KGy8YDydwzMnMm6NWqQnPPx8VEHBgYGq1SqAa+z4JyOhoEu2Naho4GzevXqgD59+vTy8PBokBHIjRs3cPr0aQDQl8lkmuo8Vy6XOxFC7q9YsYKX1LHlT33h5+cHX19fLF26FEZGRnjTWp/rg6CgIOTk5ODGjRuUUkratGkjjBw5ks/Pz4eVldUr9R7/8ccfYkJCQsmCpVIpVSqVxNHRUZg2bRqfnJwMOzs7XLlyBR06dKh1NftZVCoVVq1ahUGDBiE6OlpMTU3lpFIpXbx4MdG6YyAnhwlOSSTIycnBunXrAAA1agOujAsXmDr3zZulf967N/vz3//WaLfBwcE4efIkBg0ahHbt2mmlD5GdnQ0zMzNwHIf4+HgUFhYiKyuLJiYmijExMTzHceL748dz4du20eCmTcV3332XNzIy0qoLQxRFxMXFQRRFRPz6q5hmbExse/UShw4dylfYiqwFGRkZMDIywoEDB8SHDx9yoijC0NBQnDp1Kufs7Iw1a9ZQlVJJHB4/xuCpU2Hr5gZjc3MkJSXh0aNH6NChQ4Vz+GlpaYiPj0e3bt1KznlAQADCwsLEhQsXllzbT548gaGhIVQqFS7/3/8J7jExvO0330CQSMA3b46UlBTs2bOHajQa2Nvbk9zcXLpo0SKiUqmwdu1aTJgwAR07dkRkZCROnz4tiKIItVrNDR48mDg5OUEikaCgoAD6+vqwtbWt8P318fFBcHAwLSwsJFOmTEH7xo2ZLdiPP7J2f0NDZvn13nsswVNYyBI8AQGshTwzk7WRKxTM/s7WlgXMOTmsQp6UxNrk09IAABpHR1zZsYMiPZ32XbiQ45OSWKU8MpJV0b29mbd2ed7kADIzM7F58+Z8jUbTVCaTZdX4IngFkMvlRjzPZyxZskRq/Mz7IYpizb/XU1KY1sOVK2UV57Xg4cOH+PPPP3PUanVbmUyWXLNF6NChPbpgW4eOBs6qVatOurm5DR45cmTN78zqAUopLly4IAQEBPAA5stksh012c/q1asvchzXGwCdO3eutKKbqobCyZMnxfj4ePrxxx83yOTHq07RjbeYn5/PCYIAABg/fjzcimyWRFFEVFQUnJycGqS1mL+/Py5cuAAAWLJkSUk79otAFEVs2bJFSEtL442MjMTZs2dzv/32Gxo1akQFQcD48eNJ06qUsDdsYGJUv/4KjUaD//3vf2JeXh43e/ZstKhL26q4ONbi+3wXQ3o6s/CpwbkVBAH//ve/UaTqDI7j0K5dO2ptbU2cnZ1hbm6O6OhoxMTEwMTEBJGRkWKLFi248PBwWFpaig4ODlxUVBQ4jkNhYSHMzMwwcuRItPXzYwmIWvpji2+9hRhnZ5xu2pS2adNGHDVqVKXfIXFxcTh9+jQ4jhOHDBnCSSQSGBoa4urVq8KtW7d4ADAyMhIXL17M3blzB25ubiUJgOTkZKSuWEHbBwQQg+jomqmnP8OFCxdw+/ZtumTJksozLlu3AqtWMXsxGxtQShEUFERPnz5NevXqheHDhwMAzp8/LwQGBvI8z1NKKenbty+1trYmWVlZop+fH8dxHARBgCAI4HkeEydOhKura6lDXblyBQEBAaKhoSEZOXIkiYyMFFJSUsj8+fNLR3GUMiV1ngfGjWPz3CYmQLduwKxZTJiN4wClkgXI7u4sGfTkCZtvj4lh8+KPH7Pnde4MITgYdwICaIKzMx3t7s5xNjbMu9vEhM1qV2IxFxwcDG9v76MrVqyYVMPT8UqxZs2aE0OHDh1bJ915ly+z74gxY2p0TatUKmzYsKEgPz9/9sqVK4/UfkE6dFTN6zkcp0PHa4JcLuf09PQ8evbs+dICbVEUywr9gIm7FAXanbSZ166IwsLCYQB6EUK8CgsLa7na+iMnJwf79+8XU1NTucmTG5Td+WuFi4sLWrduzd26dQtmZmYICAjA8ePHcerUKVhZWdHHjx8TQgg4jsPkyZNrNpdch6SkpODmzZtCkec4uXDhAszMzISuXbvyLzLQBtis8MKFC3mVSgUDAwMuJycHAPD48WMCAJcvX8agQYOwb98+OnDgQGJtbV22sm5vD2RkAGDt7rNnz+Y2b95cYWWxRoSEAKNGAfHxZW+YIyKYKNa3VbkJliWtqPJoY2MjPn78mBs2bBhiYmLE+/fvIzAwkFer1TA3NxcdHR0RHx9PeJ7nHj16BIApUatUKuHdd9/l09LScPz4cUycOBHOzs7Arl0siKol3FdfoU3jxuCUSnLo0CF+xIgR5Vb38vPzIQgCrl27JqSnp/NGRkbc/v37oaenR9VqNTEzMyM9e/aEjY0N2rdvz0mlUnTr1q3UPuxTU2H/668E+fm1CrSTkpJw+fJlREVFYdy4cVU/YcECYPRo5su9bRvI1q3o1q0bMTIyQqtWrUo2GzZsGO/h4YG0tDRiZ2cHAwOD4l8wXPE8tyiKyMrKQnh4uHjo0CEuICCADho0iBQUFMDb21sEQEaNGsW5urqCEAJra2v+119/fVo11WhYIB0UxITSunRhAmtDhrCAujLefrvSh/kRI9BOpSLXd+6kWzQa4YPx47VuiU9NTVUrlcrAqrd8PVAqlceio6MHubu7V+2XXRUrVrCEyfjx1X4qpRT//POPUqVS/aMLtHW8SHTBtg4dDZuJGo3GtM7UdGvAv/71L+jr64tLlizh9u3bp7S1taXjxo0zfMZHWlmb/ctkMjUA/59++ikzNzfXHgAKCwvRkNrKRVHEunXroK+vzy1fvrxiJVoddQLHcejatSsAoGXLlkhPT0dSUhIiIyOJi4sLbG1tcePGDRw4cAAcx2H8+PFUIpGQZs2avdBzo1QqsWXLFlhaWpKbN28SjuNgbGwsLlmy5KV2PRR/X5iZmaFXr164du0aANY1EBkZCQCkWExNJpMBYEmDixcvCiaEYMzUqXx2VhaMjY3h4+MjGhkZcXXqG9+4MZubLe97LSmJ2WvVAHNzc/A8j/Hjx3ONGzcGx3Ho2bNnybmglIIQUhIRJSUlYd++fbRFixbi9OnTeY7jQCnF2bNnRVdXV87Z2RkIC2PzvnUh3HfqFNCzJ1qNGAFCCI2NjSVxcXG0Q4cOpHhm9cGDB/jzzz9BCEGLFi0gkUjQv39/uLm5lQSkXFVRXWQkMGIEqwK2bFnj5Xp5eeH69etwdHQU58yZwzVt2lS7OYImTZgw2IkTQHo6SHg4XD08ymwmlUpRWacFx3GwtraGh4cH1759e3h5eYkHDx7keZ7HoEGDiLu7O3lW38HS0hLmZmY0cNs20qt3byaYZmMDbN8ODB/ObO2WL6860NYSAwMDvPPOO9yWLVvo/v376YwZM7R6fwoKCkQADcfHs/65GBcXxxV9/mq2B5WKJb3On69R1wsABAUFiVFRUckqlaqsH5sOHfWILtjWoaNhcw1gwWed3uxqiUbDRrDVarV67dq1EkJI/qNHj6wVCoUqIiLCgBCSTCm9X0eH+/rvv//+w8zMTJGTk2Po7OxcMGbMGCMrK6u6nRWtAcnJbKxr6dKlukD7BcNxHBo1aoRGjRqVtJIDQLt27fDkyRPs378fR48eLQ50acuWLfHo0SPMmjWLmD9nz1QMpRTbt2+nTk5OZPDgwTVWQL9z5w4AoFWrVtyQIUPg7e2Nfv36NagB8+HDh2P48OH46aefUOTli5EjRyI/Px9Xr15FZmYm7t69W+ypzTePjYXm+++xfvp06OnpQaPRcB4eHnX3GczNZdXFQ4fKf3zq1KcK1NXE0NAQLi4uor+/Pzdt2rQyjz//GhwcHLBs2TICoCRiy8zMxMOHD7mxY8eyHwweDPzf/9XYx7cUt28zhW0AEolEvHTpEp+UlEQCAwMhkUhgbm4upqWlcf369UOPHj1w4MABYmdnJ3br1o3T+hqNi2PHCAioVaAtiiKuXLmCYcOGoWvXrtW/pg0NmfhdXBywaBHwxRe1eg9tbGwwffp0vrCwEBzHQV9fv/TJfPAA8PLC2w8ekMzz5yns7AjOnGGjCn/8AQgCS5zU8eiJRCLBnDlzyNatWxEUFFSmw6A8zMzM9ABk1ulCGjAymSxu9erVKfHx8c2bN29es53s2MH+zJ1bo6cnJibCy8urQK1WD5fJZPk1W4QOHTVDF2zr0NGwSeI4rlBfX/+llHkVCkXxP9sCGEYp3Q/ANioqagohRKSUbpfJZEJdHOvrr7/eKZfLr+fk5LQEEP7w4cMrGzduNBo9erRWNzD1hUqlwq5du9CoUSNRKpU2qEDqTUZPTw/W1taYP38+Ll26hJ49e8LHxweJiYliXl4ed+TIEXHq1Kmc8XMiRQUFBbh+/TqSkpJIUlISrl27Bn19fVhYWFBRFGFvby+OGjWKNzQ0rPT4WVlZOHXqFAAmEujg4IBRo0bV2+utLXPnzkVcXBwkEgm6dOkCAAgLCxM2bNhQEmgaGRnRpCZNyOm+fQE8TbaFhoZiYF0paN+9C2RlsRno8igsZFXRrKzyK98oqVCX+1hmZibatWtXo6Wp1Wrs3r2bEkKIoaEhm/WNj6+7AO2PP0pek6OjIx8bGwuO4/DJJ58gMzMT0dHR3Pjx42FXJPo0d+7c6n3faDTAoEEssF24sFZL5TgO3bt3pxcvXiReXl6glGLy5Mll5qarpHlzwN+fndd+/YAffmBrrCGlOp4yM5nyuJ0dC+hHj4b+0qU4bGVFvp4wgW1DKbB3LzBvXp0H2sVYWFigZcuWQkJCAq/N7yq1Wi0CeKMUsCmlXikpKQtqFGz7+ABTpjD18RokRpVKJfbv31+g0Wjelclk0dVfgA4dtUMXbOvQ0YCRyWT0p59+yoyLi2vUuXPnF96a6u3treR5PuHbb7+NB7C16Mc5AFbXx/FkMtk9APcAQC6XNwEw9dSpU381b94c1tbW9XHIKomIiKCUUvLBBx/oAu0GiFQqLRFdeuuttwgAXqVS4c8//xQ3btyIKVOmlAh7PXr0CHv37oWenp7w1ltv8R06dMDdu3dx48YNUEqJk5MTwsLCyNq1a9GiRQvMmDGjjA1ZkWJyqVlbGxsboW3btg1aMM/e3h729valfvbRRx/xT548wb59+wS1Wo3PPvuM/33LFk2jS5f00LQpJFIpTExMqKWlJcnKyoJlDe24SsFxgJ9fxY9LJMDatSxIeg61Wo2bN2/C29sbenp6EAQBY8aMgSAIcHZ2hqWlJczMzGh6enqNlqZWq5GTk0MWLFgAk8JCZgsVFVV3QdrcuYCHB/DZZ5gwYQJOnTqFpk2bwszMDGZmZmw+vKYoFExB+/x5oI5s2kaNGkVGjBgBURTx66+/CnFxcZyrq2v1WxyKrefeeQe4d4/Nv7u5VZhMqRSNhrXjnzwJdOjAKtpTp7LzxHEw0WigEQSoVCoY/PQTUxAvskGrT2xsbEhoaKgIoMrfExKJhCeE1H5++RWC53m7GglaZmQwEbsTJ4Du3av9dEopTpw4oVCpVAdXrlx5uPoL0KGj9uiCbR06GjiiKIopKSlc586dX9gxb926JZw4cYIHIAWw6YUd+BlkMpkol8uP8jx/Ys+ePSMWLVpkUNN239rg7+9Pe/ToQV4l+6k3HQMDA8yfP58/d+4c/euv/2fvvMOiuNY//p2ZXXZp0kGpoiCoYA9KLGgUjRq7sWuKMcZocm/yy71JNLrZ3CQmuYkmJia5thij2LHHLkUpYkGlSBFUQFF6WZZtM/P74wA20F3YBdT5PI+PujNzzpndZdnved/3+26lLC0teUtLS66wsJBp3749ZsyYUSeMu3Tpgi5dutRdO3jwYPr333/ns7OzqS+//BKhoaHo0aMH4uPj+atXr1K1Qk4kEsHNzY2bOXMmbW5u3qqFdkPUZge89957det/KTRU5PXeezg1eDBefOkl1sbGhjl+/Di3Zs0a+s0334REIkFcXByXnp6ONm3aYNasWbRIJALP82BZ9vEp+Zcv33N3fpzQCg4Gysrq2vrcvn0bGRkZtVkI7NixYxkzMzOcO3eO27t3L01RFKysrPg333yT4jgO4vv7dhtAZWUlACAuLo6b+MorNL75hkTZjcUrrwA1hn4ikQjjGmHy1CBz5gBKJRGiRoSmadA0jYEDBzIHDhyAUqmEra0t+vXrZ7jT/rx55O+xYwGFgjh+68vFi8Du3SRSfvQoMciaOvXBHu0gzytN01AUFsJepwMmNo/hd1BQEB0fH4/U1NQHPk/qw8XFhZZKpf2bZWGtB8bgUpTiYiA/H0hMJD4PjSA5ORlZWVkFGo3m3UYNICBgBASxLSDQipHL5fYAXDw9PZu1aDkxMZGlKGorz/NhACKbc+6H0LAse7asrGxsZWWlcSJrBuLt7c2dP3+eUqlUGDNmTMsWjwsYxIgRI6iAgACUlJRQJSUlTO/evR+o+64Pmqbx7rvvUhzH4eDBgzh+/DiOHz8OABQABAYGoqioCPn5+SgsLKRWr17NffTRR8/ETgzHcSgtK8OWxYvBMwz8/PwYFxcXdO/enQ4PD+fXrFlD8TwPZ2dnfsCAAcyxY8f4r776Ch9//DEiIyPZs2fPMtOmTYOfnx8AkklgYWEB+1rB6uUFhIWhUq3GuTNnEBQUVL8XxZw5wIwZwOLFiIiIYOPi4hh7e3tu9OjRdGBgYN3GgJ+fH61SqSASibBt2zbu559/ZliWZSY2QmBVVFRg3bp1AADFhQs8Tp8GanqMGw03N8AUDvWXL5O+5Hr0FG8MJ0+eRHx8fJ1j+61bt/Dzzz8zffr0QVBQkOGfy/v2kSj8kiUk00EuJ38/TFUV8O23wN695BwPD+Ji/9Zbjx1+0smTHBceTj/Sw92ESKVSmJmZ8VVVVU/8HdGuXTuwLBvYHOtqLXAcF2hv6MbV1Kmk5n5Do7qKQqlU4tChQ9UajWaKTCZTNmoQAQEjIIhtAYHWTYijo6Oqc+fOJndHO3fuHPv3338zdnZ26tLSUgmAf8hkspY2cbEH8NWkSZNaRGgDwKhRo0RdunTB1q1b4e/vD18jpWgKNA9ubm5wc3Mz+DqapjF27FiMGjUKeXl58PT0rEsdz8rKwubNm6HRaCipVMoZe80tRV5eHg4dOoS3167Frfff5x0dHeuEw8SJEymdToeKigrY29szABAQEED98ssv7Pfff8+wLMt4eXkhPDwc3bp147y8vOjdu3fDz8+Pmzp1Kn05Ohoec+fy6qNHqcM7dnAFBQVUSkoKFi5c+GjWSGJiXcQyLS2NcnJy4ubNm1fvhkatYeGsWbMYpVKJ6upqg0pOFAoFEhMTERsbCzs7O3bKlCmM+cmTDMLCDHz29ODPP0lv54AA4425eTMRrenpgInMG+Pj4zF+/Pjaem0GAK5evYqIiAju3LlztEgk4iUSCefq6sp4e3vDzc0NVlZWuHjxIpKSkjitVsvXlGNQvXv3Jq29vLxIHe6KFUR4UxR5rLgYiIsjddbdupHNie3bAX3q8DkOuH4dkgkT6H35+ezc+4zvTE1ubi7UanVdF4XH4eDgAI7jbORyuYtMJrvbDMtrUeRyuQSAV60XgV4kJZGOBY9xq38cPM9j7969Sp7n/5DJZM9NmzWB1okgtgUEWjFmZmazAgMDm8WG3MnJiQGA0tLSrwBsbwVCGwDUAHD37l2+S5cuLZbK3b59e/Tq1YvdvXs3PXr0aCow8LkKSjzXiESiR2pp76897Nq16zMR1QaA8vJyAEBp377oM3o09XB/ZpFIhPujU2ZmZnj33XeZgoIC2Nvbw8rKCgUFBdi1axeflpbGMgzDXLt2jd6wYQN0ly7Bwdqa2rB5M0QiEf3xxx9j5cqV3P/+9z+4uLhwoaGhjLW1NRl4927S/uu//8XUqVPpn3/+GUqlEk+q+bSwsHjiOQ9z9+5dnDp1CoGBgfzEiRMZ5OaStln1uJk3maVLgQYc8htFSgowYQKpAzeR0K6oqIBOp3ugRzZAugF07tyZ1ul0uH37NpWdnc2kpKTwd+7cQVVVFVXrfD9w4EC6rKwMSUlJAIDY2Fh+4MCBZBOnRw9g0yZg2zaSYt6jB5CXB+zYQQzPXnyx/oh3Q8ydC6SnwzM6GqU//siEhYVxkydPppujdeatW7dgY2PD0TT9RIFPURS8vb01mZmZIwFsNPniWh4pTdNarVYr1qvEY+dO0qItNbXRLfcuXrzI37x587ZGo/m/Rg0gIGBEBLEtINC60QLgUZPCakx+/fXXaq1WywIAz/NMeXm5uZmZWZpGo/ldJpMVGnu+xiCTyRRyubznmTNnEs+cOQN/f3/NK6+8YqbT6cDzPIqKih75EmgqQkNDmYKCApw4cYILDAx8ZgSWgGHcuXMHOTk5mDx5Mnbt2oWEhARq+PDhLb2sJpOXl4fw8HAAQIS7O9qbmUEf2SqVSuHp6Vn3f2dnZ7z77rsMQNLS09LScPbsWb6nkxPvkZBA94+Kgq2tLUQiEV5//XU6NjYWBQUF1KpVq+Dv788OGTKEsadpgOPAsizOnTsHOzs71sLCwiRRyo417bGSkpKogQMHwmnBAhJlPXDA+JNt2QIEBhpHyN+6RdqSHT4M1LjLm4LffvuNd3BwqOvx/TAikQienp7w9PTE4MGD685RqVSgaRrl5eVYu3YtRowYgaCgINA0Tc7heeDIEWDZMhLJ/vBDwMGBmJk15FTfEBUVQEQEifC3bQuRSIR58+Zh586d+OGHH3hfX18MHz6cMrjG3ADS0tLYjh076v0e9ff3t8rNzZ2IZ1xsy+VyGzMzs/PdunVjLSwsnqy08/KAYcNIXX4jjQlLS0tx9OhRlVarHSeTydSNGkRAwIgIYltAoBWj0Wgyb9++rQYxKjMaNfWm5gCmA7gL8lnAajSaCJlM9qgNcAsik8kuyeXyfgDs0tLSwrOysgBArdVqJQDw0Ucf4eH2TqaApmmEhoZizZo1tE6na3RvZoGnm+3bt/NlZWWUo6MjAgICcOvWLZNshjUnO3fu5FNTU+vuYfbNm7D44w9SL9sEaJomBnQ6HYXPP6fw6acYNmxY3XEnJ6dakzC6qKgI+/fvp1avXg1nZ2d+xDvvUMc3bOCKioqoUaNGmSwduDYC27FjRzg5OZF6Yo2JujLduQO4ujZ9nNu3icFcVBRQUx9vCnJzc6HRaKjZs2cb3Gc9JSWFPX/+PFVYWEh36tSJ79evH4W7d4mg7tIFeP11Yhg3ezapzc7NJZscjcle+uQTIC2NtIiqWaeNjQ3eeustOjc3F5GRkeyqVauYbt268WPHjjXJz2p5eTndt29fvc/39fXF4cOHh8nl8jYymazCFGtqDTAM84Gvr6/7qFGjnvwdpqICCAkBfvwRqO1zbyA17uNKnue/lslkqY0aREDAyAjfFgUEWjESiSTAw8PDqEK7qqoKmzZtUlMU9c6yZcu2GXNsUyGTyc4CgFwu76/T6d7heX4DgItisTjn+PHj9t27dzdzcHAw3B3XQFxcXODg4MDv37+faowJk8DTT0VFBeXl5YXi4mIkJycjODj4qRbbBw8e5FJTU2mApGFPnz4dbaZObVxbpobo1IlEih+TQuro6AiNRgMPDw/OuqIC7p07U8yaNfzHH39Mm7J8JCMjAwDg7+9Pos5LlgDTpplmsq+/Njxq+zA8D4SGknZXMplx1tUA0dHR6Nq1K+vq6qr3ZgfHcTh16hR79uxZpnPnzni9UydIOnak8N13wDffAAUFwOrVQP/+JHLZrRsweTJgbU36kBtCdjZ0q1djW9euXHW3brAIC6PHjRv3gOmeh4cHZs+ezaSlpSE8PJwaO3asYXPoiVqtpuo1+2sAa2tr+Pn5IT09/V8AlppkUS2MXC7vTtP04qCgIPETN2tYlrR1++03oAmZQqmpqbh9+3aBTqf7rtGDCAgYGUFsCwi0Ynie97UxQo1feno6f/LkyWqlUklVVVWZi0Siw8uWLdvY9BU2LzKZLBHA/Nr/y+XyvqmpqX9dvnx5UJcuXdSvvvpqE7/JPh6KomBtbc2JRKKnstWTQOPZvn07m52dzXAch7KyMu6DDz6gdTodzMzMnuqSguzsbAoAZs6cea8kIyICuHIF+Mc/mj5BRQXQqxdw6dJjT9NoNCgsLKRff/11eLi5AZ064c0RIxgY2i7IADiOw65du+Dm5qbr06ePCB980KQv+k/kww+JOdonnzTueq0WyMoiNe0mjGgDJA382rVrmDt3rkGfdVlZWcjevZt5pawMzjNnQtK3L/DTT8CCBcA//0lqcENDH7zoww8BZ2eD1rd27VrWZ/du2iU/H1kTJtBDhw3DyZMnsXHjRm7RokWP/EzLf9k3AAAgAElEQVSmpaWxbm5uFPTog20oOp0OKpUKrgZmLQwZMsQ8PT39A7lcvrKVeKQ0CblcTgFwAdBRLBZPAvBBSEgId3+ZSYPMn0/afDWhdZ1CocCBAweqNRrNLJlMpm30QAICRkYQ2wICrRiWZSuTkpLYgICARou7yspK7Ny5U8ey7BIA5wBY6nS6COOtsuWQyWQ5AEKWL1++v23btqObY057e3sqKyuLRTM63Qq0DJWVlYiPj+fFYjGVlpbGzJgxA+3atYOVlRUNEIOwp51hw4ZRO3fuxIULF3gfHx+ibNPSgGPHjCO2CwqAgQOBx0T9cnJysHHjRvA8D49a9+HycuJS7eXV9DU0AEVREIlEGDN6tAjz5wPffWdcA7OHGTeOtP9qLAsXElO0mBjjrakBpFIpxGIxeF7PqqKqKuCtt9Dx7bdRrNFw0osX6XWbNmH6mTPweVwHhw0bAEtLYoamJ6d+/JF/edUqJuWHH0D5+eG9tm1hb2+PK1eu8IWFhfTVq1fR+SH38prWkSbZGIuPj+esra15QzdhHRwcEBAQIEpOTv4YwMemWFtzIJfLX5BIJEsZhhnGMAxlY2OjdnJyEo8aNQqWlpZPfs6rqkg2iT6O8w3A8zzCw8OVHMf9LJPJTP8DIiBgAILYFhBoxbAs+0NGRsZOnuf1rpnTarXgeR5mZmbgOA5btmxR0zT962efffajiZfbIsjl8p4URY1sLldob29v+vLly80xlUALoFarUVpaigsXLnDnz5+n27Rpw0ulUt7Ly4v39fV95jZYUlNJWWN6ejpV9zmzYAGJNBmD+Hjg118bPHzhwgUcPHgQDMPU1m8TvvwS+Phjo4ttnudx9epVeHh4IC4ujtPpdLT27l2yzqameD8JC4vHptI/lpQU4PPPG20a1RicnJz47OxsyqO+9ks8T9J+V68m7ccSEgCpFLSzM/qtX0/zPI+XYmPZbdu3M2+88UbD7fdiYkiZgb5oNChJTIRoyBC8PGHCA4dCQ0OpsLAw7N+/n+/cufMDvzB79erFHDlyxOibpBUVFTh16hQ9a9asRl0/cOBASXJy8iK5XP6TTCa7bcy1GcKXX345gabp9osXL9a7ufwXX3zxilgs/s3c3NxhwIAB0u7du1M1/in6l76dPw+MHUs2+JpQBpacnIxbt27d0Wq1z2RKvsDTjSC2BQRaN+EURalSUlKkNWZMKCsrg4uLCxwdHetO4nke169fh0KhwMGDB7VarVb8z3/+E/v37+cLCgrA8/ySFrwHk0JR1BCe50XR0dH8+PHjTV476+LiAp1OZ+ppBFqA69evIzw8HAqFAgBoLy8vzJkzx6Q1wy1NbU/q0NDQext6CQnAxInEGbgpJCcDixc/1n07NjaWd3V1pSZNmvRAWzHUtIoyNkqlEjt37gRAatRfmzkT7gDQHBtoO3cSYfnCC4Zdd/Qo8MYbwNWrpo28P4RCoeBFItG9z1SdDrh2DXByIlHIVauAAQMAb29iTHZfzTVFUejfvz9TXl7Obt26lXrnnXfoR2qai4rIGHoaXHKnT6N6/HjkLFpEvfyvfz1y3MfHBzNnzkRYWBh17tw5vHDf8+zn54d9+/YxeXl5cHd3N/CZaJiqqiqIRCJs376dd3Jy4nv16kX36NED+n5m2Nvbo2fPnqLLly9/BeANoy3MAORyuTWAcJZlAeCJYlsul9tLJJLfzM3NX5kwYYJFx44dDTbQA0DKIuztgZUrmyS0lUolDh06VK3RaGYI6eMCrRHm888/b+k1CAgINMDgwYP5qKiof5aVlVlwHIfw8PCKa9euJSQkJDgnJCToqqqqaIZh6MTERP7IkSP5WVlZeVqtdp5IJGofGxvrUVlZGcVx3PSadOtnkpCQkLioqKiEoqKiSW5ubmI7O7vG/eLXE3Nzc8TExMDHxwd1fYEFnnqKi4uxdu1a0DSNDh06cCUlJdSECRNga2vb0kszKcePH+cUCgU1ePDgewaDUilQXU1aSzUFe3tifnW/iH6I69ev48aNG1RAQAAe8Kf49ltg61ZgtHGrQ8zMzKBWq7lbt25RM2bMoNpHRACzZgEffWTUeeqlZ0+gb1+9xSUAICOD1HlPnmwcJ3M9yczMxKVLl6gRQ4fCYu9eIrDfe4/UX3/0EWnPNHQoSYv390dDtfXt27eno6KiKHt7e7Rr1+7Bg6++ShzVx49/8oJyc/H3iRO4JpVi0tdf1/vZS1EUjh07xhUVFVFdu3ZF27Zt647RNI3i4mI2IyOD6tatm9F+QVhbW2PAgAHw8fGhtFotzp49i+joaNja2lLOetahu7u7M+fOnfOLiIgoGDRo0EVjrU1fYmJiPvb29g6uqqriIiMji0JCQs43dK5cLn9BLBZHBwYG9poxY4bU2dm5cb9vWZb0h/f0bHIrvAMHDqiKioo2ffbZZ/9r0kACAiZCiGwLCLRyKIr6sri4ePjJkye7aLXauYsXLz4ll8vFVVVVA+Li4gadO3fuPa1W6wBg2OLFi68CgFwuPwgAS5YsaVVtvEyFTCY7/MUXX0zZsmXLbrFYzHz88ceMqaKRFEXBwcGB37ZtG/Xhhx+aZA6B5uHSpUtgWRZVVVWIjo6Gs7MzN3nyZNrJyenZDWU/hJOTE52fn4/169dj2bJl5IuzgwNx5ua4xrViAoDKShLFTUt77GmDBg2iMjMzUVZWhgfSlTt1alK0qyEUCgXi4+NpAMjOzkb7uXOJ6GsOfv8daN9e/xT90lLSCmnLFuCll0y6NKDOMI7LzMykfS9fxhw7O96Roih8/jng4wOsW3fPpV7P3t48z4Nl2frTyL///rEbMXVERoKfPh3XXn+df/nzzx/bL7usrIwCSLTzYXr37s1s2bKF5zhO78izPlAUhXbt2qFdu3bU0KFDkZSUhP379yMzM5MfN24c9aS5zM3N8eqrr5pv2rRpzRdffLGS53kXmUxWZbQFPgGxWDw6KChIOnLkSPz1118/LF++fKRGo1ksk8lSas+Ry+WUSCR6z8zM7JsxY8aYBwQENG1SjYa8p195pUnDpKenIy0tTaHVah9NdRAQaCVQeptfCAgItErkcrkZAIlMJqts6bW0NP/5z3+WcBz3ZZ1oMBF3797F77//juHDhyM4ONhk8wiYBoVCgR07dnC5ubm0ra0tS9M0Bg0axHTv3r2ll9bsnDhxAjExMRgxYgTphVyLmxtw9izQ2JTb3Fzgl1+e2Kt7xYoVrJ+fHz1y5MgHRYlOB9y92zRDsXrQ6XQ4fvw4f/78eWrS4cN8l86dKaxfb9Q5GuTf/yY16AsXPvnc0lJArSap1k0VNk8gPj4eN2/eRHFyMjd62za68N//Rqe8PLSprARWrGj0uDzP48cff+TVajXmzJlDPeDWvXw5YGcHvPPOI9cVFhbi/PnzKCkpQZ+SEriMGQNFcjI2XLwIX19fdtCgQYyrq2u9n/GRkZHs+fPnmaqqKrRp04a1s7PDnDlzGJqmwfM8VqxYwQ8ZMoTq1atXo+9LHwoKCrBjxw5erVbzs2bNol1cXOqOcRwHhUKB/Px8FBQU8Ddv3lTk5OSIaJo+ptFoonmeXymTyZrty/ny5cvvzJs3z6W2/V5cXJwuPj5eC+AGy7IRWq02XyKRTLewsGg/e/ZsCzs7u6ZN+MMPpFY/PLxJwxQVFWHt2rXVGo1mqEwmi2vaogQETIcQ2RYQeMqRyWQaAJqWXkdrgKbpDp6enjqKokz62Vb7xen+NEWBp4Oqqir8+uuvsLGxwccffwypVPrMmZ7pS1RUFGJqnK179uz5oHI5eNDgdkwPsHs38NlnDR7et28fn56ezgNgBg4c+GikMTGRpCqXlzd+DfUgEokwcuRIqkOHDjh7+zZ8xo5Fs3nKv/++/v3LR44E+vQhGxYm4OrVqzgaHs5VqNX06L//xpDKSr4kLIx2r6qC1/jxJG28iZSWlqKiooIKCQlBXl7eg62xiouB+wRoLSdPnkRcXBxcXFy4rhUVaPfll/SfN2+i3NYWFhYWbEZGBpORkYGRI0ciKCjokesHDx7M9O7dGytWrEDbtm2ZjIwM/Oc//0FwcDCGDx+OoKAgxMbGsr169TLpz72zszPeffdd6sCBA1i7di2mTJmCnJwcbVpaWnVZWZmUoiitWCy+rNVqL+h0ulgAZ2QyWRNNEhqHTqezra2nNzMzQ0hIiGjgwIGi69evd757925npVLJubm50X5+fsbJCJBIgLlzmzSEWq3G5s2blSzLfiAIbYHWjiC2BQQEnhl0Oh13+7bpDV05jgNFUXAywhdSgeYlNzcXFEVx8+bNe6aNz/TBx8cHp0+fBsuyKCgoeDCNe/16Usvcr5/hA6enkzZaixbVe5jjOFy6dIkKCgqihg4dWn8LtZ49SWTdRHTasQPRYjGV26kTOppsloeQy0mk/nFeOTxP6rQ3bDDMpVtf8vOB/HxcDg9nF/70E1O+bRukgwfDysWFcu7aFejd22hT2dnZYfjw4fzly5f50tJSWqvVon///qSl20cfAfVsVsbExGDKq6/Cf/duGnPnAq+9hvccHVFZWYn8/HxGpVJhz549UKlUDc5rbW2NZcuWQafT4dSpU4iPj8e5c+cwaNAg9OrVi4qOjmaKi4vrzAFNAc/zOHDgQHVycjLP87zF1q1btWZmZhs1Gs0fANJbU19tjuPE4odc8mmaRseOHdGxY0fAWL3JCwqAUaNIW0F9ygcagOd57Nq1q7q6unrHZ599tsYoaxMQMCGC2BYQEHgmkMvlvgDemjhxosnn4nkeEokEFy9exKBBg0w+n4DxkEgkUCqVdGFhIVzqiaw9T7i5uWHu3LlYs2bNow77eXnAnTuNG9jdnbSqEtX/FWPfvn0wNzdHaGgoRA2cA5EICAsjETAT9Nrmjh+Hubc36m1rZSomTnyym/jSpcD+/cQd3VilMJWVxPV70SLw06cjo6gI1159lSmLi4OTCVPUKYpCcHAwFRwcTCUnJ+PQoUNccHAwTb/xBuDn90hLOKVSCZqm4VZZee+1r+m6YW1tXWeK1q1bN73mFovFGDFiBIYOHYr//e9//A8//EBJJBKO53k6NjYWY8aMMer9siyLoqIicByHK1euaFNTU7N1Ol1fAMrmTAs3BLlcLgJA0zSNW7du4cqVK7hx4wZbWVlJMQyD+fPnP+oi31hyc4kTfxOENgBERkZqc3Jy0jUajZH6EwoImBZBbAsICDwr+ANAenq6TqfTibp27WqyiRiGwdixYxEeHo4+ffrAohl73wo0nvLyciQkJLCWlpaUjY3N8x3WBrBhwwYuNzeX9vLy4ry8vB58PrZtIwZphlJZCXToQNpUAbhx4wb+/PNP2Nvbc/3796d79OiBK1euYMaMGQ0L7VqOHCEmSsYW29nZyF6/Hrf37OFEIlHzvQ90OtLuqCHS00mq+aJFxhHay5cDDAOMHg1u/35s1unY2yNGMGqNBlOnToWTv3/T59CTrl27IioqitqxYwc3LSyMhvTRVsxSkQiTtm9Hda9esE5Obrw530OIRCIsXLiQysnJgVqtpsPCwh51Rm8kOp0OFy9e5E+fPq2sqqqSikSiUpqmVRRFXdNoNFOb0+iskbAikYj/5ptvAIByc3Nju3fvznh6emLXrl18bGwshg8f3vRZ3nmHmKH99luThklNTUVcXFylVqsdWVNCJyDQ6hHEtoCAwLPCIQDjU1JS5iYmJo65cOECS9N0tVgslowdO1Zsbm5u1Mn8/f3h7u7ObdmyhZo3b57J+3sLNAzHcbh58yauXbvGFxcXs66urtSgQYPqajIrKiqwefNmFBYWAgDz9ttvQ1rPl/3nDalUSgOov5f4228TN3BDa4aVSuCTT+oikrXjlpSU0EeOHIFCoeABUPb6RLcSEgybWx9KSoAuXeBx9SpsbGzw7bff8n369OFDQ0NNL7pr6+BDQh49du4cMGYMiWg31guCZckmx8yZwIEDRNg7OCDf3h7H3n0XeXl5TP/gYL6kpATt2rVr1s8siqIwffp0KmPUKIrNygKzZMkja8+Oj4dYowHfqZPRhPb9MAyD69evcxRF0e3bt2/yeBqNBmvXrlVWVlaeU6vVnwA4t3jxYrbJAzcjMpmM//rrrxVTpkyx7tChAyiKqvvcdHV15fLy8mgATXuvqNWkP7ufX5OGycrKwt69exVarTZUJpM1Mu1GQKD5EdzIBQQEnjm+/vrr33ie99fpdIcBfDt+/HiYwmm6qKgI69atQ9euXY2ekigAqFQqcBwHiUQChiHfAVmWRU5ODvLy8viUlBS+qKiI5jgOUqmUc3JyoqRSKZWXl8f/61//oliWxeXLl/H3338DAHr37o0ePXoYLar1NMNxHM6ePYtjx45h2rRp8Hv4i3BUFIlsDxli2MCLFwMffYQymkZYWBhbVlbG2NraQqPR8Gq1GiqVivLy8sJrr7325I4BCxaQNfzPyO1zy8oAW1scPHhQd+HCBZGDgwO7aNEi0xvl3b1L0uMfrhW+eZNsTty4ARiakXP9OmknNmgQGeP334Ht28lmiVSKiIgIREdHw9HRkevVqxcXHBzcYkEWjuNwbuJEPq9NG2DcOGrMmDFQKpW4EB2NzosWIfrFF0FPnIgpU6YYfe6zZ89yJ0+epJ2dnblhw4YZRWwfOnRIfeXKlaMajWZ8a00T14fvvvvu4qRJk3rW1GfXUVZWhp9++gn29vastbU1Y29vj8DAQHh7e+s/+PHjwNGjpM1bE7h58ya2bNlSpdVqR8hkspgmDSYg0MwIkW0BAYFnjsWLFy+o/fcXX3yxODIy0rp79+5GD5U4Ojpi6NChOH78uCC2jYRWq8WePXu4srIyFBQU0BRFgWVZuLi4cDRN84WFhYxYLOZsbGzg7+9P9+zZE+bm5jAzM6MBItC//fZbasWKFbylpSXu3LlDDRw4EC81Q5/ipwWVSoWoqCjEx8fDx8eHc3BwePRnw82NGHUZAJedDXb9emzy9OTvFBdT9vb2jE6nw+jRo+Ho6EhlZGRg//79GDJkiH6t+SZMaFwqe0McPkyMuVJI++D8/Hza0tISs2fPbh5H+h9+IPWqn3xy77HqamDAANJiS99+33FxxNH57l0Sxb5yhdRkd+xIshHef7/u1KtXr3IODg70woULaRjL6KqR0CkpCPrmG8qeYRATE8MtX76cllRXw8raGo69eyPT1xd2BQU8mhpJfQilUokTJ07Qs2fPhqenp1Geg6qqKiQmJvIsy77+NAttANBqtSezsrICOnbs+IBLmq2tLebNm4eCggKmpKQEp0+fRmJiItzd3Xlzc3PKzs4OAwcOxGNruo8eBZpY833r1i2EhYUpdTrdOEFoCzyNCGJbQEDgmUYkEkWVlZWNDQ8P140ZM0b0sOtqU2EYBmZmZhxa+Ivss0JOTg6uXr1Kh4SEYMaMGbCysoJGo0FUVBQtkUjwyiuvoF27dg0+11KpFO+99x7y8/Op69evY+zYsUIk+yFWr17NKxQKSiwWY+bMmfU/l9HRwLp1xD34CdQ6Q8fExyPu7bfh7+jIj500iXrYrb9nz57o2bOn/gt98UUgO1v/859Et24PCFEfHx86MzOTt7GxaZ6Uant7IoZrUSpJWvvBg8CTMm9yc0nP8pUrSU33yJHAsmXExM7MrN5+6Ldv30ZRURE9So/XsFlYuBBUUBB8v/8evr6+dHJ8PN9h6lTKYtkyXJHLwe3ZgzFjxhj9tVCr1QCAsLAwXqvVUt7e3uysWbOatMFy+fJlXiQSHfjss89KjbLIFkSn022+cuXKgtDQUPHDm2Curq51Ldu6d++OnJwcKBQKSqFQcHfu3OFXrVrF9OzZEyNGjHiwLZhOB3zwAXHeb4Lre2FhIf76669qrVY7ddmyZScbPZCAQAsiiG0BAYFnmsWLF4+Ty+UfJSUl/bdbt27w8fEx6vhJSUlsx44dn9tezcbmwoULnKenJwYPHlz3zc3MzAyhoaF6j2Fvbw97e3uY0iTvaaX2yzIAODo6NrxJNHUqEXRPoKKiAitXroRYrcY/fvoJRV9/zU+ePNk4G0+nTwOzZwNFRU0fa98+QKUC5t8zMNZqtZxSqWy+2uWpU4lhWS0TJwK2tsSMriFefx3o2xcIDCRp5lVVwIULT5wqJycHf/zxB1xcXPjevXu3vKcEzxPDu1oxl5WFADc3CmvXAsOGwa2UaNaSkhJ4GdkQz87ODoMGDWKVSiX69evH/PTTT0xSUhICAwMbNR7P80hISKhSq9U/G3WhLccVnU5Xdvv2bUs3N7cGT3JwcLi/XRoNkPfZxo0bcfnyZX7BggWUTa3b/rlzQHw80KYNcnJyUF1djXbt2qHN/ZtNT6CqqgqbNm1SarXahcuWLTvY2JsTEGhpBLEtICDwTCOXywMB/Hf8+PHo0KGDUcfmeR537txhXnjhBaOO+7xy9+5dZGZm0jNmzGjppTyzuLu7o2vXrkhJScELL7zQsAgrLiZCNyqqwVP27t3LXb58mXZxceHbSaVU7Pjx/KApU4wn7EJDSU2yMYiPBxQKInhrYFmWLi8vR3l5OWye1JLLGKxcCVhaEpfwa9fI/++vf+V5siEQFQXMmwekppJ6bD8/kmp+UH+9cebMGQDAtGnTKL1S9k3N7NnkXr78EtBoiMv8J5+QunyQDbKRI0ciwEStyAYOHMgAQEFBAXieb9LrXVpaCqVSqQVwxljra0lqTNL2Z2dnv+Pm5mbQm8XT0xPz589HXFwc/+uvv1JBQUEYevcu6Uhw9iwuXrqEQ4cOwcLCglcqlRRFUTAzM+PFYjEnkUhgZWXF2NnZwdHRES4uLnB1dYVUKoVCocCmTZuUarX616VLl2400a0LCDQLgtgWEBB41kkDgL179+Lo0aOq3r1700OHDjUzxsAFBQXQaDRGF/HPAzzP4+LFiygrK0NxcTF769YtuqqqigoICOC9vb1bgTp4NqFpGkqlEgEBAXyPHj0afp4tLIjwewxFNRHnF198keq2ahWwYgUFY/YuF4mAt94CfvwRaEopQG4u8NVXjzhcd+nSBQkJCdizZw9ef/31pq1VHyZPJinfq1YBP/1EauJ5nmwE9O0LeHgA06cD770HrF5Nal0//9zgaW7duoXr16/Dy8vLoEiiSRkyBPD3J4ZZYjEQG0t8AWqgKApBQUEmX4aTkxP8/f3506dPUzNnzmzUGJWVlRCJRLcWL178VNdq349Wqz2RlZU1c+DAgQa/YVxcXDB+/Hi6e/fu2Ld7N99n+XJK9eOPyOZ5REREYNq0afD19aV4nkd1dTUqKiqo8vJypqKiAmVlZXxJSQl35coVqqKigqqurqZ4nodYLOYA/KDVamUmuF0BgWZFENsCAgLPNDKZTCuXy0UAvKqrq91jYmKOWVlZ6fr27dvkz78bN26gTZs2rFQqFdLIDSQ1NRXHjh3j3dzcKCsrK2b48OHw8vKClZWVILRNyN69e7m8vDx63Lhxj494OjoCW7eSdlJM/W/vl19+mV6/fj0S9u5Ft5MniRO2sSkqIu7hTRHbwcHAv/4F/OMfDzzs5eWFwYMHIyEhoXk8F0pLyf3MmEFqWlNSSBT7m29IpDsqikS6aRrw9GzUFEePHsXZs2cRHBzMhoaGto7PpYgIUpPeqxfQpQt5LQYPbpGlUBSFLl26UIcPH270a15cXAye51ONvLSW5sytW7ekHMfhkTaAeuItkeA9BwfqzLZt3OmzZ2nu+nXMmTMHtc7vFEXBwsICFhYWaHuvvR0FoO59WlZWhl9++YVjWfbnpUuXLmvaLQkItA4EsS0gIPDMI5PJWADZALK//PLLcydPnhzQt2/fJo9rbm4OtVpNNeULyvNIYWEhDh48iKFDh1LNEc1qKTQaDczMjJJEYTSuXbsGnU6nXz17SAiJRPr713vY3d0dNE1DolIRsdiAKG8SJ0+S6G9TSEoCGkgbDgoKwunTp5vnh/eHH0gdert2pH+5SkXSqffvB8rLifN6Tg7Z6NDpAHNzEgnXIw08OjoaERERAIAxY8bwvXr1ah1CGwC++IK4p48YQV4LI5tUGkppaSlfXV1NX79+3bA2VjWoVCqwLGsEI4HWg0wmK1i+fHl5aWmpk0NjDc3+8x8wRUUI2bmT7hEUhKqqqjpzNX2oqKjAhg0blBRFLfvss89+aNwiBARaH4LYFhAQeK5gWfYzlmUjjTFWQEAAoqKiqCNHjqDVOP62YjQaDc6dO8dFRUXR/v7+XFBQ0DOzQ5Gbm4ucnBxYWFiguLgYycnJbHl5OSORSPiuXbtSI0aMaDHhzXEcdDodzMzMIJVKKWtra/3aKy1fjielhYuUSkz49Vdw8+eDbmQ09rGMGkXSjdeuNfza6mqyUXDmDGBnV+8pJSUlYFm2iYt8AioVUFFBhDZABPWMGURI/+9/REyHhgKbNwO3bpEWYFeuEFG+dy/g6gq88QYxUlMogFdeAfLygIAA4OhR3OF55LVpg/ElJfD184PFlSsUIiOJqVpMDBHv/fqRtHVLS+KKrlKRGurcXJKu7ulJ1mhpCUilZE2WliSVvymbKBoNaf80bRrg5dXiQhsA2rVrRwHEgKsxSKVSMAzjaNRFtQIYhskpKytrnNjevZtkaFhYAABsbGwMqouvrKzE+vXrldXV1V8uWbJEENoCzxSC2BYQEHjeyACIAGlqNJqmaYwfP57666+/MGDAgNZTH9kKycjIwI4dO2Bubs7PmDED7du3fyaEtkKhwK5du7jbt2/TDg4OnEajgaWlJd+/f3+me/fuyM3NpSIiIrhVq1bh/fffp2sFt0qlQmJiIi5fvswpFAoAgJmZGWdlZSXy8vLC0KFDjbI+juPw1VdfQSQS8Z9++iml0Wj4fv366ffcUxRQUNCgUAUAnqZxYNQojHVwgKVRVvwQn3/+YLssQ6BpUvPt4dHgKfHx8Zy5uTkFI/d2ruPf/yaR67Q0UnseHchH5FoAACAASURBVE0cxlUq4kR+P9OnP3r9ypVATesqjB9PxCvDkJR0a2ugVy/E7NjBOfn7U929vCiwLBE8ZmbkeaNpoLKSiPSbN8k4YjFpOda7N7BhAykVmDkTOHWKHK+sJJH14cNJrXtJCWk7VrtpUVRE5hg9moisqipgzhwgOZmI9txccrx/f1Kn7uhIzN5aCbUpzI3dZLG1tQVFUfWnezzF8DyfXVFR0dvgCy9cAD78kDiQN8J4TqFQYMOGDcrq6ur/LlmyZLnBAwgItHIEsS0gIPC8IRaJRBqKoowSZvTw8IC/vz+7bds2vP32260ndbOVkZKSwvn4+NDTpk17Jp4jjuOQnp6OPXv2wN3dnX/vvfdgbW39iIjt2LEjvL296b/++ov7+eefOSsrK1RUVEClUtF2dnZcYGAg5eXlRVEUhbKyMvrWrVv8mTNnKF9fX3gaIVL8008/cRzH0RqNhpLL5QBAd+7cWb+L168nYtvPr97DOTk5mLhrFzwPHICFpUmkNhFvly83mMreIEVFxIiM3HO9VFVVISUlhTaJOVqvXqRG/JNPiOBOTiaC9e+/gT/+IMI7JYVEjh8HRZFIM3DvbwCorXl1d8e1M2fojk5OfL090e8vl6mvTnrSpHv/nj370ePvvgtotUR8z5xJ1lNZSTYLbGxIjXlVFYnC29mRx2xs7m0KFBWRqHYrIjMzExRFwb2e3uT64OrqCrVa7S+Xy81kMpnGyMtrMTiOK6ntSa43aWkk++X8ecDJyeA5y8vLa4X2D4sXL/7c4AEEBJ4CBLEtICDwvMHrdDqzvLw8eDwm4mUII0aMYFatWoXU1FR06dLFKGM+a2RmZtL9+vVr6WU0Go7jkJSUhNjYWFalUkGn01FKpZIePXo036dPn8duINA0jZkzZ9KJiYnQaDRwc3ODq6srzMzMHhDnXl5eKC0t5a2srDhPT0+jbEoolUq6V69eXOfOnektW7YAACz1Fcb79pG64Qa4fPw4Xigvh8QU6eO1HD8OyGRAdrZh150/T6794osGT7l69SpEIpFBdaWP5fp1Ik7//hv44AOSGm5vT47dvg0MHAh89BFw7Bjg40PStq2tm5xabWFhwWZnZzNqtRoSicQIN/IQYjH5U/teqL0n4J7oB4DaTZx+/Uj9+dmzJCpvilr+RlJYWIgDBw4AABpbmyyVSuHs7Ky6c+fOcADPTP9nrVZbbJDY5jhgzBjinv/++wbPV1hYiI0bNyo1Gs3SJUuWrDB4AAGBpwRBbAsICDxXyGSyXLlc/tOGDRv+0aFDh0pPT09rS0tL6HQ6uLm5wcXFxeDaWktLSwwbNow/dOgQ7+/vTwtmaY9CURTv6ur6VDqNR0dHIzY2lheJRPDz80P79u0ZjuMQEBAAhmH0uieRSIQn9WOvrKxETEyM0SKt8fHxYBgGw4cPpyUSCWbNmmVYf+GVK0kU87//rffwCw4OWPfWW1hYXQ27+6OuxmTWrPojro+jtBQYNAiIi3vsae3bt4dWq0VWVhb8Goje60VMDJCfTwzAJBIS1X14zbm5xIU7KYmkdoeGkqiyWm1Q/+z6WLhwIfPDDz/w27Zt41577bXWoWzXrAG++45ExXNzW3o1dWzcuBEAEBIS8tjzdDodaJpusNSoT58+bY4fP/5vuVx+SCaTPRMtwHier1Sr1Troow04jryXjx17sFe8nty+fRubNm2q1mq17y5duvTPRixXQOCpQRDbAgICzyOLAZRlZ2dfu3nzZg+xWOzFcZybRqMJBgArKyvd8OHD6cDAQL1Vc+/evamEhAS+Jmqiys3N5YqLiy0cHR1Vb775ptT8MRHC54E2bdrw2dnZlI+PT0svxSBqhDYmTZpE+fj4gKIok4mZgoICSKVS1s3NrUlzKJVKXLp0CcePH4e7uzsvkUgogKS0G0T79qRetz7Ky2H9xhvo+c03nJ2dnWl3l3r1AiIj9a/dnj+fpL9HRj72NPuaCK1TI9JfAZD09q5dgT17SLr05MnE0Kw+/v1v0lv78uV7j/3xB4mG5+UBjUxpBkjmxIsvvkjFxcUxKSkp6NKlCx7b1s3UVFUBr71GasJrxG1rQKfTAQBCQ0PRvXt38Dz/yPNUUlKCgwcPVt24cUNK0zRnb2+vdHd3l9ja2kpFNSn/HMdBo9FArVYPBMDJ5fJpMplse3PfjwkwE4meVNdQw6efkpZuZ88aPElqair27t2r1Ol005ctW7bf4AEEBJ4yBLEtICDw3CGTyZQAPq/57+b7j8nl8sEKhWJ0eHj4R87OznB5ghtzLTVmafTGjRuh0+mkAF4HYFFcXPz9tWvXEBgYaMQ7eLqoqKhAQUEBPXPmzJZeisGcOXMG06ZNQ4cOHUw+V3l5OUQiUaOjZBzH4dSpUzh37hw0Gg1omkZwcHDjVde4cST9uR72HTvGV8+ciRFTpphWaFMU0LEjqRHWV2xv2UIi8k+ApmmYmZkhNTUVAwYM0H9NWi2ptR4yBPjzT+D77x9/Ps8TN/Vu3YgL+cqV5L7atCGP+foSI7dPPtF/DQ/RuXNnXLhwgd29ezeTlpbGTZo0qeXSa8aMIeZob79N0ulbCX///Ten1WrpyMjI0lOnTkkkEgkGDBggbdOmDb1r1y6EhITw586dU6lUqu95nv+GZVlJYWFht8LCwp40TbdjGMYaAMdxnJbjuGoAxSDGekda9s6Mg0gksterDCE7G/jnP4kpmgGbOjWfT9qEhIRyrVY7QiaTXWzCcgUEnhoEsS0gICBwHzKZLFIul0cB+Oj333/HggUL4OzsrNe1bm5u8Pb25q9fv35zyZIlfwLAt99+6xceHv4PLy+v59KtPCMjA9u2bYODgwNrZWXVOlJcDUCn08HKysrk83Ach9jYWL5r166N/r0cFhaGrKwsAEBgYCA/YcIEqkkRzsOHiTB8OHrF8+j9f/9H6f74A3aPcSo3Gn/9pX+v7SFDSJusOXP0Ot3GxoY/e/YsP2DAAP3E6bFjJIJdVkai5/oEAtesIW23XnyR1JLz/D2RQlGkl7ilJZCVRTYWGoG9vT3ef/99Ji8vD+vXr6dHjhwJi5o2TM3OvHnEyfyzz0j/8LCwllnHfeTm5iIpKYnmOG4Fx3H/BsAplcoRx44dO1x7Tmxs7DmtVvsfmUxWm9evAnC65s8zj1gsDnhimUlkJMlaOHGCbBLpSXV1NXbs2KHMz89P0Wq1o2UyWWHTVisg8PQgiG0BAQGBh5DJZLxcLncE8OHvv//+6dChQ9G3b19Knwy7kpISLYBbtf9XqVQfMAwTvHLlyqDp06ejU6dOJlx560ChUEClUsHW1hb5+fl827Zt+afRqT0nJwdA442UDOHKlSuorq7mX3rppUar46qqKgQGBnITJkygm6ayaxg2DKjn3uOPHoWLmRlcnlCDbjQGDiRO2g3Ujj/AuHGAAVHqF154gTp27NiTn6u5c0k7rdWrSS04TZM/+lBZec+pedAgUqMdEHDvuJcXSUPv148cu99B3EDc3d0hlUr5o0ePYsKECc2bS15eTl6nkyeJidqnn5La3maiuroa3333Hfr3768bNmxY3Yd1dnY2tm3bptTpdPNkMtn9yv+IXC6nAXSQyWRZzbbQVohcLqdEIlHQY01Dt28nYnv9eoOEdkFBATZv3qxUq9WbNBrN+zKZTNv0FQsIPD0IYltAQECgHmQyWbFcLl/G83zi6dOnPz1x4kSvnj17Kvv06WNhbW0Na2vrR66Ji4tDcXGxGYDJ943Dy+XyYAAfJycnL+3UqdMzU7ytUCiQnZ2NO3fuoKysjJVIJFRubi7Ky8tpmqZ5lmUplmUpb2/v5vvGbSROnTqF06dPo3PnzjqGYUz6u1KhUODQoUMYMWJEk8z1eJ7nsrKyaKPV60qlwNGjxMyrhqSkJNzYtg3OO3fCorkyNbZu1S+F/PPPgf/7P+LwrSfm5uYwMzPjOI579LnXaIAJE0hkfcIEsvFA06RO2xCCgkhaNUDEdn0GjI6OwOnTpE4+M9MgMfMw/fv3p06ePAk/P7/m7Y5QUAD06HHPrXzFCuLObiJqSyVqN0EzMjIAAGVlZXU/r2lpafyuXbt0LMtOkMlkxx4eo8bc7LkW2jX4iMViut7ItkpFXP3d3YmbvgHZF7X12SzLLli6dOkmI65XQOCpQRDbAgICAg0gk8lYALsA7JLL5Z0SExPTExMTQVEUHxoaSvXt2xc0TUOlUmHNmjVsaWkpI5FI/u+TTz6589A43JdffmmXl5dnVlFR8dSnk/M8j9jYWD4iIoKytLRkLSwsaHt7e6aqqgovvPACunfvDqlUSpWXl4PnebRp0+api2rHxsbC3d0dEydONOnvyerqahw5coQXiURURUUFOI5r0AG5IdLT07F7925eq9XSffv2ZQEY7/letYpElCkKxcXFOBgWhv87dAhm//mP0aZ4Ikolcfx+7bWGz6msBDZtAhYtMkhsd+rUCbt376ZLS0vvZTDcvk3ad9VGs0tKgFdeadzab94EXn+dCGiAiM+G0rv9/UlUePp04No1/WvUH+LFF1/EyZMnsXv37uYT25GRxOztjz/uPXbiBPDmm0afiuM4REdHs6dPn+bMzMx0Hh4euvbt21vFx8ezDMOkZ2RkOMTExDj26NFDtGfPHhXLsqNkMlmk0RfybPGil5fXo7Ua+fnAb78Rl/05cwADSmoSEhK4EydOlGq12uFCfbbA84wgtgUEBAT0QCaTZcjlcn8AN3ie/9fx48c/PXbsmMX8+fNBURRKS0sZAL6ffPLJtfquZ1n2F6VS2eunn34a6Ovrq5s4caKFoS3GWgOZmZnYv38/r9PpMHnyZPj7+zco7AxqM9XKCA0NRXR0NPf999/TQUFBXP/+/WlT9DC+fPkyMjMz0a9fP1y8eBFJSUn8nDlzqLy8PNA0DVtbW7i4uOBxJQwnT57ktFot/eGHH8KodfFWViSqVRMpNzc3h8bMDKeXLuWGeng0nwHX2bMkuvw4sa1UGt6LG6SWVywWk97jBQWknjouDvjlF2JatmdPExYOIvxXrLjXa/rHH4GQEGDBgvrPHzoUiI0ljt4KBdBAD3ClUonk5GScP3+eKy0tpX18fPjBgwdTTk5OoGka7u7uyM/Pb9raDWHjRhKdr4XjiPO6CXpsnz9/HjExMTc4jntFpVJJMjMzu964ceMdrVY7EMAklmUHxsfH/5Kdna3jeX6tILSfjEQiGda+ffsHlXRMDNlsmzMHGDVKbzM0rVaLw4cPq1NSUgq1Wm2ITCYz/AdTQOAZguL1NR0REBAQEKhDLpfTFEX9yfP8rPseFstkMt0TrmtjZma2Lzg4uP/gwYPFJl6mUblx4wbCwsIQEhKC4OBggyOwTyOZmZk4cuQIq1QqmUmTJsHYrcsOHjzIKRQKetq0aVAoFNiyZQt39+5d2srKitfpdFRtqmzXrl35srIy/u7du7C1teU7d+7MaLVaxMbGgmVZAMBHH31ERKMx8fEBtm0D+vQBeB6l7u44s2gRO+bTT1tPtsLu3UQYl5QY5I7Msix++eUX3tvDgxo7cSLQvTsxWPvxR+Ot7euvSQ35oEHk/5GRQNu2JIr9OD79FDhwgPQyfuieFAoFVq1aBXNzc97e3h5isRg0TXMZGRmMSCSCra0tx/M8XVRUBJlMZrx7aYiYGJI+bm5+r449IoL0Gs/LM+pUKpUKhw8fxpUrV0DT9LqlS5fOAwC5XC4C8DWAHwGsBjBeIpHsV6vVM2UymcKoi3gG+eabb3Jee+01j3bt2pEHNm4km099+gAvvaT3OHfv3sX27duVSqXyhFqtniOTycpNs2IBgacHIbItICAg0AhkMhkHYLZcLl8FwA6AGACrx6UqrVbbOzc3V1dzTauHZVmEh4ezGRkZTN++fdG/tv70OcDX1xe+vr5MXFwcduzYAT8/P3bkyJFMU52eOY7D+fPnkZycTIfW1ERbWVnh7bffpgGAoqi6zfD8/HwcOHCAt7Cw4KdMmcJkZWXx8fHxvFKppPr164c2bdrAw8PD+EIbILXKNTW4SXFx4O3t0Wns2OYV2ioVqRPNza3flGziRCA42CChDZCWdMo7d6gxn34KpKcDCQkkXdaY7NhB+k3XUlIC5OQ8WWx//TVJOU9MBPz8iFt5DVu3bmV9fHwwZcqU+18Hhud55Ofn4+bNm/TVq1dZAMy6dev4KVOmUCYrXSkvB8aPJynj3bvfe7xjR1Lna0RiY2N1ERERrEgkuiQSiSJ0Ol3drkjNJue/AUAuly+jKMpTrVaPr6nJFngMcrncjmEYFxcXF5LZsWQJ+ZlbtAjQs+Uhz/NISEjgTp48qWJZdiHHcX8Kz72AAEEQ2wICAgJNQCaTnTPwkk48z1vb29sjLy8P7dq1A2OCVEtjUlpaitTUVOadd97Ru+/4s0bfvn2h0WgQGRnJMAzDjR8/vklh/ZSUFBw+fBhdunThe/fuXacS7zc3q/23q6sr5s+fXzdf+/btqZCQEKxbtw4lJSXsiBEjTPcGmjGjToA6JiQgfMIEvNucplsAMWqbPJkYlkmlDx5buhS4dQvYsMGwMdesgeTPPyEaP56jIiNpPM6FubFUV99z5q7l6lUgNfXJrckoCvDwIOe1b19XC52UlISioiJmxowZ9VxCwdXVFa6urggODmYKCgrw22+/UUeOHMHEiRORlJQEZ2dnMAwDGxsbmJsbwauR44CLF/HI81cbFTUS2dnZiIyMrNTpdC8uWbIk7XHnymSyJAC9H3eOwAP0b9u2rYouKzPDm2+SjIThw/X2PtBoNNi5c6cyJyfnhlarHSeTyeotpRIQeF4RxLaAgIBA83KVpunE8+fP9zx//jy6deummTBhQosVb3Mch23btrEqlQodOnRgOnToAHt7e0il0ro64cTERK5NmzaUk5NT87YSakWsXr2aLykpoTw8PLjg4OAm589nZ2dz1tbW1Kuvvtqo51QkEmHcuHH4448/mP3797NjxoxhjOZCfj/LlpH+0O+/D5evvoJ69mxcvXq1eV2uAeIyXp/YDgkBCg1o2fv++8CkScCLLyIzORkcx1EICjLuWmvZtIn0mI6KuvfYhx+S+9CXw4dJlPH4cehCQvD3339zI0eOpPXJYnB2dkb//v2RkJCAr7/+GhKJhFepVJRUKuVVKhVlaWnJOzo6clKptM57wdfXF+bm5vqViPz2G0m5T6tH+37xBYnI69OuTQ+OHj1aqdVq35HJZI8V2gKGI5FIpveytbXG3LnA1Kkkm0XPEiGlUolNmzYpS0tL92s0mjlCWy8BgUcRxLaAgIBAM1LjcN4LAORy+ar09HTT9cbRgzNnznA3btxgevfujUuXLiEuLg5cTW/cvn37wsbGBomJifSQIUOeixrt+jh16hRfUlJCde3alZ08eXKTo8g6nQ4ZGRl0QEBAk9Is27Vrh/nz5+OXX35hBg8ebBqX+2+/BWxsALEY59etQ+WlS9BoNI1yTW8SI0cCs2aRWuZa1q4ltdDDhj3+Wo2G9AZesIAYqZWX46qzM/Y6OGDOlCmm20Dq2pW0SrqfI0eIAI2N1W+M2s2F+fORMGoU79CjB7rfn679BIYNG4ZBgwZBp9PBwsKCqnndqIiICPbu3buMra0to1AoEBERwSqVSobjOJibm/OzZ8+mnpjFMmwYqdWub5Pnr78MTut/HAUFBVYAThhtQAEAgFwut+6WkjKp2759FDZtMqjlXHl5Of744w+lUqlco9VqPxTSxgUE6kcQ2wICAgItx4IuXbowGRkZqKiogIWFBQ4ePKgSi8Xid999lzGF+/X9pKenIyYmhpo1axY8PT0xYsSIumPHjx/nsrOzoVarOR8fH1Hv3s9fVmbNl0muqqqKHjp0KAYMGGCUdO2bN29CqVRixIgRTVYjlpaWYBgGRUVFphHbmZmkFdaKFei1eTNibWzYffv2MXfu3OFefvnl5lPbERGPtsLatAkQi0kEtT50OmLQxTAk0jprFrBuHQDgbmQkb25uznt7e5vuHqKigIULH3wsIIBkChiCVIrcffsQvWsXtcDKitI3gyEyMpIVi8Xo378/U9v5gKZpZGRkIDo6mgGAIUOG4OWXXwZq2sVxHIcff/yRz83NbVBs5+TkQDJjBlL8/LiSkSPpfnl5aNu27YOO+WPHkqh+E9+T1dXVSE5OBgAKQPEXX3zx67JlyxY+4TIBfaAo0aTAwC20tzctCgsDvL31vvTWrVvYsmWLUqvVfrFkyZJvTbhKAYGnHkFsCwgICLQQDMNcS0xM9L9y5UoFTdN5Wq22C8MwF6urq1/Mzs5G586dTTp/TEwMGxgYyHh6ej5yLDQ0tFaEPJ/hbBDX5/LyctrKygr9+vUz2rgdOnSApaUlDh06hDFjxjRpLKlUitDQUGzduhUuLi6stbU1srOzmfbt2/P9+/enPDw80KT08qtXgUuXgDFjIHrhBfyzb18mMTERhw4dojt16gRv7/9n77zDorq2KL7OvTN0pAkoCIoFBEUQCyooYI3YjT0qplgSE5O8NH0mTMb0aoyal5hoNJbEit0oiigiogh2ICogKiC9lyn3vD+OGlE6g2Jyft83n3DLuedOwVln7722U+PGrytnz7K5/Oc/7PecHCAioupj73VZmTOHuXmfOcP6BT9AZGQkadeuXdNNPCeHtU16553K29u0Abp1q9dQkiRha2ioFODsDLMFCwT06wdU8Zl9mGPHjokAIJfL4ejoCCMjI2RkZGDbtm3w9PSESqVCbGws7d+//30BLwgCXF1dhQMHDiA1NVXy9/cXRFFESkoK0tLSYGxsjKNhYRiv1cIwIEBQq9XaNWvWiPr6+vTNN98kenp67PkvLwfMzet1nw9TWlqKH374oUyj0USKonhJq9WeppQebdSgHAYhgkTIlkIzsyFOX30lr4/QvnbtGrZs2VKq0WieCw4O3tmEs+Rw/hFwsc3hcDhPCK1W2xWAm1arvfb++++XKZVK/ffff7/ik08++SguLu4tZ2dnw6YyTystLcXNmzfFx157+xRhb2+PefPm4ddff6UHDhyggwYNEhrrQg4wI6v+/fsjMjJSi7sRxcbg7e0NOzs7pKSkiLm5uejZsycuX74sbdiwQVSr1Zg4cWLDa6wXLmQ9p0ePvl/H2b17d4SGhtL169eTcePGoVs9xWODuHGDiX6ApYLb2zMhXVXaa5s2zM37hx+AanrZ9+nTBydOnEBsbCy8vLx0P18zM9Zn+uHrq9XAxx8zE6o6LlIcOnQIoiiSnlOmEIwcCRQWsqi5n1+Vx6ekpODAgQOSTCYTHB0d8eeff0JPT4+q1WoiSRL69++PgQMHIj09HatWrSKU0koLJsOHD0dqaiq9cuWKEB8fD0opjI2NqY2NjTb2xAnZ1NOn0fbAAeibm6PvXRf09evXSytXrsSLL74otpDJgN27KzmoN4QTJ06o1Gr15kWLFj3fqIE4lSGkLYDfQ4cPP5c1ebLUz9m5zqdev34dW7ZsKVGr1cMUCkVk002Sw/nnwMU2h8PhPCHu1m9ffOD3CgDQaDSfpaamDvzkk0/6Dh48WOrTp4+oy/rY7OxsrFu3jpqbm9P27dv/ayPXdcHW1hZz584lmzZtkr799lv4+/tLvr6+jX7ObGxsoNFodBZZdXBwgMMDjtBDhgwR76UE5+XlNfw6777Laoy7dWPRYUKAXr0w/bnnyNZt26SQkBAhMzNT8vHxEXTibl0d8+axBwAYGbH09gcdsOPigFmz2L+bNwN9+gCy6r/ieHp64sSJEwgNDaVeXl66j3AHBbGU8QdrzAHm8Pzppyz6WwexnZeXh9jYWMyYMYOIoshE/IEDLGKenPzIPVJKERISQh0dHYUJEybA2tr63q5HLlZQwFogJyQkPLIYU1BQgNatW2PWrFkghEAQBAJARsPDgdBQEDOz+8cSQvDcc8+Ja9eupcuXL8fiVq2AlSuZS3kDoZTi/PnzapVKpRuHNQ6DEFcAm3LNzV+P6ddvz0vDhtV59TApKQmbN28uVavVz3ChzeHUHf4li8PhcJoZCoWidOHChT6UUt/Q0FAxJydHZ2PHx8fj559/RseOHfH6668LNjY2Ohv7n4qFhQXmz58vTp06FRERETr5f7OiogJlZWVCeXm5LoarEo1Gg4qKivq3a5MkYPlyoKICyMtj5mRWVizCPWcOcOEC7Pr3x+s5OcICU1O0WbgQqxcvxq4xY/D71KnYsnYtVn34IT1x/Di9Z7bXaG7eBCwsmIDz8GDRa4C1/PrxR1a3PXw42+brW6PQBphwEAQBL774YtOkkru4VB15JoQtBty+Xadhfv/9d23Xrl21Dy6kYMoU5gAeEQFcvnx/syRJ2Lt3Ly0tLSXjxo17UGg/gkqlQmRkJAWA+Pj4R14kV1dXkp2dDVEU/zbCu34dpGVLkOjoRxYKRFGEgYEB1Wg0uGhry1zKG8HNmzeh0WjyAcQ3aiAOgxAZCHkVwC8AfH54663BHTp0kNX1b0NSUhL++OOPe0L7RJPOlcP5h8Ej2xwOh9N8SQCACxcuYODAgTXWxubn5yMjIwPl5eWwsrJCy5YtH+mje+DAAc25c+fEIUOGkJ49e/5r23g1FCcnJ2g0GmRlZdUoZOpCp7vpzxERERgyZIgupvcIiYmJMDAwQMeOHet2Qm4uS9X28mLR7GHDmOP3Pdav//vnmBiAUlikp8NCT0+wDwyE+u23kXbzJv6Ki8PU3btJXFQUvWVuTh3T0wk2b2YmZT4+wMCBTCx6ebFIbV3Sqe3sAKWS9ax+9llgwwZg0iRm3iZJLNr9+ed1fm4SEhKkDh06kJYtW+r+c1BYCHTuXL0R2qVLbN619PY+ffo0iouLxaFDhz6609gY2LoV0rVrKN2xAzk5OTh69Kh048YNwcfHp1an+GvXruHWrVtkzpw5aNWq1SMHX79+XerYsWPl7S+9BLi7s1r0KhgyGl7uswAAIABJREFUZIjQqlUrXF+zBont2mm9nZzE1q1bVzZOqyMJCQmSRqP5jTtc6wBCLAG8CMAawNAlSuVIfbn87eHDh9cpFeX8+fN03759pWq1eoRCoajGKIHD4VQHF9scDofTTFEoFLlKpdItOjo63MzMzKpHjx5icXExLl++TMPCwiQLCwutk5OT3pUrV8qLior0cTdVVC6XZ6rVamsrK6vSoKAgYwMDA0RGRuLs2bOy+fPnw8LC4gnf2eMnIyMD+/btkzw9PYVu3bpBLpfXewxBENCtWzf6yy+/kJEjR6Jr164NNgcTBAEGBgb466+/mkxsX7lyBQ4ODhJqqwv/6y+gdWuW3hwZCURFAampNQ9uacn+tbICunaFKQDs2AFLAF0BYNky4Ngxcig8nL40axZLn7a0BIqLWa31l18Cr7zCotKXLrGI9ezZrDa8Vy9majZ6NKDVsnNbtGBtsHbtAt5/nxmEtWvHfq4nKSkpSEpKEl5//fV6n1sn9uxhixSTJlW9PyICqCXlvry8HGFhYXT06NHE4OHe4vdYuRJ/rFmjNZk6VUzs2hWmbdvivffeQ7XHP8DlByLiD3L16lXs2bNHW1RUJFL6gM7NzAS2bavRXdzGxgaDBg2CdtEixMlkZP369dBqtXj22Wfr7Rlw7dq1EkmSjtV+JKdGCLECsAlAKICFyg8/bC+XyX4NCgoyNHugFKAqJEnCoUOHVLGxsTlqtXqoQqG49DimzOH80yCV/phyOBwOp9mhVCq7y2Sy46IoGmo0GpUoitkqlWobAD25XO6iVquXAzigUCjUD5zTUS6XL1Wr1SOB+0IRgYGBDRKaTzs///yzVFFRIZSWlkplZWXCyy+/jIam0MfFxeHgwYO0ZcuWdPr06UJdxE1VHD9+HCdPnqQLFy7UeXRVkiR888030pgxYwTn6gyQ0tMBW1vA2pqljU+dqrPeyKWlpVi2bBkdMmQIasyiUKtZlNfaGtiyhUV71WrW1uutt4A33mCLAUeOsPRxSpkAb8A8NRoN9u7dq01PTxfz8/PpokWLmia7IycHyMhgfbarYswYIDAQmDu32iHWrl2rFUUR06dPF6tb0CkoKMCK77/HGzt3wnjSJOC996qfU2wsyyS4y8WLF7F//35aXl5OvL29pWeeeUa4ePEiduzYgYCAAKlfv36CKIpsMSknB3B1hSosDKKrK2o1baSUPQQBe/bsoWlpadLcuXPrbARYWlqKb775RiVJkrlCoSir63mchyCkP4D/AvgAlMYolUpBX1//jJ+fn2ffvn1rTH3Iz8/Hli1bSnJzc89VVFSMVigUuY9n0hzOPw8e2eZwOJxmjkKhiFMqlQO0Wm0gpfTL999/X12Hc64plcpxALoBsJLJZEPPnTv39uXLlysCAgL0PTw8IJPJIJPJak05fdrJy8tDWlqa8Pzzz8PR0VH4+OOPERUVJfXt27dBNevdu3eHu7s7+fnnn+nGjRul559/Xrj/HK5cyVKIBw2qcYzi4mIcPXoUuu5frtFoEB0djZiYGMnY2Ji0b9++8gGSxGqx9+5lacG5uUx0V+Pa3VAiIiJgbW1Ne/bsWfObSy4H2rZlP8+a9fd21vuZRYEpZXOeNw8ICwMOHvx7fz3YuHGjlJ6eLtja2mLMmDFNV0YxaRJbvKiOsWOBDh2q3Z2YmIi0tDTx1VdfrTFz4sSJE9BSCnL0KM6eOweDmTMRExBA7dq0IZWyJWJjAX9/ID//vqN8SkqKVF5eLtjZ2Ulubm4CAKjVaujr62PAgAGVXrMyQcCV+fOlvdu3C6amprCzs9OOHTtWrHKRSaViCyY3bwJ6evDw8CAXLlwQ9+3bhxEjRlT/nDxASkoK9PX1T7377rtcaDcEQkQAPgC+BDANlCYBgEwmU1pZWbn06dOnxs/kpUuXsGfPnjJJkj7WaDRfKhQKHRkvcDj/TrjY5nA4nKcAhUIRByCunudoANyzBA5VKpXvajSagWFhYZuOHDliRikVJEmS3XUb1gqCoBFFURJFUSopKTEZOHAg+vfvr/N7eZyo1Wr88ccftEWLFrCzsyMAMH36dISFheHnn39Gt27dNKNGjar3/4UymQyzZ88Wvv76a5qamop27dqxHfHxLFLr51etSdelS5ewa9cutGrVShsYGKiz3m6SJGHp0qUSpVTo2bMn8fX1JffrZSWJCS0vL5amvXIli66KInvomLKyMpiYmOgmdY4QlkL+/fdAx45AdjZLP7/nTl5HUlJSBBsbG/r8803YSUqjYSnX9wzcqqJzZ9aHugokScLu3bulQYMGkRYtWtS4IJCZmSlRSoWvv/sOpgCeP3gQBT17IvTkyXuimR3o5QUUFFTKBrC0tBQAwM3NjTje7dltb2+PiooK3L59G/b29qCU4vZHH0G1fj2i33yTjvXxwdWrV5Genk6WL1+O0aNHw8XFpfKkysuBESPuL944Ojpi8uTJ2LhxI3r37l0nr4PMzExaUVHB3a4bAqvPng/ABYAPKNUCgFKpfEsUxf9OnDhRqG4Bp6KiAvv27StPTEzMUalUYxQKxdnHN3EO558LF9scDofzL+Gu2dARAPctaJVKJaGUyrRarYFWq9VXq9X6AAwIIbvCwsK61MVsqTkTHx+PzMxM8tZbb903amrXrh1eeOEFIS4uDsePH2/QzUmShGXLlmkrKipEU1PTv3esWMH+DQoCsrKA/fsBADdu3MDZs2e1qampQnFxMQkMDISXl5dOVW50dDTkcjmZP38+5HL539+ok5IAT0/WLmv7dhZJlsl0Hs1+kIyMDMnV1VW3bxxRBBYvZtHuq1cBNzfgnqCshcjISAmAMH78+KY1BkxLYzXbNdQ24+hRICUFCAh4ZNfOnTupiYkJevXqVes8R4wYIZSVlcHe3p69txcvRr8bNwhZtIies7HBgAED/h4jJgaYOZMtBgHw8fHBpUuXtGlpaQR3vR6ioqIgk8lgY2ODgoIC7Ny5UzK+epX0nDaNvPLKKyIAeHh4AIDw6aefIikpSXJxcan8GpeWAm+/XWlTh7tR/NjYWAwbNqy220JOTk6pJElJtR7IqQwhbgCGgL2eM3C3TvSjjz6apa+v/7GlpSVWrVolPfvss0KHhzIrkpOTERISUlpRURGiUqnmKRSK4sd/AxzOPxMutjkcDudfzF0Brr77KLq3XalUeurp6d1MT09vZW9v/8Tm1xgopThx4gTatGkDExOTR/a3a9cO+/btExISEtC5c+d6jX3lyhVoNBpx/vz5sLKyevSAzz5jgjApCUhKQsilS7S0tFTs168fBgwY0CQLGFlZWZDL5VCr1awu//XXmev3gQPA7t2sPru+bcAaiLm5uZCdna1FbeZs9WXCBNbuavVqtpBx7BgzSaulhvvKlSukdevW9W+DVl8++wzQ16+59VVQEDOKe4j09HTEx8eTl156idTl/fFICYQoAqWl6BEZSSJcXFBcXPz3+75bN9Yy7F6GA4DCwkLi5+d3/0LdunXD+fPnsW7dOnrnzh0yMjqauC1cSORVuKrLZDKpTZs2lSdJKcs20NNj9fd3IYRg8uTJ2LFjB/T09BBQxSLDg+Tl5WkA3Kj1CfiHolQqW8vl8iOCIGQuXLjQv04nEdITwLsAdoDSZXfHEeVy+VeGhoZzg4KCDFq2bIlTp07RzZs3o1OnTvTZZ58lhBAcOXJEffr06QKNRvNicHDw7qa7Mw7n38nTG67gcDgcTpNxNwV946+//qo9dOgQSkpKnvSUaoRSiiNHjkg7d+7Upqen48yZM/jqq6+Qn59PR44cWeU5FhYWGDVqFLZv347o6Ghotdo6XWv9+vXS7t274efnJ7Vs2bLqg+zsWCp5aCgwfz7cXF1haWlJ/f39myxTIDAwEOqiIqg7dwbOngWmTQM++YSJK3//JrlmdXTq1AmXLl0Ss7KyoNFodDewuTlL0T55ktVGh4ezmuRq+nlLkoTvvvsOaWlpZMKECbqbR3VMnQq8/HLNxyQnszZmD7FlyxZtr169pEYtCHTtCtWVK3BOT6dhL71Ec3Jy2HZ9fRZx/uOPB48mmZmZ939p3749CCG4ffs2GeTnB4/kZCJ/MGvjAUxMTKQdO3Zg2bJldMX339NrK1dKmDIFGDIE+OGHR47v3LkzpkyZgpiYGHz22Wd0586d1fZgLygoEPEvFdtKpdJOLpfHurq6OlNK3Ws9gRARhLwIYAYAJSj9Q6lUGiuVypn6+vqJrVu3nvPKK68YWVtbgxCCvn37kjlz5iAjIwNr166VLly4gDNnztxSq9VuXGhzOE0Dj2xzOBwOp0oWLVr09pIlSw5FRUXtiYqK0hs9ejQ8PT0b3O6qqSgrK8P+/ftx5coVQZIknD9/HmZmZlo/Pz+xS5cupKqo9j3uGcXt2rUL+fn50rBhw6pVwtnZ2di4cSPKy8uF6dOnw9HRsXbVPHcuMHs2Wn7zDXVbtkyQgoIg1NL2qUGEhEC2Zg0GKJXkzOHD6ADAydtb99epI5Z3W4P9cFd49e/fH+7u7o3uTw6ACbqwMCa8Q0OB//6XicjPP3+k/pwQAgMDAxQVFd2fU5ORn89qyTdurPk4Qpgp3QMcPXoUGo1GCAgIaPSHy6RFCwx1dyeZ336LFStWwMbGRvLw8BD6yOUQ3noLmDoVdzIzUVpaSu71e79Hq1attDl37gi9b90iOHeu2nr+V155RVZeXo74devIneho4MQJkvzpp3CqYUGjffv2eOutt5Cenk5++eUXlJaWYtq0aY8cV1JSYgjgVqOehKcQpVLZQk9PL9LX17dlx44dxcTExNIaTyDEHMAIAAMAvK388MNs+aefLpTJZO87ODhQb29vE2dn50f+Xrds2RIvvPAC+emnn7B7926tJEmTFApFVpPdGIfzL0f88MMPn/QcOBwOh9NM8fPzu37s2LHfAJQkJiYOOHXqVFl+fr7c3t4eek1Y81sXNBoN1q9fj3379iEzMxNDhgzB+PHj0bt3b/j7+wtt2rSp0xxtbGzg4uKC3bt3k4SEBMnBweERgZ6fn4///e9/aNGiBV588cX6iUZC0LJDB3Lh8GEYjx4N45QUoIFtxx5h9mxmHtaqFVBYiNbTpmF/YSHVMzMjTk5OurlGA7CwsIC/vz/8/f2Rn5+P2NhYREdHIyEhgUZHR8PFxYXo6+s3bOHG1hb44gvWa7ttW8DbG9i8mf1ubV1JIN6N1EoGBgbkbr1x0xEVxYR2bcZtdnbMpO5uBLugoADbtm3DhAkTiE4WIwDIvb1xc+BAtPzxRxSWlZFrFRX0zK1bVHzvPZKdnY3s7Gxcu3YNAwcOrNQKsEePHkL/1FRCPv6Y9UGvLgsjLQ2yrVvR+sQJdJwwAWH+/jh8+TJ69uxZ42eOEIIWLVpAEAR69uxZYmtrC2tra1y8eBG7d++WwsPDKaVUlCTpY39/fx2mRDR/IiMj/9uxY8ehgYGB+iqVCjExMdqwsLCv/P39HzUaJMQVwFsAJLD+2ZKent7vFhYWLwYFBRn37dtXr2XLltV+vuRyOW7cuFFRUFAQ/sEHH3zZpDfG4fzL4WKbw+FwODXi7+9f4O/vf/TYsWNLtFrtsfT09BdOnjwJR0dHWFhYNOm1tVotfvzxRxofHy+VlpYKMpkMhYWFyMvLw/Lly5Gfn48RI0ZgypQpcHR0hFwuR0P6XhsbG6Nz584IDw8nHTt2xIPp4SkpKVi7di3UajVmzJjRoHsWTUywJSsL7UpKYDVqFPD880ANEfcauXmTpQSPGgX8+Sfg6gr073/fcOvkyZPUzMwMnTp1ahYpCJ07d8aAAQMgSRIKCwtJRkYGOX36NO7cuSM5ODgQfX19UErrJ7xbtmTpyhMnshTp8eOBTz9lLc0GDwbkckiShOvXryMiIoL06dMHdnZ2TXeTAGBsDCxYULu7e0EBS+t/4w0ArKd2eXm5cPv2beru7v63g3wjsbW1hdG+ffBu1Qre//0vKSsrozc3bSJ9/vMf7OrQgTo4OFAvLy9S6XnPzQXc3VldubFx1QN/+SXw9dfsOZ89G3B3x759+yRbW1vq7e1N6vI6Ojo6ktu3byMiIgJ37tzBmTNnqKWlJbGwsBDy8vISP/jggxqK3v95KJXKFoIgbJ80aZKRkZERDA0NcfHiRW1ZWVlBeHh4jP+DZSCEjADQHUCy8sMP1xzz9x+rp6e3p2PHjp7Tpk0zqimT5x5Hjx5VX758OVmtVgf82xY1OJzHDRfbHA6Hw6kTdyOVqceOHfsegJSWltazS5cu8qaMcH/00UdQqVSkY8eOQnx8vPbMmTMkJiaGXLhwAZRSLF68GG3atNFJavsvv/xCtVotGTduHACgtLQUt27dwoYNG2BgYIB3330XptXUsNYGpRQxMTGAjQ2cly4FDA2ZUBw0qHpR8zA7dzLTNUtL4NdfmeFVFT2bz549S9VqNfH09GwWYvseTk5O8PLygoeHB3x8fHDy5EkaGRlJLl++LO3bt4/4+vrWvZ69XTsWSb5Xww0AQ4cyt22NBrCxwbWbN7Fp0ybY2NhoAwMDq215pDMCAljGgqtrzccZGDDX8kGDcO78eVy6dElYsGABLl++LMXFxcHT07NOBml1wWTSJMj694c4bx7aT59O3MePh76jI/rOmUO6dev2qDAODGQGdHc/A/ehFDhyBHjhBWDgQGa+163bfUf7s2fP0szMTCEvL09ydXWt9YkmhMDd3R3Hjh1DdnY2NBoNGTNmDMnJySlPS0v72s/PL0onT8BTwokTJ95ydnYe0KtXLznAnp927drpXbt2zY9SOuvIkSN5KWvXJnqOGzcNwOD4zp1jV73xRi9RFDfb2tqOGzZsmM2AAQPkYh3a+MXHx+Pw4cM5arW6t0KhKGjqe+Nw/u3wmm0Oh8Ph1AuFQpGnVCqD8/Ly2mzdunXirFmz6h9KrgVJknD48GEJgPDcc8/d62Ot+4bQDyCTySSNRiOeOnWK6unpkT179twXf5RS1OWLbHVkZWWhoKAA7u7uTCBWVLAdZWVAYWH1raIkCfj5ZyZydu4E7O1ZRDs8vKb7ELKzs1FeXt6gKH9Tcy8zYP78+UJ4eDiKi4uFjIwMfPLJJ3jmmWeot7d37apYEJi43b0b6NOHbZPLgUWLgLfeApYvh9OGDSCEYOrUqeJjaV8XGMgEf20QApibQ5WZiYMHD9LAwEBibGyMl156Sfz++++1a9eulcaNGydU6XLfEAwMgDt3gAsXWNR/8GDg22/Z8/QgkgR89BHQvXvl7XFxTIBv2gQsWwZUkY4/b948Yd26dUhNTSXZ2dmo1jjwAQghWLBgAW7cuAFLS0u0adMGISEhakppeCPu9qlDqVTKCSFLrl69KqSlpd3PwLCxscFrr71mfPXq1U5RBw/+r/Off/4aMXRo6oUePWihqWlQdw8PoVevXnr1KT0oKyvD7t27y9Rq9ViFQpFZ+xkcDqexcDdyDofD4dQbhUKh0Wq1X964ccMgK0v33jr5+fmIiooShg0bdk9oNzmvvvqqOHHiRJw8eVLas2cPvL29af/+/SFJEsaOHduosUNDQ7XA38Zh0NcHdu1i9btOTsDWrZVPyM9n/aTz84ElS4DUVGDtWuYuXgtlZWVSSUkJDhw48GitZzPD398fhYWFEgDIZDL8+eefJDc3t24nBwSwKP/t25W3f/01MGcOcr7+GkaFhbqectWcOwe4uNS9NGDHDoT9+KNkY2ND3d2Z6bQgCHjllVdEExMT8tNPP+HChQu6ef0IYe3fOnQApk9nwnvVKhatvkdBAdC+PdCx49+ZFrdvs1r4jz9mCxn791cptAFAT08PQUFB0Gg0dOXKlfj1119RWIfn3sLCAp6ennB0dASlFEVFRYYALuvgrp8mAiilokajeWSRiRACZ0IQlJdn4jF8uMzm66/bj5w7t8O7775rEBgYWC+hrdVqsX379jJJktYrFIp/VeYAh/Mk4WKbw+FwOA1CoVBcAvDRtm3bSijVna7Lz8/H2bNnKQB4eXnpbNy64Obmhv/85z/ivHnz8Mwzz5CioiJYWFjQDg+lateH4uJiZGdni3K5nD5STymXA5GRwIgRwPffA4cPsx7MwcHAu+/+LSbrcf3JkycLAGBlZdWs0sjvodFosH37drpy5UqqVCqRkpIiDBkyBI6OjtpBgwZJdXYNNzVl0dhDhypvJwQYORI2RkaY/McfMNBRDXSNrF5dY7bBw9z64gtcIEQYO3ZspfR2PT09TJkyhYwdOxZ79+4lcXFxuvtgtWwJ3DPnS0ysvK+kBJgxgxntUQqsWMFSxU1N2SLP5Mm1Dq+np4e33npL6NChA01NTcXSpUtx7++CJEm4desWzpw5g/j4eBRX0We8vLwcgiCoFApFuQ7u9qlBEIRvAMDZ2bm8devWlXfu388WO7y9YRAcDBd3d7Rt27beWTaSJGHLli1lN2/ePK5SqV7T2eQ5HE6tEF1+QeJwOBzOvwulUtkRwNX33ntPJynLly5dwq5du2BpaakdMGCA2KVLl8ZPshEkJiZi27Zt8Pb21g4ePLhBeeR79uyR4uLihHHjxuFeFLNKRo4EoqOZ0Jk0iYnGenLq1CkcPnwYRkZGmD9/PvT19Rsy5Sbll19+0RYVFQmurq4wNTUlzs7ODW8JlprK+lrv3v2IMdmdO3ew7fPP8UpeHsiiRSzy3FSUlLDSgDosFEiShISePakYGEhdPv642qDH1atXsXnzZri5uUnjx4/XXXBkzx5gzRrg2DFmiLZrF4vMBwezhYvFi9nP3bqx2vgGEBkZicOHD8PNzQ3l5eVISkqCnp4eNTQ0lCilKCkpEW1sbLSOjo6CmZkZ6dKlC4yMjPDpp59qKaX6CoWibk3vn2KUSqUAoD+AcABYtGjR307ulALffccW43r1Yo77DYRSin379lVcvHjxvEqlGqBQKCoaP3sOh1NXeM02h8PhcBpDllwuzwwNDTXz9vbWt2lkS6u4uDitu7u7OHr06Catz64rLi4u6Nq1Ky5duiQMHjy4QWNoNBq0a9eOuru716ye9+5lkdpG1BeHh4fT/v37Ex8fH+jK1VqXqFQqZGRkiK+++irMzc0bP6CjI9C7N6trf6jllpGREXItLRFVVkb7BQURREbW7hTeEAoLWSnAw+ns1bBv3z7YWViQ7l261Ph+sLa2hiAIuHjxojBmzJhGeQZUonVrtjAQEsJ+P30ayMoC/vc/Fp1fs4YJ7UZwb+HtypUr97eNGjWKdO3aVQSY+WB0dLQYGRkJrVaLhIQE6fnnnxf09PRUFRUVNgDSqxz4H4JSqWyrr6+/vaKiogcAdOnS5W+hrdGwbgPGxsD8+azUpIFIkoSwsDDNxYsXb6tUqmFcaHM4jx+eRs7hcDicBqNQKArUarXX5cuXI/73v/+hpKSkQeMUFBRAqVQiNTVV9PX11fEsG4eRkREKCwtJampqg87Pzs6mbdu2rVuYupFGXhqNhuTm5kpN7rzdQHbv3g0rKytJJ0L7HlOnAjt2ANrKwVBTU1MEBQUh3NGRhL37LjNSi47W3XXvUVAAPPssMyKrhezsbFy8eBFtPv8cQs+e1R4XERGBZcuWoWPHjlJwcLDuhDYA9OzJ0pM3bWLO6a6uQFISE3dbtjRaaANAjx49EBQUhJkzZ+Lll1/GggUL4Obmdn+/kZERAgIC0K9fPwBAamqqAACmpqYqAE+uQfxjQKlUdpbL5bG+vr6ezzzzDACgRYsW7M174wYrKRkyhGUXNEJoFxUVYfXq1SUxMTFnVSqVr0KhyNfJDXA4nHrBW39xOBwOp1H4+/sX+fr6rj916lR3Smnn9u3b1+k8SZIQHx+PzMxMVFRU4OLFi3j22Wfh6OjYxDOuHx06dMC1a9ek6Oho4uPjU+82Y1FRUUStVpNuOhAxteHi4oIDBw4QU1PTpu8rXU8kScLOnTsxcOBAtGrVSnerAVZWgErFat0fSns2MzND+/btcfDYMahtbGjbQ4cIGTy4TsK4zpw/DzzzTI0p5KGhodiyZQs9c+YM8fDwkLxOnCA4dIiVDjxEdnY2QkJCYGRkhKlTpxKdlgIcPQqcOMEi/AsXsjR8b2/gm29YhoAOMTc3h4WFBYyNjWFoaFjl58bBwQFpaWkwNjaW7OzsSH5+Ps3Ozk7y8/OL1OlkmglKpbKrXC6PDAwMNO/du7cAAHFxcbh165bQT08P4hdfALNmsfdFIzJTUlJSsHbt2rKioqJv1Gr1DIVCUaSre+BwOPWj+eWYcTgcDuepRK1WF5w4cQLW1tZSt27dqg3RFhYW4tChQ9rk5GQRgCSKIi0tLRXt7Oy0zs7OzSJ9/GGCgoKEzz77DCdPnqQ+Pj71Eopt27Ylt2/fltCIbLLExETs379fq9FoiCAICAwMFFwf6ue8adMmKSkpSZAkCWZmZg29VJNx4sQJyOVyeHh46D7sbmfH+kD7+T2yy97eHrNnz8Zvv/2GGAMDvODsDKMff4TB+PHVDpeXl4cLFy7A29sbenp6Nff//uADYPZs5uT9EIWFhdi/fz+Sk5MxePBg0rJlSzg6Ogqwt2eu4FWwevVq6uLiQkePHi3I5fLa770q1GoW7TczY4sQX30FKJXAunWsDnjMGFbrfvAg8N57DbuGDpDJZDAwMMD169eFn376CTY2NoZ6enoBAL58YpNqIpRKZUu5XH581KhR5ve8G+4tpPQ8fRqFf/5JW27cSFDHxcqqoJTi5MmT2mPHjpVoNJpJwcHBB3UyeQ6H02C42OZwOByOTpAkaRGAdiEhIQNMTU3h5FR1NuiWLVtobm6uOGzYMLi7uwsPCJlmKbQBJgrs7e1pXl4eBVAvsXj16lXas2fPRuWHx8TEoLCwUAwKCkJsbKx2y5YtMDAwoG3btiUajYaWlJSQ3NxcYeLEicjKykLbtm0bczmdc+rUKRw/fhxjx46td2ZAnRgyhLVPy8hgjtoPYWFhgdf0L28WAAAgAElEQVRee40UFhZiX34+HXDkCHHo0OGRVlYqlQqCIGDjxo3IyclB+F2HcRMTEzp06FDSoUMHCIIAPT09JCQkwMbGBvnz5iGBENz+6SdtWVkZWrVqJQ4dOhTm5uZYtWqVZGJiQl544QVia2v794X09YGLF1lE/AESExNRUVFBBg4cSOoktCkF8vJYD3Z3d5Yevno1c7hfvpwZ7Y0bx3728mLXkyTWt332bNYS7AnTr18/lJWVwcTEREpISBAkSfJ80nNqCvT19X/w9PQ0ftAk0drcHK/euIEzWi1+69ePjKEUDe17UFRUhB07dpSmpaUlq9XqEQqF4oZuZs7hcBoDF9scDofD0SUDAKBVFYIHAHbu3KnNzMwUZ8yYAQcHh8c6scbi6elJDhw4QPz8/GBqalqncwoLC6FSqeomnGqgqKhIGxAQILRr1460a9dO9PPzQ3R0NL1y5QoxMjJCVlYWBg4cCBcXF7g0pet2AygtLcXRo0fp+PHjyYN1uzpFJmM12UuXAl98UeUhgiDA3Nwc1qNGaTcfOiQLevZZav3++wSzZgEAbt26hbVr1wJgPYkHDRpEu3fvTo4ePUpzc3NpaGgoduzYcX+lQBRFtP/rL3S/cgUl77yj7datm6ivr4+EhARp+fLlgkwmg0ajEaZMmYJKQhsA0tNZffQ771TaHB0dTV1cXGBubv7oigSlQHIyc14fPx6YM4f9vnUrcxhv3RqYMoU92rRh6eL3ePCzlpcHJCQA2dmNcrnWFXZ2dpgxYwYACFevXkVISMg/TiQqlcp2crl8lL+/v979jSoVMGIErIYPR99PP0X0Tz9hw4YNWLhwYb27CCQkJCAkJKSMUvqdWq3+UKFQqHV9DxwOp2Fwsc3hcDgcXSEBwEsvvQRDQ8NHdubl5eH8+fPiggULYGFh8dgn11g6d+6M/fv3Iz8/v85iOyIiApIkoVevXg26piRJCA8PR25urvCgALOyskJgYKAQGBgIAOSPP/7A2bNnaf/+/ZuFM5pGo8GGDRukvLw8qlKpBAcHB+rq6tq0cwsKAjw9WX9yK6tqDxs6dKgsIyNDG+PgIA43Nwe2boVm3DisXr0abm5u9JlnniEGBgaQy+UEAEaOHElwN5tBrVYjPT0dVlZW0NfXh2zTJqBFC7hOnnw/K8PLy0v4/fff8ddff2Hw4MGwt7d/dBK+vsx9/iEGDRpE1q1bhyUffog3fHxgdvIk8NprLD2+a1dgwgQgNhYYOhT4/HMWxW/V6m9n8dpYt45FwE+dYi7kw4bV7bzHRGZmppZSmvWk56Fr9PX1f/T29pbdb4944QIwfTp7PTw9YUYIZs6cid9++w0//PADffPNN+v0WZEkCUeOHFGfOXOmQK1Wj1QoFE3gAMjhcBoDdyPncDgcjk5QKBQZgiBEbdy4USovL6+0LzU1FT/++COcnZ3p0yi0ASA8PBy2trZSmzZtaj32bssdKS4uDn369GlwG65Vq1ZpYmNj4e3tTR+u0X6Q/v37o7CwkBQVMR+ksrKyBjvDN5b169fTTz75BCqVCgMHDhSHDBlCpk2bJjS5Q7q+PutN/McftR6alZUlOAwaxJy5f/sNwv/+BwsLCykxMZEIgoDqMhHkcjkcHR1hbGzMXlNXV2Yu9hDjxo0DAJw7d67qtPnSUtb3m1Lmor5xI7ByJexv3sRLP/xAh128SI1jYoCbN1nUfvVqYO1aJrZ/+w1wc2MLC9VkkFTLjh1AWBj7+coVFmFvRiQkJBSXl5f/+KTnoUuWLFkySl9fv3///v1loBQ4fhxYsAD4/Xege3fg7vvDyckJQ4YMQbt27eoUlS4tLcXatWtLz549e1atVrtxoc3hNE94ZJvD4XA4OkOSpBFlZWW5iYmJ8LhbDxsfH48tW7bA2tpamjp16lO7yJuenk7bt29P6iIaL1y4gIiICGHAgAEICAho8DUlSSIuLi7SoEGDanze7O3tYWlpKX377beCvb299vbt2yIhBNbW1tTT0xM9evQg9/v4NiERERFITk4m8+bNg42NTdML7IdxcQG2bwdKSlgrqyo4f/48VCoVOnbsyFzJV62CkJiI0Zs2CRscHKBSqWBczbmVUKuBiROBs2eZ0H8AAwMDTJ48GVu2bEF2djZatmxZ+dwzZ5jQ7tSJ9QgPDwdatgRmzsSeyZPR7plnqGzIkL+fvMamexcXs/TzXbv+3paVxdLNmwmUUty5c8cAwNknPRddoVQqLeRy+eoxY8YYybRaZlJ37hzw559VOuLfbYVW6wc1LS0NmzZtKlWpVD+p1ep3FQqFpgmmz+FwdAAX2xwOh8PRGQqFIk+pVOLMmTPQ09NDeHi4lJmZKXTq1IlOmzbtqRXaAJCfn0/Nzc1rvYfLly9jz5496N27d52E9pYtW6SrV68KRkZGaNOmDYyNjXH79m2akZFBCCFiVlYWhg8fXmt0fN68ecLZs2eRm5srOjs7w83NDQcOHMDJkyfpoUOHiJ6eHrp27Sr5+voKTZVdcP36dbi4uGhtbW2fjNldu3Ys3frCBaBv3yoPycrKooaGhri/+NC6NWIuXIBRVBTmtGlT9xIHtRr46KNqU9bvRb8rCfe0NKCsjEU227dn4isggD3u4jZhAjl16hQGDRlSt3nUhX37gCVLWE/yewsg2dlVurc/KfLz8wGgVKFQZDzpuegKPT29b7p06WLW3sQE+O9/mWv+li0Nbj2n0Whw/PhxTVRUVIVWq50ZHBy8Q8dT5nA4OoaLbQ6Hw+HoDKVSqQcAd+7cwZYtW9CpUyfBz88Pbm5uzaKWuDGUl5cLtdVqU0qxbds2ODg4YPjw4bWOWVhYiPj4eMHPzw9paWnS7du3iSRJxMvLi7i5uSE1NZXq6+tLgiDUKl5lMhm8H4qAzpgxgwAgV69eRWlpKU6fPo3vv/8e8+fPfzTa2khu3ryJtLQ0zJ0798m6ynt5AT/9xKLBVbTs6tu3L7ly5Qr98ssvqaurKxk1ahQirlzRBqxcKdpUVLCWWMuX197neNMmFo2uhqysLJiZmUmGhoYCKGVO4cHBwNdfA5cusfHPnwdycyv16Pbw8EBYWBjJyMio1miwXoSFsQj8pEl/C20AKC8HjIwaP76OuHPnDmQy2eUnPQ9doVQqe+np6U0Z1K2bHkaOBObPB2bOrPwa1IPMzExs2rSptLy8/LhGo3lJoVDc1vGUORxOE8DFNofD4XB0hkKhUCmVSidCyIFWrVo5jR8/Xt+ggVGc5oabmxsNCQkhr732GoyqEClarRb79u2T5HI5mTVrVp2+UZeUlEAQBPj4+EAulz+iDO/29G60eO3UqRMAwMPDQ/j++++lCxcukIEDB+p0ASQqKoq2a9dOsrKyerJi29eXRYwvXnyktRfAIs6vvfYaSUpKwoYNG6DRaFBWViZaOTmxKPW6dSwVffx41pO6OrZuBcaOrXb33r17YWhoKCAkBPj0UybON2+u3I/75ZeZi/q3397fZGRkBHt7e7p3717MmjWLNLTeHwCLvo8fz/ppP5yK7uTEIq3NhOzsbKrVav8RYlupVJrIZbKd0ywtDY2nT2evezWtEOvChQsX6N69e8u0Wu0rH3zwwTodTpXD4TQxT3VKH4fD4XCaHwqFIkWtVnvm5ub+tnLlytK0tLQnPSWd4ObmRsrLy1FQUFDl/qKiIsTFxQmzZs0iQhUR1apo3bo15HI5TU5O1uVUa2TYsGHCiRMnyOrVq6V7hmqNRZIkpKSkUA8Pj+bRK33pUuDLL6vdTQhBhw4dMHbsWCQlJUEQBGRnZ7NI79q1TKQ+/zwzMqsKrZaJ5zlzqr1GH0ND2iEqCtKNGyyFuFOnykIbYH2uv/qK9b5+gIEDB5L8/Hz6ySef4OLFi3W968qkpbFa7ZycR4U2pew5MjFp2NiNJDMzE0VFRaCU3t926dKlEpVK9eQbf+sAY0pX+5eUWLXdupU5xTdQaJeWlmLz5s1l+/btu61Wq/twoc3hPH3wyDaHw+FwdI5CoagAMGfJkiVH1q5du3rQoEGGvXv3fvyGWTokPDycuri4kNatW1e5/9atWzAxMdHa2dnVS3BKkkR0ndJdEy4uLhg3bhx27dolXLp0CX2rqW2uDyEhISgrKxOarI92fXF2Blq0YGnaVUS37+Hh4QE3Nzfs3bsXu3fvRpcuXVgt95QpQFQUcPIk0KsXYGZW+cSwMNaSKyHh0UELC4HiYvRbupRc9vGhwhtvVP+mNzMD/vMfIDUV2LYNWq0WmzZtkpKSkgSZTCYAQIsWLaDRaCCKYtXO5tVxbyGgihZjyMxkBnKPwTSvsLAQ169fR0lJCTIzM8szMjJUWVlZLQCgR48e0pAhQ4Ti4mLk5ORQAIeafEJNzKo331wxKC5uXDcXFzlCQxucNp6amorNmzeXqdXqNXdN0KpZ+eFwOM0ZLrY5HA6H02QEBwdvViqVZ8PCwvZev37dYfz48UZPa1p5r169yJ9//lntfnNzc6jV6npljOXl5UGj0aCkpASWD9TtNjXu7u7YuXMn8vLyKO72kG4MosjWF8rLy6vssf7Y0dMDxoxhtdE1iG2AtfNKT0+XunbtKtw3TZPJgJUr2ePnn9m/Dy6I2NoC77336GBxccDcucDLL+PQ0qUo02gk79rKAGbMwO2oKBTGxyMsLIyWlZXBzMzsfgZFSEgILSgoIKamppg/fz70H3I+r5KsLBZR1Wqr3l9RwUzamgBJknDjxg3Ex8erLl26pFGr1VQmk4Wr1eq/tFptCoCLAC4BcLp06VLw1atXA5ycnIxEUdy9ePHisiaZ1GPiQGDg211Vqjldxo2TiwsWNEhoU0oRERGhiYiIKNNqtVODg4P3NcFUORzOY4KLbQ6Hw+E0KQqF4ppSqfS4cePG8pUrVz43ZcoUI3t7+yc9rXqTn58PU1NTCdWUYCUlJUl362vr/A07JCREsrW1JXZ2do895C9JEiwsLHRy3fPnzwNA3YTg46J3b+DVV4GhQwFr6xoPbd++PVJTUx99bV95BSgqYqLd2fnvGufISGDkyL+PO30aePddYMUKYMUK7M/OxsUzZ+pkkkc9PbF71Sq47tgB+aRJ0ty5c8V7ddrFxcW4du0asba2xm+//UZ///13YmtrK3l5eQkWFhaosp3byZPsnvPyqne9vnWLPXRMSkoKtm/fXqpWq9M0Gs1mrVa7DkDS4sWLq1L9WQBGLlmyZFpiYuIrFRUV31ZxzFPDcT+/V13S0j43W7JE1Js6tUFjFBUVYdu2baV37txJ0Gg0YxQKhe5fJA6H81jhYpvD4XA4Tc4DaeUH161btzYgIMCoT58+T1Va+dmzZyV3d3da3f6MjAzq5ORUrxRyJycnISIiAhEREVp/f//HXu9sooOa3czMTADA66+/jrrWqj8WLC1ZrWxsLDBsWI2HiqKI9PR0QZKkyvdACLBwIRPRX30FrFoFtGrF2mg98wyQnAwcPsxqvadNA9zcmAP6/v2ws7PT9u7du06vaSsjI8kzKUnwnz1bfDAaamJiAk9PTwBA//79SUxMjFar1QoxMTEQBAFmZmZSTk6O8Oqrr8LKyoo5jPfrB8TH12zuVlHB0ux1REFBAcLDw8svX75codFopgUHB++v67nBwcGbAGzS2WQeN4ToZ7Vs+b5p27YL9davF6369Kn3EFqtFtHR0VJ4eHgFgO/UarWC987mcP4ZcLHN4XA4nMdGcHDwdqVSGRceHr43KSmp7fjx442aRdpxLdy5cwcqlUoYPHhwlfuzs7ORmJgovvTSS/UaNyAgALa2tti6davo7++vg5nWHVEUUVFR0ehxSu+aiKlUqkaPpXPeegvw8WHO5NW4eqtUKsTGxgrdu3evfrHg1VeBNm2AU6eAzp1ZZNvAABg9monbpUvvtxnbv38/jY2NJYGBgXVaSaKUotTLi/zeoYP25atXRTg7V3mcr68vfH1974v31NRUJCUlCceOHcOKFSvQ3dNTGv7iiwL5+muUjh+PGqX05ctALW3s6kJRURHCw8MrLly4IBFCflCr1Z8pFIqcRg/8tEBI6xRHxxcKWrb8D/3mG6FNA4R2VlYW/vjjj5KSkpJzarX6BYVC8VcTzJTD4TwhuNjmcDgczmNFoVAkKZXK7qmpqctWrlw5c8qUKYZt2rR50tOqkcuXL8PW1lYrk8mqjFRGRUVBEARUZ55WE4cPH9ZaWVkJ0EHtdH3QarXQhVP8PYEqrymS+qSwtGT9pdevZ+7iVVBUVARJkjB69Oiaxxo7FvjtN8DPD9BoWIp5ZGQlk7GCggKcP38eAwYMgJeXV5XKvbi4GDdu3EBJSQny8/Ol5ORkkpubi1dGjBDRsyeQklKp73Z1ODo6wtHRER4eHjhz5gySr1+nG6ZNo2k3bxLN0qWYMmUKXFxcqj65rAzo0KHWa9TEjRs38Pvvv5dJkrRao9F8pFAoMhs14NMGIT1yzc3f+atr17GtP/9c38PdvV6nazQaxMTE0LCwsDJJkt7UarW/KBSKajNnOBzO0wkX2xwOh8N57NxNK5+3ZMmSg7/99ttv/v7+hn379hWba1p5Xl5ejfstLCxgamqqRQN6YstkMqKnp6cTo7K6kp+fDwDQhQt6cXExACZaLSwsGj2eznnuOWDjRtbuqor3l4WFBQwNDemaNWvorFmzhBpT4WfOBD75BMjNZW214uMBc3OgbVsAwHfffQdRFImVlRUuXrwIjUZT6ZGcnKxNTU0VTUxMNPr6+sTY2Fiws7Mjw4cPh5mjI0t5r6dRnoWFBYYeOgScOiVKx48jISEBJ0+epJGRkXBxcan6PeXoyFqRNZDLly9j586dJVqtdmxwcPDhBg/0tELI5CQnJ7+z/fqNdX//ff3OnTvX6/Tk5GTs2rWrpLy8/LRarV6gUCguNdFMORzOE4aLbQ6Hw+E8MYKDg0OUSmW3Y8eO7bl+/brThAkTmmVa+dWrV+mwYcOqFNJFRUU4fvw42t4VXHVFpVIhOTkZ2dnZQqdOnaTaz9Ad94y1zM3NGz3WvXZfhYWFjR6rSejUibXjiopiKd8PIQgCZs+eTZYuXUpOnTqFflUccx9JYmZplLLo9rJlwPLlzIU8Lg76cjmIKNLQ0FCtIAgQBAGiKEIURSKKomBtbS2OHz8eJiYmVX//cnICXFyAHTuALl3qfo9vvw1cuwZBEODm5oY2bdqQFStW0P3792sDAwMffd8eONBgsX369Gnt4cOHizUaTYBCoYhr0CBPK4TIAcy45eAw+8jYsZ5DX39dvz6f+7S0NBw8eLAkIyOjWK1Wzw8ODt7edJPlcDjNAS62ORwOh/NEUSgUyUql0uvWrVtLV65cOWvKlClGzSmtPDMzExUVFaSqHtJ37tzBrl27AADPPfdcnaPaR48eRUREBARBQL9+/ai/v/9jdRYzMjKCiYmJtGfPHsHQ0BCWlpYwe7iXdD2QyWTYvn073NzcmpdJGsCi2X5+wLVrVYptADA2Noa9vb0UGhoqnDhxQho3bpzQqSox+t57QHo6sGEDSx9ftAh44w0gLQ1QKjHB2ZnGOzhg1LRpMjg41H+uosiM1uraHi8tjfUBv3AB8PW9v7lFixaYOXMmWbdunaivry8NGjSo8osSEcFM3upJZmYmQkNDSzUajadCoUip9wBPM4TYSsAHMX37usWMGtXtuVdeMajrZyYnJwehoaGlSUlJKq1W+4EkSasUCkUzNDngcDi6Rvzwww+f9Bw4HA6H8y/H399f279///2HDx++fPHixRGCIMgcHByahVs5pRSnT5+GpaUlBEGAkZERCCEoLCzEihUrUFxcjIkTJ9Y5JTsvLw/bt2/HzJkzMXr0aLRv3548CYHarVs3Ehsbi5iYGKSnp0uenp4NfrJFUURKSgoet8lbnenUCZgxg9VdV2MM1r17d+Lj44Nr167R4uJiUmVqcHk569v9oBCXy1nq94svYsft29Tr5Emh5UcfAdOns5ruDh3q12/Z3x/YswfQ16+1ZRkMDdlxAQGP7GrRogVcXFywe/duYmhoSO3t7dkkJAlITQUmTKh3H+hr164hOTn5yOLFi3+o14n/AEq++mpelL//rL9mzOg4/aWXjE3rYDBXWFiIP//8s3z//v0VeXl5X2g0monBwcGR/v7+1TRA53A4/zR4ZJvD4XA4zYbg4OBdSqXS/fjx43uuX7/eYcKECUZGRkZPdE4mJiYwNTXV7tq1SxRFEZRSvPPOOxBFEYIgYNGiRZBV43RdFdeuXYOBgQFt27btE11JMDExwauvvoqlS5fSGzduCFlZWbCuTdxVg4eHBw4fPoyysjI0xzIAiCLw+uvAt98CX39d7WFyuRxmZma0Smf1jAwmtidNqvLcA3/+qc0uLhY67NgBaLVAQgLw4YdAUhLg6gp07MgczevC77+zPtiLF1d/zLhxwPDhwIIF1R5ia2uLCRMmYMeOHaRbt26sD3p+Pns8tMBTXFyM/fv3o7S0FMbGxjA1NYWVlRU8PDzulx1YWVlBkqRuSqWS/FvMvJRKZVu5XP42/e9/Z3bv3t1g1vDherUtApaWliIiIkIdExOjIYT8qNFoPlYoFLmPacocDqcZQSj9V/yt5HA4HM5ThFKplMvl8i9lMtncyZMnG9a3HlrXaDQalJeXw8DAAMuWLZOMjY2Jvr6+lJqaKlpZWdF58+aRugruHTt2oLy8XJo2bVqzybdetmyZRCkVXnzxRdQlYvcwkiRh6dKlkkajEd5888374qxZUVLCUr4/+QSwsXlkd1FRESIjI2lsbCyZOHEiHkkj//JLYNcuFq2ugqVLl9JOnTqRkSNH/r2RUkCtBubMYeeFhwOJiSwSXZNgu3deWRlQXaryN98AgwezSHstfPvtt1JRUZFgZ2eH8f36wWr9euDzz3HlyhXExcWBUopbt25RR0dH2rp1a6GoqEhbWFiI/Px8kp+fL5ibm2uHDx8utm/fHl999ZWqvLx8anBw8I5aL9zMUCqVAgDjuw8TMFPCQgBZAAQAvgBMAZQB8NLX1x8gSdIgLy8v0cvLS25tbY2ahLZWq8WZM2eko0ePVgDYolKpFisUittNfFscDqcZw8U2h8PhcJotS5YsCZTJZL/7+PgYDRgwQNYc0spv376N06dPw9jYGNHR0ZAkCa1atcLcuXPrdP7WrVuRk5MjzZs3r9mI7fLycnzxxRfo0aMHKonFenDlyhXs2LED77zzDougNkdWrGBp17NnP7IrLCyMnjlzhvj6+sLHx+fRcyWJOZC3qLqD9dq1a7VqtVoMCgqqerFBrQaio1nv73ffZSngnp6AnV3Vcw0KYtH0gwcrbz92jJmybdtW293e586dOwgNDUV2dja1OXeOTCwuxoYRI7SZmZmiu7u7RAhB+/bthapahRUXF+P06dNSdHS0oNFoQAiBJEkvBwcH/1iXayuVSgcACYSQFZTSFQqF4madJ95IlEolAeBMCBmpr68/RaVSeRBCIJPJNDKZTEsIgUqlkqnVahkhBJaWlqXGxsa0oqICDg4ORra2tvIuXbrAoA419NevX8eePXtKysvLz1dUVMxWKBRXmv4OORxOc4enkXM4HA6n2RIcHLxfqVR2OXny5K6kpKTOEydONDIxMXmic7K3t8e4ceMgSRJcXV2xZs0aZGRkIC8vr9bWV1evXkVCQgKCgoKajdAGAAMDA/Tt2xexsbHo27cvrKysGjzWmTNntL6+vvVugfZYGDKEOXFXVDDRfZe8vDycOnWKdOrUqepe6Vevslrqm9XrxKFDh4o///wzQkJCMHny5EcPkMuZidmpU8zJfNIkJqbXrWNGZ35+laPdX37JXNSrGsfevh43/Xc6+aZNm4isogLRJSU0KytLfO2112BkZFTje9HExAQDBw4UAgICUFFRgYiICOn06dMKpVL5Ux1TyZ0BGFFK3wVwFcAv9Zp8PVAqlZaiKO4mhFyQy+XtZTKZt1wu13N2dhY6d+5s4OTkBEII9PT0KjWFV6vVAAC5XF71SkoN3L59G4cPHy5JS0srVKvV8yile/4tKfYcDqd2mtV/9hwOh8PhPIxCobilUqm809PTV6xcubIsOTn5SU8JAHD48GGsWbPm/u9FRUU1Hh8bG4tNmzbBzMyMOjo6NvX06s3QoUNhaGhI4+PjG3S+m5sbxowZg7CwMPHKlWYa1HNxAc6ffyQVvLi4GGq1Gjdv3qSb/8/efcdVcWb/A//MzL2XIoqKNMWCLSIqoogNFUvsonHTXLMm2bimbJLvJtlfEmPC7CSxZFvWNNNMNjGWGDVR0VgRRUEsCCgiIooI0jvcMu35/XHV1QQVkJ7zfr18veTemWfOgL4uZ57nOef77/HZZ5/dWsBKEOxLwe9QyK6oqAgAEHJTVfBqcZw9Yf7xR3tV8PR04KWX7H//4QegsNB+nKen/b2HHrJ/rev2Pt+9etlbjtWCruv4+OOPdYPBoA8YOxZes2dzL774ImpTDyEnJwfvvfceYmJieFVVvQAE1eQ8URT38zx/4NqXVTe/J0mSiyRJ7SVJMlZzal0ouq4PUFX1WYvFMkVV1fYAYDAYTAkJCfKOHTuwfPlyHD58+JZWe0ajEUZj7UIoKirC2rVrzd98801RZmbmq7Is+4aHh2+jRJsQcjNaRk4IIaTFePvtt+83GAwbR4wY4RIaGmpoyjZTJSUl+OCDDzBx4kQ4OTlh6NChtz02IyMD33zzDdzd3dmzzz7LNYfl8NVZvXo1MxgMePzxx+scoCRJN4qvNcvl5JGRwNdfA2vW3PKy2WyGk5MTZFnGP//5TwwZMgTTpk2zv7lxo70Y2R32s58/fx7r16+H0WhkEyZM4EaMGFHzmHQdkGXgd78DDAZAkuxL1j097S26vv3WXpzt/vvty8e9vGp1y5s3b2YFBQVs0aJFPP+f/wDe3sC8ebUaAwD++9//ssuXL3MAEgGMEkXRXNNz33777UOMsTEABqjd0twAACAASURBVAmCEGowGF5VFMWL53ld0zSD0Wgs5nm+DMApm8224doMcZ3aY0mS5Ar7hFIpgMEA4m9+f9GiRdWvYKiBq1evYu/evVVZWVk6gJXXip/Z6jQYIaTVo2SbEEJIiyJJkpeDg8NPnTp1GvjII48416WgV31ZtmwZjEYjXnnllWr7S0dGRuLIkSPgeR5+fn56WFgYX5vK5Y0tPT0da9euxauvvlqjfarViYyMRFJSkq4oCm80Gln//v31yZMnN59l5YwBzz4LPPJItS2zAODy5cv49ttv8cADD2CAjw/Qvbt9Cflt9mtfpygKtm7dypKTk7lJkyZVv/f7bjQN2LIFEEV7Yn3woL2P9u9+Zy+IVgfvv/++Nm3aNKFfv37AM8/YHxzMnl2nsVauXFlRWlo6RRTF2Nqcd23/tA4A3t7eVdOnT2/TuXNn8DwPXddRWloKs9mM3NxcHD9+vKK4uJjpuv6arutfiKJ4T62yJEnyAXBlypQpCA4OrnUveF3Xcf78eZw8ebLq8uXLsqZp4dfioiSbEHJH1GebEEJIixIaGlq5f//+ry0Wi8OJEyeGeXl5GTt27NgksQQEBCAuLo6ZzWbWu3fvG7PBP/30E/vxxx9x+fJlLjAwEIMGDcKECRM4QWg+OecvMcbw888/62VlZVxpaSkyMzP1AwcOsI4dO3LOzs44deoUKy8v5zp06IDMzExkZ2dDEAQ4OztD07QbCYyvry+Cg4M5TdMAgEtISOADAgKaT0uw68u4t24Fpk+v9pD27dtD0zScPHlSGzZmDI9Fi4C77McH7P3G/fz8uKKiInbixAmuT58+ta/uzvOAv7/9gUBurr2Y2q5dgKMjMGtW7ca65tixY6xr1668l5cXEBNjv+8a3M91BQUFuHDhAhwcHJCWlmarqKjYFhoaml7T8yVJ4gRBuOTi4mIaNWqU8YEHHjC5urreqOzNcRycnJzQrl07dO7cGcOGDXPo27evQ3Z29nhFUcL279+/LzQ0tLT2d24XGhpaHhcXN9zPz69PbWa0LRYL4uLi9I0bN1rOnTt3trCw8B+apj0WHh4eQ72yCSE10XwfrxNCCCG3cW2m6y1JkvZv3Lhx87Bhw9pOmDDB2NjJrIuLCxRF4U6ePMmNHTsWuq4jNjYWiYmJ3IQJExAYGIimLuhWU8ePH2cXLlzgeZ5HWloac3JyYm5ubsLatWuh6/r1mW7dZrPxPM/D2dlZq6qqEjw8PJCTk4NZs2axIUOGcADA8zzGjRsHXdexatUqbdWqVcJrr72GZvOwYexYIDnZ3gO7Z89qD+nZsycOHz4slPv7o93KlTWeVeY4DjNnzuTatWunf/755/zcuXMxcODAmsdWUQEUFNjblJ0/DwwcaN+n/fLLNR/jFzp16iRcvXpVDwgI4FFcDNSiNVtaWhp++OGHSkEQDimKMknTtHYAsmoZAq9pWvcJEyZg8ODBNTrBy8sLCxcubBMdHT3kyJEjKStWrIiw2Wxibat8S5LEcRz3hMFgGF/TWglWqxVHjhxR4+LiVJ7nd9hstuWiKJ6szXUJIQSgmW1CCCEtWGhoaEZkZOTX+fn5I1NSUtz79Oljquvy57pQVRUxMTHgeR7R0dGIjY1FdnY2ZsyYgREjRjTPftPVUFUVGzZs4MaPHw8fHx/Mnz+fGz58OD9o0CAMGDAAgYGBmDJlCjd69GguMDAQo0ePxtixY/lz585pOTk5vL+/P+Li4rhTp04xV1dXzs3NDRzHgeM49OnThz927BjGjRt3xx7FjcpoBOLjgYwMIKj6Ol/t27eHk6OjXrx7N7fRaGT9hgzheJ6/cV93YjAY0KtXL85kMmHv3r0sMDCQu+O/BU0DDh+2J9d//rM94Z42DcjLA55+GnjxRWDDBmDUqFrfamVlJZKSksDzvN7fz49HXBwwdap9b3gNHD16VM7Kynp7yZIlzx04cCDSaDQW67q+PjQ0tMYxhIaGsoMHD1aUlpaOCQoKqnElMo7j0KNHDz4oKMggCMJ9OTk5T0VHR3eNjIyMDA0NVe52viRJPRwcHLa2a9fuqT/84Q/OnTp1uuPxiqLg6NGj+vfff2+9evXqVlmW57z55pufh4aG5tQ0ZkIIuRnt2SaEENLiSZLEGwyG13mef3Pu3LlO1fULbggHDhzAoUOHMHv2bPTo0QMrr1WJXrBgAXx9fRslhvpw6NAhPT4+nr344otCbfaz6roOq9UKZ2dnWK1WHDhwAImJicxms3HDhg3THB0dubS0NC43N5f761//ijZt2jTgXdxZVVUVysvLUVlZCbPZjEEeHuBeeslefOwXibCmaaioqEDcRx9paQUFQpmHBzRNA2MMBoMBHTt21EJCQoSbZ6x1XUdJScktbdMYY1i/fr2WkZEhzJo169cz3BkZ9oR6yRL7A4D33wc6dgSKioDnn7e3/xo61N6ju1s3+xLwWvy7iouLw549e+Du7q7Pnj2b9zaZ7EvUc3NrPMaxY8cQGRn57euvv/54jU+qhiRJYR4eHmuffvppl7oWNrRarfjxxx/Nly5dsgH4WlGU90VR/NUsuyRJniaT6W+MsSdGjx5tCAkJMdxpVYWmaTh16hTbv3+/lTEWbbPZXqI+2YSQ+kDJNiGEkFZDkqSRRqNx2+DBg9tNmTLF1JDLluPj47F7924WGBioT506VQDss4iCIDSf/cl3wRhDcnIyIiIiEBoailpV0L6N9PR0bN++nWmaxhwcHFhRUZEAAIMHD9YrKyuZj48Pxo0bV6sfTHl5OTZs2KC7ubkhKCiI9/T0BGMMuq7fSOB1XceBAwf0iooK5u7uzhcXF+tZWVmcg4MD69u3rxAVFQWDwcAMBgOzWCz8iBEj9PsPHODh4wM89xwAXN+njbi4OFZcXMw988UXaPv003B+6y0UFBTAYrHAbDbj0qVLemJiIm80GnUvLy/eZDIhMzNTr6ys5E0mEzOZTLqqqny/fv0we/Zs7tSpU2zXrl1c37592e+mTePw00/2pPrdd4E5c+wtvdzd7fvJk5KAhQvtM9k3L3EvKgJcXG7pD34327ZtQ3Fxsf7EE0/Ys9uUFODNN4HNm2s8RmZmJtatW3fFZrP5AnjMZDKNlWX5Q1EUE2o8CABJkhwcHBz2enl5DX344Yeda9N27JdycnKQmJionDx5UhcEIZ/n+VTGWC7HcR0ZY71UVfUNCAhgoaGhDnfaxsEYw5kzZ7Bnz54qRVFO22y2F0VRPF7nwAgh5Bco2SaEENKqSJLkZjQaf3Z0dBw4Z84cx5632ZN7r/7xj3/oI0aM4MeMGdMg4zc0i8WC/fv366dPn+ZGjRrFjRkzptZVmmsiKysLJ0+ehMViwcWLF6EoCp555hkA9v7GHTp0QHx8PLPZbCwoKIgvKytDQkICS01NZa6urqxLly7C0aNH4e3trefn5/NWqxUAbuz/9vf3169evcqVlpZyDg4OutFoZKqqcj4+PryPjw9SU1P1/Px8LiwsjOvfvz8AoLCwEJ999hnmBgXB7623kLNlCyAIyM3NxY4dOzBgwABMnToVpqoq8G3a2IuT/YLNZkNsbCzKy8t1VVUZz/OYMmWKUFlZifLycpjNZuzcuRMAmFvHjhjj6Mgl7dqFyRcvwrV/f2DxYsDH59axjx+39/PevRvw8Lj1gidOAI8+Cly4UOPvvdlsxsqVK1lYWBjn7+8PpKbax5k/v8ZjMMbw6aefVuXn5wsAHIOCgpCQkFChquowURRTazwQAEmSDEaj8UMHB4cF48ePdw4MDLynrQWKoqCoqAhFRUWwWCxwcHCAq6srvL2979o3Oy0tDbt27aqsqqq6fC3JjqxzIIQQchuUbBNCCGl1JEkyAfjIxcXl8VdeeaXGG6cZYzX+5f+9995jTz75JOfxy6SoBTh48KB2+PBhwdnZWXv00UeFuvYcrq1ly5ZBURQYDAYYDAamKArn6OiIqqoqAPa9zrquw8nJCWPHjkVhYSGysrL04cOH8wEBAYiNjUV0dLQ+YsQI3tvbG56envjmm280FxcXBAUFCX5+frhTazVVVbFx40Z25coV2Gw2juM4TMzO1i9VVfEXevcGAEybNg3BwcHA3/4GFBcDH3xQ5/tVr1xB+eef46KDA+u0ejV3xt8f7q+/juEhIb8+eN8++6zzvn32GexfBw+88grw3nvVJv+3k5iYiB07dmD27Nnwv3wZOHXKvmy9FsxmM/7xj38AAMaMGcM6dOiAn3/+uUBRlFBRFFNqM9a1FmATTSbTyn79+vWaNWuWQ2O2wysuLkZERIQ5Ozu7WFGU5xlj20RRpF+GCSENgpJtQgghrZIkSd0AXAaA0aNH66GhobftcV1UVIT169fbioqKHEwmkx4WFsb7+/vfcfzly5ez+fPnczWtcNzUrFYroqOjWWpqKoqKiriFCxeiS5cujRqDruuQZRkmkwk8z0NVVZw8eRLdunVDcXEx2rZti5iYGEyfPh3t7tLTuqbXS05Ohq+vL7KysrBt2zbdzc0N999/P+/p6YlDhw7BGBOD4KIilLz6KixWK3pfS7rx1Vf2vdyPPVa7i8qyvVVXZiZw7BjQpQvw7LM4U1aGrdu2wd3dHaGhoejbt6/9eMaAH36wX2/LFuBOy6vPngXOnAEefrhWISUnJ2Pr1q1Y5OyMTppmT9prKT8/H59++imbMmUKhg8fziUkJLCdO3eaNU17Xtf1b2qbsEqS1NZkMm339/cfHhYW1uBVDRVFweHDh9WYmBgZwDJVVf8hiqLc0NclhPy2UbJNCCGk1ZIkqQOAGYIgrOA4zuvhhx8W+vTpc8sxR44c0fft28dzHJfJGBMBfA0AgwYN0ubMmSNUN9NdUlKCVatW4Q9/+AO6du3aGLdSI1euXEF+fj4CAwNvWRKuaRp27tzJkpOTMXjwYK5Xr1745fehJZNlGSkpKSgsLATP8zh9+rRmNpt5xhh4nudsNhsMBgMmT57Mhg4dyt3yM9V14P/9P+CFF4AePeyvlZYCCQlALSpuIykJWLcOGDcOWLXKvlT7kUduvF1SUoIPPvgARqORKYrCDRkyRB8/dizvsmaNPSn/9NM7J9oAsHevfZn5pUs1j+uaDRs2wH/3bn3ggw/ymDGj1udXJzc3F5s2baqqqKgo1TTtM03TNgI4X9PEW5KkfgBSBg8ebJkxY4ZTQ81wnz9/Htu3bzcrihJls9meEUXxSoNciBBCfoGSbUIIIa2eJEkCgDUA5omiCAC4fPkycnJysHv3bgCYJYpixLVjTQC+BXAjU/L19bU5Ozs7TJs2Dd999x1yc3PRvXt39vjjj3PNpZ0VYwwrV65EWVkZunXrxiZOnMh17doVu3fvZseOHeNMJhN75JFHuJZUJb0mUlNTsWXLFubg4AA3NzfdbDZzgYGBXK9evTjGGNzd3W9sD7jtz+rLL+09t5cts3+9bBmwYwdw5MidL15RAXz/vb3wmIsL0K6dPcn28rrlMF3X8fnnn+tt27bF/Pnz+fz8fGxYs0YbFBEh9PTwgPX119E3MPDuN8sYUFhov04tCqUBwLp16xC4fbvu96c/8ZgwoVbn3jkkhqysLMTHx1vPnz+vybIMg8EQZbVa3xFFMe5u50uSNMhkMv29Q4cOYxYtWuRcn3UDzGYzIiIiLOnp6aWyLD8uiuLeehucEEJqgJJtQgghvwmSJN0H4NzIkSPh5uaGiIgIAGCCILzx5ptvrqjmeHcAAQDCABQB+Nv19x588EHcbZl5Y9N1He+88w44jgNjDIIgQNM0AIC/vz8mT55cL0uzm5Pi4mJ8/vnnbPz48Rg+fHjdn3qoqr0l1pEjQKdOgM1m3699u73sUVHA+vXAzJnAjz/al5rfIYHdt2+fnpiYiJdeeonned5+vb/8BdmMYZefn55VVMQ/9dRT8PHxuXusomgvdLZhQ61u8Z///Kf+mCzzXo88AvTqVatza4oxhvj4eGRlZSE5OdmiquqM8PDwA3c7T5IkzsHB4ciQIUOGTJ48uXZPEW7jwoUL2Lx5s0XTtG8URXlFFEVzfYxLCCG1Qck2IYSQ34x33nnnYV3XvwcAk8n09uLFi8WanCdJUlcAmde/fv311+FQy5nFxvD111+z3NxcLF68mNN1HdnZ2XB0dIS7u3tTh1avdF3Htm3bWHJyMjdw4EB95syZ/D3PiO7da68G/vvfA7NmAYmJwM1jlpQAH39sPyYsDDAY7G27XF3vOOylS5ewfv16PPXUU/D09LQn2n/4AzBxIvD448grLsann36KkSNHose1Zew39nNXJykJ2LMH+Otfa3xrxcXF+OSTT7Bk+3ZwGzbUqld3bVy8eBFr1qyBo6OjbebMmQ6bNm0Cx3Fh4eHh2+92riRJvkajMfn//u//nO6lH7uiKNizZ48tMTGxUlGUR0RR3F/nwQgh5B7Vf48PQgghpJl66623NgLwAhBW00T7mgAAePLJJ8HzPE6ePNkg8d2rRx99lNM0jTtz5gx4nkfXrl1bXaKdlpaGlStX6hkZGXjiiScQFhZ274k2AAwYAKSlATk5QEiIPdHWNGDrVnvLrV27AIsFWL4ceOop4PHH75poV1VVYePGjRg7dqw90VYU+2z4nDnAH/8IGI1wd3dHt27d9FOnTrFNmzZh/fr1iIiIQGRkJNatW8csFsutgw4aBPTrZ68qXkPx8fHw8vTUuKwsoHPnunx37ioxMVFPSEhgAGC1Wh28vLwwePBgMMa2SZJUTfn1W4mieInn+R3Jycl1jiE3NxeffPJJVVJS0h5FUfpSok0IaWqN12uBEEIIaQZEUcwDcNeZtl/4WRCE+K1btw7Qdd0UHR2N4cOH3+j13NQYYzh27BgrLS3lGGM3ZkhbmyNHjiAqKgpjxozhRo4cyd2tl3KteHsDY8YAMTHAG28Azz1nX1a+YgXwxBPApEnAvHk1Ho4xhh9++EHv1KkTQkJCeFRU2GfMX38dmDIFuLZ/nOd5PPnkkzeeFpw8eRJ79+5lNpuNA8D9/e9/R4cOHdC5c2cmCALr2bMnF7B/P4ecnBotJa+srMSJEyfYzLFjBbz4Yq33et/N1q1brampqbzFYjEYDIZMAN0B4KOPPoLBYLgIoCfHcW8CmHq3sWw2W0x+fv4sALUKkjGGuLg4ff/+/RZN055ljH1H7bwIIc0BJduEEELIXYiiqEmSNLysrGwbz/MDrVarz7vvvovu3btrAQEBwqBBg5ok8d6zZ4/G8zx8fHyEXbt2cR06dNCmT58uuFTXp7mFi4yMxLFjx/Dggw/ivvvua5iqdD17ApMnA05OwODBgCQBdVwZEBMTo+fn5+Mvf/kLj9xc+/L08PA77u0GgKFDh2Lo0KEcAOTk5GDLli2srKyMS05O5jiO486ePQvv55+Hh6enfeb9Nv/udF1HbGwsDh8+zPr166f7e3gIuHy5Tvdys5SUFBw8eLBiwIABbTVNQ0JCgiOAJwHEqKqaBmAvgIkAYDAYjjPGRE3T7lr9W5Ik3mQyzfT09KxVom2z2fDjjz9aLl26dEVV1emiKKbX5b4IIaQhULJNCCGE1IAoiiqA6YC9oBOA5zMzM4MuX768ICEhQb95drIxREVFabGxsTcyrQEDBuB3v/td85hqbwCxsbEYN24c7rvvvoa7SJs29tnr5567p2Gys7Nx8OBB/rHHHoPp0iXg5ZeBt9+2L0+vBW9vb/z5z3++8WDhyJEj2LdvH87n5DCPxx/n8Mc/2pejV2PTpk3sypUrmD59OjdgwACBO3PG/hDhHsiyjAMHDlQWFBT8VFpa2l/TtBNGo9GmKMp31/5/QJKk+zmOO88Y6221Wh8xGAxWURSfqG48SZLaApjh4OAw02QyDXFycvINCAiocTzl5eX45ptvqiorK3+UZflPoiha7+kGCSGknlGyTQghhNTStSWqHwKAJEn/zszMTPjkk0/kZ555xlSfrYsYY0hJSUG3bt3g4uJyo4UVAJhMJgEAlixZguLiYnTq1KnertvcFBYWQtM0FBYWNuyFsrOBe+w/brVasWHDBhYUFMR1y88H/vMfYPHiWifa1bleOKysrEzDM88Y7jTrXlJSoo8aNUoYOHCg/YW0NHt7sjqy2Wz47rvv5NLS0hMAFr7++usyAEiS9DqAs8uXL0+XZTmK5/n2uq5vBfAKAKiq+vjSpUtjlyxZ8tnN40mSZDQajWc6d+7c0c/Pz8XV1RW9evVCTbcG5OTkYM2aNWZFUZaqqrqClo0TQpojSrYJIYSQeyCKYqIkST4FBQVZe/bs0aZOnSrIsgxFUXAvVZUBIDExEVu3boXRaERQUBDLyMhAUVER17FjR72goID39vZWeZ43eHh41NPdNE9ffvklY4xxwcHBDXuhY8eA2bPrfDpjDD/99JPm7OyMyTwv4KuvgOefB0aMqJfwBg8ejK5du+Ljjz82DFiwALb33mMd+/fnOnXt+qtjFUW59YWKinsqjhYREWHLyclJ1DQtTBRFGQDeeeedJwAs79q1K+vevXufkpKSse7u7s7FxcXWsrKy8l69erXz8PDAli1b/vP222+XhoeHf3/TkIKu6+4zZ850qu2DorS0NPzwww8WVVWfCA8P/6HON0UIIQ2MWn8RQggh9UCSpMUAlvE8r+m6LgCAt7e3/vjjj/N1aROWl5eHTz/9FJ07d8bEiROxd+9ejTGGkJAQISsrCyEhIWiNe7N/acWKFcxms3ELFy687cOLNm3a1HhG9I7mzwfeece+d7sO4uPj2Z49e/BSt26cw+nTwNy5wLBh9x7XL3zxxRdqbm6u4U+rV+PYsGEonDaNOTs767Nnzxacri0VX758ORYtWgQ3Nzf7ST//bJ/ZHjOmTtc8cuQIDh06tHfx4sWTr7+2dOnS/HHjxrmH3GXWPi8vD6tXr7YqijJIFMW066+/++67ywICAl6eNWtWjf+DHDt2TN+3b1+FoijTRFGMrdPNEEJII6GZbUIIIaR+vAfgS13XO8FeeVnPycn5T1FRETrXYUYxKipKd3R05J966inwPI+nn376lv3ZvwWlpaWw2WycwWDAt99+W+0xjDE4OjqyhQsXcu3atav7xSwWICgIqGMl9/z8fOzatYt7EoBDaqp97/egQXWP5w7+9Kc/2X9/e/ppdElPR+7p06y4uJhbtWoVCw0N5QYOHAiO45imaf8rJBcVVedEGwBMJhM4jtOvf/3OO+88qOu6+/VtDdVhjCE/Px8HDhwwK4riDMAXwI1kW9O0TfHx8YsnTZoEpxrsJ9+3b598/PjxPEVRxlMhNEJIS0DJNiGEEFIPRFHUARRc+5MiSZI3gP907NixTuN1796dv3Llil4/TaRbFsYYzpw5g23btqF37976/Pnz7/g9WLduHfvyyy/x9NNPc3Veup+YCERHAy+9VOtTFUXB+vXr2YyMDHh3787hD38AeveuWxy10bYthoaFYejZs7zu5obY2FhERUVpu3btEjiO4woKCnBji0FcnL31WB15e3uD47h+N720ZurUqRhWzcx9Xl4e4uLirGfPnmW6rpsZYx8COAAg5heHJgCAxWK5a7J94sQJ/fjx4/myLA8RRbGBN+8TQkj9+M19gBNCCCGNxAIAVVVVdTo5Pz+fWSyW3+Tn9JEjR9iWLVvQtm3buybaAPD73/+e79Spk/7BBx/g0KFDenFxMS5fvoyrV6+ixtvljh4F+vW7+3HV2BERoQ2OimIDO3fm8PjjjZNoA/bq4k8/DVRVged5jB49Gi+//LLw+9//HhzHoaioCLp+bTJ6/HjA379Ol1FVFVFRUVbG2KXrrxmNxh1Hjx5lhYWFsFgsuHDhAlJSUrB27Vrz6tWri5OTkz+22Wz9FUV5QlXVJQCcAXhIkjRckqT2gP0Blclkirty5c6dwU6dOsX27NlTLsvyeEq0CSEtCe3ZJoQQQhqIJEkMAO6//35t1KhRNW7LVVlZiX/9618YOXIkJk+efPcTWoFrPZtRXl6O2NhYzJo1CzcqadfQuXPnsHfvXq28vFwwGAxM13Vomsb5+fnpLi4uMBqNPM/zSElJ0VVVRd++feHh4cEPHDgQhi+/tPfA7tu3VtdMTkpC3quvslGBgZzjK68AjV0VvrAQ+OknYOHCW16OjIzU4+LiuBEjRrDxoaE8FiwAPvsMcHau9SWKi4vx4YcfMgBBAIIBMADzAIwzmUy5qqp2dHBwSAFQKstyhKZpH4uiaJEkaSCAJADw8PAoLy0tNcmy7Hht2NGiKMYsW7Zs1bhx454ZPXp0tdc+fPiwdujQoWJFUUJEUTxf6+AJIaQJ0TJyQgghpIGIosi98847+Xv37nUPDg6GwVCzj93rLa5+K4k2AKxdu1bLzc3ljEYjRowYwdc20QaAfv36oV+/ftcfanC6ruPixYs4fvw4X1hYCJvNBl3Xmb+/P+/o6Ihz586xhIQEFhMTgwd37kRB165c3pUrDADn5+d31732Jfn5yHvtNfT38eEcFy8G7mXPeF0ZDMAbbwAPPgi0bw/A3n4MAM/zPHN2duYgy4CDQ50SbQC4ePEiAHBGozGiV69eHUwmE0tKSnICULJ48WLvm4+VJMkRwKDly5cvMhqN8++//37Wv39/7uzZs+2GDBmCHTt26KdOneJ5nn8TwHRBEIKrq0bOGMP+/fuV48ePX72WaGfVKXhCCGlClGwTQgghDUjX9RAAqeXl5ajp/m1XV1cA9uW7NU3QW7KCggJcunRJWLBgAXx9fettXJ7n0bt3b/S+dVn3jYpewcHBXGlpKWKiopBXVcX2nTunte/QQSgtLcXhw4cxY8YMODs7o2vXrmjbtu0tY2sVFTi3aBHzNJng9eGHHGpQ4Ks+FRcXQ1EUaJoG15gYtLlWmT4tLQ0bN25Ep06d9EmTJvFDhgwBUlPtf+ooKipKAyBMnDjRMzg4mOc4DkOHDsXatWsrJUkSAAgAhhiNxgcEQXje1dVV7dq1em3qJwAAIABJREFUq4O3t7fDpUuXzDt37nQGgJ07dwLXtjDquj5ZkqQgR0dHv169elV3TeX48ePZsiwPo6XjhJCWqvV/ghNCCCFNK81oNEZ+/PHHoSaTSZs/f77Rx8fnjidc32ebl5eHLl26NEaMTaayshK7d++Gp6en7uvr2+h71Nu3b4/pJhPg5cUNeuWVG0v9t2zZosXGxjJN01BVVWUYNmyYfv/99/McxwFlZUj761+ZUF4Ov59/5lCH1m73IjU1FRs3bgTHcRAEAd1TUzE5KgoZmzYhOjpaHzNmDDd27Nj/fS9LSoAhQ+p8vSeffFJwcXGBg4MDDwAZGRnYuXNnhSzLXQG8ajAYXnBxcXHp0qWLMGnSJOfc3Fy2efNmJTU1NdZqtf4XQCLHcT6MsU0AogCIAOJ5nn8tKCjI9MsHStHR0erRo0dzZVkeQYk2IaQlo2SbEEIIaUCiKDJJku4HMFRV1T+tXr36Tz179rRdvHjRITg4WO/Rowfft29fCML/tnRfL0BeXFzcapNtm82GTz75BOXl5ejUqZP24IMP1nhPe71LSQFGjbrlpblz596IJysrC2vXruUMBgNC/f3B/fe/yEtL43y/+QZ8IyfaO3fuZAkJCdyUKVNYcHAwBwAlmZmoWLSIHY2JYS4uLggKCrq1H5ejIzB2bJ2veb1X94YNG9S8vDxbaWlpGwBtOY5LNhgM4qRJk4zBwcE8AGRmZmLz5s1WVVUnLlmyJBYAli1b9r6Tk9Oi8vJyAPheFMVDAPD3v/99mIeHxy0/95iYGC06OrpAUZSRoijm1TloQghpBijZJoQQQhrYtbZgxwEclyTp5MWLF9twHDfyxIkTHePj4wcIguBmMBg0Pz8/bcaMGU7t27fH5MmT2ZYtW7ikpKQaVeRuSRhjOHz4MMrLyzFp0iSMHj266RJtAPDxAQIC7vC2Dx555BFu+yefMP+ICK69qysOTZyIkd7etz2nIXz11VfalStXhEWLFsHb2/tGQt2hWzd0eO897vncXA5Tpvz6xLNngfz8e7r2rl275NTUVBOAcADLAKB79+69H3roIQfnm/aCb926tVLTtCdEUYwFAEmSpjo7Oz/t4eEhWK3W/bIsfwEAy5Yte1bX9dCb98VHR0er1xLtEaIoZt9TwIQQ0gxQsk0IIYQ0IlEUP7v5a0mSvFRVnWGz2SYlJiY+GBISgnbt2mHkyJHc2bNndZPJ1KoSbVmWsXz5cgBA37592ejRo7m7nNKwGLNX8/7FzPYv9VBVhCQnszwfH1bx3HO8afNmZjKZGi32gwcPorCwUHj++edvzDTfIikJ+Ne/UG2yffEiUMd+79d5enpe/51xmZOTk/XZZ591bNu27S3T+iUlJSgvLwdjbAsASJLUxWg0fj106FCn2NjYclVV54iiqEmS1EYQhPefeuopBzc3NzDGEBUVpRw9ejTn2oz21XsKlhBCmglKtgkhhJAmJIpiLoDVkiT9yPO8/0cffdRLVVXnadOmITc3l59SXfLUgiUnJwMAOnXqpM+bN6/pHyRUVNjbZ91pH/3Zs8AXX6DHjBn8JyUlmKsosFqtXGpqKu67774GCSsvLw/nz58Hz/OIj4/XKisrhXnz5lWfaAPAo48CkycDmgYIv1go4OICjBhxT/EEBgbygYGBkGUZBoPB8fpWh5tlZWXBYDAkqqrKvfvuu38xGo1vBQcHO7dp04YZDIbIJUuWVF47VOrVq5fmfW1lQExMjHb06NGrsiwPp6XjhJDWhJJtQgghpBkQRbH47bffjmCMLeY4jkVGRsLb21vz8fFpNZ/VjDFs27YNJpMJTz31VNMn2gAQFweMGwdwt5mkTk4GVqwA5s9Hh6lTEXrkCDZu3AgnJyfdxcWlQe6hrKwMq1evhpubm84YQ0BAAD9s2DA43aniudFoj1OWgY8/vvW9e1xCfjOTyXTb964VOnMxmUxfdujQ4ZGwsDDnzp07Y926dRVWq/X6bPckBweHP48fP94RAJKSktjBgwdLrrX3okSbENKqtJoPcEIIIaSlMxgME318fNSwsDBDe3vP5Fb1OX3mzBlwHIfp06fD0dGxqcOxO3cO8PKq/r2kJHsP67feAoYPBwCMHDkSxcXFmDZtGl+fbdl0XUdmZiZ+/PFHrbKyUvD399duLtJWI7Nm2R8O/JKbG9AI+8tzc3OZ1WoN6NixY58nn3zS2eFa8bhr1fVtkiRxDg4OK6ZNm+bo5eWF9PR0REREVCqKEkp9tAkhrVGr+hAnhBBCWjKe56vatWuntW/fvtV9PiuKgi1btsDb2xsBdyhG1ui6dAH69v3160lJwDPPAF9/Ddy0VJznecyaNaveLq+qKg4fPozY2FjGcRw3ZMgQbvjw4WjXrl3ti8ZNmACUlwPp6cDNvauXLrXfSwMLDg7mjEYjBgwYcCPRvoYBMAL4J4BB/v7+uHz5Mr7//nuzoigzRFGs5gkBIYS0fK3uw5wQQghpqWw224PJyckX77vvPgc/P7+mDqdemc1mAMDgwYObOJKbMGYvKrZp062vHzsGPPccsGMH4OnZYJfXdR0rV67UTCYTP2fOHK5fv37gOO7elqZv2QLs3fu/peTl5YCfn33fdgNr06YNQkJCfvW6wWDgAQwF8JKrq6uelJSEXbt2WVRVnS2KYnSDB0YIIU2keeyXIoQQQghEUSxWVfX+zZs3y1artanDqVcZGRkMAKqqqpo6lP/JybHvZ755iXVUFPDCC0BkZIMm2gBw9uxZ6LouPP/885yfnx+42+0br4333weWL7c/SAAAiwVYsODex70Hbdq0MfI8XwwgKD8/n9++fTtTVXV2eHj4viYNjBBCGhgl24QQQkgzYjQaP9U0zXT06FG9rmNIkoTly5eztWvXsuzs5tGuePv27dzEiRMxfvz4pg7lfy5dAt591/53xuyzwu++Cxw8CLRrV++XUxQF58+fR1RUlL5q1aqKrVu3sh49eij1kmRf5+YGjB0L/Pyz/eusLPu+9Cbk6+vr4ODgME4UxZMAxgJoGx4evrdJgyKEkEZAy8gJIYSQZkRV1dU8zx88ePDgSyEhIahJEa6LFy8iKysLSUlJWnl5uQAAkyZN4rKzs/Wvv/6a8/DwUBcuXGiorl1TYygoKICmaXB2dm6S699WYiLg5AToOrB6tX02OyICqKfibVlZWThz5owyYMAAY1lZmb5t2zaZ5/lLqqpGqqqaAOCLmTNnGuvlYjd74gmgTRv734uLf90KrBFZrVaUlpZC07TBAEDLxgkhvyWUbBNCCCHNSHh4+CdLly59wdPTs8pgMLS507GyLGP16tVqcXGxwcPDQxswYAAXGBgIFxcXCIKAYcOG8RMmTMAXX3whfPvtt+jSpQv8/f3RuXPnRrkXTdMQExOD3Nxc8DyPAQMGNMp1a8zZGejXz77sOjfXnnDXMdFOS0vDunXr0L17d3P37t2dr169ak1PT3cEkBofH++pKIo7gKmiKB4EAEmSHuJ5XjeZTPX/BOSFF4BPPwXGjAEKCoAm+L5brVbs2bPHevr0ac5oNCYxxjY0ehCEENLEKNkmhBBCmhlBEAbk5OS0OXPmTLUJqq7r2L9/P06ePMm8vb35+fPn37Z6dbt27fDwww9zhw8f1tLT07mYmBi+f//+eOihhxr0Hs6cOaOfOnWKy8zM5BwdHbXx48cLd+rR3OgYA5YsAebMsbf++vvf7bPcdZCVlYV169YBAK5evfrN5cuXnwXwI4AoABvfeOONUkmSjKIoKgAgSZKHwWD4csGCBbzQELPOggB88gnQuzdQWmpfWt6I0tPTsXnzZoumad+rqvrqkiVLCho1AEIIaSYo2SaEEEKaGZvN9ozJZBp49OjR4Y6Ojnzv3r1veX/t2rV6dnY2P3fuXK5Pnz7c3fb8du3aFfPmzRMAYNeuXXpcXBy/c+dOeHl5QZZlWK1WyLIMm80GWZahKApkWYaqqlBVVVNVlWmaBk3TwBjjdF3nVFXlBEFgzz33HH99ebjFYsGJEyeQnp6uZWZmCu3bt8e8efPQs2fPplvHfDspKfbiYbIMvPIKcPYs0LOnfa82zwO12Eft7e2Nnj174uLFi9B1vTsAR1EUbTcfc1OizTk4OKwZMmSIY9euXev3nm62a5f9IUJFBeDr23DXuYksy9i9e7ft9OnTVYqiPCyK4v5GuTAhhDRTlGwTQgghzYwoikySpP9kZ2d/n5aWZuvdu/eNpsWFhYW4cuUK/8ADD6Bvdf2h72LKlCm8LMt6eno6d/HiRWY0GmE0GpnRaITJZILJZGLt2rXjTCYTbzKZOKPRKFw75ld/9u3bh6+++kp7/vnnBQBISUnBwYMHERgYKEyfPh0eHh71+F2pZ717A8OGAVev2vc1P/YYMH++fVb4ww/tyeoLL9iXla9aBfz5z8CiRYDJZH9PkoAtW1BkNCJSlqvYkSNOTp6evCoI00fExbkCyL/Nle93dHQcPWHChIad5nd0BLp3B0aMAP7v/xr0UgBw9epVbNiwwWyz2SIURXlGFMWSBr8oIYQ0c5RsE0IIIc1TMgBkZ2drR48elUeMGGECgK1bt+oDBgyAn59fnfb6chyHsLCw6+feUxnshx9+mPvnP/8pbN++XZ81axafkZGh+fr6CjNmzLiXYRuHyQTs3v2/r1NS/vf3l14CjEbgs88Amw3o2BF48EGgb18gLw/gOGiM4cyHH6LSxQWl/v7cw1u2XNk7eXL3Xunp+YEJCRfAcd0AnAOwC8BXAETZaFwWMnHi14M5ro3h+eeBN98EJkywj3v8ODBuHGC12gu2+fjc2/25uwOzZwNxcUBDzqDDXqBvw4YNZlVV/xgeHv59g16MEEJaEGr9RQghhDRDoigmC4LwYHZ29vG4uDhdVVXouo68vDyuR48ezeLz28nJCU888QQSExN5XdfRpUsXIT8/X2vquO6Zo6N9hrtfPyAgAGjfHnjqKcDf354cf/ABsior8dPcudg3eTKuduni/J9XXumePHAgtj/wQFsw1g5AGYARABYDSAGwatNDD3mbu3fv0CEw0J5QZ2QAFy8CycnAypVAairw4ovA8OH2pN7NDXjtNWDNGmDkSOD0aeAvf7HPsBcV2Ze/R0fbX//vf+1LxhMTgQsXAE0D3njDnsh36dIg3ybGGE6dOoUNGzZUKYoylRJtQgi5Fc1sE0IIIc3Um2++uVmSpD1ms3nn5s2bh/Tq1cuZMcb16dOnqUO7oUuXLnBzc2NLly7lBEGAoihCcnIy/P39mzq0BtWlSxc888wz+PrrrzVVVb8HMFXTtI6MMfuabcYYgIybTtmUuWJFxIQJE5z44GD7K9999793p0y5dtQmXDsfSE+3J/0WC+DhYV8WPmYMUFZmT9ZtNqCqyr4Uft06YOJE+3J3Jydg6VJg5kz7WA1QmE5VVWzYsMFy+fLlSk3THqWWXoQQ8muc/bOAEEIIIc2VJEltAFQCQGBgoBoWFtasHpYrioIzZ87Ax8cHubm52LZtGx5++GE0p4cC9c1iseDTTz+12Gy2rTab7fcA3GEvjJZZ3fGSJHUSBCH75ZdfNjVav3Fdt/+pQa/22pBlGWvXrjXn5ubul2X5d9eLvxFCCLlVs/qwJoQQQsiviaJY9e677yYMGjQooLkl2gBgNBoRGBgIAHB3d8eVK1cQGRmp9+nTp1ksd28IR48e1axW6z5Zln8viiLD7QuiAQA4jvtjv379tEZLtAF7VXW+fn8EVqsV3377rbmoqGibLMuPiaLY8rcNEEJIA2m1H4KEEEJIa2I0GjO9vLzuqaBZY3F0dISDg0Or/h1D13WOMXb1WqJ9R5IkGYxG48vBwcF1a+TdTJjNZnz11VfmoqKi9bIsz6dEmxBC7qxVfxASQgghrYWiKBfz8/P1po6jJnieR2vepsYYQ1pamkVV1bganvJg+/bt2zRoX+0GZrVa8dVXX5nLysq+kGX5T6Iotoh/i4QQ0pQo2SaEEEJaAE3TvklKSrJlZ2c3dSh3deXKFa19+/ZNHUaDKCkpwdq1a5GXl9eGMbbmbsdLksQbjcYPp06d6sJxLWJhwq9omob169ebKyoq1suy/FJNZvMJIYRQsk0IIYS0CKIoJiiK8sLXX3+tHDlyRG7qeO6E53lYrdZWmZAdOHDAmp6eDkEQVoqiqNbglFHOzs6Ovr6+DR5bQ9m2bZs1Nzc3VpblpynRJoSQmqNkmxBCCGkhRFFcrWnag3Fxcc062Z4wYYJw8eJFrrKysqlDqXc9e/Z0BABN02rUU9poND42dOjQNg0bVcNJTExk586dy5dleTbt0SaEkNqhZJsQQghpWc6azWZ+3bp1VRUVFU0dS7W8vb3h7++vfvjhhyw+Pr6pw6lX6enpVo7j3hJFMbYmxwuCMKl79+4tcv14cXExduzYYZVlOUwUxaqmjocQQloaSrYJIYSQFkQUxQuapnW9dOmSOT//jt2mmtScOXMMc+bM4X7++Wd8/PHHmq63/Hpa5eXlOHfuHGOMrarJ8ZIkdVAUpWuXLl0aOrR6p2kaNmzYUKXr+mJRFBObOh5CCGmJKNkmhBBCWhBJkrxMJtMhjuPad+/evanDuSM/Pz+88sorMJvNwpEjR5o6nHvCGMO6detkjuM2iaJYVMPT+ri6uloFQWjQ2BrCvn375PLy8qOapn3Q1LEQQkhLZWjqAAghhBBSK56yLPt369ZNbQnttYxGI9q2bcuuXr3aIpdSX7dz507k5eWZAHxei9PcHB0dGyqkBpOeno6TJ09WKoryKBVEI4SQuqOZbUIIIaQFubakd/CVK1f0nJycpg7nrqqqqpCXl8d169atqUOps5KSEpw4ceL6l9GSJNX0wcGR/Px87ezZsw0UWf2zWq3YtGmTRVGUh0RRLGzqeAghpCWjZJsQQghpYURRTDSZTKfz8vKa/Ubodu3a4aGHHsL+/fsRGRkJWW7WhdSrVVpaCkdHx7MA+vE8vwvAUEmS7ro6UBTFclVVJ/30009VV65cafhA68G+fftsuq7/IIpiZFPHQgghLR0l24QQQkgLZLPZ/rh3715rS5jd7t+/P8LCwhAdHY1///vfLCcnp0Ul3T4+PjAajT14np/MGMsGcBzABEmSekiS1OFO54qiGK+q6mM//PCDWVVr0pa76WRnZyMpKckqy/JLTR0LIYS0BlxL2O9FCCGEkFtJkuRgMpmSZs6c2XfgwIFNHU6NWCwWHDt2TD948CDPcRzc3d1ZWFgY17lz56YO7a4KCwuxZs0aW3l5uQPHcVmMsW6CIERqmhZ67ZBAURQTbnf+ihUr9oSEhEwICQlpltXSdF3HqlWrqoqKip4LDw//tqnjIYSQ1oCSbUIIIaQFWr58+d+9vLz+vGDBAueWVu1a0zSUl5fjgw/sha5Hjx4NV1dXuLu7IyUlBYwxnDt3TnN1dRWCgoIQEBDQxBHblZSU4OOPP7ZpmhYkiuIZSZK8ANyytMBgMPxDVdV/iKJYcPPrkiT1MRqNiS+88IJT27ZtGzXumjhx4gTbt2/fKZvNFkRF0QghpH5Qsk0IIYS0QO+9997F+fPn+/r4+DR1KHVWVFSElJQUHD9+nFmtVo4xhi5dumiMMXTv3l0wm83s9OnTHGOMeXh4cMHBwWjqWfyEhAS2detWDgB4no/WdX1MUFAQevfuDUdHR5w8eVJJS0s78tprr43/5bnLli17z9fX94V58+Y5NX7kt2ez2fD+++9bbDZbiCiK8U0dDyGEtBbU+osQQghpgWw2m09qaiqMRiM8PT2bOpw6cXNzQ0hICEJCQm6u7n3zND03atQoxMXFcWlpaWzr1q2ct7c3ioqK4O7uDoPBAMYYXF1dGy3mgIAALjk5WRUEweDl5TWmf//+8PDwuPG+oijGtLS0ap+AKIryt4yMjMdSUlKc/Pz8Gi3muzl8+LACYAcl2oQQUr9oZpsQQghpgSRJmmIwGGZxHDevZ8+ejnPnznU2mUxNHVaD+uGHH7Rz584JJpMJiqJA0zRwHIfw8PCmDg0AUFZWhs8++8xitVrnhYeHb63uGEmSRphMpsjnnnvOqTEfEtxOWVkZPvroI4uqqveJotgySqYTQkgLQck2IYQQ0oJJkuRoNBo3+Pv7T5k9e7ZjU8fTkBRFQVZWFnr06AGr1YrvvvuOqaqqP/vss02+ab2iogJffvml2WKxvP3GG2+8d6djly5d+m6XLl1eWrBggTPPN21jmC1btljOnTv30RtvvPFqkwZCCCGtELX+IoQQQlowURStmqYlGQyGVv+ZbjQa4evrC47j4OTkhNLSUq6srIz/5ptvkJGR0WRxFRYWYvXq1WaLxfKPuyXaAKCqqpSbm5t64sSJJp3xKCwsREpKiq4oyrKmjIMQQlqrVv/BTAghhLR2RqOx7aVLl7Ty8vKmDqVRvfDCC/Dz82NWqxW7du3SVVVFSkoKzpw502gxXL58GV988YWloqLir2+88cbfanKOKIqKzWb7w/79+61VVVUNHOHt7du3z8wYWy6KYmmTBUEIIa2Y8Le//a2pYyCEEELIPdi/f/8eWZad0tLSgnx8fIwuLi7gOO7uJ7ZwBoMB/fr143r27Ino6GjuyJEjOH/+PE6fPg1/f3+0adOmQa9/5swZbNq0qUpV1dnh4eEba3NuaGhoweHDh7uZzWb/vn37NnrB2oKCAuzfv9+iqupDoaGhSmNfnxBCfgso2SaEEEJauNDQUBw4cCBKURTnU6dOBXTs2NHh5grZrZ2TkxP8/PzQr18/zJgxA7Isa7t27UJVVRXXtm3bek+6zWYzdu7caT18+HCJoijjRFGMrcs4kZGRsQUFBf/n5+dnaugHA7+0b98+a35+/vtvvfXW7ka9MCGE/IZQsk0IIYS0AqGhoWzMmDGRBw4csAqCML5///6/qfaezs7OaN++PTiOQ8+ePXmO49ihQ4e4pKQkjB49+p5n+m02Gy5cuIADBw6YIyIi9KKiorWKoswURTGzrmOGhoZaDh48qJWXl48aMGCA8Z4CrAWz2Yxt27apqqr+PjQ0tOnWsRNCSCv3m/ogJoQQQn4DfkhNTX3tk08+aTN+/Pi2zamfc2PhOA6jR4/mhw0bhi+++EKPiIjgw8LC7ngOYwxlZWUoKSlBZWUlzGYzNE1DQUGB9dKlS0pFRYWjg4NDgtVqXcsYW/vmm28W1kesuq5/duHChfDi4mJ07NixPoa8qxMnTmiCIGxbsmRJXqNckBBCfqOo9RchhBDSykiSJACYZTQa1y5cuND5t7Sk/Jfi4+MRGRmp//Wvf72lKKymabh8+TIuXryoXbhwoaqoqMiR4zizwWC4DCBb1/UcXddtiqKkAYgGkCSKYoPsbV66dKnUv3////fAAw84NcT4N1MUBf/6178sNpttmCiKyQ19PUII+S2jmW1CCCGklRFFUQPw0zvvvPPnL7/88lOe5xEUFMQmTZrUqvtwV8fT0xOqqt5YQ56Tk4O4uDjr2bNnOUEQLimK8qOmafsBJIqiWC+z1bWlqurnKSkpr06bNg2Ojg37I4qPj2cADlOiTQghDY9mtgkhhJBWTJKkATzPL9V1PUwQBKZpGtehQwfdbDbbDAaD5u3tzfLy8nhPT09+7ty5Tk5ODT652qhiY2OxZ88evPTSS4iIiDBnZGSYdV3/UNO0r0RRzGrq+K5bsWLFT2PGjJk1evToBmvLqmka/v3vf5vNZvN4URSPNdR1CCGE2FGyTQghhPwGSJLkznFcJmPMkef5HF3XRwNwADAYQAWAiAULFsDX17dpA61nVVVVWLVqFaxWKwRB+FmW5QdEUbQ1dVy/JEnSUGdn50Mvv/yysyAIDXKNpKQk7Ny58/jrr78e3CAXIIQQcgtaRk4IIYT8BoiiWACgumnrc5IkcUajMbO8vLxbY8fV0IqLi2Gz2aBpGjRNe6w5JtoAIIriyffeey/9woULA++7774GucbRo0crbDbbigYZnBBCyK802FIlQgghhLQMoigyjuMOFhYWtqrlbvn5+VizZo1ZVdUZoihyoigWN3VMd2Kz2bZnZmbqDTF2YWEhCgsLdQDbG2J8Qgghv0bJNiGEEEIgy/LKmJgY1pq2l504cULWdf3foijubOpYaoIxllxUVNQgfa9PnDihAFjdUBXVCSGE/Bol24QQQggBgBSDwZARERFhaepA6ktaWppV07SfmjqOWsivqKio95ltVVVx6tQpTVGUVfU9NiGEkNujZJsQQgghEEXRLMtywJkzZ4rOnj3b1OHcM8YYKisrHQFcbupYaqGXq6trvdfTOXfuHHiePy2K4oX6HpsQQsjtUbJNCCGEEACAKIqVsiw/vH37dovVam3qcO5JRkYGBEG4AqCoqWOpKScnp9/dd999bep73Li4uAqr1fqv+h6XEELInVGyTQghhJAbRFGMZYztio2NRVpaGlrqHu6SkhJwHHdMFMUWcQOSJBllWQ7p1atXvY5bVlaG3NxcDkBLWk5PCCGtAiXbhBBCCLmFzWaLOHToENatW4dTp07d8p6iKFBVtYkiqzmz2QxFUXKaOo5amODm5qa4uLjU66ApKSlMEITdzbXlGSGEtGbUZ5sQQgj5/+3deXxV1b338d/e5+x9MpGQF7OATN4CRSgIIloea0GtV21qUR7Hii0tWoc+7avtvdZi1l1Ba+3tvVevt6BFSikyVKwTFkRFKo8IyDwFEJAkDIYxAxnOOXufvZ8/THxAQQick31CPu/Xi1eSzcla3/yVfM/aey183iuGYXQ3DMNbunTpw7169crKz8+XXbt2yaxZs7x27dpFJ0yYkGVZVtA5T6m2ttZNJBIHgs5xpiKRyLjBgwe3Sfa4a9asqYnFYn9M9rgAgNOjbAMAgBMopSpERIuIPPbYY8eeffbZ34wYMcIqLS11ROTX1dXVV77xxhvfuummmzI3btwoubm50qtXr2BDf86hQ4fqRaQs6BxnQmvAdmADAAAgAElEQVRthcPhgq9+9atGMsc9fPiwVFVVJURkcTLHBQCcGco2AAA4pYkTJz6ltX5r5cqVvzYMo63v+5Pj8fj04uLiPdXV1XX79u076Pt+u69//esZV155pWWawT+h5nme7N27Nywiq4LOcoZG5ufnJ/Ly8pI66Nq1ax0RmaGUSiR1YADAGaFsAwCAL6WUKhaRO4+7FNNaX1pSUnKFiLwiItkrVqyYt379+oFjx47N7tq1q4h8er5zaWmplJeXS05OjgwaNEgMI6mLtyf1wQcfJERkq4h8nPLJksAwjOG9evXKSOaYiURC1q5d6zqOMzmZ4wIAzhxlGwAANJlSapuIbGv4skJrfUUsFrtlxowZM8aPH5/Zvn17mTZtWl1lZWVpIpFYahjGVbFYrO/w4cOTmiMajUpFRYWEQiGJx+OydetWd9WqVdWO49zUUnYiz8jI+EbXrl3tZI750UcfiWEYW5VSHyVzXADAmaNsAwCAc9ZQbOdNmjSpzQsvvPDMkCFDso4cOVLnOM4ApZSvtf7R3r17nxo+fHjWuc7l+75s2bJFlixZcqyqqsq2LOsT3/fDhmFEPc9b6jhOoVJqXxJ+rJTTWofC4fDXu3fvntRxN23aVBuNRqcmdVAAQJNQtgEAQNI8+uijfyoqKqpatWrVHa7rTjtudXnLrl27ZN++fdJ4m/nZWrRoUXTdunUH4/H4D0VkycSJE9P/LLJTu71Dhw5mfn5+0gb0PE927twZFpHXkzYoAKDJDN9vEXdYAQCAFu6xxx77vmmav2vbtm1k6NChbdq3by95eXnSrl27E57lrq2tlfLycqmqqhLTNOWCCy6QDh06iGEYUl1dLc8880yd67oXKKWqAvxxzpnW2haRmGVZcvHFF8vQoUPP6Y0Iz/PEdV0pLy+XWbNm7f/Vr351bu9qAADOCSvbAACgWUycOHG61vovhw4dum7JkiV3mab5T4lEokdOTk7m3XffnV1dXS3vv/9+3a5du0zbtjf7vv+xiEQSicTI7OzsjOuvvz7bMAwJhUKHf/3rX7fooi0iYprmg/n5+YmBAweGysrKEjNmzAiJiIRCIV/k09vlGz4ax33+2cfGf8dfNwxDfN+XUCi0v5l/HADA57CyDQAAAqO1NkKh0B86dux47+HDh+tc133Y9/0Xjl+11lobhmHcaFnWlHg83tUwjNmFhYV3ftm46U5r3SEcDu+eMGFCdocOHUTk08J8+PBh8X1fDMMQ0zRP+fHznzd+LSIye/bsYzt27LhfKfVCkD8jALR2rGwDAIDANGyeVnj48OFcx3H+Sym15mSvEZH5WutFtm3/Lh6PvxxA1KSybfs/Bg8ebDUWbZFPV6WP//ps+L4vZWVlYRFZeo4RAQDniJVtAACAZqS1viQSibz/05/+NDMjI6nHa8vhw4dl6tSph3/1q1+dW2sHAJwzM+gAAAAArYXW2ohEItOuueaajGQXbRGRnTt3imEY7yR9YABAk1G2AQAAms/1WVlZFw0ZMsQ4/Uubbtu2bdWxWOylVIwNAGgayjYAAEAzaFjVfnL06NE5ppn8P8F835cDBw5YIrI26YMDAJqMsg0AANA8RmdkZPTs379/SgY/fPiweJ5XIyIlKZkAANAklG0AAIAU01qHIpHI06NGjcpOxaq2iMjHH38shmG83bB7OwAgYJRtAACAFDNN8/527dr1GDhwYMrmKC0trYnFYmyOBgBpgrINAACQQlrrPNM0Hy8oKMg2jJTsiyYiImVlZSIiK1M2AQCgScJBBwAAAEg2rXV2OBye5rruA0qpI0FmsSxrYr9+/cKdOnVK2Ry1tbUSjUZDIrItZZMAAJqElW0AAHA+utl13VtF5IogQ2it+4rIA1dffXVmKufZtm2bWJa1VCnlpXIeAMCZY2UbAACcV4qKisaKyIyGL98OKkfDUV8vjh49OiM3Nzelc61atepYNBp9JqWTAACahJVtAABw3pg0adLdvu+/2PBlO6VUNMA4/8u27T5Dhw5N3YPaIhKLxeTQoUMREWFzNABII5RtAABwXigqKvp5Zmbms0OHDpVIJPKiUupokHkyMjKKrrzyypQd9dVo586dEolE1iqlYimdCADQJNxGDgAA0pLWOltEcpVSn5zmdYZhGOMty3rszjvvzFizZk0sFosFuiu31vriSCQy/Gtf+1rK51q/fn1NfX39H1M+EQCgSVjZBgAAaUdr3TMcDu8Jh8Mljz/++H9qrU/6N4vW2rBt+09t27Z9avz48RldunSRnj17RmzbnqC1tpo7d0Mm27btuVdddZVtWamNEI1GZffu3ZaIvJLSiQAATUbZBgAAaUVrnRcOh1dfffXVbcaPH2+7rvszwzBOeqSVYRjfy8nJGXvvvfdmd+zYUUREBgwYIF27du1uWdakZg3ewLbtp7t37977sssuC6V6rrVr13rhcHihUqoy1XMBAJqGsg0AANKKZVn/2r9//6zLLrss3KlTJ8nMzBTf9/9Ja33C8Vla6+6hUOgPt9xyS3YkEvnsumEYMmbMmCzTNH+itb6mObNrra8Mh8N333zzzZmGkdJ90cTzPFm2bFk0Fos9ntKJAABnhbINAADSiud5/+eqq67KFPm0OP/iF7+Qiy++OGrb9mat9UgREa31AMuyVo8aNSqjS5cuXxgjJydHbr/99kzbtl+bNGnSd5sjd8NRX/997bXXZmVmpvRYbRERKSkpEc/z9iilVqd8MgBAk1G2AQBA2tBa9zQMI5yfn//ZNdM0ZcyYMRkFBQW9MzMz3/rtb39bYlnWqhtuuKHD5ZdffsrNXnv06CH33HNPZjgcfqGoqOhbqc5uGEZBZmbmRQMHDkz1VCIismnTpmg8Hv9zs0wGAGgydiMHAABpw7Ksf7300ktDn78F2zAMGTBggPTr1y/zk08+6dG2bVvJyck57XhdunSRu+66K2vmzJkvFxUV3V5YWPh6KnJrrfMty5peUFCQ8qO+REQSiYQUFxeL53kvnv7VAIAgsLINAADSRigU6tK+fftTbiwWCoWkW7duZ1S0G3Xv3l3GjRuXFYlE5j7++OMPa62T/jB1JBL546BBg7J69eqV7KFPaseOHWKa5kdKqY+bZUIAQJNRtgEAQFrQWhue513WuXPnpI/dtWtXue+++zLz8vIm2rY9S2s9IFlja63H2rZ9/bXXXhs5/auTY/Xq1TXRaPS/m2s+AEDTUbYBAEC6GBQKhXJTUbZFRPLy8mTChAnZAwYM+G5GRsaqJ5544vfnOqbWup9lWX++7bbbsmzbTkbM06qsrJTS0lJTRLiFHADSGGUbAACkhVAo9M+9evWSVB6ZZdu2FBQUZDz00EOZGRkZP37sscfO+rZyrXUf27bfuuaaazIuuOCCZEc9peXLl8cNw5imlDrWbJMCAJqMsg0AANKCZVlDevfundUcc2VlZckPfvCDrNzc3Edt2/6z1jrjTL9Xa21MmjTph5ZlrR89enTXYcOGNdvfU/X19bJ27VrPcZx/b645AQBnh7INAADSguM42/bt2xdvrvny8vLk3nvvzerVq9cttm3vaDzD+8torTtFIpH327Vr99T48eNzhg8fbqZyJf7zli5d6pim+aJSak+zTQoAOCsc/QUAANKC7/uXrVu3zi4oKGi2OSORiNx2221ZW7ZsyVqwYMGiJ598cms0Gv2TiKwRka1KqWqtdUhELgmHw2PD4fADw4YNC48aNcpujiO+jldRUSGrV692Xdd9pFknBgCcFco2AABIF7179Ojhi0jzLRU3GDBggPTt2zdrx44dQ4uLi/vv37/fraqqyvzNb35TGw6H7ezs7ETfvn0jI0aMsPPz85s7nvi+L/Pnz68Tkd8qpfY1ewAAQJNRtgEAQFrwPO9x27b/R0TO/BDtJAqHw9K/f3/p379/lsinBbeqqqqtZVmSnZ0dRKTPbN++Xfbt23fEdd0nAw0CADhjlG0AAJAuFpeWloZ930/pjuRnyjAMadu2bdAxJB6Py/z58+vi8fg4pVSzPdMOADg3bJAGAADSglJqr+d5bk1NTdBR0sqSJUucRCKxUCm1JOgsAIAzR9kGAABpIxQKVUWj0aBjpI2DBw/K6tWr47FY7IGgswAAmoayDQAA0orv+0FHSBsLFiyo9TxvolLqQNBZAABNwzPbAAAgLWitM03T7JiXlxd0lLRQUlIin3zySY3neZODzgIAaDrKNgAASAuWZf22d+/ebiQSsYLOEjTf92XhwoU18Xj852yKBgAtE7eRAwCAwGmt+xqG8aNvf/vbmUFnSQfbt2+XqqqqAyIyJ+gsAICzw8o2AAAIlNbaiEQiz11++eVW0OdZpwPP82TRokW1sVjsJ0opL+g8AICzw8o2AAAI2o2ZmZnDRo4cySKAiBQXF0t9ff3HIrIw6CwAgLNH2QYAAIHRWufZtv3sddddlx0KhYKOEzjP8+Sdd96pjcViv1BKsS07ALRgvIMMAAACE4lEZgwYMKBd3759g46SFrZs2SLRaPRjEXk76CwAgHPDyjYAAAhEUVHR9ZZlXXPddddFgs6SDjzPk8WLF7OqDQDnCco2AABodlrrr4bD4dnf/e53syyr1Z/0JSKsagPA+YayDQAAzpnWur3W2j7D146wLGvZjTfemNu7d+9UR2sRfN9vXNX+JavaAHB+4JltAABwTrTWl4nICsuypojI/V/yOiMSiTxjWdYPx4wZE+nXr1/zhUxz27dvl2g0uk9E3go6CwAgOSjbAADgrGmtu4vIChERx3G6fdlrw+HwL3Nzc+/5/ve/H8nMzGyWfC2B7/vyj3/8oyYWiz3KqjYAnD+4jRwAAJw127Yfb/j0GRG56VSv01r3Mgzj326//fZsivaJdu7cKRUVFZUi8regswAAkoeyDQAAzorWuofv+2Ovv/56sW37ehHJPdVrI5HIlJEjR9r5+fnNmDD9JRIJWbBgQa3jOA8qpRJB5wEAJA9lGwAAnBXbtv/z0ksvDQ0bNkwGDx7czbbtrVrru7TWJ2wvrrXulUgkrhoxYkQoqKzp6oMPPnDr6+s/9H3/9aCzAACSy/B9Hg0CAAAnp7UeJCL7lFJHjr8+adKkH7Rp0+aZ+++/P8u2P92EfNeuXfLuu+/WHDx40DBNc148Hp8sIiW2bf9lxIgRV3/zm99kr5jjVFRUyJQpU+ocx7lYKbU76DwAgOTilx4AADgprfXXRGS9YRg/E5GnGq9PmjTpe5FI5H/uuOOOzMaiLSLSp08f6dOnT05VVZWsWbPmrvXr199SX19vd+nSxRk5ciR/cxzH932ZP39+ne/7T1C0AeD8xC8+AABwKusbPn72LLbWuo9lWZPvvvvuzI4dO570m/Ly8mTUqFHhUaNG5TRcOqPzt1uTDRs2+Pv27St3Xfd3QWcBAKQGz2wDAIAv0FoPafh0fTgc9kREioqKrrcsa/W1116b1blz5wDTtWyVlZWyYMGCaDweH6OUigedBwCQGqxsAwCAk1nR8HHoI4884mmtLwmHw6/ccccdds+ePYPM1aIlEgmZN29ebcPt4xuCzgMASB1WtgEAwMnYIiJKKU9rbViWdX+/fv1civa5eeutt+JHjhxZ7bruE0FnAQCkFruRAwCAE2itLxKRHQ1fjrAs62f5+fnfvueee7IyMzODjNaibd68WV5//fUDjuN8VSl1NOg8AIDU4jZyAABwglAo9L3c3FyvoqLCFJEV/fr1q7/hhhsyI5FI0NFarP3798vrr79e5zjOdRRtAGgdKNsAAOAE4XD4nptuusm88MILGy+xnH0Oqqur5YUXXqh3Xfd7Sqn1p/8OAMD5gGe2AQDAZ7TWg03TbN+9e/ego5wXHMeRmTNn1jmO80RhYeHLQecBADQfyjYAAPhMJBL5+YgRIyKGYQQdpcXzfV9eeuml+urq6r+7rvtY0HkAAM2Lsg0AAERERGtteJ737YsvvjgUdJbzwbvvvuuUlpZuj8fj31NKsSMtALQyPLMNAAAafc227XB+fn7QOVq8jRs3+itXrqxwHOdapVQs6DwAgObHyjYAABAREdu2f3bppZdmcAv5udm+fbu88cYbNY7jjFZKHQo6DwAgGJRtAAAgWusLPc/730OHDuUW8nNQUlIif/vb32obivbmoPMAAILDbeQAAEAikcjzl19+eTgnJyfoKC3WgQMHZM6cOfWu696klFoVdB4AQLBY2QYAoJXTWvcXkZFXXHEFb8KfpWg02njE148KCwvfCToPACB4/FIFAAA3Dhw4MGRZVtA5WqT6+nqZPn16neM4MwoLC2cFnQcAkB4o2wAAtHKhUKhddnY2Tfss1NbWyvTp02urq6unO47zk6DzAADSB7eRAwDQyiUSiU2ffPJJTdA5WpqamhqZNm1aXXV19RTHcX7CWdoAgOOxsg0AADaVl5cHnaFFOXr0qMyYMaOuvr7+94888ogKOg8AIP1QtgEAwLba2trwsWPHpE2bNkFnSXslJSUyd+7cetd1/2XixIl/CDoPACA9cRs5AACtnFIqHg6HF2zdujXoKGlv9erV/uzZs4/F4/ECijYA4MtQtgEAgMRisVe3bdvGc9un4LquvPrqq7G33367zHGcYRzvBQA4Hco2AAAQEXmttLQ0w3XdoHOkncrKSpk6dWrd1q1bl8Tj8YFKqY+CzgQASH+UbQAAIEqpY7Ztl7JR2om2bt0qU6ZMqT969GhRPB6/Xil1LOhMAICWgQ3SAACAiIj4vv/Bvn37+nTr1i3oKIFzXVcWLlwY27RpU4XjON9RSn0YdCYAQMvCyjYAABARkVgs9l5ZWVlt0DmCVllZKc8991zt5s2b33Icpx9FGwBwNljZBgAAjVbt2bPHCzpEkBqP9UokEv/muu5/KKX8oDMBAFomyjYAAGi0pa6uLtQaz9v2fV8+/PBDb/HixTWu697MbuMAgHPFbeQAAEBERJRSCdu239+9e3fQUZqV67ryyiuvRN99990Sx3GGULQBAMlA2QYAAJ+JRqNL9+7d6wSdo7lUV1fL1KlTa7dv3/5OPB4fpJT6OOhMAIDzA2UbAAB8xvf9eRs3bnQTiUTQUVJuz549jcd6PRmPxwuUUq1+czgAQPIYvs++HwAA4P978sknN950000D+/btG3SUlFmzZo2/aNGiWtd1by0sLFwQdB4AwPmHlW0AAHCCaDT6zJo1a87LVd5EIiHz58+PvfXWW3scxxlK0QYApAplGwAAfN4ru3fvtlzXDTpHUkWjUZkxY0bdli1blsXj8YFKqY+CzgQAOH9RtgEAwAmUUodDodDBo0ePBh0laSoqKuS5556rO3DgwF9isdi1SqnqoDMBAM5vnLMNAAC+wDTNfVVVVd06duwYdJRztmfPHpk9e3a94ziPTJw48emg8wAAWgfKNgAA+ALDMGItfUfyRCIh7733nrt8+fL6RCJxG89nAwCaE2UbAAB8ge/7HbOysoKOcdai0ajMmjWr7tChQ2tc171VKfVJ0JkAAK0LZRsAAJxAa22bptm7U6dOQUc5KxUVFTJz5sza2traF+Lx+ANKqZa9RA8AaJEo2wAA4PMuadu2bTQSidhBB2kK3/dl1apV3jvvvBPzff9R13WfUkr5QecCALROlG0AAPB5X+vWrVuL+hvBdV1ZtGhRfOPGjXsdx/lnjvUCAAStRf0iBQAAzaJtTk5OJOgQZ6qmpkbmzJlTd+TIkWXxePxWpVRF0JkAAKBsAwCAz2ite5im+XXLskJBZzkThw4dkueffz7u+/6zjuP8UinlBZ0JAAARyjYAAGigtW4vIiWe50mXLl2CjnNamzZtkvnz59d7nveTiRMnPh90HgAAjkfZBgAAjR5v/KRHjx5B5vhSjuPIggULYsXFxYcdx7lRKbU+6EwAAHweZRsAgFZm0qRJ4z3P26iUWnX89Ugk0vaiiy6SwYMHS0ZGRlDxvtSRI0dk1qxZdXV1dW/F4/G7lVLHgs4EAMDJULYBAGhFtNZ5IvJ8KBRaKyJDj7tu2LZ9yaBBg+Siiy4KLuCXKCsrk9mzZ9c7jvMvnudN5lgvAEA6o2wDANC69LRt2wmHw/1/97vfbXAcx3Bdd4FhGDsyMzO79u7dO+h8J7VlyxZ57bXXal3XvaWwsPDNoPMAAHA6lG0AAFqXK3r37u0UFBRk7d69e9C8efNERAb6vi9VVVXiuq6Ew+n158HKlSsTixcvrnIcZzTPZwMAWgoz6AAAAKBZHa2rq0tkZmbK2rVr6xuuXWqapg6HwwvffPPNxmuyatUq/+mnn/Z37doVSNBEIiFvvvlmfPHixYcdxxlK0QYAtCSUbQAAWpf3ysvLLRGR4cOHZ4qImKZ5m+d520Tk2NGjRxMiIsuWLfPeeecd6datm/HXv/5VXn755cSGDRuktra2WULW1NTI1KlTa9etW/d/Hcf5qlKqpFkmBgAgSdLrPjEAAJBqF2dnZzsikvGVr3xFJkyYIMXFxT8rLy+v7dChQ8YVV1xhLV682Fu5cqU5btw46dq1q1RXV8uMGTNk06ZN0qdPH++uu+5K6Zv1Bw8elBkzZtTF4/H/cl23kI3QAAAtEWUbAIBWxDTN4d26dYs0ft2lSxfp0qWLKSJtjh07JnPnzvUqKytl/Pjx0qlTJxERyc3NlYceeij0+9//3quvrzej0WjKjgZr3HE8Ho/fV1hYODMlkwAA0AwM3+fNYgAAznda614i0kdEwl27dp37wx/+MO/4/z969KhMmzZNOnbs6N15553myTZJO3TokMydOzcRDoeNH//4x0ld3fZ9X5YvX55YsmRJXSKRuLWwsHBhMscHAKC58cw2AACtgGEY/y4ib1uW9b0LL7ww6/j/q6yslOnTp/u9e/f2x40bd9KiLSLSoUMHeeCBB0KVlZXGokWLvGS9YR+NRmXOnDl177333nbXdQdStAEA5wPKNgAArYBpmpeIiFiWNeYb3/iG1Xi9rKxMJk+eLJ07d/Zuvvlm4wzGkXHjxhkrVqwwq6urzzlXw/x1paWlc+Lx+CVKqdJzHhQAgDTAM9sAALQCiUTiFhFZc8stt2REIp89si3r1q3zunXrZtx5552hMx2rc+fO0qlTp8TUqVPNW2+91ejevXuT88TjcXnjjTei27Ztq3Uc58dKqXlNHgQAgDTGyjYAAK3DVhGRnJwcERHxPE/+/ve/J4qLi41vfetbp13RPp5pmnLfffeFhgwZYsycOVNWrFjhN+WW8vLycpk8eXLd9u3bX3UcpwdFGwBwPmJlGwCA1mGciEi7du3E93156aWXEmVlZcZ9991n5Ofnn9WAo0ePlj59+sjcuXNl7969ie985zshy7JO+Xrf92XlypX+u+++W++6LruNAwDOa6xsAwDQOjwq8umq9IcffuiXlJQY999/v3m2RbtRz5495cEHHzT2798vzz33nF9ZWXnS18ViMXnppZfqFy9efMhxnMso2gCA8x1lGwCA1mGnyKery++//76MHj3azMrKOt33nJGcnBx58MEHQ23btvWnTJkiR48ePeH/G28b37lz58uu6/ZSSm1OysQAAKQxbiMHAKB1eFpErty4caPEYjFjyJAhSR18/fr1snfvXqNnz56JnJyckMinz4UvW7bMXbp0acz3/Z9MnDjxT0mdFACANEbZBgCgdfhIROTVV1+V/v37+6ZpNmlTtFOJRqMya9Ys7+DBg2ZBQYExYMCAkMinq9kvvvhiXV1dXbHrujcrpcqSMR8AAC0FZRsAgNZhoIiIZVli23bs7bfftiKRSCgSiYht29L40bIsyc7Olvbt2592wA0bNsjChQv9bt26yUMPPSQ5OTni+74sX748sWTJkmgikfi57/tTlVJeyn86AADSDGUbAIDW4e8i8jPHcSIbNmyoFZE2pmnmhcPh9qZptjVNM9f3/dz6+vrLREQefvhhOf487uPF43GZNWuWV15ebt5www3GwIEDDcMw5ODBg/Lqq6/WHj16dIfrumOUUrub8ecDACCtGE05FxMAAJzftNZ9RWTbuHHjpGfPnl/4/+LiYpk/f77fqVMnf8yYMWZubq64rivvvfees2LFirjneQ97njeZ1WwAQGtH2QYAACcoKiqqHzt2bEb//v0/u+a6rsyZM8fbs2ePed111/lDhgwxDMOQQ4cOyV//+tfampqaZbFY7PtKqf0BRgcAIG1wGzkAADiBaZqvbN68+db+/fubIiLbt2+X1157zWvfvr088MADkpeXZ/i+L2vWrPEXLVpUn0gkfu553h+VUryDDwBAA8o2AAA4QSKRWFRSUjLGcZzIvHnzvJKSEvOaa64xhg0bZhiGITU1NTJz5sz6ysrK/Y7jfFsptTXozAAApBtuIwcAACfQWl8oIqUiIh06dPDGjh1r5ubmyt69e6WsrMxZvny57/v+867r/lwpFQ04LgAAaYmyDQAAvkBrnWea5jbP8zqLiFiW5YjIQdM0V8disd8rpd4POCIAAGmNsg0AAE5Jax0RkTYi0kNE1rHLOAAAZ4ayDQAAAABAkplBBwAAAAAA4HxD2QYAAAAAIMko2wAAAAAAJBllGwAAAACAJKNsAwAAAACQZJRtAAAAAACSjLINAAAAAECSUbYBAAAAAEgyyjYAAAAAAElG2QYAAAAAIMko2wAAAAAAJBllGwAAAACAJKNsAwAAAACQZJRtAAAAAACSjLINAAAAAECSUbYBAAAAAEgyyjYAAAAAAElG2QYAAAAAIMko2wAAAAAAJBllGwAAAACAJKNsAwAAAACQZJRtAAAAAACSjLINAAAAAECSUbYBAAAAAEgyyjYAAAAAAElG2QYAAAAAIMko2wAAAAAAJBllGwAAAACAJKNsAwAAAACQZJRtAAAAAACSjLINAAAAAECSUbYBAAAAAEgyyjYAAAAAAElG2QYAAAAAIMko2wAAAAAAJBllGwAAAACAJKNsAwAAAACQZJRtAAAAAACSjLINAJyj9QoAAAAlSURBVAAAAECSUbYBAAAAAEgyyjYAAAAAAElG2QYAAAAAIMn+H9ifxVrebqluAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams[\"figure.figsize\"] = (20,15)\n", - "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n", - "f,ax = wq.plot(rs_df, ax=ax, \n", - " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", - " node_kws=dict(marker=''))\n", - "\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n", - "f,ax = wf.plot(rs_df, ax=ax, \n", - " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", - " node_kws=dict(marker=''))\n", - "ax.set_title('Rio Grande do Sul: Nonplanar Weights')\n", - "ax.set_axis_off()\n", - "plt.savefig('rioGrandeDoSul.png')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/_sources/references.rst.txt b/docs/_sources/references.rst.txt deleted file mode 100644 index 09d2529e0..000000000 --- a/docs/_sources/references.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -.. reference for the docs - -References -========== - -.. bibliography:: _static/references.bib - :cited: diff --git a/docs/_sources/tutorial.rst.txt b/docs/_sources/tutorial.rst.txt deleted file mode 100644 index 9b9021775..000000000 --- a/docs/_sources/tutorial.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -libpysal Tutorial -================= - - -Spatial Weights ---------------- - -.. toctree:: - :glob: - - Spatial Weights - Voronoi - - -Example Datasets ----------------- - -.. toctree:: - :glob: - - Example Data - diff --git a/docs/_static/basic.css b/docs/_static/basic.css deleted file mode 100644 index 2e3cf3230..000000000 --- a/docs/_static/basic.css +++ /dev/null @@ -1,855 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; -} - -div.section::after { - display: block; - content: ''; - clear: left; -} - -/* -- relbar ---------------------------------------------------------------- */ - -div.related { - width: 100%; - font-size: 90%; -} - -div.related h3 { - display: none; -} - -div.related ul { - margin: 0; - padding: 0 0 0 10px; - list-style: none; -} - -div.related li { - display: inline; -} - -div.related li.right { - float: right; - margin-right: 5px; -} - -/* -- sidebar --------------------------------------------------------------- */ - -div.sphinxsidebarwrapper { - padding: 10px 5px 0 10px; -} - -div.sphinxsidebar { - float: left; - width: 230px; - margin-left: -100%; - font-size: 90%; - word-wrap: break-word; - overflow-wrap : break-word; -} - -div.sphinxsidebar ul { - list-style: none; -} - -div.sphinxsidebar ul ul, -div.sphinxsidebar ul.want-points { - margin-left: 20px; - list-style: square; -} - -div.sphinxsidebar ul ul { - margin-top: 0; - margin-bottom: 0; -} - -div.sphinxsidebar form { - margin-top: 10px; -} - -div.sphinxsidebar input { - border: 1px solid #98dbcc; - font-family: sans-serif; - font-size: 1em; -} - -div.sphinxsidebar #searchbox form.search { - overflow: hidden; -} - -div.sphinxsidebar #searchbox input[type="text"] { - float: left; - width: 80%; - padding: 0.25em; - box-sizing: border-box; -} - -div.sphinxsidebar #searchbox input[type="submit"] { - float: left; - width: 20%; - border-left: none; - padding: 0.25em; - box-sizing: border-box; -} - - -img { - border: 0; - max-width: 100%; -} - -/* -- search page ----------------------------------------------------------- */ - -ul.search { - margin: 10px 0 0 20px; - padding: 0; -} - -ul.search li { - padding: 5px 0 5px 20px; - background-image: url(file.png); - background-repeat: no-repeat; - background-position: 0 7px; -} - -ul.search li a { - font-weight: bold; -} - -ul.search li div.context { - color: #888; - margin: 2px 0 0 30px; - text-align: left; -} - -ul.keywordmatches li.goodmatch a { - font-weight: bold; -} - -/* -- index page ------------------------------------------------------------ */ - -table.contentstable { - width: 90%; - margin-left: auto; - margin-right: auto; -} - -table.contentstable p.biglink { - line-height: 150%; -} - -a.biglink { - font-size: 1.3em; -} - -span.linkdescr { - font-style: italic; - padding-top: 5px; - font-size: 90%; -} - -/* -- general index --------------------------------------------------------- */ - -table.indextable { - width: 100%; -} - -table.indextable td { - text-align: left; - vertical-align: top; -} - -table.indextable ul { - margin-top: 0; - margin-bottom: 0; - list-style-type: none; -} - -table.indextable > tbody > tr > td > ul { - padding-left: 0em; -} - -table.indextable tr.pcap { - height: 10px; -} - -table.indextable tr.cap { - margin-top: 10px; - background-color: #f2f2f2; -} - -img.toggler { - margin-right: 3px; - margin-top: 3px; - cursor: pointer; -} - -div.modindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -div.genindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -/* -- domain module index --------------------------------------------------- */ - -table.modindextable td { - padding: 2px; - border-collapse: collapse; -} - -/* -- general body styles --------------------------------------------------- */ - -div.body { - min-width: 450px; - max-width: 800px; -} - -div.body p, div.body dd, div.body li, div.body blockquote { - -moz-hyphens: auto; - -ms-hyphens: auto; - -webkit-hyphens: auto; - hyphens: auto; -} - -a.headerlink { - visibility: hidden; -} - -a.brackets:before, -span.brackets > a:before{ - content: "["; -} - -a.brackets:after, -span.brackets > a:after { - content: "]"; -} - -h1:hover > a.headerlink, -h2:hover > a.headerlink, -h3:hover > a.headerlink, -h4:hover > a.headerlink, -h5:hover > a.headerlink, -h6:hover > a.headerlink, -dt:hover > a.headerlink, -caption:hover > a.headerlink, -p.caption:hover > a.headerlink, -div.code-block-caption:hover > a.headerlink { - visibility: visible; -} - -div.body p.caption { - text-align: inherit; -} - -div.body td { - text-align: left; -} - -.first { - margin-top: 0 !important; -} - -p.rubric { - margin-top: 30px; - font-weight: bold; -} - -img.align-left, .figure.align-left, object.align-left { - clear: left; - float: left; - margin-right: 1em; -} - -img.align-right, .figure.align-right, object.align-right { - clear: right; - float: right; - margin-left: 1em; -} - -img.align-center, .figure.align-center, object.align-center { - display: block; - margin-left: auto; - margin-right: auto; -} - -img.align-default, .figure.align-default { - display: block; - margin-left: auto; - margin-right: auto; -} - -.align-left { - text-align: left; -} - -.align-center { - text-align: center; -} - -.align-default { - text-align: center; -} - -.align-right { - text-align: right; -} - -/* -- sidebars -------------------------------------------------------------- */ - -div.sidebar { - margin: 0 0 0.5em 1em; - border: 1px solid #ddb; - padding: 7px; - background-color: #ffe; - width: 40%; - float: right; - clear: right; - overflow-x: auto; -} - -p.sidebar-title { - font-weight: bold; -} - -div.admonition, div.topic, blockquote { - clear: left; -} - -/* -- topics ---------------------------------------------------------------- */ - -div.topic { - border: 1px solid #ccc; - padding: 7px; - margin: 10px 0 10px 0; -} - -p.topic-title { - font-size: 1.1em; - font-weight: bold; - margin-top: 10px; -} - -/* -- admonitions ----------------------------------------------------------- */ - -div.admonition { - margin-top: 10px; - margin-bottom: 10px; - padding: 7px; -} - -div.admonition dt { - font-weight: bold; -} - -p.admonition-title { - margin: 0px 10px 5px 0px; - font-weight: bold; -} - -div.body p.centered { - text-align: center; - margin-top: 25px; -} - -/* -- content of sidebars/topics/admonitions -------------------------------- */ - -div.sidebar > :last-child, -div.topic > :last-child, -div.admonition > :last-child { - margin-bottom: 0; -} - -div.sidebar::after, -div.topic::after, -div.admonition::after, -blockquote::after { - display: block; - content: ''; - clear: both; -} - -/* -- tables ---------------------------------------------------------------- */ - -table.docutils { - margin-top: 10px; - margin-bottom: 10px; - border: 0; - border-collapse: collapse; -} - -table.align-center { - margin-left: auto; - margin-right: auto; -} - -table.align-default { - margin-left: auto; - margin-right: auto; -} - -table caption span.caption-number { - font-style: italic; -} - -table caption span.caption-text { -} - -table.docutils td, table.docutils th { - padding: 1px 8px 1px 5px; - border-top: 0; - border-left: 0; - border-right: 0; - border-bottom: 1px solid #aaa; -} - -table.footnote td, table.footnote th { - border: 0 !important; -} - -th { - text-align: left; - padding-right: 5px; -} - -table.citation { - border-left: solid 1px gray; - margin-left: 1px; -} - -table.citation td { - border-bottom: none; -} - -th > :first-child, -td > :first-child { - margin-top: 0px; -} - -th > :last-child, -td > :last-child { - margin-bottom: 0px; -} - -/* -- figures --------------------------------------------------------------- */ - -div.figure { - margin: 0.5em; - padding: 0.5em; -} - -div.figure p.caption { - padding: 0.3em; -} - -div.figure p.caption span.caption-number { - font-style: italic; -} - -div.figure p.caption span.caption-text { -} - -/* -- field list styles ----------------------------------------------------- */ - -table.field-list td, table.field-list th { - border: 0 !important; -} - -.field-list ul { - margin: 0; - padding-left: 1em; -} - -.field-list p { - margin: 0; -} - -.field-name { - -moz-hyphens: manual; - -ms-hyphens: manual; - -webkit-hyphens: manual; - hyphens: manual; -} - -/* -- hlist styles ---------------------------------------------------------- */ - -table.hlist { - margin: 1em 0; -} - -table.hlist td { - vertical-align: top; -} - - -/* -- other body styles ----------------------------------------------------- */ - -ol.arabic { - list-style: decimal; -} - -ol.loweralpha { - list-style: lower-alpha; -} - -ol.upperalpha { - list-style: upper-alpha; -} - -ol.lowerroman { - list-style: lower-roman; -} - -ol.upperroman { - list-style: upper-roman; -} - -:not(li) > ol > li:first-child > :first-child, -:not(li) > ul > li:first-child > :first-child { - margin-top: 0px; -} - -:not(li) > ol > li:last-child > :last-child, -:not(li) > ul > li:last-child > :last-child { - margin-bottom: 0px; -} - -ol.simple ol p, -ol.simple ul p, -ul.simple ol p, -ul.simple ul p { - margin-top: 0; -} - -ol.simple > li:not(:first-child) > p, -ul.simple > li:not(:first-child) > p { - margin-top: 0; -} - -ol.simple p, -ul.simple p { - margin-bottom: 0; -} - -dl.footnote > dt, -dl.citation > dt { - float: left; - margin-right: 0.5em; -} - -dl.footnote > dd, -dl.citation > dd { - margin-bottom: 0em; -} - -dl.footnote > dd:after, -dl.citation > dd:after { - content: ""; - clear: both; -} - -dl.field-list { - display: grid; - grid-template-columns: fit-content(30%) auto; -} - -dl.field-list > dt { - font-weight: bold; - word-break: break-word; - padding-left: 0.5em; - padding-right: 5px; -} - -dl.field-list > dt:after { - content: ":"; -} - -dl.field-list > dd { - padding-left: 0.5em; - margin-top: 0em; - margin-left: 0em; - margin-bottom: 0em; -} - -dl { - margin-bottom: 15px; -} - -dd > :first-child { - margin-top: 0px; -} - -dd ul, dd table { - margin-bottom: 10px; -} - -dd { - margin-top: 3px; - margin-bottom: 10px; - margin-left: 30px; -} - -dl > dd:last-child, -dl > dd:last-child > :last-child { - margin-bottom: 0; -} - -dt:target, span.highlighted { - background-color: #fbe54e; -} - -rect.highlighted { - fill: #fbe54e; -} - -dl.glossary dt { - font-weight: bold; - font-size: 1.1em; -} - -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - -.versionmodified { - font-style: italic; -} - -.system-message { - background-color: #fda; - padding: 5px; - border: 3px solid red; -} - -.footnote:target { - background-color: #ffa; -} - -.line-block { - display: block; - margin-top: 1em; - margin-bottom: 1em; -} - -.line-block .line-block { - margin-top: 0; - margin-bottom: 0; - margin-left: 1.5em; -} - -.guilabel, .menuselection { - font-family: sans-serif; -} - -.accelerator { - text-decoration: underline; -} - -.classifier { - font-style: oblique; -} - -.classifier:before { - font-style: normal; - margin: 0.5em; - content: ":"; -} - -abbr, acronym { - border-bottom: dotted 1px; - cursor: help; -} - -/* -- code displays --------------------------------------------------------- */ - -pre { - overflow: auto; - overflow-y: hidden; /* fixes display issues on Chrome browsers */ -} - -pre, div[class|="highlight"] { - clear: both; -} - -span.pre { - -moz-hyphens: none; - -ms-hyphens: none; - -webkit-hyphens: none; - hyphens: none; -} - -div[class^="highlight-"] { - margin: 1em 0; -} - -td.linenos pre { - border: 0; - background-color: transparent; - color: #aaa; -} - -table.highlighttable { - display: block; -} - -table.highlighttable tbody { - display: block; -} - -table.highlighttable tr { - display: flex; -} - -table.highlighttable td { - margin: 0; - padding: 0; -} - -table.highlighttable td.linenos { - padding-right: 0.5em; -} - -table.highlighttable td.code { - flex: 1; - overflow: hidden; -} - -.highlight .hll { - display: block; -} - -div.highlight pre, -table.highlighttable pre { - margin: 0; -} - -div.code-block-caption + div { - margin-top: 0; -} - -div.code-block-caption { - margin-top: 1em; - padding: 2px 5px; - font-size: small; -} - -div.code-block-caption code { - background-color: transparent; -} - -table.highlighttable td.linenos, -div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; -} - -div.code-block-caption span.caption-number { - padding: 0.1em 0.3em; - font-style: italic; -} - -div.code-block-caption span.caption-text { -} - -div.literal-block-wrapper { - margin: 1em 0; -} - -code.descname { - background-color: transparent; - font-weight: bold; - font-size: 1.2em; -} - -code.descclassname { - background-color: transparent; -} - -code.xref, a code { - background-color: transparent; - font-weight: bold; -} - -h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { - background-color: transparent; -} - -.viewcode-link { - float: right; -} - -.viewcode-back { - float: right; - font-family: sans-serif; -} - -div.viewcode-block:target { - margin: -1px -10px; - padding: 0 10px; -} - -/* -- math display ---------------------------------------------------------- */ - -img.math { - vertical-align: middle; -} - -div.body div.math p { - text-align: center; -} - -span.eqno { - float: right; -} - -span.eqno a.headerlink { - position: absolute; - z-index: 1; -} - -div.math:hover a.headerlink { - visibility: visible; -} - -/* -- printout stylesheet --------------------------------------------------- */ - -@media print { - div.document, - div.documentwrapper, - div.bodywrapper { - margin: 0 !important; - width: 100%; - } - - div.sphinxsidebar, - div.related, - div.footer, - #top-link { - display: none; - } -} \ No newline at end of file diff --git a/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.css b/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.css deleted file mode 100644 index 09e88ce3f..000000000 --- a/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.css +++ /dev/null @@ -1,1109 +0,0 @@ -/*! - * Bootstrap Responsive v2.3.2 - * - * Copyright 2012 Twitter, Inc - * Licensed under the Apache License v2.0 - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Designed and built with all the love in the world @twitter by @mdo and @fat. - */ - -.clearfix { - *zoom: 1; -} - -.clearfix:before, -.clearfix:after { - display: table; - line-height: 0; - content: ""; -} - -.clearfix:after { - clear: both; -} - -.hide-text { - font: 0/0 a; - color: transparent; - text-shadow: none; - background-color: transparent; - border: 0; -} - -.input-block-level { - display: block; - width: 100%; - min-height: 30px; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; -} - -@-ms-viewport { - width: device-width; -} - -.hidden { - display: none; - visibility: hidden; -} - -.visible-phone { - display: none !important; -} - -.visible-tablet { - display: none !important; -} - -.hidden-desktop { - display: none !important; -} - -.visible-desktop { - display: inherit !important; -} - -@media (min-width: 768px) and (max-width: 979px) { - .hidden-desktop { - display: inherit !important; - } - .visible-desktop { - display: none !important ; - } - .visible-tablet { - display: inherit !important; - } - .hidden-tablet { - display: none !important; - } -} - -@media (max-width: 767px) { - .hidden-desktop { - display: inherit !important; - } - .visible-desktop { - display: none !important; - } - .visible-phone { - display: inherit !important; - } - .hidden-phone { - display: none !important; - } -} - -.visible-print { - display: none !important; -} - -@media print { - .visible-print { - display: inherit !important; - } - .hidden-print { - display: none !important; - } -} - -@media (min-width: 1200px) { - .row { - margin-left: -30px; - *zoom: 1; - } - .row:before, - .row:after { - display: table; - line-height: 0; - content: ""; - } - .row:after { - clear: both; - } - [class*="span"] { - float: left; - min-height: 1px; - margin-left: 30px; - } - .container, - .navbar-static-top .container, - .navbar-fixed-top .container, - .navbar-fixed-bottom .container { - width: 1170px; - } - .span12 { - width: 1170px; - } - .span11 { - width: 1070px; - } - .span10 { - width: 970px; - } - .span9 { - width: 870px; - } - .span8 { - width: 770px; - } - .span7 { - width: 670px; - } - .span6 { - width: 570px; - } - .span5 { - width: 470px; - } - .span4 { - width: 370px; - } - .span3 { - width: 270px; - } - .span2 { - width: 170px; - } - .span1 { - width: 70px; - } - .offset12 { - margin-left: 1230px; - } - .offset11 { - margin-left: 1130px; - } - .offset10 { - margin-left: 1030px; - } - .offset9 { - margin-left: 930px; - } - .offset8 { - margin-left: 830px; - } - .offset7 { - margin-left: 730px; - } - .offset6 { - margin-left: 630px; - } - .offset5 { - margin-left: 530px; - } - .offset4 { - margin-left: 430px; - } - .offset3 { - margin-left: 330px; - } - .offset2 { - margin-left: 230px; - } - .offset1 { - margin-left: 130px; - } - .row-fluid { - width: 100%; - *zoom: 1; - } - .row-fluid:before, - .row-fluid:after { - display: table; - line-height: 0; - content: ""; - } - .row-fluid:after { - clear: both; - } - .row-fluid [class*="span"] { - display: block; - float: left; - width: 100%; - min-height: 30px; - margin-left: 2.564102564102564%; - *margin-left: 2.5109110747408616%; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - } - .row-fluid [class*="span"]:first-child { - margin-left: 0; - } - .row-fluid .controls-row [class*="span"] + [class*="span"] { - margin-left: 2.564102564102564%; - } - .row-fluid .span12 { - width: 100%; - *width: 99.94680851063829%; - } - .row-fluid .span11 { - width: 91.45299145299145%; - *width: 91.39979996362975%; - } - .row-fluid .span10 { - width: 82.90598290598291%; - *width: 82.8527914166212%; - } - .row-fluid .span9 { - width: 74.35897435897436%; - *width: 74.30578286961266%; - } - .row-fluid .span8 { - width: 65.81196581196582%; - *width: 65.75877432260411%; - } - .row-fluid .span7 { - width: 57.26495726495726%; - *width: 57.21176577559556%; - } - .row-fluid .span6 { - width: 48.717948717948715%; - *width: 48.664757228587014%; - } - .row-fluid .span5 { - width: 40.17094017094017%; - *width: 40.11774868157847%; - } - .row-fluid .span4 { - width: 31.623931623931625%; - *width: 31.570740134569924%; - } - .row-fluid .span3 { - width: 23.076923076923077%; - *width: 23.023731587561375%; - } - .row-fluid .span2 { - width: 14.52991452991453%; - *width: 14.476723040552828%; - } - .row-fluid .span1 { - width: 5.982905982905983%; - *width: 5.929714493544281%; - } - .row-fluid .offset12 { - margin-left: 105.12820512820512%; - *margin-left: 105.02182214948171%; - } - .row-fluid .offset12:first-child { - margin-left: 102.56410256410257%; - *margin-left: 102.45771958537915%; - } - .row-fluid .offset11 { - margin-left: 96.58119658119658%; - *margin-left: 96.47481360247316%; - } - .row-fluid .offset11:first-child { - margin-left: 94.01709401709402%; - *margin-left: 93.91071103837061%; - } - .row-fluid .offset10 { - margin-left: 88.03418803418803%; - *margin-left: 87.92780505546462%; - } - .row-fluid .offset10:first-child { - margin-left: 85.47008547008548%; - *margin-left: 85.36370249136206%; - } - .row-fluid .offset9 { - margin-left: 79.48717948717949%; - *margin-left: 79.38079650845607%; - } - .row-fluid .offset9:first-child { - margin-left: 76.92307692307693%; - *margin-left: 76.81669394435352%; - } - .row-fluid .offset8 { - margin-left: 70.94017094017094%; - *margin-left: 70.83378796144753%; - } - .row-fluid .offset8:first-child { - margin-left: 68.37606837606839%; - *margin-left: 68.26968539734497%; - } - .row-fluid .offset7 { - margin-left: 62.393162393162385%; - *margin-left: 62.28677941443899%; - } - .row-fluid .offset7:first-child { - margin-left: 59.82905982905982%; - *margin-left: 59.72267685033642%; - } - .row-fluid .offset6 { - margin-left: 53.84615384615384%; - *margin-left: 53.739770867430444%; - } - .row-fluid .offset6:first-child { - margin-left: 51.28205128205128%; - *margin-left: 51.175668303327875%; - } - .row-fluid .offset5 { - margin-left: 45.299145299145295%; - *margin-left: 45.1927623204219%; - } - .row-fluid .offset5:first-child { - margin-left: 42.73504273504273%; - *margin-left: 42.62865975631933%; - } - .row-fluid .offset4 { - margin-left: 36.75213675213675%; - *margin-left: 36.645753773413354%; - } - .row-fluid .offset4:first-child { - margin-left: 34.18803418803419%; - *margin-left: 34.081651209310785%; - } - .row-fluid .offset3 { - margin-left: 28.205128205128204%; - *margin-left: 28.0987452264048%; - } - .row-fluid .offset3:first-child { - margin-left: 25.641025641025642%; - *margin-left: 25.53464266230224%; - } - .row-fluid .offset2 { - margin-left: 19.65811965811966%; - *margin-left: 19.551736679396257%; - } - .row-fluid .offset2:first-child { - margin-left: 17.094017094017094%; - *margin-left: 16.98763411529369%; - } - .row-fluid .offset1 { - margin-left: 11.11111111111111%; - *margin-left: 11.004728132387708%; - } - .row-fluid .offset1:first-child { - margin-left: 8.547008547008547%; - *margin-left: 8.440625568285142%; - } - input, - textarea, - .uneditable-input { - margin-left: 0; - } - .controls-row [class*="span"] + [class*="span"] { - margin-left: 30px; - } - input.span12, - textarea.span12, - .uneditable-input.span12 { - width: 1156px; - } - input.span11, - textarea.span11, - .uneditable-input.span11 { - width: 1056px; - } - input.span10, - textarea.span10, - .uneditable-input.span10 { - width: 956px; - } - input.span9, - textarea.span9, - .uneditable-input.span9 { - width: 856px; - } - input.span8, - textarea.span8, - .uneditable-input.span8 { - width: 756px; - } - input.span7, - textarea.span7, - .uneditable-input.span7 { - width: 656px; - } - input.span6, - textarea.span6, - .uneditable-input.span6 { - width: 556px; - } - input.span5, - textarea.span5, - .uneditable-input.span5 { - width: 456px; - } - input.span4, - textarea.span4, - .uneditable-input.span4 { - width: 356px; - } - input.span3, - textarea.span3, - .uneditable-input.span3 { - width: 256px; - } - input.span2, - textarea.span2, - .uneditable-input.span2 { - width: 156px; - } - input.span1, - textarea.span1, - .uneditable-input.span1 { - width: 56px; - } - .thumbnails { - margin-left: -30px; - } - .thumbnails > li { - margin-left: 30px; - } - .row-fluid .thumbnails { - margin-left: 0; - } -} - -@media (min-width: 768px) and (max-width: 979px) { - .row { - margin-left: -20px; - *zoom: 1; - } - .row:before, - .row:after { - display: table; - line-height: 0; - content: ""; - } - .row:after { - clear: both; - } - [class*="span"] { - float: left; - min-height: 1px; - margin-left: 20px; - } - .container, - .navbar-static-top .container, - .navbar-fixed-top .container, - .navbar-fixed-bottom .container { - width: 724px; - } - .span12 { - width: 724px; - } - .span11 { - width: 662px; - } - .span10 { - width: 600px; - } - .span9 { - width: 538px; - } - .span8 { - width: 476px; - } - .span7 { - width: 414px; - } - .span6 { - width: 352px; - } - .span5 { - width: 290px; - } - .span4 { - width: 228px; - } - .span3 { - width: 166px; - } - .span2 { - width: 104px; - } - .span1 { - width: 42px; - } - .offset12 { - margin-left: 764px; - } - .offset11 { - margin-left: 702px; - } - .offset10 { - margin-left: 640px; - } - .offset9 { - margin-left: 578px; - } - .offset8 { - margin-left: 516px; - } - .offset7 { - margin-left: 454px; - } - .offset6 { - margin-left: 392px; - } - .offset5 { - margin-left: 330px; - } - .offset4 { - margin-left: 268px; - } - .offset3 { - margin-left: 206px; - } - .offset2 { - margin-left: 144px; - } - .offset1 { - margin-left: 82px; - } - .row-fluid { - width: 100%; - *zoom: 1; - } - .row-fluid:before, - .row-fluid:after { - display: table; - line-height: 0; - content: ""; - } - .row-fluid:after { - clear: both; - } - .row-fluid [class*="span"] { - display: block; - float: left; - width: 100%; - min-height: 30px; - margin-left: 2.7624309392265194%; - *margin-left: 2.709239449864817%; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - } - .row-fluid [class*="span"]:first-child { - margin-left: 0; - } - .row-fluid .controls-row [class*="span"] + [class*="span"] { - margin-left: 2.7624309392265194%; - } - .row-fluid .span12 { - width: 100%; - *width: 99.94680851063829%; - } - .row-fluid .span11 { - width: 91.43646408839778%; - *width: 91.38327259903608%; - } - .row-fluid .span10 { - width: 82.87292817679558%; - *width: 82.81973668743387%; - } - .row-fluid .span9 { - width: 74.30939226519337%; - *width: 74.25620077583166%; - } - .row-fluid .span8 { - width: 65.74585635359117%; - *width: 65.69266486422946%; - } - .row-fluid .span7 { - width: 57.18232044198895%; - *width: 57.12912895262725%; - } - .row-fluid .span6 { - width: 48.61878453038674%; - *width: 48.56559304102504%; - } - .row-fluid .span5 { - width: 40.05524861878453%; - *width: 40.00205712942283%; - } - .row-fluid .span4 { - width: 31.491712707182323%; - *width: 31.43852121782062%; - } - .row-fluid .span3 { - width: 22.92817679558011%; - *width: 22.87498530621841%; - } - .row-fluid .span2 { - width: 14.3646408839779%; - *width: 14.311449394616199%; - } - .row-fluid .span1 { - width: 5.801104972375691%; - *width: 5.747913483013988%; - } - .row-fluid .offset12 { - margin-left: 105.52486187845304%; - *margin-left: 105.41847889972962%; - } - .row-fluid .offset12:first-child { - margin-left: 102.76243093922652%; - *margin-left: 102.6560479605031%; - } - .row-fluid .offset11 { - margin-left: 96.96132596685082%; - *margin-left: 96.8549429881274%; - } - .row-fluid .offset11:first-child { - margin-left: 94.1988950276243%; - *margin-left: 94.09251204890089%; - } - .row-fluid .offset10 { - margin-left: 88.39779005524862%; - *margin-left: 88.2914070765252%; - } - .row-fluid .offset10:first-child { - margin-left: 85.6353591160221%; - *margin-left: 85.52897613729868%; - } - .row-fluid .offset9 { - margin-left: 79.8342541436464%; - *margin-left: 79.72787116492299%; - } - .row-fluid .offset9:first-child { - margin-left: 77.07182320441989%; - *margin-left: 76.96544022569647%; - } - .row-fluid .offset8 { - margin-left: 71.2707182320442%; - *margin-left: 71.16433525332079%; - } - .row-fluid .offset8:first-child { - margin-left: 68.50828729281768%; - *margin-left: 68.40190431409427%; - } - .row-fluid .offset7 { - margin-left: 62.70718232044199%; - *margin-left: 62.600799341718584%; - } - .row-fluid .offset7:first-child { - margin-left: 59.94475138121547%; - *margin-left: 59.838368402492065%; - } - .row-fluid .offset6 { - margin-left: 54.14364640883978%; - *margin-left: 54.037263430116376%; - } - .row-fluid .offset6:first-child { - margin-left: 51.38121546961326%; - *margin-left: 51.27483249088986%; - } - .row-fluid .offset5 { - margin-left: 45.58011049723757%; - *margin-left: 45.47372751851417%; - } - .row-fluid .offset5:first-child { - margin-left: 42.81767955801105%; - *margin-left: 42.71129657928765%; - } - .row-fluid .offset4 { - margin-left: 37.01657458563536%; - *margin-left: 36.91019160691196%; - } - .row-fluid .offset4:first-child { - margin-left: 34.25414364640884%; - *margin-left: 34.14776066768544%; - } - .row-fluid .offset3 { - margin-left: 28.45303867403315%; - *margin-left: 28.346655695309746%; - } - .row-fluid .offset3:first-child { - margin-left: 25.69060773480663%; - *margin-left: 25.584224756083227%; - } - .row-fluid .offset2 { - margin-left: 19.88950276243094%; - *margin-left: 19.783119783707537%; - } - .row-fluid .offset2:first-child { - margin-left: 17.12707182320442%; - *margin-left: 17.02068884448102%; - } - .row-fluid .offset1 { - margin-left: 11.32596685082873%; - *margin-left: 11.219583872105325%; - } - .row-fluid .offset1:first-child { - margin-left: 8.56353591160221%; - *margin-left: 8.457152932878806%; - } - input, - textarea, - .uneditable-input { - margin-left: 0; - } - .controls-row [class*="span"] + [class*="span"] { - margin-left: 20px; - } - input.span12, - textarea.span12, - .uneditable-input.span12 { - width: 710px; - } - input.span11, - textarea.span11, - .uneditable-input.span11 { - width: 648px; - } - input.span10, - textarea.span10, - .uneditable-input.span10 { - width: 586px; - } - input.span9, - textarea.span9, - .uneditable-input.span9 { - width: 524px; - } - input.span8, - textarea.span8, - .uneditable-input.span8 { - width: 462px; - } - input.span7, - textarea.span7, - .uneditable-input.span7 { - width: 400px; - } - input.span6, - textarea.span6, - .uneditable-input.span6 { - width: 338px; - } - input.span5, - textarea.span5, - .uneditable-input.span5 { - width: 276px; - } - input.span4, - textarea.span4, - .uneditable-input.span4 { - width: 214px; - } - input.span3, - textarea.span3, - .uneditable-input.span3 { - width: 152px; - } - input.span2, - textarea.span2, - .uneditable-input.span2 { - width: 90px; - } - input.span1, - textarea.span1, - .uneditable-input.span1 { - width: 28px; - } -} - -@media (max-width: 767px) { - body { - padding-right: 20px; - padding-left: 20px; - } - .navbar-fixed-top, - .navbar-fixed-bottom, - .navbar-static-top { - margin-right: -20px; - margin-left: -20px; - } - .container-fluid { - padding: 0; - } - .dl-horizontal dt { - float: none; - width: auto; - clear: none; - text-align: left; - } - .dl-horizontal dd { - margin-left: 0; - } - .container { - width: auto; - } - .row-fluid { - width: 100%; - } - .row, - .thumbnails { - margin-left: 0; - } - .thumbnails > li { - float: none; - margin-left: 0; - } - [class*="span"], - .uneditable-input[class*="span"], - .row-fluid [class*="span"] { - display: block; - float: none; - width: 100%; - margin-left: 0; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - } - .span12, - .row-fluid .span12 { - width: 100%; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - } - .row-fluid [class*="offset"]:first-child { - margin-left: 0; - } - .input-large, - .input-xlarge, - .input-xxlarge, - input[class*="span"], - select[class*="span"], - textarea[class*="span"], - .uneditable-input { - display: block; - width: 100%; - min-height: 30px; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - } - .input-prepend input, - .input-append input, - .input-prepend input[class*="span"], - .input-append input[class*="span"] { - display: inline-block; - width: auto; - } - .controls-row [class*="span"] + [class*="span"] { - margin-left: 0; - } - .modal { - position: fixed; - top: 20px; - right: 20px; - left: 20px; - width: auto; - margin: 0; - } - .modal.fade { - top: -100px; - } - .modal.fade.in { - top: 20px; - } -} - -@media (max-width: 480px) { - .nav-collapse { - -webkit-transform: translate3d(0, 0, 0); - } - .page-header h1 small { - display: block; - line-height: 20px; - } - input[type="checkbox"], - input[type="radio"] { - border: 1px solid #ccc; - } - .form-horizontal .control-label { - float: none; - width: auto; - padding-top: 0; - text-align: left; - } - .form-horizontal .controls { - margin-left: 0; - } - .form-horizontal .control-list { - padding-top: 0; - } - .form-horizontal .form-actions { - padding-right: 10px; - padding-left: 10px; - } - .media .pull-left, - .media .pull-right { - display: block; - float: none; - margin-bottom: 10px; - } - .media-object { - margin-right: 0; - margin-left: 0; - } - .modal { - top: 10px; - right: 10px; - left: 10px; - } - .modal-header .close { - padding: 10px; - margin: -10px; - } - .carousel-caption { - position: static; - } -} - -@media (max-width: 979px) { - body { - padding-top: 0; - } - .navbar-fixed-top, - .navbar-fixed-bottom { - position: static; - } - .navbar-fixed-top { - margin-bottom: 20px; - } - .navbar-fixed-bottom { - margin-top: 20px; - } - .navbar-fixed-top .navbar-inner, - .navbar-fixed-bottom .navbar-inner { - padding: 5px; - } - .navbar .container { - width: auto; - padding: 0; - } - .navbar .brand { - padding-right: 10px; - padding-left: 10px; - margin: 0 0 0 -5px; - } - .nav-collapse { - clear: both; - } - .nav-collapse .nav { - float: none; - margin: 0 0 10px; - } - .nav-collapse .nav > li { - float: none; - } - .nav-collapse .nav > li > a { - margin-bottom: 2px; - } - .nav-collapse .nav > .divider-vertical { - display: none; - } - .nav-collapse .nav .nav-header { - color: #777777; - text-shadow: none; - } - .nav-collapse .nav > li > a, - .nav-collapse .dropdown-menu a { - padding: 9px 15px; - font-weight: bold; - color: #777777; - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; - } - .nav-collapse .btn { - padding: 4px 10px 4px; - font-weight: normal; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - } - .nav-collapse .dropdown-menu li + li a { - margin-bottom: 2px; - } - .nav-collapse .nav > li > a:hover, - .nav-collapse .nav > li > a:focus, - .nav-collapse .dropdown-menu a:hover, - .nav-collapse .dropdown-menu a:focus { - background-color: #f2f2f2; - } - .navbar-inverse .nav-collapse .nav > li > a, - .navbar-inverse .nav-collapse .dropdown-menu a { - color: #999999; - } - .navbar-inverse .nav-collapse .nav > li > a:hover, - .navbar-inverse .nav-collapse .nav > li > a:focus, - .navbar-inverse .nav-collapse .dropdown-menu a:hover, - .navbar-inverse .nav-collapse .dropdown-menu a:focus { - background-color: #111111; - } - .nav-collapse.in .btn-group { - padding: 0; - margin-top: 5px; - } - .nav-collapse .dropdown-menu { - position: static; - top: auto; - left: auto; - display: none; - float: none; - max-width: none; - padding: 0; - margin: 0 15px; - background-color: transparent; - border: none; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; - -webkit-box-shadow: none; - -moz-box-shadow: none; - box-shadow: none; - } - .nav-collapse .open > .dropdown-menu { - display: block; - } - .nav-collapse .dropdown-menu:before, - .nav-collapse .dropdown-menu:after { - display: none; - } - .nav-collapse .dropdown-menu .divider { - display: none; - } - .nav-collapse .nav > li > .dropdown-menu:before, - .nav-collapse .nav > li > .dropdown-menu:after { - display: none; - } - .nav-collapse .navbar-form, - .nav-collapse .navbar-search { - float: none; - padding: 10px 15px; - margin: 10px 0; - border-top: 1px solid #f2f2f2; - border-bottom: 1px solid #f2f2f2; - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); - -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); - } - .navbar-inverse .nav-collapse .navbar-form, - .navbar-inverse .nav-collapse .navbar-search { - border-top-color: #111111; - border-bottom-color: #111111; - } - .navbar .nav-collapse .nav.pull-right { - float: none; - margin-left: 0; - } - .nav-collapse, - .nav-collapse.collapse { - height: 0; - overflow: hidden; - } - .navbar .btn-navbar { - display: block; - } - .navbar-static .navbar-inner { - padding-right: 10px; - padding-left: 10px; - } -} - -@media (min-width: 980px) { - .nav-collapse.collapse { - height: auto !important; - overflow: visible !important; - } -} diff --git a/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.min.css b/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.min.css deleted file mode 100644 index f4ede63f3..000000000 --- a/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.min.css +++ /dev/null @@ -1,9 +0,0 @@ -/*! - * Bootstrap Responsive v2.3.2 - * - * Copyright 2012 Twitter, Inc - * Licensed under the Apache License v2.0 - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Designed and built with all the love in the world @twitter by @mdo and @fat. - */.