Skip to content

Add power-law normalization #1204

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 5 commits into from
Apr 4, 2014
Merged
Changes from 1 commit
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
Next Next commit
power_norm_demo: Initial commit
  • Loading branch information
bgamari committed Mar 10, 2014
commit 48998c1595e291239cd049a881a09a95d98534a3
27 changes: 27 additions & 0 deletions examples/api/power_norm_demo.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,27 @@
#!/usr/bin/python

from matplotlib import pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
from numpy.random import multivariate_normal

data = np.vstack([multivariate_normal([10, 10], [[3, 5],[4, 2]], size=100000),
multivariate_normal([30, 20], [[2, 3],[1, 3]], size=1000)
])

gammas = [0.8, 0.5, 0.3]
xgrid = np.floor((len(gammas) + 1.) / 2)
ygrid = np.ceil((len(gammas) + 1.) / 2)

plt.subplot(xgrid, ygrid, 1)
plt.title('Linear normalization')
plt.hist2d(data[:,0], data[:,1], bins=100)

for i, gamma in enumerate(gammas):
plt.subplot(xgrid, ygrid, i + 2)
plt.title('Power law normalization\n$(\gamma=%1.1f)$' % gamma)
plt.hist2d(data[:, 0], data[:, 1],
bins=100, norm=mcolors.PowerNorm(gamma))

plt.subplots_adjust(hspace=0.39)
plt.show()