Nuitka Developer Manual
~~~~~~~~~~~~~~~~~~~~~~~

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The purpose of this developer manual is to present the current design of
Nuitka, the project rules, and the motivations for choices made. It is intended
to be a guide to the source code, and to give explanations that don't fit into
the source code in comments form.

It should be used as a reference for the process of planning and documenting
decisions we made. Therefore we are e.g. presenting here the type inference
plans before implementing them. And we update them as we proceed.

It grows out of discussions and presentations made at conferences as well as
private conversations or issue tracker.

Milestones
==========

1. Feature parity with CPython, understand all the language construct and
   behave absolutely compatible.

   Feature parity has been reached for CPython 2.6 and 2.7. We do not target
   any older CPython release. For CPython 3.3 up to 3.8 it also has been
   reached. We do not target the older and practically unused CPython 3.0 to
   3.2 releases.

   This milestone was reached. Dropping support for Python 2.6 and 3.3 is an
   option, should this prove to be any benefit. Currently it is not, as it
   extends the test coverage only.

2. Create the most efficient native code from this. This means to be fast with
   the basic Python object handling.

   This milestone was reached, although of course, micro optimizations to this
   are happening all the time.

3. Then do constant propagation, determine as many values and useful
   constraints as possible at compile time and create more efficient code.

   This milestone is considered almost reached. We continue to discover new
   things, but the infrastructure is there, and these are easy to add.

4. Type inference, detect and special case the handling of strings, integers,
   lists in the program.

   This milestone is considered in progress.

5. Add interfacing to C code, so Nuitka can turn a ``ctypes`` binding into an
   efficient binding as written with C.

   This milestone is planned only.

6. Add hints module with a useful Python implementation that the compiler can
   use to learn about types from the programmer.

   This milestone is planned only.


Version Numbers
===============

For Nuitka we use a defensive version numbering system to indicate that it is
not yet ready for everything. We have defined milestones and the version
numbers should express which of these, we consider done.

- So far:

  Before milestone 1, we used ``0.1.x`` version numbers. After reaching it, we
  used ``0.2.x`` version numbers.

  Before milestone 2 and 3, we used ``0.3.x`` version numbers. After almost
  reaching 3, and beginning with 4, we use "0.4.x" version numbers. Due to an
  interface change, ``0.5.x`` version numbers are being used.

  Due to reaching type inference in code generation, even if only starting,
  the ``0.6.x`` version numbers were started to be used. This stage should
  allow quick progress in performance for individual releases.

- Future:

  With ``ctypes`` bindings in a usable state it will be ``0.7.x``.

- Final:

  We will then round it up and call it Nuitka ``1.0`` when this works as
  expected for a bunch of people. The plan is to reach this goal during 2021.
  This is based on positive assumptions that may not hold up though.

Of course, all of this may be subject to change.


Current State
=============

Nuitka top level works like this:

- ``nuitka.tree.Building`` outputs node tree
- ``nuitka.optimization`` enhances it as best as it can
- ``nuitka.finalization`` prepares the tree for code generation
- ``nuitka.codegen.CodeGeneration`` orchestrates the creation of code snippets
- ``nuitka.codegen.*Codes`` knows how specific code kinds are created
- ``nuitka.MainControl`` keeps it all together

This design is intended to last.

Regarding types, the state is:

- Types are always ``PyObject *``, and only a few C types, e.g. ``nuitka_bool``
  and ``nuitka_void`` and more are coming. Even for objects, often it's know
  that things are e.g. really a ``PyTupleObject **``, but no C type is available
  for that yet.

- There are a some specific use of types beyond "compile time constant",
  that are encoded in type and value shapes, which can be used to predict some
  operations, conditions, etc. if they raise, and result types they give.

- In code generation, the supported C types are used, and sometimes we have
  specialized code generation, e.g. a binary operation that takes an ``int``
  and a ``float`` and produces a ``float`` value. There will be fallbacks to
  less specific types.

The expansion with more C types is currently in progress, and there will also
be alternative C types, where e.g. ``PyObject *`` and ``C long`` are in an enum
that indicates which value is valid, and where special code will be available
that can avoid creating the ``PyObject **`` unless the later overflows.


Setting up the Development Environment for Nuitka
=================================================

Currently there are very different kinds of files that we need support for.
This is best addressed with an IDE. We cover here how to setup the most common
one.

Visual Studio Code
------------------

Download Visual Studio Code from here: https://code.visualstudio.com/download

At this time, this is the recommended IDE for Linux and Windows. This is going
to cover the plugins to install. Configuration is part of the ``.vscode`` in
your Nuitka checkout. If you are not familiar with Eclipse, this is Free
Software IDE,designed to be universally extended, and it truly is. There are
plugins available for nearly everything.

The extensions to be installed are part of the Visual Code recommendations in
``.vscode/extensions.json`` and you will be prompted about that and ought to
install these.

Another one we found useful to collaborate:

- Live Share (``ms-vsliveshare.vsliveshare``)

Eclipse / PyCharm
-----------------

Don't use these anymore, we consider Visual Studio Code to be far superior for
delivering a nice out of the box environment.


Commit and Code Hygiene
=======================

In Nuitka we have tools to autoformat code, you can execute them manually, but
it's probably best to execute them at commit time, to make sure when we share
code, it's already well format, and to avoid noise doing cleanups.

The kinds of changes also often cause unnecessary merge conflicts, while the
autoformat is designed to format code also in a way that it avoids merge
conflicts in the normal case, e.g. by doing imports one item per line.

In order to set up hooks, you need to execute these commands:

.. code-block:: sh

   # Where python is the one you use with Nuitka, this then gets all
   # development requirements, can be full PATH.
   python -m pip install -r requirements-devel.txt
   python ./misc/install-git-hooks.py


These commands will make sure that the ``autoformat-nuitka-source`` is run on
every staged file content at the time you do the commit. For C files, it may
complain unavailability of ``clang-format``, follow it's advice. You may call
the above tool at all times, without arguments to format call Nuitka source
code.

Should you encounter problems with applying the changes to the checked out
file, you can always execute it with ``COMMIT_UNCHECKED=1`` environment set.

Coding Rules Python
===================

These rules should generally be adhered when working on Nuitka code. It's not
library code and it's optimized for readability, and avoids all performance
optimization for itself.

Tool to format
--------------

There is a tool ``bin/autoformat-nuitka-source`` which is to apply automatic
formatting to code as much as possible. It uses ``black`` (internally) for
consistent code formatting. The imports are sorted with ``isort`` for proper
order.

The tool (mostly ``black`` and ``isort``) encodes all formatting rules, and
makes the decisions for us. The idea being that we can focus on actual code and
do not have to care as much about other things. It also deals with Windows new
lines, trailing space, etc. and even sorts pylint disable statements.

Identifiers
-----------

Classes are camel case with leading upper case. Functions and methods are with
leading verb in lower case, but also camel case. Variables and arguments are
lower case with ``_`` as a separator.

.. code-block:: python

   class SomeClass:

      def doSomething(some_parameter):
         some_var = ("foo", "bar")

Base classes that are abstract have their name end with ``Base``, so that a
meta class can use that convention, and readers immediately know, that it will
not be instantiated like that.

Function calls use keyword argument preferably. These are slower in CPython,
but more readable:

.. code-block:: python

   getSequenceCreationCode(
        sequence_kind=sequence_kind,
        element_identifiers=identifiers,
        context=context
   )

When the names don't add much value, sequential calls can be done:

.. code-block:: python

   context.setLoopContinueTarget(handler_start_target)

Here, ``setLoopContinueTarget`` will be so well known that the reader is
expected to know the argument names and their meaning, but it would be still
better to add them. But in this instance, the variable name already indicates
that it is.


Module/Package Names
--------------------

Normal modules are named in camel case with leading upper case, because of
their role as singleton classes. The difference between a module and a class is
small enough and in the source code they are also used similarly.

For the packages, no real code is allowed in their ``__init__.py`` and they
must be lower case, like e.g. ``nuitka`` or ``codegen``. This is to distinguish
them from the modules.

Packages shall only be used to group things. In ``nuitka.codegen`` the code
generation packages are located, while the main interface is
``nuitka.codegen.CodeGeneration`` and may then use most of the entries as local
imports.

There is no code in packages themselves. For programs, we use ``__main__``
package to carry the actual code.

Names of modules should be plurals if they contain classes. Example is that a
``Nodes`` module that contains a ``Node`` class.


Prefer list contractions over built-ins
---------------------------------------

This concerns ``map``, ``filter``, and ``apply``. Usage of these built-ins is
highly discouraged within Nuitka source code. Using them is considered worth a
warning by "PyLint" e.g. "Used built-in function 'map'". We should use list
contractions instead, because they are more readable.

List contractions are a generalization for all of them. We love readability and
with Nuitka as a compiler, there won't be any performance difference at all.

There are cases where a list contraction is faster because you can avoid to
make a function call. And there may be cases, where map is faster, if a
function must be called. These calls can be very expensive in CPython, and if
you introduce a function, just for ``map``, then it might be slower.

But of course, Nuitka is the project to free us from what is faster and to
allow us to use what is more readable, so whatever is faster, we don't care. We
make all options equally fast and let people choose.

For Nuitka the choice is list contractions as these are more easily changed and
readable.

Look at this code examples from Python:

.. code-block:: python

   class A:
       def getX(self):
           return 1
       x = property(getX)

   class B(A):
      def getX(self):
         return 2


   A().x == 1 # True
   B().x == 1 # True (!)

This pretty much is what makes properties bad. One would hope ``B().x`` to be
``2``, but instead it's not changed. Because of the way properties take the
functions and not members, and because they then are not part of the class, they
cannot be overloaded without re-declaring them.

Overloading is then not at all obvious anymore. Now imagine having a setter and
only overloading the getter. How to update the property easily?

So, that's not likable about them. And then we are also for clarity in these
internal APIs too. Properties try and hide the fact that code needs to run and
may do things. So let's not use them.

For an external API you may exactly want to hide things, but internally that
has no use, and in Nuitka, every API is internal API. One exception may be the
``hints`` module, which will gladly use such tricks for an easier write syntax.

Coding Rules C
==============

For the static C parts, e.g. compiled types, helper codes, the ``clang-format``
from LLVM project is used, the tool ``autoformat-nuitka-source`` does this for
us.

We always have blocks for conditional statements to avoid typical mistakes made
by adding a statement to a branch, forgetting to make it a block.


The "git flow" model
====================

* The flow is used for releases and occasionally subsequent hot fixes.

  A few feature branches were used so far. It allows for quick delivery of
  fixes to both the stable and the development version, supported by a git
  plug-in, that can be installed via "apt-get install git-flow".

* Stable (master branch)

  The stable version, is expected to pass all the tests at all times and is
  fully supported. As soon as bugs are discovered, they are fixed as hot fixes,
  and then merged to develop by the "git flow" automatically.

* Development (develop branch)

  The future release, supposedly in almost ready for release state at nearly
  all times, but this is as strict. It is not officially supported, and may
  have problems and at times inconsistencies. Normally this branch is supposed
  to not be rebased. For severe problems it may be done though.

* Factory (default feature branch)

  Code under construction. We publish commits there, that may not hold up in
  testing, and before it enters develop branch. Factory may have severe
  regressions frequently, and commits become **rebased all the time**, so do
  not base your patches on it, please prefer the ``develop`` branch for that,
  unless of course, it's about factory code itself.

* Personal branches (jorj, orsiris, others as well)

  Same as factory, but not integrated as factory normally is, and not rebased
  all the time. For some branches, they will be rebased as a service when we
  update develop.

* Feature Branches

  We are not currently using these. They could be used for long lived changes
  that extend for multiple release cycles and are not ready yet. Currently we
  perform all changes in steps that can be included in releases or delay making
  those changes.


Nuitka "git/github" Workflow
============================

  * Forking and cloning

  You need to have git installed and GitHub account. Goto Nuitka repository
  <https://github.com/Nuitka/Nuitka> and fork the repository.

  To clone it to your local machine execute the following your git bash:

    .. code-block:: sh

        git clone https://github.com/your-user-name/Nuitka.git
        cd Nuitka
        git remote add upstream https://github.com/Nuitka/Nuitka.git

  * Create a Branch

    .. code-block:: sh

        git checkout develop
        git pull --rebase upstream
        git checkout -b feature_branch

  If you are having merge conflicts while doing the previous step, then check
  out (DON'T FORGET TO SAVE YOUR CHANGES FIRST IF ANY):
  <https://stackoverflow.com/questions/1125968/how-do-i-force-git-pull-to-overwrite-local-files>

  * In case you have an existing branch rebase it to develop

    .. code-block:: sh

        git fetch upstream
        git rebase upstream/develop

  Fix the merge conflicts if any, stash them and continue:

    .. code-block:: sh

        git rebase --continue

   If anything goes wrong while rebasing:

    .. code-block:: sh

        git rebase --abort

  * Making changes

    .. code-block:: sh

        git commit -a -m "Commit Message"
        git push -u origin # once, later always:
        git push


API Documentation and Guidelines
================================

There is API documentation generated with ``doxygen``, available at `this
location <https://nuitka.net/apidoc>`__ .

To ensure meaningful ``doxygen`` output, the following guidelines must be
observed when creating or updating Python source:

Use of Standard Python ``"__doc__"`` Strings
--------------------------------------------

Every class and every method **must be documented** via the standard Python
delimiters (``""" ... """``) in the usual way.

Special ``doxygen`` Anatomy of ``"__doc__"``
--------------------------------------------

* Immediately after the leading ``"""``, and after 1 space on the same line,
  enter a brief description or title of the class or method. This must be 1
  line and be followed by at least 1 empty line.

* Depending on the item, choose from the following "sections" to describe what
  the item is and does.

  Each section name is coded on its own line, aligned with the leading ``"""``
  and followed by a colon ":". Anything following the section, must start on a
  new line and be indented by 4 spaces relative to the section. Except for the
  first section (``Notes:``) after the title, sections need not be preceded by
  empty lines -- but it is good practice to still do that.

    - ``Notes:`` detailed description of the item, any length.

      May contain line breaks with each new line starting aligned with previous
      one. The text will automatically be joined across line breaks and be
      reformatted in the browser.

      If you describe details for a class, you can do so **without** using this
      section header and all formatting will still work fine.
      If you however omit the ``Notes:`` for methods, then the text will
      be interpreted **as code,** be shown in an ugly monospaced font, and no
      automatic line breaks will occur in the browser.

    - ``Args:`` positional arguments.

      Each argument then follows, starting on a new line and indented by 4
      spaces. The argument name must be followed by a colon ``:`` or double
      hash ``--``, followed by a description of arbitrary length.

      The description can be separated by line breaks.
    - ``Kwargs:`` keyword arguments. Same rules as for args.
    - ``Returns:`` description of what will be returned if applicable (any
      length).
    - ``Yields:`` synonymous for ``Returns:``.
    - ``Raises:`` name any exceptions that may be raised.
    - ``Examples:`` specify any example code.

.. code-block:: python

    def foo(p1, p2, kw1=None, kw2=None):
        """ This is an example method.

