Update app.py

app.py

@@ -573,34 +573,39 @@ def server(input, output, session):
         colour_df[[0],[1]] = 'white'
         colour_df[[1],[1]] = 'white'
 
-        if df_combined_t.values[[10],[1]] < 0:
-            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(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]])))
 
+        if 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=0.8, vmax=1.2)
+            colour_df[[10],[0]] = tuple(cmap_flip(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_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]])))
+
+        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"])
+            norm = Normalize(vmin=0, vmax=1)
+            colour_df[[10],[0]] = tuple(cmap_y(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:
-                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]])))
-            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]])))
 
+        if 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=0.8, vmax=1.2)
+            colour_df[[10],[1]] = tuple(cmap_flip(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_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]])))
+
+        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"])
+            norm = Normalize(vmin=0, vmax=1)
+            colour_df[[10],[1]] = tuple(cmap_y(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)
         ax3 = plt.subplot(3,3,5)