Update app.py

app.py

@@ -571,19 +571,26 @@ def server(input, output, session):
         colour_df[[1],[0]] = 'white'
         colour_df[[0],[1]] = 'white'
         colour_df[[1],[1]] = 'white'
-        if df_combined_t.values[[10],[0]] < 0:
-            if df_combined_t.values[[10],[1]] < 0:
-                #cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
-                norm = Normalize(vmin=-1.2, vmax=-0.8)
-                colour_df[[10],[0]] = tuple(colormap(norm(-df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))
 
         if df_combined_t.values[[10],[1]] < 0:
-            if df_combined_t.values[[10],[2]] < 0:
-                #cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
+            if df_combined_t.values[[10],[0]] < 0:
+                cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
                 norm = Normalize(vmin=-1.2, vmax=-0.8)
-                colour_df[[10],[1]] = tuple(colormap(norm(-df_combined_t.values[[10],[1]] / df_combined_t.values[[10],[2]])))
+                colour_df[[10],[0]] = tuple(cmap_flip(norm(-df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))
+            else:
+                norm = Normalize(vmin=0.8, vmax=1.2)
+                colour_df[[10],[0]] = tuple(colormap(norm(-df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))
+
 
 
+        if df_combined_t.values[[10],[2]] < 0:
+            if df_combined_t.values[[10],[1]] < 0:
+                cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
+                norm = Normalize(vmin=-1.2, vmax=0.8)
+                colour_df[[10],[1]] = tuple(cmap_flip(norm(-df_combined_t.values[[10],[1]] / df_combined_t.values[[10],[2]])))
+            else:
+                norm = Normalize(vmin=0.8, vmax=1.2)
+                colour_df[[10],[1]] = tuple(colormap(norm(-df_combined_t.values[[10],[1]] / df_combined_t.values[[10],[2]])))