Remove global variables

app.py

@@ -21,9 +21,6 @@
     "https://cdn.jsdelivr.net/gh/AnnMarieW/dash-bootstrap-templates@V1.0.4/dbc.min.css"
 )
 
-# GLOBAL VARS
-SUMMARY_ALL = None
-
 # APP
 app = dash.Dash(
     APP_TITLE,
@@ -184,20 +181,19 @@ def callback_download_table(_, table_data, table_columns):
     prevent_initial_call=True,
 )
 def callback_analyze(_, table_data, table_columns):
-    global SUMMARY_ALL
 
     button_viz_analysis_disabled = True
     button_viz_analysis_outline = True
     row_button_download_analysis_style = {"visibility": "hidden"}
 
     try:
         dataframe = pyfunc.transform_to_dataframe(table_data, table_columns)
-        SUMMARY_ALL = pyfunc.generate_summary_all(dataframe, n_days=["16D", "MS", "YS"])
+        summary_all = pyfunc.generate_summary_all(dataframe, n_days=["16D", "MS", "YS"])
         tables = [
             pylayoutfunc.create_table_summary(
                 summary, f"table-analyze-{counter}", deletable=False
             )
-            for counter, summary in enumerate(SUMMARY_ALL)
+            for counter, summary in enumerate(summary_all)
         ]
 
         children = pylayoutfunc.create_tabcard_table_layout(tables)
@@ -218,43 +214,106 @@ def callback_analyze(_, table_data, table_columns):
 @app.callback(
     Output("download-analysis-csv", "data"),
     Input("button-download-analysis-csv", "n_clicks"),
+    State("table-analyze-0", "data"),
+    State("table-analyze-0", "columns"),
+    State("table-analyze-1", "data"),
+    State("table-analyze-1", "columns"),
+    State("table-analyze-2", "data"),
+    State("table-analyze-2", "columns"),
     prevent_initial_call=True,
 )
-def callback_download_results(_):
+def callback_download_results(
+    _,
+    biweekly_data,
+    biweekly_columns,
+    monthly_data,
+    monthly_columns,
+    yearly_data,
+    yearly_columns,
+):
+
+    biweekly = (biweekly_data, biweekly_columns)
+    monthly = (monthly_data, monthly_columns)
+    yearly = (yearly_data, yearly_columns)
+
+    summary_all = []
+    for period in (biweekly, monthly, yearly):
+        data, columns = period
+        dataframe = pyfunc.transform_to_dataframe(
+            data,
+            columns,
+            multiindex=True,
+            apply_numeric=False,
+            parse_dates=["max_date"],
+        )
+        summary_all.append(dataframe)
+
+    dataframe_all = pd.concat(
+        summary_all, axis=1, keys=["Biweekly", "Monthly", "Yearly"]
+    )
 
-    dataframe = pd.concat(SUMMARY_ALL, axis=1, keys=["Biweekly", "Monthly", "Yearly"])
-    return dcc.send_data_frame(dataframe.to_csv, "results.csv")
+    return dcc.send_data_frame(dataframe_all.to_csv, "results.csv")
 
 
 @app.callback(
     Output("tab-graph-analysis", "children"),
     Input("button-viz-analysis", "n_clicks"),
+    State("table-analyze-0", "data"),
+    State("table-analyze-0", "columns"),
+    State("table-analyze-1", "data"),
+    State("table-analyze-1", "columns"),
+    State("table-analyze-2", "data"),
+    State("table-analyze-2", "columns"),
     prevent_initial_call=True,
 )
-def callback_troubleshoot(_):
+def callback_graph_analysis(
+    _,
+    biweekly_data,
+    biweekly_columns,
+    monthly_data,
+    monthly_columns,
+    yearly_data,
+    yearly_columns,
+):
     from itertools import product
 
     label_periods = ["Biweekly", "Monthly", "Yearly"]
     label_maxsum = ["Max + Sum"]
     label_raindry = ["Dry + Rain"]
     label_ufunc = label_maxsum + label_raindry
 
