Update aliases to reflect package name (#547)

* Change `import pytensor.tensor as at` to `as pt` everywhere in the docs

* Change `import pytensor.tensor as at` to `as pt` everywhere

Change `import pytensor.scalar as aes` to `as ps` everywhere

Change `import pytensor.tensor.random as aer` to `as ptr` everywhere

Change test variables with `_at` suffix or `at_` prefix to `_pt` and `pt_`, respectively

* More renaming

* Rename remaining instances of `aes` and `aer`

doc/extending/creating_a_numba_jax_op.rst

@@ -135,16 +135,16 @@ Here's a small example of a test for :class:`Eye`:
 
 .. code:: python
 
-   import pytensor.tensor as at
+   import pytensor.tensor as pt
 
    def test_jax_Eye():
        """Test JAX conversion of the `Eye` `Op`."""
 
        # Create a symbolic input for `Eye`
-       x_at = at.scalar()
+       x_at = pt.scalar()
 
        # Create a variable that is the output of an `Eye` `Op`
-       eye_var = at.eye(x_at)
+       eye_var = pt.eye(x_at)
 
        # Create an PyTensor `FunctionGraph`
        out_fg = FunctionGraph(outputs=[eye_var])

doc/extending/creating_an_op.rst

@@ -786,7 +786,7 @@ signature:
 .. testcode:: asop
 
     import pytensor
-    import pytensor.tensor as at
+    import pytensor.tensor as pt
     import numpy as np
     from pytensor import function
     from pytensor.compile.ops import as_op
@@ -797,17 +797,17 @@ signature:
         return [ashp[:-1] + bshp[-1:]]
 
 
-    @as_op(itypes=[at.matrix, at.matrix],
-           otypes=[at.matrix], infer_shape=infer_shape_numpy_dot)
+    @as_op(itypes=[pt.matrix, pt.matrix],
+           otypes=[pt.matrix], infer_shape=infer_shape_numpy_dot)
     def numpy_dot(a, b):
        return np.dot(a, b)
 
 You can try it as follows:
 
 .. testcode:: asop
 
-    x = at.matrix()
-    y = at.matrix()
+    x = pt.matrix()
+    y = pt.matrix()
     f = function([x, y], numpy_dot(x, y))
     inp1 = np.random.random_sample((5, 4))
     inp2 = np.random.random_sample((4, 7))

doc/extending/extending_pytensor_solution_1.py

@@ -14,8 +14,8 @@
 
 class ProdOp(Op):
     def make_node(self, x, y):
-        x = at.as_tensor_variable(x)
-        y = at.as_tensor_variable(y)
+        x = pt.as_tensor_variable(x)
+        y = pt.as_tensor_variable(y)
         outdim = x.type.ndim
         output = TensorType(
             dtype=pytensor.scalar.upcast(x.dtype, y.dtype), shape=(None,) * outdim
@@ -39,8 +39,8 @@ def grad(self, inputs, output_grads):
 
 class SumDiffOp(Op):
     def make_node(self, x, y):
-        x = at.as_tensor_variable(x)
-        y = at.as_tensor_variable(y)
+        x = pt.as_tensor_variable(x)
+        y = pt.as_tensor_variable(y)
         outdim = x.type.ndim
         output1 = TensorType(
             dtype=pytensor.scalar.upcast(x.dtype, y.dtype), shape=(None,) * outdim
@@ -62,20 +62,16 @@ def infer_shape(self, fgraph, node, i0_shapes):
     def grad(self, inputs, output_grads):
         og1, og2 = output_grads
         if og1 is None:
-            og1 = at.zeros_like(og2)
+            og1 = pt.zeros_like(og2)
         if og2 is None:
-            og2 = at.zeros_like(og1)
+            og2 = pt.zeros_like(og1)
         return [og1 + og2, og1 - og2]
 
 
 # 3. Testing apparatus
-
-import numpy as np
-
 from tests import unittest_tools as utt
-from pytensor import tensor as at
+from pytensor import tensor as pt
 from pytensor.graph.basic import Apply
-from pytensor.graph.op import Op
 from pytensor.tensor.type import dmatrix, matrix
 
