use workaround for attribute formatting

docs/conf.py

@@ -67,3 +67,42 @@ def setup(app):
     gallery_dirs=["examples"],
     pattern=".+.ipynb",
 )
+
+# -- Extensions to the  Napoleon GoogleDocstring class ---------------------
+# michaelgoerz.net/notes/extending-sphinx-napoleon-docstring-sections.html
+from sphinx.ext.napoleon.docstring import GoogleDocstring  # noqa: E402
+
+
+def parse_keys_section(self, section):
+    return self._format_fields("Keys", self._consume_fields())
+
+
+GoogleDocstring._parse_keys_section = parse_keys_section
+
+
+def parse_attributes_section(self, section):
+    return self._format_fields("Attributes", self._consume_fields())
+
+
+GoogleDocstring._parse_attributes_section = parse_attributes_section
+
+
+def parse_class_attributes_section(self, section):
+    return self._format_fields("Class Attributes", self._consume_fields())
+
+
+GoogleDocstring._parse_class_attributes_section = parse_class_attributes_section
+
+
+def patched_parse(self):
+    """
+    we now patch the parse method to guarantee that the the above methods are
+    assigned to the _section dict
+    """
+    self._sections["keys"] = self._parse_keys_section
+    self._sections["class attributes"] = self._parse_class_attributes_section
+    self._unpatched_parse()
+
+
+GoogleDocstring._unpatched_parse = GoogleDocstring._parse
+GoogleDocstring._parse = patched_parse

pysindy/optimizers/sindy_optimizer.py

@@ -24,10 +24,10 @@ class SINDyOptimizer(BaseEstimator):
     unbias : boolean, optional (default True)
         Whether to perform an extra step of unregularized linear regression to unbias
         the coefficients for the identified support.
-        For example, if `optimizer=STLSQ(alpha=0.1)` is used then the learned
+        For example, if ``optimizer=STLSQ(alpha=0.1)`` is used then the learned
         coefficients will be biased toward 0 due to the L2 regularization.
-        Setting `unbias=True` will trigger an additional step wherein the nonzero
-        coefficients learned by the `STLSQ` object will be updated using an
+        Setting ``unbias=True`` will trigger an additional step wherein the nonzero
+        coefficients learned by the optimizer object will be updated using an
         unregularized least-squares fit.
     """
 

pysindy/optimizers/sr3.py

@@ -17,8 +17,8 @@ class SR3(BaseOptimizer):
 
     .. math::
 
-        0.5\\|y-Xw\\|^2_2 + lambda \\times R(v)
-        + (0.5 / nu)\\|w-v\\|^2_2
+        0.5\\|y-Xw\\|^2_2 + \\lambda \\times R(v)
+        + (0.5 / \\nu)\\|w-v\\|^2_2
 
     where :math:`R(v)` is a regularization function. See the following reference
     for more details:

pysindy/optimizers/stlsq.py

@@ -12,7 +12,7 @@ class STLSQ(BaseOptimizer):
     """Sequentially thresholded least squares algorithm.
 
     Attempts to minimize the objective function
-    :math:`\\|y - Xw\\|^2_2 + alpha \\times \\|w\\|^2_2`
+    :math:`\\|y - Xw\\|^2_2 + \\alpha \\|w\\|^2_2`
     by iteratively performing least squares and masking out
     elements of the weight that are below a given threshold.