PyExtJS
Python Extension Packages in Javascript and NodeJS
Install / Use
/learn @fernandezajp/PyExtJSREADME
PyExtJS
<sup>(Python Extension Packages in Javascript)</sup>
Contents
- <a href="#what-is-pyextjs">What is PyExtJs?</a>
- <a href="#installation">Installation</a>
- <a href="#sourcecode">Latest source code</a>
- <a href="#bugs">Bug reports</a>
- <a href="#gettingstarted">Getting Started</a>
- <a href="#numpy">numpy</a>
- <a href="#array">array</a>
- <a href="#linspace">linspace</a>
- <a href="#logspace">logspace</a>
- <a href="#exp">exp</a>
- <a href="#arange">arange</a>
- <a href="#power">power</a>
- <a href="#reshape">reshape</a>
- <a href="#shape">shape</a>
- <a href="#size">size</a>
- <a href="#ndim">ndim</a>
- <a href="#strides">strides</a>
- <a href="#dtype">dtype</a>
- <a href="#ravel">ravel</a>
- <a href="#transpose">T / transpose</a>
- <a href="#dot">dot</a>
- <a href="#zeros">zeros</a>
- <a href="#ones">ones</a>
- <a href="#random">random</a>
- <a href="#polyfit">polyfit</a>
- <a href="#scipy">scipy</a>
- <a href="#interpolate">interpolate</a>
- <a href="#linregress">linregress</a>
- <a href="#numpy">numpy</a>
<a id="what-is-pyextjs">What is PyExtJs?</a>
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
<a id="installation">Installation</a>
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>
<a id="sourcecode">Latest source code</a>
<br> The latest development version of Scipy's sources are always available at:<a id="bugs">Bug reports</a>
<br> To search for bugs or report them, please use the Scipy Bug Tracker at:<a id="gettingstarted">Getting Started</a>
<a id="numpy">numpy</a>
importing a library
Python
>>> import numpy as np
> np = numpy;
<a id="array">Using array</a>
Python
>>> import numpy
>>> numpy.array([[1,2],[3,4]])
array([[1, 2], [3, 4]])
> numpy.array([[1,2],[3,4]]);
[[1, 2], [3, 4]]
<a id="linspace">Using linspace</a>
Python
>>> import numpy
>>> numpy.linspace(2.0, 3.0, 5)
array([ 2. , 2.25, 2.5 , 2.75, 3. ])
> numpy.linspace(2.0, 3.0, 5);
[2, 2.25, 2.5, 2.75, 3]
<a id="logspace">Using logspace</a>
Python
>>> import numpy
>>> numpy.logspace(2.0, 3.0, 5)
array([100.,177.827941,316.22776602,562.34132519,1000.])
> numpy.logspace(2.0, 3.0, 5);
[100, 177.82794100389228, 316.22776601683796, 562.341325190349, 1000]
<a id="exp">Using exp</a>
Python
>>> import numpy
>>> numpy.exp(2.4)
11.023176380641601
>>> numpy.exp([2.4, 3.1])
array([ 11.02317638, 22.19795128])
> numpy.exp(2.4);
11.0231763806416
> numpy.exp([2.4, 3.2])
[11.0231763806416, 24.53253019710935]
<a id="arange">Using arange</a>
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])
> numpy.arange(3);
[0, 1, 2]
> numpy.arange(3,7)
[3, 4, 5, 6]
> numpy.arange(3,7,2)
[3, 5]
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.])
> 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]
<a id="shape">Using shape</a>
Python
>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.shape
(3, 2)
> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a.shape;
[3, 2]
<a id="size">Using size</a>
Python
>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.size
6
> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a.size;
6
<a id="ndim">Using ndim</a>
Python
>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.ndim
2
> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a.ndim;
2
<a id="strides">Using strides</a>
Python
>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.strides
(16, 8)
> 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
<a id="dtype">Using dtype</a>
Python
>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.dtype
dtype('int64')
> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a.dtype;
"Number"
<a id="ravel">Using ravel</a>
Python
>>> import numpy
>>> a = numpy.arange(6).reshape((3, 2))
>>> a.ravel()
array([0, 1, 2, 3, 4, 5])
> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a.ravel();
[0, 1, 2, 3, 4, 5]
<a id="transpose">Using T or transpose</a>
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]])
> 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]]
<a id="dot">Using dot</a>
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]])
> 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]]
<a id="zeros">Using zeros</a>
Python
>>> import numpy
>>> numpy.zeros(5)
array([ 0., 0., 0., 0., 0.])
>>> numpy.zeros((3, 2))
array([[ 0., 0.],
[ 0., 0.],
[ 0., 0.]])
> numpy.zeros(5);
[0, 0, 0, 0, 0]
> numpy.zeros([3, 2]);
[[0, 0],[0, 0],[0, 0]]
<a id="ones">Using ones</a>
Python
>>> import numpy
>>> numpy.ones(5)
array([ 1., 1., 1., 1., 1.])
>>> numpy.ones((3, 2))
array([[ 1., 1.],
[ 1., 1.],
[ 1., 1.]])
> numpy.ones(5);
[1, 1, 1, 1, 1]
> numpy.ones([3, 2]);
[[1, 1],[1, 1],[1, 1]]
<a id="random">Using random</a>
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]])
> 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]]
<a id="polyfit">Using polyfit</a>
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])
> 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]
<a id="scipy">scipy</a>
<a id="interpolate">Using interpolate</a>
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)
> 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, -2.33