        Notes:
            It does one or the other indispensable things based on some parameters
            and proudly returns a dictionary.

        Args:
            p1: parameter one
            p2: parameter two

        Kwargs:
            kw1: keyword one
            kw2: keyword two

        Returns:
            A dictionary calculated from the input.

        Raises:
            ValueError, IndexError

        Examples:
            >>> foo(1, 2, kw1=3, kw2=4)
            {'a': 4, 'b': 6}
        """

Checking the Source
===================

The static checking for errors is currently done with ``PyLint``. In the
future, Nuitka itself will gain the ability to present its findings in a
similar way, but this is not a priority, and we are not there yet.

So, we currently use ``PyLint`` with options defined in a script.

.. code-block:: sh

   ./bin/check-nuitka-with-pylint

The above command is expected to give no warnings. It is also run on our CI and
we will not merge branches that do not pass.

Running the Tests
=================

This section describes how to run Nuitka tests.

Running all Tests
-----------------

The top level access to the tests is as simple as this:

.. code-block:: sh

   ./tests/run-tests

For fine grained control, it has the following options::

  --skip-basic-tests    The basic tests, execute these to check if Nuitka is
                        healthy. Default is True.
  --skip-syntax-tests   The syntax tests, execute these to check if Nuitka
                        handles Syntax errors fine. Default is True.
  --skip-program-tests  The programs tests, execute these to check if Nuitka
                        handles programs, e.g. import recursions, etc. fine.
                        Default is True.
  --skip-package-tests  The packages tests, execute these to check if Nuitka
                        handles packages, e.g. import recursions, etc. fine.
                        Default is True.
  --skip-optimizations-tests
                        The optimization tests, execute these to check if
                        Nuitka does optimize certain constructs fully away.
                        Default is True.
  --skip-standalone-tests
                        The standalone tests, execute these to check if Nuitka
                        standalone mode, e.g. not referring to outside,
                        important 3rd library packages like PyQt fine. Default
                        is True.
  --skip-reflection-test
                        The reflection test compiles Nuitka with Nuitka, and
                        then Nuitka with the compile Nuitka and compares the
                        outputs. Default is True.
  --skip-cpython26-tests
                        The standard CPython2.6 test suite. Execute this for
                        all corner cases to be covered. With Python 2.7 this
                        covers exception behavior quite well. Default is True.
  --skip-cpython27-tests
                        The standard CPython2.7 test suite. Execute this for
                        all corner cases to be covered. With Python 2.6 these
                        are not run. Default is True.
  --skip-cpython32-tests
                        The standard CPython3.2 test suite. Execute this for
                        all corner cases to be covered. With Python 2.6 these
                        are not run. Default is True.
  --skip-cpython33-tests
                        The standard CPython3.3 test suite. Execute this for
                        all corner cases to be covered. With Python 2.x these
                        are not run. Default is True.
  --skip-cpython34-tests
                        The standard CPython3.4 test suite. Execute this for
                        all corner cases to be covered. With Python 2.x these
                        are not run. Default is True.
  --skip-cpython35-tests
                        The standard CPython3.5 test suite. Execute this for
                        all corner cases to be covered. With Python 2.x these
                        are not run. Default is True.
  --skip-cpython36-tests
                        The standard CPython3.6 test suite. Execute this for
                        all corner cases to be covered. With Python 2.x these
                        are not run. Default is True.
  --skip-cpython37-tests
                        The standard CPython3.7 test suite. Execute this for
                        all corner cases to be covered. With Python 2.x these
                        are not run. Default is True.
  --skip-cpython38-tests
                        The standard CPython3.8 test suite. Execute this for
                        all corner cases to be covered. With Python 2.x these
                        are not run. Default is True.
  --skip-cpython39-tests
                        The standard CPython3.8 test suite. Execute this for
                        all corner cases to be covered. With Python 2.x these
                        are not run. Default is True.
  --no-python2.6        Do not use Python 2.6 even if available on the system.
                        Default is False.
  --no-python2.7        Do not use Python 2.7 even if available on the system.
                        Default is False.
  --no-python3.3        Do not use Python 3.3 even if available on the system.
                        Default is False.
  --no-python3.4        Do not use Python 3.4 even if available on the system.
                        Default is False.
  --no-python3.5        Do not use Python 3.5 even if available on the system.
                        Default is False.
  --no-python3.6        Do not use Python 3.6 even if available on the system.
                        Default is False.
  --no-python3.7        Do not use Python 3.7 even if available on the system.
                        Default is False.
  --no-python3.8        Do not use Python 3.8 even if available on the system.
                        Default is False.
  --no-python3.9        Do not use Python 3.9 even if available on the system.
                        Default is False.
  --coverage            Make a coverage analysis, that does not really check.
                        Default is False.


You will only run the CPython test suites, if you have the submodules of the
Nuitka git repository checked out. Otherwise, these will be skipped with a
warning that they are not available.

The policy is generally, that ``./test/run-tests`` running and passing all
the tests on Linux and Windows shall be considered sufficient for a release,
but of course, depending on changes going on, that might have to be expanded.


Basic Tests
-----------

You can run the "basic" tests like this:

.. code-block:: sh

   ./tests/basics/run_all.py search

These tests normally give sufficient coverage to assume that a change is
correct, if these "basic" tests pass. The most important constructs and
built-ins are exercised.

To control the Python version used for testing, you can set the ``PYTHON``
environment variable to e.g. ``python3.5`` (can also be full path), or simply
execute the ``run_all.py`` script directly with the intended version, as it is
portable across all supported Python versions, and defaults testing with the
Python version is run with.

Syntax Tests
------------

Then there are "syntax" tests, i.e. language constructs that need to give a
syntax error.

It sometimes so happens that Nuitka must do this itself, because the
``ast.parse`` doesn't see the problem and raises no ``SyntaxError`` of its
own. These cases are then covered by tests to make sure they work as expected.

Using the ``global`` statement on a function argument is an example of
this. These tests make sure that the errors of Nuitka and CPython are totally
the same for this:

.. code-block:: sh

   ./tests/syntax/run_all.py search

Program Tests
-------------

Then there are small "programs" tests, that e.g. exercise many kinds of import
tricks and are designed to reveal problems with inter-module behavior. These
can be run like this:

.. code-block:: sh

   ./tests/programs/run_all.py search

Generated Tests
---------------

There are tests, which are generated from Jinja2 templates. They aim at e.g.
combining at types with operations, in-place or not, or large constants. These
can be run like this:

.. code-block:: sh

   ./tests/generated/run_all.py search


Compile Nuitka with Nuitka
--------------------------

And there is the "compile itself" or "reflected" test. This test makes Nuitka
compile itself and compare the resulting C++ when running compiled to
non-compiled, which helps to find in-determinism.

The test compiles every module of Nuitka into an extension module and all of
Nuitka into a single binary.

That test case also gives good coverage of the ``import`` mechanisms, because
Nuitka uses a lot of packages and imports between them.

.. code-block:: sh

   ./tests/reflected/compile_itself.py

Internal/Plugin API
===================

The documentation from the source code for both the Python and the C parts are
published as `Nuitka API <https://nuitka.net/apidoc>`__ and argumently in a
relatively bad shape as we started generating those with Doxygen only
relatively late.

.. code-block:: sh

   doxygen ./doc/Doxyfile
   xdg-open html

Improvements have already been implemented for plugins: The plugin base
class defined in ``PluginBase.py`` (which is used as a template for all
plugins) is fully documented in Doxygen now. The
same is true for the recently added standard plugins ``NumpyPlugin.py`` and
``TkinterPlugin.py``. These will be uploaded very soon.

Going forward, this will also happen for the remaining standard plugins.

Please find `here <https://github.com/Nuitka/Nuitka/blob/develop/UserPlugin-Creation.rst>`__
a detailed description of how to write your own plugin.

To learn about plugin option specification consult
`this document <https://github.com/Nuitka/Nuitka/blob/develop/Using-Plugin-Options.rst>`__.


Working with the CPython suites
===============================

The CPython test suites are different branches of the same submodule. When you
update your git checkout, they will frequently become detached. In this case,
simply execute this command:

.. code-block: sh

    git submodule foreach 'git fetch && git checkout $(basename $(pwd)) && \
    git reset --hard origin/$(basename $(pwd))'

When adding a test suite, for a new version, proceed like this:

.. code-block: sh

   # Switch to a new branch.
   git checkout CPython38
   git branch CPython39
   git checkout CPython39

   # Delete all but root commit
   git rebase -i root
   rm -rf test
   cp ~/repos/Nuitka-references/final/Python-3.9.0/Lib/test test
   git add test

   # Update commit message to mention proper Python version.
   git commit --amend

   # Push to github, setting upstream for branch.
   git push -u

   # Cherry pick the removal commits from previous branches.
   git log origin/CPython38 --reverse --oneline | grep ' Removed' | cut -d' ' -f1 | xargs git cherry-pick
   # While being prompted for merge conflicts with the deleted files:
   git status | sed -n 's/deleted by them://p' | xargs git rm --ignore-unmatch x ; git cherry-pick --continue

   # Push to github, this is useful.
   git push

   # Cherry pick the first commit of run_all.py, the copy it from the last state, and amend the commits.
   git log --reverse origin/CPython38 --oneline -- run_all.py | head -1 | cut -d' ' -f1 | xargs git cherry-pick
   git checkout origin/CPython38 -- run_all.py
   git commit --amend run_all.py

   # Same for .gitignore
   git log --reverse origin/CPython38 --oneline -- .gitignore | head -1 | cut -d' ' -f1 | xargs git cherry-pick
   git checkout origin/CPython38 -- .gitignore
   git commit --amend .gitignore

   # Now cherry-pick all commits of test support, these disable network, audio, GUI, random filenames and more
   # and are crucial for determistic outputs and non-reliance on outside stuff.
   git log --reverse origin/CPython38 --oneline -- tests/support/__init__.py | cut -d' ' -f1 | xargs git cherry-pick

   git push


Design Descriptions
===================

These should be a lot more and contain graphics from presentations given. It
will be filled in, but not now.

Nuitka Logo
-----------

The logo was submitted by "dr. Equivalent". It's source is contained in
``doc/Logo`` where 3 variants of the logo in SVG are placed.

* Symbol only (symbol)

  .. image:: doc/images/Nuitka-Logo-Symbol.png

* Text next to symbol (horizontal)

  .. image:: doc/images/Nuitka-Logo-Horizontal.png

* Text beneath symbol (vertical)

  .. image:: doc/images/Nuitka-Logo-Vertical.png

From these logos, PNG images, and "favicons", and are derived.

The exact ImageMagick commands are in ``nuitka/tools/release/Documentation``,
but are not executed each time, the commands are also replicated here:

.. code-block:: sh

   convert -background none doc/Logo/Nuitka-Logo-Symbol.svg doc/images/Nuitka-Logo-Symbol.png
   convert -background none doc/Logo/Nuitka-Logo-Vertical.svg doc/images/Nuitka-Logo-Vertical.png
   convert -background none doc/Logo/Nuitka-Logo-Horizontal.svg doc/images/Nuitka-Logo-Horizontal.png

   optipng -o2 doc/images/Nuitka-Logo-Symbol.png
   optipng -o2 doc/images/Nuitka-Logo-Vertical.png
   optipng -o2 doc/images/Nuitka-Logo-Horizontal.png


Choice of the Target Language
-----------------------------

* Choosing the target language was important decision. factors
  were:

  * The portability of Nuitka is decided here
  * How difficult is it to generate the code?
  * Does the Python C-API have bindings?
  * Is that language known?
  * Does the language aid to find bugs?

The *decision for C11* is ultimately one for portability, general knowledge of
the language and for control over created code, e.g. being able to edit and try
that quickly.

The current status is to use pure C11. All code compiles as C11, and also in
terms of workaround to missing compiler support as C++03. This is mostly
needed, because MSVC does not support C. Naturally we are not using any C++
features, just the allowances of C++ features that made it into C11, which is
e.g. allowing late definitions of variables.


Use of Scons internally
-----------------------

Nuitka does not involve Scons in its user interface at all; Scons is purely
used internally. Nuitka itself, being pure Python, will run without any build
process just fine.

Nuitka simply prepares ``<program>.build`` folders with lots of files and tasks
scons to execute the final build, after which Nuitka again will take control
and do more work as necessary.

.. note::

   When we speak of "standalone" mode, this is handled outside of Scons, and
   after it, creating the ".dist" folder. This is done in ``nuitka.MainControl``
   module.

For interfacing to Scons, there is the module ``nuitka.build.SconsInterface``
that will support calling ``scons`` - potentially from one of two inline copies
(one for before / one for Python 3.5 or later). These are mainly used on
Windows or when using source releases - and passing arguments to it. These
arguments are passed as ``key=value``, and decoded in the scons file of Nuitka.

The scons file is named ``SingleExe.scons`` for lack of better name. It's
really wrong now, but we have yet to find a better name. It once expressed the
intention to be used to create executables, but the same works for modules too,
as in terms of building, and to Scons, things really are the same.

The scons file supports operation in multiple modes for many things, and
modules is just one of them. It runs outside of Nuitka process scope, even with
a different Python version potentially, so all the information must be passed
on the command line.

What follows is the (lengthy) list of arguments that the scons file processes:

* ``source_dir``

  Where is the generated C source code. Scons will just compile everything it
  finds there. No list of files is passed, but instead this directory is being
  scanned.

* ``nuitka_src``

  Where do the include files and static C parts of Nuitka live. These provide
  e.g. the implementation of compiled function, generators, and other helper
  codes, this will point to where ``nuitka.build`` package lives normally.

* ``module_mode``

  Build a module instead of a program.

* ``result_base``

  This is not a full name, merely the basename for the result to be produced,
  but with path included, and the suffix comes from module or executable mode.

* ``debug_mode``

  Enable debug mode, which is a mode, where Nuitka tries to help identify
  errors in itself, and will generate less optimal code. This also asks for
  warnings, and makes the build fail if there are any. Scons will pass
  different compiler options in this case.

* ``python_debug``

  Compile and link against Python debug mode, which does assertions and extra
  checks, to identify errors, mostly related to reference counting. May make
  the build fail, if no debug build library of CPython is available. On Windows
  it is possible to install it for CPython3.5 or higher.

* ``full_compat_mode``

  Full compatibility, even where it's stupid, i.e. do not provide information,
  even if available, in order to assert maximum compatibility. Intended to
  control the level of compatibility to absurd.

* ``experimental_mode``

  Do things that are not yet accepted to be safe.

* ``lto_mode``

  Make use of link time optimization of gcc compiler if available and known
  good with the compiler in question. So far, this was not found to make major
  differences.

* ``win_disable_console``

  Windows subsystem mode: Disable console for windows builds.

* ``unstriped_mode``

  Unstriped mode: Do not remove debug symbols.

* ``clang_mode``

  Clang compiler mode, default on macOS X and FreeBSD, optional on Linux.

* ``mingw_mode``

  MinGW compiler mode, optional and useful on Windows only.

* ``standalone_mode``

  Building a standalone distribution for the binary.

* ``show_scons``

  Show scons mode, output information about Scons operation. This will e.g.
  also output the actual compiler used, output from compilation process, and
  generally debug information relating to be build process.

