work on OP contribution

apps/app_components.py

@@ -438,15 +438,6 @@ def get_op_contribution_component():
                 ],
                 value='daily',
             ),
-            dcc.RadioItems(
-                id='op-contribution-estimator',
-                className='options-items',
-                options=[
-                    {'label': 'Mean', 'value': 'mean'},
-                    {'label': 'Median', 'value': 'median'},
-                ],
-                value='median',
-            ),
             dcc.Loading(
                 dcc.Graph(
                     id="op-contribution-graph",

apps/app_results.py

@@ -1387,9 +1387,8 @@ def update_betacoeff_graph(sources, stations, species):
                  Input('station-dropdown', 'value'),
                  Input('OP-dropdown', 'value'),
                  Input('op-contribution-plottype', 'value'),
-                 Input('op-contribution-estimator', 'value')
              ])
-def update_op_contribution_graph(stations, species, plot_type, estimator):
+def update_op_contribution_graph(stations, species, plot_type):
     """TODO: Docstring for update_op_contribution_graph.
     :returns: TODO
 
@@ -1437,34 +1436,42 @@ def update_op_contribution_graph(stations, species, plot_type, estimator):
 
     contrib = contrib.dropna(how="all", axis=1)
 
-    print(plot_type)
     contrib = ac.add_season(contrib)
     contrib = contrib.reset_index().melt(id_vars=["station", "Date", "season", "month"],
                                          var_name="factor", value_name="OP")
-    if plot_type == "daily":
-        if estimator == "median":
-            contrib = contrib.groupby(["factor"]).median().reset_index()
-        elif estimator == "mean":
-            contrib = contrib.groupby(["factor"]).mean().reset_index()
 
+    if plot_type == "daily":
+        data = contrib.groupby(["factor"])\
+                .describe()["OP"][["mean", "50%"]]\
+                .reset_index()\
+                .melt(id_vars=["factor"], value_name="OP",
+                      var_name="estimator")
         fig = px.bar(
-            contrib, x="OP", y="factor", color="factor",
+            data, x="OP", y="factor", color="factor",
             orientation='h',
+            facet_row="estimator",
             color_discrete_map=colors,
-            height=500
+            category_orders={"estimator": ["50%", "mean"]},
+            height=1000
         )
+        fig.update_yaxes(title_text="Median value<br>OP {} (nmol.min⁻¹.m⁻³)".format(species[0]), row=1)
+        fig.update_yaxes(title_text="Mean value<br>OP {} (nmol.min⁻¹.m⁻³)".format(species[0]), row=2)
     elif plot_type == "seasonal":
-        if estimator == "median":
-            contrib = contrib.groupby(["season", "station", "factor"]).median().reset_index()
-        elif estimator == "mean":
-            contrib = contrib.groupby(["season", "station", "factor"]).mean().reset_index()
-
+        data = contrib.groupby(["season", "factor"])\
+                .describe()["OP"][["mean", "50%"]]\
+                .reset_index()\
+                .melt(id_vars=["season", "factor"], value_name="OP",
+                      var_name="estimator")
         fig = px.bar(
-            contrib, x="season", y="OP", color="factor",
+            data, x="season", y="OP", color="factor",
             color_discrete_map=colors,
-            facet_row="station",
-            height=500*len(stations)
+            barmode="group",
+            facet_row="estimator",
+            category_orders={"season": ["Winter", "Spring", "Summer", "Fall"]},
+            height=1000, #*len(stations),
         )
+        fig.update_yaxes(title_text="Median value<br>OP {} (nmol.min⁻¹.m⁻³)".format(species[0]), row=1)
+        fig.update_yaxes(title_text="Mean value<br>OP {} (nmol.min⁻¹.m⁻³)".format(species[0]), row=2)
     elif plot_type == "ts":
         fig = px.area(
             contrib, x="Date", y="OP", color="factor",