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‎Examples

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SciPy (pronounced "Sigh Pie") is an open source [[Python]] library used for scientific computing and technical computing.

==The SciPy Library/Package==
The SciPy package of key algorithms and functions core to Python's scientific computing capabilities. Available sub-packages include:
* '''constants''': physical constants and conversion factors
* '''cluster''': hierarchical clustering, vector quantization, K-means
* '''fftpack''': Discrete Fourier Transform algorithms
* '''integrate''': numerical integration routines
* '''interpolate''': interpolation tools
* '''io''': data input and output
* '''lib''': Python wrappers to external libraries
* '''linalg''': linear algebra routines
* '''misc''': miscellaneous utilities (e.g. image reading/writing)
* '''ndimage''': various functions for multi-dimensional image processing
* '''optimize''': optimization algorithms including linear programming
* '''signal''': signal processing tools
* '''sparse''': sparse matrix and related algorithms
* '''spatial''': KD-trees, nearest neighbors, distance functions
* '''special''': special functions
* '''stats''': statistical functions
* '''weave''': tool for writing C/C++ code as Python multi-line strings

==Examples==

''Note: These examples will be biased towards preparation for deep learning and machine learning courses (not to show you every feature of all these libraries).''

<pre>
from scipy.stats import norm
import numpy as np
</pre>

; Gaussian [[:wikipedia:Probability density function|probability density function]] (PDF)
<pre>
norm.pdf(0)
norm.pdf(0, loc=5, scale=10) # scale => stdev

r = np.random.randn(10)
norm.pdf(r) # calculate the PDF of all values r at the same time

# Log of Gaussian PDF is much faster to calculate, since there are no exponentials
# adding is a cheaper operation than multiplying
norm.logpdf(r)
# CDF
</pre>

; Guassian [[:wikipedia:Cumulative distribution function|cumulative distribution function]] (CDF)
<pre>
norm.cdf(r)
norm.logcdf(r)
</pre>

; Sampling from a Gaussian distribution (1D)
<pre>
r = np.random.randn(10000)
plt.hist(r, bins=100)
plt.show()

r = 10 * np.random.randn(10000) + 5 # 10 = stdev; 5 = mean
plt.hist(r, bins=100)
plt.show()
</pre>

; Sampling from a Gaussian distribution (spherical and axis-aligned elliptical)
<pre>
r = np.random.randn(10000, 2)
plt.scatter(r[:,0], r[:,1])
plt.show()

# stdev
r[:,1] = 5 * r[:,1] + 2
plt.scatter(r[:,0], r[:,1])
plt.show()

plt.scatter(r[:,0], r[:,1])
plt.axis('equal')
plt.show()
</pre>

; Sampling from a General Multivariate Normal
; Other interesting SciPy functions

==See also==
* [[Python/NumPy|NumPy]]
* [[Python/matplotlib|matplotlib]]
* [[Python/pandas|pandas]]
* scikit-learn
* scikit-image

==External links==
*[https://www.scipy.org/ Official website]

[[Category:Scripting languages]]

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 ; Sampling from a General Multivariate Normal
 ; Other interesting SciPy functions
 ==See also==

Python/SciPy - Revision history

Christoph: /* External links */

Christoph: /* Examples */

Christoph: Created page with "SciPy (pronounced "Sigh Pie") is an open source Python library used for scientific computing and technical computing. ==The SciPy Library/Package== The SciPy package of k..."