Disable sphinx-autodoc rendering for type hints (#33)

Author: crusaderky

.github/workflows/docs.yml

@@ -38,7 +38,7 @@ jobs:
         run: python -m pip install --no-deps -e .
 
       - name: Build docs
-        run: sphinx-build -n -j auto -b html -d build/doctrees doc build/html
+        run: sphinx-build -W -n -j auto -b html -d build/doctrees doc build/html
 
       - uses: actions/upload-artifact@v4
         with:

.readthedocs.yaml

@@ -16,4 +16,5 @@ python:
 sphinx:
   builder: html
   configuration: doc/conf.py
+  # This is deliberately laxer than the setting in CI
   fail_on_warning: false

doc/conf.py

@@ -297,6 +297,9 @@
     "scipy": ("https://docs.scipy.org/doc/scipy/reference/", None),
 }
 
+# Configuration for sphinx-autodoc
+autodoc_typehints = "none"
+
 # Hack around intersphinx issue caused by :show-inheritance:
 import dask.array  # isort:skip  # noqa: E402
 

pyscenarios/_sobol/_sobol.py

@@ -69,7 +69,7 @@ def sobol(
     `Stephen Joe and Frances Y. Kuo <https://web.maths.unsw.edu.au/~fkuo/sobol/>`_,
     using directions from the file ``new-joe-kuo-6.21201`` linked above.
 
-    :param size:
+    :param int | tuple[int, int] size:
         number of samples (cannot be greater than :math:`2^{32}`) to extract
         from a single dimension, or tuple (samples, dimensions).
         To guarantee uniform distribution, the number of samples should

pyscenarios/copula.py

@@ -53,7 +53,7 @@ def gaussian_copula(
         >>> y = numpy.random.standard_normal(size=(samples, cov.shape[0]))
         >>> p = (l @ y.T).T
 
-    :param ArrayLike cov:
+    :param cov:
         covariance matrix, a.k.a. correlation matrix. It must be a
         Hermitian, positive-definite matrix in any square array-like format.
         The width of cov determines the number of dimensions of the output.
@@ -151,12 +151,12 @@ def t_copula(
         >>> u = scipy.stats.t.cdf(z, df=df)
         >>> t = scipy.stats.norm.ppf(u)
 
-    :param ArrayLike cov:
+    :param cov:
         covariance matrix, a.k.a. correlation matrix. It must be a
         Hermitian, positive-definite matrix in any square array-like format.
         The width of cov determines the number of dimensions of the output.
 
-    :param ArrayLike df:
+    :param df:
         Number of degrees of freedom. Can be either a scalar int for
         Student T Copula, or a one-dimensional NumPy array or array-like with
         one point per dimension for IT Copula.

pyscenarios/stats.py

@@ -19,12 +19,12 @@ def tail_dependence(
         1D array-like or Dask array containing samples from a
         uniform (0, 1) distribution.
     :param y:
-        other array to compare against
+        other 1D array-like or Dask array to compare against
     :param q:
-        quantile(s) (0 < q < 1).
+        quantile(s) (`0 < q < 1`).
         Either a scalar or a ND array-like or Dask array.
     :returns:
-        Array of the same shape and type as q, containing:
+        Array of the same shape and type as `q`, containing:
 
         .. math::