* ``python_prefix``

  Home of Python to be compiled against, used to locate headers and libraries.

* ``target_arch``

  Target architecture to build. Only meaningful on Windows.

* ``python_version``

  The major version of Python built against.

* ``abiflags``

  The flags needed for the Python ABI chosen. Might be necessary to find the
  folders for Python installations on some systems.

* ``icon_path``

  The icon to use for Windows programs if given.



Locating Modules and Packages
-----------------------------

The search for modules used is driven by ``nuitka.importing.Importing`` module.

* Quoting the ``nuitka.importing.Importing`` documentation:

  Locating modules and package source on disk.

  The actual import of a module would already execute code that changes things.
  Imagine a module that does ``os.system()``, it would be done during
  compilation. People often connect to databases, and these kind of things, at
  import time.

  Therefore CPython exhibits the interfaces in an ``imp`` module in standard
  library, which one can use those to know ahead of time, what file import
  would load. For us unfortunately there is nothing in CPython that is easily
  accessible and gives us this functionality for packages and search paths
  exactly like CPython does, so we implement here a multi step search process
  that is compatible.

  This approach is much safer of course and there is no loss. To determine if
  it's from the standard library, one can abuse the attribute ``__file__`` of
  the ``os`` module like it's done in ``isStandardLibraryPath`` of this module.

  End quoting the ``nuitka.importing.Importing`` documentation.

* Role

  This module serves the recursion into modules and analysis if a module is a
  known one. It will give warnings for modules attempted to be located, but not
  found. These warnings are controlled by a while list inside the module.

The decision making and caching are located in the ``nuitka.tree`` package, in
modules ``nuitka.tree.Recursion`` and ``nuitka.tree.ImportCache``. Each module
is only considered once (then cached), and we need to obey lots of user
choices, e.g. to compile a standard library or not.


Hooking for module ``import`` process
-------------------------------------

Currently, in generated code, for every ``import`` a normal ``__import__()``
built-in call is executed. The
``nuitka/build/static_src/MetaPathBasedLoader.c`` file provides the
implementation of a ``sys.meta_path`` hook.

This meta path based importer allows us to have the Nuitka provided module
imported even when imported by non-compiled code.

.. note::

   Of course, it would make sense to compile time detect which module it is that
   is being imported and then to make it directly. At this time, we don't have
   this inter-module optimization yet, mid-term it should become easy to add.

Supporting ``__class__`` of Python3
-----------------------------------

In Python3 the handling of ``__class__`` and ``super`` is different from
Python2. It used to be a normal variable, and now the following things have
changed.

* The use of the ``super`` variable name triggers the addition of a closure
  variable ``__class__``, as can be witnessed by the following code:

  .. code-block:: python

     class X:
        def f1(self):
           print(locals())

        def f2(self):
           print(locals())
           super # Just using the name, not even calling it.

     x = X()
     x.f1()
     x.f2()

  Output is:

     {'self': <__main__.X object at 0x7f1773762390>}
     {'self': <__main__.X object at 0x7f1773762390>, '__class__': <class '__main__.X'>}


* This value of ``__class__`` is also available in the child functions.

* The parser marks up code objects usage of "super". It doesn't have to be a
  call, it can also be a local variable. If the ``super`` built-in is assigned
  to another name and that is used without arguments, it won't work unless
  ``__class__`` is taken as a closure variable.

* As can be seen in the CPython3 code, the closure value is added after the
  class creation is performed.

* It appears, that only functions locally defined to the class are affected and
  take the closure.

This left Nuitka with the strange problem, of how to emulate that.

The solution is this:

* Under Python3, usage of ``__class__`` as a reference in a child function body
  is mandatory. It remains that way until all variable names have been
  resolved.

* When recognizing calls to ``super`` without arguments, make the arguments
    into variable reference to ``__class__`` and potentially ``self`` (actually
    first argument name).

* After all variables have been known, and no suspicious unresolved calls to
  anything named ``super`` are down, then unused references are optimized away
  by the normal unused closure variable.

* Class dictionary definitions are added.

  These are special direct function calls, ready to propagate also "bases" and
  "metaclass" values, which need to be calculated outside.

  The function bodies used for classes will automatically store ``__class__``
  as a shared local variable, if anything uses it. And if it's not assigned by
  user code, it doesn't show up in the "locals()" used for dictionary creation.

  Existing ``__class__`` local variable values are in fact provided as closure,
  and overridden with the built class , but they should be used for the closure
  giving, before the class is finished.

  So ``__class__`` will be local variable of the class body, until the class is
  built, then it will be the ``__class__`` itself.


Frame Stack
-----------

In Python, every function, class, and module has a frame. It creates created
when the scope is entered, and there is a stack of these at run time, which
becomes visible in tracebacks in case of exceptions.

The choice of Nuitka is to make this an explicit element of the node tree, that
are as such subject to optimization. In cases, where they are not needed, they
may be removed.

Consider the following code.

.. code-block:: python

   def f():
       if someNotRaisingCall():
           return somePotentiallyRaisingCall()
       else:
           return None

In this example, the frame is not needed for all the code, because the
condition checked wouldn't possibly raise at all. The idea is the make the
frame guard explicit and then to reduce its scope whenever possible.

So we start out with code like this one:

.. code-block:: python

   def f():
       with frame_guard("f"):
           if someNotRaisingCall():
               return somePotentiallyRaisingCall()
           else:
               return None

This is to be optimized into:

.. code-block:: python

   def f():
       if someNotRaisingCall():
           with frame_guard("f"):
               return somePotentiallyRaisingCall()
       else:
           return None


Notice how the frame guard taking is limited and may be avoided, or in best
cases, it might be removed completely. Also this will play a role when in-lining
function. The frame stack entry will then be automatically preserved without
extra care.

.. note::

    In the actual code, ``nuitka.nodes.FrameNodes.StatementsFrame`` is represents
    this as a set of statements to be guarded by a frame presence.

Parameter Parsing
-----------------

The parsing of parameters is very convoluted in Python, and doing it in a
compatible way is not that easy. This is a description of the required process,
for an easier overview.

Input
+++++

The input is an argument ``tuple`` (the type is fixed), which contains the
positional arguments, and potentially an argument ``dict`` (type is fixed as
well, but could also be ``NULL``, indicating that there are no keyword
arguments.

Keyword dictionary
++++++++++++++++++

The keyword argument dictionary is checked first. Anything in there, that
cannot be associated, either raise an error, or is added to a potentially given
star dict argument. So there are two major cases.

* No star dict argument: Iterate over dictionary, and assign or raise errors.

  This check covers extra arguments given.

* With star dict argument: Iterate over dictionary, and assign or raise errors.

  Interesting case for optimization are no positional arguments, then no check
  is needed, and the keyword argument dictionary could be used as the star
  argument. Should it change, a copy is needed though.

What's noteworthy here, is that in comparison to the keywords, we can hope that
they are the same value as we use. The interning of strings increases chances
for non-compiled code to do that, esp. for short names.

We then can do a simple ``is`` comparison and only fall back to real string
`==` comparisons, after all of these failed. That means more code, but also a
lot faster code in the positive case.

Argument tuple
++++++++++++++

After this completed, the argument tuple is up for processing. The first thing
it needs to do is to check if it's too many of them, and then to complain.

For arguments in Python2, there is the possibility of them being nested, in
which case they cannot be provided in the keyword dictionary, and merely should
get picked from the argument tuple.

Otherwise, the length of the argument tuple should be checked against its
position and if possible, values should be taken from there. If it's already
set (from the keyword dictionary), raise an error instead.


SSA form for Nuitka
-------------------

The SSA form is critical to how optimization works. The so called trace
collections builds up traces. These are facts about how this works:

   * Assignments draw from a counter unique for the variable, which becomes the
     variable version. This happens during tree building phase.

   * References are associated with the version of the variable active.

     This can be a merge of branches. Trace collection does do that and
     provides nodes with the currently active trace for a variable.

The data structures used for trace collection need to be relatively compact as
the trace information can become easily much more data than the program itself.

Every trace collection has these:

   * variable_actives

     Dictionary, where per "variable" the currently used version is. Used to
     track situations changes in branches. This is the main input for merge
     process.

   * variable_traces

     Dictionary, where "variable" and "version" form the key. The values are
     objects with or without an assignment, and a list of usages, which starts
     out empty.

     These objects have usages appended to them. In "onVariableSet", a new
     version is allocated, which gives a new object for the dictionary, with an
     empty usages list, because each write starts a new version. In
     "onVariableUsage" the version is detected from the current version. It may
     be not set yet, which means, it's a read of an undefined value (local
     variable, not a parameter name), or unknown in case of global variable.

     These objects may be told that their value has escaped. This should
     influence the value friend they attached to the initial assignment. Each
     usage may have a current value friend state that is different.

When merging branches of conditional statements, the merge shall apply as
follows:

  * Branches have their own collection

    Thee have potentially deviating sets of ``variable_actives``. These are
    children of an outer collections.

  * Case a) One branch only.

    For that branch a collection is performed. As usual new assignments
    generate a new version making it "active", references then related to these
    "active" versions.

    Then, when the branch is merged, for all "active" variables, it is
    considered, if that is a change related to before the branch. If it's not
    the same, a merge trace with the branch condition is created with the one
    active in the collection before that statement.

  * Case b) Two branches.

    When there are two branches, they both as are treated as above, except for
    the merge.

    When merging, a difference in active variables between the two branches
    creates the merge trace.

  .. note::

     For conditional expressions, there are always only two branches. Even if
     you think you have more than one branch, you do not. It's always nested
     branches, already when it comes out of the parser.

Trace structure, there are different kinds of traces.

 * Initial write of the version

   There may be an initial write for each version. It can only occur at the
   start of the scope, but not later, and there is only one. This might be
   known to be "initialized" (parameter variables of functions are like that)
   or "uninitialized", or "unknown".

 * Merge of other one or two other versions

   This combines two or more previous versions. In cases of loop exits or
   entries, there are multiple branches to combine potentially. These branches
   can have vastly different properties.

 * Becoming unknown.

   When control flow escapes, e.g. for a module variable, any write can occur
   to it, and it's value cannot be trusted to be unchanged. These are then
   traced as unknown.

All traces have a base class ``ValueTraceBase`` which provides the interface to
query facts about the state of a variable in that trace. It's e.g. of some
interest, if a variable must have a value or must not. This allows to e.g. omit
checks, know what exceptions might raise.

Loop SSA
--------

For loops we have the addition difficulty that we need would need to look ahead
what types a variable has at loop exit, but that is a recursive dependency.

Our solution is to consider the variable types at loop entry. When these
change, we drop all gained information from inside the loop. We may e.g. think
that a variable is a ``int`` or ``float``, but later recognize that it can only
be a float. Derivations from ``int`` must be discarded, and the loop analysis
restarted.

Then during the loop, we assign an incomplete loop trace shape to the variable,
which e.g. says it was an ``int`` initially and additional type shapes, e.g.
``int or long`` are then derived. If at the end of the loop, a type produced no
new types, we know we are finished and mark the trace as a complete loop trace.

If it is not, and next time, we have the same initial types, we add the ones
derived from this to the starting values, and see if this gives more types.



Python Slots in Optimization
----------------------------

Basic Slot Idea
+++++++++++++++

For almost all the operations in Python, a form of overloading is
available. That is what makes it so powerful.

So when you write an expression like this one:

.. code-block:: python

   1.0 + something

This something will not just blindly work when it's a float, but
go through a slot mechanism, which then can be overloaded.

.. code-block:: python

   class SomeStrangeFloat:
      def __float__(self):
         return 3.14

   something = SomeStrangeFloat()
   # ...
   1.0 + float(something) // 4.140000000000001

Here it is the case, that this is used by user code, but more often
this is used internally. Not all types have all slots, e.g. `list`
does not have ``__float__`` and therefore will refuse an addition to
a `float` value, based on that.

Another slot is working here, that we didn't mention yet, and that
is ``__add__`` which for some times will be these kinds of conversions
or it will not do that kind of thing, e.g. something do hard checks,
which is why this fails to work:

.. code-block:: python

   [] + ()

As a deliberate choice, there is no `__list__` slot used. The Python
designers are aiming at solving many things with slots, but they
also accept limitations.

There are many slots that are frequently used, most often behind
your back (``__iter__``, ``__next__``, ``__lt__``, etc.). The list
is large, and tends to grow with Python releases, but it is not
endless.

Representation in Nuitka
++++++++++++++++++++++++

So a slot in Nuitka typically has an owning node. We use ``__len__``
as an example here. In the ``computeExpression`` the ``len`` node
named ``ExpressionBuiltinLen`` has to defer the decision what it
computes to its argument.

.. code-block:: python

    def computeExpression(self, trace_collection):
        return self.subnode_value.computeExpressionLen(
            len_node=self, trace_collection=trace_collection
        )

That decision then, in the absence of any type knowledge, must be
done absolutely carefully and conservative, as could see anything
executing here.

That examples this code in ``ExpressionBase`` which every expression
by default uses:

.. code-block:: python

    def computeExpressionLen(self, len_node, trace_collection):
        shape = self.getValueShape()

        has_len = shape.hasShapeSlotLen()

        if has_len is False:
            return makeRaiseTypeErrorExceptionReplacementFromTemplateAndValue(
                template="object of type '%s' has no len()",
                operation="len",
                original_node=len_node,
                value_node=self,
            )
        elif has_len is True:
            iter_length = self.getIterationLength()

            if iter_length is not None:
                from .ConstantRefNodes import makeConstantRefNode

                result = makeConstantRefNode(
                    constant=int(iter_length),  # make sure to downcast long
                    source_ref=len_node.getSourceReference(),
                )

                result = wrapExpressionWithNodeSideEffects(
                    new_node=result, old_node=self
                )

                return (
                    result,
                    "new_constant",
                    "Predicted 'len' result from value shape.",
                )

        self.onContentEscapes(trace_collection)

        # Any code could be run, note that.
        trace_collection.onControlFlowEscape(self)

        # Any exception may be raised.
        trace_collection.onExceptionRaiseExit(BaseException)

        return len_node, None, None

Notice how by default, known ``__len__`` but unpredictable or even
unknown if a ``__len__`` slot is there, the code indicates that
its contents and the control flow escapes (could change things
behind out back) and any exception could happen.

Other expressions can know better, e.g. for compile time constants
we can be a whole lot more certain:

.. code-block:: python

    def computeExpressionLen(self, len_node, trace_collection):
        return trace_collection.getCompileTimeComputationResult(
            node=len_node,
            computation=lambda: len(self.getCompileTimeConstant()),
            description="""Compile time constant len value pre-computed.""",
        )

In this case, we are using a function that will produce a concrete
value or the exception that the `computation` function raised. In
this case, we can let the Python interpreter that runs Nuitka do
all the hard work. This lives in ``CompileTimeConstantExpressionBase``
and is the base for all kinds of constant values, or even built-in
references like the name ``len`` itself and would be used in case
of doing ``len(len)`` which obviously gives an exception.

