Pr/1.4.7

.github/workflows/push_to_main.yml

@@ -41,37 +41,6 @@ jobs:
  - name: Test with pytest (excluding abaqus tests)
  run: |
  python -m pytest -S abaqus
- publish-sphinx-documentation:
- runs-on: ubuntu-latest
- steps:
- - name: Checkout
- uses: actions/checkout@v3
- - name: Set up Python
- uses: actions/setup-python@v4
- with:
- python-version: "3.8"
- cache: "pip" # caching pip dependencies
- - name: Install package dependencies
- run: |
- python -m pip install --upgrade pip
- pip install -r requirements.txt
- - name: Install documentation requirements
- run: |
- pip install -r docs/requirements.txt
- - name: Build documentation
- run: |
- sphinx-build -b html ./docs/source ./docs/build/html
- - name: Upload artifacts
- uses: actions/upload-artifact@v3
- with:
- name: html-docs
- path: docs/build/html/
- - name: Deploy
- uses: peaceiris/actions-gh-pages@v3
- with:
- github_token: ${{ secrets.GITHUB_TOKEN }}
- publish_dir: docs/build/html
- force_orphan: false
  build-package:
  strategy:
  matrix:

VERSION

@@ -1 +1 @@
-1.4.6
+1.4.7

docs/source/conf.py

@@ -25,8 +25,8 @@
 project = 'f3dasm'
 author = 'Martin van der Schelling'
 copyright = '2022, Martin van der Schelling'
-version = '1.4.6'
-release = '1.4.6'
+version = '1.4.7'
+release = '1.4.7'
 
 
 # -- General configuration ----------------------------------------------------

docs/source/rst_doc_files/classes/datageneration/datagenerator.rst

@@ -38,7 +38,7 @@ There are three methods available of handeling the :class:`~f3dasm.ExperimentSam
 * :code:`sequential`: regular for-loop over each of the :class:`~f3dasm.ExperimentSample` objects in order
 * :code:`parallel`: utilizing the multiprocessing capabilities (with the `pathos <https://pathos.readthedocs.io/en/latest/pathos.html>`_ multiprocessing library), each :class:`~f3dasm.ExperimentSample` object is run in a separate core
 * :code:`cluster`: utilizing the multiprocessing capabilities, each :class:`~f3dasm.ExperimentSample` object is run in a separate node. After completion of an sample, the node will automatically pick the next available sample. More information on this mode can be found in the :ref:`cluster-mode` section.
-
+* :code:`cluster_parallel`: Combination of the :code:`cluster` and :code:`parallel` mode. Each node will run multiple samples in parallel.
 
 Implement your simulator
 ^^^^^^^^^^^^^^^^^^^^^^^^

docs/source/rst_doc_files/classes/sampling/sampling.rst

@@ -21,9 +21,6 @@ The function should return the samples (``input_data``) in one of the following
 * A :class:`~pandas.DataFrame` object
 * A :class:`~numpy.ndarray` object
 
-.. note::
-
- ...
 
 
 .. _implemented samplers:
@@ -36,7 +33,6 @@ To use the sampler in the data-driven process, you should pass the function to t
 .. code-block:: python
 
  from f3dasm.design import ExperimentData, Domain
- from 
 
  domain = Domain(...)
  # Create the ExperimentData object
@@ -63,4 +59,5 @@ Name Method
 ``"random"`` Random Uniform sampling `numpy.random.uniform <https://numpy.org/doc/stable/reference/random/generated/numpy.random.uniform.html>`_
 ``"latin"`` Latin Hypercube sampling `SALib.latin <https://salib.readthedocs.io/en/latest/api/SALib.sample.html?highlight=latin%20hypercube#SALib.sample.latin.sample>`_
 ``"sobol"`` Sobol Sequence sampling `SALib.sobol_sequence <https://salib.readthedocs.io/en/latest/api/SALib.sample.html?highlight=sobol%20sequence#SALib.sample.sobol_sequence.sample>`_
+``"grid"`` Grid Search sampling `itertools.product <https://docs.python.org/3/library/itertools.html#itertools.product>`_
 ======================== ====================================================================== ===========================================================================================================

docs/source/rst_doc_files/general/overview.rst

@@ -3,9 +3,9 @@
 Overview
 ========
 
-.. _design-of-experiments: https://bessagroup.github.io/f3dasm/classes/design/experimentdata.html
-.. _sampling: https://bessagroup.github.io/f3dasm/classes/sampling/sampling.html
-.. _optimizing: https://bessagroup.github.io/f3dasm/classes/optimization/optimizers.html
+.. _design-of-experiments: https://f3dasm.readthedocs.io/en/latest/rst_doc_files/classes/design/experimentdata.html
+.. _sampling: https://f3dasm.readthedocs.io/en/latest/rst_doc_files/classes/sampling/sampling.html
+.. _optimizing: https://f3dasm.readthedocs.io/en/latest/rst_doc_files/classes/optimization/optimizers.html
 .. _parallelizing: <URL for parallelizing>
 .. _TORQUE system: https://hpc-wiki.info/hpc/Torque
 

paper/paper.md

@@ -116,7 +116,7 @@ An overview of the different levels of abstraction is given in \autoref{fig:f3da
 
