99fe730f9963f87aaf5d614bb74d60fc3d8e8173,examples/connectivity/plot_signal_extraction.py,,,#,27

Before Change


from matplotlib import pyplot as plt
plt.figure(figsize=(10, 10))
// Mask the main diagonal for visualization:
correlation_matrix[range(correlation_matrix.shape[-1]),
                   range(correlation_matrix.shape[-1])] = 0

plt.imshow(correlation_matrix, interpolation="nearest", cmap="RdBu_r",
           vmax=0.8, vmin=-0.8)

After Change


from matplotlib import pyplot as plt
plt.figure(figsize=(10, 10))
// Mask the main diagonal for visualization:
np.fill_diagonal(correlation_matrix, 0)

plt.imshow(correlation_matrix, interpolation="nearest", cmap="RdBu_r",
           vmax=0.8, vmin=-0.8)
Italian Trulli
In pattern: SUPERPATTERN

Frequency: 3

Non-data size: 4

Instances


Project Name: nilearn/nilearn
Commit Name: 99fe730f9963f87aaf5d614bb74d60fc3d8e8173
Time: 2015-11-08
Author: arokem@gmail.com
File Name: examples/connectivity/plot_signal_extraction.py
Class Name:
Method Name:


Project Name: nilearn/nilearn
Commit Name: 99fe730f9963f87aaf5d614bb74d60fc3d8e8173
Time: 2015-11-08
Author: arokem@gmail.com
File Name: examples/connectivity/plot_probabilistic_atlas_extraction.py
Class Name:
Method Name:


Project Name: nilearn/nilearn
Commit Name: d5af4c37893a7ead45a966eb89a3e4018a97e556
Time: 2015-10-06
Author: sb238920@is223297.intra.cea.fr
File Name: nilearn/connectivity/connectivity_matrices.py
Class Name:
Method Name: sym_to_vec