Merge branch 'master' into lps_weights_docs

.github/workflows/release_and_publish.yml

@@ -54,16 +54,16 @@
        run: |
          export PKG=$(ls dist/)
          set -- $PKG
-         echo "name=whl_path" >> $GITHUB_ENV
+         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: ${{ steps.create_release.outputs.upload_url }}
-         asset_path: dist/${{ env.whl_path }}
-         asset_name: ${{ env.whl_path }}
+         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

README.rst

@@ -3,7 +3,7 @@ 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
+    :target: https://github.com/pysal/libpysal/actions?query=workflow%3A.github%2Fworkflows%2Funittests.yml
 
 .. image:: https://codecov.io/gh/pysal/libpysal/branch/master/graph/badge.svg
     :target: https://codecov.io/gh/pysal/libpysal
@@ -12,10 +12,10 @@ Python Spatial Analysis Library Core
     :target: https://badge.fury.io/py/libpysal
 
 .. image:: https://img.shields.io/conda/vn/conda-forge/libpysal.svg
-   :target: https://anaconda.org/conda-forge/libpysal
+    :target: https://anaconda.org/conda-forge/libpysal
 
 .. image:: https://zenodo.org/badge/81501824.svg
-   :target: https://zenodo.org/badge/latestdoi/81501824
+    :target: https://zenodo.org/badge/latestdoi/81501824
 
 
 ****************
@@ -69,14 +69,13 @@ the `Contributors List <https://github.com/pysal/libpysal/graphs/contributors>`_
 Bug reports
 ***********
 
-To search for or report bugs, please see libpysal's issues_.
+To search for or report bugs, please see `libpysal's issues <https://github.com/pysal/libpysal/issues>`_.
 
-.. _issues :  http://github.com/pysal/libpysal/issues
 
 *******************
 License information
 *******************
 
-See the `LICENSE <https://github.com/pysal/libpysal/blob/master/LICENSE.txt>`_ for information on the history of this
-software, terms & conditions for usage, and a DISCLAIMER OF ALL
-WARRANTIES.
+See the `LICENSE <https://github.com/pysal/libpysal/blob/master/LICENSE.txt>`_
+for information on the history of this software, terms & conditions for usage,
+and a DISCLAIMER OF ALL WARRANTIES.

ci/38.yaml

@@ -15,4 +15,6 @@ dependencies:
   # optional
   - geopandas>=0.7.0
   - numba
+  - xarray
+  - joblib
   - zstd

environment.yml

@@ -0,0 +1,34 @@
+name: libpysal
+channels:
+  - conda-forge
+dependencies:
+  - jupyterlab
+  - beautifulsoup4
+  - pandas
+  - requests
+  - jinja2
+  - numpydoc
+  - nbsphinx
+  - pandoc
+  - sphinx>=1.4.3
+  - sphinxcontrib-bibtex
+  - sphinx_bootstrap_theme>=0.7.0
+  - bokeh>=0.11.1
+  - folium>=0.2.1
+  - geojson>=1.3.2
+  - geopandas>=0.2
+  - matplotlib>=1.5.1
+  - mplleaflet>=0.0.5
+  - numba
+  - numexpr
+  - networkx
+  - scikit-learn>=0.17.1
+  - seaborn>=0.7.0
+  - sqlalchemy
+  - statsmodels>=0.6.1
+  - xarray
+  - codecov
+  - pytest
+  - pytest-cov
+  - descartes
+  - mapclassify

libpysal/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "4.3.1"
+__version__ = "4.3.5"
 
 # __version__ has to be define in the first line
 

libpysal/weights/contiguity.py

@@ -228,8 +228,21 @@ def from_dataframe(
         return w
 
     @classmethod
-    def from_xarray(cls, da, z_value=None, coords_labels={}, sparse=False, **kwargs):
-        """Construct a weights object from a ``xarray.DataArray``.
+    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 or ``pandas.MultiIndex``.
 
         Parameters
         ----------
@@ -246,14 +259,29 @@ def from_xarray(cls, da, z_value=None, coords_labels={}, sparse=False, **kwargs)
         sparse : bool
             The type of weight object. Default is ``False``.
             For sparse set to ``True``.
+        k : int
+            Order of contiguity, this will select all neighbors up to ``k``th order.
+            Default is ``1``.
+        include_nodata : bool
+            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}
-            An instance of spatial weights class `W` or `WSP`.
-
+            An instance of spatial weights class `W` or `WSP` with an index attribute.
+        
+        Notes
+        -----
+        1. Lower order contiguities are also selected.
+        2. Returned object contains and ``index`` attribute that includes a 
+        ``pandas.MultiIndex`` object from the ``xarray.DataArray``.
+        
         See Also
         --------
 
@@ -263,9 +291,9 @@ def from_xarray(cls, da, z_value=None, coords_labels={}, sparse=False, **kwargs)
         """
 
         if sparse:
-            w = da2WSP(da, "rook", z_value, coords_labels)
+            w = da2WSP(da, "rook", z_value, coords_labels, k, include_nodata)
         else:
-            w = da2W(da, "rook", z_value, coords_labels, **kwargs)
+            w = da2W(da, "rook", z_value, coords_labels, k, include_nodata, **kwargs)
 
         return w
 
@@ -474,8 +502,21 @@ def from_dataframe(cls, df, geom_col=None, **kwargs):
         return w
 
     @classmethod
-    def from_xarray(cls, da, z_value=None, coords_labels={}, sparse=False, **kwargs):
-        """Construct a weights object from a ``xarray.DataArray``.
+    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`` or ``pandas.MultiIndex``.
 
         Parameters
         ----------
@@ -492,14 +533,29 @@ def from_xarray(cls, da, z_value=None, coords_labels={}, sparse=False, **kwargs)
         sparse : bool
             The type of weight object. Default is ``False``.
             For sparse set to ``True``.
+        k : int
+            Order of contiguity, this will select all neighbors up to ``k``th order.
+            Default is ``1``.
+        include_nodata : bool
+            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}
-            An instance of spatial weights class `W` or `WSP`.
-
+            An 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 ``xarray.DataArray``.
+        
         See Also
         --------
 
@@ -509,9 +565,9 @@ def from_xarray(cls, da, z_value=None, coords_labels={}, sparse=False, **kwargs)
         """
 
         if sparse:
-            w = da2WSP(da, "queen", z_value, coords_labels)
+            w = da2WSP(da, "queen", z_value, coords_labels, k, include_nodata)
         else:
-            w = da2W(da, "queen", z_value, coords_labels, **kwargs)
+            w = da2W(da, "queen", z_value, coords_labels, k, include_nodata, **kwargs)
         return w