 
@@ -182,8 +178,8 @@ def infer_shape_numpy_dot(fgraph, node, input_shapes):
 
 
 @as_op(
-    itypes=[at.fmatrix, at.fmatrix],
-    otypes=[at.fmatrix],
+    itypes=[pt.fmatrix, pt.fmatrix],
+    otypes=[pt.fmatrix],
     infer_shape=infer_shape_numpy_dot,
 )
 def numpy_add(a, b):
@@ -197,17 +193,17 @@ def infer_shape_numpy_add_sub(fgraph, node, input_shapes):
 
 
 @as_op(
-    itypes=[at.fmatrix, at.fmatrix],
-    otypes=[at.fmatrix],
+    itypes=[pt.fmatrix, pt.fmatrix],
+    otypes=[pt.fmatrix],
     infer_shape=infer_shape_numpy_add_sub,
 )
 def numpy_add(a, b):
     return np.add(a, b)
 
 
 @as_op(
-    itypes=[at.fmatrix, at.fmatrix],
-    otypes=[at.fmatrix],
+    itypes=[pt.fmatrix, pt.fmatrix],
+    otypes=[pt.fmatrix],
     infer_shape=infer_shape_numpy_add_sub,
 )
 def numpy_sub(a, b):

doc/extending/graph_rewriting.rst

@@ -443,7 +443,7 @@ The following is an example that distributes dot products across additions.
 .. code::
 
     import pytensor
-    import pytensor.tensor as at
+    import pytensor.tensor as pt
     from pytensor.graph.rewriting.kanren import KanrenRelationSub
     from pytensor.graph.rewriting.basic import EquilibriumGraphRewriter
     from pytensor.graph.rewriting.utils import rewrite_graph
@@ -462,7 +462,7 @@ The following is an example that distributes dot products across additions.
     )
 
     # Tell `kanren` that `add` is associative
-    fact(associative, at.add)
+    fact(associative, pt.add)
 
 
     def dot_distributeo(in_lv, out_lv):
@@ -473,13 +473,13 @@ The following is an example that distributes dot products across additions.
             # Make sure the input is a `_dot`
             eq(in_lv, etuple(_dot, A_lv, add_term_lv)),
             # Make sure the term being `_dot`ed is an `add`
-            heado(at.add, add_term_lv),
+            heado(pt.add, add_term_lv),
             # Flatten the associative pairings of `add` operations
             assoc_flatten(add_term_lv, add_flat_lv),
             # Get the flattened `add` arguments
             tailo(add_cdr_lv, add_flat_lv),
             # Add all the `_dot`ed arguments and set the output
-            conso(at.add, dot_cdr_lv, out_lv),
+            conso(pt.add, dot_cdr_lv, out_lv),
             # Apply the `_dot` to all the flattened `add` arguments
             mapo(lambda x, y: conso(_dot, etuple(A_lv, x), y), add_cdr_lv, dot_cdr_lv),
         )
@@ -490,10 +490,10 @@ The following is an example that distributes dot products across additions.
 
 Below, we apply `dot_distribute_rewrite` to a few example graphs.  First we create simple test graph:
 
->>> x_at = at.vector("x")
->>> y_at = at.vector("y")
->>> A_at = at.matrix("A")
->>> test_at = A_at.dot(x_at + y_at)
+>>> x_at = pt.vector("x")
+>>> y_at = pt.vector("y")
+>>> A_at = pt.matrix("A")
+>>> test_at = A_pt.dot(x_at + y_at)
 >>> print(pytensor.pprint(test_at))
 (A @ (x + y))
 
@@ -506,18 +506,18 @@ Next we apply the rewrite to the graph:
 We see that the dot product has been distributed, as desired.  Now, let's try a
 few more test cases:
 
