cython-npm 0.2.4
Cython project management like npm in nodejs
License: MIT

cython-npm
==========

Cython project management like npm in nodejs. This project is inspired
by npm in nodejs.

Installation
~~~~~~~~~~~~

You can easily install by:

::

    pip install cython-npm

What problems does it solve ?
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

When using cython, we face the problem of compile cython file. We can do
it easily by:

.. code:: python

    import pyximport; pyximport.install()

But that it is not recommended to let **pyximport** build code on end
user side as it *hooks into their import system*. The best way to cater
for end users is to provide pre-built binary packages. So this project
compiles .pyx file and provides pre-built binary packages for easy of
use.

Quickstart:
~~~~~~~~~~~

Basic use to Complie file or folder

.. code:: python

    from cython_npm.cythoncompile import export
    export('examplefile.pyx')
    export('./examplefolder')
    # then import them to use
    import examplefile
    from examplefolder import *

You should do this code once time only.

Create install file like package.json
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can also compile many files or folders at once time. Create a file
name ``install.py`` in the root of your project/package and write the
code below:

.. code:: python

    from cython_npm.cythoncompile import install
    Manymodules = [
        # put your modules list here
        'examplefile.pyx',
        './examplefolder'
    ]
    install(Manymodules)

Run the file before start your project

::

    python install.py

Or add first line ``import install`` in startup file of your project.
Use install or export in parent folder will compile all .pyx file in
subdirectories. ### Using require('path') as nodejs You can also
relative or fullpath import in python by ``require`` function. For
example:

.. code:: python

    from cython_npm.cythoncompile import require

    # import .pyx file. Will cause error if it is not compiled by export() yet. 
    # Default value of recompile is True, only apply for .py file. To import .pyx, change recompile=False
    examplefile = require('../parentpackage', recompile=False) # import cython package from parent folder
    examplefile.somefunction()

    # it also support relative import .py file
    examplefile = require('../parentpackage')
    examplefile.somefunction()

Using requirepyx('path'): ``requirepyx`` is simillar to ``require``
except: \* Use for cython file ('.pyx') only \* Equivalent to
export('.pyx file') and require('.pyx file') Example:

.. code:: python

    from cython_npm.cythoncompile import export
    export('examplefile')
    require('examplefile',recompile=False)
    # The code above is the same as:
    from cython_npm.cythoncompile import requirepyx
    requirepyx('examplefile')

Using typecheck
~~~~~~~~~~~~~~~

Another utils is typecheck support to raise error in typing module (from
python 3.3):

.. code:: python

    from cython_npm.typecheck import typecheck

    @type_check
    def checkstr(s: Any)->(None, str):
        return None, s

    x,y = checkstr('tuan')
    print(x,y)

    try:
        checkstr(120)
    except Exception as error:
        print(error)
        traceback.print_exc()

    # That will raise an error of TypeError
    checkstr(200)

Example: Cython vs speed test battle
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This example compare the speed between cython vs python, Swift, Go and
Code differences in doing a short calculation. Cython\_npm is used in
the test. This test is forked from 'marcinkliks', the original code and
test is here: `Swift vs Go vs Python
battle <http://www.marcinkliks.pl/2015/02/22/swift-vs-others/>`__. Note:
We use Swift and Go test results as pattern and do not retest them. Go
to see in test folder in github for more examples

Testing condition: \* Python version: Python 3.6.3 :: Anaconda, Inc.

-  About computer: MacBook Pro (13-inch, 2016, Two Thunderbolt 3 ports),
   2 GHz Intel Core i5, 256GB SSD

| Hypothesis:
| \* Is Cython really fast (compare to other language) ? \* How does
  Code differences affect performance ?

Test process and results as shown below:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

0. Recall the speed of Swift: 0m0.416s, Go: 0m0.592s and Pypy: 0m2.633s

1. Test pure python code:

   .. code:: python

       sum = 0

       for e in range(30):
           sum = 0
           x = []

           for i in range(1000000):
               x.append(i)

           y = []
           for i in range(1000000 - 1):
               y.append(x[i] + x[i+1])

           i = 0
           for i in range(0, 1000000, 100):
               sum += y[i]

       print(sum)

   Speed test result is same/similar to original test

   ::

       time python test_python.py
       9999010000

       real    0m12.825s
       user    0m11.721s
       sys     0m1.061s

2. Test cython code: Create run.py with code:

   .. code:: python

       from cython_npm.cythoncompile import export
       export('test_cython.pyx') # will do once time
       import test_cython

   Code in **test\_cython.pyx**:

   .. code:: python

       cdef long sum = 0
       cdef int i
       cdef int e
       for e in range(30):
           sum = 0
           x = []

           for i in range(1000000):
               x.append(i)

           y = []
           for i in range(1000000 - 1):
               y.append(x[i] + x[i+1])

           i = 0
           for i in range(0, 1000000, 100):
               sum += y[i]

       print(sum)

   Speed test result: time python run.py

   ::

       time python run.py
       9999010000

       real    0m5.803s
       user    0m4.496s
       sys     0m1.211s

3. Test cython code with list optimization and cache: create similar
   run.py. Code in **test\_cythoncache.pyx**:

   .. code:: python

       from functools import lru_cache
       @lru_cache(maxsize=128)
       def dotest():
           cdef long mysum = 0
           cdef int i
           cdef int e
           for e in range(30):
               mysum = 0
               x = [i for i in range(1000000)]

               y = [x[i] + x[i+1] for i in range(1000000-1)]

               i = 0
               for i in range(0, 1000000, 100):
                   mysum += y[i]

           print(mysum)
       dotest()

   Speed test result:

   ::

       time python run.py
       9999010000

       real    0m3.373s
       user    0m2.360s
       sys     0m1.001s

4. Test cython code with cache and C array: create similar run.py. Code
   in **test\_cythoncache.pyx**:

   .. code:: python

       from functools import lru_cache
       @lru_cache(maxsize=128)
       def dotest():
           cdef long mysum = 0
           cdef int i
           cdef int e
           cdef int x[1000000]
           cdef int y[1000000]
           for e in range(30):
               mysum = 0
               for i in range(1000000):
                   x[i] = i

               # y = []
               for i in range(1000000 - 1):
                   y[i] = (x[i] + x[i+1])

               i = 0
               for i in range(0, 1000000, 100):
                   mysum += y[i]

           print(mysum)
       dotest()

   Speed test result:

   ::

       time python run.py
       9999010000

       real    0m0.085s
       user    0m0.067s
       sys     0m0.015s

Conclusions
^^^^^^^^^^^

-  With a slight change, Cython make pure python code faster by 2X time.
   But it is very slow compare to Swift and Go
-  Appling some optimal technical, Cython make python nearly 4X time
   faster than the original code. It may be the acceptable result. Pypy
   result seems very attractive too.
-  Using C array, Cython make the code become very fast. It consumes
   only 0.085s to complete as 4X time faster than Swift, 6X time faster
   than Go. It maybe the fastest but it is unusable in real life.
-  After all, i wish cython and cython\_npm could give you more usefull
   options in coding