Add doc warnings, fix small formatting issues, and remove outdated sections

doc/dev_start_guide.rst

@@ -4,6 +4,10 @@
 Developer Start Guide
 =====================
 
+.. warning::
+   This document is outdated.
+
+
 Contributing
 ============
 
@@ -243,9 +247,6 @@ Documentation and docstrings
   `Numpy docstring standard
   <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`_.
 
-* Split the docstrings in sections, according to the `Allowed docstring
-  sections in Napoleon`_
-
 * To cross-reference other objects (e.g. reference other classes or methods) in
   the docstrings, use the
   `cross-referencing objects <http://www.sphinx-doc.org/en/stable/domains.html#cross-referencing-python-objects>`_

doc/extending/cop.rst

 .. _cop:
 
-====================================
+=====================================
 Implementing the arithmetic COps in C
-====================================
+=====================================
 
 Now that we have set up our ``double`` type properly to allow C
 implementations for operations that work on it, all we have to do now

doc/extending/extending_aesara.rst

@@ -7,14 +7,14 @@ Creating a new Op: Python implementation
 So suppose you have looked through the library documentation and you don't see
 a function that does what you want.
 
-If you can implement something in terms of existing ``Op``s, you should do that.
+If you can implement something in terms of an existing ``Op``, you should do that.
 Odds are your function that uses existing Aesara expressions is short,
 has no bugs, and potentially profits from optimizations that have already been
 implemented.
 
-However, if you cannot implement an ``Op`` in terms of existing ``Op``s, you have to
-write a new one. Don't worry, Aesara was designed to make it easy to add new
-``Op``s, Types, and Optimizations.
+However, if you cannot implement an ``Op`` in terms of an existing ``Op``, you have to
+write a new one. Don't worry, Aesara was designed to make it easy to add a new
+``Op``, ``Type``, and ``Optimization``.
 
 .. These first few pages will walk you through the definition of a new :ref:`type`,
 .. ``double``, and a basic arithmetic :ref:`operations <op>` on that `Type`.
@@ -46,7 +46,7 @@ As an illustration, this tutorial shows how to write a simple Python-based
 Aesara Graphs refresher
 -----------------------
 
-.. image:: ../hpcs2011_tutorial/pics/apply_node.png
+.. image:: apply.png
     :width: 500 px
 
 Aesara represents symbolic mathematical computations as graphs. Those graphs
@@ -145,9 +145,9 @@ or :func:`make_thunk`.
   It takes several arguments:
 
     - ``node`` is a reference to an Apply node which was previously
-      obtained via the ``Op``'s :func:`make_node` method. It is typically not
-      used in simple ``Op``s, but it contains symbolic information that
-      could be required for complex ``Op``s.
+      obtained via the :func:`make_node` method. It is typically not
+      used in a simple ``Op``, but it contains symbolic information that
+      could be required by a complex ``Op``.
     - ``inputs`` is a list of references to data which can be operated on using
       non-symbolic statements, (i.e., statements in Python, Numpy).
     - ``output_storage`` is a list of storage cells where the output

doc/extending/extending_aesara_c.rst

@@ -1029,7 +1029,7 @@ an apply-specific function.
 
 
 Using GDB to debug COp's C code
-==============================
+===============================
 
 When debugging C code, it can be useful to use GDB for code compiled
 by Aesara.

doc/extending/index.rst

@@ -51,3 +51,4 @@ with Aesara itself.
     unittest
     scan
     extending_faq
+    jax_op

doc/extending/jax_op.rst

 Tutorial on adding JAX Ops to Aesara
-=============================
+====================================
 
-A core feature of Aesara, previously named Aesara, is the JAX
-backend. To support the backend JAX ops need be added to Aesara once to
-be supported. This tutorial will explain each step.
+Aesara is able to convert its graphs into JAX compiled functions. In order to do
+this, each ``Op`` in the graph must have a JAX implementation.  This tutorial
+will explain how JAX implementations are created for an ``Op``.
 
 Step 1: Identify the Aesara Op you’d like to JAXify
 ===================================================

doc/extending/other_ops.rst

@@ -137,7 +137,7 @@ You can extract the 4 fields with
 :func:`Aesara.sparse.basic.csm_shape` to extract the individual
 fields.
 
-You can look at the `AddSD`_ sparse `Op` for an example with C code. It implements
+You can look at the `AddSD` sparse `Op` for an example with C code. It implements
 the addition of a sparse matrix with a dense matrix.
 
 Sparse Tests
@@ -200,7 +200,7 @@ To allow consistent interface of Ops that support OpenMP, we have some
 helper code. Doing this also allows to enable/disable OpenMP globally
 or per op for fine-grained control.
 