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;line-height:0;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}@-ms-viewport{width:device-width}.hidden{display:none;visibility:hidden}.visible-phone{display:none!important}.visible-tablet{display:none!important}.hidden-desktop{display:none!important}.visible-desktop{display:inherit!important}@media(min-width:768px) and (max-width:979px){.hidden-desktop{display:inherit!important}.visible-desktop{display:none!important}.visible-tablet{display:inherit!important}.hidden-tablet{display:none!important}}@media(max-width:767px){.hidden-desktop{display:inherit!important}.visible-desktop{display:none!important}.visible-phone{display:inherit!important}.hidden-phone{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:inherit!important}.hidden-print{display:none!important}}@media(min-width:1200px){.row{margin-left:-30px;*zoom:1}.row:before,.row:after{display:table;line-height:0;content:""}.row:after{clear:both}[class*="span"]{float:left;min-height:1px;margin-left:30px}.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:1170px}.span12{width:1170px}.span11{width:1070px}.span10{width:970px}.span9{width:870px}.span8{width:770px}.span7{width:670px}.span6{width:570px}.span5{width:470px}.span4{width:370px}.span3{width:270px}.span2{width:170px}.span1{width:70px}.offset12{margin-left:1230px}.offset11{margin-left:1130px}.offset10{margin-left:1030px}.offset9{margin-left:930px}.offset8{margin-left:830px}.offset7{margin-left:730px}.offset6{margin-left:630px}.offset5{margin-left:530px}.offset4{margin-left:430px}.offset3{margin-left:330px}.offset2{margin-left:230px}.offset1{margin-left:130px}.row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;line-height:0;content:""}.row-fluid:after{clear:both}.row-fluid [class*="span"]{display:block;float:left;width:100%;min-height:30px;margin-left:2.564102564102564%;*margin-left:2.5109110747408616%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.row-fluid [class*="span"]:first-child{margin-left:0}.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.564102564102564%}.row-fluid .span12{width:100%;*width:99.94680851063829%}.row-fluid .span11{width:91.45299145299145%;*width:91.39979996362975%}.row-fluid .span10{width:82.90598290598291%;*width:82.8527914166212%}.row-fluid .span9{width:74.35897435897436%;*width:74.30578286961266%}.row-fluid .span8{width:65.81196581196582%;*width:65.75877432260411%}.row-fluid .span7{width:57.26495726495726%;*width:57.21176577559556%}.row-fluid .span6{width:48.717948717948715%;*width:48.664757228587014%}.row-fluid .span5{width:40.17094017094017%;*width:40.11774868157847%}.row-fluid .span4{width:31.623931623931625%;*width:31.570740134569924%}.row-fluid .span3{width:23.076923076923077%;*width:23.023731587561375%}.row-fluid .span2{width:14.52991452991453%;*width:14.476723040552828%}.row-fluid .span1{width:5.982905982905983%;*width:5.929714493544281%}.row-fluid .offset12{margin-left:105.12820512820512%;*margin-left:105.02182214948171%}.row-fluid .offset12:first-child{margin-left:102.56410256410257%;*margin-left:102.45771958537915%}.row-fluid .offset11{margin-left:96.58119658119658%;*margin-left:96.47481360247316%}.row-fluid .offset11:first-child{margin-left:94.01709401709402%;*margin-left:93.91071103837061%}.row-fluid .offset10{margin-left:88.03418803418803%;*margin-left:87.92780505546462%}.row-fluid .offset10:first-child{margin-left:85.47008547008548%;*margin-left:85.36370249136206%}.row-fluid .offset9{margin-left:79.48717948717949%;*margin-left:79.38079650845607%}.row-fluid .offset9:first-child{margin-left:76.92307692307693%;*margin-left:76.81669394435352%}.row-fluid .offset8{margin-left:70.94017094017094%;*margin-left:70.83378796144753%}.row-fluid .offset8:first-child{margin-left:68.37606837606839%;*margin-left:68.26968539734497%}.row-fluid .offset7{margin-left:62.393162393162385%;*margin-left:62.28677941443899%}.row-fluid .offset7:first-child{margin-left:59.82905982905982%;*margin-left:59.72267685033642%}.row-fluid .offset6{margin-left:53.84615384615384%;*margin-left:53.739770867430444%}.row-fluid .offset6:first-child{margin-left:51.28205128205128%;*margin-left:51.175668303327875%}.row-fluid .offset5{margin-left:45.299145299145295%;*margin-left:45.1927623204219%}.row-fluid .offset5:first-child{margin-left:42.73504273504273%;*margin-left:42.62865975631933%}.row-fluid .offset4{margin-left:36.75213675213675%;*margin-left:36.645753773413354%}.row-fluid .offset4:first-child{margin-left:34.18803418803419%;*margin-left:34.081651209310785%}.row-fluid .offset3{margin-left:28.205128205128204%;*margin-left:28.0987452264048%}.row-fluid .offset3:first-child{margin-left:25.641025641025642%;*margin-left:25.53464266230224%}.row-fluid .offset2{margin-left:19.65811965811966%;*margin-left:19.551736679396257%}.row-fluid .offset2:first-child{margin-left:17.094017094017094%;*margin-left:16.98763411529369%}.row-fluid .offset1{margin-left:11.11111111111111%;*margin-left:11.004728132387708%}.row-fluid .offset1:first-child{margin-left:8.547008547008547%;*margin-left:8.440625568285142%}input,textarea,.uneditable-input{margin-left:0}.controls-row [class*="span"]+[class*="span"]{margin-left:30px}input.span12,textarea.span12,.uneditable-input.span12{width:1156px}input.span11,textarea.span11,.uneditable-input.span11{width:1056px}input.span10,textarea.span10,.uneditable-input.span10{width:956px}input.span9,textarea.span9,.uneditable-input.span9{width:856px}input.span8,textarea.span8,.uneditable-input.span8{width:756px}input.span7,textarea.span7,.uneditable-input.span7{width:656px}input.span6,textarea.span6,.uneditable-input.span6{width:556px}input.span5,textarea.span5,.uneditable-input.span5{width:456px}input.span4,textarea.span4,.uneditable-input.span4{width:356px}input.span3,textarea.span3,.uneditable-input.span3{width:256px}input.span2,textarea.span2,.uneditable-input.span2{width:156px}input.span1,textarea.span1,.uneditable-input.span1{width:56px}.thumbnails{margin-left:-30px}.thumbnails>li{margin-left:30px}.row-fluid .thumbnails{margin-left:0}}@media(min-width:768px) and (max-width:979px){.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;line-height:0;content:""}.row:after{clear:both}[class*="span"]{float:left;min-height:1px;margin-left:20px}.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:724px}.span12{width:724px}.span11{width:662px}.span10{width:600px}.span9{width:538px}.span8{width:476px}.span7{width:414px}.span6{width:352px}.span5{width:290px}.span4{width:228px}.span3{width:166px}.span2{width:104px}.span1{width:42px}.offset12{margin-left:764px}.offset11{margin-left:702px}.offset10{margin-left:640px}.offset9{margin-left:578px}.offset8{margin-left:516px}.offset7{margin-left:454px}.offset6{margin-left:392px}.offset5{margin-left:330px}.offset4{margin-left:268px}.offset3{margin-left:206px}.offset2{margin-left:144px}.offset1{margin-left:82px}.row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;line-height:0;content:""}.row-fluid:after{clear:both}.row-fluid [class*="span"]{display:block;float:left;width:100%;min-height:30px;margin-left:2.7624309392265194%;*margin-left:2.709239449864817%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.row-fluid [class*="span"]:first-child{margin-left:0}.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.7624309392265194%}.row-fluid .span12{width:100%;*width:99.94680851063829%}.row-fluid .span11{width:91.43646408839778%;*width:91.38327259903608%}.row-fluid .span10{width:82.87292817679558%;*width:82.81973668743387%}.row-fluid .span9{width:74.30939226519337%;*width:74.25620077583166%}.row-fluid .span8{width:65.74585635359117%;*width:65.69266486422946%}.row-fluid .span7{width:57.18232044198895%;*width:57.12912895262725%}.row-fluid .span6{width:48.61878453038674%;*width:48.56559304102504%}.row-fluid .span5{width:40.05524861878453%;*width:40.00205712942283%}.row-fluid .span4{width:31.491712707182323%;*width:31.43852121782062%}.row-fluid .span3{width:22.92817679558011%;*width:22.87498530621841%}.row-fluid .span2{width:14.3646408839779%;*width:14.311449394616199%}.row-fluid .span1{width:5.801104972375691%;*width:5.747913483013988%}.row-fluid .offset12{margin-left:105.52486187845304%;*margin-left:105.41847889972962%}.row-fluid .offset12:first-child{margin-left:102.76243093922652%;*margin-left:102.6560479605031%}.row-fluid .offset11{margin-left:96.96132596685082%;*margin-left:96.8549429881274%}.row-fluid .offset11:first-child{margin-left:94.1988950276243%;*margin-left:94.09251204890089%}.row-fluid .offset10{margin-left:88.39779005524862%;*margin-left:88.2914070765252%}.row-fluid .offset10:first-child{margin-left:85.6353591160221%;*margin-left:85.52897613729868%}.row-fluid .offset9{margin-left:79.8342541436464%;*margin-left:79.72787116492299%}.row-fluid .offset9:first-child{margin-left:77.07182320441989%;*margin-left:76.96544022569647%}.row-fluid .offset8{margin-left:71.2707182320442%;*margin-left:71.16433525332079%}.row-fluid .offset8:first-child{margin-left:68.50828729281768%;*margin-left:68.40190431409427%}.row-fluid .offset7{margin-left:62.70718232044199%;*margin-left:62.600799341718584%}.row-fluid .offset7:first-child{margin-left:59.94475138121547%;*margin-left:59.838368402492065%}.row-fluid .offset6{margin-left:54.14364640883978%;*margin-left:54.037263430116376%}.row-fluid .offset6:first-child{margin-left:51.38121546961326%;*margin-left:51.27483249088986%}.row-fluid .offset5{margin-left:45.58011049723757%;*margin-left:45.47372751851417%}.row-fluid .offset5:first-child{margin-left:42.81767955801105%;*margin-left:42.71129657928765%}.row-fluid .offset4{margin-left:37.01657458563536%;*margin-left:36.91019160691196%}.row-fluid .offset4:first-child{margin-left:34.25414364640884%;*margin-left:34.14776066768544%}.row-fluid .offset3{margin-left:28.45303867403315%;*margin-left:28.346655695309746%}.row-fluid .offset3:first-child{margin-left:25.69060773480663%;*margin-left:25.584224756083227%}.row-fluid .offset2{margin-left:19.88950276243094%;*margin-left:19.783119783707537%}.row-fluid .offset2:first-child{margin-left:17.12707182320442%;*margin-left:17.02068884448102%}.row-fluid .offset1{margin-left:11.32596685082873%;*margin-left:11.219583872105325%}.row-fluid .offset1:first-child{margin-left:8.56353591160221%;*margin-left:8.457152932878806%}input,textarea,.uneditable-input{margin-left:0}.controls-row [class*="span"]+[class*="span"]{margin-left:20px}input.span12,textarea.span12,.uneditable-input.span12{width:710px}input.span11,textarea.span11,.uneditable-input.span11{width:648px}input.span10,textarea.span10,.uneditable-input.span10{width:586px}input.span9,textarea.span9,.uneditable-input.span9{width:524px}input.span8,textarea.span8,.uneditable-input.span8{width:462px}input.span7,textarea.span7,.uneditable-input.span7{width:400px}input.span6,textarea.span6,.uneditable-input.span6{width:338px}input.span5,textarea.span5,.uneditable-input.span5{width:276px}input.span4,textarea.span4,.uneditable-input.span4{width:214px}input.span3,textarea.span3,.uneditable-input.span3{width:152px}input.span2,textarea.span2,.uneditable-input.span2{width:90px}input.span1,textarea.span1,.uneditable-input.span1{width:28px}}@media(max-width:767px){body{padding-right:20px;padding-left:20px}.navbar-fixed-top,.navbar-fixed-bottom,.navbar-static-top{margin-right:-20px;margin-left:-20px}.container-fluid{padding:0}.dl-horizontal dt{float:none;width:auto;clear:none;text-align:left}.dl-horizontal dd{margin-left:0}.container{width:auto}.row-fluid{width:100%}.row,.thumbnails{margin-left:0}.thumbnails>li{float:none;margin-left:0}[class*="span"],.uneditable-input[class*="span"],.row-fluid [class*="span"]{display:block;float:none;width:100%;margin-left:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.span12,.row-fluid .span12{width:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.row-fluid [class*="offset"]:first-child{margin-left:0}.input-large,.input-xlarge,.input-xxlarge,input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.input-prepend input,.input-append input,.input-prepend input[class*="span"],.input-append input[class*="span"]{display:inline-block;width:auto}.controls-row [class*="span"]+[class*="span"]{margin-left:0}.modal{position:fixed;top:20px;right:20px;left:20px;width:auto;margin:0}.modal.fade{top:-100px}.modal.fade.in{top:20px}}@media(max-width:480px){.nav-collapse{-webkit-transform:translate3d(0,0,0)}.page-header h1 small{display:block;line-height:20px}input[type="checkbox"],input[type="radio"]{border:1px solid #ccc}.form-horizontal .control-label{float:none;width:auto;padding-top:0;text-align:left}.form-horizontal .controls{margin-left:0}.form-horizontal .control-list{padding-top:0}.form-horizontal .form-actions{padding-right:10px;padding-left:10px}.media .pull-left,.media .pull-right{display:block;float:none;margin-bottom:10px}.media-object{margin-right:0;margin-left:0}.modal{top:10px;right:10px;left:10px}.modal-header .close{padding:10px;margin:-10px}.carousel-caption{position:static}}@media(max-width:979px){body{padding-top:0}.navbar-fixed-top,.navbar-fixed-bottom{position:static}.navbar-fixed-top{margin-bottom:20px}.navbar-fixed-bottom{margin-top:20px}.navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding:5px}.navbar .container{width:auto;padding:0}.navbar .brand{padding-right:10px;padding-left:10px;margin:0 0 0 -5px}.nav-collapse{clear:both}.nav-collapse .nav{float:none;margin:0 0 10px}.nav-collapse .nav>li{float:none}.nav-collapse .nav>li>a{margin-bottom:2px}.nav-collapse .nav>.divider-vertical{display:none}.nav-collapse .nav .nav-header{color:#777;text-shadow:none}.nav-collapse .nav>li>a,.nav-collapse .dropdown-menu a{padding:9px 15px;font-weight:bold;color:#777;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.nav-collapse .btn{padding:4px 10px 4px;font-weight:normal;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.nav-collapse .dropdown-menu li+li a{margin-bottom:2px}.nav-collapse .nav>li>a:hover,.nav-collapse .nav>li>a:focus,.nav-collapse .dropdown-menu a:hover,.nav-collapse .dropdown-menu a:focus{background-color:#f2f2f2}.navbar-inverse .nav-collapse .nav>li>a,.navbar-inverse .nav-collapse .dropdown-menu a{color:#999}.navbar-inverse .nav-collapse .nav>li>a:hover,.navbar-inverse .nav-collapse .nav>li>a:focus,.navbar-inverse .nav-collapse .dropdown-menu a:hover,.navbar-inverse .nav-collapse .dropdown-menu a:focus{background-color:#111}.nav-collapse.in .btn-group{padding:0;margin-top:5px}.nav-collapse .dropdown-menu{position:static;top:auto;left:auto;display:none;float:none;max-width:none;padding:0;margin:0 15px;background-color:transparent;border:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}.nav-collapse .open>.dropdown-menu{display:block}.nav-collapse .dropdown-menu:before,.nav-collapse .dropdown-menu:after{display:none}.nav-collapse .dropdown-menu .divider{display:none}.nav-collapse .nav>li>.dropdown-menu:before,.nav-collapse .nav>li>.dropdown-menu:after{display:none}.nav-collapse .navbar-form,.nav-collapse .navbar-search{float:none;padding:10px 15px;margin:10px 0;border-top:1px solid #f2f2f2;border-bottom:1px solid #f2f2f2;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.1);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.1);box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.1)}.navbar-inverse .nav-collapse .navbar-form,.navbar-inverse .nav-collapse .navbar-search{border-top-color:#111;border-bottom-color:#111}.navbar .nav-collapse .nav.pull-right{float:none;margin-left:0}.nav-collapse,.nav-collapse.collapse{height:0;overflow:hidden}.navbar .btn-navbar{display:block}.navbar-static .navbar-inner{padding-right:10px;padding-left:10px}}@media(min-width:980px){.nav-collapse.collapse{height:auto!important;overflow:visible!important}} diff --git a/docs/_static/bootstrap-2.3.2/css/bootstrap.css b/docs/_static/bootstrap-2.3.2/css/bootstrap.css deleted file mode 100644 index b725064aa..000000000 --- a/docs/_static/bootstrap-2.3.2/css/bootstrap.css +++ /dev/null @@ -1,6167 +0,0 @@ -/*! - * Bootstrap v2.3.2 - * - * Copyright 2012 Twitter, Inc - * Licensed under the Apache License v2.0 - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Designed and built with all the love in the world @twitter by @mdo and @fat. - */ - -.clearfix { - *zoom: 1; -} - -.clearfix:before, -.clearfix:after { - display: table; - line-height: 0; - content: ""; -} - -.clearfix:after { - clear: both; -} - -.hide-text { - font: 0/0 a; - color: transparent; - text-shadow: none; - background-color: transparent; - border: 0; -} - -.input-block-level { - display: block; - width: 100%; - min-height: 30px; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; -} - -article, -aside, -details, -figcaption, -figure, -footer, -header, -hgroup, -nav, -section { - display: block; -} - -audio, -canvas, -video { - display: inline-block; - *display: inline; - *zoom: 1; -} - -audio:not([controls]) { - display: none; -} - -html { - font-size: 100%; - -webkit-text-size-adjust: 100%; - -ms-text-size-adjust: 100%; -} - -a:focus { - outline: thin dotted #333; - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} - -a:hover, -a:active { - outline: 0; -} - -sub, -sup { - position: relative; - font-size: 75%; - line-height: 0; - vertical-align: baseline; -} - -sup { - top: -0.5em; -} - -sub { - bottom: -0.25em; -} - -img { - width: auto\9; - height: auto; - max-width: 100%; - vertical-align: middle; - border: 0; - -ms-interpolation-mode: bicubic; -} - -#map_canvas img, -.google-maps img { - max-width: none; -} - -button, -input, -select, -textarea { - margin: 0; - font-size: 100%; - vertical-align: middle; -} - -button, -input { - *overflow: visible; - line-height: normal; -} - -button::-moz-focus-inner, -input::-moz-focus-inner { - padding: 0; - border: 0; -} - -button, -html input[type="button"], -input[type="reset"], -input[type="submit"] { - cursor: pointer; - -webkit-appearance: button; -} - -label, -select, -button, -input[type="button"], -input[type="reset"], -input[type="submit"], -input[type="radio"], -input[type="checkbox"] { - cursor: pointer; -} - -input[type="search"] { - -webkit-box-sizing: content-box; - -moz-box-sizing: content-box; - box-sizing: content-box; - -webkit-appearance: textfield; -} - -input[type="search"]::-webkit-search-decoration, -input[type="search"]::-webkit-search-cancel-button { - -webkit-appearance: none; -} - -textarea { - overflow: auto; - vertical-align: top; -} - -@media print { - * { - color: #000 !important; - text-shadow: none !important; - background: transparent !important; - box-shadow: none !important; - } - a, - a:visited { - text-decoration: underline; - } - a[href]:after { - content: " (" attr(href) ")"; - } - abbr[title]:after { - content: " (" attr(title) ")"; - } - .ir a:after, - a[href^="javascript:"]:after, - a[href^="#"]:after { - content: ""; - } - pre, - blockquote { - border: 1px solid #999; - page-break-inside: avoid; - } - thead { - display: table-header-group; - } - tr, - img { - page-break-inside: avoid; - } - img { - max-width: 100% !important; - } - @page { - margin: 0.5cm; - } - p, - h2, - h3 { - orphans: 3; - widows: 3; - } - h2, - h3 { - page-break-after: avoid; - } -} - -body { - margin: 0; - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; - font-size: 14px; - line-height: 20px; - color: #333333; - background-color: #ffffff; -} - -a { - color: #0088cc; - text-decoration: none; -} - -a:hover, -a:focus { - color: #005580; - text-decoration: underline; -} - -.img-rounded { - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} - -.img-polaroid { - padding: 4px; - background-color: #fff; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.2); - -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1); - -moz-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1); - box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1); -} - -.img-circle { - -webkit-border-radius: 500px; - -moz-border-radius: 500px; - border-radius: 500px; -} - -.row { - margin-left: -20px; - *zoom: 1; -} - -.row:before, -.row:after { - display: table; - line-height: 0; - content: ""; -} - -.row:after { - clear: both; -} - -[class*="span"] { - float: left; - min-height: 1px; - margin-left: 20px; -} - -.container, -.navbar-static-top .container, -.navbar-fixed-top .container, -.navbar-fixed-bottom .container { - width: 940px; -} - -.span12 { - width: 940px; -} - -.span11 { - width: 860px; -} - -.span10 { - width: 780px; -} - -.span9 { - width: 700px; -} - -.span8 { - width: 620px; -} - -.span7 { - width: 540px; -} - -.span6 { - width: 460px; -} - -.span5 { - width: 380px; -} - -.span4 { - width: 300px; -} - -.span3 { - width: 220px; -} - -.span2 { - width: 140px; -} - -.span1 { - width: 60px; -} - -.offset12 { - margin-left: 980px; -} - -.offset11 { - margin-left: 900px; -} - -.offset10 { - margin-left: 820px; -} - -.offset9 { - margin-left: 740px; -} - -.offset8 { - margin-left: 660px; -} - -.offset7 { - margin-left: 580px; -} - -.offset6 { - margin-left: 500px; -} - -.offset5 { - margin-left: 420px; -} - -.offset4 { - margin-left: 340px; -} - -.offset3 { - margin-left: 260px; -} - -.offset2 { - margin-left: 180px; -} - -.offset1 { - margin-left: 100px; -} - -.row-fluid { - width: 100%; - *zoom: 1; -} - -.row-fluid:before, -.row-fluid:after { - display: table; - line-height: 0; - content: ""; -} - -.row-fluid:after { - clear: both; -} - -.row-fluid [class*="span"] { - display: block; - float: left; - width: 100%; - min-height: 30px; - margin-left: 2.127659574468085%; - *margin-left: 2.074468085106383%; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; -} - -.row-fluid [class*="span"]:first-child { - margin-left: 0; -} - -.row-fluid .controls-row [class*="span"] + [class*="span"] { - margin-left: 2.127659574468085%; -} - -.row-fluid .span12 { - width: 100%; - *width: 99.94680851063829%; -} - -.row-fluid .span11 { - width: 91.48936170212765%; - *width: 91.43617021276594%; -} - -.row-fluid .span10 { - width: 82.97872340425532%; - *width: 82.92553191489361%; -} - -.row-fluid .span9 { - width: 74.46808510638297%; - *width: 74.41489361702126%; -} - -.row-fluid .span8 { - width: 65.95744680851064%; - *width: 65.90425531914893%; -} - -.row-fluid .span7 { - width: 57.44680851063829%; - *width: 57.39361702127659%; -} - -.row-fluid .span6 { - width: 48.93617021276595%; - *width: 48.88297872340425%; -} - -.row-fluid .span5 { - width: 40.42553191489362%; - *width: 40.37234042553192%; -} - -.row-fluid .span4 { - width: 31.914893617021278%; - *width: 31.861702127659576%; -} - -.row-fluid .span3 { - width: 23.404255319148934%; - *width: 23.351063829787233%; -} - -.row-fluid .span2 { - width: 14.893617021276595%; - *width: 14.840425531914894%; -} - -.row-fluid .span1 { - width: 6.382978723404255%; - *width: 6.329787234042553%; -} - -.row-fluid .offset12 { - margin-left: 104.25531914893617%; - *margin-left: 104.14893617021275%; -} - -.row-fluid .offset12:first-child { - margin-left: 102.12765957446808%; - *margin-left: 102.02127659574467%; -} - -.row-fluid .offset11 { - margin-left: 95.74468085106382%; - *margin-left: 95.6382978723404%; -} - -.row-fluid .offset11:first-child { - margin-left: 93.61702127659574%; - *margin-left: 93.51063829787232%; -} - -.row-fluid .offset10 { - margin-left: 87.23404255319149%; - *margin-left: 87.12765957446807%; -} - -.row-fluid .offset10:first-child { - margin-left: 85.1063829787234%; - *margin-left: 84.99999999999999%; -} - -.row-fluid .offset9 { - margin-left: 78.72340425531914%; - *margin-left: 78.61702127659572%; -} - -.row-fluid .offset9:first-child { - margin-left: 76.59574468085106%; - *margin-left: 76.48936170212764%; -} - -.row-fluid .offset8 { - margin-left: 70.2127659574468%; - *margin-left: 70.10638297872339%; -} - -.row-fluid .offset8:first-child { - margin-left: 68.08510638297872%; - *margin-left: 67.9787234042553%; -} - -.row-fluid .offset7 { - margin-left: 61.70212765957446%; - *margin-left: 61.59574468085106%; -} - -.row-fluid .offset7:first-child { - margin-left: 59.574468085106375%; - *margin-left: 59.46808510638297%; -} - -.row-fluid .offset6 { - margin-left: 53.191489361702125%; - *margin-left: 53.085106382978715%; -} - -.row-fluid .offset6:first-child { - margin-left: 51.063829787234035%; - *margin-left: 50.95744680851063%; -} - -.row-fluid .offset5 { - margin-left: 44.68085106382979%; - *margin-left: 44.57446808510638%; -} - -.row-fluid .offset5:first-child { - margin-left: 42.5531914893617%; - *margin-left: 42.4468085106383%; -} - -.row-fluid .offset4 { - margin-left: 36.170212765957444%; - *margin-left: 36.06382978723405%; -} - -.row-fluid .offset4:first-child { - margin-left: 34.04255319148936%; - *margin-left: 33.93617021276596%; -} - -.row-fluid .offset3 { - margin-left: 27.659574468085104%; - *margin-left: 27.5531914893617%; -} - -.row-fluid .offset3:first-child { - margin-left: 25.53191489361702%; - *margin-left: 25.425531914893618%; -} - -.row-fluid .offset2 { - margin-left: 19.148936170212764%; - *margin-left: 19.04255319148936%; -} - -.row-fluid .offset2:first-child { - margin-left: 17.02127659574468%; - *margin-left: 16.914893617021278%; -} - -.row-fluid .offset1 { - margin-left: 10.638297872340425%; - *margin-left: 10.53191489361702%; -} - -.row-fluid .offset1:first-child { - margin-left: 8.51063829787234%; - *margin-left: 8.404255319148938%; -} - -[class*="span"].hide, -.row-fluid [class*="span"].hide { - display: none; -} - -[class*="span"].pull-right, -.row-fluid [class*="span"].pull-right { - float: right; -} - -.container { - margin-right: auto; - margin-left: auto; - *zoom: 1; -} - -.container:before, -.container:after { - display: table; - line-height: 0; - content: ""; -} - -.container:after { - clear: both; -} - -.container-fluid { - padding-right: 20px; - padding-left: 20px; - *zoom: 1; -} - -.container-fluid:before, -.container-fluid:after { - display: table; - line-height: 0; - content: ""; -} - -.container-fluid:after { - clear: both; -} - -p { - margin: 0 0 10px; -} - -.lead { - margin-bottom: 20px; - font-size: 21px; - font-weight: 200; - line-height: 30px; -} - -small { - font-size: 85%; -} - -strong { - font-weight: bold; -} - -em { - font-style: italic; -} - -cite { - font-style: normal; -} - -.muted { - color: #999999; -} - -a.muted:hover, -a.muted:focus { - color: #808080; -} - -.text-warning { - color: #c09853; -} - -a.text-warning:hover, -a.text-warning:focus { - color: #a47e3c; -} - -.text-error { - color: #b94a48; -} - -a.text-error:hover, -a.text-error:focus { - color: #953b39; -} - -.text-info { - color: #3a87ad; -} - -a.text-info:hover, -a.text-info:focus { - color: #2d6987; -} - -.text-success { - color: #468847; -} - -a.text-success:hover, -a.text-success:focus { - color: #356635; -} - -.text-left { - text-align: left; -} - -.text-right { - text-align: right; -} - -.text-center { - text-align: center; -} - -h1, -h2, -h3, -h4, -h5, -h6 { - margin: 10px 0; - font-family: inherit; - font-weight: bold; - line-height: 20px; - color: inherit; - text-rendering: optimizelegibility; -} - -h1 small, -h2 small, -h3 small, -h4 small, -h5 small, -h6 small { - font-weight: normal; - line-height: 1; - color: #999999; -} - -h1, -h2, -h3 { - line-height: 40px; -} - -h1 { - font-size: 38.5px; -} - -h2 { - font-size: 31.5px; -} - -h3 { - font-size: 24.5px; -} - -h4 { - font-size: 17.5px; -} - -h5 { - font-size: 14px; -} - -h6 { - font-size: 11.9px; -} - -h1 small { - font-size: 24.5px; -} - -h2 small { - font-size: 17.5px; -} - -h3 small { - font-size: 14px; -} - -h4 small { - font-size: 14px; -} - -.page-header { - padding-bottom: 9px; - margin: 20px 0 30px; - border-bottom: 1px solid #eeeeee; -} - -ul, -ol { - padding: 0; - margin: 0 0 10px 25px; -} - -ul ul, -ul ol, -ol ol, -ol ul { - margin-bottom: 0; -} - -li { - line-height: 20px; -} - -ul.unstyled, -ol.unstyled { - margin-left: 0; - list-style: none; -} - -ul.inline, -ol.inline { - margin-left: 0; - list-style: none; -} - -ul.inline > li, -ol.inline > li { - display: inline-block; - *display: inline; - padding-right: 5px; - padding-left: 5px; - *zoom: 1; -} - -dl { - margin-bottom: 20px; -} - -dt, -dd { - line-height: 20px; -} - -dt { - font-weight: bold; -} - -dd { - margin-left: 10px; -} - -.dl-horizontal { - *zoom: 1; -} - -.dl-horizontal:before, -.dl-horizontal:after { - display: table; - line-height: 0; - content: ""; -} - -.dl-horizontal:after { - clear: both; -} - -.dl-horizontal dt { - float: left; - width: 160px; - overflow: hidden; - clear: left; - text-align: right; - text-overflow: ellipsis; - white-space: nowrap; -} - -.dl-horizontal dd { - margin-left: 180px; -} - -hr { - margin: 20px 0; - border: 0; - border-top: 1px solid #eeeeee; - border-bottom: 1px solid #ffffff; -} - -abbr[title], -abbr[data-original-title] { - cursor: help; - border-bottom: 1px dotted #999999; -} - -abbr.initialism { - font-size: 90%; - text-transform: uppercase; -} - -blockquote { - padding: 0 0 0 15px; - margin: 0 0 20px; - border-left: 5px solid #eeeeee; -} - -blockquote p { - margin-bottom: 0; - font-size: 17.5px; - font-weight: 300; - line-height: 1.25; -} - -blockquote small { - display: block; - line-height: 20px; - color: #999999; -} - -blockquote small:before { - content: '\2014 \00A0'; -} - -blockquote.pull-right { - float: right; - padding-right: 15px; - padding-left: 0; - border-right: 5px solid #eeeeee; - border-left: 0; -} - -blockquote.pull-right p, -blockquote.pull-right small { - text-align: right; -} - -blockquote.pull-right small:before { - content: ''; -} - -blockquote.pull-right small:after { - content: '\00A0 \2014'; -} - -q:before, -q:after, -blockquote:before, -blockquote:after { - content: ""; -} - -address { - display: block; - margin-bottom: 20px; - font-style: normal; - line-height: 20px; -} - -code, -pre { - padding: 0 3px 2px; - font-family: Monaco, Menlo, Consolas, "Courier New", monospace; - font-size: 12px; - color: #333333; - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; -} - -code { - padding: 2px 4px; - color: #d14; - white-space: nowrap; - background-color: #f7f7f9; - border: 1px solid #e1e1e8; -} - -pre { - display: block; - padding: 9.5px; - margin: 0 0 10px; - font-size: 13px; - line-height: 20px; - word-break: break-all; - word-wrap: break-word; - white-space: pre; - white-space: pre-wrap; - background-color: #f5f5f5; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.15); - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -pre.prettyprint { - margin-bottom: 20px; -} - -pre code { - padding: 0; - color: inherit; - white-space: pre; - white-space: pre-wrap; - background-color: transparent; - border: 0; -} - -.pre-scrollable { - max-height: 340px; - overflow-y: scroll; -} - -form { - margin: 0 0 20px; -} - -fieldset { - padding: 0; - margin: 0; - border: 0; -} - -legend { - display: block; - width: 100%; - padding: 0; - margin-bottom: 20px; - font-size: 21px; - line-height: 40px; - color: #333333; - border: 0; - border-bottom: 1px solid #e5e5e5; -} - -legend small { - font-size: 15px; - color: #999999; -} - -label, -input, -button, -select, -textarea { - font-size: 14px; - font-weight: normal; - line-height: 20px; -} - -input, -button, -select, -textarea { - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; -} - -label { - display: block; - margin-bottom: 5px; -} - -select, -textarea, -input[type="text"], -input[type="password"], -input[type="datetime"], -input[type="datetime-local"], -input[type="date"], -input[type="month"], -input[type="time"], -input[type="week"], -input[type="number"], -input[type="email"], -input[type="url"], -input[type="search"], -input[type="tel"], -input[type="color"], -.uneditable-input { - display: inline-block; - height: 20px; - padding: 4px 6px; - margin-bottom: 10px; - font-size: 14px; - line-height: 20px; - color: #555555; - vertical-align: middle; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -input, -textarea, -.uneditable-input { - width: 206px; -} - -textarea { - height: auto; -} - -textarea, -input[type="text"], -input[type="password"], -input[type="datetime"], -input[type="datetime-local"], -input[type="date"], -input[type="month"], -input[type="time"], -input[type="week"], -input[type="number"], -input[type="email"], -input[type="url"], -input[type="search"], -input[type="tel"], -input[type="color"], -.uneditable-input { - background-color: #ffffff; - border: 1px solid #cccccc; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - -webkit-transition: border linear 0.2s, box-shadow linear 0.2s; - -moz-transition: border linear 0.2s, box-shadow linear 0.2s; - -o-transition: border linear 0.2s, box-shadow linear 0.2s; - transition: border linear 0.2s, box-shadow linear 0.2s; -} - -textarea:focus, -input[type="text"]:focus, -input[type="password"]:focus, -input[type="datetime"]:focus, -input[type="datetime-local"]:focus, -input[type="date"]:focus, -input[type="month"]:focus, -input[type="time"]:focus, -input[type="week"]:focus, -input[type="number"]:focus, -input[type="email"]:focus, -input[type="url"]:focus, -input[type="search"]:focus, -input[type="tel"]:focus, -input[type="color"]:focus, -.uneditable-input:focus { - border-color: rgba(82, 168, 236, 0.8); - outline: 0; - outline: thin dotted \9; - /* IE6-9 */ - - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); -} - -input[type="radio"], -input[type="checkbox"] { - margin: 4px 0 0; - margin-top: 1px \9; - *margin-top: 0; - line-height: normal; -} - -input[type="file"], -input[type="image"], -input[type="submit"], -input[type="reset"], -input[type="button"], -input[type="radio"], -input[type="checkbox"] { - width: auto; -} - -select, -input[type="file"] { - height: 30px; - /* In IE7, the height of the select element cannot be changed by height, only font-size */ - - *margin-top: 4px; - /* For IE7, add top margin to align select with labels */ - - line-height: 30px; -} - -select { - width: 220px; - background-color: #ffffff; - border: 1px solid #cccccc; -} - -select[multiple], -select[size] { - height: auto; -} - -select:focus, -input[type="file"]:focus, -input[type="radio"]:focus, -input[type="checkbox"]:focus { - outline: thin dotted #333; - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} - -.uneditable-input, -.uneditable-textarea { - color: #999999; - cursor: not-allowed; - background-color: #fcfcfc; - border-color: #cccccc; - -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); - -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); - box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); -} - -.uneditable-input { - overflow: hidden; - white-space: nowrap; -} - -.uneditable-textarea { - width: auto; - height: auto; -} - -input:-moz-placeholder, -textarea:-moz-placeholder { - color: #999999; -} - -input:-ms-input-placeholder, -textarea:-ms-input-placeholder { - color: #999999; -} - -input::-webkit-input-placeholder, -textarea::-webkit-input-placeholder { - color: #999999; -} - -.radio, -.checkbox { - min-height: 20px; - padding-left: 20px; -} - -.radio input[type="radio"], -.checkbox input[type="checkbox"] { - float: left; - margin-left: -20px; -} - -.controls > .radio:first-child, -.controls > .checkbox:first-child { - padding-top: 5px; -} - -.radio.inline, -.checkbox.inline { - display: inline-block; - padding-top: 5px; - margin-bottom: 0; - vertical-align: middle; -} - -.radio.inline + .radio.inline, -.checkbox.inline + .checkbox.inline { - margin-left: 10px; -} - -.input-mini { - width: 60px; -} - -.input-small { - width: 90px; -} - -.input-medium { - width: 150px; -} - -.input-large { - width: 210px; -} - -.input-xlarge { - width: 270px; -} - -.input-xxlarge { - width: 530px; -} - -input[class*="span"], -select[class*="span"], -textarea[class*="span"], -.uneditable-input[class*="span"], -.row-fluid input[class*="span"], -.row-fluid select[class*="span"], -.row-fluid textarea[class*="span"], -.row-fluid .uneditable-input[class*="span"] { - float: none; - margin-left: 0; -} - -.input-append input[class*="span"], -.input-append .uneditable-input[class*="span"], -.input-prepend input[class*="span"], -.input-prepend .uneditable-input[class*="span"], -.row-fluid input[class*="span"], -.row-fluid select[class*="span"], -.row-fluid textarea[class*="span"], -.row-fluid .uneditable-input[class*="span"], -.row-fluid .input-prepend [class*="span"], -.row-fluid .input-append [class*="span"] { - display: inline-block; -} - -input, -textarea, -.uneditable-input { - margin-left: 0; -} - -.controls-row [class*="span"] + [class*="span"] { - margin-left: 20px; -} - -input.span12, -textarea.span12, -.uneditable-input.span12 { - width: 926px; -} - -input.span11, -textarea.span11, -.uneditable-input.span11 { - width: 846px; -} - -input.span10, -textarea.span10, -.uneditable-input.span10 { - width: 766px; -} - -input.span9, -textarea.span9, -.uneditable-input.span9 { - width: 686px; -} - -input.span8, -textarea.span8, -.uneditable-input.span8 { - width: 606px; -} - -input.span7, -textarea.span7, -.uneditable-input.span7 { - width: 526px; -} - -input.span6, -textarea.span6, -.uneditable-input.span6 { - width: 446px; -} - -input.span5, -textarea.span5, -.uneditable-input.span5 { - width: 366px; -} - -input.span4, -textarea.span4, -.uneditable-input.span4 { - width: 286px; -} - -input.span3, -textarea.span3, -.uneditable-input.span3 { - width: 206px; -} - -input.span2, -textarea.span2, -.uneditable-input.span2 { - width: 126px; -} - -input.span1, -textarea.span1, -.uneditable-input.span1 { - width: 46px; -} - -.controls-row { - *zoom: 1; -} - -.controls-row:before, -.controls-row:after { - display: table; - line-height: 0; - content: ""; -} - -.controls-row:after { - clear: both; -} - -.controls-row [class*="span"], -.row-fluid .controls-row [class*="span"] { - float: left; -} - -.controls-row .checkbox[class*="span"], -.controls-row .radio[class*="span"] { - padding-top: 5px; -} - -input[disabled], -select[disabled], -textarea[disabled], -input[readonly], -select[readonly], -textarea[readonly] { - cursor: not-allowed; - background-color: #eeeeee; -} - -input[type="radio"][disabled], -input[type="checkbox"][disabled], -input[type="radio"][readonly], -input[type="checkbox"][readonly] { - background-color: transparent; -} - -.control-group.warning .control-label, -.control-group.warning .help-block, -.control-group.warning .help-inline { - color: #c09853; -} - -.control-group.warning .checkbox, -.control-group.warning .radio, -.control-group.warning input, -.control-group.warning select, -.control-group.warning textarea { - color: #c09853; -} - -.control-group.warning input, -.control-group.warning select, -.control-group.warning textarea { - border-color: #c09853; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} - -.control-group.warning input:focus, -.control-group.warning select:focus, -.control-group.warning textarea:focus { - border-color: #a47e3c; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e; - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e; -} - -.control-group.warning .input-prepend .add-on, -.control-group.warning .input-append .add-on { - color: #c09853; - background-color: #fcf8e3; - border-color: #c09853; -} - -.control-group.error .control-label, -.control-group.error .help-block, -.control-group.error .help-inline { - color: #b94a48; -} - -.control-group.error .checkbox, -.control-group.error .radio, -.control-group.error input, -.control-group.error select, -.control-group.error textarea { - color: #b94a48; -} - -.control-group.error input, -.control-group.error select, -.control-group.error textarea { - border-color: #b94a48; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} - -.control-group.error input:focus, -.control-group.error select:focus, -.control-group.error textarea:focus { - border-color: #953b39; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392; - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392; -} - -.control-group.error .input-prepend .add-on, -.control-group.error .input-append .add-on { - color: #b94a48; - background-color: #f2dede; - border-color: #b94a48; -} - -.control-group.success .control-label, -.control-group.success .help-block, -.control-group.success .help-inline { - color: #468847; -} - -.control-group.success .checkbox, -.control-group.success .radio, -.control-group.success input, -.control-group.success select, -.control-group.success textarea { - color: #468847; -} - -.control-group.success input, -.control-group.success select, -.control-group.success textarea { - border-color: #468847; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} - -.control-group.success input:focus, -.control-group.success select:focus, -.control-group.success textarea:focus { - border-color: #356635; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b; - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b; -} - -.control-group.success .input-prepend .add-on, -.control-group.success .input-append .add-on { - color: #468847; - background-color: #dff0d8; - border-color: #468847; -} - -.control-group.info .control-label, -.control-group.info .help-block, -.control-group.info .help-inline { - color: #3a87ad; -} - -.control-group.info .checkbox, -.control-group.info .radio, -.control-group.info input, -.control-group.info select, -.control-group.info textarea { - color: #3a87ad; -} - -.control-group.info input, -.control-group.info select, -.control-group.info textarea { - border-color: #3a87ad; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} - -.control-group.info input:focus, -.control-group.info select:focus, -.control-group.info textarea:focus { - border-color: #2d6987; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3; - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3; -} - -.control-group.info .input-prepend .add-on, -.control-group.info .input-append .add-on { - color: #3a87ad; - background-color: #d9edf7; - border-color: #3a87ad; -} - -input:focus:invalid, -textarea:focus:invalid, -select:focus:invalid { - color: #b94a48; - border-color: #ee5f5b; -} - -input:focus:invalid:focus, -textarea:focus:invalid:focus, -select:focus:invalid:focus { - border-color: #e9322d; - -webkit-box-shadow: 0 0 6px #f8b9b7; - -moz-box-shadow: 0 0 6px #f8b9b7; - box-shadow: 0 0 6px #f8b9b7; -} - -.form-actions { - padding: 19px 20px 20px; - margin-top: 20px; - margin-bottom: 20px; - background-color: #f5f5f5; - border-top: 1px solid #e5e5e5; - *zoom: 1; -} - -.form-actions:before, -.form-actions:after { - display: table; - line-height: 0; - content: ""; -} - -.form-actions:after { - clear: both; -} - -.help-block, -.help-inline { - color: #595959; -} - -.help-block { - display: block; - margin-bottom: 10px; -} - -.help-inline { - display: inline-block; - *display: inline; - padding-left: 5px; - vertical-align: middle; - *zoom: 1; -} - -.input-append, -.input-prepend { - display: inline-block; - margin-bottom: 10px; - font-size: 0; - white-space: nowrap; - vertical-align: middle; -} - -.input-append input, -.input-prepend input, -.input-append select, -.input-prepend select, -.input-append .uneditable-input, -.input-prepend .uneditable-input, -.input-append .dropdown-menu, -.input-prepend .dropdown-menu, -.input-append .popover, -.input-prepend .popover { - font-size: 14px; -} - -.input-append input, -.input-prepend input, -.input-append select, -.input-prepend select, -.input-append .uneditable-input, -.input-prepend .uneditable-input { - position: relative; - margin-bottom: 0; - *margin-left: 0; - vertical-align: top; - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} - -.input-append input:focus, -.input-prepend input:focus, -.input-append select:focus, -.input-prepend select:focus, -.input-append .uneditable-input:focus, -.input-prepend .uneditable-input:focus { - z-index: 2; -} - -.input-append .add-on, -.input-prepend .add-on { - display: inline-block; - width: auto; - height: 20px; - min-width: 16px; - padding: 4px 5px; - font-size: 14px; - font-weight: normal; - line-height: 20px; - text-align: center; - text-shadow: 0 1px 0 #ffffff; - background-color: #eeeeee; - border: 1px solid #ccc; -} - -.input-append .add-on, -.input-prepend .add-on, -.input-append .btn, -.input-prepend .btn, -.input-append .btn-group > .dropdown-toggle, -.input-prepend .btn-group > .dropdown-toggle { - vertical-align: top; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.input-append .active, -.input-prepend .active { - background-color: #a9dba9; - border-color: #46a546; -} - -.input-prepend .add-on, -.input-prepend .btn { - margin-right: -1px; -} - -.input-prepend .add-on:first-child, -.input-prepend .btn:first-child { - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} - -.input-append input, -.input-append select, -.input-append .uneditable-input { - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} - -.input-append input + .btn-group .btn:last-child, -.input-append select + .btn-group .btn:last-child, -.input-append .uneditable-input + .btn-group .btn:last-child { - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} - -.input-append .add-on, -.input-append .btn, -.input-append .btn-group { - margin-left: -1px; -} - -.input-append .add-on:last-child, -.input-append .btn:last-child, -.input-append .btn-group:last-child > .dropdown-toggle { - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} - -.input-prepend.input-append input, -.input-prepend.input-append select, -.input-prepend.input-append .uneditable-input { - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.input-prepend.input-append input + .btn-group .btn, -.input-prepend.input-append select + .btn-group .btn, -.input-prepend.input-append .uneditable-input + .btn-group .btn { - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} - -.input-prepend.input-append .add-on:first-child, -.input-prepend.input-append .btn:first-child { - margin-right: -1px; - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} - -.input-prepend.input-append .add-on:last-child, -.input-prepend.input-append .btn:last-child { - margin-left: -1px; - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} - -.input-prepend.input-append .btn-group:first-child { - margin-left: 0; -} - -input.search-query { - padding-right: 14px; - padding-right: 4px \9; - padding-left: 14px; - padding-left: 4px \9; - /* IE7-8 doesn't have border-radius, so don't indent the padding */ - - margin-bottom: 0; - -webkit-border-radius: 15px; - -moz-border-radius: 15px; - border-radius: 15px; -} - -/* Allow for input prepend/append in search forms */ - -.form-search .input-append .search-query, -.form-search .input-prepend .search-query { - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.form-search .input-append .search-query { - -webkit-border-radius: 14px 0 0 14px; - -moz-border-radius: 14px 0 0 14px; - border-radius: 14px 0 0 14px; -} - -.form-search .input-append .btn { - -webkit-border-radius: 0 14px 14px 0; - -moz-border-radius: 0 14px 14px 0; - border-radius: 0 14px 14px 0; -} - -.form-search .input-prepend .search-query { - -webkit-border-radius: 0 14px 14px 0; - -moz-border-radius: 0 14px 14px 0; - border-radius: 0 14px 14px 0; -} - -.form-search .input-prepend .btn { - -webkit-border-radius: 14px 0 0 14px; - -moz-border-radius: 14px 0 0 14px; - border-radius: 14px 0 0 14px; -} - -.form-search input, -.form-inline input, -.form-horizontal input, -.form-search textarea, -.form-inline textarea, -.form-horizontal textarea, -.form-search select, -.form-inline select, -.form-horizontal select, -.form-search .help-inline, -.form-inline .help-inline, -.form-horizontal .help-inline, -.form-search .uneditable-input, -.form-inline .uneditable-input, -.form-horizontal .uneditable-input, -.form-search .input-prepend, -.form-inline .input-prepend, -.form-horizontal .input-prepend, -.form-search .input-append, -.form-inline .input-append, -.form-horizontal .input-append { - display: inline-block; - *display: inline; - margin-bottom: 0; - vertical-align: middle; - *zoom: 1; -} - -.form-search .hide, -.form-inline .hide, -.form-horizontal .hide { - display: none; -} - -.form-search label, -.form-inline label, -.form-search .btn-group, -.form-inline .btn-group { - display: inline-block; -} - -.form-search .input-append, -.form-inline .input-append, -.form-search .input-prepend, -.form-inline .input-prepend { - margin-bottom: 0; -} - -.form-search .radio, -.form-search .checkbox, -.form-inline .radio, -.form-inline .checkbox { - padding-left: 0; - margin-bottom: 0; - vertical-align: middle; -} - -.form-search .radio input[type="radio"], -.form-search .checkbox input[type="checkbox"], -.form-inline .radio input[type="radio"], -.form-inline .checkbox input[type="checkbox"] { - float: left; - margin-right: 3px; - margin-left: 0; -} - -.control-group { - margin-bottom: 10px; -} - -legend + .control-group { - margin-top: 20px; - -webkit-margin-top-collapse: separate; -} - -.form-horizontal .control-group { - margin-bottom: 20px; - *zoom: 1; -} - -.form-horizontal .control-group:before, -.form-horizontal .control-group:after { - display: table; - line-height: 0; - content: ""; -} - -.form-horizontal .control-group:after { - clear: both; -} - -.form-horizontal .control-label { - float: left; - width: 160px; - padding-top: 5px; - text-align: right; -} - -.form-horizontal .controls { - *display: inline-block; - *padding-left: 20px; - margin-left: 180px; - *margin-left: 0; -} - -.form-horizontal .controls:first-child { - *padding-left: 180px; -} - -.form-horizontal .help-block { - margin-bottom: 0; -} - -.form-horizontal input + .help-block, -.form-horizontal select + .help-block, -.form-horizontal textarea + .help-block, -.form-horizontal .uneditable-input + .help-block, -.form-horizontal .input-prepend + .help-block, -.form-horizontal .input-append + .help-block { - margin-top: 10px; -} - -.form-horizontal .form-actions { - padding-left: 180px; -} - -table { - max-width: 100%; - background-color: transparent; - border-collapse: collapse; - border-spacing: 0; -} - -.table { - width: 100%; - margin-bottom: 20px; -} - -.table th, -.table td { - padding: 8px; - line-height: 20px; - text-align: left; - vertical-align: top; - border-top: 1px solid #dddddd; -} - -.table th { - font-weight: bold; -} - -.table thead th { - vertical-align: bottom; -} - -.table caption + thead tr:first-child th, -.table caption + thead tr:first-child td, -.table colgroup + thead tr:first-child th, -.table colgroup + thead tr:first-child td, -.table thead:first-child tr:first-child th, -.table thead:first-child tr:first-child td { - border-top: 0; -} - -.table tbody + tbody { - border-top: 2px solid #dddddd; -} - -.table .table { - background-color: #ffffff; -} - -.table-condensed th, -.table-condensed td { - padding: 4px 5px; -} - -.table-bordered { - border: 1px solid #dddddd; - border-collapse: separate; - *border-collapse: collapse; - border-left: 0; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -.table-bordered th, -.table-bordered td { - border-left: 1px solid #dddddd; -} - -.table-bordered caption + thead tr:first-child th, -.table-bordered caption + tbody tr:first-child th, -.table-bordered caption + tbody tr:first-child td, -.table-bordered colgroup + thead tr:first-child th, -.table-bordered colgroup + tbody tr:first-child th, -.table-bordered colgroup + tbody tr:first-child td, -.table-bordered thead:first-child tr:first-child th, -.table-bordered tbody:first-child tr:first-child th, -.table-bordered tbody:first-child tr:first-child td { - border-top: 0; -} - -.table-bordered thead:first-child tr:first-child > th:first-child, -.table-bordered tbody:first-child tr:first-child > td:first-child, -.table-bordered tbody:first-child tr:first-child > th:first-child { - -webkit-border-top-left-radius: 4px; - border-top-left-radius: 4px; - -moz-border-radius-topleft: 4px; -} - -.table-bordered thead:first-child tr:first-child > th:last-child, -.table-bordered tbody:first-child tr:first-child > td:last-child, -.table-bordered tbody:first-child tr:first-child > th:last-child { - -webkit-border-top-right-radius: 4px; - border-top-right-radius: 4px; - -moz-border-radius-topright: 4px; -} - -.table-bordered thead:last-child tr:last-child > th:first-child, -.table-bordered tbody:last-child tr:last-child > td:first-child, -.table-bordered tbody:last-child tr:last-child > th:first-child, -.table-bordered tfoot:last-child tr:last-child > td:first-child, -.table-bordered tfoot:last-child tr:last-child > th:first-child { - -webkit-border-bottom-left-radius: 4px; - border-bottom-left-radius: 4px; - -moz-border-radius-bottomleft: 4px; -} - -.table-bordered thead:last-child tr:last-child > th:last-child, -.table-bordered tbody:last-child tr:last-child > td:last-child, -.table-bordered tbody:last-child tr:last-child > th:last-child, -.table-bordered tfoot:last-child tr:last-child > td:last-child, -.table-bordered tfoot:last-child tr:last-child > th:last-child { - -webkit-border-bottom-right-radius: 4px; - border-bottom-right-radius: 4px; - -moz-border-radius-bottomright: 4px; -} - -.table-bordered tfoot + tbody:last-child tr:last-child td:first-child { - -webkit-border-bottom-left-radius: 0; - border-bottom-left-radius: 0; - -moz-border-radius-bottomleft: 0; -} - -.table-bordered tfoot + tbody:last-child tr:last-child td:last-child { - -webkit-border-bottom-right-radius: 0; - border-bottom-right-radius: 0; - -moz-border-radius-bottomright: 0; -} - -.table-bordered caption + thead tr:first-child th:first-child, -.table-bordered caption + tbody tr:first-child td:first-child, -.table-bordered colgroup + thead tr:first-child th:first-child, -.table-bordered colgroup + tbody tr:first-child td:first-child { - -webkit-border-top-left-radius: 4px; - border-top-left-radius: 4px; - -moz-border-radius-topleft: 4px; -} - -.table-bordered caption + thead tr:first-child th:last-child, -.table-bordered caption + tbody tr:first-child td:last-child, -.table-bordered colgroup + thead tr:first-child th:last-child, -.table-bordered colgroup + tbody tr:first-child td:last-child { - -webkit-border-top-right-radius: 4px; - border-top-right-radius: 4px; - -moz-border-radius-topright: 4px; -} - -.table-striped tbody > tr:nth-child(odd) > td, -.table-striped tbody > tr:nth-child(odd) > th { - background-color: #f9f9f9; -} - -.table-hover tbody tr:hover > td, -.table-hover tbody tr:hover > th { - background-color: #f5f5f5; -} - -table td[class*="span"], -table th[class*="span"], -.row-fluid table td[class*="span"], -.row-fluid table th[class*="span"] { - display: table-cell; - float: none; - margin-left: 0; -} - -.table td.span1, -.table th.span1 { - float: none; - width: 44px; - margin-left: 0; -} - -.table td.span2, -.table th.span2 { - float: none; - width: 124px; - margin-left: 0; -} - -.table td.span3, -.table th.span3 { - float: none; - width: 204px; - margin-left: 0; -} - -.table td.span4, -.table th.span4 { - float: none; - width: 284px; - margin-left: 0; -} - -.table td.span5, -.table th.span5 { - float: none; - width: 364px; - margin-left: 0; -} - -.table td.span6, -.table th.span6 { - float: none; - width: 444px; - margin-left: 0; -} - -.table td.span7, -.table th.span7 { - float: none; - width: 524px; - margin-left: 0; -} - -.table td.span8, -.table th.span8 { - float: none; - width: 604px; - margin-left: 0; -} - -.table td.span9, -.table th.span9 { - float: none; - width: 684px; - margin-left: 0; -} - -.table td.span10, -.table th.span10 { - float: none; - width: 764px; - margin-left: 0; -} - -.table td.span11, -.table th.span11 { - float: none; - width: 844px; - margin-left: 0; -} - -.table td.span12, -.table th.span12 { - float: none; - width: 924px; - margin-left: 0; -} - -.table tbody tr.success > td { - background-color: #dff0d8; -} - -.table tbody tr.error > td { - background-color: #f2dede; -} - -.table tbody tr.warning > td { - background-color: #fcf8e3; -} - -.table tbody tr.info > td { - background-color: #d9edf7; -} - -.table-hover tbody tr.success:hover > td { - background-color: #d0e9c6; -} - -.table-hover tbody tr.error:hover > td { - background-color: #ebcccc; -} - -.table-hover tbody tr.warning:hover > td { - background-color: #faf2cc; -} - -.table-hover tbody tr.info:hover > td { - background-color: #c4e3f3; -} - -[class^="icon-"], -[class*=" icon-"] { - display: inline-block; - width: 14px; - height: 14px; - margin-top: 1px; - *margin-right: .3em; - line-height: 14px; - vertical-align: text-top; - background-image: url("../img/glyphicons-halflings.png"); - background-position: 14px 14px; - background-repeat: no-repeat; -} - -/* White icons with optional class, or on hover/focus/active states of certain elements */ - -.icon-white, -.nav-pills > .active > a > [class^="icon-"], -.nav-pills > .active > a > [class*=" icon-"], -.nav-list > .active > a > [class^="icon-"], -.nav-list > .active > a > [class*=" icon-"], -.navbar-inverse .nav > .active > a > [class^="icon-"], -.navbar-inverse .nav > .active > a > [class*=" icon-"], -.dropdown-menu > li > a:hover > [class^="icon-"], -.dropdown-menu > li > a:focus > [class^="icon-"], -.dropdown-menu > li > a:hover > [class*=" icon-"], -.dropdown-menu > li > a:focus > [class*=" icon-"], -.dropdown-menu > .active > a > [class^="icon-"], -.dropdown-menu > .active > a > [class*=" icon-"], -.dropdown-submenu:hover > a > [class^="icon-"], -.dropdown-submenu:focus > a > [class^="icon-"], -.dropdown-submenu:hover > a > [class*=" icon-"], -.dropdown-submenu:focus > a > [class*=" icon-"] { - background-image: url("../img/glyphicons-halflings-white.png"); -} - -.icon-glass { - background-position: 0 0; -} - -.icon-music { - background-position: -24px 0; -} - -.icon-search { - background-position: -48px 0; -} - -.icon-envelope { - background-position: -72px 0; -} - -.icon-heart { - background-position: -96px 0; -} - -.icon-star { - background-position: -120px 0; -} - -.icon-star-empty { - background-position: -144px 0; -} - -.icon-user { - background-position: -168px 0; -} - -.icon-film { - background-position: -192px 0; -} - -.icon-th-large { - background-position: -216px 0; -} - -.icon-th { - background-position: -240px 0; -} - -.icon-th-list { - background-position: -264px 0; -} - -.icon-ok { - background-position: -288px 0; -} - -.icon-remove { - background-position: -312px 0; -} - -.icon-zoom-in { - background-position: -336px 0; -} - -.icon-zoom-out { - background-position: -360px 0; -} - -.icon-off { - background-position: -384px 0; -} - -.icon-signal { - background-position: -408px 0; -} - -.icon-cog { - background-position: -432px 0; -} - -.icon-trash { - background-position: -456px 0; -} - -.icon-home { - background-position: 0 -24px; -} - -.icon-file { - background-position: -24px -24px; -} - -.icon-time { - background-position: -48px -24px; -} - -.icon-road { - background-position: -72px -24px; -} - -.icon-download-alt { - background-position: -96px -24px; -} - -.icon-download { - background-position: -120px -24px; -} - -.icon-upload { - background-position: -144px -24px; -} - -.icon-inbox { - background-position: -168px -24px; -} - -.icon-play-circle { - background-position: -192px -24px; -} - -.icon-repeat { - background-position: -216px -24px; -} - -.icon-refresh { - background-position: -240px -24px; -} - -.icon-list-alt { - background-position: -264px -24px; -} - -.icon-lock { - background-position: -287px -24px; -} - -.icon-flag { - background-position: -312px -24px; -} - -.icon-headphones { - background-position: -336px -24px; -} - -.icon-volume-off { - background-position: -360px -24px; -} - -.icon-volume-down { - background-position: -384px -24px; -} - -.icon-volume-up { - background-position: -408px -24px; -} - -.icon-qrcode { - background-position: -432px -24px; -} - -.icon-barcode { - background-position: -456px -24px; -} - -.icon-tag { - background-position: 0 -48px; -} - -.icon-tags { - background-position: -25px -48px; -} - -.icon-book { - background-position: -48px -48px; -} - -.icon-bookmark { - background-position: -72px -48px; -} - -.icon-print { - background-position: -96px -48px; -} - -.icon-camera { - background-position: -120px -48px; -} - -.icon-font { - background-position: -144px -48px; -} - -.icon-bold { - background-position: -167px -48px; -} - -.icon-italic { - background-position: -192px -48px; -} - -.icon-text-height { - background-position: -216px -48px; -} - -.icon-text-width { - background-position: -240px -48px; -} - -.icon-align-left { - background-position: -264px -48px; -} - -.icon-align-center { - background-position: -288px -48px; -} - -.icon-align-right { - background-position: -312px -48px; -} - -.icon-align-justify { - background-position: -336px -48px; -} - -.icon-list { - background-position: -360px -48px; -} - -.icon-indent-left { - background-position: -384px -48px; -} - -.icon-indent-right { - background-position: -408px -48px; -} - -.icon-facetime-video { - background-position: -432px -48px; -} - -.icon-picture { - background-position: -456px -48px; -} - -.icon-pencil { - background-position: 0 -72px; -} - -.icon-map-marker { - background-position: -24px -72px; -} - -.icon-adjust { - background-position: -48px -72px; -} - -.icon-tint { - background-position: -72px -72px; -} - -.icon-edit { - background-position: -96px -72px; -} - -.icon-share { - background-position: -120px -72px; -} - -.icon-check { - background-position: -144px -72px; -} - -.icon-move { - background-position: -168px -72px; -} - -.icon-step-backward { - background-position: -192px -72px; -} - -.icon-fast-backward { - background-position: -216px -72px; -} - -.icon-backward { - background-position: -240px -72px; -} - -.icon-play { - background-position: -264px -72px; -} - -.icon-pause { - background-position: -288px -72px; -} - -.icon-stop { - background-position: -312px -72px; -} - -.icon-forward { - background-position: -336px -72px; -} - -.icon-fast-forward { - background-position: -360px -72px; -} - -.icon-step-forward { - background-position: -384px -72px; -} - -.icon-eject { - background-position: -408px -72px; -} - -.icon-chevron-left { - background-position: -432px -72px; -} - -.icon-chevron-right { - background-position: -456px -72px; -} - -.icon-plus-sign { - background-position: 0 -96px; -} - -.icon-minus-sign { - background-position: -24px -96px; -} - -.icon-remove-sign { - background-position: -48px -96px; -} - -.icon-ok-sign { - background-position: -72px -96px; -} - -.icon-question-sign { - background-position: -96px -96px; -} - -.icon-info-sign { - background-position: -120px -96px; -} - -.icon-screenshot { - background-position: -144px -96px; -} - -.icon-remove-circle { - background-position: -168px -96px; -} - -.icon-ok-circle { - background-position: -192px -96px; -} - -.icon-ban-circle { - background-position: -216px -96px; -} - -.icon-arrow-left { - background-position: -240px -96px; -} - -.icon-arrow-right { - background-position: -264px -96px; -} - -.icon-arrow-up { - background-position: -289px -96px; -} - -.icon-arrow-down { - background-position: -312px -96px; -} - -.icon-share-alt { - background-position: -336px -96px; -} - -.icon-resize-full { - background-position: -360px -96px; -} - -.icon-resize-small { - background-position: -384px -96px; -} - -.icon-plus { - background-position: -408px -96px; -} - -.icon-minus { - background-position: -433px -96px; -} - -.icon-asterisk { - background-position: -456px -96px; -} - -.icon-exclamation-sign { - background-position: 0 -120px; -} - -.icon-gift { - background-position: -24px -120px; -} - -.icon-leaf { - background-position: -48px -120px; -} - -.icon-fire { - background-position: -72px -120px; -} - -.icon-eye-open { - background-position: -96px -120px; -} - -.icon-eye-close { - background-position: -120px -120px; -} - -.icon-warning-sign { - background-position: -144px -120px; -} - -.icon-plane { - background-position: -168px -120px; -} - -.icon-calendar { - background-position: -192px -120px; -} - -.icon-random { - width: 16px; - background-position: -216px -120px; -} - -.icon-comment { - background-position: -240px -120px; -} - -.icon-magnet { - background-position: -264px -120px; -} - -.icon-chevron-up { - background-position: -288px -120px; -} - -.icon-chevron-down { - background-position: -313px -119px; -} - -.icon-retweet { - background-position: -336px -120px; -} - -.icon-shopping-cart { - background-position: -360px -120px; -} - -.icon-folder-close { - width: 16px; - background-position: -384px -120px; -} - -.icon-folder-open { - width: 16px; - background-position: -408px -120px; -} - -.icon-resize-vertical { - background-position: -432px -119px; -} - -.icon-resize-horizontal { - background-position: -456px -118px; -} - -.icon-hdd { - background-position: 0 -144px; -} - -.icon-bullhorn { - background-position: -24px -144px; -} - -.icon-bell { - background-position: -48px -144px; -} - -.icon-certificate { - background-position: -72px -144px; -} - -.icon-thumbs-up { - background-position: -96px -144px; -} - -.icon-thumbs-down { - background-position: -120px -144px; -} - -.icon-hand-right { - background-position: -144px -144px; -} - -.icon-hand-left { - background-position: -168px -144px; -} - -.icon-hand-up { - background-position: -192px -144px; -} - -.icon-hand-down { - background-position: -216px -144px; -} - -.icon-circle-arrow-right { - background-position: -240px -144px; -} - -.icon-circle-arrow-left { - background-position: -264px -144px; -} - -.icon-circle-arrow-up { - background-position: -288px -144px; -} - -.icon-circle-arrow-down { - background-position: -312px -144px; -} - -.icon-globe { - background-position: -336px -144px; -} - -.icon-wrench { - background-position: -360px -144px; -} - -.icon-tasks { - background-position: -384px -144px; -} - -.icon-filter { - background-position: -408px -144px; -} - -.icon-briefcase { - background-position: -432px -144px; -} - -.icon-fullscreen { - background-position: -456px -144px; -} - -.dropup, -.dropdown { - position: relative; -} - -.dropdown-toggle { - *margin-bottom: -3px; -} - -.dropdown-toggle:active, -.open .dropdown-toggle { - outline: 0; -} - -.caret { - display: inline-block; - width: 0; - height: 0; - vertical-align: top; - border-top: 4px solid #000000; - border-right: 4px solid transparent; - border-left: 4px solid transparent; - content: ""; -} - -.dropdown .caret { - margin-top: 8px; - margin-left: 2px; -} - -.dropdown-menu { - position: absolute; - top: 100%; - left: 0; - z-index: 1000; - display: none; - float: left; - min-width: 160px; - padding: 5px 0; - margin: 2px 0 0; - list-style: none; - background-color: #ffffff; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.2); - *border-right-width: 2px; - *border-bottom-width: 2px; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; - -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -webkit-background-clip: padding-box; - -moz-background-clip: padding; - background-clip: padding-box; -} - -.dropdown-menu.pull-right { - right: 0; - left: auto; -} - -.dropdown-menu .divider { - *width: 100%; - height: 1px; - margin: 9px 1px; - *margin: -5px 0 5px; - overflow: hidden; - background-color: #e5e5e5; - border-bottom: 1px solid #ffffff; -} - -.dropdown-menu > li > a { - display: block; - padding: 3px 20px; - clear: both; - font-weight: normal; - line-height: 20px; - color: #333333; - white-space: nowrap; -} - -.dropdown-menu > li > a:hover, -.dropdown-menu > li > a:focus, -.dropdown-submenu:hover > a, -.dropdown-submenu:focus > a { - color: #ffffff; - text-decoration: none; - background-color: #0081c2; - background-image: -moz-linear-gradient(top, #0088cc, #0077b3); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); - background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); - background-image: -o-linear-gradient(top, #0088cc, #0077b3); - background-image: linear-gradient(to bottom, #0088cc, #0077b3); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); -} - -.dropdown-menu > .active > a, -.dropdown-menu > .active > a:hover, -.dropdown-menu > .active > a:focus { - color: #ffffff; - text-decoration: none; - background-color: #0081c2; - background-image: -moz-linear-gradient(top, #0088cc, #0077b3); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); - background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); - background-image: -o-linear-gradient(top, #0088cc, #0077b3); - background-image: linear-gradient(to bottom, #0088cc, #0077b3); - background-repeat: repeat-x; - outline: 0; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); -} - -.dropdown-menu > .disabled > a, -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - color: #999999; -} - -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - text-decoration: none; - cursor: default; - background-color: transparent; - background-image: none; - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); -} - -.open { - *z-index: 1000; -} - -.open > .dropdown-menu { - display: block; -} - -.dropdown-backdrop { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 990; -} - -.pull-right > .dropdown-menu { - right: 0; - left: auto; -} - -.dropup .caret, -.navbar-fixed-bottom .dropdown .caret { - border-top: 0; - border-bottom: 4px solid #000000; - content: ""; -} - -.dropup .dropdown-menu, -.navbar-fixed-bottom .dropdown .dropdown-menu { - top: auto; - bottom: 100%; - margin-bottom: 1px; -} - -.dropdown-submenu { - position: relative; -} - -.dropdown-submenu > .dropdown-menu { - top: 0; - left: 100%; - margin-top: -6px; - margin-left: -1px; - -webkit-border-radius: 0 6px 6px 6px; - -moz-border-radius: 0 6px 6px 6px; - border-radius: 0 6px 6px 6px; -} - -.dropdown-submenu:hover > .dropdown-menu { - display: block; -} - -.dropup .dropdown-submenu > .dropdown-menu { - top: auto; - bottom: 0; - margin-top: 0; - margin-bottom: -2px; - -webkit-border-radius: 5px 5px 5px 0; - -moz-border-radius: 5px 5px 5px 0; - border-radius: 5px 5px 5px 0; -} - -.dropdown-submenu > a:after { - display: block; - float: right; - width: 0; - height: 0; - margin-top: 5px; - margin-right: -10px; - border-color: transparent; - border-left-color: #cccccc; - border-style: solid; - border-width: 5px 0 5px 5px; - content: " "; -} - -.dropdown-submenu:hover > a:after { - border-left-color: #ffffff; -} - -.dropdown-submenu.pull-left { - float: none; -} - -.dropdown-submenu.pull-left > .dropdown-menu { - left: -100%; - margin-left: 10px; - -webkit-border-radius: 6px 0 6px 6px; - -moz-border-radius: 6px 0 6px 6px; - border-radius: 6px 0 6px 6px; -} - -.dropdown .dropdown-menu .nav-header { - padding-right: 20px; - padding-left: 20px; -} - -.typeahead { - z-index: 1051; - margin-top: 2px; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -.well { - min-height: 20px; - padding: 19px; - margin-bottom: 20px; - background-color: #f5f5f5; - border: 1px solid #e3e3e3; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); -} - -.well blockquote { - border-color: #ddd; - border-color: rgba(0, 0, 0, 0.15); -} - -.well-large { - padding: 24px; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} - -.well-small { - padding: 9px; - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; -} - -.fade { - opacity: 0; - -webkit-transition: opacity 0.15s linear; - -moz-transition: opacity 0.15s linear; - -o-transition: opacity 0.15s linear; - transition: opacity 0.15s linear; -} - -.fade.in { - opacity: 1; -} - -.collapse { - position: relative; - height: 0; - overflow: hidden; - -webkit-transition: height 0.35s ease; - -moz-transition: height 0.35s ease; - -o-transition: height 0.35s ease; - transition: height 0.35s ease; -} - -.collapse.in { - height: auto; -} - -.close { - float: right; - font-size: 20px; - font-weight: bold; - line-height: 20px; - color: #000000; - text-shadow: 0 1px 0 #ffffff; - opacity: 0.2; - filter: alpha(opacity=20); -} - -.close:hover, -.close:focus { - color: #000000; - text-decoration: none; - cursor: pointer; - opacity: 0.4; - filter: alpha(opacity=40); -} - -button.close { - padding: 0; - cursor: pointer; - background: transparent; - border: 0; - -webkit-appearance: none; -} - -.btn { - display: inline-block; - *display: inline; - padding: 4px 12px; - margin-bottom: 0; - *margin-left: .3em; - font-size: 14px; - line-height: 20px; - color: #333333; - text-align: center; - text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75); - vertical-align: middle; - cursor: pointer; - background-color: #f5f5f5; - *background-color: #e6e6e6; - background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6)); - background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6); - background-image: -o-linear-gradient(top, #ffffff, #e6e6e6); - background-image: linear-gradient(to bottom, #ffffff, #e6e6e6); - background-repeat: repeat-x; - border: 1px solid #cccccc; - *border: 0; - border-color: #e6e6e6 #e6e6e6 #bfbfbf; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - border-bottom-color: #b3b3b3; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); - *zoom: 1; - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn:hover, -.btn:focus, -.btn:active, -.btn.active, -.btn.disabled, -.btn[disabled] { - color: #333333; - background-color: #e6e6e6; - *background-color: #d9d9d9; -} - -.btn:active, -.btn.active { - background-color: #cccccc \9; -} - -.btn:first-child { - *margin-left: 0; -} - -.btn:hover, -.btn:focus { - color: #333333; - text-decoration: none; - background-position: 0 -15px; - -webkit-transition: background-position 0.1s linear; - -moz-transition: background-position 0.1s linear; - -o-transition: background-position 0.1s linear; - transition: background-position 0.1s linear; -} - -.btn:focus { - outline: thin dotted #333; - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} - -.btn.active, -.btn:active { - background-image: none; - outline: 0; - -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn.disabled, -.btn[disabled] { - cursor: default; - background-image: none; - opacity: 0.65; - filter: alpha(opacity=65); - -webkit-box-shadow: none; - -moz-box-shadow: none; - box-shadow: none; -} - -.btn-large { - padding: 11px 19px; - font-size: 17.5px; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} - -.btn-large [class^="icon-"], -.btn-large [class*=" icon-"] { - margin-top: 4px; -} - -.btn-small { - padding: 2px 10px; - font-size: 11.9px; - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; -} - -.btn-small [class^="icon-"], -.btn-small [class*=" icon-"] { - margin-top: 0; -} - -.btn-mini [class^="icon-"], -.btn-mini [class*=" icon-"] { - margin-top: -1px; -} - -.btn-mini { - padding: 0 6px; - font-size: 10.5px; - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; -} - -.btn-block { - display: block; - width: 100%; - padding-right: 0; - padding-left: 0; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; -} - -.btn-block + .btn-block { - margin-top: 5px; -} - -input[type="submit"].btn-block, -input[type="reset"].btn-block, -input[type="button"].btn-block { - width: 100%; -} - -.btn-primary.active, -.btn-warning.active, -.btn-danger.active, -.btn-success.active, -.btn-info.active, -.btn-inverse.active { - color: rgba(255, 255, 255, 0.75); -} - -.btn-primary { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #006dcc; - *background-color: #0044cc; - background-image: -moz-linear-gradient(top, #0088cc, #0044cc); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0044cc)); - background-image: -webkit-linear-gradient(top, #0088cc, #0044cc); - background-image: -o-linear-gradient(top, #0088cc, #0044cc); - background-image: linear-gradient(to bottom, #0088cc, #0044cc); - background-repeat: repeat-x; - border-color: #0044cc #0044cc #002a80; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); -} - -.btn-primary:hover, -.btn-primary:focus, -.btn-primary:active, -.btn-primary.active, -.btn-primary.disabled, -.btn-primary[disabled] { - color: #ffffff; - background-color: #0044cc; - *background-color: #003bb3; -} - -.btn-primary:active, -.btn-primary.active { - background-color: #003399 \9; -} - -.btn-warning { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #faa732; - *background-color: #f89406; - background-image: -moz-linear-gradient(top, #fbb450, #f89406); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); - background-image: -webkit-linear-gradient(top, #fbb450, #f89406); - background-image: -o-linear-gradient(top, #fbb450, #f89406); - background-image: linear-gradient(to bottom, #fbb450, #f89406); - background-repeat: repeat-x; - border-color: #f89406 #f89406 #ad6704; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); -} - -.btn-warning:hover, -.btn-warning:focus, -.btn-warning:active, -.btn-warning.active, -.btn-warning.disabled, -.btn-warning[disabled] { - color: #ffffff; - background-color: #f89406; - *background-color: #df8505; -} - -.btn-warning:active, -.btn-warning.active { - background-color: #c67605 \9; -} - -.btn-danger { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #da4f49; - *background-color: #bd362f; - background-image: -moz-linear-gradient(top, #ee5f5b, #bd362f); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f)); - background-image: -webkit-linear-gradient(top, #ee5f5b, #bd362f); - background-image: -o-linear-gradient(top, #ee5f5b, #bd362f); - background-image: linear-gradient(to bottom, #ee5f5b, #bd362f); - background-repeat: repeat-x; - border-color: #bd362f #bd362f #802420; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); -} - -.btn-danger:hover, -.btn-danger:focus, -.btn-danger:active, -.btn-danger.active, -.btn-danger.disabled, -.btn-danger[disabled] { - color: #ffffff; - background-color: #bd362f; - *background-color: #a9302a; -} - -.btn-danger:active, -.btn-danger.active { - background-color: #942a25 \9; -} - -.btn-success { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #5bb75b; - *background-color: #51a351; - background-image: -moz-linear-gradient(top, #62c462, #51a351); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351)); - background-image: -webkit-linear-gradient(top, #62c462, #51a351); - background-image: -o-linear-gradient(top, #62c462, #51a351); - background-image: linear-gradient(to bottom, #62c462, #51a351); - background-repeat: repeat-x; - border-color: #51a351 #51a351 #387038; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); -} - -.btn-success:hover, -.btn-success:focus, -.btn-success:active, -.btn-success.active, -.btn-success.disabled, -.btn-success[disabled] { - color: #ffffff; - background-color: #51a351; - *background-color: #499249; -} - -.btn-success:active, -.btn-success.active { - background-color: #408140 \9; -} - -.btn-info { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #49afcd; - *background-color: #2f96b4; - background-image: -moz-linear-gradient(top, #5bc0de, #2f96b4); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4)); - background-image: -webkit-linear-gradient(top, #5bc0de, #2f96b4); - background-image: -o-linear-gradient(top, #5bc0de, #2f96b4); - background-image: linear-gradient(to bottom, #5bc0de, #2f96b4); - background-repeat: repeat-x; - border-color: #2f96b4 #2f96b4 #1f6377; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); -} - -.btn-info:hover, -.btn-info:focus, -.btn-info:active, -.btn-info.active, -.btn-info.disabled, -.btn-info[disabled] { - color: #ffffff; - background-color: #2f96b4; - *background-color: #2a85a0; -} - -.btn-info:active, -.btn-info.active { - background-color: #24748c \9; -} - -.btn-inverse { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #363636; - *background-color: #222222; - background-image: -moz-linear-gradient(top, #444444, #222222); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#444444), to(#222222)); - background-image: -webkit-linear-gradient(top, #444444, #222222); - background-image: -o-linear-gradient(top, #444444, #222222); - background-image: linear-gradient(to bottom, #444444, #222222); - background-repeat: repeat-x; - border-color: #222222 #222222 #000000; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); -} - -.btn-inverse:hover, -.btn-inverse:focus, -.btn-inverse:active, -.btn-inverse.active, -.btn-inverse.disabled, -.btn-inverse[disabled] { - color: #ffffff; - background-color: #222222; - *background-color: #151515; -} - -.btn-inverse:active, -.btn-inverse.active { - background-color: #080808 \9; -} - -button.btn, -input[type="submit"].btn { - *padding-top: 3px; - *padding-bottom: 3px; -} - -button.btn::-moz-focus-inner, -input[type="submit"].btn::-moz-focus-inner { - padding: 0; - border: 0; -} - -button.btn.btn-large, -input[type="submit"].btn.btn-large { - *padding-top: 7px; - *padding-bottom: 7px; -} - -button.btn.btn-small, -input[type="submit"].btn.btn-small { - *padding-top: 3px; - *padding-bottom: 3px; -} - -button.btn.btn-mini, -input[type="submit"].btn.btn-mini { - *padding-top: 1px; - *padding-bottom: 1px; -} - -.btn-link, -.btn-link:active, -.btn-link[disabled] { - background-color: transparent; - background-image: none; - -webkit-box-shadow: none; - -moz-box-shadow: none; - box-shadow: none; -} - -.btn-link { - color: #0088cc; - cursor: pointer; - border-color: transparent; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.btn-link:hover, -.btn-link:focus { - color: #005580; - text-decoration: underline; - background-color: transparent; -} - -.btn-link[disabled]:hover, -.btn-link[disabled]:focus { - color: #333333; - text-decoration: none; -} - -.btn-group { - position: relative; - display: inline-block; - *display: inline; - *margin-left: .3em; - font-size: 0; - white-space: nowrap; - vertical-align: middle; - *zoom: 1; -} - -.btn-group:first-child { - *margin-left: 0; -} - -.btn-group + .btn-group { - margin-left: 5px; -} - -.btn-toolbar { - margin-top: 10px; - margin-bottom: 10px; - font-size: 0; -} - -.btn-toolbar > .btn + .btn, -.btn-toolbar > .btn-group + .btn, -.btn-toolbar > .btn + .btn-group { - margin-left: 5px; -} - -.btn-group > .btn { - position: relative; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.btn-group > .btn + .btn { - margin-left: -1px; -} - -.btn-group > .btn, -.btn-group > .dropdown-menu, -.btn-group > .popover { - font-size: 14px; -} - -.btn-group > .btn-mini { - font-size: 10.5px; -} - -.btn-group > .btn-small { - font-size: 11.9px; -} - -.btn-group > .btn-large { - font-size: 17.5px; -} - -.btn-group > .btn:first-child { - margin-left: 0; - -webkit-border-bottom-left-radius: 4px; - border-bottom-left-radius: 4px; - -webkit-border-top-left-radius: 4px; - border-top-left-radius: 4px; - -moz-border-radius-bottomleft: 4px; - -moz-border-radius-topleft: 4px; -} - -.btn-group > .btn:last-child, -.btn-group > .dropdown-toggle { - -webkit-border-top-right-radius: 4px; - border-top-right-radius: 4px; - -webkit-border-bottom-right-radius: 4px; - border-bottom-right-radius: 4px; - -moz-border-radius-topright: 4px; - -moz-border-radius-bottomright: 4px; -} - -.btn-group > .btn.large:first-child { - margin-left: 0; - -webkit-border-bottom-left-radius: 6px; - border-bottom-left-radius: 6px; - -webkit-border-top-left-radius: 6px; - border-top-left-radius: 6px; - -moz-border-radius-bottomleft: 6px; - -moz-border-radius-topleft: 6px; -} - -.btn-group > .btn.large:last-child, -.btn-group > .large.dropdown-toggle { - -webkit-border-top-right-radius: 6px; - border-top-right-radius: 6px; - -webkit-border-bottom-right-radius: 6px; - border-bottom-right-radius: 6px; - -moz-border-radius-topright: 6px; - -moz-border-radius-bottomright: 6px; -} - -.btn-group > .btn:hover, -.btn-group > .btn:focus, -.btn-group > .btn:active, -.btn-group > .btn.active { - z-index: 2; -} - -.btn-group .dropdown-toggle:active, -.btn-group.open .dropdown-toggle { - outline: 0; -} - -.btn-group > .btn + .dropdown-toggle { - *padding-top: 5px; - padding-right: 8px; - *padding-bottom: 5px; - padding-left: 8px; - -webkit-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn-group > .btn-mini + .dropdown-toggle { - *padding-top: 2px; - padding-right: 5px; - *padding-bottom: 2px; - padding-left: 5px; -} - -.btn-group > .btn-small + .dropdown-toggle { - *padding-top: 5px; - *padding-bottom: 4px; -} - -.btn-group > .btn-large + .dropdown-toggle { - *padding-top: 7px; - padding-right: 12px; - *padding-bottom: 7px; - padding-left: 12px; -} - -.btn-group.open .dropdown-toggle { - background-image: none; - -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn-group.open .btn.dropdown-toggle { - background-color: #e6e6e6; -} - -.btn-group.open .btn-primary.dropdown-toggle { - background-color: #0044cc; -} - -.btn-group.open .btn-warning.dropdown-toggle { - background-color: #f89406; -} - -.btn-group.open .btn-danger.dropdown-toggle { - background-color: #bd362f; -} - -.btn-group.open .btn-success.dropdown-toggle { - background-color: #51a351; -} - -.btn-group.open .btn-info.dropdown-toggle { - background-color: #2f96b4; -} - -.btn-group.open .btn-inverse.dropdown-toggle { - background-color: #222222; -} - -.btn .caret { - margin-top: 8px; - margin-left: 0; -} - -.btn-large .caret { - margin-top: 6px; -} - -.btn-large .caret { - border-top-width: 5px; - border-right-width: 5px; - border-left-width: 5px; -} - -.btn-mini .caret, -.btn-small .caret { - margin-top: 8px; -} - -.dropup .btn-large .caret { - border-bottom-width: 5px; -} - -.btn-primary .caret, -.btn-warning .caret, -.btn-danger .caret, -.btn-info .caret, -.btn-success .caret, -.btn-inverse .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; -} - -.btn-group-vertical { - display: inline-block; - *display: inline; - /* IE7 inline-block hack */ - - *zoom: 1; -} - -.btn-group-vertical > .btn { - display: block; - float: none; - max-width: 100%; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.btn-group-vertical > .btn + .btn { - margin-top: -1px; - margin-left: 0; -} - -.btn-group-vertical > .btn:first-child { - -webkit-border-radius: 4px 4px 0 0; - -moz-border-radius: 4px 4px 0 0; - border-radius: 4px 4px 0 0; -} - -.btn-group-vertical > .btn:last-child { - -webkit-border-radius: 0 0 4px 4px; - -moz-border-radius: 0 0 4px 4px; - border-radius: 0 0 4px 4px; -} - -.btn-group-vertical > .btn-large:first-child { - -webkit-border-radius: 6px 6px 0 0; - -moz-border-radius: 6px 6px 0 0; - border-radius: 6px 6px 0 0; -} - -.btn-group-vertical > .btn-large:last-child { - -webkit-border-radius: 0 0 6px 6px; - -moz-border-radius: 0 0 6px 6px; - border-radius: 0 0 6px 6px; -} - -.alert { - padding: 8px 35px 8px 14px; - margin-bottom: 20px; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); - background-color: #fcf8e3; - border: 1px solid #fbeed5; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -.alert, -.alert h4 { - color: #c09853; -} - -.alert h4 { - margin: 0; -} - -.alert .close { - position: relative; - top: -2px; - right: -21px; - line-height: 20px; -} - -.alert-success { - color: #468847; - background-color: #dff0d8; - border-color: #d6e9c6; -} - -.alert-success h4 { - color: #468847; -} - -.alert-danger, -.alert-error { - color: #b94a48; - background-color: #f2dede; - border-color: #eed3d7; -} - -.alert-danger h4, -.alert-error h4 { - color: #b94a48; -} - -.alert-info { - color: #3a87ad; - background-color: #d9edf7; - border-color: #bce8f1; -} - -.alert-info h4 { - color: #3a87ad; -} - -.alert-block { - padding-top: 14px; - padding-bottom: 14px; -} - -.alert-block > p, -.alert-block > ul { - margin-bottom: 0; -} - -.alert-block p + p { - margin-top: 5px; -} - -.nav { - margin-bottom: 20px; - margin-left: 0; - list-style: none; -} - -.nav > li > a { - display: block; -} - -.nav > li > a:hover, -.nav > li > a:focus { - text-decoration: none; - background-color: #eeeeee; -} - -.nav > li > a > img { - max-width: none; -} - -.nav > .pull-right { - float: right; -} - -.nav-header { - display: block; - padding: 3px 15px; - font-size: 11px; - font-weight: bold; - line-height: 20px; - color: #999999; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); - text-transform: uppercase; -} - -.nav li + .nav-header { - margin-top: 9px; -} - -.nav-list { - padding-right: 15px; - padding-left: 15px; - margin-bottom: 0; -} - -.nav-list > li > a, -.nav-list .nav-header { - margin-right: -15px; - margin-left: -15px; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); -} - -.nav-list > li > a { - padding: 3px 15px; -} - -.nav-list > .active > a, -.nav-list > .active > a:hover, -.nav-list > .active > a:focus { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2); - background-color: #0088cc; -} - -.nav-list [class^="icon-"], -.nav-list [class*=" icon-"] { - margin-right: 2px; -} - -.nav-list .divider { - *width: 100%; - height: 1px; - margin: 9px 1px; - *margin: -5px 0 5px; - overflow: hidden; - background-color: #e5e5e5; - border-bottom: 1px solid #ffffff; -} - -.nav-tabs, -.nav-pills { - *zoom: 1; -} - -.nav-tabs:before, -.nav-pills:before, -.nav-tabs:after, -.nav-pills:after { - display: table; - line-height: 0; - content: ""; -} - -.nav-tabs:after, -.nav-pills:after { - clear: both; -} - -.nav-tabs > li, -.nav-pills > li { - float: left; -} - -.nav-tabs > li > a, -.nav-pills > li > a { - padding-right: 12px; - padding-left: 12px; - margin-right: 2px; - line-height: 14px; -} - -.nav-tabs { - border-bottom: 1px solid #ddd; -} - -.nav-tabs > li { - margin-bottom: -1px; -} - -.nav-tabs > li > a { - padding-top: 8px; - padding-bottom: 8px; - line-height: 20px; - border: 1px solid transparent; - -webkit-border-radius: 4px 4px 0 0; - -moz-border-radius: 4px 4px 0 0; - border-radius: 4px 4px 0 0; -} - -.nav-tabs > li > a:hover, -.nav-tabs > li > a:focus { - border-color: #eeeeee #eeeeee #dddddd; -} - -.nav-tabs > .active > a, -.nav-tabs > .active > a:hover, -.nav-tabs > .active > a:focus { - color: #555555; - cursor: default; - background-color: #ffffff; - border: 1px solid #ddd; - border-bottom-color: transparent; -} - -.nav-pills > li > a { - padding-top: 8px; - padding-bottom: 8px; - margin-top: 2px; - margin-bottom: 2px; - -webkit-border-radius: 5px; - -moz-border-radius: 5px; - border-radius: 5px; -} - -.nav-pills > .active > a, -.nav-pills > .active > a:hover, -.nav-pills > .active > a:focus { - color: #ffffff; - background-color: #0088cc; -} - -.nav-stacked > li { - float: none; -} - -.nav-stacked > li > a { - margin-right: 0; -} - -.nav-tabs.nav-stacked { - border-bottom: 0; -} - -.nav-tabs.nav-stacked > li > a { - border: 1px solid #ddd; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.nav-tabs.nav-stacked > li:first-child > a { - -webkit-border-top-right-radius: 4px; - border-top-right-radius: 4px; - -webkit-border-top-left-radius: 4px; - border-top-left-radius: 4px; - -moz-border-radius-topright: 4px; - -moz-border-radius-topleft: 4px; -} - -.nav-tabs.nav-stacked > li:last-child > a { - -webkit-border-bottom-right-radius: 4px; - border-bottom-right-radius: 4px; - -webkit-border-bottom-left-radius: 4px; - border-bottom-left-radius: 4px; - -moz-border-radius-bottomright: 4px; - -moz-border-radius-bottomleft: 4px; -} - -.nav-tabs.nav-stacked > li > a:hover, -.nav-tabs.nav-stacked > li > a:focus { - z-index: 2; - border-color: #ddd; -} - -.nav-pills.nav-stacked > li > a { - margin-bottom: 3px; -} - -.nav-pills.nav-stacked > li:last-child > a { - margin-bottom: 1px; -} - -.nav-tabs .dropdown-menu { - -webkit-border-radius: 0 0 6px 6px; - -moz-border-radius: 0 0 6px 6px; - border-radius: 0 0 6px 6px; -} - -.nav-pills .dropdown-menu { - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} - -.nav .dropdown-toggle .caret { - margin-top: 6px; - border-top-color: #0088cc; - border-bottom-color: #0088cc; -} - -.nav .dropdown-toggle:hover .caret, -.nav .dropdown-toggle:focus .caret { - border-top-color: #005580; - border-bottom-color: #005580; -} - -/* move down carets for tabs */ - -.nav-tabs .dropdown-toggle .caret { - margin-top: 8px; -} - -.nav .active .dropdown-toggle .caret { - border-top-color: #fff; - border-bottom-color: #fff; -} - -.nav-tabs .active .dropdown-toggle .caret { - border-top-color: #555555; - border-bottom-color: #555555; -} - -.nav > .dropdown.active > a:hover, -.nav > .dropdown.active > a:focus { - cursor: pointer; -} - -.nav-tabs .open .dropdown-toggle, -.nav-pills .open .dropdown-toggle, -.nav > li.dropdown.open.active > a:hover, -.nav > li.dropdown.open.active > a:focus { - color: #ffffff; - background-color: #999999; - border-color: #999999; -} - -.nav li.dropdown.open .caret, -.nav li.dropdown.open.active .caret, -.nav li.dropdown.open a:hover .caret, -.nav li.dropdown.open a:focus .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; - opacity: 1; - filter: alpha(opacity=100); -} - -.tabs-stacked .open > a:hover, -.tabs-stacked .open > a:focus { - border-color: #999999; -} - -.tabbable { - *zoom: 1; -} - -.tabbable:before, -.tabbable:after { - display: table; - line-height: 0; - content: ""; -} - -.tabbable:after { - clear: both; -} - -.tab-content { - overflow: auto; -} - -.tabs-below > .nav-tabs, -.tabs-right > .nav-tabs, -.tabs-left > .nav-tabs { - border-bottom: 0; -} - -.tab-content > .tab-pane, -.pill-content > .pill-pane { - display: none; -} - -.tab-content > .active, -.pill-content > .active { - display: block; -} - -.tabs-below > .nav-tabs { - border-top: 1px solid #ddd; -} - -.tabs-below > .nav-tabs > li { - margin-top: -1px; - margin-bottom: 0; -} - -.tabs-below > .nav-tabs > li > a { - -webkit-border-radius: 0 0 4px 4px; - -moz-border-radius: 0 0 4px 4px; - border-radius: 0 0 4px 4px; -} - -.tabs-below > .nav-tabs > li > a:hover, -.tabs-below > .nav-tabs > li > a:focus { - border-top-color: #ddd; - border-bottom-color: transparent; -} - -.tabs-below > .nav-tabs > .active > a, -.tabs-below > .nav-tabs > .active > a:hover, -.tabs-below > .nav-tabs > .active > a:focus { - border-color: transparent #ddd #ddd #ddd; -} - -.tabs-left > .nav-tabs > li, -.tabs-right > .nav-tabs > li { - float: none; -} - -.tabs-left > .nav-tabs > li > a, -.tabs-right > .nav-tabs > li > a { - min-width: 74px; - margin-right: 0; - margin-bottom: 3px; -} - -.tabs-left > .nav-tabs { - float: left; - margin-right: 19px; - border-right: 1px solid #ddd; -} - -.tabs-left > .nav-tabs > li > a { - margin-right: -1px; - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} - -.tabs-left > .nav-tabs > li > a:hover, -.tabs-left > .nav-tabs > li > a:focus { - border-color: #eeeeee #dddddd #eeeeee #eeeeee; -} - -.tabs-left > .nav-tabs .active > a, -.tabs-left > .nav-tabs .active > a:hover, -.tabs-left > .nav-tabs .active > a:focus { - border-color: #ddd transparent #ddd #ddd; - *border-right-color: #ffffff; -} - -.tabs-right > .nav-tabs { - float: right; - margin-left: 19px; - border-left: 1px solid #ddd; -} - -.tabs-right > .nav-tabs > li > a { - margin-left: -1px; - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} - -.tabs-right > .nav-tabs > li > a:hover, -.tabs-right > .nav-tabs > li > a:focus { - border-color: #eeeeee #eeeeee #eeeeee #dddddd; -} - -.tabs-right > .nav-tabs .active > a, -.tabs-right > .nav-tabs .active > a:hover, -.tabs-right > .nav-tabs .active > a:focus { - border-color: #ddd #ddd #ddd transparent; - *border-left-color: #ffffff; -} - -.nav > .disabled > a { - color: #999999; -} - -.nav > .disabled > a:hover, -.nav > .disabled > a:focus { - text-decoration: none; - cursor: default; - background-color: transparent; -} - -.navbar { - *position: relative; - *z-index: 2; - margin-bottom: 20px; - overflow: visible; -} - -.navbar-inner { - min-height: 40px; - padding-right: 20px; - padding-left: 20px; - background-color: #fafafa; - background-image: -moz-linear-gradient(top, #ffffff, #f2f2f2); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f2f2f2)); - background-image: -webkit-linear-gradient(top, #ffffff, #f2f2f2); - background-image: -o-linear-gradient(top, #ffffff, #f2f2f2); - background-image: linear-gradient(to bottom, #ffffff, #f2f2f2); - background-repeat: repeat-x; - border: 1px solid #d4d4d4; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0); - *zoom: 1; - -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); - -moz-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); - box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); -} - -.navbar-inner:before, -.navbar-inner:after { - display: table; - line-height: 0; - content: ""; -} - -.navbar-inner:after { - clear: both; -} - -.navbar .container { - width: auto; -} - -.nav-collapse.collapse { - height: auto; - overflow: visible; -} - -.navbar .brand { - display: block; - float: left; - padding: 10px 20px 10px; - margin-left: -20px; - font-size: 20px; - font-weight: 200; - color: #777777; - text-shadow: 0 1px 0 #ffffff; -} - -.navbar .brand:hover, -.navbar .brand:focus { - text-decoration: none; -} - -.navbar-text { - margin-bottom: 0; - line-height: 40px; - color: #777777; -} - -.navbar-link { - color: #777777; -} - -.navbar-link:hover, -.navbar-link:focus { - color: #333333; -} - -.navbar .divider-vertical { - height: 40px; - margin: 0 9px; - border-right: 1px solid #ffffff; - border-left: 1px solid #f2f2f2; -} - -.navbar .btn, -.navbar .btn-group { - margin-top: 5px; -} - -.navbar .btn-group .btn, -.navbar .input-prepend .btn, -.navbar .input-append .btn, -.navbar .input-prepend .btn-group, -.navbar .input-append .btn-group { - margin-top: 0; -} - -.navbar-form { - margin-bottom: 0; - *zoom: 1; -} - -.navbar-form:before, -.navbar-form:after { - display: table; - line-height: 0; - content: ""; -} - -.navbar-form:after { - clear: both; -} - -.navbar-form input, -.navbar-form select, -.navbar-form .radio, -.navbar-form .checkbox { - margin-top: 5px; -} - -.navbar-form input, -.navbar-form select, -.navbar-form .btn { - display: inline-block; - margin-bottom: 0; -} - -.navbar-form input[type="image"], -.navbar-form input[type="checkbox"], -.navbar-form input[type="radio"] { - margin-top: 3px; -} - -.navbar-form .input-append, -.navbar-form .input-prepend { - margin-top: 5px; - white-space: nowrap; -} - -.navbar-form .input-append input, -.navbar-form .input-prepend input { - margin-top: 0; -} - -.navbar-search { - position: relative; - float: left; - margin-top: 5px; - margin-bottom: 0; -} - -.navbar-search .search-query { - padding: 4px 14px; - margin-bottom: 0; - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; - font-size: 13px; - font-weight: normal; - line-height: 1; - -webkit-border-radius: 15px; - -moz-border-radius: 15px; - border-radius: 15px; -} - -.navbar-static-top { - position: static; - margin-bottom: 0; -} - -.navbar-static-top .navbar-inner { - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.navbar-fixed-top, -.navbar-fixed-bottom { - position: fixed; - right: 0; - left: 0; - z-index: 1030; - margin-bottom: 0; -} - -.navbar-fixed-top .navbar-inner, -.navbar-static-top .navbar-inner { - border-width: 0 0 1px; -} - -.navbar-fixed-bottom .navbar-inner { - border-width: 1px 0 0; -} - -.navbar-fixed-top .navbar-inner, -.navbar-fixed-bottom .navbar-inner { - padding-right: 0; - padding-left: 0; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.navbar-static-top .container, -.navbar-fixed-top .container, -.navbar-fixed-bottom .container { - width: 940px; -} - -.navbar-fixed-top { - top: 0; -} - -.navbar-fixed-top .navbar-inner, -.navbar-static-top .navbar-inner { - -webkit-box-shadow: 0 1px 10px rgba(0, 0, 0, 0.1); - -moz-box-shadow: 0 1px 10px rgba(0, 0, 0, 0.1); - box-shadow: 0 1px 10px rgba(0, 0, 0, 0.1); -} - -.navbar-fixed-bottom { - bottom: 0; -} - -.navbar-fixed-bottom .navbar-inner { - -webkit-box-shadow: 0 -1px 10px rgba(0, 0, 0, 0.1); - -moz-box-shadow: 0 -1px 10px rgba(0, 0, 0, 0.1); - box-shadow: 0 -1px 10px rgba(0, 0, 0, 0.1); -} - -.navbar .nav { - position: relative; - left: 0; - display: block; - float: left; - margin: 0 10px 0 0; -} - -.navbar .nav.pull-right { - float: right; - margin-right: 0; -} - -.navbar .nav > li { - float: left; -} - -.navbar .nav > li > a { - float: none; - padding: 10px 15px 10px; - color: #777777; - text-decoration: none; - text-shadow: 0 1px 0 #ffffff; -} - -.navbar .nav .dropdown-toggle .caret { - margin-top: 8px; -} - -.navbar .nav > li > a:focus, -.navbar .nav > li > a:hover { - color: #333333; - text-decoration: none; - background-color: transparent; -} - -.navbar .nav > .active > a, -.navbar .nav > .active > a:hover, -.navbar .nav > .active > a:focus { - color: #555555; - text-decoration: none; - background-color: #e5e5e5; - -webkit-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); - -moz-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); - box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); -} - -.navbar .btn-navbar { - display: none; - float: right; - padding: 7px 10px; - margin-right: 5px; - margin-left: 5px; - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #ededed; - *background-color: #e5e5e5; - background-image: -moz-linear-gradient(top, #f2f2f2, #e5e5e5); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5)); - background-image: -webkit-linear-gradient(top, #f2f2f2, #e5e5e5); - background-image: -o-linear-gradient(top, #f2f2f2, #e5e5e5); - background-image: linear-gradient(to bottom, #f2f2f2, #e5e5e5); - background-repeat: repeat-x; - border-color: #e5e5e5 #e5e5e5 #bfbfbf; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2', endColorstr='#ffe5e5e5', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); - -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); -} - -.navbar .btn-navbar:hover, -.navbar .btn-navbar:focus, -.navbar .btn-navbar:active, -.navbar .btn-navbar.active, -.navbar .btn-navbar.disabled, -.navbar .btn-navbar[disabled] { - color: #ffffff; - background-color: #e5e5e5; - *background-color: #d9d9d9; -} - -.navbar .btn-navbar:active, -.navbar .btn-navbar.active { - background-color: #cccccc \9; -} - -.navbar .btn-navbar .icon-bar { - display: block; - width: 18px; - height: 2px; - background-color: #f5f5f5; - -webkit-border-radius: 1px; - -moz-border-radius: 1px; - border-radius: 1px; - -webkit-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); - -moz-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); - box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); -} - -.btn-navbar .icon-bar + .icon-bar { - margin-top: 3px; -} - -.navbar .nav > li > .dropdown-menu:before { - position: absolute; - top: -7px; - left: 9px; - display: inline-block; - border-right: 7px solid transparent; - border-bottom: 7px solid #ccc; - border-left: 7px solid transparent; - border-bottom-color: rgba(0, 0, 0, 0.2); - content: ''; -} - -.navbar .nav > li > .dropdown-menu:after { - position: absolute; - top: -6px; - left: 10px; - display: inline-block; - border-right: 6px solid transparent; - border-bottom: 6px solid #ffffff; - border-left: 6px solid transparent; - content: ''; -} - -.navbar-fixed-bottom .nav > li > .dropdown-menu:before { - top: auto; - bottom: -7px; - border-top: 7px solid #ccc; - border-bottom: 0; - border-top-color: rgba(0, 0, 0, 0.2); -} - -.navbar-fixed-bottom .nav > li > .dropdown-menu:after { - top: auto; - bottom: -6px; - border-top: 6px solid #ffffff; - border-bottom: 0; -} - -.navbar .nav li.dropdown > a:hover .caret, -.navbar .nav li.dropdown > a:focus .caret { - border-top-color: #333333; - border-bottom-color: #333333; -} - -.navbar .nav li.dropdown.open > .dropdown-toggle, -.navbar .nav li.dropdown.active > .dropdown-toggle, -.navbar .nav li.dropdown.open.active > .dropdown-toggle { - color: #555555; - background-color: #e5e5e5; -} - -.navbar .nav li.dropdown > .dropdown-toggle .caret { - border-top-color: #777777; - border-bottom-color: #777777; -} - -.navbar .nav li.dropdown.open > .dropdown-toggle .caret, -.navbar .nav li.dropdown.active > .dropdown-toggle .caret, -.navbar .nav li.dropdown.open.active > .dropdown-toggle .caret { - border-top-color: #555555; - border-bottom-color: #555555; -} - -.navbar .pull-right > li > .dropdown-menu, -.navbar .nav > li > .dropdown-menu.pull-right { - right: 0; - left: auto; -} - -.navbar .pull-right > li > .dropdown-menu:before, -.navbar .nav > li > .dropdown-menu.pull-right:before { - right: 12px; - left: auto; -} - -.navbar .pull-right > li > .dropdown-menu:after, -.navbar .nav > li > .dropdown-menu.pull-right:after { - right: 13px; - left: auto; -} - -.navbar .pull-right > li > .dropdown-menu .dropdown-menu, -.navbar .nav > li > .dropdown-menu.pull-right .dropdown-menu { - right: 100%; - left: auto; - margin-right: -1px; - margin-left: 0; - -webkit-border-radius: 6px 0 6px 6px; - -moz-border-radius: 6px 0 6px 6px; - border-radius: 6px 0 6px 6px; -} - -.navbar-inverse .navbar-inner { - background-color: #1b1b1b; - background-image: -moz-linear-gradient(top, #222222, #111111); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#222222), to(#111111)); - background-image: -webkit-linear-gradient(top, #222222, #111111); - background-image: -o-linear-gradient(top, #222222, #111111); - background-image: linear-gradient(to bottom, #222222, #111111); - background-repeat: repeat-x; - border-color: #252525; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222', endColorstr='#ff111111', GradientType=0); -} - -.navbar-inverse .brand, -.navbar-inverse .nav > li > a { - color: #999999; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); -} - -.navbar-inverse .brand:hover, -.navbar-inverse .nav > li > a:hover, -.navbar-inverse .brand:focus, -.navbar-inverse .nav > li > a:focus { - color: #ffffff; -} - -.navbar-inverse .brand { - color: #999999; -} - -.navbar-inverse .navbar-text { - color: #999999; -} - -.navbar-inverse .nav > li > a:focus, -.navbar-inverse .nav > li > a:hover { - color: #ffffff; - background-color: transparent; -} - -.navbar-inverse .nav .active > a, -.navbar-inverse .nav .active > a:hover, -.navbar-inverse .nav .active > a:focus { - color: #ffffff; - background-color: #111111; -} - -.navbar-inverse .navbar-link { - color: #999999; -} - -.navbar-inverse .navbar-link:hover, -.navbar-inverse .navbar-link:focus { - color: #ffffff; -} - -.navbar-inverse .divider-vertical { - border-right-color: #222222; - border-left-color: #111111; -} - -.navbar-inverse .nav li.dropdown.open > .dropdown-toggle, -.navbar-inverse .nav li.dropdown.active > .dropdown-toggle, -.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle { - color: #ffffff; - background-color: #111111; -} - -.navbar-inverse .nav li.dropdown > a:hover .caret, -.navbar-inverse .nav li.dropdown > a:focus .