with selection of the places

ratios.py

@@ -1,24 +1,56 @@
 import pandas as pd
+import dash
+import dash_core_components as dcc
+import dash_html_components as html
+from dash.dependencies import Input, Output
 import plotly.graph_objects as go
 
 # Read the CSV file into a Pandas DataFrame
 file_path = "texts_by_period_and_location_saparated_by_periods_in_columns.csv"
 df = pd.read_csv(file_path)
 
+# Initialize the Dash app
+app = dash.Dash(__name__)
+
 # Define the columns to summarize
 columns_to_summarize = ["ancient", "old", "middle", "new", "late"]
 
-# Group the data by the "place" column and calculate sums within each group
-grouped = df.groupby("place")[columns_to_summarize].sum().reset_index()
-
-# Create a stacked bar chart using Plotly
+# Create the figure
 fig = go.Figure()
 
-# Add a bar trace for each category
-for col in columns_to_summarize:
-    fig.add_trace(go.Bar(x=grouped["place"], y=grouped[col], name=col, text=grouped[col], textposition='auto'))
+# Add an initial trace for demonstration
+fig.add_trace(go.Bar(x=["Place 1", "Place 2", "Place 3"],
+                     y=[5, 7, 4], name="ancient"))
 
-# Customize the layout
+# Define the app layout
+app.layout = html.Div([
+    html.H1("Interactive Bar Graph with Filtering"),
+    dcc.Checklist(
+        id="place-selector",
+        options=[{"label": place, "value": place} for place in df["place"].unique()],
+        value=df["place"].unique().tolist(),
+        labelStyle={'display': 'block'}
+    ),
+    dcc.Checklist(
+        id="column-selector",
+        options=[{"label": col, "value": col} for col in columns_to_summarize],
+        value=columns_to_summarize,
+        labelStyle={'display': 'block'}
+    ),
+    dcc.Graph(id="bar-graph", figure=fig)
+])
+
+# Define callback functions to update the graph
+@app.callback(
+    Output("bar-graph", "figure"),
+    [Input("place-selector", "value"), Input("column-selector", "value")]
+)
+def update_graph(selected_places, selected_columns):
+    filtered_df = df[(df["place"].isin(selected_places)) & (df[selected_columns].sum(axis=1) > 0)]
+    grouped = filtered_df.groupby("place")[selected_columns].sum().reset_index()
+    fig = go.Figure()
+    for col in selected_columns:
+        fig.add_trace(go.Bar(x=grouped["place"], y=grouped[col], name=col, text=grouped[col], textposition='auto'))
     fig.update_layout(
         title="Summary of Categories by Place with Selection",
         xaxis_title="Place",
@@ -26,33 +58,7 @@ fig.update_layout(
         xaxis=dict(categoryorder='total descending'),
         barmode='stack'
     )
+    return fig
 
-# Enable selection by clicking
-fig.update_traces(selector=dict(mode='lasso'))
-
-# Enable searching for place names
-fig.update_layout(updatemenus=[
-    {
-        'buttons': [
-            {
-                'args': [{'visible': [True] * len(grouped)}],
-                'label': 'Show All',
-                'method': 'restyle'
-            },
-            {
-                'args': [{'visible': [grouped["place"] == place for place in grouped["place"]]}],
-                'label': 'Select Place',
-                'method': 'restyle'
-            }
-        ],
-        'direction': 'down',
-        'showactive': False,
-        'x': 0.1,
-        'xanchor': 'left',
-        'y': 1.1,
-        'yanchor': 'top'
-    }
-])
-
-# Show the interactive graph
-fig.show()
+if __name__ == "__main__":
+    app.run_server(debug=True)