Fix: get back exam graphs

recopytex/dashboard/exam_analysis/app.py

@@ -11,7 +11,6 @@ from pathlib import Path
 from datetime import datetime
 import pandas as pd
 import numpy as np
-import dash_bootstrap_components as dbc
 
 
 from ... import flat_df_students, pp_q_scores
@@ -75,22 +74,13 @@ layout = html.Div(
                 ),
                 html.Div(
                     [
-                        "Evaluation: ",
+                        html.Div(
-                        dbc.Select(id="csv"),
-                    ]
-                ),
-            ],
-        ),
-        html.H2("Résultats"),
-        dbc.Row(
-            [
-                dbc.Col(
                             dash_table.DataTable(
                                 id="final_score_table",
                                 columns=[
-                            {"id": "Eleve", "name": "Élève"},
+                                    {"id": "Élève", "name": "Élève"},
                                     {"id": "Note", "name": "Note"},
-                            {"id": "Bareme", "name": "Barème"},
+                                    {"id": "Barème", "name": "Barème"},
                                 ],
                                 data=[],
                                 style_data_conditional=[
@@ -99,16 +89,38 @@ layout = html.Div(
                                         "backgroundColor": "rgb(248, 248, 248)",
                                     }
                                 ],
-                        style_header={
-                            "backgroundColor": "rgb(230, 230, 230)",
-                            "fontWeight": "bold",
-                        },
                                 style_data={
                                     "width": "100px",
                                     "maxWidth": "100px",
                                     "minWidth": "100px",
                                 },
-                    )
+                            ),
+                            id="final_score_table_container",
+                        ),
+                        html.Div(
+                            [
+                                dash_table.DataTable(
+                                    id="final_score_describe",
+                                    columns=[
+                                        {"id": "count", "name": "count"},
+                                        {"id": "mean", "name": "mean"},
+                                        {"id": "std", "name": "std"},
+                                        {"id": "min", "name": "min"},
+                                        {"id": "25%", "name": "25%"},
+                                        {"id": "50%", "name": "50%"},
+                                        {"id": "75%", "name": "75%"},
+                                        {"id": "max", "name": "max"},
+                                    ],
+                                ),
+                                dcc.Graph(
+                                    id="fig_assessment_hist",
+                                ),
+                                dcc.Graph(id="fig_competences"),
+                            ],
+                            id="desc_plots",
+                        ),
+                    ],
+                    id="analysis",
                 ),
                 html.Div(
                     [
@@ -218,7 +230,7 @@ def update_final_scores_hist(data):
     assessment_scores = pd.DataFrame.from_records(data)
 
     if assessment_scores.empty:
-        return [{'data': [], 'layout':[]}]
+        return [{"data": [], "layout": []}]
 
     ranges = np.linspace(
         -0.5,
@@ -265,7 +277,7 @@ def update_competence_fig(data):
     scores = flat_df_students(scores).dropna(subset=["Score"])
 
     if scores.empty:
-        return [{'data': [], 'layout':[]}]
+        return [{"data": [], "layout": []}]
 
     scores = pp_q_scores(scores)
     pt = pd.pivot_table(