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; -} - -.navbar-inverse .nav li.dropdown > .dropdown-toggle .caret { - border-top-color: #999999; - border-bottom-color: #999999; -} - -.navbar-inverse .nav li.dropdown.open > .dropdown-toggle .caret, -.navbar-inverse .nav li.dropdown.active > .dropdown-toggle .caret, -.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; -} - -.navbar-inverse .navbar-search .search-query { - color: #ffffff; - background-color: #515151; - border-color: #111111; - -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); - -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); - box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); - -webkit-transition: none; - -moz-transition: none; - -o-transition: none; - transition: none; -} - -.navbar-inverse .navbar-search .search-query:-moz-placeholder { - color: #cccccc; -} - -.navbar-inverse .navbar-search .search-query:-ms-input-placeholder { - color: #cccccc; -} - -.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder { - color: #cccccc; -} - -.navbar-inverse .navbar-search .search-query:focus, -.navbar-inverse .navbar-search .search-query.focused { - padding: 5px 15px; - color: #333333; - text-shadow: 0 1px 0 #ffffff; - background-color: #ffffff; - border: 0; - outline: 0; - -webkit-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); - -moz-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); - box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); -} - -.navbar-inverse .btn-navbar { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #0e0e0e; - *background-color: #040404; - background-image: -moz-linear-gradient(top, #151515, #040404); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404)); - background-image: -webkit-linear-gradient(top, #151515, #040404); - background-image: -o-linear-gradient(top, #151515, #040404); - background-image: linear-gradient(to bottom, #151515, #040404); - background-repeat: repeat-x; - border-color: #040404 #040404 #000000; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); -} - -.navbar-inverse .btn-navbar:hover, -.navbar-inverse .btn-navbar:focus, -.navbar-inverse .btn-navbar:active, -.navbar-inverse .btn-navbar.active, -.navbar-inverse .btn-navbar.disabled, -.navbar-inverse .btn-navbar[disabled] { - color: #ffffff; - background-color: #040404; - *background-color: #000000; -} - -.navbar-inverse .btn-navbar:active, -.navbar-inverse .btn-navbar.active { - background-color: #000000 \9; -} - -.breadcrumb { - padding: 8px 15px; - margin: 0 0 20px; - list-style: none; - background-color: #f5f5f5; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -.breadcrumb > li { - display: inline-block; - *display: inline; - text-shadow: 0 1px 0 #ffffff; - *zoom: 1; -} - -.breadcrumb > li > .divider { - padding: 0 5px; - color: #ccc; -} - -.breadcrumb > .active { - color: #999999; -} - -.pagination { - margin: 20px 0; -} - -.pagination ul { - display: inline-block; - *display: inline; - margin-bottom: 0; - margin-left: 0; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - *zoom: 1; - -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.pagination ul > li { - display: inline; -} - -.pagination ul > li > a, -.pagination ul > li > span { - float: left; - padding: 4px 12px; - line-height: 20px; - text-decoration: none; - background-color: #ffffff; - border: 1px solid #dddddd; - border-left-width: 0; -} - -.pagination ul > li > a:hover, -.pagination ul > li > a:focus, -.pagination ul > .active > a, -.pagination ul > .active > span { - background-color: #f5f5f5; -} - -.pagination ul > .active > a, -.pagination ul > .active > span { - color: #999999; - cursor: default; -} - -.pagination ul > .disabled > span, -.pagination ul > .disabled > a, -.pagination ul > .disabled > a:hover, -.pagination ul > .disabled > a:focus { - color: #999999; - cursor: default; - background-color: transparent; -} - -.pagination ul > li:first-child > a, -.pagination ul > li:first-child > span { - border-left-width: 1px; - -webkit-border-bottom-left-radius: 4px; - border-bottom-left-radius: 4px; - -webkit-border-top-left-radius: 4px; - border-top-left-radius: 4px; - -moz-border-radius-bottomleft: 4px; - -moz-border-radius-topleft: 4px; -} - -.pagination ul > li:last-child > a, -.pagination ul > li:last-child > span { - -webkit-border-top-right-radius: 4px; - border-top-right-radius: 4px; - -webkit-border-bottom-right-radius: 4px; - border-bottom-right-radius: 4px; - -moz-border-radius-topright: 4px; - -moz-border-radius-bottomright: 4px; -} - -.pagination-centered { - text-align: center; -} - -.pagination-right { - text-align: right; -} - -.pagination-large ul > li > a, -.pagination-large ul > li > span { - padding: 11px 19px; - font-size: 17.5px; -} - -.pagination-large ul > li:first-child > a, -.pagination-large ul > li:first-child > span { - -webkit-border-bottom-left-radius: 6px; - border-bottom-left-radius: 6px; - -webkit-border-top-left-radius: 6px; - border-top-left-radius: 6px; - -moz-border-radius-bottomleft: 6px; - -moz-border-radius-topleft: 6px; -} - -.pagination-large ul > li:last-child > a, -.pagination-large ul > li:last-child > span { - -webkit-border-top-right-radius: 6px; - border-top-right-radius: 6px; - -webkit-border-bottom-right-radius: 6px; - border-bottom-right-radius: 6px; - -moz-border-radius-topright: 6px; - -moz-border-radius-bottomright: 6px; -} - -.pagination-mini ul > li:first-child > a, -.pagination-small ul > li:first-child > a, -.pagination-mini ul > li:first-child > span, -.pagination-small ul > li:first-child > span { - -webkit-border-bottom-left-radius: 3px; - border-bottom-left-radius: 3px; - -webkit-border-top-left-radius: 3px; - border-top-left-radius: 3px; - -moz-border-radius-bottomleft: 3px; - -moz-border-radius-topleft: 3px; -} - -.pagination-mini ul > li:last-child > a, -.pagination-small ul > li:last-child > a, -.pagination-mini ul > li:last-child > span, -.pagination-small ul > li:last-child > span { - -webkit-border-top-right-radius: 3px; - border-top-right-radius: 3px; - -webkit-border-bottom-right-radius: 3px; - border-bottom-right-radius: 3px; - -moz-border-radius-topright: 3px; - -moz-border-radius-bottomright: 3px; -} - -.pagination-small ul > li > a, -.pagination-small ul > li > span { - padding: 2px 10px; - font-size: 11.9px; -} - -.pagination-mini ul > li > a, -.pagination-mini ul > li > span { - padding: 0 6px; - font-size: 10.5px; -} - -.pager { - margin: 20px 0; - text-align: center; - list-style: none; - *zoom: 1; -} - -.pager:before, -.pager:after { - display: table; - line-height: 0; - content: ""; -} - -.pager:after { - clear: both; -} - -.pager li { - display: inline; -} - -.pager li > a, -.pager li > span { - display: inline-block; - padding: 5px 14px; - background-color: #fff; - border: 1px solid #ddd; - -webkit-border-radius: 15px; - -moz-border-radius: 15px; - border-radius: 15px; -} - -.pager li > a:hover, -.pager li > a:focus { - text-decoration: none; - background-color: #f5f5f5; -} - -.pager .next > a, -.pager .next > span { - float: right; -} - -.pager .previous > a, -.pager .previous > span { - float: left; -} - -.pager .disabled > a, -.pager .disabled > a:hover, -.pager .disabled > a:focus, -.pager .disabled > span { - color: #999999; - cursor: default; - background-color: #fff; -} - -.modal-backdrop { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 1040; - background-color: #000000; -} - -.modal-backdrop.fade { - opacity: 0; -} - -.modal-backdrop, -.modal-backdrop.fade.in { - opacity: 0.8; - filter: alpha(opacity=80); -} - -.modal { - position: fixed; - top: 10%; - left: 50%; - z-index: 1050; - width: 560px; - margin-left: -280px; - background-color: #ffffff; - border: 1px solid #999; - border: 1px solid rgba(0, 0, 0, 0.3); - *border: 1px solid #999; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; - outline: none; - -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); - -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); - box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); - -webkit-background-clip: padding-box; - -moz-background-clip: padding-box; - background-clip: padding-box; -} - -.modal.fade { - top: -25%; - -webkit-transition: opacity 0.3s linear, top 0.3s ease-out; - -moz-transition: opacity 0.3s linear, top 0.3s ease-out; - -o-transition: opacity 0.3s linear, top 0.3s ease-out; - transition: opacity 0.3s linear, top 0.3s ease-out; -} - -.modal.fade.in { - top: 10%; -} - -.modal-header { - padding: 9px 15px; - border-bottom: 1px solid #eee; -} - -.modal-header .close { - margin-top: 2px; -} - -.modal-header h3 { - margin: 0; - line-height: 30px; -} - -.modal-body { - position: relative; - max-height: 400px; - padding: 15px; - overflow-y: auto; -} - -.modal-form { - margin-bottom: 0; -} - -.modal-footer { - padding: 14px 15px 15px; - margin-bottom: 0; - text-align: right; - background-color: #f5f5f5; - border-top: 1px solid #ddd; - -webkit-border-radius: 0 0 6px 6px; - -moz-border-radius: 0 0 6px 6px; - border-radius: 0 0 6px 6px; - *zoom: 1; - -webkit-box-shadow: inset 0 1px 0 #ffffff; - -moz-box-shadow: inset 0 1px 0 #ffffff; - box-shadow: inset 0 1px 0 #ffffff; -} - -.modal-footer:before, -.modal-footer:after { - display: table; - line-height: 0; - content: ""; -} - -.modal-footer:after { - clear: both; -} - -.modal-footer .btn + .btn { - margin-bottom: 0; - margin-left: 5px; -} - -.modal-footer .btn-group .btn + .btn { - margin-left: -1px; -} - -.modal-footer .btn-block + .btn-block { - margin-left: 0; -} - -.tooltip { - position: absolute; - z-index: 1030; - display: block; - font-size: 11px; - line-height: 1.4; - opacity: 0; - filter: alpha(opacity=0); - visibility: visible; -} - -.tooltip.in { - opacity: 0.8; - filter: alpha(opacity=80); -} - -.tooltip.top { - padding: 5px 0; - margin-top: -3px; -} - -.tooltip.right { - padding: 0 5px; - margin-left: 3px; -} - -.tooltip.bottom { - padding: 5px 0; - margin-top: 3px; -} - -.tooltip.left { - padding: 0 5px; - margin-left: -3px; -} - -.tooltip-inner { - max-width: 200px; - padding: 8px; - color: #ffffff; - text-align: center; - text-decoration: none; - background-color: #000000; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -.tooltip-arrow { - position: absolute; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; -} - -.tooltip.top .tooltip-arrow { - bottom: 0; - left: 50%; - margin-left: -5px; - border-top-color: #000000; - border-width: 5px 5px 0; -} - -.tooltip.right .tooltip-arrow { - top: 50%; - left: 0; - margin-top: -5px; - border-right-color: #000000; - border-width: 5px 5px 5px 0; -} - -.tooltip.left .tooltip-arrow { - top: 50%; - right: 0; - margin-top: -5px; - border-left-color: #000000; - border-width: 5px 0 5px 5px; -} - -.tooltip.bottom .tooltip-arrow { - top: 0; - left: 50%; - margin-left: -5px; - border-bottom-color: #000000; - border-width: 0 5px 5px; -} - -.popover { - position: absolute; - top: 0; - left: 0; - z-index: 1010; - display: none; - max-width: 276px; - padding: 1px; - text-align: left; - white-space: normal; - background-color: #ffffff; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.2); - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; - -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -webkit-background-clip: padding-box; - -moz-background-clip: padding; - background-clip: padding-box; -} - -.popover.top { - margin-top: -10px; -} - -.popover.right { - margin-left: 10px; -} - -.popover.bottom { - margin-top: 10px; -} - -.popover.left { - margin-left: -10px; -} - -.popover-title { - padding: 8px 14px; - margin: 0; - font-size: 14px; - font-weight: normal; - line-height: 18px; - background-color: #f7f7f7; - border-bottom: 1px solid #ebebeb; - -webkit-border-radius: 5px 5px 0 0; - -moz-border-radius: 5px 5px 0 0; - border-radius: 5px 5px 0 0; -} - -.popover-title:empty { - display: none; -} - -.popover-content { - padding: 9px 14px; -} - -.popover .arrow, -.popover .arrow:after { - position: absolute; - display: block; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; -} - -.popover .arrow { - border-width: 11px; -} - -.popover .arrow:after { - border-width: 10px; - content: ""; -} - -.popover.top .arrow { - bottom: -11px; - left: 50%; - margin-left: -11px; - border-top-color: #999; - border-top-color: rgba(0, 0, 0, 0.25); - border-bottom-width: 0; -} - -.popover.top .arrow:after { - bottom: 1px; - margin-left: -10px; - border-top-color: #ffffff; - border-bottom-width: 0; -} - -.popover.right .arrow { - top: 50%; - left: -11px; - margin-top: -11px; - border-right-color: #999; - border-right-color: rgba(0, 0, 0, 0.25); - border-left-width: 0; -} - -.popover.right .arrow:after { - bottom: -10px; - left: 1px; - border-right-color: #ffffff; - border-left-width: 0; -} - -.popover.bottom .arrow { - top: -11px; - left: 50%; - margin-left: -11px; - border-bottom-color: #999; - border-bottom-color: rgba(0, 0, 0, 0.25); - border-top-width: 0; -} - -.popover.bottom .arrow:after { - top: 1px; - margin-left: -10px; - border-bottom-color: #ffffff; - border-top-width: 0; -} - -.popover.left .arrow { - top: 50%; - right: -11px; - margin-top: -11px; - border-left-color: #999; - border-left-color: rgba(0, 0, 0, 0.25); - border-right-width: 0; -} - -.popover.left .arrow:after { - right: 1px; - bottom: -10px; - border-left-color: #ffffff; - border-right-width: 0; -} - -.thumbnails { - margin-left: -20px; - list-style: none; - *zoom: 1; -} - -.thumbnails:before, -.thumbnails:after { - display: table; - line-height: 0; - content: ""; -} - -.thumbnails:after { - clear: both; -} - -.row-fluid .thumbnails { - margin-left: 0; -} - -.thumbnails > li { - float: left; - margin-bottom: 20px; - margin-left: 20px; -} - -.thumbnail { - display: block; - padding: 4px; - line-height: 20px; - border: 1px solid #ddd; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); - -moz-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); - box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); - -webkit-transition: all 0.2s ease-in-out; - -moz-transition: all 0.2s ease-in-out; - -o-transition: all 0.2s ease-in-out; - transition: all 0.2s ease-in-out; -} - -a.thumbnail:hover, -a.thumbnail:focus { - border-color: #0088cc; - -webkit-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); - -moz-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); - box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); -} - -.thumbnail > img { - display: block; - max-width: 100%; - margin-right: auto; - margin-left: auto; -} - -.thumbnail .caption { - padding: 9px; - color: #555555; -} - -.media, -.media-body { - overflow: hidden; - *overflow: visible; - zoom: 1; -} - -.media, -.media .media { - margin-top: 15px; -} - -.media:first-child { - margin-top: 0; -} - -.media-object { - display: block; -} - -.media-heading { - margin: 0 0 5px; -} - -.media > .pull-left { - margin-right: 10px; -} - -.media > .pull-right { - margin-left: 10px; -} - -.media-list { - margin-left: 0; - list-style: none; -} - -.label, -.badge { - display: inline-block; - padding: 2px 4px; - font-size: 11.844px; - font-weight: bold; - line-height: 14px; - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - white-space: nowrap; - vertical-align: baseline; - background-color: #999999; -} - -.label { - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; -} - -.badge { - padding-right: 9px; - padding-left: 9px; - -webkit-border-radius: 9px; - -moz-border-radius: 9px; - border-radius: 9px; -} - -.label:empty, -.badge:empty { - display: none; -} - -a.label:hover, -a.label:focus, -a.badge:hover, -a.badge:focus { - color: #ffffff; - text-decoration: none; - cursor: pointer; -} - -.label-important, -.badge-important { - background-color: #b94a48; -} - -.label-important[href], -.badge-important[href] { - background-color: #953b39; -} - -.label-warning, -.badge-warning { - background-color: #f89406; -} - -.label-warning[href], -.badge-warning[href] { - background-color: #c67605; -} - -.label-success, -.badge-success { - background-color: #468847; -} - -.label-success[href], -.badge-success[href] { - background-color: #356635; -} - -.label-info, -.badge-info { - background-color: #3a87ad; -} - -.label-info[href], -.badge-info[href] { - background-color: #2d6987; -} - -.label-inverse, -.badge-inverse { - background-color: #333333; -} - -.label-inverse[href], -.badge-inverse[href] { - background-color: #1a1a1a; -} - -.btn .label, -.btn .badge { - position: relative; - top: -1px; -} - -.btn-mini .label, -.btn-mini .badge { - top: 0; -} - -@-webkit-keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} - -@-moz-keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} - -@-ms-keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} - -@-o-keyframes progress-bar-stripes { - from { - background-position: 0 0; - } - to { - background-position: 40px 0; - } -} - -@keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} - -.progress { - height: 20px; - margin-bottom: 20px; - overflow: hidden; - background-color: #f7f7f7; - background-image: -moz-linear-gradient(top, #f5f5f5, #f9f9f9); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9)); - background-image: -webkit-linear-gradient(top, #f5f5f5, #f9f9f9); - background-image: -o-linear-gradient(top, #f5f5f5, #f9f9f9); - background-image: linear-gradient(to bottom, #f5f5f5, #f9f9f9); - background-repeat: repeat-x; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0); - -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); - -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); - box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); -} - -.progress .bar { - float: left; - width: 0; - height: 100%; - font-size: 12px; - color: #ffffff; - text-align: center; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #0e90d2; - background-image: -moz-linear-gradient(top, #149bdf, #0480be); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be)); - background-image: -webkit-linear-gradient(top, #149bdf, #0480be); - background-image: -o-linear-gradient(top, #149bdf, #0480be); - background-image: linear-gradient(to bottom, #149bdf, #0480be); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0); - -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); - -moz-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); - box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - -webkit-transition: width 0.6s ease; - -moz-transition: width 0.6s ease; - -o-transition: width 0.6s ease; - transition: width 0.6s ease; -} - -.progress .bar + .bar { - -webkit-box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15); - -moz-box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15); - box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15); -} - -.progress-striped .bar { - background-color: #149bdf; - background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - -webkit-background-size: 40px 40px; - -moz-background-size: 40px 40px; - -o-background-size: 40px 40px; - background-size: 40px 40px; -} - -.progress.active .bar { - -webkit-animation: progress-bar-stripes 2s linear infinite; - -moz-animation: progress-bar-stripes 2s linear infinite; - -ms-animation: progress-bar-stripes 2s linear infinite; - -o-animation: progress-bar-stripes 2s linear infinite; - animation: progress-bar-stripes 2s linear infinite; -} - -.progress-danger .bar, -.progress .bar-danger { - background-color: #dd514c; - background-image: -moz-linear-gradient(top, #ee5f5b, #c43c35); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35)); - background-image: -webkit-linear-gradient(top, #ee5f5b, #c43c35); - background-image: -o-linear-gradient(top, #ee5f5b, #c43c35); - background-image: linear-gradient(to bottom, #ee5f5b, #c43c35); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0); -} - -.progress-danger.progress-striped .bar, -.progress-striped .bar-danger { - background-color: #ee5f5b; - background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); -} - -.progress-success .bar, -.progress .bar-success { - background-color: #5eb95e; - background-image: -moz-linear-gradient(top, #62c462, #57a957); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957)); - background-image: -webkit-linear-gradient(top, #62c462, #57a957); - background-image: -o-linear-gradient(top, #62c462, #57a957); - background-image: linear-gradient(to bottom, #62c462, #57a957); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0); -} - -.progress-success.progress-striped .bar, -.progress-striped .bar-success { - background-color: #62c462; - background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); -} - -.progress-info .bar, -.progress .bar-info { - background-color: #4bb1cf; - background-image: -moz-linear-gradient(top, #5bc0de, #339bb9); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9)); - background-image: -webkit-linear-gradient(top, #5bc0de, #339bb9); - background-image: -o-linear-gradient(top, #5bc0de, #339bb9); - background-image: linear-gradient(to bottom, #5bc0de, #339bb9); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0); -} - -.progress-info.progress-striped .bar, -.progress-striped .bar-info { - background-color: #5bc0de; - background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); -} - -.progress-warning .bar, -.progress .bar-warning { - background-color: #faa732; - background-image: -moz-linear-gradient(top, #fbb450, #f89406); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); - background-image: -webkit-linear-gradient(top, #fbb450, #f89406); - background-image: -o-linear-gradient(top, #fbb450, #f89406); - background-image: linear-gradient(to bottom, #fbb450, #f89406); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0); -} - -.progress-warning.progress-striped .bar, -.progress-striped .bar-warning { - background-color: #fbb450; - background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); -} - -.accordion { - margin-bottom: 20px; -} - -.accordion-group { - margin-bottom: 2px; - border: 1px solid #e5e5e5; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -.accordion-heading { - border-bottom: 0; -} - -.accordion-heading .accordion-toggle { - display: block; - padding: 8px 15px; -} - -.accordion-toggle { - cursor: pointer; -} - -.accordion-inner { - padding: 9px 15px; - border-top: 1px solid #e5e5e5; -} - -.carousel { - position: relative; - margin-bottom: 20px; - line-height: 1; -} - -.carousel-inner { - position: relative; - width: 100%; - overflow: hidden; -} - -.carousel-inner > .item { - position: relative; - display: none; - -webkit-transition: 0.6s ease-in-out left; - -moz-transition: 0.6s ease-in-out left; - -o-transition: 0.6s ease-in-out left; - transition: 0.6s ease-in-out left; -} - -.carousel-inner > .item > img, -.carousel-inner > .item > a > img { - display: block; - line-height: 1; -} - -.carousel-inner > .active, -.carousel-inner > .next, -.carousel-inner > .prev { - display: block; -} - -.carousel-inner > .active { - left: 0; -} - -.carousel-inner > .next, -.carousel-inner > .prev { - position: absolute; - top: 0; - width: 100%; -} - -.carousel-inner > .next { - left: 100%; -} - -.carousel-inner > .prev { - left: -100%; -} - -.carousel-inner > .next.left, -.carousel-inner > .prev.right { - left: 0; -} - -.carousel-inner > .active.left { - left: -100%; -} - -.carousel-inner > .active.right { - left: 100%; -} - -.carousel-control { - position: absolute; - top: 40%; - left: 15px; - width: 40px; - height: 40px; - margin-top: -20px; - font-size: 60px; - font-weight: 100; - line-height: 30px; - color: #ffffff; - text-align: center; - background: #222222; - border: 3px solid #ffffff; - -webkit-border-radius: 23px; - -moz-border-radius: 23px; - border-radius: 23px; - opacity: 0.5; - filter: alpha(opacity=50); -} - -.carousel-control.right { - right: 15px; - left: auto; -} - -.carousel-control:hover, -.carousel-control:focus { - color: #ffffff; - text-decoration: none; - opacity: 0.9; - filter: alpha(opacity=90); -} - -.carousel-indicators { - position: absolute; - top: 15px; - right: 15px; - z-index: 5; - margin: 0; - list-style: none; -} - -.carousel-indicators li { - display: block; - float: left; - width: 10px; - height: 10px; - margin-left: 5px; - text-indent: -999px; - background-color: #ccc; - background-color: rgba(255, 255, 255, 0.25); - border-radius: 5px; -} - -.carousel-indicators .active { - background-color: #fff; -} - -.carousel-caption { - position: absolute; - right: 0; - bottom: 0; - left: 0; - padding: 15px; - background: #333333; - background: rgba(0, 0, 0, 0.75); -} - -.carousel-caption h4, -.carousel-caption p { - line-height: 20px; - color: #ffffff; -} - -.carousel-caption h4 { - margin: 0 0 5px; -} - -.carousel-caption p { - margin-bottom: 0; -} - -.hero-unit { - padding: 60px; - margin-bottom: 30px; - font-size: 18px; - font-weight: 200; - line-height: 30px; - color: inherit; - background-color: #eeeeee; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} - -.hero-unit h1 { - margin-bottom: 0; - font-size: 60px; - line-height: 1; - letter-spacing: -1px; - color: inherit; -} - -.hero-unit li { - line-height: 30px; -} - -.pull-right { - float: right; -} - -.pull-left { - float: left; -} - -.hide { - display: none; -} - -.show { - display: block; -} - -.invisible { - visibility: hidden; -} - -.affix { - position: fixed; -} diff --git a/docs/_static/bootstrap-2.3.2/css/bootstrap.min.css b/docs/_static/bootstrap-2.3.2/css/bootstrap.min.css deleted file mode 100644 index b6428e695..000000000 --- a/docs/_static/bootstrap-2.3.2/css/bootstrap.min.css +++ /dev/null @@ -1,9 +0,0 @@ -/*! - * Bootstrap v2.3.2 - * - * Copyright 2012 Twitter, Inc - * Licensed under the Apache License v2.0 - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Designed and built with all the love in the world @twitter by @mdo and @fat. - */.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;line-height:0;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}a:hover,a:active{outline:0}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-0.5em}sub{bottom:-0.25em}img{width:auto\9;height:auto;max-width:100%;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}#map_canvas img,.google-maps img{max-width:none}button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}button,input{*overflow:visible;line-height:normal}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}button,html input[type="button"],input[type="reset"],input[type="submit"]{cursor:pointer;-webkit-appearance:button}label,select,button,input[type="button"],input[type="reset"],input[type="submit"],input[type="radio"],input[type="checkbox"]{cursor:pointer}input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}textarea{overflow:auto;vertical-align:top}@media print{*{color:#000!important;text-shadow:none!important;background:transparent!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100%!important}@page{margin:.5cm}p,h2,h3{orphans:3;widows:3}h2,h3{page-break-after:avoid}}body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:20px;color:#333;background-color:#fff}a{color:#08c;text-decoration:none}a:hover,a:focus{color:#005580;text-decoration:underline}.img-rounded{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.1);box-shadow:0 1px 3px rgba(0,0,0,0.1)}.img-circle{-webkit-border-radius:500px;-moz-border-radius:500px;border-radius:500px}.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;line-height:0;content:""}.row:after{clear:both}[class*="span"]{float:left;min-height:1px;margin-left:20px}.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}.span12{width:940px}.span11{width:860px}.span10{width:780px}.span9{width:700px}.span8{width:620px}.span7{width:540px}.span6{width:460px}.span5{width:380px}.span4{width:300px}.span3{width:220px}.span2{width:140px}.span1{width:60px}.offset12{margin-left:980px}.offset11{margin-left:900px}.offset10{margin-left:820px}.offset9{margin-left:740px}.offset8{margin-left:660px}.offset7{margin-left:580px}.offset6{margin-left:500px}.offset5{margin-left:420px}.offset4{margin-left:340px}.offset3{margin-left:260px}.offset2{margin-left:180px}.offset1{margin-left:100px}.row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;line-height:0;content:""}.row-fluid:after{clear:both}.row-fluid [class*="span"]{display:block;float:left;width:100%;min-height:30px;margin-left:2.127659574468085%;*margin-left:2.074468085106383%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.row-fluid [class*="span"]:first-child{margin-left:0}.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.127659574468085%}.row-fluid .span12{width:100%;*width:99.94680851063829%}.row-fluid .span11{width:91.48936170212765%;*width:91.43617021276594%}.row-fluid .span10{width:82.97872340425532%;*width:82.92553191489361%}.row-fluid .span9{width:74.46808510638297%;*width:74.41489361702126%}.row-fluid .span8{width:65.95744680851064%;*width:65.90425531914893%}.row-fluid .span7{width:57.44680851063829%;*width:57.39361702127659%}.row-fluid .span6{width:48.93617021276595%;*width:48.88297872340425%}.row-fluid .span5{width:40.42553191489362%;*width:40.37234042553192%}.row-fluid .span4{width:31.914893617021278%;*width:31.861702127659576%}.row-fluid .span3{width:23.404255319148934%;*width:23.351063829787233%}.row-fluid .span2{width:14.893617021276595%;*width:14.840425531914894%}.row-fluid .span1{width:6.382978723404255%;*width:6.329787234042553%}.row-fluid .offset12{margin-left:104.25531914893617%;*margin-left:104.14893617021275%}.row-fluid .offset12:first-child{margin-left:102.12765957446808%;*margin-left:102.02127659574467%}.row-fluid .offset11{margin-left:95.74468085106382%;*margin-left:95.6382978723404%}.row-fluid .offset11:first-child{margin-left:93.61702127659574%;*margin-left:93.51063829787232%}.row-fluid .offset10{margin-left:87.23404255319149%;*margin-left:87.12765957446807%}.row-fluid .offset10:first-child{margin-left:85.1063829787234%;*margin-left:84.99999999999999%}.row-fluid .offset9{margin-left:78.72340425531914%;*margin-left:78.61702127659572%}.row-fluid .offset9:first-child{margin-left:76.59574468085106%;*margin-left:76.48936170212764%}.row-fluid .offset8{margin-left:70.2127659574468%;*margin-left:70.10638297872339%}.row-fluid .offset8:first-child{margin-left:68.08510638297872%;*margin-left:67.9787234042553%}.row-fluid .offset7{margin-left:61.70212765957446%;*margin-left:61.59574468085106%}.row-fluid .offset7:first-child{margin-left:59.574468085106375%;*margin-left:59.46808510638297%}.row-fluid .offset6{margin-left:53.191489361702125%;*margin-left:53.085106382978715%}.row-fluid .offset6:first-child{margin-left:51.063829787234035%;*margin-left:50.95744680851063%}.row-fluid .offset5{margin-left:44.68085106382979%;*margin-left:44.57446808510638%}.row-fluid .offset5:first-child{margin-left:42.5531914893617%;*margin-left:42.4468085106383%}.row-fluid .offset4{margin-left:36.170212765957444%;*margin-left:36.06382978723405%}.row-fluid .offset4:first-child{margin-left:34.04255319148936%;*margin-left:33.93617021276596%}.row-fluid .offset3{margin-left:27.659574468085104%;*margin-left:27.5531914893617%}.row-fluid .offset3:first-child{margin-left:25.53191489361702%;*margin-left:25.425531914893618%}.row-fluid .offset2{margin-left:19.148936170212764%;*margin-left:19.04255319148936%}.row-fluid .offset2:first-child{margin-left:17.02127659574468%;*margin-left:16.914893617021278%}.row-fluid .offset1{margin-left:10.638297872340425%;*margin-left:10.53191489361702%}.row-fluid .offset1:first-child{margin-left:8.51063829787234%;*margin-left:8.404255319148938%}[class*="span"].hide,.row-fluid [class*="span"].hide{display:none}[class*="span"].pull-right,.row-fluid [class*="span"].pull-right{float:right}.container{margin-right:auto;margin-left:auto;*zoom:1}.container:before,.container:after{display:table;line-height:0;content:""}.container:after{clear:both}.container-fluid{padding-right:20px;padding-left:20px;*zoom:1}.container-fluid:before,.container-fluid:after{display:table;line-height:0;content:""}.container-fluid:after{clear:both}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:21px;font-weight:200;line-height:30px}small{font-size:85%}strong{font-weight:bold}em{font-style:italic}cite{font-style:normal}.muted{color:#999}a.muted:hover,a.muted:focus{color:#808080}.text-warning{color:#c09853}a.text-warning:hover,a.text-warning:focus{color:#a47e3c}.text-error{color:#b94a48}a.text-error:hover,a.text-error:focus{color:#953b39}.text-info{color:#3a87ad}a.text-info:hover,a.text-info:focus{color:#2d6987}.text-success{color:#468847}a.text-success:hover,a.text-success:focus{color:#356635}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}h1,h2,h3,h4,h5,h6{margin:10px 0;font-family:inherit;font-weight:bold;line-height:20px;color:inherit;text-rendering:optimizelegibility}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small{font-weight:normal;line-height:1;color:#999}h1,h2,h3{line-height:40px}h1{font-size:38.5px}h2{font-size:31.5px}h3{font-size:24.5px}h4{font-size:17.5px}h5{font-size:14px}h6{font-size:11.9px}h1 small{font-size:24.5px}h2 small{font-size:17.5px}h3 small{font-size:14px}h4 small{font-size:14px}.page-header{padding-bottom:9px;margin:20px 0 30px;border-bottom:1px solid #eee}ul,ol{padding:0;margin:0 0 10px 25px}ul ul,ul ol,ol ol,ol ul{margin-bottom:0}li{line-height:20px}ul.unstyled,ol.unstyled{margin-left:0;list-style:none}ul.inline,ol.inline{margin-left:0;list-style:none}ul.inline>li,ol.inline>li{display:inline-block;*display:inline;padding-right:5px;padding-left:5px;*zoom:1}dl{margin-bottom:20px}dt,dd{line-height:20px}dt{font-weight:bold}dd{margin-left:10px}.dl-horizontal{*zoom:1}.dl-horizontal:before,.dl-horizontal:after{display:table;line-height:0;content:""}.dl-horizontal:after{clear:both}.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}hr{margin:20px 0;border:0;border-top:1px solid #eee;border-bottom:1px solid #fff}abbr[title],abbr[data-original-title]{cursor:help;border-bottom:1px dotted #999}abbr.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:0 0 0 15px;margin:0 0 20px;border-left:5px solid #eee}blockquote p{margin-bottom:0;font-size:17.5px;font-weight:300;line-height:1.25}blockquote small{display:block;line-height:20px;color:#999}blockquote small:before{content:'\2014 \00A0'}blockquote.pull-right{float:right;padding-right:15px;padding-left:0;border-right:5px solid #eee;border-left:0}blockquote.pull-right p,blockquote.pull-right small{text-align:right}blockquote.pull-right small:before{content:''}blockquote.pull-right small:after{content:'\00A0 \2014'}q:before,q:after,blockquote:before,blockquote:after{content:""}address{display:block;margin-bottom:20px;font-style:normal;line-height:20px}code,pre{padding:0 3px 2px;font-family:Monaco,Menlo,Consolas,"Courier New",monospace;font-size:12px;color:#333;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}code{padding:2px 4px;color:#d14;white-space:nowrap;background-color:#f7f7f9;border:1px solid #e1e1e8}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:20px;word-break:break-all;word-wrap:break-word;white-space:pre;white-space:pre-wrap;background-color:#f5f5f5;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.15);-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}pre.prettyprint{margin-bottom:20px}pre code{padding:0;color:inherit;white-space:pre;white-space:pre-wrap;background-color:transparent;border:0}.pre-scrollable{max-height:340px;overflow-y:scroll}form{margin:0 0 20px}fieldset{padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:40px;color:#333;border:0;border-bottom:1px solid #e5e5e5}legend small{font-size:15px;color:#999}label,input,button,select,textarea{font-size:14px;font-weight:normal;line-height:20px}input,button,select,textarea{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif}label{display:block;margin-bottom:5px}select,textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{display:inline-block;height:20px;padding:4px 6px;margin-bottom:10px;font-size:14px;line-height:20px;color:#555;vertical-align:middle;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}input,textarea,.uneditable-input{width:206px}textarea{height:auto}textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{background-color:#fff;border:1px solid #ccc;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-webkit-transition:border linear .2s,box-shadow linear .2s;-moz-transition:border linear .2s,box-shadow linear .2s;-o-transition:border linear .2s,box-shadow linear .2s;transition:border linear .2s,box-shadow linear .2s}textarea:focus,input[type="text"]:focus,input[type="password"]:focus,input[type="datetime"]:focus,input[type="datetime-local"]:focus,input[type="date"]:focus,input[type="month"]:focus,input[type="time"]:focus,input[type="week"]:focus,input[type="number"]:focus,input[type="email"]:focus,input[type="url"]:focus,input[type="search"]:focus,input[type="tel"]:focus,input[type="color"]:focus,.uneditable-input:focus{border-color:rgba(82,168,236,0.8);outline:0;outline:thin dotted \9;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 8px rgba(82,168,236,0.6);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 8px rgba(82,168,236,0.6);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 8px rgba(82,168,236,0.6)}input[type="radio"],input[type="checkbox"]{margin:4px 0 0;margin-top:1px \9;*margin-top:0;line-height:normal}input[type="file"],input[type="image"],input[type="submit"],input[type="reset"],input[type="button"],input[type="radio"],input[type="checkbox"]{width:auto}select,input[type="file"]{height:30px;*margin-top:4px;line-height:30px}select{width:220px;background-color:#fff;border:1px solid #ccc}select[multiple],select[size]{height:auto}select:focus,input[type="file"]:focus,input[type="radio"]:focus,input[type="checkbox"]:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.uneditable-input,.uneditable-textarea{color:#999;cursor:not-allowed;background-color:#fcfcfc;border-color:#ccc;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);box-shadow:inset 0 1px 2px rgba(0,0,0,0.025)}.uneditable-input{overflow:hidden;white-space:nowrap}.uneditable-textarea{width:auto;height:auto}input:-moz-placeholder,textarea:-moz-placeholder{color:#999}input:-ms-input-placeholder,textarea:-ms-input-placeholder{color:#999}input::-webkit-input-placeholder,textarea::-webkit-input-placeholder{color:#999}.radio,.checkbox{min-height:20px;padding-left:20px}.radio input[type="radio"],.checkbox input[type="checkbox"]{float:left;margin-left:-20px}.controls>.radio:first-child,.controls>.checkbox:first-child{padding-top:5px}.radio.inline,.checkbox.inline{display:inline-block;padding-top:5px;margin-bottom:0;vertical-align:middle}.radio.inline+.radio.inline,.checkbox.inline+.checkbox.inline{margin-left:10px}.input-mini{width:60px}.input-small{width:90px}.input-medium{width:150px}.input-large{width:210px}.input-xlarge{width:270px}.input-xxlarge{width:530px}input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"]{float:none;margin-left:0}.input-append input[class*="span"],.input-append .uneditable-input[class*="span"],.input-prepend input[class*="span"],.input-prepend .uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"],.row-fluid .input-prepend [class*="span"],.row-fluid .input-append [class*="span"]{display:inline-block}input,textarea,.uneditable-input{margin-left:0}.controls-row [class*="span"]+[class*="span"]{margin-left:20px}input.span12,textarea.span12,.uneditable-input.span12{width:926px}input.span11,textarea.span11,.uneditable-input.span11{width:846px}input.span10,textarea.span10,.uneditable-input.span10{width:766px}input.span9,textarea.span9,.uneditable-input.span9{width:686px}input.span8,textarea.span8,.uneditable-input.span8{width:606px}input.span7,textarea.span7,.uneditable-input.span7{width:526px}input.span6,textarea.span6,.uneditable-input.span6{width:446px}input.span5,textarea.span5,.uneditable-input.span5{width:366px}input.span4,textarea.span4,.uneditable-input.span4{width:286px}input.span3,textarea.span3,.uneditable-input.span3{width:206px}input.span2,textarea.span2,.uneditable-input.span2{width:126px}input.span1,textarea.span1,.uneditable-input.span1{width:46px}.controls-row{*zoom:1}.controls-row:before,.controls-row:after{display:table;line-height:0;content:""}.controls-row:after{clear:both}.controls-row [class*="span"],.row-fluid .controls-row [class*="span"]{float:left}.controls-row .checkbox[class*="span"],.controls-row .radio[class*="span"]{padding-top:5px}input[disabled],select[disabled],textarea[disabled],input[readonly],select[readonly],textarea[readonly]{cursor:not-allowed;background-color:#eee}input[type="radio"][disabled],input[type="checkbox"][disabled],input[type="radio"][readonly],input[type="checkbox"][readonly]{background-color:transparent}.control-group.warning .control-label,.control-group.warning .help-block,.control-group.warning .help-inline{color:#c09853}.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea{color:#c09853}.control-group.warning input,.control-group.warning select,.control-group.warning textarea{border-color:#c09853;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus{border-color:#a47e3c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e}.control-group.warning .input-prepend .add-on,.control-group.warning .input-append .add-on{color:#c09853;background-color:#fcf8e3;border-color:#c09853}.control-group.error .control-label,.control-group.error .help-block,.control-group.error .help-inline{color:#b94a48}.control-group.error .checkbox,.control-group.error .radio,.control-group.error input,.control-group.error select,.control-group.error textarea{color:#b94a48}.control-group.error input,.control-group.error select,.control-group.error textarea{border-color:#b94a48;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.error input:focus,.control-group.error select:focus,.control-group.error textarea:focus{border-color:#953b39;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392}.control-group.error .input-prepend .add-on,.control-group.error .input-append .add-on{color:#b94a48;background-color:#f2dede;border-color:#b94a48}.control-group.success .control-label,.control-group.success .help-block,.control-group.success .help-inline{color:#468847}.control-group.success .checkbox,.control-group.success .radio,.control-group.success input,.control-group.success select,.control-group.success textarea{color:#468847}.control-group.success input,.control-group.success select,.control-group.success textarea{border-color:#468847;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.success input:focus,.control-group.success select:focus,.control-group.success textarea:focus{border-color:#356635;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b}.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#468847;background-color:#dff0d8;border-color:#468847}.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3a87ad}.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3a87ad}.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3a87ad;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#2d6987;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3}.control-group.info .input-prepend .add-on,.control-group.info .input-append .add-on{color:#3a87ad;background-color:#d9edf7;border-color:#3a87ad}input:focus:invalid,textarea:focus:invalid,select:focus:invalid{color:#b94a48;border-color:#ee5f5b}input:focus:invalid:focus,textarea:focus:invalid:focus,select:focus:invalid:focus{border-color:#e9322d;-webkit-box-shadow:0 0 6px #f8b9b7;-moz-box-shadow:0 0 6px #f8b9b7;box-shadow:0 0 6px #f8b9b7}.form-actions{padding:19px 20px 20px;margin-top:20px;margin-bottom:20px;background-color:#f5f5f5;border-top:1px solid #e5e5e5;*zoom:1}.form-actions:before,.form-actions:after{display:table;line-height:0;content:""}.form-actions:after{clear:both}.help-block,.help-inline{color:#595959}.help-block{display:block;margin-bottom:10px}.help-inline{display:inline-block;*display:inline;padding-left:5px;vertical-align:middle;*zoom:1}.input-append,.input-prepend{display:inline-block;margin-bottom:10px;font-size:0;white-space:nowrap;vertical-align:middle}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input,.input-append .dropdown-menu,.input-prepend .dropdown-menu,.input-append .popover,.input-prepend .popover{font-size:14px}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input{position:relative;margin-bottom:0;*margin-left:0;vertical-align:top;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-append input:focus,.input-prepend input:focus,.input-append select:focus,.input-prepend select:focus,.input-append .uneditable-input:focus,.input-prepend .uneditable-input:focus{z-index:2}.input-append .add-on,.input-prepend .add-on{display:inline-block;width:auto;height:20px;min-width:16px;padding:4px 5px;font-size:14px;font-weight:normal;line-height:20px;text-align:center;text-shadow:0 1px 0 #fff;background-color:#eee;border:1px solid #ccc}.input-append .add-on,.input-prepend .add-on,.input-append .btn,.input-prepend .btn,.input-append .btn-group>.dropdown-toggle,.input-prepend .btn-group>.dropdown-toggle{vertical-align:top;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-append .active,.input-prepend .active{background-color:#a9dba9;border-color:#46a546}.input-prepend .add-on,.input-prepend .btn{margin-right:-1px}.input-prepend .add-on:first-child,.input-prepend .btn:first-child{-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input,.input-append select,.input-append .uneditable-input{-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input+.btn-group .btn:last-child,.input-append select+.btn-group .btn:last-child,.input-append .uneditable-input+.btn-group .btn:last-child{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-append .add-on,.input-append .btn,.input-append .btn-group{margin-left:-1px}.input-append .add-on:last-child,.input-append .btn:last-child,.input-append .btn-group:last-child>.dropdown-toggle{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-prepend.input-append input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-prepend.input-append input+.btn-group .btn,.input-prepend.input-append select+.btn-group .btn,.input-prepend.input-append .uneditable-input+.btn-group .btn{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child{margin-right:-1px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child{margin-left:-1px;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-prepend.input-append .btn-group:first-child{margin-left:0}input.search-query{padding-right:14px;padding-right:4px \9;padding-left:14px;padding-left:4px \9;margin-bottom:0;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}.form-search .input-append .search-query,.form-search .input-prepend .search-query{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.form-search .input-append .search-query{-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}.form-search .input-append .btn{-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}.form-search .input-prepend .search-query{-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}.form-search .input-prepend .btn{-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append{display:inline-block;*display:inline;margin-bottom:0;vertical-align:middle;*zoom:1}.form-search .hide,.form-inline .hide,.form-horizontal .hide{display:none}.form-search label,.form-inline label,.form-search .btn-group,.form-inline .btn-group{display:inline-block}.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend{margin-bottom:0}.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox{padding-left:0;margin-bottom:0;vertical-align:middle}.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:left;margin-right:3px;margin-left:0}.control-group{margin-bottom:10px}legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate}.form-horizontal .control-group{margin-bottom:20px;*zoom:1}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;line-height:0;content:""}.form-horizontal .control-group:after{clear:both}.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right}.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0}.form-horizontal .controls:first-child{*padding-left:180px}.form-horizontal .help-block{margin-bottom:0}.form-horizontal input+.help-block,.form-horizontal select+.help-block,.form-horizontal textarea+.help-block,.form-horizontal .uneditable-input+.help-block,.form-horizontal .input-prepend+.help-block,.form-horizontal .input-append+.help-block{margin-top:10px}.form-horizontal .form-actions{padding-left:180px}table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0}.table{width:100%;margin-bottom:20px}.table th,.table td{padding:8px;line-height:20px;text-align:left;vertical-align:top;border-top:1px solid #ddd}.table th{font-weight:bold}.table thead th{vertical-align:bottom}.table caption+thead tr:first-child th,.table caption+thead tr:first-child td,.table colgroup+thead tr:first-child th,.table colgroup+thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td{border-top:0}.table tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed th,.table-condensed td{padding:4px 5px}.table-bordered{border:1px solid #ddd;border-collapse:separate;*border-collapse:collapse;border-left:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.table-bordered th,.table-bordered td{border-left:1px solid #ddd}.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0}.table-bordered thead:first-child tr:first-child>th:first-child,.table-bordered tbody:first-child tr:first-child>td:first-child,.table-bordered tbody:first-child tr:first-child>th:first-child{-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-topleft:4px}.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-moz-border-radius-topright:4px}.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px}.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px}.table-bordered tfoot+tbody:last-child tr:last-child td:first-child{-webkit-border-bottom-left-radius:0;border-bottom-left-radius:0;-moz-border-radius-bottomleft:0}.table-bordered tfoot+tbody:last-child tr:last-child td:last-child{-webkit-border-bottom-right-radius:0;border-bottom-right-radius:0;-moz-border-radius-bottomright:0}.table-bordered caption+thead tr:first-child th:first-child,.table-bordered caption+tbody tr:first-child td:first-child,.table-bordered colgroup+thead tr:first-child th:first-child,.table-bordered colgroup+tbody tr:first-child td:first-child{-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-topleft:4px}.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-moz-border-radius-topright:4px}.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5}table td[class*="span"],table th[class*="span"],.row-fluid table td[class*="span"],.row-fluid table th[class*="span"]{display:table-cell;float:none;margin-left:0}.table td.span1,.table th.span1{float:none;width:44px;margin-left:0}.table td.span2,.table th.span2{float:none;width:124px;margin-left:0}.table td.span3,.table th.span3{float:none;width:204px;margin-left:0}.table td.span4,.table th.span4{float:none;width:284px;margin-left:0}.table td.span5,.table th.span5{float:none;width:364px;margin-left:0}.table td.span6,.table th.span6{float:none;width:444px;margin-left:0}.table td.span7,.table th.span7{float:none;width:524px;margin-left:0}.table td.span8,.table th.span8{float:none;width:604px;margin-left:0}.table td.span9,.table th.span9{float:none;width:684px;margin-left:0}.table td.span10,.table th.span10{float:none;width:764px;margin-left:0}.table td.span11,.table th.span11{float:none;width:844px;margin-left:0}.table td.span12,.table th.span12{float:none;width:924px;margin-left:0}.table tbody tr.success>td{background-color:#dff0d8}.table tbody tr.error>td{background-color:#f2dede}.table tbody tr.warning>td{background-color:#fcf8e3}.table tbody tr.info>td{background-color:#d9edf7}.table-hover tbody tr.success:hover>td{background-color:#d0e9c6}.table-hover tbody tr.error:hover>td{background-color:#ebcccc}.table-hover tbody tr.warning:hover>td{background-color:#faf2cc}.table-hover tbody tr.info:hover>td{background-color:#c4e3f3}[class^="icon-"],[class*=" icon-"]{display:inline-block;width:14px;height:14px;margin-top:1px;*margin-right:.3em;line-height:14px;vertical-align:text-top;background-image:url("../img/glyphicons-halflings.png");background-position:14px 14px;background-repeat:no-repeat}.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:focus>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>li>a:focus>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:focus>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"],.dropdown-submenu:focus>a>[class*=" icon-"]{background-image:url("../img/glyphicons-halflings-white.png")}.icon-glass{background-position:0 0}.icon-music{background-position:-24px 0}.icon-search{background-position:-48px 0}.icon-envelope{background-position:-72px 0}.icon-heart{background-position:-96px 0}.icon-star{background-position:-120px 0}.icon-star-empty{background-position:-144px 0}.icon-user{background-position:-168px 0}.icon-film{background-position:-192px 0}.icon-th-large{background-position:-216px 0}.icon-th{background-position:-240px 0}.icon-th-list{background-position:-264px 0}.icon-ok{background-position:-288px 0}.icon-remove{background-position:-312px 0}.icon-zoom-in{background-position:-336px 0}.icon-zoom-out{background-position:-360px 0}.icon-off{background-position:-384px 0}.icon-signal{background-position:-408px 0}.icon-cog{background-position:-432px 0}.icon-trash{background-position:-456px 0}.icon-home{background-position:0 -24px}.icon-file{background-position:-24px -24px}.icon-time{background-position:-48px -24px}.icon-road{background-position:-72px -24px}.icon-download-alt{background-position:-96px -24px}.icon-download{background-position:-120px -24px}.icon-upload{background-position:-144px -24px}.icon-inbox{background-position:-168px -24px}.icon-play-circle{background-position:-192px -24px}.icon-repeat{background-position:-216px -24px}.icon-refresh{background-position:-240px -24px}.icon-list-alt{background-position:-264px -24px}.icon-lock{background-position:-287px -24px}.icon-flag{background-position:-312px -24px}.icon-headphones{background-position:-336px -24px}.icon-volume-off{background-position:-360px -24px}.icon-volume-down{background-position:-384px -24px}.icon-volume-up{background-position:-408px -24px}.icon-qrcode{background-position:-432px -24px}.icon-barcode{background-position:-456px -24px}.icon-tag{background-position:0 -48px}.icon-tags{background-position:-25px -48px}.icon-book{background-position:-48px -48px}.icon-bookmark{background-position:-72px -48px}.icon-print{background-position:-96px -48px}.icon-camera{background-position:-120px -48px}.icon-font{background-position:-144px -48px}.icon-bold{background-position:-167px -48px}.icon-italic{background-position:-192px -48px}.icon-text-height{background-position:-216px -48px}.icon-text-width{background-position:-240px -48px}.icon-align-left{background-position:-264px -48px}.icon-align-center{background-position:-288px -48px}.icon-align-right{background-position:-312px -48px}.icon-align-justify{background-position:-336px -48px}.icon-list{background-position:-360px -48px}.icon-indent-left{background-position:-384px -48px}.icon-indent-right{background-position:-408px -48px}.icon-facetime-video{background-position:-432px -48px}.icon-picture{background-position:-456px -48px}.icon-pencil{background-position:0 -72px}.icon-map-marker{background-position:-24px -72px}.icon-adjust{background-position:-48px -72px}.icon-tint{background-position:-72px -72px}.icon-edit{background-position:-96px -72px}.icon-share{background-position:-120px -72px}.icon-check{background-position:-144px -72px}.icon-move{background-position:-168px -72px}.icon-step-backward{background-position:-192px -72px}.icon-fast-backward{background-position:-216px -72px}.icon-backward{background-position:-240px -72px}.icon-play{background-position:-264px -72px}.icon-pause{background-position:-288px -72px}.icon-stop{background-position:-312px -72px}.icon-forward{background-position:-336px -72px}.icon-fast-forward{background-position:-360px -72px}.icon-step-forward{background-position:-384px -72px}.icon-eject{background-position:-408px -72px}.icon-chevron-left{background-position:-432px -72px}.icon-chevron-right{background-position:-456px -72px}.icon-plus-sign{background-position:0 -96px}.icon-minus-sign{background-position:-24px -96px}.icon-remove-sign{background-position:-48px -96px}.icon-ok-sign{background-position:-72px -96px}.icon-question-sign{background-position:-96px -96px}.icon-info-sign{background-position:-120px -96px}.icon-screenshot{background-position:-144px -96px}.icon-remove-circle{background-position:-168px -96px}.icon-ok-circle{background-position:-192px -96px}.icon-ban-circle{background-position:-216px -96px}.icon-arrow-left{background-position:-240px -96px}.icon-arrow-right{background-position:-264px -96px}.icon-arrow-up{background-position:-289px -96px}.icon-arrow-down{background-position:-312px -96px}.icon-share-alt{background-position:-336px -96px}.icon-resize-full{background-position:-360px -96px}.icon-resize-small{background-position:-384px -96px}.icon-plus{background-position:-408px -96px}.icon-minus{background-position:-433px -96px}.icon-asterisk{background-position:-456px -96px}.icon-exclamation-sign{background-position:0 -120px}.icon-gift{background-position:-24px -120px}.icon-leaf{background-position:-48px -120px}.icon-fire{background-position:-72px -120px}.icon-eye-open{background-position:-96px -120px}.icon-eye-close{background-position:-120px -120px}.icon-warning-sign{background-position:-144px -120px}.icon-plane{background-position:-168px -120px}.icon-calendar{background-position:-192px -120px}.icon-random{width:16px;background-position:-216px -120px}.icon-comment{background-position:-240px -120px}.icon-magnet{background-position:-264px -120px}.icon-chevron-up{background-position:-288px -120px}.icon-chevron-down{background-position:-313px -119px}.icon-retweet{background-position:-336px -120px}.icon-shopping-cart{background-position:-360px -120px}.icon-folder-close{width:16px;background-position:-384px -120px}.icon-folder-open{width:16px;background-position:-408px -120px}.icon-resize-vertical{background-position:-432px -119px}.icon-resize-horizontal{background-position:-456px -118px}.icon-hdd{background-position:0 -144px}.icon-bullhorn{background-position:-24px -144px}.icon-bell{background-position:-48px -144px}.icon-certificate{background-position:-72px -144px}.icon-thumbs-up{background-position:-96px -144px}.icon-thumbs-down{background-position:-120px -144px}.icon-hand-right{background-position:-144px -144px}.icon-hand-left{background-position:-168px -144px}.icon-hand-up{background-position:-192px -144px}.icon-hand-down{background-position:-216px -144px}.icon-circle-arrow-right{background-position:-240px -144px}.icon-circle-arrow-left{background-position:-264px -144px}.icon-circle-arrow-up{background-position:-288px -144px}.icon-circle-arrow-down{background-position:-312px -144px}.icon-globe{background-position:-336px -144px}.icon-wrench{background-position:-360px -144px}.icon-tasks{background-position:-384px -144px}.icon-filter{background-position:-408px -144px}.icon-briefcase{background-position:-432px -144px}.icon-fullscreen{background-position:-456px -144px}.dropup,.dropdown{position:relative}.dropdown-toggle{*margin-bottom:-3px}.dropdown-toggle:active,.open .dropdown-toggle{outline:0}.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000;border-right:4px solid transparent;border-left:4px solid transparent;content:""}.dropdown .caret{margin-top:8px;margin-left:2px}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:20px;color:#333;white-space:nowrap}.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{color:#fff;text-decoration:none;background-color:#0081c2;background-image:-moz-linear-gradient(top,#08c,#0077b3);background-image:-webkit-gradient(linear,0 0,0 100%,from(#08c),to(#0077b3));background-image:-webkit-linear-gradient(top,#08c,#0077b3);background-image:-o-linear-gradient(top,#08c,#0077b3);background-image:linear-gradient(to bottom,#08c,#0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc',endColorstr='#ff0077b3',GradientType=0)}.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;background-color:#0081c2;background-image:-moz-linear-gradient(top,#08c,#0077b3);background-image:-webkit-gradient(linear,0 0,0 100%,from(#08c),to(#0077b3));background-image:-webkit-linear-gradient(top,#08c,#0077b3);background-image:-o-linear-gradient(top,#08c,#0077b3);background-image:linear-gradient(to bottom,#08c,#0077b3);background-repeat:repeat-x;outline:0;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc',endColorstr='#ff0077b3',GradientType=0)}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999}.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;cursor:default;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open{*z-index:1000}.open>.dropdown-menu{display:block}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000;content:""}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}.dropdown-submenu{position:relative}.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px}.dropdown-submenu:hover>.dropdown-menu{display:block}.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0}.dropdown-submenu>a:after{display:block;float:right;width:0;height:0;margin-top:5px;margin-right:-10px;border-color:transparent;border-left-color:#ccc;border-style:solid;border-width:5px 0 5px 5px;content:" "}.dropdown-submenu:hover>a:after{border-left-color:#fff}.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}.dropdown .dropdown-menu .nav-header{padding-right:20px;padding-left:20px}.typeahead{z-index:1051;margin-top:2px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);box-shadow:inset 0 1px 1px rgba(0,0,0,0.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,0.15)}.well-large{padding:24px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.well-small{padding:9px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.fade{opacity:0;-webkit-transition:opacity .15s linear;-moz-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;-moz-transition:height .35s ease;-o-transition:height .35s ease;transition:height .35s ease}.collapse.in{height:auto}.close{float:right;font-size:20px;font-weight:bold;line-height:20px;color:#000;text-shadow:0 1px 0 #fff;opacity:.2;filter:alpha(opacity=20)}.close:hover,.close:focus{color:#000;text-decoration:none;cursor:pointer;opacity:.4;filter:alpha(opacity=40)}button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none}.btn{display:inline-block;*display:inline;padding:4px 12px;margin-bottom:0;*margin-left:.3em;font-size:14px;line-height:20px;color:#333;text-align:center;text-shadow:0 1px 1px rgba(255,255,255,0.75);vertical-align:middle;cursor:pointer;background-color:#f5f5f5;*background-color:#e6e6e6;background-image:-moz-linear-gradient(top,#fff,#e6e6e6);background-image:-webkit-gradient(linear,0 0,0 100%,from(#fff),to(#e6e6e6));background-image:-webkit-linear-gradient(top,#fff,#e6e6e6);background-image:-o-linear-gradient(top,#fff,#e6e6e6);background-image:linear-gradient(to bottom,#fff,#e6e6e6);background-repeat:repeat-x;border:1px solid #ccc;*border:0;border-color:#e6e6e6 #e6e6e6 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff',endColorstr='#ffe6e6e6',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);*zoom:1;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05)}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333;background-color:#e6e6e6;*background-color:#d9d9d9}.btn:active,.btn.active{background-color:#ccc \9}.btn:first-child{*margin-left:0}.btn:hover,.btn:focus{color:#333;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position .1s linear;-moz-transition:background-position .1s linear;-o-transition:background-position .1s linear;transition:background-position .1s linear}.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05)}.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}.btn-large{padding:11px 19px;font-size:17.5px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px}.btn-small{padding:2px 10px;font-size:11.9px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0}.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px}.btn-mini{padding:0 6px;font-size:10.5px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.btn-block{display:block;width:100%;padding-right:0;padding-left:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.btn-block+.btn-block{margin-top:5px}input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255,255,255,0.75)}.btn-primary{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#006dcc;*background-color:#04c;background-image:-moz-linear-gradient(top,#08c,#04c);background-image:-webkit-gradient(linear,0 0,0 100%,from(#08c),to(#04c));background-image:-webkit-linear-gradient(top,#08c,#04c);background-image:-o-linear-gradient(top,#08c,#04c);background-image:linear-gradient(to bottom,#08c,#04c);background-repeat:repeat-x;border-color:#04c #04c #002a80;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc',endColorstr='#ff0044cc',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#fff;background-color:#04c;*background-color:#003bb3}.btn-primary:active,.btn-primary.active{background-color:#039 \9}.btn-warning{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#faa732;*background-color:#f89406;background-image:-moz-linear-gradient(top,#fbb450,#f89406);background-image:-webkit-gradient(linear,0 0,0 100%,from(#fbb450),to(#f89406));background-image:-webkit-linear-gradient(top,#fbb450,#f89406);background-image:-o-linear-gradient(top,#fbb450,#f89406);background-image:linear-gradient(to bottom,#fbb450,#f89406);background-repeat:repeat-x;border-color:#f89406 #f89406 #ad6704;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450',endColorstr='#fff89406',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#fff;background-color:#f89406;*background-color:#df8505}.btn-warning:active,.btn-warning.active{background-color:#c67605 \9}.btn-danger{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#da4f49;*background-color:#bd362f;background-image:-moz-linear-gradient(top,#ee5f5b,#bd362f);background-image:-webkit-gradient(linear,0 0,0 100%,from(#ee5f5b),to(#bd362f));background-image:-webkit-linear-gradient(top,#ee5f5b,#bd362f);background-image:-o-linear-gradient(top,#ee5f5b,#bd362f);background-image:linear-gradient(to bottom,#ee5f5b,#bd362f);background-repeat:repeat-x;border-color:#bd362f #bd362f #802420;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b',endColorstr='#ffbd362f',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#fff;background-color:#bd362f;*background-color:#a9302a}.btn-danger:active,.btn-danger.active{background-color:#942a25 \9}.btn-success{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#5bb75b;*background-color:#51a351;background-image:-moz-linear-gradient(top,#62c462,#51a351);background-image:-webkit-gradient(linear,0 0,0 100%,from(#62c462),to(#51a351));background-image:-webkit-linear-gradient(top,#62c462,#51a351);background-image:-o-linear-gradient(top,#62c462,#51a351);background-image:linear-gradient(to bottom,#62c462,#51a351);background-repeat:repeat-x;border-color:#51a351 #51a351 #387038;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462',endColorstr='#ff51a351',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#fff;background-color:#51a351;*background-color:#499249}.btn-success:active,.btn-success.active{background-color:#408140 \9}.btn-info{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#49afcd;*background-color:#2f96b4;background-image:-moz-linear-gradient(top,#5bc0de,#2f96b4);background-image:-webkit-gradient(linear,0 0,0 100%,from(#5bc0de),to(#2f96b4));background-image:-webkit-linear-gradient(top,#5bc0de,#2f96b4);background-image:-o-linear-gradient(top,#5bc0de,#2f96b4);background-image:linear-gradient(to bottom,#5bc0de,#2f96b4);background-repeat:repeat-x;border-color:#2f96b4 #2f96b4 #1f6377;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de',endColorstr='#ff2f96b4',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#fff;background-color:#2f96b4;*background-color:#2a85a0}.btn-info:active,.btn-info.active{background-color:#24748c \9}.btn-inverse{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#363636;*background-color:#222;background-image:-moz-linear-gradient(top,#444,#222);background-image:-webkit-gradient(linear,0 0,0 100%,from(#444),to(#222));background-image:-webkit-linear-gradient(top,#444,#222);background-image:-o-linear-gradient(top,#444,#222);background-image:linear-gradient(to bottom,#444,#222);background-repeat:repeat-x;border-color:#222 #222 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444',endColorstr='#ff222222',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#fff;background-color:#222;*background-color:#151515}.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9}button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0}button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px}button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px}button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px}.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}.btn-link{color:#08c;cursor:pointer;border-color:transparent;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.btn-link:hover,.btn-link:focus{color:#005580;text-decoration:underline;background-color:transparent}.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#333;text-decoration:none}.btn-group{position:relative;display:inline-block;*display:inline;*margin-left:.3em;font-size:0;white-space:nowrap;vertical-align:middle;*zoom:1}.btn-group:first-child{*margin-left:0}.btn-group+.btn-group{margin-left:5px}.btn-toolbar{margin-top:10px;margin-bottom:10px;font-size:0}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px}.btn-group>.btn{position:relative;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.btn-group>.btn+.btn{margin-left:-1px}.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:14px}.btn-group>.btn-mini{font-size:10.5px}.btn-group>.btn-small{font-size:11.9px}.btn-group>.btn-large{font-size:17.5px}.btn-group>.btn:first-child{margin-left:0;-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-bottomleft:4px;-moz-border-radius-topleft:4px}.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-moz-border-radius-topright:4px;-moz-border-radius-bottomright:4px}.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-bottom-left-radius:6px;border-bottom-left-radius:6px;-webkit-border-top-left-radius:6px;border-top-left-radius:6px;-moz-border-radius-bottomleft:6px;-moz-border-radius-topleft:6px}.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;border-bottom-right-radius:6px;-moz-border-radius-topright:6px;-moz-border-radius-bottomright:6px}.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{*padding-top:5px;padding-right:8px;*padding-bottom:5px;padding-left:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,0.125),inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,0.125),inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 1px 0 0 rgba(255,255,255,0.125),inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05)}.btn-group>.btn-mini+.dropdown-toggle{*padding-top:2px;padding-right:5px;*padding-bottom:2px;padding-left:5px}.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px}.btn-group>.btn-large+.dropdown-toggle{*padding-top:7px;padding-right:12px;*padding-bottom:7px;padding-left:12px}.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05)}.btn-group.open .btn.dropdown-toggle{background-color:#e6e6e6}.btn-group.open .btn-primary.dropdown-toggle{background-color:#04c}.btn-group.open .btn-warning.dropdown-toggle{background-color:#f89406}.btn-group.open .btn-danger.dropdown-toggle{background-color:#bd362f}.btn-group.open .btn-success.dropdown-toggle{background-color:#51a351}.btn-group.open .btn-info.dropdown-toggle{background-color:#2f96b4}.btn-group.open .btn-inverse.dropdown-toggle{background-color:#222}.btn .caret{margin-top:8px;margin-left:0}.btn-large .caret{margin-top:6px}.btn-large .caret{border-top-width:5px;border-right-width:5px;border-left-width:5px}.btn-mini .caret,.btn-small .caret{margin-top:8px}.dropup .btn-large .caret{border-bottom-width:5px}.btn-primary .caret,.btn-warning .caret,.btn-danger .caret,.btn-info .caret,.btn-success .caret,.btn-inverse .caret{border-top-color:#fff;border-bottom-color:#fff}.btn-group-vertical{display:inline-block;*display:inline;*zoom:1}.btn-group-vertical>.btn{display:block;float:none;max-width:100%;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.btn-group-vertical>.btn+.btn{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:first-child{-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}.btn-group-vertical>.btn:last-child{-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.btn-group-vertical>.btn-large:first-child{-webkit-border-radius:6px 6px 0 0;-moz-border-radius:6px 6px 0 0;border-radius:6px 6px 0 0}.btn-group-vertical>.btn-large:last-child{-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}.alert{padding:8px 35px 8px 14px;margin-bottom:20px;text-shadow:0 1px 0 rgba(255,255,255,0.5);background-color:#fcf8e3;border:1px solid #fbeed5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.alert,.alert h4{color:#c09853}.alert h4{margin:0}.alert .close{position:relative;top:-2px;right:-21px;line-height:20px}.alert-success{color:#468847;background-color:#dff0d8;border-color:#d6e9c6}.alert-success h4{color:#468847}.alert-danger,.alert-error{color:#b94a48;background-color:#f2dede;border-color:#eed3d7}.alert-danger h4,.alert-error h4{color:#b94a48}.alert-info{color:#3a87ad;background-color:#d9edf7;border-color:#bce8f1}.alert-info h4{color:#3a87ad}.alert-block{padding-top:14px;padding-bottom:14px}.alert-block>p,.alert-block>ul{margin-bottom:0}.alert-block p+p{margin-top:5px}.nav{margin-bottom:20px;margin-left:0;list-style:none}.nav>li>a{display:block}.nav>li>a:hover,.nav>li>a:focus{text-decoration:none;background-color:#eee}.nav>li>a>img{max-width:none}.nav>.pull-right{float:right}.nav-header{display:block;padding:3px 15px;font-size:11px;font-weight:bold;line-height:20px;color:#999;text-shadow:0 1px 0 rgba(255,255,255,0.5);text-transform:uppercase}.nav li+.nav-header{margin-top:9px}.nav-list{padding-right:15px;padding-left:15px;margin-bottom:0}.nav-list>li>a,.nav-list .nav-header{margin-right:-15px;margin-left:-15px;text-shadow:0 1px 0 rgba(255,255,255,0.5)}.nav-list>li>a{padding:3px 15px}.nav-list>.active>a,.nav-list>.active>a:hover,.nav-list>.active>a:focus{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.2);background-color:#08c}.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{margin-right:2px}.nav-list .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}.nav-tabs,.nav-pills{*zoom:1}.nav-tabs:before,.nav-pills:before,.nav-tabs:after,.nav-pills:after{display:table;line-height:0;content:""}.nav-tabs:after,.nav-pills:after{clear:both}.nav-tabs>li,.nav-pills>li{float:left}.nav-tabs>li>a,.nav-pills>li>a{padding-right:12px;padding-left:12px;margin-right:2px;line-height:14px}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{margin-bottom:-1px}.nav-tabs>li>a{padding-top:8px;padding-bottom:8px;line-height:20px;border:1px solid transparent;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover,.nav-tabs>li>a:focus{border-color:#eee #eee #ddd}.nav-tabs>.active>a,.nav-tabs>.active>a:hover,.nav-tabs>.active>a:focus{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-pills>li>a{padding-top:8px;padding-bottom:8px;margin-top:2px;margin-bottom:2px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}.nav-pills>.active>a,.nav-pills>.active>a:hover,.nav-pills>.active>a:focus{color:#fff;background-color:#08c}.nav-stacked>li{float:none}.nav-stacked>li>a{margin-right:0}.nav-tabs.nav-stacked{border-bottom:0}.nav-tabs.nav-stacked>li>a{border:1px solid #ddd;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.nav-tabs.nav-stacked>li:first-child>a{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-topright:4px;-moz-border-radius-topleft:4px}.nav-tabs.nav-stacked>li:last-child>a{-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-moz-border-radius-bottomright:4px;-moz-border-radius-bottomleft:4px}.nav-tabs.nav-stacked>li>a:hover,.nav-tabs.nav-stacked>li>a:focus{z-index:2;border-color:#ddd}.nav-pills.nav-stacked>li>a{margin-bottom:3px}.nav-pills.nav-stacked>li:last-child>a{margin-bottom:1px}.nav-tabs .dropdown-menu{-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}.nav-pills .dropdown-menu{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.nav .dropdown-toggle .caret{margin-top:6px;border-top-color:#08c;border-bottom-color:#08c}.nav .dropdown-toggle:hover .caret,.nav .dropdown-toggle:focus .caret{border-top-color:#005580;border-bottom-color:#005580}.nav-tabs .dropdown-toggle .caret{margin-top:8px}.nav .active .dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}.nav-tabs .active .dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}.nav>.dropdown.active>a:hover,.nav>.dropdown.active>a:focus{cursor:pointer}.nav-tabs .open .dropdown-toggle,.nav-pills .open .dropdown-toggle,.nav>li.dropdown.open.active>a:hover,.nav>li.dropdown.open.active>a:focus{color:#fff;background-color:#999;border-color:#999}.nav li.dropdown.open .caret,.nav li.dropdown.open.active .caret,.nav li.dropdown.open a:hover .caret,.nav li.dropdown.open a:focus .caret{border-top-color:#fff;border-bottom-color:#fff;opacity:1;filter:alpha(opacity=100)}.tabs-stacked .open>a:hover,.tabs-stacked .open>a:focus{border-color:#999}.tabbable{*zoom:1}.tabbable:before,.tabbable:after{display:table;line-height:0;content:""}.tabbable:after{clear:both}.tab-content{overflow:auto}.tabs-below>.nav-tabs,.tabs-right>.nav-tabs,.tabs-left>.nav-tabs{border-bottom:0}.tab-content>.tab-pane,.pill-content>.pill-pane{display:none}.tab-content>.active,.pill-content>.active{display:block}.tabs-below>.nav-tabs{border-top:1px solid #ddd}.tabs-below>.nav-tabs>li{margin-top:-1px;margin-bottom:0}.tabs-below>.nav-tabs>li>a{-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-top-color:#ddd;border-bottom-color:transparent}.tabs-below>.nav-tabs>.active>a,.tabs-below>.nav-tabs>.active>a:hover,.tabs-below>.nav-tabs>.active>a:focus{border-color:transparent #ddd #ddd #ddd}.tabs-left>.nav-tabs>li,.tabs-right>.nav-tabs>li{float:none}.tabs-left>.nav-tabs>li>a,.tabs-right>.nav-tabs>li>a{min-width:74px;margin-right:0;margin-bottom:3px}.tabs-left>.nav-tabs{float:left;margin-right:19px;border-right:1px solid #ddd}.tabs-left>.nav-tabs>li>a{margin-right:-1px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.tabs-left>.nav-tabs>li>a:hover,.tabs-left>.nav-tabs>li>a:focus{border-color:#eee #ddd #eee #eee}.tabs-left>.nav-tabs .active>a,.tabs-left>.nav-tabs .active>a:hover,.tabs-left>.nav-tabs .active>a:focus{border-color:#ddd transparent #ddd #ddd;*border-right-color:#fff}.tabs-right>.nav-tabs{float:right;margin-left:19px;border-left:1px solid #ddd}.tabs-right>.nav-tabs>li>a{margin-left:-1px;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.tabs-right>.nav-tabs>li>a:hover,.tabs-right>.nav-tabs>li>a:focus{border-color:#eee #eee #eee #ddd}.tabs-right>.nav-tabs .active>a,.tabs-right>.nav-tabs .active>a:hover,.tabs-right>.nav-tabs .active>a:focus{border-color:#ddd #ddd #ddd transparent;*border-left-color:#fff}.nav>.disabled>a{color:#999}.nav>.disabled>a:hover,.nav>.disabled>a:focus{text-decoration:none;cursor:default;background-color:transparent}.navbar{*position:relative;*z-index:2;margin-bottom:20px;overflow:visible}.navbar-inner{min-height:40px;padding-right:20px;padding-left:20px;background-color:#fafafa;background-image:-moz-linear-gradient(top,#fff,#f2f2f2);background-image:-webkit-gradient(linear,0 0,0 100%,from(#fff),to(#f2f2f2));background-image:-webkit-linear-gradient(top,#fff,#f2f2f2);background-image:-o-linear-gradient(top,#fff,#f2f2f2);background-image:linear-gradient(to bottom,#fff,#f2f2f2);background-repeat:repeat-x;border:1px solid #d4d4d4;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff',endColorstr='#fff2f2f2',GradientType=0);*zoom:1;-webkit-box-shadow:0 1px 4px rgba(0,0,0,0.065);-moz-box-shadow:0 1px 4px rgba(0,0,0,0.065);box-shadow:0 1px 4px rgba(0,0,0,0.065)}.navbar-inner:before,.navbar-inner:after{display:table;line-height:0;content:""}.navbar-inner:after{clear:both}.navbar .container{width:auto}.nav-collapse.collapse{height:auto;overflow:visible}.navbar .brand{display:block;float:left;padding:10px 20px 10px;margin-left:-20px;font-size:20px;font-weight:200;color:#777;text-shadow:0 1px 0 #fff}.navbar .brand:hover,.navbar .brand:focus{text-decoration:none}.navbar-text{margin-bottom:0;line-height:40px;color:#777}.navbar-link{color:#777}.navbar-link:hover,.navbar-link:focus{color:#333}.navbar .divider-vertical{height:40px;margin:0 9px;border-right:1px solid #fff;border-left:1px solid #f2f2f2}.navbar .btn,.navbar .btn-group{margin-top:5px}.navbar .btn-group .btn,.navbar .input-prepend .btn,.navbar .input-append .btn,.navbar .input-prepend .btn-group,.navbar .input-append .btn-group{margin-top:0}.navbar-form{margin-bottom:0;*zoom:1}.navbar-form:before,.navbar-form:after{display:table;line-height:0;content:""}.navbar-form:after{clear:both}.navbar-form input,.navbar-form select,.navbar-form .radio,.navbar-form .checkbox{margin-top:5px}.navbar-form input,.navbar-form select,.navbar-form .btn{display:inline-block;margin-bottom:0}.navbar-form input[type="image"],.navbar-form input[type="checkbox"],.navbar-form input[type="radio"]{margin-top:3px}.navbar-form .input-append,.navbar-form .input-prepend{margin-top:5px;white-space:nowrap}.navbar-form .input-append input,.navbar-form .input-prepend input{margin-top:0}.navbar-search{position:relative;float:left;margin-top:5px;margin-bottom:0}.navbar-search .search-query{padding:4px 14px;margin-bottom:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;font-weight:normal;line-height:1;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}.navbar-static-top{position:static;margin-bottom:0}.navbar-static-top .navbar-inner{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.navbar-fixed-top,.navbar-fixed-bottom{position:fixed;right:0;left:0;z-index:1030;margin-bottom:0}.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{border-width:0 0 1px}.navbar-fixed-bottom .navbar-inner{border-width:1px 0 0}.navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding-right:0;padding-left:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}.navbar-fixed-top{top:0}.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{-webkit-box-shadow:0 1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 10px rgba(0,0,0,0.1);box-shadow:0 1px 10px rgba(0,0,0,0.1)}.navbar-fixed-bottom{bottom:0}.navbar-fixed-bottom .navbar-inner{-webkit-box-shadow:0 -1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,0.1);box-shadow:0 -1px 10px rgba(0,0,0,0.1)}.navbar .nav{position:relative;left:0;display:block;float:left;margin:0 10px 0 0}.navbar .nav.pull-right{float:right;margin-right:0}.navbar .nav>li{float:left}.navbar .nav>li>a{float:none;padding:10px 15px 10px;color:#777;text-decoration:none;text-shadow:0 1px 0 #fff}.navbar .nav .dropdown-toggle .caret{margin-top:8px}.navbar .nav>li>a:focus,.navbar .nav>li>a:hover{color:#333;text-decoration:none;background-color:transparent}.navbar .nav>.active>a,.navbar .nav>.active>a:hover,.navbar .nav>.active>a:focus{color:#555;text-decoration:none;background-color:#e5e5e5;-webkit-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);-moz-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);box-shadow:inset 0 3px 8px rgba(0,0,0,0.125)}.navbar .btn-navbar{display:none;float:right;padding:7px 10px;margin-right:5px;margin-left:5px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#ededed;*background-color:#e5e5e5;background-image:-moz-linear-gradient(top,#f2f2f2,#e5e5e5);background-image:-webkit-gradient(linear,0 0,0 100%,from(#f2f2f2),to(#e5e5e5));background-image:-webkit-linear-gradient(top,#f2f2f2,#e5e5e5);background-image:-o-linear-gradient(top,#f2f2f2,#e5e5e5);background-image:linear-gradient(to bottom,#f2f2f2,#e5e5e5);background-repeat:repeat-x;border-color:#e5e5e5 #e5e5e5 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2',endColorstr='#ffe5e5e5',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.075);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.075);box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.075)}.navbar .btn-navbar:hover,.navbar .btn-navbar:focus,.navbar .btn-navbar:active,.navbar .btn-navbar.active,.navbar .btn-navbar.disabled,.navbar .btn-navbar[disabled]{color:#fff;background-color:#e5e5e5;*background-color:#d9d9d9}.navbar .btn-navbar:active,.navbar .btn-navbar.active{background-color:#ccc \9}.navbar .btn-navbar .icon-bar{display:block;width:18px;height:2px;background-color:#f5f5f5;-webkit-border-radius:1px;-moz-border-radius:1px;border-radius:1px;-webkit-box-shadow:0 1px 0 rgba(0,0,0,0.25);-moz-box-shadow:0 1px 0 rgba(0,0,0,0.25);box-shadow:0 1px 0 rgba(0,0,0,0.25)}.btn-navbar .icon-bar+.icon-bar{margin-top:3px}.navbar .nav>li>.dropdown-menu:before{position:absolute;top:-7px;left:9px;display:inline-block;border-right:7px solid transparent;border-bottom:7px solid #ccc;border-left:7px solid transparent;border-bottom-color:rgba(0,0,0,0.2);content:''}.navbar .nav>li>.dropdown-menu:after{position:absolute;top:-6px;left:10px;display:inline-block;border-right:6px solid transparent;border-bottom:6px solid #fff;border-left:6px solid transparent;content:''}.navbar-fixed-bottom .nav>li>.dropdown-menu:before{top:auto;bottom:-7px;border-top:7px solid #ccc;border-bottom:0;border-top-color:rgba(0,0,0,0.2)}.navbar-fixed-bottom .nav>li>.dropdown-menu:after{top:auto;bottom:-6px;border-top:6px solid #fff;border-bottom:0}.navbar .nav li.dropdown>a:hover .caret,.navbar .nav li.dropdown>a:focus .caret{border-top-color:#333;border-bottom-color:#333}.navbar .nav li.dropdown.open>.dropdown-toggle,.navbar .nav li.dropdown.active>.dropdown-toggle,.navbar .nav li.dropdown.open.active>.dropdown-toggle{color:#555;background-color:#e5e5e5}.navbar .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#777;border-bottom-color:#777}.navbar .nav li.dropdown.open>.dropdown-toggle .caret,.navbar .nav li.dropdown.active>.dropdown-toggle .caret,.navbar .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}.navbar .pull-right>li>.dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right{right:0;left:auto}.navbar .pull-right>li>.dropdown-menu:before,.navbar .nav>li>.dropdown-menu.pull-right:before{right:12px;left:auto}.navbar .pull-right>li>.dropdown-menu:after,.navbar .nav>li>.dropdown-menu.pull-right:after{right:13px;left:auto}.navbar .pull-right>li>.dropdown-menu .dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right .dropdown-menu{right:100%;left:auto;margin-right:-1px;margin-left:0;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}.navbar-inverse .navbar-inner{background-color:#1b1b1b;background-image:-moz-linear-gradient(top,#222,#111);background-image:-webkit-gradient(linear,0 0,0 100%,from(#222),to(#111));background-image:-webkit-linear-gradient(top,#222,#111);background-image:-o-linear-gradient(top,#222,#111);background-image:linear-gradient(to bottom,#222,#111);background-repeat:repeat-x;border-color:#252525;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222',endColorstr='#ff111111',GradientType=0)}.navbar-inverse .brand,.navbar-inverse .nav>li>a{color:#999;text-shadow:0 -1px 0 rgba(0,0,0,0.25)}.navbar-inverse .brand:hover,.navbar-inverse .nav>li>a:hover,.navbar-inverse .brand:focus,.navbar-inverse .nav>li>a:focus{color:#fff}.navbar-inverse .brand{color:#999}.navbar-inverse .navbar-text{color:#999}.navbar-inverse .nav>li>a:focus,.navbar-inverse .nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .nav .active>a,.navbar-inverse .nav .active>a:hover,.navbar-inverse .nav .active>a:focus{color:#fff;background-color:#111}.navbar-inverse .navbar-link{color:#999}.navbar-inverse .navbar-link:hover,.navbar-inverse .navbar-link:focus{color:#fff}.navbar-inverse .divider-vertical{border-right-color:#222;border-left-color:#111}.navbar-inverse .nav li.dropdown.open>.dropdown-toggle,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle{color:#fff;background-color:#111}.navbar-inverse .nav li.dropdown>a:hover .caret,.navbar-inverse .nav li.dropdown>a:focus .caret{border-top-color:#fff;border-bottom-color:#fff}.navbar-inverse .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#999;border-bottom-color:#999}.navbar-inverse .nav li.dropdown.open>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}.navbar-inverse .navbar-search .search-query{color:#fff;background-color:#515151;border-color:#111;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1),0 1px 0 rgba(255,255,255,0.15);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1),0 1px 0 rgba(255,255,255,0.15);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1),0 1px 0 rgba(255,255,255,0.15);-webkit-transition:none;-moz-transition:none;-o-transition:none;transition:none}.navbar-inverse .navbar-search .search-query:-moz-placeholder{color:#ccc}.navbar-inverse .navbar-search .search-query:-ms-input-placeholder{color:#ccc}.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder{color:#ccc}.navbar-inverse .navbar-search .search-query:focus,.navbar-inverse .navbar-search .search-query.focused{padding:5px 15px;color:#333;text-shadow:0 1px 0 #fff;background-color:#fff;border:0;outline:0;-webkit-box-shadow:0 0 3px rgba(0,0,0,0.15);-moz-box-shadow:0 0 3px rgba(0,0,0,0.15);box-shadow:0 0 3px rgba(0,0,0,0.15)}.navbar-inverse .btn-navbar{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e0e0e;*background-color:#040404;background-image:-moz-linear-gradient(top,#151515,#040404);background-image:-webkit-gradient(linear,0 0,0 100%,from(#151515),to(#040404));background-image:-webkit-linear-gradient(top,#151515,#040404);background-image:-o-linear-gradient(top,#151515,#040404);background-image:linear-gradient(to bottom,#151515,#040404);background-repeat:repeat-x;border-color:#040404 #040404 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515',endColorstr='#ff040404',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#fff;background-color:#040404;*background-color:#000}.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000 \9}.breadcrumb{padding:8px 15px;margin:0 0 20px;list-style:none;background-color:#f5f5f5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.breadcrumb>li{display:inline-block;*display:inline;text-shadow:0 1px 0 #fff;*zoom:1}.breadcrumb>li>.divider{padding:0 5px;color:#ccc}.breadcrumb>.active{color:#999}.pagination{margin:20px 0}.pagination ul{display:inline-block;*display:inline;margin-bottom:0;margin-left:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*zoom:1;-webkit-box-shadow:0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:0 1px 2px rgba(0,0,0,0.05);box-shadow:0 1px 2px rgba(0,0,0,0.05)}.pagination ul>li{display:inline}.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:20px;text-decoration:none;background-color:#fff;border:1px solid #ddd;border-left-width:0}.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5}.pagination ul>.active>a,.pagination ul>.active>span{color:#999;cursor:default}.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#999;cursor:default;background-color:transparent}.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-bottomleft:4px;-moz-border-radius-topleft:4px}.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-moz-border-radius-topright:4px;-moz-border-radius-bottomright:4px}.pagination-centered{text-align:center}.pagination-right{text-align:right}.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:17.5px}.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-bottom-left-radius:6px;border-bottom-left-radius:6px;-webkit-border-top-left-radius:6px;border-top-left-radius:6px;-moz-border-radius-bottomleft:6px;-moz-border-radius-topleft:6px}.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;border-bottom-right-radius:6px;-moz-border-radius-topright:6px;-moz-border-radius-bottomright:6px}.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-bottom-left-radius:3px;border-bottom-left-radius:3px;-webkit-border-top-left-radius:3px;border-top-left-radius:3px;-moz-border-radius-bottomleft:3px;-moz-border-radius-topleft:3px}.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;border-bottom-right-radius:3px;-moz-border-radius-topright:3px;-moz-border-radius-bottomright:3px}.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:11.9px}.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:10.5px}.pager{margin:20px 0;text-align:center;list-style:none;*zoom:1}.pager:before,.pager:after{display:table;line-height:0;content:""}.pager:after{clear:both}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999;cursor:default;background-color:#fff}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}.modal-backdrop,.modal-backdrop.fade.in{opacity:.8;filter:alpha(opacity=80)}.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#fff;border:1px solid #999;border:1px solid rgba(0,0,0,0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;outline:0;-webkit-box-shadow:0 3px 7px rgba(0,0,0,0.3);-moz-box-shadow:0 3px 7px rgba(0,0,0,0.3);box-shadow:0 3px 7px rgba(0,0,0,0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box}.modal.fade{top:-25%;-webkit-transition:opacity .3s linear,top .3s ease-out;-moz-transition:opacity .3s linear,top .3s ease-out;-o-transition:opacity .3s linear,top .3s ease-out;transition:opacity .3s linear,top .3s ease-out}.modal.fade.in{top:10%}.modal-header{padding:9px 15px;border-bottom:1px solid #eee}.modal-header .close{margin-top:2px}.modal-header h3{margin:0;line-height:30px}.modal-body{position:relative;max-height:400px;padding:15px;overflow-y:auto}.modal-form{margin-bottom:0}.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;*zoom:1;-webkit-box-shadow:inset 0 1px 0 #fff;-moz-box-shadow:inset 0 1px 0 #fff;box-shadow:inset 0 1px 0 #fff}.modal-footer:before,.modal-footer:after{display:table;line-height:0;content:""}.modal-footer:after{clear:both}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.tooltip{position:absolute;z-index:1030;display:block;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0);visibility:visible}.tooltip.in{opacity:.8;filter:alpha(opacity=80)}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:8px;color:#fff;text-align:center;text-decoration:none;background-color:#000;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-top-color:#000;border-width:5px 5px 0}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-right-color:#000;border-width:5px 5px 5px 0}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-left-color:#000;border-width:5px 0 5px 5px}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-bottom-color:#000;border-width:0 5px 5px}.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;white-space:normal;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0}.popover-title:empty{display:none}.popover-content{padding:9px 14px}.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover .arrow{border-width:11px}.popover .arrow:after{border-width:10px;content:""}.popover.top .arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,0.25);border-bottom-width:0}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-top-color:#fff;border-bottom-width:0}.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,0.25);border-left-width:0}.popover.right .arrow:after{bottom:-10px;left:1px;border-right-color:#fff;border-left-width:0}.popover.bottom .arrow{top:-11px;left:50%;margin-left:-11px;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,0.25);border-top-width:0}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-bottom-color:#fff;border-top-width:0}.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-left-color:#999;border-left-color:rgba(0,0,0,0.25);border-right-width:0}.popover.left .arrow:after{right:1px;bottom:-10px;border-left-color:#fff;border-right-width:0}.thumbnails{margin-left:-20px;list-style:none;*zoom:1}.thumbnails:before,.thumbnails:after{display:table;line-height:0;content:""}.thumbnails:after{clear:both}.row-fluid .thumbnails{margin-left:0}.thumbnails>li{float:left;margin-bottom:20px;margin-left:20px}.thumbnail{display:block;padding:4px;line-height:20px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.055);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.055);box-shadow:0 1px 3px rgba(0,0,0,0.055);-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}a.thumbnail:hover,a.thumbnail:focus{border-color:#08c;-webkit-box-shadow:0 1px 4px rgba(0,105,214,0.25);-moz-box-shadow:0 1px 4px rgba(0,105,214,0.25);box-shadow:0 1px 4px rgba(0,105,214,0.25)}.thumbnail>img{display:block;max-width:100%;margin-right:auto;margin-left:auto}.thumbnail .caption{padding:9px;color:#555}.media,.media-body{overflow:hidden;*overflow:visible;zoom:1}.media,.media .media{margin-top:15px}.media:first-child{margin-top:0}.media-object{display:block}.media-heading{margin:0 0 5px}.media>.pull-left{margin-right:10px}.media>.pull-right{margin-left:10px}.media-list{margin-left:0;list-style:none}.label,.badge{display:inline-block;padding:2px 4px;font-size:11.844px;font-weight:bold;line-height:14px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);white-space:nowrap;vertical-align:baseline;background-color:#999}.label{-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.badge{padding-right:9px;padding-left:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px}.label:empty,.badge:empty{display:none}a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}.label-important,.badge-important{background-color:#b94a48}.label-important[href],.badge-important[href]{background-color:#953b39}.label-warning,.badge-warning{background-color:#f89406}.label-warning[href],.badge-warning[href]{background-color:#c67605}.label-success,.badge-success{background-color:#468847}.label-success[href],.badge-success[href]{background-color:#356635}.label-info,.badge-info{background-color:#3a87ad}.label-info[href],.badge-info[href]{background-color:#2d6987}.label-inverse,.badge-inverse{background-color:#333}.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a}.btn .label,.btn .badge{position:relative;top:-1px}.btn-mini .label,.btn-mini .badge{top:0}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:0 0}to{background-position:40px 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f7f7f7;background-image:-moz-linear-gradient(top,#f5f5f5,#f9f9f9);background-image:-webkit-gradient(linear,0 0,0 100%,from(#f5f5f5),to(#f9f9f9));background-image:-webkit-linear-gradient(top,#f5f5f5,#f9f9f9);background-image:-o-linear-gradient(top,#f5f5f5,#f9f9f9);background-image:linear-gradient(to bottom,#f5f5f5,#f9f9f9);background-repeat:repeat-x;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5',endColorstr='#fff9f9f9',GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1)}.progress .bar{float:left;width:0;height:100%;font-size:12px;color:#fff;text-align:center;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top,#149bdf,#0480be);background-image:-webkit-gradient(linear,0 0,0 100%,from(#149bdf),to(#0480be));background-image:-webkit-linear-gradient(top,#149bdf,#0480be);background-image:-o-linear-gradient(top,#149bdf,#0480be);background-image:linear-gradient(to bottom,#149bdf,#0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf',endColorstr='#ff0480be',GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width .6s ease;-moz-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,0.15),inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,0.15),inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 1px 0 0 rgba(0,0,0,0.15),inset 0 -1px 0 rgba(0,0,0,0.15)}.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px}.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top,#ee5f5b,#c43c35);background-image:-webkit-gradient(linear,0 0,0 100%,from(#ee5f5b),to(#c43c35));background-image:-webkit-linear-gradient(top,#ee5f5b,#c43c35);background-image:-o-linear-gradient(top,#ee5f5b,#c43c35);background-image:linear-gradient(to bottom,#ee5f5b,#c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b',endColorstr='#ffc43c35',GradientType=0)}.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top,#62c462,#57a957);background-image:-webkit-gradient(linear,0 0,0 100%,from(#62c462),to(#57a957));background-image:-webkit-linear-gradient(top,#62c462,#57a957);background-image:-o-linear-gradient(top,#62c462,#57a957);background-image:linear-gradient(to bottom,#62c462,#57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462',endColorstr='#ff57a957',GradientType=0)}.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top,#5bc0de,#339bb9);background-image:-webkit-gradient(linear,0 0,0 100%,from(#5bc0de),to(#339bb9));background-image:-webkit-linear-gradient(top,#5bc0de,#339bb9);background-image:-o-linear-gradient(top,#5bc0de,#339bb9);background-image:linear-gradient(to bottom,#5bc0de,#339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de',endColorstr='#ff339bb9',GradientType=0)}.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.progress-warning .bar,.progress .bar-warning{background-color:#faa732;background-image:-moz-linear-gradient(top,#fbb450,#f89406);background-image:-webkit-gradient(linear,0 0,0 100%,from(#fbb450),to(#f89406));background-image:-webkit-linear-gradient(top,#fbb450,#f89406);background-image:-o-linear-gradient(top,#fbb450,#f89406);background-image:linear-gradient(to bottom,#fbb450,#f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450',endColorstr='#fff89406',GradientType=0)}.progress-warning.progress-striped .bar,.progress-striped .bar-warning{background-color:#fbb450;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.accordion{margin-bottom:20px}.accordion-group{margin-bottom:2px;border:1px solid #e5e5e5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.accordion-heading{border-bottom:0}.accordion-heading .accordion-toggle{display:block;padding:8px 15px}.accordion-toggle{cursor:pointer}.accordion-inner{padding:9px 15px;border-top:1px solid #e5e5e5}.carousel{position:relative;margin-bottom:20px;line-height:1}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-moz-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>img,.carousel-inner>.item>a>img{display:block;line-height:1}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:40%;left:15px;width:40px;height:40px;margin-top:-20px;font-size:60px;font-weight:100;line-height:30px;color:#fff;text-align:center;background:#222;border:3px solid #fff;-webkit-border-radius:23px;-moz-border-radius:23px;border-radius:23px;opacity:.5;filter:alpha(opacity=50)}.carousel-control.right{right:15px;left:auto}.carousel-control:hover,.carousel-control:focus{color:#fff;text-decoration:none;opacity:.9;filter:alpha(opacity=90)}.carousel-indicators{position:absolute;top:15px;right:15px;z-index:5;margin:0;list-style:none}.carousel-indicators li{display:block;float:left;width:10px;height:10px;margin-left:5px;text-indent:-999px;background-color:#ccc;background-color:rgba(255,255,255,0.25);border-radius:5px}.carousel-indicators .active{background-color:#fff}.carousel-caption{position:absolute;right:0;bottom:0;left:0;padding:15px;background:#333;background:rgba(0,0,0,0.75)}.carousel-caption h4,.carousel-caption p{line-height:20px;color:#fff}.carousel-caption h4{margin:0 0 5px}.carousel-caption p{margin-bottom:0}.hero-unit{padding:60px;margin-bottom:30px;font-size:18px;font-weight:200;line-height:30px;color:inherit;background-color:#eee;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.hero-unit h1{margin-bottom:0;font-size:60px;line-height:1;letter-spacing:-1px;color:inherit}.hero-unit li{line-height:30px}.pull-right{float:right}.pull-left{float:left}.hide{display:none}.show{display:block}.invisible{visibility:hidden}.affix{position:fixed} diff --git a/docs/_static/bootstrap-2.3.2/img/glyphicons-halflings-white.png b/docs/_static/bootstrap-2.3.2/img/glyphicons-halflings-white.png deleted file mode 100644 index 3bf6484a2..000000000 Binary files a/docs/_static/bootstrap-2.3.2/img/glyphicons-halflings-white.png and /dev/null differ diff --git a/docs/_static/bootstrap-2.3.2/img/glyphicons-halflings.png b/docs/_static/bootstrap-2.3.2/img/glyphicons-halflings.png deleted file mode 100644 index a99699932..000000000 Binary files a/docs/_static/bootstrap-2.3.2/img/glyphicons-halflings.png and /dev/null differ diff --git a/docs/_static/bootstrap-2.3.2/js/bootstrap.js b/docs/_static/bootstrap-2.3.2/js/bootstrap.js deleted file mode 100644 index 638bb1877..000000000 --- a/docs/_static/bootstrap-2.3.2/js/bootstrap.js +++ /dev/null @@ -1,2287 +0,0 @@ -/* =================================================== - * bootstrap-transition.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#transitions - * =================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* CSS TRANSITION SUPPORT (http://www.modernizr.com/) - * ======================================================= */ - - $(function () { - - $.support.transition = (function () { - - var transitionEnd = (function () { - - var el = document.createElement('bootstrap') - , transEndEventNames = { - 'WebkitTransition' : 'webkitTransitionEnd' - , 'MozTransition' : 'transitionend' - , 'OTransition' : 'oTransitionEnd otransitionend' - , 'transition' : 'transitionend' - } - , name - - for (name in transEndEventNames){ - if (el.style[name] !== undefined) { - return transEndEventNames[name] - } - } - - }()) - - return transitionEnd && { - end: transitionEnd - } - - })() - - }) - -}(window.$jqTheme || window.jQuery); -/* ========================================================== - * bootstrap-alert.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#alerts - * ========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* ALERT CLASS DEFINITION - * ====================== */ - - var dismiss = '[data-dismiss="alert"]' - , Alert = function (el) { - $(el).on('click', dismiss, this.close) - } - - Alert.prototype.close = function (e) { - var $this = $(this) - , selector = $this.attr('data-target') - , $parent - - if (!selector) { - selector = $this.attr('href') - selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - $parent = $(selector) - - e && e.preventDefault() - - $parent.length || ($parent = $this.hasClass('alert') ? $this : $this.parent()) - - $parent.trigger(e = $.Event('close')) - - if (e.isDefaultPrevented()) return - - $parent.removeClass('in') - - function removeElement() { - $parent - .trigger('closed') - .remove() - } - - $.support.transition && $parent.hasClass('fade') ? - $parent.on($.support.transition.end, removeElement) : - removeElement() - } - - - /* ALERT PLUGIN DEFINITION - * ======================= */ - - var old = $.fn.alert - - $.fn.alert = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('alert') - if (!data) $this.data('alert', (data = new Alert(this))) - if (typeof option == 'string') data[option].call($this) - }) - } - - $.fn.alert.Constructor = Alert - - - /* ALERT NO CONFLICT - * ================= */ - - $.fn.alert.noConflict = function () { - $.fn.alert = old - return this - } - - - /* ALERT DATA-API - * ============== */ - - $(document).on('click.alert.data-api', dismiss, Alert.prototype.close) - -}(window.$jqTheme || window.jQuery); -/* ============================================================ - * bootstrap-button.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#buttons - * ============================================================ - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* BUTTON PUBLIC CLASS DEFINITION - * ============================== */ - - var Button = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.button.defaults, options) - } - - Button.prototype.setState = function (state) { - var d = 'disabled' - , $el = this.$element - , data = $el.data() - , val = $el.is('input') ? 'val' : 'html' - - state = state + 'Text' - data.resetText || $el.data('resetText', $el[val]()) - - $el[val](data[state] || this.options[state]) - - // push to event loop to allow forms to submit - setTimeout(function () { - state == 'loadingText' ? - $el.addClass(d).attr(d, d) : - $el.removeClass(d).removeAttr(d) - }, 0) - } - - Button.prototype.toggle = function () { - var $parent = this.$element.closest('[data-toggle="buttons-radio"]') - - $parent && $parent - .find('.active') - .removeClass('active') - - this.$element.toggleClass('active') - } - - - /* BUTTON PLUGIN DEFINITION - * ======================== */ - - var old = $.fn.button - - $.fn.button = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('button') - , options = typeof option == 'object' && option - if (!data) $this.data('button', (data = new Button(this, options))) - if (option == 'toggle') data.toggle() - else if (option) data.setState(option) - }) - } - - $.fn.button.defaults = { - loadingText: 'loading...' - } - - $.fn.button.Constructor = Button - - - /* BUTTON NO CONFLICT - * ================== */ - - $.fn.button.noConflict = function () { - $.fn.button = old - return this - } - - - /* BUTTON DATA-API - * =============== */ - - $(document).on('click.button.data-api', '[data-toggle^=button]', function (e) { - var $btn = $(e.target) - if (!$btn.hasClass('btn')) $btn = $btn.closest('.btn') - $btn.button('toggle') - }) - -}(window.$jqTheme || window.jQuery); -/* ========================================================== - * bootstrap-carousel.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#carousel - * ========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* CAROUSEL CLASS DEFINITION - * ========================= */ - - var Carousel = function (element, options) { - this.$element = $(element) - this.$indicators = this.$element.find('.carousel-indicators') - this.options = options - this.options.pause == 'hover' && this.$element - .on('mouseenter', $.proxy(this.pause, this)) - .on('mouseleave', $.proxy(this.cycle, this)) - } - - Carousel.prototype = { - - cycle: function (e) { - if (!e) this.paused = false - if (this.interval) clearInterval(this.interval); - this.options.interval - && !this.paused - && (this.interval = setInterval($.proxy(this.next, this), this.options.interval)) - return this - } - - , getActiveIndex: function () { - this.$active = this.$element.find('.item.active') - this.$items = this.$active.parent().children() - return this.$items.index(this.$active) - } - - , to: function (pos) { - var activeIndex = this.getActiveIndex() - , that = this - - if (pos > (this.$items.length - 1) || pos < 0) return - - if (this.sliding) { - return this.$element.one('slid', function () { - that.to(pos) - }) - } - - if (activeIndex == pos) { - return this.pause().cycle() - } - - return this.slide(pos > activeIndex ? 'next' : 'prev', $(this.$items[pos])) - } - - , pause: function (e) { - if (!e) this.paused = true - if (this.$element.find('.next, .prev').length && $.support.transition.end) { - this.$element.trigger($.support.transition.end) - this.cycle(true) - } - clearInterval(this.interval) - this.interval = null - return this - } - - , next: function () { - if (this.sliding) return - return this.slide('next') - } - - , prev: function () { - if (this.sliding) return - return this.slide('prev') - } - - , slide: function (type, next) { - var $active = this.$element.find('.item.active') - , $next = next || $active[type]() - , isCycling = this.interval - , direction = type == 'next' ? 'left' : 'right' - , fallback = type == 'next' ? 'first' : 'last' - , that = this - , e - - this.sliding = true - - isCycling && this.pause() - - $next = $next.length ? $next : this.$element.find('.item')[fallback]() - - e = $.Event('slide', { - relatedTarget: $next[0] - , direction: direction - }) - - if ($next.hasClass('active')) return - - if (this.$indicators.length) { - this.$indicators.find('.active').removeClass('active') - this.$element.one('slid', function () { - var $nextIndicator = $(that.$indicators.children()[that.getActiveIndex()]) - $nextIndicator && $nextIndicator.addClass('active') - }) - } - - if ($.support.transition && this.$element.hasClass('slide')) { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $next.addClass(type) - $next[0].offsetWidth // force reflow - $active.addClass(direction) - $next.addClass(direction) - this.$element.one($.support.transition.end, function () { - $next.removeClass([type, direction].join(' ')).addClass('active') - $active.removeClass(['active', direction].join(' ')) - that.sliding = false - setTimeout(function () { that.$element.trigger('slid') }, 0) - }) - } else { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $active.removeClass('active') - $next.addClass('active') - this.sliding = false - this.$element.trigger('slid') - } - - isCycling && this.cycle() - - return this - } - - } - - - /* CAROUSEL PLUGIN DEFINITION - * ========================== */ - - var old = $.fn.carousel - - $.fn.carousel = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('carousel') - , options = $.extend({}, $.fn.carousel.defaults, typeof option == 'object' && option) - , action = typeof option == 'string' ? option : options.slide - if (!data) $this.data('carousel', (data = new Carousel(this, options))) - if (typeof option == 'number') data.to(option) - else if (action) data[action]() - else if (options.interval) data.pause().cycle() - }) - } - - $.fn.carousel.defaults = { - interval: 5000 - , pause: 'hover' - } - - $.fn.carousel.Constructor = Carousel - - - /* CAROUSEL NO CONFLICT - * ==================== */ - - $.fn.carousel.noConflict = function () { - $.fn.carousel = old - return this - } - - /* CAROUSEL DATA-API - * ================= */ - - $(document).on('click.carousel.data-api', '[data-slide], [data-slide-to]', function (e) { - var $this = $(this), href - , $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 - , options = $.extend({}, $target.data(), $this.data()) - , slideIndex - - $target.carousel(options) - - if (slideIndex = $this.attr('data-slide-to')) { - $target.data('carousel').pause().to(slideIndex).cycle() - } - - e.preventDefault() - }) - -}(window.$jqTheme || window.jQuery); -/* ============================================================= - * bootstrap-collapse.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#collapse - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* COLLAPSE PUBLIC CLASS DEFINITION - * ================================ */ - - var Collapse = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.collapse.defaults, options) - - if (this.options.parent) { - this.$parent = $(this.options.parent) - } - - this.options.toggle && this.toggle() - } - - Collapse.prototype = { - - constructor: Collapse - - , dimension: function () { - var hasWidth = this.$element.hasClass('width') - return hasWidth ? 'width' : 'height' - } - - , show: function () { - var dimension - , scroll - , actives - , hasData - - if (this.transitioning || this.$element.hasClass('in')) return - - dimension = this.dimension() - scroll = $.camelCase(['scroll', dimension].join('-')) - actives = this.$parent && this.$parent.find('> .accordion-group > .in') - - if (actives && actives.length) { - hasData = actives.data('collapse') - if (hasData && hasData.transitioning) return - actives.collapse('hide') - hasData || actives.data('collapse', null) - } - - this.$element[dimension](0) - this.transition('addClass', $.Event('show'), 'shown') - $.support.transition && this.$element[dimension](this.$element[0][scroll]) - } - - , hide: function () { - var dimension - if (this.transitioning || !this.$element.hasClass('in')) return - dimension = this.dimension() - this.reset(this.$element[dimension]()) - this.transition('removeClass', $.Event('hide'), 'hidden') - this.$element[dimension](0) - } - - , reset: function (size) { - var dimension = this.dimension() - - this.$element - .removeClass('collapse') - [dimension](size || 'auto') - [0].offsetWidth - - this.$element[size !== null ? 'addClass' : 'removeClass']('collapse') - - return this - } - - , transition: function (method, startEvent, completeEvent) { - var that = this - , complete = function () { - if (startEvent.type == 'show') that.reset() - that.transitioning = 0 - that.$element.trigger(completeEvent) - } - - this.$element.trigger(startEvent) - - if (startEvent.isDefaultPrevented()) return - - this.transitioning = 1 - - this.$element[method]('in') - - $.support.transition && this.$element.hasClass('collapse') ? - this.$element.one($.support.transition.end, complete) : - complete() - } - - , toggle: function () { - this[this.$element.hasClass('in') ? 'hide' : 'show']() - } - - } - - - /* COLLAPSE PLUGIN DEFINITION - * ========================== */ - - var old = $.fn.collapse - - $.fn.collapse = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('collapse') - , options = $.extend({}, $.fn.collapse.defaults, $this.data(), typeof option == 'object' && option) - if (!data) $this.data('collapse', (data = new Collapse(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.collapse.defaults = { - toggle: true - } - - $.fn.collapse.Constructor = Collapse - - - /* COLLAPSE NO CONFLICT - * ==================== */ - - $.fn.collapse.noConflict = function () { - $.fn.collapse = old - return this - } - - - /* COLLAPSE DATA-API - * ================= */ - - $(document).on('click.collapse.data-api', '[data-toggle=collapse]', function (e) { - var $this = $(this), href - , target = $this.attr('data-target') - || e.preventDefault() - || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') //strip for ie7 - , option = $(target).data('collapse') ? 'toggle' : $this.data() - $this[$(target).hasClass('in') ? 'addClass' : 'removeClass']('collapsed') - $(target).collapse(option) - }) - -}(window.$jqTheme || window.jQuery); -/* ============================================================ - * bootstrap-dropdown.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#dropdowns - * ============================================================ - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* DROPDOWN CLASS DEFINITION - * ========================= */ - - var toggle = '[data-toggle=dropdown]' - , Dropdown = function (element) { - var $el = $(element).on('click.dropdown.data-api', this.toggle) - $('html').on('click.dropdown.data-api', function () { - $el.parent().removeClass('open') - }) - } - - Dropdown.prototype = { - - constructor: Dropdown - - , toggle: function (e) { - var $this = $(this) - , $parent - , isActive - - if ($this.is('.disabled, :disabled')) return - - $parent = getParent($this) - - isActive = $parent.hasClass('open') - - clearMenus() - - if (!isActive) { - if ('ontouchstart' in document.documentElement) { - // if mobile we we use a backdrop because click events don't delegate - $('
