Fixes by format action

.github/workflows/documentation.yml

@@ -18,14 +18,13 @@ jobs:
       - name: Install dependencies
         run: |
           pip install sphinx sphinx_rtd_theme myst_parser
-          # WICHTIG: Wenn dein Code Abhängigkeiten hat (z.B. numpy, scipy), 
-          # installiere sie hier, sonst kann 'autodoc' den Code nicht importieren!
-          # pip install -e . 
+          # WICHTIG: autodoc importiert den Code, um die Docstrings zu lesen.
+          # Ohne diese Zeile schlaegt der Import fehl und die Seiten bleiben leer.
+          pip install -e .
 
       - name: Sphinx build
         run: |
           cd docs
-          sphinx-apidoc -o ./source ../src -f
           make html
 
       - name: Deploy to GitHub Pages

docs/source/Analysis.rst

@@ -1,18 +1,8 @@
-Analysis
-=======================
-
-.. py:function:: get_variances(a, lam = None, Dict = False)
-
-   This function returns the variances of the approximated ANOVA terms for all lam, if lam == None. Otherwise for the provided lam. Depending on Dict, it returns the approximated ANOVA terms as a vector or as a dict.
-
-.. py:function:: get_GSI(a, lam = None, Dict = False)
-
-   This function returns the global sensitivity indices of the approximation for all lam, if lam == None. Otherwise for the provided lam. Depending on Dict, it returns the approximated ANOVA terms as a vector or as a dict.
-
-.. py:function:: get_AttributeRanking(a, lam = None)
-
-   This function returns the attribute ranking of the approximation for all reg. parameters lam, if lam == None, as a dictionary of vectors of length ``a.d``. Otherwise for the provided lam as a vector of length ``a.d``.
-
-.. py:function:: get_ShapleyValues(a, lam = None)
-
-   This function returns the Shapley values of the approximation for all reg. parameters lam, if lam == None, as a dictionary of vectors of length ``a.d``. Otherwise for the provided lam as a vector of length ``a.d``.
+Analysis
+========
+
+.. automodule:: pyANOVAapprox.analysis
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :member-order: bysource

