jack-low / pyextjs

Python Extension Packages in Javascript and NodeJS

PyExtJS

^{(Python Extension Packages in Javascript)}

Contents

• What is PyExtJs?
• Installation
• Latest source code
• Bug reports
• Getting Started
  • numpy
    • array
    • linspace
    • logspace
    • exp
    • arange
    • power
    • reshape
    • shape
    • size
    • ndim
    • strides
    • dtype
    • ravel
    • T / transpose
    • dot
    • zeros
    • ones
    • random
    • polyfit
  • scipy
  • interpolate
  • linregress

What is PyExtJs?

Python Extension Packages in Javascript is open-source implementation of some common libraries used in the scientific python programming.
The main goal of this project is to improve migration of python language to javascript.

License

Copyright 2016 Alvaro Fernandez

License: MIT/X11

Installation

Just include the following libraries in your html.

<script type="text/javascript" src="ss.js"></script>
<script type="text/javascript" src="Numpy.js"></script>
<script type="text/javascript" src="PolySolve.js"></script>
<script type="text/javascript" src="Scipy.js"></script>

Latest source code

The latest development version of Scipy's sources are always available at:

https://github.com/fernandezajp/PyExtJs

Bug reports

To search for bugs or report them, please use the Scipy Bug Tracker at:

https://github.com/fernandezajp/PyExtJs/issues

Getting Started

numpy

importing a library

Python

>>> import numpy as np

Javascript with PyExtJS

> np = numpy;

Using array

Python

>>> import numpy
>>> numpy.array([[1,2],[3,4]])
    array([[1, 2], [3, 4]])

Javascript with PyExtJS

> numpy.array([[1,2],[3,4]]);
  [[1, 2], [3, 4]]

Using linspace

Python

>>> import numpy
>>> numpy.linspace(2.0, 3.0, 5)
    array([ 2.  ,  2.25,  2.5 ,  2.75,  3.  ])

Javascript with PyExtJS

> numpy.linspace(2.0, 3.0, 5);
  [2, 2.25, 2.5, 2.75, 3]

Using logspace

Python

>>> import numpy
>>> numpy.logspace(2.0, 3.0, 5)
    array([100.,177.827941,316.22776602,562.34132519,1000.])

Javascript with PyExtJS

> numpy.logspace(2.0, 3.0, 5);
  [100, 177.82794100389228, 316.22776601683796, 562.341325190349, 1000]

Using exp

Python

>>> import numpy
>>> numpy.exp(2.4)
    11.023176380641601
>>> numpy.exp([2.4, 3.1])
    array([ 11.02317638,  22.19795128])

Javascript with PyExtJS

> numpy.exp(2.4);
  11.0231763806416
> numpy.exp([2.4, 3.2])
  [11.0231763806416, 24.53253019710935]

Using arange

Python

>>> import numpy
>>> numpy.arange(3)
    array([0, 1, 2])
>>> numpy.arange(3,7)
    array([3, 4, 5, 6])
>>> numpy.arange(3,7,2)
    array([3, 5])

Javascript with PyExtJS

> numpy.arange(3);
  [0, 1, 2]
> numpy.arange(3,7)
  [3, 4, 5, 6]
> numpy.arange(3,7,2)
  [3, 5]

#### *Using power*

Python

>>> import numpy
>>> x1 = range(6)
>>> numpy.power(x1, 3)
    array([  0,   1,   8,  27,  64, 125])
>>> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]
>>> np.power(x1, x2)
    array([  0.,   1.,   8.,  27.,  16.,   5.])

Javascript with PyExtJS

> x1 = numpy.range(6);
  [0, 1, 2, 3, 4, 5]
> numpy.power(x1, 3);
  [0, 1, 8, 27, 64, 125]
> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0];
  [1, 2, 3, 3, 2, 1]
> numpy.power(x1, x2);
  [0, 1, 8, 27, 16, 5]

Using shape

Python

>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.shape
    (3, 2)

Javascript with PyExtJS

> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a.shape;
  [3, 2]

Using size

Python

>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.size
    6

Javascript with PyExtJS

> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a.size;
  6

Using ndim

Python

>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.ndim
    2

Javascript with PyExtJS

> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a.ndim;
  2

Using strides

Python

>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.strides
    (16, 8)

Javascript with PyExtJS

> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a.strides;
  [2, 1]
# Very important: in Javascript the element has 1 byte

Using dtype

Python

>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.dtype
    dtype('int64')

Javascript with PyExtJS

> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a.dtype;
  "Number"

Using ravel

Python

>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.ravel()
    array([0, 1, 2, 3, 4, 5])

