using dash-cytoscape

test-notebooks/Analyse_5-1_Timeline.ipynb

@@ -3087,7 +3087,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

test-notebooks/Preprocess_Pipeline.ipynb

@@ -1077,7 +1077,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

test-notebooks/Token_Analysis.ipynb

@@ -2267,7 +2267,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

test-notebooks/dashboard/app.py

@@ -1,195 +0,0 @@
-import time
-import webbrowser
-from pathlib import Path
-from threading import Thread
-from typing import cast
-
-import pandas as pd
-import plotly.express as px
-from dash import (
-    Dash,
-    Input,
-    Output,
-    State,
-    callback,
-    dash_table,
-    dcc,
-    html,
-)
-from lang_main import CALLER_PATH
-from lang_main.io import load_pickle
-from lang_main.types import ObjectID, TimelineCandidates
-from pandas import DataFrame
-
-# df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder_unfiltered.csv')
-
-# ** data
-# p_df = Path(r'.\test-notebooks\dashboard\Pipe-TargetFeature_Step-3_remove_NA.pkl')
-p_df = CALLER_PATH.joinpath('./Pipe-TargetFeature_Step-3_remove_NA.pkl')
-# p_tl = Path(
-#     r'.\test-notebooks\dashboard\Pipe-Timeline_Analysis_Step-4_get_timeline_candidates.pkl'
-# )
-p_tl = CALLER_PATH.joinpath('./Pipe-Timeline_Analysis_Step-4_get_timeline_candidates.pkl')
-ret = cast(DataFrame, load_pickle(p_df))
-data = ret[0]
-ret = cast(tuple[TimelineCandidates, dict[ObjectID, str]], load_pickle(p_tl))
-cands = ret[0]
-texts = ret[1]
-
-# p_df = Path(r'.\test-notebooks\dashboard\data.pkl')
-# p_cands = Path(r'.\test-notebooks\dashboard\map_candidates.pkl')
-# p_map = Path(r'.\test-notebooks\dashboard\map_texts.pkl')
-# data = cast(DataFrame, load_pickle(p_df))
-# cands = cast(TimelineCandidates, load_pickle(p_cands))
-# texts = cast(dict[ObjectID, str], load_pickle(p_map))
-
-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
-
-
-def _start_webbrowser():
-    host = '127.0.0.1'
-    port = '8050'
-    adress = f'http://{host}:{port}/'
-    time.sleep(2)
-    webbrowser.open_new(adress)
-
-
-def main():
-    webbrowser_thread = Thread(target=_start_webbrowser, daemon=True)
-    webbrowser_thread.start()
-    app.run(debug=True)
-
-
-if __name__ == '__main__':
-    main()

test-notebooks/timeline_analysis.ipynb

@@ -2327,7 +2327,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,