').insertBefore($(this)).on('click', clearMenus) - } - $parent.toggleClass('open') - } - - $this.focus() - - return false - } - - , keydown: function (e) { - var $this - , $items - , $active - , $parent - , isActive - , index - - if (!/(38|40|27)/.test(e.keyCode)) return - - $this = $(this) - - e.preventDefault() - e.stopPropagation() - - if ($this.is('.disabled, :disabled')) return - - $parent = getParent($this) - - isActive = $parent.hasClass('open') - - if (!isActive || (isActive && e.keyCode == 27)) { - if (e.which == 27) $parent.find(toggle).focus() - return $this.click() - } - - $items = $('[role=menu] li:not(.divider):visible a', $parent) - - if (!$items.length) return - - index = $items.index($items.filter(':focus')) - - if (e.keyCode == 38 && index > 0) index-- // up - if (e.keyCode == 40 && index < $items.length - 1) index++ // down - if (!~index) index = 0 - - $items - .eq(index) - .focus() - } - - } - - function clearMenus() { - $('.dropdown-backdrop').remove() - $(toggle).each(function () { - getParent($(this)).removeClass('open') - }) - } - - function getParent($this) { - var selector = $this.attr('data-target') - , $parent - - if (!selector) { - selector = $this.attr('href') - selector = selector && /#/.test(selector) && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - $parent = selector && $(selector) - - if (!$parent || !$parent.length) $parent = $this.parent() - - return $parent - } - - - /* DROPDOWN PLUGIN DEFINITION - * ========================== */ - - var old = $.fn.dropdown - - $.fn.dropdown = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('dropdown') - if (!data) $this.data('dropdown', (data = new Dropdown(this))) - if (typeof option == 'string') data[option].call($this) - }) - } - - $.fn.dropdown.Constructor = Dropdown - - - /* DROPDOWN NO CONFLICT - * ==================== */ - - $.fn.dropdown.noConflict = function () { - $.fn.dropdown = old - return this - } - - - /* APPLY TO STANDARD DROPDOWN ELEMENTS - * =================================== */ - - $(document) - .on('click.dropdown.data-api', clearMenus) - .on('click.dropdown.data-api', '.dropdown form', function (e) { e.stopPropagation() }) - .on('click.dropdown.data-api' , toggle, Dropdown.prototype.toggle) - .on('keydown.dropdown.data-api', toggle + ', [role=menu]' , Dropdown.prototype.keydown) - -}(window.$jqTheme || window.jQuery); -/* ========================================================= - * bootstrap-modal.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#modals - * ========================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================= */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* MODAL CLASS DEFINITION - * ====================== */ - - var Modal = function (element, options) { - this.options = options - this.$element = $(element) - .delegate('[data-dismiss="modal"]', 'click.dismiss.modal', $.proxy(this.hide, this)) - this.options.remote && this.$element.find('.modal-body').load(this.options.remote) - } - - Modal.prototype = { - - constructor: Modal - - , toggle: function () { - return this[!this.isShown ? 'show' : 'hide']() - } - - , show: function () { - var that = this - , e = $.Event('show') - - this.$element.trigger(e) - - if (this.isShown || e.isDefaultPrevented()) return - - this.isShown = true - - this.escape() - - this.backdrop(function () { - var transition = $.support.transition && that.$element.hasClass('fade') - - if (!that.$element.parent().length) { - that.$element.appendTo(document.body) //don't move modals dom position - } - - that.$element.show() - - if (transition) { - that.$element[0].offsetWidth // force reflow - } - - that.$element - .addClass('in') - .attr('aria-hidden', false) - - that.enforceFocus() - - transition ? - that.$element.one($.support.transition.end, function () { that.$element.focus().trigger('shown') }) : - that.$element.focus().trigger('shown') - - }) - } - - , hide: function (e) { - e && e.preventDefault() - - var that = this - - e = $.Event('hide') - - this.$element.trigger(e) - - if (!this.isShown || e.isDefaultPrevented()) return - - this.isShown = false - - this.escape() - - $(document).off('focusin.modal') - - this.$element - .removeClass('in') - .attr('aria-hidden', true) - - $.support.transition && this.$element.hasClass('fade') ? - this.hideWithTransition() : - this.hideModal() - } - - , enforceFocus: function () { - var that = this - $(document).on('focusin.modal', function (e) { - if (that.$element[0] !== e.target && !that.$element.has(e.target).length) { - that.$element.focus() - } - }) - } - - , escape: function () { - var that = this - if (this.isShown && this.options.keyboard) { - this.$element.on('keyup.dismiss.modal', function ( e ) { - e.which == 27 && that.hide() - }) - } else if (!this.isShown) { - this.$element.off('keyup.dismiss.modal') - } - } - - , hideWithTransition: function () { - var that = this - , timeout = setTimeout(function () { - that.$element.off($.support.transition.end) - that.hideModal() - }, 500) - - this.$element.one($.support.transition.end, function () { - clearTimeout(timeout) - that.hideModal() - }) - } - - , hideModal: function () { - var that = this - this.$element.hide() - this.backdrop(function () { - that.removeBackdrop() - that.$element.trigger('hidden') - }) - } - - , removeBackdrop: function () { - this.$backdrop && this.$backdrop.remove() - this.$backdrop = null - } - - , backdrop: function (callback) { - var that = this - , animate = this.$element.hasClass('fade') ? 'fade' : '' - - if (this.isShown && this.options.backdrop) { - var doAnimate = $.support.transition && animate - - this.$backdrop = $('
') - .appendTo(document.body) - - this.$backdrop.click( - this.options.backdrop == 'static' ? - $.proxy(this.$element[0].focus, this.$element[0]) - : $.proxy(this.hide, this) - ) - - if (doAnimate) this.$backdrop[0].offsetWidth // force reflow - - this.$backdrop.addClass('in') - - if (!callback) return - - doAnimate ? - this.$backdrop.one($.support.transition.end, callback) : - callback() - - } else if (!this.isShown && this.$backdrop) { - this.$backdrop.removeClass('in') - - $.support.transition && this.$element.hasClass('fade')? - this.$backdrop.one($.support.transition.end, callback) : - callback() - - } else if (callback) { - callback() - } - } - } - - - /* MODAL PLUGIN DEFINITION - * ======================= */ - - var old = $.fn.modal - - $.fn.modal = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('modal') - , options = $.extend({}, $.fn.modal.defaults, $this.data(), typeof option == 'object' && option) - if (!data) $this.data('modal', (data = new Modal(this, options))) - if (typeof option == 'string') data[option]() - else if (options.show) data.show() - }) - } - - $.fn.modal.defaults = { - backdrop: true - , keyboard: true - , show: true - } - - $.fn.modal.Constructor = Modal - - - /* MODAL NO CONFLICT - * ================= */ - - $.fn.modal.noConflict = function () { - $.fn.modal = old - return this - } - - - /* MODAL DATA-API - * ============== */ - - $(document).on('click.modal.data-api', '[data-toggle="modal"]', function (e) { - var $this = $(this) - , href = $this.attr('href') - , $target = $($this.attr('data-target') || (href && href.replace(/.*(?=#[^\s]+$)/, ''))) //strip for ie7 - , option = $target.data('modal') ? 'toggle' : $.extend({ remote:!/#/.test(href) && href }, $target.data(), $this.data()) - - e.preventDefault() - - $target - .modal(option) - .one('hide', function () { - $this.focus() - }) - }) - -}(window.$jqTheme || window.jQuery); -/* =========================================================== - * bootstrap-tooltip.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#tooltips - * Inspired by the original jQuery.tipsy by Jason Frame - * =========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* TOOLTIP PUBLIC CLASS DEFINITION - * =============================== */ - - var Tooltip = function (element, options) { - this.init('tooltip', element, options) - } - - Tooltip.prototype = { - - constructor: Tooltip - - , init: function (type, element, options) { - var eventIn - , eventOut - , triggers - , trigger - , i - - this.type = type - this.$element = $(element) - this.options = this.getOptions(options) - this.enabled = true - - triggers = this.options.trigger.split(' ') - - for (i = triggers.length; i--;) { - trigger = triggers[i] - if (trigger == 'click') { - this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this)) - } else if (trigger != 'manual') { - eventIn = trigger == 'hover' ? 'mouseenter' : 'focus' - eventOut = trigger == 'hover' ? 'mouseleave' : 'blur' - this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this)) - this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this)) - } - } - - this.options.selector ? - (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) : - this.fixTitle() - } - - , getOptions: function (options) { - options = $.extend({}, $.fn[this.type].defaults, this.$element.data(), options) - - if (options.delay && typeof options.delay == 'number') { - options.delay = { - show: options.delay - , hide: options.delay - } - } - - return options - } - - , enter: function (e) { - var defaults = $.fn[this.type].defaults - , options = {} - , self - - this._options && $.each(this._options, function (key, value) { - if (defaults[key] != value) options[key] = value - }, this) - - self = $(e.currentTarget)[this.type](options).data(this.type) - - if (!self.options.delay || !self.options.delay.show) return self.show() - - clearTimeout(this.timeout) - self.hoverState = 'in' - this.timeout = setTimeout(function() { - if (self.hoverState == 'in') self.show() - }, self.options.delay.show) - } - - , leave: function (e) { - var self = $(e.currentTarget)[this.type](this._options).data(this.type) - - if (this.timeout) clearTimeout(this.timeout) - if (!self.options.delay || !self.options.delay.hide) return self.hide() - - self.hoverState = 'out' - this.timeout = setTimeout(function() { - if (self.hoverState == 'out') self.hide() - }, self.options.delay.hide) - } - - , show: function () { - var $tip - , pos - , actualWidth - , actualHeight - , placement - , tp - , e = $.Event('show') - - if (this.hasContent() && this.enabled) { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $tip = this.tip() - this.setContent() - - if (this.options.animation) { - $tip.addClass('fade') - } - - placement = typeof this.options.placement == 'function' ? - this.options.placement.call(this, $tip[0], this.$element[0]) : - this.options.placement - - $tip - .detach() - .css({ top: 0, left: 0, display: 'block' }) - - this.options.container ? $tip.appendTo(this.options.container) : $tip.insertAfter(this.$element) - - pos = this.getPosition() - - actualWidth = $tip[0].offsetWidth - actualHeight = $tip[0].offsetHeight - - switch (placement) { - case 'bottom': - tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2} - break - case 'top': - tp = {top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2} - break - case 'left': - tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth} - break - case 'right': - tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width} - break - } - - this.applyPlacement(tp, placement) - this.$element.trigger('shown') - } - } - - , applyPlacement: function(offset, placement){ - var $tip = this.tip() - , width = $tip[0].offsetWidth - , height = $tip[0].offsetHeight - , actualWidth - , actualHeight - , delta - , replace - - $tip - .offset(offset) - .addClass(placement) - .addClass('in') - - actualWidth = $tip[0].offsetWidth - actualHeight = $tip[0].offsetHeight - - if (placement == 'top' && actualHeight != height) { - offset.top = offset.top + height - actualHeight - replace = true - } - - if (placement == 'bottom' || placement == 'top') { - delta = 0 - - if (offset.left < 0){ - delta = offset.left * -2 - offset.left = 0 - $tip.offset(offset) - actualWidth = $tip[0].offsetWidth - actualHeight = $tip[0].offsetHeight - } - - this.replaceArrow(delta - width + actualWidth, actualWidth, 'left') - } else { - this.replaceArrow(actualHeight - height, actualHeight, 'top') - } - - if (replace) $tip.offset(offset) - } - - , replaceArrow: function(delta, dimension, position){ - this - .arrow() - .css(position, delta ? (50 * (1 - delta / dimension) + "%") : '') - } - - , setContent: function () { - var $tip = this.tip() - , title = this.getTitle() - - $tip.find('.tooltip-inner')[this.options.html ? 'html' : 'text'](title) - $tip.removeClass('fade in top bottom left right') - } - - , hide: function () { - var that = this - , $tip = this.tip() - , e = $.Event('hide') - - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - - $tip.removeClass('in') - - function removeWithAnimation() { - var timeout = setTimeout(function () { - $tip.off($.support.transition.end).detach() - }, 500) - - $tip.one($.support.transition.end, function () { - clearTimeout(timeout) - $tip.detach() - }) - } - - $.support.transition && this.$tip.hasClass('fade') ? - removeWithAnimation() : - $tip.detach() - - this.$element.trigger('hidden') - - return this - } - - , fixTitle: function () { - var $e = this.$element - if ($e.attr('title') || typeof($e.attr('data-original-title')) != 'string') { - $e.attr('data-original-title', $e.attr('title') || '').attr('title', '') - } - } - - , hasContent: function () { - return this.getTitle() - } - - , getPosition: function () { - var el = this.$element[0] - return $.extend({}, (typeof el.getBoundingClientRect == 'function') ? el.getBoundingClientRect() : { - width: el.offsetWidth - , height: el.offsetHeight - }, this.$element.offset()) - } - - , getTitle: function () { - var title - , $e = this.$element - , o = this.options - - title = $e.attr('data-original-title') - || (typeof o.title == 'function' ? o.title.call($e[0]) : o.title) - - return title - } - - , tip: function () { - return this.$tip = this.$tip || $(this.options.template) - } - - , arrow: function(){ - return this.$arrow = this.$arrow || this.tip().find(".tooltip-arrow") - } - - , validate: function () { - if (!this.$element[0].parentNode) { - this.hide() - this.$element = null - this.options = null - } - } - - , enable: function () { - this.enabled = true - } - - , disable: function () { - this.enabled = false - } - - , toggleEnabled: function () { - this.enabled = !this.enabled - } - - , toggle: function (e) { - var self = e ? $(e.currentTarget)[this.type](this._options).data(this.type) : this - self.tip().hasClass('in') ? self.hide() : self.show() - } - - , destroy: function () { - this.hide().$element.off('.' + this.type).removeData(this.type) - } - - } - - - /* TOOLTIP PLUGIN DEFINITION - * ========================= */ - - var old = $.fn.tooltip - - $.fn.tooltip = function ( option ) { - return this.each(function () { - var $this = $(this) - , data = $this.data('tooltip') - , options = typeof option == 'object' && option - if (!data) $this.data('tooltip', (data = new Tooltip(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.tooltip.Constructor = Tooltip - - $.fn.tooltip.defaults = { - animation: true - , placement: 'top' - , selector: false - , template: '
' - , trigger: 'hover focus' - , title: '' - , delay: 0 - , html: false - , container: false - } - - - /* TOOLTIP NO CONFLICT - * =================== */ - - $.fn.tooltip.noConflict = function () { - $.fn.tooltip = old - return this - } - -}(window.$jqTheme || window.jQuery); -/* =========================================================== - * bootstrap-popover.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#popovers - * =========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * =========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* POPOVER PUBLIC CLASS DEFINITION - * =============================== */ - - var Popover = function (element, options) { - this.init('popover', element, options) - } - - - /* NOTE: POPOVER EXTENDS BOOTSTRAP-TOOLTIP.js - ========================================== */ - - Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype, { - - constructor: Popover - - , setContent: function () { - var $tip = this.tip() - , title = this.getTitle() - , content = this.getContent() - - $tip.find('.popover-title')[this.options.html ? 'html' : 'text'](title) - $tip.find('.popover-content')[this.options.html ? 'html' : 'text'](content) - - $tip.removeClass('fade top bottom left right in') - } - - , hasContent: function () { - return this.getTitle() || this.getContent() - } - - , getContent: function () { - var content - , $e = this.$element - , o = this.options - - content = (typeof o.content == 'function' ? o.content.call($e[0]) : o.content) - || $e.attr('data-content') - - return content - } - - , tip: function () { - if (!this.$tip) { - this.$tip = $(this.options.template) - } - return this.$tip - } - - , destroy: function () { - this.hide().$element.off('.' + this.type).removeData(this.type) - } - - }) - - - /* POPOVER PLUGIN DEFINITION - * ======================= */ - - var old = $.fn.popover - - $.fn.popover = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('popover') - , options = typeof option == 'object' && option - if (!data) $this.data('popover', (data = new Popover(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.popover.Constructor = Popover - - $.fn.popover.defaults = $.extend({} , $.fn.tooltip.defaults, { - placement: 'right' - , trigger: 'click' - , content: '' - , template: '

' - }) - - - /* POPOVER NO CONFLICT - * =================== */ - - $.fn.popover.noConflict = function () { - $.fn.popover = old - return this - } - -}(window.$jqTheme || window.jQuery); -/* ============================================================= - * bootstrap-scrollspy.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#scrollspy - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* SCROLLSPY CLASS DEFINITION - * ========================== */ - - function ScrollSpy(element, options) { - var process = $.proxy(this.process, this) - , $element = $(element).is('body') ? $(window) : $(element) - , href - this.options = $.extend({}, $.fn.scrollspy.defaults, options) - this.$scrollElement = $element.on('scroll.scroll-spy.data-api', process) - this.selector = (this.options.target - || ((href = $(element).attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 - || '') + ' .nav li > a' - this.$body = $('body') - this.refresh() - this.process() - } - - ScrollSpy.prototype = { - - constructor: ScrollSpy - - , refresh: function () { - var self = this - , $targets - - this.offsets = $([]) - this.targets = $([]) - - $targets = this.$body - .find(this.selector) - .map(function () { - var $el = $(this) - , href = $el.data('target') || $el.attr('href') - , $href = /^#\w/.test(href) && $(href) - return ( $href - && $href.length - && [[ $href.position().top + (!$.isWindow(self.$scrollElement.get(0)) && self.$scrollElement.scrollTop()), href ]] ) || null - }) - .sort(function (a, b) { return a[0] - b[0] }) - .each(function () { - self.offsets.push(this[0]) - self.targets.push(this[1]) - }) - } - - , process: function () { - var scrollTop = this.$scrollElement.scrollTop() + this.options.offset - , scrollHeight = this.$scrollElement[0].scrollHeight || this.$body[0].scrollHeight - , maxScroll = scrollHeight - this.$scrollElement.height() - , offsets = this.offsets - , targets = this.targets - , activeTarget = this.activeTarget - , i - - if (scrollTop >= maxScroll) { - return activeTarget != (i = targets.last()[0]) - && this.activate ( i ) - } - - for (i = offsets.length; i--;) { - activeTarget != targets[i] - && scrollTop >= offsets[i] - && (!offsets[i + 1] || scrollTop <= offsets[i + 1]) - && this.activate( targets[i] ) - } - } - - , activate: function (target) { - var active - , selector - - this.activeTarget = target - - $(this.selector) - .parent('.active') - .removeClass('active') - - selector = this.selector - + '[data-target="' + target + '"],' - + this.selector + '[href="' + target + '"]' - - active = $(selector) - .parent('li') - .addClass('active') - - if (active.parent('.dropdown-menu').length) { - active = active.closest('li.dropdown').addClass('active') - } - - active.trigger('activate') - } - - } - - - /* SCROLLSPY PLUGIN DEFINITION - * =========================== */ - - var old = $.fn.scrollspy - - $.fn.scrollspy = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('scrollspy') - , options = typeof option == 'object' && option - if (!data) $this.data('scrollspy', (data = new ScrollSpy(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.scrollspy.Constructor = ScrollSpy - - $.fn.scrollspy.defaults = { - offset: 10 - } - - - /* SCROLLSPY NO CONFLICT - * ===================== */ - - $.fn.scrollspy.noConflict = function () { - $.fn.scrollspy = old - return this - } - - - /* SCROLLSPY DATA-API - * ================== */ - - $(window).on('load', function () { - $('[data-spy="scroll"]').each(function () { - var $spy = $(this) - $spy.scrollspy($spy.data()) - }) - }) - -}(window.$jqTheme || window.jQuery); -/* ======================================================== - * bootstrap-tab.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#tabs - * ======================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ======================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* TAB CLASS DEFINITION - * ==================== */ - - var Tab = function (element) { - this.element = $(element) - } - - Tab.prototype = { - - constructor: Tab - - , show: function () { - var $this = this.element - , $ul = $this.closest('ul:not(.dropdown-menu)') - , selector = $this.attr('data-target') - , previous - , $target - , e - - if (!selector) { - selector = $this.attr('href') - selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - if ( $this.parent('li').hasClass('active') ) return - - previous = $ul.find('.active:last a')[0] - - e = $.Event('show', { - relatedTarget: previous - }) - - $this.trigger(e) - - if (e.isDefaultPrevented()) return - - $target = $(selector) - - this.activate($this.parent('li'), $ul) - this.activate($target, $target.parent(), function () { - $this.trigger({ - type: 'shown' - , relatedTarget: previous - }) - }) - } - - , activate: function ( element, container, callback) { - var $active = container.find('> .active') - , transition = callback - && $.support.transition - && $active.hasClass('fade') - - function next() { - $active - .removeClass('active') - .find('> .dropdown-menu > .active') - .removeClass('active') - - element.addClass('active') - - if (transition) { - element[0].offsetWidth // reflow for transition - element.addClass('in') - } else { - element.removeClass('fade') - } - - if ( element.parent('.dropdown-menu') ) { - element.closest('li.dropdown').addClass('active') - } - - callback && callback() - } - - transition ? - $active.one($.support.transition.end, next) : - next() - - $active.removeClass('in') - } - } - - - /* TAB PLUGIN DEFINITION - * ===================== */ - - var old = $.fn.tab - - $.fn.tab = function ( option ) { - return this.each(function () { - var $this = $(this) - , data = $this.data('tab') - if (!data) $this.data('tab', (data = new Tab(this))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.tab.Constructor = Tab - - - /* TAB NO CONFLICT - * =============== */ - - $.fn.tab.noConflict = function () { - $.fn.tab = old - return this - } - - - /* TAB DATA-API - * ============ */ - - $(document).on('click.tab.data-api', '[data-toggle="tab"], [data-toggle="pill"]', function (e) { - e.preventDefault() - $(this).tab('show') - }) - -}(window.$jqTheme || window.jQuery); -/* ============================================================= - * bootstrap-typeahead.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#typeahead - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function($){ - - "use strict"; // jshint ;_; - - - /* TYPEAHEAD PUBLIC CLASS DEFINITION - * ================================= */ - - var Typeahead = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.typeahead.defaults, options) - this.matcher = this.options.matcher || this.matcher - this.sorter = this.options.sorter || this.sorter - this.highlighter = this.options.highlighter || this.highlighter - this.updater = this.options.updater || this.updater - this.source = this.options.source - this.$menu = $(this.options.menu) - this.shown = false - this.listen() - } - - Typeahead.prototype = { - - constructor: Typeahead - - , select: function () { - var val = this.$menu.find('.active').attr('data-value') - this.$element - .val(this.updater(val)) - .change() - return this.hide() - } - - , updater: function (item) { - return item - } - - , show: function () { - var pos = $.extend({}, this.$element.position(), { - height: this.$element[0].offsetHeight - }) - - this.$menu - .insertAfter(this.$element) - .css({ - top: pos.top + pos.height - , left: pos.left - }) - .show() - - this.shown = true - return this - } - - , hide: function () { - this.$menu.hide() - this.shown = false - return this - } - - , lookup: function (event) { - var items - - this.query = this.$element.val() - - if (!this.query || this.query.length < this.options.minLength) { - return this.shown ? this.hide() : this - } - - items = $.isFunction(this.source) ? this.source(this.query, $.proxy(this.process, this)) : this.source - - return items ? this.process(items) : this - } - - , process: function (items) { - var that = this - - items = $.grep(items, function (item) { - return that.matcher(item) - }) - - items = this.sorter(items) - - if (!items.length) { - return this.shown ? this.hide() : this - } - - return this.render(items.slice(0, this.options.items)).show() - } - - , matcher: function (item) { - return ~item.toLowerCase().indexOf(this.query.toLowerCase()) - } - - , sorter: function (items) { - var beginswith = [] - , caseSensitive = [] - , caseInsensitive = [] - , item - - while (item = items.shift()) { - if (!item.toLowerCase().indexOf(this.query.toLowerCase())) beginswith.push(item) - else if (~item.indexOf(this.query)) caseSensitive.push(item) - else caseInsensitive.push(item) - } - - return beginswith.concat(caseSensitive, caseInsensitive) - } - - , highlighter: function (item) { - var query = this.query.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g, '\\$&') - return item.replace(new RegExp('(' + query + ')', 'ig'), function ($1, match) { - return '' + match + '' - }) - } - - , render: function (items) { - var that = this - - items = $(items).map(function (i, item) { - i = $(that.options.item).attr('data-value', item) - i.find('a').html(that.highlighter(item)) - return i[0] - }) - - items.first().addClass('active') - this.$menu.html(items) - return this - } - - , next: function (event) { - var active = this.$menu.find('.active').removeClass('active') - , next = active.next() - - if (!next.length) { - next = $(this.$menu.find('li')[0]) - } - - next.addClass('active') - } - - , prev: function (event) { - var active = this.$menu.find('.active').removeClass('active') - , prev = active.prev() - - if (!prev.length) { - prev = this.$menu.find('li').last() - } - - prev.addClass('active') - } - - , listen: function () { - this.$element - .on('focus', $.proxy(this.focus, this)) - .on('blur', $.proxy(this.blur, this)) - .on('keypress', $.proxy(this.keypress, this)) - .on('keyup', $.proxy(this.keyup, this)) - - if (this.eventSupported('keydown')) { - this.$element.on('keydown', $.proxy(this.keydown, this)) - } - - this.$menu - .on('click', $.proxy(this.click, this)) - .on('mouseenter', 'li', $.proxy(this.mouseenter, this)) - .on('mouseleave', 'li', $.proxy(this.mouseleave, this)) - } - - , eventSupported: function(eventName) { - var isSupported = eventName in this.$element - if (!isSupported) { - this.$element.setAttribute(eventName, 'return;') - isSupported = typeof this.$element[eventName] === 'function' - } - return isSupported - } - - , move: function (e) { - if (!this.shown) return - - switch(e.keyCode) { - case 9: // tab - case 13: // enter - case 27: // escape - e.preventDefault() - break - - case 38: // up arrow - e.preventDefault() - this.prev() - break - - case 40: // down arrow - e.preventDefault() - this.next() - break - } - - e.stopPropagation() - } - - , keydown: function (e) { - this.suppressKeyPressRepeat = ~$.inArray(e.keyCode, [40,38,9,13,27]) - this.move(e) - } - - , keypress: function (e) { - if (this.suppressKeyPressRepeat) return - this.move(e) - } - - , keyup: function (e) { - switch(e.keyCode) { - case 40: // down arrow - case 38: // up arrow - case 16: // shift - case 17: // ctrl - case 18: // alt - break - - case 9: // tab - case 13: // enter - if (!this.shown) return - this.select() - break - - case 27: // escape - if (!this.shown) return - this.hide() - break - - default: - this.lookup() - } - - e.stopPropagation() - e.preventDefault() - } - - , focus: function (e) { - this.focused = true - } - - , blur: function (e) { - this.focused = false - if (!this.mousedover && this.shown) this.hide() - } - - , click: function (e) { - e.stopPropagation() - e.preventDefault() - this.select() - this.$element.focus() - } - - , mouseenter: function (e) { - this.mousedover = true - this.$menu.find('.active').removeClass('active') - $(e.currentTarget).addClass('active') - } - - , mouseleave: function (e) { - this.mousedover = false - if (!this.focused && this.shown) this.hide() - } - - } - - - /* TYPEAHEAD PLUGIN DEFINITION - * =========================== */ - - var old = $.fn.typeahead - - $.fn.typeahead = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('typeahead') - , options = typeof option == 'object' && option - if (!data) $this.data('typeahead', (data = new Typeahead(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.typeahead.defaults = { - source: [] - , items: 8 - , menu: '
    ' - , item: '
    • ' - , minLength: 1 - } - - $.fn.typeahead.Constructor = Typeahead - - - /* TYPEAHEAD NO CONFLICT - * =================== */ - - $.fn.typeahead.noConflict = function () { - $.fn.typeahead = old - return this - } - - - /* TYPEAHEAD DATA-API - * ================== */ - - $(document).on('focus.typeahead.data-api', '[data-provide="typeahead"]', function (e) { - var $this = $(this) - if ($this.data('typeahead')) return - $this.typeahead($this.data()) - }) - -}(window.$jqTheme || window.jQuery); -/* ========================================================== - * bootstrap-affix.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#affix - * ========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* AFFIX CLASS DEFINITION - * ====================== */ - - var Affix = function (element, options) { - this.options = $.extend({}, $.fn.affix.defaults, options) - this.$window = $(window) - .on('scroll.affix.data-api', $.proxy(this.checkPosition, this)) - .on('click.affix.data-api', $.proxy(function () { setTimeout($.proxy(this.checkPosition, this), 1) }, this)) - this.$element = $(element) - this.checkPosition() - } - - Affix.prototype.checkPosition = function () { - if (!this.$element.is(':visible')) return - - var scrollHeight = $(document).height() - , scrollTop = this.$window.scrollTop() - , position = this.$element.offset() - , offset = this.options.offset - , offsetBottom = offset.bottom - , offsetTop = offset.top - , reset = 'affix affix-top affix-bottom' - , affix - - if (typeof offset != 'object') offsetBottom = offsetTop = offset - if (typeof offsetTop == 'function') offsetTop = offset.top() - if (typeof offsetBottom == 'function') offsetBottom = offset.bottom() - - affix = this.unpin != null && (scrollTop + this.unpin <= position.top) ? - false : offsetBottom != null && (position.top + this.$element.height() >= scrollHeight - offsetBottom) ? - 'bottom' : offsetTop != null && scrollTop <= offsetTop ? - 'top' : false - - if (this.affixed === affix) return - - this.affixed = affix - this.unpin = affix == 'bottom' ? position.top - scrollTop : null - - this.$element.removeClass(reset).addClass('affix' + (affix ? '-' + affix : '')) - } - - - /* AFFIX PLUGIN DEFINITION - * ======================= */ - - var old = $.fn.affix - - $.fn.affix = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('affix') - , options = typeof option == 'object' && option - if (!data) $this.data('affix', (data = new Affix(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.affix.Constructor = Affix - - $.fn.affix.defaults = { - offset: 0 - } - - - /* AFFIX NO CONFLICT - * ================= */ - - $.fn.affix.noConflict = function () { - $.fn.affix = old - return this - } - - - /* AFFIX DATA-API - * ============== */ - - $(window).on('load', function () { - $('[data-spy="affix"]').each(function () { - var $spy = $(this) - , data = $spy.data() - - data.offset = data.offset || {} - - data.offsetBottom && (data.offset.bottom = data.offsetBottom) - data.offsetTop && (data.offset.top = data.offsetTop) - - $spy.affix(data) - }) - }) - - -}(window.$jqTheme || window.jQuery); diff --git a/docs/_static/bootstrap-2.3.2/js/bootstrap.min.js b/docs/_static/bootstrap-2.3.2/js/bootstrap.min.js deleted file mode 100644 index 3a3394bd4..000000000 --- a/docs/_static/bootstrap-2.3.2/js/bootstrap.min.js +++ /dev/null @@ -1,6 +0,0 @@ -/** -* Bootstrap.js v2.3.2 by @fat & @mdo -* Copyright 2012 Twitter, Inc. -* http://www.apache.org/licenses/LICENSE-2.0.txt -*/ -!function(e){"use strict";e(function(){e.support.transition=function(){var e=function(){var e=document.createElement("bootstrap"),t={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"},n;for(n in t)if(e.style[n]!==undefined)return t[n]}();return e&&{end:e}}()})}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t='[data-dismiss="alert"]',n=function(n){e(n).on("click",t,this.close)};n.prototype.close=function(t){function s(){i.trigger("closed").remove()}var n=e(this),r=n.attr("data-target"),i;r||(r=n.attr("href"),r=r&&r.replace(/.*(?=#[^\s]*$)/,"")),i=e(r),t&&t.preventDefault(),i.length||(i=n.hasClass("alert")?n:n.parent()),i.trigger(t=e.Event("close"));if(t.isDefaultPrevented())return;i.removeClass("in"),e.support.transition&&i.hasClass("fade")?i.on(e.support.transition.end,s):s()};var r=e.fn.alert;e.fn.alert=function(t){return this.each(function(){var r=e(this),i=r.data("alert");i||r.data("alert",i=new n(this)),typeof t=="string"&&i[t].call(r)})},e.fn.alert.Constructor=n,e.fn.alert.noConflict=function(){return e.fn.alert=r,this},e(document).on("click.alert.data-api",t,n.prototype.close)}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(t,n){this.$element=e(t),this.options=e.extend({},e.fn.button.defaults,n)};t.prototype.setState=function(e){var t="disabled",n=this.$element,r=n.data(),i=n.is("input")?"val":"html";e+="Text",r.resetText||n.data("resetText",n[i]()),n[i](r[e]||this.options[e]),setTimeout(function(){e=="loadingText"?n.addClass(t).attr(t,t):n.removeClass(t).removeAttr(t)},0)},t.prototype.toggle=function(){var e=this.$element.closest('[data-toggle="buttons-radio"]');e&&e.find(".active").removeClass("active"),this.$element.toggleClass("active")};var n=e.fn.button;e.fn.button=function(n){return this.each(function(){var r=e(this),i=r.data("button"),s=typeof n=="object"&&n;i||r.data("button",i=new t(this,s)),n=="toggle"?i.toggle():n&&i.setState(n)})},e.fn.button.defaults={loadingText:"loading..."},e.fn.button.Constructor=t,e.fn.button.noConflict=function(){return e.fn.button=n,this},e(document).on("click.button.data-api","[data-toggle^=button]",function(t){var n=e(t.target);n.hasClass("btn")||(n=n.closest(".btn")),n.button("toggle")})}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(t,n){this.$element=e(t),this.$indicators=this.$element.find(".carousel-indicators"),this.options=n,this.options.pause=="hover"&&this.$element.on("mouseenter",e.proxy(this.pause,this)).on("mouseleave",e.proxy(this.cycle,this))};t.prototype={cycle:function(t){return t||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(e.proxy(this.next,this),this.options.interval)),this},getActiveIndex:function(){return this.$active=this.$element.find(".item.active"),this.$items=this.$active.parent().children(),this.$items.index(this.$active)},to:function(t){var n=this.getActiveIndex(),r=this;if(t>this.$items.length-1||t<0)return;return this.sliding?this.$element.one("slid",function(){r.to(t)}):n==t?this.pause().cycle():this.slide(t>n?"next":"prev",e(this.$items[t]))},pause:function(t){return t||(this.paused=!0),this.$element.find(".next, .prev").length&&e.support.transition.end&&(this.$element.trigger(e.support.transition.end),this.cycle(!0)),clearInterval(this.interval),this.interval=null,this},next:function(){if(this.sliding)return;return this.slide("next")},prev:function(){if(this.sliding)return;return this.slide("prev")},slide:function(t,n){var r=this.$element.find(".item.active"),i=n||r[t](),s=this.interval,o=t=="next"?"left":"right",u=t=="next"?"first":"last",a=this,f;this.sliding=!0,s&&this.pause(),i=i.length?i:this.$element.find(".item")[u](),f=e.Event("slide",{relatedTarget:i[0],direction:o});if(i.hasClass("active"))return;this.$indicators.length&&(this.$indicators.find(".active").removeClass("active"),this.$element.one("slid",function(){var t=e(a.$indicators.children()[a.getActiveIndex()]);t&&t.addClass("active")}));if(e.support.transition&&this.$element.hasClass("slide")){this.$element.trigger(f);if(f.isDefaultPrevented())return;i.addClass(t),i[0].offsetWidth,r.addClass(o),i.addClass(o),this.$element.one(e.support.transition.end,function(){i.removeClass([t,o].join(" ")).addClass("active"),r.removeClass(["active",o].join(" ")),a.sliding=!1,setTimeout(function(){a.$element.trigger("slid")},0)})}else{this.$element.trigger(f);if(f.isDefaultPrevented())return;r.removeClass("active"),i.addClass("active"),this.sliding=!1,this.$element.trigger("slid")}return s&&this.cycle(),this}};var n=e.fn.carousel;e.fn.carousel=function(n){return this.each(function(){var r=e(this),i=r.data("carousel"),s=e.extend({},e.fn.carousel.defaults,typeof n=="object"&&n),o=typeof n=="string"?n:s.slide;i||r.data("carousel",i=new t(this,s)),typeof n=="number"?i.to(n):o?i[o]():s.interval&&i.pause().cycle()})},e.fn.carousel.defaults={interval:5e3,pause:"hover"},e.fn.carousel.Constructor=t,e.fn.carousel.noConflict=function(){return e.fn.carousel=n,this},e(document).on("click.carousel.data-api","[data-slide], [data-slide-to]",function(t){var n=e(this),r,i=e(n.attr("data-target")||(r=n.attr("href"))&&r.replace(/.*(?=#[^\s]+$)/,"")),s=e.extend({},i.data(),n.data()),o;i.carousel(s),(o=n.attr("data-slide-to"))&&i.data("carousel").pause().to(o).cycle(),t.preventDefault()})}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(t,n){this.$element=e(t),this.options=e.extend({},e.fn.collapse.defaults,n),this.options.parent&&(this.$parent=e(this.options.parent)),this.options.toggle&&this.toggle()};t.prototype={constructor:t,dimension:function(){var e=this.$element.hasClass("width");return e?"width":"height"},show:function(){var t,n,r,i;if(this.transitioning||this.$element.hasClass("in"))return;t=this.dimension(),n=e.camelCase(["scroll",t].join("-")),r=this.$parent&&this.$parent.find("> .accordion-group > .in");if(r&&r.length){i=r.data("collapse");if(i&&i.transitioning)return;r.collapse("hide"),i||r.data("collapse",null)}this.$element[t](0),this.transition("addClass",e.Event("show"),"shown"),e.support.transition&&this.$element[t](this.$element[0][n])},hide:function(){var t;if(this.transitioning||!this.$element.hasClass("in"))return;t=this.dimension(),this.reset(this.$element[t]()),this.transition("removeClass",e.Event("hide"),"hidden"),this.$element[t](0)},reset:function(e){var t=this.dimension();return this.$element.removeClass("collapse")[t](e||"auto")[0].offsetWidth,this.$element[e!==null?"addClass":"removeClass"]("collapse"),this},transition:function(t,n,r){var i=this,s=function(){n.type=="show"&&i.reset(),i.transitioning=0,i.$element.trigger(r)};this.$element.trigger(n);if(n.isDefaultPrevented())return;this.transitioning=1,this.$element[t]("in"),e.support.transition&&this.$element.hasClass("collapse")?this.$element.one(e.support.transition.end,s):s()},toggle:function(){this[this.$element.hasClass("in")?"hide":"show"]()}};var n=e.fn.collapse;e.fn.collapse=function(n){return this.each(function(){var r=e(this),i=r.data("collapse"),s=e.extend({},e.fn.collapse.defaults,r.data(),typeof n=="object"&&n);i||r.data("collapse",i=new t(this,s)),typeof n=="string"&&i[n]()})},e.fn.collapse.defaults={toggle:!0},e.fn.collapse.Constructor=t,e.fn.collapse.noConflict=function(){return e.fn.collapse=n,this},e(document).on("click.collapse.data-api","[data-toggle=collapse]",function(t){var n=e(this),r,i=n.attr("data-target")||t.preventDefault()||(r=n.attr("href"))&&r.replace(/.*(?=#[^\s]+$)/,""),s=e(i).data("collapse")?"toggle":n.data();n[e(i).hasClass("in")?"addClass":"removeClass"]("collapsed"),e(i).collapse(s)})}(window.$jqTheme||window.jQuery),!function(e){"use strict";function r(){e(".dropdown-backdrop").remove(),e(t).each(function(){i(e(this)).removeClass("open")})}function i(t){var n=t.attr("data-target"),r;n||(n=t.attr("href"),n=n&&/#/.test(n)&&n.replace(/.*(?=#[^\s]*$)/,"")),r=n&&e(n);if(!r||!r.length)r=t.parent();return r}var t="[data-toggle=dropdown]",n=function(t){var n=e(t).on("click.dropdown.data-api",this.toggle);e("html").on("click.dropdown.data-api",function(){n.parent().removeClass("open")})};n.prototype={constructor:n,toggle:function(t){var n=e(this),s,o;if(n.is(".disabled, :disabled"))return;return s=i(n),o=s.hasClass("open"),r(),o||("ontouchstart"in document.documentElement&&e('
    ').insertBefore(e(this)).on("click",r),s.toggleClass("open")),n.focus(),!1},keydown:function(n){var r,s,o,u,a,f;if(!/(38|40|27)/.test(n.keyCode))return;r=e(this),n.preventDefault(),n.stopPropagation();if(r.is(".disabled, :disabled"))return;u=i(r),a=u.hasClass("open");if(!a||a&&n.keyCode==27)return n.which==27&&u.find(t).focus(),r.click();s=e("[role=menu] li:not(.divider):visible a",u);if(!s.length)return;f=s.index(s.filter(":focus")),n.keyCode==38&&f>0&&f--,n.keyCode==40&&f').appendTo(document.body),this.$backdrop.click(this.options.backdrop=="static"?e.proxy(this.$element[0].focus,this.$element[0]):e.proxy(this.hide,this)),i&&this.$backdrop[0].offsetWidth,this.$backdrop.addClass("in");if(!t)return;i?this.$backdrop.one(e.support.transition.end,t):t()}else!this.isShown&&this.$backdrop?(this.$backdrop.removeClass("in"),e.support.transition&&this.$element.hasClass("fade")?this.$backdrop.one(e.support.transition.end,t):t()):t&&t()}};var n=e.fn.modal;e.fn.modal=function(n){return this.each(function(){var r=e(this),i=r.data("modal"),s=e.extend({},e.fn.modal.defaults,r.data(),typeof n=="object"&&n);i||r.data("modal",i=new t(this,s)),typeof n=="string"?i[n]():s.show&&i.show()})},e.fn.modal.defaults={backdrop:!0,keyboard:!0,show:!0},e.fn.modal.Constructor=t,e.fn.modal.noConflict=function(){return e.fn.modal=n,this},e(document).on("click.modal.data-api",'[data-toggle="modal"]',function(t){var n=e(this),r=n.attr("href"),i=e(n.attr("data-target")||r&&r.replace(/.*(?=#[^\s]+$)/,"")),s=i.data("modal")?"toggle":e.extend({remote:!/#/.test(r)&&r},i.data(),n.data());t.preventDefault(),i.modal(s).one("hide",function(){n.focus()})})}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(e,t){this.init("tooltip",e,t)};t.prototype={constructor:t,init:function(t,n,r){var i,s,o,u,a;this.type=t,this.$element=e(n),this.options=this.getOptions(r),this.enabled=!0,o=this.options.trigger.split(" ");for(a=o.length;a--;)u=o[a],u=="click"?this.$element.on("click."+this.type,this.options.selector,e.proxy(this.toggle,this)):u!="manual"&&(i=u=="hover"?"mouseenter":"focus",s=u=="hover"?"mouseleave":"blur",this.$element.on(i+"."+this.type,this.options.selector,e.proxy(this.enter,this)),this.$element.on(s+"."+this.type,this.options.selector,e.proxy(this.leave,this)));this.options.selector?this._options=e.extend({},this.options,{trigger:"manual",selector:""}):this.fixTitle()},getOptions:function(t){return t=e.extend({},e.fn[this.type].defaults,this.$element.data(),t),t.delay&&typeof t.delay=="number"&&(t.delay={show:t.delay,hide:t.delay}),t},enter:function(t){var n=e.fn[this.type].defaults,r={},i;this._options&&e.each(this._options,function(e,t){n[e]!=t&&(r[e]=t)},this),i=e(t.currentTarget)[this.type](r).data(this.type);if(!i.options.delay||!i.options.delay.show)return i.show();clearTimeout(this.timeout),i.hoverState="in",this.timeout=setTimeout(function(){i.hoverState=="in"&&i.show()},i.options.delay.show)},leave:function(t){var n=e(t.currentTarget)[this.type](this._options).data(this.type);this.timeout&&clearTimeout(this.timeout);if(!n.options.delay||!n.options.delay.hide)return n.hide();n.hoverState="out",this.timeout=setTimeout(function(){n.hoverState=="out"&&n.hide()},n.options.delay.hide)},show:function(){var t,n,r,i,s,o,u=e.Event("show");if(this.hasContent()&&this.enabled){this.$element.trigger(u);if(u.isDefaultPrevented())return;t=this.tip(),this.setContent(),this.options.animation&&t.addClass("fade"),s=typeof this.options.placement=="function"?this.options.placement.call(this,t[0],this.$element[0]):this.options.placement,t.detach().css({top:0,left:0,display:"block"}),this.options.container?t.appendTo(this.options.container):t.insertAfter(this.$element),n=this.getPosition(),r=t[0].offsetWidth,i=t[0].offsetHeight;switch(s){case"bottom":o={top:n.top+n.height,left:n.left+n.width/2-r/2};break;case"top":o={top:n.top-i,left:n.left+n.width/2-r/2};break;case"left":o={top:n.top+n.height/2-i/2,left:n.left-r};break;case"right":o={top:n.top+n.height/2-i/2,left:n.left+n.width}}this.applyPlacement(o,s),this.$element.trigger("shown")}},applyPlacement:function(e,t){var n=this.tip(),r=n[0].offsetWidth,i=n[0].offsetHeight,s,o,u,a;n.offset(e).addClass(t).addClass("in"),s=n[0].offsetWidth,o=n[0].offsetHeight,t=="top"&&o!=i&&(e.top=e.top+i-o,a=!0),t=="bottom"||t=="top"?(u=0,e.left<0&&(u=e.left*-2,e.left=0,n.offset(e),s=n[0].offsetWidth,o=n[0].offsetHeight),this.replaceArrow(u-r+s,s,"left")):this.replaceArrow(o-i,o,"top"),a&&n.offset(e)},replaceArrow:function(e,t,n){this.arrow().css(n,e?50*(1-e/t)+"%":"")},setContent:function(){var e=this.tip(),t=this.getTitle();e.find(".tooltip-inner")[this.options.html?"html":"text"](t),e.removeClass("fade in top bottom left right")},hide:function(){function i(){var t=setTimeout(function(){n.off(e.support.transition.end).detach()},500);n.one(e.support.transition.end,function(){clearTimeout(t),n.detach()})}var t=this,n=this.tip(),r=e.Event("hide");this.$element.trigger(r);if(r.isDefaultPrevented())return;return n.removeClass("in"),e.support.transition&&this.$tip.hasClass("fade")?i():n.detach(),this.$element.trigger("hidden"),this},fixTitle:function(){var e=this.$element;(e.attr("title")||typeof e.attr("data-original-title")!="string")&&e.attr("data-original-title",e.attr("title")||"").attr("title","")},hasContent:function(){return this.getTitle()},getPosition:function(){var t=this.$element[0];return e.extend({},typeof t.getBoundingClientRect=="function"?t.getBoundingClientRect():{width:t.offsetWidth,height:t.offsetHeight},this.$element.offset())},getTitle:function(){var e,t=this.$element,n=this.options;return e=t.attr("data-original-title")||(typeof n.title=="function"?n.title.call(t[0]):n.title),e},tip:function(){return this.$tip=this.$tip||e(this.options.template)},arrow:function(){return this.$arrow=this.$arrow||this.tip().find(".tooltip-arrow")},validate:function(){this.$element[0].parentNode||(this.hide(),this.$element=null,this.options=null)},enable:function(){this.enabled=!0},disable:function(){this.enabled=!1},toggleEnabled:function(){this.enabled=!this.enabled},toggle:function(t){var n=t?e(t.currentTarget)[this.type](this._options).data(this.type):this;n.tip().hasClass("in")?n.hide():n.show()},destroy:function(){this.hide().$element.off("."+this.type).removeData(this.type)}};var n=e.fn.tooltip;e.fn.tooltip=function(n){return this.each(function(){var r=e(this),i=r.data("tooltip"),s=typeof n=="object"&&n;i||r.data("tooltip",i=new t(this,s)),typeof n=="string"&&i[n]()})},e.fn.tooltip.Constructor=t,e.fn.tooltip.defaults={animation:!0,placement:"top",selector:!1,template:'
    ',trigger:"hover focus",title:"",delay:0,html:!1,container:!1},e.fn.tooltip.noConflict=function(){return e.fn.tooltip=n,this}}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(e,t){this.init("popover",e,t)};t.prototype=e.extend({},e.fn.tooltip.Constructor.prototype,{constructor:t,setContent:function(){var e=this.tip(),t=this.getTitle(),n=this.getContent();e.find(".popover-title")[this.options.html?"html":"text"](t),e.find(".popover-content")[this.options.html?"html":"text"](n),e.removeClass("fade top bottom left right in")},hasContent:function(){return this.getTitle()||this.getContent()},getContent:function(){var e,t=this.$element,n=this.options;return e=(typeof n.content=="function"?n.content.call(t[0]):n.content)||t.attr("data-content"),e},tip:function(){return this.$tip||(this.$tip=e(this.options.template)),this.$tip},destroy:function(){this.hide().$element.off("."+this.type).removeData(this.type)}});var n=e.fn.popover;e.fn.popover=function(n){return this.each(function(){var r=e(this),i=r.data("popover"),s=typeof n=="object"&&n;i||r.data("popover",i=new t(this,s)),typeof n=="string"&&i[n]()})},e.fn.popover.Constructor=t,e.fn.popover.defaults=e.extend({},e.fn.tooltip.defaults,{placement:"right",trigger:"click",content:"",template:'

    '}),e.fn.popover.noConflict=function(){return e.fn.popover=n,this}}(window.$jqTheme||window.jQuery),!function(e){"use strict";function t(t,n){var r=e.proxy(this.process,this),i=e(t).is("body")?e(window):e(t),s;this.options=e.extend({},e.fn.scrollspy.defaults,n),this.$scrollElement=i.on("scroll.scroll-spy.data-api",r),this.selector=(this.options.target||(s=e(t).attr("href"))&&s.replace(/.*(?=#[^\s]+$)/,"")||"")+" .nav li > a",this.$body=e("body"),this.refresh(),this.process()}t.prototype={constructor:t,refresh:function(){var t=this,n;this.offsets=e([]),this.targets=e([]),n=this.$body.find(this.selector).map(function(){var n=e(this),r=n.data("target")||n.attr("href"),i=/^#\w/.test(r)&&e(r);return i&&i.length&&[[i.position().top+(!e.isWindow(t.$scrollElement.get(0))&&t.$scrollElement.scrollTop()),r]]||null}).sort(function(e,t){return e[0]-t[0]}).each(function(){t.offsets.push(this[0]),t.targets.push(this[1])})},process:function(){var e=this.$scrollElement.scrollTop()+this.options.offset,t=this.$scrollElement[0].scrollHeight||this.$body[0].scrollHeight,n=t-this.$scrollElement.height(),r=this.offsets,i=this.targets,s=this.activeTarget,o;if(e>=n)return s!=(o=i.last()[0])&&this.activate(o);for(o=r.length;o--;)s!=i[o]&&e>=r[o]&&(!r[o+1]||e<=r[o+1])&&this.activate(i[o])},activate:function(t){var n,r;this.activeTarget=t,e(this.selector).parent(".active").removeClass("active"),r=this.selector+'[data-target="'+t+'"],'+this.selector+'[href="'+t+'"]',n=e(r).parent("li").addClass("active"),n.parent(".dropdown-menu").length&&(n=n.closest("li.dropdown").addClass("active")),n.trigger("activate")}};var n=e.fn.scrollspy;e.fn.scrollspy=function(n){return this.each(function(){var r=e(this),i=r.data("scrollspy"),s=typeof n=="object"&&n;i||r.data("scrollspy",i=new t(this,s)),typeof n=="string"&&i[n]()})},e.fn.scrollspy.Constructor=t,e.fn.scrollspy.defaults={offset:10},e.fn.scrollspy.noConflict=function(){return e.fn.scrollspy=n,this},e(window).on("load",function(){e('[data-spy="scroll"]').each(function(){var t=e(this);t.scrollspy(t.data())})})}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(t){this.element=e(t)};t.prototype={constructor:t,show:function(){var t=this.element,n=t.closest("ul:not(.dropdown-menu)"),r=t.attr("data-target"),i,s,o;r||(r=t.attr("href"),r=r&&r.replace(/.*(?=#[^\s]*$)/,""));if(t.parent("li").hasClass("active"))return;i=n.find(".active:last a")[0],o=e.Event("show",{relatedTarget:i}),t.trigger(o);if(o.isDefaultPrevented())return;s=e(r),this.activate(t.parent("li"),n),this.activate(s,s.parent(),function(){t.trigger({type:"shown",relatedTarget:i})})},activate:function(t,n,r){function o(){i.removeClass("active").find("> .dropdown-menu > .active").removeClass("active"),t.addClass("active"),s?(t[0].offsetWidth,t.addClass("in")):t.removeClass("fade"),t.parent(".dropdown-menu")&&t.closest("li.dropdown").addClass("active"),r&&r()}var i=n.find("> .active"),s=r&&e.support.transition&&i.hasClass("fade");s?i.one(e.support.transition.end,o):o(),i.removeClass("in")}};var n=e.fn.tab;e.fn.tab=function(n){return this.each(function(){var r=e(this),i=r.data("tab");i||r.data("tab",i=new t(this)),typeof n=="string"&&i[n]()})},e.fn.tab.Constructor=t,e.fn.tab.noConflict=function(){return e.fn.tab=n,this},e(document).on("click.tab.data-api",'[data-toggle="tab"], [data-toggle="pill"]',function(t){t.preventDefault(),e(this).tab("show")})}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(t,n){this.$element=e(t),this.options=e.extend({},e.fn.typeahead.defaults,n),this.matcher=this.options.matcher||this.matcher,this.sorter=this.options.sorter||this.sorter,this.highlighter=this.options.highlighter||this.highlighter,this.updater=this.options.updater||this.updater,this.source=this.options.source,this.$menu=e(this.options.menu),this.shown=!1,this.listen()};t.prototype={constructor:t,select:function(){var e=this.$menu.find(".active").attr("data-value");return this.$element.val(this.updater(e)).change(),this.hide()},updater:function(e){return e},show:function(){var t=e.extend({},this.$element.position(),{height:this.$element[0].offsetHeight});return this.$menu.insertAfter(this.$element).css({top:t.top+t.height,left:t.left}).show(),this.shown=!0,this},hide:function(){return this.$menu.hide(),this.shown=!1,this},lookup:function(t){var n;return this.query=this.$element.val(),!this.query||this.query.length"+t+""})},render:function(t){var n=this;return t=e(t).map(function(t,r){return t=e(n.options.item).attr("data-value",r),t.find("a").html(n.highlighter(r)),t[0]}),t.first().addClass("active"),this.$menu.html(t),this},next:function(t){var n=this.$menu.find(".active").removeClass("active"),r=n.next();r.length||(r=e(this.$menu.find("li")[0])),r.addClass("active")},prev:function(e){var t=this.$menu.find(".active").removeClass("active"),n=t.prev();n.length||(n=this.$menu.find("li").last()),n.addClass("active")},listen:function(){this.$element.on("focus",e.proxy(this.focus,this)).on("blur",e.proxy(this.blur,this)).on("keypress",e.proxy(this.keypress,this)).on("keyup",e.proxy(this.keyup,this)),this.eventSupported("keydown")&&this.$element.on("keydown",e.proxy(this.keydown,this)),this.$menu.on("click",e.proxy(this.click,this)).on("mouseenter","li",e.proxy(this.mouseenter,this)).on("mouseleave","li",e.proxy(this.mouseleave,this))},eventSupported:function(e){var t=e in this.$element;return t||(this.$element.setAttribute(e,"return;"),t=typeof this.$element[e]=="function"),t},move:function(e){if(!this.shown)return;switch(e.keyCode){case 9:case 13:case 27:e.preventDefault();break;case 38:e.preventDefault(),this.prev();break;case 40:e.preventDefault(),this.next()}e.stopPropagation()},keydown:function(t){this.suppressKeyPressRepeat=~e.inArray(t.keyCode,[40,38,9,13,27]),this.move(t)},keypress:function(e){if(this.suppressKeyPressRepeat)return;this.move(e)},keyup:function(e){switch(e.keyCode){case 40:case 38:case 16:case 17:case 18:break;case 9:case 13:if(!this.shown)return;this.select();break;case 27:if(!this.shown)return;this.hide();break;default:this.lookup()}e.stopPropagation(),e.preventDefault()},focus:function(e){this.focused=!0},blur:function(e){this.focused=!1,!this.mousedover&&this.shown&&this.hide()},click:function(e){e.stopPropagation(),e.preventDefault(),this.select(),this.$element.focus()},mouseenter:function(t){this.mousedover=!0,this.$menu.find(".active").removeClass("active"),e(t.currentTarget).addClass("active")},mouseleave:function(e){this.mousedover=!1,!this.focused&&this.shown&&this.hide()}};var n=e.fn.typeahead;e.fn.typeahead=function(n){return this.each(function(){var r=e(this),i=r.data("typeahead"),s=typeof n=="object"&&n;i||r.data("typeahead",i=new t(this,s)),typeof n=="string"&&i[n]()})},e.fn.typeahead.defaults={source:[],items:8,menu:'
      ',item:'
      • ',minLength:1},e.fn.typeahead.Constructor=t,e.fn.typeahead.noConflict=function(){return e.fn.typeahead=n,this},e(document).on("focus.typeahead.data-api",'[data-provide="typeahead"]',function(t){var n=e(this);if(n.data("typeahead"))return;n.typeahead(n.data())})}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(t,n){this.options=e.extend({},e.fn.affix.defaults,n),this.$window=e(window).on("scroll.affix.data-api",e.proxy(this.checkPosition,this)).on("click.affix.data-api",e.proxy(function(){setTimeout(e.proxy(this.checkPosition,this),1)},this)),this.$element=e(t),this.checkPosition()};t.prototype.checkPosition=function(){if(!this.$element.is(":visible"))return;var t=e(document).height(),n=this.$window.scrollTop(),r=this.$element.offset(),i=this.options.offset,s=i.bottom,o=i.top,u="affix affix-top affix-bottom",a;typeof i!="object"&&(s=o=i),typeof o=="function"&&(o=i.top()),typeof s=="function"&&(s=i.bottom()),a=this.unpin!=null&&n+this.unpin<=r.top?!1:s!=null&&r.top+this.$element.height()>=t-s?"bottom":o!=null&&n<=o?"top":!1;if(this.affixed===a)return;this.affixed=a,this.unpin=a=="bottom"?r.top-n:null,this.$element.removeClass(u).addClass("affix"+(a?"-"+a:""))};var n=e.fn.affix;e.fn.affix=function(n){return this.each(function(){var r=e(this),i=r.data("affix"),s=typeof n=="object"&&n;i||r.data("affix",i=new t(this,s)),typeof n=="string"&&i[n]()})},e.fn.affix.Constructor=t,e.fn.affix.defaults={offset:0},e.fn.affix.noConflict=function(){return e.fn.affix=n,this},e(window).on("load",function(){e('[data-spy="affix"]').each(function(){var t=e(this),n=t.data();n.offset=n.offset||{},n.offsetBottom&&(n.offset.bottom=n.offsetBottom),n.offsetTop&&(n.offset.top=n.offsetTop),t.affix(n)})})}(window.$jqTheme||window.jQuery); \ No newline at end of file diff --git a/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.css b/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.css deleted file mode 100644 index 31d888266..000000000 --- a/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.css +++ /dev/null @@ -1,587 +0,0 @@ -/*! - * Bootstrap v3.3.7 (http://getbootstrap.com) - * Copyright 2011-2016 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - */ -.btn-default, -.btn-primary, -.btn-success, -.btn-info, -.btn-warning, -.btn-danger { - text-shadow: 0 -1px 0 rgba(0, 0, 0, .2); - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075); -} -.btn-default:active, -.btn-primary:active, -.btn-success:active, -.btn-info:active, -.btn-warning:active, -.btn-danger:active, -.btn-default.active, -.btn-primary.active, -.btn-success.active, -.btn-info.active, -.btn-warning.active, -.btn-danger.active { - -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); - box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); -} -.btn-default.disabled, -.btn-primary.disabled, -.btn-success.disabled, -.btn-info.disabled, -.btn-warning.disabled, -.btn-danger.disabled, -.btn-default[disabled], -.btn-primary[disabled], -.btn-success[disabled], -.btn-info[disabled], -.btn-warning[disabled], -.btn-danger[disabled], -fieldset[disabled] .btn-default, -fieldset[disabled] .btn-primary, -fieldset[disabled] .btn-success, -fieldset[disabled] .btn-info, -fieldset[disabled] .btn-warning, -fieldset[disabled] .btn-danger { - -webkit-box-shadow: none; - box-shadow: none; -} -.btn-default .badge, -.btn-primary .badge, -.btn-success .badge, -.btn-info .badge, -.btn-warning .badge, -.btn-danger .badge { - text-shadow: none; -} -.btn:active, -.btn.active { - background-image: none; -} -.btn-default { - text-shadow: 0 1px 0 #fff; - background-image: -webkit-linear-gradient(top, #fff 0%, #e0e0e0 100%); - background-image: -o-linear-gradient(top, #fff 0%, #e0e0e0 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#e0e0e0)); - background-image: linear-gradient(to bottom, #fff 0%, #e0e0e0 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); - background-repeat: repeat-x; - border-color: #dbdbdb; - border-color: #ccc; -} -.btn-default:hover, -.btn-default:focus { - background-color: #e0e0e0; - background-position: 0 -15px; -} -.btn-default:active, -.btn-default.active { - background-color: #e0e0e0; - border-color: #dbdbdb; -} -.btn-default.disabled, -.btn-default[disabled], -fieldset[disabled] .btn-default, -.btn-default.disabled:hover, -.btn-default[disabled]:hover, -fieldset[disabled] .btn-default:hover, -.btn-default.disabled:focus, -.btn-default[disabled]:focus, -fieldset[disabled] .btn-default:focus, -.btn-default.disabled.focus, -.btn-default[disabled].focus, -fieldset[disabled] .btn-default.focus, -.btn-default.disabled:active, -.btn-default[disabled]:active, -fieldset[disabled] .btn-default:active, -.btn-default.disabled.active, -.btn-default[disabled].active, -fieldset[disabled] .btn-default.active { - background-color: #e0e0e0; - background-image: none; -} -.btn-primary { - background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%); - background-image: -o-linear-gradient(top, #337ab7 0%, #265a88 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#265a88)); - background-image: linear-gradient(to bottom, #337ab7 0%, #265a88 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); - background-repeat: repeat-x; - border-color: #245580; -} -.btn-primary:hover, -.btn-primary:focus { - background-color: #265a88; - background-position: 0 -15px; -} -.btn-primary:active, -.btn-primary.active { - background-color: #265a88; - border-color: #245580; -} -.btn-primary.disabled, -.btn-primary[disabled], -fieldset[disabled] .btn-primary, -.btn-primary.disabled:hover, -.btn-primary[disabled]:hover, -fieldset[disabled] .btn-primary:hover, -.btn-primary.disabled:focus, -.btn-primary[disabled]:focus, -fieldset[disabled] .btn-primary:focus, -.btn-primary.disabled.focus, -.btn-primary[disabled].focus, -fieldset[disabled] .btn-primary.focus, -.btn-primary.disabled:active, -.btn-primary[disabled]:active, -fieldset[disabled] .btn-primary:active, -.btn-primary.disabled.active, -.btn-primary[disabled].active, -fieldset[disabled] .btn-primary.active { - background-color: #265a88; - background-image: none; -} -.btn-success { - background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%); - background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#419641)); - background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); - background-repeat: repeat-x; - border-color: #3e8f3e; -} -.btn-success:hover, -.btn-success:focus { - background-color: #419641; - background-position: 0 -15px; -} -.btn-success:active, -.btn-success.active { - background-color: #419641; - border-color: #3e8f3e; -} -.btn-success.disabled, -.btn-success[disabled], -fieldset[disabled] .btn-success, -.btn-success.disabled:hover, -.btn-success[disabled]:hover, -fieldset[disabled] .btn-success:hover, -.btn-success.disabled:focus, -.btn-success[disabled]:focus, -fieldset[disabled] .btn-success:focus, -.btn-success.disabled.focus, -.btn-success[disabled].focus, -fieldset[disabled] .btn-success.focus, -.btn-success.disabled:active, -.btn-success[disabled]:active, -fieldset[disabled] .btn-success:active, -.btn-success.disabled.active, -.btn-success[disabled].active, -fieldset[disabled] .btn-success.active { - background-color: #419641; - background-image: none; -} -.btn-info { - background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%); - background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#2aabd2)); - background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); - background-repeat: repeat-x; - border-color: #28a4c9; -} -.btn-info:hover, -.btn-info:focus { - background-color: #2aabd2; - background-position: 0 -15px; -} -.btn-info:active, -.btn-info.active { - background-color: #2aabd2; - border-color: #28a4c9; -} -.btn-info.disabled, -.btn-info[disabled], -fieldset[disabled] .btn-info, -.btn-info.disabled:hover, -.btn-info[disabled]:hover, -fieldset[disabled] .btn-info:hover, -.btn-info.disabled:focus, -.btn-info[disabled]:focus, -fieldset[disabled] .btn-info:focus, -.btn-info.disabled.focus, -.btn-info[disabled].focus, -fieldset[disabled] .btn-info.focus, -.btn-info.disabled:active, -.btn-info[disabled]:active, -fieldset[disabled] .btn-info:active, -.btn-info.disabled.active, -.btn-info[disabled].active, -fieldset[disabled] .btn-info.active { - background-color: #2aabd2; - background-image: none; -} -.btn-warning { - background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%); - background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#eb9316)); - background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); - background-repeat: repeat-x; - border-color: #e38d13; -} -.btn-warning:hover, -.btn-warning:focus { - background-color: #eb9316; - background-position: 0 -15px; -} -.btn-warning:active, -.btn-warning.active { - background-color: #eb9316; - border-color: #e38d13; -} -.btn-warning.disabled, -.btn-warning[disabled], -fieldset[disabled] .btn-warning, -.btn-warning.disabled:hover, -.btn-warning[disabled]:hover, -fieldset[disabled] .btn-warning:hover, -.btn-warning.disabled:focus, -.btn-warning[disabled]:focus, -fieldset[disabled] .btn-warning:focus, -.btn-warning.disabled.focus, -.btn-warning[disabled].focus, -fieldset[disabled] .btn-warning.focus, -.btn-warning.disabled:active, -.btn-warning[disabled]:active, -fieldset[disabled] .btn-warning:active, -.btn-warning.disabled.active, -.btn-warning[disabled].active, -fieldset[disabled] .btn-warning.active { - background-color: #eb9316; - background-image: none; -} -.btn-danger { - background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%); - background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c12e2a)); - background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); - background-repeat: repeat-x; - border-color: #b92c28; -} -.btn-danger:hover, -.btn-danger:focus { - background-color: #c12e2a; - background-position: 0 -15px; -} -.btn-danger:active, -.btn-danger.active { - background-color: #c12e2a; - border-color: #b92c28; -} -.btn-danger.disabled, -.btn-danger[disabled], -fieldset[disabled] .btn-danger, -.btn-danger.disabled:hover, -.btn-danger[disabled]:hover, -fieldset[disabled] .btn-danger:hover, -.btn-danger.disabled:focus, -.btn-danger[disabled]:focus, -fieldset[disabled] .btn-danger:focus, -.btn-danger.disabled.focus, -.btn-danger[disabled].focus, -fieldset[disabled] .btn-danger.focus, -.btn-danger.disabled:active, -.btn-danger[disabled]:active, -fieldset[disabled] .btn-danger:active, -.btn-danger.disabled.active, -.btn-danger[disabled].active, -fieldset[disabled] .btn-danger.active { - background-color: #c12e2a; - background-image: none; -} -.thumbnail, -.img-thumbnail { - -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .075); - box-shadow: 0 1px 2px rgba(0, 0, 0, .075); -} -.dropdown-menu > li > a:hover, -.dropdown-menu > li > a:focus { - background-color: #e8e8e8; - background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%); - background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8)); - background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0); - background-repeat: repeat-x; -} -.dropdown-menu > .active > a, -.dropdown-menu > .active > a:hover, -.dropdown-menu > .active > a:focus { - background-color: #2e6da4; - background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%); - background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4)); - background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0); - background-repeat: repeat-x; -} -.navbar-default { - background-image: -webkit-linear-gradient(top, #fff 0%, #f8f8f8 100%); - background-image: -o-linear-gradient(top, #fff 0%, #f8f8f8 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#f8f8f8)); - background-image: linear-gradient(to bottom, #fff 0%, #f8f8f8 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); - background-repeat: repeat-x; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075); -} -.navbar-default .navbar-nav > .open > a, -.navbar-default .navbar-nav > .active > a { - background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%); - background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#dbdbdb), to(#e2e2e2)); - background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0); - background-repeat: repeat-x; - -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, .075); - box-shadow: inset 0 3px 9px rgba(0, 0, 0, .075); -} -.navbar-brand, -.navbar-nav > li > a { - text-shadow: 0 1px 0 rgba(255, 255, 255, .25); -} -.navbar-inverse { - background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%); - background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#3c3c3c), to(#222)); - background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); - background-repeat: repeat-x; - border-radius: 4px; -} -.navbar-inverse .navbar-nav > .open > a, -.navbar-inverse .navbar-nav > .active > a { - background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%); - background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#080808), to(#0f0f0f)); - background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0); - background-repeat: repeat-x; - -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, .25); - box-shadow: inset 0 3px 9px rgba(0, 0, 0, .25); -} -.navbar-inverse .navbar-brand, -.navbar-inverse .navbar-nav > li > a { - text-shadow: 0 -1px 0 rgba(0, 0, 0, .25); -} -.navbar-static-top, -.navbar-fixed-top, -.navbar-fixed-bottom { - border-radius: 0; -} -@media (max-width: 767px) { - .navbar .navbar-nav .open .dropdown-menu > .active > a, - .navbar .navbar-nav .open .dropdown-menu > .active > a:hover, - .navbar .navbar-nav .open .dropdown-menu > .active > a:focus { - color: #fff; - background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%); - background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4)); - background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0); - background-repeat: repeat-x; - } -} -.alert { - text-shadow: 0 1px 0 rgba(255, 255, 255, .2); - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05); -} -.alert-success { - background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%); - background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#c8e5bc)); - background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0); - background-repeat: repeat-x; - border-color: #b2dba1; -} -.alert-info { - background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%); - background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#b9def0)); - background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0); - background-repeat: repeat-x; - border-color: #9acfea; -} -.alert-warning { - background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%); - background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#f8efc0)); - background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0); - background-repeat: repeat-x; - border-color: #f5e79e; -} -.alert-danger { - background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%); - background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#e7c3c3)); - background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0); - background-repeat: repeat-x; - border-color: #dca7a7; -} -.progress { - background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%); - background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#ebebeb), to(#f5f5f5)); - background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0); - background-repeat: repeat-x; -} -.progress-bar { - background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%); - background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#286090)); - background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0); - background-repeat: repeat-x; -} -.progress-bar-success { - background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%); - background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#449d44)); - background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0); - background-repeat: repeat-x; -} -.progress-bar-info { - background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%); - background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#31b0d5)); - background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0); - background-repeat: repeat-x; -} -.progress-bar-warning { - background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%); - background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#ec971f)); - background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0); - background-repeat: repeat-x; -} -.progress-bar-danger { - background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%); - background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c9302c)); - background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0); - background-repeat: repeat-x; -} -.progress-bar-striped { - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); -} -.list-group { - border-radius: 4px; - -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .075); - box-shadow: 0 1px 2px rgba(0, 0, 0, .075); -} -.list-group-item.active, -.list-group-item.active:hover, -.list-group-item.active:focus { - text-shadow: 0 -1px 0 #286090; - background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%); - background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2b669a)); - background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0); - background-repeat: repeat-x; - border-color: #2b669a; -} -.list-group-item.active .badge, -.list-group-item.active:hover .badge, -.list-group-item.active:focus .badge { - text-shadow: none; -} -.panel { - -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .05); - box-shadow: 0 1px 2px rgba(0, 0, 0, .05); -} -.panel-default > .panel-heading { - background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%); - background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8)); - background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0); - background-repeat: repeat-x; -} -.panel-primary > .panel-heading { - background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%); - background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4)); - background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0); - background-repeat: repeat-x; -} -.panel-success > .panel-heading { - background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%); - background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#d0e9c6)); - background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0); - background-repeat: repeat-x; -} -.panel-info > .panel-heading { - background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%); - background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#c4e3f3)); - background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0); - background-repeat: repeat-x; -} -.panel-warning > .panel-heading { - background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%); - background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#faf2cc)); - background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0); - background-repeat: repeat-x; -} -.panel-danger > .panel-heading { - background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%); - background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#ebcccc)); - background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0); - background-repeat: repeat-x; -} -.well { - background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%); - background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%); - background-image: -webkit-gradient(linear, left top, left bottom, from(#e8e8e8), to(#f5f5f5)); - background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0); - background-repeat: repeat-x; - border-color: #dcdcdc; - -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1); - box-shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1); -} -/*# sourceMappingURL=bootstrap-theme.css.map */ diff --git a/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.css.map b/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.css.map deleted file mode 100644 index d876f60fb..000000000 --- a/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.css.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"sources":["bootstrap-theme.css","less/theme.less","less/mixins/vendor-prefixes.less","less/mixins/gradients.less","less/mixins/reset-filter.less"],"names":[],"mappings":"AAAA;;;;GAIG;ACeH;;;;;;EAME,yCAAA;EC2CA,4FAAA;EACQ,oFAAA;CFvDT;ACgBC;;;;;;;;;;;;ECsCA,yDAAA;EACQ,iDAAA;CFxCT;ACMC;;;;;;;;;;;;;;;;;;ECiCA,yBAAA;EACQ,iBAAA;CFnBT;AC/BD;;;;;;EAuBI,kBAAA;CDgBH;ACyBC;;EAEE,uBAAA;CDvBH;AC4BD;EErEI,sEAAA;EACA,iEAAA;EACA,2FAAA;EAAA,oEAAA;EAEA,uHAAA;ECnBF,oEAAA;EH4CA,4BAAA;EACA,sBAAA;EAuC2C,0BAAA;EAA2B,mBAAA;CDjBvE;ACpBC;;EAEE,0BAAA;EACA,6BAAA;CDsBH;ACnBC;;EAEE,0BAAA;EACA,sBAAA;CDqBH;ACfG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CD6BL;ACbD;EEtEI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EAEA,uHAAA;ECnBF,oEAAA;EH4CA,4BAAA;EACA,sBAAA;CD8DD;AC5DC;;EAEE,0BAAA;EACA,6BAAA;CD8DH;AC3DC;;EAEE,0BAAA;EACA,sBAAA;CD6DH;ACvDG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CDqEL;ACpDD;EEvEI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EAEA,uHAAA;ECnBF,oEAAA;EH4CA,4BAAA;EACA,sBAAA;CDsGD;ACpGC;;EAEE,0BAAA;EACA,6BAAA;CDsGH;ACnGC;;EAEE,0BAAA;EACA,sBAAA;CDqGH;AC/FG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CD6GL;AC3FD;EExEI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EAEA,uHAAA;ECnBF,oEAAA;EH4CA,4BAAA;EACA,sBAAA;CD8ID;AC5IC;;EAEE,0BAAA;EACA,6BAAA;CD8IH;AC3IC;;EAEE,0BAAA;EACA,sBAAA;CD6IH;ACvIG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CDqJL;AClID;EEzEI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EAEA,uHAAA;ECnBF,oEAAA;EH4CA,4BAAA;EACA,sBAAA;CDsLD;ACpLC;;EAEE,0BAAA;EACA,6BAAA;CDsLH;ACnLC;;EAEE,0BAAA;EACA,sBAAA;CDqLH;AC/KG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CD6LL;ACzKD;EE1EI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EAEA,uHAAA;ECnBF,oEAAA;EH4CA,4BAAA;EACA,sBAAA;CD8ND;AC5NC;;EAEE,0BAAA;EACA,6BAAA;CD8NH;AC3NC;;EAEE,0BAAA;EACA,sBAAA;CD6NH;ACvNG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CDqOL;AC1MD;;EClCE,mDAAA;EACQ,2CAAA;CFgPT;ACrMD;;EE3FI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;EF0FF,0BAAA;CD2MD;ACzMD;;;EEhGI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;EFgGF,0BAAA;CD+MD;ACtMD;EE7GI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;ECnBF,oEAAA;EH+HA,mBAAA;ECjEA,4FAAA;EACQ,oFAAA;CF8QT;ACjND;;EE7GI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;ED2CF,yDAAA;EACQ,iDAAA;CFwRT;AC9MD;;EAEE,+CAAA;CDgND;AC5MD;EEhII,sEAAA;EACA,iEAAA;EACA,2FAAA;EAAA,oEAAA;EACA,4BAAA;EACA,uHAAA;ECnBF,oEAAA;EHkJA,mBAAA;CDkND;ACrND;;EEhII,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;ED2CF,wDAAA;EACQ,gDAAA;CF+ST;AC/ND;;EAYI,0CAAA;CDuNH;AClND;;;EAGE,iBAAA;CDoND;AC/LD;EAfI;;;IAGE,YAAA;IE7JF,yEAAA;IACA,oEAAA;IACA,8FAAA;IAAA,uEAAA;IACA,4BAAA;IACA,uHAAA;GH+WD;CACF;AC3MD;EACE,8CAAA;EC3HA,2FAAA;EACQ,mFAAA;CFyUT;ACnMD;EEtLI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;EF8KF,sBAAA;CD+MD;AC1MD;EEvLI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;EF8KF,sBAAA;CDuND;ACjND;EExLI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;EF8KF,sBAAA;CD+ND;ACxND;EEzLI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;EF8KF,sBAAA;CDuOD;ACxND;EEjMI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CH4ZH;ACrND;EE3MI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CHmaH;AC3ND;EE5MI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CH0aH;ACjOD;EE7MI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CHibH;ACvOD;EE9MI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CHwbH;AC7OD;EE/MI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CH+bH;AChPD;EElLI,8MAAA;EACA,yMAAA;EACA,sMAAA;CHqaH;AC5OD;EACE,mBAAA;EC9KA,mDAAA;EACQ,2CAAA;CF6ZT;AC7OD;;;EAGE,8BAAA;EEnOE,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;EFiOF,sBAAA;CDmPD;ACxPD;;;EAQI,kBAAA;CDqPH;AC3OD;ECnME,kDAAA;EACQ,0CAAA;CFibT;ACrOD;EE5PI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CHoeH;AC3OD;EE7PI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CH2eH;ACjPD;EE9PI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CHkfH;ACvPD;EE/PI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CHyfH;AC7PD;EEhQI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CHggBH;ACnQD;EEjQI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;CHugBH;ACnQD;EExQI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,4BAAA;EACA,uHAAA;EFsQF,sBAAA;EC3NA,0FAAA;EACQ,kFAAA;CFqeT","file":"bootstrap-theme.css","sourcesContent":["/*!\n * Bootstrap v3.3.7 (http://getbootstrap.com)\n * Copyright 2011-2016 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2);\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.btn-default:active,\n.btn-primary:active,\n.btn-success:active,\n.btn-info:active,\n.btn-warning:active,\n.btn-danger:active,\n.btn-default.active,\n.btn-primary.active,\n.btn-success.active,\n.btn-info.active,\n.btn-warning.active,\n.btn-danger.active {\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-default.disabled,\n.btn-primary.disabled,\n.btn-success.disabled,\n.btn-info.disabled,\n.btn-warning.disabled,\n.btn-danger.disabled,\n.btn-default[disabled],\n.btn-primary[disabled],\n.btn-success[disabled],\n.btn-info[disabled],\n.btn-warning[disabled],\n.btn-danger[disabled],\nfieldset[disabled] .btn-default,\nfieldset[disabled] .btn-primary,\nfieldset[disabled] .btn-success,\nfieldset[disabled] .btn-info,\nfieldset[disabled] .btn-warning,\nfieldset[disabled] .btn-danger {\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn-default .badge,\n.btn-primary .badge,\n.btn-success .badge,\n.btn-info .badge,\n.btn-warning .badge,\n.btn-danger .badge {\n text-shadow: none;\n}\n.btn:active,\n.btn.active {\n background-image: none;\n}\n.btn-default {\n background-image: -webkit-linear-gradient(top, #fff 0%, #e0e0e0 100%);\n background-image: -o-linear-gradient(top, #fff 0%, #e0e0e0 100%);\n background-image: linear-gradient(to bottom, #fff 0%, #e0e0e0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #dbdbdb;\n text-shadow: 0 1px 0 #fff;\n border-color: #ccc;\n}\n.btn-default:hover,\n.btn-default:focus {\n background-color: #e0e0e0;\n background-position: 0 -15px;\n}\n.btn-default:active,\n.btn-default.active {\n background-color: #e0e0e0;\n border-color: #dbdbdb;\n}\n.btn-default.disabled,\n.btn-default[disabled],\nfieldset[disabled] .btn-default,\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus,\n.btn-default.disabled:active,\n.btn-default[disabled]:active,\nfieldset[disabled] .btn-default:active,\n.btn-default.disabled.active,\n.btn-default[disabled].active,\nfieldset[disabled] .btn-default.active {\n background-color: #e0e0e0;\n background-image: none;\n}\n.btn-primary {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #265a88 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #265a88 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #245580;\n}\n.btn-primary:hover,\n.btn-primary:focus {\n background-color: #265a88;\n background-position: 0 -15px;\n}\n.btn-primary:active,\n.btn-primary.active {\n background-color: #265a88;\n border-color: #245580;\n}\n.btn-primary.disabled,\n.btn-primary[disabled],\nfieldset[disabled] .btn-primary,\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus,\n.btn-primary.disabled:active,\n.btn-primary[disabled]:active,\nfieldset[disabled] .btn-primary:active,\n.btn-primary.disabled.active,\n.btn-primary[disabled].active,\nfieldset[disabled] .btn-primary.active {\n background-color: #265a88;\n background-image: none;\n}\n.btn-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%);\n background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #3e8f3e;\n}\n.btn-success:hover,\n.btn-success:focus {\n background-color: #419641;\n background-position: 0 -15px;\n}\n.btn-success:active,\n.btn-success.active {\n background-color: #419641;\n border-color: #3e8f3e;\n}\n.btn-success.disabled,\n.btn-success[disabled],\nfieldset[disabled] .btn-success,\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus,\n.btn-success.disabled:active,\n.btn-success[disabled]:active,\nfieldset[disabled] .btn-success:active,\n.btn-success.disabled.active,\n.btn-success[disabled].active,\nfieldset[disabled] .btn-success.active {\n background-color: #419641;\n background-image: none;\n}\n.btn-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n background-color: #2aabd2;\n background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n background-color: #2aabd2;\n border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info[disabled],\nfieldset[disabled] .btn-info,\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus,\n.btn-info.disabled:active,\n.btn-info[disabled]:active,\nfieldset[disabled] .btn-info:active,\n.btn-info.disabled.active,\n.btn-info[disabled].active,\nfieldset[disabled] .btn-info.active {\n background-color: #2aabd2;\n background-image: none;\n}\n.btn-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n background-color: #eb9316;\n background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n background-color: #eb9316;\n border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning[disabled],\nfieldset[disabled] .btn-warning,\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus,\n.btn-warning.disabled:active,\n.btn-warning[disabled]:active,\nfieldset[disabled] .btn-warning:active,\n.btn-warning.disabled.active,\n.btn-warning[disabled].active,\nfieldset[disabled] .btn-warning.active {\n background-color: #eb9316;\n background-image: none;\n}\n.btn-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n background-color: #c12e2a;\n background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n background-color: #c12e2a;\n border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger[disabled],\nfieldset[disabled] .btn-danger,\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus,\n.btn-danger.disabled:active,\n.btn-danger[disabled]:active,\nfieldset[disabled] .btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n background-color: #c12e2a;\n background-image: none;\n}\n.thumbnail,\n.img-thumbnail {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-color: #e8e8e8;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-color: #2e6da4;\n}\n.navbar-default {\n background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n@media (max-width: 767px) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n }\n}\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n border-color: #b2dba1;\n}\n.alert-info {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n border-color: #9acfea;\n}\n.alert-warning {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n border-color: #f5e79e;\n}\n.alert-danger {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n border-color: #dca7a7;\n}\n.progress {\n background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n}\n.progress-bar {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n}\n.progress-bar-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n}\n.progress-bar-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n}\n.progress-bar-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n}\n.progress-bar-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n}\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 #286090;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n text-shadow: none;\n}\n.panel {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n}\n.panel-primary > .panel-heading {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n}\n.panel-success > .panel-heading {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n}\n.panel-info > .panel-heading {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n}\n.panel-warning > .panel-heading {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n}\n.panel-danger > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n}\n.well {\n background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n border-color: #dcdcdc;\n -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","/*!\n * Bootstrap v3.3.7 (http://getbootstrap.com)\n * Copyright 2011-2016 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n\n//\n// Load core variables and mixins\n// --------------------------------------------------\n\n@import \"variables.less\";\n@import \"mixins.less\";\n\n\n//\n// Buttons\n// --------------------------------------------------\n\n// Common styles\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n text-shadow: 0 -1px 0 rgba(0,0,0,.2);\n @shadow: inset 0 1px 0 rgba(255,255,255,.15), 0 1px 1px rgba(0,0,0,.075);\n .box-shadow(@shadow);\n\n // Reset the shadow\n &:active,\n &.active {\n .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n .box-shadow(none);\n }\n\n .badge {\n text-shadow: none;\n }\n}\n\n// Mixin for generating new styles\n.btn-styles(@btn-color: #555) {\n #gradient > .vertical(@start-color: @btn-color; @end-color: darken(@btn-color, 12%));\n .reset-filter(); // Disable gradients for IE9 because filter bleeds through rounded corners; see https://github.com/twbs/bootstrap/issues/10620\n background-repeat: repeat-x;\n border-color: darken(@btn-color, 14%);\n\n &:hover,\n &:focus {\n background-color: darken(@btn-color, 12%);\n background-position: 0 -15px;\n }\n\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n border-color: darken(@btn-color, 14%);\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n &,\n &:hover,\n &:focus,\n &.focus,\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n background-image: none;\n }\n }\n}\n\n// Common styles\n.btn {\n // Remove the gradient for the pressed/active state\n &:active,\n &.active {\n background-image: none;\n }\n}\n\n// Apply the mixin to the buttons\n.btn-default { .btn-styles(@btn-default-bg); text-shadow: 0 1px 0 #fff; border-color: #ccc; }\n.btn-primary { .btn-styles(@btn-primary-bg); }\n.btn-success { .btn-styles(@btn-success-bg); }\n.btn-info { .btn-styles(@btn-info-bg); }\n.btn-warning { .btn-styles(@btn-warning-bg); }\n.btn-danger { .btn-styles(@btn-danger-bg); }\n\n\n//\n// Images\n// --------------------------------------------------\n\n.thumbnail,\n.img-thumbnail {\n .box-shadow(0 1px 2px rgba(0,0,0,.075));\n}\n\n\n//\n// Dropdowns\n// --------------------------------------------------\n\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-hover-bg; @end-color: darken(@dropdown-link-hover-bg, 5%));\n background-color: darken(@dropdown-link-hover-bg, 5%);\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n background-color: darken(@dropdown-link-active-bg, 5%);\n}\n\n\n//\n// Navbar\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n #gradient > .vertical(@start-color: lighten(@navbar-default-bg, 10%); @end-color: @navbar-default-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered\n border-radius: @navbar-border-radius;\n @shadow: inset 0 1px 0 rgba(255,255,255,.15), 0 1px 5px rgba(0,0,0,.075);\n .box-shadow(@shadow);\n\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: darken(@navbar-default-link-active-bg, 5%); @end-color: darken(@navbar-default-link-active-bg, 2%));\n .box-shadow(inset 0 3px 9px rgba(0,0,0,.075));\n }\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255,255,255,.25);\n}\n\n// Inverted navbar\n.navbar-inverse {\n #gradient > .vertical(@start-color: lighten(@navbar-inverse-bg, 10%); @end-color: @navbar-inverse-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n border-radius: @navbar-border-radius;\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n .box-shadow(inset 0 3px 9px rgba(0,0,0,.25));\n }\n\n .navbar-brand,\n .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0,0,0,.25);\n }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a {\n &,\n &:hover,\n &:focus {\n color: #fff;\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n }\n }\n}\n\n\n//\n// Alerts\n// --------------------------------------------------\n\n// Common styles\n.alert {\n text-shadow: 0 1px 0 rgba(255,255,255,.2);\n @shadow: inset 0 1px 0 rgba(255,255,255,.25), 0 1px 2px rgba(0,0,0,.05);\n .box-shadow(@shadow);\n}\n\n// Mixin for generating new styles\n.alert-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 7.5%));\n border-color: darken(@color, 15%);\n}\n\n// Apply the mixin to the alerts\n.alert-success { .alert-styles(@alert-success-bg); }\n.alert-info { .alert-styles(@alert-info-bg); }\n.alert-warning { .alert-styles(@alert-warning-bg); }\n.alert-danger { .alert-styles(@alert-danger-bg); }\n\n\n//\n// Progress bars\n// --------------------------------------------------\n\n// Give the progress background some depth\n.progress {\n #gradient > .vertical(@start-color: darken(@progress-bg, 4%); @end-color: @progress-bg)\n}\n\n// Mixin for generating new styles\n.progress-bar-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 10%));\n}\n\n// Apply the mixin to the progress bars\n.progress-bar { .progress-bar-styles(@progress-bar-bg); }\n.progress-bar-success { .progress-bar-styles(@progress-bar-success-bg); }\n.progress-bar-info { .progress-bar-styles(@progress-bar-info-bg); }\n.progress-bar-warning { .progress-bar-styles(@progress-bar-warning-bg); }\n.progress-bar-danger { .progress-bar-styles(@progress-bar-danger-bg); }\n\n// Reset the striped class because our mixins don't do multiple gradients and\n// the above custom styles override the new `.progress-bar-striped` in v3.2.0.\n.progress-bar-striped {\n #gradient > .striped();\n}\n\n\n//\n// List groups\n// --------------------------------------------------\n\n.list-group {\n border-radius: @border-radius-base;\n .box-shadow(0 1px 2px rgba(0,0,0,.075));\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 darken(@list-group-active-bg, 10%);\n #gradient > .vertical(@start-color: @list-group-active-bg; @end-color: darken(@list-group-active-bg, 7.5%));\n border-color: darken(@list-group-active-border, 7.5%);\n\n .badge {\n text-shadow: none;\n }\n}\n\n\n//\n// Panels\n// --------------------------------------------------\n\n// Common styles\n.panel {\n .box-shadow(0 1px 2px rgba(0,0,0,.05));\n}\n\n// Mixin for generating new styles\n.panel-heading-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 5%));\n}\n\n// Apply the mixin to the panel headings only\n.panel-default > .panel-heading { .panel-heading-styles(@panel-default-heading-bg); }\n.panel-primary > .panel-heading { .panel-heading-styles(@panel-primary-heading-bg); }\n.panel-success > .panel-heading { .panel-heading-styles(@panel-success-heading-bg); }\n.panel-info > .panel-heading { .panel-heading-styles(@panel-info-heading-bg); }\n.panel-warning > .panel-heading { .panel-heading-styles(@panel-warning-heading-bg); }\n.panel-danger > .panel-heading { .panel-heading-styles(@panel-danger-heading-bg); }\n\n\n//\n// Wells\n// --------------------------------------------------\n\n.well {\n #gradient > .vertical(@start-color: darken(@well-bg, 5%); @end-color: @well-bg);\n border-color: darken(@well-bg, 10%);\n @shadow: inset 0 1px 3px rgba(0,0,0,.05), 0 1px 0 rgba(255,255,255,.1);\n .box-shadow(@shadow);\n}\n","// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n -webkit-animation: @animation;\n -o-animation: @animation;\n animation: @animation;\n}\n.animation-name(@name) {\n -webkit-animation-name: @name;\n animation-name: @name;\n}\n.animation-duration(@duration) {\n -webkit-animation-duration: @duration;\n animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n -webkit-animation-timing-function: @timing-function;\n animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n -webkit-animation-delay: @delay;\n animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n -webkit-animation-iteration-count: @iteration-count;\n animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n -webkit-animation-direction: @direction;\n animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n -webkit-animation-fill-mode: @fill-mode;\n animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n -webkit-backface-visibility: @visibility;\n -moz-backface-visibility: @visibility;\n backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n -webkit-box-sizing: @boxmodel;\n -moz-box-sizing: @boxmodel;\n box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n -webkit-column-count: @column-count;\n -moz-column-count: @column-count;\n column-count: @column-count;\n -webkit-column-gap: @column-gap;\n -moz-column-gap: @column-gap;\n column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n word-wrap: break-word;\n -webkit-hyphens: @mode;\n -moz-hyphens: @mode;\n -ms-hyphens: @mode; // IE10+\n -o-hyphens: @mode;\n hyphens: @mode;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n // Firefox\n &::-moz-placeholder {\n color: @color;\n opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n }\n &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n &::-webkit-input-placeholder { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n -webkit-transform: scale(@ratio);\n -ms-transform: scale(@ratio); // IE9 only\n -o-transform: scale(@ratio);\n transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n -webkit-transform: scale(@ratioX, @ratioY);\n -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n -o-transform: scale(@ratioX, @ratioY);\n transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n -webkit-transform: scaleX(@ratio);\n -ms-transform: scaleX(@ratio); // IE9 only\n -o-transform: scaleX(@ratio);\n transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n -webkit-transform: scaleY(@ratio);\n -ms-transform: scaleY(@ratio); // IE9 only\n -o-transform: scaleY(@ratio);\n transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n -webkit-transform: skewX(@x) skewY(@y);\n -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n -o-transform: skewX(@x) skewY(@y);\n transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n -webkit-transform: translate(@x, @y);\n -ms-transform: translate(@x, @y); // IE9 only\n -o-transform: translate(@x, @y);\n transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n -webkit-transform: translate3d(@x, @y, @z);\n transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n -webkit-transform: rotate(@degrees);\n -ms-transform: rotate(@degrees); // IE9 only\n -o-transform: rotate(@degrees);\n transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n -webkit-transform: rotateX(@degrees);\n -ms-transform: rotateX(@degrees); // IE9 only\n -o-transform: rotateX(@degrees);\n transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n -webkit-transform: rotateY(@degrees);\n -ms-transform: rotateY(@degrees); // IE9 only\n -o-transform: rotateY(@degrees);\n transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n -webkit-perspective: @perspective;\n -moz-perspective: @perspective;\n perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n -webkit-perspective-origin: @perspective;\n -moz-perspective-origin: @perspective;\n perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n -webkit-transform-origin: @origin;\n -moz-transform-origin: @origin;\n -ms-transform-origin: @origin; // IE9 only\n transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n -webkit-transition: @transition;\n -o-transition: @transition;\n transition: @transition;\n}\n.transition-property(@transition-property) {\n -webkit-transition-property: @transition-property;\n transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n -webkit-transition-delay: @transition-delay;\n transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n -webkit-transition-duration: @transition-duration;\n transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n -webkit-transition-timing-function: @timing-function;\n transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n -webkit-transition: -webkit-transform @transition;\n -moz-transition: -moz-transform @transition;\n -o-transition: -o-transform @transition;\n transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n -webkit-user-select: @select;\n -moz-user-select: @select;\n -ms-user-select: @select; // IE10+\n user-select: @select;\n}\n","// Gradients\n\n#gradient {\n\n // Horizontal gradient, from left to right\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down\n }\n\n // Vertical gradient, from top to bottom\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down\n }\n\n .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n background-repeat: repeat-x;\n background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n }\n .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n background-repeat: no-repeat;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n }\n .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-repeat: no-repeat;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n }\n .radial(@inner-color: #555; @outer-color: #333) {\n background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n background-image: radial-gradient(circle, @inner-color, @outer-color);\n background-repeat: no-repeat;\n }\n .striped(@color: rgba(255,255,255,.15); @angle: 45deg) {\n background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]} \ No newline at end of file diff --git a/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.min.css b/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.min.css deleted file mode 100644 index 5e3940195..000000000 --- a/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.min.css +++ /dev/null @@ -1,6 +0,0 @@ -/*! - * Bootstrap v3.3.7 (http://getbootstrap.com) - * Copyright 2011-2016 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-danger.disabled,.btn-danger[disabled],.btn-default.disabled,.btn-default[disabled],.btn-info.disabled,.btn-info[disabled],.btn-primary.disabled,.btn-primary[disabled],.btn-success.disabled,.btn-success[disabled],.btn-warning.disabled,.btn-warning[disabled],fieldset[disabled] .btn-danger,fieldset[disabled] .btn-default,fieldset[disabled] .btn-info,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-success,fieldset[disabled] .btn-warning{-webkit-box-shadow:none;box-shadow:none}.btn-danger .badge,.btn-default .badge,.btn-info .badge,.btn-primary .badge,.btn-success .badge,.btn-warning .badge{text-shadow:none}.btn.active,.btn:active{background-image:none}.btn-default{text-shadow:0 1px 0 #fff;background-image:-webkit-linear-gradient(top,#fff 0,#e0e0e0 100%);background-image:-o-linear-gradient(top,#fff 0,#e0e0e0 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fff),to(#e0e0e0));background-image:linear-gradient(to bottom,#fff 0,#e0e0e0 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#dbdbdb;border-color:#ccc}.btn-default:focus,.btn-default:hover{background-color:#e0e0e0;background-position:0 -15px}.btn-default.active,.btn-default:active{background-color:#e0e0e0;border-color:#dbdbdb}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#e0e0e0;background-image:none}.btn-primary{background-image:-webkit-linear-gradient(top,#337ab7 0,#265a88 100%);background-image:-o-linear-gradient(top,#337ab7 0,#265a88 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#265a88));background-image:linear-gradient(to bottom,#337ab7 0,#265a88 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#245580}.btn-primary:focus,.btn-primary:hover{background-color:#265a88;background-position:0 -15px}.btn-primary.active,.btn-primary:active{background-color:#265a88;border-color:#245580}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#265a88;background-image:none}.btn-success{background-image:-webkit-linear-gradient(top,#5cb85c 0,#419641 100%);background-image:-o-linear-gradient(top,#5cb85c 0,#419641 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5cb85c),to(#419641));background-image:linear-gradient(to bottom,#5cb85c 0,#419641 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#3e8f3e}.btn-success:focus,.btn-success:hover{background-color:#419641;background-position:0 -15px}.btn-success.active,.btn-success:active{background-color:#419641;border-color:#3e8f3e}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#419641;background-image:none}.btn-info{background-image:-webkit-linear-gradient(top,#5bc0de 0,#2aabd2 100%);background-image:-o-linear-gradient(top,#5bc0de 0,#2aabd2 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5bc0de),to(#2aabd2));background-image:linear-gradient(to bottom,#5bc0de 0,#2aabd2 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#28a4c9}.btn-info:focus,.btn-info:hover{background-color:#2aabd2;background-position:0 -15px}.btn-info.active,.btn-info:active{background-color:#2aabd2;border-color:#28a4c9}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#2aabd2;background-image:none}.btn-warning{background-image:-webkit-linear-gradient(top,#f0ad4e 0,#eb9316 100%);background-image:-o-linear-gradient(top,#f0ad4e 0,#eb9316 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f0ad4e),to(#eb9316));background-image:linear-gradient(to bottom,#f0ad4e 0,#eb9316 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#e38d13}.btn-warning:focus,.btn-warning:hover{background-color:#eb9316;background-position:0 -15px}.btn-warning.active,.btn-warning:active{background-color:#eb9316;border-color:#e38d13}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#eb9316;background-image:none}.btn-danger{background-image:-webkit-linear-gradient(top,#d9534f 0,#c12e2a 100%);background-image:-o-linear-gradient(top,#d9534f 0,#c12e2a 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9534f),to(#c12e2a));background-image:linear-gradient(to bottom,#d9534f 0,#c12e2a 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#b92c28}.btn-danger:focus,.btn-danger:hover{background-color:#c12e2a;background-position:0 -15px}.btn-danger.active,.btn-danger:active{background-color:#c12e2a;border-color:#b92c28}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#c12e2a;background-image:none}.img-thumbnail,.thumbnail{-webkit-box-shadow:0 1px 2px rgba(0,0,0,.075);box-shadow:0 1px 2px rgba(0,0,0,.075)}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{background-color:#e8e8e8;background-image:-webkit-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-o-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f5f5f5),to(#e8e8e8));background-image:linear-gradient(to bottom,#f5f5f5 0,#e8e8e8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);background-repeat:repeat-x}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{background-color:#2e6da4;background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);background-repeat:repeat-x}.navbar-default{background-image:-webkit-linear-gradient(top,#fff 0,#f8f8f8 100%);background-image:-o-linear-gradient(top,#fff 0,#f8f8f8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fff),to(#f8f8f8));background-image:linear-gradient(to bottom,#fff 0,#f8f8f8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-radius:4px;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 5px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 5px rgba(0,0,0,.075)}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.open>a{background-image:-webkit-linear-gradient(top,#dbdbdb 0,#e2e2e2 100%);background-image:-o-linear-gradient(top,#dbdbdb 0,#e2e2e2 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dbdbdb),to(#e2e2e2));background-image:linear-gradient(to bottom,#dbdbdb 0,#e2e2e2 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);background-repeat:repeat-x;-webkit-box-shadow:inset 0 3px 9px rgba(0,0,0,.075);box-shadow:inset 0 3px 9px rgba(0,0,0,.075)}.navbar-brand,.navbar-nav>li>a{text-shadow:0 1px 0 rgba(255,255,255,.25)}.navbar-inverse{background-image:-webkit-linear-gradient(top,#3c3c3c 0,#222 100%);background-image:-o-linear-gradient(top,#3c3c3c 0,#222 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#3c3c3c),to(#222));background-image:linear-gradient(to bottom,#3c3c3c 0,#222 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-radius:4px}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.open>a{background-image:-webkit-linear-gradient(top,#080808 0,#0f0f0f 100%);background-image:-o-linear-gradient(top,#080808 0,#0f0f0f 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#080808),to(#0f0f0f));background-image:linear-gradient(to bottom,#080808 0,#0f0f0f 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);background-repeat:repeat-x;-webkit-box-shadow:inset 0 3px 9px rgba(0,0,0,.25);box-shadow:inset 0 3px 9px rgba(0,0,0,.25)}.navbar-inverse .navbar-brand,.navbar-inverse .navbar-nav>li>a{text-shadow:0 -1px 0 rgba(0,0,0,.25)}.navbar-fixed-bottom,.navbar-fixed-top,.navbar-static-top{border-radius:0}@media (max-width:767px){.navbar .navbar-nav .open .dropdown-menu>.active>a,.navbar .