13- Using SciPy (Scientific Python) library in Python

SciPy denotes Scientific Python and it is valuable for scientific analysis.

SciPy installation:

pip install SciPy

Examples of the functions in SciPy:

• Integration: (scipy.integrate)

• Optimization/Fitting: (scipy.optimize)

• Interpolation: (scipy.interpolate)

• Signal Processing: (scipy.signal)

• Spatial data structures and algorithms: (scipy.spatial)

• Statistics: (scipy.stats)

• Multi-dimensional image processing: (scipy.ndimage)

Importing from SciPy

from scipy import optimize
from scipy import spatial
# first form
from scipy import stats
# second form
from scipy.stats import distributions

T-Test

from scipy.stats import ttest_ind
x = ([1, 3, 5, 7, 11])
y = ([2, 4, 6, 8, 4])
res = ttest_ind(x, y)
print(res)

Statistical Description of Data

import numpy as np
from scipy.stats import describe

x = np.random.normal(size=50)
res = describe(x)

print(res)
# DescribeResult(nobs=50, minmax=(-2.5511668761037507, 1.7772593602939395), mean=0.02937722230632724, variance=0.9754504451804601, skewness=-0.12226936632001595, kurtosis=-0.39363575297869824)

Interpolation

import numpy as np
x = np.array([0., 1.,5., 8., 10.])
y = np.array([0., 4., 1., 6., 8.])
f = interp1d(x, y)
f(3)
# array(2.5)

Integration

import scipy.integrate
f= lambda x:np.exp(x**1)
# print results
i = scipy.integrate.quad(f, 1, 2) # quad -- General purpose integration.
print(i)
# (4.670774270471606, 5.1856011379043454e-14)

Input and Output

Scipy.io package provides multiple methods to handle inputs and outputs of multiple formats such as:

• Matlab

• Netcdf

• IDL

• Arff

• Matrix Market

• Wave
  

import scipy.io as syio

# Save the mat file
n = 14031977
syio.savemat('test.mat', {'test': n})

# Load the mat File
matf_contents = syio.loadmat('test.mat')
print(matf_contents)

# printing the contents of mat file.
matf_contents = syio.whosmat('test.mat')
print(matf_contents)

#{'__header__': b'MATLAB 5.0 MAT-file Platform: nt, Created on: Mon Jun 28 16:15:59 2021', '__version__': '1.0', '__globals__': [], 'test': array([[14031977]])}
[('test', (1, 1), 'int32')]

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