 # Documentation and collaborative development
 
-To improve the usability of the `f3dasm` frame­work, thorough documentation has been included with the Sphinx package. Documentation for this package can be accessed on [the homepage](https://bessagroup.github.io/f3dasm/) and will be kept up to date with the latest release of the package.
+To improve the usability of the `f3dasm` frame­work, thorough documentation has been included with the Sphinx package. Documentation for this package can be accessed on [the homepage](https://f3dasm.readthedocs.io/en/latest/) and will be kept up to date with the latest release of the package.
 
 The `f3dasm` framework relies on the collaborative efforts of scientists and developers to expand its capabilities. Therefore, it is essential to have a well-defined software development process in place. This is achieved by maintaining strict branching policies, and incorporating comprehensive testing suites and automatic continuous integration with GitHub Workflows.
 
@@ -138,7 +138,7 @@ To maintain the integrity of the framework, various (automatic) validation proce
 
 # Availability
 
-`f3dasm` and its extensions are available as a `pip` package and is compatible with Python 3.8 to 3.10 and all major operating systems (Linux, MacOS and Windows). Detailed installation instructions can be found on the ['Getting Started'](https://bessagroup.github.io/f3dasm/general/gettingstarted.html) documentation page. 
+`f3dasm` and its extensions are available as a `pip` package and is compatible with Python 3.8 to 3.10 and all major operating systems (Linux, MacOS and Windows). Detailed installation instructions can be found on the ['Getting Started'](https://f3dasm.readthedocs.io/en/latest/rst_doc_files/general/gettingstarted.html) documentation page. 
 
 
 # Acknowledgements

src/f3dasm/_src/datageneration/datagenerator.py

@@ -12,7 +12,7 @@
 import sys
 from abc import abstractmethod
 from functools import partial
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Dict, List, Optional
 
 if sys.version_info < (3, 8): # NOQA
  from typing_extensions import Protocol # NOQA
@@ -187,3 +187,69 @@ def add_post_process(self, func: Callable, **kwargs):
  The keyword arguments to pass to the post-processing function
  """
  self.post_process = partial(func, **kwargs)
+
+
+def convert_function(f: Callable,
+ input: List[str],
+ output: Optional[List[str]] = None,
+ kwargs: Optional[Dict[str, Any]] = None,
+ to_disk: Optional[List[str]] = None) -> DataGenerator:
+ """
+ Converts a given function `f` into a `DataGenerator` object.
+
+ Parameters
+ ----------
+ f : Callable
+ The function to be converted.
+ input : List[str]
+ A list of argument names required by the function.
+ output : Optional[List[str]], optional
+ A list of names for the return values of the function.
+ Defaults to None.
+ kwargs : Optional[Dict[str, Any]], optional
+ Additional keyword arguments passed to the function. Defaults to None.
+ to_disk : Optional[List[str]], optional
+ The list of output names where the value needs to be stored on disk.
+ Defaults to None.
+
+ Returns
+ -------
+ DataGenerator
+ A converted `DataGenerator` object.
+
+ Notes
+ -----
+
+ The function `f` can have any number of arguments and any number of returns
+ as long as they are consistent with the `input` and `output` arguments that
+ are given to this function.
+ """
+
+ kwargs = kwargs if kwargs is not None else {}
+ to_disk = to_disk if to_disk is not None else []
+ output = output if output is not None else []
+
+ class TempDataGenerator(DataGenerator):
+ def execute(self, **_kwargs) -> None:
+ _input = {input_name: self.experiment_sample.get(input_name)
+ for input_name in input}
+ _output = f(**_input, **kwargs)
+
+ # check if output is empty
+ if output is None:
+ return
+
+ if len(output) == 1:
+ _output = (_output,)
+
+ for name, value in zip(output, _output):
+ if name in to_disk:
+ self.experiment_sample.store(name=name,
+ object=value,
+ to_disk=True)
+ else:
+ self.experiment_sample.store(name=name,
+ object=value,
+ to_disk=False)
+
+ return TempDataGenerator()

src/f3dasm/_src/datageneration/functions/adapters/augmentor.py

@@ -77,6 +77,10 @@ def augment(self, input: np.ndarray) -> np.ndarray:
 
  scale = abs(self.noise * yy)
 