-Your Op needs to inherit from ``Aesara.graph.op.OpenMPOp``. If it overrides
+Your Op needs to inherit from ``aesara.graph.op.OpenMPOp``. If it overrides
 the ``__init__()`` method, it must have an ``openmp=None`` parameter
 and must call ``super(MyOpClass, self).__init__(openmp=openmp)``.
 
@@ -263,11 +263,11 @@ along with pointers to the relevant documentation.
         can split a sparse variable into its parts as TensorVariables. Those
         can then be used as inputs to an op with C code.
 
-*       :class:`Generic <Aesara.graph.type.Generic>` : Aesara type that
+*       :class:`Generic <aesara.graph.type.Generic>` : Aesara type that
         represents a simple Python Object. Variables of this Aesara type are
         represented in C as objects of class `PyObject
         <https://docs.python.org/2/c-api/structures.html#c.PyObject>`_.
 
-*       :class:`CDataType <Aesara.graph.type.CDataType>` :  Aesara type that
+*       :class:`CDataType <aesara.graph.type.CDataType>` :  Aesara type that
         represents a C data type. The C type associated with this Aesara type
         depends on the data being represented.

doc/extending/unittest.rst

@@ -4,6 +4,9 @@
 Unit Testing
 ============
 
+.. warning::
+   This document is very outdated.
+
 Aesara relies heavily on unit testing. Its importance cannot be
 stressed enough!
 
@@ -45,20 +48,17 @@ Mostly `pytest aesara/`
 Folder Layout
 -------------
 
-"tests" directories are scattered throughout aesara. Each tests
-subfolder is meant to contain the unittests which validate the .py
-files in the parent folder.
-
 Files containing unittests should be prefixed with the word "test".
 
 Optimally every python module should have a unittest file associated
-with it, as shown below. Unittests testing functionality of module
-<module>.py should therefore be stored in tests/test_<module>.py::
+with it, as shown below. Unit tests that test functionality of module
+``<module>.py`` should therefore be stored in
+``tests/<sub-package>/test_<module>.py``::
 
     Aesara/aesara/tensor/basic.py
     Aesara/aesara/tensor/elemwise.py
-    Aesara/aesara/tensor/tests/test_basic.py
-    Aesara/aesara/tensor/tests/test_elemwise.py
+    Aesara/tests/tensor/test_basic.py
+    Aesara/tests/tensor/test_elemwise.py
 
 
 How to Write a Unittest
@@ -67,16 +67,14 @@ How to Write a Unittest
 Test Cases and Methods
 ----------------------
 
-Unittests should be grouped "logically" into test cases, which are
-meant to group all unittests operating on the same element and/or
-concept. Test cases are implemented as Python classes which inherit
-from unittest.TestCase
+Unit tests should be grouped "logically" into test cases, which are
+meant to group all unit tests operating on the same element and/or
+concept.
 
-Test cases contain multiple test methods. These should be prefixed
-with the word "test".
+Test cases should be functions or classes prefixed with the word "test".
 
 Test methods should be as specific as possible and cover a particular
-aspect of the problem. For example, when testing the TensorDot Op, one
+aspect of the problem. For example, when testing the ``Dot`` ``Op``, one
 test method could check for validity, while another could verify that
 the proper errors are raised when inputs have invalid dimensions.
 
@@ -84,224 +82,30 @@ Test method names should be as explicit as possible, so that users can
 see at first glance, what functionality is being tested and what tests
 need to be added.
 
-Example:
-
-.. code-block:: python
-
-    import unittest
-
-    class TestTensorDot(unittest.TestCase):
-        def test_validity(self):
-            # do stuff
-            ...
-        def test_invalid_dims(self):
-            # do more stuff
-            ...
-
-
-Test cases can define a special setUp method, which will get called
-before each test method is executed. This is a good place to put
-functionality which is shared amongst all test methods in the test
-case (i.e initializing data, parameters, seeding random number
-generators -- more on this later)
-
-.. testcode:: writeUnitest
-
-    import unittest
-
-    class TestTensorDot(unittest.TestCase):
-        def setUp(self):
-            # data which will be used in various test methods
-            self.avals = numpy.array([[1,5,3],[2,4,1]])
-            self.bvals = numpy.array([[2,3,1,8],[4,2,1,1],[1,4,8,5]])
-
-Similarly, test cases can define a tearDown method, which will be
-implicitely called at the end of each test method.
-
-
 Checking for correctness
 ------------------------
 
 When checking for correctness of mathematical expressions, the user
-should preferably compare aesara's output to the equivalent numpy
+should preferably compare aesara's output to the equivalent NumPy
 implementation.
 
