enhanced timeline analysis

test-notebooks/dashboard/app.py

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+from typing import cast
+
+from dash import (
+    Dash,
+    html,
+    dcc,
+    callback,
+    Output,
+    Input,
+    State,
+    dash_table,
+)
+import plotly.express as px
+import pandas as pd
+from pandas import DataFrame
+
+from lang_main import load_pickle
+from lang_main.types import TimelineCandidates, ObjectID
+
+#df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder_unfiltered.csv')
+
+# ** data
+data = cast(DataFrame, load_pickle('./data.pkl'))
+cands = cast(TimelineCandidates, load_pickle('./map_candidates.pkl'))
+texts = cast(dict[ObjectID, str], load_pickle('./map_texts.pkl'))
+table_feats = [
+    'ErstellungsDatum',
+    'ErledigungsDatum',
+    'VorgangsTypName',
+    'VorgangsBeschreibung',
+]
+table_feats_dates = [
+    'ErstellungsDatum',
+    'ErledigungsDatum',
+]
+
+# ** graph config
+markers = {
+    'size': 12,
+    'color': 'yellow',
+    'line': {
+        'width': 2,
+        'color': 'red',
+    },
+}
+hover_data = {
+    'ErstellungsDatum': '|%d.%m.%Y',
+    'VorgangsBeschreibung': True,
+}
+
+
+app = Dash(prevent_initial_callbacks=True)
+
+app.layout = [
+    html.H1(children='Demo Zeitreihenanalyse', style={'textAlign':'center'}),
+    html.Div(children=[
+        html.H2('Wählen Sie ein Objekt aus (ObjektID):'),
+        dcc.Dropdown(
+            list(cands.keys()),
+            id='dropdown-selection',
+            placeholder="ObjektID auswählen...",
+        )
+    ]),
+    html.Div(children=[
+        html.H3(id='object_text'),
+        dcc.Dropdown(id='choice-candidates'),
+        dcc.Graph(id='graph-output'),
+    ]),
+    html.Div(children=[
+        dash_table.DataTable(id='table-candidates')
+    ]),
+]
+
+@callback(
+    Output('object_text', 'children'),
+    Input('dropdown-selection', 'value'),
+    prevent_initial_call=True,
+)
+def update_obj_text(obj_id):
+    obj_id = int(obj_id)
+    obj_text = texts[obj_id]
+    headline = f'HObjektText: {obj_text}'
+    return headline
+
+@callback(
+    Output('choice-candidates', 'options'),
+    Input('dropdown-selection', 'value'),
+    prevent_initial_call=True,
+)
+def update_choice_candidates(obj_id):
+    obj_id = int(obj_id)
+    cands_obj_id = cands[obj_id]
+    choices = list(range(1, len(cands_obj_id)+1))
+    return choices
+
+@callback(
+    Output('graph-output', 'figure'),
+    Input('choice-candidates', 'value'),
+    State('dropdown-selection', 'value'),
+    prevent_initial_call=True,
+)
+def update_timeline(index, obj_id):
+    obj_id = int(obj_id)
+    # title
+    obj_text = texts[obj_id]
+    title = f'HObjektText: {obj_text}'
+    # cands
+    cands_obj_id = cands[obj_id]
+    cands_choice = cands_obj_id[int(index)-1]
+    # data
+    df = data.loc[list(cands_choice)].sort_index()
+    # figure
+    fig = px.line(
+        data_frame=df,
+        x='ErstellungsDatum',
+        y='ObjektID',
+        title=title,
+        hover_data=hover_data,
+    )
+    fig.update_traces(
+        mode='markers+lines',
+        marker=markers,
+        marker_symbol='diamond'
+    )
+    fig.update_xaxes(
+        tickformat="%B\n%Y",
+        rangeslider_visible=True,
+    )
+    fig.update_yaxes(type='category')
+    fig.update_layout(hovermode="x unified")
+    return fig
+
+@callback(
+    [Output('table-candidates', 'data'),
+     Output('table-candidates', 'columns')],
+    Input('choice-candidates', 'value'),
+    State('dropdown-selection', 'value'),
+    prevent_initial_call=True,
+)
+def update_table_candidates(index, obj_id):
+    obj_id = int(obj_id)
+    # cands
+    cands_obj_id = cands[obj_id]
+    cands_choice = cands_obj_id[int(index)-1]
+    # data
+    df = data.loc[list(cands_choice)].sort_index()
+    df = (df
+          .filter(items=table_feats, axis=1)
+          .sort_values(by='ErstellungsDatum', ascending=True))
+    cols = [{"name": i, "id": i} for i in df.columns]
+    # convert dates to strings
+    for col in table_feats_dates:
+        df[col] = df[col].dt.strftime(r'%Y-%m-%d')
+    
+    table_data = df.to_dict('records')
+    return table_data, cols
+
+if __name__ == '__main__':
+    app.run(debug=True)