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PyTables can be used to easily perform calculations on large data sets. The operator is similar to numpy’s arange() and the like. The module allows you to efficiently manipulate large data arrays, as well as compute simple statistics.
PyTablesis designed for arrays and tables that contain large amounts of data. It’s primary goal is to make it easier to access large data sets within your applications. PyTablesis also lightweight, and can easily be incorporated into your code.
GitHub Page:

A:

I had used code from youtube to do this:
import numpy as np
import matplotlib.pyplot as plt
from math import sin, cos, pi
from scipy.optimize import fmin_powell

x = [0, 1, 2, 3]
y = [0, 1, 2, 3]

#convert to numpy arrays
x = np.array(x)
y = np.array(y)

x = np.linspace(0, np.pi, 100, endpoint=False)

#array_sin and array_cos are arrays with 100 equally spaced values between 0-pi
array_sin = np.sin(x)
array_cos = np.cos(x)

#result is a list of the above, and an array holding the sum
result = [x, y]

def func_numerical(x, y, tau):
x0 = x[0]
y0 = y[0]

x1 = x[1]
y1 = y[1]

x2 = x[2]
y2 = y[2]

x3 = x[3]
y3 = y[3]

c = tau / (x0**2 + y0**2)

d = 1 – c

#sum(array_sin*array_cos)
f1 = (x1 * y2 – x2 * y1) / d
f2 = (x1 * y3 – x3 * y1) / d

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