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);background-repeat:repeat-x}}.alert{text-shadow:0 1px 0 rgba(255,255,255,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.25),0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.25),0 1px 2px rgba(0,0,0,.05)}.alert-success{background-image:-webkit-linear-gradient(top,#dff0d8 0,#c8e5bc 100%);background-image:-o-linear-gradient(top,#dff0d8 0,#c8e5bc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dff0d8),to(#c8e5bc));background-image:linear-gradient(to bottom,#dff0d8 0,#c8e5bc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);background-repeat:repeat-x;border-color:#b2dba1}.alert-info{background-image:-webkit-linear-gradient(top,#d9edf7 0,#b9def0 100%);background-image:-o-linear-gradient(top,#d9edf7 0,#b9def0 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9edf7),to(#b9def0));background-image:linear-gradient(to bottom,#d9edf7 0,#b9def0 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);background-repeat:repeat-x;border-color:#9acfea}.alert-warning{background-image:-webkit-linear-gradient(top,#fcf8e3 0,#f8efc0 100%);background-image:-o-linear-gradient(top,#fcf8e3 0,#f8efc0 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fcf8e3),to(#f8efc0));background-image:linear-gradient(to bottom,#fcf8e3 0,#f8efc0 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);background-repeat:repeat-x;border-color:#f5e79e}.alert-danger{background-image:-webkit-linear-gradient(top,#f2dede 0,#e7c3c3 100%);background-image:-o-linear-gradient(top,#f2dede 0,#e7c3c3 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f2dede),to(#e7c3c3));background-image:linear-gradient(to bottom,#f2dede 0,#e7c3c3 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);background-repeat:repeat-x;border-color:#dca7a7}.progress{background-image:-webkit-linear-gradient(top,#ebebeb 0,#f5f5f5 100%);background-image:-o-linear-gradient(top,#ebebeb 0,#f5f5f5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#ebebeb),to(#f5f5f5));background-image:linear-gradient(to bottom,#ebebeb 0,#f5f5f5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);background-repeat:repeat-x}.progress-bar{background-image:-webkit-linear-gradient(top,#337ab7 0,#286090 100%);background-image:-o-linear-gradient(top,#337ab7 0,#286090 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#286090));background-image:linear-gradient(to bottom,#337ab7 0,#286090 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);background-repeat:repeat-x}.progress-bar-success{background-image:-webkit-linear-gradient(top,#5cb85c 0,#449d44 100%);background-image:-o-linear-gradient(top,#5cb85c 0,#449d44 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5cb85c),to(#449d44));background-image:linear-gradient(to bottom,#5cb85c 0,#449d44 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);background-repeat:repeat-x}.progress-bar-info{background-image:-webkit-linear-gradient(top,#5bc0de 0,#31b0d5 100%);background-image:-o-linear-gradient(top,#5bc0de 0,#31b0d5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5bc0de),to(#31b0d5));background-image:linear-gradient(to bottom,#5bc0de 0,#31b0d5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);background-repeat:repeat-x}.progress-bar-warning{background-image:-webkit-linear-gradient(top,#f0ad4e 0,#ec971f 100%);background-image:-o-linear-gradient(top,#f0ad4e 0,#ec971f 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f0ad4e),to(#ec971f));background-image:linear-gradient(to bottom,#f0ad4e 0,#ec971f 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);background-repeat:repeat-x}.progress-bar-danger{background-image:-webkit-linear-gradient(top,#d9534f 0,#c9302c 100%);background-image:-o-linear-gradient(top,#d9534f 0,#c9302c 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9534f),to(#c9302c));background-image:linear-gradient(to bottom,#d9534f 0,#c9302c 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);background-repeat:repeat-x}.progress-bar-striped{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.list-group{border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,.075);box-shadow:0 1px 2px rgba(0,0,0,.075)}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{text-shadow:0 -1px 0 #286090;background-image:-webkit-linear-gradient(top,#337ab7 0,#2b669a 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2b669a 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2b669a));background-image:linear-gradient(to bottom,#337ab7 0,#2b669a 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);background-repeat:repeat-x;border-color:#2b669a}.list-group-item.active .badge,.list-group-item.active:focus .badge,.list-group-item.active:hover .badge{text-shadow:none}.panel{-webkit-box-shadow:0 1px 2px rgba(0,0,0,.05);box-shadow:0 1px 2px rgba(0,0,0,.05)}.panel-default>.panel-heading{background-image:-webkit-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-o-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f5f5f5),to(#e8e8e8));background-image:linear-gradient(to bottom,#f5f5f5 0,#e8e8e8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);background-repeat:repeat-x}.panel-primary>.panel-heading{background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);background-repeat:repeat-x}.panel-success>.panel-heading{background-image:-webkit-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-o-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dff0d8),to(#d0e9c6));background-image:linear-gradient(to bottom,#dff0d8 0,#d0e9c6 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);background-repeat:repeat-x}.panel-info>.panel-heading{background-image:-webkit-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-o-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9edf7),to(#c4e3f3));background-image:linear-gradient(to bottom,#d9edf7 0,#c4e3f3 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);background-repeat:repeat-x}.panel-warning>.panel-heading{background-image:-webkit-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-o-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fcf8e3),to(#faf2cc));background-image:linear-gradient(to bottom,#fcf8e3 0,#faf2cc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);background-repeat:repeat-x}.panel-danger>.panel-heading{background-image:-webkit-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-o-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f2dede),to(#ebcccc));background-image:linear-gradient(to bottom,#f2dede 0,#ebcccc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);background-repeat:repeat-x}.well{background-image:-webkit-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-o-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#e8e8e8),to(#f5f5f5));background-image:linear-gradient(to bottom,#e8e8e8 0,#f5f5f5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);background-repeat:repeat-x;border-color:#dcdcdc;-webkit-box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1)} -/*# sourceMappingURL=bootstrap-theme.min.css.map */ \ No newline at end of file diff --git a/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.min.css.map b/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.min.css.map deleted file mode 100644 index 94813e900..000000000 --- a/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.min.css.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"sources":["less/theme.less","less/mixins/vendor-prefixes.less","less/mixins/gradients.less","less/mixins/reset-filter.less"],"names":[],"mappings":";;;;AAmBA,YAAA,aAAA,UAAA,aAAA,aAAA,aAME,YAAA,EAAA,KAAA,EAAA,eC2CA,mBAAA,MAAA,EAAA,IAAA,EAAA,sBAAA,EAAA,IAAA,IAAA,iBACQ,WAAA,MAAA,EAAA,IAAA,EAAA,sBAAA,EAAA,IAAA,IAAA,iBDvCR,mBAAA,mBAAA,oBAAA,oBAAA,iBAAA,iBAAA,oBAAA,oBAAA,oBAAA,oBAAA,oBAAA,oBCsCA,mBAAA,MAAA,EAAA,IAAA,IAAA,iBACQ,WAAA,MAAA,EAAA,IAAA,IAAA,iBDlCR,qBAAA,sBAAA,sBAAA,uBAAA,mBAAA,oBAAA,sBAAA,uBAAA,sBAAA,uBAAA,sBAAA,uBAAA,+BAAA,gCAAA,6BAAA,gCAAA,gCAAA,gCCiCA,mBAAA,KACQ,WAAA,KDlDV,mBAAA,oBAAA,iBAAA,oBAAA,oBAAA,oBAuBI,YAAA,KAyCF,YAAA,YAEE,iBAAA,KAKJ,aErEI,YAAA,EAAA,IAAA,EAAA,KACA,iBAAA,iDACA,iBAAA,4CAAA,iBAAA,qEAEA,iBAAA,+CCnBF,OAAA,+GH4CA,OAAA,0DACA,kBAAA,SAuC2C,aAAA,QAA2B,aAAA,KArCtE,mBAAA,mBAEE,iBAAA,QACA,oBAAA,EAAA,MAGF,oBAAA,oBAEE,iBAAA,QACA,aAAA,QAMA,sBAAA,6BAAA,4BAAA,6BAAA,4BAAA,4BAAA,uBAAA,8BAAA,6BAAA,8BAAA,6BAAA,6BAAA,gCAAA,uCAAA,sCAAA,uCAAA,sCAAA,sCAME,iBAAA,QACA,iBAAA,KAgBN,aEtEI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDAEA,OAAA,+GCnBF,OAAA,0DH4CA,kBAAA,SACA,aAAA,QAEA,mBAAA,mBAEE,iBAAA,QACA,oBAAA,EAAA,MAGF,oBAAA,oBAEE,iBAAA,QACA,aAAA,QAMA,sBAAA,6BAAA,4BAAA,6BAAA,4BAAA,4BAAA,uBAAA,8BAAA,6BAAA,8BAAA,6BAAA,6BAAA,gCAAA,uCAAA,sCAAA,uCAAA,sCAAA,sCAME,iBAAA,QACA,iBAAA,KAiBN,aEvEI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDAEA,OAAA,+GCnBF,OAAA,0DH4CA,kBAAA,SACA,aAAA,QAEA,mBAAA,mBAEE,iBAAA,QACA,oBAAA,EAAA,MAGF,oBAAA,oBAEE,iBAAA,QACA,aAAA,QAMA,sBAAA,6BAAA,4BAAA,6BAAA,4BAAA,4BAAA,uBAAA,8BAAA,6BAAA,8BAAA,6BAAA,6BAAA,gCAAA,uCAAA,sCAAA,uCAAA,sCAAA,sCAME,iBAAA,QACA,iBAAA,KAkBN,UExEI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDAEA,OAAA,+GCnBF,OAAA,0DH4CA,kBAAA,SACA,aAAA,QAEA,gBAAA,gBAEE,iBAAA,QACA,oBAAA,EAAA,MAGF,iBAAA,iBAEE,iBAAA,QACA,aAAA,QAMA,mBAAA,0BAAA,yBAAA,0BAAA,yBAAA,yBAAA,oBAAA,2BAAA,0BAAA,2BAAA,0BAAA,0BAAA,6BAAA,oCAAA,mCAAA,oCAAA,mCAAA,mCAME,iBAAA,QACA,iBAAA,KAmBN,aEzEI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDAEA,OAAA,+GCnBF,OAAA,0DH4CA,kBAAA,SACA,aAAA,QAEA,mBAAA,mBAEE,iBAAA,QACA,oBAAA,EAAA,MAGF,oBAAA,oBAEE,iBAAA,QACA,aAAA,QAMA,sBAAA,6BAAA,4BAAA,6BAAA,4BAAA,4BAAA,uBAAA,8BAAA,6BAAA,8BAAA,6BAAA,6BAAA,gCAAA,uCAAA,sCAAA,uCAAA,sCAAA,sCAME,iBAAA,QACA,iBAAA,KAoBN,YE1EI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDAEA,OAAA,+GCnBF,OAAA,0DH4CA,kBAAA,SACA,aAAA,QAEA,kBAAA,kBAEE,iBAAA,QACA,oBAAA,EAAA,MAGF,mBAAA,mBAEE,iBAAA,QACA,aAAA,QAMA,qBAAA,4BAAA,2BAAA,4BAAA,2BAAA,2BAAA,sBAAA,6BAAA,4BAAA,6BAAA,4BAAA,4BAAA,+BAAA,sCAAA,qCAAA,sCAAA,qCAAA,qCAME,iBAAA,QACA,iBAAA,KA2BN,eAAA,WClCE,mBAAA,EAAA,IAAA,IAAA,iBACQ,WAAA,EAAA,IAAA,IAAA,iBD2CV,0BAAA,0BE3FI,iBAAA,QACA,iBAAA,oDACA,iBAAA,+CAAA,iBAAA,wEACA,iBAAA,kDACA,OAAA,+GF0FF,kBAAA,SAEF,yBAAA,+BAAA,+BEhGI,iBAAA,QACA,iBAAA,oDACA,iBAAA,+CAAA,iBAAA,wEACA,iBAAA,kDACA,OAAA,+GFgGF,kBAAA,SASF,gBE7GI,iBAAA,iDACA,iBAAA,4CACA,iBAAA,qEAAA,iBAAA,+CACA,OAAA,+GACA,OAAA,0DCnBF,kBAAA,SH+HA,cAAA,ICjEA,mBAAA,MAAA,EAAA,IAAA,EAAA,sBAAA,EAAA,IAAA,IAAA,iBACQ,WAAA,MAAA,EAAA,IAAA,EAAA,sBAAA,EAAA,IAAA,IAAA,iBD6DV,sCAAA,oCE7GI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SD2CF,mBAAA,MAAA,EAAA,IAAA,IAAA,iBACQ,WAAA,MAAA,EAAA,IAAA,IAAA,iBD0EV,cAAA,iBAEE,YAAA,EAAA,IAAA,EAAA,sBAIF,gBEhII,iBAAA,iDACA,iBAAA,4CACA,iBAAA,qEAAA,iBAAA,+CACA,OAAA,+GACA,OAAA,0DCnBF,kBAAA,SHkJA,cAAA,IAHF,sCAAA,oCEhII,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SD2CF,mBAAA,MAAA,EAAA,IAAA,IAAA,gBACQ,WAAA,MAAA,EAAA,IAAA,IAAA,gBDgFV,8BAAA,iCAYI,YAAA,EAAA,KAAA,EAAA,gBAKJ,qBAAA,kBAAA,mBAGE,cAAA,EAqBF,yBAfI,mDAAA,yDAAA,yDAGE,MAAA,KE7JF,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,UFqKJ,OACE,YAAA,EAAA,IAAA,EAAA,qBC3HA,mBAAA,MAAA,EAAA,IAAA,EAAA,sBAAA,EAAA,IAAA,IAAA,gBACQ,WAAA,MAAA,EAAA,IAAA,EAAA,sBAAA,EAAA,IAAA,IAAA,gBDsIV,eEtLI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF8KF,aAAA,QAKF,YEvLI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF8KF,aAAA,QAMF,eExLI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF8KF,aAAA,QAOF,cEzLI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF8KF,aAAA,QAeF,UEjMI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SFuMJ,cE3MI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SFwMJ,sBE5MI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SFyMJ,mBE7MI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF0MJ,sBE9MI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF2MJ,qBE/MI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF+MJ,sBElLI,iBAAA,yKACA,iBAAA,oKACA,iBAAA,iKFyLJ,YACE,cAAA,IC9KA,mBAAA,EAAA,IAAA,IAAA,iBACQ,WAAA,EAAA,IAAA,IAAA,iBDgLV,wBAAA,8BAAA,8BAGE,YAAA,EAAA,KAAA,EAAA,QEnOE,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SFiOF,aAAA,QALF,+BAAA,qCAAA,qCAQI,YAAA,KAUJ,OCnME,mBAAA,EAAA,IAAA,IAAA,gBACQ,WAAA,EAAA,IAAA,IAAA,gBD4MV,8BE5PI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SFyPJ,8BE7PI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF0PJ,8BE9PI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF2PJ,2BE/PI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF4PJ,8BEhQI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SF6PJ,6BEjQI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SFoQJ,MExQI,iBAAA,oDACA,iBAAA,+CACA,iBAAA,wEAAA,iBAAA,kDACA,OAAA,+GACA,kBAAA,SFsQF,aAAA,QC3NA,mBAAA,MAAA,EAAA,IAAA,IAAA,gBAAA,EAAA,IAAA,EAAA,qBACQ,WAAA,MAAA,EAAA,IAAA,IAAA,gBAAA,EAAA,IAAA,EAAA","sourcesContent":["/*!\n * Bootstrap v3.3.7 (http://getbootstrap.com)\n * Copyright 2011-2016 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n\n//\n// Load core variables and mixins\n// --------------------------------------------------\n\n@import \"variables.less\";\n@import \"mixins.less\";\n\n\n//\n// Buttons\n// --------------------------------------------------\n\n// Common styles\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n text-shadow: 0 -1px 0 rgba(0,0,0,.2);\n @shadow: inset 0 1px 0 rgba(255,255,255,.15), 0 1px 1px rgba(0,0,0,.075);\n .box-shadow(@shadow);\n\n // Reset the shadow\n &:active,\n &.active {\n .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n .box-shadow(none);\n }\n\n .badge {\n text-shadow: none;\n }\n}\n\n// Mixin for generating new styles\n.btn-styles(@btn-color: #555) {\n #gradient > .vertical(@start-color: @btn-color; @end-color: darken(@btn-color, 12%));\n .reset-filter(); // Disable gradients for IE9 because filter bleeds through rounded corners; see https://github.com/twbs/bootstrap/issues/10620\n background-repeat: repeat-x;\n border-color: darken(@btn-color, 14%);\n\n &:hover,\n &:focus {\n background-color: darken(@btn-color, 12%);\n background-position: 0 -15px;\n }\n\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n border-color: darken(@btn-color, 14%);\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n &,\n &:hover,\n &:focus,\n &.focus,\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n background-image: none;\n }\n }\n}\n\n// Common styles\n.btn {\n // Remove the gradient for the pressed/active state\n &:active,\n &.active {\n background-image: none;\n }\n}\n\n// Apply the mixin to the buttons\n.btn-default { .btn-styles(@btn-default-bg); text-shadow: 0 1px 0 #fff; border-color: #ccc; }\n.btn-primary { .btn-styles(@btn-primary-bg); }\n.btn-success { .btn-styles(@btn-success-bg); }\n.btn-info { .btn-styles(@btn-info-bg); }\n.btn-warning { .btn-styles(@btn-warning-bg); }\n.btn-danger { .btn-styles(@btn-danger-bg); }\n\n\n//\n// Images\n// --------------------------------------------------\n\n.thumbnail,\n.img-thumbnail {\n .box-shadow(0 1px 2px rgba(0,0,0,.075));\n}\n\n\n//\n// Dropdowns\n// --------------------------------------------------\n\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-hover-bg; @end-color: darken(@dropdown-link-hover-bg, 5%));\n background-color: darken(@dropdown-link-hover-bg, 5%);\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n background-color: darken(@dropdown-link-active-bg, 5%);\n}\n\n\n//\n// Navbar\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n #gradient > .vertical(@start-color: lighten(@navbar-default-bg, 10%); @end-color: @navbar-default-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered\n border-radius: @navbar-border-radius;\n @shadow: inset 0 1px 0 rgba(255,255,255,.15), 0 1px 5px rgba(0,0,0,.075);\n .box-shadow(@shadow);\n\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: darken(@navbar-default-link-active-bg, 5%); @end-color: darken(@navbar-default-link-active-bg, 2%));\n .box-shadow(inset 0 3px 9px rgba(0,0,0,.075));\n }\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255,255,255,.25);\n}\n\n// Inverted navbar\n.navbar-inverse {\n #gradient > .vertical(@start-color: lighten(@navbar-inverse-bg, 10%); @end-color: @navbar-inverse-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n border-radius: @navbar-border-radius;\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n .box-shadow(inset 0 3px 9px rgba(0,0,0,.25));\n }\n\n .navbar-brand,\n .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0,0,0,.25);\n }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a {\n &,\n &:hover,\n &:focus {\n color: #fff;\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n }\n }\n}\n\n\n//\n// Alerts\n// --------------------------------------------------\n\n// Common styles\n.alert {\n text-shadow: 0 1px 0 rgba(255,255,255,.2);\n @shadow: inset 0 1px 0 rgba(255,255,255,.25), 0 1px 2px rgba(0,0,0,.05);\n .box-shadow(@shadow);\n}\n\n// Mixin for generating new styles\n.alert-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 7.5%));\n border-color: darken(@color, 15%);\n}\n\n// Apply the mixin to the alerts\n.alert-success { .alert-styles(@alert-success-bg); }\n.alert-info { .alert-styles(@alert-info-bg); }\n.alert-warning { .alert-styles(@alert-warning-bg); }\n.alert-danger { .alert-styles(@alert-danger-bg); }\n\n\n//\n// Progress bars\n// --------------------------------------------------\n\n// Give the progress background some depth\n.progress {\n #gradient > .vertical(@start-color: darken(@progress-bg, 4%); @end-color: @progress-bg)\n}\n\n// Mixin for generating new styles\n.progress-bar-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 10%));\n}\n\n// Apply the mixin to the progress bars\n.progress-bar { .progress-bar-styles(@progress-bar-bg); }\n.progress-bar-success { .progress-bar-styles(@progress-bar-success-bg); }\n.progress-bar-info { .progress-bar-styles(@progress-bar-info-bg); }\n.progress-bar-warning { .progress-bar-styles(@progress-bar-warning-bg); }\n.progress-bar-danger { .progress-bar-styles(@progress-bar-danger-bg); }\n\n// Reset the striped class because our mixins don't do multiple gradients and\n// the above custom styles override the new `.progress-bar-striped` in v3.2.0.\n.progress-bar-striped {\n #gradient > .striped();\n}\n\n\n//\n// List groups\n// --------------------------------------------------\n\n.list-group {\n border-radius: @border-radius-base;\n .box-shadow(0 1px 2px rgba(0,0,0,.075));\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 darken(@list-group-active-bg, 10%);\n #gradient > .vertical(@start-color: @list-group-active-bg; @end-color: darken(@list-group-active-bg, 7.5%));\n border-color: darken(@list-group-active-border, 7.5%);\n\n .badge {\n text-shadow: none;\n }\n}\n\n\n//\n// Panels\n// --------------------------------------------------\n\n// Common styles\n.panel {\n .box-shadow(0 1px 2px rgba(0,0,0,.05));\n}\n\n// Mixin for generating new styles\n.panel-heading-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 5%));\n}\n\n// Apply the mixin to the panel headings only\n.panel-default > .panel-heading { .panel-heading-styles(@panel-default-heading-bg); }\n.panel-primary > .panel-heading { .panel-heading-styles(@panel-primary-heading-bg); }\n.panel-success > .panel-heading { .panel-heading-styles(@panel-success-heading-bg); }\n.panel-info > .panel-heading { .panel-heading-styles(@panel-info-heading-bg); }\n.panel-warning > .panel-heading { .panel-heading-styles(@panel-warning-heading-bg); }\n.panel-danger > .panel-heading { .panel-heading-styles(@panel-danger-heading-bg); }\n\n\n//\n// Wells\n// --------------------------------------------------\n\n.well {\n #gradient > .vertical(@start-color: darken(@well-bg, 5%); @end-color: @well-bg);\n border-color: darken(@well-bg, 10%);\n @shadow: inset 0 1px 3px rgba(0,0,0,.05), 0 1px 0 rgba(255,255,255,.1);\n .box-shadow(@shadow);\n}\n","// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n -webkit-animation: @animation;\n -o-animation: @animation;\n animation: @animation;\n}\n.animation-name(@name) {\n -webkit-animation-name: @name;\n animation-name: @name;\n}\n.animation-duration(@duration) {\n -webkit-animation-duration: @duration;\n animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n -webkit-animation-timing-function: @timing-function;\n animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n -webkit-animation-delay: @delay;\n animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n -webkit-animation-iteration-count: @iteration-count;\n animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n -webkit-animation-direction: @direction;\n animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n -webkit-animation-fill-mode: @fill-mode;\n animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n -webkit-backface-visibility: @visibility;\n -moz-backface-visibility: @visibility;\n backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n -webkit-box-sizing: @boxmodel;\n -moz-box-sizing: @boxmodel;\n box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n -webkit-column-count: @column-count;\n -moz-column-count: @column-count;\n column-count: @column-count;\n -webkit-column-gap: @column-gap;\n -moz-column-gap: @column-gap;\n column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n word-wrap: break-word;\n -webkit-hyphens: @mode;\n -moz-hyphens: @mode;\n -ms-hyphens: @mode; // IE10+\n -o-hyphens: @mode;\n hyphens: @mode;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n // Firefox\n &::-moz-placeholder {\n color: @color;\n opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n }\n &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n &::-webkit-input-placeholder { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n -webkit-transform: scale(@ratio);\n -ms-transform: scale(@ratio); // IE9 only\n -o-transform: scale(@ratio);\n transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n -webkit-transform: scale(@ratioX, @ratioY);\n -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n -o-transform: scale(@ratioX, @ratioY);\n transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n -webkit-transform: scaleX(@ratio);\n -ms-transform: scaleX(@ratio); // IE9 only\n -o-transform: scaleX(@ratio);\n transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n -webkit-transform: scaleY(@ratio);\n -ms-transform: scaleY(@ratio); // IE9 only\n -o-transform: scaleY(@ratio);\n transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n -webkit-transform: skewX(@x) skewY(@y);\n -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n -o-transform: skewX(@x) skewY(@y);\n transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n -webkit-transform: translate(@x, @y);\n -ms-transform: translate(@x, @y); // IE9 only\n -o-transform: translate(@x, @y);\n transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n -webkit-transform: translate3d(@x, @y, @z);\n transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n -webkit-transform: rotate(@degrees);\n -ms-transform: rotate(@degrees); // IE9 only\n -o-transform: rotate(@degrees);\n transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n -webkit-transform: rotateX(@degrees);\n -ms-transform: rotateX(@degrees); // IE9 only\n -o-transform: rotateX(@degrees);\n transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n -webkit-transform: rotateY(@degrees);\n -ms-transform: rotateY(@degrees); // IE9 only\n -o-transform: rotateY(@degrees);\n transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n -webkit-perspective: @perspective;\n -moz-perspective: @perspective;\n perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n -webkit-perspective-origin: @perspective;\n -moz-perspective-origin: @perspective;\n perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n -webkit-transform-origin: @origin;\n -moz-transform-origin: @origin;\n -ms-transform-origin: @origin; // IE9 only\n transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n -webkit-transition: @transition;\n -o-transition: @transition;\n transition: @transition;\n}\n.transition-property(@transition-property) {\n -webkit-transition-property: @transition-property;\n transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n -webkit-transition-delay: @transition-delay;\n transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n -webkit-transition-duration: @transition-duration;\n transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n -webkit-transition-timing-function: @timing-function;\n transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n -webkit-transition: -webkit-transform @transition;\n -moz-transition: -moz-transform @transition;\n -o-transition: -o-transform @transition;\n transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n -webkit-user-select: @select;\n -moz-user-select: @select;\n -ms-user-select: @select; // IE10+\n user-select: @select;\n}\n","// Gradients\n\n#gradient {\n\n // Horizontal gradient, from left to right\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down\n }\n\n // Vertical gradient, from top to bottom\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down\n }\n\n .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n background-repeat: repeat-x;\n background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n }\n .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n background-repeat: no-repeat;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n }\n .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-repeat: no-repeat;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n }\n .radial(@inner-color: #555; @outer-color: #333) {\n background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n background-image: radial-gradient(circle, @inner-color, @outer-color);\n background-repeat: no-repeat;\n }\n .striped(@color: rgba(255,255,255,.15); @angle: 45deg) {\n background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]} \ No newline at end of file diff --git a/docs/_static/bootstrap-3.3.7/css/bootstrap.css b/docs/_static/bootstrap-3.3.7/css/bootstrap.css deleted file mode 100644 index 6167622ce..000000000 --- a/docs/_static/bootstrap-3.3.7/css/bootstrap.css +++ /dev/null @@ -1,6757 +0,0 @@ -/*! - * Bootstrap v3.3.7 (http://getbootstrap.com) - * Copyright 2011-2016 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - */ -/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */ -html { - font-family: sans-serif; - -webkit-text-size-adjust: 100%; - -ms-text-size-adjust: 100%; -} -body { - margin: 0; -} -article, -aside, -details, -figcaption, -figure, -footer, -header, -hgroup, -main, -menu, -nav, -section, -summary { - display: block; -} -audio, -canvas, -progress, -video { - display: inline-block; - vertical-align: baseline; -} -audio:not([controls]) { - display: none; - height: 0; -} -[hidden], -template { - display: none; -} -a { - background-color: transparent; -} -a:active, -a:hover { - outline: 0; -} -abbr[title] { - border-bottom: 1px dotted; -} -b, -strong { - font-weight: bold; -} -dfn { - font-style: italic; -} -h1 { - margin: .67em 0; - font-size: 2em; -} -mark { - color: #000; - background: #ff0; -} -small { - font-size: 80%; -} -sub, -sup { - position: relative; - font-size: 75%; - line-height: 0; - vertical-align: baseline; -} -sup { - top: -.5em; -} -sub { - bottom: -.25em; -} -img { - border: 0; -} -svg:not(:root) { - overflow: hidden; -} -figure { - margin: 1em 40px; -} -hr { - height: 0; - -webkit-box-sizing: content-box; - -moz-box-sizing: content-box; - box-sizing: content-box; -} -pre { - overflow: auto; -} -code, -kbd, -pre, -samp { - font-family: monospace, monospace; - font-size: 1em; -} -button, -input, -optgroup, -select, -textarea { - margin: 0; - font: inherit; - color: inherit; -} -button { - overflow: visible; -} -button, -select { - text-transform: none; -} -button, -html input[type="button"], -input[type="reset"], -input[type="submit"] { - -webkit-appearance: button; - cursor: pointer; -} -button[disabled], -html input[disabled] { - cursor: default; -} -button::-moz-focus-inner, -input::-moz-focus-inner { - padding: 0; - border: 0; -} -input { - line-height: normal; -} -input[type="checkbox"], -input[type="radio"] { - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - padding: 0; -} -input[type="number"]::-webkit-inner-spin-button, -input[type="number"]::-webkit-outer-spin-button { - height: auto; -} -input[type="search"] { - -webkit-box-sizing: content-box; - -moz-box-sizing: content-box; - box-sizing: content-box; - -webkit-appearance: textfield; -} -input[type="search"]::-webkit-search-cancel-button, -input[type="search"]::-webkit-search-decoration { - -webkit-appearance: none; -} -fieldset { - padding: .35em .625em .75em; - margin: 0 2px; - border: 1px solid #c0c0c0; -} -legend { - padding: 0; - border: 0; -} -textarea { - overflow: auto; -} -optgroup { - font-weight: bold; -} -table { - border-spacing: 0; - border-collapse: collapse; -} -td, -th { - padding: 0; -} -/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */ -@media print { - *, - *:before, - *:after { - color: #000 !important; - text-shadow: none !important; - background: transparent !important; - -webkit-box-shadow: none !important; - box-shadow: none !important; - } - a, - a:visited { - text-decoration: underline; - } - a[href]:after { - content: " (" attr(href) ")"; - } - abbr[title]:after { - content: " (" attr(title) ")"; - } - a[href^="#"]:after, - a[href^="javascript:"]:after { - content: ""; - } - pre, - blockquote { - border: 1px solid #999; - - page-break-inside: avoid; - } - thead { - display: table-header-group; - } - tr, - img { - page-break-inside: avoid; - } - img { - max-width: 100% !important; - } - p, - h2, - h3 { - orphans: 3; - widows: 3; - } - h2, - h3 { - page-break-after: avoid; - } - .navbar { - display: none; - } - .btn > .caret, - .dropup > .btn > .caret { - border-top-color: #000 !important; - } - .label { - border: 1px solid #000; - } - .table { - border-collapse: collapse !important; - } - .table td, - .table th { - background-color: #fff !important; - } - .table-bordered th, - .table-bordered td { - border: 1px solid #ddd !important; - } -} -@font-face { - font-family: 'Glyphicons Halflings'; - - src: url('../fonts/glyphicons-halflings-regular.eot'); - src: url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../fonts/glyphicons-halflings-regular.woff') format('woff'), url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg'); -} -.glyphicon { - position: relative; - top: 1px; - display: inline-block; - font-family: 'Glyphicons Halflings'; - font-style: normal; - font-weight: normal; - line-height: 1; - - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} -.glyphicon-asterisk:before { - content: "\002a"; -} -.glyphicon-plus:before { - content: "\002b"; -} -.glyphicon-euro:before, -.glyphicon-eur:before { - content: "\20ac"; -} -.glyphicon-minus:before { - content: "\2212"; -} -.glyphicon-cloud:before { - content: "\2601"; -} -.glyphicon-envelope:before { - content: "\2709"; -} -.glyphicon-pencil:before { - content: "\270f"; -} -.glyphicon-glass:before { - content: "\e001"; -} -.glyphicon-music:before { - content: "\e002"; -} -.glyphicon-search:before { - content: "\e003"; -} -.glyphicon-heart:before { - content: "\e005"; -} -.glyphicon-star:before { - content: "\e006"; -} -.glyphicon-star-empty:before { - content: "\e007"; -} -.glyphicon-user:before { - content: "\e008"; -} -.glyphicon-film:before { - content: "\e009"; -} -.glyphicon-th-large:before { - content: "\e010"; -} -.glyphicon-th:before { - content: "\e011"; -} -.glyphicon-th-list:before { - content: "\e012"; -} -.glyphicon-ok:before { - content: "\e013"; -} -.glyphicon-remove:before { - content: "\e014"; -} -.glyphicon-zoom-in:before { - content: "\e015"; -} -.glyphicon-zoom-out:before { - content: "\e016"; -} -.glyphicon-off:before { - content: "\e017"; -} -.glyphicon-signal:before { - content: "\e018"; -} -.glyphicon-cog:before { - content: "\e019"; -} -.glyphicon-trash:before { - content: "\e020"; -} -.glyphicon-home:before { - content: "\e021"; -} -.glyphicon-file:before { - content: "\e022"; -} -.glyphicon-time:before { - content: "\e023"; -} -.glyphicon-road:before { - content: "\e024"; -} -.glyphicon-download-alt:before { - content: "\e025"; -} -.glyphicon-download:before { - content: "\e026"; -} -.glyphicon-upload:before { - content: "\e027"; -} -.glyphicon-inbox:before { - content: "\e028"; -} -.glyphicon-play-circle:before { - content: "\e029"; -} -.glyphicon-repeat:before { - content: "\e030"; -} -.glyphicon-refresh:before { - content: "\e031"; -} -.glyphicon-list-alt:before { - content: "\e032"; -} -.glyphicon-lock:before { - content: "\e033"; -} -.glyphicon-flag:before { - content: "\e034"; -} -.glyphicon-headphones:before { - content: "\e035"; -} -.glyphicon-volume-off:before { - content: "\e036"; -} -.glyphicon-volume-down:before { - content: "\e037"; -} -.glyphicon-volume-up:before { - content: "\e038"; -} -.glyphicon-qrcode:before { - content: "\e039"; -} -.glyphicon-barcode:before { - content: "\e040"; -} -.glyphicon-tag:before { - content: "\e041"; -} -.glyphicon-tags:before { - content: "\e042"; -} -.glyphicon-book:before { - content: "\e043"; -} -.glyphicon-bookmark:before { - content: "\e044"; -} -.glyphicon-print:before { - content: "\e045"; -} -.glyphicon-camera:before { - content: "\e046"; -} -.glyphicon-font:before { - content: "\e047"; -} -.glyphicon-bold:before { - content: "\e048"; -} -.glyphicon-italic:before { - content: "\e049"; -} -.glyphicon-text-height:before { - content: "\e050"; -} -.glyphicon-text-width:before { - content: "\e051"; -} -.glyphicon-align-left:before { - content: "\e052"; -} -.glyphicon-align-center:before { - content: "\e053"; -} -.glyphicon-align-right:before { - content: "\e054"; -} -.glyphicon-align-justify:before { - content: "\e055"; -} -.glyphicon-list:before { - content: "\e056"; -} -.glyphicon-indent-left:before { - content: "\e057"; -} -.glyphicon-indent-right:before { - content: "\e058"; -} -.glyphicon-facetime-video:before { - content: "\e059"; -} -.glyphicon-picture:before { - content: "\e060"; -} -.glyphicon-map-marker:before { - content: "\e062"; -} -.glyphicon-adjust:before { - content: "\e063"; -} -.glyphicon-tint:before { - content: "\e064"; -} -.glyphicon-edit:before { - content: "\e065"; -} -.glyphicon-share:before { - content: "\e066"; -} -.glyphicon-check:before { - content: "\e067"; -} -.glyphicon-move:before { - content: "\e068"; -} -.glyphicon-step-backward:before { - content: "\e069"; -} -.glyphicon-fast-backward:before { - content: "\e070"; -} -.glyphicon-backward:before { - content: "\e071"; -} -.glyphicon-play:before { - content: "\e072"; -} -.glyphicon-pause:before { - content: "\e073"; -} -.glyphicon-stop:before { - content: "\e074"; -} -.glyphicon-forward:before { - content: "\e075"; -} -.glyphicon-fast-forward:before { - content: "\e076"; -} -.glyphicon-step-forward:before { - content: "\e077"; -} -.glyphicon-eject:before { - content: "\e078"; -} -.glyphicon-chevron-left:before { - content: "\e079"; -} -.glyphicon-chevron-right:before { - content: "\e080"; -} -.glyphicon-plus-sign:before { - content: "\e081"; -} -.glyphicon-minus-sign:before { - content: "\e082"; -} -.glyphicon-remove-sign:before { - content: "\e083"; -} -.glyphicon-ok-sign:before { - content: "\e084"; -} -.glyphicon-question-sign:before { - content: "\e085"; -} -.glyphicon-info-sign:before { - content: "\e086"; -} -.glyphicon-screenshot:before { - content: "\e087"; -} -.glyphicon-remove-circle:before { - content: "\e088"; -} -.glyphicon-ok-circle:before { - content: "\e089"; -} -.glyphicon-ban-circle:before { - content: "\e090"; -} -.glyphicon-arrow-left:before { - content: "\e091"; -} -.glyphicon-arrow-right:before { - content: "\e092"; -} -.glyphicon-arrow-up:before { - content: "\e093"; -} -.glyphicon-arrow-down:before { - content: "\e094"; -} -.glyphicon-share-alt:before { - content: "\e095"; -} -.glyphicon-resize-full:before { - content: "\e096"; -} -.glyphicon-resize-small:before { - content: "\e097"; -} -.glyphicon-exclamation-sign:before { - content: "\e101"; -} -.glyphicon-gift:before { - content: "\e102"; -} -.glyphicon-leaf:before { - content: "\e103"; -} -.glyphicon-fire:before { - content: "\e104"; -} -.glyphicon-eye-open:before { - content: "\e105"; -} -.glyphicon-eye-close:before { - content: "\e106"; -} -.glyphicon-warning-sign:before { - content: "\e107"; -} -.glyphicon-plane:before { - content: "\e108"; -} -.glyphicon-calendar:before { - content: "\e109"; -} -.glyphicon-random:before { - content: "\e110"; -} -.glyphicon-comment:before { - content: "\e111"; -} -.glyphicon-magnet:before { - content: "\e112"; -} -.glyphicon-chevron-up:before { - content: "\e113"; -} -.glyphicon-chevron-down:before { - content: "\e114"; -} -.glyphicon-retweet:before { - content: "\e115"; -} -.glyphicon-shopping-cart:before { - content: "\e116"; -} -.glyphicon-folder-close:before { - content: "\e117"; -} -.glyphicon-folder-open:before { - content: "\e118"; -} -.glyphicon-resize-vertical:before { - content: "\e119"; -} -.glyphicon-resize-horizontal:before { - content: "\e120"; -} -.glyphicon-hdd:before { - content: "\e121"; -} -.glyphicon-bullhorn:before { - content: "\e122"; -} -.glyphicon-bell:before { - content: "\e123"; -} -.glyphicon-certificate:before { - content: "\e124"; -} -.glyphicon-thumbs-up:before { - content: "\e125"; -} -.glyphicon-thumbs-down:before { - content: "\e126"; -} -.glyphicon-hand-right:before { - content: "\e127"; -} -.glyphicon-hand-left:before { - content: "\e128"; -} -.glyphicon-hand-up:before { - content: "\e129"; -} -.glyphicon-hand-down:before { - content: "\e130"; -} -.glyphicon-circle-arrow-right:before { - content: "\e131"; -} -.glyphicon-circle-arrow-left:before { - content: "\e132"; -} -.glyphicon-circle-arrow-up:before { - content: "\e133"; -} -.glyphicon-circle-arrow-down:before { - content: "\e134"; -} -.glyphicon-globe:before { - content: "\e135"; -} -.glyphicon-wrench:before { - content: "\e136"; -} -.glyphicon-tasks:before { - content: "\e137"; -} -.glyphicon-filter:before { - content: "\e138"; -} -.glyphicon-briefcase:before { - content: "\e139"; -} -.glyphicon-fullscreen:before { - content: "\e140"; -} -.glyphicon-dashboard:before { - content: "\e141"; -} -.glyphicon-paperclip:before { - content: "\e142"; -} -.glyphicon-heart-empty:before { - content: "\e143"; -} -.glyphicon-link:before { - content: "\e144"; -} -.glyphicon-phone:before { - content: "\e145"; -} -.glyphicon-pushpin:before { - content: "\e146"; -} -.glyphicon-usd:before { - content: "\e148"; -} -.glyphicon-gbp:before { - content: "\e149"; -} -.glyphicon-sort:before { - content: "\e150"; -} -.glyphicon-sort-by-alphabet:before { - content: "\e151"; -} -.glyphicon-sort-by-alphabet-alt:before { - content: "\e152"; -} -.glyphicon-sort-by-order:before { - content: "\e153"; -} -.glyphicon-sort-by-order-alt:before { - content: "\e154"; -} -.glyphicon-sort-by-attributes:before { - content: "\e155"; -} -.glyphicon-sort-by-attributes-alt:before { - content: "\e156"; -} -.glyphicon-unchecked:before { - content: "\e157"; -} -.glyphicon-expand:before { - content: "\e158"; -} -.glyphicon-collapse-down:before { - content: "\e159"; -} -.glyphicon-collapse-up:before { - content: "\e160"; -} -.glyphicon-log-in:before { - content: "\e161"; -} -.glyphicon-flash:before { - content: "\e162"; -} -.glyphicon-log-out:before { - content: "\e163"; -} -.glyphicon-new-window:before { - content: "\e164"; -} -.glyphicon-record:before { - content: "\e165"; -} -.glyphicon-save:before { - content: "\e166"; -} -.glyphicon-open:before { - content: "\e167"; -} -.glyphicon-saved:before { - content: "\e168"; -} -.glyphicon-import:before { - content: "\e169"; -} -.glyphicon-export:before { - content: "\e170"; -} -.glyphicon-send:before { - content: "\e171"; -} -.glyphicon-floppy-disk:before { - content: "\e172"; -} -.glyphicon-floppy-saved:before { - content: "\e173"; -} -.glyphicon-floppy-remove:before { - content: "\e174"; -} -.glyphicon-floppy-save:before { - content: "\e175"; -} -.glyphicon-floppy-open:before { - content: "\e176"; -} -.glyphicon-credit-card:before { - content: "\e177"; -} -.glyphicon-transfer:before { - content: "\e178"; -} -.glyphicon-cutlery:before { - content: "\e179"; -} -.glyphicon-header:before { - content: "\e180"; -} -.glyphicon-compressed:before { - content: "\e181"; -} -.glyphicon-earphone:before { - content: "\e182"; -} -.glyphicon-phone-alt:before { - content: "\e183"; -} -.glyphicon-tower:before { - content: "\e184"; -} -.glyphicon-stats:before { - content: "\e185"; -} -.glyphicon-sd-video:before { - content: "\e186"; -} -.glyphicon-hd-video:before { - content: "\e187"; -} -.glyphicon-subtitles:before { - content: "\e188"; -} -.glyphicon-sound-stereo:before { - content: "\e189"; -} -.glyphicon-sound-dolby:before { - content: "\e190"; -} -.glyphicon-sound-5-1:before { - content: "\e191"; -} -.glyphicon-sound-6-1:before { - content: "\e192"; -} -.glyphicon-sound-7-1:before { - content: "\e193"; -} -.glyphicon-copyright-mark:before { - content: "\e194"; -} -.glyphicon-registration-mark:before { - content: "\e195"; -} -.glyphicon-cloud-download:before { - content: "\e197"; -} -.glyphicon-cloud-upload:before { - content: "\e198"; -} -.glyphicon-tree-conifer:before { - content: "\e199"; -} -.glyphicon-tree-deciduous:before { - content: "\e200"; -} -.glyphicon-cd:before { - content: "\e201"; -} -.glyphicon-save-file:before { - content: "\e202"; -} -.glyphicon-open-file:before { - content: "\e203"; -} -.glyphicon-level-up:before { - content: "\e204"; -} -.glyphicon-copy:before { - content: "\e205"; -} -.glyphicon-paste:before { - content: "\e206"; -} -.glyphicon-alert:before { - content: "\e209"; -} -.glyphicon-equalizer:before { - content: "\e210"; -} -.glyphicon-king:before { - content: "\e211"; -} -.glyphicon-queen:before { - content: "\e212"; -} -.glyphicon-pawn:before { - content: "\e213"; -} -.glyphicon-bishop:before { - content: "\e214"; -} -.glyphicon-knight:before { - content: "\e215"; -} -.glyphicon-baby-formula:before { - content: "\e216"; -} -.glyphicon-tent:before { - content: "\26fa"; -} -.glyphicon-blackboard:before { - content: "\e218"; -} -.glyphicon-bed:before { - content: "\e219"; -} -.glyphicon-apple:before { - content: "\f8ff"; -} -.glyphicon-erase:before { - content: "\e221"; -} -.glyphicon-hourglass:before { - content: "\231b"; -} -.glyphicon-lamp:before { - content: "\e223"; -} -.glyphicon-duplicate:before { - content: "\e224"; -} -.glyphicon-piggy-bank:before { - content: "\e225"; -} -.glyphicon-scissors:before { - content: "\e226"; -} -.glyphicon-bitcoin:before { - content: "\e227"; -} -.glyphicon-btc:before { - content: "\e227"; -} -.glyphicon-xbt:before { - content: "\e227"; -} -.glyphicon-yen:before { - content: "\00a5"; -} -.glyphicon-jpy:before { - content: "\00a5"; -} -.glyphicon-ruble:before { - content: "\20bd"; -} -.glyphicon-rub:before { - content: "\20bd"; -} -.glyphicon-scale:before { - content: "\e230"; -} -.glyphicon-ice-lolly:before { - content: "\e231"; -} -.glyphicon-ice-lolly-tasted:before { - content: "\e232"; -} -.glyphicon-education:before { - content: "\e233"; -} -.glyphicon-option-horizontal:before { - content: "\e234"; -} -.glyphicon-option-vertical:before { - content: "\e235"; -} -.glyphicon-menu-hamburger:before { - content: "\e236"; -} -.glyphicon-modal-window:before { - content: "\e237"; -} -.glyphicon-oil:before { - content: "\e238"; -} -.glyphicon-grain:before { - content: "\e239"; -} -.glyphicon-sunglasses:before { - content: "\e240"; -} -.glyphicon-text-size:before { - content: "\e241"; -} -.glyphicon-text-color:before { - content: "\e242"; -} -.glyphicon-text-background:before { - content: "\e243"; -} -.glyphicon-object-align-top:before { - content: "\e244"; -} -.glyphicon-object-align-bottom:before { - content: "\e245"; -} -.glyphicon-object-align-horizontal:before { - content: "\e246"; -} -.glyphicon-object-align-left:before { - content: "\e247"; -} -.glyphicon-object-align-vertical:before { - content: "\e248"; -} -.glyphicon-object-align-right:before { - content: "\e249"; -} -.glyphicon-triangle-right:before { - content: "\e250"; -} -.glyphicon-triangle-left:before { - content: "\e251"; -} -.glyphicon-triangle-bottom:before { - content: "\e252"; -} -.glyphicon-triangle-top:before { - content: "\e253"; -} -.glyphicon-console:before { - content: "\e254"; -} -.glyphicon-superscript:before { - content: "\e255"; -} -.glyphicon-subscript:before { - content: "\e256"; -} -.glyphicon-menu-left:before { - content: "\e257"; -} -.glyphicon-menu-right:before { - content: "\e258"; -} -.glyphicon-menu-down:before { - content: "\e259"; -} -.glyphicon-menu-up:before { - content: "\e260"; -} -* { - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; -} -*:before, -*:after { - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; -} -html { - font-size: 10px; - - -webkit-tap-highlight-color: rgba(0, 0, 0, 0); -} -body { - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; - font-size: 14px; - line-height: 1.42857143; - color: #333; - background-color: #fff; -} -input, -button, -select, -textarea { - font-family: inherit; - font-size: inherit; - line-height: inherit; -} -a { - color: #337ab7; - text-decoration: none; -} -a:hover, -a:focus { - color: #23527c; - text-decoration: underline; -} -a:focus { - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -figure { - margin: 0; -} -img { - vertical-align: middle; -} -.img-responsive, -.thumbnail > img, -.thumbnail a > img, -.carousel-inner > .item > img, -.carousel-inner > .item > a > img { - display: block; - max-width: 100%; - height: auto; -} -.img-rounded { - border-radius: 6px; -} -.img-thumbnail { - display: inline-block; - max-width: 100%; - height: auto; - padding: 4px; - line-height: 1.42857143; - background-color: #fff; - border: 1px solid #ddd; - border-radius: 4px; - -webkit-transition: all .2s ease-in-out; - -o-transition: all .2s ease-in-out; - transition: all .2s ease-in-out; -} -.img-circle { - border-radius: 50%; -} -hr { - margin-top: 20px; - margin-bottom: 20px; - border: 0; - border-top: 1px solid #eee; -} -.sr-only { - position: absolute; - width: 1px; - height: 1px; - padding: 0; - margin: -1px; - overflow: hidden; - clip: rect(0, 0, 0, 0); - border: 0; -} -.sr-only-focusable:active, -.sr-only-focusable:focus { - position: static; - width: auto; - height: auto; - margin: 0; - overflow: visible; - clip: auto; -} -[role="button"] { - cursor: pointer; -} -h1, -h2, -h3, -h4, -h5, -h6, -.h1, -.h2, -.h3, -.h4, -.h5, -.h6 { - font-family: inherit; - font-weight: 500; - line-height: 1.1; - color: inherit; -} -h1 small, -h2 small, -h3 small, -h4 small, -h5 small, -h6 small, -.h1 small, -.h2 small, -.h3 small, -.h4 small, -.h5 small, -.h6 small, -h1 .small, -h2 .small, -h3 .small, -h4 .small, -h5 .small, -h6 .small, -.h1 .small, -.h2 .small, -.h3 .small, -.h4 .small, -.h5 .small, -.h6 .small { - font-weight: normal; - line-height: 1; - color: #777; -} -h1, -.h1, -h2, -.h2, -h3, -.h3 { - margin-top: 20px; - margin-bottom: 10px; -} -h1 small, -.h1 small, -h2 small, -.h2 small, -h3 small, -.h3 small, -h1 .small, -.h1 .small, -h2 .small, -.h2 .small, -h3 .small, -.h3 .small { - font-size: 65%; -} -h4, -.h4, -h5, -.h5, -h6, -.h6 { - margin-top: 10px; - margin-bottom: 10px; -} -h4 small, -.h4 small, -h5 small, -.h5 small, -h6 small, -.h6 small, -h4 .small, -.h4 .small, -h5 .small, -.h5 .small, -h6 .small, -.h6 .small { - font-size: 75%; -} -h1, -.h1 { - font-size: 36px; -} -h2, -.h2 { - font-size: 30px; -} -h3, -.h3 { - font-size: 24px; -} -h4, -.h4 { - font-size: 18px; -} -h5, -.h5 { - font-size: 14px; -} -h6, -.h6 { - font-size: 12px; -} -p { - margin: 0 0 10px; -} -.lead { - margin-bottom: 20px; - font-size: 16px; - font-weight: 300; - line-height: 1.4; -} -@media (min-width: 768px) { - .lead { - font-size: 21px; - } -} -small, -.small { - font-size: 85%; -} -mark, -.mark { - padding: .2em; - background-color: #fcf8e3; -} -.text-left { - text-align: left; -} -.text-right { - text-align: right; -} -.text-center { - text-align: center; -} -.text-justify { - text-align: justify; -} -.text-nowrap { - white-space: nowrap; -} -.text-lowercase { - text-transform: lowercase; -} -.text-uppercase { - text-transform: uppercase; -} -.text-capitalize { - text-transform: capitalize; -} -.text-muted { - color: #777; -} -.text-primary { - color: #337ab7; -} -a.text-primary:hover, -a.text-primary:focus { - color: #286090; -} -.text-success { - color: #3c763d; -} -a.text-success:hover, -a.text-success:focus { - color: #2b542c; -} -.text-info { - color: #31708f; -} -a.text-info:hover, -a.text-info:focus { - color: #245269; -} -.text-warning { - color: #8a6d3b; -} -a.text-warning:hover, -a.text-warning:focus { - color: #66512c; -} -.text-danger { - color: #a94442; -} -a.text-danger:hover, -a.text-danger:focus { - color: #843534; -} -.bg-primary { - color: #fff; - background-color: #337ab7; -} -a.bg-primary:hover, -a.bg-primary:focus { - background-color: #286090; -} -.bg-success { - background-color: #dff0d8; -} -a.bg-success:hover, -a.bg-success:focus { - background-color: #c1e2b3; -} -.bg-info { - background-color: #d9edf7; -} -a.bg-info:hover, -a.bg-info:focus { - background-color: #afd9ee; -} -.bg-warning { - background-color: #fcf8e3; -} -a.bg-warning:hover, -a.bg-warning:focus { - background-color: #f7ecb5; -} -.bg-danger { - background-color: #f2dede; -} -a.bg-danger:hover, -a.bg-danger:focus { - background-color: #e4b9b9; -} -.page-header { - padding-bottom: 9px; - margin: 40px 0 20px; - border-bottom: 1px solid #eee; -} -ul, -ol { - margin-top: 0; - margin-bottom: 10px; -} -ul ul, -ol ul, -ul ol, -ol ol { - margin-bottom: 0; -} -.list-unstyled { - padding-left: 0; - list-style: none; -} -.list-inline { - padding-left: 0; - margin-left: -5px; - list-style: none; -} -.list-inline > li { - display: inline-block; - padding-right: 5px; - padding-left: 5px; -} -dl { - margin-top: 0; - margin-bottom: 20px; -} -dt, -dd { - line-height: 1.42857143; -} -dt { - font-weight: bold; -} -dd { - margin-left: 0; -} -@media (min-width: 768px) { - .dl-horizontal dt { - float: left; - width: 160px; - overflow: hidden; - clear: left; - text-align: right; - text-overflow: ellipsis; - white-space: nowrap; - } - .dl-horizontal dd { - margin-left: 180px; - } -} -abbr[title], -abbr[data-original-title] { - cursor: help; - border-bottom: 1px dotted #777; -} -.initialism { - font-size: 90%; - text-transform: uppercase; -} -blockquote { - padding: 10px 20px; - margin: 0 0 20px; - font-size: 17.5px; - border-left: 5px solid #eee; -} -blockquote p:last-child, -blockquote ul:last-child, -blockquote ol:last-child { - margin-bottom: 0; -} -blockquote footer, -blockquote small, -blockquote .small { - display: block; - font-size: 80%; - line-height: 1.42857143; - color: #777; -} -blockquote footer:before, -blockquote small:before, -blockquote .small:before { - content: '\2014 \00A0'; -} -.blockquote-reverse, -blockquote.pull-right { - padding-right: 15px; - padding-left: 0; - text-align: right; - border-right: 5px solid #eee; - border-left: 0; -} -.blockquote-reverse footer:before, -blockquote.pull-right footer:before, -.blockquote-reverse small:before, -blockquote.pull-right small:before, -.blockquote-reverse .small:before, -blockquote.pull-right .small:before { - content: ''; -} -.blockquote-reverse footer:after, -blockquote.pull-right footer:after, -.blockquote-reverse small:after, -blockquote.pull-right small:after, -.blockquote-reverse .small:after, -blockquote.pull-right .small:after { - content: '\00A0 \2014'; -} -address { - margin-bottom: 20px; - font-style: normal; - line-height: 1.42857143; -} -code, -kbd, -pre, -samp { - font-family: Menlo, Monaco, Consolas, "Courier New", monospace; -} -code { - padding: 2px 4px; - font-size: 90%; - color: #c7254e; - background-color: #f9f2f4; - border-radius: 4px; -} -kbd { - padding: 2px 4px; - font-size: 90%; - color: #fff; - background-color: #333; - border-radius: 3px; - -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25); - box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25); -} -kbd kbd { - padding: 0; - font-size: 100%; - font-weight: bold; - -webkit-box-shadow: none; - box-shadow: none; -} -pre { - display: block; - padding: 9.5px; - margin: 0 0 10px; - font-size: 13px; - line-height: 1.42857143; - color: #333; - word-break: break-all; - word-wrap: break-word; - background-color: #f5f5f5; - border: 1px solid #ccc; - border-radius: 4px; -} -pre code { - padding: 0; - font-size: inherit; - color: inherit; - white-space: pre-wrap; - background-color: transparent; - border-radius: 0; -} -.pre-scrollable { - max-height: 340px; - overflow-y: scroll; -} -.container { - padding-right: 15px; - padding-left: 15px; - margin-right: auto; - margin-left: auto; -} -@media (min-width: 768px) { - .container { - width: 750px; - } -} -@media (min-width: 992px) { - .container { - width: 970px; - } -} -@media (min-width: 1200px) { - .container { - width: 1170px; - } -} -.container-fluid { - padding-right: 15px; - padding-left: 15px; - margin-right: auto; - margin-left: auto; -} -.row { - margin-right: -15px; - margin-left: -15px; -} -.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { - position: relative; - min-height: 1px; - padding-right: 15px; - padding-left: 15px; -} -.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { - float: left; -} -.col-xs-12 { - width: 100%; -} -.col-xs-11 { - width: 91.66666667%; -} -.col-xs-10 { - width: 83.33333333%; -} -.col-xs-9 { - width: 75%; -} -.col-xs-8 { - width: 66.66666667%; -} -.col-xs-7 { - width: 58.33333333%; -} -.col-xs-6 { - width: 50%; -} -.col-xs-5 { - width: 41.66666667%; -} -.col-xs-4 { - width: 33.33333333%; -} -.col-xs-3 { - width: 25%; -} -.col-xs-2 { - width: 16.66666667%; -} -.col-xs-1 { - width: 8.33333333%; -} -.col-xs-pull-12 { - right: 100%; -} -.col-xs-pull-11 { - right: 91.66666667%; -} -.col-xs-pull-10 { - right: 83.33333333%; -} -.col-xs-pull-9 { - right: 75%; -} -.col-xs-pull-8 { - right: 66.66666667%; -} -.col-xs-pull-7 { - right: 58.33333333%; -} -.col-xs-pull-6 { - right: 50%; -} -.col-xs-pull-5 { - right: 41.66666667%; -} -.col-xs-pull-4 { - right: 33.33333333%; -} -.col-xs-pull-3 { - right: 25%; -} -.col-xs-pull-2 { - right: 16.66666667%; -} -.col-xs-pull-1 { - right: 8.33333333%; -} -.col-xs-pull-0 { - right: auto; -} -.col-xs-push-12 { - left: 100%; -} -.col-xs-push-11 { - left: 91.66666667%; -} -.col-xs-push-10 { - left: 83.33333333%; -} -.col-xs-push-9 { - left: 75%; -} -.col-xs-push-8 { - left: 66.66666667%; -} -.col-xs-push-7 { - left: 58.33333333%; -} -.col-xs-push-6 { - left: 50%; -} -.col-xs-push-5 { - left: 41.66666667%; -} -.col-xs-push-4 { - left: 33.33333333%; -} -.col-xs-push-3 { - left: 25%; -} -.col-xs-push-2 { - left: 16.66666667%; -} -.col-xs-push-1 { - left: 8.33333333%; -} -.col-xs-push-0 { - left: auto; -} -.col-xs-offset-12 { - margin-left: 100%; -} -.col-xs-offset-11 { - margin-left: 91.66666667%; -} -.col-xs-offset-10 { - margin-left: 83.33333333%; -} -.col-xs-offset-9 { - margin-left: 75%; -} -.col-xs-offset-8 { - margin-left: 66.66666667%; -} -.col-xs-offset-7 { - margin-left: 58.33333333%; -} -.col-xs-offset-6 { - margin-left: 50%; -} -.col-xs-offset-5 { - margin-left: 41.66666667%; -} -.col-xs-offset-4 { - margin-left: 33.33333333%; -} -.col-xs-offset-3 { - margin-left: 25%; -} -.col-xs-offset-2 { - margin-left: 16.66666667%; -} -.col-xs-offset-1 { - margin-left: 8.33333333%; -} -.col-xs-offset-0 { - margin-left: 0; -} -@media (min-width: 768px) { - .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { - float: left; - } - .col-sm-12 { - width: 100%; - } - .col-sm-11 { - width: 91.66666667%; - } - .col-sm-10 { - width: 83.33333333%; - } - .col-sm-9 { - width: 75%; - } - .col-sm-8 { - width: 66.66666667%; - } - .col-sm-7 { - width: 58.33333333%; - } - .col-sm-6 { - width: 50%; - } - .col-sm-5 { - width: 41.66666667%; - } - .col-sm-4 { - width: 33.33333333%; - } - .col-sm-3 { - width: 25%; - } - .col-sm-2 { - width: 16.66666667%; - } - .col-sm-1 { - width: 8.33333333%; - } - .col-sm-pull-12 { - right: 100%; - } - .col-sm-pull-11 { - right: 91.66666667%; - } - .col-sm-pull-10 { - right: 83.33333333%; - } - .col-sm-pull-9 { - right: 75%; - } - .col-sm-pull-8 { - right: 66.66666667%; - } - .col-sm-pull-7 { - right: 58.33333333%; - } - .col-sm-pull-6 { - right: 50%; - } - .col-sm-pull-5 { - right: 41.66666667%; - } - .col-sm-pull-4 { - right: 33.33333333%; - } - .col-sm-pull-3 { - right: 25%; - } - .col-sm-pull-2 { - right: 16.66666667%; - } - .col-sm-pull-1 { - right: 8.33333333%; - } - .col-sm-pull-0 { - right: auto; - } - .col-sm-push-12 { - left: 100%; - } - .col-sm-push-11 { - left: 91.66666667%; - } - .col-sm-push-10 { - left: 83.33333333%; - } - .col-sm-push-9 { - left: 75%; - } - .col-sm-push-8 { - left: 66.66666667%; - } - .col-sm-push-7 { - left: 58.33333333%; - } - .col-sm-push-6 { - left: 50%; - } - .col-sm-push-5 { - left: 41.66666667%; - } - .col-sm-push-4 { - left: 33.33333333%; - } - .col-sm-push-3 { - left: 25%; - } - .col-sm-push-2 { - left: 16.66666667%; - } - .col-sm-push-1 { - left: 8.33333333%; - } - .col-sm-push-0 { - left: auto; - } - .col-sm-offset-12 { - margin-left: 100%; - } - .col-sm-offset-11 { - margin-left: 91.66666667%; - } - .col-sm-offset-10 { - margin-left: 83.33333333%; - } - .col-sm-offset-9 { - margin-left: 75%; - } - .col-sm-offset-8 { - margin-left: 66.66666667%; - } - .col-sm-offset-7 { - margin-left: 58.33333333%; - } - .col-sm-offset-6 { - margin-left: 50%; - } - .col-sm-offset-5 { - margin-left: 41.66666667%; - } - .col-sm-offset-4 { - margin-left: 33.33333333%; - } - .col-sm-offset-3 { - margin-left: 25%; - } - .col-sm-offset-2 { - margin-left: 16.66666667%; - } - .col-sm-offset-1 { - margin-left: 8.33333333%; - } - .col-sm-offset-0 { - margin-left: 0; - } -} -@media (min-width: 992px) { - .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { - float: left; - } - .col-md-12 { - width: 100%; - } - .col-md-11 { - width: 91.66666667%; - } - .col-md-10 { - width: 83.33333333%; - } - .col-md-9 { - width: 75%; - } - .col-md-8 { - width: 66.66666667%; - } - .col-md-7 { - width: 58.33333333%; - } - .col-md-6 { - width: 50%; - } - .col-md-5 { - width: 41.66666667%; - } - .col-md-4 { - width: 33.33333333%; - } - .col-md-3 { - width: 25%; - } - .col-md-2 { - width: 16.66666667%; - } - .col-md-1 { - width: 8.33333333%; - } - .col-md-pull-12 { - right: 100%; - } - .col-md-pull-11 { - right: 91.66666667%; - } - .col-md-pull-10 { - right: 83.33333333%; - } - .col-md-pull-9 { - right: 75%; - } - .col-md-pull-8 { - right: 66.66666667%; - } - .col-md-pull-7 { - right: 58.33333333%; - } - .col-md-pull-6 { - right: 50%; - } - .col-md-pull-5 { - right: 41.66666667%; - } - .col-md-pull-4 { - right: 33.33333333%; - } - .col-md-pull-3 { - right: 25%; - } - .col-md-pull-2 { - right: 16.66666667%; - } - .col-md-pull-1 { - right: 8.33333333%; - } - .col-md-pull-0 { - right: auto; - } - .col-md-push-12 { - left: 100%; - } - .col-md-push-11 { - left: 91.66666667%; - } - .col-md-push-10 { - left: 83.33333333%; - } - .col-md-push-9 { - left: 75%; - } - .col-md-push-8 { - left: 66.66666667%; - } - .col-md-push-7 { - left: 58.33333333%; - } - .col-md-push-6 { - left: 50%; - } - .col-md-push-5 { - left: 41.66666667%; - } - .col-md-push-4 { - left: 33.33333333%; - } - .col-md-push-3 { - left: 25%; - } - .col-md-push-2 { - left: 16.66666667%; - } - .col-md-push-1 { - left: 8.33333333%; - } - .col-md-push-0 { - left: auto; - } - .col-md-offset-12 { - margin-left: 100%; - } - .col-md-offset-11 { - margin-left: 91.66666667%; - } - .col-md-offset-10 { - margin-left: 83.33333333%; - } - .col-md-offset-9 { - margin-left: 75%; - } - .col-md-offset-8 { - margin-left: 66.66666667%; - } - .col-md-offset-7 { - margin-left: 58.33333333%; - } - .col-md-offset-6 { - margin-left: 50%; - } - .col-md-offset-5 { - margin-left: 41.66666667%; - } - .col-md-offset-4 { - margin-left: 33.33333333%; - } - .col-md-offset-3 { - margin-left: 25%; - } - .col-md-offset-2 { - margin-left: 16.66666667%; - } - .col-md-offset-1 { - margin-left: 8.33333333%; - } - .col-md-offset-0 { - margin-left: 0; - } -} -@media (min-width: 1200px) { - .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { - float: left; - } - .col-lg-12 { - width: 100%; - } - .col-lg-11 { - width: 91.66666667%; - } - .col-lg-10 { - width: 83.33333333%; - } - .col-lg-9 { - width: 75%; - } - .col-lg-8 { - width: 66.66666667%; - } - .col-lg-7 { - width: 58.33333333%; - } - .col-lg-6 { - width: 50%; - } - .col-lg-5 { - width: 41.66666667%; - } - .col-lg-4 { - width: 33.33333333%; - } - .col-lg-3 { - width: 25%; - } - .col-lg-2 { - width: 16.66666667%; - } - .col-lg-1 { - width: 8.33333333%; - } - .col-lg-pull-12 { - right: 100%; - } - .col-lg-pull-11 { - right: 91.66666667%; - } - .col-lg-pull-10 { - right: 83.33333333%; - } - .col-lg-pull-9 { - right: 75%; - } - .col-lg-pull-8 { - right: 66.66666667%; - } - .col-lg-pull-7 { - right: 58.33333333%; - } - .col-lg-pull-6 { - right: 50%; - } - .col-lg-pull-5 { - right: 41.66666667%; - } - .col-lg-pull-4 { - right: 33.33333333%; - } - .col-lg-pull-3 { - right: 25%; - } - .col-lg-pull-2 { - right: 16.66666667%; - } - .col-lg-pull-1 { - right: 8.33333333%; - } - .col-lg-pull-0 { - right: auto; - } - .col-lg-push-12 { - left: 100%; - } - .col-lg-push-11 { - left: 91.66666667%; - } - .col-lg-push-10 { - left: 83.33333333%; - } - .col-lg-push-9 { - left: 75%; - } - .col-lg-push-8 { - left: 66.66666667%; - } - .col-lg-push-7 { - left: 58.33333333%; - } - .col-lg-push-6 { - left: 50%; - } - .col-lg-push-5 { - left: 41.66666667%; - } - .col-lg-push-4 { - left: 33.33333333%; - } - .col-lg-push-3 { - left: 25%; - } - .col-lg-push-2 { - left: 16.66666667%; - } - .col-lg-push-1 { - left: 8.33333333%; - } - .col-lg-push-0 { - left: auto; - } - .col-lg-offset-12 { - margin-left: 100%; - } - .col-lg-offset-11 { - margin-left: 91.66666667%; - } - .col-lg-offset-10 { - margin-left: 83.33333333%; - } - .col-lg-offset-9 { - margin-left: 75%; - } - .col-lg-offset-8 { - margin-left: 66.66666667%; - } - .col-lg-offset-7 { - margin-left: 58.33333333%; - } - .col-lg-offset-6 { - margin-left: 50%; - } - .col-lg-offset-5 { - margin-left: 41.66666667%; - } - .col-lg-offset-4 { - margin-left: 33.33333333%; - } - .col-lg-offset-3 { - margin-left: 25%; - } - .col-lg-offset-2 { - margin-left: 16.66666667%; - } - .col-lg-offset-1 { - margin-left: 8.33333333%; - } - .col-lg-offset-0 { - margin-left: 0; - } -} -table { - background-color: transparent; -} -caption { - padding-top: 8px; - padding-bottom: 8px; - color: #777; - text-align: left; -} -th { - text-align: left; -} -.table { - width: 100%; - max-width: 100%; - margin-bottom: 20px; -} -.table > thead > tr > th, -.table > tbody > tr > th, -.table > tfoot > tr > th, -.table > thead > tr > td, -.table > tbody > tr > td, -.table > tfoot > tr > td { - padding: 8px; - line-height: 1.42857143; - vertical-align: top; - border-top: 1px solid #ddd; -} -.table > thead > tr > th { - vertical-align: bottom; - border-bottom: 2px solid #ddd; -} -.table > caption + thead > tr:first-child > th, -.table > colgroup + thead > tr:first-child > th, -.table > thead:first-child > tr:first-child > th, -.table > caption + thead > tr:first-child > td, -.table > colgroup + thead > tr:first-child > td, -.table > thead:first-child > tr:first-child > td { - border-top: 0; -} -.table > tbody + tbody { - border-top: 2px solid #ddd; -} -.table .table { - background-color: #fff; -} -.table-condensed > thead > tr > th, -.table-condensed > tbody > tr > th, -.table-condensed > tfoot > tr > th, -.table-condensed > thead > tr > td, -.table-condensed > tbody > tr > td, -.table-condensed > tfoot > tr > td { - padding: 5px; -} -.table-bordered { - border: 1px solid #ddd; -} -.table-bordered > thead > tr > th, -.table-bordered > tbody > tr > th, -.table-bordered > tfoot > tr > th, -.table-bordered > thead > tr > td, -.table-bordered > tbody > tr > td, -.table-bordered > tfoot > tr > td { - border: 1px solid #ddd; -} -.table-bordered > thead > tr > th, -.table-bordered > thead > tr > td { - border-bottom-width: 2px; -} -.table-striped > tbody > tr:nth-of-type(odd) { - background-color: #f9f9f9; -} -.table-hover > tbody > tr:hover { - background-color: #f5f5f5; -} -table col[class*="col-"] { - position: static; - display: table-column; - float: none; -} -table td[class*="col-"], -table th[class*="col-"] { - position: static; - display: table-cell; - float: none; -} -.table > thead > tr > td.active, -.table > tbody > tr > td.active, -.table > tfoot > tr > td.active, -.table > thead > tr > th.active, -.table > tbody > tr > th.active, -.table > tfoot > tr > th.active, -.table > thead > tr.active > td, -.table > tbody > tr.active > td, -.table > tfoot > tr.active > td, -.table > thead > tr.active > th, -.table > tbody > tr.active > th, -.table > tfoot > tr.active > th { - background-color: #f5f5f5; -} -.table-hover > tbody > tr > td.active:hover, -.table-hover > tbody > tr > th.active:hover, -.table-hover > tbody > tr.active:hover > td, -.table-hover > tbody > tr:hover > .active, -.table-hover > tbody > tr.active:hover > th { - background-color: #e8e8e8; -} -.table > thead > tr > td.success, -.table > tbody > tr > td.success, -.table > tfoot > tr > td.success, -.table > thead > tr > th.success, -.table > tbody > tr > th.success, -.table > tfoot > tr > th.success, -.table > thead > tr.success > td, -.table > tbody > tr.success > td, -.table > tfoot > tr.success > td, -.table > thead > tr.success > th, -.table > tbody > tr.success > th, -.table > tfoot > tr.success > th { - background-color: #dff0d8; -} -.table-hover > tbody > tr > td.success:hover, -.table-hover > tbody > tr > th.success:hover, -.table-hover > tbody > tr.success:hover > td, -.table-hover > tbody > tr:hover > .success, -.table-hover > tbody > tr.success:hover > th { - background-color: #d0e9c6; -} -.table > thead > tr > td.info, -.table > tbody > tr > td.info, -.table > tfoot > tr > td.info, -.table > thead > tr > th.info, -.table > tbody > tr > th.info, -.table > tfoot > tr > th.info, -.table > thead > tr.info > td, -.table > tbody > tr.info > td, -.table > tfoot > tr.info > td, -.table > thead > tr.info > th, -.table > tbody > tr.info > th, -.table > tfoot > tr.info > th { - background-color: #d9edf7; -} -.table-hover > tbody > tr > td.info:hover, -.table-hover > tbody > tr > th.info:hover, -.table-hover > tbody > tr.info:hover > td, -.table-hover > tbody > tr:hover > .info, -.table-hover > tbody > tr.info:hover > th { - background-color: #c4e3f3; -} -.table > thead > tr > td.warning, -.table > tbody > tr > td.warning, -.table > tfoot > tr > td.warning, -.table > thead > tr > th.warning, -.table > tbody > tr > th.warning, -.table > tfoot > tr > th.warning, -.table > thead > tr.warning > td, -.table > tbody > tr.warning > td, -.table > tfoot > tr.warning > td, -.table > thead > tr.warning > th, -.table > tbody > tr.warning > th, -.table > tfoot > tr.warning > th { - background-color: #fcf8e3; -} -.table-hover > tbody > tr > td.warning:hover, -.table-hover > tbody > tr > th.warning:hover, -.table-hover > tbody > tr.warning:hover > td, -.table-hover > tbody > tr:hover > .warning, -.table-hover > tbody > tr.warning:hover > th { - background-color: #faf2cc; -} -.table > thead > tr > td.danger, -.table > tbody > tr > td.danger, -.table > tfoot > tr > td.danger, -.table > thead > tr > th.danger, -.table > tbody > tr > th.danger, -.table > tfoot > tr > th.danger, -.table > thead > tr.danger > td, -.table > tbody > tr.danger > td, -.table > tfoot > tr.danger > td, -.table > thead > tr.danger > th, -.table > tbody > tr.danger > th, -.table > tfoot > tr.danger > th { - background-color: #f2dede; -} -.table-hover > tbody > tr > td.danger:hover, -.table-hover > tbody > tr > th.danger:hover, -.table-hover > tbody > tr.danger:hover > td, -.table-hover > tbody > tr:hover > .danger, -.table-hover > tbody > tr.danger:hover > th { - background-color: #ebcccc; -} -.table-responsive { - min-height: .01%; - overflow-x: auto; -} -@media screen and (max-width: 767px) { - .table-responsive { - width: 100%; - margin-bottom: 15px; - overflow-y: hidden; - -ms-overflow-style: -ms-autohiding-scrollbar; - border: 1px solid #ddd; - } - .table-responsive > .table { - margin-bottom: 0; - } - .table-responsive > .table > thead > tr > th, - .table-responsive > .table > tbody > tr > th, - .table-responsive > .table > tfoot > tr > th, - .table-responsive > .table > thead > tr > td, - .table-responsive > .table > tbody > tr > td, - .table-responsive > .table > tfoot > tr > td { - white-space: nowrap; - } - .table-responsive > .table-bordered { - border: 0; - } - .table-responsive > .table-bordered > thead > tr > th:first-child, - .table-responsive > .table-bordered > tbody > tr > th:first-child, - .table-responsive > .table-bordered > tfoot > tr > th:first-child, - .table-responsive > .table-bordered > thead > tr > td:first-child, - .table-responsive > .table-bordered > tbody > tr > td:first-child, - .table-responsive > .table-bordered > tfoot > tr > td:first-child { - border-left: 0; - } - .table-responsive > .table-bordered > thead > tr > th:last-child, - .table-responsive > .table-bordered > tbody > tr > th:last-child, - .table-responsive > .table-bordered > tfoot > tr > th:last-child, - .table-responsive > .table-bordered > thead > tr > td:last-child, - .table-responsive > .table-bordered > tbody > tr > td:last-child, - .table-responsive > .table-bordered > tfoot > tr > td:last-child { - border-right: 0; - } - .table-responsive > .table-bordered > tbody > tr:last-child > th, - .table-responsive > .table-bordered > tfoot > tr:last-child > th, - .table-responsive > .table-bordered > tbody > tr:last-child > td, - .table-responsive > .table-bordered > tfoot > tr:last-child > td { - border-bottom: 0; - } -} -fieldset { - min-width: 0; - padding: 0; - margin: 0; - border: 0; -} -legend { - display: block; - width: 100%; - padding: 0; - margin-bottom: 20px; - font-size: 21px; - line-height: inherit; - color: #333; - border: 0; - border-bottom: 1px solid #e5e5e5; -} -label { - display: inline-block; - max-width: 100%; - margin-bottom: 5px; - font-weight: bold; -} -input[type="search"] { - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; -} -input[type="radio"], -input[type="checkbox"] { - margin: 4px 0 0; - margin-top: 1px \9; - line-height: normal; -} -input[type="file"] { - display: block; -} -input[type="range"] { - display: block; - width: 100%; -} -select[multiple], -select[size] { - height: auto; -} -input[type="file"]:focus, -input[type="radio"]:focus, -input[type="checkbox"]:focus { - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -output { - display: block; - padding-top: 7px; - font-size: 14px; - line-height: 1.42857143; - color: #555; -} -.form-control { - display: block; - width: 100%; - height: 34px; - padding: 6px 12px; - font-size: 14px; - line-height: 1.42857143; - color: #555; - background-color: #fff; - background-image: none; - border: 1px solid #ccc; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); - -webkit-transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s; - -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; - transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -} -.form-control:focus { - border-color: #66afe9; - outline: 0; - -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, .6); - box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, .6); -} -.form-control::-moz-placeholder { - color: #999; - opacity: 1; -} -.form-control:-ms-input-placeholder { - color: #999; -} -.form-control::-webkit-input-placeholder { - color: #999; -} -.form-control::-ms-expand { - background-color: transparent; - border: 0; -} -.form-control[disabled], -.form-control[readonly], -fieldset[disabled] .form-control { - background-color: #eee; - opacity: 1; -} -.form-control[disabled], -fieldset[disabled] .form-control { - cursor: not-allowed; -} -textarea.form-control { - height: auto; -} -input[type="search"] { - -webkit-appearance: none; -} -@media screen and (-webkit-min-device-pixel-ratio: 0) { - input[type="date"].form-control, - input[type="time"].form-control, - input[type="datetime-local"].form-control, - input[type="month"].form-control { - line-height: 34px; - } - input[type="date"].input-sm, - input[type="time"].input-sm, - input[type="datetime-local"].input-sm, - input[type="month"].input-sm, - .input-group-sm input[type="date"], - .input-group-sm input[type="time"], - .input-group-sm input[type="datetime-local"], - .input-group-sm input[type="month"] { - line-height: 30px; - } - input[type="date"].input-lg, - input[type="time"].input-lg, - input[type="datetime-local"].input-lg, - input[type="month"].input-lg, - .input-group-lg input[type="date"], - .input-group-lg input[type="time"], - .input-group-lg input[type="datetime-local"], - .input-group-lg input[type="month"] { - line-height: 46px; - } -} -.form-group { - margin-bottom: 15px; -} -.radio, -.checkbox { - position: relative; - display: block; - margin-top: 10px; - margin-bottom: 10px; -} -.radio label, -.checkbox label { - min-height: 20px; - padding-left: 20px; - margin-bottom: 0; - font-weight: normal; - cursor: pointer; -} -.radio input[type="radio"], -.radio-inline input[type="radio"], -.checkbox input[type="checkbox"], -.checkbox-inline input[type="checkbox"] { - position: absolute; - margin-top: 4px \9; - margin-left: -20px; -} -.radio + .radio, -.checkbox + .checkbox { - margin-top: -5px; -} -.radio-inline, -.checkbox-inline { - position: relative; - display: inline-block; - padding-left: 20px; - margin-bottom: 0; - font-weight: normal; - vertical-align: middle; - cursor: pointer; -} -.radio-inline + .radio-inline, -.checkbox-inline + .checkbox-inline { - margin-top: 0; - margin-left: 10px; -} -input[type="radio"][disabled], -input[type="checkbox"][disabled], -input[type="radio"].disabled, -input[type="checkbox"].disabled, -fieldset[disabled] input[type="radio"], -fieldset[disabled] input[type="checkbox"] { - cursor: not-allowed; -} -.radio-inline.disabled, -.checkbox-inline.disabled, -fieldset[disabled] .radio-inline, -fieldset[disabled] .checkbox-inline { - cursor: not-allowed; -} -.radio.disabled label, -.checkbox.disabled label, -fieldset[disabled] .radio label, -fieldset[disabled] .checkbox label { - cursor: not-allowed; -} -.form-control-static { - min-height: 34px; - padding-top: 7px; - padding-bottom: 7px; - margin-bottom: 0; -} -.form-control-static.input-lg, -.form-control-static.input-sm { - padding-right: 0; - padding-left: 0; -} -.input-sm { - height: 30px; - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -select.input-sm { - height: 30px; - line-height: 30px; -} -textarea.input-sm, -select[multiple].input-sm { - height: auto; -} -.form-group-sm .form-control { - height: 30px; - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -.form-group-sm select.form-control { - height: 30px; - line-height: 30px; -} -.form-group-sm textarea.form-control, -.form-group-sm select[multiple].form-control { - height: auto; -} -.form-group-sm .form-control-static { - height: 30px; - min-height: 32px; - padding: 6px 10px; - font-size: 12px; - line-height: 1.5; -} -.input-lg { - height: 46px; - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -select.input-lg { - height: 46px; - line-height: 46px; -} -textarea.input-lg, -select[multiple].input-lg { - height: auto; -} -.form-group-lg .form-control { - height: 46px; - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -.form-group-lg select.form-control { - height: 46px; - line-height: 46px; -} -.form-group-lg textarea.form-control, -.form-group-lg select[multiple].form-control { - height: auto; -} -.form-group-lg .form-control-static { - height: 46px; - min-height: 38px; - padding: 11px 16px; - font-size: 18px; - line-height: 1.3333333; -} -.has-feedback { - position: relative; -} -.has-feedback .form-control { - padding-right: 42.5px; -} -.form-control-feedback { - position: absolute; - top: 0; - right: 0; - z-index: 2; - display: block; - width: 34px; - height: 34px; - line-height: 34px; - text-align: center; - pointer-events: none; -} -.input-lg + .form-control-feedback, -.input-group-lg + .form-control-feedback, -.form-group-lg .form-control + .form-control-feedback { - width: 46px; - height: 46px; - line-height: 46px; -} -.input-sm + .form-control-feedback, -.input-group-sm + .form-control-feedback, -.form-group-sm .form-control + .form-control-feedback { - width: 30px; - height: 30px; - line-height: 30px; -} -.has-success .help-block, -.has-success .control-label, -.has-success .radio, -.has-success .checkbox, -.has-success .radio-inline, -.has-success .checkbox-inline, -.has-success.radio label, -.has-success.checkbox label, -.has-success.radio-inline label, -.has-success.checkbox-inline label { - color: #3c763d; -} -.has-success .form-control { - border-color: #3c763d; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); -} -.has-success .form-control:focus { - border-color: #2b542c; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #67b168; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #67b168; -} -.has-success .input-group-addon { - color: #3c763d; - background-color: #dff0d8; - border-color: #3c763d; -} -.has-success .form-control-feedback { - color: #3c763d; -} -.has-warning .help-block, -.has-warning .control-label, -.has-warning .radio, -.has-warning .checkbox, -.has-warning .radio-inline, -.has-warning .checkbox-inline, -.has-warning.radio label, -.has-warning.checkbox label, -.has-warning.radio-inline label, -.has-warning.checkbox-inline label { - color: #8a6d3b; -} -.has-warning .form-control { - border-color: #8a6d3b; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); -} -.has-warning .form-control:focus { - border-color: #66512c; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #c0a16b; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #c0a16b; -} -.has-warning .input-group-addon { - color: #8a6d3b; - background-color: #fcf8e3; - border-color: #8a6d3b; -} -.has-warning .form-control-feedback { - color: #8a6d3b; -} -.has-error .help-block, -.has-error .control-label, -.has-error .radio, -.has-error .checkbox, -.has-error .radio-inline, -.has-error .checkbox-inline, -.has-error.radio label, -.has-error.checkbox label, -.has-error.radio-inline label, -.has-error.checkbox-inline label { - color: #a94442; -} -.has-error .form-control { - border-color: #a94442; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); -} -.has-error .form-control:focus { - border-color: #843534; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #ce8483; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #ce8483; -} -.has-error .input-group-addon { - color: #a94442; - background-color: #f2dede; - border-color: #a94442; -} -.has-error .form-control-feedback { - color: #a94442; -} -.has-feedback label ~ .form-control-feedback { - top: 25px; -} -.has-feedback label.sr-only ~ .form-control-feedback { - top: 0; -} -.help-block { - display: block; - margin-top: 5px; - margin-bottom: 10px; - color: #737373; -} -@media (min-width: 768px) { - .form-inline .form-group { - display: inline-block; - margin-bottom: 0; - vertical-align: middle; - } - .form-inline .form-control { - display: inline-block; - width: auto; - vertical-align: middle; - } - .form-inline .form-control-static { - display: inline-block; - } - .form-inline .input-group { - display: inline-table; - vertical-align: middle; - } - .form-inline .input-group .input-group-addon, - .form-inline .input-group .input-group-btn, - .form-inline .input-group .form-control { - width: auto; - } - .form-inline .input-group > .form-control { - width: 100%; - } - .form-inline .control-label { - margin-bottom: 0; - vertical-align: middle; - } - .form-inline .radio, - .form-inline .checkbox { - display: inline-block; - margin-top: 0; - margin-bottom: 0; - vertical-align: middle; - } - .form-inline .radio label, - .form-inline .checkbox label { - padding-left: 0; - } - .form-inline .radio input[type="radio"], - .form-inline .checkbox input[type="checkbox"] { - position: relative; - margin-left: 0; - } - .form-inline .has-feedback .form-control-feedback { - top: 0; - } -} -.form-horizontal .radio, -.form-horizontal .checkbox, -.form-horizontal .radio-inline, -.form-horizontal .checkbox-inline { - padding-top: 7px; - margin-top: 0; - margin-bottom: 0; -} -.form-horizontal .radio, -.form-horizontal .checkbox { - min-height: 27px; -} -.form-horizontal .form-group { - margin-right: -15px; - margin-left: -15px; -} -@media (min-width: 768px) { - .form-horizontal .control-label { - padding-top: 7px; - margin-bottom: 0; - text-align: right; - } -} -.form-horizontal .has-feedback .form-control-feedback { - right: 15px; -} -@media (min-width: 768px) { - .form-horizontal .form-group-lg .control-label { - padding-top: 11px; - font-size: 18px; - } -} -@media (min-width: 768px) { - .form-horizontal .form-group-sm .control-label { - padding-top: 6px; - font-size: 12px; - } -} -.btn { - display: inline-block; - padding: 6px 12px; - margin-bottom: 0; - font-size: 14px; - font-weight: normal; - line-height: 1.42857143; - text-align: center; - white-space: nowrap; - vertical-align: middle; - -ms-touch-action: manipulation; - touch-action: manipulation; - cursor: pointer; - -webkit-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - user-select: none; - background-image: none; - border: 1px solid transparent; - border-radius: 4px; -} -.btn:focus, -.btn:active:focus, -.btn.active:focus, -.btn.focus, -.btn:active.focus, -.btn.active.focus { - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -.btn:hover, -.btn:focus, -.btn.focus { - color: #333; - text-decoration: none; -} -.btn:active, -.btn.active { - background-image: none; - outline: 0; - -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); - box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); -} -.btn.disabled, -.btn[disabled], -fieldset[disabled] .btn { - cursor: not-allowed; - filter: alpha(opacity=65); - -webkit-box-shadow: none; - box-shadow: none; - opacity: .65; -} -a.btn.disabled, -fieldset[disabled] a.btn { - pointer-events: none; -} -.btn-default { - color: #333; - background-color: #fff; - border-color: #ccc; -} -.btn-default:focus, -.btn-default.focus { - color: #333; - background-color: #e6e6e6; - border-color: #8c8c8c; -} -.btn-default:hover { - color: #333; - background-color: #e6e6e6; - border-color: #adadad; -} -.btn-default:active, -.btn-default.active, -.open > .dropdown-toggle.btn-default { - color: #333; - background-color: #e6e6e6; - border-color: #adadad; -} -.btn-default:active:hover, -.btn-default.active:hover, -.open > .dropdown-toggle.btn-default:hover, -.btn-default:active:focus, -.btn-default.active:focus, -.open > .dropdown-toggle.btn-default:focus, -.btn-default:active.focus, -.btn-default.active.focus, -.open > .dropdown-toggle.btn-default.focus { - color: #333; - background-color: #d4d4d4; - border-color: #8c8c8c; -} -.btn-default:active, -.btn-default.active, -.open > .dropdown-toggle.btn-default { - background-image: none; -} -.btn-default.disabled:hover, -.btn-default[disabled]:hover, -fieldset[disabled] .btn-default:hover, -.btn-default.disabled:focus, -.btn-default[disabled]:focus, -fieldset[disabled] .btn-default:focus, -.btn-default.disabled.focus, -.btn-default[disabled].focus, -fieldset[disabled] .btn-default.focus { - background-color: #fff; - border-color: #ccc; -} -.btn-default .badge { - color: #fff; - background-color: #333; -} -.btn-primary { - color: #fff; - background-color: #337ab7; - border-color: #2e6da4; -} -.btn-primary:focus, -.btn-primary.focus { - color: #fff; - background-color: #286090; - border-color: #122b40; -} -.btn-primary:hover { - color: #fff; - background-color: #286090; - border-color: #204d74; -} -.btn-primary:active, -.btn-primary.active, -.open > .dropdown-toggle.btn-primary { - color: #fff; - background-color: #286090; - border-color: #204d74; -} -.btn-primary:active:hover, -.btn-primary.active:hover, -.open > .dropdown-toggle.btn-primary:hover, -.btn-primary:active:focus, -.btn-primary.active:focus, -.open > .dropdown-toggle.btn-primary:focus, -.btn-primary:active.focus, -.btn-primary.active.focus, -.open > .dropdown-toggle.btn-primary.focus { - color: #fff; - background-color: #204d74; - border-color: #122b40; -} -.btn-primary:active, -.btn-primary.active, -.open > .dropdown-toggle.btn-primary { - background-image: none; -} -.btn-primary.disabled:hover, -.btn-primary[disabled]:hover, -fieldset[disabled] .btn-primary:hover, -.btn-primary.disabled:focus, -.btn-primary[disabled]:focus, -fieldset[disabled] .btn-primary:focus, -.btn-primary.disabled.focus, -.btn-primary[disabled].focus, -fieldset[disabled] .btn-primary.focus { - background-color: #337ab7; - border-color: #2e6da4; -} -.btn-primary .badge { - color: #337ab7; - background-color: #fff; -} -.btn-success { - color: #fff; - background-color: #5cb85c; - border-color: #4cae4c; -} -.btn-success:focus, -.btn-success.focus { - color: #fff; - background-color: #449d44; - border-color: #255625; -} -.btn-success:hover { - color: #fff; - background-color: #449d44; - border-color: #398439; -} -.btn-success:active, -.btn-success.active, -.open > .dropdown-toggle.btn-success { - color: #fff; - background-color: #449d44; - border-color: #398439; -} -.btn-success:active:hover, -.btn-success.active:hover, -.open > .dropdown-toggle.btn-success:hover, -.btn-success:active:focus, -.btn-success.active:focus, -.open > .dropdown-toggle.btn-success:focus, -.btn-success:active.focus, -.btn-success.active.focus, -.open > .dropdown-toggle.btn-success.focus { - color: #fff; - background-color: #398439; - border-color: #255625; -} -.btn-success:active, -.btn-success.active, -.open > .dropdown-toggle.btn-success { - background-image: none; -} -.btn-success.disabled:hover, -.btn-success[disabled]:hover, -fieldset[disabled] .btn-success:hover, -.btn-success.disabled:focus, -.btn-success[disabled]:focus, -fieldset[disabled] .btn-success:focus, -.btn-success.disabled.focus, -.btn-success[disabled].focus, -fieldset[disabled] .btn-success.focus { - background-color: #5cb85c; - border-color: #4cae4c; -} -.btn-success .badge { - color: #5cb85c; - background-color: #fff; -} -.btn-info { - color: #fff; - background-color: #5bc0de; - border-color: #46b8da; -} -.btn-info:focus, -.btn-info.focus { - color: #fff; - background-color: #31b0d5; - border-color: #1b6d85; -} -.btn-info:hover { - color: #fff; - background-color: #31b0d5; - border-color: #269abc; -} -.btn-info:active, -.btn-info.active, -.open > .dropdown-toggle.btn-info { - color: #fff; - background-color: #31b0d5; - border-color: #269abc; -} -.btn-info:active:hover, -.btn-info.active:hover, -.open > .dropdown-toggle.btn-info:hover, -.btn-info:active:focus, -.btn-info.active:focus, -.open > .dropdown-toggle.btn-info:focus, -.btn-info:active.focus, -.btn-info.active.focus, -.open > .dropdown-toggle.btn-info.focus { - color: #fff; - background-color: #269abc; - border-color: #1b6d85; -} -.btn-info:active, -.btn-info.active, -.open > .dropdown-toggle.btn-info { - background-image: none; -} -.btn-info.disabled:hover, -.btn-info[disabled]:hover, -fieldset[disabled] .btn-info:hover, -.btn-info.disabled:focus, -.btn-info[disabled]:focus, -fieldset[disabled] .btn-info:focus, -.btn-info.disabled.focus, -.btn-info[disabled].focus, -fieldset[disabled] .btn-info.focus { - background-color: #5bc0de; - border-color: #46b8da; -} -.btn-info .badge { - color: #5bc0de; - background-color: #fff; -} -.btn-warning { - color: #fff; - background-color: #f0ad4e; - border-color: #eea236; -} -.btn-warning:focus, -.btn-warning.focus { - color: #fff; - background-color: #ec971f; - border-color: #985f0d; -} -.btn-warning:hover { - color: #fff; - background-color: #ec971f; - border-color: #d58512; -} -.btn-warning:active, -.btn-warning.active, -.open > .dropdown-toggle.btn-warning { - color: #fff; - background-color: #ec971f; - border-color: #d58512; -} -.btn-warning:active:hover, -.btn-warning.active:hover, -.open > .dropdown-toggle.btn-warning:hover, -.btn-warning:active:focus, -.btn-warning.active:focus, -.open > .dropdown-toggle.btn-warning:focus, -.btn-warning:active.focus, -.btn-warning.active.focus, -.open > .dropdown-toggle.btn-warning.focus { - color: #fff; - background-color: #d58512; - border-color: #985f0d; -} -.btn-warning:active, -.btn-warning.active, -.open > .dropdown-toggle.btn-warning { - background-image: none; -} -.btn-warning.disabled:hover, -.btn-warning[disabled]:hover, -fieldset[disabled] .btn-warning:hover, -.btn-warning.disabled:focus, -.btn-warning[disabled]:focus, -fieldset[disabled] .btn-warning:focus, -.btn-warning.disabled.focus, -.btn-warning[disabled].focus, -fieldset[disabled] .btn-warning.focus { - background-color: #f0ad4e; - border-color: #eea236; -} -.btn-warning .badge { - color: #f0ad4e; - background-color: #fff; -} -.btn-danger { - color: #fff; - background-color: #d9534f; - border-color: #d43f3a; -} -.btn-danger:focus, -.btn-danger.focus { - color: #fff; - background-color: #c9302c; - border-color: #761c19; -} -.btn-danger:hover { - color: #fff; - background-color: #c9302c; - border-color: #ac2925; -} -.btn-danger:active, -.btn-danger.active, -.open > .dropdown-toggle.btn-danger { - color: #fff; - background-color: #c9302c; - border-color: #ac2925; -} -.btn-danger:active:hover, -.btn-danger.active:hover, -.open > .dropdown-toggle.btn-danger:hover, -.btn-danger:active:focus, -.btn-danger.active:focus, -.open > .dropdown-toggle.btn-danger:focus, -.btn-danger:active.focus, -.btn-danger.active.focus, -.open > .dropdown-toggle.btn-danger.focus { - color: #fff; - background-color: #ac2925; - border-color: #761c19; -} -.btn-danger:active, -.btn-danger.active, -.open > .dropdown-toggle.btn-danger { - background-image: none; -} -.btn-danger.disabled:hover, -.btn-danger[disabled]:hover, -fieldset[disabled] .btn-danger:hover, -.btn-danger.disabled:focus, -.btn-danger[disabled]:focus, -fieldset[disabled] .btn-danger:focus, -.btn-danger.disabled.focus, -.btn-danger[disabled].focus, -fieldset[disabled] .btn-danger.focus { - background-color: #d9534f; - border-color: #d43f3a; -} -.btn-danger .badge { - color: #d9534f; - background-color: #fff; -} -.btn-link { - font-weight: normal; - color: #337ab7; - border-radius: 0; -} -.btn-link, -.btn-link:active, -.btn-link.active, -.btn-link[disabled], -fieldset[disabled] .btn-link { - background-color: transparent; - -webkit-box-shadow: none; - box-shadow: none; -} -.btn-link, -.btn-link:hover, -.btn-link:focus, -.btn-link:active { - border-color: transparent; -} -.btn-link:hover, -.btn-link:focus { - color: #23527c; - text-decoration: underline; - background-color: transparent; -} -.btn-link[disabled]:hover, -fieldset[disabled] .btn-link:hover, -.btn-link[disabled]:focus, -fieldset[disabled] .btn-link:focus { - color: #777; - text-decoration: none; -} -.btn-lg, -.btn-group-lg > .btn { - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -.btn-sm, -.btn-group-sm > .btn { - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -.btn-xs, -.btn-group-xs > .btn { - padding: 1px 5px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -.btn-block { - display: block; - width: 100%; -} -.btn-block + .btn-block { - margin-top: 5px; -} -input[type="submit"].btn-block, -input[type="reset"].btn-block, -input[type="button"].btn-block { - width: 100%; -} -.fade { - opacity: 0; - -webkit-transition: opacity .15s linear; - -o-transition: opacity .15s linear; - transition: opacity .15s linear; -} -.fade.in { - opacity: 1; -} -.collapse { - display: none; -} -.collapse.in { - display: block; -} -tr.collapse.in { - display: table-row; -} -tbody.collapse.in { - display: table-row-group; -} -.collapsing { - position: relative; - height: 0; - overflow: hidden; - -webkit-transition-timing-function: ease; - -o-transition-timing-function: ease; - transition-timing-function: ease; - -webkit-transition-duration: .35s; - -o-transition-duration: .35s; - transition-duration: .35s; - -webkit-transition-property: height, visibility; - -o-transition-property: height, visibility; - transition-property: height, visibility; -} -.caret { - display: inline-block; - width: 0; - height: 0; - margin-left: 2px; - vertical-align: middle; - border-top: 4px dashed; - border-top: 4px solid \9; - border-right: 4px solid transparent; - border-left: 4px solid transparent; -} -.dropup, -.dropdown { - position: relative; -} -.dropdown-toggle:focus { - outline: 0; -} -.dropdown-menu { - position: absolute; - top: 100%; - left: 0; - z-index: 1000; - display: none; - float: left; - min-width: 160px; - padding: 5px 0; - margin: 2px 0 0; - font-size: 14px; - text-align: left; - list-style: none; - background-color: #fff; - -webkit-background-clip: padding-box; - background-clip: padding-box; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, .15); - border-radius: 4px; - -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, .175); - box-shadow: 0 6px 12px rgba(0, 0, 0, .175); -} -.dropdown-menu.pull-right { - right: 0; - left: auto; -} -.dropdown-menu .divider { - height: 1px; - margin: 9px 0; - overflow: hidden; - background-color: #e5e5e5; -} -.dropdown-menu > li > a { - display: block; - padding: 3px 20px; - clear: both; - font-weight: normal; - line-height: 1.42857143; - color: #333; - white-space: nowrap; -} -.dropdown-menu > li > a:hover, -.dropdown-menu > li > a:focus { - color: #262626; - text-decoration: none; - background-color: #f5f5f5; -} -.dropdown-menu > .active > a, -.dropdown-menu > .active > a:hover, -.dropdown-menu > .active > a:focus { - color: #fff; - text-decoration: none; - background-color: #337ab7; - outline: 0; -} -.dropdown-menu > .disabled > a, -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - color: #777; -} -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - text-decoration: none; - cursor: not-allowed; - background-color: transparent; - background-image: none; - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); -} -.open > .dropdown-menu { - display: block; -} -.open > a { - outline: 0; -} -.dropdown-menu-right { - right: 0; - left: auto; -} -.dropdown-menu-left { - right: auto; - left: 0; -} -.dropdown-header { - display: block; - padding: 3px 20px; - font-size: 12px; - line-height: 1.42857143; - color: #777; - white-space: nowrap; -} -.dropdown-backdrop { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 990; -} -.pull-right > .dropdown-menu { - right: 0; - left: auto; -} -.dropup .caret, -.navbar-fixed-bottom .dropdown .caret { - content: ""; - border-top: 0; - border-bottom: 4px dashed; - border-bottom: 4px solid \9; -} -.dropup .dropdown-menu, -.navbar-fixed-bottom .dropdown .dropdown-menu { - top: auto; - bottom: 100%; - margin-bottom: 2px; -} -@media (min-width: 768px) { - .navbar-right .dropdown-menu { - right: 0; - left: auto; - } - .navbar-right .dropdown-menu-left { - right: auto; - left: 0; - } -} -.btn-group, -.btn-group-vertical { - position: relative; - display: inline-block; - vertical-align: middle; -} -.btn-group > .btn, -.btn-group-vertical > .btn { - position: relative; - float: left; -} -.btn-group > .btn:hover, -.btn-group-vertical > .btn:hover, -.btn-group > .btn:focus, -.btn-group-vertical > .btn:focus, -.btn-group > .btn:active, -.btn-group-vertical > .btn:active, -.btn-group > .btn.active, -.btn-group-vertical > .btn.active { - z-index: 2; -} -.btn-group .btn + .btn, -.btn-group .btn + .btn-group, -.btn-group .btn-group + .btn, -.btn-group .btn-group + .btn-group { - margin-left: -1px; -} -.btn-toolbar { - margin-left: -5px; -} -.btn-toolbar .btn, -.btn-toolbar .btn-group, -.btn-toolbar .input-group { - float: left; -} -.btn-toolbar > .btn, -.btn-toolbar > .btn-group, -.btn-toolbar > .input-group { - margin-left: 5px; -} -.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { - border-radius: 0; -} -.btn-group > .btn:first-child { - margin-left: 0; -} -.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} -.btn-group > .btn:last-child:not(:first-child), -.btn-group > .dropdown-toggle:not(:first-child) { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group > .btn-group { - float: left; -} -.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { - border-radius: 0; -} -.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, -.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} -.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group .dropdown-toggle:active, -.btn-group.open .dropdown-toggle { - outline: 0; -} -.btn-group > .btn + .dropdown-toggle { - padding-right: 8px; - padding-left: 8px; -} -.btn-group > .btn-lg + .dropdown-toggle { - padding-right: 12px; - padding-left: 12px; -} -.btn-group.open .dropdown-toggle { - -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); - box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); -} -.btn-group.open .dropdown-toggle.btn-link { - -webkit-box-shadow: none; - box-shadow: none; -} -.btn .caret { - margin-left: 0; -} -.btn-lg .caret { - border-width: 5px 5px 0; - border-bottom-width: 0; -} -.dropup .btn-lg .caret { - border-width: 0 5px 5px; -} -.btn-group-vertical > .btn, -.btn-group-vertical > .btn-group, -.btn-group-vertical > .btn-group > .btn { - display: block; - float: none; - width: 100%; - max-width: 100%; -} -.btn-group-vertical > .btn-group > .btn { - float: none; -} -.btn-group-vertical > .btn + .btn, -.btn-group-vertical > .btn + .btn-group, -.btn-group-vertical > .btn-group + .btn, -.btn-group-vertical > .btn-group + .btn-group { - margin-top: -1px; - margin-left: 0; -} -.btn-group-vertical > .btn:not(:first-child):not(:last-child) { - border-radius: 0; -} -.btn-group-vertical > .btn:first-child:not(:last-child) { - border-top-left-radius: 4px; - border-top-right-radius: 4px; - border-bottom-right-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group-vertical > .btn:last-child:not(:first-child) { - border-top-left-radius: 0; - border-top-right-radius: 0; - border-bottom-right-radius: 4px; - border-bottom-left-radius: 4px; -} -.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { - border-radius: 0; -} -.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, -.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { - border-bottom-right-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { - border-top-left-radius: 0; - border-top-right-radius: 0; -} -.btn-group-justified { - display: table; - width: 100%; - table-layout: fixed; - border-collapse: separate; -} -.btn-group-justified > .btn, -.btn-group-justified > .btn-group { - display: table-cell; - float: none; - width: 1%; -} -.btn-group-justified > .btn-group .btn { - width: 100%; -} -.btn-group-justified > .btn-group .dropdown-menu { - left: auto; -} -[data-toggle="buttons"] > .btn input[type="radio"], -[data-toggle="buttons"] > .btn-group > .btn input[type="radio"], -[data-toggle="buttons"] > .btn input[type="checkbox"], -[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { - position: absolute; - clip: rect(0, 0, 0, 0); - pointer-events: none; -} -.input-group { - position: relative; - display: table; - border-collapse: separate; -} -.input-group[class*="col-"] { - float: none; - padding-right: 0; - padding-left: 0; -} -.input-group .form-control { - position: relative; - z-index: 2; - float: left; - width: 100%; - margin-bottom: 0; -} -.input-group .form-control:focus { - z-index: 3; -} -.input-group-lg > .form-control, -.input-group-lg > .input-group-addon, -.input-group-lg > .input-group-btn > .btn { - height: 46px; - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -select.input-group-lg > .form-control, -select.input-group-lg > .input-group-addon, -select.input-group-lg > .input-group-btn > .btn { - height: 46px; - line-height: 46px; -} -textarea.input-group-lg > .form-control, -textarea.input-group-lg > .input-group-addon, -textarea.input-group-lg > .input-group-btn > .btn, -select[multiple].input-group-lg > .form-control, -select[multiple].input-group-lg > .input-group-addon, -select[multiple].input-group-lg > .input-group-btn > .btn { - height: auto; -} -.input-group-sm > .form-control, -.input-group-sm > .input-group-addon, -.input-group-sm > .input-group-btn > .btn { - height: 30px; - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -select.input-group-sm > .form-control, -select.input-group-sm > .input-group-addon, -select.input-group-sm > .input-group-btn > .btn { - height: 30px; - line-height: 30px; -} -textarea.input-group-sm > .form-control, -textarea.input-group-sm > .input-group-addon, -textarea.input-group-sm > .input-group-btn > .btn, -select[multiple].input-group-sm > .form-control, -select[multiple].input-group-sm > .input-group-addon, -select[multiple].input-group-sm > .input-group-btn > .btn { - height: auto; -} -.input-group-addon, -.input-group-btn, -.input-group .form-control { - display: table-cell; -} -.input-group-addon:not(:first-child):not(:last-child), -.input-group-btn:not(:first-child):not(:last-child), -.input-group .form-control:not(:first-child):not(:last-child) { - border-radius: 0; -} -.input-group-addon, -.input-group-btn { - width: 1%; - white-space: nowrap; - vertical-align: middle; -} -.input-group-addon { - padding: 6px 12px; - font-size: 14px; - font-weight: normal; - line-height: 1; - color: #555; - text-align: center; - background-color: #eee; - border: 1px solid #ccc; - border-radius: 4px; -} -.input-group-addon.input-sm { - padding: 5px 10px; - font-size: 12px; - border-radius: 3px; -} -.input-group-addon.input-lg { - padding: 10px 16px; - font-size: 18px; - border-radius: 6px; -} -.input-group-addon input[type="radio"], -.input-group-addon input[type="checkbox"] { - margin-top: 0; -} -.input-group .form-control:first-child, -.input-group-addon:first-child, -.input-group-btn:first-child > .btn, -.input-group-btn:first-child > .btn-group > .btn, -.input-group-btn:first-child > .dropdown-toggle, -.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), -.input-group-btn:last-child > .btn-group:not(:last-child) > .btn { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} -.input-group-addon:first-child { - border-right: 0; -} -.input-group .form-control:last-child, -.input-group-addon:last-child, -.input-group-btn:last-child > .btn, -.input-group-btn:last-child > .btn-group > .btn, -.input-group-btn:last-child > .dropdown-toggle, -.input-group-btn:first-child > .btn:not(:first-child), -.input-group-btn:first-child > .btn-group:not(:first-child) > .btn { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} -.input-group-addon:last-child { - border-left: 0; -} -.input-group-btn { - position: relative; - font-size: 0; - white-space: nowrap; -} -.input-group-btn > .btn { - position: relative; -} -.input-group-btn > .btn + .btn { - margin-left: -1px; -} -.input-group-btn > .btn:hover, -.input-group-btn > .btn:focus, -.input-group-btn > .btn:active { - z-index: 2; -} -.input-group-btn:first-child > .btn, -.input-group-btn:first-child > .btn-group { - margin-right: -1px; -} -.input-group-btn:last-child > .btn, -.input-group-btn:last-child > .btn-group { - z-index: 2; - margin-left: -1px; -} -.nav { - padding-left: 0; - margin-bottom: 0; - list-style: none; -} -.nav > li { - position: relative; - display: block; -} -.nav > li > a { - position: relative; - display: block; - padding: 10px 15px; -} -.nav > li > a:hover, -.nav > li > a:focus { - text-decoration: none; - background-color: #eee; -} -.nav > li.disabled > a { - color: #777; -} -.nav > li.disabled > a:hover, -.nav > li.disabled > a:focus { - color: #777; - text-decoration: none; - cursor: not-allowed; - background-color: transparent; -} -.nav .open > a, -.nav .open > a:hover, -.nav .open > a:focus { - background-color: #eee; - border-color: #337ab7; -} -.nav .nav-divider { - height: 1px; - margin: 9px 0; - overflow: hidden; - background-color: #e5e5e5; -} -.nav > li > a > img { - max-width: none; -} -.nav-tabs { - border-bottom: 1px solid #ddd; -} -.nav-tabs > li { - float: left; - margin-bottom: -1px; -} -.nav-tabs > li > a { - margin-right: 2px; - line-height: 1.42857143; - border: 1px solid transparent; - border-radius: 4px 4px 0 0; -} -.nav-tabs > li > a:hover { - border-color: #eee #eee #ddd; -} -.nav-tabs > li.active > a, -.nav-tabs > li.active > a:hover, -.nav-tabs > li.active > a:focus { - color: #555; - cursor: default; - background-color: #fff; - border: 1px solid #ddd; - border-bottom-color: transparent; -} -.nav-tabs.nav-justified { - width: 100%; - border-bottom: 0; -} -.nav-tabs.nav-justified > li { - float: none; -} -.nav-tabs.nav-justified > li > a { - margin-bottom: 5px; - text-align: center; -} -.nav-tabs.nav-justified > .dropdown .dropdown-menu { - top: auto; - left: auto; -} -@media (min-width: 768px) { - .nav-tabs.nav-justified > li { - display: table-cell; - width: 1%; - } - .nav-tabs.nav-justified > li > a { - margin-bottom: 0; - } -} -.nav-tabs.nav-justified > li > a { - margin-right: 0; - border-radius: 4px; -} -.nav-tabs.nav-justified > .active > a, -.nav-tabs.nav-justified > .active > a:hover, -.nav-tabs.nav-justified > .active > a:focus { - border: 1px solid #ddd; -} -@media (min-width: 768px) { - .nav-tabs.nav-justified > li > a { - border-bottom: 1px solid #ddd; - border-radius: 4px 4px 0 0; - } - .nav-tabs.nav-justified > .active > a, - .nav-tabs.nav-justified > .active > a:hover, - .nav-tabs.nav-justified > .active > a:focus { - border-bottom-color: #fff; - } -} -.nav-pills > li { - float: left; -} -.nav-pills > li > a { - border-radius: 4px; -} -.nav-pills > li + li { - margin-left: 2px; -} -.nav-pills > li.active > a, -.nav-pills > li.active > a:hover, -.nav-pills > li.active > a:focus { - color: #fff; - background-color: #337ab7; -} -.nav-stacked > li { - float: none; -} -.nav-stacked > li + li { - margin-top: 2px; - margin-left: 0; -} -.nav-justified { - width: 100%; -} -.nav-justified > li { - float: none; -} -.nav-justified > li > a { - margin-bottom: 5px; - text-align: center; -} -.nav-justified > .dropdown .dropdown-menu { - top: auto; - left: auto; -} -@media (min-width: 768px) { - .nav-justified > li { - display: table-cell; - width: 1%; - } - .nav-justified > li > a { - margin-bottom: 0; - } -} -.nav-tabs-justified { - border-bottom: 0; -} -.nav-tabs-justified > li > a { - margin-right: 0; - border-radius: 4px; -} -.nav-tabs-justified > .active > a, -.nav-tabs-justified > .active > a:hover, -.nav-tabs-justified > .active > a:focus { - border: 1px solid #ddd; -} -@media (min-width: 768px) { - .nav-tabs-justified > li > a { - border-bottom: 1px solid #ddd; - border-radius: 4px 4px 0 0; - } - .nav-tabs-justified > .active > a, - .nav-tabs-justified > .active > a:hover, - .nav-tabs-justified > .active > a:focus { - border-bottom-color: #fff; - } -} -.tab-content > .tab-pane { - display: none; -} -.tab-content > .active { - display: block; -} -.nav-tabs .dropdown-menu { - margin-top: -1px; - border-top-left-radius: 0; - border-top-right-radius: 0; -} -.navbar { - position: relative; - min-height: 50px; - margin-bottom: 20px; - border: 1px solid transparent; -} -@media (min-width: 768px) { - .navbar { - border-radius: 4px; - } -} -@media (min-width: 768px) { - .navbar-header { - float: left; - } -} -.navbar-collapse { - padding-right: 15px; - padding-left: 15px; - overflow-x: visible; - -webkit-overflow-scrolling: touch; - border-top: 1px solid transparent; - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1); -} -.navbar-collapse.in { - overflow-y: auto; -} -@media (min-width: 768px) { - .navbar-collapse { - width: auto; - border-top: 0; - -webkit-box-shadow: none; - box-shadow: none; - } - .navbar-collapse.collapse { - display: block !important; - height: auto !important; - padding-bottom: 0; - overflow: visible !important; - } - .navbar-collapse.in { - overflow-y: visible; - } - .navbar-fixed-top .navbar-collapse, - .navbar-static-top .navbar-collapse, - .navbar-fixed-bottom .navbar-collapse { - padding-right: 0; - padding-left: 0; - } -} -.navbar-fixed-top .navbar-collapse, -.navbar-fixed-bottom .navbar-collapse { - max-height: 340px; -} -@media (max-device-width: 480px) and (orientation: landscape) { - .navbar-fixed-top .navbar-collapse, - .navbar-fixed-bottom .navbar-collapse { - max-height: 200px; - } -} -.container > .navbar-header, -.container-fluid > .navbar-header, -.container > .navbar-collapse, -.container-fluid > .navbar-collapse { - margin-right: -15px; - margin-left: -15px; -} -@media (min-width: 768px) { - .container > .navbar-header, - .container-fluid > .navbar-header, - .container > .navbar-collapse, - .container-fluid > .navbar-collapse { - margin-right: 0; - margin-left: 0; - } -} -.navbar-static-top { - z-index: 1000; - border-width: 0 0 1px; -} -@media (min-width: 768px) { - .navbar-static-top { - border-radius: 0; - } -} -.navbar-fixed-top, -.navbar-fixed-bottom { - position: fixed; - right: 0; - left: 0; - z-index: 1030; -} -@media (min-width: 768px) { - .navbar-fixed-top, - .navbar-fixed-bottom { - border-radius: 0; - } -} -.navbar-fixed-top { - top: 0; - border-width: 0 0 1px; -} -.navbar-fixed-bottom { - bottom: 0; - margin-bottom: 0; - border-width: 1px 0 0; -} -.navbar-brand { - float: left; - height: 50px; - padding: 15px 15px; - font-size: 18px; - line-height: 20px; -} -.navbar-brand:hover, -.navbar-brand:focus { - text-decoration: none; -} -.navbar-brand > img { - display: block; -} -@media (min-width: 768px) { - .navbar > .container .navbar-brand, - .navbar > .container-fluid .navbar-brand { - margin-left: -15px; - } -} -.navbar-toggle { - position: relative; - float: right; - padding: 9px 10px; - margin-top: 8px; - margin-right: 15px; - margin-bottom: 8px; - background-color: transparent; - background-image: none; - border: 1px solid transparent; - border-radius: 4px; -} -.navbar-toggle:focus { - outline: 0; -} -.navbar-toggle .icon-bar { - display: block; - width: 22px; - height: 2px; - border-radius: 1px; -} -.navbar-toggle .icon-bar + .icon-bar { - margin-top: 4px; -} -@media (min-width: 768px) { - .navbar-toggle { - display: none; - } -} -.navbar-nav { - margin: 7.5px -15px; -} -.navbar-nav > li > a { - padding-top: 10px; - padding-bottom: 10px; - line-height: 20px; -} -@media (max-width: 767px) { - .navbar-nav .open .dropdown-menu { - position: static; - float: none; - width: auto; - margin-top: 0; - background-color: transparent; - border: 0; - -webkit-box-shadow: none; - box-shadow: none; - } - .navbar-nav .open .dropdown-menu > li > a, - .navbar-nav .open .dropdown-menu .dropdown-header { - padding: 5px 15px 5px 25px; - } - .navbar-nav .open .dropdown-menu > li > a { - line-height: 20px; - } - .navbar-nav .open .dropdown-menu > li > a:hover, - .navbar-nav .open .dropdown-menu > li > a:focus { - background-image: none; - } -} -@media (min-width: 768px) { - .navbar-nav { - float: left; - margin: 0; - } - .navbar-nav > li { - float: left; - } - .navbar-nav > li > a { - padding-top: 15px; - padding-bottom: 15px; - } -} -.navbar-form { - padding: 10px 15px; - margin-top: 8px; - margin-right: -15px; - margin-bottom: 8px; - margin-left: -15px; - border-top: 1px solid transparent; - border-bottom: 1px solid transparent; - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1), 0 1px 0 rgba(255, 255, 255, .1); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1), 0 1px 0 rgba(255, 255, 255, .1); -} -@media (min-width: 768px) { - .navbar-form .form-group { - display: inline-block; - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .form-control { - display: inline-block; - width: auto; - vertical-align: middle; - } - .navbar-form .form-control-static { - display: inline-block; - } - .navbar-form .input-group { - display: inline-table; - vertical-align: middle; - } - .navbar-form .input-group .input-group-addon, - .navbar-form .input-group .input-group-btn, - .navbar-form .input-group .form-control { - width: auto; - } - .navbar-form .input-group > .form-control { - width: 100%; - } - .navbar-form .control-label { - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .radio, - .navbar-form .checkbox { - display: inline-block; - margin-top: 0; - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .radio label, - .navbar-form .checkbox label { - padding-left: 0; - } - .navbar-form .radio input[type="radio"], - .navbar-form .checkbox input[type="checkbox"] { - position: relative; - margin-left: 0; - } - .navbar-form .has-feedback .form-control-feedback { - top: 0; - } -} -@media (max-width: 767px) { - .navbar-form .form-group { - margin-bottom: 5px; - } - .navbar-form .form-group:last-child { - margin-bottom: 0; - } -} -@media (min-width: 768px) { - .navbar-form { - width: auto; - padding-top: 0; - padding-bottom: 0; - margin-right: 0; - margin-left: 0; - border: 0; - -webkit-box-shadow: none; - box-shadow: none; - } -} -.navbar-nav > li > .dropdown-menu { - margin-top: 0; - border-top-left-radius: 0; - border-top-right-radius: 0; -} -.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { - margin-bottom: 0; - border-top-left-radius: 4px; - border-top-right-radius: 4px; - border-bottom-right-radius: 0; - border-bottom-left-radius: 0; -} -.navbar-btn { - margin-top: 8px; - margin-bottom: 8px; -} -.navbar-btn.btn-sm { - margin-top: 10px; - margin-bottom: 10px; -} -.navbar-btn.btn-xs { - margin-top: 14px; - margin-bottom: 14px; -} -.navbar-text { - margin-top: 15px; - margin-bottom: 15px; -} -@media (min-width: 768px) { - .navbar-text { - float: left; - margin-right: 15px; - margin-left: 15px; - } -} -@media (min-width: 768px) { - .navbar-left { - float: left !important; - } - .navbar-right { - float: right !important; - margin-right: -15px; - } - .navbar-right ~ .navbar-right { - margin-right: 0; - } -} -.navbar-default { - background-color: #f8f8f8; - border-color: #e7e7e7; -} -.navbar-default .navbar-brand { - color: #777; -} -.navbar-default .navbar-brand:hover, -.navbar-default .navbar-brand:focus { - color: #5e5e5e; - background-color: transparent; -} -.navbar-default .navbar-text { - color: #777; -} -.navbar-default .navbar-nav > li > a { - color: #777; -} -.navbar-default .navbar-nav > li > a:hover, -.navbar-default .navbar-nav > li > a:focus { - color: #333; - background-color: transparent; -} -.navbar-default .navbar-nav > .active > a, -.navbar-default .navbar-nav > .active > a:hover, -.navbar-default .navbar-nav > .active > a:focus { - color: #555; - background-color: #e7e7e7; -} -.navbar-default .navbar-nav > .disabled > a, -.navbar-default .navbar-nav > .disabled > a:hover, -.navbar-default .navbar-nav > .disabled > a:focus { - color: #ccc; - background-color: transparent; -} -.navbar-default .navbar-toggle { - border-color: #ddd; -} -.navbar-default .navbar-toggle:hover, -.navbar-default .navbar-toggle:focus { - background-color: #ddd; -} -.navbar-default .navbar-toggle .icon-bar { - background-color: #888; -} -.navbar-default .navbar-collapse, -.navbar-default .navbar-form { - border-color: #e7e7e7; -} -.navbar-default .navbar-nav > .open > a, -.navbar-default .navbar-nav > .open > a:hover, -.navbar-default .navbar-nav > .open > a:focus { - color: #555; - background-color: #e7e7e7; -} -@media (max-width: 767px) { - .navbar-default .navbar-nav .open .dropdown-menu > li > a { - color: #777; - } - .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, - .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { - color: #333; - background-color: transparent; - } - .navbar-default .navbar-nav .open .dropdown-menu > .active > a, - .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, - .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { - color: #555; - background-color: #e7e7e7; - } - .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, - .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, - .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { - color: #ccc; - background-color: transparent; - } -} -.navbar-default .navbar-link { - color: #777; -} -.navbar-default .navbar-link:hover { - color: #333; -} -.navbar-default .btn-link { - color: #777; -} -.navbar-default .btn-link:hover, -.navbar-default .btn-link:focus { - color: #333; -} -.navbar-default .btn-link[disabled]:hover, -fieldset[disabled] .navbar-default .btn-link:hover, -.navbar-default .btn-link[disabled]:focus, -fieldset[disabled] .navbar-default .btn-link:focus { - color: #ccc; -} -.navbar-inverse { - background-color: #222; - border-color: #080808; -} -.navbar-inverse .navbar-brand { - color: #9d9d9d; -} -.navbar-inverse .navbar-brand:hover, -.navbar-inverse .navbar-brand:focus { - color: #fff; - background-color: transparent; -} -.navbar-inverse .navbar-text { - color: #9d9d9d; -} -.navbar-inverse .navbar-nav > li > a { - color: #9d9d9d; -} -.navbar-inverse .navbar-nav > li > a:hover, -.navbar-inverse .navbar-nav > li > a:focus { - color: #fff; - background-color: transparent; -} -.navbar-inverse .navbar-nav > .active > a, -.navbar-inverse .navbar-nav > .active > a:hover, -.navbar-inverse .navbar-nav > .active > a:focus { - color: #fff; - background-color: #080808; -} -.navbar-inverse .navbar-nav > .disabled > a, -.navbar-inverse .navbar-nav > .disabled > a:hover, -.navbar-inverse .navbar-nav > .disabled > a:focus { - color: #444; - background-color: transparent; -} -.navbar-inverse .navbar-toggle { - border-color: #333; -} -.navbar-inverse .navbar-toggle:hover, -.navbar-inverse .navbar-toggle:focus { - background-color: #333; -} -.navbar-inverse .navbar-toggle .icon-bar { - background-color: #fff; -} -.navbar-inverse .navbar-collapse, -.navbar-inverse .navbar-form { - border-color: #101010; -} -.navbar-inverse .navbar-nav > .open > a, -.navbar-inverse .navbar-nav > .open > a:hover, -.navbar-inverse .navbar-nav > .open > a:focus { - color: #fff; - background-color: #080808; -} -@media (max-width: 767px) { - .