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app/apps/app_components.py

@@ -245,7 +245,7 @@ def get_boxplot_options_component(id=None):
     )
     return boxplot_options_component
 
-def get_timeserie_component():
+def get_rawdata_monthly_component():
     monthly_boxplot_component = [
         get_boxplot_options_component(id="monthly"),
         dcc.Loading(
@@ -255,6 +255,9 @@ def get_timeserie_component():
             )
         )
     ]
+    return monthly_boxplot_component
+
+def get_rawdata_seasonal_component():
     seasonal_boxplot_component = [
         get_boxplot_options_component(id="seasonal"),
         dcc.Loading(
@@ -264,6 +267,9 @@ def get_timeserie_component():
             )
         )
     ]
+    return seasonal_boxplot_component
+
+def get_rawdata_timeserie_component():
     timeserie_component = [
         dcc.Loading(
             dcc.Graph(
@@ -272,25 +278,7 @@ def get_timeserie_component():
             )
         )
     ]
-    rawdata_component = html.Div(
-        id='tab-timeserie',
-        children=[
-            html.Div(
-                children=[
-                    dcc.Tabs(
-                        value="timeserie",
-                        id="tab-rawdata",
-                        children=[
-                            dcc.Tab(label="Timeserie", value="timeserie", children=timeserie_component),
-                            dcc.Tab(label="Monthly", value="monthly", children=monthly_boxplot_component),
-                            dcc.Tab(label="Seasonal", value="seasonal", children=seasonal_boxplot_component),
-                        ]
-                    ),
-                ]
-            )
-        ]
-    )
-    return rawdata_component
+    return timeserie_component
 
 def get_profile_component():
     profile_component = html.Div(
@@ -395,19 +383,21 @@ def get_specie_repartition_component():
     specie_repartition_component = html.Div(
         id="specie-repartition-component",
         children=[
-        dcc.Graph(
-            id="repartition-graph",
-            figure={
-                'data': [],
-                'layout': {
-                    'title': 'Specie repartition among factors (Loading...)',
-                }
-            }
-        )
+            dcc.Loading(
+                dcc.Graph(
+                    id="repartition-graph",
+                    figure={
+                        'data': [],
+                        'layout': {
+                            'title': 'Specie repartition among factors (Loading...)',
+                        }
+                    }
+                )
+            )
     ])
     return specie_repartition_component
 
-def get_model_component():
+def get_op_beta_component():
     """Get the OP model
     :returns: TODO
 
@@ -415,12 +405,15 @@ def get_model_component():
     model_component = html.Div(
         id="model-component",
         children=[
-            dcc.Graph(
-                id="beta-coefficient-graph",
-                figure={
-                    'data': [],
-                    'layout': {'title': 'Intrisic OP'}
-                }
+            html.H3("Intrinsic OP of the PMF factors"),
+            dcc.Loading(
+                dcc.Graph(
+                    id="beta-coefficient-graph",
+                    figure={
+                        'data': [],
+                        'layout': {'title': 'Intrisic OP'}
+                    }
+                )
             )
         ],
     )
@@ -434,12 +427,34 @@ def get_op_contribution_component():
     contribution_component = html.Div(
         id="op-contribution-component",
         children=[
-            dcc.Graph(
-                id="op-contribution-graph",
-                figure={
-                    'data': [],
-                    'layout': {'title': 'OP contribution'}
-                }
+            html.H3("Contribution of PMF factors to the OP"),
+            dcc.RadioItems(
+                id='op-contribution-plottype',
+                className='options-items',
+                options=[
+                    {'label': 'Daily barplot', 'value': 'daily'},
+                    {'label': 'Seasonal barplot', 'value': 'seasonal'},
+                    {'label': 'Daily timeserie', 'value': 'ts'}
+                ],
+                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",
+                    figure={
+                        'data': [],
+                        'layout': {'title': 'OP contribution (Loading...)'}
+                    }
+                )
             )
         ]
     )
@@ -931,3 +946,55 @@ def add_month(df, season=False):
     if season:
         df_tmp["season"] = df_tmp["month_number"].replace(month_to_season)
     return df_tmp
+
+def add_season(df, month=True, month_to_season=None):
+    """
+    Add a season column to the DataFrame df.
+
+    parameters
+    ----------
+
+    df: Pandas DataFrame.
+        The DataFrame to work with.
+    month : add month number, default True
+
+    return
+    ------
+
+    dfnew: a new pandas DataFrame with a 'season' columns.
+
+    """
+
+    if month_to_season is None:
+        month_to_season = {1:'Winter', 2:'Winter', 3:'Spring', 4:'Spring', 5:'Spring', 6:'Summer',
+                           7:'Summer', 8:'Summer', 9:'Fall', 10:'Fall', 11:'Fall',
+                           12:'Winter'}
+
+    dfnew = df.copy()
+
+    # ensure we have date in index
+    if isinstance(dfnew.index, pd.DatetimeIndex):
+        dfnew["Date"] = dfnew.index
+        dropDate = True
+    elif 'Date' in dfnew.columns:
+        dfnew["Date"] = pd.to_datetime(dfnew["Date"])
+        dropDate = False
+    else:
+        print("No date given")
+        return
+
+    # add a new column with the number of the month (Jan=1, etc)
+    dfnew["month"] = dfnew["Date"].apply(lambda x: x.month)
+    # sort it. This is not mandatory.
+    dfnew.sort_values(by="month", inplace=True)
+
+    # add the season based on the month number
+    dfnew["season"] = dfnew["month"].replace(month_to_season)
+
+    if not month:
+        dfnew.drop(columns=["month"], inplace=True)
+    if dropDate:
+        dfnew.drop(columns=["Date"], inplace=True)
+
+    # and return the new dataframe
+    return dfnew