Javascript with PyExtJS

> a = numpy.arange(6).reshape([3, 2]);
  [[0, 1], [2, 3], [4, 5]]
> a.ravel();
  [0, 1, 2, 3, 4, 5]

Using T or transpose

Python

>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.T
    array([[0, 2, 4],
           [1, 3, 5]])
>>> a.transpose()
    array([[0, 2, 4],
           [1, 3, 5]])

Javascript with PyExtJS

> a = numpy.arange(6).reshape([3, 2]);
  [[0, 1], [2, 3], [4, 5]]
> a.T;
  [[0, 2, 4], [1, 3, 5]]
> a.transpose();
  [[0, 2, 4], [1, 3, 5]]

Using dot

Python

>>> import numpy
>>> a = numpy.arange(6).reshape(3, 2)
>>> b = numpy.arange(6,12).reshape(2, 3)
>>> a.dot(b)
    array([[ 9, 10, 11],
           [39, 44, 49],
           [69, 78, 87]])

Javascript with PyExtJS

> a = numpy.arange(6).reshape(3, 2);
  [[0, 1], [2, 3], [4, 5]]
> b = numpy.arange(6,12).reshape(2, 3);
  [[6, 7, 8], [9, 10, 11]]
> a.dot(b);
  [[9, 10, 11], [39, 44, 49], [69, 78, 87]]

Using zeros

Python

>>> import numpy
>>> numpy.zeros(5)
    array([ 0.,  0.,  0.,  0.,  0.])
>>> numpy.zeros((3, 2))
    array([[ 0.,  0.],
          [ 0.,  0.],
          [ 0.,  0.]])

Javascript with PyExtJS

> numpy.zeros(5);
  [0, 0, 0, 0, 0]
> numpy.zeros([3, 2]);
  [[0, 0],[0, 0],[0, 0]]

Using ones

Python

>>> import numpy
>>> numpy.ones(5)
    array([ 1.,  1.,  1.,  1.,  1.])
>>> numpy.ones((3, 2))
    array([[ 1.,  1.],
          [ 1.,  1.],
          [ 1.,  1.]])

Javascript with PyExtJS

> numpy.ones(5);
  [1, 1, 1, 1, 1]
> numpy.ones([3, 2]);
  [[1, 1],[1, 1],[1, 1]]

Using random

Python

>>> import numpy
>>> numpy.random.random()
    0.298740136734731
>>> numpy.random.random(2)
    array([ 0.05538307,  0.74942997])
>>> numpy.random.random([2,2])
    array([[ 0.51655267,  0.57323634],
           [ 0.82552349,  0.10818737]])

Javascript with PyExtJS

> numpy.random.random();
  0.298740136734731
> numpy.random.random(2);
  [0.05538307, 0.74942997]
> numpy.random.random([2,2]);
  [[0.51655267, 0.57323634], [0.82552349, 0.10818737]]

Using polyfit

Python

>>> import numpy
>>> x = numpy.array([0.0, 1.0, 2.0, 3.0,  4.0,  5.0])
>>> y = numpy.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
>>> numpy.polyfit(x, y, 3)
    array([ 0.08703704, -0.81349206,  1.69312169, -0.03968254])

Javascript with PyExtJS

> x = [0.0, 1.0, 2.0, 3.0,  4.0,  5.0];
  [0, 1, 2, 3, 4, 5]
> y = [0.0, 0.8, 0.9, 0.1, -0.8, -1.0];
  [0, 0.8, 0.9, 0.1, -0.8, -1]
> numpy.polyfit(x, y, 3);
  [0.0870370370370341, -0.8134920634920405, 1.6931216931216477, -0.039682539682528106]

scipy

Using interpolate

Python

>>> import numpy
>>> from scipy import interpolate
>>> x = numpy.arange(0, 10)
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> y = numpy.exp(-x/3.0)
    array([ 1.        ,  0.71653131,  0.51341712,  0.36787944,  0.26359714,
    0.1888756 ,  0.13533528,  0.09697197,  0.06948345,  0.04978707])
>>> f = interpolate.interp1d(x, y)
>>> f(2.4)
    array(0.4552020478881322)  

Javascript with PyExtJS

> x = numpy.arange(0, 10);
  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
> tmp = x.map(function(v) {
    return -v/3.0;
  });
  [-0, -0.3333333333333333, -0.6666666666666666, -1, -1.3333333333333333, -1.6666666666666667, -2, -2.3333333333333335, -2.6666666666666665, -3]
> y = numpy.exp(tmp);
  [1, 0.7165313105737892, 0.513417119032592, 0.3678794411714424, 0.26359713811572677, 0.1888756028375618, 0.1353352832366127, 0.09697196786440504, 0.06948345122280153, 0.04978706836786395]
> var interpolation = new interpolate();
  undefined
> interpolation.interp1d(x, y);
  undefined
> interpolation.eval(2.4);
  0.4552020478881322
> interpolation.eval([3.1, 2.4]);
  [0.35745121086587084, 0.4552020478881322]