->>> z_at = at.vector("z")
->>> w_at = at.vector("w")
->>> test_at = A_at.dot((x_at + y_at) + (z_at + w_at))
+>>> z_at = pt.vector("z")
+>>> w_at = pt.vector("w")
+>>> test_at = A_pt.dot((x_at + y_at) + (z_at + w_at))
 >>> print(pytensor.pprint(test_at))
 (A @ ((x + y) + (z + w)))
 >>> res = rewrite_graph(test_at, include=[], custom_rewrite=dot_distribute_rewrite, clone=False)
 >>> print(pytensor.pprint(res))
 (((A @ x) + (A @ y)) + ((A @ z) + (A @ w)))
 
->>> B_at = at.matrix("B")
->>> w_at = at.vector("w")
->>> test_at = A_at.dot(x_at + (y_at + B_at.dot(z_at + w_at)))
+>>> B_at = pt.matrix("B")
+>>> w_at = pt.vector("w")
+>>> test_at = A_pt.dot(x_at + (y_at + B_pt.dot(z_at + w_at)))
 >>> print(pytensor.pprint(test_at))
 (A @ (x + (y + ((B @ z) + (B @ w)))))
 >>> res = rewrite_graph(test_at, include=[], custom_rewrite=dot_distribute_rewrite, clone=False)

doc/extending/graphstructures.rst

@@ -28,10 +28,10 @@ The following illustrates these elements:
 
 .. testcode::
 
-   import pytensor.tensor as at
+   import pytensor.tensor as pt
 
-   x = at.dmatrix('x')
-   y = at.dmatrix('y')
+   x = pt.dmatrix('x')
+   y = pt.dmatrix('y')
    z = x + y
 
 **Diagram**

doc/extending/tips.rst

@@ -20,10 +20,10 @@ simple function:
 
 .. code::
 
-   from pytensor import tensor as at
+   from pytensor import tensor as pt
 
    def sum_square_difference(a, b):
-       return at.sum((a - b)**2)
+       return pt.sum((a - b)**2)
 
 Even without taking PyTensor's rewrites into account, it is likely
 to work just as well as a custom implementation. It also supports all

doc/extending/unittest.rst

@@ -98,13 +98,13 @@ Example:
 .. code-block:: python
 
     import numpy as np
-    import pytensor.tensor as at
+    import pytensor.tensor as pt
 
 
     def test_dot_validity():
-        a = at.dmatrix('a')
-        b = at.dmatrix('b')
-        c = at.dot(a, b)
+        a = pt.dmatrix('a')
+        b = pt.dmatrix('b')
+        c = pt.dot(a, b)
 
         c_fn = pytensor.function([a, b], [c])
 
@@ -187,7 +187,7 @@ symbolic variable:
 
     def test_verify_exprgrad():
         def fun(x,y,z):
-            return (x + at.cos(y)) / (4 * z)**2
+            return (x + pt.cos(y)) / (4 * z)**2
 
         x_val = np.asarray([[1], [1.1], [1.2]])
         y_val = np.asarray([0.1, 0.2])
@@ -207,7 +207,7 @@ Here is an example showing how to use :func:`verify_grad` on an :class:`Op` inst
         """
         a_val = np.asarray([[0,1,2],[3,4,5]], dtype='float64')
         rng = np.random.default_rng(42)
-        pytensor.gradient.verify_grad(at.Flatten(), [a_val], rng=rng)
+        pytensor.gradient.verify_grad(pt.Flatten(), [a_val], rng=rng)
 
 .. note::
 

doc/glossary.rst

@@ -6,7 +6,7 @@ Glossary
 .. testsetup::
 
    import pytensor
-   import pytensor.tensor as at
+   import pytensor.tensor as pt
 
 .. glossary::
 
@@ -31,7 +31,7 @@ Glossary
         A variable with an immutable value.
         For example, when you type
 
-        >>> x = at.ivector()
+        >>> x = pt.ivector()
         >>> y = x + 3
 
         Then a `constant` is created to represent the ``3`` in the graph.
@@ -151,7 +151,7 @@ Glossary
         The the main data structure you work with when using PyTensor.
         For example,
 
-        >>> x = at.ivector()
+        >>> x = pt.ivector()
         >>> y = -x**2
 