Other overloads do not currently exist in Nuitka, but through the
iteration length, most cases could be addressed, e.g. ``list``
nodes typical know their element counts.

The C side
----------

When a slot is not optimized away at compile time however, we need
to generate actual code for it. We figure out what this could be
by looking at the original CPython implementation.

.. code-block:: C

   PyObject *builtin_len(PyObject *self, PyObject *v) {
       Py_ssize_t res;

       res = PyObject_Size(v);
       if (res < 0 && PyErr_Occurred())
           return NULL;
       return PyInt_FromSsize_t(res);
   }

We find a pointer to ``PyObject_Size`` which is a generic Python
C/API function used in the ``builtin_len`` implementation:

.. code-block:: C

   Py_ssize_t PyObject_Size(PyObject *o) {
       PySequenceMethods *m;

       if (o == NULL) {
           null_error();
           return -1;
       }

       m = o->ob_type->tp_as_sequence;
       if (m && m->sq_length)
           return m->sq_length(o);

       return PyMapping_Size(o);
   }

On the C level, every Python object (the ``PyObject *``) as a type
named ``ob_type`` and most of its elements are slots. Sometimes
they form a group, here ``tp_as_sequence`` and then it may or may
not contain a function. This one is tried in preference. Then, if
that fails, next up the mapping size is tried.

.. code-block:: C

   Py_ssize_t PyMapping_Size(PyObject *o) {
       PyMappingMethods *m;

       if (o == NULL) {
           null_error();
           return -1;
       }

       m = o->ob_type->tp_as_mapping;
       if (m && m->mp_length)
           return m->mp_length(o);

       type_error("object of type '%.200s' has no len()", o);
       return -1;
   }

This is the same principle, except with ``tp_as_mapping`` and
``mp_length`` used.

So from this, we can tell how ``len`` gets at what could be a
Python class ``__len__`` or other built-in types.

In principle, every slot needs to be dealt with in Nuitka, and
it is assumed that currently all slots are supported on at least
a very defensive level, to avoid unnoticed escapes of control
flow.

Built-in call optimization
--------------------------

For calls to built-in names, there is typically a function in
Python that delegates to the type constructor (e.g. when we talk
about ``int`` that just creates an object passing the arguments
of the call) or its own special implementation as we saw with the
`len`.

For each built-in called, we have a specialized node, that presents
to optimization the actions of the built-in. What are the impact,
what are the results. We have seen the resulting example for ``len``
above, but how do we get there.

In Python, built-in names are used only if there is no module
level variable of the name, and of course no local variable of
that name.

Therefore, optimization of a built-in name is only done if it
turns out the actually assigned in other code, and then when
the call comes, arguments are checked and a relatively static
node is created.

Code Generation towards C
-------------------------

Currently, Nuitka uses Pure C and no C++ patterns at all. The use of C11
requires on some platforms to compile the C11 using a C++ compiler, which
works relatively well, but also limits the amount of C11 that can be used.

Exceptions
++++++++++

To handle and work with exceptions, every construct that can raise has either a
``bool`` or ``int`` return code or ``PyObject *`` with ``NULL`` return value.
This is very much in line with that the Python C-API does.

Every helper function that contains code that might raise needs these
variables. After a failed call, our variant of ``PyErr_Fetch`` called
``FETCH_ERROR_OCCURRED`` must be used to catch the defined error, unless some
quick exception cases apply. The quick exception means, ``NULL`` return from
C-API without a set exception means e.g. ``StopIteration``.

As an optimization, functions that raise exceptions, but are known not to do
so, for whatever reason, could only be asserted to not do so.

Statement Temporary Variables
+++++++++++++++++++++++++++++

For statements and larger constructs the context object track temporary values,
that represent references. For some, these should be released at the end of the
statement, or they represent a leak.

The larger scope temporary variables, are tracked in the function or module
context, where they are supposed to have explicit ``del`` to release their
references.

Local Variables Storage
+++++++++++++++++++++++

Closure variables taken are to be released when the function object is later
destroyed. For in-lined calls, variables are just passed, and it does not
become an issue to release anything.

For function exit, owned variables, local or shared to other functions, must
be released. This cannot be a ``del`` operation, as it also involves setting
a value, which would be wrong for shared variables (and wasteful to local
variables, as that would be its last usage). Therefore we need a special
operation that simply releases the reference to the cell or object variable.

Exit Targets
++++++++++++

Each error or other exit releases statement temporary values and then executes
a ``goto`` to the exit target. These targets need to be setup. The
``try``/``except`` will e.g. catch error exits.

Other exits are ``continue``, ``break``, and ``return`` exits. They all work
alike.

Generally, the exits stack of with constructs that need to register themselves
for some exit types. A loop e.g. registers the ``continue`` exit, and a
contained ``try``/``finally`` too, so it can execute the final code should it
be needed.

Frames
++++++

Frames are containers for variable declarations and cleanups. As such, frames
provide error exits and success exits, which remove the frame from the frame
stack, and then proceed to the parent exit.

With the use of non ``PyObject **`` C types, but frame exception exits, the
need to convert those types becomes apparent. Exceptions should still resolve
the C version. When using different C types at frame exception exits, there
is a need to trace the active type, so it can be used in the correct form.

Abortive Statements
+++++++++++++++++++

The way ``try``/``finally`` is handled, copies of the ``finally`` block are
made, and optimized independently for each abort method. The ones there are
of course, ``return``, ``continue``, and ``break``, but also implicit and
explicit ``raise`` of an exception.

Code trailing an abortive statement can be discarded, and the control flow
will follow these "exits".

Constant Preparation
--------------------

Early versions of Nuitka, created all constants for the whole program for ready
access to generated code, before the program launches. It did so in a single
file, but that approach didn't scale well.

Problems were

* Even unused code contributed to start-up time, this can become a lot for
  large programs, especially in standalone mode.

* The massive amount of constant creation codes gave backend C compilers a
  much harder time than necessary to analyse it all at once.

The current approach is as follows. Code generation detects constants used in
only one module, and declared ``static`` there, if the module is the only user,
or ``extern`` if it is not. Some values are forced to be global, as they are
used pre-main or in helpers.

These ``extern`` values are globally created before anything is used. The
``static`` values are created when the module is loaded, i.e. something did
import it.

We trace used constants per module, and for nested ones, we also associate
them. The global constants code is special in that it can only use ``static``
for nested values it exclusively uses, and has to export values that others
use.


Language Conversions to make things simpler
-------------------------------------------

There are some cases, where the Python language has things that can in fact be
expressed in a simpler or more general way, and where we choose to do that at
either tree building or optimization time.


The ``assert`` statement
++++++++++++++++++++++++

The ``assert`` statement is a special statement in Python, allowed by the
syntax. It has two forms, with and without a second argument. The later is
probably less known, as is the fact that raise statements can have multiple
arguments too.

The handling in Nuitka is:

.. code-block:: python

   assert value
   # Absolutely the same as:
   if not value:
       raise AssertionError

.. code-block:: python

   assert value, raise_arg
   # Absolutely the same as:
   if not value:
       raise AssertionError, raise_arg

This makes assertions absolutely the same as a raise exception in a conditional
statement.

This transformation is performed at tree building already, so Nuitka never
knows about ``assert`` as an element and standard optimizations apply. If e.g.
the truth value of the assertion can be predicted, the conditional statement
will have the branch statically executed or removed.


The "comparison chain" expressions
++++++++++++++++++++++++++++++++++


In Nuitka we have the concept of an outline, and therefore we can make
the following re-formulation instead:

.. code-block:: python

   a < b() > c < d

   def _comparison_chain(): # So called "outline" function
       tmp_a = a
       tmp_b = b()

       tmp = tmp_a < tmp_b

       if not tmp:
           return tmp

       del tmp_a
       tmp_c = c

       tmp = tmp_b > tmp_c

       if not tmp:
           return tmp

       del tmp_b

       return tmp_c < d

   _comparison_chain()

This transformation is performed at tree building already. The temporary
variables keep the value for the use of the same expression. Only the last
expression needs no temporary variable to keep it.

What we got from this, is making the checks of the comparison chain explicit
and comparisons in Nuitka to be internally always about two operands only.


The ``execfile`` built-in
+++++++++++++++++++++++++

Handling is:

.. code-block:: python

   execfile(filename)
   # Basically the same as:
   exec compile(open(filename).read()), filename, "exec"

.. note::

   This allows optimizations to discover the file opening nature easily and
   apply file embedding or whatever we will have there one day.

This transformation is performed when the ``execfile`` built-in is detected as
such during optimization.


Generator expressions with ``yield``
++++++++++++++++++++++++++++++++++++

These are converted at tree building time into a generator function body that
yields from the iterator given, which is the put into a for loop to iterate,
created a lambda function of and then called with the first iterator.

That eliminates the generator expression for this case. It's a bizarre
construct and with this trick needs no special code generation.

This is a complex example, demonstrating multiple cases of yield in unexpected
cases:

.. code-block:: python

   x = ((yield i) for i in (1,2) if not (yield))
   # Basically the same as:
   def x():
       for i in (1,2):
           if not (yield):
               yield(yield i)

Function Decorators
+++++++++++++++++++

When one learns about decorators, you see that:

.. code-block:: python

   @decorator
   def function():
      pass
   # Is basically the same as:
   def function():
      pass
   function = decorator(function)

The only difference is the assignment to function. In the ``@decorator`` case,
if the decorator fails with an exception, the name ``function`` is not assigned
yet, but kept in a temporary variable.

Therefore in Nuitka this assignment is more similar to that of a lambda
expression, where the assignment to the name is only at the end, which also
has the extra benefit of not treating real function and lambda functions any
different.

This removes the need for optimization and code generation to support
decorators at all. And it should make the two variants optimize equally well.


Functions nested arguments
++++++++++++++++++++++++++

Nested arguments are a Python2 only feature supported by Nuitka. Consider this
example:

.. code-block:: python

   def function(a,(b,c)):
      return a, b, c

We solve this, by kind of wrapping the function with another function that does
the unpacking and gives the errors that come from this:

.. code-block:: python

   def function(a, _1):
      def _tmp(a, b, c):
         return a, b, c

      a, b = _1
      return _tmp(a, b, c)

The ``".1"`` is the variable name used by CPython internally, and actually
works if you use keyword arguments via star dictionary. So this is very
compatible and actually the right kind of re-formulation, but it removes the
need from the code that does parameter parsing to deal with these.

Obviously, there is no frame for ``_tmp``, just one for ``function`` and we do
not use local variables, but temporary functions.

In-place Assignments
++++++++++++++++++++

In-place assignments are re-formulated to an expression using temporary
variables.

These are not as much a reformulation of ``+=`` to ``+``, but instead one which
makes it explicit that the assign target may change its value.

.. code-block:: python

   a += b

.. code-block:: python

   _tmp = a.__iadd__(b)

   if a is not _tmp:
       a = _tmp

Using ``__iadd__`` here to express that for the ``+``, the in-place variant
``iadd`` is used instead. The ``is`` check may be optimized away depending on
type and value knowledge later on.


Complex Assignments
+++++++++++++++++++

Complex assignments are defined as those with multiple targets to assign from a
single source and are re-formulated to such using a temporary variable and
multiple simple assignments instead.

.. code-block:: python

   a = b = c

.. code-block:: python

   _tmp = c
   a = _tmp
   b = _tmp
   del _tmp


This is possible, because in Python, if one assignment fails, it can just be
interrupted, so in fact, they are sequential, and all that is required is to not
calculate ``c`` twice, which the temporary variable takes care of. Were ``b``
a more complex expression, e.g. ``b.some_attribute`` that might raise an
exception, ``a`` would still be assigned.


Unpacking Assignments
+++++++++++++++++++++

Unpacking assignments are re-formulated to use temporary variables as well.

.. code-block:: python

   a, b.attr, c[ind] = d = e, f, g = h()

Becomes this:

.. code-block:: python

   _tmp = h()

   _iter1 = iter(_tmp)
   _tmp1 = unpack(_iter1, 3)
   _tmp2 = unpack(_iter1, 3)
   _tmp3 = unpack(_iter1, 3)
   unpack_check(_iter1)
   a = _tmp1
   b.attr = _tmp2
   c[ind] = _tmp3
   d = _tmp
   _iter2 = iter(_tmp)
   _tmp4 = unpack(_iter2, 3)
   _tmp5 = unpack(_iter2, 3)
   _tmp6 = unpack(_iter2, 3)
   unpack_check(_iter1)
   e = _tmp4
   f = _tmp5
   g = _tmp6

That way, the unpacking is decomposed into multiple simple statements. It will
be the job of optimizations to try and remove unnecessary unpacking, in case
e.g. the source is a known tuple or list creation.

.. note::

   The ``unpack`` is a special node which is a form of ``next`` that will raise
   a ``ValueError`` when it cannot get the next value, rather than a
   ``StopIteration``. The message text contains the number of values to unpack,
   therefore the integer argument.

.. note::

   The ``unpack_check`` is a special node that raises a ``ValueError`` exception
   if the iterator is not finished, i.e. there are more values to unpack. Again
   the number of values to unpack is provided to construct the error message.


With Statements
+++++++++++++++

The ``with`` statements are re-formulated to use temporary variables as
well. The taking and calling of ``__enter__`` and ``__exit__`` with arguments,
is presented with standard operations instead. The promise to call ``__exit__``
is fulfilled by ``try``/``except`` clause instead.

.. code-block:: python

    with some_context as x:
        something(x)

.. code-block:: python

    tmp_source = some_context

    # Actually it needs to be "special look-up" for Python2.7, so attribute
    # look-up won't be exactly what is there.
    tmp_exit = tmp_source.__exit__

    # This one must be held for the whole with statement, it may be assigned
    # or not, in our example it is. If an exception occurs when calling
    # ``__enter__``, the ``__exit__`` should not be called.
    tmp_enter_result = tmp_source.__enter__()

    # Indicator variable to know if "tmp_exit" has been called.
    tmp_indicator = False

    try:
        # Now the assignment is to be done, if there is any name for the
        # manager given, this may become multiple assignment statements and
        # even unpacking ones.
        x = tmp_enter_result

        # Then the code of the "with" block.
        something(x)
    except Exception:
        # Note: This part of the code must not set line numbers, which we
        # indicate with special source code references, which we call "internal".
        # Otherwise the line of the frame would get corrupted.

        tmp_indicator = True

        if not tmp_exit(*sys.exc_info()):
            raise
    finally:
         if not tmp_indicator:
            # Call the exit if no exception occurred with all arguments
            # as "None".
            tmp_exit(None, None, None)

.. note::

   We don't refer really to ``sys.exc_info()`` at all, instead, we have
   fast references to the current exception type, value and trace, taken
   directly from the caught exception object on the C level.

   If we had the ability to optimize ``sys.exc_info()`` to do that, we could use
   the same transformation, but right now we don't have it.


For Loops
+++++++++

The ``for`` loops use normal assignments and handle the iterator that is
implicit in the code explicitly.

