Update correlation_explorer_app.py

correlation_explorer_app.py

@@ -13,7 +13,7 @@ df_global = pd.read_csv("merged_global.csv")
 df_hemi = pd.read_csv("hemispheric_merged.csv")
 # Merge by Year and Month 
 df = pd.merge(df_global, df_hemi, on=["year", "month"], suffixes=("", "_hemi"))
-# Add Season column
+# For the seaosnal analysis categorizing the months
 def get_season(month):
     return {
         12: "DJF", 1: "DJF", 2: "DJF",
@@ -80,11 +80,13 @@ app.layout = dbc.Container([
     dbc.Row([
         dbc.Col([
             html.Label("X-axis Variable"),
-            dcc.Dropdown(id='x-axis-dropdown')
+            html.P("Select the variable for the X-axis"),
+            dcc.Dropdown(id='x-axis-dropdown', placeholder="Choose a variable for X-axis")
         ], md=6),
         dbc.Col([
             html.Label("Y-axis Variable"),
-            dcc.Dropdown(id='y-axis-dropdown')
+            html.P("Select the variable for the Y-axis"),
+            dcc.Dropdown(id='y-axis-dropdown', placeholder="Choose a variable for Y-axis")
         ], md=6)
     ], className="mb-3"),
 
@@ -122,7 +124,7 @@ app.layout = dbc.Container([
             ]),
             html.P("Switch views (Global, Northern, Southern Hemisphere) and select indicators and years to compare."),
             html.P("The scatter plot shows how the selected variables change together, with a regression trendline and R² value."),
-            html.P("Pearson's r (seen on the heatmap and above the scatter) helps you evaluate correlation strength."),
+            html.P("Pearson's r (shown in the heatmap and above the scatter) helps you evaluate correlation strength."),
             html.P("The correlation heatmap reveals how all indicators relate within your selected range.")
         ])
     ], className="mb-4"),
@@ -156,11 +158,16 @@ def update_variable_options(scope):
     Input('scope-selector', 'value'),
     Input('theme-toggle', 'value')
 )
-def update_visuals(x_var, y_var, year_range, mode, scope, dark_mode):
+def update_visuals(x_var, y_var, year_range,view_mode, scope, dark_mode):
+
     vars_dict = get_variables(scope)
     dff = df[(df["year"] >= year_range[0]) & (df["year"] <= year_range[1])]
-
-    if mode == "Seasonal":
+    """
+    Updating both charts based on user inputs
+    Supports theme switching and monthly/seasonal toggling.
+    """
+# If the view mode switched one the seasonal the month columns are not needed
+    if view_mode == "Seasonal":
         dff = dff.groupby(['year', 'Season']).mean(numeric_only=True).reset_index()
     else:
         dff = dff.copy()
@@ -187,7 +194,7 @@ def update_visuals(x_var, y_var, year_range, mode, scope, dark_mode):
     )
     fig.update_traces(
         hovertemplate=f"<b>Year</b>: %{{customdata[0]}}<br><b>Month/Season</b>: %{{customdata[1]}}<br><b>{vars_dict[x_var]}</b>: %{{x:.3f}}<br><b>{vars_dict[y_var]}</b>: %{{y:.3f}}",
-        customdata=dff[["year", "Season"]] if mode == "Seasonal" else dff[["year", "month"]]
+        customdata=dff[["year", "Season"]] if view_mode == "Seasonal" else dff[["year", "month"]]
     )
 # Add R² if OLS exists
     try:
@@ -218,4 +225,7 @@ def update_visuals(x_var, y_var, year_range, mode, scope, dark_mode):
 server = app.server  # Required for gunicorn
 # 5. Run app
 if __name__ == '__main__':
-    app.run_server(debug=True, host='0.0.0.0', port=7860)
+    app.run_server(debug=True, host='0.0.0.0', port=7860)
+dbc.Row([
+    dbc.Col(html.Footer("Created by Irem R. as part of Bachelor Thesis at Riga Technical University — 2025", className="text-center text-muted"), width=12)
+], className="mt-4")