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { - border-color: #080808; - } - .navbar-inverse .navbar-nav .open .dropdown-menu .divider { - background-color: #080808; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { - color: #9d9d9d; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, - .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { - color: #fff; - background-color: transparent; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, - .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, - .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { - color: #fff; - background-color: #080808; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, - .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, - .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { - color: #444; - background-color: transparent; - } -} -.navbar-inverse .navbar-link { - color: #9d9d9d; -} -.navbar-inverse .navbar-link:hover { - color: #fff; -} -.navbar-inverse .btn-link { - color: #9d9d9d; -} -.navbar-inverse .btn-link:hover, -.navbar-inverse .btn-link:focus { - color: #fff; -} -.navbar-inverse .btn-link[disabled]:hover, -fieldset[disabled] .navbar-inverse .btn-link:hover, -.navbar-inverse .btn-link[disabled]:focus, -fieldset[disabled] .navbar-inverse .btn-link:focus { - color: #444; -} -.breadcrumb { - padding: 8px 15px; - margin-bottom: 20px; - list-style: none; - background-color: #f5f5f5; - border-radius: 4px; -} -.breadcrumb > li { - display: inline-block; -} -.breadcrumb > li + li:before { - padding: 0 5px; - color: #ccc; - content: "/\00a0"; -} -.breadcrumb > .active { - color: #777; -} -.pagination { - display: inline-block; - padding-left: 0; - margin: 20px 0; - border-radius: 4px; -} -.pagination > li { - display: inline; -} -.pagination > li > a, -.pagination > li > span { - position: relative; - float: left; - padding: 6px 12px; - margin-left: -1px; - line-height: 1.42857143; - color: #337ab7; - text-decoration: none; - background-color: #fff; - border: 1px solid #ddd; -} -.pagination > li:first-child > a, -.pagination > li:first-child > span { - margin-left: 0; - border-top-left-radius: 4px; - border-bottom-left-radius: 4px; -} -.pagination > li:last-child > a, -.pagination > li:last-child > span { - border-top-right-radius: 4px; - border-bottom-right-radius: 4px; -} -.pagination > li > a:hover, -.pagination > li > span:hover, -.pagination > li > a:focus, -.pagination > li > span:focus { - z-index: 2; - color: #23527c; - background-color: #eee; - border-color: #ddd; -} -.pagination > .active > a, -.pagination > .active > span, -.pagination > .active > a:hover, -.pagination > .active > span:hover, -.pagination > .active > a:focus, -.pagination > .active > span:focus { - z-index: 3; - color: #fff; - cursor: default; - background-color: #337ab7; - border-color: #337ab7; -} -.pagination > .disabled > span, -.pagination > .disabled > span:hover, -.pagination > .disabled > span:focus, -.pagination > .disabled > a, -.pagination > .disabled > a:hover, -.pagination > .disabled > a:focus { - color: #777; - cursor: not-allowed; - background-color: #fff; - border-color: #ddd; -} -.pagination-lg > li > a, -.pagination-lg > li > span { - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; -} -.pagination-lg > li:first-child > a, -.pagination-lg > li:first-child > span { - border-top-left-radius: 6px; - border-bottom-left-radius: 6px; -} -.pagination-lg > li:last-child > a, -.pagination-lg > li:last-child > span { - border-top-right-radius: 6px; - border-bottom-right-radius: 6px; -} -.pagination-sm > li > a, -.pagination-sm > li > span { - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; -} -.pagination-sm > li:first-child > a, -.pagination-sm > li:first-child > span { - border-top-left-radius: 3px; - border-bottom-left-radius: 3px; -} -.pagination-sm > li:last-child > a, -.pagination-sm > li:last-child > span { - border-top-right-radius: 3px; - border-bottom-right-radius: 3px; -} -.pager { - padding-left: 0; - margin: 20px 0; - text-align: center; - list-style: none; -} -.pager li { - display: inline; -} -.pager li > a, -.pager li > span { - display: inline-block; - padding: 5px 14px; - background-color: #fff; - border: 1px solid #ddd; - border-radius: 15px; -} -.pager li > a:hover, -.pager li > a:focus { - text-decoration: none; - background-color: #eee; -} -.pager .next > a, -.pager .next > span { - float: right; -} -.pager .previous > a, -.pager .previous > span { - float: left; -} -.pager .disabled > a, -.pager .disabled > a:hover, -.pager .disabled > a:focus, -.pager .disabled > span { - color: #777; - cursor: not-allowed; - background-color: #fff; -} -.label { - display: inline; - padding: .2em .6em .3em; - font-size: 75%; - font-weight: bold; - line-height: 1; - color: #fff; - text-align: center; - white-space: nowrap; - vertical-align: baseline; - border-radius: .25em; -} -a.label:hover, -a.label:focus { - color: #fff; - text-decoration: none; - cursor: pointer; -} -.label:empty { - display: none; -} -.btn .label { - position: relative; - top: -1px; -} -.label-default { - background-color: #777; -} -.label-default[href]:hover, -.label-default[href]:focus { - background-color: #5e5e5e; -} -.label-primary { - background-color: #337ab7; -} -.label-primary[href]:hover, -.label-primary[href]:focus { - background-color: #286090; -} -.label-success { - background-color: #5cb85c; -} -.label-success[href]:hover, -.label-success[href]:focus { - background-color: #449d44; -} -.label-info { - background-color: #5bc0de; -} -.label-info[href]:hover, -.label-info[href]:focus { - background-color: #31b0d5; -} -.label-warning { - background-color: #f0ad4e; -} -.label-warning[href]:hover, -.label-warning[href]:focus { - background-color: #ec971f; -} -.label-danger { - background-color: #d9534f; -} -.label-danger[href]:hover, -.label-danger[href]:focus { - background-color: #c9302c; -} -.badge { - display: inline-block; - min-width: 10px; - padding: 3px 7px; - font-size: 12px; - font-weight: bold; - line-height: 1; - color: #fff; - text-align: center; - white-space: nowrap; - vertical-align: middle; - background-color: #777; - border-radius: 10px; -} -.badge:empty { - display: none; -} -.btn .badge { - position: relative; - top: -1px; -} -.btn-xs .badge, -.btn-group-xs > .btn .badge { - top: 0; - padding: 1px 5px; -} -a.badge:hover, -a.badge:focus { - color: #fff; - text-decoration: none; - cursor: pointer; -} -.list-group-item.active > .badge, -.nav-pills > .active > a > .badge { - color: #337ab7; - background-color: #fff; -} -.list-group-item > .badge { - float: right; -} -.list-group-item > .badge + .badge { - margin-right: 5px; -} -.nav-pills > li > a > .badge { - margin-left: 3px; -} -.jumbotron { - padding-top: 30px; - padding-bottom: 30px; - margin-bottom: 30px; - color: inherit; - background-color: #eee; -} -.jumbotron h1, -.jumbotron .h1 { - color: inherit; -} -.jumbotron p { - margin-bottom: 15px; - font-size: 21px; - font-weight: 200; -} -.jumbotron > hr { - border-top-color: #d5d5d5; -} -.container .jumbotron, -.container-fluid .jumbotron { - padding-right: 15px; - padding-left: 15px; - border-radius: 6px; -} -.jumbotron .container { - max-width: 100%; -} -@media screen and (min-width: 768px) { - .jumbotron { - padding-top: 48px; - padding-bottom: 48px; - } - .container .jumbotron, - .container-fluid .jumbotron { - padding-right: 60px; - padding-left: 60px; - } - .jumbotron h1, - .jumbotron .h1 { - font-size: 63px; - } -} -.thumbnail { - display: block; - padding: 4px; - margin-bottom: 20px; - line-height: 1.42857143; - background-color: #fff; - border: 1px solid #ddd; - border-radius: 4px; - -webkit-transition: border .2s ease-in-out; - -o-transition: border .2s ease-in-out; - transition: border .2s ease-in-out; -} -.thumbnail > img, -.thumbnail a > img { - margin-right: auto; - margin-left: auto; -} -a.thumbnail:hover, -a.thumbnail:focus, -a.thumbnail.active { - border-color: #337ab7; -} -.thumbnail .caption { - padding: 9px; - color: #333; -} -.alert { - padding: 15px; - margin-bottom: 20px; - border: 1px solid transparent; - border-radius: 4px; -} -.alert h4 { - margin-top: 0; - color: inherit; -} -.alert .alert-link { - font-weight: bold; -} -.alert > p, -.alert > ul { - margin-bottom: 0; -} -.alert > p + p { - margin-top: 5px; -} -.alert-dismissable, -.alert-dismissible { - padding-right: 35px; -} -.alert-dismissable .close, -.alert-dismissible .close { - position: relative; - top: -2px; - right: -21px; - color: inherit; -} -.alert-success { - color: #3c763d; - background-color: #dff0d8; - border-color: #d6e9c6; -} -.alert-success hr { - border-top-color: #c9e2b3; -} -.alert-success .alert-link { - color: #2b542c; -} -.alert-info { - color: #31708f; - background-color: #d9edf7; - border-color: #bce8f1; -} -.alert-info hr { - border-top-color: #a6e1ec; -} -.alert-info .alert-link { - color: #245269; -} -.alert-warning { - color: #8a6d3b; - background-color: #fcf8e3; - border-color: #faebcc; -} -.alert-warning hr { - border-top-color: #f7e1b5; -} -.alert-warning .alert-link { - color: #66512c; -} -.alert-danger { - color: #a94442; - background-color: #f2dede; - border-color: #ebccd1; -} -.alert-danger hr { - border-top-color: #e4b9c0; -} -.alert-danger .alert-link { - color: #843534; -} -@-webkit-keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} -@-o-keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} -@keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} -.progress { - height: 20px; - margin-bottom: 20px; - overflow: hidden; - background-color: #f5f5f5; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, .1); - box-shadow: inset 0 1px 2px rgba(0, 0, 0, .1); -} -.progress-bar { - float: left; - width: 0; - height: 100%; - font-size: 12px; - line-height: 20px; - color: #fff; - text-align: center; - background-color: #337ab7; - -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .15); - box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .15); - -webkit-transition: width .6s ease; - -o-transition: width .6s ease; - transition: width .6s ease; -} -.progress-striped .progress-bar, -.progress-bar-striped { - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - -webkit-background-size: 40px 40px; - background-size: 40px 40px; -} -.progress.active .progress-bar, -.progress-bar.active { - -webkit-animation: progress-bar-stripes 2s linear infinite; - -o-animation: progress-bar-stripes 2s linear infinite; - animation: progress-bar-stripes 2s linear infinite; -} -.progress-bar-success { - background-color: #5cb85c; -} -.progress-striped .progress-bar-success { - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); -} -.progress-bar-info { - background-color: #5bc0de; -} -.progress-striped .progress-bar-info { - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); -} -.progress-bar-warning { - background-color: #f0ad4e; -} -.progress-striped .progress-bar-warning { - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); -} -.progress-bar-danger { - background-color: #d9534f; -} -.progress-striped .progress-bar-danger { - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); -} -.media { - margin-top: 15px; -} -.media:first-child { - margin-top: 0; -} -.media, -.media-body { - overflow: hidden; - zoom: 1; -} -.media-body { - width: 10000px; -} -.media-object { - display: block; -} -.media-object.img-thumbnail { - max-width: none; -} -.media-right, -.media > .pull-right { - padding-left: 10px; -} -.media-left, -.media > .pull-left { - padding-right: 10px; -} -.media-left, -.media-right, -.media-body { - display: table-cell; - vertical-align: top; -} -.media-middle { - vertical-align: middle; -} -.media-bottom { - vertical-align: bottom; -} -.media-heading { - margin-top: 0; - margin-bottom: 5px; -} -.media-list { - padding-left: 0; - list-style: none; -} -.list-group { - padding-left: 0; - margin-bottom: 20px; -} -.list-group-item { - position: relative; - display: block; - padding: 10px 15px; - margin-bottom: -1px; - background-color: #fff; - border: 1px solid #ddd; -} -.list-group-item:first-child { - border-top-left-radius: 4px; - border-top-right-radius: 4px; -} -.list-group-item:last-child { - margin-bottom: 0; - border-bottom-right-radius: 4px; - border-bottom-left-radius: 4px; -} -a.list-group-item, -button.list-group-item { - color: #555; -} -a.list-group-item .list-group-item-heading, -button.list-group-item .list-group-item-heading { - color: #333; -} -a.list-group-item:hover, -button.list-group-item:hover, -a.list-group-item:focus, -button.list-group-item:focus { - color: #555; - text-decoration: none; - background-color: #f5f5f5; -} -button.list-group-item { - width: 100%; - text-align: left; -} -.list-group-item.disabled, -.list-group-item.disabled:hover, -.list-group-item.disabled:focus { - color: #777; - cursor: not-allowed; - background-color: #eee; -} -.list-group-item.disabled .list-group-item-heading, -.list-group-item.disabled:hover .list-group-item-heading, -.list-group-item.disabled:focus .list-group-item-heading { - color: inherit; -} -.list-group-item.disabled .list-group-item-text, -.list-group-item.disabled:hover .list-group-item-text, -.list-group-item.disabled:focus .list-group-item-text { - color: #777; -} -.list-group-item.active, -.list-group-item.active:hover, -.list-group-item.active:focus { - z-index: 2; - color: #fff; - background-color: #337ab7; - border-color: #337ab7; -} -.list-group-item.active .list-group-item-heading, -.list-group-item.active:hover .list-group-item-heading, -.list-group-item.active:focus .list-group-item-heading, -.list-group-item.active .list-group-item-heading > small, -.list-group-item.active:hover .list-group-item-heading > small, -.list-group-item.active:focus .list-group-item-heading > small, -.list-group-item.active .list-group-item-heading > .small, -.list-group-item.active:hover .list-group-item-heading > .small, -.list-group-item.active:focus .list-group-item-heading > .small { - color: inherit; -} -.list-group-item.active .list-group-item-text, -.list-group-item.active:hover .list-group-item-text, -.list-group-item.active:focus .list-group-item-text { - color: #c7ddef; -} -.list-group-item-success { - color: #3c763d; - background-color: #dff0d8; -} -a.list-group-item-success, -button.list-group-item-success { - color: #3c763d; -} -a.list-group-item-success .list-group-item-heading, -button.list-group-item-success .list-group-item-heading { - color: inherit; -} -a.list-group-item-success:hover, -button.list-group-item-success:hover, -a.list-group-item-success:focus, -button.list-group-item-success:focus { - color: #3c763d; - background-color: #d0e9c6; -} -a.list-group-item-success.active, -button.list-group-item-success.active, -a.list-group-item-success.active:hover, -button.list-group-item-success.active:hover, -a.list-group-item-success.active:focus, -button.list-group-item-success.active:focus { - color: #fff; - background-color: #3c763d; - border-color: #3c763d; -} -.list-group-item-info { - color: #31708f; - background-color: #d9edf7; -} -a.list-group-item-info, -button.list-group-item-info { - color: #31708f; -} -a.list-group-item-info .list-group-item-heading, -button.list-group-item-info .list-group-item-heading { - color: inherit; -} -a.list-group-item-info:hover, -button.list-group-item-info:hover, -a.list-group-item-info:focus, -button.list-group-item-info:focus { - color: #31708f; - background-color: #c4e3f3; -} -a.list-group-item-info.active, -button.list-group-item-info.active, -a.list-group-item-info.active:hover, -button.list-group-item-info.active:hover, -a.list-group-item-info.active:focus, -button.list-group-item-info.active:focus { - color: #fff; - background-color: #31708f; - border-color: #31708f; -} -.list-group-item-warning { - color: #8a6d3b; - background-color: #fcf8e3; -} -a.list-group-item-warning, -button.list-group-item-warning { - color: #8a6d3b; -} -a.list-group-item-warning .list-group-item-heading, -button.list-group-item-warning .list-group-item-heading { - color: inherit; -} -a.list-group-item-warning:hover, -button.list-group-item-warning:hover, -a.list-group-item-warning:focus, -button.list-group-item-warning:focus { - color: #8a6d3b; - background-color: #faf2cc; -} -a.list-group-item-warning.active, -button.list-group-item-warning.active, -a.list-group-item-warning.active:hover, -button.list-group-item-warning.active:hover, -a.list-group-item-warning.active:focus, -button.list-group-item-warning.active:focus { - color: #fff; - background-color: #8a6d3b; - border-color: #8a6d3b; -} -.list-group-item-danger { - color: #a94442; - background-color: #f2dede; -} -a.list-group-item-danger, -button.list-group-item-danger { - color: #a94442; -} -a.list-group-item-danger .list-group-item-heading, -button.list-group-item-danger .list-group-item-heading { - color: inherit; -} -a.list-group-item-danger:hover, -button.list-group-item-danger:hover, -a.list-group-item-danger:focus, -button.list-group-item-danger:focus { - color: #a94442; - background-color: #ebcccc; -} -a.list-group-item-danger.active, -button.list-group-item-danger.active, -a.list-group-item-danger.active:hover, -button.list-group-item-danger.active:hover, -a.list-group-item-danger.active:focus, -button.list-group-item-danger.active:focus { - color: #fff; - background-color: #a94442; - border-color: #a94442; -} -.list-group-item-heading { - margin-top: 0; - margin-bottom: 5px; -} -.list-group-item-text { - margin-bottom: 0; - line-height: 1.3; -} -.panel { - margin-bottom: 20px; - background-color: #fff; - border: 1px solid transparent; - border-radius: 4px; - -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, .05); - box-shadow: 0 1px 1px rgba(0, 0, 0, .05); -} -.panel-body { - padding: 15px; -} -.panel-heading { - padding: 10px 15px; - border-bottom: 1px solid transparent; - border-top-left-radius: 3px; - border-top-right-radius: 3px; -} -.panel-heading > .dropdown .dropdown-toggle { - color: inherit; -} -.panel-title { - margin-top: 0; - margin-bottom: 0; - font-size: 16px; - color: inherit; -} -.panel-title > a, -.panel-title > small, -.panel-title > .small, -.panel-title > small > a, -.panel-title > .small > a { - color: inherit; -} -.panel-footer { - padding: 10px 15px; - background-color: #f5f5f5; - border-top: 1px solid #ddd; - border-bottom-right-radius: 3px; - border-bottom-left-radius: 3px; -} -.panel > .list-group, -.panel > .panel-collapse > .list-group { - margin-bottom: 0; -} -.panel > .list-group .list-group-item, -.panel > .panel-collapse > .list-group .list-group-item { - border-width: 1px 0; - border-radius: 0; -} -.panel > .list-group:first-child .list-group-item:first-child, -.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child { - border-top: 0; - border-top-left-radius: 3px; - border-top-right-radius: 3px; -} -.panel > .list-group:last-child .list-group-item:last-child, -.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child { - border-bottom: 0; - border-bottom-right-radius: 3px; - border-bottom-left-radius: 3px; -} -.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child { - border-top-left-radius: 0; - border-top-right-radius: 0; -} -.panel-heading + .list-group .list-group-item:first-child { - border-top-width: 0; -} -.list-group + .panel-footer { - border-top-width: 0; -} -.panel > .table, -.panel > .table-responsive > .table, -.panel > .panel-collapse > .table { - margin-bottom: 0; -} -.panel > .table caption, -.panel > .table-responsive > .table caption, -.panel > .panel-collapse > .table caption { - padding-right: 15px; - padding-left: 15px; -} -.panel > .table:first-child, -.panel > .table-responsive:first-child > .table:first-child { - border-top-left-radius: 3px; - border-top-right-radius: 3px; -} -.panel > .table:first-child > thead:first-child > tr:first-child, -.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child, -.panel > .table:first-child > tbody:first-child > tr:first-child, -.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child { - border-top-left-radius: 3px; - border-top-right-radius: 3px; -} -.panel > .table:first-child > thead:first-child > tr:first-child td:first-child, -.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child, -.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, -.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child, -.panel > .table:first-child > thead:first-child > tr:first-child th:first-child, -.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child, -.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, -.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child { - border-top-left-radius: 3px; -} -.panel > .table:first-child > thead:first-child > tr:first-child td:last-child, -.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child, -.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, -.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child, -.panel > .table:first-child > thead:first-child > tr:first-child th:last-child, -.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child, -.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, -.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child { - border-top-right-radius: 3px; -} -.panel > .table:last-child, -.panel > .table-responsive:last-child > .table:last-child { - border-bottom-right-radius: 3px; - border-bottom-left-radius: 3px; -} -.panel > .table:last-child > tbody:last-child > tr:last-child, -.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child, -.panel > .table:last-child > tfoot:last-child > tr:last-child, -.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child { - border-bottom-right-radius: 3px; - border-bottom-left-radius: 3px; -} -.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, -.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child, -.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, -.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child, -.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, -.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child, -.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, -.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child { - border-bottom-left-radius: 3px; -} -.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, -.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child, -.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, -.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child, -.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, -.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child, -.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, -.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child { - border-bottom-right-radius: 3px; -} -.panel > .panel-body + .table, -.panel > .panel-body + .table-responsive, -.panel > .table + .panel-body, -.panel > .table-responsive + .panel-body { - border-top: 1px solid #ddd; -} -.panel > .table > tbody:first-child > tr:first-child th, -.panel > .table > tbody:first-child > tr:first-child td { - border-top: 0; -} -.panel > .table-bordered, -.panel > .table-responsive > .table-bordered { - border: 0; -} -.panel > .table-bordered > thead > tr > th:first-child, -.panel > .table-responsive > .table-bordered > thead > tr > th:first-child, -.panel > .table-bordered > tbody > tr > th:first-child, -.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, -.panel > .table-bordered > tfoot > tr > th:first-child, -.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, -.panel > .table-bordered > thead > tr > td:first-child, -.panel > .table-responsive > .table-bordered > thead > tr > td:first-child, -.panel > .table-bordered > tbody > tr > td:first-child, -.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, -.panel > .table-bordered > tfoot > tr > td:first-child, -.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { - border-left: 0; -} -.panel > .table-bordered > thead > tr > th:last-child, -.panel > .table-responsive > .table-bordered > thead > tr > th:last-child, -.panel > .table-bordered > tbody > tr > th:last-child, -.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, -.panel > .table-bordered > tfoot > tr > th:last-child, -.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, -.panel > .table-bordered > thead > tr > td:last-child, -.panel > .table-responsive > .table-bordered > thead > tr > td:last-child, -.panel > .table-bordered > tbody > tr > td:last-child, -.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, -.panel > .table-bordered > tfoot > tr > td:last-child, -.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { - border-right: 0; -} -.panel > .table-bordered > thead > tr:first-child > td, -.panel > .table-responsive > .table-bordered > thead > tr:first-child > td, -.panel > .table-bordered > tbody > tr:first-child > td, -.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, -.panel > .table-bordered > thead > tr:first-child > th, -.panel > .table-responsive > .table-bordered > thead > tr:first-child > th, -.panel > .table-bordered > tbody > tr:first-child > th, -.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { - border-bottom: 0; -} -.panel > .table-bordered > tbody > tr:last-child > td, -.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, -.panel > .table-bordered > tfoot > tr:last-child > td, -.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, -.panel > .table-bordered > tbody > tr:last-child > th, -.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, -.panel > .table-bordered > tfoot > tr:last-child > th, -.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { - border-bottom: 0; -} -.panel > .table-responsive { - margin-bottom: 0; - border: 0; -} -.panel-group { - margin-bottom: 20px; -} -.panel-group .panel { - margin-bottom: 0; - border-radius: 4px; -} -.panel-group .panel + .panel { - margin-top: 5px; -} -.panel-group .panel-heading { - border-bottom: 0; -} -.panel-group .panel-heading + .panel-collapse > .panel-body, -.panel-group .panel-heading + .panel-collapse > .list-group { - border-top: 1px solid #ddd; -} -.panel-group .panel-footer { - border-top: 0; -} -.panel-group .panel-footer + .panel-collapse .panel-body { - border-bottom: 1px solid #ddd; -} -.panel-default { - border-color: #ddd; -} -.panel-default > .panel-heading { - color: #333; - background-color: #f5f5f5; - border-color: #ddd; -} -.panel-default > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #ddd; -} -.panel-default > .panel-heading .badge { - color: #f5f5f5; - background-color: #333; -} -.panel-default > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #ddd; -} -.panel-primary { - border-color: #337ab7; -} -.panel-primary > .panel-heading { - color: #fff; - background-color: #337ab7; - border-color: #337ab7; -} -.panel-primary > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #337ab7; -} -.panel-primary > .panel-heading .badge { - color: #337ab7; - background-color: #fff; -} -.panel-primary > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #337ab7; -} -.panel-success { - border-color: #d6e9c6; -} -.panel-success > .panel-heading { - color: #3c763d; - background-color: #dff0d8; - border-color: #d6e9c6; -} -.panel-success > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #d6e9c6; -} -.panel-success > .panel-heading .badge { - color: #dff0d8; - background-color: #3c763d; -} -.panel-success > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #d6e9c6; -} -.panel-info { - border-color: #bce8f1; -} -.panel-info > .panel-heading { - color: #31708f; - background-color: #d9edf7; - border-color: #bce8f1; -} -.panel-info > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #bce8f1; -} -.panel-info > .panel-heading .badge { - color: #d9edf7; - background-color: #31708f; -} -.panel-info > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #bce8f1; -} -.panel-warning { - border-color: #faebcc; -} -.panel-warning > .panel-heading { - color: #8a6d3b; - background-color: #fcf8e3; - border-color: #faebcc; -} -.panel-warning > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #faebcc; -} -.panel-warning > .panel-heading .badge { - color: #fcf8e3; - background-color: #8a6d3b; -} -.panel-warning > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #faebcc; -} -.panel-danger { - border-color: #ebccd1; -} -.panel-danger > .panel-heading { - color: #a94442; - background-color: #f2dede; - border-color: #ebccd1; -} -.panel-danger > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #ebccd1; -} -.panel-danger > .panel-heading .badge { - color: #f2dede; - background-color: #a94442; -} -.panel-danger > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #ebccd1; -} -.embed-responsive { - position: relative; - display: block; - height: 0; - padding: 0; - overflow: hidden; -} -.embed-responsive .embed-responsive-item, -.embed-responsive iframe, -.embed-responsive embed, -.embed-responsive object, -.embed-responsive video { - position: absolute; - top: 0; - bottom: 0; - left: 0; - width: 100%; - height: 100%; - border: 0; -} -.embed-responsive-16by9 { - padding-bottom: 56.25%; -} -.embed-responsive-4by3 { - padding-bottom: 75%; -} -.well { - min-height: 20px; - padding: 19px; - margin-bottom: 20px; - background-color: #f5f5f5; - border: 1px solid #e3e3e3; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .05); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, .05); -} -.well blockquote { - border-color: #ddd; - border-color: rgba(0, 0, 0, .15); -} -.well-lg { - padding: 24px; - border-radius: 6px; -} -.well-sm { - padding: 9px; - border-radius: 3px; -} -.close { - float: right; - font-size: 21px; - font-weight: bold; - line-height: 1; - color: #000; - text-shadow: 0 1px 0 #fff; - filter: alpha(opacity=20); - opacity: .2; -} -.close:hover, -.close:focus { - color: #000; - text-decoration: none; - cursor: pointer; - filter: alpha(opacity=50); - opacity: .5; -} -button.close { - -webkit-appearance: none; - padding: 0; - cursor: pointer; - background: transparent; - border: 0; -} -.modal-open { - overflow: hidden; -} -.modal { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 1050; - display: none; - overflow: hidden; - -webkit-overflow-scrolling: touch; - outline: 0; -} -.modal.fade .modal-dialog { - -webkit-transition: -webkit-transform .3s ease-out; - -o-transition: -o-transform .3s ease-out; - transition: transform .3s ease-out; - -webkit-transform: translate(0, -25%); - -ms-transform: translate(0, -25%); - -o-transform: translate(0, -25%); - transform: translate(0, -25%); -} -.modal.in .modal-dialog { - -webkit-transform: translate(0, 0); - -ms-transform: translate(0, 0); - -o-transform: translate(0, 0); - transform: translate(0, 0); -} -.modal-open .modal { - overflow-x: hidden; - overflow-y: auto; -} -.modal-dialog { - position: relative; - width: auto; - margin: 10px; -} -.modal-content { - position: relative; - background-color: #fff; - -webkit-background-clip: padding-box; - background-clip: padding-box; - border: 1px solid #999; - border: 1px solid rgba(0, 0, 0, .2); - border-radius: 6px; - outline: 0; - -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, .5); - box-shadow: 0 3px 9px rgba(0, 0, 0, .5); -} -.modal-backdrop { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 1040; - background-color: #000; -} -.modal-backdrop.fade { - filter: alpha(opacity=0); - opacity: 0; -} -.modal-backdrop.in { - filter: alpha(opacity=50); - opacity: .5; -} -.modal-header { - padding: 15px; - border-bottom: 1px solid #e5e5e5; -} -.modal-header .close { - margin-top: -2px; -} -.modal-title { - margin: 0; - line-height: 1.42857143; -} -.modal-body { - position: relative; - padding: 15px; -} -.modal-footer { - padding: 15px; - text-align: right; - border-top: 1px solid #e5e5e5; -} -.modal-footer .btn + .btn { - margin-bottom: 0; - margin-left: 5px; -} -.modal-footer .btn-group .btn + .btn { - margin-left: -1px; -} -.modal-footer .btn-block + .btn-block { - margin-left: 0; -} -.modal-scrollbar-measure { - position: absolute; - top: -9999px; - width: 50px; - height: 50px; - overflow: scroll; -} -@media (min-width: 768px) { - .modal-dialog { - width: 600px; - margin: 30px auto; - } - .modal-content { - -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, .5); - box-shadow: 0 5px 15px rgba(0, 0, 0, .5); - } - .modal-sm { - width: 300px; - } -} -@media (min-width: 992px) { - .modal-lg { - width: 900px; - } -} -.tooltip { - position: absolute; - z-index: 1070; - display: block; - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; - font-size: 12px; - font-style: normal; - font-weight: normal; - line-height: 1.42857143; - text-align: left; - text-align: start; - text-decoration: none; - text-shadow: none; - text-transform: none; - letter-spacing: normal; - word-break: normal; - word-spacing: normal; - word-wrap: normal; - white-space: normal; - filter: alpha(opacity=0); - opacity: 0; - - line-break: auto; -} -.tooltip.in { - filter: alpha(opacity=90); - opacity: .9; -} -.tooltip.top { - padding: 5px 0; - margin-top: -3px; -} -.tooltip.right { - padding: 0 5px; - margin-left: 3px; -} -.tooltip.bottom { - padding: 5px 0; - margin-top: 3px; -} -.tooltip.left { - padding: 0 5px; - margin-left: -3px; -} -.tooltip-inner { - max-width: 200px; - padding: 3px 8px; - color: #fff; - text-align: center; - background-color: #000; - border-radius: 4px; -} -.tooltip-arrow { - position: absolute; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; -} -.tooltip.top .tooltip-arrow { - bottom: 0; - left: 50%; - margin-left: -5px; - border-width: 5px 5px 0; - border-top-color: #000; -} -.tooltip.top-left .tooltip-arrow { - right: 5px; - bottom: 0; - margin-bottom: -5px; - border-width: 5px 5px 0; - border-top-color: #000; -} -.tooltip.top-right .tooltip-arrow { - bottom: 0; - left: 5px; - margin-bottom: -5px; - border-width: 5px 5px 0; - border-top-color: #000; -} -.tooltip.right .tooltip-arrow { - top: 50%; - left: 0; - margin-top: -5px; - border-width: 5px 5px 5px 0; - border-right-color: #000; -} -.tooltip.left .tooltip-arrow { - top: 50%; - right: 0; - margin-top: -5px; - border-width: 5px 0 5px 5px; - border-left-color: #000; -} -.tooltip.bottom .tooltip-arrow { - top: 0; - left: 50%; - margin-left: -5px; - border-width: 0 5px 5px; - border-bottom-color: #000; -} -.tooltip.bottom-left .tooltip-arrow { - top: 0; - right: 5px; - margin-top: -5px; - border-width: 0 5px 5px; - border-bottom-color: #000; -} -.tooltip.bottom-right .tooltip-arrow { - top: 0; - left: 5px; - margin-top: -5px; - border-width: 0 5px 5px; - border-bottom-color: #000; -} -.popover { - position: absolute; - top: 0; - left: 0; - z-index: 1060; - display: none; - max-width: 276px; - padding: 1px; - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; - font-size: 14px; - font-style: normal; - font-weight: normal; - line-height: 1.42857143; - text-align: left; - text-align: start; - text-decoration: none; - text-shadow: none; - text-transform: none; - letter-spacing: normal; - word-break: normal; - word-spacing: normal; - word-wrap: normal; - white-space: normal; - background-color: #fff; - -webkit-background-clip: padding-box; - background-clip: padding-box; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, .2); - border-radius: 6px; - -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, .2); - box-shadow: 0 5px 10px rgba(0, 0, 0, .2); - - line-break: auto; -} -.popover.top { - margin-top: -10px; -} -.popover.right { - margin-left: 10px; -} -.popover.bottom { - margin-top: 10px; -} -.popover.left { - margin-left: -10px; -} -.popover-title { - padding: 8px 14px; - margin: 0; - font-size: 14px; - background-color: #f7f7f7; - border-bottom: 1px solid #ebebeb; - border-radius: 5px 5px 0 0; -} -.popover-content { - padding: 9px 14px; -} -.popover > .arrow, -.popover > .arrow:after { - position: absolute; - display: block; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; -} -.popover > .arrow { - border-width: 11px; -} -.popover > .arrow:after { - content: ""; - border-width: 10px; -} -.popover.top > .arrow { - bottom: -11px; - left: 50%; - margin-left: -11px; - border-top-color: #999; - border-top-color: rgba(0, 0, 0, .25); - border-bottom-width: 0; -} -.popover.top > .arrow:after { - bottom: 1px; - margin-left: -10px; - content: " "; - border-top-color: #fff; - border-bottom-width: 0; -} -.popover.right > .arrow { - top: 50%; - left: -11px; - margin-top: -11px; - border-right-color: #999; - border-right-color: rgba(0, 0, 0, .25); - border-left-width: 0; -} -.popover.right > .arrow:after { - bottom: -10px; - left: 1px; - content: " "; - border-right-color: #fff; - border-left-width: 0; -} -.popover.bottom > .arrow { - top: -11px; - left: 50%; - margin-left: -11px; - border-top-width: 0; - border-bottom-color: #999; - border-bottom-color: rgba(0, 0, 0, .25); -} -.popover.bottom > .arrow:after { - top: 1px; - margin-left: -10px; - content: " "; - border-top-width: 0; - border-bottom-color: #fff; -} -.popover.left > .arrow { - top: 50%; - right: -11px; - margin-top: -11px; - border-right-width: 0; - border-left-color: #999; - border-left-color: rgba(0, 0, 0, .25); -} -.popover.left > .arrow:after { - right: 1px; - bottom: -10px; - content: " "; - border-right-width: 0; - border-left-color: #fff; -} -.carousel { - position: relative; -} -.carousel-inner { - position: relative; - width: 100%; - overflow: hidden; -} -.carousel-inner > .item { - position: relative; - display: none; - -webkit-transition: .6s ease-in-out left; - -o-transition: .6s ease-in-out left; - transition: .6s ease-in-out left; -} -.carousel-inner > .item > img, -.carousel-inner > .item > a > img { - line-height: 1; -} -@media all and (transform-3d), (-webkit-transform-3d) { - .carousel-inner > .item { - -webkit-transition: -webkit-transform .6s ease-in-out; - -o-transition: -o-transform .6s ease-in-out; - transition: transform .6s ease-in-out; - - -webkit-backface-visibility: hidden; - backface-visibility: hidden; - -webkit-perspective: 1000px; - perspective: 1000px; - } - .carousel-inner > .item.next, - .carousel-inner > .item.active.right { - left: 0; - -webkit-transform: translate3d(100%, 0, 0); - transform: translate3d(100%, 0, 0); - } - .carousel-inner > .item.prev, - .carousel-inner > .item.active.left { - left: 0; - -webkit-transform: translate3d(-100%, 0, 0); - transform: translate3d(-100%, 0, 0); - } - .carousel-inner > .item.next.left, - .carousel-inner > .item.prev.right, - .carousel-inner > .item.active { - left: 0; - -webkit-transform: translate3d(0, 0, 0); - transform: translate3d(0, 0, 0); - } -} -.carousel-inner > .active, -.carousel-inner > .next, -.carousel-inner > .prev { - display: block; -} -.carousel-inner > .active { - left: 0; -} -.carousel-inner > .next, -.carousel-inner > .prev { - position: absolute; - top: 0; - width: 100%; -} -.carousel-inner > .next { - left: 100%; -} -.carousel-inner > .prev { - left: -100%; -} -.carousel-inner > .next.left, -.carousel-inner > .prev.right { - left: 0; -} -.carousel-inner > .active.left { - left: -100%; -} -.carousel-inner > .active.right { - left: 100%; -} -.carousel-control { - position: absolute; - top: 0; - bottom: 0; - left: 0; - width: 15%; - font-size: 20px; - color: #fff; - text-align: center; - text-shadow: 0 1px 2px rgba(0, 0, 0, .6); - background-color: rgba(0, 0, 0, 0); - filter: alpha(opacity=50); - opacity: .5; -} -.carousel-control.left { - background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%); - background-image: -o-linear-gradient(left, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%); - background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, .5)), to(rgba(0, 0, 0, .0001))); - background-image: linear-gradient(to right, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); - background-repeat: repeat-x; -} -.carousel-control.right { - right: 0; - left: auto; - background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, .0001) 0%, rgba(0, 0, 0, .5) 100%); - background-image: -o-linear-gradient(left, rgba(0, 0, 0, .0001) 0%, rgba(0, 0, 0, .5) 100%); - background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, .0001)), to(rgba(0, 0, 0, .5))); - background-image: linear-gradient(to right, rgba(0, 0, 0, .0001) 0%, rgba(0, 0, 0, .5) 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); - background-repeat: repeat-x; -} -.carousel-control:hover, -.carousel-control:focus { - color: #fff; - text-decoration: none; - filter: alpha(opacity=90); - outline: 0; - opacity: .9; -} -.carousel-control .icon-prev, -.carousel-control .icon-next, -.carousel-control .glyphicon-chevron-left, -.carousel-control .glyphicon-chevron-right { - position: absolute; - top: 50%; - z-index: 5; - display: inline-block; - margin-top: -10px; -} -.carousel-control .icon-prev, -.carousel-control .glyphicon-chevron-left { - left: 50%; - margin-left: -10px; -} -.carousel-control .icon-next, -.carousel-control .glyphicon-chevron-right { - right: 50%; - margin-right: -10px; -} -.carousel-control .icon-prev, -.carousel-control .icon-next { - width: 20px; - height: 20px; - font-family: serif; - line-height: 1; -} -.carousel-control .icon-prev:before { - content: '\2039'; -} -.carousel-control .icon-next:before { - content: '\203a'; -} -.carousel-indicators { - position: absolute; - bottom: 10px; - left: 50%; - z-index: 15; - width: 60%; - padding-left: 0; - margin-left: -30%; - text-align: center; - list-style: none; -} -.carousel-indicators li { - display: inline-block; - width: 10px; - height: 10px; - margin: 1px; - text-indent: -999px; - cursor: pointer; - background-color: #000 \9; - background-color: rgba(0, 0, 0, 0); - border: 1px solid #fff; - border-radius: 10px; -} -.carousel-indicators .active { - width: 12px; - height: 12px; - margin: 0; - background-color: #fff; -} -.carousel-caption { - position: absolute; - right: 15%; - bottom: 20px; - left: 15%; - z-index: 10; - padding-top: 20px; - padding-bottom: 20px; - color: #fff; - text-align: center; - text-shadow: 0 1px 2px rgba(0, 0, 0, .6); -} -.carousel-caption .btn { - text-shadow: none; -} -@media screen and (min-width: 768px) { - .carousel-control .glyphicon-chevron-left, - .carousel-control .glyphicon-chevron-right, - .carousel-control .icon-prev, - .carousel-control .icon-next { - width: 30px; - height: 30px; - margin-top: -10px; - font-size: 30px; - } - .carousel-control .glyphicon-chevron-left, - .carousel-control .icon-prev { - margin-left: -10px; - } - .carousel-control .glyphicon-chevron-right, - .carousel-control .icon-next { - margin-right: -10px; - } - .carousel-caption { - right: 20%; - left: 20%; - padding-bottom: 30px; - } - .carousel-indicators { - bottom: 20px; - } -} -.clearfix:before, -.clearfix:after, -.dl-horizontal dd:before, -.dl-horizontal dd:after, -.container:before, -.container:after, -.container-fluid:before, -.container-fluid:after, -.row:before, -.row:after, -.form-horizontal .form-group:before, -.form-horizontal .form-group:after, -.btn-toolbar:before, -.btn-toolbar:after, -.btn-group-vertical > .btn-group:before, -.btn-group-vertical > .btn-group:after, -.nav:before, -.nav:after, -.navbar:before, -.navbar:after, -.navbar-header:before, -.navbar-header:after, -.navbar-collapse:before, -.navbar-collapse:after, -.pager:before, -.pager:after, -.panel-body:before, -.panel-body:after, -.modal-header:before, -.modal-header:after, -.modal-footer:before, -.modal-footer:after { - display: table; - content: " "; -} -.clearfix:after, -.dl-horizontal dd:after, -.container:after, -.container-fluid:after, -.row:after, -.form-horizontal .form-group:after, -.btn-toolbar:after, -.btn-group-vertical > .btn-group:after, -.nav:after, -.navbar:after, -.navbar-header:after, -.navbar-collapse:after, -.pager:after, -.panel-body:after, -.modal-header:after, -.modal-footer:after { - clear: both; -} -.center-block { - display: block; - margin-right: auto; - margin-left: auto; -} -.pull-right { - float: right !important; -} -.pull-left { - float: left !important; -} -.hide { - display: none !important; -} -.show { - display: block !important; -} -.invisible { - visibility: hidden; -} -.text-hide { - font: 0/0 a; - color: transparent; - text-shadow: none; - background-color: transparent; - border: 0; -} -.hidden { - display: none !important; -} -.affix { - position: fixed; -} -@-ms-viewport { - width: device-width; -} -.visible-xs, -.visible-sm, -.visible-md, -.visible-lg { - display: none !important; -} -.visible-xs-block, -.visible-xs-inline, -.visible-xs-inline-block, -.visible-sm-block, -.visible-sm-inline, -.visible-sm-inline-block, -.visible-md-block, -.visible-md-inline, -.visible-md-inline-block, -.visible-lg-block, -.visible-lg-inline, -.visible-lg-inline-block { - display: none !important; -} -@media (max-width: 767px) { - .visible-xs { - display: block !important; - } - table.visible-xs { - display: table !important; - } - tr.visible-xs { - display: table-row !important; - } - th.visible-xs, - td.visible-xs { - display: table-cell !important; - } -} -@media (max-width: 767px) { - .visible-xs-block { - display: block !important; - } -} -@media (max-width: 767px) { - .visible-xs-inline { - display: inline !important; - } -} -@media (max-width: 767px) { - .visible-xs-inline-block { - display: inline-block !important; - } -} -@media (min-width: 768px) and (max-width: 991px) { - .visible-sm { - display: block !important; - } - table.visible-sm { - display: table !important; - } - tr.visible-sm { - display: table-row !important; - } - th.visible-sm, - td.visible-sm { - display: table-cell !important; - } -} -@media (min-width: 768px) and (max-width: 991px) { - .visible-sm-block { - display: block !important; - } -} -@media (min-width: 768px) and (max-width: 991px) { - .visible-sm-inline { - display: inline !important; - } -} -@media (min-width: 768px) and (max-width: 991px) { - .visible-sm-inline-block { - display: inline-block !important; - } -} -@media (min-width: 992px) and (max-width: 1199px) { - .visible-md { - display: block !important; - } - table.visible-md { - display: table !important; - } - tr.visible-md { - display: table-row !important; - } - th.visible-md, - td.visible-md { - display: table-cell !important; - } -} -@media (min-width: 992px) and (max-width: 1199px) { - .visible-md-block { - display: block !important; - } -} -@media (min-width: 992px) and (max-width: 1199px) { - .visible-md-inline { - display: inline !important; - } -} -@media (min-width: 992px) and (max-width: 1199px) { - .visible-md-inline-block { - display: inline-block !important; - } -} -@media (min-width: 1200px) { - .visible-lg { - display: block !important; - } - table.visible-lg { - display: table !important; - } - tr.visible-lg { - display: table-row !important; - } - th.visible-lg, - td.visible-lg { - display: table-cell !important; - } -} -@media (min-width: 1200px) { - .visible-lg-block { - display: block !important; - } -} -@media (min-width: 1200px) { - .visible-lg-inline { - display: inline !important; - } -} -@media (min-width: 1200px) { - .visible-lg-inline-block { - display: inline-block !important; - } -} -@media (max-width: 767px) { - .hidden-xs { - display: none !important; - } -} -@media (min-width: 768px) and (max-width: 991px) { - .hidden-sm { - display: none !important; - } -} -@media (min-width: 992px) and (max-width: 1199px) { - .hidden-md { - display: none !important; - } -} -@media (min-width: 1200px) { - .hidden-lg { - display: none !important; - } -} -.visible-print { - display: none !important; -} -@media print { - .visible-print { - display: block !important; - } - table.visible-print { - display: table !important; - } - tr.visible-print { - display: table-row !important; - } - th.visible-print, - td.visible-print { - display: table-cell !important; - } -} -.visible-print-block { - display: none !important; -} -@media print { - .visible-print-block { - display: block !important; - } -} -.visible-print-inline { - display: none !important; -} -@media print { - .visible-print-inline { - display: inline !important; - } -} -.visible-print-inline-block { - display: none !important; -} -@media print { - .visible-print-inline-block { - display: inline-block !important; - } -} -@media print { - .hidden-print { - display: none !important; - } -} -/*# sourceMappingURL=bootstrap.css.map */ diff --git a/docs/_static/bootstrap-3.3.7/css/bootstrap.css.map b/docs/_static/bootstrap-3.3.7/css/bootstrap.css.map deleted file mode 100644 index f010c82d1..000000000 --- a/docs/_static/bootstrap-3.3.7/css/bootstrap.css.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"sources":["bootstrap.css","less/normalize.less","less/print.less","less/glyphicons.less","less/scaffolding.less","less/mixins/vendor-prefixes.less","less/mixins/tab-focus.less","less/mixins/image.less","less/type.less","less/mixins/text-emphasis.less","less/mixins/background-variant.less","less/mixins/text-overflow.less","less/code.less","less/grid.less","less/mixins/grid.less","less/mixins/grid-framework.less","less/tables.less","less/mixins/table-row.less","less/forms.less","less/mixins/forms.less","less/buttons.less","less/mixins/buttons.less","less/mixins/opacity.less","less/component-animations.less","less/dropdowns.less","less/mixins/nav-divider.less","less/mixins/reset-filter.less","less/button-groups.less","less/mixins/border-radius.less","less/input-groups.less","less/navs.less","less/navbar.less","less/mixins/nav-vertical-align.less","less/utilities.less","less/breadcrumbs.less","less/pagination.less","less/mixins/pagination.less","less/pager.less","less/labels.less","less/mixins/labels.less","less/badges.less","less/jumbotron.less","less/thumbnails.less","less/alerts.less","less/mixins/alerts.less","less/progress-bars.less","less/mixins/gradients.less","less/mixins/progress-bar.less","less/media.less","less/list-group.less","less/mixins/list-group.less","less/panels.less","less/mixins/panels.less","less/responsive-embed.less","less/wells.less","less/close.less","less/modals.less","less/tooltip.less","less/mixins/reset-text.less","less/popovers.less","less/carousel.less","less/mixins/clearfix.less","less/mixins/center-block.less","less/mixins/hide-text.less","less/responsive-utilities.less","less/mixins/responsive-visibility.less"],"names":[],"mappings":"AAAA;;;;GAIG;AACH,4EAA4E;ACG5E;EACE,wBAAA;EACA,2BAAA;EACA,+BAAA;CDDD;ACQD;EACE,UAAA;CDND;ACmBD;;;;;;;;;;;;;EAaE,eAAA;CDjBD;ACyBD;;;;EAIE,sBAAA;EACA,yBAAA;CDvBD;AC+BD;EACE,cAAA;EACA,UAAA;CD7BD;ACqCD;;EAEE,cAAA;CDnCD;AC6CD;EACE,8BAAA;CD3CD;ACmDD;;EAEE,WAAA;CDjDD;AC2DD;EACE,0BAAA;CDzDD;ACgED;;EAEE,kBAAA;CD9DD;ACqED;EACE,mBAAA;CDnED;AC2ED;EACE,eAAA;EACA,iBAAA;CDzED;ACgFD;EACE,iBAAA;EACA,YAAA;CD9ED;ACqFD;EACE,eAAA;CDnFD;AC0FD;;EAEE,eAAA;EACA,eAAA;EACA,mBAAA;EACA,yBAAA;CDxFD;AC2FD;EACE,YAAA;CDzFD;AC4FD;EACE,gBAAA;CD1FD;ACoGD;EACE,UAAA;CDlGD;ACyGD;EACE,iBAAA;CDvGD;ACiHD;EACE,iBAAA;CD/GD;ACsHD;EACE,gCAAA;KAAA,6BAAA;UAAA,wBAAA;EACA,UAAA;CDpHD;AC2HD;EACE,eAAA;CDzHD;ACgID;;;;EAIE,kCAAA;EACA,eAAA;CD9HD;ACgJD;;;;;EAKE,eAAA;EACA,cAAA;EACA,UAAA;CD9ID;ACqJD;EACE,kBAAA;CDnJD;AC6JD;;EAEE,qBAAA;CD3JD;ACsKD;;;;EAIE,2BAAA;EACA,gBAAA;CDpKD;AC2KD;;EAEE,gBAAA;CDzKD;ACgLD;;EAEE,UAAA;EACA,WAAA;CD9KD;ACsLD;EACE,oBAAA;CDpLD;AC+LD;;EAEE,+BAAA;KAAA,4BAAA;UAAA,uBAAA;EACA,WAAA;CD7LD;ACsMD;;EAEE,aAAA;CDpMD;AC4MD;EACE,8BAAA;EACA,gCAAA;KAAA,6BAAA;UAAA,wBAAA;CD1MD;ACmND;;EAEE,yBAAA;CDjND;ACwND;EACE,0BAAA;EACA,cAAA;EACA,+BAAA;CDtND;AC8ND;EACE,UAAA;EACA,WAAA;CD5ND;ACmOD;EACE,eAAA;CDjOD;ACyOD;EACE,kBAAA;CDvOD;ACiPD;EACE,0BAAA;EACA,kBAAA;CD/OD;ACkPD;;EAEE,WAAA;CDhPD;AACD,qFAAqF;AElFrF;EA7FI;;;IAGI,mCAAA;IACA,uBAAA;IACA,oCAAA;YAAA,4BAAA;IACA,6BAAA;GFkLL;EE/KC;;IAEI,2BAAA;GFiLL;EE9KC;IACI,6BAAA;GFgLL;EE7KC;IACI,8BAAA;GF+KL;EE1KC;;IAEI,YAAA;GF4KL;EEzKC;;IAEI,uBAAA;IACA,yBAAA;GF2KL;EExKC;IACI,4BAAA;GF0KL;EEvKC;;IAEI,yBAAA;GFyKL;EEtKC;IACI,2BAAA;GFwKL;EErKC;;;IAGI,WAAA;IACA,UAAA;GFuKL;EEpKC;;IAEI,wBAAA;GFsKL;EEhKC;IACI,cAAA;GFkKL;EEhKC;;IAGQ,kCAAA;GFiKT;EE9JC;IACI,uBAAA;GFgKL;EE7JC;IACI,qCAAA;GF+JL;EEhKC;;IAKQ,kCAAA;GF+JT;EE5JC;;IAGQ,kCAAA;GF6JT;CACF;AGnPD;EACE,oCAAA;EACA,sDAAA;EACA,gYAAA;CHqPD;AG7OD;EACE,mBAAA;EACA,SAAA;EACA,sBAAA;EACA,oCAAA;EACA,mBAAA;EACA,oBAAA;EACA,eAAA;EACA,oCAAA;EACA,mCAAA;CH+OD;AG3OmC;EAAW,iBAAA;CH8O9C;AG7OmC;EAAW,iBAAA;CHgP9C;AG9OmC;;EAAW,iBAAA;CHkP9C;AGjPmC;EAAW,iBAAA;CHoP9C;AGnPmC;EAAW,iBAAA;CHsP9C;AGrPmC;EAAW,iBAAA;CHwP9C;AGvPmC;EAAW,iBAAA;CH0P9C;AGzPmC;EAAW,iBAAA;CH4P9C;AG3PmC;EAAW,iBAAA;CH8P9C;AG7PmC;EAAW,iBAAA;CHgQ9C;AG/PmC;EAAW,iBAAA;CHkQ9C;AGjQmC;EAAW,iBAAA;CHoQ9C;AGnQmC;EAAW,iBAAA;CHsQ9C;AGrQmC;EAAW,iBAAA;CHwQ9C;AGvQmC;EAAW,iBAAA;CH0Q9C;AGzQmC;EAAW,iBAAA;CH4Q9C;AG3QmC;EAAW,iBAAA;CH8Q9C;AG7QmC;EAAW,iBAAA;CHgR9C;AG/QmC;EAAW,iBAAA;CHkR9C;AGjRmC;EAAW,iBAAA;CHoR9C;AGnRmC;EAAW,iBAAA;CHsR9C;AGrRmC;EAAW,iBAAA;CHwR9C;AGvRmC;EAAW,iBAAA;CH0R9C;AGzRmC;EAAW,iBAAA;CH4R9C;AG3RmC;EAAW,iBAAA;CH8R9C;AG7RmC;EAAW,iBAAA;CHgS9C;AG/RmC;EAAW,iBAAA;CHkS9C;AGjSmC;EAAW,iBAAA;CHoS9C;AGnSmC;EAAW,iBAAA;CHsS9C;AGrSmC;EAAW,iBAAA;CHwS9C;AGvSmC;EAAW,iBAAA;CH0S9C;AGzSmC;EAAW,iBAAA;CH4S9C;AG3SmC;EAAW,iBAAA;CH8S9C;AG7SmC;EAAW,iBAAA;CHgT9C;AG/SmC;EAAW,iBAAA;CHkT9C;AGjTmC;EAAW,iBAAA;CHoT9C;AGnTmC;EAAW,iBAAA;CHsT9C;AGrTmC;EAAW,iBAAA;CHwT9C;AGvTmC;EAAW,iBAAA;CH0T9C;AGzTmC;EAAW,iBAAA;CH4T9C;AG3TmC;EAAW,iBAAA;CH8T9C;AG7TmC;EAAW,iBAAA;CHgU9C;AG/TmC;EAAW,iBAAA;CHkU9C;AGjUmC;EAAW,iBAAA;CHoU9C;AGnUmC;EAAW,iBAAA;CHsU9C;AGrUmC;EAAW,iBAAA;CHwU9C;AGvUmC;EAAW,iBAAA;CH0U9C;AGzUmC;EAAW,iBAAA;CH4U9C;AG3UmC;EAAW,iBAAA;CH8U9C;AG7UmC;EAAW,iBAAA;CHgV9C;AG/UmC;EAAW,iBAAA;CHkV9C;AGjVmC;EAAW,iBAAA;CHoV9C;AGnVmC;EAAW,iBAAA;CHsV9C;AGrVmC;EAAW,iBAAA;CHwV9C;AGvVmC;EAAW,iBAAA;CH0V9C;AGzVmC;EAAW,iBAAA;CH4V9C;AG3VmC;EAAW,iBAAA;CH8V9C;AG7VmC;EAAW,iBAAA;CHgW9C;AG/VmC;EAAW,iBAAA;CHkW9C;AGjWmC;EAAW,iBAAA;CHoW9C;AGnWmC;EAAW,iBAAA;CHsW9C;AGrWmC;EAAW,iBAAA;CHwW9C;AGvWmC;EAAW,iBAAA;CH0W9C;AGzWmC;EAAW,iBAAA;CH4W9C;AG3WmC;EAAW,iBAAA;CH8W9C;AG7WmC;EAAW,iBAAA;CHgX9C;AG/WmC;EAAW,iBAAA;CHkX9C;AGjXmC;EAAW,iBAAA;CHoX9C;AGnXmC;EAAW,iBAAA;CHsX9C;AGrXmC;EAAW,iBAAA;CHwX9C;AGvXmC;EAAW,iBAAA;CH0X9C;AGzXmC;EAAW,iBAAA;CH4X9C;AG3XmC;EAAW,iBAAA;CH8X9C;AG7XmC;EAAW,iBAAA;CHgY9C;AG/XmC;EAAW,iBAAA;CHkY9C;AGjYmC;EAAW,iBAAA;CHoY9C;AGnYmC;EAAW,iBAAA;CHsY9C;AGrYmC;EAAW,iBAAA;CHwY9C;AGvYmC;EAAW,iBAAA;CH0Y9C;AGzYmC;EAAW,iBAAA;CH4Y9C;AG3YmC;EAAW,iBAAA;CH8Y9C;AG7YmC;EAAW,iBAAA;CHgZ9C;AG/YmC;EAAW,iBAAA;CHkZ9C;AGjZmC;EAAW,iBAAA;CHoZ9C;AGnZmC;EAAW,iBAAA;CHsZ9C;AGrZmC;EAAW,iBAAA;CHwZ9C;AGvZmC;EAAW,iBAAA;CH0Z9C;AGzZmC;EAAW,iBAAA;CH4Z9C;AG3ZmC;EAAW,iBAAA;CH8Z9C;AG7ZmC;EAAW,iBAAA;CHga9C;AG/ZmC;EAAW,iBAAA;CHka9C;AGjamC;EAAW,iBAAA;CHoa9C;AGnamC;EAAW,iBAAA;CHsa9C;AGramC;EAAW,iBAAA;CHwa9C;AGvamC;EAAW,iBAAA;CH0a9C;AGzamC;EAAW,iBAAA;CH4a9C;AG3amC;EAAW,iBAAA;CH8a9C;AG7amC;EAAW,iBAAA;CHgb9C;AG/amC;EAAW,iBAAA;CHkb9C;AGjbmC;EAAW,iBAAA;CHob9C;AGnbmC;EAAW,iBAAA;CHsb9C;AGrbmC;EAAW,iBAAA;CHwb9C;AGvbmC;EAAW,iBAAA;CH0b9C;AGzbmC;EAAW,iBAAA;CH4b9C;AG3bmC;EAAW,iBAAA;CH8b9C;AG7bmC;EAAW,iBAAA;CHgc9C;AG/bmC;EAAW,iBAAA;CHkc9C;AGjcmC;EAAW,iBAAA;CHoc9C;AGncmC;EAAW,iBAAA;CHsc9C;AGrcmC;EAAW,iBAAA;CHwc9C;AGvcmC;EAAW,iBAAA;CH0c9C;AGzcmC;EAAW,iBAAA;CH4c9C;AG3cmC;EAAW,iBAAA;CH8c9C;AG7cmC;EAAW,iBAAA;CHgd9C;AG/cmC;EAAW,iBAAA;CHkd9C;AGjdmC;EAAW,iBAAA;CHod9C;AGndmC;EAAW,iBAAA;CHsd9C;AGrdmC;EAAW,iBAAA;CHwd9C;AGvdmC;EAAW,iBAAA;CH0d9C;AGzdmC;EAAW,iBAAA;CH4d9C;AG3dmC;EAAW,iBAAA;CH8d9C;AG7dmC;EAAW,iBAAA;CHge9C;AG/dmC;EAAW,iBAAA;CHke9C;AGjemC;EAAW,iBAAA;CHoe9C;AGnemC;EAAW,iBAAA;CHse9C;AGremC;EAAW,iBAAA;CHwe9C;AGvemC;EAAW,iBAAA;CH0e9C;AGzemC;EAAW,iBAAA;CH4e9C;AG3emC;EAAW,iBAAA;CH8e9C;AG7emC;EAAW,iBAAA;CHgf9C;AG/emC;EAAW,iBAAA;CHkf9C;AGjfmC;EAAW,iBAAA;CHof9C;AGnfmC;EAAW,iBAAA;CHsf9C;AGrfmC;EAAW,iBAAA;CHwf9C;AGvfmC;EAAW,iBAAA;CH0f9C;AGzfmC;EAAW,iBAAA;CH4f9C;AG3fmC;EAAW,iBAAA;CH8f9C;AG7fmC;EAAW,iBAAA;CHggB9C;AG/fmC;EAAW,iBAAA;CHkgB9C;AGjgBmC;EAAW,iBAAA;CHogB9C;AGngBmC;EAAW,iBAAA;CHsgB9C;AGrgBmC;EAAW,iBAAA;CHwgB9C;AGvgBmC;EAAW,iBAAA;CH0gB9C;AGzgBmC;EAAW,iBAAA;CH4gB9C;AG3gBmC;EAAW,iBAAA;CH8gB9C;AG7gBmC;EAAW,iBAAA;CHghB9C;AG/gBmC;EAAW,iBAAA;CHkhB9C;AGjhBmC;EAAW,iBAAA;CHohB9C;AGnhBmC;EAAW,iBAAA;CHshB9C;AGrhBmC;EAAW,iBAAA;CHwhB9C;AGvhBmC;EAAW,iBAAA;CH0hB9C;AGzhBmC;EAAW,iBAAA;CH4hB9C;AG3hBmC;EAAW,iBAAA;CH8hB9C;AG7hBmC;EAAW,iBAAA;CHgiB9C;AG/hBmC;EAAW,iBAAA;CHkiB9C;AGjiBmC;EAAW,iBAAA;CHoiB9C;AGniBmC;EAAW,iBAAA;CHsiB9C;AGriBmC;EAAW,iBAAA;CHwiB9C;AGviBmC;EAAW,iBAAA;CH0iB9C;AGziBmC;EAAW,iBAAA;CH4iB9C;AG3iBmC;EAAW,iBAAA;CH8iB9C;AG7iBmC;EAAW,iBAAA;CHgjB9C;AG/iBmC;EAAW,iBAAA;CHkjB9C;AGjjBmC;EAAW,iBAAA;CHojB9C;AGnjBmC;EAAW,iBAAA;CHsjB9C;AGrjBmC;EAAW,iBAAA;CHwjB9C;AGvjBmC;EAAW,iBAAA;CH0jB9C;AGzjBmC;EAAW,iBAAA;CH4jB9C;AG3jBmC;EAAW,iBAAA;CH8jB9C;AG7jBmC;EAAW,iBAAA;CHgkB9C;AG/jBmC;EAAW,iBAAA;CHkkB9C;AGjkBmC;EAAW,iBAAA;CHokB9C;AGnkBmC;EAAW,iBAAA;CHskB9C;AGrkBmC;EAAW,iBAAA;CHwkB9C;AGvkBmC;EAAW,iBAAA;CH0kB9C;AGzkBmC;EAAW,iBAAA;CH4kB9C;AG3kBmC;EAAW,iBAAA;CH8kB9C;AG7kBmC;EAAW,iBAAA;CHglB9C;AG/kBmC;EAAW,iBAAA;CHklB9C;AGjlBmC;EAAW,iBAAA;CHolB9C;AGnlBmC;EAAW,iBAAA;CHslB9C;AGrlBmC;EAAW,iBAAA;CHwlB9C;AGvlBmC;EAAW,iBAAA;CH0lB9C;AGzlBmC;EAAW,iBAAA;CH4lB9C;AG3lBmC;EAAW,iBAAA;CH8lB9C;AG7lBmC;EAAW,iBAAA;CHgmB9C;AG/lBmC;EAAW,iBAAA;CHkmB9C;AGjmBmC;EAAW,iBAAA;CHomB9C;AGnmBmC;EAAW,iBAAA;CHsmB9C;AGrmBmC;EAAW,iBAAA;CHwmB9C;AGvmBmC;EAAW,iBAAA;CH0mB9C;AGzmBmC;EAAW,iBAAA;CH4mB9C;AG3mBmC;EAAW,iBAAA;CH8mB9C;AG7mBmC;EAAW,iBAAA;CHgnB9C;AG/mBmC;EAAW,iBAAA;CHknB9C;AGjnBmC;EAAW,iBAAA;CHonB9C;AGnnBmC;EAAW,iBAAA;CHsnB9C;AGrnBmC;EAAW,iBAAA;CHwnB9C;AGvnBmC;EAAW,iBAAA;CH0nB9C;AGznBmC;EAAW,iBAAA;CH4nB9C;AG3nBmC;EAAW,iBAAA;CH8nB9C;AG7nBmC;EAAW,iBAAA;CHgoB9C;AG/nBmC;EAAW,iBAAA;CHkoB9C;AGjoBmC;EAAW,iBAAA;CHooB9C;AGnoBmC;EAAW,iBAAA;CHsoB9C;AGroBmC;EAAW,iBAAA;CHwoB9C;AG/nBmC;EAAW,iBAAA;CHkoB9C;AGjoBmC;EAAW,iBAAA;CHooB9C;AGnoBmC;EAAW,iBAAA;CHsoB9C;AGroBmC;EAAW,iBAAA;CHwoB9C;AGvoBmC;EAAW,iBAAA;CH0oB9C;AGzoBmC;EAAW,iBAAA;CH4oB9C;AG3oBmC;EAAW,iBAAA;CH8oB9C;AG7oBmC;EAAW,iBAAA;CHgpB9C;AG/oBmC;EAAW,iBAAA;CHkpB9C;AGjpBmC;EAAW,iBAAA;CHopB9C;AGnpBmC;EAAW,iBAAA;CHspB9C;AGrpBmC;EAAW,iBAAA;CHwpB9C;AGvpBmC;EAAW,iBAAA;CH0pB9C;AGzpBmC;EAAW,iBAAA;CH4pB9C;AG3pBmC;EAAW,iBAAA;CH8pB9C;AG7pBmC;EAAW,iBAAA;CHgqB9C;AG/pBmC;EAAW,iBAAA;CHkqB9C;AGjqBmC;EAAW,iBAAA;CHoqB9C;AGnqBmC;EAAW,iBAAA;CHsqB9C;AGrqBmC;EAAW,iBAAA;CHwqB9C;AGvqBmC;EAAW,iBAAA;CH0qB9C;AGzqBmC;EAAW,iBAAA;CH4qB9C;AG3qBmC;EAAW,iBAAA;CH8qB9C;AG7qBmC;EAAW,iBAAA;CHgrB9C;AG/qBmC;EAAW,iBAAA;CHkrB9C;AGjrBmC;EAAW,iBAAA;CHorB9C;AGnrBmC;EAAW,iBAAA;CHsrB9C;AGrrBmC;EAAW,iBAAA;CHwrB9C;AGvrBmC;EAAW,iBAAA;CH0rB9C;AGzrBmC;EAAW,iBAAA;CH4rB9C;AG3rBmC;EAAW,iBAAA;CH8rB9C;AG7rBmC;EAAW,iBAAA;CHgsB9C;AG/rBmC;EAAW,iBAAA;CHksB9C;AGjsBmC;EAAW,iBAAA;CHosB9C;AGnsBmC;EAAW,iBAAA;CHssB9C;AGrsBmC;EAAW,iBAAA;CHwsB9C;AGvsBmC;EAAW,iBAAA;CH0sB9C;AGzsBmC;EAAW,iBAAA;CH4sB9C;AG3sBmC;EAAW,iBAAA;CH8sB9C;AG7sBmC;EAAW,iBAAA;CHgtB9C;AG/sBmC;EAAW,iBAAA;CHktB9C;AGjtBmC;EAAW,iBAAA;CHotB9C;AGntBmC;EAAW,iBAAA;CHstB9C;AGrtBmC;EAAW,iBAAA;CHwtB9C;AGvtBmC;EAAW,iBAAA;CH0tB9C;AGztBmC;EAAW,iBAAA;CH4tB9C;AG3tBmC;EAAW,iBAAA;CH8tB9C;AG7tBmC;EAAW,iBAAA;CHguB9C;AG/tBmC;EAAW,iBAAA;CHkuB9C;AGjuBmC;EAAW,iBAAA;CHouB9C;AGnuBmC;EAAW,iBAAA;CHsuB9C;AGruBmC;EAAW,iBAAA;CHwuB9C;AGvuBmC;EAAW,iBAAA;CH0uB9C;AGzuBmC;EAAW,iBAAA;CH4uB9C;AG3uBmC;EAAW,iBAAA;CH8uB9C;AG7uBmC;EAAW,iBAAA;CHgvB9C;AIthCD;ECgEE,+BAAA;EACG,4BAAA;EACK,uBAAA;CLy9BT;AIxhCD;;EC6DE,+BAAA;EACG,4BAAA;EACK,uBAAA;CL+9BT;AIthCD;EACE,gBAAA;EACA,8CAAA;CJwhCD;AIrhCD;EACE,4DAAA;EACA,gBAAA;EACA,wBAAA;EACA,eAAA;EACA,uBAAA;CJuhCD;AInhCD;;;;EAIE,qBAAA;EACA,mBAAA;EACA,qBAAA;CJqhCD;AI/gCD;EACE,eAAA;EACA,sBAAA;CJihCD;AI/gCC;;EAEE,eAAA;EACA,2BAAA;CJihCH;AI9gCC;EEnDA,2CAAA;EACA,qBAAA;CNokCD;AIvgCD;EACE,UAAA;CJygCD;AIngCD;EACE,uBAAA;CJqgCD;AIjgCD;;;;;EGvEE,eAAA;EACA,gBAAA;EACA,aAAA;CP+kCD;AIrgCD;EACE,mBAAA;CJugCD;AIjgCD;EACE,aAAA;EACA,wBAAA;EACA,uBAAA;EACA,uBAAA;EACA,mBAAA;EC6FA,yCAAA;EACK,oCAAA;EACG,iCAAA;EEvLR,sBAAA;EACA,gBAAA;EACA,aAAA;CP+lCD;AIjgCD;EACE,mBAAA;CJmgCD;AI7/BD;EACE,iBAAA;EACA,oBAAA;EACA,UAAA;EACA,8BAAA;CJ+/BD;AIv/BD;EACE,mBAAA;EACA,WAAA;EACA,YAAA;EACA,aAAA;EACA,WAAA;EACA,iBAAA;EACA,uBAAA;EACA,UAAA;CJy/BD;AIj/BC;;EAEE,iBAAA;EACA,YAAA;EACA,aAAA;EACA,UAAA;EACA,kBAAA;EACA,WAAA;CJm/BH;AIx+BD;EACE,gBAAA;CJ0+BD;AQjoCD;;;;;;;;;;;;EAEE,qBAAA;EACA,iBAAA;EACA,iBAAA;EACA,eAAA;CR6oCD;AQlpCD;;;;;;;;;;;;;;;;;;;;;;;;EASI,oBAAA;EACA,eAAA;EACA,eAAA;CRmqCH;AQ/pCD;;;;;;EAGE,iBAAA;EACA,oBAAA;CRoqCD;AQxqCD;;;;;;;;;;;;EAQI,eAAA;CR8qCH;AQ3qCD;;;;;;EAGE,iBAAA;EACA,oBAAA;CRgrCD;AQprCD;;;;;;;;;;;;EAQI,eAAA;CR0rCH;AQtrCD;;EAAU,gBAAA;CR0rCT;AQzrCD;;EAAU,gBAAA;CR6rCT;AQ5rCD;;EAAU,gBAAA;CRgsCT;AQ/rCD;;EAAU,gBAAA;CRmsCT;AQlsCD;;EAAU,gBAAA;CRssCT;AQrsCD;;EAAU,gBAAA;CRysCT;AQnsCD;EACE,iBAAA;CRqsCD;AQlsCD;EACE,oBAAA;EACA,gBAAA;EACA,iBAAA;EACA,iBAAA;CRosCD;AQ/rCD;EAwOA;IA1OI,gBAAA;GRqsCD;CACF;AQ7rCD;;EAEE,eAAA;CR+rCD;AQ5rCD;;EAEE,0BAAA;EACA,cAAA;CR8rCD;AQ1rCD;EAAuB,iBAAA;CR6rCtB;AQ5rCD;EAAuB,kBAAA;CR+rCtB;AQ9rCD;EAAuB,mBAAA;CRisCtB;AQhsCD;EAAuB,oBAAA;CRmsCtB;AQlsCD;EAAuB,oBAAA;CRqsCtB;AQlsCD;EAAuB,0BAAA;CRqsCtB;AQpsCD;EAAuB,0BAAA;CRusCtB;AQtsCD;EAAuB,2BAAA;CRysCtB;AQtsCD;EACE,eAAA;CRwsCD;AQtsCD;ECrGE,eAAA;CT8yCD;AS7yCC;;EAEE,eAAA;CT+yCH;AQ1sCD;ECxGE,eAAA;CTqzCD;ASpzCC;;EAEE,eAAA;CTszCH;AQ9sCD;EC3GE,eAAA;CT4zCD;AS3zCC;;EAEE,eAAA;CT6zCH;AQltCD;EC9GE,eAAA;CTm0CD;ASl0CC;;EAEE,eAAA;CTo0CH;AQttCD;ECjHE,eAAA;CT00CD;ASz0CC;;EAEE,eAAA;CT20CH;AQttCD;EAGE,YAAA;EE3HA,0BAAA;CVk1CD;AUj1CC;;EAEE,0BAAA;CVm1CH;AQxtCD;EE9HE,0BAAA;CVy1CD;AUx1CC;;EAEE,0BAAA;CV01CH;AQ5tCD;EEjIE,0BAAA;CVg2CD;AU/1CC;;EAEE,0BAAA;CVi2CH;AQhuCD;EEpIE,0BAAA;CVu2CD;AUt2CC;;EAEE,0BAAA;CVw2CH;AQpuCD;EEvIE,0BAAA;CV82CD;AU72CC;;EAEE,0BAAA;CV+2CH;AQnuCD;EACE,oBAAA;EACA,oBAAA;EACA,iCAAA;CRquCD;AQ7tCD;;EAEE,cAAA;EACA,oBAAA;CR+tCD;AQluCD;;;;EAMI,iBAAA;CRkuCH;AQ3tCD;EACE,gBAAA;EACA,iBAAA;CR6tCD;AQztCD;EALE,gBAAA;EACA,iBAAA;EAMA,kBAAA;CR4tCD;AQ9tCD;EAKI,sBAAA;EACA,kBAAA;EACA,mBAAA;CR4tCH;AQvtCD;EACE,cAAA;EACA,oBAAA;CRytCD;AQvtCD;;EAEE,wBAAA;CRytCD;AQvtCD;EACE,kBAAA;CRytCD;AQvtCD;EACE,eAAA;CRytCD;AQhsCD;EA6EA;IAvFM,YAAA;IACA,aAAA;IACA,YAAA;IACA,kBAAA;IGtNJ,iBAAA;IACA,wBAAA;IACA,oBAAA;GXq6CC;EQ7nCH;IAhFM,mBAAA;GRgtCH;CACF;AQvsCD;;EAGE,aAAA;EACA,kCAAA;CRwsCD;AQtsCD;EACE,eAAA;EA9IqB,0BAAA;CRu1CtB;AQpsCD;EACE,mBAAA;EACA,iBAAA;EACA,kBAAA;EACA,+BAAA;CRssCD;AQjsCG;;;EACE,iBAAA;CRqsCL;AQ/sCD;;;EAmBI,eAAA;EACA,eAAA;EACA,wBAAA;EACA,eAAA;CRisCH;AQ/rCG;;;EACE,uBAAA;CRmsCL;AQ3rCD;;EAEE,oBAAA;EACA,gBAAA;EACA,gCAAA;EACA,eAAA;EACA,kBAAA;CR6rCD;AQvrCG;;;;;;EAAW,YAAA;CR+rCd;AQ9rCG;;;;;;EACE,uBAAA;CRqsCL;AQ/rCD;EACE,oBAAA;EACA,mBAAA;EACA,wBAAA;CRisCD;AYv+CD;;;;EAIE,+DAAA;CZy+CD;AYr+CD;EACE,iBAAA;EACA,eAAA;EACA,eAAA;EACA,0BAAA;EACA,mBAAA;CZu+CD;AYn+CD;EACE,iBAAA;EACA,eAAA;EACA,YAAA;EACA,uBAAA;EACA,mBAAA;EACA,uDAAA;UAAA,+CAAA;CZq+CD;AY3+CD;EASI,WAAA;EACA,gBAAA;EACA,kBAAA;EACA,yBAAA;UAAA,iBAAA;CZq+CH;AYh+CD;EACE,eAAA;EACA,eAAA;EACA,iBAAA;EACA,gBAAA;EACA,wBAAA;EACA,sBAAA;EACA,sBAAA;EACA,eAAA;EACA,0BAAA;EACA,uBAAA;EACA,mBAAA;CZk+CD;AY7+CD;EAeI,WAAA;EACA,mBAAA;EACA,eAAA;EACA,sBAAA;EACA,8BAAA;EACA,iBAAA;CZi+CH;AY59CD;EACE,kBAAA;EACA,mBAAA;CZ89CD;AaxhDD;ECHE,mBAAA;EACA,kBAAA;EACA,mBAAA;EACA,oBAAA;Cd8hDD;AaxhDC;EAqEF;IAvEI,aAAA;Gb8hDD;CACF;Aa1hDC;EAkEF;IApEI,aAAA;GbgiDD;CACF;Aa5hDD;EA+DA;IAjEI,cAAA;GbkiDD;CACF;AazhDD;ECvBE,mBAAA;EACA,kBAAA;EACA,mBAAA;EACA,oBAAA;CdmjDD;AathDD;ECvBE,mBAAA;EACA,oBAAA;CdgjDD;AehjDG;EACE,mBAAA;EAEA,gBAAA;EAEA,mBAAA;EACA,oBAAA;CfgjDL;AehiDG;EACE,YAAA;CfkiDL;Ae3hDC;EACE,YAAA;Cf6hDH;Ae9hDC;EACE,oBAAA;CfgiDH;AejiDC;EACE,oBAAA;CfmiDH;AepiDC;EACE,WAAA;CfsiDH;AeviDC;EACE,oBAAA;CfyiDH;Ae1iDC;EACE,oBAAA;Cf4iDH;Ae7iDC;EACE,WAAA;Cf+iDH;AehjDC;EACE,oBAAA;CfkjDH;AenjDC;EACE,oBAAA;CfqjDH;AetjDC;EACE,WAAA;CfwjDH;AezjDC;EACE,oBAAA;Cf2jDH;Ae5jDC;EACE,mBAAA;Cf8jDH;AehjDC;EACE,YAAA;CfkjDH;AenjDC;EACE,oBAAA;CfqjDH;AetjDC;EACE,oBAAA;CfwjDH;AezjDC;EACE,WAAA;Cf2jDH;Ae5jDC;EACE,oBAAA;Cf8jDH;Ae/jDC;EACE,oBAAA;CfikDH;AelkDC;EACE,WAAA;CfokDH;AerkDC;EACE,oBAAA;CfukDH;AexkDC;EACE,oBAAA;Cf0kDH;Ae3kDC;EACE,WAAA;Cf6kDH;Ae9kDC;EACE,oBAAA;CfglDH;AejlDC;EACE,mBAAA;CfmlDH;Ae/kDC;EACE,YAAA;CfilDH;AejmDC;EACE,WAAA;CfmmDH;AepmDC;EACE,mBAAA;CfsmDH;AevmDC;EACE,mBAAA;CfymDH;Ae1mDC;EACE,UAAA;Cf4mDH;Ae7mDC;EACE,mBAAA;Cf+mDH;AehnDC;EACE,mBAAA;CfknDH;AennDC;EACE,UAAA;CfqnDH;AetnDC;EACE,mBAAA;CfwnDH;AeznDC;EACE,mBAAA;Cf2nDH;Ae5nDC;EACE,UAAA;Cf8nDH;Ae/nDC;EACE,mBAAA;CfioDH;AeloDC;EACE,kBAAA;CfooDH;AehoDC;EACE,WAAA;CfkoDH;AepnDC;EACE,kBAAA;CfsnDH;AevnDC;EACE,0BAAA;CfynDH;Ae1nDC;EACE,0BAAA;Cf4nDH;Ae7nDC;EACE,iBAAA;Cf+nDH;AehoDC;EACE,0BAAA;CfkoDH;AenoDC;EACE,0BAAA;CfqoDH;AetoDC;EACE,iBAAA;CfwoDH;AezoDC;EACE,0BAAA;Cf2oDH;Ae5oDC;EACE,0BAAA;Cf8oDH;Ae/oDC;EACE,iBAAA;CfipDH;AelpDC;EACE,0BAAA;CfopDH;AerpDC;EACE,yBAAA;CfupDH;AexpDC;EACE,gBAAA;Cf0pDH;Aa1pDD;EElCI;IACE,YAAA;Gf+rDH;EexrDD;IACE,YAAA;Gf0rDD;Ee3rDD;IACE,oBAAA;Gf6rDD;Ee9rDD;IACE,oBAAA;GfgsDD;EejsDD;IACE,WAAA;GfmsDD;EepsDD;IACE,oBAAA;GfssDD;EevsDD;IACE,oBAAA;GfysDD;Ee1sDD;IACE,WAAA;Gf4sDD;Ee7sDD;IACE,oBAAA;Gf+sDD;EehtDD;IACE,oBAAA;GfktDD;EentDD;IACE,WAAA;GfqtDD;EettDD;IACE,oBAAA;GfwtDD;EeztDD;IACE,mBAAA;Gf2tDD;Ee7sDD;IACE,YAAA;Gf+sDD;EehtDD;IACE,oBAAA;GfktDD;EentDD;IACE,oBAAA;GfqtDD;EettDD;IACE,WAAA;GfwtDD;EeztDD;IACE,oBAAA;Gf2tDD;Ee5tDD;IACE,oBAAA;Gf8tDD;Ee/tDD;IACE,WAAA;GfiuDD;EeluDD;IACE,oBAAA;GfouDD;EeruDD;IACE,oBAAA;GfuuDD;EexuDD;IACE,WAAA;Gf0uDD;Ee3uDD;IACE,oBAAA;Gf6uDD;Ee9uDD;IACE,mBAAA;GfgvDD;Ee5uDD;IACE,YAAA;Gf8uDD;Ee9vDD;IACE,WAAA;GfgwDD;EejwDD;IACE,mBAAA;GfmwDD;EepwDD;IACE,mBAAA;GfswDD;EevwDD;IACE,UAAA;GfywDD;Ee1wDD;IACE,mBAAA;Gf4wDD;Ee7wDD;IACE,mBAAA;Gf+wDD;EehxDD;IACE,UAAA;GfkxDD;EenxDD;IACE,mBAAA;GfqxDD;EetxDD;IACE,mBAAA;GfwxDD;EezxDD;IACE,UAAA;Gf2xDD;Ee5xDD;IACE,mBAAA;Gf8xDD;Ee/xDD;IACE,kBAAA;GfiyDD;Ee7xDD;IACE,WAAA;Gf+xDD;EejxDD;IACE,kBAAA;GfmxDD;EepxDD;IACE,0BAAA;GfsxDD;EevxDD;IACE,0BAAA;GfyxDD;Ee1xDD;IACE,iBAAA;Gf4xDD;Ee7xDD;IACE,0BAAA;Gf+xDD;EehyDD;IACE,0BAAA;GfkyDD;EenyDD;IACE,iBAAA;GfqyDD;EetyDD;IACE,0BAAA;GfwyDD;EezyDD;IACE,0BAAA;Gf2yDD;Ee5yDD;IACE,iBAAA;Gf8yDD;Ee/yDD;IACE,0BAAA;GfizDD;EelzDD;IACE,yBAAA;GfozDD;EerzDD;IACE,gBAAA;GfuzDD;CACF;Aa/yDD;EE3CI;IACE,YAAA;Gf61DH;Eet1DD;IACE,YAAA;Gfw1DD;Eez1DD;IACE,oBAAA;Gf21DD;Ee51DD;IACE,oBAAA;Gf81DD;Ee/1DD;IACE,WAAA;Gfi2DD;Eel2DD;IACE,oBAAA;Gfo2DD;Eer2DD;IACE,oBAAA;Gfu2DD;Eex2DD;IACE,WAAA;Gf02DD;Ee32DD;IACE,oBAAA;Gf62DD;Ee92DD;IACE,oBAAA;Gfg3DD;Eej3DD;IACE,WAAA;Gfm3DD;Eep3DD;IACE,oBAAA;Gfs3DD;Eev3DD;IACE,mBAAA;Gfy3DD;Ee32DD;IACE,YAAA;Gf62DD;Ee92DD;IACE,oBAAA;Gfg3DD;Eej3DD;IACE,oBAAA;Gfm3DD;Eep3DD;IACE,WAAA;Gfs3DD;Eev3DD;IACE,oBAAA;Gfy3DD;Ee13DD;IACE,oBAAA;Gf43DD;Ee73DD;IACE,WAAA;Gf+3DD;Eeh4DD;IACE,oBAAA;Gfk4DD;Een4DD;IACE,oBAAA;Gfq4DD;Eet4DD;IACE,WAAA;Gfw4DD;Eez4DD;IACE,oBAAA;Gf24DD;Ee54DD;IACE,mBAAA;Gf84DD;Ee14DD;IACE,YAAA;Gf44DD;Ee55DD;IACE,WAAA;Gf85DD;Ee/5DD;IACE,mBAAA;Gfi6DD;Eel6DD;IACE,mBAAA;Gfo6DD;Eer6DD;IACE,UAAA;Gfu6DD;Eex6DD;IACE,mBAAA;Gf06DD;Ee36DD;IACE,mBAAA;Gf66DD;Ee96DD;IACE,UAAA;Gfg7DD;Eej7DD;IACE,mBAAA;Gfm7DD;Eep7DD;IACE,mBAAA;Gfs7DD;Eev7DD;IACE,UAAA;Gfy7DD;Ee17DD;IACE,mBAAA;Gf47DD;Ee77DD;IACE,kBAAA;Gf+7DD;Ee37DD;IACE,WAAA;Gf67DD;Ee/6DD;IACE,kBAAA;Gfi7DD;Eel7DD;IACE,0BAAA;Gfo7DD;Eer7DD;IACE,0BAAA;Gfu7DD;Eex7DD;IACE,iBAAA;Gf07DD;Ee37DD;IACE,0BAAA;Gf67DD;Ee97DD;IACE,0BAAA;Gfg8DD;Eej8DD;IACE,iBAAA;Gfm8DD;Eep8DD;IACE,0BAAA;Gfs8DD;Eev8DD;IACE,0BAAA;Gfy8DD;Ee18DD;IACE,iBAAA;Gf48DD;Ee78DD;IACE,0BAAA;Gf+8DD;Eeh9DD;IACE,yBAAA;Gfk9DD;Een9DD;IACE,gBAAA;Gfq9DD;CACF;Aa18DD;EE9CI;IACE,YAAA;Gf2/DH;Eep/DD;IACE,YAAA;Gfs/DD;Eev/DD;IACE,oBAAA;Gfy/DD;Ee1/DD;IACE,oBAAA;Gf4/DD;Ee7/DD;IACE,WAAA;Gf+/DD;EehgED;IACE,oBAAA;GfkgED;EengED;IACE,oBAAA;GfqgED;EetgED;IACE,WAAA;GfwgED;EezgED;IACE,oBAAA;Gf2gED;Ee5gED;IACE,oBAAA;Gf8gED;Ee/gED;IACE,WAAA;GfihED;EelhED;IACE,oBAAA;GfohED;EerhED;IACE,mBAAA;GfuhED;EezgED;IACE,YAAA;Gf2gED;Ee5gED;IACE,oBAAA;Gf8gED;Ee/gED;IACE,oBAAA;GfihED;EelhED;IACE,WAAA;GfohED;EerhED;IACE,oBAAA;GfuhED;EexhED;IACE,oBAAA;Gf0hED;Ee3hED;IACE,WAAA;Gf6hED;Ee9hED;IACE,oBAAA;GfgiED;EejiED;IACE,oBAAA;GfmiED;EepiED;IACE,WAAA;GfsiED;EeviED;IACE,oBAAA;GfyiED;Ee1iED;IACE,mBAAA;Gf4iED;EexiED;IACE,YAAA;Gf0iED;Ee1jED;IACE,WAAA;Gf4jED;Ee7jED;IACE,mBAAA;Gf+jED;EehkED;IACE,mBAAA;GfkkED;EenkED;IACE,UAAA;GfqkED;EetkED;IACE,mBAAA;GfwkED;EezkED;IACE,mBAAA;Gf2kED;Ee5kED;IACE,UAAA;Gf8kED;Ee/kED;IACE,mBAAA;GfilED;EellED;IACE,mBAAA;GfolED;EerlED;IACE,UAAA;GfulED;EexlED;IACE,mBAAA;Gf0lED;Ee3lED;IACE,kBAAA;Gf6lED;EezlED;IACE,WAAA;Gf2lED;Ee7kED;IACE,kBAAA;Gf+kED;EehlED;IACE,0BAAA;GfklED;EenlED;IACE,0BAAA;GfqlED;EetlED;IACE,iBAAA;GfwlED;EezlED;IACE,0BAAA;Gf2lED;Ee5lED;IACE,0BAAA;Gf8lED;Ee/lED;IACE,iBAAA;GfimED;EelmED;IACE,0BAAA;GfomED;EermED;IACE,0BAAA;GfumED;EexmED;IACE,iBAAA;Gf0mED;Ee3mED;IACE,0BAAA;Gf6mED;Ee9mED;IACE,yBAAA;GfgnED;EejnED;IACE,gBAAA;GfmnED;CACF;AgBvrED;EACE,8BAAA;ChByrED;AgBvrED;EACE,iBAAA;EACA,oBAAA;EACA,eAAA;EACA,iBAAA;ChByrED;AgBvrED;EACE,iBAAA;ChByrED;AgBnrED;EACE,YAAA;EACA,gBAAA;EACA,oBAAA;ChBqrED;AgBxrED;;;;;;EAWQ,aAAA;EACA,wBAAA;EACA,oBAAA;EACA,2BAAA;ChBqrEP;AgBnsED;EAoBI,uBAAA;EACA,8BAAA;ChBkrEH;AgBvsED;;;;;;EA8BQ,cAAA;ChBirEP;AgB/sED;EAoCI,2BAAA;ChB8qEH;AgBltED;EAyCI,uBAAA;ChB4qEH;AgBrqED;;;;;;EAOQ,aAAA;ChBsqEP;AgB3pED;EACE,uBAAA;ChB6pED;AgB9pED;;;;;;EAQQ,uBAAA;ChB8pEP;AgBtqED;;EAeM,yBAAA;ChB2pEL;AgBjpED;EAEI,0BAAA;ChBkpEH;AgBzoED;EAEI,0BAAA;ChB0oEH;AgBjoED;EACE,iBAAA;EACA,YAAA;EACA,sBAAA;ChBmoED;AgB9nEG;;EACE,iBAAA;EACA,YAAA;EACA,oBAAA;ChBioEL;AiB7wEC;;;;;;;;;;;;EAOI,0BAAA;CjBoxEL;AiB9wEC;;;;;EAMI,0BAAA;CjB+wEL;AiBlyEC;;;;;;;;;;;;EAOI,0BAAA;CjByyEL;AiBnyEC;;;;;EAMI,0BAAA;CjBoyEL;AiBvzEC;;;;;;;;;;;;EAOI,0BAAA;CjB8zEL;AiBxzEC;;;;;EAMI,0BAAA;CjByzEL;AiB50EC;;;;;;;;;;;;EAOI,0BAAA;CjBm1EL;AiB70EC;;;;;EAMI,0BAAA;CjB80EL;AiBj2EC;;;;;;;;;;;;EAOI,0BAAA;CjBw2EL;AiBl2EC;;;;;EAMI,0BAAA;CjBm2EL;AgBjtED;EACE,iBAAA;EACA,kBAAA;ChBmtED;AgBtpED;EACA;IA3DI,YAAA;IACA,oBAAA;IACA,mBAAA;IACA,6CAAA;IACA,uBAAA;GhBotED;EgB7pEH;IAnDM,iBAAA;GhBmtEH;EgBhqEH;;;;;;IA1CY,oBAAA;GhBktET;EgBxqEH;IAlCM,UAAA;GhB6sEH;EgB3qEH;;;;;;IAzBY,eAAA;GhB4sET;EgBnrEH;;;;;;IArBY,gBAAA;GhBgtET;EgB3rEH;;;;IARY,iBAAA;GhBysET;CACF;AkBn6ED;EACE,WAAA;EACA,UAAA;EACA,UAAA;EAIA,aAAA;ClBk6ED;AkB/5ED;EACE,eAAA;EACA,YAAA;EACA,WAAA;EACA,oBAAA;EACA,gBAAA;EACA,qBAAA;EACA,eAAA;EACA,UAAA;EACA,iCAAA;ClBi6ED;AkB95ED;EACE,sBAAA;EACA,gBAAA;EACA,mBAAA;EACA,kBAAA;ClBg6ED;AkBr5ED;Eb4BE,+BAAA;EACG,4BAAA;EACK,uBAAA;CL43ET;AkBr5ED;;EAEE,gBAAA;EACA,mBAAA;EACA,oBAAA;ClBu5ED;AkBp5ED;EACE,eAAA;ClBs5ED;AkBl5ED;EACE,eAAA;EACA,YAAA;ClBo5ED;AkBh5ED;;EAEE,aAAA;ClBk5ED;AkB94ED;;;EZrEE,2CAAA;EACA,qBAAA;CNw9ED;AkB74ED;EACE,eAAA;EACA,iBAAA;EACA,gBAAA;EACA,wBAAA;EACA,eAAA;ClB+4ED;AkBr3ED;EACE,eAAA;EACA,YAAA;EACA,aAAA;EACA,kBAAA;EACA,gBAAA;EACA,wBAAA;EACA,eAAA;EACA,uBAAA;EACA,uBAAA;EACA,uBAAA;EACA,mBAAA;EbxDA,yDAAA;EACQ,iDAAA;EAyHR,uFAAA;EACK,0EAAA;EACG,uEAAA;CLwzET;AmBh8EC;EACE,sBAAA;EACA,WAAA;EdUF,uFAAA;EACQ,+EAAA;CLy7ET;AKx5EC;EACE,YAAA;EACA,WAAA;CL05EH;AKx5EC;EAA0B,YAAA;CL25E3B;AK15EC;EAAgC,YAAA;CL65EjC;AkBj4EC;EACE,UAAA;EACA,8BAAA;ClBm4EH;AkB33EC;;;EAGE,0BAAA;EACA,WAAA;ClB63EH;AkB13EC;;EAEE,oBAAA;ClB43EH;AkBx3EC;EACE,aAAA;ClB03EH;AkB92ED;EACE,yBAAA;ClBg3ED;AkBx0ED;EAtBI;;;;IACE,kBAAA;GlBo2EH;EkBj2EC;;;;;;;;IAEE,kBAAA;GlBy2EH;EkBt2EC;;;;;;;;IAEE,kBAAA;GlB82EH;CACF;AkBp2ED;EACE,oBAAA;ClBs2ED;AkB91ED;;EAEE,mBAAA;EACA,eAAA;EACA,iBAAA;EACA,oBAAA;ClBg2ED;AkBr2ED;;EAQI,iBAAA;EACA,mBAAA;EACA,iBAAA;EACA,oBAAA;EACA,gBAAA;ClBi2EH;AkB91ED;;;;EAIE,mBAAA;EACA,mBAAA;EACA,mBAAA;ClBg2ED;AkB71ED;;EAEE,iBAAA;ClB+1ED;AkB31ED;;EAEE,mBAAA;EACA,sBAAA;EACA,mBAAA;EACA,iBAAA;EACA,uBAAA;EACA,oBAAA;EACA,gBAAA;ClB61ED;AkB31ED;;EAEE,cAAA;EACA,kBAAA;ClB61ED;AkBp1EC;;;;;;EAGE,oBAAA;ClBy1EH;AkBn1EC;;;;EAEE,oBAAA;ClBu1EH;AkBj1EC;;;;EAGI,oBAAA;ClBo1EL;AkBz0ED;EAEE,iBAAA;EACA,oBAAA;EAEA,iBAAA;EACA,iBAAA;ClBy0ED;AkBv0EC;;EAEE,gBAAA;EACA,iBAAA;ClBy0EH;AkB5zED;ECnQE,aAAA;EACA,kBAAA;EACA,gBAAA;EACA,iBAAA;EACA,mBAAA;CnBkkFD;AmBhkFC;EACE,aAAA;EACA,kBAAA;CnBkkFH;AmB/jFC;;EAEE,aAAA;CnBikFH;AkBx0ED;EAEI,aAAA;EACA,kBAAA;EACA,gBAAA;EACA,iBAAA;EACA,mBAAA;ClBy0EH;AkB/0ED;EASI,aAAA;EACA,kBAAA;ClBy0EH;AkBn1ED;;EAcI,aAAA;ClBy0EH;AkBv1ED;EAiBI,aAAA;EACA,iBAAA;EACA,kBAAA;EACA,gBAAA;EACA,iBAAA;ClBy0EH;AkBr0ED;EC/RE,aAAA;EACA,mBAAA;EACA,gBAAA;EACA,uBAAA;EACA,mBAAA;CnBumFD;AmBrmFC;EACE,aAAA;EACA,kBAAA;CnBumFH;AmBpmFC;;EAEE,aAAA;CnBsmFH;AkBj1ED;EAEI,aAAA;EACA,mBAAA;EACA,gBAAA;EACA,uBAAA;EACA,mBAAA;ClBk1EH;AkBx1ED;EASI,aAAA;EACA,kBAAA;ClBk1EH;AkB51ED;;EAcI,aAAA;ClBk1EH;AkBh2ED;EAiBI,aAAA;EACA,iBAAA;EACA,mBAAA;EACA,gBAAA;EACA,uBAAA;ClBk1EH;AkBz0ED;EAEE,mBAAA;ClB00ED;AkB50ED;EAMI,sBAAA;ClBy0EH;AkBr0ED;EACE,mBAAA;EACA,OAAA;EACA,SAAA;EACA,WAAA;EACA,eAAA;EACA,YAAA;EACA,aAAA;EACA,kBAAA;EACA,mBAAA;EACA,qBAAA;ClBu0ED;AkBr0ED;;;EAGE,YAAA;EACA,aAAA;EACA,kBAAA;ClBu0ED;AkBr0ED;;;EAGE,YAAA;EACA,aAAA;EACA,kBAAA;ClBu0ED;AkBn0ED;;;;;;;;;;EC1ZI,eAAA;CnByuFH;AkB/0ED;ECtZI,sBAAA;Ed+CF,yDAAA;EACQ,iDAAA;CL0rFT;AmBxuFG;EACE,sBAAA;Ed4CJ,0EAAA;EACQ,kEAAA;CL+rFT;AkBz1ED;EC5YI,eAAA;EACA,sBAAA;EACA,0BAAA;CnBwuFH;AkB91ED;ECtYI,eAAA;CnBuuFH;AkB91ED;;;;;;;;;;EC7ZI,eAAA;CnBuwFH;AkB12ED;ECzZI,sBAAA;Ed+CF,yDAAA;EACQ,iDAAA;CLwtFT;AmBtwFG;EACE,sBAAA;Ed4CJ,0EAAA;EACQ,kEAAA;CL6tFT;AkBp3ED;EC/YI,eAAA;EACA,sBAAA;EACA,0BAAA;CnBswFH;AkBz3ED;ECzYI,eAAA;CnBqwFH;AkBz3ED;;;;;;;;;;EChaI,eAAA;CnBqyFH;AkBr4ED;EC5ZI,sBAAA;Ed+CF,yDAAA;EACQ,iDAAA;CLsvFT;AmBpyFG;EACE,sBAAA;Ed4CJ,0EAAA;EACQ,kEAAA;CL2vFT;AkB/4ED;EClZI,eAAA;EACA,sBAAA;EACA,0BAAA;CnBoyFH;AkBp5ED;EC5YI,eAAA;CnBmyFH;AkBh5EC;EACE,UAAA;ClBk5EH;AkBh5EC;EACE,OAAA;ClBk5EH;AkBx4ED;EACE,eAAA;EACA,gBAAA;EACA,oBAAA;EACA,eAAA;ClB04ED;AkBvzED;EAwEA;IAtIM,sBAAA;IACA,iBAAA;IACA,uBAAA;GlBy3EH;EkBrvEH;IA/HM,sBAAA;IACA,YAAA;IACA,uBAAA;GlBu3EH;EkB1vEH;IAxHM,sBAAA;GlBq3EH;EkB7vEH;IApHM,sBAAA;IACA,uBAAA;GlBo3EH;EkBjwEH;;;IA9GQ,YAAA;GlBo3EL;EkBtwEH;IAxGM,YAAA;GlBi3EH;EkBzwEH;IApGM,iBAAA;IACA,uBAAA;GlBg3EH;EkB7wEH;;IA5FM,sBAAA;IACA,cAAA;IACA,iBAAA;IACA,uBAAA;GlB62EH;EkBpxEH;;IAtFQ,gBAAA;GlB82EL;EkBxxEH;;IAjFM,mBAAA;IACA,eAAA;GlB62EH;EkB7xEH;IA3EM,OAAA;GlB22EH;CACF;AkBj2ED;;;;EASI,cAAA;EACA,iBAAA;EACA,iBAAA;ClB81EH;AkBz2ED;;EAiBI,iBAAA;ClB41EH;AkB72ED;EJthBE,mBAAA;EACA,oBAAA;Cds4FD;AkB10EC;EAyBF;IAnCM,kBAAA;IACA,iBAAA;IACA,iBAAA;GlBw1EH;CACF;AkBx3ED;EAwCI,YAAA;ClBm1EH;AkBr0EC;EAUF;IAdQ,kBAAA;IACA,gBAAA;GlB60EL;CACF;AkBn0EC;EAEF;IANQ,iBAAA;IACA,gBAAA;GlB20EL;CACF;AoBp6FD;EACE,sBAAA;EACA,iBAAA;EACA,oBAAA;EACA,mBAAA;EACA,uBAAA;EACA,+BAAA;MAAA,2BAAA;EACA,gBAAA;EACA,uBAAA;EACA,8BAAA;EACA,oBAAA;EC0CA,kBAAA;EACA,gBAAA;EACA,wBAAA;EACA,mBAAA;EhB+JA,0BAAA;EACG,uBAAA;EACC,sBAAA;EACI,kBAAA;CL+tFT;AoBv6FG;;;;;;EdnBF,2CAAA;EACA,qBAAA;CNk8FD;AoB16FC;;;EAGE,YAAA;EACA,sBAAA;CpB46FH;AoBz6FC;;EAEE,WAAA;EACA,uBAAA;Ef2BF,yDAAA;EACQ,iDAAA;CLi5FT;AoBz6FC;;;EAGE,oBAAA;EE7CF,cAAA;EAGA,0BAAA;EjB8DA,yBAAA;EACQ,iBAAA;CL05FT;AoBz6FG;;EAEE,qBAAA;CpB26FL;AoBl6FD;EC3DE,YAAA;EACA,uBAAA;EACA,mBAAA;CrBg+FD;AqB99FC;;EAEE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBg+FP;AqB99FC;EACE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBg+FP;AqB99FC;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBg+FP;AqB99FG;;;;;;;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBs+FT;AqBn+FC;;;EAGE,uBAAA;CrBq+FH;AqBh+FG;;;;;;;;;EAGE,uBAAA;EACI,mBAAA;CrBw+FT;AoBv9FD;ECZI,YAAA;EACA,uBAAA;CrBs+FH;AoBx9FD;EC9DE,YAAA;EACA,0BAAA;EACA,sBAAA;CrByhGD;AqBvhGC;;EAEE,YAAA;EACA,0BAAA;EACI,sBAAA;CrByhGP;AqBvhGC;EACE,YAAA;EACA,0BAAA;EACI,sBAAA;CrByhGP;AqBvhGC;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrByhGP;AqBvhGG;;;;;;;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrB+hGT;AqB5hGC;;;EAGE,uBAAA;CrB8hGH;AqBzhGG;;;;;;;;;EAGE,0BAAA;EACI,sBAAA;CrBiiGT;AoB7gGD;ECfI,eAAA;EACA,uBAAA;CrB+hGH;AoB7gGD;EClEE,YAAA;EACA,0BAAA;EACA,sBAAA;CrBklGD;AqBhlGC;;EAEE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBklGP;AqBhlGC;EACE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBklGP;AqBhlGC;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBklGP;AqBhlGG;;;;;;;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBwlGT;AqBrlGC;;;EAGE,uBAAA;CrBulGH;AqBllGG;;;;;;;;;EAGE,0BAAA;EACI,sBAAA;CrB0lGT;AoBlkGD;ECnBI,eAAA;EACA,uBAAA;CrBwlGH;AoBlkGD;ECtEE,YAAA;EACA,0BAAA;EACA,sBAAA;CrB2oGD;AqBzoGC;;EAEE,YAAA;EACA,0BAAA;EACI,sBAAA;CrB2oGP;AqBzoGC;EACE,YAAA;EACA,0BAAA;EACI,sBAAA;CrB2oGP;AqBzoGC;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrB2oGP;AqBzoGG;;;;;;;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBipGT;AqB9oGC;;;EAGE,uBAAA;CrBgpGH;AqB3oGG;;;;;;;;;EAGE,0BAAA;EACI,sBAAA;CrBmpGT;AoBvnGD;ECvBI,eAAA;EACA,uBAAA;CrBipGH;AoBvnGD;EC1EE,YAAA;EACA,0BAAA;EACA,sBAAA;CrBosGD;AqBlsGC;;EAEE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBosGP;AqBlsGC;EACE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBosGP;AqBlsGC;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBosGP;AqBlsGG;;;;;;;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrB0sGT;AqBvsGC;;;EAGE,uBAAA;CrBysGH;AqBpsGG;;;;;;;;;EAGE,0BAAA;EACI,sBAAA;CrB4sGT;AoB5qGD;EC3BI,eAAA;EACA,uBAAA;CrB0sGH;AoB5qGD;EC9EE,YAAA;EACA,0BAAA;EACA,sBAAA;CrB6vGD;AqB3vGC;;EAEE,YAAA;EACA,0BAAA;EACI,sBAAA;CrB6vGP;AqB3vGC;EACE,YAAA;EACA,0BAAA;EACI,sBAAA;CrB6vGP;AqB3vGC;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrB6vGP;AqB3vGG;;;;;;;;;EAGE,YAAA;EACA,0BAAA;EACI,sBAAA;CrBmwGT;AqBhwGC;;;EAGE,uBAAA;CrBkwGH;AqB7vGG;;;;;;;;;EAGE,0BAAA;EACI,sBAAA;CrBqwGT;AoBjuGD;EC/BI,eAAA;EACA,uBAAA;CrBmwGH;AoB5tGD;EACE,eAAA;EACA,oBAAA;EACA,iBAAA;CpB8tGD;AoB5tGC;;;;;EAKE,8BAAA;EfnCF,yBAAA;EACQ,iBAAA;CLkwGT;AoB7tGC;;;;EAIE,0BAAA;CpB+tGH;AoB7tGC;;EAEE,eAAA;EACA,2BAAA;EACA,8BAAA;CpB+tGH;AoB3tGG;;;;EAEE,eAAA;EACA,sBAAA;CpB+tGL;AoBttGD;;ECxEE,mBAAA;EACA,gBAAA;EACA,uBAAA;EACA,mBAAA;CrBkyGD;AoBztGD;;EC5EE,kBAAA;EACA,gBAAA;EACA,iBAAA;EACA,mBAAA;CrByyGD;AoB5tGD;;EChFE,iBAAA;EACA,gBAAA;EACA,iBAAA;EACA,mBAAA;CrBgzGD;AoB3tGD;EACE,eAAA;EACA,YAAA;CpB6tGD;AoBztGD;EACE,gBAAA;CpB2tGD;AoBptGC;;;EACE,YAAA;CpBwtGH;AuBl3GD;EACE,WAAA;ElBoLA,yCAAA;EACK,oCAAA;EACG,iCAAA;CLisGT;AuBr3GC;EACE,WAAA;CvBu3GH;AuBn3GD;EACE,cAAA;CvBq3GD;AuBn3GC;EAAY,eAAA;CvBs3Gb;AuBr3GC;EAAY,mBAAA;CvBw3Gb;AuBv3GC;EAAY,yBAAA;CvB03Gb;AuBv3GD;EACE,mBAAA;EACA,UAAA;EACA,iBAAA;ElBuKA,gDAAA;EACQ,2CAAA;KAAA,wCAAA;EAOR,mCAAA;EACQ,8BAAA;KAAA,2BAAA;EAGR,yCAAA;EACQ,oCAAA;KAAA,iCAAA;CL2sGT;AwBr5GD;EACE,sBAAA;EACA,SAAA;EACA,UAAA;EACA,iBAAA;EACA,uBAAA;EACA,uBAAA;EACA,yBAAA;EACA,oCAAA;EACA,mCAAA;CxBu5GD;AwBn5GD;;EAEE,mBAAA;CxBq5GD;AwBj5GD;EACE,WAAA;CxBm5GD;AwB/4GD;EACE,mBAAA;EACA,UAAA;EACA,QAAA;EACA,cAAA;EACA,cAAA;EACA,YAAA;EACA,iBAAA;EACA,eAAA;EACA,gBAAA;EACA,iBAAA;EACA,gBAAA;EACA,iBAAA;EACA,uBAAA;EACA,uBAAA;EACA,sCAAA;EACA,mBAAA;EnBsBA,oDAAA;EACQ,4CAAA;EmBrBR,qCAAA;UAAA,6BAAA;CxBk5GD;AwB74GC;EACE,SAAA;EACA,WAAA;CxB+4GH;AwBx6GD;ECzBE,YAAA;EACA,cAAA;EACA,iBAAA;EACA,0BAAA;CzBo8GD;AwB96GD;EAmCI,eAAA;EACA,kBAAA;EACA,YAAA;EACA,oBAAA;EACA,wBAAA;EACA,eAAA;EACA,oBAAA;CxB84GH;AwBx4GC;;EAEE,sBAAA;EACA,eAAA;EACA,0BAAA;CxB04GH;AwBp4GC;;;EAGE,YAAA;EACA,sBAAA;EACA,WAAA;EACA,0BAAA;CxBs4GH;AwB73GC;;;EAGE,eAAA;CxB+3GH;AwB33GC;;EAEE,sBAAA;EACA,8BAAA;EACA,uBAAA;EE3GF,oEAAA;EF6GE,oBAAA;CxB63GH;AwBx3GD;EAGI,eAAA;CxBw3GH;AwB33GD;EAQI,WAAA;CxBs3GH;AwB92GD;EACE,WAAA;EACA,SAAA;CxBg3GD;AwBx2GD;EACE,QAAA;EACA,YAAA;CxB02GD;AwBt2GD;EACE,eAAA;EACA,kBAAA;EACA,gBAAA;EACA,wBAAA;EACA,eAAA;EACA,oBAAA;CxBw2GD;AwBp2GD;EACE,gBAAA;EACA,QAAA;EACA,SAAA;EACA,UAAA;EACA,OAAA;EACA,aAAA;CxBs2GD;AwBl2GD;EACE,SAAA;EACA,WAAA;CxBo2GD;AwB51GD;;EAII,cAAA;EACA,0BAAA;EACA,4BAAA;EACA,YAAA;CxB41GH;AwBn2GD;;EAWI,UAAA;EACA,aAAA;EACA,mBAAA;CxB41GH;AwBv0GD;EAXE;IApEA,WAAA;IACA,SAAA;GxB05GC;EwBv1GD;IA1DA,QAAA;IACA,YAAA;GxBo5GC;CACF;A2BpiHD;;EAEE,mBAAA;EACA,sBAAA;EACA,uBAAA;C3BsiHD;A2B1iHD;;EAMI,mBAAA;EACA,YAAA;C3BwiHH;A2BtiHG;;;;;;;;EAIE,WAAA;C3B4iHL;A2BtiHD;;;;EAKI,kBAAA;C3BuiHH;A2BliHD;EACE,kBAAA;C3BoiHD;A2BriHD;;;EAOI,YAAA;C3BmiHH;A2B1iHD;;;EAYI,iBAAA;C3BmiHH;A2B/hHD;EACE,iBAAA;C3BiiHD;A2B7hHD;EACE,eAAA;C3B+hHD;A2B9hHC;EClDA,8BAAA;EACG,2BAAA;C5BmlHJ;A2B7hHD;;EC/CE,6BAAA;EACG,0BAAA;C5BglHJ;A2B5hHD;EACE,YAAA;C3B8hHD;A2B5hHD;EACE,iBAAA;C3B8hHD;A2B5hHD;;ECnEE,8BAAA;EACG,2BAAA;C5BmmHJ;A2B3hHD;ECjEE,6BAAA;EACG,0BAAA;C5B+lHJ;A2B1hHD;;EAEE,WAAA;C3B4hHD;A2B3gHD;EACE,kBAAA;EACA,mBAAA;C3B6gHD;A2B3gHD;EACE,mBAAA;EACA,oBAAA;C3B6gHD;A2BxgHD;EtB/CE,yDAAA;EACQ,iDAAA;CL0jHT;A2BxgHC;EtBnDA,yBAAA;EACQ,iBAAA;CL8jHT;A2BrgHD;EACE,eAAA;C3BugHD;A2BpgHD;EACE,wBAAA;EACA,uBAAA;C3BsgHD;A2BngHD;EACE,wBAAA;C3BqgHD;A2B9/GD;;;EAII,eAAA;EACA,YAAA;EACA,YAAA;EACA,gBAAA;C3B+/GH;A2BtgHD;EAcM,YAAA;C3B2/GL;A2BzgHD;;;;EAsBI,iBAAA;EACA,eAAA;C3By/GH;A2Bp/GC;EACE,iBAAA;C3Bs/GH;A2Bp/GC;EC3KA,6BAAA;EACC,4BAAA;EAOD,8BAAA;EACC,6BAAA;C5B4pHF;A2Bt/GC;EC/KA,2BAAA;EACC,0BAAA;EAOD,gCAAA;EACC,+BAAA;C5BkqHF;A2Bv/GD;EACE,iBAAA;C3By/GD;A2Bv/GD;;EC/KE,8BAAA;EACC,6BAAA;C5B0qHF;A2Bt/GD;EC7LE,2BAAA;EACC,0BAAA;C5BsrHF;A2Bl/GD;EACE,eAAA;EACA,YAAA;EACA,oBAAA;EACA,0BAAA;C3Bo/GD;A2Bx/GD;;EAOI,YAAA;EACA,oBAAA;EACA,UAAA;C3Bq/GH;A2B9/GD;EAYI,YAAA;C3Bq/GH;A2BjgHD;EAgBI,WAAA;C3Bo/GH;A2Bn+GD;;;;EAKM,mBAAA;EACA,uBAAA;EACA,qBAAA;C3Bo+GL;A6B9sHD;EACE,mBAAA;EACA,eAAA;EACA,0BAAA;C7BgtHD;A6B7sHC;EACE,YAAA;EACA,gBAAA;EACA,iBAAA;C7B+sHH;A6BxtHD;EAeI,mBAAA;EACA,WAAA;EAKA,YAAA;EAEA,YAAA;EACA,iBAAA;C7BusHH;A6BrsHG;EACE,WAAA;C7BusHL;A6B7rHD;;;EV0BE,aAAA;EACA,mBAAA;EACA,gBAAA;EACA,uBAAA;EACA,mBAAA;CnBwqHD;AmBtqHC;;;EACE,aAAA;EACA,kBAAA;CnB0qHH;AmBvqHC;;;;;;EAEE,aAAA;CnB6qHH;A6B/sHD;;;EVqBE,aAAA;EACA,kBAAA;EACA,gBAAA;EACA,iBAAA;EACA,mBAAA;CnB+rHD;AmB7rHC;;;EACE,aAAA;EACA,kBAAA;CnBisHH;AmB9rHC;;;;;;EAEE,aAAA;CnBosHH;A6B7tHD;;;EAGE,oBAAA;C7B+tHD;A6B7tHC;;;EACE,iBAAA;C7BiuHH;A6B7tHD;;EAEE,UAAA;EACA,oBAAA;EACA,uBAAA;C7B+tHD;A6B1tHD;EACE,kBAAA;EACA,gBAAA;EACA,oBAAA;EACA,eAAA;EACA,eAAA;EACA,mBAAA;EACA,0BAAA;EACA,uBAAA;EACA,mBAAA;C7B4tHD;A6BztHC;EACE,kBAAA;EACA,gBAAA;EACA,mBAAA;C7B2tHH;A6BztHC;EACE,mBAAA;EACA,gBAAA;EACA,mBAAA;C7B2tHH;A6B/uHD;;EA0BI,cAAA;C7BytHH;A6BptHD;;;;;;;EDpGE,8BAAA;EACG,2BAAA;C5Bi0HJ;A6BrtHD;EACE,gBAAA;C7ButHD;A6BrtHD;;;;;;;EDxGE,6BAAA;EACG,0BAAA;C5Bs0HJ;A6BttHD;EACE,eAAA;C7BwtHD;A6BntHD;EACE,mBAAA;EAGA,aAAA;EACA,oBAAA;C7BmtHD;A6BxtHD;EAUI,mBAAA;C7BitHH;A6B3tHD;EAYM,kBAAA;C7BktHL;A6B/sHG;;;EAGE,WAAA;C7BitHL;A6B5sHC;;EAGI,mBAAA;C7B6sHL;A6B1sHC;;EAGI,WAAA;EACA,kBAAA;C7B2sHL;A8B12HD;EACE,iBAAA;EACA,gBAAA;EACA,iBAAA;C9B42HD;A8B/2HD;EAOI,mBAAA;EACA,eAAA;C9B22HH;A8Bn3HD;EAWM,mBAAA;EACA,eAAA;EACA,mBAAA;C9B22HL;A8B12HK;;EAEE,sBAAA;EACA,0BAAA;C9B42HP;A8Bv2HG;EACE,eAAA;C9By2HL;A8Bv2HK;;EAEE,eAAA;EACA,sBAAA;EACA,8BAAA;EACA,oBAAA;C9By2HP;A8Bl2HG;;;EAGE,0BAAA;EACA,sBAAA;C9Bo2HL;A8B74HD;ELHE,YAAA;EACA,cAAA;EACA,iBAAA;EACA,0BAAA;CzBm5HD;A8Bn5HD;EA0DI,gBAAA;C9B41HH;A8Bn1HD;EACE,8BAAA;C9Bq1HD;A8Bt1HD;EAGI,YAAA;EAEA,oBAAA;C9Bq1HH;A8B11HD;EASM,kBAAA;EACA,wBAAA;EACA,8BAAA;EACA,2BAAA;C9Bo1HL;A8Bn1HK;EACE,mCAAA;C9Bq1HP;A8B/0HK;;;EAGE,eAAA;EACA,uBAAA;EACA,uBAAA;EACA,iCAAA;EACA,gBAAA;C9Bi1HP;A8B50HC;EAqDA,YAAA;EA8BA,iBAAA;C9B6vHD;A8Bh1HC;EAwDE,YAAA;C9B2xHH;A8Bn1HC;EA0DI,mBAAA;EACA,mBAAA;C9B4xHL;A8Bv1HC;EAgEE,UAAA;EACA,WAAA;C9B0xHH;A8B9wHD;EA0DA;IAjEM,oBAAA;IACA,UAAA;G9ByxHH;E8BztHH;IA9DQ,iBAAA;G9B0xHL;CACF;A8Bp2HC;EAuFE,gBAAA;EACA,mBAAA;C9BgxHH;A8Bx2HC;;;EA8FE,uBAAA;C9B+wHH;A8BjwHD;EA2BA;IApCM,8BAAA;IACA,2BAAA;G9B8wHH;E8B3uHH;;;IA9BM,0BAAA;G9B8wHH;CACF;A8B/2HD;EAEI,YAAA;C9Bg3HH;A8Bl3HD;EAMM,mBAAA;C9B+2HL;A8Br3HD;EASM,iBAAA;C9B+2HL;A8B12HK;;;EAGE,YAAA;EACA,0BAAA;C9B42HP;A8Bp2HD;EAEI,YAAA;C9Bq2HH;A8Bv2HD;EAIM,gBAAA;EACA,eAAA;C9Bs2HL;A8B11HD;EACE,YAAA;C9B41HD;A8B71HD;EAII,YAAA;C9B41HH;A8Bh2HD;EAMM,mBAAA;EACA,mBAAA;C9B61HL;A8Bp2HD;EAYI,UAAA;EACA,WAAA;C9B21HH;A8B/0HD;EA0DA;IAjEM,oBAAA;IACA,UAAA;G9B01HH;E8B1xHH;IA9DQ,iBAAA;G9B21HL;CACF;A8Bn1HD;EACE,iBAAA;C9Bq1HD;A8Bt1HD;EAKI,gBAAA;EACA,mBAAA;C9Bo1HH;A8B11HD;;;EAYI,uBAAA;C9Bm1HH;A8Br0HD;EA2BA;IApCM,8BAAA;IACA,2BAAA;G9Bk1HH;E8B/yHH;;;IA9BM,0BAAA;G9Bk1HH;CACF;A8Bz0HD;EAEI,cAAA;C9B00HH;A8B50HD;EAKI,eAAA;C9B00HH;A8Bj0HD;EAEE,iBAAA;EF3OA,2BAAA;EACC,0BAAA;C5B8iIF;A+BxiID;EACE,mBAAA;EACA,iBAAA;EACA,oBAAA;EACA,8BAAA;C/B0iID;A+BliID;EA8nBA;IAhoBI,mBAAA;G/BwiID;CACF;A+BzhID;EAgnBA;IAlnBI,YAAA;G/B+hID;CACF;A+BjhID;EACE,oBAAA;EACA,oBAAA;EACA,mBAAA;EACA,kCAAA;EACA,2DAAA;UAAA,mDAAA;EAEA,kCAAA;C/BkhID;A+BhhIC;EACE,iBAAA;C/BkhIH;A+Bt/HD;EA6jBA;IArlBI,YAAA;IACA,cAAA;IACA,yBAAA;YAAA,iBAAA;G/BkhID;E+BhhIC;IACE,0BAAA;IACA,wBAAA;IACA,kBAAA;IACA,6BAAA;G/BkhIH;E+B/gIC;IACE,oBAAA;G/BihIH;E+B5gIC;;;IAGE,gBAAA;IACA,iBAAA;G/B8gIH;CACF;A+B1gID;;EAGI,kBAAA;C/B2gIH;A+BtgIC;EAmjBF;;IArjBM,kBAAA;G/B6gIH;CACF;A+BpgID;;;;EAII,oBAAA;EACA,mBAAA;C/BsgIH;A+BhgIC;EAgiBF;;;;IAniBM,gBAAA;IACA,eAAA;G/B0gIH;CACF;A+B9/HD;EACE,cAAA;EACA,sBAAA;C/BggID;A+B3/HD;EA8gBA;IAhhBI,iBAAA;G/BigID;CACF;A+B7/HD;;EAEE,gBAAA;EACA,SAAA;EACA,QAAA;EACA,cAAA;C/B+/HD;A+Bz/HD;EAggBA;;IAlgBI,iBAAA;G/BggID;CACF;A+B9/HD;EACE,OAAA;EACA,sBAAA;C/BggID;A+B9/HD;EACE,UAAA;EACA,iBAAA;EACA,sBAAA;C/BggID;A+B1/HD;EACE,YAAA;EACA,mBAAA;EACA,gBAAA;EACA,kBAAA;EACA,aAAA;C/B4/HD;A+B1/HC;;EAEE,sBAAA;C/B4/HH;A+BrgID;EAaI,eAAA;C/B2/HH;A+Bl/HD;EALI;;IAEE,mBAAA;G/B0/HH;CACF;A+Bh/HD;EACE,mBAAA;EACA,aAAA;EACA,mBAAA;EACA,kBAAA;EC9LA,gBAAA;EACA,mBAAA;ED+LA,8BAAA;EACA,uBAAA;EACA,8BAAA;EACA,mBAAA;C/Bm/HD;A+B/+HC;EACE,WAAA;C/Bi/HH;A+B//HD;EAmBI,eAAA;EACA,YAAA;EACA,YAAA;EACA,mBAAA;C/B++HH;A+BrgID;EAyBI,gBAAA;C/B++HH;A+Bz+HD;EAqbA;IAvbI,cAAA;G/B++HD;CACF;A+Bt+HD;EACE,oBAAA;C/Bw+HD;A+Bz+HD;EAII,kBAAA;EACA,qBAAA;EACA,kBAAA;C/Bw+HH;A+B58HC;EA2YF;IAjaM,iBAAA;IACA,YAAA;IACA,YAAA;IACA,cAAA;IACA,8BAAA;IACA,UAAA;IACA,yBAAA;YAAA,iBAAA;G/Bs+HH;E+B3kHH;;IAxZQ,2BAAA;G/Bu+HL;E+B/kHH;IArZQ,kBAAA;G/Bu+HL;E+Bt+HK;;IAEE,uBAAA;G/Bw+HP;CACF;A+Bt9HD;EA+XA;IA1YI,YAAA;IACA,UAAA;G/Bq+HD;E+B5lHH;IAtYM,YAAA;G/Bq+HH;E+B/lHH;IApYQ,kBAAA;IACA,qBAAA;G/Bs+HL;CACF;A+B39HD;EACE,mBAAA;EACA,oBAAA;EACA,mBAAA;EACA,kCAAA;EACA,qCAAA;E1B9NA,6FAAA;EACQ,qFAAA;E2B/DR,gBAAA;EACA,mBAAA;ChC4vID;AkBtuHD;EAwEA;IAtIM,sBAAA;IACA,iBAAA;IACA,uBAAA;GlBwyHH;EkBpqHH;IA/HM,sBAAA;IACA,YAAA;IACA,uBAAA;GlBsyHH;EkBzqHH;IAxHM,sBAAA;GlBoyHH;EkB5qHH;IApHM,sBAAA;IACA,uBAAA;GlBmyHH;EkBhrHH;;;IA9GQ,YAAA;GlBmyHL;EkBrrHH;IAxGM,YAAA;GlBgyHH;EkBxrHH;IApGM,iBAAA;IACA,uBAAA;GlB+xHH;EkB5rHH;;IA5FM,sBAAA;IACA,cAAA;IACA,iBAAA;IACA,uBAAA;GlB4xHH;EkBnsHH;;IAtFQ,gBAAA;GlB6xHL;EkBvsHH;;IAjFM,mBAAA;IACA,eAAA;GlB4xHH;EkB5sHH;IA3EM,OAAA;GlB0xHH;CACF;A+BpgIC;EAmWF;IAzWM,mBAAA;G/B8gIH;E+B5gIG;IACE,iBAAA;G/B8gIL;CACF;A+B7/HD;EAoVA;IA5VI,YAAA;IACA,UAAA;IACA,eAAA;IACA,gBAAA;IACA,eAAA;IACA,kBAAA;I1BzPF,yBAAA;IACQ,iBAAA;GLmwIP;CACF;A+BngID;EACE,cAAA;EHpUA,2BAAA;EACC,0BAAA;C5B00IF;A+BngID;EACE,iBAAA;EHzUA,6BAAA;EACC,4BAAA;EAOD,8BAAA;EACC,6BAAA;C5By0IF;A+B//HD;EChVE,gBAAA;EACA,mBAAA;ChCk1ID;A+BhgIC;ECnVA,iBAAA;EACA,oBAAA;ChCs1ID;A+BjgIC;ECtVA,iBAAA;EACA,oBAAA;ChC01ID;A+B3/HD;EChWE,iBAAA;EACA,oBAAA;ChC81ID;A+Bv/HD;EAsSA;IA1SI,YAAA;IACA,kBAAA;IACA,mBAAA;G/B+/HD;CACF;A+Bl+HD;EAhBE;IExWA,uBAAA;GjC81IC;E+Br/HD;IE5WA,wBAAA;IF8WE,oBAAA;G/Bu/HD;E+Bz/HD;IAKI,gBAAA;G/Bu/HH;CACF;A+B9+HD;EACE,0BAAA;EACA,sBAAA;C/Bg/HD;A+Bl/HD;EAKI,YAAA;C/Bg/HH;A+B/+HG;;EAEE,eAAA;EACA,8BAAA;C/Bi/HL;A+B1/HD;EAcI,YAAA;C/B++HH;A+B7/HD;EAmBM,YAAA;C/B6+HL;A+B3+HK;;EAEE,YAAA;EACA,8BAAA;C/B6+HP;A+Bz+HK;;;EAGE,YAAA;EACA,0BAAA;C/B2+HP;A+Bv+HK;;;EAGE,YAAA;EACA,8BAAA;C/By+HP;A+BjhID;EA8CI,mBAAA;C/Bs+HH;A+Br+HG;;EAEE,uBAAA;C/Bu+HL;A+BxhID;EAoDM,uBAAA;C/Bu+HL;A+B3hID;;EA0DI,sBAAA;C/Bq+HH;A+B99HK;;;EAGE,0BAAA;EACA,YAAA;C/Bg+HP;A+B/7HC;EAoKF;IA7LU,YAAA;G/B49HP;E+B39HO;;IAEE,YAAA;IACA,8BAAA;G/B69HT;E+Bz9HO;;;IAGE,YAAA;IACA,0BAAA;G/B29HT;E+Bv9HO;;;IAGE,YAAA;IACA,8BAAA;G/By9HT;CACF;A+B3jID;EA8GI,YAAA;C/Bg9HH;A+B/8HG;EACE,YAAA;C/Bi9HL;A+BjkID;EAqHI,YAAA;C/B+8HH;A+B98HG;;EAEE,YAAA;C/Bg9HL;A+B58HK;;;;EAEE,YAAA;C/Bg9HP;A+Bx8HD;EACE,uBAAA;EACA,sBAAA;C/B08HD;A+B58HD;EAKI,eAAA;C/B08HH;A+Bz8HG;;EAEE,YAAA;EACA,8BAAA;C/B28HL;A+Bp9HD;EAcI,eAAA;C/By8HH;A+Bv9HD;EAmBM,eAAA;C/Bu8HL;A+Br8HK;;EAEE,YAAA;EACA,8BAAA;C/Bu8HP;A+Bn8HK;;;EAGE,YAAA;EACA,0BAAA;C/Bq8HP;A+Bj8HK;;;EAGE,YAAA;EACA,8BAAA;C/Bm8HP;A+B3+HD;EA+CI,mBAAA;C/B+7HH;A+B97HG;;EAEE,uBAAA;C/Bg8HL;A+Bl/HD;EAqDM,uBAAA;C/Bg8HL;A+Br/HD;;EA2DI,sBAAA;C/B87HH;A+Bx7HK;;;EAGE,0BAAA;EACA,YAAA;C/B07HP;A+Bn5HC;EAwBF;IAvDU,sBAAA;G/Bs7HP;E+B/3HH;IApDU,0BAAA;G/Bs7HP;E+Bl4HH;IAjDU,eAAA;G/Bs7HP;E+Br7HO;;IAEE,YAAA;IACA,8BAAA;G/Bu7HT;E+Bn7HO;;;IAGE,YAAA;IACA,0BAAA;G/Bq7HT;E+Bj7HO;;;IAGE,YAAA;IACA,8BAAA;G/Bm7HT;CACF;A+B3hID;EA+GI,eAAA;C/B+6HH;A+B96HG;EACE,YAAA;C/Bg7HL;A+BjiID;EAsHI,eAAA;C/B86HH;A+B76HG;;EAEE,YAAA;C/B+6HL;A+B36HK;;;;EAEE,YAAA;C/B+6HP;AkCzjJD;EACE,kBAAA;EACA,oBAAA;EACA,iBAAA;EACA,0BAAA;EACA,mBAAA;ClC2jJD;AkChkJD;EAQI,sBAAA;ClC2jJH;AkCnkJD;EAWM,kBAAA;EACA,eAAA;EACA,YAAA;ClC2jJL;AkCxkJD;EAkBI,eAAA;ClCyjJH;AmC7kJD;EACE,sBAAA;EACA,gBAAA;EACA,eAAA;EACA,mBAAA;CnC+kJD;AmCnlJD;EAOI,gBAAA;CnC+kJH;AmCtlJD;;EAUM,mBAAA;EACA,YAAA;EACA,kBAAA;EACA,wBAAA;EACA,sBAAA;EACA,eAAA;EACA,uBAAA;EACA,uBAAA;EACA,kBAAA;CnCglJL;AmC9kJG;;EAGI,eAAA;EPXN,+BAAA;EACG,4BAAA;C5B2lJJ;AmC7kJG;;EPvBF,gCAAA;EACG,6BAAA;C5BwmJJ;AmCxkJG;;;;EAEE,WAAA;EACA,eAAA;EACA,0BAAA;EACA,mBAAA;CnC4kJL;AmCtkJG;;;;;;EAGE,WAAA;EACA,YAAA;EACA,0BAAA;EACA,sBAAA;EACA,gBAAA;CnC2kJL;AmCloJD;;;;;;EAkEM,eAAA;EACA,uBAAA;EACA,mBAAA;EACA,oBAAA;CnCwkJL;AmC/jJD;;EC3EM,mBAAA;EACA,gBAAA;EACA,uBAAA;CpC8oJL;AoC5oJG;;ERKF,+BAAA;EACG,4BAAA;C5B2oJJ;AoC3oJG;;ERTF,gCAAA;EACG,6BAAA;C5BwpJJ;AmC1kJD;;EChFM,kBAAA;EACA,gBAAA;EACA,iBAAA;CpC8pJL;AoC5pJG;;ERKF,+BAAA;EACG,4BAAA;C5B2pJJ;AoC3pJG;;ERTF,gCAAA;EACG,6BAAA;C5BwqJJ;AqC3qJD;EACE,gBAAA;EACA,eAAA;EACA,iBAAA;EACA,mBAAA;CrC6qJD;AqCjrJD;EAOI,gBAAA;CrC6qJH;AqCprJD;;EAUM,sBAAA;EACA,kBAAA;EACA,uBAAA;EACA,uBAAA;EACA,oBAAA;CrC8qJL;AqC5rJD;;EAmBM,sBAAA;EACA,0BAAA;CrC6qJL;AqCjsJD;;EA2BM,aAAA;CrC0qJL;AqCrsJD;;EAkCM,YAAA;CrCuqJL;AqCzsJD;;;;EA2CM,eAAA;EACA,uBAAA;EACA,oBAAA;CrCoqJL;AsCltJD;EACE,gBAAA;EACA,wBAAA;EACA,eAAA;EACA,kBAAA;EACA,eAAA;EACA,YAAA;EACA,mBAAA;EACA,oBAAA;EACA,yBAAA;EACA,qBAAA;CtCotJD;AsChtJG;;EAEE,YAAA;EACA,sBAAA;EACA,gBAAA;CtCktJL;AsC7sJC;EACE,cAAA;CtC+sJH;AsC3sJC;EACE,mBAAA;EACA,UAAA;CtC6sJH;AsCtsJD;ECtCE,0BAAA;CvC+uJD;AuC5uJG;;EAEE,0BAAA;CvC8uJL;AsCzsJD;EC1CE,0BAAA;CvCsvJD;AuCnvJG;;EAEE,0BAAA;CvCqvJL;AsC5sJD;EC9CE,0BAAA;CvC6vJD;AuC1vJG;;EAEE,0BAAA;CvC4vJL;AsC/sJD;EClDE,0BAAA;CvCowJD;AuCjwJG;;EAEE,0BAAA;CvCmwJL;AsCltJD;ECtDE,0BAAA;CvC2wJD;AuCxwJG;;EAEE,0BAAA;CvC0wJL;AsCrtJD;EC1DE,0BAAA;CvCkxJD;AuC/wJG;;EAEE,0BAAA;CvCixJL;AwCnxJD;EACE,sBAAA;EACA,gBAAA;EACA,iBAAA;EACA,gBAAA;EACA,kBAAA;EACA,YAAA;EACA,eAAA;EACA,uBAAA;EACA,oBAAA;EACA,mBAAA;EACA,0BAAA;EACA,oBAAA;CxCqxJD;AwClxJC;EACE,cAAA;CxCoxJH;AwChxJC;EACE,mBAAA;EACA,UAAA;CxCkxJH;AwC/wJC;;EAEE,OAAA;EACA,iBAAA;CxCixJH;AwC5wJG;;EAEE,YAAA;EACA,sBAAA;EACA,gBAAA;CxC8wJL;AwCzwJC;;EAEE,eAAA;EACA,uBAAA;CxC2wJH;AwCxwJC;EACE,aAAA;CxC0wJH;AwCvwJC;EACE,kBAAA;CxCywJH;AwCtwJC;EACE,iBAAA;CxCwwJH;AyCl0JD;EACE,kBAAA;EACA,qBAAA;EACA,oBAAA;EACA,eAAA;EACA,0BAAA;CzCo0JD;AyCz0JD;;EASI,eAAA;CzCo0JH;AyC70JD;EAaI,oBAAA;EACA,gBAAA;EACA,iBAAA;CzCm0JH;AyCl1JD;EAmBI,0BAAA;CzCk0JH;AyC/zJC;;EAEE,mBAAA;EACA,mBAAA;EACA,oBAAA;CzCi0JH;AyC31JD;EA8BI,gBAAA;CzCg0JH;AyC9yJD;EACA;IAfI,kBAAA;IACA,qBAAA;GzCg0JD;EyC9zJC;;IAEE,mBAAA;IACA,oBAAA;GzCg0JH;EyCvzJH;;IAJM,gBAAA;GzC+zJH;CACF;A0C52JD;EACE,eAAA;EACA,aAAA;EACA,oBAAA;EACA,wBAAA;EACA,uBAAA;EACA,uBAAA;EACA,mBAAA;ErCiLA,4CAAA;EACK,uCAAA;EACG,oCAAA;CL8rJT;A0Cx3JD;;EAaI,kBAAA;EACA,mBAAA;C1C+2JH;A0C32JC;;;EAGE,sBAAA;C1C62JH;A0Cl4JD;EA0BI,aAAA;EACA,eAAA;C1C22JH;A2Cp4JD;EACE,cAAA;EACA,oBAAA;EACA,8BAAA;EACA,mBAAA;C3Cs4JD;A2C14JD;EAQI,cAAA;EAEA,eAAA;C3Co4JH;A2C94JD;EAeI,kBAAA;C3Ck4JH;A2Cj5JD;;EAqBI,iBAAA;C3Cg4JH;A2Cr5JD;EAyBI,gBAAA;C3C+3JH;A2Cv3JD;;EAEE,oBAAA;C3Cy3JD;A2C33JD;;EAMI,mBAAA;EACA,UAAA;EACA,aAAA;EACA,eAAA;C3Cy3JH;A2Cj3JD;ECvDE,0BAAA;EACA,sBAAA;EACA,eAAA;C5C26JD;A2Ct3JD;EClDI,0BAAA;C5C26JH;A2Cz3JD;EC/CI,eAAA;C5C26JH;A2Cx3JD;EC3DE,0BAAA;EACA,sBAAA;EACA,eAAA;C5Cs7JD;A2C73JD;ECtDI,0BAAA;C5Cs7JH;A2Ch4JD;ECnDI,eAAA;C5Cs7JH;A2C/3JD;EC/DE,0BAAA;EACA,sBAAA;EACA,eAAA;C5Ci8JD;A2Cp4JD;EC1DI,0BAAA;C5Ci8JH;A2Cv4JD;ECvDI,eAAA;C5Ci8JH;A2Ct4JD;ECnEE,0BAAA;EACA,sBAAA;EACA,eAAA;C5C48JD;A2C34JD;EC9DI,0BAAA;C5C48JH;A2C94JD;EC3DI,eAAA;C5C48JH;A6C98JD;EACE;IAAQ,4BAAA;G7Ci9JP;E6Ch9JD;IAAQ,yBAAA;G7Cm9JP;CACF;A6Ch9JD;EACE;IAAQ,4BAAA;G7Cm9JP;E6Cl9JD;IAAQ,yBAAA;G7Cq9JP;CACF;A6Cx9JD;EACE;IAAQ,4BAAA;G7Cm9JP;E6Cl9JD;IAAQ,yBAAA;G7Cq9JP;CACF;A6C98JD;EACE,iBAAA;EACA,aAAA;EACA,oBAAA;EACA,0BAAA;EACA,mBAAA;ExCsCA,uDAAA;EACQ,+CAAA;CL26JT;A6C78JD;EACE,YAAA;EACA,UAAA;EACA,aAAA;EACA,gBAAA;EACA,kBAAA;EACA,YAAA;EACA,mBAAA;EACA,0BAAA;ExCyBA,uDAAA;EACQ,+CAAA;EAyHR,oCAAA;EACK,+BAAA;EACG,4BAAA;CL+zJT;A6C18JD;;ECCI,8MAAA;EACA,yMAAA;EACA,sMAAA;EDAF,mCAAA;UAAA,2BAAA;C7C88JD;A6Cv8JD;;ExC5CE,2DAAA;EACK,sDAAA;EACG,mDAAA;CLu/JT;A6Cp8JD;EErEE,0BAAA;C/C4gKD;A+CzgKC;EDgDE,8MAAA;EACA,yMAAA;EACA,sMAAA;C9C49JH;A6Cx8JD;EEzEE,0BAAA;C/CohKD;A+CjhKC;EDgDE,8MAAA;EACA,yMAAA;EACA,sMAAA;C9Co+JH;A6C58JD;EE7EE,0BAAA;C/C4hKD;A+CzhKC;EDgDE,8MAAA;EACA,yMAAA;EACA,sMAAA;C9C4+JH;A6Ch9JD;EEjFE,0BAAA;C/CoiKD;A+CjiKC;EDgDE,8MAAA;EACA,yMAAA;EACA,sMAAA;C9Co/JH;AgD5iKD;EAEE,iBAAA;ChD6iKD;AgD3iKC;EACE,cAAA;ChD6iKH;AgDziKD;;EAEE,QAAA;EACA,iBAAA;ChD2iKD;AgDxiKD;EACE,eAAA;ChD0iKD;AgDviKD;EACE,eAAA;ChDyiKD;AgDtiKC;EACE,gBAAA;ChDwiKH;AgDpiKD;;EAEE,mBAAA;ChDsiKD;AgDniKD;;EAEE,oBAAA;ChDqiKD;AgDliKD;;;EAGE,oBAAA;EACA,oBAAA;ChDoiKD;AgDjiKD;EACE,uBAAA;ChDmiKD;AgDhiKD;EACE,uBAAA;ChDkiKD;AgD9hKD;EACE,cAAA;EACA,mBAAA;ChDgiKD;AgD1hKD;EACE,gBAAA;EACA,iBAAA;ChD4hKD;AiDnlKD;EAEE,oBAAA;EACA,gBAAA;CjDolKD;AiD5kKD;EACE,mBAAA;EACA,eAAA;EACA,mBAAA;EAEA,oBAAA;EACA,uBAAA;EACA,uBAAA;CjD6kKD;AiD1kKC;ErB3BA,6BAAA;EACC,4BAAA;C5BwmKF;AiD3kKC;EACE,iBAAA;ErBvBF,gCAAA;EACC,+BAAA;C5BqmKF;AiDpkKD;;EAEE,YAAA;CjDskKD;AiDxkKD;;EAKI,YAAA;CjDukKH;AiDnkKC;;;;EAEE,sBAAA;EACA,YAAA;EACA,0BAAA;CjDukKH;AiDnkKD;EACE,YAAA;EACA,iBAAA;CjDqkKD;AiDhkKC;;;EAGE,0BAAA;EACA,eAAA;EACA,oBAAA;CjDkkKH;AiDvkKC;;;EASI,eAAA;CjDmkKL;AiD5kKC;;;EAYI,eAAA;CjDqkKL;AiDhkKC;;;EAGE,WAAA;EACA,YAAA;EACA,0BAAA;EACA,sBAAA;CjDkkKH;AiDxkKC;;;;;;;;;EAYI,eAAA;CjDukKL;AiDnlKC;;;EAeI,eAAA;CjDykKL;AkD3qKC;EACE,eAAA;EACA,0BAAA;ClD6qKH;AkD3qKG;;EAEE,eAAA;ClD6qKL;AkD/qKG;;EAKI,eAAA;ClD8qKP;AkD3qKK;;;;EAEE,eAAA;EACA,0BAAA;ClD+qKP;AkD7qKK;;;;;;EAGE,YAAA;EACA,0BAAA;EACA,sBAAA;ClDkrKP;AkDxsKC;EACE,eAAA;EACA,0BAAA;ClD0sKH;AkDxsKG;;EAEE,eAAA;ClD0sKL;AkD5sKG;;EAKI,eAAA;ClD2sKP;AkDxsKK;;;;EAEE,eAAA;EACA,0BAAA;ClD4sKP;AkD1sKK;;;;;;EAGE,YAAA;EACA,0BAAA;EACA,sBAAA;ClD+sKP;AkDruKC;EACE,eAAA;EACA,0BAAA;ClDuuKH;AkDruKG;;EAEE,eAAA;ClDuuKL;AkDzuKG;;EAKI,eAAA;ClDwuKP;AkDruKK;;;;EAEE,eAAA;EACA,0BAAA;ClDyuKP;AkDvuKK;;;;;;EAGE,YAAA;EACA,0BAAA;EACA,sBAAA;ClD4uKP;AkDlwKC;EACE,eAAA;EACA,0BAAA;ClDowKH;AkDlwKG;;EAEE,eAAA;ClDowKL;AkDtwKG;;EAKI,eAAA;ClDqwKP;AkDlwKK;;;;EAEE,eAAA;EACA,0BAAA;ClDswKP;AkDpwKK;;;;;;EAGE,YAAA;EACA,0BAAA;EACA,sBAAA;ClDywKP;AiDxqKD;EACE,cAAA;EACA,mBAAA;CjD0qKD;AiDxqKD;EACE,iBAAA;EACA,iBAAA;CjD0qKD;AmDpyKD;EACE,oBAAA;EACA,uBAAA;EACA,8BAAA;EACA,mBAAA;E9C0DA,kDAAA;EACQ,0CAAA;CL6uKT;AmDnyKD;EACE,cAAA;CnDqyKD;AmDhyKD;EACE,mBAAA;EACA,qCAAA;EvBpBA,6BAAA;EACC,4BAAA;C5BuzKF;AmDtyKD;EAMI,eAAA;CnDmyKH;AmD9xKD;EACE,cAAA;EACA,iBAAA;EACA,gBAAA;EACA,eAAA;CnDgyKD;AmDpyKD;;;;;EAWI,eAAA;CnDgyKH;AmD3xKD;EACE,mBAAA;EACA,0BAAA;EACA,2BAAA;EvBxCA,gCAAA;EACC,+BAAA;C5Bs0KF;AmDrxKD;;EAGI,iBAAA;CnDsxKH;AmDzxKD;;EAMM,oBAAA;EACA,iBAAA;CnDuxKL;AmDnxKG;;EAEI,cAAA;EvBvEN,6BAAA;EACC,4BAAA;C5B61KF;AmDjxKG;;EAEI,iBAAA;EvBvEN,gCAAA;EACC,+BAAA;C5B21KF;AmD1yKD;EvB1DE,2BAAA;EACC,0BAAA;C5Bu2KF;AmD7wKD;EAEI,oBAAA;CnD8wKH;AmD3wKD;EACE,oBAAA;CnD6wKD;AmDrwKD;;;EAII,iBAAA;CnDswKH;AmD1wKD;;;EAOM,mBAAA;EACA,oBAAA;CnDwwKL;AmDhxKD;;EvBzGE,6BAAA;EACC,4BAAA;C5B63KF;AmDrxKD;;;;EAmBQ,4BAAA;EACA,6BAAA;CnDwwKP;AmD5xKD;;;;;;;;EAwBU,4BAAA;CnD8wKT;AmDtyKD;;;;;;;;EA4BU,6BAAA;CnDoxKT;AmDhzKD;;EvBjGE,gCAAA;EACC,+BAAA;C5Bq5KF;AmDrzKD;;;;EAyCQ,+BAAA;EACA,gCAAA;CnDkxKP;AmD5zKD;;;;;;;;EA8CU,+BAAA;CnDwxKT;AmDt0KD;;;;;;;;EAkDU,gCAAA;CnD8xKT;AmDh1KD;;;;EA2DI,2BAAA;CnD2xKH;AmDt1KD;;EA+DI,cAAA;CnD2xKH;AmD11KD;;EAmEI,UAAA;CnD2xKH;AmD91KD;;;;;;;;;;;;EA0EU,eAAA;CnDkyKT;AmD52KD;;;;;;;;;;;;EA8EU,gBAAA;CnD4yKT;AmD13KD;;;;;;;;EAuFU,iBAAA;CnD6yKT;AmDp4KD;;;;;;;;EAgGU,iBAAA;CnD8yKT;AmD94KD;EAsGI,UAAA;EACA,iBAAA;CnD2yKH;AmDjyKD;EACE,oBAAA;CnDmyKD;AmDpyKD;EAKI,iBAAA;EACA,mBAAA;CnDkyKH;AmDxyKD;EASM,gBAAA;CnDkyKL;AmD3yKD;EAcI,iBAAA;CnDgyKH;AmD9yKD;;EAkBM,2BAAA;CnDgyKL;AmDlzKD;EAuBI,cAAA;CnD8xKH;AmDrzKD;EAyBM,8BAAA;CnD+xKL;AmDxxKD;EC1PE,mBAAA;CpDqhLD;AoDnhLC;EACE,eAAA;EACA,0BAAA;EACA,mBAAA;CpDqhLH;AoDxhLC;EAMI,uBAAA;CpDqhLL;AoD3hLC;EASI,eAAA;EACA,0BAAA;CpDqhLL;AoDlhLC;EAEI,0BAAA;CpDmhLL;AmDvyKD;EC7PE,sBAAA;CpDuiLD;AoDriLC;EACE,YAAA;EACA,0BAAA;EACA,sBAAA;CpDuiLH;AoD1iLC;EAMI,0BAAA;CpDuiLL;AoD7iLC;EASI,eAAA;EACA,uBAAA;CpDuiLL;AoDpiLC;EAEI,6BAAA;CpDqiLL;AmDtzKD;EChQE,sBAAA;CpDyjLD;AoDvjLC;EACE,eAAA;EACA,0BAAA;EACA,sBAAA;CpDyjLH;AoD5jLC;EAMI,0BAAA;CpDyjLL;AoD/jLC;EASI,eAAA;EACA,0BAAA;CpDyjLL;AoDtjLC;EAEI,6BAAA;CpDujLL;AmDr0KD;ECnQE,sBAAA;CpD2kLD;AoDzkLC;EACE,eAAA;EACA,0BAAA;EACA,sBAAA;CpD2kLH;AoD9kLC;EAMI,0BAAA;CpD2kLL;AoDjlLC;EASI,eAAA;EACA,0BAAA;CpD2kLL;AoDxkLC;EAEI,6BAAA;CpDykLL;AmDp1KD;ECtQE,sBAAA;CpD6lLD;AoD3lLC;EACE,eAAA;EACA,0BAAA;EACA,sBAAA;CpD6lLH;AoDhmLC;EAMI,0BAAA;CpD6lLL;AoDnmLC;EASI,eAAA;EACA,0BAAA;CpD6lLL;AoD1lLC;EAEI,6BAAA;CpD2lLL;AmDn2KD;ECzQE,sBAAA;CpD+mLD;AoD7mLC;EACE,eAAA;EACA,0BAAA;EACA,sBAAA;CpD+mLH;AoDlnLC;EAMI,0BAAA;CpD+mLL;AoDrnLC;EASI,eAAA;EACA,0BAAA;CpD+mLL;AoD5mLC;EAEI,6BAAA;CpD6mLL;AqD7nLD;EACE,mBAAA;EACA,eAAA;EACA,UAAA;EACA,WAAA;EACA,iBAAA;CrD+nLD;AqDpoLD;;;;;EAYI,mBAAA;EACA,OAAA;EACA,QAAA;EACA,UAAA;EACA,aAAA;EACA,YAAA;EACA,UAAA;CrD+nLH;AqD1nLD;EACE,uBAAA;CrD4nLD;AqDxnLD;EACE,oBAAA;CrD0nLD;AsDrpLD;EACE,iBAAA;EACA,cAAA;EACA,oBAAA;EACA,0BAAA;EACA,0BAAA;EACA,mBAAA;EjDwDA,wDAAA;EACQ,gDAAA;CLgmLT;AsD/pLD;EASI,mBAAA;EACA,kCAAA;CtDypLH;AsDppLD;EACE,cAAA;EACA,mBAAA;CtDspLD;AsDppLD;EACE,aAAA;EACA,mBAAA;CtDspLD;AuD5qLD;EACE,aAAA;EACA,gBAAA;EACA,kBAAA;EACA,eAAA;EACA,YAAA;EACA,0BAAA;EjCRA,aAAA;EAGA,0BAAA;CtBqrLD;AuD7qLC;;EAEE,YAAA;EACA,sBAAA;EACA,gBAAA;EjCfF,aAAA;EAGA,0BAAA;CtB6rLD;AuDzqLC;EACE,WAAA;EACA,gBAAA;EACA,wBAAA;EACA,UAAA;EACA,yBAAA;CvD2qLH;AwDhsLD;EACE,iBAAA;CxDksLD;AwD9rLD;EACE,cAAA;EACA,iBAAA;EACA,gBAAA;EACA,OAAA;EACA,SAAA;EACA,UAAA;EACA,QAAA;EACA,cAAA;EACA,kCAAA;EAIA,WAAA;CxD6rLD;AwD1rLC;EnD+GA,sCAAA;EACI,kCAAA;EACC,iCAAA;EACG,8BAAA;EAkER,oDAAA;EAEK,0CAAA;EACG,oCAAA;CL6gLT;AwDhsLC;EnD2GA,mCAAA;EACI,+BAAA;EACC,8BAAA;EACG,2BAAA;CLwlLT;AwDpsLD;EACE,mBAAA;EACA,iBAAA;CxDssLD;AwDlsLD;EACE,mBAAA;EACA,YAAA;EACA,aAAA;CxDosLD;AwDhsLD;EACE,mBAAA;EACA,uBAAA;EACA,uBAAA;EACA,qCAAA;EACA,mBAAA;EnDaA,iDAAA;EACQ,yCAAA;EmDZR,qCAAA;UAAA,6BAAA;EAEA,WAAA;CxDksLD;AwD9rLD;EACE,gBAAA;EACA,OAAA;EACA,SAAA;EACA,UAAA;EACA,QAAA;EACA,cAAA;EACA,uBAAA;CxDgsLD;AwD9rLC;ElCrEA,WAAA;EAGA,yBAAA;CtBowLD;AwDjsLC;ElCtEA,aAAA;EAGA,0BAAA;CtBwwLD;AwDhsLD;EACE,cAAA;EACA,iCAAA;CxDksLD;AwD9rLD;EACE,iBAAA;CxDgsLD;AwD5rLD;EACE,UAAA;EACA,wBAAA;CxD8rLD;AwDzrLD;EACE,mBAAA;EACA,cAAA;CxD2rLD;AwDvrLD;EACE,cAAA;EACA,kBAAA;EACA,8BAAA;CxDyrLD;AwD5rLD;EAQI,iBAAA;EACA,iBAAA;CxDurLH;AwDhsLD;EAaI,kBAAA;CxDsrLH;AwDnsLD;EAiBI,eAAA;CxDqrLH;AwDhrLD;EACE,mBAAA;EACA,aAAA;EACA,YAAA;EACA,aAAA;EACA,iBAAA;CxDkrLD;AwDhqLD;EAZE;IACE,aAAA;IACA,kBAAA;GxD+qLD;EwD7qLD;InDvEA,kDAAA;IACQ,0CAAA;GLuvLP;EwD5qLD;IAAY,aAAA;GxD+qLX;CACF;AwD1qLD;EAFE;IAAY,aAAA;GxDgrLX;CACF;AyD/zLD;EACE,mBAAA;EACA,cAAA;EACA,eAAA;ECRA,4DAAA;EAEA,mBAAA;EACA,oBAAA;EACA,uBAAA;EACA,iBAAA;EACA,wBAAA;EACA,iBAAA;EACA,kBAAA;EACA,sBAAA;EACA,kBAAA;EACA,qBAAA;EACA,oBAAA;EACA,mBAAA;EACA,qBAAA;EACA,kBAAA;EDHA,gBAAA;EnCVA,WAAA;EAGA,yBAAA;CtBs1LD;AyD30LC;EnCdA,aAAA;EAGA,0BAAA;CtB01LD;AyD90LC;EAAW,iBAAA;EAAmB,eAAA;CzDk1L/B;AyDj1LC;EAAW,iBAAA;EAAmB,eAAA;CzDq1L/B;AyDp1LC;EAAW,gBAAA;EAAmB,eAAA;CzDw1L/B;AyDv1LC;EAAW,kBAAA;EAAmB,eAAA;CzD21L/B;AyDv1LD;EACE,iBAAA;EACA,iBAAA;EACA,YAAA;EACA,mBAAA;EACA,uBAAA;EACA,mBAAA;CzDy1LD;AyDr1LD;EACE,mBAAA;EACA,SAAA;EACA,UAAA;EACA,0BAAA;EACA,oBAAA;CzDu1LD;AyDn1LC;EACE,UAAA;EACA,UAAA;EACA,kBAAA;EACA,wBAAA;EACA,uBAAA;CzDq1LH;AyDn1LC;EACE,UAAA;EACA,WAAA;EACA,oBAAA;EACA,wBAAA;EACA,uBAAA;CzDq1LH;AyDn1LC;EACE,UAAA;EACA,UAAA;EACA,oBAAA;EACA,wBAAA;EACA,uBAAA;CzDq1LH;AyDn1LC;EACE,SAAA;EACA,QAAA;EACA,iBAAA;EACA,4BAAA;EACA,yBAAA;CzDq1LH;AyDn1LC;EACE,SAAA;EACA,SAAA;EACA,iBAAA;EACA,4BAAA;EACA,wBAAA;CzDq1LH;AyDn1LC;EACE,OAAA;EACA,UAAA;EACA,kBAAA;EACA,wBAAA;EACA,0BAAA;CzDq1LH;AyDn1LC;EACE,OAAA;EACA,WAAA;EACA,iBAAA;EACA,wBAAA;EACA,0BAAA;CzDq1LH;AyDn1LC;EACE,OAAA;EACA,UAAA;EACA,iBAAA;EACA,wBAAA;EACA,0BAAA;CzDq1LH;A2Dl7LD;EACE,mBAAA;EACA,OAAA;EACA,QAAA;EACA,cAAA;EACA,cAAA;EACA,iBAAA;EACA,aAAA;EDXA,4DAAA;EAEA,mBAAA;EACA,oBAAA;EACA,uBAAA;EACA,iBAAA;EACA,wBAAA;EACA,iBAAA;EACA,kBAAA;EACA,sBAAA;EACA,kBAAA;EACA,qBAAA;EACA,oBAAA;EACA,mBAAA;EACA,qBAAA;EACA,kBAAA;ECAA,gBAAA;EAEA,uBAAA;EACA,qCAAA;UAAA,6BAAA;EACA,uBAAA;EACA,qCAAA;EACA,mBAAA;EtD8CA,kDAAA;EACQ,0CAAA;CLk5LT;A2D77LC;EAAY,kBAAA;C3Dg8Lb;A2D/7LC;EAAY,kBAAA;C3Dk8Lb;A2Dj8LC;EAAY,iBAAA;C3Do8Lb;A2Dn8LC;EAAY,mBAAA;C3Ds8Lb;A2Dn8LD;EACE,UAAA;EACA,kBAAA;EACA,gBAAA;EACA,0BAAA;EACA,iCAAA;EACA,2BAAA;C3Dq8LD;A2Dl8LD;EACE,kBAAA;C3Do8LD;A2D57LC;;EAEE,mBAAA;EACA,eAAA;EACA,SAAA;EACA,UAAA;EACA,0BAAA;EACA,oBAAA;C3D87LH;A2D37LD;EACE,mBAAA;C3D67LD;A2D37LD;EACE,mBAAA;EACA,YAAA;C3D67LD;A2Dz7LC;EACE,UAAA;EACA,mBAAA;EACA,uBAAA;EACA,0BAAA;EACA,sCAAA;EACA,cAAA;C3D27LH;A2D17LG;EACE,aAAA;EACA,YAAA;EACA,mBAAA;EACA,uBAAA;EACA,uBAAA;C3D47LL;A2Dz7LC;EACE,SAAA;EACA,YAAA;EACA,kBAAA;EACA,qBAAA;EACA,4BAAA;EACA,wCAAA;C3D27LH;A2D17LG;EACE,aAAA;EACA,UAAA;EACA,cAAA;EACA,qBAAA;EACA,yBAAA;C3D47LL;A2Dz7LC;EACE,UAAA;EACA,mBAAA;EACA,oBAAA;EACA,6BAAA;EACA,yCAAA;EACA,WAAA;C3D27LH;A2D17LG;EACE,aAAA;EACA,SAAA;EACA,mBAAA;EACA,oBAAA;EACA,0BAAA;C3D47LL;A2Dx7LC;EACE,SAAA;EACA,aAAA;EACA,kBAAA;EACA,sBAAA;EACA,2BAAA;EACA,uCAAA;C3D07LH;A2Dz7LG;EACE,aAAA;EACA,WAAA;EACA,sBAAA;EACA,wBAAA;EACA,cAAA;C3D27LL;A4DpjMD;EACE,mBAAA;C5DsjMD;A4DnjMD;EACE,mBAAA;EACA,iBAAA;EACA,YAAA;C5DqjMD;A4DxjMD;EAMI,cAAA;EACA,mBAAA;EvD6KF,0CAAA;EACK,qCAAA;EACG,kCAAA;CLy4LT;A4D/jMD;;EAcM,eAAA;C5DqjML;A4D3hMC;EA4NF;IvD3DE,uDAAA;IAEK,6CAAA;IACG,uCAAA;IA7JR,oCAAA;IAEQ,4BAAA;IA+GR,4BAAA;IAEQ,oBAAA;GL86LP;E4DzjMG;;IvDmHJ,2CAAA;IACQ,mCAAA;IuDjHF,QAAA;G5D4jML;E4D1jMG;;IvD8GJ,4CAAA;IACQ,oCAAA;IuD5GF,QAAA;G5D6jML;E4D3jMG;;;IvDyGJ,wCAAA;IACQ,gCAAA;IuDtGF,QAAA;G5D8jML;CACF;A4DpmMD;;;EA6CI,eAAA;C5D4jMH;A4DzmMD;EAiDI,QAAA;C5D2jMH;A4D5mMD;;EAsDI,mBAAA;EACA,OAAA;EACA,YAAA;C5D0jMH;A4DlnMD;EA4DI,WAAA;C5DyjMH;A4DrnMD;EA+DI,YAAA;C5DyjMH;A4DxnMD;;EAmEI,QAAA;C5DyjMH;A4D5nMD;EAuEI,YAAA;C5DwjMH;A4D/nMD;EA0EI,WAAA;C5DwjMH;A4DhjMD;EACE,mBAAA;EACA,OAAA;EACA,QAAA;EACA,UAAA;EACA,WAAA;EtC9FA,aAAA;EAGA,0BAAA;EsC6FA,gBAAA;EACA,YAAA;EACA,mBAAA;EACA,0CAAA;EACA,mCAAA;C5DmjMD;A4D9iMC;EdnGE,mGAAA;EACA,8FAAA;EACA,qHAAA;EAAA,+FAAA;EACA,4BAAA;EACA,uHAAA;C9CopMH;A4DljMC;EACE,WAAA;EACA,SAAA;EdxGA,mGAAA;EACA,8FAAA;EACA,qHAAA;EAAA,+FAAA;EACA,4BAAA;EACA,uHAAA;C9C6pMH;A4DpjMC;;EAEE,WAAA;EACA,YAAA;EACA,sBAAA;EtCvHF,aAAA;EAGA,0BAAA;CtB4qMD;A4DtlMD;;;;EAuCI,mBAAA;EACA,SAAA;EACA,kBAAA;EACA,WAAA;EACA,sBAAA;C5DqjMH;A4DhmMD;;EA+CI,UAAA;EACA,mBAAA;C5DqjMH;A4DrmMD;;EAoDI,WAAA;EACA,oBAAA;C5DqjMH;A4D1mMD;;EAyDI,YAAA;EACA,aAAA;EACA,eAAA;EACA,mBAAA;C5DqjMH;A4DhjMG;EACE,iBAAA;C5DkjML;A4D9iMG;EACE,iBAAA;C5DgjML;A4DtiMD;EACE,mBAAA;EACA,aAAA;EACA,UAAA;EACA,YAAA;EACA,WAAA;EACA,kBAAA;EACA,gBAAA;EACA,iBAAA;EACA,mBAAA;C5DwiMD;A4DjjMD;EAYI,sBAAA;EACA,YAAA;EACA,aAAA;EACA,YAAA;EACA,oBAAA;EACA,uBAAA;EACA,oBAAA;EACA,gBAAA;EAWA,0BAAA;EACA,mCAAA;C5D8hMH;A4D7jMD;EAkCI,UAAA;EACA,YAAA;EACA,aAAA;EACA,uBAAA;C5D8hMH;A4DvhMD;EACE,mBAAA;EACA,UAAA;EACA,WAAA;EACA,aAAA;EACA,YAAA;EACA,kBAAA;EACA,qBAAA;EACA,YAAA;EACA,mBAAA;EACA,0CAAA;C5DyhMD;A4DxhMC;EACE,kBAAA;C5D0hMH;A4Dj/LD;EAhCE;;;;IAKI,YAAA;IACA,aAAA;IACA,kBAAA;IACA,gBAAA;G5DmhMH;E4D3hMD;;IAYI,mBAAA;G5DmhMH;E4D/hMD;;IAgBI,oBAAA;G5DmhMH;E4D9gMD;IACE,UAAA;IACA,WAAA;IACA,qBAAA;G5DghMD;E4D5gMD;IACE,aAAA;G5D8gMD;CACF;A6D7wMC;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;EAEE,aAAA;EACA,eAAA;C7D6yMH;A6D3yMC;;;;;;;;;;;;;;;;EACE,YAAA;C7D4zMH;AiCp0MD;E6BRE,eAAA;EACA,kBAAA;EACA,mBAAA;C9D+0MD;AiCt0MD;EACE,wBAAA;CjCw0MD;AiCt0MD;EACE,uBAAA;CjCw0MD;AiCh0MD;EACE,yBAAA;CjCk0MD;AiCh0MD;EACE,0BAAA;CjCk0MD;AiCh0MD;EACE,mBAAA;CjCk0MD;AiCh0MD;E8BzBE,YAAA;EACA,mBAAA;EACA,kBAAA;EACA,8BAAA;EACA,UAAA;C/D41MD;AiC9zMD;EACE,yBAAA;CjCg0MD;AiCzzMD;EACE,gBAAA;CjC2zMD;AgE51MD;EACE,oBAAA;ChE81MD;AgEx1MD;;;;ECdE,yBAAA;CjE42MD;AgEv1MD;;;;;;;;;;;;EAYE,yBAAA;ChEy1MD;AgEl1MD;EA6IA;IC7LE,0BAAA;GjEs4MC;EiEr4MD;IAAU,0BAAA;GjEw4MT;EiEv4MD;IAAU,8BAAA;GjE04MT;EiEz4MD;;IACU,+BAAA;GjE44MT;CACF;AgE51MD;EAwIA;IA1II,0BAAA;GhEk2MD;CACF;AgE51MD;EAmIA;IArII,2BAAA;GhEk2MD;CACF;AgE51MD;EA8HA;IAhII,iCAAA;GhEk2MD;CACF;AgE31MD;EAwHA;IC7LE,0BAAA;GjEo6MC;EiEn6MD;IAAU,0BAAA;GjEs6MT;EiEr6MD;IAAU,8BAAA;GjEw6MT;EiEv6MD;;IACU,+BAAA;GjE06MT;CACF;AgEr2MD;EAmHA;IArHI,0BAAA;GhE22MD;CACF;AgEr2MD;EA8GA;IAhHI,2BAAA;GhE22MD;CACF;AgEr2MD;EAyGA;IA3GI,iCAAA;GhE22MD;CACF;AgEp2MD;EAmGA;IC7LE,0BAAA;GjEk8MC;EiEj8MD;IAAU,0BAAA;GjEo8MT;EiEn8MD;IAAU,8BAAA;GjEs8MT;EiEr8MD;;IACU,+BAAA;GjEw8MT;CACF;AgE92MD;EA8FA;IAhGI,0BAAA;GhEo3MD;CACF;AgE92MD;EAyFA;IA3FI,2BAAA;GhEo3MD;CACF;AgE92MD;EAoFA;IAtFI,iCAAA;GhEo3MD;CACF;AgE72MD;EA8EA;IC7LE,0BAAA;GjEg+MC;EiE/9MD;IAAU,0BAAA;GjEk+MT;EiEj+MD;IAAU,8BAAA;GjEo+MT;EiEn+MD;;IACU,+BAAA;GjEs+MT;CACF;AgEv3MD;EAyEA;IA3EI,0BAAA;GhE63MD;CACF;AgEv3MD;EAoEA;IAtEI,2BAAA;GhE63MD;CACF;AgEv3MD;EA+DA;IAjEI,iCAAA;GhE63MD;CACF;AgEt3MD;EAyDA;ICrLE,yBAAA;GjEs/MC;CACF;AgEt3MD;EAoDA;ICrLE,yBAAA;GjE2/MC;CACF;AgEt3MD;EA+CA;ICrLE,yBAAA;GjEggNC;CACF;AgEt3MD;EA0CA;ICrLE,yBAAA;GjEqgNC;CACF;AgEn3MD;ECnJE,yBAAA;CjEygND;AgEh3MD;EA4BA;IC7LE,0BAAA;GjEqhNC;EiEphND;IAAU,0BAAA;GjEuhNT;EiEthND;IAAU,8BAAA;GjEyhNT;EiExhND;;IACU,+BAAA;GjE2hNT;CACF;AgE93MD;EACE,yBAAA;ChEg4MD;AgE33MD;EAqBA;IAvBI,0BAAA;GhEi4MD;CACF;AgE/3MD;EACE,yBAAA;ChEi4MD;AgE53MD;EAcA;IAhBI,2BAAA;GhEk4MD;CACF;AgEh4MD;EACE,yBAAA;ChEk4MD;AgE73MD;EAOA;IATI,iCAAA;GhEm4MD;CACF;AgE53MD;EACA;ICrLE,yBAAA;GjEojNC;CACF","file":"bootstrap.css","sourcesContent":["/*!