.. code-block:: python

    for x, y in iterable:
        if something(x):
            break
    else:
        otherwise()

This is roughly equivalent to the following code:

.. code-block:: python

    _iter = iter(iterable)
    _no_break_indicator = False

    while 1:
         try:
            _tmp_value = next(_iter)
         except StopIteration:
            # Set the indicator that the else branch may be executed.
            _no_break_indicator = True

            # Optimization should be able to tell that the else branch is run
            # only once.
            break

         # Normal assignment re-formulation applies to this assignment of course.
         x, y = _tmp_value
         del _tmp_value

         if something(x):
             break

    if _no_break_indicator:
        otherwise()

.. note::

   The ``_iter`` temporary variable is of course also in a ``try/finally``
   construct, to make sure it releases after its used. The ``x, y`` assignment
   is of course subject to unpacking re-formulation.

   The ``try``/``except`` is detected to allow to use a variant of ``next`` that
   does not raise an exception, but to be fast check about the ``NULL`` return
   from ``next`` built-in. So no actual exception handling is happening in this
   case.


While Loops
+++++++++++

Quoting the ``nuitka.tree.ReformulationWhileLoopStatements`` documentation:

Reformulation of while loop statements.

Loops in Nuitka have no condition attached anymore, so while loops are
re-formulated like this:

.. code-block:: python

    while condition:
        something()

.. code-block:: python

    while 1:
        if not condition:
            break

        something()

This is to totally remove the specialization of loops, with the condition moved
to the loop body in an initial conditional statement, which contains a ``break``
statement.

That achieves, that only ``break`` statements exit the loop, and allow for
optimization to remove always true loop conditions, without concerning code
generation about it, and to detect such a situation, consider e.g. endless
loops.

.. note::

   Loop analysis (not yet done) can then work on a reduced problem (which
   ``break`` statements are executed under what conditions) and is then
   automatically very general.

   The fact that the loop body may not be entered at all, is still optimized,
   but also in the general sense. Explicit breaks at the loop start and loop
   conditions are the same.

End quoting the ``nuitka.tree.ReformulationWhileLoopStatements`` documentation:


Exception Handlers
++++++++++++++++++

Exception handlers in Python may assign the caught exception value to a
variable in the handler definition. And the different handlers are represented
as conditional checks on the result of comparison operations.

.. code-block:: python


   try:
       block()
   except A as e:
       handlerA(e)
   except B as e:
       handlerB(e)
   else:
      handlerElse()

.. code-block:: python

    try:
       block()
    except:
       # These are special nodes that access the exception, and don't really
       # use the "sys" module.
       tmp_exc_type = sys.exc_info()[0]
       tmp_exc_value = sys.exc_info()[1]

       # exception_matches is a comparison operation, also a special node.
       if exception_matches(tmp_exc_type, (A,)):
          e = tmp_exc_value
          handlerA(e)
       elif exception_matches(tmp_exc_type, (B,)):
          e = tmp_exc_value
          handlerB(e)
       else:
          handlerElse()

For Python3, the assigned ``e`` variables get deleted at the end of the handler
block. Should that value be already deleted, that ``del`` does not raise,
therefore it's tolerant. This has to be done in any case, so for Python3 it is
even more complex.

.. code-block:: python

    try:
        block()
    except:
        # These are special nodes that access the exception, and don't really
        # use the "sys" module.
        tmp_exc_type = sys.exc_info()[0]
        tmp_exc_value = sys.exc_info()[1]

        # exception_matches is a comparison operation, also a special node.
        if exception_matches(tmp_exc_type, (A,)):
            try:
                e = tmp_exc_value
                handlerA(e)
            finally:
                del e
        elif exception_matches(tmp_exc_type, (B,)):
            try:
                e = tmp_exc_value
                handlerB(e)
            finally:
                del e
        else:
            handlerElse()

Should there be no ``else:`` branch, a default re-raise statement is used
instead.

And of course, the values of the current exception type and value, both use
special references, that access the C++ and don't go via ``sys.exc_info`` at
all, nodes called ``CaughtExceptionTypeRef`` and ``CaughtExceptionValueRef``.

This means, that the different handlers and their catching run time behavior
are all explicit and reduced the branches.


Statement ``try``/``except`` with ``else``
++++++++++++++++++++++++++++++++++++++++++

Much like ``else`` branches of loops, an indicator variable is used to indicate
the entry into any of the exception handlers.

Therefore, the ``else`` becomes a real conditional statement in the node tree,
checking the indicator variable and guarding the execution of the ``else``
branch.


Class Creation (Python2)
++++++++++++++++++++++++

Classes in Python2 have a body that only serves to build the class dictionary
and is a normal function otherwise. This is expressed with the following
re-formulation:

.. code-block:: python

   # in module "SomeModule"
   # ...

   class SomeClass(SomeBase, AnotherBase):
       """ This is the class documentation. """

       some_member = 3

.. code-block:: python

   def _makeSomeClass():
       # The module name becomes a normal local variable too.
       __module__ = "SomeModule"

       # The doc string becomes a normal local variable.
       __doc__ = """ This is the class documentation. """

       some_member = 3

       return locals()

       # force locals to be a writable dictionary, will be optimized away, but
       # that property will stick. This is only to express, that locals(), where
       # used will be writable to.
       exec ""

   SomeClass = make_class("SomeClass", (SomeBase, AnotherBase), _makeSomeClass())

That is roughly the same, except that ``_makeSomeClass`` is *not* visible to
its child functions when it comes to closure taking, which we cannot express in
Python language at all.

Therefore, class bodies are just special function bodies that create a
dictionary for use in class creation. They don't really appear after the tree
building stage anymore. The type inference will of course have to become able to
understand ``make_class`` quite well, so it can recognize the created class
again.


Class Creation (Python3)
++++++++++++++++++++++++

In Python3, classes are a complicated way to write a function call, that can
interact with its body. The body starts with a dictionary provided by the
metaclass, so that is different, because it can ``__prepare__`` a non-empty
locals for it, which is hidden away in "prepare_class_dict" below.

What's noteworthy, is that this dictionary, could e.g. be an ``OrderDict``. I
am not sure, what ``__prepare__`` is allowed to return.

.. code-block:: python3

   # in module "SomeModule"
   # ...

   class SomeClass(SomeBase, AnotherBase, metaclass = SomeMetaClass):
       """ This is the class documentation. """

       some_member = 3

.. code-block:: python

   # Non-keyword arguments, need to be evaluated first.
   tmp_bases = (SomeBase, AnotherBase)

   # Keyword arguments go next, __metaclass__ is just one of them. In principle
   # we need to forward the others as well, but this is ignored for the sake of
   # brevity.
   tmp_metaclass = select_metaclass(tmp_bases, SomeMetaClass)

   tmp_prepared = tmp_metaclass.__prepare__("SomeClass", tmp_bases)

   # The function that creates the class dictionary. Receives temporary variables
   # to work with.
   def _makeSomeClass():
       # This has effect, currently I don't know how to express that in Python3
       # syntax, but we will have a node that does that.
       locals().replace(tmp_prepared)

       # The module name becomes a normal local variable too.
       __module__ = "SomeModule"

       # The doc string becomes a normal local variable.
       __doc__ = """ This is the class documentation. """

       some_member = 3

       # Create the class, share the potential closure variable "__class__"
       # with others.
       __class__ = tmp_metaclass("SomeClass", tmp_bases, locals())

       return __class__

   # Build and assign the class.
   SomeClass = _makeSomeClass()


Generator Expressions
+++++++++++++++++++++

There are re-formulated as functions.

Generally they are turned into calls of function bodies with (potentially
nested) for loops:

.. code-block:: python

    gen = (x*2 for x in range(8) if cond())

.. code-block:: python

    def _gen_helper(__iterator):
       for x in __iterator:
          if cond():
              yield x*2

    gen = _gen_helper(range(8))


List Contractions
+++++++++++++++++

The list contractions of Python2 are different from those of Python3, in that
they don't actually do any closure variable taking, and that no function object
ever exists.

.. code-block:: python

   list_value = [x*2 for x in range(8) if cond()]

.. code-block:: python

    def _listcontr_helper(__iterator):
       result = []

       for x in __iterator:
          if cond():
              result.append(x*2)

       return result

    list_value = _listcontr_helper(range(8))

The difference is that with Python3, the function "_listcontr_helper" is really
there and named ``<listcontraction>`` (or ``<listcomp>`` as of Python3.7 or
higher), whereas with Python2 the function is only an outline, so it can readily
access the containing name space.


Set Contractions
++++++++++++++++

The set contractions of Python2.7 are like list contractions in Python3, in
that they produce an actual helper function:

.. code-block:: python

   set_value = {x*2 for x in range(8) if cond()}

.. code-block:: python

    def _setcontr_helper(__iterator):
       result = set()

       for x in __iterator:
          if cond():
              result.add(x*2)

       return result

    set_value = _setcontr_helper(range(8))


Dictionary Contractions
+++++++++++++++++++++++

The dictionary contractions of are like list contractions in Python3, in that
they produce an actual helper function:

.. code-block:: python

   dict_value = {x: x*2 for x in range(8) if cond()}

.. code-block:: python

    def _dictcontr_helper(__iterator):
       result = {}

       for x in __iterator:
          if cond():
              result[x] = x*2

       return result

    set_value = _dictcontr_helper(range(8))


Boolean expressions ``and`` and ``or``
++++++++++++++++++++++++++++++++++++++

The short circuit operators ``or`` and ``and`` tend to be only less general
that the ``if``/``else`` expressions, but have dedicated nodes. We used to have
a re-formulation towards those, but we now do these via dedicated nodes too.

These new nodes, present the evaluation of the left value, checking for its
truth value, and depending on it, to pick it, or use the right value.


Simple Calls
++++++++++++

As seen below, even complex calls are simple calls. In simple calls of Python
there is still some hidden semantic going on, that we expose.

.. code-block:: python

   func(arg1, arg2, named1 = arg3, named2 = arg4)

On the C-API level there is a tuple and dictionary built. This one is exposed:

.. code-block:: python

   func(*(arg1, arg2), **{"named1" : arg3, "named2" : arg4})

A called function will access this tuple and the dictionary to parse the
arguments, once that is also re-formulated (argument parsing), it can then lead
to simple in-lining. This way calls only have 2 arguments with constant
semantics, that fits perfectly with the C-API where it is the same, so it is
actually easier for code generation.

Although the above looks like a complex call, it actually is not. No checks are
needed for the types of the star arguments and it's directly translated to
``PyObject_Call``.


Complex Calls
+++++++++++++

The call operator in Python allows to provide arguments in 4 forms.

* Positional (or normal) arguments
* Named (or keyword) arguments
* Star list arguments
* Star dictionary arguments

The evaluation order is precisely that. An example would be:

.. code-block:: python

   something(pos1, pos2, name1 = named1, name2 = named2, *star_list, **star_dict)

The task here is that first all the arguments are evaluated, left to right, and
then they are merged into only two, that is positional and named arguments
only. for this, the star list argument and the star dictionary arguments, are
merged with the positional and named arguments.

What's peculiar, is that if both the star list and dictionary arguments are
present, the merging is first done for star dictionary, and only after that for
the star list argument. This makes a difference, because in case of an error,
the star argument raises first.

.. code-block:: python

   something(*1, **2)

This raises "TypeError: something() argument after ** must be a mapping, not
int" as opposed to a possibly more expected "TypeError: something() argument
after * must be a sequence, not int."

That doesn't matter much though, because the value is to be evaluated first
anyway, and the check is only performed afterwards. If the star list argument
calculation gives an error, this one is raised before checking the star
dictionary argument.

So, what we do, is we convert complex calls by the way of special functions,
which handle the dirty work for us. The optimization is then tasked to do the
difficult stuff. Our example becomes this:

.. code-block:: python

   def _complex_call(called, pos, kw, star_list_arg, star_dict_arg):
       # Raises errors in case of duplicate arguments or tmp_star_dict not
       # being a mapping.
       tmp_merged_dict = merge_star_dict_arguments(called, tmp_named, mapping_check(called, tmp_star_dict))

       # Raises an error if tmp_star_list is not a sequence.
       tmp_pos_merged = merge_pos_arguments(called, tmp_pos, tmp_star_list)

       # On the C-API level, this is what it looks like.
       return called(*tmp_pos_merged, **tmp_merged_dict)

   returned = _complex_call(
       called        = something,
       pos           = (pos1, pos2),
       named         = {
           "name1" : named1,
           "name2" : named2
       },
       star_list_arg = star_list,
       star_dict_arg = star_dict
   )


The call to ``_complex_call`` is be a direct function call with no parameter
parsing overhead. And the call in its end, is a special call operation, which
relates to the "PyObject_Call" C-API.


Print statements
++++++++++++++++

The ``print`` statement exists only in Python2. It implicitly converts its
arguments to strings before printing them. In order to make this accessible and
compile time optimized, this is made visible in the node tree.

.. code-block:: python

    print arg1, "1", 1

This is in Nuitka converted so that the code generation for ``print`` doesn't
do any conversions itself anymore and relies on the string nature of its input.

.. code-block:: python

    print str(arg1), "1", str(1)

Only string objects are spared from the ``str`` built-in wrapper, because that
would only cause noise in optimization stage. Later optization can then find it
unnecessary for certain arguments.

Additionally, each ``print`` may have a target, and multiple arguments, which
we break down as well for dumber code generation. The target is evaluated first
and should be a file, kept referenced throughout the whole print statement.

.. code-block:: python

    print >>target_file, str(arg1), "1", str(1)

This is being reformulated to:

    try:
        tmp_target = target_file

        print >>tmp_target, str(arg1),
        print >>tmp_target, "1",
        print >>tmp_target, str(1),
        print >>tmp_target
    finally:
        del tmp_target

This allows code generation to not deal with arbitrary amount of arguments to
``print``. It also separates the newline indicator from the rest of things,
which makes sense too, having it as a special node, as it's behavior with
regards to soft-space is different of course.

And finally, for ``print`` without a target, we still assume that a target was
given, which would be ``sys.stdout`` in a rather hard-coded way (no variable
look-ups involved).

Reformulations during Optimization
----------------------------------

Builtin ``zip`` for Python2
+++++++++++++++++++++++++++

.. code-block:: python

    def _zip(a, b, c): # Potentially more arguments.
