rework

apps/app_components.py

@@ -410,6 +410,31 @@ def get_specie_repartition_component():
     )
     return specie_repartition_component
 
+def get_op_obs_vs_model_component():
+    component = html.Div(
+        children=[
+            dcc.Markdown("""
+                         ### Observation vs. model OP
+                         """),
+            # dcc.RadioItems(
+            #     id="op-obsvsmodel-options",
+            #     options=[
+            #         {"label": "Trendline", "value": "trendline"},
+            #         {"label": "Add uncertainties", "value": "error_xy"},
+            #         {"label": "None", "value": "nothing"},
+            #     ]
+            # ),
+            dcc.Loading(
+                dcc.Graph(
+                    id="op-obs-vs-model-graph",
+                    figure={"data": [], "layout": {}}
+                )
+            )
+        ]
+    )
+
+    return component
+
 def get_op_beta_component():
     """Get the OP model
     :returns: TODO
@@ -484,9 +509,16 @@ def get_op_contribution_ts_component():
                     {'label': 'Monthly timeserie', 'value': 'monthly'},
                     {'label': 'Quaterly timeserie', 'value': 'quaterly'},
                 ],
-                value='monthly',
+                value='daily',
             ),
             html.Br(),
+            dcc.Dropdown(
+                id='op-contribution-ts-station-dropdown',
+                className='dropdown',
+                options=[{"label": "CHAM", "value": "CHAM"}],
+                value=["CHAM"],
+                multi=False,
+            ),
             dcc.RadioItems(
                 id='op-contribution-ts-estimator',
                 className='options-items',
@@ -496,7 +528,17 @@ def get_op_contribution_ts_component():
                 ],
                 value='median',
             ),
-            dcc.Markdown("**Daily** is the daily reconstructed OP from the regression model. **Monthly** (mean or median) are the monthly aggregated mean or median for each station. **Quaterly** is the seasonal (4 seasons: DJF, MAM, JJA, SON) mean or median aggratation per station.  This plot may take some time to generate..."),
+            dcc.Markdown("""
+                         **Daily** is the daily reconstructed OP from the
+                         regression model. You have to chose only one station from
+                         the little dropdown above. The default one is "CHAM".
+
+                         **Monthly** (mean or median) are the monthly aggregated mean or median for each station. **Quaterly** is the seasonal (4 seasons: DJF, MAM, JJA, SON) mean or median aggratation per station.  This plot may take some time to generate...
+
+                         The bars represent the contribution of each source to
+                         the OP (in nmol.min⁻¹.m⁻³) and the line and dot are the
+                         observed OP.
+                         """),
             dcc.Loading(
                 dcc.Graph(
                     id="op-contribution-ts-graph",
@@ -756,18 +798,18 @@ def plot_ts(df, station, var, groupby):
                 for groupName, dfgroup in df.groupby(group):
                     if all(dfgroup.loc[:, v].isnull()):
                         continue
-                    dfgroup = dfgroup.sort_values(by="date")
+                    dfgroup = dfgroup.sort_values(by="Date")
                     traces.append(go.Scatter(
-                        x=dfgroup.loc[:, "date"],
+                        x=dfgroup.loc[:, "Date"],
                         y=dfgroup.loc[:, v],
                         mode="lines+markers",
                         name="{}-{}-({})".format(station, v, groupName)
                     ))
     else:
         for v in var:
-            dfgroup = df.sort_values(by="date")
+            dfgroup = df.sort_values(by="Date")
             traces.append(go.Scatter(
-                x=dfgroup.loc[:, "date"],
+                x=dfgroup.loc[:, "Date"],
                 y=dfgroup.loc[:, v],
                 mode="lines+markers",
                 name="{}-{}".format(station, v)
@@ -788,7 +830,7 @@ def plot_box(df, trace_name, x_var, y_var, groupby=None, plot_type="box"):
     :returns: a list of plotly traces
     """
     traces=[]
-    df = df.sort_values(by="station")
+    df = df.sort_values(by="Station")
     if len(groupby) != 0:
         for v in y_var:
             for group in groupby:
@@ -836,26 +878,26 @@ def get_contribution(factors, species, stations):
     if len(factors) > 0:
         conn = sqlite3.connect(DBPATH)
         contrib = pd.read_sql(
-            """SELECT "{factors}" FROM SRC WHERE station in ('{stations}');""".format(
-                factors='", "'.join(factors + ["station", "date"]),
+            """SELECT "{factors}" FROM SRC WHERE Station IN ('{stations}');""".format(
+                factors='", "'.join(factors + ["Station", "Date"]),
                 stations="', '".join(stations)
             ),
             con=conn,
-            parse_dates=["date"],
-            index_col=["station", "date"]
+            parse_dates=["Date"],
+            index_col=["Station", "Date"]
         )
         conn.close()
 
     if len(species) > 0:
         conn = sqlite3.connect(DBPATH)
         sp = pd.read_sql(
-            "SELECT {sp} FROM OP WHERE station in ('{stations}');".format(
-                sp=", ".join(species + ["station", "date"]),
+            "SELECT {sp} FROM OP WHERE Station IN ('{stations}');".format(
+                sp=", ".join(species + ["Station", "Date"]),
                 stations="', '".join(stations)
             ),
             con=conn,
-            parse_dates=["date"],
-            index_col=["station", "date"]
+            parse_dates=["Date"],
+            index_col=["Station", "Date"]
         )
         conn.close()
 
@@ -866,7 +908,7 @@ def get_contribution(factors, species, stations):
     elif (not sp.empty) and contrib.empty:
         df = sp
     else:
-        df = contrib.merge(sp, on=["station", "date"], how="outer")
+        df = contrib.merge(sp, on=["Station", "Date"], how="outer")
     return df.reset_index()
 
 def get_SID_PD():
@@ -879,8 +921,8 @@ def get_SID_PD():
     SID = pd.read_sql("SELECT * FROM SID;", con=con)
     PD = pd.read_sql("SELECT * FROM PD;", con=con)
     con.close()
-    SID = SID.set_index(["profile", "station"])
-    PD = PD.set_index(["profile", "station"])
+    SID = SID.set_index(["Profile", "Station"])
+    PD = PD.set_index(["Profile", "Station"])
     return (SID, PD)
     
 
@@ -994,13 +1036,13 @@ def add_month(df, season=False):
 
     df_tmp = df.copy()
     # ensure we have date in index
-    if 'date' not in df_tmp.columns:
+    if 'Date' not in df_tmp.columns:
         print("No date given")
         return
     else:
-        df_tmp.date = pd.to_datetime(df_tmp["date"])
+        df_tmp["Date"] = pd.to_datetime(df_tmp["Date"])
 
-    df_tmp["month_number"] = df_tmp.date.apply(lambda x: x.month)
+    df_tmp["month_number"] = df_tmp["Date"].apply(lambda x: x.month)
     df_tmp["month"] = df_tmp["month_number"].replace(number_to_name)
     df_tmp.sort_values(by="month", inplace=True)
     if season:

assets/local.css

@@ -14,6 +14,10 @@
     color: darkolivegreen;
 }
 
+#op-contribution-ts-station-dropdown {
+    max-width: 150px;
+}
+
 .equation {
     text-align: center;
 }