Image Cleaning notebook update

jupyter_notebooks/Image_Cleaning_Atom_Finding.ipynb

@@ -516,11 +516,11 @@
 We want a large enough number of clusters so that K-means identifies fine nuances in the data. At the same time, we want to minimize computational time by reducing the number of clusters. We recommend 32 - 64 clusters.
 
 %% Cell type:code id: tags:
 
 ``` python
-clean_components = 12
+clean_components = 32
 num_clusters = 32
 
 # Check for existing Clustering results
 estimator = px.Cluster(h5_U, 'KMeans', num_comps=clean_components, n_clusters=num_clusters)
 do_cluster = False
@@ -543,11 +543,11 @@
         if new_clean.step is None:
             new_clean = range(new_clean.start, new_clean.stop)
         else:
             new_clean = range(new_clean.start, new_clean.stop, new_clean.step)
     
-    if all(h5_kmeans.attrs['components_used']==new_clean):
+    if np.array_equal(h5_kmeans.attrs['components_used'], new_clean):
         print( 'Clustering results used the same components as those requested.')
     else:
         do_cluster = True
         print( 'Clustering results used the different components from those requested.')
 
@@ -615,16 +615,10 @@
 fig.axes[0].set_xlabel('Cluster number', fontsize=20) 
 fig.axes[0].set_ylabel('Cluster separation', fontsize=20)
 px.plot_utils.set_tick_font_size(fig.axes[0], 12)
 ```
 
-%% Cell type:code id: tags:
-
-``` python
-vert_line.get_xdata()
-```
-
 %% Cell type:markdown id: tags:
 
 ## Identifiying the principal patterns
 Here, we will interactively identify N windows, each centered on a distinct class / kind of atom.
 
@@ -742,25 +736,25 @@
 for window_center, ax_row in zip(motif_win_centers, np.atleast_2d(axes)):
     indices = (slice(window_center[0] - half_wind, window_center[0] + half_wind), 
                slice(window_center[1] - half_wind, window_center[1] + half_wind))
     motifs.append(labels_mat[indices])
     
-    ax_row[0].hold(True)
+#     ax_row[0].hold(True)
     ax_row[0].imshow(cropped_clean_image, interpolation='none',cmap=px.plot_utils.cmap_jet_white_center(), origin='lower')
     ax_row[0].add_patch(patches.Rectangle((window_center[1] - int(0.5*motif_win_size), 
                                            window_center[0] - int(0.5*motif_win_size)),
                                           motif_win_size, motif_win_size, fill=False,
                                          color='black', linewidth=2))
-    ax_row[0].hold(False)
-    ax_row[1].hold(True)
+#     ax_row[0].hold(False)
+#     ax_row[1].hold(True)
     ax_row[1].imshow(cropped_clean_image[indices], interpolation='none',cmap=px.plot_utils.cmap_jet_white_center(),
                      vmax=np.max(cropped_clean_image), vmin=np.min(cropped_clean_image), origin='lower')
     ax_row[1].plot([0, motif_win_size-2],[int(0.5*motif_win_size), int(0.5*motif_win_size)], 'k--')
     ax_row[1].plot([int(0.5*motif_win_size), int(0.5*motif_win_size)], [0, motif_win_size-2], 'k--')
     # ax_row[1].axis('tight')
     ax_row[1].set_title('Selected window for motif around (row {}, col {})'.format(window_center[0], window_center[1]))
-    ax_row[1].hold(False)
+#     ax_row[1].hold(False)
     ax_row[2].imshow(labels_mat[indices], interpolation='none',cmap=px.plot_utils.cmap_jet_white_center(),
                      vmax=num_clusters-1, vmin=0, origin='lower')
     ax_row[2].set_title('Motif from K-means labels');
 ```
 
@@ -801,20 +795,20 @@
 ``` python
 show_legend = True
 
 base_color_map = plt.cm.get_cmap('jet')
 fig = plt.figure(figsize=(8, 8))
-im = plt.imshow(double_cropped_image, cmap="gray", origin='lower')
+plt.imshow(double_cropped_image, cmap="gray", origin='lower')
 
 if num_motifs > 1:
     motif_colors = [base_color_map(int(255 * motif_ind / (num_motifs - 1))) for motif_ind in range(num_motifs)]
 else:
     motif_colors = [base_color_map(0)]
 handles = list()
 for motif_ind, current_solid_color, match_mat in zip(range(num_motifs), motif_colors, motif_match_coeffs):
     my_cmap = px.plot_utils.make_linear_alpha_cmap('fdfd', current_solid_color, 1)
-    im = plt.imshow(match_mat, cmap=my_cmap, origin='lower');
+    plt.imshow(match_mat, cmap=my_cmap, origin='lower');
     current_solid_color = list(current_solid_color)
     current_solid_color[3] = 0.5 # maximum alpha value
     handles.append(patches.Patch(color=current_solid_color, label='Motif {}'.format(motif_ind)))
 if show_legend:
     plt.legend(handles=handles, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0., fontsize=14)
@@ -842,14 +836,15 @@
 thresholds = [0.25 for x in range(num_motifs)]
 thresholded_maps = list()
 motif_imgs = list()
 
 base_color_map = plt.cm.jet
-fig, axis = plt.subplots(figsize=(10, 10))
-plt.hold(True);
+fig = plt.figure(figsize=(10, 10))
 plt.imshow(double_cropped_image, cmap="gray")
+axis = plt.gca()
 handles = list()
+
 if num_motifs > 1:
     motif_colors = [base_color_map(int(255 * motif_ind / (num_motifs - 1))) for motif_ind in range(num_motifs)]
 else:
     motif_colors = [base_color_map(0)]
 
@@ -868,11 +863,10 @@
 axis.set_xticklabels([])
 axis.set_yticklabels([])
 axis.get_xaxis().set_visible(False)
 axis.get_yaxis().set_visible(False)
 plt.legend(handles=handles, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
-plt.hold(False);
 
 def threshold_images(thresholds):
     # thresholded_maps = list()
     # empty the thresholded maps:
     del thresholded_maps[:]
@@ -925,11 +919,11 @@
 %% Cell type:code id: tags:
 
 ``` python
 # overlay atom positions on original image
 fig, axis = plt.subplots(figsize=(8,8))
-axis.hold(True)
+
 col_map = plt.cm.jet
 axis.imshow(double_cropped_image, interpolation='none',cmap="gray")
 legend_handles = list()
 for atom_type_ind, atom_centroids in enumerate(atom_labels):    
     axis.scatter(atom_centroids[:,1], atom_centroids[:,0], color=col_map(int(255 * atom_type_ind / (num_motifs-1))),