       # First assign, to preserve order of execution,
       # the arguments might be complex expressions.
       tmp_arg1 = a
       tmp_arg2 = b
       tmp_arg3 = c
       # more arguments here ...

       tmp_iter_1 = iter(tmp_arg1)
       tmp_iter_2 = iter(tmp_arg2)
       tmp_iter_3 = iter(tmp_arg3)
       # more arguments here ...

       # could be more
       tmp_result = []
       try:
           while 1:
               tmp_result.append(
                   (
                        next(tmp_iter_1),
                        next(tmp_iter_2),
                        next(tmp_iter_3),
                        # more arguments here ...
                   )
                )
       except StopIteration:
           pass

       return tmp_result

Builtin ``map`` for Python2
+++++++++++++++++++++++++++

.. code-block:: python

    def _map():
        # TODO: Not done yet.
        pass


Builtin ``min``
+++++++++++++++

.. code-block:: python

    # TODO: keyfunc (Python2/3), defaults (Python3)
    def _min(a, b, c): # Potentially more arguments.
        tmp_arg1 = a
        tmp_arg2 = b
        tmp_arg3 = c
        # more arguments here ...

        result = tmp_arg1
        if keyfunc is None: # can be decided during re-formulation
            tmp_key_result = keyfunc(result)
            tmp_key_candidate = keyfunc(tmp_arg2)
            if tmp_key_candidate < tmp_key_result:
                result = tmp_arg2
                tmp_key_result = tmp_key_candidate
            tmp_key_candidate = keyfunc(tmp_arg3)
            if tmp_key_candidate < tmp_key_result:
                result = tmp_arg3
                tmp_key_result = tmp_key_candidate
            # more arguments here ...
        else:
            if tmp_arg2 < result:
                result = tmp_arg2
            if tmp_arg3 < result:
                result = tmp_arg3
            # more arguments here ...

        return result


Builtin ``max``
+++++++++++++++

See ``min`` just with ``>`` instead of ``<``.

Call to ``dir`` without arguments
+++++++++++++++++++++++++++++++++

This expression is reformulated to ``locals().keys()`` for Python2, and
``list(locals.keys())`` for Python3.

Calls to functions with known signatures
++++++++++++++++++++++++++++++++++++++++

As a necessary step for inlining function calls, we need to change
calls to variable references to function references.

.. code-block:: python


    def f(arg1, arg2):
        return some_op(arg1, arg2)

    # ... other code

    x = f(a, b+c)

In the optimization it is turned into

.. code-block:: python


    # ... other code

    x = lambda arg1, arg2 : some_op(arg1, arg2)(a, b+c)

.. note::

    The `lambda` stands here for a reference to the function, rather than a
    variable reference, this is the normal forward propagation of values, and
    does not imply duplicating or moving any code at all.

At this point, we still have not resolved the actual call arguments to the
variable names, still a Python level function is created, and called, and
arguments are parsed to a tuple, and from a tuple. For simplicity sake, we have
left out keyword arguments out of the equation for now, but they are even more
costly.

So now, what we want to do, is to re-formulate the call into what we call an
outline body, which is a inline function, and that does the parameter parsing
already and contains the function code too. In this inlining, there still is a
function, but it's technically not a Python function anymore, just something
that is an expression whose value is determined by control flow and the
function call.

.. code-block:: python

    # ... other code

    def _f():
        tmp_arg1 = arg1
        tmp_arg2 = b+c
        return tmp_arg1+tmp_arg2
    x = _f()

With this, a function is considered inlined, because it becomes part of the
abstract execution, and the actual code is duplicated.

The point is, that matching the signature of the function to the actual
arguments given, is pretty straight forward in many cases, but there are two
forms of complications that can happen. One is default values, because they
need to assigned or not, and the other is keyword arguments, because they allow
to reorder arguments.

Lets consider an example with default values first.

.. code-block:: python


    def f(arg1, arg2=some_default()):
        return some_op(arg1, arg2)

    # ... other code

    x = f(a, b+c)


Since the point, at which defaults are taken, we must execute them at that
point and make them available.

.. code-block:: python

    tmp_defaults = (some_default,) # that was f.__defaults__

    # ... other code

    def _f():
        tmp_arg1 = arg1
        tmp_arg2 = tmp_defaults[0]
        return tmp_arg1+tmp_arg2
    x = _f()

Now, one where keyword arguments are ordered the other way.

.. code-block:: python


    def f(arg1, arg2):
        return some_op(arg1, arg2)

    # ... other code

    x = f(arg2=b+c, arg1=a) # "b+c" is evaluated before "a"

The solution is an extra level of temporary variables. We remember the argument
order by names and then assign parameters from it:

.. code-block:: python

    # ... other code

    def _f():
        tmp_given_value1 = b+c
        tmp_given_value2 = a
        tmp_arg1 = tmp_given_value2
        tmp_arg2 = tmp_given_value1
        return tmp_arg1+tmp_arg2
    x = _f()

Obviously, optimization of Nuitka can decide, that e.g. should ``a`` or ``b+c``
not have side effects, to optimize these with standard variable tracing away.

Nodes that serve special purposes
---------------------------------

Try statements
++++++++++++++

In Python, there is ``try``/``except`` and ``try``/``finally``. In Nuitka there
is only a ``try``, which then has blocks to handle exceptions, ``continue``, or
``break``, or ``return``. There is no ``else`` to this node type.

This is more low level and universal. Code for the different handlers can be
different. User provided ``finally`` blocks become copied into the different
handlers.

Releases
++++++++

When a function exits, the local variables are to be released. The same applies
to temporary variables used in re-formulations. These releases cause a
reference to the object to the released, but no value change. They are
typically the last use of the object in the function.

The are similar to ``del``, but make no value change. For shared variables this
effect is most visible.

Side Effects
++++++++++++

When an exception is bound to occur, and this can be determined at compile
time, Nuitka will not generate the code the leads to the exception, but
directly just raise it. But not in all cases, this is the full thing.

Consider this code:

.. code-block:: python

   f(a(), 1 / 0)

The second argument will create a ``ZeroDivisionError`` exception, but before
that ``a()`` must be executed, but the call to ``f`` will never happen and no
code is needed for that, but the name look-up must still succeed. This then
leads to code that is internally like this:

.. code-block:: python

   f(a(), raise_ZeroDivisionError())

which is then modeled as:

.. code-block:: python

   side_effect(a(), f, raise_ZeroDivisionError())

where we can consider ``side_effect`` to be a function that returns the last
expression. Of course, if this is not part of another expression, but close to
statement level, side effects, can be converted to multiple statements simply.

Another use case, is that the value of an expression can be predicted, but that
the language still requires things to happen, consider this:

.. code-block:: python

   a = len(
      (
         f(),
         g()
      )
   )

We can tell that ``a`` will be 2, but the call to ``f`` and ``g`` must still be
performed, so it becomes:

.. code-block:: python

   a = side_effects(f(), g(), 2)

Modelling side effects explicitly has the advantage of recognizing them easily
and allowing to drop the call to the tuple building and checking its length,
only to release it.


Caught Exception Type/Value References
++++++++++++++++++++++++++++++++++++++

When catching an exception, these are not directly put to ``sys.exc_info()``,
but remain as mere C variables. From there, they can be accessed with these
nodes, or if published then from the thread state.


Hard Module Imports
+++++++++++++++++++

These are module look-ups that don't depend on any local variable for the
module to be looked up, but with hard-coded names. These may be the result of
optimization gaining such level of certainty.

Currently they are used to represent ``sys.stdout`` usage for ``print``
statements, but other usages will follow.


Locals Dict Update Statement
++++++++++++++++++++++++++++

For the ``exec`` re-formulation, we apply an explicit sync back to locals as
an explicit node. It helps us to tell the affected local variable traces that
they might be affected. It represents the bit of ``exec`` in Python2, that
treats ``None`` as the locals argument as an indication to copy back.


Plan to add "ctypes" support
============================

Add interfacing to C code, so Nuitka can turn a ``ctypes`` binding into an
efficient binding as if it were written manually with Python C-API or better.

Goals/Allowances to the task
----------------------------

1. Goal: Must not directly use any pre-existing C/C++ language file headers,
   only generate declarations in generated C code ourselves. We would rather
   write or use tools that turn an existing a C header to some ``ctypes``
   declarations if it needs to be, but not mix and use declarations from
   existing header code.

   ..note::

      The "cffi" interface maybe won't have the issue, but it's not something
      we need to write or test the code for.

2. Allowance: May use ``ctypes`` module at compile time to ask things about
   ``ctypes`` and its types.

3. Goal: Should make use of ``ctypes``, to e.g. not hard code in Nuitka what
   ``ctypes.c_int()`` gives on the current platform, unless there is a specific
   benefit.

4. Allowance: Not all ``ctypes`` usages must be supported immediately.

5. Goal: Try and be as general as possible.

   For the compiler, ``ctypes`` support should be hidden behind a generic
   interface of some sort. Supporting ``math`` module should be the same thing.

Type Inference - The Discussion
-------------------------------

Main initial goal is to forward value knowledge. When you have ``a = b``, that
means that a and b now "alias". And if you know the value of ``b`` you can
assume to know the value of ``a``. This is called "aliasing".

When assigning ``a`` to something new, that won't change ``b`` at all. But when
an attribute is set, a method called of it, that might impact the actual value,
referenced by both. We need to understand mutable vs. immutable though, as some
things are not affectable by aliasing in any way.

.. code-block:: python

   a = 3
   b = a

   b += 4 # a is not changed

   a = [3]
   b = a

   b += [4] # a is changed indeed

If we cannot tell, we must assume that ``a`` might be changed. It's either
``b`` or what ``a`` was before. If the type is not mutable, we can assume the
aliasing to be broken up, and if it is, we can assume both to be the same value
still.

When that value is a compile time constant, we will want to push it forward,
and we do that with "(Constant) Value Propagation", which is implemented
already. We avoid too large constants, and we properly trace value assignments,
but not yet aliases.

In order to fully benefit from type knowledge, the new type system must be able
to be fully friends with existing built-in types, but for classes to also work
with it, it should not be tied to them.  The behavior of a type ``long``,
``str``, etc. ought to be implemented as far as possible with the built-in
``long``, ``str`` at compiled time as well.

.. note::

   This "use the real thing" concept extends beyond builtin types, e.g.
   ``ctypes.c_int()`` should also be used, but we must be aware of platform
   dependencies. The maximum size of ``ctypes.c_int`` values would be an example
   of that. Of course that may not be possible for everything.

   This approach has well proven itself with built-in functions already, where
   we use real built-ins where possible to make computations. We have the
   problem though that built-ins may have problems to execute everything with
   reasonable compile time cost.

Another example, consider the following code:

.. code-block:: python

   len("a" * 1000000000000)

To predict this code, calculating it at compile time using constant operations,
while feasible, puts an unacceptable burden on the compilation.

Esp. we wouldn't want to produce such a huge constant and stream it, the C++
code would become too huge. So, we need to stop the ``*`` operator from being
used at compile time and cope with reduced knowledge, already here:

.. code-block:: python

   "a" * 10000000000000

Instead, we would probably say that for this expression:

   - The result is a ``str`` or a C level ``PyStringObject *``.
   - We know its length exactly, it's ``10000000000000``.
   - Can predict every of its elements when sub-scripted, sliced, etc., if need
     be, with a function we may create.

Similar is true for this horrible (in Python2) thing:

.. code-block:: python

   range(10000000000000)

So it's a rather general problem, this time we know:

   - The result is a ``list`` or C level ``PyListObject *``
   - We know its length exactly, ``10000000000000``
   - Can predict every of its elements when index, sliced, etc., if need be,
     with a function.

Again, we wouldn't want to create the list. Therefore Nuitka avoids executing
these calculation, when they result in constants larger than a threshold of
e.g. 256 elements. This concept has to be also applied to large integers and
more CPU and memory traps.

Now lets look at a more complete use case:

.. code-block:: python

   for x in range(10000000000000):
       doSomething()

Looking at this example, one traditional way to look at it, would be to turn
``range`` into ``xrange``, and to note that ``x`` is unused. That would already
perform better. But really better is to notice that ``range()`` generated
values are not used at all, but only the length of the expression matters.

And even if ``x`` were used, only the ability to predict the value from a
function would be interesting, so we would use that computation function
instead of having an iteration source. Being able to predict from a function
could mean to have Python code to do it, as well as C code to do it. Then code
for the loop can be generated without any CPython library usage at all.

.. note::

   Of course, it would only make sense where such calculations are "O(1)"
   complexity, i.e. do not require recursion like "n!" does.

The other thing is that CPython appears to at - run time - take length hints
from objects for some operations, and there it would help too, to track length
of objects, and provide it, to outside code.

Back to the original example:

.. code-block:: python

   len("a" * 1000000000000)

The theme here, is that when we can't compute all intermediate expressions, and
we sure can't do it in the general case. But we can still, predict some of
properties of an expression result, more or less.

Here we have ``len`` to look at an argument that we know the size of. Great. We
need to ask if there are any side effects, and if there are, we need to
maintain them of course. This is already done by existing optimization if an
operation generates an exception.

.. note::

   The optimization of ``len`` has been implemented and works for all kinds of
   container creation and ranges.

Applying this to "ctypes"
-------------------------

The *not so specific* problem to be solved to understand ``ctypes``
declarations is maybe as follows:

.. code-block:: python

   import ctypes

This leads to Nuitka in its tree to have an assignment from a ``__import__``
expression to the variable ``ctypes``. It can be predicted by default to be a
module object, and even better, it can be known as ``ctypes`` from standard
library with more or less certainty. See the section about "Importing".

So that part is "easy", and it's what will happen. During optimization, when
the module ``__import__`` expression is examined, it should say:

   - ``ctypes`` is a module
   - ``ctypes`` is from standard library (if it is, might not be true)
   - ``ctypes`` then has code behind it, called ``ModuleFriend`` that knows
     things about it attributes, that should be asked.

The later is the generic interface, and the optimization should connect the
two, of course via package and module full names. It will need a
``ModuleFriendRegistry``, from which it can be pulled. It would be nice if we
can avoid ``ctypes`` to be loaded into Nuitka unless necessary, so these need
to be more like a plug-in, loaded only if necessary, i.e. the user code
actually uses ``ctypes``.

Coming back to the original expression, it also contains an assignment
expression, because it re-formulated to be more like this:

.. code-block:: python

   ctypes = __import__("ctypes")

The assigned to object, simply gets the type inferred propagated as part of an
SSA form. Ideally, we could be sure that nothing in the program changes the
variable, and therefore have only one version of that variable.

For module variables, when the execution leaves the module to unknown code, or
unclear code, it might change the variable. Therefore, likely we will often
only assume that it could still be ``ctypes``, but also something else.

Depending on how well we control module variable assignment, we can decide this
more of less quickly. With "compiled modules" types, the expectation is that
it's merely a quick C `==` comparison check. The module friend should offer
code to allow a check if it applies, for uncertain cases.

Then when we come to uses of it:

.. code-block:: python

   ctypes.c_int()

At this point, using SSA, we are more of less sure, that ``ctypes`` is at that
point the module, and that we know what it's ``c_int`` attribute is, at compile
time, and what it's call result is. We will use the module friend to help with
that. It will attach knowledge about the result of that expression during the
SSA collection process.

This is more like a value forward propagation than anything else. In fact,
constant propagation should only be the special case of it, and one design goal
of Nuitka was always to cover these two cases with the same code.

Excursion to Functions
----------------------

In order to decide what this means to functions and their call boundaries, if
we propagate forward, how to handle this:

.. code-block:: python

   def my_append(a, b):
      a.append(b)

      return a

We annotate that ``a`` is first a "unknown but defined parameter object", then
later on something that definitely has an ``append`` attribute, when returned,
as otherwise an exception occurs.

The type of ``a`` changes to that after ``a.append`` look-up succeeds. It might
be many kinds of an object, but e.g. it could have a higher probability of
being a ``PyListObject``. And we would know it cannot be a ``PyStringObject``,
as that one has no ``append`` method, and would have raised an exception
therefore.

.. note::

   If classes, i.e. other types in the program, have an ``append`` attribute, it
   should play a role too, there needs to be a way to plug-in to this decisions.

.. note::

   On the other hand, types without ``append`` attribute can be eliminated.

Therefore, functions through SSA provide an automatic analysis on their return
state, or return value types, or a quick way to predict return value
properties, based on input value knowledge.

So this could work:

.. code-block:: python

   b = my_append([], 3)

   assert b == [3] # Could be decided now

Goal: The structure we use makes it easy to tell what ``my_append`` may be. So,
there should be a means to ask it about call results with given type/value
information. We need to be able to tell, if evaluating ``my_append`` makes
sense with given parameters or not, if it does impact the return value.

We should e.g. be able to make ``my_append`` tell, one or more of these:

  - Returns the first parameter value as return value (unless it raises an
    exception).

  - The return value has the same type as ``a`` (unless it raises an
    exception).

  - The return value has an ``append`` attribute.

  - The return value might be a ``list`` object.

  - The return value may not be a ``str`` object.

  - The function will raise if first argument has no ``append`` attribute.

The exactness of statements may vary. But some things may be more
interesting. If e.g. the aliasing of a parameter value to the return value is
known exactly, then information about it need to all be given up, but some can
survive.

It would be nice, if ``my_append`` had sufficient information, so we could
specialize with ``list`` and ``int`` from the parameters, and then e.g. know at
least some things that it does in that case. Such specialization would have to
be decided if it makes sense. In the alternative, it could be done for each
variant anyway, as there won't be that many of them.

Doing this "forward" analysis appears to be best suited for functions and
therefore long term. We will try it that way.

Excursion to Loops
------------------

.. code-block:: python

   a = 1

   while 1:   # think loop: here
       b = a + 1
       a = b

       if cond():
          break

   print(a)

The handling of loops (both ``for`` and ``while`` are re-formulated to this
kind of loops with ``break`` statements) has its own problem. The loop start
and may have an assumption from before it started, that ``a`` is constant, but
that is only true for the first iteration. So, we can't pass knowledge from
outside loop forward directly into the for loop body.

So the collection for loops needs to be two pass for loops. First, to collect
assignments, and merge these into the start state, before entering the loop
body. The need to make two passes is special to loops.

For a start, it is done like this. At loop entry, all pre-existing, but written
traces, are turned into loop merges. Knowledge is not completely removed about
everything assigned or changed in the loop, but then it's not trusted anymore.

From that basis, the ``break`` exits are analysed, and merged, building up the
post loop state, and ``continue`` exits of the loop replacing the unknown part
of the loop entry state. The loop end is considered a ``continue`` for this
purpose.


Excursion to Conditions
-----------------------

.. code-block:: python

   if cond:
      x = 1
   else:
      x = 2

   b = x < 3

The above code contains a condition, and these have the problem, that when
exiting the conditional block, a merge must be done, of the ``x`` versions. It
could be either one. The merge may trace the condition under which a choice is
taken. That way, we could decide pairs of traces under the same condition.

These merges of SSA variable "versions", represent alternative values. They
pose difficulties, and might have to be reduced to commonality. In the above
example, the ``<`` operator will have to check for each version, and then to
decide that both indeed give the same result.

The trace collection tracks variable changes in conditional branches, and then
merges the existing state at conditional statement exits.

.. note::

   A branch is considered "exiting" if it is not abortive. Should it end in a
   ``raise``, ``break``, ``continue``, or ``return``, there is no need to merge
   that branch, as execution of that branch is terminated.

   Should both branches be abortive, that makes things really simple, as there
   is no need to even continue.

   Should only one branch exist, but be abortive, then no merge is needed, and
   the collection can assume after the conditional statement, that the branch
   was not taken, and continue.

When exiting both the branches, these branches must both be merged, with their
new information.

In the above case:

   - The "yes" branch knows variable ``x`` is an ``int`` of constant value
     ``1``

   - The "no" branch knows variable ``x`` is an ``int`` of constant value ``2``

That might be collapsed to:

   - The variable ``x`` is an integer of value in ``(1,2)``

Given this, we then should be able to precompute the value of this:

.. code-block:: python

   b = x < 3

The comparison operator can therefore decide and tell:

    - The variable ``b`` is a boolean of constant value ``True``.

Were it unable to decide, it would still be able to say:

    - The variable ``b`` is a boolean.

For conditional statements optimization, it's also noteworthy, that the
condition is known to pass or not pass the truth check, inside branches, and in
the case of non-exiting single branches, after the statement it's not true.

We may want to take advantage of it. Consider e.g.

.. code-block:: python

   if type(a) is list:
       a.append(x)
   else:
       a += (x,)

In this case, the knowledge that ``a`` is a list, could be used to generate
better code and with the definite knowledge that ``a`` is of type list. With
that knowledge the ``append`` attribute call will become the ``list`` built-in
type operation.

Excursion to ``return`` statements
----------------------------------

The ``return`` statement (like ``break``, ``continue``, ``raise``) is
"aborting" to control flow. It is always the last statement of inspected block.
When there statements to follow it, optimization will remove it as "dead code".

If all branches of a conditional statement are "aborting", the statement is
decided "aborting" too. If a loop doesn't abort with a break, it should be
considered "aborting" too.


Excursion to ``yield`` expressions
----------------------------------

The ``yield`` expression can be treated like a normal function call, and as
such invalidates some known constraints just as much as they do. It executes
outside code for an unknown amount of time, and then returns, with little about
the outside world known anymore, if it's accessible from there.


Mixed Types
-----------

Consider the following inside a function or module:

.. code-block:: python

   if cond is not None:
      a = [x for x in something() if cond(x)]
   else:
      a = ()

A programmer will often not make a difference between ``list`` and ``tuple``.
In fact, using a ``tuple`` is a good way to express that something won't be
changed later, as these are mutable.

.. note::

   Better programming style, would be to use this:

   .. code-block:: python

      if cond is not None:
         a = tuple(x for x in something() if cond(x))
      else:
         a = ()

   People don't do it, because they dislike the performance hit encountered by
   the generator expression being used to initialize the tuple. But it would be
   more consistent, and so Nuitka is using it, and of course one day Nuitka
   ought to be able to make no difference in performance for it.

To Nuitka though this means, that if ``cond`` is not predictable, after the
conditional statement we may either have a ``tuple`` or a ``list`` type object
in ``a``. In order to represent that without resorting to "I know nothing about
it", we need a kind of ``min``/``max`` operating mechanism that is capable of
say what is common with multiple alternative values.

.. note::

   At this time, we don't really have that mechanism to find the commonality
   between values.

Back to "ctypes"
----------------

.. code-block:: python

   v = ctypes.c_int()

Coming back to this example, we needed to propagate ``ctypes``, then we can
propagate "something" from ``ctypes.int`` and then known what this gives with a
call and no arguments, so the walk of the nodes, and diverse operations should
be addressed by a module friend.

In case a module friend doesn't know what to do, it needs to say so by
default. This should be enforced by a base class and give a warning or note.

Now to the interface
--------------------

The following is the intended interface:

- Iteration with node methods ``computeStatement`` and ``computeExpression``.

  These traverse modules and functions (i.e. scopes) and visit everything in
  the order that Python executes it. The visiting object is ``TraceCollection``
  and pass forward. Some node types, e.g. ``StatementConditional`` new create
  branch trace collections and handle the SSA merging at exit.

- Replacing nodes during the visit.

  Both ``computeStatement`` and ``computeExpression`` are tasked to return
  potential replacements of themselves, together with "tags" (meaningless now),
  and a "message", used for verbose tracing.

  The replacement node of ``+`` operator, may e.g. be the pre-computed constant
  result, wrapped in side effects of the node, or the expression raised, again
  wrapped in side effects.

- Assignments and references affect SSA.

  The SSA tree is initialized every time a scope is visited. Then during
  traversal, traces are built up. Every assignment and merge starts a new trace
  for that matter. References to a given variable version are traced that way.

- Value escapes are traced too.

  When an operation hands over a value to outside code, it indicates so to the
  trace collection. This is for it to know, when e.g. a constant value, might
  be mutated meanwhile.

- Nodes can be queried about their properties.

  There is a type shape and a value shape that each node can be asked about.
  The type shape offers methods that allow to check if certain operations are
  at all supported or not. These can always return ``True`` (yes), ``False``
  (no), and ``None`` (cannot decide). In the case of the later, optimizations
  may not be able do much about it. Lets call these values "tri-state".

  There is also the value shape of a node. This can go deeper, and be more
  specific to a given node.

  The default implementation will be very pessimistic. Specific node types and
  shapes may then declare, that they e.g. have no side effects, will not raise
  for certain operations, have a known truth value, have a known iteration
  length, can predict their iteration values, etc.

- Nodes are linked to certain states.

  During the collect, a variable reference, is linked to a certain trace state,
  and that can be used by parent operations.

  .. code-block:: python

     a = 1
     b = a + a

  In this example, the references to ``a``, can look-up the ``1`` in the trace,
  and base value shape response to ``+`` on it. For compile time evaluation,
  it may also ask ``isCompileTimeConstant()`` and if both nodes will respond
  ``True``, then "getCompileTimeConstant()" will return ``1``, which will be
  be used in computation.

  Then ``extractSideEffects()`` for the ``a`` reference will return ``()`` and
  therefore, the result ``2`` will not be wrapped.

  An alternative approach would be ``hasTypeSlotAdd()`` on the both nodes, and
  they both do, to see if the selection mechanism used by CPython can be used
  to find which types ``+`` should be used.

- Class for module import expression ``ExpressionImportModule``.

  This one just knows that something is imported, but not how or what it is
  assigned to. It will be able in a recursive compile, to provide the module as
  an assignment source, or the module variables or submodules as an attribute
  source when referenced from a variable trace or in an expression.

- Base class for module friend ``ModuleFriendBase``.

  This is intended to provide something to overload, which e.g. can handle
  ``math`` in a better way.

- Module ``ModuleFriendRegistry``

  Provides a register function with ``name`` and instances of
  ``ValueFriendModuleBase`` to be registered. Recursed to modules should
  integrate with that too. The registry could well be done with a metaclass
  approach.

- The module friends should each live in a module of their own.

  With a naming policy to be determined. These modules should add themselves
  via above mechanism to ``ModuleFriendRegistry`` and all shall be imported and
  register. Importing of e.g. ``ctypes`` should be delayed to when the friend
  is actually used. A meta class should aid this task.

  The delay will avoid unnecessary blot of the compiler at run time, if no such
  module is used. For "qt" and other complex stuff, this will be a must.

- The walk should initially be single pass, and not maintain history.

  Instead optimization that needs to look at multiple things, e.g. "unused
  assignment", will look at the whole SSA collection afterwards.


Discussing with examples
------------------------

The following examples:

.. code-block:: python

   # Assignment, the source decides the type of the assigned expression
   a = b

   # Operator "attribute look-up", the looked up expression "ctypes" decides
   # via its trace.
   ctypes.c_int

   # Call operator, the called expressions decides with help of arguments,
   # which have been walked, before the call itself.
   called_expression_of_any_complexity()

   # import gives a module any case, and the "ModuleRegistry" may say more.
   import ctypes

   # From import need not give module, "x" decides what it is.
   from x import y

   # Operations are decided by arguments, and CPython operator rules between
   # argument states.
   a + b

The optimization is mostly performed by walking of the tree and performing
trace collection. When it encounters assignments and references to them, it
considers current state of traces and uses it for ``computeExpression``.

.. note::

   Assignments to attributes, indexes, slices, etc. will also need to follow the
   flow of ``append``, so it cannot escape attention that a list may be
   modified. Usages of ``append`` that we cannot be sure about, must be traced
   to exist, and disallow the list to be considered known value again.

Code Generation Impact
----------------------

Right now, code generation assumes that everything is a ``PyObject *``, i.e. a
Python object, and does not take knowledge of ``int`` or other types into
consideration at all, and it should remain like that for some time to come.

Instead, ``ctypes`` value friend will be asked give ``Identifiers``, like other
codes do too. And these need to be able to convert themselves to objects to
work with the other things.

But Code Generation should no longer require that operations must be performed
on that level. Imagine e.g. the following calls:

.. code-block:: python

   c_call(other_c_call())

Value returned by "other_c_call()" of say ``c_int`` type, should be possible to
be fed directly into another call. That should be easy by having a ``asIntC()``
in the identifier classes, which the ``ctypes`` Identifiers handle without
conversions.

Code Generation should one day also become able to tell that all uses of a
variable have only ``c_int`` value, and use ``int`` instead of
``PyObjectLocalVariable`` more or less directly. We could consider
``PyIntLocalVariable`` of similar complexity as ``int`` after the C++ compiler
performed its in-lining.

Such decisions would be prepared by finalization, which then would track the
history of values throughout a function or part of it.

Initial Implementation
----------------------

The basic interface will be added to *all* expressions and a node may override
it, potentially using trace collection state, as attached during
``computeExpression``.

Goal 1 (Reached)
++++++++++++++++

Initially most things will only be able to give up on about anything. And it
will be little more than a tool to do simple look-ups in a general form. It
will then be the first goal to turn the following code into better performing
one:

.. code-block:: python


   a = 3
   b = 7
   c = a / b
   print(c)

to:

.. code-block:: python

   a = 3
   b = 7
   c = 3 / 7
   print(c)

and then:

.. code-block:: python

   a = 3
   b = 7
   c = 0
   print(c)

and then:

.. code-block:: python

   a = 3
   b = 7
   c = 0
   print(0)

This depends on SSA form to be able to tell us the values of ``a``, ``b``, and
``c`` to be written to by constants, which can be forward propagated at no
cost.

Goal 2 (Reached)
++++++++++++++++

The assignments to ``a``, ``b``, and ``c`` shall all become prey to "unused"
assignment analysis in the next step. They are all only assigned to, and the
assignment source has no effect, so they can be simply dropped.

.. code-block:: python

   print(0)

In the SSA form, these are then assignments without references. These
assignments, can be removed if the assignment source has no side effect. Or at
least they could be made "anonymous", i.e. use a temporary variable instead of
the named one. That would have to take into account though, that the old
version still needs a release.

The most general form would first merely remove assignments that have no
impact, and leave the value as a side effect, so we arrive at this first:

.. code-block:: python

   3
   7
   0
   print(0)

When applying the removal of expression only statements without effect, this
gives us:

.. code-block:: python

   print(0)

which is the perfect result. Doing it in one step would only be an
optimization at the cost of generalization.

In order to be able to manipulate nodes related to a variable trace, we need to
attach the nodes that did it. Consider this:

.. code-block:: python

   if cond():
       x = 1
   elif other():
       x = 3

   # Not using "x".
   print(0)

In the above case, the merge of the value traces, should say that ``x`` may be
undefined, or one of ``1`` or ``3``, but since ``x`` is not used, apply the
"dead value" trick to each branch.

