scikit-learn · ogrisel · Jul 5, 2018
diff --git a/examples/applications/plot_stock_market.py b/examples/applications/plot_stock_market.py
@@ -278,7 +278,7 @@ def quotes_historical_google(symbol, start_date, end_date):
 
 # Plot the nodes using the coordinates of our embedding
 plt.scatter(embedding[0], embedding[1], s=100 * d ** 2, c=labels,
-            cmap=plt.cm.spectral)
+            cmap=plt.cm.nipy_spectral)
 
 # Plot the edges
 start_idx, end_idx = np.where(non_zero)
@@ -321,7 +321,7 @@ def quotes_historical_google(symbol, start_date, end_date):
              horizontalalignment=horizontalalignment,
              verticalalignment=verticalalignment,
              bbox=dict(facecolor='w',
-                       edgecolor=plt.cm.spectral(label / float(n_labels)),
+                       edgecolor=plt.cm.nipy_spectral(label / float(n_labels)),
                        alpha=.6))
 
 plt.xlim(embedding[0].min() - .15 * embedding[0].ptp(),

diff --git a/examples/cluster/plot_agglomerative_clustering.py b/examples/cluster/plot_agglomerative_clustering.py
@@ -61,7 +61,7 @@
             model.fit(X)
             elapsed_time = time.time() - t0
             plt.scatter(X[:, 0], X[:, 1], c=model.labels_,
-                        cmap=plt.cm.spectral)
+                        cmap=plt.cm.nipy_spectral)
             plt.title('linkage=%s (time %.2fs)' % (linkage, elapsed_time),
                       fontdict=dict(verticalalignment='top'))
             plt.axis('equal')

diff --git a/examples/cluster/plot_digits_agglomeration.py b/examples/cluster/plot_digits_agglomeration.py
@@ -54,7 +54,7 @@
 
 plt.subplot(3, 4, 10)
 plt.imshow(np.reshape(agglo.labels_, images[0].shape),
-           interpolation='nearest', cmap=plt.cm.spectral)
+           interpolation='nearest', cmap=plt.cm.nipy_spectral)
 plt.xticks(())
 plt.yticks(())
 plt.title('Labels')

diff --git a/examples/cluster/plot_digits_linkage.py b/examples/cluster/plot_digits_linkage.py
@@ -61,7 +61,7 @@ def plot_clustering(X_red, X, labels, title=None):
     plt.figure(figsize=(6, 4))
     for i in range(X_red.shape[0]):
         plt.text(X_red[i, 0], X_red[i, 1], str(y[i]),
-                 color=plt.cm.spectral(labels[i] / 10.),
+                 color=plt.cm.nipy_spectral(labels[i] / 10.),
                  fontdict={'weight': 'bold', 'size': 9})
 
     plt.xticks([])

diff --git a/examples/cluster/plot_face_segmentation.py b/examples/cluster/plot_face_segmentation.py
@@ -71,7 +71,7 @@
     plt.imshow(face, cmap=plt.cm.gray)
     for l in range(N_REGIONS):
         plt.contour(labels == l, contours=1,
-                    colors=[plt.cm.spectral(l / float(N_REGIONS))])
+                    colors=[plt.cm.nipy_spectral(l / float(N_REGIONS))])
     plt.xticks(())
     plt.yticks(())
     title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))

diff --git a/examples/cluster/plot_face_ward_segmentation.py b/examples/cluster/plot_face_ward_segmentation.py
@@ -61,7 +61,7 @@
 plt.imshow(face, cmap=plt.cm.gray)
 for l in range(n_clusters):
     plt.contour(label == l, contours=1,
-                colors=[plt.cm.spectral(l / float(n_clusters)), ])
+                colors=[plt.cm.nipy_spectral(l / float(n_clusters)), ])
 plt.xticks(())
 plt.yticks(())
 plt.show()
diff --git a/examples/cluster/plot_kmeans_silhouette_analysis.py b/examples/cluster/plot_kmeans_silhouette_analysis.py
@@ -95,7 +95,7 @@
         size_cluster_i = ith_cluster_silhouette_values.shape[0]
         y_upper = y_lower + size_cluster_i
 
-        color = cm.spectral(float(i) / n_clusters)
+        color = cm.nipy_spectral(float(i) / n_clusters)
         ax1.fill_betweenx(np.arange(y_lower, y_upper),
                           0, ith_cluster_silhouette_values,
                           facecolor=color, edgecolor=color, alpha=0.7)
@@ -117,7 +117,7 @@
     ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
 
     # 2nd Plot showing the actual clusters formed
-    colors = cm.spectral(cluster_labels.astype(float) / n_clusters)
+    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)
     ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,
                 c=colors, edgecolor='k')
 

diff --git a/examples/cluster/plot_kmeans_stability_low_dim_dense.py b/examples/cluster/plot_kmeans_stability_low_dim_dense.py
@@ -108,7 +108,7 @@ def make_data(random_state, n_samples_per_center, grid_size, scale):
 fig = plt.figure()
 for k in range(n_clusters):
     my_members = km.labels_ == k
-    color = cm.spectral(float(k) / n_clusters, 1)
+    color = cm.nipy_spectral(float(k) / n_clusters, 1)
     plt.plot(X[my_members, 0], X[my_members, 1], 'o', marker='.', c=color)
     cluster_center = km.cluster_centers_[k]
     plt.plot(cluster_center[0], cluster_center[1], 'o',

diff --git a/examples/decomposition/plot_pca_iris.py b/examples/decomposition/plot_pca_iris.py
@@ -50,7 +50,7 @@
               bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
 # Reorder the labels to have colors matching the cluster results
 y = np.choose(y, [1, 2, 0]).astype(np.float)
-ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.spectral,
+ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral,
            edgecolor='k')
 
 ax.w_xaxis.set_ticklabels([])