+ if isinstance(input, float):
+ # convert to numpy float
+ input = np.float64(input)
+
  noise: np.ndarray = np.random.normal(
  loc=0.0, scale=scale, size=input.shape)
  y_noise = input + float(noise)

src/f3dasm/_src/datageneration/functions/adapters/pybenchfunction.py

@@ -89,9 +89,15 @@ def _configure_offset(self):
 
  unscaled_offset = np.atleast_1d(
  [
+ # np.random.uniform(
+ # low=-abs(g[d] - self.scale_bounds[d, 0]),
+ # high=abs(g[d] - self.scale_bounds[d, 1]))
+
+ # This is added so we only create offsets in one quadrant
+
  np.random.uniform(
  low=-abs(g[d] - self.scale_bounds[d, 0]),
- high=abs(g[d] - self.scale_bounds[d, 1]))
+ high=0.0)
  for d in range(self.dimensionality)
  ]
  )

src/f3dasm/_src/datageneration/functions/function.py

@@ -101,8 +101,8 @@ def execute(self, experiment_sample: ExperimentSample) -> ExperimentSample:
  x = x._value
  if isinstance(x, ArrayBox):
  x = x._value
-
- experiment_sample["y"] = float(self(x).ravel().astype(np.float32))
+ y = np.nan_to_num(self(x), nan=np.nan)
+ experiment_sample["y"] = float(y.ravel().astype(np.float64))
  return experiment_sample
 
  def _run(

src/f3dasm/_src/datageneration/functions/pybenchfunction.py

@@ -269,7 +269,7 @@ class Bartels(PyBenchFunction):
  continuous = True
  convex = False
  separable = False
- differentiable = False
+ differentiable = True
  multimodal = True
  randomized_term = False
  parametric = False
@@ -660,7 +660,7 @@ class BukinN6(PyBenchFunction):
  continuous = True
  convex = True
  separable = False
- differentiable = False
+ differentiable = True
  multimodal = True
  randomized_term = False
  parametric = False
@@ -737,7 +737,7 @@ class CrossInTray(PyBenchFunction):
  continuous = True
  convex = False
  separable = False
- differentiable = False
+ differentiable = True
  multimodal = True
  randomized_term = False
  parametric = False
@@ -1249,7 +1249,7 @@ class HolderTable(PyBenchFunction):
  continuous = True
  convex = False
  separable = False
- differentiable = False
+ differentiable = True
  multimodal = True
  randomized_term = False
  parametric = False
@@ -1650,7 +1650,7 @@ class Powell(PyBenchFunction):
  continuous = True
  convex = True
  separable = True
- differentiable = False
+ differentiable = True
  multimodal = False
  randomized_term = False
  parametric = False
@@ -2116,7 +2116,7 @@ class Schwefel(PyBenchFunction):
  continuous = True
  convex = False
  separable = True
- differentiable = False
+ differentiable = True
  multimodal = True
  randomized_term = False
  parametric = False
@@ -2155,7 +2155,7 @@ class Schwefel2_20(PyBenchFunction):
  continuous = True
  convex = True
  separable = True
- differentiable = False
+ differentiable = True
  multimodal = False
  randomized_term = False
  parametric = False
@@ -2193,7 +2193,7 @@ class Schwefel2_21(PyBenchFunction):
  continuous = True
  convex = True
  separable = True
- differentiable = False
+ differentiable = True
  multimodal = False
  randomized_term = False
  parametric = False
@@ -2231,7 +2231,7 @@ class Schwefel2_22(PyBenchFunction):
  continuous = True
  convex = True
  separable = True
- differentiable = False
+ differentiable = True
  multimodal = False
  randomized_term = False
  parametric = False
@@ -2270,7 +2270,7 @@ class Schwefel2_23(PyBenchFunction):
  continuous = True
  convex = True
  separable = True
- differentiable = False
+ differentiable = True
  multimodal = False
  randomized_term = False
  parametric = False
@@ -2490,7 +2490,7 @@ class Sphere(PyBenchFunction):
  continuous = True
  convex = True
  separable = True
- differentiable = False
+ differentiable = True
  multimodal = False
  randomized_term = False
  parametric = False
@@ -2750,7 +2750,7 @@ class XinSheYangN2(PyBenchFunction):
  continuous = False
  convex = False
  separable = False
- differentiable = False
+ differentiable = True
  multimodal = True
  randomized_term = False
  parametric = False
@@ -2828,7 +2828,7 @@ class XinSheYangN4(PyBenchFunction):
  continuous = True
  convex = True
  separable = False
- differentiable = False
+ differentiable = True
  multimodal = False
  randomized_term = False
  parametric = False
@@ -2866,7 +2866,7 @@ class Zakharov(PyBenchFunction):
  continuous = True
  convex = False
  separable = False
- differentiable = False
+ differentiable = True
  multimodal = False
  randomized_term = False
  parametric = False