The removal of the "merge" of the 3 ``x`` versions, should exhibit that the
other versions are also only assigned to, and can be removed. These merges of
course appear as usages of the ``x`` versions.

Goal 3
++++++

Then third goal is to understand all of this:

.. code-block:: python

   def f():
      a = []

      print(a)

      for i in range(1000):
          print(a)

          a.append(i)

      return len(a)

.. note::

   There are many operations in this, and all of them should be properly
   handled, or at least ignored in safe way.

The first goal code gave us that the ``list`` has an annotation from the
assignment of ``[]`` and that it will be copied to ``a`` until the for loop in
encountered. Then it must be removed, because the ``for`` loop somehow says so.

The ``a`` may change its value, due to the unknown attribute look-up of it
already, not even the call. The for loop must be able to say "may change value"
due to that, of course also due to the call of that attribute too.

The code should therefore become equivalent to:

.. code-block:: python

   def f():
      a = []

      print([])

      for i in range(1000):
          print(a)

          a.append(i)

      return len(a)

But no other changes must occur, especially not to the ``return`` statement, it
must not assume ``a`` to be constant "[]" but an unknown ``a`` instead.

With that, we would handle this code correctly and have some form constant
value propagation in place, handle loops at least correctly, and while it is
not much, it is important demonstration of the concept.

Goal 4
++++++

The fourth goal is to understand the following:

.. code-block:: python

   def f(cond):
       y = 3

       if cond:
           x = 1
       else:
           x = 2

       return x < y

In this we have a branch, and we will be required to keep track of both the
branches separately, and then to merge with the original knowledge. After the
conditional statement we will know that "x" is an "int" with possible values in
``(1,2)``, which can be used to predict that the return value is always
``True``.

The forth goal will therefore be that the "ValueFriendConstantList" knows that
append changes ``a`` value, but it remains a list, and that the size increases by
one. It should provide an other value friend "ValueFriendList" for "a" due to
that.

In order to do that, such code must be considered:

.. code-block:: python

   a = []

   a.append(1)
   a.append(2)

   print(len(a))

It will be good, if ``len`` still knows that ``a`` is a list object, but not
the constant list anymore.

From here, work should be done to demonstrate the correctness of it with the
basic tests applied to discover undetected issues.

Fifth and optional goal: Extra bonus points for being able to track and predict
``append`` to update the constant list in a known way. Using ``list.append``
that should be done and lead to a constant result of ``len`` being used.

The sixth and challenging goal will be to make the code generation be impacted
by the value friends types. It should have a knowledge that ``PyList_Append``
does the job of append and use ``PyList_Size`` for ``len``. The "ValueFriends"
should aid the code generation too.

Last and right now optional goal will be to make ``range`` have a value friend,
that can interact with iteration of the for loop, and ``append`` of the
``list`` value friend, so it knows it's possible to iterate 5000 times, and
that "a" has then after the "loop" this size, so ``len(a)`` could be predicted.
For during the loop, about a the range of its length should be known to be less
than 5000. That would make the code of goal 2 completely analyzed at compile
time.

Limitations for now
-------------------

- Aim only for limited examples. For ``ctypes`` that means to compile time
  evaluate:

  .. code-block:: python

     print(ctypes.c_int(17) + ctypes.c_long(19))

  Later then call to "libc" or something else universally available,
  e.g. "strlen()" or "strcmp()" from full blown declarations of the callable.

- We won't have the ability to test that optimization are actually performed,
  we will check the generated code by hand.

  With time, we will add XML based checks with "xpath" queries, expressed as
  hints, but that is some work that will be based on this work here. The
  "hints" fits into the "ValueFriends" concept nicely or so the hope is.

- No inter-function optimization functions yet

  Of course, once in place, it will make the ``ctypes`` annotation even more
  usable. Using ``ctypes`` objects inside functions, while creating them on the
  module level, is therefore not immediately going to work.

- No loops yet

  Loops break value propagation. For the ``ctypes`` use case, this won't be
  much of a difficulty. Due to the strangeness of the task, it should be
  tackled later on at a higher priority.

- Not too much.

  Try and get simple things to work now. We shall see, what kinds of
  constraints really make the most sense. Understanding ``list``
  subscript/slice values e.g. is not strictly useful for much code and should
  not block us.

.. note::

   This design is not likely to be the final one.

.. raw:: pdf

   PageBreak


How to make Features Experimental
=================================

Every experimental feature needs a name. We have a rule to pick a name with
lower case and ``_`` as separators. An example of with would be the name
``jinja_generated_add`` that has been used.

Command Line
------------

Experimental features are enabled with the command line argument

.. code-block:: sh

   nuitka --experimental=jinja_generated_add ...

In C code
---------

In Scons, all experimental features automatically are converted into C
defines, and can be used like this:

.. code-block:: C

   #ifdef _NUITKA_EXPERIMENTAL_JINJA_GENERATED_ADD
   #include "HelpersOperationGeneratedBinaryAdd.c"
   #else
   #include "HelpersOperationBinaryAdd.c"
   #endif

The C pre-processor is the only thing that makes an experimental feature
usable.

In Python
---------

You can query experimental features using ``Options.isExperimental()`` with
e.g. code like this:

.. code-block:: python

   if Options.isExperimental("use_feature"):
      experimental_code()
   else:
      standard_code()

When to use it
--------------

Often we need to keep feature in parallel because they are not finished, or
need to be tested after merge and should not break. Then we can do code changes
that will not make a difference except when the experimental flag is given on
the command line to Nuitka.

The testing of Nuitka is very heavy weight when e.g. all Python code is
compiled, and very often, it is interesting to compare behavior with and
without a change.

When to remove it
-----------------

When a feature becomes default, we might choose to keep the old variant around,
but normally we do not. Then we remove the ``if`` and ``#if`` checks and drop
the old code.

At this time, large scale testing will have demonstrated the viability of the
code.

Adding dependencies to Nuitka
=============================

First of all, there is an important distinction to make, runtime or development
time. The first kind of dependency is used when Nuitka is executing.

Adding a Runtime Dependency
---------------------------

This is the kind of dependency that is the most scrutinized. As we want Nuitka
to run on latest greatest Python as well as relatively old ones, we have to be
very careful with these ones.

There is also a distinction of optional dependencies. Right now e.g. the
``lxml`` package is relatively optional, and Nuitka can work without it being
installed, because e.g. on some platforms it will not be easy to do so. That
bar has lifted somewhat, but it means e.g. that XML based optimization tests
are not run with all Python versions.

The list of runtime dependencies is in ``requirements.txt`` and it is for
those the case, that they are not really required to be installed by the
user, consider this snippet:

   # Folders to use for cache files.
   appdirs == 1.4.4

   # Scons is the backend building tool to turn C files to binaries.
   scons == 3.1.2

For both these dependencies, there is either an inline copy (Scons) that we
handle to use in case, Scons is not available (in fact we have a version that
works with Python 2.6 and 2.7 still), and also for appdirs. But since inline
copies are against the rules on some platforms that still do not contain the
package, we even have our own wrapper which provides a minimal fallback.

.. note::

   Therefore, please if you consider adding one of these, get in touch with
   @Nuitka-pushers first and get a green light.

Adding a Development Dependency
-------------------------------

A typical example of a development dependency is ``black`` which is used by
our autoformat, and then in turn by the git pre-commit hook. It is used to
format source code, and doesn't have a role at run time of the actual compiler
code of Nuitka.

Much less strict rules apply to these in comparison to runtime dependencies.
Generally please take care that the tool must be well maintained an available
on newer Pythons. Then we can use it, no problem normally. But if it's really
big, say all of SciPy, we might want to justify it a bit better.

The list of development dependencies is in ``requirements-devel.txt`` and it
is for example like this:

   # API doc, doxygen helper for Python
   doxypypy == 0.8.8.6 ; python_version >= '2.7'

So the ``doxypypy`` likely practically anything requires 2.7 or higher, but
since we still run tests on Python 2.6, the installation would fail with that
version, so we need to make a version requirement. Sometimes we use older
versions for Python2 than for Python3, ``pylint`` being a notable candidate,
but generally we ought to avoid that. For many tools only being available
for currently 3.6 or higher is good enough, esp. if they are run as standalone
tools, like ``autoformat-nuitka-source`` is.

Idea Bin
========

This an area where to drop random ideas on our minds, to later sort it out, and
out it into action, which could be code changes, plan changes, issues created,
etc.

* Make "SELECT_METACLASS" meta class selection transparent.

  Looking at the "SELECT_METACLASS" it should become an anonymous helper
  function. In that way, the optimization process can remove choices at compile
  time, and e.g. in-line the effect of a meta class, if it is known.

  This of course makes most sense, if we have the optimizations in place that
  will allow this to actually happen.

* Keeping track of iterations

  The trace collection trace should become the place, where variables or
  values track their use state. The iterator should keep track of the "next()"
  calls made to it, so it can tell which value to given in that case.

  That would solve the "iteration of constants" as a side effect and it would
  allow to tell that they can be removed.

  That would mean to go back in the tree and modify it long after.

  .. code-block:: python

     a = iter((2, 3))
     b = next(a)
     c = next(a)
     del a

  It would be sweet if we could recognize that as:

  .. code-block:: python

     a = iter((2,3))
     b = side_effect(next(a), 2)
     c = side_effect(next(a), 3)
     del a

  That trivially becomes:

  .. code-block:: python

     a = iter((2, 3))
     next(a)
     b = 2
     next(a)
     c = 3
     del a

  When the ``del a`` is examined at the end of scope, or due to another
  assignment to the same variable, ending the trace, we would have to consider
  of the ``next`` uses, and retrofit the information that they had no effect.

  .. code-block:: python

     a = iter((2, 3))
     b = 2
     b = 3
     del a

* Aliasing

  Each time an assignment is made, an alias is created. A value may have
  different names.

  .. code-block:: python

     a = iter(range(9))
     b = a
     c = next(b)
     d = next(a)

  If we fail to detect the aliasing nature, we will calculate ``d`` wrongly. We
  may incref and decref values to trace it.

  Aliasing is automatically traced already in SSA form. The ``b`` is assigned
  to version of ``a``. So, that should allow to replace it with this:

  .. code-block:: python

     a = iter(range(9))
     c = next(a)
     d = next(a)

  Which then will be properly handled.

* Tail recursion optimization.

  Functions that return the results of calls, can be optimized. The Stackless
  Python does it already.

* Integrate with "upx" compression.

  Calling "upx" on the created binaries, would be easy.

* In-lining constant "exec" and "eval".

  It should be possible to re-formulate at least cases without "locals" or
  "globals" given.

  .. code-block:: python

     def f():
        a = 1
        b = 2

        exec("""a+=b;c=1""")

        return a, c

  Should become this here:

  .. code-block:: python

     def f():
        a = 1
        b = 2

        a += b  #
        c = 1   # MaybeLocalVariables for everything except known local ones.

        return a, c

  If this holds up, inlining ``exec`` should be relatively easy.

* Original and overloaded built-ins

  This is about making things visible in the node tree. In Nuitka things that
  are not visible in the node tree tend to be wrong. We already pushed around
  information to the node tree a lot.

  Later versions, Nuitka will become able to determine it has to be the
  original built-in at compile time, then a condition that checks will be
  optimized away, together with the slow path. Or the other path, if it won't
  be. Then it will be optimized away, or if doubt exists, it will be correct.
  That is the goal.

  Right now, the change would mean to effectively disable all built-in call
  optimization, which is why we don't immediately do it.

  Making the compatible version, will also require a full listing of all
  built-ins, which is typing work merely, but not needed now. And a way to stop
  built-in optimization from optimizing built-in calls that it used in a
  wrap. Probably just some flag to indicate it when it visits it to skip
  it. That's for later.

  But should we have that both, I figure, we could not raise a ``RuntimeError``
  error, but just do the correct thing, in all cases. An earlier step may raise
  ``RuntimeError`` error, when built-in module values are written to, that we
  don't support.

.. raw:: pdf

   PageBreak

* Recursion checks are expensive.

  If the "caller" or the "called" can declare that it cannot be called by
  itself, we could leave it out.

  TODO: Are they really that expensive? Unnecessary yes, but expensive may not
  be true.

.. raw:: pdf

   PageBreak

Prongs of Action
================

In this chapter, we keep track of prongs of action currently ongoing. This can
get detailed and shows things we strive for.

Builtin optimization
--------------------

Definitely want to get built-in names under full control, so that variable
references to module variables do not have a twofold role. Currently they
reference the module variable and also the potential built-in as a fallback.

In terms of generated code size and complexity for modules with many variables
and uses of them that is horrible. But ``some_var`` (normally) cannot be a
built-in and therefore needs no code to check for that each time.

This is also critical to getting to whole program optimization. Being certain
what is what there on module level, will enable more definitely knowledge about
data flows and module interfaces.


Class Creation Overhead Reduction
---------------------------------

This is more of a meta goal. Some work for the metaclass has already been done,
but that is Python2 only currently. Being able to to decide built-ins and to
distinguish between global only variables, and built-ins more clearly will help
this a lot.

In the end, empty classes should be able to be statically converted to calls
to ``type`` with static dictionaries. The inlining of class creation function
is also needed for this, but on Python3 cannot happen yet.

Memory Usage at Compile Time
----------------------------

We will need to store more and more information in the future. Getting the tree
to be tight shaped is therefore an effort, where we will be spending time too.

The mix-ins prevent slots usage, so lets try and get rid of those. The
"children having" should become more simple and faster code. I am even thinking
of even generating code in the meta class, so it's both optimal and doesn't
need that mix-in any more. This is going to be ugly then.

Coverage Testing
----------------

And then there is coverage, it should be taken and merged from all Python
versions and OSes, but I never managed to merge between Windows and Linux
for unknown reasons.

Python3 Performance
-------------------

The Python3 lock for thread state is making it slower by a lot. I have only
experimental code that just ignores the lock, but it likely only works on
Linux, and I wonder why there is that lock in the first place.

Ignoring the locks cannot be good. But what updates that thread state pointer
ever without a thread change, and is this what abiflags are about in this
context, are there some that allow us to ignore the locks.

An important bit would be to use a thread state once acquired for as much
as possible, currently exception helpers do not accept it as an argument,
but that ought to become an option, that way saving and restoring an
exception will be much faster, not to mention checking and dropping non
interesting, or rewriting exceptions.

Onefile compression on Windows
------------------------------

We need to add compression on that platform too. This should use zstd and
probably just needs integration into our build. The Python side already
is capable of producing compressed payload.

Caching of Python level compilation
-----------------------------------

While the C compilation result is already cached with ccache and friends now,
we need to also cover our bases and save the resulting node tree of potential
expensive optimization on the module level.

Updates for this Manual
=======================

This document is written in REST. That is an ASCII format which is readable as
ASCII, but used to generate PDF or HTML documents.

You will find the current source under:
https://github.com/Nuitka/Nuitka/blob/develop/Developer_Manual.rst

And the current PDF under:
https://nuitka.net/doc/Developer_Manual.pdf