 Example:
 
 .. code-block:: python
 
-    class TestTensorDot(unittest.TestCase):
-        def setUp(self):
-            ...
-
-        def test_validity(self):
-            a = T.dmatrix('a')
-            b = T.dmatrix('b')
-            c = T.dot(a, b)
-            f = aesara.function([a, b], [c])
-            cmp = f(self.avals, self.bvals) == numpy.dot(self.avals, self.bvals)
-            self.assertTrue(numpy.all(cmp))
-
-Avoid hard-coding variables, as in the following case:
-
-.. code-block:: python
-
-    self.assertTrue(numpy.all(f(self.avals, self.bvals) == numpy.array([[25, 25, 30, 28], [21, 18, 14, 25]])))
-
-This makes the test case less manageable and forces the user to update
-the variables each time the input is changed or possibly when the
-module being tested changes (after a bug fix for example). It also
-constrains the test case to specific input/output data pairs. The
-section on random values covers why this might not be such a good
-idea.
-
-Here is a list of useful functions, as defined by TestCase:
-
-* checking the state of boolean variables: assert,
-  assertTrue, assertFalse
-
-* checking for (in)equality constraints: assertEqual,
-  assertNotEqual
-
-* checking for (in)equality constraints up to a given precision (very
-  useful in aesara): assertAlmostEqual,
-  assertNotAlmostEqual
-
-
-Checking for errors
--------------------
-
-On top of verifying that your code provides the correct output, it is
-equally important to test that it fails in the appropriate manner,
-raising the appropriate exceptions, etc. Silent failures are deadly,
-as they can go unnoticed for a long time and a hard to detect
-"after-the-fact".
-
-Example:
-
-.. code-block:: python
-
-    import unittest
-
-    class TestTensorDot(unittest.TestCase):
-        ...
-        def test_3D_dot_fail(self):
-            def func():
-                a = TensorType('float64', (False,False,False)) # create 3d tensor
-                b = dmatrix()
-                c = dot(a,b) # we expect this to fail
-            # above should fail as dot operates on 2D tensors only
-            self.assertRaises(TypeError, func)
-
-Useful function, as defined by TestCase:
-
-* assertRaises
-
-
-Test Cases and Aesara Modes
----------------------------
-
-When compiling aesara functions or modules, a mode parameter can be
-given to specify which linker and optimizer to use.
-
-Example:
-
-.. code-block:: python
-
-    from aesara import function
-
-    f = function([a,b],[c],mode='FAST_RUN')
-
-Whenever possible, unit tests should omit this parameter. Leaving
-out the mode will ensure that unit tests use the default mode.
-This default mode is set to
-the configuration variable :attr:`config.mode`, which defaults to
-'FAST_RUN', and can be set by various mechanisms (see :mod:`config`).
-
-In particular, the enviromnment variable :envvar:`AESARA_FLAGS`
-allows the user to easily switch the mode in which unittests are
-run. For example to run all tests in all modes from a BASH script,
-type this:
-
-.. code-block:: bash
-
-    AESARA_FLAGS='mode=FAST_COMPILE' pytest
-    AESARA_FLAGS='mode=FAST_RUN' pytest
-    AESARA_FLAGS='mode=DebugMode' pytest
-
-.. _random_value_in_tests:
-
-Using Random Values in Test Cases
----------------------------------
-
-``numpy.random`` is often used in unit tests to initialize large data
-structures, for use as inputs to the function or module being
-tested. When doing this, it is imperative that the random number
-generator be seeded at the be beginning of each unit test. This will
-ensure that unittest behaviour is consistent from one execution to
-another (i.e., always pass or always fail).
-
-Instead of using ``numpy.random.seed`` to do this, we encourage users to
-do the following:
-
-.. testcode::
-
-    from tests import unittest_tools
-
-    class TestTensorDot(unittest.TestCase):
-        def setUp(self):
-            unittest_tools.seed_rng()
-            # OR ... call with an explicit seed
-            unittest_tools.seed_rng(234234)  # use only if really necessary!
-
-The behaviour of ``seed_rng`` is as follows:
-
-* If an explicit seed is given, it will be used for seeding numpy's rng.
-
-* If not, it will use ``config.unittests__rseed`` (its default value is ``666``).
-
-* If ``config.unittests__rseed`` is set to ``"random"``, it will seed the rng with
-  None, which is equivalent to seeding with a random seed.
-
-
-The main advantage of using ``unittest_tools.seed_rng`` is that it allows
-us to change the seed used in the unitests, without having to manually
-edit all the files. For example, this allows the nightly build to run
-``pytest`` repeatedly, changing the seed on every run (hence achieving
-a higher confidence that the variables are correct), while still
-making sure unittests are deterministic.
-
-Users who prefer their unittests to be random (when run on their local
-machine) can simply set ``config.unittests__rseed`` to ``'random'`` (see
-:mod:`config`).
-
-Similarly, to provide a seed to ``numpy.random.RandomState``, simply use:
-
-.. testcode::
-
-    import numpy
-
-    rng = numpy.random.RandomState(unittest_tools.fetch_seed())
-    # OR providing an explicit seed
-    rng = numpy.random.RandomState(unittest_tools.fetch_seed(1231))  # again not recommended
-
-Note that the ability to change the seed from one test to another,
-is incompatible with the method of hard-coding the baseline variables
-(against which we compare the aesara outputs). These must then be
-determined "algorithmically". Although this represents more work, the
-test suite will be better because of it.
-
-To help you check that the boundaries provided to ``numpy.random`` are
-correct and your tests will pass those corner cases, you can check
-``utt.MockRandomState``. Code using ``utt.MockRandomState`` should not
-be committed, it is just a tool to help adjust the sampling range.
+    def test_dot_validity():
+        a = aet.dmatrix('a')
+        b = aet.dmatrix('b')
+        c = aet.dot(a, b)
+        f = aesara.function([a, b], [c])
+        assert np.array_equal(f(self.avals, self.bvals), numpy.dot(self.avals, self.bvals))
 
