Update app.py

app.py

@@ -575,9 +575,13 @@ def server(input, output, session):
 
         if df_combined_t.values[[10],[1]] < 0:
             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],[0]] = tuple(cmap_flip(norm(-df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))
+                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(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]])))
             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]])))
@@ -586,14 +590,16 @@ def server(input, output, session):
 
         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]])))
+                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(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],[0]] = tuple(colormap(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]])))
-
-
+                colour_df[[10],[0]] = tuple(colormap(norm(-df_combined_t.values[[10],[1]] / df_combined_t.values[[10],[2]])))
 
         ax1 = plt.subplot(1,3,1)
         ax2 = plt.subplot(3,3,2)