Update app.py

app.py

@@ -584,7 +584,7 @@ def server(input, output, session):
                     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]])))
+                colour_df[[10],[0]] = tuple(colormap(norm(df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))
 
 
 
@@ -593,13 +593,13 @@ def server(input, output, session):
                 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]])))
+                    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],[0]] = tuple(colormap(norm(-df_combined_t.values[[10],[1]] / df_combined_t.values[[10],[2]])))
+                    colour_df[[10],[1]] = 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],[0]] = tuple(colormap(norm(-df_combined_t.values[[10],[1]] / df_combined_t.values[[10],[2]])))
+                colour_df[[10],[1]] = 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)