 
-Creating an Op UnitTest
+Creating an Op Unit Test
 =======================
 
 A few tools have been developed to help automate the development of
-unitests for Aesara Ops.
+unit tests for Aesara Ops.
 
 
 .. _validating_grad:

doc/index.rst

@@ -16,6 +16,11 @@ arrays efficiently. Aesara features:
 Aesara is based on `Theano`_, which has been powering large-scale computationally
 intensive scientific investigations since 2007.
 
+
+.. warning::
+
+   Much of the documentation hasn't been updated and is simply the old Theano documentation.
+
 Download
 ========
 
@@ -69,7 +74,6 @@ Roughly in order of what you'll want to check out:
    :maxdepth: 1
    :hidden:
 
-   NEWS
    introduction
    requirements
    install

doc/install.rst

@@ -12,7 +12,6 @@ Supported platforms:
   install_macos
   install_windows
   install_centos6
-  install_others
 
 Once your setup is complete and if you installed the GPU libraries, head to :ref:`testing_the_gpu` to find how to verify
 everything is working properly.

doc/internal/index.rst

@@ -8,8 +8,5 @@ Internal Documentation
 .. toctree::
    :maxdepth: 2
 
-   release
-   dev_start_guide
    metadocumentation
-   python
    how_to_release

doc/internal/metadocumentation.rst

@@ -85,31 +85,3 @@ It will not appear in the output generated.
 
     .. TODO: Check it out, this won't appear.
     .. Nor will this.
-
-
-How documentation is built
-----------------------------------------------
-
-TBD
-
-pylint
----------------------------------------
-
-pylint output is not autogenerated anymore.
-
-Pylint documentation is generated using pylintrc file: ``Aesara/doc/pylintrc``
-
-
-.. _metadocumentation_nightly_build:
-
-The nightly build/tests process
----------------------------------------
-
-We use GitHub Actions to run daily builds and test for Aesara.
-
-TO WRITE
----------------------------------------
-
-*There is other stuff to document here, e.g.:*
-
- * We also want examples of good documentation, to show people how to write ReST.

doc/introduction.rst

@@ -9,8 +9,7 @@ Aesara is a Python library that lets you define, optimize, and evaluate
 mathematical expressions, especially ones with multi-dimensional arrays
 (numpy.ndarray).  Using Aesara it is
 possible to attain speeds rivaling hand-crafted C implementations for problems
-involving large amounts of data.  It can also surpass C on a CPU by many orders
-of magnitude by taking advantage of recent GPUs.
+involving large amounts of data.
 
 Aesara combines aspects of a computer algebra system (CAS) with aspects of an
 optimizing compiler. It can also generate customized C code for many
@@ -25,7 +24,6 @@ Aesara's compiler applies many optimizations of varying complexity to
 these symbolic expressions. These optimizations include, but are not
 limited to:
 
-* use of GPU for computations
 * constant folding
 * merging of similar subgraphs, to avoid redundant calculation
 * arithmetic simplification (e.g. ``x*y/x -> y``, ``--x -> x``)

doc/library/scan.rst

@@ -682,4 +682,4 @@ reference
 .. autofunction:: aesara.foldl
 .. autofunction:: aesara.foldr
 .. autofunction:: aesara.scan
-.. autofunction:: aesara.scan_checkpoints
+.. autofunction:: aesara.scan.scan_checkpoints

doc/library/tensor/basic.rst

@@ -465,7 +465,7 @@ TensorVariable
     you'll want to call.
 
 
-.. autoclass:: _tensor_py_operators
+.. autoclass:: var._tensor_py_operators
    :members:
 
     This mix-in class adds convenient attributes, methods, and support
@@ -1749,7 +1749,7 @@ Linear Algebra
 Gradient / Differentiation
 ==========================
 
-.. automodule:: Aesara.gradient
+.. automodule:: aesara.gradient
     :members: grad
     :noindex: