Metadata-Version: 1.1
Name: fluidpythran
Version: 0.0.7
Summary: Pythran annotations in Python files
Home-page: UNKNOWN
Author: UNKNOWN
Author-email: UNKNOWN
License: CeCILL-B License
Description: FluidPythran: easily speedup your Python code with Pythran
        ==========================================================
        
        |release| |docs| |coverage|
        
        .. |release| image:: https://img.shields.io/pypi/v/fluidpythran.svg
           :target: https://pypi.python.org/pypi/fluidpythran/
           :alt: Latest version
        
        .. |docs| image:: https://readthedocs.org/projects/fluidpythran/badge/?version=latest
           :target: http://fluidpythran.readthedocs.org
           :alt: Documentation status
        
        .. |coverage| image:: https://codecov.io/bb/fluiddyn/fluidpythran/branch/default/graph/badge.svg
           :target: https://codecov.io/bb/fluiddyn/fluidpythran/branch/default/
           :alt: Code coverage
        
        
        .. warning ::
        
           FluidPythran is in a very early stage. Remarks and suggestions are very
           welcome.
        
           FluidPythran just starts to be used in `FluidSim
           <https://bitbucket.org/fluiddyn/fluidsim>`_ (for example in `this file
           <https://bitbucket.org/fluiddyn/fluidsim/src/default/fluidsim/base/time_stepping/pseudo_spect.py>`_).
        
        FluidPythran is a pure Python package (requiring Python >= 3.6 or Pypy3) to
        help to write Python code that *can* use `Pythran
        <https://github.com/serge-sans-paille/pythran>`_ if it is available.
        
        Let's recall that "Pythran is an ahead-of-time (AOT) compiler for a subset of
        the Python language, with a focus on scientific computing. It takes a Python
        module annotated with a few interface description and turns it into a native
        Python module with the same interface, but (hopefully) faster."
        
        Pythran is able to produce **very efficient C++ code and binaries from high
        level Numpy code**. If the algorithm is easier to express without loops, don't
        write loops!
        
        Pythran always releases the GIL and can use SIMD instructions and OpenMP!
        
        **Pythran is not a hard dependency of FluidPythran:** Python code using
        FluidPythran run fine without Pythran and without compilation (and of course
        without speedup)!
        
        Overview
        --------
        
        Python + Numpy + Pythran is a great combo to easily write highly efficient
        scientific programs and libraries.
        
        To use Pythran, one needs to isolate the numerical kernels functions in modules
        that are compiled by Pythran. The C++ code produced by Pythran never uses the
        Python interpreter. It means that only a subset of what is doable in Python can
        be done in Pythran files. Some `language features
        <https://pythran.readthedocs.io/en/latest/MANUAL.html#disclaimer>`_ are not
        supported by Pythran (for example no classes) and most of the extension
        packages cannot be used in Pythran files (basically `only Numpy and some Scipy
        functions <https://pythran.readthedocs.io/en/latest/SUPPORT.html>`_).
        
        Another cause of frustration for Python developers when using Pythran is
        related to manual writting of Pythran function signatures in comments, which
        can not be automated. Pythran uses C++ templates but Pythran users can not
        think with this concept. We would like to be able to **express the templated
        nature of Pythran with modern Python syntax** (in particular **type
        annotations**). Finally, another limitation is that it is not possible to use
        Pythran for **just-in-time** (JIT) compilation so one needs to manually write
        all argument types.
        
        With FluidPythran, we try to overcome these limitations. FluidPythran provides
        few supplementary Pythran commands and a small Python API to define Pythran
        functions without writing the Pythran modules. The code of the numerical
        kernels can stay in the modules and in the classes where they were written. The
        Pythran files (i.e. the files compiled by Pythran), which are usually written
        by the user, are produced automatically by FluidPythran.
        
        Bonus: There are FluidPythran syntaxes for both **ahead-of-time** and
        **just-in-time** compilations!
        
        At run time, FluidPythran uses when possible the pythranized functions, but
        let's stress again that codes using FluidPythran work fine without Pythran (of
        course without speedup)!
        
        To summarize, a **strategy to quickly develop a very efficient scientific
        application/library** with Python could be:
        
        - Use modern Python coding, standard Numpy/Scipy for the computations and all
          the cool libraries you want.
        
        - Profile your applications on real cases, detect the bottlenecks and apply
          standard optimizations with Numpy.
        
        - Add few lines of FluidPythran to compile the hot spots.
        
        **Implementation details:** Under the hood, FluidPythran creates Pythran files
        (one per module for AOT compilation and one per function for JIT compilation)
        that can be compiled at build, import or run times depending of the cases. Note
        that the developers can still read the Pythran files if needed.
        
        Installation
        ------------
        
        .. code ::
        
           pip install fluidpythran
        
        A short tour of FluidPythran syntaxes
        -------------------------------------
        
        Command :code:`# pythran def`
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code :: python
        
            import h5py
            import mpi4py
        
            from fluidpythran import pythran_def
        
            # pythran def myfunc(int, float)
        
            @pythran_def
            def myfunc(a, b):
                return a * b
        
            ...
        
        Most of this code looks familiar to Pythran users. The differences:
        
        - One can use (for example) h5py and mpi4py (of course not in the Pythran
          functions).
        
        - :code:`# pythran def` instead of :code:`# pythran export` (to stress that it
          is not the same command).
        
        - A tiny bit of Python... The decorator :code:`@pythran_def` replaces the
          Python function by the pythranized function if FluidPythran has been used to
          produced the associated Pythran file.
        
        Pythran using type annotations
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The previous example can be rewritten without Pythran commands:
        
        .. code :: python
        
            import h5py
            import mpi4py
        
            from fluidpythran import pythran_def
        
            @pythran_def
            def myfunc(a: int, b: float):
                return a * b
        
            ...
        
        Nice but very limited... So it is possible to mix type hints and :code:`#
        pythran def` commands. Moreover, one can also elegantly define many Pythran
        signatures with type variables (see `these examples in the documentation
        <https://fluidpythran.readthedocs.io/en/latest/examples/type_hints.html>`_).
        
        
        Command :code:`# pythran block`
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        FluidPythran blocks can be used with classes and more generally in functions
        with lines that cannot be compiled by Pythran.
        
        .. code :: python
        
            from fluidpythran import FluidPythran
        
            fp = FluidPythran()
        
            class MyClass:
        
                ...
        
                def func(self, n):
                    a, b = self.something_that_cannot_be_pythranized()
        
                    if fp.is_pythranized:
                        result = fp.use_pythranized_block("name_block")
                    else:
                        # pythran block (
                        #     float a, b;
                        #     int n
                        # ) -> result
        
                        # pythran block (
                        #     complex a, b;
                        #     int n
                        # ) -> result
        
                        result = a**n + b**n
        
                    return self.another_func_that_cannot_be_pythranized(result)
        
        For blocks, we need a little bit more of Python.
        
        - At import time, we have :code:`fp = FluidPythran()`, which detects which
          Pythran module should be used and imports it. This is done at import time
          since we want to be very fast at run time.
        
        - In the function, we define a block with three lines of Python and special
          Pythran annotations (:code:`# pythran block`). The 3 lines of Python are used
          (i) at run time to choose between the two branches (:code:`is_pythranized` or
          not) and (ii) at compile time to detect the blocks.
        
        Note that the annotations in the command :code:`# pythran block` are different
        (and somehow easier to write) than in the standard command :code:`# pythran
        export`.
        
        `Blocks can now also be defined with type hints!
        <https://fluidpythran.readthedocs.io/en/latest/examples/blocks.html>`_
        
        Cached Just-In-Time compilation
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        With FluidPythran, one can use the Ahead-Of-Time compiler Pythran in a
        Just-In-Time mode. It is really the **easiest way to speedup a function with
        Pythran**, just by adding a decorator! It is a "work in progress" so (i) it can
        be buggy and (ii) the API is not great, but it is a good start!
        
        .. code :: python
        
            import numpy as np
        
            # pythran import numpy as numpy
        
            from fluidpythran import cachedjit, used_by_cachedjit
        
            @used_by_cachedjit("func1")
            def func0(a, b):
                return a + b
        
            @cachedjit
            def func1(a, b):
                return np.exp(a) * b * func0(a, b)
        
        Note that the :code:`@cachedjit` decorator takes into account type hints (see
        `the example in the documentation
        <https://fluidpythran.readthedocs.io/en/latest/examples/using_cachedjit.html>`_).
        
        
        **Implementation details for just-in-time compilation:** A Pythran file is
        produced for each "cachedjited" function (function decorated with
        :code:`@cachedjit`). The file is compiled at the first call of the function and
        the compiled version is used as soon as it is ready. The warmup can be quite
        long but the compiled version is saved and can be reused (without warmup!) by
        another process.
        
        Python classes: :code:`@pythran_def` for methods
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Just a NotImplemented idea! See https://bitbucket.org/fluiddyn/fluidpythran/issues/3
        
        For simple methods only using simple attributes, if could be simple and *very*
        useful to support this:
        
        .. code :: python
        
            from fluidpythran import Type, NDim, Array, pythran_def
        
            import numpy as np
        
            T = Type(int, np.float64)
            N = NDim(1)
        
            A1 = Array[T, N]
            A2 = Array[float, N+1]
        
            class MyClass:
        
                arr0: A1
                arr1: A1
                arr2: A2
        
                def __init__(self, n, dtype=int):
                    self.arr0 = np.zeros(n, dtype=dtype)
                    self.arr1 = np.zeros(n, dtype=dtype)
                    self.arr2 = np.zeros(n)
        
                @pythran_def
                def compute(self, alpha: int):
                    tmp = (self.arr0 + self.arr1).mean()
                    return tmp ** alpha * self.arr2
        
        Make the Pythran files
        ----------------------
        
        There is a command-line tool :code:`fluidpythran` which makes the associated
        Pythran files from Python files with annotations and fluidpythran code.
        
        There is also a function :code:`make_pythran_files` that can be used in a
        setup.py like this:
        
        .. code ::
        
            from pathlib import Path
        
            from fluidpythran.dist import make_pythran_files
        
            here = Path(__file__).parent.absolute()
        
            paths = ["fluidsim/base/time_stepping/pseudo_spect.py"]
            make_pythran_files([here / path for path in paths])
        
        Note that FluidPythran never uses Pythran. Compiling the associated Pythran
        file can be done if wanted (see for example how it is done in the example
        package `example_package_fluidpythran
        <https://bitbucket.org/fluiddyn/example_package_fluidpythran>`_ or in
        `fluidsim's setup.py
        <https://bitbucket.org/fluiddyn/fluidsim/src/default/setup.py>`_).
        
        License
        -------
        
        FluidDyn is distributed under the CeCILL-B_ License, a BSD compatible
        french license.
        
        .. _CeCILL-B: http://www.cecill.info/index.en.html
        
Keywords: pythran
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
