# Scipy

Gregor von Laszewski (laszewski@gmail.com)

SciPy is a library built around NumPy and has a number of off-the-shelf algorithms and operations implemented. These include algorithms from calculus (such as integration), statistics, linear algebra, image-processing, signal processing, machine learning.

To achieve this, SciPy bundles a number of useful open-source software for mathematics, science, and engineering. It includes the following packages:

NumPy,

for managing N-dimensional arrays

SciPy library,

to access fundamental scientific computing capabilities

Matplotlib,

to conduct 2D plotting

IPython,

for an Interactive console (see jupyter)

Sympy,

for symbolic mathematics

pandas,

for providing data structures and analysis

## Introduction

First, we add the usual scientific computing modules with the typical abbreviations, including sp for scipy. We could invoke scipy’s statistical package as sp.stats, but for the sake of laziness, we abbreviate that too.

import numpy as np # import numpy
import scipy as sp # import scipy
from scipy import stats # refer directly to stats rather than sp.stats
import matplotlib as mpl # for visualization
from matplotlib import pyplot as plt # refer directly to pyplot
# rather than mpl.pyplot


Now we create some random data to play with. We generate 100 samples from a Gaussian distribution centered at zero.

s = sp.randn(100)


How many elements are in the set?

print ('There are',len(s),'elements in the set')


What is the mean (average) of the set?

print ('The mean of the set is',s.mean())


What is the minimum of the set?

print ('The minimum of the set is',s.min())


What is the maximum of the set?

print ('The maximum of the set is',s.max())


We can use the scipy functions too. What’s the median?

print ('The median of the set is',sp.median(s))


What about the standard deviation and variance?

print ('The standard deviation is',sp.std(s),
'and the variance is',sp.var(s))


Isn’t the variance the square of the standard deviation?

    print ('The square of the standard deviation is',sp.std(s)**2)


How close are the measures? The differences are close as the following calculation shows

    print ('The difference is',abs(sp.std(s)**2 - sp.var(s)))

print ('And in decimal form, the difference is %0.16f' %
(abs(sp.std(s)**2 - sp.var(s))))


How does this look as a histogram? See Figure 1, Figure 2, Figure 3

plt.hist(s) # yes, one line of code for a histogram
plt.show()


Figure 1: Histogram 1

plt.clf() # clear out the previous plot

plt.hist(s)
plt.title("Histogram Example")
plt.xlabel("Value")
plt.ylabel("Frequency")

plt.show()


Figure 2: Histogram 2

Typically we do not include titles when we prepare images for inclusion in LaTeX. There we use the caption to describe what the figure is about.

plt.clf() # clear out the previous plot

plt.hist(s)
plt.xlabel("Value")
plt.ylabel("Frequency")

plt.show()


Figure 3: Histogram 3

Let us try out some linear regression or curve fitting. See @#fig:scipy-output_30_0

import random

def F(x):
return 2*x - 2

return x + random.uniform(-1,1)

X = range(0,10,1)

Y = []
for i in range(len(X)):

plt.clf() # clear out the old figure
plt.plot(X,Y,'.')
plt.show()


Figure 4: Result 1

Now let’s try linear regression to fit the curve.

m, b, r, p, est_std_err = stats.linregress(X,Y)


What is the slope and y-intercept of the fitted curve?

print ('The slope is',m,'and the y-intercept is', b)

def Fprime(x): # the fitted curve
return m*x + b


Now let’s see how well the curve fits the data. We’ll call the fitted curve F'.

X = range(0,10,1)

Yprime = []
for i in range(len(X)):
Yprime.append(Fprime(X[i]))

plt.clf() # clear out the old figure

# the observed points, blue dots
plt.plot(X, Y, '.', label='observed points')

# the interpolated curve, connected red line
plt.plot(X, Yprime, 'r-', label='estimated points')

plt.title("Linear Regression Example") # title
plt.xlabel("x") # horizontal axis title
plt.ylabel("y") # vertical axis title
# legend labels to plot
plt.legend(['obsered points', 'estimated points'])

# comment out so that you can save the figure
#plt.show()


To save images into a PDF file for inclusion into LaTeX documents you can save the images as follows. Other formats such as png are also possible, but the quality is naturally not sufficient for inclusion in papers and documents. For that, you certainly want to use PDF. The save of the figure has to occur before you use the show() command. See Figure 5

plt.savefig("regression.pdf", bbox_inches='tight')

plt.savefig('regression.png')

plt.show()


Figure 5: Result 2

## References

https://www.scipy.org/getting-started.html#learning-to-work-with-scipy

Additional material and inspiration for this section are from

[![No

• [] Prasanth. “Simple statistics with SciPy.” Comfort at 1 AU. February
• [] SciPy Cookbook. Lasted updated: 2015.

create bibtex entries

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