Update app.py

app.py

@@ -581,8 +581,9 @@ def server(input, output, session):
 
         elif df_combined_t.values[[10],[0]] < 0 and 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]])))
+            cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#4285F4"])
+            norm = Normalize(vmin=-1, vmax=0)
+            colour_df[[10],[0]] = tuple(cmap_y(norm(df_combined_t.values[[10],[0]] - df_combined_t.values[[10],[1]])))
 
         elif df_combined_t.values[[10],[0]] > 0 and df_combined_t.values[[10],[1]] < 0:
             cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#FBBC04"])
@@ -597,8 +598,9 @@ def server(input, output, session):
 
         elif df_combined_t.values[[10],[1]] < 0 and df_combined_t.values[[10],[2]] > 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]])))
+            cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#4285F4"])
+            norm = Normalize(vmin=-1, vmax=0)
+            colour_df[[10],[1]] = tuple(cmap_y(norm(df_combined_t.values[[10],[1]] - df_combined_t.values[[10],[2]])))
 
         elif df_combined_t.values[[10],[1]] > 0 and df_combined_t.values[[10],[2]] < 0:
             cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#FBBC04"])