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I have a distribution and I wish to integrate over a custom range selected by the user's mouse click on the distribution's plot.

import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from scipy.integrate import trapz

# Probability Density Function
pdf = stats.norm.pdf

#adjust the location and scale of the distribution
loc1, scale1, size1 = (20, 1.5, 500)
loc2, scale2, size2 = (28, 2.5, 500)

# Probability Density Function
pdf = stats.norm.pdf
x2 = np.concatenate([np.random.normal(loc=loc1, scale=scale1, size=size1),np.random.normal(loc=loc2, scale=scale2, size=size2)])
x_eval = np.linspace(x2.min() - 1, x2.max() + 1, 1000)
bimodal_pdf = pdf(x_eval, loc=loc1, scale=scale1) * float(size1) / x2.size + pdf(x_eval, loc=loc2, scale=scale2) * float(size2) / x2.size

plt.figure()
plt.plot(x_eval,bimodal_pdf)
plt.show()

At this point I would like to be able to select the lower and upper bounds of x over which the integral of y will be calculated.

ie.

a = User mouse click x position 1
b = User mouse click x position 2

area = trapz(y[a,b], x[a:b])


print 'the area under curve between x1 and x2 = ' + str(area)
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    matplotlib has an event handling system that can give you info about mouse clicks, etc. have you tried it? matplotlib.org/users/event_handling.html Commented Feb 28, 2014 at 0:42

1 Answer 1

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If you want the simplest possible option, use point1, point2 = plt.ginput(2).

point1 and point2 will be tuples of x,y, so you'd want a, b = point1[0], point2[0] in your example.

As a quick example:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.set(title='Click Twice', xlabel='X', ylabel='Y')

point1, point2 = fig.ginput(2) # Or equivalently, "plt.ginput"

ax.autoscale(False)
ax.axvspan(point1[0], point2[0], color='red', alpha=0.5)
fig.canvas.draw()

plt.show()
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1 Comment

Thanks, this is very nearly it! Is there a way of returning the indices of the x array, rather than the value itself?

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