         ``x`` and ``y`` are both :class:`Variable`\s, i.e. instances of the :class:`Variable` class.

doc/introduction.rst

@@ -66,11 +66,11 @@ its features, but it illustrates concretely what PyTensor is.
 .. code-block:: python
 
     import pytensor
-    from pytensor import tensor as at
+    from pytensor import tensor as pt
 
     # declare two symbolic floating-point scalars
-    a = at.dscalar()
-    b = at.dscalar()
+    a = pt.dscalar()
+    b = pt.dscalar()
 
     # create a simple expression
     c = a + b

doc/library/compile/debugmode.rst

@@ -28,10 +28,10 @@ a cluster.
 .. testcode::
 
     import pytensor
-    from pytensor import tensor as at
+    from pytensor import tensor as pt
     from pytensor.compile.debugmode import DebugMode
 
-    x = at.dscalar('x')
+    x = pt.dscalar('x')
 
     f = pytensor.function([x], 10*x, mode='DebugMode')
 

doc/library/compile/io.rst

@@ -80,10 +80,10 @@ A non-None `value` argument makes an In() instance an optional parameter
 of the compiled function.  For example, in the following code we are
 defining an arity-2 function ``inc``.
 
->>> import pytensor.tensor as at
+>>> import pytensor.tensor as pt
 >>> from pytensor import function
 >>> from pytensor.compile.io import In
->>> u, x, s = at.scalars('u', 'x', 's')
+>>> u, x, s = pt.scalars('u', 'x', 's')
 >>> inc = function([u, In(x, value=3), In(s, update=(s+x*u), value=10.0)], [])
 
 Since we provided a ``value`` for ``s`` and ``x``, we can call it with just a value for ``u`` like this:
@@ -183,8 +183,8 @@ method to access values by indexing a Function directly by typing
 To show some examples of these access methods...
 
 
->>> from pytensor import tensor as at, function
->>> a, b, c = at.scalars('xys') # set the internal names of graph nodes
+>>> from pytensor import tensor as pt, function
+>>> a, b, c = pt.scalars('xys') # set the internal names of graph nodes
 >>> # Note that the name of c is 's', not 'c'!
 >>> fn = function([a, b, ((c, c+a+b), 10.0)], [])
 
@@ -236,12 +236,12 @@ Every element of the inputs list will be upgraded to an In instance if necessary
 Example:
 
 >>> import pytensor
->>> from pytensor import tensor as at
+>>> from pytensor import tensor as pt
 >>> from pytensor.compile.io import In
->>> x = at.scalar()
->>> y = at.scalar('y')
->>> z = at.scalar('z')
->>> w = at.scalar('w')
+>>> x = pt.scalar()
+>>> y = pt.scalar('y')
+>>> z = pt.scalar('z')
+>>> w = pt.scalar('w')
 
 >>> fn = pytensor.function(inputs=[x, y, In(z, value=42), ((w, w+x), 0)],
 ...                      outputs=x + y + z)
@@ -308,7 +308,7 @@ If a list of ``Variable`` or ``Out`` instances is given as argument, then the co
 
 >>> import numpy
 >>> from pytensor.compile.io import Out
->>> x, y, s = at.matrices('xys')
+>>> x, y, s = pt.matrices('xys')
 
 >>> # print a list of 2 ndarrays
 >>> fn1 = pytensor.function([x], [x+x, Out((x+x).T, borrow=True)])

doc/library/compile/nanguardmode.rst

@@ -25,12 +25,12 @@ of abnormal values: NaNs, Infs, and abnormally big values.
 
     import numpy as np
     import pytensor
-    import pytensor.tensor as at
+    import pytensor.tensor as pt
     from pytensor.compile.nanguardmode import NanGuardMode
 
-    x = at.matrix()
+    x = pt.matrix()
     w = pytensor.shared(np.random.standard_normal((5, 7)).astype(pytensor.config.floatX))
-    y = at.dot(x, w)
+    y = pt.dot(x, w)
     fun = pytensor.function(
         [x], y,
         mode=NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True)