Separated graphs

py/clustering.py

@@ -83,34 +83,39 @@ print(mdagg.describe())
 
 # Algorithm via 
 # <https://stackoverflow.com/questions/38153829/custom-cluster-colors-of-scipy-dendrogram-in-python-link-color-func>
-ldict = {icp_id:cpal[cluster] for icp_id, cluster in zip(clustdf.icp_id, clustdf.cluster)}
-link_cols = {}
-for i, i12 in enumerate(lobj[:,:2].astype(int)):
-  c1, c2 = (link_cols[x] if x > len(lobj) else ldict[clustdf.icp_id[x]]
-    for x in i12)
-  link_cols[i+1+len(lobj)] = c1 if c1 == c2 else '#000000'
-
-plt.figure(figsize = (25, 10))
-plt.title('ICP Clustering Dendrogram')
-plt.xlabel('ICP ID/(Number of ICPs)')
-plt.ylabel('distance')
-dendrogram(
-    lobj,
-    labels = cmat.index.values,
-    leaf_rotation=90,
-    leaf_font_size=8,
-    # show_leaf_counts = True,
-    # truncate_mode = 'lastp',
-    # p = 50,
-    # show_contracted = True,
-    link_color_func = lambda x: link_cols[x],
-    color_threshold = None
-)
-plt.show()
+# ldict = {icp_id:cpal[cluster] for icp_id, cluster in zip(clustdf.icp_id, clustdf.cluster)}
+# link_cols = {}
+# for i, i12 in enumerate(lobj[:,:2].astype(int)):
+#   c1, c2 = (link_cols[x] if x > len(lobj) else ldict[clustdf.icp_id[x]]
+#     for x in i12)
+#   link_cols[i+1+len(lobj)] = c1 if c1 == c2 else '#000000'
+# 
+# plt.figure(figsize = (25, 10))
+# plt.title('ICP Clustering Dendrogram')
+# plt.xlabel('ICP ID/(Number of ICPs)')
+# plt.ylabel('distance')
+# dendrogram(
+#     lobj,
+#     labels = cmat.index.values,
+#     leaf_rotation=90,
+#     leaf_font_size=8,
+#     # show_leaf_counts = True,
+#     # truncate_mode = 'lastp',
+#     # p = 50,
+#     # show_contracted = True,
+#     link_color_func = lambda x: link_cols[x],
+#     color_threshold = None
+# )
+# plt.show()
 
 sns.set()
-ax = sns.lineplot(x = 'read_time', y = 'kwh_tot_mean', hue = 'cluster', data = mdagg, palette = cpal)
-for c in clabs:
+
+f, axes = plt.subplots(3,3)
+print(f)
+print(axes)
+
+for i, c in enumerate(clabs):
     fds = mdagg[mdagg.cluster == c]
-    ax.fill_between(fds.read_time.dt.to_pydatetime(), fds.kwh_tot_CI_low, fds.kwh_tot_CI_high, alpha = 0.1, color = cpal[c])
+    sns.lineplot(x = 'read_time', y = 'kwh_tot_mean', color = cpal[c], ax = axes[i//3][i%3], data = fds)
+    axes[i//3][i%3].fill_between(fds.read_time.dt.to_pydatetime(), fds.kwh_tot_CI_low, fds.kwh_tot_CI_high, alpha = 0.1, color = cpal[c])
 plt.show()