\n * Bootstrap v3.3.7 (http://getbootstrap.com)\n * Copyright 2011-2016 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\nhtml {\n font-family: sans-serif;\n -ms-text-size-adjust: 100%;\n -webkit-text-size-adjust: 100%;\n}\nbody {\n margin: 0;\n}\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n display: block;\n}\naudio,\ncanvas,\nprogress,\nvideo {\n display: inline-block;\n vertical-align: baseline;\n}\naudio:not([controls]) {\n display: none;\n height: 0;\n}\n[hidden],\ntemplate {\n display: none;\n}\na {\n background-color: transparent;\n}\na:active,\na:hover {\n outline: 0;\n}\nabbr[title] {\n border-bottom: 1px dotted;\n}\nb,\nstrong {\n font-weight: bold;\n}\ndfn {\n font-style: italic;\n}\nh1 {\n font-size: 2em;\n margin: 0.67em 0;\n}\nmark {\n background: #ff0;\n color: #000;\n}\nsmall {\n font-size: 80%;\n}\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\nsup {\n top: -0.5em;\n}\nsub {\n bottom: -0.25em;\n}\nimg {\n border: 0;\n}\nsvg:not(:root) {\n overflow: hidden;\n}\nfigure {\n margin: 1em 40px;\n}\nhr {\n box-sizing: content-box;\n height: 0;\n}\npre {\n overflow: auto;\n}\ncode,\nkbd,\npre,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n color: inherit;\n font: inherit;\n margin: 0;\n}\nbutton {\n overflow: visible;\n}\nbutton,\nselect {\n text-transform: none;\n}\nbutton,\nhtml input[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button;\n cursor: pointer;\n}\nbutton[disabled],\nhtml input[disabled] {\n cursor: default;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\ninput {\n line-height: normal;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box;\n padding: 0;\n}\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\ninput[type=\"search\"] {\n -webkit-appearance: textfield;\n box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\nfieldset {\n border: 1px solid #c0c0c0;\n margin: 0 2px;\n padding: 0.35em 0.625em 0.75em;\n}\nlegend {\n border: 0;\n padding: 0;\n}\ntextarea {\n overflow: auto;\n}\noptgroup {\n font-weight: bold;\n}\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\ntd,\nth {\n padding: 0;\n}\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n@media print {\n *,\n *:before,\n *:after {\n background: transparent !important;\n color: #000 !important;\n box-shadow: none !important;\n text-shadow: none !important;\n }\n a,\n a:visited {\n text-decoration: underline;\n }\n a[href]:after {\n content: \" (\" attr(href) \")\";\n }\n abbr[title]:after {\n content: \" (\" attr(title) \")\";\n }\n a[href^=\"#\"]:after,\n a[href^=\"javascript:\"]:after {\n content: \"\";\n }\n pre,\n blockquote {\n border: 1px solid #999;\n page-break-inside: avoid;\n }\n thead {\n display: table-header-group;\n }\n tr,\n img {\n page-break-inside: avoid;\n }\n img {\n max-width: 100% !important;\n }\n p,\n h2,\n h3 {\n orphans: 3;\n widows: 3;\n }\n h2,\n h3 {\n page-break-after: avoid;\n }\n .navbar {\n display: none;\n }\n .btn > .caret,\n .dropup > .btn > .caret {\n border-top-color: #000 !important;\n }\n .label {\n border: 1px solid #000;\n }\n .table {\n border-collapse: collapse !important;\n }\n .table td,\n .table th {\n background-color: #fff !important;\n }\n .table-bordered th,\n .table-bordered td {\n border: 1px solid #ddd !important;\n }\n}\n@font-face {\n font-family: 'Glyphicons Halflings';\n src: url('../fonts/glyphicons-halflings-regular.eot');\n src: url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../fonts/glyphicons-halflings-regular.woff') format('woff'), url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');\n}\n.glyphicon {\n position: relative;\n top: 1px;\n display: inline-block;\n font-family: 'Glyphicons Halflings';\n font-style: normal;\n font-weight: normal;\n line-height: 1;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n.glyphicon-asterisk:before {\n content: \"\\002a\";\n}\n.glyphicon-plus:before {\n content: \"\\002b\";\n}\n.glyphicon-euro:before,\n.glyphicon-eur:before {\n content: \"\\20ac\";\n}\n.glyphicon-minus:before {\n content: \"\\2212\";\n}\n.glyphicon-cloud:before {\n content: \"\\2601\";\n}\n.glyphicon-envelope:before {\n content: \"\\2709\";\n}\n.glyphicon-pencil:before {\n content: \"\\270f\";\n}\n.glyphicon-glass:before {\n content: \"\\e001\";\n}\n.glyphicon-music:before {\n content: \"\\e002\";\n}\n.glyphicon-search:before {\n content: \"\\e003\";\n}\n.glyphicon-heart:before {\n content: \"\\e005\";\n}\n.glyphicon-star:before {\n content: \"\\e006\";\n}\n.glyphicon-star-empty:before {\n content: \"\\e007\";\n}\n.glyphicon-user:before {\n content: \"\\e008\";\n}\n.glyphicon-film:before {\n content: \"\\e009\";\n}\n.glyphicon-th-large:before {\n content: \"\\e010\";\n}\n.glyphicon-th:before {\n content: \"\\e011\";\n}\n.glyphicon-th-list:before {\n content: \"\\e012\";\n}\n.glyphicon-ok:before {\n content: \"\\e013\";\n}\n.glyphicon-remove:before {\n content: \"\\e014\";\n}\n.glyphicon-zoom-in:before {\n content: \"\\e015\";\n}\n.glyphicon-zoom-out:before {\n content: \"\\e016\";\n}\n.glyphicon-off:before {\n content: \"\\e017\";\n}\n.glyphicon-signal:before {\n content: \"\\e018\";\n}\n.glyphicon-cog:before {\n content: \"\\e019\";\n}\n.glyphicon-trash:before {\n content: \"\\e020\";\n}\n.glyphicon-home:before {\n content: \"\\e021\";\n}\n.glyphicon-file:before {\n content: \"\\e022\";\n}\n.glyphicon-time:before {\n content: \"\\e023\";\n}\n.glyphicon-road:before {\n content: \"\\e024\";\n}\n.glyphicon-download-alt:before {\n content: \"\\e025\";\n}\n.glyphicon-download:before {\n content: \"\\e026\";\n}\n.glyphicon-upload:before {\n content: \"\\e027\";\n}\n.glyphicon-inbox:before {\n content: \"\\e028\";\n}\n.glyphicon-play-circle:before {\n content: \"\\e029\";\n}\n.glyphicon-repeat:before {\n content: \"\\e030\";\n}\n.glyphicon-refresh:before {\n content: \"\\e031\";\n}\n.glyphicon-list-alt:before {\n content: \"\\e032\";\n}\n.glyphicon-lock:before {\n content: \"\\e033\";\n}\n.glyphicon-flag:before {\n content: \"\\e034\";\n}\n.glyphicon-headphones:before {\n content: \"\\e035\";\n}\n.glyphicon-volume-off:before {\n content: \"\\e036\";\n}\n.glyphicon-volume-down:before {\n content: \"\\e037\";\n}\n.glyphicon-volume-up:before {\n content: \"\\e038\";\n}\n.glyphicon-qrcode:before {\n content: \"\\e039\";\n}\n.glyphicon-barcode:before {\n content: \"\\e040\";\n}\n.glyphicon-tag:before {\n content: \"\\e041\";\n}\n.glyphicon-tags:before {\n content: \"\\e042\";\n}\n.glyphicon-book:before {\n content: \"\\e043\";\n}\n.glyphicon-bookmark:before {\n content: \"\\e044\";\n}\n.glyphicon-print:before {\n content: \"\\e045\";\n}\n.glyphicon-camera:before {\n content: \"\\e046\";\n}\n.glyphicon-font:before {\n content: \"\\e047\";\n}\n.glyphicon-bold:before {\n content: \"\\e048\";\n}\n.glyphicon-italic:before {\n content: \"\\e049\";\n}\n.glyphicon-text-height:before {\n content: \"\\e050\";\n}\n.glyphicon-text-width:before {\n content: \"\\e051\";\n}\n.glyphicon-align-left:before {\n content: \"\\e052\";\n}\n.glyphicon-align-center:before {\n content: \"\\e053\";\n}\n.glyphicon-align-right:before {\n content: \"\\e054\";\n}\n.glyphicon-align-justify:before {\n content: \"\\e055\";\n}\n.glyphicon-list:before {\n content: \"\\e056\";\n}\n.glyphicon-indent-left:before {\n content: \"\\e057\";\n}\n.glyphicon-indent-right:before {\n content: \"\\e058\";\n}\n.glyphicon-facetime-video:before {\n content: \"\\e059\";\n}\n.glyphicon-picture:before {\n content: \"\\e060\";\n}\n.glyphicon-map-marker:before {\n content: \"\\e062\";\n}\n.glyphicon-adjust:before {\n content: \"\\e063\";\n}\n.glyphicon-tint:before {\n content: \"\\e064\";\n}\n.glyphicon-edit:before {\n content: \"\\e065\";\n}\n.glyphicon-share:before {\n content: \"\\e066\";\n}\n.glyphicon-check:before {\n content: \"\\e067\";\n}\n.glyphicon-move:before {\n content: \"\\e068\";\n}\n.glyphicon-step-backward:before {\n content: \"\\e069\";\n}\n.glyphicon-fast-backward:before {\n content: \"\\e070\";\n}\n.glyphicon-backward:before {\n content: \"\\e071\";\n}\n.glyphicon-play:before {\n content: \"\\e072\";\n}\n.glyphicon-pause:before {\n content: \"\\e073\";\n}\n.glyphicon-stop:before {\n content: \"\\e074\";\n}\n.glyphicon-forward:before {\n content: \"\\e075\";\n}\n.glyphicon-fast-forward:before {\n content: \"\\e076\";\n}\n.glyphicon-step-forward:before {\n content: \"\\e077\";\n}\n.glyphicon-eject:before {\n content: \"\\e078\";\n}\n.glyphicon-chevron-left:before {\n content: \"\\e079\";\n}\n.glyphicon-chevron-right:before {\n content: \"\\e080\";\n}\n.glyphicon-plus-sign:before {\n content: \"\\e081\";\n}\n.glyphicon-minus-sign:before {\n content: \"\\e082\";\n}\n.glyphicon-remove-sign:before {\n content: \"\\e083\";\n}\n.glyphicon-ok-sign:before {\n content: \"\\e084\";\n}\n.glyphicon-question-sign:before {\n content: \"\\e085\";\n}\n.glyphicon-info-sign:before {\n content: \"\\e086\";\n}\n.glyphicon-screenshot:before {\n content: \"\\e087\";\n}\n.glyphicon-remove-circle:before {\n content: \"\\e088\";\n}\n.glyphicon-ok-circle:before {\n content: \"\\e089\";\n}\n.glyphicon-ban-circle:before {\n content: \"\\e090\";\n}\n.glyphicon-arrow-left:before {\n content: \"\\e091\";\n}\n.glyphicon-arrow-right:before {\n content: \"\\e092\";\n}\n.glyphicon-arrow-up:before {\n content: \"\\e093\";\n}\n.glyphicon-arrow-down:before {\n content: \"\\e094\";\n}\n.glyphicon-share-alt:before {\n content: \"\\e095\";\n}\n.glyphicon-resize-full:before {\n content: \"\\e096\";\n}\n.glyphicon-resize-small:before {\n content: \"\\e097\";\n}\n.glyphicon-exclamation-sign:before {\n content: \"\\e101\";\n}\n.glyphicon-gift:before {\n content: \"\\e102\";\n}\n.glyphicon-leaf:before {\n content: \"\\e103\";\n}\n.glyphicon-fire:before {\n content: \"\\e104\";\n}\n.glyphicon-eye-open:before {\n content: \"\\e105\";\n}\n.glyphicon-eye-close:before {\n content: \"\\e106\";\n}\n.glyphicon-warning-sign:before {\n content: \"\\e107\";\n}\n.glyphicon-plane:before {\n content: \"\\e108\";\n}\n.glyphicon-calendar:before {\n content: \"\\e109\";\n}\n.glyphicon-random:before {\n content: \"\\e110\";\n}\n.glyphicon-comment:before {\n content: \"\\e111\";\n}\n.glyphicon-magnet:before {\n content: \"\\e112\";\n}\n.glyphicon-chevron-up:before {\n content: \"\\e113\";\n}\n.glyphicon-chevron-down:before {\n content: \"\\e114\";\n}\n.glyphicon-retweet:before {\n content: \"\\e115\";\n}\n.glyphicon-shopping-cart:before {\n content: \"\\e116\";\n}\n.glyphicon-folder-close:before {\n content: \"\\e117\";\n}\n.glyphicon-folder-open:before {\n content: \"\\e118\";\n}\n.glyphicon-resize-vertical:before {\n content: \"\\e119\";\n}\n.glyphicon-resize-horizontal:before {\n content: \"\\e120\";\n}\n.glyphicon-hdd:before {\n content: \"\\e121\";\n}\n.glyphicon-bullhorn:before {\n content: \"\\e122\";\n}\n.glyphicon-bell:before {\n content: \"\\e123\";\n}\n.glyphicon-certificate:before {\n content: \"\\e124\";\n}\n.glyphicon-thumbs-up:before {\n content: \"\\e125\";\n}\n.glyphicon-thumbs-down:before {\n content: \"\\e126\";\n}\n.glyphicon-hand-right:before {\n content: \"\\e127\";\n}\n.glyphicon-hand-left:before {\n content: \"\\e128\";\n}\n.glyphicon-hand-up:before {\n content: \"\\e129\";\n}\n.glyphicon-hand-down:before {\n content: \"\\e130\";\n}\n.glyphicon-circle-arrow-right:before {\n content: \"\\e131\";\n}\n.glyphicon-circle-arrow-left:before {\n content: \"\\e132\";\n}\n.glyphicon-circle-arrow-up:before {\n content: \"\\e133\";\n}\n.glyphicon-circle-arrow-down:before {\n content: \"\\e134\";\n}\n.glyphicon-globe:before {\n content: \"\\e135\";\n}\n.glyphicon-wrench:before {\n content: \"\\e136\";\n}\n.glyphicon-tasks:before {\n content: \"\\e137\";\n}\n.glyphicon-filter:before {\n content: \"\\e138\";\n}\n.glyphicon-briefcase:before {\n content: \"\\e139\";\n}\n.glyphicon-fullscreen:before {\n content: \"\\e140\";\n}\n.glyphicon-dashboard:before {\n content: \"\\e141\";\n}\n.glyphicon-paperclip:before {\n content: \"\\e142\";\n}\n.glyphicon-heart-empty:before {\n content: \"\\e143\";\n}\n.glyphicon-link:before {\n content: \"\\e144\";\n}\n.glyphicon-phone:before {\n content: \"\\e145\";\n}\n.glyphicon-pushpin:before {\n content: \"\\e146\";\n}\n.glyphicon-usd:before {\n content: \"\\e148\";\n}\n.glyphicon-gbp:before {\n content: \"\\e149\";\n}\n.glyphicon-sort:before {\n content: \"\\e150\";\n}\n.glyphicon-sort-by-alphabet:before {\n content: \"\\e151\";\n}\n.glyphicon-sort-by-alphabet-alt:before {\n content: \"\\e152\";\n}\n.glyphicon-sort-by-order:before {\n content: \"\\e153\";\n}\n.glyphicon-sort-by-order-alt:before {\n content: \"\\e154\";\n}\n.glyphicon-sort-by-attributes:before {\n content: \"\\e155\";\n}\n.glyphicon-sort-by-attributes-alt:before {\n content: \"\\e156\";\n}\n.glyphicon-unchecked:before {\n content: \"\\e157\";\n}\n.glyphicon-expand:before {\n content: \"\\e158\";\n}\n.glyphicon-collapse-down:before {\n content: \"\\e159\";\n}\n.glyphicon-collapse-up:before {\n content: \"\\e160\";\n}\n.glyphicon-log-in:before {\n content: \"\\e161\";\n}\n.glyphicon-flash:before {\n content: \"\\e162\";\n}\n.glyphicon-log-out:before {\n content: \"\\e163\";\n}\n.glyphicon-new-window:before {\n content: \"\\e164\";\n}\n.glyphicon-record:before {\n content: \"\\e165\";\n}\n.glyphicon-save:before {\n content: \"\\e166\";\n}\n.glyphicon-open:before {\n content: \"\\e167\";\n}\n.glyphicon-saved:before {\n content: \"\\e168\";\n}\n.glyphicon-import:before {\n content: \"\\e169\";\n}\n.glyphicon-export:before {\n content: \"\\e170\";\n}\n.glyphicon-send:before {\n content: \"\\e171\";\n}\n.glyphicon-floppy-disk:before {\n content: \"\\e172\";\n}\n.glyphicon-floppy-saved:before {\n content: \"\\e173\";\n}\n.glyphicon-floppy-remove:before {\n content: \"\\e174\";\n}\n.glyphicon-floppy-save:before {\n content: \"\\e175\";\n}\n.glyphicon-floppy-open:before {\n content: \"\\e176\";\n}\n.glyphicon-credit-card:before {\n content: \"\\e177\";\n}\n.glyphicon-transfer:before {\n content: \"\\e178\";\n}\n.glyphicon-cutlery:before {\n content: \"\\e179\";\n}\n.glyphicon-header:before {\n content: \"\\e180\";\n}\n.glyphicon-compressed:before {\n content: \"\\e181\";\n}\n.glyphicon-earphone:before {\n content: \"\\e182\";\n}\n.glyphicon-phone-alt:before {\n content: \"\\e183\";\n}\n.glyphicon-tower:before {\n content: \"\\e184\";\n}\n.glyphicon-stats:before {\n content: \"\\e185\";\n}\n.glyphicon-sd-video:before {\n content: \"\\e186\";\n}\n.glyphicon-hd-video:before {\n content: \"\\e187\";\n}\n.glyphicon-subtitles:before {\n content: \"\\e188\";\n}\n.glyphicon-sound-stereo:before {\n content: \"\\e189\";\n}\n.glyphicon-sound-dolby:before {\n content: \"\\e190\";\n}\n.glyphicon-sound-5-1:before {\n content: \"\\e191\";\n}\n.glyphicon-sound-6-1:before {\n content: \"\\e192\";\n}\n.glyphicon-sound-7-1:before {\n content: \"\\e193\";\n}\n.glyphicon-copyright-mark:before {\n content: \"\\e194\";\n}\n.glyphicon-registration-mark:before {\n content: \"\\e195\";\n}\n.glyphicon-cloud-download:before {\n content: \"\\e197\";\n}\n.glyphicon-cloud-upload:before {\n content: \"\\e198\";\n}\n.glyphicon-tree-conifer:before {\n content: \"\\e199\";\n}\n.glyphicon-tree-deciduous:before {\n content: \"\\e200\";\n}\n.glyphicon-cd:before {\n content: \"\\e201\";\n}\n.glyphicon-save-file:before {\n content: \"\\e202\";\n}\n.glyphicon-open-file:before {\n content: \"\\e203\";\n}\n.glyphicon-level-up:before {\n content: \"\\e204\";\n}\n.glyphicon-copy:before {\n content: \"\\e205\";\n}\n.glyphicon-paste:before {\n content: \"\\e206\";\n}\n.glyphicon-alert:before {\n content: \"\\e209\";\n}\n.glyphicon-equalizer:before {\n content: \"\\e210\";\n}\n.glyphicon-king:before {\n content: \"\\e211\";\n}\n.glyphicon-queen:before {\n content: \"\\e212\";\n}\n.glyphicon-pawn:before {\n content: \"\\e213\";\n}\n.glyphicon-bishop:before {\n content: \"\\e214\";\n}\n.glyphicon-knight:before {\n content: \"\\e215\";\n}\n.glyphicon-baby-formula:before {\n content: \"\\e216\";\n}\n.glyphicon-tent:before {\n content: \"\\26fa\";\n}\n.glyphicon-blackboard:before {\n content: \"\\e218\";\n}\n.glyphicon-bed:before {\n content: \"\\e219\";\n}\n.glyphicon-apple:before {\n content: \"\\f8ff\";\n}\n.glyphicon-erase:before {\n content: \"\\e221\";\n}\n.glyphicon-hourglass:before {\n content: \"\\231b\";\n}\n.glyphicon-lamp:before {\n content: \"\\e223\";\n}\n.glyphicon-duplicate:before {\n content: \"\\e224\";\n}\n.glyphicon-piggy-bank:before {\n content: \"\\e225\";\n}\n.glyphicon-scissors:before {\n content: \"\\e226\";\n}\n.glyphicon-bitcoin:before {\n content: \"\\e227\";\n}\n.glyphicon-btc:before {\n content: \"\\e227\";\n}\n.glyphicon-xbt:before {\n content: \"\\e227\";\n}\n.glyphicon-yen:before {\n content: \"\\00a5\";\n}\n.glyphicon-jpy:before {\n content: \"\\00a5\";\n}\n.glyphicon-ruble:before {\n content: \"\\20bd\";\n}\n.glyphicon-rub:before {\n content: \"\\20bd\";\n}\n.glyphicon-scale:before {\n content: \"\\e230\";\n}\n.glyphicon-ice-lolly:before {\n content: \"\\e231\";\n}\n.glyphicon-ice-lolly-tasted:before {\n content: \"\\e232\";\n}\n.glyphicon-education:before {\n content: \"\\e233\";\n}\n.glyphicon-option-horizontal:before {\n content: \"\\e234\";\n}\n.glyphicon-option-vertical:before {\n content: \"\\e235\";\n}\n.glyphicon-menu-hamburger:before {\n content: \"\\e236\";\n}\n.glyphicon-modal-window:before {\n content: \"\\e237\";\n}\n.glyphicon-oil:before {\n content: \"\\e238\";\n}\n.glyphicon-grain:before {\n content: \"\\e239\";\n}\n.glyphicon-sunglasses:before {\n content: \"\\e240\";\n}\n.glyphicon-text-size:before {\n content: \"\\e241\";\n}\n.glyphicon-text-color:before {\n content: \"\\e242\";\n}\n.glyphicon-text-background:before {\n content: \"\\e243\";\n}\n.glyphicon-object-align-top:before {\n content: \"\\e244\";\n}\n.glyphicon-object-align-bottom:before {\n content: \"\\e245\";\n}\n.glyphicon-object-align-horizontal:before {\n content: \"\\e246\";\n}\n.glyphicon-object-align-left:before {\n content: \"\\e247\";\n}\n.glyphicon-object-align-vertical:before {\n content: \"\\e248\";\n}\n.glyphicon-object-align-right:before {\n content: \"\\e249\";\n}\n.glyphicon-triangle-right:before {\n content: \"\\e250\";\n}\n.glyphicon-triangle-left:before {\n content: \"\\e251\";\n}\n.glyphicon-triangle-bottom:before {\n content: \"\\e252\";\n}\n.glyphicon-triangle-top:before {\n content: \"\\e253\";\n}\n.glyphicon-console:before {\n content: \"\\e254\";\n}\n.glyphicon-superscript:before {\n content: \"\\e255\";\n}\n.glyphicon-subscript:before {\n content: \"\\e256\";\n}\n.glyphicon-menu-left:before {\n content: \"\\e257\";\n}\n.glyphicon-menu-right:before {\n content: \"\\e258\";\n}\n.glyphicon-menu-down:before {\n content: \"\\e259\";\n}\n.glyphicon-menu-up:before {\n content: \"\\e260\";\n}\n* {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\n*:before,\n*:after {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\nhtml {\n font-size: 10px;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\nbody {\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-size: 14px;\n line-height: 1.42857143;\n color: #333333;\n background-color: #fff;\n}\ninput,\nbutton,\nselect,\ntextarea {\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\na {\n color: #337ab7;\n text-decoration: none;\n}\na:hover,\na:focus {\n color: #23527c;\n text-decoration: underline;\n}\na:focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\nfigure {\n margin: 0;\n}\nimg {\n vertical-align: middle;\n}\n.img-responsive,\n.thumbnail > img,\n.thumbnail a > img,\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n display: block;\n max-width: 100%;\n height: auto;\n}\n.img-rounded {\n border-radius: 6px;\n}\n.img-thumbnail {\n padding: 4px;\n line-height: 1.42857143;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 4px;\n -webkit-transition: all 0.2s ease-in-out;\n -o-transition: all 0.2s ease-in-out;\n transition: all 0.2s ease-in-out;\n display: inline-block;\n max-width: 100%;\n height: auto;\n}\n.img-circle {\n border-radius: 50%;\n}\nhr {\n margin-top: 20px;\n margin-bottom: 20px;\n border: 0;\n border-top: 1px solid #eeeeee;\n}\n.sr-only {\n position: absolute;\n width: 1px;\n height: 1px;\n margin: -1px;\n padding: 0;\n overflow: hidden;\n clip: rect(0, 0, 0, 0);\n border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n position: static;\n width: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n clip: auto;\n}\n[role=\"button\"] {\n cursor: pointer;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\n.h1,\n.h2,\n.h3,\n.h4,\n.h5,\n.h6 {\n font-family: inherit;\n font-weight: 500;\n line-height: 1.1;\n color: inherit;\n}\nh1 small,\nh2 small,\nh3 small,\nh4 small,\nh5 small,\nh6 small,\n.h1 small,\n.h2 small,\n.h3 small,\n.h4 small,\n.h5 small,\n.h6 small,\nh1 .small,\nh2 .small,\nh3 .small,\nh4 .small,\nh5 .small,\nh6 .small,\n.h1 .small,\n.h2 .small,\n.h3 .small,\n.h4 .small,\n.h5 .small,\n.h6 .small {\n font-weight: normal;\n line-height: 1;\n color: #777777;\n}\nh1,\n.h1,\nh2,\n.h2,\nh3,\n.h3 {\n margin-top: 20px;\n margin-bottom: 10px;\n}\nh1 small,\n.h1 small,\nh2 small,\n.h2 small,\nh3 small,\n.h3 small,\nh1 .small,\n.h1 .small,\nh2 .small,\n.h2 .small,\nh3 .small,\n.h3 .small {\n font-size: 65%;\n}\nh4,\n.h4,\nh5,\n.h5,\nh6,\n.h6 {\n margin-top: 10px;\n margin-bottom: 10px;\n}\nh4 small,\n.h4 small,\nh5 small,\n.h5 small,\nh6 small,\n.h6 small,\nh4 .small,\n.h4 .small,\nh5 .small,\n.h5 .small,\nh6 .small,\n.h6 .small {\n font-size: 75%;\n}\nh1,\n.h1 {\n font-size: 36px;\n}\nh2,\n.h2 {\n font-size: 30px;\n}\nh3,\n.h3 {\n font-size: 24px;\n}\nh4,\n.h4 {\n font-size: 18px;\n}\nh5,\n.h5 {\n font-size: 14px;\n}\nh6,\n.h6 {\n font-size: 12px;\n}\np {\n margin: 0 0 10px;\n}\n.lead {\n margin-bottom: 20px;\n font-size: 16px;\n font-weight: 300;\n line-height: 1.4;\n}\n@media (min-width: 768px) {\n .lead {\n font-size: 21px;\n }\n}\nsmall,\n.small {\n font-size: 85%;\n}\nmark,\n.mark {\n background-color: #fcf8e3;\n padding: .2em;\n}\n.text-left {\n text-align: left;\n}\n.text-right {\n text-align: right;\n}\n.text-center {\n text-align: center;\n}\n.text-justify {\n text-align: justify;\n}\n.text-nowrap {\n white-space: nowrap;\n}\n.text-lowercase {\n text-transform: lowercase;\n}\n.text-uppercase {\n text-transform: uppercase;\n}\n.text-capitalize {\n text-transform: capitalize;\n}\n.text-muted {\n color: #777777;\n}\n.text-primary {\n color: #337ab7;\n}\na.text-primary:hover,\na.text-primary:focus {\n color: #286090;\n}\n.text-success {\n color: #3c763d;\n}\na.text-success:hover,\na.text-success:focus {\n color: #2b542c;\n}\n.text-info {\n color: #31708f;\n}\na.text-info:hover,\na.text-info:focus {\n color: #245269;\n}\n.text-warning {\n color: #8a6d3b;\n}\na.text-warning:hover,\na.text-warning:focus {\n color: #66512c;\n}\n.text-danger {\n color: #a94442;\n}\na.text-danger:hover,\na.text-danger:focus {\n color: #843534;\n}\n.bg-primary {\n color: #fff;\n background-color: #337ab7;\n}\na.bg-primary:hover,\na.bg-primary:focus {\n background-color: #286090;\n}\n.bg-success {\n background-color: #dff0d8;\n}\na.bg-success:hover,\na.bg-success:focus {\n background-color: #c1e2b3;\n}\n.bg-info {\n background-color: #d9edf7;\n}\na.bg-info:hover,\na.bg-info:focus {\n background-color: #afd9ee;\n}\n.bg-warning {\n background-color: #fcf8e3;\n}\na.bg-warning:hover,\na.bg-warning:focus {\n background-color: #f7ecb5;\n}\n.bg-danger {\n background-color: #f2dede;\n}\na.bg-danger:hover,\na.bg-danger:focus {\n background-color: #e4b9b9;\n}\n.page-header {\n padding-bottom: 9px;\n margin: 40px 0 20px;\n border-bottom: 1px solid #eeeeee;\n}\nul,\nol {\n margin-top: 0;\n margin-bottom: 10px;\n}\nul ul,\nol ul,\nul ol,\nol ol {\n margin-bottom: 0;\n}\n.list-unstyled {\n padding-left: 0;\n list-style: none;\n}\n.list-inline {\n padding-left: 0;\n list-style: none;\n margin-left: -5px;\n}\n.list-inline > li {\n display: inline-block;\n padding-left: 5px;\n padding-right: 5px;\n}\ndl {\n margin-top: 0;\n margin-bottom: 20px;\n}\ndt,\ndd {\n line-height: 1.42857143;\n}\ndt {\n font-weight: bold;\n}\ndd {\n margin-left: 0;\n}\n@media (min-width: 768px) {\n .dl-horizontal dt {\n float: left;\n width: 160px;\n clear: left;\n text-align: right;\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n }\n .dl-horizontal dd {\n margin-left: 180px;\n }\n}\nabbr[title],\nabbr[data-original-title] {\n cursor: help;\n border-bottom: 1px dotted #777777;\n}\n.initialism {\n font-size: 90%;\n text-transform: uppercase;\n}\nblockquote {\n padding: 10px 20px;\n margin: 0 0 20px;\n font-size: 17.5px;\n border-left: 5px solid #eeeeee;\n}\nblockquote p:last-child,\nblockquote ul:last-child,\nblockquote ol:last-child {\n margin-bottom: 0;\n}\nblockquote footer,\nblockquote small,\nblockquote .small {\n display: block;\n font-size: 80%;\n line-height: 1.42857143;\n color: #777777;\n}\nblockquote footer:before,\nblockquote small:before,\nblockquote .small:before {\n content: '\\2014 \\00A0';\n}\n.blockquote-reverse,\nblockquote.pull-right {\n padding-right: 15px;\n padding-left: 0;\n border-right: 5px solid #eeeeee;\n border-left: 0;\n text-align: right;\n}\n.blockquote-reverse footer:before,\nblockquote.pull-right footer:before,\n.blockquote-reverse small:before,\nblockquote.pull-right small:before,\n.blockquote-reverse .small:before,\nblockquote.pull-right .small:before {\n content: '';\n}\n.blockquote-reverse footer:after,\nblockquote.pull-right footer:after,\n.blockquote-reverse small:after,\nblockquote.pull-right small:after,\n.blockquote-reverse .small:after,\nblockquote.pull-right .small:after {\n content: '\\00A0 \\2014';\n}\naddress {\n margin-bottom: 20px;\n font-style: normal;\n line-height: 1.42857143;\n}\ncode,\nkbd,\npre,\nsamp {\n font-family: Menlo, Monaco, Consolas, \"Courier New\", monospace;\n}\ncode {\n padding: 2px 4px;\n font-size: 90%;\n color: #c7254e;\n background-color: #f9f2f4;\n border-radius: 4px;\n}\nkbd {\n padding: 2px 4px;\n font-size: 90%;\n color: #fff;\n background-color: #333;\n border-radius: 3px;\n box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\nkbd kbd {\n padding: 0;\n font-size: 100%;\n font-weight: bold;\n box-shadow: none;\n}\npre {\n display: block;\n padding: 9.5px;\n margin: 0 0 10px;\n font-size: 13px;\n line-height: 1.42857143;\n word-break: break-all;\n word-wrap: break-word;\n color: #333333;\n background-color: #f5f5f5;\n border: 1px solid #ccc;\n border-radius: 4px;\n}\npre code {\n padding: 0;\n font-size: inherit;\n color: inherit;\n white-space: pre-wrap;\n background-color: transparent;\n border-radius: 0;\n}\n.pre-scrollable {\n max-height: 340px;\n overflow-y: scroll;\n}\n.container {\n margin-right: auto;\n margin-left: auto;\n padding-left: 15px;\n padding-right: 15px;\n}\n@media (min-width: 768px) {\n .container {\n width: 750px;\n }\n}\n@media (min-width: 992px) {\n .container {\n width: 970px;\n }\n}\n@media (min-width: 1200px) {\n .container {\n width: 1170px;\n }\n}\n.container-fluid {\n margin-right: auto;\n margin-left: auto;\n padding-left: 15px;\n padding-right: 15px;\n}\n.row {\n margin-left: -15px;\n margin-right: -15px;\n}\n.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {\n position: relative;\n min-height: 1px;\n padding-left: 15px;\n padding-right: 15px;\n}\n.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {\n float: left;\n}\n.col-xs-12 {\n width: 100%;\n}\n.col-xs-11 {\n width: 91.66666667%;\n}\n.col-xs-10 {\n width: 83.33333333%;\n}\n.col-xs-9 {\n width: 75%;\n}\n.col-xs-8 {\n width: 66.66666667%;\n}\n.col-xs-7 {\n width: 58.33333333%;\n}\n.col-xs-6 {\n width: 50%;\n}\n.col-xs-5 {\n width: 41.66666667%;\n}\n.col-xs-4 {\n width: 33.33333333%;\n}\n.col-xs-3 {\n width: 25%;\n}\n.col-xs-2 {\n width: 16.66666667%;\n}\n.col-xs-1 {\n width: 8.33333333%;\n}\n.col-xs-pull-12 {\n right: 100%;\n}\n.col-xs-pull-11 {\n right: 91.66666667%;\n}\n.col-xs-pull-10 {\n right: 83.33333333%;\n}\n.col-xs-pull-9 {\n right: 75%;\n}\n.col-xs-pull-8 {\n right: 66.66666667%;\n}\n.col-xs-pull-7 {\n right: 58.33333333%;\n}\n.col-xs-pull-6 {\n right: 50%;\n}\n.col-xs-pull-5 {\n right: 41.66666667%;\n}\n.col-xs-pull-4 {\n right: 33.33333333%;\n}\n.col-xs-pull-3 {\n right: 25%;\n}\n.col-xs-pull-2 {\n right: 16.66666667%;\n}\n.col-xs-pull-1 {\n right: 8.33333333%;\n}\n.col-xs-pull-0 {\n right: auto;\n}\n.col-xs-push-12 {\n left: 100%;\n}\n.col-xs-push-11 {\n left: 91.66666667%;\n}\n.col-xs-push-10 {\n left: 83.33333333%;\n}\n.col-xs-push-9 {\n left: 75%;\n}\n.col-xs-push-8 {\n left: 66.66666667%;\n}\n.col-xs-push-7 {\n left: 58.33333333%;\n}\n.col-xs-push-6 {\n left: 50%;\n}\n.col-xs-push-5 {\n left: 41.66666667%;\n}\n.col-xs-push-4 {\n left: 33.33333333%;\n}\n.col-xs-push-3 {\n left: 25%;\n}\n.col-xs-push-2 {\n left: 16.66666667%;\n}\n.col-xs-push-1 {\n left: 8.33333333%;\n}\n.col-xs-push-0 {\n left: auto;\n}\n.col-xs-offset-12 {\n margin-left: 100%;\n}\n.col-xs-offset-11 {\n margin-left: 91.66666667%;\n}\n.col-xs-offset-10 {\n margin-left: 83.33333333%;\n}\n.col-xs-offset-9 {\n margin-left: 75%;\n}\n.col-xs-offset-8 {\n margin-left: 66.66666667%;\n}\n.col-xs-offset-7 {\n margin-left: 58.33333333%;\n}\n.col-xs-offset-6 {\n margin-left: 50%;\n}\n.col-xs-offset-5 {\n margin-left: 41.66666667%;\n}\n.col-xs-offset-4 {\n margin-left: 33.33333333%;\n}\n.col-xs-offset-3 {\n margin-left: 25%;\n}\n.col-xs-offset-2 {\n margin-left: 16.66666667%;\n}\n.col-xs-offset-1 {\n margin-left: 8.33333333%;\n}\n.col-xs-offset-0 {\n margin-left: 0%;\n}\n@media (min-width: 768px) {\n .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {\n float: left;\n }\n .col-sm-12 {\n width: 100%;\n }\n .col-sm-11 {\n width: 91.66666667%;\n }\n .col-sm-10 {\n width: 83.33333333%;\n }\n .col-sm-9 {\n width: 75%;\n }\n .col-sm-8 {\n width: 66.66666667%;\n }\n .col-sm-7 {\n width: 58.33333333%;\n }\n .col-sm-6 {\n width: 50%;\n }\n .col-sm-5 {\n width: 41.66666667%;\n }\n .col-sm-4 {\n width: 33.33333333%;\n }\n .col-sm-3 {\n width: 25%;\n }\n .col-sm-2 {\n width: 16.66666667%;\n }\n .col-sm-1 {\n width: 8.33333333%;\n }\n .col-sm-pull-12 {\n right: 100%;\n }\n .col-sm-pull-11 {\n right: 91.66666667%;\n }\n .col-sm-pull-10 {\n right: 83.33333333%;\n }\n .col-sm-pull-9 {\n right: 75%;\n }\n .col-sm-pull-8 {\n right: 66.66666667%;\n }\n .col-sm-pull-7 {\n right: 58.33333333%;\n }\n .col-sm-pull-6 {\n right: 50%;\n }\n .col-sm-pull-5 {\n right: 41.66666667%;\n }\n .col-sm-pull-4 {\n right: 33.33333333%;\n }\n .col-sm-pull-3 {\n right: 25%;\n }\n .col-sm-pull-2 {\n right: 16.66666667%;\n }\n .col-sm-pull-1 {\n right: 8.33333333%;\n }\n .col-sm-pull-0 {\n right: auto;\n }\n .col-sm-push-12 {\n left: 100%;\n }\n .col-sm-push-11 {\n left: 91.66666667%;\n }\n .col-sm-push-10 {\n left: 83.33333333%;\n }\n .col-sm-push-9 {\n left: 75%;\n }\n .col-sm-push-8 {\n left: 66.66666667%;\n }\n .col-sm-push-7 {\n left: 58.33333333%;\n }\n .col-sm-push-6 {\n left: 50%;\n }\n .col-sm-push-5 {\n left: 41.66666667%;\n }\n .col-sm-push-4 {\n left: 33.33333333%;\n }\n .col-sm-push-3 {\n left: 25%;\n }\n .col-sm-push-2 {\n left: 16.66666667%;\n }\n .col-sm-push-1 {\n left: 8.33333333%;\n }\n .col-sm-push-0 {\n left: auto;\n }\n .col-sm-offset-12 {\n margin-left: 100%;\n }\n .col-sm-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-sm-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-sm-offset-9 {\n margin-left: 75%;\n }\n .col-sm-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-sm-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-sm-offset-6 {\n margin-left: 50%;\n }\n .col-sm-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-sm-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-sm-offset-3 {\n margin-left: 25%;\n }\n .col-sm-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-sm-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-sm-offset-0 {\n margin-left: 0%;\n }\n}\n@media (min-width: 992px) {\n .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {\n float: left;\n }\n .col-md-12 {\n width: 100%;\n }\n .col-md-11 {\n width: 91.66666667%;\n }\n .col-md-10 {\n width: 83.33333333%;\n }\n .col-md-9 {\n width: 75%;\n }\n .col-md-8 {\n width: 66.66666667%;\n }\n .col-md-7 {\n width: 58.33333333%;\n }\n .col-md-6 {\n width: 50%;\n }\n .col-md-5 {\n width: 41.66666667%;\n }\n .col-md-4 {\n width: 33.33333333%;\n }\n .col-md-3 {\n width: 25%;\n }\n .col-md-2 {\n width: 16.66666667%;\n }\n .col-md-1 {\n width: 8.33333333%;\n }\n .col-md-pull-12 {\n right: 100%;\n }\n .col-md-pull-11 {\n right: 91.66666667%;\n }\n .col-md-pull-10 {\n right: 83.33333333%;\n }\n .col-md-pull-9 {\n right: 75%;\n }\n .col-md-pull-8 {\n right: 66.66666667%;\n }\n .col-md-pull-7 {\n right: 58.33333333%;\n }\n .col-md-pull-6 {\n right: 50%;\n }\n .col-md-pull-5 {\n right: 41.66666667%;\n }\n .col-md-pull-4 {\n right: 33.33333333%;\n }\n .col-md-pull-3 {\n right: 25%;\n }\n .col-md-pull-2 {\n right: 16.66666667%;\n }\n .col-md-pull-1 {\n right: 8.33333333%;\n }\n .col-md-pull-0 {\n right: auto;\n }\n .col-md-push-12 {\n left: 100%;\n }\n .col-md-push-11 {\n left: 91.66666667%;\n }\n .col-md-push-10 {\n left: 83.33333333%;\n }\n .col-md-push-9 {\n left: 75%;\n }\n .col-md-push-8 {\n left: 66.66666667%;\n }\n .col-md-push-7 {\n left: 58.33333333%;\n }\n .col-md-push-6 {\n left: 50%;\n }\n .col-md-push-5 {\n left: 41.66666667%;\n }\n .col-md-push-4 {\n left: 33.33333333%;\n }\n .col-md-push-3 {\n left: 25%;\n }\n .col-md-push-2 {\n left: 16.66666667%;\n }\n .col-md-push-1 {\n left: 8.33333333%;\n }\n .col-md-push-0 {\n left: auto;\n }\n .col-md-offset-12 {\n margin-left: 100%;\n }\n .col-md-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-md-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-md-offset-9 {\n margin-left: 75%;\n }\n .col-md-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-md-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-md-offset-6 {\n margin-left: 50%;\n }\n .col-md-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-md-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-md-offset-3 {\n margin-left: 25%;\n }\n .col-md-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-md-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-md-offset-0 {\n margin-left: 0%;\n }\n}\n@media (min-width: 1200px) {\n .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {\n float: left;\n }\n .col-lg-12 {\n width: 100%;\n }\n .col-lg-11 {\n width: 91.66666667%;\n }\n .col-lg-10 {\n width: 83.33333333%;\n }\n .col-lg-9 {\n width: 75%;\n }\n .col-lg-8 {\n width: 66.66666667%;\n }\n .col-lg-7 {\n width: 58.33333333%;\n }\n .col-lg-6 {\n width: 50%;\n }\n .col-lg-5 {\n width: 41.66666667%;\n }\n .col-lg-4 {\n width: 33.33333333%;\n }\n .col-lg-3 {\n width: 25%;\n }\n .col-lg-2 {\n width: 16.66666667%;\n }\n .col-lg-1 {\n width: 8.33333333%;\n }\n .col-lg-pull-12 {\n right: 100%;\n }\n .col-lg-pull-11 {\n right: 91.66666667%;\n }\n .col-lg-pull-10 {\n right: 83.33333333%;\n }\n .col-lg-pull-9 {\n right: 75%;\n }\n .col-lg-pull-8 {\n right: 66.66666667%;\n }\n .col-lg-pull-7 {\n right: 58.33333333%;\n }\n .col-lg-pull-6 {\n right: 50%;\n }\n .col-lg-pull-5 {\n right: 41.66666667%;\n }\n .col-lg-pull-4 {\n right: 33.33333333%;\n }\n .col-lg-pull-3 {\n right: 25%;\n }\n .col-lg-pull-2 {\n right: 16.66666667%;\n }\n .col-lg-pull-1 {\n right: 8.33333333%;\n }\n .col-lg-pull-0 {\n right: auto;\n }\n .col-lg-push-12 {\n left: 100%;\n }\n .col-lg-push-11 {\n left: 91.66666667%;\n }\n .col-lg-push-10 {\n left: 83.33333333%;\n }\n .col-lg-push-9 {\n left: 75%;\n }\n .col-lg-push-8 {\n left: 66.66666667%;\n }\n .col-lg-push-7 {\n left: 58.33333333%;\n }\n .col-lg-push-6 {\n left: 50%;\n }\n .col-lg-push-5 {\n left: 41.66666667%;\n }\n .col-lg-push-4 {\n left: 33.33333333%;\n }\n .col-lg-push-3 {\n left: 25%;\n }\n .col-lg-push-2 {\n left: 16.66666667%;\n }\n .col-lg-push-1 {\n left: 8.33333333%;\n }\n .col-lg-push-0 {\n left: auto;\n }\n .col-lg-offset-12 {\n margin-left: 100%;\n }\n .col-lg-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-lg-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-lg-offset-9 {\n margin-left: 75%;\n }\n .col-lg-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-lg-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-lg-offset-6 {\n margin-left: 50%;\n }\n .col-lg-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-lg-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-lg-offset-3 {\n margin-left: 25%;\n }\n .col-lg-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-lg-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-lg-offset-0 {\n margin-left: 0%;\n }\n}\ntable {\n background-color: transparent;\n}\ncaption {\n padding-top: 8px;\n padding-bottom: 8px;\n color: #777777;\n text-align: left;\n}\nth {\n text-align: left;\n}\n.table {\n width: 100%;\n max-width: 100%;\n margin-bottom: 20px;\n}\n.table > thead > tr > th,\n.table > tbody > tr > th,\n.table > tfoot > tr > th,\n.table > thead > tr > td,\n.table > tbody > tr > td,\n.table > tfoot > tr > td {\n padding: 8px;\n line-height: 1.42857143;\n vertical-align: top;\n border-top: 1px solid #ddd;\n}\n.table > thead > tr > th {\n vertical-align: bottom;\n border-bottom: 2px solid #ddd;\n}\n.table > caption + thead > tr:first-child > th,\n.table > colgroup + thead > tr:first-child > th,\n.table > thead:first-child > tr:first-child > th,\n.table > caption + thead > tr:first-child > td,\n.table > colgroup + thead > tr:first-child > td,\n.table > thead:first-child > tr:first-child > td {\n border-top: 0;\n}\n.table > tbody + tbody {\n border-top: 2px solid #ddd;\n}\n.table .table {\n background-color: #fff;\n}\n.table-condensed > thead > tr > th,\n.table-condensed > tbody > tr > th,\n.table-condensed > tfoot > tr > th,\n.table-condensed > thead > tr > td,\n.table-condensed > tbody > tr > td,\n.table-condensed > tfoot > tr > td {\n padding: 5px;\n}\n.table-bordered {\n border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > tbody > tr > th,\n.table-bordered > tfoot > tr > th,\n.table-bordered > thead > tr > td,\n.table-bordered > tbody > tr > td,\n.table-bordered > tfoot > tr > td {\n border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > thead > tr > td {\n border-bottom-width: 2px;\n}\n.table-striped > tbody > tr:nth-of-type(odd) {\n background-color: #f9f9f9;\n}\n.table-hover > tbody > tr:hover {\n background-color: #f5f5f5;\n}\ntable col[class*=\"col-\"] {\n position: static;\n float: none;\n display: table-column;\n}\ntable td[class*=\"col-\"],\ntable th[class*=\"col-\"] {\n position: static;\n float: none;\n display: table-cell;\n}\n.table > thead > tr > td.active,\n.table > tbody > tr > td.active,\n.table > tfoot > tr > td.active,\n.table > thead > tr > th.active,\n.table > tbody > tr > th.active,\n.table > tfoot > tr > th.active,\n.table > thead > tr.active > td,\n.table > tbody > tr.active > td,\n.table > tfoot > tr.active > td,\n.table > thead > tr.active > th,\n.table > tbody > tr.active > th,\n.table > tfoot > tr.active > th {\n background-color: #f5f5f5;\n}\n.table-hover > tbody > tr > td.active:hover,\n.table-hover > tbody > tr > th.active:hover,\n.table-hover > tbody > tr.active:hover > td,\n.table-hover > tbody > tr:hover > .active,\n.table-hover > tbody > tr.active:hover > th {\n background-color: #e8e8e8;\n}\n.table > thead > tr > td.success,\n.table > tbody > tr > td.success,\n.table > tfoot > tr > td.success,\n.table > thead > tr > th.success,\n.table > tbody > tr > th.success,\n.table > tfoot > tr > th.success,\n.table > thead > tr.success > td,\n.table > tbody > tr.success > td,\n.table > tfoot > tr.success > td,\n.table > thead > tr.success > th,\n.table > tbody > tr.success > th,\n.table > tfoot > tr.success > th {\n background-color: #dff0d8;\n}\n.table-hover > tbody > tr > td.success:hover,\n.table-hover > tbody > tr > th.success:hover,\n.table-hover > tbody > tr.success:hover > td,\n.table-hover > tbody > tr:hover > .success,\n.table-hover > tbody > tr.success:hover > th {\n background-color: #d0e9c6;\n}\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n background-color: #d9edf7;\n}\n.table-hover > tbody > tr > td.info:hover,\n.table-hover > tbody > tr > th.info:hover,\n.table-hover > tbody > tr.info:hover > td,\n.table-hover > tbody > tr:hover > .info,\n.table-hover > tbody > tr.info:hover > th {\n background-color: #c4e3f3;\n}\n.table > thead > tr > td.warning,\n.table > tbody > tr > td.warning,\n.table > tfoot > tr > td.warning,\n.table > thead > tr > th.warning,\n.table > tbody > tr > th.warning,\n.table > tfoot > tr > th.warning,\n.table > thead > tr.warning > td,\n.table > tbody > tr.warning > td,\n.table > tfoot > tr.warning > td,\n.table > thead > tr.warning > th,\n.table > tbody > tr.warning > th,\n.table > tfoot > tr.warning > th {\n background-color: #fcf8e3;\n}\n.table-hover > tbody > tr > td.warning:hover,\n.table-hover > tbody > tr > th.warning:hover,\n.table-hover > tbody > tr.warning:hover > td,\n.table-hover > tbody > tr:hover > .warning,\n.table-hover > tbody > tr.warning:hover > th {\n background-color: #faf2cc;\n}\n.table > thead > tr > td.danger,\n.table > tbody > tr > td.danger,\n.table > tfoot > tr > td.danger,\n.table > thead > tr > th.danger,\n.table > tbody > tr > th.danger,\n.table > tfoot > tr > th.danger,\n.table > thead > tr.danger > td,\n.table > tbody > tr.danger > td,\n.table > tfoot > tr.danger > td,\n.table > thead > tr.danger > th,\n.table > tbody > tr.danger > th,\n.table > tfoot > tr.danger > th {\n background-color: #f2dede;\n}\n.table-hover > tbody > tr > td.danger:hover,\n.table-hover > tbody > tr > th.danger:hover,\n.table-hover > tbody > tr.danger:hover > td,\n.table-hover > tbody > tr:hover > .danger,\n.table-hover > tbody > tr.danger:hover > th {\n background-color: #ebcccc;\n}\n.table-responsive {\n overflow-x: auto;\n min-height: 0.01%;\n}\n@media screen and (max-width: 767px) {\n .table-responsive {\n width: 100%;\n margin-bottom: 15px;\n overflow-y: hidden;\n -ms-overflow-style: -ms-autohiding-scrollbar;\n border: 1px solid #ddd;\n }\n .table-responsive > .table {\n margin-bottom: 0;\n }\n .table-responsive > .table > thead > tr > th,\n .table-responsive > .table > tbody > tr > th,\n .table-responsive > .table > tfoot > tr > th,\n .table-responsive > .table > thead > tr > td,\n .table-responsive > .table > tbody > tr > td,\n .table-responsive > .table > tfoot > tr > td {\n white-space: nowrap;\n }\n .table-responsive > .table-bordered {\n border: 0;\n }\n .table-responsive > .table-bordered > thead > tr > th:first-child,\n .table-responsive > .table-bordered > tbody > tr > th:first-child,\n .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n .table-responsive > .table-bordered > thead > tr > td:first-child,\n .table-responsive > .table-bordered > tbody > tr > td:first-child,\n .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n border-left: 0;\n }\n .table-responsive > .table-bordered > thead > tr > th:last-child,\n .table-responsive > .table-bordered > tbody > tr > th:last-child,\n .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n .table-responsive > .table-bordered > thead > tr > td:last-child,\n .table-responsive > .table-bordered > tbody > tr > td:last-child,\n .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n border-right: 0;\n }\n .table-responsive > .table-bordered > tbody > tr:last-child > th,\n .table-responsive > .table-bordered > tfoot > tr:last-child > th,\n .table-responsive > .table-bordered > tbody > tr:last-child > td,\n .table-responsive > .table-bordered > tfoot > tr:last-child > td {\n border-bottom: 0;\n }\n}\nfieldset {\n padding: 0;\n margin: 0;\n border: 0;\n min-width: 0;\n}\nlegend {\n display: block;\n width: 100%;\n padding: 0;\n margin-bottom: 20px;\n font-size: 21px;\n line-height: inherit;\n color: #333333;\n border: 0;\n border-bottom: 1px solid #e5e5e5;\n}\nlabel {\n display: inline-block;\n max-width: 100%;\n margin-bottom: 5px;\n font-weight: bold;\n}\ninput[type=\"search\"] {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n margin: 4px 0 0;\n margin-top: 1px \\9;\n line-height: normal;\n}\ninput[type=\"file\"] {\n display: block;\n}\ninput[type=\"range\"] {\n display: block;\n width: 100%;\n}\nselect[multiple],\nselect[size] {\n height: auto;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\noutput {\n display: block;\n padding-top: 7px;\n font-size: 14px;\n line-height: 1.42857143;\n color: #555555;\n}\n.form-control {\n display: block;\n width: 100%;\n height: 34px;\n padding: 6px 12px;\n font-size: 14px;\n line-height: 1.42857143;\n color: #555555;\n background-color: #fff;\n background-image: none;\n border: 1px solid #ccc;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n}\n.form-control:focus {\n border-color: #66afe9;\n outline: 0;\n -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);\n box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);\n}\n.form-control::-moz-placeholder {\n color: #999;\n opacity: 1;\n}\n.form-control:-ms-input-placeholder {\n color: #999;\n}\n.form-control::-webkit-input-placeholder {\n color: #999;\n}\n.form-control::-ms-expand {\n border: 0;\n background-color: transparent;\n}\n.form-control[disabled],\n.form-control[readonly],\nfieldset[disabled] .form-control {\n background-color: #eeeeee;\n opacity: 1;\n}\n.form-control[disabled],\nfieldset[disabled] .form-control {\n cursor: not-allowed;\n}\ntextarea.form-control {\n height: auto;\n}\ninput[type=\"search\"] {\n -webkit-appearance: none;\n}\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n input[type=\"date\"].form-control,\n input[type=\"time\"].form-control,\n input[type=\"datetime-local\"].form-control,\n input[type=\"month\"].form-control {\n line-height: 34px;\n }\n input[type=\"date\"].input-sm,\n input[type=\"time\"].input-sm,\n input[type=\"datetime-local\"].input-sm,\n input[type=\"month\"].input-sm,\n .input-group-sm input[type=\"date\"],\n .input-group-sm input[type=\"time\"],\n .input-group-sm input[type=\"datetime-local\"],\n .input-group-sm input[type=\"month\"] {\n line-height: 30px;\n }\n input[type=\"date\"].input-lg,\n input[type=\"time\"].input-lg,\n input[type=\"datetime-local\"].input-lg,\n input[type=\"month\"].input-lg,\n .input-group-lg input[type=\"date\"],\n .input-group-lg input[type=\"time\"],\n .input-group-lg input[type=\"datetime-local\"],\n .input-group-lg input[type=\"month\"] {\n line-height: 46px;\n }\n}\n.form-group {\n margin-bottom: 15px;\n}\n.radio,\n.checkbox {\n position: relative;\n display: block;\n margin-top: 10px;\n margin-bottom: 10px;\n}\n.radio label,\n.checkbox label {\n min-height: 20px;\n padding-left: 20px;\n margin-bottom: 0;\n font-weight: normal;\n cursor: pointer;\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n position: absolute;\n margin-left: -20px;\n margin-top: 4px \\9;\n}\n.radio + .radio,\n.checkbox + .checkbox {\n margin-top: -5px;\n}\n.radio-inline,\n.checkbox-inline {\n position: relative;\n display: inline-block;\n padding-left: 20px;\n margin-bottom: 0;\n vertical-align: middle;\n font-weight: normal;\n cursor: pointer;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n margin-top: 0;\n margin-left: 10px;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled],\ninput[type=\"radio\"].disabled,\ninput[type=\"checkbox\"].disabled,\nfieldset[disabled] input[type=\"radio\"],\nfieldset[disabled] input[type=\"checkbox\"] {\n cursor: not-allowed;\n}\n.radio-inline.disabled,\n.checkbox-inline.disabled,\nfieldset[disabled] .radio-inline,\nfieldset[disabled] .checkbox-inline {\n cursor: not-allowed;\n}\n.radio.disabled label,\n.checkbox.disabled label,\nfieldset[disabled] .radio label,\nfieldset[disabled] .checkbox label {\n cursor: not-allowed;\n}\n.form-control-static {\n padding-top: 7px;\n padding-bottom: 7px;\n margin-bottom: 0;\n min-height: 34px;\n}\n.form-control-static.input-lg,\n.form-control-static.input-sm {\n padding-left: 0;\n padding-right: 0;\n}\n.input-sm {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\nselect.input-sm {\n height: 30px;\n line-height: 30px;\n}\ntextarea.input-sm,\nselect[multiple].input-sm {\n height: auto;\n}\n.form-group-sm .form-control {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.form-group-sm select.form-control {\n height: 30px;\n line-height: 30px;\n}\n.form-group-sm textarea.form-control,\n.form-group-sm select[multiple].form-control {\n height: auto;\n}\n.form-group-sm .form-control-static {\n height: 30px;\n min-height: 32px;\n padding: 6px 10px;\n font-size: 12px;\n line-height: 1.5;\n}\n.input-lg {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\nselect.input-lg {\n height: 46px;\n line-height: 46px;\n}\ntextarea.input-lg,\nselect[multiple].input-lg {\n height: auto;\n}\n.form-group-lg .form-control {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\n.form-group-lg select.form-control {\n height: 46px;\n line-height: 46px;\n}\n.form-group-lg textarea.form-control,\n.form-group-lg select[multiple].form-control {\n height: auto;\n}\n.form-group-lg .form-control-static {\n height: 46px;\n min-height: 38px;\n padding: 11px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n}\n.has-feedback {\n position: relative;\n}\n.has-feedback .form-control {\n padding-right: 42.5px;\n}\n.form-control-feedback {\n position: absolute;\n top: 0;\n right: 0;\n z-index: 2;\n display: block;\n width: 34px;\n height: 34px;\n line-height: 34px;\n text-align: center;\n pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n width: 46px;\n height: 46px;\n line-height: 46px;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n width: 30px;\n height: 30px;\n line-height: 30px;\n}\n.has-success .help-block,\n.has-success .control-label,\n.has-success .radio,\n.has-success .checkbox,\n.has-success .radio-inline,\n.has-success .checkbox-inline,\n.has-success.radio label,\n.has-success.checkbox label,\n.has-success.radio-inline label,\n.has-success.checkbox-inline label {\n color: #3c763d;\n}\n.has-success .form-control {\n border-color: #3c763d;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-success .form-control:focus {\n border-color: #2b542c;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n}\n.has-success .input-group-addon {\n color: #3c763d;\n border-color: #3c763d;\n background-color: #dff0d8;\n}\n.has-success .form-control-feedback {\n color: #3c763d;\n}\n.has-warning .help-block,\n.has-warning .control-label,\n.has-warning .radio,\n.has-warning .checkbox,\n.has-warning .radio-inline,\n.has-warning .checkbox-inline,\n.has-warning.radio label,\n.has-warning.checkbox label,\n.has-warning.radio-inline label,\n.has-warning.checkbox-inline label {\n color: #8a6d3b;\n}\n.has-warning .form-control {\n border-color: #8a6d3b;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-warning .form-control:focus {\n border-color: #66512c;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n}\n.has-warning .input-group-addon {\n color: #8a6d3b;\n border-color: #8a6d3b;\n background-color: #fcf8e3;\n}\n.has-warning .form-control-feedback {\n color: #8a6d3b;\n}\n.has-error .help-block,\n.has-error .control-label,\n.has-error .radio,\n.has-error .checkbox,\n.has-error .radio-inline,\n.has-error .checkbox-inline,\n.has-error.radio label,\n.has-error.checkbox label,\n.has-error.radio-inline label,\n.has-error.checkbox-inline label {\n color: #a94442;\n}\n.has-error .form-control {\n border-color: #a94442;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-error .form-control:focus {\n border-color: #843534;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n}\n.has-error .input-group-addon {\n color: #a94442;\n border-color: #a94442;\n background-color: #f2dede;\n}\n.has-error .form-control-feedback {\n color: #a94442;\n}\n.has-feedback label ~ .form-control-feedback {\n top: 25px;\n}\n.has-feedback label.sr-only ~ .form-control-feedback {\n top: 0;\n}\n.help-block {\n display: block;\n margin-top: 5px;\n margin-bottom: 10px;\n color: #737373;\n}\n@media (min-width: 768px) {\n .form-inline .form-group {\n display: inline-block;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .form-control {\n display: inline-block;\n width: auto;\n vertical-align: middle;\n }\n .form-inline .form-control-static {\n display: inline-block;\n }\n .form-inline .input-group {\n display: inline-table;\n vertical-align: middle;\n }\n .form-inline .input-group .input-group-addon,\n .form-inline .input-group .input-group-btn,\n .form-inline .input-group .form-control {\n width: auto;\n }\n .form-inline .input-group > .form-control {\n width: 100%;\n }\n .form-inline .control-label {\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .radio,\n .form-inline .checkbox {\n display: inline-block;\n margin-top: 0;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .radio label,\n .form-inline .checkbox label {\n padding-left: 0;\n }\n .form-inline .radio input[type=\"radio\"],\n .form-inline .checkbox input[type=\"checkbox\"] {\n position: relative;\n margin-left: 0;\n }\n .form-inline .has-feedback .form-control-feedback {\n top: 0;\n }\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox,\n.form-horizontal .radio-inline,\n.form-horizontal .checkbox-inline {\n margin-top: 0;\n margin-bottom: 0;\n padding-top: 7px;\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox {\n min-height: 27px;\n}\n.form-horizontal .form-group {\n margin-left: -15px;\n margin-right: -15px;\n}\n@media (min-width: 768px) {\n .form-horizontal .control-label {\n text-align: right;\n margin-bottom: 0;\n padding-top: 7px;\n }\n}\n.form-horizontal .has-feedback .form-control-feedback {\n right: 15px;\n}\n@media (min-width: 768px) {\n .form-horizontal .form-group-lg .control-label {\n padding-top: 11px;\n font-size: 18px;\n }\n}\n@media (min-width: 768px) {\n .form-horizontal .form-group-sm .control-label {\n padding-top: 6px;\n font-size: 12px;\n }\n}\n.btn {\n display: inline-block;\n margin-bottom: 0;\n font-weight: normal;\n text-align: center;\n vertical-align: middle;\n touch-action: manipulation;\n cursor: pointer;\n background-image: none;\n border: 1px solid transparent;\n white-space: nowrap;\n padding: 6px 12px;\n font-size: 14px;\n line-height: 1.42857143;\n border-radius: 4px;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n}\n.btn:focus,\n.btn:active:focus,\n.btn.active:focus,\n.btn.focus,\n.btn:active.focus,\n.btn.active.focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n.btn:hover,\n.btn:focus,\n.btn.focus {\n color: #333;\n text-decoration: none;\n}\n.btn:active,\n.btn.active {\n outline: 0;\n background-image: none;\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn.disabled,\n.btn[disabled],\nfieldset[disabled] .btn {\n cursor: not-allowed;\n opacity: 0.65;\n filter: alpha(opacity=65);\n -webkit-box-shadow: none;\n box-shadow: none;\n}\na.btn.disabled,\nfieldset[disabled] a.btn {\n pointer-events: none;\n}\n.btn-default {\n color: #333;\n background-color: #fff;\n border-color: #ccc;\n}\n.btn-default:focus,\n.btn-default.focus {\n color: #333;\n background-color: #e6e6e6;\n border-color: #8c8c8c;\n}\n.btn-default:hover {\n color: #333;\n background-color: #e6e6e6;\n border-color: #adadad;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n color: #333;\n background-color: #e6e6e6;\n border-color: #adadad;\n}\n.btn-default:active:hover,\n.btn-default.active:hover,\n.open > .dropdown-toggle.btn-default:hover,\n.btn-default:active:focus,\n.btn-default.active:focus,\n.open > .dropdown-toggle.btn-default:focus,\n.btn-default:active.focus,\n.btn-default.active.focus,\n.open > .dropdown-toggle.btn-default.focus {\n color: #333;\n background-color: #d4d4d4;\n border-color: #8c8c8c;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n background-image: none;\n}\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus {\n background-color: #fff;\n border-color: #ccc;\n}\n.btn-default .badge {\n color: #fff;\n background-color: #333;\n}\n.btn-primary {\n color: #fff;\n background-color: #337ab7;\n border-color: #2e6da4;\n}\n.btn-primary:focus,\n.btn-primary.focus {\n color: #fff;\n background-color: #286090;\n border-color: #122b40;\n}\n.btn-primary:hover {\n color: #fff;\n background-color: #286090;\n border-color: #204d74;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n color: #fff;\n background-color: #286090;\n border-color: #204d74;\n}\n.btn-primary:active:hover,\n.btn-primary.active:hover,\n.open > .dropdown-toggle.btn-primary:hover,\n.btn-primary:active:focus,\n.btn-primary.active:focus,\n.open > .dropdown-toggle.btn-primary:focus,\n.btn-primary:active.focus,\n.btn-primary.active.focus,\n.open > .dropdown-toggle.btn-primary.focus {\n color: #fff;\n background-color: #204d74;\n border-color: #122b40;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n background-image: none;\n}\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus {\n background-color: #337ab7;\n border-color: #2e6da4;\n}\n.btn-primary .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.btn-success {\n color: #fff;\n background-color: #5cb85c;\n border-color: #4cae4c;\n}\n.btn-success:focus,\n.btn-success.focus {\n color: #fff;\n background-color: #449d44;\n border-color: #255625;\n}\n.btn-success:hover {\n color: #fff;\n background-color: #449d44;\n border-color: #398439;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n color: #fff;\n background-color: #449d44;\n border-color: #398439;\n}\n.btn-success:active:hover,\n.btn-success.active:hover,\n.open > .dropdown-toggle.btn-success:hover,\n.btn-success:active:focus,\n.btn-success.active:focus,\n.open > .dropdown-toggle.btn-success:focus,\n.btn-success:active.focus,\n.btn-success.active.focus,\n.open > .dropdown-toggle.btn-success.focus {\n color: #fff;\n background-color: #398439;\n border-color: #255625;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n background-image: none;\n}\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus {\n background-color: #5cb85c;\n border-color: #4cae4c;\n}\n.btn-success .badge {\n color: #5cb85c;\n background-color: #fff;\n}\n.btn-info {\n color: #fff;\n background-color: #5bc0de;\n border-color: #46b8da;\n}\n.btn-info:focus,\n.btn-info.focus {\n color: #fff;\n background-color: #31b0d5;\n border-color: #1b6d85;\n}\n.btn-info:hover {\n color: #fff;\n background-color: #31b0d5;\n border-color: #269abc;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n color: #fff;\n background-color: #31b0d5;\n border-color: #269abc;\n}\n.btn-info:active:hover,\n.btn-info.active:hover,\n.open > .dropdown-toggle.btn-info:hover,\n.btn-info:active:focus,\n.btn-info.active:focus,\n.open > .dropdown-toggle.btn-info:focus,\n.btn-info:active.focus,\n.btn-info.active.focus,\n.open > .dropdown-toggle.btn-info.focus {\n color: #fff;\n background-color: #269abc;\n border-color: #1b6d85;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n background-image: none;\n}\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus {\n background-color: #5bc0de;\n border-color: #46b8da;\n}\n.btn-info .badge {\n color: #5bc0de;\n background-color: #fff;\n}\n.btn-warning {\n color: #fff;\n background-color: #f0ad4e;\n border-color: #eea236;\n}\n.btn-warning:focus,\n.btn-warning.focus {\n color: #fff;\n background-color: #ec971f;\n border-color: #985f0d;\n}\n.btn-warning:hover {\n color: #fff;\n background-color: #ec971f;\n border-color: #d58512;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n color: #fff;\n background-color: #ec971f;\n border-color: #d58512;\n}\n.btn-warning:active:hover,\n.btn-warning.active:hover,\n.open > .dropdown-toggle.btn-warning:hover,\n.btn-warning:active:focus,\n.btn-warning.active:focus,\n.open > .dropdown-toggle.btn-warning:focus,\n.btn-warning:active.focus,\n.btn-warning.active.focus,\n.open > .dropdown-toggle.btn-warning.focus {\n color: #fff;\n background-color: #d58512;\n border-color: #985f0d;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n background-image: none;\n}\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus {\n background-color: #f0ad4e;\n border-color: #eea236;\n}\n.btn-warning .badge {\n color: #f0ad4e;\n background-color: #fff;\n}\n.btn-danger {\n color: #fff;\n background-color: #d9534f;\n border-color: #d43f3a;\n}\n.btn-danger:focus,\n.btn-danger.focus {\n color: #fff;\n background-color: #c9302c;\n border-color: #761c19;\n}\n.btn-danger:hover {\n color: #fff;\n background-color: #c9302c;\n border-color: #ac2925;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n color: #fff;\n background-color: #c9302c;\n border-color: #ac2925;\n}\n.btn-danger:active:hover,\n.btn-danger.active:hover,\n.open > .dropdown-toggle.btn-danger:hover,\n.btn-danger:active:focus,\n.btn-danger.active:focus,\n.open > .dropdown-toggle.btn-danger:focus,\n.btn-danger:active.focus,\n.btn-danger.active.focus,\n.open > .dropdown-toggle.btn-danger.focus {\n color: #fff;\n background-color: #ac2925;\n border-color: #761c19;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n background-image: none;\n}\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus {\n background-color: #d9534f;\n border-color: #d43f3a;\n}\n.btn-danger .badge {\n color: #d9534f;\n background-color: #fff;\n}\n.btn-link {\n color: #337ab7;\n font-weight: normal;\n border-radius: 0;\n}\n.btn-link,\n.btn-link:active,\n.btn-link.active,\n.btn-link[disabled],\nfieldset[disabled] .btn-link {\n background-color: transparent;\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn-link,\n.btn-link:hover,\n.btn-link:focus,\n.btn-link:active {\n border-color: transparent;\n}\n.btn-link:hover,\n.btn-link:focus {\n color: #23527c;\n text-decoration: underline;\n background-color: transparent;\n}\n.btn-link[disabled]:hover,\nfieldset[disabled] .btn-link:hover,\n.btn-link[disabled]:focus,\nfieldset[disabled] .btn-link:focus {\n color: #777777;\n text-decoration: none;\n}\n.btn-lg,\n.btn-group-lg > .btn {\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\n.btn-sm,\n.btn-group-sm > .btn {\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.btn-xs,\n.btn-group-xs > .btn {\n padding: 1px 5px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.btn-block {\n display: block;\n width: 100%;\n}\n.btn-block + .btn-block {\n margin-top: 5px;\n}\ninput[type=\"submit\"].btn-block,\ninput[type=\"reset\"].btn-block,\ninput[type=\"button\"].btn-block {\n width: 100%;\n}\n.fade {\n opacity: 0;\n -webkit-transition: opacity 0.15s linear;\n -o-transition: opacity 0.15s linear;\n transition: opacity 0.15s linear;\n}\n.fade.in {\n opacity: 1;\n}\n.collapse {\n display: none;\n}\n.collapse.in {\n display: block;\n}\ntr.collapse.in {\n display: table-row;\n}\ntbody.collapse.in {\n display: table-row-group;\n}\n.collapsing {\n position: relative;\n height: 0;\n overflow: hidden;\n -webkit-transition-property: height, visibility;\n transition-property: height, visibility;\n -webkit-transition-duration: 0.35s;\n transition-duration: 0.35s;\n -webkit-transition-timing-function: ease;\n transition-timing-function: ease;\n}\n.caret {\n display: inline-block;\n width: 0;\n height: 0;\n margin-left: 2px;\n vertical-align: middle;\n border-top: 4px dashed;\n border-top: 4px solid \\9;\n border-right: 4px solid transparent;\n border-left: 4px solid transparent;\n}\n.dropup,\n.dropdown {\n position: relative;\n}\n.dropdown-toggle:focus {\n outline: 0;\n}\n.dropdown-menu {\n position: absolute;\n top: 100%;\n left: 0;\n z-index: 1000;\n display: none;\n float: left;\n min-width: 160px;\n padding: 5px 0;\n margin: 2px 0 0;\n list-style: none;\n font-size: 14px;\n text-align: left;\n background-color: #fff;\n border: 1px solid #ccc;\n border: 1px solid rgba(0, 0, 0, 0.15);\n border-radius: 4px;\n -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n background-clip: padding-box;\n}\n.dropdown-menu.pull-right {\n right: 0;\n left: auto;\n}\n.dropdown-menu .divider {\n height: 1px;\n margin: 9px 0;\n overflow: hidden;\n background-color: #e5e5e5;\n}\n.dropdown-menu > li > a {\n display: block;\n padding: 3px 20px;\n clear: both;\n font-weight: normal;\n line-height: 1.42857143;\n color: #333333;\n white-space: nowrap;\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n text-decoration: none;\n color: #262626;\n background-color: #f5f5f5;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n color: #fff;\n text-decoration: none;\n outline: 0;\n background-color: #337ab7;\n}\n.dropdown-menu > .disabled > a,\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n color: #777777;\n}\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n text-decoration: none;\n background-color: transparent;\n background-image: none;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n cursor: not-allowed;\n}\n.open > .dropdown-menu {\n display: block;\n}\n.open > a {\n outline: 0;\n}\n.dropdown-menu-right {\n left: auto;\n right: 0;\n}\n.dropdown-menu-left {\n left: 0;\n right: auto;\n}\n.dropdown-header {\n display: block;\n padding: 3px 20px;\n font-size: 12px;\n line-height: 1.42857143;\n color: #777777;\n white-space: nowrap;\n}\n.dropdown-backdrop {\n position: fixed;\n left: 0;\n right: 0;\n bottom: 0;\n top: 0;\n z-index: 990;\n}\n.pull-right > .dropdown-menu {\n right: 0;\n left: auto;\n}\n.dropup .caret,\n.navbar-fixed-bottom .dropdown .caret {\n border-top: 0;\n border-bottom: 4px dashed;\n border-bottom: 4px solid \\9;\n content: \"\";\n}\n.dropup .dropdown-menu,\n.navbar-fixed-bottom .dropdown .dropdown-menu {\n top: auto;\n bottom: 100%;\n margin-bottom: 2px;\n}\n@media (min-width: 768px) {\n .navbar-right .dropdown-menu {\n left: auto;\n right: 0;\n }\n .navbar-right .dropdown-menu-left {\n left: 0;\n right: auto;\n }\n}\n.btn-group,\n.btn-group-vertical {\n position: relative;\n display: inline-block;\n vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n position: relative;\n float: left;\n}\n.btn-group > .btn:hover,\n.btn-group-vertical > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group-vertical > .btn:focus,\n.btn-group > .btn:active,\n.btn-group-vertical > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn.active {\n z-index: 2;\n}\n.btn-group .btn + .btn,\n.btn-group .btn + .btn-group,\n.btn-group .btn-group + .btn,\n.btn-group .btn-group + .btn-group {\n margin-left: -1px;\n}\n.btn-toolbar {\n margin-left: -5px;\n}\n.btn-toolbar .btn,\n.btn-toolbar .btn-group,\n.btn-toolbar .input-group {\n float: left;\n}\n.btn-toolbar > .btn,\n.btn-toolbar > .btn-group,\n.btn-toolbar > .input-group {\n margin-left: 5px;\n}\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n border-radius: 0;\n}\n.btn-group > .btn:first-child {\n margin-left: 0;\n}\n.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n border-bottom-right-radius: 0;\n border-top-right-radius: 0;\n}\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n border-bottom-left-radius: 0;\n border-top-left-radius: 0;\n}\n.btn-group > .btn-group {\n float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n border-bottom-right-radius: 0;\n border-top-right-radius: 0;\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n border-bottom-left-radius: 0;\n border-top-left-radius: 0;\n}\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n outline: 0;\n}\n.btn-group > .btn + .dropdown-toggle {\n padding-left: 8px;\n padding-right: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n padding-left: 12px;\n padding-right: 12px;\n}\n.btn-group.open .dropdown-toggle {\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-group.open .dropdown-toggle.btn-link {\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn .caret {\n margin-left: 0;\n}\n.btn-lg .caret {\n border-width: 5px 5px 0;\n border-bottom-width: 0;\n}\n.dropup .btn-lg .caret {\n border-width: 0 5px 5px;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group,\n.btn-group-vertical > .btn-group > .btn {\n display: block;\n float: none;\n width: 100%;\n max-width: 100%;\n}\n.btn-group-vertical > .btn-group > .btn {\n float: none;\n}\n.btn-group-vertical > .btn + .btn,\n.btn-group-vertical > .btn + .btn-group,\n.btn-group-vertical > .btn-group + .btn,\n.btn-group-vertical > .btn-group + .btn-group {\n margin-top: -1px;\n margin-left: 0;\n}\n.btn-group-vertical > .btn:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.btn-group-vertical > .btn:first-child:not(:last-child) {\n border-top-right-radius: 4px;\n border-top-left-radius: 4px;\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn:last-child:not(:first-child) {\n border-top-right-radius: 0;\n border-top-left-radius: 0;\n border-bottom-right-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n border-top-right-radius: 0;\n border-top-left-radius: 0;\n}\n.btn-group-justified {\n display: table;\n width: 100%;\n table-layout: fixed;\n border-collapse: separate;\n}\n.btn-group-justified > .btn,\n.btn-group-justified > .btn-group {\n float: none;\n display: table-cell;\n width: 1%;\n}\n.btn-group-justified > .btn-group .btn {\n width: 100%;\n}\n.btn-group-justified > .btn-group .dropdown-menu {\n left: auto;\n}\n[data-toggle=\"buttons\"] > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn input[type=\"checkbox\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"checkbox\"] {\n position: absolute;\n clip: rect(0, 0, 0, 0);\n pointer-events: none;\n}\n.input-group {\n position: relative;\n display: table;\n border-collapse: separate;\n}\n.input-group[class*=\"col-\"] {\n float: none;\n padding-left: 0;\n padding-right: 0;\n}\n.input-group .form-control {\n position: relative;\n z-index: 2;\n float: left;\n width: 100%;\n margin-bottom: 0;\n}\n.input-group .form-control:focus {\n z-index: 3;\n}\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\nselect.input-group-lg > .form-control,\nselect.input-group-lg > .input-group-addon,\nselect.input-group-lg > .input-group-btn > .btn {\n height: 46px;\n line-height: 46px;\n}\ntextarea.input-group-lg > .form-control,\ntextarea.input-group-lg > .input-group-addon,\ntextarea.input-group-lg > .input-group-btn > .btn,\nselect[multiple].input-group-lg > .form-control,\nselect[multiple].input-group-lg > .input-group-addon,\nselect[multiple].input-group-lg > .input-group-btn > .btn {\n height: auto;\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\nselect.input-group-sm > .form-control,\nselect.input-group-sm > .input-group-addon,\nselect.input-group-sm > .input-group-btn > .btn {\n height: 30px;\n line-height: 30px;\n}\ntextarea.input-group-sm > .form-control,\ntextarea.input-group-sm > .input-group-addon,\ntextarea.input-group-sm > .input-group-btn > .btn,\nselect[multiple].input-group-sm > .form-control,\nselect[multiple].input-group-sm > .input-group-addon,\nselect[multiple].input-group-sm > .input-group-btn > .btn {\n height: auto;\n}\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n display: table-cell;\n}\n.input-group-addon:not(:first-child):not(:last-child),\n.input-group-btn:not(:first-child):not(:last-child),\n.input-group .form-control:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.input-group-addon,\n.input-group-btn {\n width: 1%;\n white-space: nowrap;\n vertical-align: middle;\n}\n.input-group-addon {\n padding: 6px 12px;\n font-size: 14px;\n font-weight: normal;\n line-height: 1;\n color: #555555;\n text-align: center;\n background-color: #eeeeee;\n border: 1px solid #ccc;\n border-radius: 4px;\n}\n.input-group-addon.input-sm {\n padding: 5px 10px;\n font-size: 12px;\n border-radius: 3px;\n}\n.input-group-addon.input-lg {\n padding: 10px 16px;\n font-size: 18px;\n border-radius: 6px;\n}\n.input-group-addon input[type=\"radio\"],\n.input-group-addon input[type=\"checkbox\"] {\n margin-top: 0;\n}\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n border-bottom-right-radius: 0;\n border-top-right-radius: 0;\n}\n.input-group-addon:first-child {\n border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n border-bottom-left-radius: 0;\n border-top-left-radius: 0;\n}\n.input-group-addon:last-child {\n border-left: 0;\n}\n.input-group-btn {\n position: relative;\n font-size: 0;\n white-space: nowrap;\n}\n.input-group-btn > .btn {\n position: relative;\n}\n.input-group-btn > .btn + .btn {\n margin-left: -1px;\n}\n.input-group-btn > .btn:hover,\n.input-group-btn > .btn:focus,\n.input-group-btn > .btn:active {\n z-index: 2;\n}\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group {\n margin-right: -1px;\n}\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group {\n z-index: 2;\n margin-left: -1px;\n}\n.nav {\n margin-bottom: 0;\n padding-left: 0;\n list-style: none;\n}\n.nav > li {\n position: relative;\n display: block;\n}\n.nav > li > a {\n position: relative;\n display: block;\n padding: 10px 15px;\n}\n.nav > li > a:hover,\n.nav > li > a:focus {\n text-decoration: none;\n background-color: #eeeeee;\n}\n.nav > li.disabled > a {\n color: #777777;\n}\n.nav > li.disabled > a:hover,\n.nav > li.disabled > a:focus {\n color: #777777;\n text-decoration: none;\n background-color: transparent;\n cursor: not-allowed;\n}\n.nav .open > a,\n.nav .open > a:hover,\n.nav .open > a:focus {\n background-color: #eeeeee;\n border-color: #337ab7;\n}\n.nav .nav-divider {\n height: 1px;\n margin: 9px 0;\n overflow: hidden;\n background-color: #e5e5e5;\n}\n.nav > li > a > img {\n max-width: none;\n}\n.nav-tabs {\n border-bottom: 1px solid #ddd;\n}\n.nav-tabs > li {\n float: left;\n margin-bottom: -1px;\n}\n.nav-tabs > li > a {\n margin-right: 2px;\n line-height: 1.42857143;\n border: 1px solid transparent;\n border-radius: 4px 4px 0 0;\n}\n.nav-tabs > li > a:hover {\n border-color: #eeeeee #eeeeee #ddd;\n}\n.nav-tabs > li.active > a,\n.nav-tabs > li.active > a:hover,\n.nav-tabs > li.active > a:focus {\n color: #555555;\n background-color: #fff;\n border: 1px solid #ddd;\n border-bottom-color: transparent;\n cursor: default;\n}\n.nav-tabs.nav-justified {\n width: 100%;\n border-bottom: 0;\n}\n.nav-tabs.nav-justified > li {\n float: none;\n}\n.nav-tabs.nav-justified > li > a {\n text-align: center;\n margin-bottom: 5px;\n}\n.nav-tabs.nav-justified > .dropdown .dropdown-menu {\n top: auto;\n left: auto;\n}\n@media (min-width: 768px) {\n .nav-tabs.nav-justified > li {\n display: table-cell;\n width: 1%;\n }\n .nav-tabs.nav-justified > li > a {\n margin-bottom: 0;\n }\n}\n.nav-tabs.nav-justified > li > a {\n margin-right: 0;\n border-radius: 4px;\n}\n.nav-tabs.nav-justified > .active > a,\n.nav-tabs.nav-justified > .active > a:hover,\n.nav-tabs.nav-justified > .active > a:focus {\n border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n .nav-tabs.nav-justified > li > a {\n border-bottom: 1px solid #ddd;\n border-radius: 4px 4px 0 0;\n }\n .nav-tabs.nav-justified > .active > a,\n .nav-tabs.nav-justified > .active > a:hover,\n .nav-tabs.nav-justified > .active > a:focus {\n border-bottom-color: #fff;\n }\n}\n.nav-pills > li {\n float: left;\n}\n.nav-pills > li > a {\n border-radius: 4px;\n}\n.nav-pills > li + li {\n margin-left: 2px;\n}\n.nav-pills > li.active > a,\n.nav-pills > li.active > a:hover,\n.nav-pills > li.active > a:focus {\n color: #fff;\n background-color: #337ab7;\n}\n.nav-stacked > li {\n float: none;\n}\n.nav-stacked > li + li {\n margin-top: 2px;\n margin-left: 0;\n}\n.nav-justified {\n width: 100%;\n}\n.nav-justified > li {\n float: none;\n}\n.nav-justified > li > a {\n text-align: center;\n margin-bottom: 5px;\n}\n.nav-justified > .dropdown .dropdown-menu {\n top: auto;\n left: auto;\n}\n@media (min-width: 768px) {\n .nav-justified > li {\n display: table-cell;\n width: 1%;\n }\n .nav-justified > li > a {\n margin-bottom: 0;\n }\n}\n.nav-tabs-justified {\n border-bottom: 0;\n}\n.nav-tabs-justified > li > a {\n margin-right: 0;\n border-radius: 4px;\n}\n.nav-tabs-justified > .active > a,\n.nav-tabs-justified > .active > a:hover,\n.nav-tabs-justified > .active > a:focus {\n border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n .nav-tabs-justified > li > a {\n border-bottom: 1px solid #ddd;\n border-radius: 4px 4px 0 0;\n }\n .nav-tabs-justified > .active > a,\n .nav-tabs-justified > .active > a:hover,\n .nav-tabs-justified > .active > a:focus {\n border-bottom-color: #fff;\n }\n}\n.tab-content > .tab-pane {\n display: none;\n}\n.tab-content > .active {\n display: block;\n}\n.nav-tabs .dropdown-menu {\n margin-top: -1px;\n border-top-right-radius: 0;\n border-top-left-radius: 0;\n}\n.navbar {\n position: relative;\n min-height: 50px;\n margin-bottom: 20px;\n border: 1px solid transparent;\n}\n@media (min-width: 768px) {\n .navbar {\n border-radius: 4px;\n }\n}\n@media (min-width: 768px) {\n .navbar-header {\n float: left;\n }\n}\n.navbar-collapse {\n overflow-x: visible;\n padding-right: 15px;\n padding-left: 15px;\n border-top: 1px solid transparent;\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);\n -webkit-overflow-scrolling: touch;\n}\n.navbar-collapse.in {\n overflow-y: auto;\n}\n@media (min-width: 768px) {\n .navbar-collapse {\n width: auto;\n border-top: 0;\n box-shadow: none;\n }\n .navbar-collapse.collapse {\n display: block !important;\n height: auto !important;\n padding-bottom: 0;\n overflow: visible !important;\n }\n .navbar-collapse.in {\n overflow-y: visible;\n }\n .navbar-fixed-top .navbar-collapse,\n .navbar-static-top .navbar-collapse,\n .navbar-fixed-bottom .navbar-collapse {\n padding-left: 0;\n padding-right: 0;\n }\n}\n.navbar-fixed-top .navbar-collapse,\n.navbar-fixed-bottom .navbar-collapse {\n max-height: 340px;\n}\n@media (max-device-width: 480px) and (orientation: landscape) {\n .navbar-fixed-top .navbar-collapse,\n .navbar-fixed-bottom .navbar-collapse {\n max-height: 200px;\n }\n}\n.container > .navbar-header,\n.container-fluid > .navbar-header,\n.container > .navbar-collapse,\n.container-fluid > .navbar-collapse {\n margin-right: -15px;\n margin-left: -15px;\n}\n@media (min-width: 768px) {\n .container > .navbar-header,\n .container-fluid > .navbar-header,\n .container > .navbar-collapse,\n .container-fluid > .navbar-collapse {\n margin-right: 0;\n margin-left: 0;\n }\n}\n.navbar-static-top {\n z-index: 1000;\n border-width: 0 0 1px;\n}\n@media (min-width: 768px) {\n .navbar-static-top {\n border-radius: 0;\n }\n}\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n position: fixed;\n right: 0;\n left: 0;\n z-index: 1030;\n}\n@media (min-width: 768px) {\n .navbar-fixed-top,\n .navbar-fixed-bottom {\n border-radius: 0;\n }\n}\n.navbar-fixed-top {\n top: 0;\n border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n bottom: 0;\n margin-bottom: 0;\n border-width: 1px 0 0;\n}\n.navbar-brand {\n float: left;\n padding: 15px 15px;\n font-size: 18px;\n line-height: 20px;\n height: 50px;\n}\n.navbar-brand:hover,\n.navbar-brand:focus {\n text-decoration: none;\n}\n.navbar-brand > img {\n display: block;\n}\n@media (min-width: 768px) {\n .navbar > .container .navbar-brand,\n .navbar > .container-fluid .navbar-brand {\n margin-left: -15px;\n }\n}\n.navbar-toggle {\n position: relative;\n float: right;\n margin-right: 15px;\n padding: 9px 10px;\n margin-top: 8px;\n margin-bottom: 8px;\n background-color: transparent;\n background-image: none;\n border: 1px solid transparent;\n border-radius: 4px;\n}\n.navbar-toggle:focus {\n outline: 0;\n}\n.navbar-toggle .icon-bar {\n display: block;\n width: 22px;\n height: 2px;\n border-radius: 1px;\n}\n.navbar-toggle .icon-bar + .icon-bar {\n margin-top: 4px;\n}\n@media (min-width: 768px) {\n .navbar-toggle {\n display: none;\n }\n}\n.navbar-nav {\n margin: 7.5px -15px;\n}\n.navbar-nav > li > a {\n padding-top: 10px;\n padding-bottom: 10px;\n line-height: 20px;\n}\n@media (max-width: 767px) {\n .navbar-nav .open .dropdown-menu {\n position: static;\n float: none;\n width: auto;\n margin-top: 0;\n background-color: transparent;\n border: 0;\n box-shadow: none;\n }\n .navbar-nav .open .dropdown-menu > li > a,\n .navbar-nav .open .dropdown-menu .dropdown-header {\n padding: 5px 15px 5px 25px;\n }\n .navbar-nav .open .dropdown-menu > li > a {\n line-height: 20px;\n }\n .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-nav .open .dropdown-menu > li > a:focus {\n background-image: none;\n }\n}\n@media (min-width: 768px) {\n .navbar-nav {\n float: left;\n margin: 0;\n }\n .navbar-nav > li {\n float: left;\n }\n .navbar-nav > li > a {\n padding-top: 15px;\n padding-bottom: 15px;\n }\n}\n.navbar-form {\n margin-left: -15px;\n margin-right: -15px;\n padding: 10px 15px;\n border-top: 1px solid transparent;\n border-bottom: 1px solid transparent;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n margin-top: 8px;\n margin-bottom: 8px;\n}\n@media (min-width: 768px) {\n .navbar-form .form-group {\n display: inline-block;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .form-control {\n display: inline-block;\n width: auto;\n vertical-align: middle;\n }\n .navbar-form .form-control-static {\n display: inline-block;\n }\n .navbar-form .input-group {\n display: inline-table;\n vertical-align: middle;\n }\n .navbar-form .input-group .input-group-addon,\n .navbar-form .input-group .input-group-btn,\n .navbar-form .input-group .form-control {\n width: auto;\n }\n .navbar-form .input-group > .form-control {\n width: 100%;\n }\n .navbar-form .control-label {\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .radio,\n .navbar-form .checkbox {\n display: inline-block;\n margin-top: 0;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .radio label,\n .navbar-form .checkbox label {\n padding-left: 0;\n }\n .navbar-form .radio input[type=\"radio\"],\n .navbar-form .checkbox input[type=\"checkbox\"] {\n position: relative;\n margin-left: 0;\n }\n .navbar-form .has-feedback .form-control-feedback {\n top: 0;\n }\n}\n@media (max-width: 767px) {\n .navbar-form .form-group {\n margin-bottom: 5px;\n }\n .navbar-form .form-group:last-child {\n margin-bottom: 0;\n }\n}\n@media (min-width: 768px) {\n .navbar-form {\n width: auto;\n border: 0;\n margin-left: 0;\n margin-right: 0;\n padding-top: 0;\n padding-bottom: 0;\n -webkit-box-shadow: none;\n box-shadow: none;\n }\n}\n.navbar-nav > li > .dropdown-menu {\n margin-top: 0;\n border-top-right-radius: 0;\n border-top-left-radius: 0;\n}\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n margin-bottom: 0;\n border-top-right-radius: 4px;\n border-top-left-radius: 4px;\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.navbar-btn {\n margin-top: 8px;\n margin-bottom: 8px;\n}\n.navbar-btn.btn-sm {\n margin-top: 10px;\n margin-bottom: 10px;\n}\n.navbar-btn.btn-xs {\n margin-top: 14px;\n margin-bottom: 14px;\n}\n.navbar-text {\n margin-top: 15px;\n margin-bottom: 15px;\n}\n@media (min-width: 768px) {\n .navbar-text {\n float: left;\n margin-left: 15px;\n margin-right: 15px;\n }\n}\n@media (min-width: 768px) {\n .navbar-left {\n float: left !important;\n }\n .navbar-right {\n float: right !important;\n margin-right: -15px;\n }\n .navbar-right ~ .navbar-right {\n margin-right: 0;\n }\n}\n.navbar-default {\n background-color: #f8f8f8;\n border-color: #e7e7e7;\n}\n.navbar-default .navbar-brand {\n color: #777;\n}\n.navbar-default .navbar-brand:hover,\n.navbar-default .navbar-brand:focus {\n color: #5e5e5e;\n background-color: transparent;\n}\n.navbar-default .navbar-text {\n color: #777;\n}\n.navbar-default .navbar-nav > li > a {\n color: #777;\n}\n.navbar-default .navbar-nav > li > a:hover,\n.navbar-default .navbar-nav > li > a:focus {\n color: #333;\n background-color: transparent;\n}\n.navbar-default .navbar-nav > .active > a,\n.navbar-default .navbar-nav > .active > a:hover,\n.navbar-default .navbar-nav > .active > a:focus {\n color: #555;\n background-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .disabled > a,\n.navbar-default .navbar-nav > .disabled > a:hover,\n.navbar-default .navbar-nav > .disabled > a:focus {\n color: #ccc;\n background-color: transparent;\n}\n.navbar-default .navbar-toggle {\n border-color: #ddd;\n}\n.navbar-default .navbar-toggle:hover,\n.navbar-default .navbar-toggle:focus {\n background-color: #ddd;\n}\n.navbar-default .navbar-toggle .icon-bar {\n background-color: #888;\n}\n.navbar-default .navbar-collapse,\n.navbar-default .navbar-form {\n border-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .open > a:hover,\n.navbar-default .navbar-nav > .open > a:focus {\n background-color: #e7e7e7;\n color: #555;\n}\n@media (max-width: 767px) {\n .navbar-default .navbar-nav .open .dropdown-menu > li > a {\n color: #777;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {\n color: #333;\n background-color: transparent;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a,\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #555;\n background-color: #e7e7e7;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n color: #ccc;\n background-color: transparent;\n }\n}\n.navbar-default .navbar-link {\n color: #777;\n}\n.navbar-default .navbar-link:hover {\n color: #333;\n}\n.navbar-default .btn-link {\n color: #777;\n}\n.navbar-default .btn-link:hover,\n.navbar-default .btn-link:focus {\n color: #333;\n}\n.navbar-default .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-default .btn-link:hover,\n.navbar-default .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-default .btn-link:focus {\n color: #ccc;\n}\n.navbar-inverse {\n background-color: #222;\n border-color: #080808;\n}\n.navbar-inverse .navbar-brand {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-brand:hover,\n.navbar-inverse .navbar-brand:focus {\n color: #fff;\n background-color: transparent;\n}\n.navbar-inverse .navbar-text {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a:hover,\n.navbar-inverse .navbar-nav > li > a:focus {\n color: #fff;\n background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .active > a,\n.navbar-inverse .navbar-nav > .active > a:hover,\n.navbar-inverse .navbar-nav > .active > a:focus {\n color: #fff;\n background-color: #080808;\n}\n.navbar-inverse .navbar-nav > .disabled > a,\n.navbar-inverse .navbar-nav > .disabled > a:hover,\n.navbar-inverse .navbar-nav > .disabled > a:focus {\n color: #444;\n background-color: transparent;\n}\n.navbar-inverse .navbar-toggle {\n border-color: #333;\n}\n.navbar-inverse .navbar-toggle:hover,\n.navbar-inverse .navbar-toggle:focus {\n background-color: #333;\n}\n.navbar-inverse .navbar-toggle .icon-bar {\n background-color: #fff;\n}\n.navbar-inverse .navbar-collapse,\n.navbar-inverse .navbar-form {\n border-color: #101010;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .open > a:hover,\n.navbar-inverse .navbar-nav > .open > a:focus {\n background-color: #080808;\n color: #fff;\n}\n@media (max-width: 767px) {\n .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {\n border-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu .divider {\n background-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {\n color: #9d9d9d;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {\n color: #fff;\n background-color: transparent;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n color: #444;\n background-color: transparent;\n }\n}\n.navbar-inverse .navbar-link {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-link:hover {\n color: #fff;\n}\n.navbar-inverse .btn-link {\n color: #9d9d9d;\n}\n.navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link:focus {\n color: #fff;\n}\n.navbar-inverse .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-inverse .btn-link:focus {\n color: #444;\n}\n.breadcrumb {\n padding: 8px 15px;\n margin-bottom: 20px;\n list-style: none;\n background-color: #f5f5f5;\n border-radius: 4px;\n}\n.breadcrumb > li {\n display: inline-block;\n}\n.breadcrumb > li + li:before {\n content: \"/\\00a0\";\n padding: 0 5px;\n color: #ccc;\n}\n.breadcrumb > .active {\n color: #777777;\n}\n.pagination {\n display: inline-block;\n padding-left: 0;\n margin: 20px 0;\n border-radius: 4px;\n}\n.pagination > li {\n display: inline;\n}\n.pagination > li > a,\n.pagination > li > span {\n position: relative;\n float: left;\n padding: 6px 12px;\n line-height: 1.42857143;\n text-decoration: none;\n color: #337ab7;\n background-color: #fff;\n border: 1px solid #ddd;\n margin-left: -1px;\n}\n.pagination > li:first-child > a,\n.pagination > li:first-child > span {\n margin-left: 0;\n border-bottom-left-radius: 4px;\n border-top-left-radius: 4px;\n}\n.pagination > li:last-child > a,\n.pagination > li:last-child > span {\n border-bottom-right-radius: 4px;\n border-top-right-radius: 4px;\n}\n.pagination > li > a:hover,\n.pagination > li > span:hover,\n.pagination > li > a:focus,\n.pagination > li > span:focus {\n z-index: 2;\n color: #23527c;\n background-color: #eeeeee;\n border-color: #ddd;\n}\n.pagination > .active > a,\n.pagination > .active > span,\n.pagination > .active > a:hover,\n.pagination > .active > span:hover,\n.pagination > .active > a:focus,\n.pagination > .active > span:focus {\n z-index: 3;\n color: #fff;\n background-color: #337ab7;\n border-color: #337ab7;\n cursor: default;\n}\n.pagination > .disabled > span,\n.pagination > .disabled > span:hover,\n.pagination > .disabled > span:focus,\n.pagination > .disabled > a,\n.pagination > .disabled > a:hover,\n.pagination > .disabled > a:focus {\n color: #777777;\n background-color: #fff;\n border-color: #ddd;\n cursor: not-allowed;\n}\n.pagination-lg > li > a,\n.pagination-lg > li > span {\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n}\n.pagination-lg > li:first-child > a,\n.pagination-lg > li:first-child > span {\n border-bottom-left-radius: 6px;\n border-top-left-radius: 6px;\n}\n.pagination-lg > li:last-child > a,\n.pagination-lg > li:last-child > span {\n border-bottom-right-radius: 6px;\n border-top-right-radius: 6px;\n}\n.pagination-sm > li > a,\n.pagination-sm > li > span {\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n}\n.pagination-sm > li:first-child > a,\n.pagination-sm > li:first-child > span {\n border-bottom-left-radius: 3px;\n border-top-left-radius: 3px;\n}\n.pagination-sm > li:last-child > a,\n.pagination-sm > li:last-child > span {\n border-bottom-right-radius: 3px;\n border-top-right-radius: 3px;\n}\n.pager {\n padding-left: 0;\n margin: 20px 0;\n list-style: none;\n text-align: center;\n}\n.pager li {\n display: inline;\n}\n.pager li > a,\n.pager li > span {\n display: inline-block;\n padding: 5px 14px;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 15px;\n}\n.pager li > a:hover,\n.pager li > a:focus {\n text-decoration: none;\n background-color: #eeeeee;\n}\n.pager .next > a,\n.pager .next > span {\n float: right;\n}\n.pager .previous > a,\n.pager .previous > span {\n float: left;\n}\n.pager .disabled > a,\n.pager .disabled > a:hover,\n.pager .disabled > a:focus,\n.pager .disabled > span {\n color: #777777;\n background-color: #fff;\n cursor: not-allowed;\n}\n.label {\n display: inline;\n padding: .2em .6em .3em;\n font-size: 75%;\n font-weight: bold;\n line-height: 1;\n color: #fff;\n text-align: center;\n white-space: nowrap;\n vertical-align: baseline;\n border-radius: .25em;\n}\na.label:hover,\na.label:focus {\n color: #fff;\n text-decoration: none;\n cursor: pointer;\n}\n.label:empty {\n display: none;\n}\n.btn .label {\n position: relative;\n top: -1px;\n}\n.label-default {\n background-color: #777777;\n}\n.label-default[href]:hover,\n.label-default[href]:focus {\n background-color: #5e5e5e;\n}\n.label-primary {\n background-color: #337ab7;\n}\n.label-primary[href]:hover,\n.label-primary[href]:focus {\n background-color: #286090;\n}\n.label-success {\n background-color: #5cb85c;\n}\n.label-success[href]:hover,\n.label-success[href]:focus {\n background-color: #449d44;\n}\n.label-info {\n background-color: #5bc0de;\n}\n.label-info[href]:hover,\n.label-info[href]:focus {\n background-color: #31b0d5;\n}\n.label-warning {\n background-color: #f0ad4e;\n}\n.label-warning[href]:hover,\n.label-warning[href]:focus {\n background-color: #ec971f;\n}\n.label-danger {\n background-color: #d9534f;\n}\n.label-danger[href]:hover,\n.label-danger[href]:focus {\n background-color: #c9302c;\n}\n.badge {\n display: inline-block;\n min-width: 10px;\n padding: 3px 7px;\n font-size: 12px;\n font-weight: bold;\n color: #fff;\n line-height: 1;\n vertical-align: middle;\n white-space: nowrap;\n text-align: center;\n background-color: #777777;\n border-radius: 10px;\n}\n.badge:empty {\n display: none;\n}\n.btn .badge {\n position: relative;\n top: -1px;\n}\n.btn-xs .badge,\n.btn-group-xs > .btn .badge {\n top: 0;\n padding: 1px 5px;\n}\na.badge:hover,\na.badge:focus {\n color: #fff;\n text-decoration: none;\n cursor: pointer;\n}\n.list-group-item.active > .badge,\n.nav-pills > .active > a > .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.list-group-item > .badge {\n float: right;\n}\n.list-group-item > .badge + .badge {\n margin-right: 5px;\n}\n.nav-pills > li > a > .badge {\n margin-left: 3px;\n}\n.jumbotron {\n padding-top: 30px;\n padding-bottom: 30px;\n margin-bottom: 30px;\n color: inherit;\n background-color: #eeeeee;\n}\n.jumbotron h1,\n.jumbotron .h1 {\n color: inherit;\n}\n.jumbotron p {\n margin-bottom: 15px;\n font-size: 21px;\n font-weight: 200;\n}\n.jumbotron > hr {\n border-top-color: #d5d5d5;\n}\n.container .jumbotron,\n.container-fluid .jumbotron {\n border-radius: 6px;\n padding-left: 15px;\n padding-right: 15px;\n}\n.jumbotron .container {\n max-width: 100%;\n}\n@media screen and (min-width: 768px) {\n .jumbotron {\n padding-top: 48px;\n padding-bottom: 48px;\n }\n .container .jumbotron,\n .container-fluid .jumbotron {\n padding-left: 60px;\n padding-right: 60px;\n }\n .jumbotron h1,\n .jumbotron .h1 {\n font-size: 63px;\n }\n}\n.thumbnail {\n display: block;\n padding: 4px;\n margin-bottom: 20px;\n line-height: 1.42857143;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 4px;\n -webkit-transition: border 0.2s ease-in-out;\n -o-transition: border 0.2s ease-in-out;\n transition: border 0.2s ease-in-out;\n}\n.thumbnail > img,\n.thumbnail a > img {\n margin-left: auto;\n margin-right: auto;\n}\na.thumbnail:hover,\na.thumbnail:focus,\na.thumbnail.active {\n border-color: #337ab7;\n}\n.thumbnail .caption {\n padding: 9px;\n color: #333333;\n}\n.alert {\n padding: 15px;\n margin-bottom: 20px;\n border: 1px solid transparent;\n border-radius: 4px;\n}\n.alert h4 {\n margin-top: 0;\n color: inherit;\n}\n.alert .alert-link {\n font-weight: bold;\n}\n.alert > p,\n.alert > ul {\n margin-bottom: 0;\n}\n.alert > p + p {\n margin-top: 5px;\n}\n.alert-dismissable,\n.alert-dismissible {\n padding-right: 35px;\n}\n.alert-dismissable .close,\n.alert-dismissible .close {\n position: relative;\n top: -2px;\n right: -21px;\n color: inherit;\n}\n.alert-success {\n background-color: #dff0d8;\n border-color: #d6e9c6;\n color: #3c763d;\n}\n.alert-success hr {\n border-top-color: #c9e2b3;\n}\n.alert-success .alert-link {\n color: #2b542c;\n}\n.alert-info {\n background-color: #d9edf7;\n border-color: #bce8f1;\n color: #31708f;\n}\n.alert-info hr {\n border-top-color: #a6e1ec;\n}\n.alert-info .alert-link {\n color: #245269;\n}\n.alert-warning {\n background-color: #fcf8e3;\n border-color: #faebcc;\n color: #8a6d3b;\n}\n.alert-warning hr {\n border-top-color: #f7e1b5;\n}\n.alert-warning .alert-link {\n color: #66512c;\n}\n.alert-danger {\n background-color: #f2dede;\n border-color: #ebccd1;\n color: #a94442;\n}\n.alert-danger hr {\n border-top-color: #e4b9c0;\n}\n.alert-danger .alert-link {\n color: #843534;\n}\n@-webkit-keyframes progress-bar-stripes {\n from {\n background-position: 40px 0;\n }\n to {\n background-position: 0 0;\n }\n}\n@keyframes progress-bar-stripes {\n from {\n background-position: 40px 0;\n }\n to {\n background-position: 0 0;\n }\n}\n.progress {\n overflow: hidden;\n height: 20px;\n margin-bottom: 20px;\n background-color: #f5f5f5;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n}\n.progress-bar {\n float: left;\n width: 0%;\n height: 100%;\n font-size: 12px;\n line-height: 20px;\n color: #fff;\n text-align: center;\n background-color: #337ab7;\n -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n -webkit-transition: width 0.6s ease;\n -o-transition: width 0.6s ease;\n transition: width 0.6s ease;\n}\n.progress-striped .progress-bar,\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-size: 40px 40px;\n}\n.progress.active .progress-bar,\n.progress-bar.active {\n -webkit-animation: progress-bar-stripes 2s linear infinite;\n -o-animation: progress-bar-stripes 2s linear infinite;\n animation: progress-bar-stripes 2s linear infinite;\n}\n.progress-bar-success {\n background-color: #5cb85c;\n}\n.progress-striped .progress-bar-success {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-info {\n background-color: #5bc0de;\n}\n.progress-striped .progress-bar-info {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-warning {\n background-color: #f0ad4e;\n}\n.progress-striped .progress-bar-warning {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-danger {\n background-color: #d9534f;\n}\n.progress-striped .progress-bar-danger {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.media {\n margin-top: 15px;\n}\n.media:first-child {\n margin-top: 0;\n}\n.media,\n.media-body {\n zoom: 1;\n overflow: hidden;\n}\n.media-body {\n width: 10000px;\n}\n.media-object {\n display: block;\n}\n.media-object.img-thumbnail {\n max-width: none;\n}\n.media-right,\n.media > .pull-right {\n padding-left: 10px;\n}\n.media-left,\n.media > .pull-left {\n padding-right: 10px;\n}\n.media-left,\n.media-right,\n.media-body {\n display: table-cell;\n vertical-align: top;\n}\n.media-middle {\n vertical-align: middle;\n}\n.media-bottom {\n vertical-align: bottom;\n}\n.media-heading {\n margin-top: 0;\n margin-bottom: 5px;\n}\n.media-list {\n padding-left: 0;\n list-style: none;\n}\n.list-group {\n margin-bottom: 20px;\n padding-left: 0;\n}\n.list-group-item {\n position: relative;\n display: block;\n padding: 10px 15px;\n margin-bottom: -1px;\n background-color: #fff;\n border: 1px solid #ddd;\n}\n.list-group-item:first-child {\n border-top-right-radius: 4px;\n border-top-left-radius: 4px;\n}\n.list-group-item:last-child {\n margin-bottom: 0;\n border-bottom-right-radius: 4px;\n border-bottom-left-radius: 4px;\n}\na.list-group-item,\nbutton.list-group-item {\n color: #555;\n}\na.list-group-item .list-group-item-heading,\nbutton.list-group-item .list-group-item-heading {\n color: #333;\n}\na.list-group-item:hover,\nbutton.list-group-item:hover,\na.list-group-item:focus,\nbutton.list-group-item:focus {\n text-decoration: none;\n color: #555;\n background-color: #f5f5f5;\n}\nbutton.list-group-item {\n width: 100%;\n text-align: left;\n}\n.list-group-item.disabled,\n.list-group-item.disabled:hover,\n.list-group-item.disabled:focus {\n background-color: #eeeeee;\n color: #777777;\n cursor: not-allowed;\n}\n.list-group-item.disabled .list-group-item-heading,\n.list-group-item.disabled:hover .list-group-item-heading,\n.list-group-item.disabled:focus .list-group-item-heading {\n color: inherit;\n}\n.list-group-item.disabled .list-group-item-text,\n.list-group-item.disabled:hover .list-group-item-text,\n.list-group-item.disabled:focus .list-group-item-text {\n color: #777777;\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n z-index: 2;\n color: #fff;\n background-color: #337ab7;\n border-color: #337ab7;\n}\n.list-group-item.active .list-group-item-heading,\n.list-group-item.active:hover .list-group-item-heading,\n.list-group-item.active:focus .list-group-item-heading,\n.list-group-item.active .list-group-item-heading > small,\n.list-group-item.active:hover .list-group-item-heading > small,\n.list-group-item.active:focus .list-group-item-heading > small,\n.list-group-item.active .list-group-item-heading > .small,\n.list-group-item.active:hover .list-group-item-heading > .small,\n.list-group-item.active:focus .list-group-item-heading > .small {\n color: inherit;\n}\n.list-group-item.active .list-group-item-text,\n.list-group-item.active:hover .list-group-item-text,\n.list-group-item.active:focus .list-group-item-text {\n color: #c7ddef;\n}\n.list-group-item-success {\n color: #3c763d;\n background-color: #dff0d8;\n}\na.list-group-item-success,\nbutton.list-group-item-success {\n color: #3c763d;\n}\na.list-group-item-success .list-group-item-heading,\nbutton.list-group-item-success .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-success:hover,\nbutton.list-group-item-success:hover,\na.list-group-item-success:focus,\nbutton.list-group-item-success:focus {\n color: #3c763d;\n background-color: #d0e9c6;\n}\na.list-group-item-success.active,\nbutton.list-group-item-success.active,\na.list-group-item-success.active:hover,\nbutton.list-group-item-success.active:hover,\na.list-group-item-success.active:focus,\nbutton.list-group-item-success.active:focus {\n color: #fff;\n background-color: #3c763d;\n border-color: #3c763d;\n}\n.list-group-item-info {\n color: #31708f;\n background-color: #d9edf7;\n}\na.list-group-item-info,\nbutton.list-group-item-info {\n color: #31708f;\n}\na.list-group-item-info .list-group-item-heading,\nbutton.list-group-item-info .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-info:hover,\nbutton.list-group-item-info:hover,\na.list-group-item-info:focus,\nbutton.list-group-item-info:focus {\n color: #31708f;\n background-color: #c4e3f3;\n}\na.list-group-item-info.active,\nbutton.list-group-item-info.active,\na.list-group-item-info.active:hover,\nbutton.list-group-item-info.active:hover,\na.list-group-item-info.active:focus,\nbutton.list-group-item-info.active:focus {\n color: #fff;\n background-color: #31708f;\n border-color: #31708f;\n}\n.list-group-item-warning {\n color: #8a6d3b;\n background-color: #fcf8e3;\n}\na.list-group-item-warning,\nbutton.list-group-item-warning {\n color: #8a6d3b;\n}\na.list-group-item-warning .list-group-item-heading,\nbutton.list-group-item-warning .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-warning:hover,\nbutton.list-group-item-warning:hover,\na.list-group-item-warning:focus,\nbutton.list-group-item-warning:focus {\n color: #8a6d3b;\n background-color: #faf2cc;\n}\na.list-group-item-warning.active,\nbutton.list-group-item-warning.active,\na.list-group-item-warning.active:hover,\nbutton.list-group-item-warning.active:hover,\na.list-group-item-warning.active:focus,\nbutton.list-group-item-warning.active:focus {\n color: #fff;\n background-color: #8a6d3b;\n border-color: #8a6d3b;\n}\n.list-group-item-danger {\n color: #a94442;\n background-color: #f2dede;\n}\na.list-group-item-danger,\nbutton.list-group-item-danger {\n color: #a94442;\n}\na.list-group-item-danger .list-group-item-heading,\nbutton.list-group-item-danger .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-danger:hover,\nbutton.list-group-item-danger:hover,\na.list-group-item-danger:focus,\nbutton.list-group-item-danger:focus {\n color: #a94442;\n background-color: #ebcccc;\n}\na.list-group-item-danger.active,\nbutton.list-group-item-danger.active,\na.list-group-item-danger.active:hover,\nbutton.list-group-item-danger.active:hover,\na.list-group-item-danger.active:focus,\nbutton.list-group-item-danger.active:focus {\n color: #fff;\n background-color: #a94442;\n border-color: #a94442;\n}\n.list-group-item-heading {\n margin-top: 0;\n margin-bottom: 5px;\n}\n.list-group-item-text {\n margin-bottom: 0;\n line-height: 1.3;\n}\n.panel {\n margin-bottom: 20px;\n background-color: #fff;\n border: 1px solid transparent;\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.panel-body {\n padding: 15px;\n}\n.panel-heading {\n padding: 10px 15px;\n border-bottom: 1px solid transparent;\n border-top-right-radius: 3px;\n border-top-left-radius: 3px;\n}\n.panel-heading > .dropdown .dropdown-toggle {\n color: inherit;\n}\n.panel-title {\n margin-top: 0;\n margin-bottom: 0;\n font-size: 16px;\n color: inherit;\n}\n.panel-title > a,\n.panel-title > small,\n.panel-title > .small,\n.panel-title > small > a,\n.panel-title > .small > a {\n color: inherit;\n}\n.panel-footer {\n padding: 10px 15px;\n background-color: #f5f5f5;\n border-top: 1px solid #ddd;\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .list-group,\n.panel > .panel-collapse > .list-group {\n margin-bottom: 0;\n}\n.panel > .list-group .list-group-item,\n.panel > .panel-collapse > .list-group .list-group-item {\n border-width: 1px 0;\n border-radius: 0;\n}\n.panel > .list-group:first-child .list-group-item:first-child,\n.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {\n border-top: 0;\n border-top-right-radius: 3px;\n border-top-left-radius: 3px;\n}\n.panel > .list-group:last-child .list-group-item:last-child,\n.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {\n border-bottom: 0;\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {\n border-top-right-radius: 0;\n border-top-left-radius: 0;\n}\n.panel-heading + .list-group .list-group-item:first-child {\n border-top-width: 0;\n}\n.list-group + .panel-footer {\n border-top-width: 0;\n}\n.panel > .table,\n.panel > .table-responsive > .table,\n.panel > .panel-collapse > .table {\n margin-bottom: 0;\n}\n.panel > .table caption,\n.panel > .table-responsive > .table caption,\n.panel > .panel-collapse > .table caption {\n padding-left: 15px;\n padding-right: 15px;\n}\n.panel > .table:first-child,\n.panel > .table-responsive:first-child > .table:first-child {\n border-top-right-radius: 3px;\n border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {\n border-top-left-radius: 3px;\n border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {\n border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {\n border-top-right-radius: 3px;\n}\n.panel > .table:last-child,\n.panel > .table-responsive:last-child > .table:last-child {\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {\n border-bottom-left-radius: 3px;\n border-bottom-right-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {\n border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {\n border-bottom-right-radius: 3px;\n}\n.panel > .panel-body + .table,\n.panel > .panel-body + .table-responsive,\n.panel > .table + .panel-body,\n.panel > .table-responsive + .panel-body {\n border-top: 1px solid #ddd;\n}\n.panel > .table > tbody:first-child > tr:first-child th,\n.panel > .table > tbody:first-child > tr:first-child td {\n border-top: 0;\n}\n.panel > .table-bordered,\n.panel > .table-responsive > .table-bordered {\n border: 0;\n}\n.panel > .table-bordered > thead > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,\n.panel > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-bordered > thead > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,\n.panel > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-bordered > tfoot > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n border-left: 0;\n}\n.panel > .table-bordered > thead > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,\n.panel > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-bordered > thead > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,\n.panel > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-bordered > tfoot > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n border-right: 0;\n}\n.panel > .table-bordered > thead > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,\n.panel > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-bordered > thead > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,\n.panel > .table-bordered > tbody > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {\n border-bottom: 0;\n}\n.panel > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-bordered > tfoot > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {\n border-bottom: 0;\n}\n.panel > .table-responsive {\n border: 0;\n margin-bottom: 0;\n}\n.panel-group {\n margin-bottom: 20px;\n}\n.panel-group .panel {\n margin-bottom: 0;\n border-radius: 4px;\n}\n.panel-group .panel + .panel {\n margin-top: 5px;\n}\n.panel-group .panel-heading {\n border-bottom: 0;\n}\n.panel-group .panel-heading + .panel-collapse > .panel-body,\n.panel-group .panel-heading + .panel-collapse > .list-group {\n border-top: 1px solid #ddd;\n}\n.panel-group .panel-footer {\n border-top: 0;\n}\n.panel-group .panel-footer + .panel-collapse .panel-body {\n border-bottom: 1px solid #ddd;\n}\n.panel-default {\n border-color: #ddd;\n}\n.panel-default > .panel-heading {\n color: #333333;\n background-color: #f5f5f5;\n border-color: #ddd;\n}\n.panel-default > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #ddd;\n}\n.panel-default > .panel-heading .badge {\n color: #f5f5f5;\n background-color: #333333;\n}\n.panel-default > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #ddd;\n}\n.panel-primary {\n border-color: #337ab7;\n}\n.panel-primary > .panel-heading {\n color: #fff;\n background-color: #337ab7;\n border-color: #337ab7;\n}\n.panel-primary > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #337ab7;\n}\n.panel-primary > .panel-heading .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.panel-primary > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #337ab7;\n}\n.panel-success {\n border-color: #d6e9c6;\n}\n.panel-success > .panel-heading {\n color: #3c763d;\n background-color: #dff0d8;\n border-color: #d6e9c6;\n}\n.panel-success > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #d6e9c6;\n}\n.panel-success > .panel-heading .badge {\n color: #dff0d8;\n background-color: #3c763d;\n}\n.panel-success > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #d6e9c6;\n}\n.panel-info {\n border-color: #bce8f1;\n}\n.panel-info > .panel-heading {\n color: #31708f;\n background-color: #d9edf7;\n border-color: #bce8f1;\n}\n.panel-info > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #bce8f1;\n}\n.panel-info > .panel-heading .badge {\n color: #d9edf7;\n background-color: #31708f;\n}\n.panel-info > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #bce8f1;\n}\n.panel-warning {\n border-color: #faebcc;\n}\n.panel-warning > .panel-heading {\n color: #8a6d3b;\n background-color: #fcf8e3;\n border-color: #faebcc;\n}\n.panel-warning > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #faebcc;\n}\n.panel-warning > .panel-heading .badge {\n color: #fcf8e3;\n background-color: #8a6d3b;\n}\n.panel-warning > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #faebcc;\n}\n.panel-danger {\n border-color: #ebccd1;\n}\n.panel-danger > .panel-heading {\n color: #a94442;\n background-color: #f2dede;\n border-color: #ebccd1;\n}\n.panel-danger > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #ebccd1;\n}\n.panel-danger > .panel-heading .badge {\n color: #f2dede;\n background-color: #a94442;\n}\n.panel-danger > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #ebccd1;\n}\n.embed-responsive {\n position: relative;\n display: block;\n height: 0;\n padding: 0;\n overflow: hidden;\n}\n.embed-responsive .embed-responsive-item,\n.embed-responsive iframe,\n.embed-responsive embed,\n.embed-responsive object,\n.embed-responsive video {\n position: absolute;\n top: 0;\n left: 0;\n bottom: 0;\n height: 100%;\n width: 100%;\n border: 0;\n}\n.embed-responsive-16by9 {\n padding-bottom: 56.25%;\n}\n.embed-responsive-4by3 {\n padding-bottom: 75%;\n}\n.well {\n min-height: 20px;\n padding: 19px;\n margin-bottom: 20px;\n background-color: #f5f5f5;\n border: 1px solid #e3e3e3;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.well blockquote {\n border-color: #ddd;\n border-color: rgba(0, 0, 0, 0.15);\n}\n.well-lg {\n padding: 24px;\n border-radius: 6px;\n}\n.well-sm {\n padding: 9px;\n border-radius: 3px;\n}\n.close {\n float: right;\n font-size: 21px;\n font-weight: bold;\n line-height: 1;\n color: #000;\n text-shadow: 0 1px 0 #fff;\n opacity: 0.2;\n filter: alpha(opacity=20);\n}\n.close:hover,\n.close:focus {\n color: #000;\n text-decoration: none;\n cursor: pointer;\n opacity: 0.5;\n filter: alpha(opacity=50);\n}\nbutton.close {\n padding: 0;\n cursor: pointer;\n background: transparent;\n border: 0;\n -webkit-appearance: none;\n}\n.modal-open {\n overflow: hidden;\n}\n.modal {\n display: none;\n overflow: hidden;\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1050;\n -webkit-overflow-scrolling: touch;\n outline: 0;\n}\n.modal.fade .modal-dialog {\n -webkit-transform: translate(0, -25%);\n -ms-transform: translate(0, -25%);\n -o-transform: translate(0, -25%);\n transform: translate(0, -25%);\n -webkit-transition: -webkit-transform 0.3s ease-out;\n -moz-transition: -moz-transform 0.3s ease-out;\n -o-transition: -o-transform 0.3s ease-out;\n transition: transform 0.3s ease-out;\n}\n.modal.in .modal-dialog {\n -webkit-transform: translate(0, 0);\n -ms-transform: translate(0, 0);\n -o-transform: translate(0, 0);\n transform: translate(0, 0);\n}\n.modal-open .modal {\n overflow-x: hidden;\n overflow-y: auto;\n}\n.modal-dialog {\n position: relative;\n width: auto;\n margin: 10px;\n}\n.modal-content {\n position: relative;\n background-color: #fff;\n border: 1px solid #999;\n border: 1px solid rgba(0, 0, 0, 0.2);\n border-radius: 6px;\n -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n background-clip: padding-box;\n outline: 0;\n}\n.modal-backdrop {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1040;\n background-color: #000;\n}\n.modal-backdrop.fade {\n opacity: 0;\n filter: alpha(opacity=0);\n}\n.modal-backdrop.in {\n opacity: 0.5;\n filter: alpha(opacity=50);\n}\n.modal-header {\n padding: 15px;\n border-bottom: 1px solid #e5e5e5;\n}\n.modal-header .close {\n margin-top: -2px;\n}\n.modal-title {\n margin: 0;\n line-height: 1.42857143;\n}\n.modal-body {\n position: relative;\n padding: 15px;\n}\n.modal-footer {\n padding: 15px;\n text-align: right;\n border-top: 1px solid #e5e5e5;\n}\n.modal-footer .btn + .btn {\n margin-left: 5px;\n margin-bottom: 0;\n}\n.modal-footer .btn-group .btn + .btn {\n margin-left: -1px;\n}\n.modal-footer .btn-block + .btn-block {\n margin-left: 0;\n}\n.modal-scrollbar-measure {\n position: absolute;\n top: -9999px;\n width: 50px;\n height: 50px;\n overflow: scroll;\n}\n@media (min-width: 768px) {\n .modal-dialog {\n width: 600px;\n margin: 30px auto;\n }\n .modal-content {\n -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n }\n .modal-sm {\n width: 300px;\n }\n}\n@media (min-width: 992px) {\n .modal-lg {\n width: 900px;\n }\n}\n.tooltip {\n position: absolute;\n z-index: 1070;\n display: block;\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-style: normal;\n font-weight: normal;\n letter-spacing: normal;\n line-break: auto;\n line-height: 1.42857143;\n text-align: left;\n text-align: start;\n text-decoration: none;\n text-shadow: none;\n text-transform: none;\n white-space: normal;\n word-break: normal;\n word-spacing: normal;\n word-wrap: normal;\n font-size: 12px;\n opacity: 0;\n filter: alpha(opacity=0);\n}\n.tooltip.in {\n opacity: 0.9;\n filter: alpha(opacity=90);\n}\n.tooltip.top {\n margin-top: -3px;\n padding: 5px 0;\n}\n.tooltip.right {\n margin-left: 3px;\n padding: 0 5px;\n}\n.tooltip.bottom {\n margin-top: 3px;\n padding: 5px 0;\n}\n.tooltip.left {\n margin-left: -3px;\n padding: 0 5px;\n}\n.tooltip-inner {\n max-width: 200px;\n padding: 3px 8px;\n color: #fff;\n text-align: center;\n background-color: #000;\n border-radius: 4px;\n}\n.tooltip-arrow {\n position: absolute;\n width: 0;\n height: 0;\n border-color: transparent;\n border-style: solid;\n}\n.tooltip.top .tooltip-arrow {\n bottom: 0;\n left: 50%;\n margin-left: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.top-left .tooltip-arrow {\n bottom: 0;\n right: 5px;\n margin-bottom: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.top-right .tooltip-arrow {\n bottom: 0;\n left: 5px;\n margin-bottom: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.right .tooltip-arrow {\n top: 50%;\n left: 0;\n margin-top: -5px;\n border-width: 5px 5px 5px 0;\n border-right-color: #000;\n}\n.tooltip.left .tooltip-arrow {\n top: 50%;\n right: 0;\n margin-top: -5px;\n border-width: 5px 0 5px 5px;\n border-left-color: #000;\n}\n.tooltip.bottom .tooltip-arrow {\n top: 0;\n left: 50%;\n margin-left: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.tooltip.bottom-left .tooltip-arrow {\n top: 0;\n right: 5px;\n margin-top: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.tooltip.bottom-right .tooltip-arrow {\n top: 0;\n left: 5px;\n margin-top: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.popover {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 1060;\n display: none;\n max-width: 276px;\n padding: 1px;\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-style: normal;\n font-weight: normal;\n letter-spacing: normal;\n line-break: auto;\n line-height: 1.42857143;\n text-align: left;\n text-align: start;\n text-decoration: none;\n text-shadow: none;\n text-transform: none;\n white-space: normal;\n word-break: normal;\n word-spacing: normal;\n word-wrap: normal;\n font-size: 14px;\n background-color: #fff;\n background-clip: padding-box;\n border: 1px solid #ccc;\n border: 1px solid rgba(0, 0, 0, 0.2);\n border-radius: 6px;\n -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n}\n.popover.top {\n margin-top: -10px;\n}\n.popover.right {\n margin-left: 10px;\n}\n.popover.bottom {\n margin-top: 10px;\n}\n.popover.left {\n margin-left: -10px;\n}\n.popover-title {\n margin: 0;\n padding: 8px 14px;\n font-size: 14px;\n background-color: #f7f7f7;\n border-bottom: 1px solid #ebebeb;\n border-radius: 5px 5px 0 0;\n}\n.popover-content {\n padding: 9px 14px;\n}\n.popover > .arrow,\n.popover > .arrow:after {\n position: absolute;\n display: block;\n width: 0;\n height: 0;\n border-color: transparent;\n border-style: solid;\n}\n.popover > .arrow {\n border-width: 11px;\n}\n.popover > .arrow:after {\n border-width: 10px;\n content: \"\";\n}\n.popover.top > .arrow {\n left: 50%;\n margin-left: -11px;\n border-bottom-width: 0;\n border-top-color: #999999;\n border-top-color: rgba(0, 0, 0, 0.25);\n bottom: -11px;\n}\n.popover.top > .arrow:after {\n content: \" \";\n bottom: 1px;\n margin-left: -10px;\n border-bottom-width: 0;\n border-top-color: #fff;\n}\n.popover.right > .arrow {\n top: 50%;\n left: -11px;\n margin-top: -11px;\n border-left-width: 0;\n border-right-color: #999999;\n border-right-color: rgba(0, 0, 0, 0.25);\n}\n.popover.right > .arrow:after {\n content: \" \";\n left: 1px;\n bottom: -10px;\n border-left-width: 0;\n border-right-color: #fff;\n}\n.popover.bottom > .arrow {\n left: 50%;\n margin-left: -11px;\n border-top-width: 0;\n border-bottom-color: #999999;\n border-bottom-color: rgba(0, 0, 0, 0.25);\n top: -11px;\n}\n.popover.bottom > .arrow:after {\n content: \" \";\n top: 1px;\n margin-left: -10px;\n border-top-width: 0;\n border-bottom-color: #fff;\n}\n.popover.left > .arrow {\n top: 50%;\n right: -11px;\n margin-top: -11px;\n border-right-width: 0;\n border-left-color: #999999;\n border-left-color: rgba(0, 0, 0, 0.25);\n}\n.popover.left > .arrow:after {\n content: \" \";\n right: 1px;\n border-right-width: 0;\n border-left-color: #fff;\n bottom: -10px;\n}\n.carousel {\n position: relative;\n}\n.carousel-inner {\n position: relative;\n overflow: hidden;\n width: 100%;\n}\n.carousel-inner > .item {\n display: none;\n position: relative;\n -webkit-transition: 0.6s ease-in-out left;\n -o-transition: 0.6s ease-in-out left;\n transition: 0.6s ease-in-out left;\n}\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n line-height: 1;\n}\n@media all and (transform-3d), (-webkit-transform-3d) {\n .carousel-inner > .item {\n -webkit-transition: -webkit-transform 0.6s ease-in-out;\n -moz-transition: -moz-transform 0.6s ease-in-out;\n -o-transition: -o-transform 0.6s ease-in-out;\n transition: transform 0.6s ease-in-out;\n -webkit-backface-visibility: hidden;\n -moz-backface-visibility: hidden;\n backface-visibility: hidden;\n -webkit-perspective: 1000px;\n -moz-perspective: 1000px;\n perspective: 1000px;\n }\n .carousel-inner > .item.next,\n .carousel-inner > .item.active.right {\n -webkit-transform: translate3d(100%, 0, 0);\n transform: translate3d(100%, 0, 0);\n left: 0;\n }\n .carousel-inner > .item.prev,\n .carousel-inner > .item.active.left {\n -webkit-transform: translate3d(-100%, 0, 0);\n transform: translate3d(-100%, 0, 0);\n left: 0;\n }\n .carousel-inner > .item.next.left,\n .carousel-inner > .item.prev.right,\n .carousel-inner > .item.active {\n -webkit-transform: translate3d(0, 0, 0);\n transform: translate3d(0, 0, 0);\n left: 0;\n }\n}\n.carousel-inner > .active,\n.carousel-inner > .next,\n.carousel-inner > .prev {\n display: block;\n}\n.carousel-inner > .active {\n left: 0;\n}\n.carousel-inner > .next,\n.carousel-inner > .prev {\n position: absolute;\n top: 0;\n width: 100%;\n}\n.carousel-inner > .next {\n left: 100%;\n}\n.carousel-inner > .prev {\n left: -100%;\n}\n.carousel-inner > .next.left,\n.carousel-inner > .prev.right {\n left: 0;\n}\n.carousel-inner > .active.left {\n left: -100%;\n}\n.carousel-inner > .active.right {\n left: 100%;\n}\n.carousel-control {\n position: absolute;\n top: 0;\n left: 0;\n bottom: 0;\n width: 15%;\n opacity: 0.5;\n filter: alpha(opacity=50);\n font-size: 20px;\n color: #fff;\n text-align: center;\n text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n background-color: rgba(0, 0, 0, 0);\n}\n.carousel-control.left {\n background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);\n}\n.carousel-control.right {\n left: auto;\n right: 0;\n background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);\n}\n.carousel-control:hover,\n.carousel-control:focus {\n outline: 0;\n color: #fff;\n text-decoration: none;\n opacity: 0.9;\n filter: alpha(opacity=90);\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-left,\n.carousel-control .glyphicon-chevron-right {\n position: absolute;\n top: 50%;\n margin-top: -10px;\n z-index: 5;\n display: inline-block;\n}\n.carousel-control .icon-prev,\n.carousel-control .glyphicon-chevron-left {\n left: 50%;\n margin-left: -10px;\n}\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-right {\n right: 50%;\n margin-right: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next {\n width: 20px;\n height: 20px;\n line-height: 1;\n font-family: serif;\n}\n.carousel-control .icon-prev:before {\n content: '\\2039';\n}\n.carousel-control .icon-next:before {\n content: '\\203a';\n}\n.carousel-indicators {\n position: absolute;\n bottom: 10px;\n left: 50%;\n z-index: 15;\n width: 60%;\n margin-left: -30%;\n padding-left: 0;\n list-style: none;\n text-align: center;\n}\n.carousel-indicators li {\n display: inline-block;\n width: 10px;\n height: 10px;\n margin: 1px;\n text-indent: -999px;\n border: 1px solid #fff;\n border-radius: 10px;\n cursor: pointer;\n background-color: #000 \\9;\n background-color: rgba(0, 0, 0, 0);\n}\n.carousel-indicators .active {\n margin: 0;\n width: 12px;\n height: 12px;\n background-color: #fff;\n}\n.carousel-caption {\n position: absolute;\n left: 15%;\n right: 15%;\n bottom: 20px;\n z-index: 10;\n padding-top: 20px;\n padding-bottom: 20px;\n color: #fff;\n text-align: center;\n text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n}\n.carousel-caption .btn {\n text-shadow: none;\n}\n@media screen and (min-width: 768px) {\n .carousel-control .glyphicon-chevron-left,\n .carousel-control .glyphicon-chevron-right,\n .carousel-control .icon-prev,\n .carousel-control .icon-next {\n width: 30px;\n height: 30px;\n margin-top: -10px;\n font-size: 30px;\n }\n .carousel-control .glyphicon-chevron-left,\n .carousel-control .icon-prev {\n margin-left: -10px;\n }\n .carousel-control .glyphicon-chevron-right,\n .carousel-control .icon-next {\n margin-right: -10px;\n }\n .carousel-caption {\n left: 20%;\n right: 20%;\n padding-bottom: 30px;\n }\n .carousel-indicators {\n bottom: 20px;\n }\n}\n.clearfix:before,\n.clearfix:after,\n.dl-horizontal dd:before,\n.dl-horizontal dd:after,\n.container:before,\n.container:after,\n.container-fluid:before,\n.container-fluid:after,\n.row:before,\n.row:after,\n.form-horizontal .form-group:before,\n.form-horizontal .form-group:after,\n.btn-toolbar:before,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:before,\n.btn-group-vertical > .btn-group:after,\n.nav:before,\n.nav:after,\n.navbar:before,\n.navbar:after,\n.navbar-header:before,\n.navbar-header:after,\n.navbar-collapse:before,\n.navbar-collapse:after,\n.pager:before,\n.pager:after,\n.panel-body:before,\n.panel-body:after,\n.modal-header:before,\n.modal-header:after,\n.modal-footer:before,\n.modal-footer:after {\n content: \" \";\n display: table;\n}\n.clearfix:after,\n.dl-horizontal dd:after,\n.container:after,\n.container-fluid:after,\n.row:after,\n.form-horizontal .form-group:after,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:after,\n.nav:after,\n.navbar:after,\n.navbar-header:after,\n.navbar-collapse:after,\n.pager:after,\n.panel-body:after,\n.modal-header:after,\n.modal-footer:after {\n clear: both;\n}\n.center-block {\n display: block;\n margin-left: auto;\n margin-right: auto;\n}\n.pull-right {\n float: right !important;\n}\n.pull-left {\n float: left !important;\n}\n.hide {\n display: none !important;\n}\n.show {\n display: block !important;\n}\n.invisible {\n visibility: hidden;\n}\n.text-hide {\n font: 0/0 a;\n color: transparent;\n text-shadow: none;\n background-color: transparent;\n border: 0;\n}\n.hidden {\n display: none !important;\n}\n.affix {\n position: fixed;\n}\n@-ms-viewport {\n width: device-width;\n}\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n display: none !important;\n}\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n display: none !important;\n}\n@media (max-width: 767px) {\n .visible-xs {\n display: block !important;\n }\n table.visible-xs {\n display: table !important;\n }\n tr.visible-xs {\n display: table-row !important;\n }\n th.visible-xs,\n td.visible-xs {\n display: table-cell !