Using linregress

Python

>>> from scipy import stats
>>> x = [0.6,0.1,0.7,0.7,0.3,0.6,0.1,0.6,0.7,0.8]
>>> y = [0.8,0.8,0.2,0.1,0.8,0.3,0.5,0.9,0.1,0.2]
>>> slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
>>> slope
    -0.72818791946308714
>>> intercept
    0.84865771812080526  

Javascript with PyExtJS

> var Stats = new stats();
  undefined
> x = [ 0.6, 0.1, 0.7, 0.7, 0.3, 0.6, 0.1, 0.6, 0.7, 0.8 ];
  [0.6, 0.1, 0.7, 0.7, 0.3, 0.6, 0.1, 0.6, 0.7, 0.8]
> y = [ 0.8, 0.8, 0.2, 0.1, 0.8, 0.3, 0.5, 0.9, 0.1, 0.2 ];
  [0.8, 0.8, 0.2, 0.1, 0.8, 0.3, 0.5, 0.9, 0.1, 0.2]
> Stats.linregress(x, y);
  undefined
> Stats.get_slope();
  -0.7281879194630861
> Stats.get_intercept();
  0.8486577181208048

Performance

This is very important, the test was executed in a MacBookPro i5

The python Code:

import time
import numpy

def test():
    x = numpy.array([0.0, 1.0, 2.0, 3.0,  4.0,  5.0])
    y = numpy.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])

    start = time.time()
    for num in range(1,10000):
        numpy.polyfit(x, y, 3)
    end = time.time()

    microsecs = end - start
    print microsecs * 1000

test()

The Javascript Code:

function test() {
    x = [0.0, 1.0, 2.0, 3.0,  4.0,  5.0];
    y = [0.0, 0.8, 0.9, 0.1, -0.8, -1.0];

    var start = +new Date();
    for (var i=0;i<10000;i++)
        numpy.polyfit(x, y, 3)
    var end =  +new Date();
    var diff = end - start;
    alert(diff);
}

test();

Python: 1604 milliseconds
Javascript: 14 milliseconds

Javascript! Very fast!!! PyExtJS

^{(Python Extension Packages in Javascript)}

Contents

• What is PyExtJs?
• Installation
• Latest source code
• Bug reports
• Wiki
  • Array creation routines
  • Array manipulation routines
  • Mathematical functions
• Performance

What is PyExtJs?

Python Extension Packages in Javascript is open-source implementation of some common libraries used in the scientific python programming.
The main goal of this project is to improve migration of python language to javascript.

License

Copyright 2016 Alvaro Fernandez

License: MIT/X11

Installation

on node.js

	$ npm install pyextjs  

	> require('pyextjs');

	> numpy.linspace(2.0,3.0,5);

on the browser

Just include the following libraries in your html.

<!doctype html>
<html>
  <head>
    <script type="text/javascript" src="../js/ss.js"></script>
    <script type="text/javascript" src="../js/Numpy.js"></script>
    <script type="text/javascript" src="../js/PolySolve.js"></script>
    <script type="text/javascript" src="../js/Scipy.js"></script>
    <script type="text/javascript">

       // Use Numpy & Scipy like python in javascript

       function ready() {
           var ls = numpy.linspace(2.0,3.0,5);
       }

    </script>
  </head>
</html>

Latest source code

The latest development version of Scipy's sources are always available at:

https://github.com/fernandezajp/PyExtJs

Bug reports

To search for bugs or report them, please use the Scipy Bug Tracker at:

https://github.com/fernandezajp/PyExtJs/issues

##Performance

This is very important, the test was executed in a MacBookPro i5

The python Code:

import time
import numpy

def test():
    x = numpy.array([0.0, 1.0, 2.0, 3.0,  4.0,  5.0])
    y = numpy.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])

    start = time.time()
    for num in range(1,10000):
        numpy.polyfit(x, y, 3)
    end = time.time()

    microsecs = end - start
    print microsecs * 1000

test()

The Javascript Code:

function test() {
    x = [0.0, 1.0, 2.0, 3.0,  4.0,  5.0];
    y = [0.0, 0.8, 0.9, 0.1, -0.8, -1.0];

    var start = +new Date();
    for (var i=0;i<10000;i++)
        numpy.polyfit(x, y, 3)
    var end =  +new Date();
    var diff = end - start;
    alert(diff);
}

test();

Python: 1604 milliseconds
Javascript: 14 milliseconds

Javascript! Very fast!!!