+    biweekly = (biweekly_data, biweekly_columns)
+    monthly = (monthly_data, monthly_columns)
+    yearly = (yearly_data, yearly_columns)
+
+    summary_all = []
+    for summary_period in (biweekly, monthly, yearly):
+        data, columns = summary_period
+        dataframe = pyfunc.transform_to_dataframe(
+            data,
+            columns,
+            multiindex=True,
+            apply_numeric=False,
+            parse_dates=["max_date"],
+        )
+        summary_all.append(dataframe)
+
     graphs_maxsum = [
         pyfigure.figure_summary_maxsum(
             summary,
             title=f"<b>{period}: {title}</b>",
             period=period,
             subplot_titles=["Max", "Sum"],
         )
-        for summary, title, period in zip(SUMMARY_ALL, label_maxsum * 3, label_periods)
+        for summary, title, period in zip(summary_all, label_maxsum * 3, label_periods)
     ]
     graphs_raindry = [
         pyfigure.figure_summary_raindry(
             summary, title=f"<b>{period}: {title}</b>", period=period
         )
-        for summary, title, period in zip(SUMMARY_ALL, label_raindry * 3, label_periods)
+        for summary, title, period in zip(summary_all, label_raindry * 3, label_periods)
     ]
-    graph_maxdate = [pyfigure.figure_summary_maxdate(SUMMARY_ALL)]
+    graph_maxdate = [pyfigure.figure_summary_maxdate(summary_all)]
 
     all_graphs = graphs_maxsum + graphs_raindry + graph_maxdate
     labels = [": ".join(i) for i in product(label_ufunc, label_periods)]

pyfunc.py

@@ -58,7 +58,7 @@ def max_date(vector):
         if vector.any():
             return vector.idxmax().date()
         else:
-            return np.nan
+            return pd.NaT
 
     def max(vector):
         return vector.max()
@@ -70,7 +70,7 @@ def max(vector):
         dataframe, ufunc=ufunc, ufunc_col=ufunc_col, n_days=n_days
     )
 
-    return summary
+    return summary.infer_objects()
 
 
 def generate_summary_all(dataframe, n_days: list = None):
@@ -84,10 +84,15 @@ def generate_summary_all(dataframe, n_days: list = None):
 
 
 def transform_to_dataframe(
-    table_data, table_columns, multiindex: bool = False, apply_numeric: bool = True
+    table_data,
+    table_columns,
+    multiindex: bool = False,
+    apply_numeric: bool = True,
+    parse_dates: list = None,
 ):
 
     dataframe = pd.DataFrame(table_data)
+
     if multiindex is True:
         dataframe.columns = pd.MultiIndex.from_tuples(
             [item["name"] for item in table_columns]
@@ -98,9 +103,31 @@ def transform_to_dataframe(
     dataframe["DATE"] = pd.to_datetime(dataframe.DATE)
     dataframe = dataframe.set_index("DATE").sort_index()
 
+    if multiindex is True:
+        # removing date (index.name) from top level multiindex
+        dataframe.columns = pd.MultiIndex.from_tuples(dataframe.columns.to_flat_index())
+
     if apply_numeric is True:
         dataframe = dataframe.apply(pd.to_numeric, errors="coerce")
     else:
         dataframe = dataframe.infer_objects()
 
+    if parse_dates is not None:
+        if multiindex:
+            for col_dates in parse_dates:
+                col_parsing = [
+                    col_tuple
+                    for col_tuple in dataframe.columns
+                    if col_dates in col_tuple
+                ]
+                for col_dates in col_parsing:
+                    dataframe[col_dates] = pd.to_datetime(
+                        dataframe[col_dates], errors="coerce"
+                    )
+        else:
+            for col_dates in parse_dates:
+                dataframe[col_dates] = pd.to_datetime(
+                    dataframe[col_dates], errors="coerce"
+                )
+
     return dataframe