Prevent Elemwise docstring from being appened to wrapped functions

Author: fonnesbeck

pytensor/tensor/basic.py

@@ -772,18 +772,6 @@ def switch(cond, ift, iff):
     iff : TensorVariable
         Values selected at `False` elements of `cond`.
 
-    Examples
-    --------
-    This example demonstrates how `switch` can be used in PyMC to model a
-    categorical variable.
-
-    .. code:: python
-
-        import pymc as pm
-
-        with pm.Model():
-            x = pm.Categorical('x', np.array([0.1, 0.9]))
-            y = pm.Bernoulli('y', p=pm.math.switch(x, 0.9, 0.1), shape=10)
     """
 
 

pytensor/tensor/elemwise.py

@@ -1704,7 +1704,7 @@ def construct(symbol):
             rval = Elemwise(scalar_op, nfunc_spec=(nfunc and (nfunc, nin, nout)))
 
         if getattr(symbol, "__doc__"):
-            rval.__doc__ = symbol.__doc__ + "\n\n    " + rval.__doc__
+            rval.__doc__ = symbol.__doc__
 
         # for the meaning of this see the ./epydoc script
         # it makes epydoc display rval as if it were a function, not an object

pytensor/tensor/extra_ops.py

@@ -410,19 +410,6 @@ def cumsum(x, axis=None):
     axis
         The axis along which the cumulative sum is computed.
         The default (None) is to compute the cumsum over the flattened array.
-    # noqa W293
-    Example
-    -------
-    Usage in PyMC:
-
-    .. code-block:: python
-
-    with pm.Model() as model:
-        x0 = pm.Normal('x0')
-        x = pm.Normal('x', mu=0, sd=1, shape=10)
-        # Gaussian random walk
-        grw = pm.Deterministic('grw', x0 + pm.math.cumsum(x))
-
     """
     return CumOp(axis=axis, mode="add")(x)
 
@@ -439,22 +426,6 @@ def cumprod(x, axis=None):
     axis
         The axis along which the cumulative product is computed.
         The default (None) is to compute the `cumprod` over the flattened array.
-    # noqa W293
-    Example
-    -------
-    Usage in PyMC:
-
-    .. code-block:: python
-
-    import pymc as pm
-
-    with pm.Model() as model:
-        x = pm.Normal('x', shape=(10, 3))
-        # Product of x
-        prod_x = pm.Deterministic('prod_x', pm.math.cumprod(x, axis=0))
-
-    .. versionadded:: 0.7
-
     """
     return CumOp(axis=axis, mode="mul")(x)
 

pytensor/tensor/math.py

@@ -1945,17 +1945,6 @@ def dot(l, r):
     """Return a symbolic dot product.
 
     This is designed to work with both sparse and dense tensors types.
-
-    Example usage with PyMC:
-
-    .. code:: python
-
-        import pymc as pm
-
-        with pm.Model() as model:
-            x = pm.Normal('x', mu=0, sd=1, shape=2)
-            y = pm.Normal('y', mu=0, sd=1, shape=2)
-            z = pt.math.dot(x, y)
     """
 
     if not isinstance(l, Variable):
@@ -2675,17 +2664,6 @@ def prod(
         If this is set to True, the axes which are reduced are left in
         the result as dimensions with size one. With this option, the result
         will broadcast correctly against the original tensor.
-    # noqa W293
-    Example
-    -------
-    .. code-block:: python
-
-        import pymc as pm
-
-        with pm.Model() as model:
-            n = pm.Poisson('n', 1, shape=(2, 3))
-            prod_n = pm.Deterministic('prod_n', pm.math.prod(n, axis=0))
-
     """
 
     out = Prod(

pytensor/tensor/special.py

@@ -499,20 +499,6 @@ def softmax(c, axis=None):
     -------
     TensorVariable
         The softmax of the input tensor along the specified axis.
-
-    Examples
-    --------
-    In PyMC, you can use this function to compute a softmax over a vector of
-    probabilities representing the likelihood of each class in a multiclass
-    classification problem. Here is an example::
-
-        import pymc as pm
-
-        with pm.Model() as model:
-            weights = pm.Gamma('weights', 1, 1, shape=3)
-            softmax_prob = pm.math.softmax(weights)
-            outcome = pm.Categorical('outcome', p=softmax_prob)
-
     """
     c = as_tensor_variable(c)
     return Softmax(axis=axis)(c)