docs/source/Approximation.rst

@@ -1,107 +1,17 @@
-Approximation
-=======================
-
-.. py:class:: approx(X, y, U, N, basis="cos")
-
-   A class to hold the scattered data function approximation.
-
-   This class represents an approximation object constructed from scattered data nodes, function values, an ANOVA decomposition class, and a choice of basis functions. It supports multiple bases and regularization parameters.
-
-   .. rubric:: Attributes:
-
-   .. py:attribute:: basis
-
-      **Type:** `str`
-
-      Basis of the function space. Supported values are:
-
-      - `"per"`: exponential functions
-      - `"cos"`: cosine functions
-      - `"cheb"`: Chebyshev basis
-      - `"std"`: transformed exponential functions
-      - `"chui1"`: Haar wavelets
-      - `"chui2"`: Chui-Wang wavelets of order 2
-      - `"chui3"`: Chui-Wang wavelets of order 3
-      - `"chui4"`: Chui-Wang wavelets of order 4
-
-   .. py:attribute:: X
-
-      **Type:** `Numpy array wit dtype float`
-
-      The scattered data nodes with `M` rows and `d` columns.
-
-   .. py:attribute:: y
-
-      **Type:** `Numpy array wit dtype float or complex`
-
-      A vector of `M` function values. Complex-valued for `basis = "per"`, real-valued otherwise.
-
-   .. py:attribute:: U
-
-      **Type:** `List[Tuple[int]]`
-
-      A vector containing subsets of coordinate indices representing the ANOVA decomposition.
-
-   .. py:attribute:: N
-
-      **Type:** `List[int]`
-
-      Bandwidths for each ANOVA term.
-
-   .. py:attribute:: trafo
-
-      **Type:** `GroupedTransform`
-
-      Holds the grouped transformation.
-
-   .. py:attribute:: fc
-
-      **Type:** `Dict[float, GroupedCoefficients]`
-
-      holds the GroupedCoefficients after approximation for every different regularization parameters
-
-   .. rubric:: Constructor:
-
-   .. py:method:: approx(X, y, U, N, basis="cos")
-
-
-   .. rubric:: Additional Constructor:
-
-   .. py:method:: approx(X, y, ds, N, basis="cos")
-
-
-   .. rubric:: Functions:
-
-   .. py:method:: approximate(self, lam, max_iter=50, weights=None, verbose=False, solver=None, tol=1e-8)
-
-      If ``lam`` is a ``np.ndarray`` of dtype float, this function computes the approximation for the regularization parameters contained in ``lam``.
-
-      If ``lam`` is a float, this function computes the approximation for the regularization parameter ``lam``.
-
-   .. py:method:: evaluate(self, lam=None, X=None)
-
-      This function evaluates the approximation with optional node matrix ``X`` and regularization ``lam``.
-
-      - If both ``X`` and ``lam`` are given: evaluate at ``X`` for specific ``lam``.
-      - If only ``X`` is given: evaluate at ``X`` for all ``lam``.
-      - If only ``lam`` is given: evaluate at ``self.X`` for specific ``lam``.
-      - If neither are given: evaluate at ``self.X`` for all ``lam``.
-
-   .. py:method:: evaluateANOVAterms(self, X, lam=None)
-
-      This function evaluates the single ANOVA terms of the approximation on the nodes of matrix ``X`` and regularization ``lam``.
-
-      - If ``lam`` is given: evaluate at ``X`` for specific ``lam``.
-      - If ``lam`` is not given: evaluate at ``X`` for all ``lam``.
-
-   .. py:method:: evaluateSHAPterms(self, X, lam=None)
-
-      This function evaluates for each dimension the Shapley contribution to the overall approximation on the nodes of matrix ``X`` and regularization ``lam``.
-
-      - If ``lam`` is given: evaluate at ``X`` for specific ``lam``.
-      - If ``lam`` is not given: evaluate at ``X`` for all ``lam``.
-
-
-
-
-
+Approximation
+=============
+
+.. automodule:: pyANOVAapprox.approx
+   :no-members:
+
+.. autoclass:: pyANOVAapprox.approx.approx
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :member-order: bysource
+
+.. autoclass:: pyANOVAapprox.approx.approx_setting
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :member-order: bysource

docs/source/_static/custom.css

@@ -0,0 +1,26 @@
+/* Custom overrides to widen the main content area for sphinx_rtd_theme */
+
+/* Increase the maximum content width */
+.wy-nav-content,
+.rst-content {
+  max-width: 100vw;
+  height: 100vh;
+}
+
+/* Slightly reduce left padding for large screens so content uses more space */
+@media (min-width: 1200px) {
+  .wy-nav-content {
+    padding-left: 20px;
+    padding-right: 20px;
+  }
+}
+
+/* Ensure the main document content uses more horizontal space */
+.rst-content .section, .document {
+  margin-left: 0;
+  margin-right: 0;
+}
+
+.rubric {
+    font-size: 20px;
+}

docs/source/conf.py

@@ -26,16 +26,40 @@
     "sphinx.ext.autodoc",
     "sphinx.ext.napoleon",  # Unterstützt Google/NumPy-Style Docstrings
     "sphinx.ext.mathjax",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.intersphinx",
     "myst_parser",
 ]
 
-templates_path = ["_templates"]
+templates_path = []
 exclude_patterns = []
 
+# -- autodoc -----------------------------------------------------------------
+autodoc_default_options = {
+    "members": True,
+    "undoc-members": True,
+    "show-inheritance": True,
+    "member-order": "bysource",
+}
+
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3", None),
+    "numpy": ("https://numpy.org/doc/stable/", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy/", None),
+}
+
 
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
 # Auf das im Workflow genutzte Theme umgestellt
 html_theme = "sphinx_rtd_theme"
 html_static_path = ["_static"]
+html_theme_options = {
+    "collapse_navigation": False,
+    "sticky_navigation": True,
+    "navigation_depth": 5,
+}
+html_css_files = [
+    "custom.css",
+]

docs/source/index.rst

@@ -16,5 +16,5 @@ documentation for details.
    :caption: Contents:
 
    Analysis
-   modules
+   Approximation
    Errors