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-block {\n display: block !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-inline {\n display: inline !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm {\n display: block !important;\n }\n table.visible-sm {\n display: table !important;\n }\n tr.visible-sm {\n display: table-row !important;\n }\n th.visible-sm,\n td.visible-sm {\n display: table-cell !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-block {\n display: block !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-inline {\n display: inline !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md {\n display: block !important;\n }\n table.visible-md {\n display: table !important;\n }\n tr.visible-md {\n display: table-row !important;\n }\n th.visible-md,\n td.visible-md {\n display: table-cell !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-block {\n display: block !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-inline {\n display: inline !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg {\n display: block !important;\n }\n table.visible-lg {\n display: table !important;\n }\n tr.visible-lg {\n display: table-row !important;\n }\n th.visible-lg,\n td.visible-lg {\n display: table-cell !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-block {\n display: block !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-inline {\n display: inline !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-inline-block {\n display: inline-block !important;\n }\n}\n@media (max-width: 767px) {\n .hidden-xs {\n display: none !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .hidden-sm {\n display: none !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .hidden-md {\n display: none !important;\n }\n}\n@media (min-width: 1200px) {\n .hidden-lg {\n display: none !important;\n }\n}\n.visible-print {\n display: none !important;\n}\n@media print {\n .visible-print {\n display: block !important;\n }\n table.visible-print {\n display: table !important;\n }\n tr.visible-print {\n display: table-row !important;\n }\n th.visible-print,\n td.visible-print {\n display: table-cell !important;\n }\n}\n.visible-print-block {\n display: none !important;\n}\n@media print {\n .visible-print-block {\n display: block !important;\n }\n}\n.visible-print-inline {\n display: none !important;\n}\n@media print {\n .visible-print-inline {\n display: inline !important;\n }\n}\n.visible-print-inline-block {\n display: none !important;\n}\n@media print {\n .visible-print-inline-block {\n display: inline-block !important;\n }\n}\n@media print {\n .hidden-print {\n display: none !important;\n }\n}\n/*# sourceMappingURL=bootstrap.css.map */","/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\n\n//\n// 1. Set default font family to sans-serif.\n// 2. Prevent iOS and IE text size adjust after device orientation change,\n// without disabling user zoom.\n//\n\nhtml {\n font-family: sans-serif; // 1\n -ms-text-size-adjust: 100%; // 2\n -webkit-text-size-adjust: 100%; // 2\n}\n\n//\n// Remove default margin.\n//\n\nbody {\n margin: 0;\n}\n\n// HTML5 display definitions\n// ==========================================================================\n\n//\n// Correct `block` display not defined for any HTML5 element in IE 8/9.\n// Correct `block` display not defined for `details` or `summary` in IE 10/11\n// and Firefox.\n// Correct `block` display not defined for `main` in IE 11.\n//\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n display: block;\n}\n\n//\n// 1. Correct `inline-block` display not defined in IE 8/9.\n// 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n//\n\naudio,\ncanvas,\nprogress,\nvideo {\n display: inline-block; // 1\n vertical-align: baseline; // 2\n}\n\n//\n// Prevent modern browsers from displaying `audio` without controls.\n// Remove excess height in iOS 5 devices.\n//\n\naudio:not([controls]) {\n display: none;\n height: 0;\n}\n\n//\n// Address `[hidden]` styling not present in IE 8/9/10.\n// Hide the `template` element in IE 8/9/10/11, Safari, and Firefox < 22.\n//\n\n[hidden],\ntemplate {\n display: none;\n}\n\n// Links\n// ==========================================================================\n\n//\n// Remove the gray background color from active links in IE 10.\n//\n\na {\n background-color: transparent;\n}\n\n//\n// Improve readability of focused elements when they are also in an\n// active/hover state.\n//\n\na:active,\na:hover {\n outline: 0;\n}\n\n// Text-level semantics\n// ==========================================================================\n\n//\n// Address styling not present in IE 8/9/10/11, Safari, and Chrome.\n//\n\nabbr[title] {\n border-bottom: 1px dotted;\n}\n\n//\n// Address style set to `bolder` in Firefox 4+, Safari, and Chrome.\n//\n\nb,\nstrong {\n font-weight: bold;\n}\n\n//\n// Address styling not present in Safari and Chrome.\n//\n\ndfn {\n font-style: italic;\n}\n\n//\n// Address variable `h1` font-size and margin within `section` and `article`\n// contexts in Firefox 4+, Safari, and Chrome.\n//\n\nh1 {\n font-size: 2em;\n margin: 0.67em 0;\n}\n\n//\n// Address styling not present in IE 8/9.\n//\n\nmark {\n background: #ff0;\n color: #000;\n}\n\n//\n// Address inconsistent and variable font size in all browsers.\n//\n\nsmall {\n font-size: 80%;\n}\n\n//\n// Prevent `sub` and `sup` affecting `line-height` in all browsers.\n//\n\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\n\nsup {\n top: -0.5em;\n}\n\nsub {\n bottom: -0.25em;\n}\n\n// Embedded content\n// ==========================================================================\n\n//\n// Remove border when inside `a` element in IE 8/9/10.\n//\n\nimg {\n border: 0;\n}\n\n//\n// Correct overflow not hidden in IE 9/10/11.\n//\n\nsvg:not(:root) {\n overflow: hidden;\n}\n\n// Grouping content\n// ==========================================================================\n\n//\n// Address margin not present in IE 8/9 and Safari.\n//\n\nfigure {\n margin: 1em 40px;\n}\n\n//\n// Address differences between Firefox and other browsers.\n//\n\nhr {\n box-sizing: content-box;\n height: 0;\n}\n\n//\n// Contain overflow in all browsers.\n//\n\npre {\n overflow: auto;\n}\n\n//\n// Address odd `em`-unit font size rendering in all browsers.\n//\n\ncode,\nkbd,\npre,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\n\n// Forms\n// ==========================================================================\n\n//\n// Known limitation: by default, Chrome and Safari on OS X allow very limited\n// styling of `select`, unless a `border` property is set.\n//\n\n//\n// 1. Correct color not being inherited.\n// Known issue: affects color of disabled elements.\n// 2. Correct font properties not being inherited.\n// 3. Address margins set differently in Firefox 4+, Safari, and Chrome.\n//\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n color: inherit; // 1\n font: inherit; // 2\n margin: 0; // 3\n}\n\n//\n// Address `overflow` set to `hidden` in IE 8/9/10/11.\n//\n\nbutton {\n overflow: visible;\n}\n\n//\n// Address inconsistent `text-transform` inheritance for `button` and `select`.\n// All other form control elements do not inherit `text-transform` values.\n// Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.\n// Correct `select` style inheritance in Firefox.\n//\n\nbutton,\nselect {\n text-transform: none;\n}\n\n//\n// 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n// and `video` controls.\n// 2. Correct inability to style clickable `input` types in iOS.\n// 3. Improve usability and consistency of cursor style between image-type\n// `input` and others.\n//\n\nbutton,\nhtml input[type=\"button\"], // 1\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button; // 2\n cursor: pointer; // 3\n}\n\n//\n// Re-set default cursor for disabled elements.\n//\n\nbutton[disabled],\nhtml input[disabled] {\n cursor: default;\n}\n\n//\n// Remove inner padding and border in Firefox 4+.\n//\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\n\n//\n// Address Firefox 4+ setting `line-height` on `input` using `!important` in\n// the UA stylesheet.\n//\n\ninput {\n line-height: normal;\n}\n\n//\n// It's recommended that you don't attempt to style these elements.\n// Firefox's implementation doesn't respect box-sizing, padding, or width.\n//\n// 1. Address box sizing set to `content-box` in IE 8/9/10.\n// 2. Remove excess padding in IE 8/9/10.\n//\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box; // 1\n padding: 0; // 2\n}\n\n//\n// Fix the cursor style for Chrome's increment/decrement buttons. For certain\n// `font-size` values of the `input`, it causes the cursor style of the\n// decrement button to change from `default` to `text`.\n//\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n//\n// 1. Address `appearance` set to `searchfield` in Safari and Chrome.\n// 2. Address `box-sizing` set to `border-box` in Safari and Chrome.\n//\n\ninput[type=\"search\"] {\n -webkit-appearance: textfield; // 1\n box-sizing: content-box; //2\n}\n\n//\n// Remove inner padding and search cancel button in Safari and Chrome on OS X.\n// Safari (but not Chrome) clips the cancel button when the search input has\n// padding (and `textfield` appearance).\n//\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n//\n// Define consistent border, margin, and padding.\n//\n\nfieldset {\n border: 1px solid #c0c0c0;\n margin: 0 2px;\n padding: 0.35em 0.625em 0.75em;\n}\n\n//\n// 1. Correct `color` not being inherited in IE 8/9/10/11.\n// 2. Remove padding so people aren't caught out if they zero out fieldsets.\n//\n\nlegend {\n border: 0; // 1\n padding: 0; // 2\n}\n\n//\n// Remove default vertical scrollbar in IE 8/9/10/11.\n//\n\ntextarea {\n overflow: auto;\n}\n\n//\n// Don't inherit the `font-weight` (applied by a rule above).\n// NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n//\n\noptgroup {\n font-weight: bold;\n}\n\n// Tables\n// ==========================================================================\n\n//\n// Remove most spacing between table cells.\n//\n\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\n\ntd,\nth {\n padding: 0;\n}\n","/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n\n// ==========================================================================\n// Print styles.\n// Inlined to avoid the additional HTTP request: h5bp.com/r\n// ==========================================================================\n\n@media print {\n *,\n *:before,\n *:after {\n background: transparent !important;\n color: #000 !important; // Black prints faster: h5bp.com/s\n box-shadow: none !important;\n text-shadow: none !important;\n }\n\n a,\n a:visited {\n text-decoration: underline;\n }\n\n a[href]:after {\n content: \" (\" attr(href) \")\";\n }\n\n abbr[title]:after {\n content: \" (\" attr(title) \")\";\n }\n\n // Don't show links that are fragment identifiers,\n // or use the `javascript:` pseudo protocol\n a[href^=\"#\"]:after,\n a[href^=\"javascript:\"]:after {\n content: \"\";\n }\n\n pre,\n blockquote {\n border: 1px solid #999;\n page-break-inside: avoid;\n }\n\n thead {\n display: table-header-group; // h5bp.com/t\n }\n\n tr,\n img {\n page-break-inside: avoid;\n }\n\n img {\n max-width: 100% !important;\n }\n\n p,\n h2,\n h3 {\n orphans: 3;\n widows: 3;\n }\n\n h2,\n h3 {\n page-break-after: avoid;\n }\n\n // Bootstrap specific changes start\n\n // Bootstrap components\n .navbar {\n display: none;\n }\n .btn,\n .dropup > .btn {\n > .caret {\n border-top-color: #000 !important;\n }\n }\n .label {\n border: 1px solid #000;\n }\n\n .table {\n border-collapse: collapse !important;\n\n td,\n th {\n background-color: #fff !important;\n }\n }\n .table-bordered {\n th,\n td {\n border: 1px solid #ddd !important;\n }\n }\n\n // Bootstrap specific changes end\n}\n","//\n// Glyphicons for Bootstrap\n//\n// Since icons are fonts, they can be placed anywhere text is placed and are\n// thus automatically sized to match the surrounding child. To use, create an\n// inline element with the appropriate classes, like so:\n//\n// Star\n\n// Import the fonts\n@font-face {\n font-family: 'Glyphicons Halflings';\n src: url('@{icon-font-path}@{icon-font-name}.eot');\n src: url('@{icon-font-path}@{icon-font-name}.eot?#iefix') format('embedded-opentype'),\n url('@{icon-font-path}@{icon-font-name}.woff2') format('woff2'),\n url('@{icon-font-path}@{icon-font-name}.woff') format('woff'),\n url('@{icon-font-path}@{icon-font-name}.ttf') format('truetype'),\n url('@{icon-font-path}@{icon-font-name}.svg#@{icon-font-svg-id}') format('svg');\n}\n\n// Catchall baseclass\n.glyphicon {\n position: relative;\n top: 1px;\n display: inline-block;\n font-family: 'Glyphicons Halflings';\n font-style: normal;\n font-weight: normal;\n line-height: 1;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n\n// Individual icons\n.glyphicon-asterisk { &:before { content: \"\\002a\"; } }\n.glyphicon-plus { &:before { content: \"\\002b\"; } }\n.glyphicon-euro,\n.glyphicon-eur { &:before { content: \"\\20ac\"; } }\n.glyphicon-minus { &:before { content: \"\\2212\"; } }\n.glyphicon-cloud { &:before { content: \"\\2601\"; } }\n.glyphicon-envelope { &:before { content: \"\\2709\"; } }\n.glyphicon-pencil { &:before { content: \"\\270f\"; } }\n.glyphicon-glass { &:before { content: \"\\e001\"; } }\n.glyphicon-music { &:before { content: \"\\e002\"; } }\n.glyphicon-search { &:before { content: \"\\e003\"; } }\n.glyphicon-heart { &:before { content: \"\\e005\"; } }\n.glyphicon-star { &:before { content: \"\\e006\"; } }\n.glyphicon-star-empty { &:before { content: \"\\e007\"; } }\n.glyphicon-user { &:before { content: \"\\e008\"; } }\n.glyphicon-film { &:before { content: \"\\e009\"; } }\n.glyphicon-th-large { &:before { content: \"\\e010\"; } }\n.glyphicon-th { &:before { content: \"\\e011\"; } }\n.glyphicon-th-list { &:before { content: \"\\e012\"; } }\n.glyphicon-ok { &:before { content: \"\\e013\"; } }\n.glyphicon-remove { &:before { content: \"\\e014\"; } }\n.glyphicon-zoom-in { &:before { content: \"\\e015\"; } }\n.glyphicon-zoom-out { &:before { content: \"\\e016\"; } }\n.glyphicon-off { &:before { content: \"\\e017\"; } }\n.glyphicon-signal { &:before { content: \"\\e018\"; } }\n.glyphicon-cog { &:before { content: \"\\e019\"; } }\n.glyphicon-trash { &:before { content: \"\\e020\"; } }\n.glyphicon-home { &:before { content: \"\\e021\"; } }\n.glyphicon-file { &:before { content: \"\\e022\"; } }\n.glyphicon-time { &:before { content: \"\\e023\"; } }\n.glyphicon-road { &:before { content: \"\\e024\"; } }\n.glyphicon-download-alt { &:before { content: \"\\e025\"; } }\n.glyphicon-download { &:before { content: \"\\e026\"; } }\n.glyphicon-upload { &:before { content: \"\\e027\"; } }\n.glyphicon-inbox { &:before { content: \"\\e028\"; } }\n.glyphicon-play-circle { &:before { content: \"\\e029\"; } }\n.glyphicon-repeat { &:before { content: \"\\e030\"; } }\n.glyphicon-refresh { &:before { content: \"\\e031\"; } }\n.glyphicon-list-alt { &:before { content: \"\\e032\"; } }\n.glyphicon-lock { &:before { content: \"\\e033\"; } }\n.glyphicon-flag { &:before { content: \"\\e034\"; } }\n.glyphicon-headphones { &:before { content: \"\\e035\"; } }\n.glyphicon-volume-off { &:before { content: \"\\e036\"; } }\n.glyphicon-volume-down { &:before { content: \"\\e037\"; } }\n.glyphicon-volume-up { &:before { content: \"\\e038\"; } }\n.glyphicon-qrcode { &:before { content: \"\\e039\"; } }\n.glyphicon-barcode { &:before { content: \"\\e040\"; } }\n.glyphicon-tag { &:before { content: \"\\e041\"; } }\n.glyphicon-tags { &:before { content: \"\\e042\"; } }\n.glyphicon-book { &:before { content: \"\\e043\"; } }\n.glyphicon-bookmark { &:before { content: \"\\e044\"; } }\n.glyphicon-print { &:before { content: \"\\e045\"; } }\n.glyphicon-camera { &:before { content: \"\\e046\"; } }\n.glyphicon-font { &:before { content: \"\\e047\"; } }\n.glyphicon-bold { &:before { content: \"\\e048\"; } }\n.glyphicon-italic { &:before { content: \"\\e049\"; } }\n.glyphicon-text-height { &:before { content: \"\\e050\"; } }\n.glyphicon-text-width { &:before { content: \"\\e051\"; } }\n.glyphicon-align-left { &:before { content: \"\\e052\"; } }\n.glyphicon-align-center { &:before { content: \"\\e053\"; } }\n.glyphicon-align-right { &:before { content: \"\\e054\"; } }\n.glyphicon-align-justify { &:before { content: \"\\e055\"; } }\n.glyphicon-list { &:before { content: \"\\e056\"; } }\n.glyphicon-indent-left { &:before { content: \"\\e057\"; } }\n.glyphicon-indent-right { &:before { content: \"\\e058\"; } }\n.glyphicon-facetime-video { &:before { content: \"\\e059\"; } }\n.glyphicon-picture { &:before { content: \"\\e060\"; } }\n.glyphicon-map-marker { &:before { content: \"\\e062\"; } }\n.glyphicon-adjust { &:before { content: \"\\e063\"; } }\n.glyphicon-tint { &:before { content: \"\\e064\"; } }\n.glyphicon-edit { &:before { content: \"\\e065\"; } }\n.glyphicon-share { &:before { content: \"\\e066\"; } }\n.glyphicon-check { &:before { content: \"\\e067\"; } }\n.glyphicon-move { &:before { content: \"\\e068\"; } }\n.glyphicon-step-backward { &:before { content: \"\\e069\"; } }\n.glyphicon-fast-backward { &:before { content: \"\\e070\"; } }\n.glyphicon-backward { &:before { content: \"\\e071\"; } }\n.glyphicon-play { &:before { content: \"\\e072\"; } }\n.glyphicon-pause { &:before { content: \"\\e073\"; } }\n.glyphicon-stop { &:before { content: \"\\e074\"; } }\n.glyphicon-forward { &:before { content: \"\\e075\"; } }\n.glyphicon-fast-forward { &:before { content: \"\\e076\"; } }\n.glyphicon-step-forward { &:before { content: \"\\e077\"; } }\n.glyphicon-eject { &:before { content: \"\\e078\"; } }\n.glyphicon-chevron-left { &:before { content: \"\\e079\"; } }\n.glyphicon-chevron-right { &:before { content: \"\\e080\"; } }\n.glyphicon-plus-sign { &:before { content: \"\\e081\"; } }\n.glyphicon-minus-sign { &:before { content: \"\\e082\"; } }\n.glyphicon-remove-sign { &:before { content: \"\\e083\"; } }\n.glyphicon-ok-sign { &:before { content: \"\\e084\"; } }\n.glyphicon-question-sign { &:before { content: \"\\e085\"; } }\n.glyphicon-info-sign { &:before { content: \"\\e086\"; } }\n.glyphicon-screenshot { &:before { content: \"\\e087\"; } }\n.glyphicon-remove-circle { &:before { content: \"\\e088\"; } }\n.glyphicon-ok-circle { &:before { content: \"\\e089\"; } }\n.glyphicon-ban-circle { &:before { content: \"\\e090\"; } }\n.glyphicon-arrow-left { &:before { content: \"\\e091\"; } }\n.glyphicon-arrow-right { &:before { content: \"\\e092\"; } }\n.glyphicon-arrow-up { &:before { content: \"\\e093\"; } }\n.glyphicon-arrow-down { &:before { content: \"\\e094\"; } }\n.glyphicon-share-alt { &:before { content: \"\\e095\"; } }\n.glyphicon-resize-full { &:before { content: \"\\e096\"; } }\n.glyphicon-resize-small { &:before { content: \"\\e097\"; } }\n.glyphicon-exclamation-sign { &:before { content: \"\\e101\"; } }\n.glyphicon-gift { &:before { content: \"\\e102\"; } }\n.glyphicon-leaf { &:before { content: \"\\e103\"; } }\n.glyphicon-fire { &:before { content: \"\\e104\"; } }\n.glyphicon-eye-open { &:before { content: \"\\e105\"; } }\n.glyphicon-eye-close { &:before { content: \"\\e106\"; } }\n.glyphicon-warning-sign { &:before { content: \"\\e107\"; } }\n.glyphicon-plane { &:before { content: \"\\e108\"; } }\n.glyphicon-calendar { &:before { content: \"\\e109\"; } }\n.glyphicon-random { &:before { content: \"\\e110\"; } }\n.glyphicon-comment { &:before { content: \"\\e111\"; } }\n.glyphicon-magnet { &:before { content: \"\\e112\"; } }\n.glyphicon-chevron-up { &:before { content: \"\\e113\"; } }\n.glyphicon-chevron-down { &:before { content: \"\\e114\"; } }\n.glyphicon-retweet { &:before { content: \"\\e115\"; } }\n.glyphicon-shopping-cart { &:before { content: \"\\e116\"; } }\n.glyphicon-folder-close { &:before { content: \"\\e117\"; } }\n.glyphicon-folder-open { &:before { content: \"\\e118\"; } }\n.glyphicon-resize-vertical { &:before { content: \"\\e119\"; } }\n.glyphicon-resize-horizontal { &:before { content: \"\\e120\"; } }\n.glyphicon-hdd { &:before { content: \"\\e121\"; } }\n.glyphicon-bullhorn { &:before { content: \"\\e122\"; } }\n.glyphicon-bell { &:before { content: \"\\e123\"; } }\n.glyphicon-certificate { &:before { content: \"\\e124\"; } }\n.glyphicon-thumbs-up { &:before { content: \"\\e125\"; } }\n.glyphicon-thumbs-down { &:before { content: \"\\e126\"; } }\n.glyphicon-hand-right { &:before { content: \"\\e127\"; } }\n.glyphicon-hand-left { &:before { content: \"\\e128\"; } }\n.glyphicon-hand-up { &:before { content: \"\\e129\"; } }\n.glyphicon-hand-down { &:before { content: \"\\e130\"; } }\n.glyphicon-circle-arrow-right { &:before { content: \"\\e131\"; } }\n.glyphicon-circle-arrow-left { &:before { content: \"\\e132\"; } }\n.glyphicon-circle-arrow-up { &:before { content: \"\\e133\"; } }\n.glyphicon-circle-arrow-down { &:before { content: \"\\e134\"; } }\n.glyphicon-globe { &:before { content: \"\\e135\"; } }\n.glyphicon-wrench { &:before { content: \"\\e136\"; } }\n.glyphicon-tasks { &:before { content: \"\\e137\"; } }\n.glyphicon-filter { &:before { content: \"\\e138\"; } }\n.glyphicon-briefcase { &:before { content: \"\\e139\"; } }\n.glyphicon-fullscreen { &:before { content: \"\\e140\"; } }\n.glyphicon-dashboard { &:before { content: \"\\e141\"; } }\n.glyphicon-paperclip { &:before { content: \"\\e142\"; } }\n.glyphicon-heart-empty { &:before { content: \"\\e143\"; } }\n.glyphicon-link { &:before { content: \"\\e144\"; } }\n.glyphicon-phone { &:before { content: \"\\e145\"; } }\n.glyphicon-pushpin { &:before { content: \"\\e146\"; } }\n.glyphicon-usd { &:before { content: \"\\e148\"; } }\n.glyphicon-gbp { &:before { content: \"\\e149\"; } }\n.glyphicon-sort { &:before { content: \"\\e150\"; } }\n.glyphicon-sort-by-alphabet { &:before { content: \"\\e151\"; } }\n.glyphicon-sort-by-alphabet-alt { &:before { content: \"\\e152\"; } }\n.glyphicon-sort-by-order { &:before { content: \"\\e153\"; } }\n.glyphicon-sort-by-order-alt { &:before { content: \"\\e154\"; } }\n.glyphicon-sort-by-attributes { &:before { content: \"\\e155\"; } }\n.glyphicon-sort-by-attributes-alt { &:before { content: \"\\e156\"; } }\n.glyphicon-unchecked { &:before { content: \"\\e157\"; } }\n.glyphicon-expand { &:before { content: \"\\e158\"; } }\n.glyphicon-collapse-down { &:before { content: \"\\e159\"; } }\n.glyphicon-collapse-up { &:before { content: \"\\e160\"; } }\n.glyphicon-log-in { &:before { content: \"\\e161\"; } }\n.glyphicon-flash { &:before { content: \"\\e162\"; } }\n.glyphicon-log-out { &:before { content: \"\\e163\"; } }\n.glyphicon-new-window { &:before { content: \"\\e164\"; } }\n.glyphicon-record { &:before { content: \"\\e165\"; } }\n.glyphicon-save { &:before { content: \"\\e166\"; } }\n.glyphicon-open { &:before { content: \"\\e167\"; } }\n.glyphicon-saved { &:before { content: \"\\e168\"; } }\n.glyphicon-import { &:before { content: \"\\e169\"; } }\n.glyphicon-export { &:before { content: \"\\e170\"; } }\n.glyphicon-send { &:before { content: \"\\e171\"; } }\n.glyphicon-floppy-disk { &:before { content: \"\\e172\"; } }\n.glyphicon-floppy-saved { &:before { content: \"\\e173\"; } }\n.glyphicon-floppy-remove { &:before { content: \"\\e174\"; } }\n.glyphicon-floppy-save { &:before { content: \"\\e175\"; } }\n.glyphicon-floppy-open { &:before { content: \"\\e176\"; } }\n.glyphicon-credit-card { &:before { content: \"\\e177\"; } }\n.glyphicon-transfer { &:before { content: \"\\e178\"; } }\n.glyphicon-cutlery { &:before { content: \"\\e179\"; } }\n.glyphicon-header { &:before { content: \"\\e180\"; } }\n.glyphicon-compressed { &:before { content: \"\\e181\"; } }\n.glyphicon-earphone { &:before { content: \"\\e182\"; } }\n.glyphicon-phone-alt { &:before { content: \"\\e183\"; } }\n.glyphicon-tower { &:before { content: \"\\e184\"; } }\n.glyphicon-stats { &:before { content: \"\\e185\"; } }\n.glyphicon-sd-video { &:before { content: \"\\e186\"; } }\n.glyphicon-hd-video { &:before { content: \"\\e187\"; } }\n.glyphicon-subtitles { &:before { content: \"\\e188\"; } }\n.glyphicon-sound-stereo { &:before { content: \"\\e189\"; } }\n.glyphicon-sound-dolby { &:before { content: \"\\e190\"; } }\n.glyphicon-sound-5-1 { &:before { content: \"\\e191\"; } }\n.glyphicon-sound-6-1 { &:before { content: \"\\e192\"; } }\n.glyphicon-sound-7-1 { &:before { content: \"\\e193\"; } }\n.glyphicon-copyright-mark { &:before { content: \"\\e194\"; } }\n.glyphicon-registration-mark { &:before { content: \"\\e195\"; } }\n.glyphicon-cloud-download { &:before { content: \"\\e197\"; } }\n.glyphicon-cloud-upload { &:before { content: \"\\e198\"; } }\n.glyphicon-tree-conifer { &:before { content: \"\\e199\"; } }\n.glyphicon-tree-deciduous { &:before { content: \"\\e200\"; } }\n.glyphicon-cd { &:before { content: \"\\e201\"; } }\n.glyphicon-save-file { &:before { content: \"\\e202\"; } }\n.glyphicon-open-file { &:before { content: \"\\e203\"; } }\n.glyphicon-level-up { &:before { content: \"\\e204\"; } }\n.glyphicon-copy { &:before { content: \"\\e205\"; } }\n.glyphicon-paste { &:before { content: \"\\e206\"; } }\n// The following 2 Glyphicons are omitted for the time being because\n// they currently use Unicode codepoints that are outside the\n// Basic Multilingual Plane (BMP). Older buggy versions of WebKit can't handle\n// non-BMP codepoints in CSS string escapes, and thus can't display these two icons.\n// Notably, the bug affects some older versions of the Android Browser.\n// More info: https://github.com/twbs/bootstrap/issues/10106\n// .glyphicon-door { &:before { content: \"\\1f6aa\"; } }\n// .glyphicon-key { &:before { content: \"\\1f511\"; } }\n.glyphicon-alert { &:before { content: \"\\e209\"; } }\n.glyphicon-equalizer { &:before { content: \"\\e210\"; } }\n.glyphicon-king { &:before { content: \"\\e211\"; } }\n.glyphicon-queen { &:before { content: \"\\e212\"; } }\n.glyphicon-pawn { &:before { content: \"\\e213\"; } }\n.glyphicon-bishop { &:before { content: \"\\e214\"; } }\n.glyphicon-knight { &:before { content: \"\\e215\"; } }\n.glyphicon-baby-formula { &:before { content: \"\\e216\"; } }\n.glyphicon-tent { &:before { content: \"\\26fa\"; } }\n.glyphicon-blackboard { &:before { content: \"\\e218\"; } }\n.glyphicon-bed { &:before { content: \"\\e219\"; } }\n.glyphicon-apple { &:before { content: \"\\f8ff\"; } }\n.glyphicon-erase { &:before { content: \"\\e221\"; } }\n.glyphicon-hourglass { &:before { content: \"\\231b\"; } }\n.glyphicon-lamp { &:before { content: \"\\e223\"; } }\n.glyphicon-duplicate { &:before { content: \"\\e224\"; } }\n.glyphicon-piggy-bank { &:before { content: \"\\e225\"; } }\n.glyphicon-scissors { &:before { content: \"\\e226\"; } }\n.glyphicon-bitcoin { &:before { content: \"\\e227\"; } }\n.glyphicon-btc { &:before { content: \"\\e227\"; } }\n.glyphicon-xbt { &:before { content: \"\\e227\"; } }\n.glyphicon-yen { &:before { content: \"\\00a5\"; } }\n.glyphicon-jpy { &:before { content: \"\\00a5\"; } }\n.glyphicon-ruble { &:before { content: \"\\20bd\"; } }\n.glyphicon-rub { &:before { content: \"\\20bd\"; } }\n.glyphicon-scale { &:before { content: \"\\e230\"; } }\n.glyphicon-ice-lolly { &:before { content: \"\\e231\"; } }\n.glyphicon-ice-lolly-tasted { &:before { content: \"\\e232\"; } }\n.glyphicon-education { &:before { content: \"\\e233\"; } }\n.glyphicon-option-horizontal { &:before { content: \"\\e234\"; } }\n.glyphicon-option-vertical { &:before { content: \"\\e235\"; } }\n.glyphicon-menu-hamburger { &:before { content: \"\\e236\"; } }\n.glyphicon-modal-window { &:before { content: \"\\e237\"; } }\n.glyphicon-oil { &:before { content: \"\\e238\"; } }\n.glyphicon-grain { &:before { content: \"\\e239\"; } }\n.glyphicon-sunglasses { &:before { content: \"\\e240\"; } }\n.glyphicon-text-size { &:before { content: \"\\e241\"; } }\n.glyphicon-text-color { &:before { content: \"\\e242\"; } }\n.glyphicon-text-background { &:before { content: \"\\e243\"; } }\n.glyphicon-object-align-top { &:before { content: \"\\e244\"; } }\n.glyphicon-object-align-bottom { &:before { content: \"\\e245\"; } }\n.glyphicon-object-align-horizontal{ &:before { content: \"\\e246\"; } }\n.glyphicon-object-align-left { &:before { content: \"\\e247\"; } }\n.glyphicon-object-align-vertical { &:before { content: \"\\e248\"; } }\n.glyphicon-object-align-right { &:before { content: \"\\e249\"; } }\n.glyphicon-triangle-right { &:before { content: \"\\e250\"; } }\n.glyphicon-triangle-left { &:before { content: \"\\e251\"; } }\n.glyphicon-triangle-bottom { &:before { content: \"\\e252\"; } }\n.glyphicon-triangle-top { &:before { content: \"\\e253\"; } }\n.glyphicon-console { &:before { content: \"\\e254\"; } }\n.glyphicon-superscript { &:before { content: \"\\e255\"; } }\n.glyphicon-subscript { &:before { content: \"\\e256\"; } }\n.glyphicon-menu-left { &:before { content: \"\\e257\"; } }\n.glyphicon-menu-right { &:before { content: \"\\e258\"; } }\n.glyphicon-menu-down { &:before { content: \"\\e259\"; } }\n.glyphicon-menu-up { &:before { content: \"\\e260\"; } }\n","//\n// Scaffolding\n// --------------------------------------------------\n\n\n// Reset the box-sizing\n//\n// Heads up! This reset may cause conflicts with some third-party widgets.\n// For recommendations on resolving such conflicts, see\n// http://getbootstrap.com/getting-started/#third-box-sizing\n* {\n .box-sizing(border-box);\n}\n*:before,\n*:after {\n .box-sizing(border-box);\n}\n\n\n// Body reset\n\nhtml {\n font-size: 10px;\n -webkit-tap-highlight-color: rgba(0,0,0,0);\n}\n\nbody {\n font-family: @font-family-base;\n font-size: @font-size-base;\n line-height: @line-height-base;\n color: @text-color;\n background-color: @body-bg;\n}\n\n// Reset fonts for relevant elements\ninput,\nbutton,\nselect,\ntextarea {\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\n\n\n// Links\n\na {\n color: @link-color;\n text-decoration: none;\n\n &:hover,\n &:focus {\n color: @link-hover-color;\n text-decoration: @link-hover-decoration;\n }\n\n &:focus {\n .tab-focus();\n }\n}\n\n\n// Figures\n//\n// We reset this here because previously Normalize had no `figure` margins. This\n// ensures we don't break anyone's use of the element.\n\nfigure {\n margin: 0;\n}\n\n\n// Images\n\nimg {\n vertical-align: middle;\n}\n\n// Responsive images (ensure images don't scale beyond their parents)\n.img-responsive {\n .img-responsive();\n}\n\n// Rounded corners\n.img-rounded {\n border-radius: @border-radius-large;\n}\n\n// Image thumbnails\n//\n// Heads up! This is mixin-ed into thumbnails.less for `.thumbnail`.\n.img-thumbnail {\n padding: @thumbnail-padding;\n line-height: @line-height-base;\n background-color: @thumbnail-bg;\n border: 1px solid @thumbnail-border;\n border-radius: @thumbnail-border-radius;\n .transition(all .2s ease-in-out);\n\n // Keep them at most 100% wide\n .img-responsive(inline-block);\n}\n\n// Perfect circle\n.img-circle {\n border-radius: 50%; // set radius in percents\n}\n\n\n// Horizontal rules\n\nhr {\n margin-top: @line-height-computed;\n margin-bottom: @line-height-computed;\n border: 0;\n border-top: 1px solid @hr-border;\n}\n\n\n// Only display content to screen readers\n//\n// See: http://a11yproject.com/posts/how-to-hide-content\n\n.sr-only {\n position: absolute;\n width: 1px;\n height: 1px;\n margin: -1px;\n padding: 0;\n overflow: hidden;\n clip: rect(0,0,0,0);\n border: 0;\n}\n\n// Use in conjunction with .sr-only to only display content when it's focused.\n// Useful for \"Skip to main content\" links; see http://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n// Credit: HTML5 Boilerplate\n\n.sr-only-focusable {\n &:active,\n &:focus {\n position: static;\n width: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n clip: auto;\n }\n}\n\n\n// iOS \"clickable elements\" fix for role=\"button\"\n//\n// Fixes \"clickability\" issue (and more generally, the firing of events such as focus as well)\n// for traditionally non-focusable elements with role=\"button\"\n// see https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n[role=\"button\"] {\n cursor: pointer;\n}\n","// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n -webkit-animation: @animation;\n -o-animation: @animation;\n animation: @animation;\n}\n.animation-name(@name) {\n -webkit-animation-name: @name;\n animation-name: @name;\n}\n.animation-duration(@duration) {\n -webkit-animation-duration: @duration;\n animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n -webkit-animation-timing-function: @timing-function;\n animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n -webkit-animation-delay: @delay;\n animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n -webkit-animation-iteration-count: @iteration-count;\n animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n -webkit-animation-direction: @direction;\n animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n -webkit-animation-fill-mode: @fill-mode;\n animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n -webkit-backface-visibility: @visibility;\n -moz-backface-visibility: @visibility;\n backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n -webkit-box-sizing: @boxmodel;\n -moz-box-sizing: @boxmodel;\n box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n -webkit-column-count: @column-count;\n -moz-column-count: @column-count;\n column-count: @column-count;\n -webkit-column-gap: @column-gap;\n -moz-column-gap: @column-gap;\n column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n word-wrap: break-word;\n -webkit-hyphens: @mode;\n -moz-hyphens: @mode;\n -ms-hyphens: @mode; // IE10+\n -o-hyphens: @mode;\n hyphens: @mode;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n // Firefox\n &::-moz-placeholder {\n color: @color;\n opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n }\n &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n &::-webkit-input-placeholder { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n -webkit-transform: scale(@ratio);\n -ms-transform: scale(@ratio); // IE9 only\n -o-transform: scale(@ratio);\n transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n -webkit-transform: scale(@ratioX, @ratioY);\n -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n -o-transform: scale(@ratioX, @ratioY);\n transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n -webkit-transform: scaleX(@ratio);\n -ms-transform: scaleX(@ratio); // IE9 only\n -o-transform: scaleX(@ratio);\n transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n -webkit-transform: scaleY(@ratio);\n -ms-transform: scaleY(@ratio); // IE9 only\n -o-transform: scaleY(@ratio);\n transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n -webkit-transform: skewX(@x) skewY(@y);\n -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n -o-transform: skewX(@x) skewY(@y);\n transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n -webkit-transform: translate(@x, @y);\n -ms-transform: translate(@x, @y); // IE9 only\n -o-transform: translate(@x, @y);\n transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n -webkit-transform: translate3d(@x, @y, @z);\n transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n -webkit-transform: rotate(@degrees);\n -ms-transform: rotate(@degrees); // IE9 only\n -o-transform: rotate(@degrees);\n transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n -webkit-transform: rotateX(@degrees);\n -ms-transform: rotateX(@degrees); // IE9 only\n -o-transform: rotateX(@degrees);\n transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n -webkit-transform: rotateY(@degrees);\n -ms-transform: rotateY(@degrees); // IE9 only\n -o-transform: rotateY(@degrees);\n transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n -webkit-perspective: @perspective;\n -moz-perspective: @perspective;\n perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n -webkit-perspective-origin: @perspective;\n -moz-perspective-origin: @perspective;\n perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n -webkit-transform-origin: @origin;\n -moz-transform-origin: @origin;\n -ms-transform-origin: @origin; // IE9 only\n transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n -webkit-transition: @transition;\n -o-transition: @transition;\n transition: @transition;\n}\n.transition-property(@transition-property) {\n -webkit-transition-property: @transition-property;\n transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n -webkit-transition-delay: @transition-delay;\n transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n -webkit-transition-duration: @transition-duration;\n transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n -webkit-transition-timing-function: @timing-function;\n transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n -webkit-transition: -webkit-transform @transition;\n -moz-transition: -moz-transform @transition;\n -o-transition: -o-transform @transition;\n transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n -webkit-user-select: @select;\n -moz-user-select: @select;\n -ms-user-select: @select; // IE10+\n user-select: @select;\n}\n","// WebKit-style focus\n\n.tab-focus() {\n // WebKit-specific. Other browsers will keep their default outline style.\n // (Initially tried to also force default via `outline: initial`,\n // but that seems to erroneously remove the outline in Firefox altogether.)\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n","// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n.img-responsive(@display: block) {\n display: @display;\n max-width: 100%; // Part 1: Set a maximum relative to the parent\n height: auto; // Part 2: Scale the height according to the width, otherwise you get stretching\n}\n\n\n// Retina image\n//\n// Short retina mixin for setting background-image and -size. Note that the\n// spelling of `min--moz-device-pixel-ratio` is intentional.\n.img-retina(@file-1x; @file-2x; @width-1x; @height-1x) {\n background-image: url(\"@{file-1x}\");\n\n @media\n only screen and (-webkit-min-device-pixel-ratio: 2),\n only screen and ( min--moz-device-pixel-ratio: 2),\n only screen and ( -o-min-device-pixel-ratio: 2/1),\n only screen and ( min-device-pixel-ratio: 2),\n only screen and ( min-resolution: 192dpi),\n only screen and ( min-resolution: 2dppx) {\n background-image: url(\"@{file-2x}\");\n background-size: @width-1x @height-1x;\n }\n}\n","//\n// Typography\n// --------------------------------------------------\n\n\n// Headings\n// -------------------------\n\nh1, h2, h3, h4, h5, h6,\n.h1, .h2, .h3, .h4, .h5, .h6 {\n font-family: @headings-font-family;\n font-weight: @headings-font-weight;\n line-height: @headings-line-height;\n color: @headings-color;\n\n small,\n .small {\n font-weight: normal;\n line-height: 1;\n color: @headings-small-color;\n }\n}\n\nh1, .h1,\nh2, .h2,\nh3, .h3 {\n margin-top: @line-height-computed;\n margin-bottom: (@line-height-computed / 2);\n\n small,\n .small {\n font-size: 65%;\n }\n}\nh4, .h4,\nh5, .h5,\nh6, .h6 {\n margin-top: (@line-height-computed / 2);\n margin-bottom: (@line-height-computed / 2);\n\n small,\n .small {\n font-size: 75%;\n }\n}\n\nh1, .h1 { font-size: @font-size-h1; }\nh2, .h2 { font-size: @font-size-h2; }\nh3, .h3 { font-size: @font-size-h3; }\nh4, .h4 { font-size: @font-size-h4; }\nh5, .h5 { font-size: @font-size-h5; }\nh6, .h6 { font-size: @font-size-h6; }\n\n\n// Body text\n// -------------------------\n\np {\n margin: 0 0 (@line-height-computed / 2);\n}\n\n.lead {\n margin-bottom: @line-height-computed;\n font-size: floor((@font-size-base * 1.15));\n font-weight: 300;\n line-height: 1.4;\n\n @media (min-width: @screen-sm-min) {\n font-size: (@font-size-base * 1.5);\n }\n}\n\n\n// Emphasis & misc\n// -------------------------\n\n// Ex: (12px small font / 14px base font) * 100% = about 85%\nsmall,\n.small {\n font-size: floor((100% * @font-size-small / @font-size-base));\n}\n\nmark,\n.mark {\n background-color: @state-warning-bg;\n padding: .2em;\n}\n\n// Alignment\n.text-left { text-align: left; }\n.text-right { text-align: right; }\n.text-center { text-align: center; }\n.text-justify { text-align: justify; }\n.text-nowrap { white-space: nowrap; }\n\n// Transformation\n.text-lowercase { text-transform: lowercase; }\n.text-uppercase { text-transform: uppercase; }\n.text-capitalize { text-transform: capitalize; }\n\n// Contextual colors\n.text-muted {\n color: @text-muted;\n}\n.text-primary {\n .text-emphasis-variant(@brand-primary);\n}\n.text-success {\n .text-emphasis-variant(@state-success-text);\n}\n.text-info {\n .text-emphasis-variant(@state-info-text);\n}\n.text-warning {\n .text-emphasis-variant(@state-warning-text);\n}\n.text-danger {\n .text-emphasis-variant(@state-danger-text);\n}\n\n// Contextual backgrounds\n// For now we'll leave these alongside the text classes until v4 when we can\n// safely shift things around (per SemVer rules).\n.bg-primary {\n // Given the contrast here, this is the only class to have its color inverted\n // automatically.\n color: #fff;\n .bg-variant(@brand-primary);\n}\n.bg-success {\n .bg-variant(@state-success-bg);\n}\n.bg-info {\n .bg-variant(@state-info-bg);\n}\n.bg-warning {\n .bg-variant(@state-warning-bg);\n}\n.bg-danger {\n .bg-variant(@state-danger-bg);\n}\n\n\n// Page header\n// -------------------------\n\n.page-header {\n padding-bottom: ((@line-height-computed / 2) - 1);\n margin: (@line-height-computed * 2) 0 @line-height-computed;\n border-bottom: 1px solid @page-header-border-color;\n}\n\n\n// Lists\n// -------------------------\n\n// Unordered and Ordered lists\nul,\nol {\n margin-top: 0;\n margin-bottom: (@line-height-computed / 2);\n ul,\n ol {\n margin-bottom: 0;\n }\n}\n\n// List options\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n.list-unstyled {\n padding-left: 0;\n list-style: none;\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n .list-unstyled();\n margin-left: -5px;\n\n > li {\n display: inline-block;\n padding-left: 5px;\n padding-right: 5px;\n }\n}\n\n// Description Lists\ndl {\n margin-top: 0; // Remove browser default\n margin-bottom: @line-height-computed;\n}\ndt,\ndd {\n line-height: @line-height-base;\n}\ndt {\n font-weight: bold;\n}\ndd {\n margin-left: 0; // Undo browser default\n}\n\n// Horizontal description lists\n//\n// Defaults to being stacked without any of the below styles applied, until the\n// grid breakpoint is reached (default of ~768px).\n\n.dl-horizontal {\n dd {\n &:extend(.clearfix all); // Clear the floated `dt` if an empty `dd` is present\n }\n\n @media (min-width: @dl-horizontal-breakpoint) {\n dt {\n float: left;\n width: (@dl-horizontal-offset - 20);\n clear: left;\n text-align: right;\n .text-overflow();\n }\n dd {\n margin-left: @dl-horizontal-offset;\n }\n }\n}\n\n\n// Misc\n// -------------------------\n\n// Abbreviations and acronyms\nabbr[title],\n// Add data-* attribute to help out our tooltip plugin, per https://github.com/twbs/bootstrap/issues/5257\nabbr[data-original-title] {\n cursor: help;\n border-bottom: 1px dotted @abbr-border-color;\n}\n.initialism {\n font-size: 90%;\n .text-uppercase();\n}\n\n// Blockquotes\nblockquote {\n padding: (@line-height-computed / 2) @line-height-computed;\n margin: 0 0 @line-height-computed;\n font-size: @blockquote-font-size;\n border-left: 5px solid @blockquote-border-color;\n\n p,\n ul,\n ol {\n &:last-child {\n margin-bottom: 0;\n }\n }\n\n // Note: Deprecated small and .small as of v3.1.0\n // Context: https://github.com/twbs/bootstrap/issues/11660\n footer,\n small,\n .small {\n display: block;\n font-size: 80%; // back to default font-size\n line-height: @line-height-base;\n color: @blockquote-small-color;\n\n &:before {\n content: '\\2014 \\00A0'; // em dash, nbsp\n }\n }\n}\n\n// Opposite alignment of blockquote\n//\n// Heads up: `blockquote.pull-right` has been deprecated as of v3.1.0.\n.blockquote-reverse,\nblockquote.pull-right {\n padding-right: 15px;\n padding-left: 0;\n border-right: 5px solid @blockquote-border-color;\n border-left: 0;\n text-align: right;\n\n // Account for citation\n footer,\n small,\n .small {\n &:before { content: ''; }\n &:after {\n content: '\\00A0 \\2014'; // nbsp, em dash\n }\n }\n}\n\n// Addresses\naddress {\n margin-bottom: @line-height-computed;\n font-style: normal;\n line-height: @line-height-base;\n}\n","// Typography\n\n.text-emphasis-variant(@color) {\n color: @color;\n a&:hover,\n a&:focus {\n color: darken(@color, 10%);\n }\n}\n","// Contextual backgrounds\n\n.bg-variant(@color) {\n background-color: @color;\n a&:hover,\n a&:focus {\n background-color: darken(@color, 10%);\n }\n}\n","// Text overflow\n// Requires inline-block or block for proper styling\n\n.text-overflow() {\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n}\n","//\n// Code (inline and block)\n// --------------------------------------------------\n\n\n// Inline and block code styles\ncode,\nkbd,\npre,\nsamp {\n font-family: @font-family-monospace;\n}\n\n// Inline code\ncode {\n padding: 2px 4px;\n font-size: 90%;\n color: @code-color;\n background-color: @code-bg;\n border-radius: @border-radius-base;\n}\n\n// User input typically entered via keyboard\nkbd {\n padding: 2px 4px;\n font-size: 90%;\n color: @kbd-color;\n background-color: @kbd-bg;\n border-radius: @border-radius-small;\n box-shadow: inset 0 -1px 0 rgba(0,0,0,.25);\n\n kbd {\n padding: 0;\n font-size: 100%;\n font-weight: bold;\n box-shadow: none;\n }\n}\n\n// Blocks of code\npre {\n display: block;\n padding: ((@line-height-computed - 1) / 2);\n margin: 0 0 (@line-height-computed / 2);\n font-size: (@font-size-base - 1); // 14px to 13px\n line-height: @line-height-base;\n word-break: break-all;\n word-wrap: break-word;\n color: @pre-color;\n background-color: @pre-bg;\n border: 1px solid @pre-border-color;\n border-radius: @border-radius-base;\n\n // Account for some code outputs that place code tags in pre tags\n code {\n padding: 0;\n font-size: inherit;\n color: inherit;\n white-space: pre-wrap;\n background-color: transparent;\n border-radius: 0;\n }\n}\n\n// Enable scrollable blocks of code\n.pre-scrollable {\n max-height: @pre-scrollable-max-height;\n overflow-y: scroll;\n}\n","//\n// Grid system\n// --------------------------------------------------\n\n\n// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n.container {\n .container-fixed();\n\n @media (min-width: @screen-sm-min) {\n width: @container-sm;\n }\n @media (min-width: @screen-md-min) {\n width: @container-md;\n }\n @media (min-width: @screen-lg-min) {\n width: @container-lg;\n }\n}\n\n\n// Fluid container\n//\n// Utilizes the mixin meant for fixed width containers, but without any defined\n// width for fluid, full width layouts.\n\n.container-fluid {\n .container-fixed();\n}\n\n\n// Row\n//\n// Rows contain and clear the floats of your columns.\n\n.row {\n .make-row();\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n.make-grid-columns();\n\n\n// Extra small grid\n//\n// Columns, offsets, pushes, and pulls for extra small devices like\n// smartphones.\n\n.make-grid(xs);\n\n\n// Small grid\n//\n// Columns, offsets, pushes, and pulls for the small device range, from phones\n// to tablets.\n\n@media (min-width: @screen-sm-min) {\n .make-grid(sm);\n}\n\n\n// Medium grid\n//\n// Columns, offsets, pushes, and pulls for the desktop device range.\n\n@media (min-width: @screen-md-min) {\n .make-grid(md);\n}\n\n\n// Large grid\n//\n// Columns, offsets, pushes, and pulls for the large desktop device range.\n\n@media (min-width: @screen-lg-min) {\n .make-grid(lg);\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n// Centered container element\n.container-fixed(@gutter: @grid-gutter-width) {\n margin-right: auto;\n margin-left: auto;\n padding-left: floor((@gutter / 2));\n padding-right: ceil((@gutter / 2));\n &:extend(.clearfix all);\n}\n\n// Creates a wrapper for a series of columns\n.make-row(@gutter: @grid-gutter-width) {\n margin-left: ceil((@gutter / -2));\n margin-right: floor((@gutter / -2));\n &:extend(.clearfix all);\n}\n\n// Generate the extra small columns\n.make-xs-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n float: left;\n width: percentage((@columns / @grid-columns));\n min-height: 1px;\n padding-left: (@gutter / 2);\n padding-right: (@gutter / 2);\n}\n.make-xs-column-offset(@columns) {\n margin-left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-push(@columns) {\n left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-pull(@columns) {\n right: percentage((@columns / @grid-columns));\n}\n\n// Generate the small columns\n.make-sm-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-left: (@gutter / 2);\n padding-right: (@gutter / 2);\n\n @media (min-width: @screen-sm-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-offset(@columns) {\n @media (min-width: @screen-sm-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-push(@columns) {\n @media (min-width: @screen-sm-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-pull(@columns) {\n @media (min-width: @screen-sm-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n\n// Generate the medium columns\n.make-md-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-left: (@gutter / 2);\n padding-right: (@gutter / 2);\n\n @media (min-width: @screen-md-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-offset(@columns) {\n @media (min-width: @screen-md-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-push(@columns) {\n @media (min-width: @screen-md-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-pull(@columns) {\n @media (min-width: @screen-md-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n\n// Generate the large columns\n.make-lg-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-left: (@gutter / 2);\n padding-right: (@gutter / 2);\n\n @media (min-width: @screen-lg-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-offset(@columns) {\n @media (min-width: @screen-lg-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-push(@columns) {\n @media (min-width: @screen-lg-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-pull(@columns) {\n @media (min-width: @screen-lg-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n","// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `@grid-columns`.\n\n.make-grid-columns() {\n // Common styles for all sizes of grid columns, widths 1-12\n .col(@index) { // initial\n @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n .col((@index + 1), @item);\n }\n .col(@index, @list) when (@index =< @grid-columns) { // general; \"=<\" isn't a typo\n @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n .col((@index + 1), ~\"@{list}, @{item}\");\n }\n .col(@index, @list) when (@index > @grid-columns) { // terminal\n @{list} {\n position: relative;\n // Prevent columns from collapsing when empty\n min-height: 1px;\n // Inner gutter via padding\n padding-left: ceil((@grid-gutter-width / 2));\n padding-right: floor((@grid-gutter-width / 2));\n }\n }\n .col(1); // kickstart it\n}\n\n.float-grid-columns(@class) {\n .col(@index) { // initial\n @item: ~\".col-@{class}-@{index}\";\n .col((@index + 1), @item);\n }\n .col(@index, @list) when (@index =< @grid-columns) { // general\n @item: ~\".col-@{class}-@{index}\";\n .col((@index + 1), ~\"@{list}, @{item}\");\n }\n .col(@index, @list) when (@index > @grid-columns) { // terminal\n @{list} {\n float: left;\n }\n }\n .col(1); // kickstart it\n}\n\n.calc-grid-column(@index, @class, @type) when (@type = width) and (@index > 0) {\n .col-@{class}-@{index} {\n width: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index > 0) {\n .col-@{class}-push-@{index} {\n left: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index = 0) {\n .col-@{class}-push-0 {\n left: auto;\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index > 0) {\n .col-@{class}-pull-@{index} {\n right: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index = 0) {\n .col-@{class}-pull-0 {\n right: auto;\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = offset) {\n .col-@{class}-offset-@{index} {\n margin-left: percentage((@index / @grid-columns));\n }\n}\n\n// Basic looping in LESS\n.loop-grid-columns(@index, @class, @type) when (@index >= 0) {\n .calc-grid-column(@index, @class, @type);\n // next iteration\n .loop-grid-columns((@index - 1), @class, @type);\n}\n\n// Create grid for specific class\n.make-grid(@class) {\n .float-grid-columns(@class);\n .loop-grid-columns(@grid-columns, @class, width);\n .loop-grid-columns(@grid-columns, @class, pull);\n .loop-grid-columns(@grid-columns, @class, push);\n .loop-grid-columns(@grid-columns, @class, offset);\n}\n","//\n// Tables\n// --------------------------------------------------\n\n\ntable {\n background-color: @table-bg;\n}\ncaption {\n padding-top: @table-cell-padding;\n padding-bottom: @table-cell-padding;\n color: @text-muted;\n text-align: left;\n}\nth {\n text-align: left;\n}\n\n\n// Baseline styles\n\n.table {\n width: 100%;\n max-width: 100%;\n margin-bottom: @line-height-computed;\n // Cells\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n padding: @table-cell-padding;\n line-height: @line-height-base;\n vertical-align: top;\n border-top: 1px solid @table-border-color;\n }\n }\n }\n // Bottom align for column headings\n > thead > tr > th {\n vertical-align: bottom;\n border-bottom: 2px solid @table-border-color;\n }\n // Remove top border from thead by default\n > caption + thead,\n > colgroup + thead,\n > thead:first-child {\n > tr:first-child {\n > th,\n > td {\n border-top: 0;\n }\n }\n }\n // Account for multiple tbody instances\n > tbody + tbody {\n border-top: 2px solid @table-border-color;\n }\n\n // Nesting\n .table {\n background-color: @body-bg;\n }\n}\n\n\n// Condensed table w/ half padding\n\n.table-condensed {\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n padding: @table-condensed-cell-padding;\n }\n }\n }\n}\n\n\n// Bordered version\n//\n// Add borders all around the table and between all the columns.\n\n.table-bordered {\n border: 1px solid @table-border-color;\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n border: 1px solid @table-border-color;\n }\n }\n }\n > thead > tr {\n > th,\n > td {\n border-bottom-width: 2px;\n }\n }\n}\n\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n.table-striped {\n > tbody > tr:nth-of-type(odd) {\n background-color: @table-bg-accent;\n }\n}\n\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n > tbody > tr:hover {\n background-color: @table-bg-hover;\n }\n}\n\n\n// Table cell sizing\n//\n// Reset default table behavior\n\ntable col[class*=\"col-\"] {\n position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n float: none;\n display: table-column;\n}\ntable {\n td,\n th {\n &[class*=\"col-\"] {\n position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n float: none;\n display: table-cell;\n }\n }\n}\n\n\n// Table backgrounds\n//\n// Exact selectors below required to override `.table-striped` and prevent\n// inheritance to nested tables.\n\n// Generate the contextual variants\n.table-row-variant(active; @table-bg-active);\n.table-row-variant(success; @state-success-bg);\n.table-row-variant(info; @state-info-bg);\n.table-row-variant(warning; @state-warning-bg);\n.table-row-variant(danger; @state-danger-bg);\n\n\n// Responsive tables\n//\n// Wrap your tables in `.table-responsive` and we'll make them mobile friendly\n// by enabling horizontal scrolling. Only applies <768px. Everything above that\n// will display normally.\n\n.table-responsive {\n overflow-x: auto;\n min-height: 0.01%; // Workaround for IE9 bug (see https://github.com/twbs/bootstrap/issues/14837)\n\n @media screen and (max-width: @screen-xs-max) {\n width: 100%;\n margin-bottom: (@line-height-computed * 0.75);\n overflow-y: hidden;\n -ms-overflow-style: -ms-autohiding-scrollbar;\n border: 1px solid @table-border-color;\n\n // Tighten up spacing\n > .table {\n margin-bottom: 0;\n\n // Ensure the content doesn't wrap\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n white-space: nowrap;\n }\n }\n }\n }\n\n // Special overrides for the bordered tables\n > .table-bordered {\n border: 0;\n\n // Nuke the appropriate borders so that the parent can handle them\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th:first-child,\n > td:first-child {\n border-left: 0;\n }\n > th:last-child,\n > td:last-child {\n border-right: 0;\n }\n }\n }\n\n // Only nuke the last row's bottom-border in `tbody` and `tfoot` since\n // chances are there will be only one `tr` in a `thead` and that would\n // remove the border altogether.\n > tbody,\n > tfoot {\n > tr:last-child {\n > th,\n > td {\n border-bottom: 0;\n }\n }\n }\n\n }\n }\n}\n","// Tables\n\n.table-row-variant(@state; @background) {\n // Exact selectors below required to override `.table-striped` and prevent\n // inheritance to nested tables.\n .table > thead > tr,\n .table > tbody > tr,\n .table > tfoot > tr {\n > td.@{state},\n > th.@{state},\n &.@{state} > td,\n &.@{state} > th {\n background-color: @background;\n }\n }\n\n // Hover states for `.table-hover`\n // Note: this is not available for cells or rows within `thead` or `tfoot`.\n .table-hover > tbody > tr {\n > td.@{state}:hover,\n > th.@{state}:hover,\n &.@{state}:hover > td,\n &:hover > .@{state},\n &.@{state}:hover > th {\n background-color: darken(@background, 5%);\n }\n }\n}\n","//\n// Forms\n// --------------------------------------------------\n\n\n// Normalize non-controls\n//\n// Restyle and baseline non-control form elements.\n\nfieldset {\n padding: 0;\n margin: 0;\n border: 0;\n // Chrome and Firefox set a `min-width: min-content;` on fieldsets,\n // so we reset that to ensure it behaves more like a standard block element.\n // See https://github.com/twbs/bootstrap/issues/12359.\n min-width: 0;\n}\n\nlegend {\n display: block;\n width: 100%;\n padding: 0;\n margin-bottom: @line-height-computed;\n font-size: (@font-size-base * 1.5);\n line-height: inherit;\n color: @legend-color;\n border: 0;\n border-bottom: 1px solid @legend-border-color;\n}\n\nlabel {\n display: inline-block;\n max-width: 100%; // Force IE8 to wrap long content (see https://github.com/twbs/bootstrap/issues/13141)\n margin-bottom: 5px;\n font-weight: bold;\n}\n\n\n// Normalize form controls\n//\n// While most of our form styles require extra classes, some basic normalization\n// is required to ensure optimum display with or without those classes to better\n// address browser inconsistencies.\n\n// Override content-box in Normalize (* isn't specific enough)\ninput[type=\"search\"] {\n .box-sizing(border-box);\n}\n\n// Position radios and checkboxes better\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n margin: 4px 0 0;\n margin-top: 1px \\9; // IE8-9\n line-height: normal;\n}\n\ninput[type=\"file\"] {\n display: block;\n}\n\n// Make range inputs behave like textual form controls\ninput[type=\"range\"] {\n display: block;\n width: 100%;\n}\n\n// Make multiple select elements height not fixed\nselect[multiple],\nselect[size] {\n height: auto;\n}\n\n// Focus for file, radio, and checkbox\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n .tab-focus();\n}\n\n// Adjust output element\noutput {\n display: block;\n padding-top: (@padding-base-vertical + 1);\n font-size: @font-size-base;\n line-height: @line-height-base;\n color: @input-color;\n}\n\n\n// Common form controls\n//\n// Shared size and type resets for form controls. Apply `.form-control` to any\n// of the following form controls:\n//\n// select\n// textarea\n// input[type=\"text\"]\n// input[type=\"password\"]\n// input[type=\"datetime\"]\n// input[type=\"datetime-local\"]\n// input[type=\"date\"]\n// input[type=\"month\"]\n// input[type=\"time\"]\n// input[type=\"week\"]\n// input[type=\"number\"]\n// input[type=\"email\"]\n// input[type=\"url\"]\n// input[type=\"search\"]\n// input[type=\"tel\"]\n// input[type=\"color\"]\n\n.form-control {\n display: block;\n width: 100%;\n height: @input-height-base; // Make inputs at least the height of their button counterpart (base line-height + padding + border)\n padding: @padding-base-vertical @padding-base-horizontal;\n font-size: @font-size-base;\n line-height: @line-height-base;\n color: @input-color;\n background-color: @input-bg;\n background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n border: 1px solid @input-border;\n border-radius: @input-border-radius; // Note: This has no effect on s in CSS.\n .box-shadow(inset 0 1px 1px rgba(0,0,0,.075));\n .transition(~\"border-color ease-in-out .15s, box-shadow ease-in-out .15s\");\n\n // Customize the `:focus` state to imitate native WebKit styles.\n .form-control-focus();\n\n // Placeholder\n .placeholder();\n\n // Unstyle the caret on ``\n// element gets special love because it's special, and that's a fact!\n.input-size(@input-height; @padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n height: @input-height;\n padding: @padding-vertical @padding-horizontal;\n font-size: @font-size;\n line-height: @line-height;\n border-radius: @border-radius;\n\n select& {\n height: @input-height;\n line-height: @input-height;\n }\n\n textarea&,\n select[multiple]& {\n height: auto;\n }\n}\n","//\n// Buttons\n// --------------------------------------------------\n\n\n// Base styles\n// --------------------------------------------------\n\n.btn {\n display: inline-block;\n margin-bottom: 0; // For input.btn\n font-weight: @btn-font-weight;\n text-align: center;\n vertical-align: middle;\n touch-action: manipulation;\n cursor: pointer;\n background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n border: 1px solid transparent;\n white-space: nowrap;\n .button-size(@padding-base-vertical; @padding-base-horizontal; @font-size-base; @line-height-base; @btn-border-radius-base);\n .user-select(none);\n\n &,\n &:active,\n &.active {\n &:focus,\n &.focus {\n .tab-focus();\n }\n }\n\n &:hover,\n &:focus,\n &.focus {\n color: @btn-default-color;\n text-decoration: none;\n }\n\n &:active,\n &.active {\n outline: 0;\n background-image: none;\n .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n cursor: @cursor-disabled;\n .opacity(.65);\n .box-shadow(none);\n }\n\n a& {\n &.disabled,\n fieldset[disabled] & {\n pointer-events: none; // Future-proof disabling of clicks on `` elements\n }\n }\n}\n\n\n// Alternate buttons\n// --------------------------------------------------\n\n.btn-default {\n .button-variant(@btn-default-color; @btn-default-bg; @btn-default-border);\n}\n.btn-primary {\n .button-variant(@btn-primary-color; @btn-primary-bg; @btn-primary-border);\n}\n// Success appears as green\n.btn-success {\n .button-variant(@btn-success-color; @btn-success-bg; @btn-success-border);\n}\n// Info appears as blue-green\n.btn-info {\n .button-variant(@btn-info-color; @btn-info-bg; @btn-info-border);\n}\n// Warning appears as orange\n.btn-warning {\n .button-variant(@btn-warning-color; @btn-warning-bg; @btn-warning-border);\n}\n// Danger and error appear as red\n.btn-danger {\n .button-variant(@btn-danger-color; @btn-danger-bg; @btn-danger-border);\n}\n\n\n// Link buttons\n// -------------------------\n\n// Make a button look and behave like a link\n.btn-link {\n color: @link-color;\n font-weight: normal;\n border-radius: 0;\n\n &,\n &:active,\n &.active,\n &[disabled],\n fieldset[disabled] & {\n background-color: transparent;\n .box-shadow(none);\n }\n &,\n &:hover,\n &:focus,\n &:active {\n border-color: transparent;\n }\n &:hover,\n &:focus {\n color: @link-hover-color;\n text-decoration: @link-hover-decoration;\n background-color: transparent;\n }\n &[disabled],\n fieldset[disabled] & {\n &:hover,\n &:focus {\n color: @btn-link-disabled-color;\n text-decoration: none;\n }\n }\n}\n\n\n// Button Sizes\n// --------------------------------------------------\n\n.btn-lg {\n // line-height: ensure even-numbered height of button next to large input\n .button-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @btn-border-radius-large);\n}\n.btn-sm {\n // line-height: ensure proper height of button next to small input\n .button-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n.btn-xs {\n .button-size(@padding-xs-vertical; @padding-xs-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n\n\n// Block button\n// --------------------------------------------------\n\n.btn-block {\n display: block;\n width: 100%;\n}\n\n// Vertically space out multiple block buttons\n.btn-block + .btn-block {\n margin-top: 5px;\n}\n\n// Specificity overrides\ninput[type=\"submit\"],\ninput[type=\"reset\"],\ninput[type=\"button\"] {\n &.btn-block {\n width: 100%;\n }\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n.button-variant(@color; @background; @border) {\n color: @color;\n background-color: @background;\n border-color: @border;\n\n &:focus,\n &.focus {\n color: @color;\n background-color: darken(@background, 10%);\n border-color: darken(@border, 25%);\n }\n &:hover {\n color: @color;\n background-color: darken(@background, 10%);\n border-color: darken(@border, 12%);\n }\n &:active,\n &.active,\n .open > .dropdown-toggle& {\n color: @color;\n background-color: darken(@background, 10%);\n border-color: darken(@border, 12%);\n\n &:hover,\n &:focus,\n &.focus {\n color: @color;\n background-color: darken(@background, 17%);\n border-color: darken(@border, 25%);\n }\n }\n &:active,\n &.active,\n .open > .dropdown-toggle& {\n background-image: none;\n }\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n &:hover,\n &:focus,\n &.focus {\n background-color: @background;\n border-color: @border;\n }\n }\n\n .badge {\n color: @background;\n background-color: @color;\n }\n}\n\n// Button sizes\n.button-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n padding: @padding-vertical @padding-horizontal;\n font-size: @font-size;\n line-height: @line-height;\n border-radius: @border-radius;\n}\n","// Opacity\n\n.opacity(@opacity) {\n opacity: @opacity;\n // IE8 filter\n @opacity-ie: (@opacity * 100);\n filter: ~\"alpha(opacity=@{opacity-ie})\";\n}\n","//\n// Component animations\n// --------------------------------------------------\n\n// Heads up!\n//\n// We don't use the `.opacity()` mixin here since it causes a bug with text\n// fields in IE7-8. Source: https://github.com/twbs/bootstrap/pull/3552.\n\n.fade {\n opacity: 0;\n .transition(opacity .15s linear);\n &.in {\n opacity: 1;\n }\n}\n\n.collapse {\n display: none;\n\n &.in { display: block; }\n tr&.in { display: table-row; }\n tbody&.in { display: table-row-group; }\n}\n\n.collapsing {\n position: relative;\n height: 0;\n overflow: hidden;\n .transition-property(~\"height, visibility\");\n .transition-duration(.35s);\n .transition-timing-function(ease);\n}\n","//\n// Dropdown menus\n// --------------------------------------------------\n\n\n// Dropdown arrow/caret\n.caret {\n display: inline-block;\n width: 0;\n height: 0;\n margin-left: 2px;\n vertical-align: middle;\n border-top: @caret-width-base dashed;\n border-top: @caret-width-base solid ~\"\\9\"; // IE8\n border-right: @caret-width-base solid transparent;\n border-left: @caret-width-base solid transparent;\n}\n\n// The dropdown wrapper (div)\n.dropup,\n.dropdown {\n position: relative;\n}\n\n// Prevent the focus on the dropdown toggle when closing dropdowns\n.dropdown-toggle:focus {\n outline: 0;\n}\n\n// The dropdown menu (ul)\n.dropdown-menu {\n position: absolute;\n top: 100%;\n left: 0;\n z-index: @zindex-dropdown;\n display: none; // none by default, but block on \"open\" of the menu\n float: left;\n min-width: 160px;\n padding: 5px 0;\n margin: 2px 0 0; // override default ul\n list-style: none;\n font-size: @font-size-base;\n text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n background-color: @dropdown-bg;\n border: 1px solid @dropdown-fallback-border; // IE8 fallback\n border: 1px solid @dropdown-border;\n border-radius: @border-radius-base;\n .box-shadow(0 6px 12px rgba(0,0,0,.175));\n background-clip: padding-box;\n\n // Aligns the dropdown menu to right\n //\n // Deprecated as of 3.1.0 in favor of `.dropdown-menu-[dir]`\n &.pull-right {\n right: 0;\n left: auto;\n }\n\n // Dividers (basically an hr) within the dropdown\n .divider {\n .nav-divider(@dropdown-divider-bg);\n }\n\n // Links within the dropdown menu\n > li > a {\n display: block;\n padding: 3px 20px;\n clear: both;\n font-weight: normal;\n line-height: @line-height-base;\n color: @dropdown-link-color;\n white-space: nowrap; // prevent links from randomly breaking onto new lines\n }\n}\n\n// Hover/Focus state\n.dropdown-menu > li > a {\n &:hover,\n &:focus {\n text-decoration: none;\n color: @dropdown-link-hover-color;\n background-color: @dropdown-link-hover-bg;\n }\n}\n\n// Active state\n.dropdown-menu > .active > a {\n &,\n &:hover,\n &:focus {\n color: @dropdown-link-active-color;\n text-decoration: none;\n outline: 0;\n background-color: @dropdown-link-active-bg;\n }\n}\n\n// Disabled state\n//\n// Gray out text and ensure the hover/focus state remains gray\n\n.dropdown-menu > .disabled > a {\n &,\n &:hover,\n &:focus {\n color: @dropdown-link-disabled-color;\n }\n\n // Nuke hover/focus effects\n &:hover,\n &:focus {\n text-decoration: none;\n background-color: transparent;\n background-image: none; // Remove CSS gradient\n .reset-filter();\n cursor: @cursor-disabled;\n }\n}\n\n// Open state for the dropdown\n.open {\n // Show the menu\n > .dropdown-menu {\n display: block;\n }\n\n // Remove the outline when :focus is triggered\n > a {\n outline: 0;\n }\n}\n\n// Menu positioning\n//\n// Add extra class to `.dropdown-menu` to flip the alignment of the dropdown\n// menu with the parent.\n.dropdown-menu-right {\n left: auto; // Reset the default from `.dropdown-menu`\n right: 0;\n}\n// With v3, we enabled auto-flipping if you have a dropdown within a right\n// aligned nav component. To enable the undoing of that, we provide an override\n// to restore the default dropdown menu alignment.\n//\n// This is only for left-aligning a dropdown menu within a `.navbar-right` or\n// `.pull-right` nav component.\n.dropdown-menu-left {\n left: 0;\n right: auto;\n}\n\n// Dropdown section headers\n.dropdown-header {\n display: block;\n padding: 3px 20px;\n font-size: @font-size-small;\n line-height: @line-height-base;\n color: @dropdown-header-color;\n white-space: nowrap; // as with > li > a\n}\n\n// Backdrop to catch body clicks on mobile, etc.\n.dropdown-backdrop {\n position: fixed;\n left: 0;\n right: 0;\n bottom: 0;\n top: 0;\n z-index: (@zindex-dropdown - 10);\n}\n\n// Right aligned dropdowns\n.pull-right > .dropdown-menu {\n right: 0;\n left: auto;\n}\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n//\n// Just add .dropup after the standard .dropdown class and you're set, bro.\n// TODO: abstract this so that the navbar fixed styles are not placed here?\n\n.dropup,\n.navbar-fixed-bottom .dropdown {\n // Reverse the caret\n .caret {\n border-top: 0;\n border-bottom: @caret-width-base dashed;\n border-bottom: @caret-width-base solid ~\"\\9\"; // IE8\n content: \"\";\n }\n // Different positioning for bottom up menu\n .dropdown-menu {\n top: auto;\n bottom: 100%;\n margin-bottom: 2px;\n }\n}\n\n\n// Component alignment\n//\n// Reiterate per navbar.less and the modified component alignment there.\n\n@media (min-width: @grid-float-breakpoint) {\n .navbar-right {\n .dropdown-menu {\n .dropdown-menu-right();\n }\n // Necessary for overrides of the default right aligned menu.\n // Will remove come v4 in all likelihood.\n .dropdown-menu-left {\n .dropdown-menu-left();\n }\n }\n}\n","// Horizontal dividers\n//\n// Dividers (basically an hr) within dropdowns and nav lists\n\n.nav-divider(@color: #e5e5e5) {\n height: 1px;\n margin: ((@line-height-computed / 2) - 1) 0;\n overflow: hidden;\n background-color: @color;\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n","//\n// Button groups\n// --------------------------------------------------\n\n// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n position: relative;\n display: inline-block;\n vertical-align: middle; // match .btn alignment given font-size hack above\n > .btn {\n position: relative;\n float: left;\n // Bring the \"active\" button to the front\n &:hover,\n &:focus,\n &:active,\n &.active {\n z-index: 2;\n }\n }\n}\n\n// Prevent double borders when buttons are next to each other\n.btn-group {\n .btn + .btn,\n .btn + .btn-group,\n .btn-group + .btn,\n .btn-group + .btn-group {\n margin-left: -1px;\n }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n margin-left: -5px; // Offset the first child's margin\n &:extend(.clearfix all);\n\n .btn,\n .btn-group,\n .input-group {\n float: left;\n }\n > .btn,\n > .btn-group,\n > .input-group {\n margin-left: 5px;\n }\n}\n\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n border-radius: 0;\n}\n\n// Set corners individual because sometimes a single button can be in a .btn-group and we need :first-child and :last-child to both match\n.btn-group > .btn:first-child {\n margin-left: 0;\n &:not(:last-child):not(.dropdown-toggle) {\n .border-right-radius(0);\n }\n}\n// Need .dropdown-toggle since :last-child doesn't apply, given that a .dropdown-menu is used immediately after it\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n .border-left-radius(0);\n}\n\n// Custom edits for including btn-groups within btn-groups (useful for including dropdown buttons within a btn-group)\n.btn-group > .btn-group {\n float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) {\n > .btn:last-child,\n > .dropdown-toggle {\n .border-right-radius(0);\n }\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n .border-left-radius(0);\n}\n\n// On active and open, don't show outline\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n outline: 0;\n}\n\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-xs > .btn { &:extend(.btn-xs); }\n.btn-group-sm > .btn { &:extend(.btn-sm); }\n.btn-group-lg > .btn { &:extend(.btn-lg); }\n\n\n// Split button dropdowns\n// ----------------------\n\n// Give the line between buttons some depth\n.btn-group > .btn + .dropdown-toggle {\n padding-left: 8px;\n padding-right: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n padding-left: 12px;\n padding-right: 12px;\n}\n\n// The clickable button for toggling the menu\n// Remove the gradient and set the same inset shadow as the :active state\n.btn-group.open .dropdown-toggle {\n .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n\n // Show no shadow for `.btn-link` since it has no other button styles.\n &.btn-link {\n .box-shadow(none);\n }\n}\n\n\n// Reposition the caret\n.btn .caret {\n margin-left: 0;\n}\n// Carets in other button sizes\n.btn-lg .caret {\n border-width: @caret-width-large @caret-width-large 0;\n border-bottom-width: 0;\n}\n// Upside down carets for .dropup\n.dropup .btn-lg .caret {\n border-width: 0 @caret-width-large @caret-width-large;\n}\n\n\n// Vertical button groups\n// ----------------------\n\n.btn-group-vertical {\n > .btn,\n > .btn-group,\n > .btn-group > .btn {\n display: block;\n float: none;\n width: 100%;\n max-width: 100%;\n }\n\n // Clear floats so dropdown menus can be properly placed\n > .btn-group {\n &:extend(.clearfix all);\n > .btn {\n float: none;\n }\n }\n\n > .btn + .btn,\n > .btn + .btn-group,\n > .btn-group + .btn,\n > .btn-group + .btn-group {\n margin-top: -1px;\n margin-left: 0;\n }\n}\n\n.btn-group-vertical > .btn {\n &:not(:first-child):not(:last-child) {\n border-radius: 0;\n }\n &:first-child:not(:last-child) {\n .border-top-radius(@btn-border-radius-base);\n .border-bottom-radius(0);\n }\n &:last-child:not(:first-child) {\n .border-top-radius(0);\n .border-bottom-radius(@btn-border-radius-base);\n }\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) {\n > .btn:last-child,\n > .dropdown-toggle {\n .border-bottom-radius(0);\n }\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n .border-top-radius(0);\n}\n\n\n// Justified button groups\n// ----------------------\n\n.btn-group-justified {\n display: table;\n width: 100%;\n table-layout: fixed;\n border-collapse: separate;\n > .btn,\n > .btn-group {\n float: none;\n display: table-cell;\n width: 1%;\n }\n > .btn-group .btn {\n width: 100%;\n }\n\n > .btn-group .dropdown-menu {\n left: auto;\n }\n}\n\n\n// Checkbox and radio options\n//\n// In order to support the browser's form validation feedback, powered by the\n// `required` attribute, we have to \"hide\" the inputs via `clip`. We cannot use\n// `display: none;` or `visibility: hidden;` as that also hides the popover.\n// Simply visually hiding the inputs via `opacity` would leave them clickable in\n// certain cases which is prevented by using `clip` and `pointer-events`.\n// This way, we ensure a DOM element is visible to position the popover from.\n//\n// See https://github.com/twbs/bootstrap/pull/12794 and\n// https://github.com/twbs/bootstrap/pull/14559 for more information.\n\n[data-toggle=\"buttons\"] {\n > .btn,\n > .btn-group > .btn {\n input[type=\"radio\"],\n input[type=\"checkbox\"] {\n position: absolute;\n clip: rect(0,0,0,0);\n pointer-events: none;\n }\n }\n}\n","// Single side border-radius\n\n.border-top-radius(@radius) {\n border-top-right-radius: @radius;\n border-top-left-radius: @radius;\n}\n.border-right-radius(@radius) {\n border-bottom-right-radius: @radius;\n border-top-right-radius: @radius;\n}\n.border-bottom-radius(@radius) {\n border-bottom-right-radius: @radius;\n border-bottom-left-radius: @radius;\n}\n.border-left-radius(@radius) {\n border-bottom-left-radius: @radius;\n border-top-left-radius: @radius;\n}\n","//\n// Input groups\n// --------------------------------------------------\n\n// Base styles\n// -------------------------\n.input-group {\n position: relative; // For dropdowns\n display: table;\n border-collapse: separate; // prevent input groups from inheriting border styles from table cells when placed within a table\n\n // Undo padding and float of grid classes\n &[class*=\"col-\"] {\n float: none;\n padding-left: 0;\n padding-right: 0;\n }\n\n .form-control {\n // Ensure that the input is always above the *appended* addon button for\n // proper border colors.\n position: relative;\n z-index: 2;\n\n // IE9 fubars the placeholder attribute in text inputs and the arrows on\n // select elements in input groups. To fix it, we float the input. Details:\n // https://github.com/twbs/bootstrap/issues/11561#issuecomment-28936855\n float: left;\n\n width: 100%;\n margin-bottom: 0;\n\n &:focus {\n z-index: 3;\n }\n }\n}\n\n// Sizing options\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n .input-lg();\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n .input-sm();\n}\n\n\n// Display as table-cell\n// -------------------------\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n display: table-cell;\n\n &:not(:first-child):not(:last-child) {\n border-radius: 0;\n }\n}\n// Addon and addon wrapper for buttons\n.input-group-addon,\n.input-group-btn {\n width: 1%;\n white-space: nowrap;\n vertical-align: middle; // Match the inputs\n}\n\n// Text input groups\n// -------------------------\n.input-group-addon {\n padding: @padding-base-vertical @padding-base-horizontal;\n font-size: @font-size-base;\n font-weight: normal;\n line-height: 1;\n color: @input-color;\n text-align: center;\n background-color: @input-group-addon-bg;\n border: 1px solid @input-group-addon-border-color;\n border-radius: @input-border-radius;\n\n // Sizing\n &.input-sm {\n padding: @padding-small-vertical @padding-small-horizontal;\n font-size: @font-size-small;\n border-radius: @input-border-radius-small;\n }\n &.input-lg {\n padding: @padding-large-vertical @padding-large-horizontal;\n font-size: @font-size-large;\n border-radius: @input-border-radius-large;\n }\n\n // Nuke default margins from checkboxes and radios to vertically center within.\n input[type=\"radio\"],\n input[type=\"checkbox\"] {\n margin-top: 0;\n }\n}\n\n// Reset rounded corners\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n .border-right-radius(0);\n}\n.input-group-addon:first-child {\n border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n .border-left-radius(0);\n}\n.input-group-addon:last-child {\n border-left: 0;\n}\n\n// Button input groups\n// -------------------------\n.input-group-btn {\n position: relative;\n // Jankily prevent input button groups from wrapping with `white-space` and\n // `font-size` in combination with `inline-block` on buttons.\n font-size: 0;\n white-space: nowrap;\n\n // Negative margin for spacing, position for bringing hovered/focused/actived\n // element above the siblings.\n > .btn {\n position: relative;\n + .btn {\n margin-left: -1px;\n }\n // Bring the \"active\" button to the front\n &:hover,\n &:focus,\n &:active {\n z-index: 2;\n }\n }\n\n // Negative margin to only have a 1px border between the two\n &:first-child {\n > .btn,\n > .btn-group {\n margin-right: -1px;\n }\n }\n &:last-child {\n > .btn,\n > .btn-group {\n z-index: 2;\n margin-left: -1px;\n }\n }\n}\n","//\n// Navs\n// --------------------------------------------------\n\n\n// Base class\n// --------------------------------------------------\n\n.nav {\n margin-bottom: 0;\n padding-left: 0; // Override default ul/ol\n list-style: none;\n &:extend(.clearfix all);\n\n > li {\n position: relative;\n display: block;\n\n > a {\n position: relative;\n display: block;\n padding: @nav-link-padding;\n &:hover,\n &:focus {\n text-decoration: none;\n background-color: @nav-link-hover-bg;\n }\n }\n\n // Disabled state sets text to gray and nukes hover/tab effects\n &.disabled > a {\n color: @nav-disabled-link-color;\n\n &:hover,\n &:focus {\n color: @nav-disabled-link-hover-color;\n text-decoration: none;\n background-color: transparent;\n cursor: @cursor-disabled;\n }\n }\n }\n\n // Open dropdowns\n .open > a {\n &,\n &:hover,\n &:focus {\n background-color: @nav-link-hover-bg;\n border-color: @link-color;\n }\n }\n\n // Nav dividers (deprecated with v3.0.1)\n //\n // This should have been removed in v3 with the dropping of `.nav-list`, but\n // we missed it. We don't currently support this anywhere, but in the interest\n // of maintaining backward compatibility in case you use it, it's deprecated.\n .nav-divider {\n .nav-divider();\n }\n\n // Prevent IE8 from misplacing imgs\n //\n // See https://github.com/h5bp/html5-boilerplate/issues/984#issuecomment-3985989\n > li > a > img {\n max-width: none;\n }\n}\n\n\n// Tabs\n// -------------------------\n\n// Give the tabs something to sit on\n.nav-tabs {\n border-bottom: 1px solid @nav-tabs-border-color;\n > li {\n float: left;\n // Make the list-items overlay the bottom border\n margin-bottom: -1px;\n\n // Actual tabs (as links)\n > a {\n margin-right: 2px;\n line-height: @line-height-base;\n border: 1px solid transparent;\n border-radius: @border-radius-base @border-radius-base 0 0;\n &:hover {\n border-color: @nav-tabs-link-hover-border-color @nav-tabs-link-hover-border-color @nav-tabs-border-color;\n }\n }\n\n // Active state, and its :hover to override normal :hover\n &.active > a {\n &,\n &:hover,\n &:focus {\n color: @nav-tabs-active-link-hover-color;\n background-color: @nav-tabs-active-link-hover-bg;\n border: 1px solid @nav-tabs-active-link-hover-border-color;\n border-bottom-color: transparent;\n cursor: default;\n }\n }\n }\n // pulling this in mainly for less shorthand\n &.nav-justified {\n .nav-justified();\n .nav-tabs-justified();\n }\n}\n\n\n// Pills\n// -------------------------\n.nav-pills {\n > li {\n float: left;\n\n // Links rendered as pills\n > a {\n border-radius: @nav-pills-border-radius;\n }\n + li {\n margin-left: 2px;\n }\n\n // Active state\n &.active > a {\n &,\n &:hover,\n &:focus {\n color: @nav-pills-active-link-hover-color;\n background-color: @nav-pills-active-link-hover-bg;\n }\n }\n }\n}\n\n\n// Stacked pills\n.nav-stacked {\n > li {\n float: none;\n + li {\n margin-top: 2px;\n margin-left: 0; // no need for this gap between nav items\n }\n }\n}\n\n\n// Nav variations\n// --------------------------------------------------\n\n// Justified nav links\n// -------------------------\n\n.nav-justified {\n width: 100%;\n\n > li {\n float: none;\n > a {\n text-align: center;\n margin-bottom: 5px;\n }\n }\n\n > .dropdown .dropdown-menu {\n top: auto;\n left: auto;\n }\n\n @media (min-width: @screen-sm-min) {\n > li {\n display: table-cell;\n width: 1%;\n > a {\n margin-bottom: 0;\n }\n }\n }\n}\n\n// Move borders to anchors instead of bottom of list\n//\n// Mixin for adding on top the shared `.nav-justified` styles for our tabs\n.nav-tabs-justified {\n border-bottom: 0;\n\n > li > a {\n // Override margin from .nav-tabs\n margin-right: 0;\n border-radius: @border-radius-base;\n }\n\n > .active > a,\n > .active > a:hover,\n > .active > a:focus {\n border: 1px solid @nav-tabs-justified-link-border-color;\n }\n\n @media (min-width: @screen-sm-min) {\n > li > a {\n border-bottom: 1px solid @nav-tabs-justified-link-border-color;\n border-radius: @border-radius-base @border-radius-base 0 0;\n }\n > .active > a,\n > .active > a:hover,\n > .active > a:focus {\n border-bottom-color: @nav-tabs-justified-active-link-border-color;\n }\n }\n}\n\n\n// Tabbable tabs\n// -------------------------\n\n// Hide tabbable panes to start, show them when `.active`\n.tab-content {\n > .tab-pane {\n display: none;\n }\n > .active {\n display: block;\n }\n}\n\n\n// Dropdowns\n// -------------------------\n\n// Specific dropdowns\n.nav-tabs .dropdown-menu {\n // make dropdown border overlap tab border\n margin-top: -1px;\n // Remove the top rounded corners here since there is a hard edge above the menu\n .border-top-radius(0);\n}\n","//\n// Navbars\n// --------------------------------------------------\n\n\n// Wrapper and base class\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n position: relative;\n min-height: @navbar-height; // Ensure a navbar always shows (e.g., without a .navbar-brand in collapsed mode)\n margin-bottom: @navbar-margin-bottom;\n border: 1px solid transparent;\n\n // Prevent floats from breaking the navbar\n &:extend(.clearfix all);\n\n @media (min-width: @grid-float-breakpoint) {\n border-radius: @navbar-border-radius;\n }\n}\n\n\n// Navbar heading\n//\n// Groups `.navbar-brand` and `.navbar-toggle` into a single component for easy\n// styling of responsive aspects.\n\n.navbar-header {\n &:extend(.clearfix all);\n\n @media (min-width: @grid-float-breakpoint) {\n float: left;\n }\n}\n\n\n// Navbar collapse (body)\n//\n// Group your navbar content into this for easy collapsing and expanding across\n// various device sizes. By default, this content is collapsed when <768px, but\n// will expand past that for a horizontal display.\n//\n// To start (on mobile devices) the navbar links, forms, and buttons are stacked\n// vertically and include a `max-height` to overflow in case you have too much\n// content for the user's viewport.\n\n.navbar-collapse {\n overflow-x: visible;\n padding-right: @navbar-padding-horizontal;\n padding-left: @navbar-padding-horizontal;\n border-top: 1px solid transparent;\n box-shadow: inset 0 1px 0 rgba(255,255,255,.1);\n &:extend(.clearfix all);\n -webkit-overflow-scrolling: touch;\n\n &.in {\n overflow-y: auto;\n }\n\n @media (min-width: @grid-float-breakpoint) {\n width: auto;\n border-top: 0;\n box-shadow: none;\n\n &.collapse {\n display: block !important;\n height: auto !important;\n padding-bottom: 0; // Override default setting\n overflow: visible !important;\n }\n\n &.in {\n overflow-y: visible;\n }\n\n // Undo the collapse side padding for navbars with containers to ensure\n // alignment of right-aligned contents.\n .navbar-fixed-top &,\n .navbar-static-top &,\n .navbar-fixed-bottom & {\n padding-left: 0;\n padding-right: 0;\n }\n }\n}\n\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n .navbar-collapse {\n max-height: @navbar-collapse-max-height;\n\n @media (max-device-width: @screen-xs-min) and (orientation: landscape) {\n max-height: 200px;\n }\n }\n}\n\n\n// Both navbar header and collapse\n//\n// When a container is present, change the behavior of the header and collapse.\n\n.container,\n.container-fluid {\n > .navbar-header,\n > .navbar-collapse {\n margin-right: -@navbar-padding-horizontal;\n margin-left: -@navbar-padding-horizontal;\n\n @media (min-width: @grid-float-breakpoint) {\n margin-right: 0;\n margin-left: 0;\n }\n }\n}\n\n\n//\n// Navbar alignment options\n//\n// Display the navbar across the entirety of the page or fixed it to the top or\n// bottom of the page.\n\n// Static top (unfixed, but 100% wide) navbar\n.navbar-static-top {\n z-index: @zindex-navbar;\n border-width: 0 0 1px;\n\n @media (min-width: @grid-float-breakpoint) {\n border-radius: 0;\n }\n}\n\n// Fix the top/bottom navbars when screen real estate supports it\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n position: fixed;\n right: 0;\n left: 0;\n z-index: @zindex-navbar-fixed;\n\n // Undo the rounded corners\n @media (min-width: @grid-float-breakpoint) {\n border-radius: 0;\n }\n}\n.navbar-fixed-top {\n top: 0;\n border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n bottom: 0;\n margin-bottom: 0; // override .navbar defaults\n border-width: 1px 0 0;\n}\n\n\n// Brand/project name\n\n.navbar-brand {\n float: left;\n padding: @navbar-padding-vertical @navbar-padding-horizontal;\n font-size: @font-size-large;\n line-height: @line-height-computed;\n height: @navbar-height;\n\n &:hover,\n &:focus {\n text-decoration: none;\n }\n\n > img {\n display: block;\n }\n\n @media (min-width: @grid-float-breakpoint) {\n .navbar > .container &,\n .navbar > .container-fluid & {\n margin-left: -@navbar-padding-horizontal;\n }\n }\n}\n\n\n// Navbar toggle\n//\n// Custom button for toggling the `.navbar-collapse`, powered by the collapse\n// JavaScript plugin.\n\n.navbar-toggle {\n position: relative;\n float: right;\n margin-right: @navbar-padding-horizontal;\n padding: 9px 10px;\n .navbar-vertical-align(34px);\n background-color: transparent;\n background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n border: 1px solid transparent;\n border-radius: @border-radius-base;\n\n // We remove the `outline` here, but later compensate by attaching `:hover`\n // styles to `:focus`.\n &:focus {\n outline: 0;\n }\n\n // Bars\n .icon-bar {\n display: block;\n width: 22px;\n height: 2px;\n border-radius: 1px;\n }\n .icon-bar + .icon-bar {\n margin-top: 4px;\n }\n\n @media (min-width: @grid-float-breakpoint) {\n display: none;\n }\n}\n\n\n// Navbar nav links\n//\n// Builds on top of the `.nav` components with its own modifier class to make\n// the nav the full height of the horizontal nav (above 768px).\n\n.navbar-nav {\n margin: (@navbar-padding-vertical / 2) -@navbar-padding-horizontal;\n\n > li > a {\n padding-top: 10px;\n padding-bottom: 10px;\n line-height: @line-height-computed;\n }\n\n @media (max-width: @grid-float-breakpoint-max) {\n // Dropdowns get custom display when collapsed\n .open .dropdown-menu {\n position: static;\n float: none;\n width: auto;\n margin-top: 0;\n background-color: transparent;\n border: 0;\n box-shadow: none;\n > li > a,\n .dropdown-header {\n padding: 5px 15px 5px 25px;\n }\n > li > a {\n line-height: @line-height-computed;\n &:hover,\n &:focus {\n background-image: none;\n }\n }\n }\n }\n\n // Uncollapse the nav\n @media (min-width: @grid-float-breakpoint) {\n float: left;\n margin: 0;\n\n > li {\n float: left;\n > a {\n padding-top: @navbar-padding-vertical;\n padding-bottom: @navbar-padding-vertical;\n }\n }\n }\n}\n\n\n// Navbar form\n//\n// Extension of the `.form-inline` with some extra flavor for optimum display in\n// our navbars.\n\n.navbar-form {\n margin-left: -@navbar-padding-horizontal;\n margin-right: -@navbar-padding-horizontal;\n padding: 10px @navbar-padding-horizontal;\n border-top: 1px solid transparent;\n border-bottom: 1px solid transparent;\n @shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);\n .box-shadow(@shadow);\n\n // Mixin behavior for optimum display\n .form-inline();\n\n .form-group {\n @media (max-width: @grid-float-breakpoint-max) {\n margin-bottom: 5px;\n\n &:last-child {\n margin-bottom: 0;\n }\n }\n }\n\n // Vertically center in expanded, horizontal navbar\n .navbar-vertical-align(@input-height-base);\n\n // Undo 100% width for pull classes\n @media (min-width: @grid-float-breakpoint) {\n width: auto;\n border: 0;\n margin-left: 0;\n margin-right: 0;\n padding-top: 0;\n padding-bottom: 0;\n .box-shadow(none);\n }\n}\n\n\n// Dropdown menus\n\n// Menu position and menu carets\n.navbar-nav > li > .dropdown-menu {\n margin-top: 0;\n .border-top-radius(0);\n}\n// Menu position and menu caret support for dropups via extra dropup class\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n margin-bottom: 0;\n .border-top-radius(@navbar-border-radius);\n .border-bottom-radius(0);\n}\n\n\n// Buttons in navbars\n//\n// Vertically center a button within a navbar (when *not* in a form).\n\n.navbar-btn {\n .navbar-vertical-align(@input-height-base);\n\n &.btn-sm {\n .navbar-vertical-align(@input-height-small);\n }\n &.btn-xs {\n .navbar-vertical-align(22);\n }\n}\n\n\n// Text in navbars\n//\n// Add a class to make any element properly align itself vertically within the navbars.\n\n.navbar-text {\n .navbar-vertical-align(@line-height-computed);\n\n @media (min-width: @grid-float-breakpoint) {\n float: left;\n margin-left: @navbar-padding-horizontal;\n margin-right: @navbar-padding-horizontal;\n }\n}\n\n\n// Component alignment\n//\n// Repurpose the pull utilities as their own navbar utilities to avoid specificity\n// issues with parents and chaining. Only do this when the navbar is uncollapsed\n// though so that navbar contents properly stack and align in mobile.\n//\n// Declared after the navbar components to ensure more specificity on the margins.\n\n@media (min-width: @grid-float-breakpoint) {\n .navbar-left { .pull-left(); }\n .navbar-right {\n .pull-right();\n margin-right: -@navbar-padding-horizontal;\n\n ~ .navbar-right {\n margin-right: 0;\n }\n }\n}\n\n\n// Alternate navbars\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n background-color: @navbar-default-bg;\n border-color: @navbar-default-border;\n\n .navbar-brand {\n color: @navbar-default-brand-color;\n &:hover,\n &:focus {\n color: @navbar-default-brand-hover-color;\n background-color: @navbar-default-brand-hover-bg;\n }\n }\n\n .navbar-text {\n color: @navbar-default-color;\n }\n\n .navbar-nav {\n > li > a {\n color: @navbar-default-link-color;\n\n &:hover,\n &:focus {\n color: @navbar-default-link-hover-color;\n background-color: @navbar-default-link-hover-bg;\n }\n }\n > .active > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-default-link-active-color;\n background-color: @navbar-default-link-active-bg;\n }\n }\n > .disabled > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-default-link-disabled-color;\n background-color: @navbar-default-link-disabled-bg;\n }\n }\n }\n\n .navbar-toggle {\n border-color: @navbar-default-toggle-border-color;\n &:hover,\n &:focus {\n background-color: @navbar-default-toggle-hover-bg;\n }\n .icon-bar {\n background-color: @navbar-default-toggle-icon-bar-bg;\n }\n }\n\n .navbar-collapse,\n .navbar-form {\n border-color: @navbar-default-border;\n }\n\n // Dropdown menu items\n .navbar-nav {\n // Remove background color from open dropdown\n > .open > a {\n &,\n &:hover,\n &:focus {\n background-color: @navbar-default-link-active-bg;\n color: @navbar-default-link-active-color;\n }\n }\n\n @media (max-width: @grid-float-breakpoint-max) {\n // Dropdowns get custom display when collapsed\n .open .dropdown-menu {\n > li > a {\n color: @navbar-default-link-color;\n &:hover,\n &:focus {\n color: @navbar-default-link-hover-color;\n background-color: @navbar-default-link-hover-bg;\n }\n }\n > .active > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-default-link-active-color;\n background-color: @navbar-default-link-active-bg;\n }\n }\n > .disabled > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-default-link-disabled-color;\n background-color: @navbar-default-link-disabled-bg;\n }\n }\n }\n }\n }\n\n\n // Links in navbars\n //\n // Add a class to ensure links outside the navbar nav are colored correctly.\n\n .navbar-link {\n color: @navbar-default-link-color;\n &:hover {\n color: @navbar-default-link-hover-color;\n }\n }\n\n .btn-link {\n color: @navbar-default-link-color;\n &:hover,\n &:focus {\n color: @navbar-default-link-hover-color;\n }\n &[disabled],\n fieldset[disabled] & {\n &:hover,\n &:focus {\n color: @navbar-default-link-disabled-color;\n }\n }\n }\n}\n\n// Inverse navbar\n\n.navbar-inverse {\n background-color: @navbar-inverse-bg;\n border-color: @navbar-inverse-border;\n\n .navbar-brand {\n color: @navbar-inverse-brand-color;\n &:hover,\n &:focus {\n color: @navbar-inverse-brand-hover-color;\n background-color: @navbar-inverse-brand-hover-bg;\n }\n }\n\n .navbar-text {\n color: @navbar-inverse-color;\n }\n\n .navbar-nav {\n > li > a {\n color: @navbar-inverse-link-color;\n\n &:hover,\n &:focus {\n color: @navbar-inverse-link-hover-color;\n background-color: @navbar-inverse-link-hover-bg;\n }\n }\n > .active > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-inverse-link-active-color;\n background-color: @navbar-inverse-link-active-bg;\n }\n }\n > .disabled > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-inverse-link-disabled-color;\n background-color: @navbar-inverse-link-disabled-bg;\n }\n }\n }\n\n // Darken the responsive nav toggle\n .navbar-toggle {\n border-color: @navbar-inverse-toggle-border-color;\n &:hover,\n &:focus {\n background-color: @navbar-inverse-toggle-hover-bg;\n }\n .icon-bar {\n background-color: @navbar-inverse-toggle-icon-bar-bg;\n }\n }\n\n .navbar-collapse,\n .navbar-form {\n border-color: darken(@navbar-inverse-bg, 7%);\n }\n\n // Dropdowns\n .navbar-nav {\n > .open > a {\n &,\n &:hover,\n &:focus {\n background-color: @navbar-inverse-link-active-bg;\n color: @navbar-inverse-link-active-color;\n }\n }\n\n @media (max-width: @grid-float-breakpoint-max) {\n // Dropdowns get custom display\n .open .dropdown-menu {\n > .dropdown-header {\n border-color: @navbar-inverse-border;\n }\n .divider {\n background-color: @navbar-inverse-border;\n }\n > li > a {\n color: @navbar-inverse-link-color;\n &:hover,\n &:focus {\n color: @navbar-inverse-link-hover-color;\n background-color: @navbar-inverse-link-hover-bg;\n }\n }\n > .active > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-inverse-link-active-color;\n background-color: @navbar-inverse-link-active-bg;\n }\n }\n > .disabled > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-inverse-link-disabled-color;\n background-color: @navbar-inverse-link-disabled-bg;\n }\n }\n }\n }\n }\n\n .navbar-link {\n color: @navbar-inverse-link-color;\n &:hover {\n color: @navbar-inverse-link-hover-color;\n }\n }\n\n .btn-link {\n color: @navbar-inverse-link-color;\n &:hover,\n &:focus {\n color: @navbar-inverse-link-hover-color;\n }\n &[disabled],\n fieldset[disabled] & {\n &:hover,\n &:focus {\n color: @navbar-inverse-link-disabled-color;\n }\n }\n }\n}\n","// Navbar vertical align\n//\n// Vertically center elements in the navbar.\n// Example: an element has a height of 30px, so write out `.navbar-vertical-align(30px);` to calculate the appropriate top margin.\n\n.navbar-vertical-align(@element-height) {\n margin-top: ((@navbar-height - @element-height) / 2);\n margin-bottom: ((@navbar-height - @element-height) / 2);\n}\n","//\n// Utility classes\n// --------------------------------------------------\n\n\n// Floats\n// -------------------------\n\n.clearfix {\n .clearfix();\n}\n.center-block {\n .center-block();\n}\n.pull-right {\n float: right !important;\n}\n.pull-left {\n float: left !important;\n}\n\n\n// Toggling content\n// -------------------------\n\n// Note: Deprecated .hide in favor of .hidden or .sr-only (as appropriate) in v3.0.1\n.hide {\n display: none !important;\n}\n.show {\n display: block !important;\n}\n.invisible {\n visibility: hidden;\n}\n.text-hide {\n .text-hide();\n}\n\n\n// Hide from screenreaders and browsers\n//\n// Credit: HTML5 Boilerplate\n\n.hidden {\n display: none !important;\n}\n\n\n// For Affix plugin\n// -------------------------\n\n.affix {\n position: fixed;\n}\n","//\n// Breadcrumbs\n// --------------------------------------------------\n\n\n.breadcrumb {\n padding: @breadcrumb-padding-vertical @breadcrumb-padding-horizontal;\n margin-bottom: @line-height-computed;\n list-style: none;\n background-color: @breadcrumb-bg;\n border-radius: @border-radius-base;\n\n > li {\n display: inline-block;\n\n + li:before {\n content: \"@{breadcrumb-separator}\\00a0\"; // Unicode space added since inline-block means non-collapsing white-space\n padding: 0 5px;\n color: @breadcrumb-color;\n }\n }\n\n > .active {\n color: @breadcrumb-active-color;\n }\n}\n","//\n// Pagination (multiple pages)\n// --------------------------------------------------\n.pagination {\n display: inline-block;\n padding-left: 0;\n margin: @line-height-computed 0;\n border-radius: @border-radius-base;\n\n > li {\n display: inline; // Remove list-style and block-level defaults\n > a,\n > span {\n position: relative;\n float: left; // Collapse white-space\n padding: @padding-base-vertical @padding-base-horizontal;\n line-height: @line-height-base;\n text-decoration: none;\n color: @pagination-color;\n background-color: @pagination-bg;\n border: 1px solid @pagination-border;\n margin-left: -1px;\n }\n &:first-child {\n > a,\n > span {\n margin-left: 0;\n .border-left-radius(@border-radius-base);\n }\n }\n &:last-child {\n > a,\n > span {\n .border-right-radius(@border-radius-base);\n }\n }\n }\n\n > li > a,\n > li > span {\n &:hover,\n &:focus {\n z-index: 2;\n color: @pagination-hover-color;\n background-color: @pagination-hover-bg;\n border-color: @pagination-hover-border;\n }\n }\n\n > .active > a,\n > .active > span {\n &,\n &:hover,\n &:focus {\n z-index: 3;\n color: @pagination-active-color;\n background-color: @pagination-active-bg;\n border-color: @pagination-active-border;\n cursor: default;\n }\n }\n\n > .disabled {\n > span,\n > span:hover,\n > span:focus,\n > a,\n > a:hover,\n > a:focus {\n color: @pagination-disabled-color;\n background-color: @pagination-disabled-bg;\n border-color: @pagination-disabled-border;\n cursor: @cursor-disabled;\n }\n }\n}\n\n// Sizing\n// --------------------------------------------------\n\n// Large\n.pagination-lg {\n .pagination-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @border-radius-large);\n}\n\n// Small\n.pagination-sm {\n .pagination-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @border-radius-small);\n}\n","// Pagination\n\n.pagination-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n > li {\n > a,\n > span {\n padding: @padding-vertical @padding-horizontal;\n font-size: @font-size;\n line-height: @line-height;\n }\n &:first-child {\n > a,\n > span {\n .border-left-radius(@border-radius);\n }\n }\n &:last-child {\n > a,\n > span {\n .border-right-radius(@border-radius);\n }\n }\n }\n}\n","//\n// Pager pagination\n// --------------------------------------------------\n\n\n.pager {\n padding-left: 0;\n margin: @line-height-computed 0;\n list-style: none;\n text-align: center;\n &:extend(.clearfix all);\n li {\n display: inline;\n > a,\n > span {\n display: inline-block;\n padding: 5px 14px;\n background-color: @pager-bg;\n border: 1px solid @pager-border;\n border-radius: @pager-border-radius;\n }\n\n > a:hover,\n > a:focus {\n text-decoration: none;\n background-color: @pager-hover-bg;\n }\n }\n\n .next {\n > a,\n > span {\n float: right;\n }\n }\n\n .previous {\n > a,\n > span {\n float: left;\n }\n }\n\n .disabled {\n > a,\n > a:hover,\n > a:focus,\n > span {\n color: @pager-disabled-color;\n background-color: @pager-bg;\n cursor: @cursor-disabled;\n }\n }\n}\n","//\n// Labels\n// --------------------------------------------------\n\n.label {\n display: inline;\n padding: .2em .6em .3em;\n font-size: 75%;\n font-weight: bold;\n line-height: 1;\n color: @label-color;\n text-align: center;\n white-space: nowrap;\n vertical-align: baseline;\n border-radius: .25em;\n\n // Add hover effects, but only for links\n a& {\n &:hover,\n &:focus {\n color: @label-link-hover-color;\n text-decoration: none;\n cursor: pointer;\n }\n }\n\n // Empty labels collapse automatically (not available in IE8)\n &:empty {\n display: none;\n }\n\n // Quick fix for labels in buttons\n .btn & {\n position: relative;\n top: -1px;\n }\n}\n\n// Colors\n// Contextual variations (linked labels get darker on :hover)\n\n.label-default {\n .label-variant(@label-default-bg);\n}\n\n.label-primary {\n .label-variant(@label-primary-bg);\n}\n\n.label-success {\n .label-variant(@label-success-bg);\n}\n\n.label-info {\n .label-variant(@label-info-bg);\n}\n\n.label-warning {\n .label-variant(@label-warning-bg);\n}\n\n.label-danger {\n .label-variant(@label-danger-bg);\n}\n","// Labels\n\n.label-variant(@color) {\n background-color: @color;\n\n &[href] {\n &:hover,\n &:focus {\n background-color: darken(@color, 10%);\n }\n }\n}\n","//\n// Badges\n// --------------------------------------------------\n\n\n// Base class\n.badge {\n display: inline-block;\n min-width: 10px;\n padding: 3px 7px;\n font-size: @font-size-small;\n font-weight: @badge-font-weight;\n color: @badge-color;\n line-height: @badge-line-height;\n vertical-align: middle;\n white-space: nowrap;\n text-align: center;\n background-color: @badge-bg;\n border-radius: @badge-border-radius;\n\n // Empty badges collapse automatically (not available in IE8)\n &:empty {\n display: none;\n }\n\n // Quick fix for badges in buttons\n .btn & {\n position: relative;\n top: -1px;\n }\n\n .btn-xs &,\n .btn-group-xs > .btn & {\n top: 0;\n padding: 1px 5px;\n }\n\n // Hover state, but only for links\n a& {\n &:hover,\n &:focus {\n color: @badge-link-hover-color;\n text-decoration: none;\n cursor: pointer;\n }\n }\n\n // Account for badges in navs\n .list-group-item.active > &,\n .nav-pills > .active > a > & {\n color: @badge-active-color;\n background-color: @badge-active-bg;\n }\n\n .list-group-item > & {\n float: right;\n }\n\n .list-group-item > & + & {\n margin-right: 5px;\n }\n\n .nav-pills > li > a > & {\n margin-left: 3px;\n }\n}\n","//\n// Jumbotron\n// --------------------------------------------------\n\n\n.jumbotron {\n padding-top: @jumbotron-padding;\n padding-bottom: @jumbotron-padding;\n margin-bottom: @jumbotron-padding;\n color: @jumbotron-color;\n background-color: @jumbotron-bg;\n\n h1,\n .h1 {\n color: @jumbotron-heading-color;\n }\n\n p {\n margin-bottom: (@jumbotron-padding / 2);\n font-size: @jumbotron-font-size;\n font-weight: 200;\n }\n\n > hr {\n border-top-color: darken(@jumbotron-bg, 10%);\n }\n\n .container &,\n .container-fluid & {\n border-radius: @border-radius-large; // Only round corners at higher resolutions if contained in a container\n padding-left: (@grid-gutter-width / 2);\n padding-right: (@grid-gutter-width / 2);\n }\n\n .container {\n max-width: 100%;\n }\n\n @media screen and (min-width: @screen-sm-min) {\n padding-top: (@jumbotron-padding * 1.6);\n padding-bottom: (@jumbotron-padding * 1.6);\n\n .container &,\n .container-fluid & {\n padding-left: (@jumbotron-padding * 2);\n padding-right: (@jumbotron-padding * 2);\n }\n\n h1,\n .h1 {\n font-size: @jumbotron-heading-font-size;\n }\n }\n}\n","//\n// Thumbnails\n// --------------------------------------------------\n\n\n// Mixin and adjust the regular image class\n.thumbnail {\n display: block;\n padding: @thumbnail-padding;\n margin-bottom: @line-height-computed;\n line-height: @line-height-base;\n background-color: @thumbnail-bg;\n border: 1px solid @thumbnail-border;\n border-radius: @thumbnail-border-radius;\n .transition(border .2s ease-in-out);\n\n > img,\n a > img {\n &:extend(.img-responsive);\n margin-left: auto;\n margin-right: auto;\n }\n\n // Add a hover state for linked versions only\n a&:hover,\n a&:focus,\n a&.active {\n border-color: @link-color;\n }\n\n // Image captions\n .caption {\n padding: @thumbnail-caption-padding;\n color: @thumbnail-caption-color;\n }\n}\n","//\n// Alerts\n// --------------------------------------------------\n\n\n// Base styles\n// -------------------------\n\n.alert {\n padding: @alert-padding;\n margin-bottom: @line-height-computed;\n border: 1px solid transparent;\n border-radius: @alert-border-radius;\n\n // Headings for larger alerts\n h4 {\n margin-top: 0;\n // Specified for the h4 to prevent conflicts of changing @headings-color\n color: inherit;\n }\n\n // Provide class for links that match alerts\n .alert-link {\n font-weight: @alert-link-font-weight;\n }\n\n // Improve alignment and spacing of inner content\n > p,\n > ul {\n margin-bottom: 0;\n }\n\n > p + p {\n margin-top: 5px;\n }\n}\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n.alert-dismissable, // The misspelled .alert-dismissable was deprecated in 3.2.0.\n.alert-dismissible {\n padding-right: (@alert-padding + 20);\n\n // Adjust close link position\n .close {\n position: relative;\n top: -2px;\n right: -21px;\n color: inherit;\n }\n}\n\n// Alternate styles\n//\n// Generate contextual modifier classes for colorizing the alert.\n\n.alert-success {\n .alert-variant(@alert-success-bg; @alert-success-border; @alert-success-text);\n}\n\n.alert-info {\n .alert-variant(@alert-info-bg; @alert-info-border; @alert-info-text);\n}\n\n.alert-warning {\n .alert-variant(@alert-warning-bg; @alert-warning-border; @alert-warning-text);\n}\n\n.alert-danger {\n .alert-variant(@alert-danger-bg; @alert-danger-border; @alert-danger-text);\n}\n","// Alerts\n\n.alert-variant(@background; @border; @text-color) {\n background-color: @background;\n border-color: @border;\n color: @text-color;\n\n hr {\n border-top-color: darken(@border, 5%);\n }\n .alert-link {\n color: darken(@text-color, 10%);\n }\n}\n","//\n// Progress bars\n// --------------------------------------------------\n\n\n// Bar animations\n// -------------------------\n\n// WebKit\n@-webkit-keyframes progress-bar-stripes {\n from { background-position: 40px 0; }\n to { background-position: 0 0; }\n}\n\n// Spec and IE10+\n@keyframes progress-bar-stripes {\n from { background-position: 40px 0; }\n to { background-position: 0 0; }\n}\n\n\n// Bar itself\n// -------------------------\n\n// Outer container\n.progress {\n overflow: hidden;\n height: @line-height-computed;\n margin-bottom: @line-height-computed;\n background-color: @progress-bg;\n border-radius: @progress-border-radius;\n .box-shadow(inset 0 1px 2px rgba(0,0,0,.1));\n}\n\n// Bar of progress\n.progress-bar {\n float: left;\n width: 0%;\n height: 100%;\n font-size: @font-size-small;\n line-height: @line-height-computed;\n color: @progress-bar-color;\n text-align: center;\n background-color: @progress-bar-bg;\n .box-shadow(inset 0 -1px 0 rgba(0,0,0,.15));\n .transition(width .6s ease);\n}\n\n// Striped bars\n//\n// `.progress-striped .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar-striped` class, which you just add to an existing\n// `.progress-bar`.\n.progress-striped .progress-bar,\n.progress-bar-striped {\n #gradient > .striped();\n background-size: 40px 40px;\n}\n\n// Call animation for the active one\n//\n// `.progress.active .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar.active` approach.\n.progress.active .progress-bar,\n.progress-bar.active {\n .animation(progress-bar-stripes 2s linear infinite);\n}\n\n\n// Variations\n// -------------------------\n\n.progress-bar-success {\n .progress-bar-variant(@progress-bar-success-bg);\n}\n\n.progress-bar-info {\n .progress-bar-variant(@progress-bar-info-bg);\n}\n\n.progress-bar-warning {\n .progress-bar-variant(@progress-bar-warning-bg);\n}\n\n.progress-bar-danger {\n .progress-bar-variant(@progress-bar-danger-bg);\n}\n","// Gradients\n\n#gradient {\n\n // Horizontal gradient, from left to right\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down\n }\n\n // Vertical gradient, from top to bottom\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down\n }\n\n .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n background-repeat: repeat-x;\n background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n }\n .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n background-repeat: no-repeat;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n }\n .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-repeat: no-repeat;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n }\n .radial(@inner-color: #555; @outer-color: #333) {\n background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n background-image: radial-gradient(circle, @inner-color, @outer-color);\n background-repeat: no-repeat;\n }\n .striped(@color: rgba(255,255,255,.15); @angle: 45deg) {\n background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n }\n}\n","// Progress bars\n\n.progress-bar-variant(@color) {\n background-color: @color;\n\n // Deprecated parent class requirement as of v3.2.0\n .progress-striped & {\n #gradient > .striped();\n }\n}\n",".media {\n // Proper spacing between instances of .media\n margin-top: 15px;\n\n &:first-child {\n margin-top: 0;\n }\n}\n\n.media,\n.media-body {\n zoom: 1;\n overflow: hidden;\n}\n\n.media-body {\n width: 10000px;\n}\n\n.media-object {\n display: block;\n\n // Fix collapse in webkit from max-width: 100% and display: table-cell.\n &.img-thumbnail {\n max-width: none;\n }\n}\n\n.media-right,\n.media > .pull-right {\n padding-left: 10px;\n}\n\n.media-left,\n.media > .pull-left {\n padding-right: 10px;\n}\n\n.media-left,\n.media-right,\n.media-body {\n display: table-cell;\n vertical-align: top;\n}\n\n.media-middle {\n vertical-align: middle;\n}\n\n.media-bottom {\n vertical-align: bottom;\n}\n\n// Reset margins on headings for tighter default spacing\n.media-heading {\n margin-top: 0;\n margin-bottom: 5px;\n}\n\n// Media list variation\n//\n// Undo default ul/ol styles\n.media-list {\n padding-left: 0;\n list-style: none;\n}\n","//\n// List groups\n// --------------------------------------------------\n\n\n// Base class\n//\n// Easily usable on