-
Notifications
You must be signed in to change notification settings - Fork 0
/
Histograma.py
36 lines (26 loc) · 1.07 KB
/
Histograma.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import scipy.stats as sci
# example data
for l in range(len(D.keys())):
data = D[D.keys()[l]][6][-3000:-1]
while data.max(axis = 0) > np.std(data)*3 or data.min(axis = 0) < -np.std(data)*3:
data = np.delete(data, np.argmax(data, axis=0) , axis = 0)
data = np.delete(data, np.argmin(data, axis=0) , axis = 0)
mu = np.mean( data) # mean of distribution
sigma = np.sqrt(np.var( data )) # standard deviation of distribution
x = data
num_bins = 50
# the histogram of the data
n, bins, patches = plt.hist(x, num_bins, normed=2, facecolor='green', alpha=0.5,rwidth=0.2)
# add a 'best fit' line
y = mlab.normpdf(bins, mu, sigma)
plt.plot(bins, y, 'r--')
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('%s' % D.keys()[l])
# Tweak spacing to prevent clipping of ylabel
plt.subplots_adjust(left=0.15)
plt.show()
norm = sci.normaltest(data,nan_policy='omit')