Feat: add competence fig and better error management

recopytex/dashboard/exam.py

@@ -75,7 +75,7 @@ app.layout = html.Div(
                         columns=[
                             {"id": "Élève", "name": "Élève"},
                             {"id": "Note", "name": "Note"},
-                            {"id": "Barème", "name": "Bareme"},
+                            {"id": "Barème", "name": "Barème"},
                         ],
                         data=[],
                         style_data_conditional=[
@@ -99,11 +99,21 @@ app.layout = 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"),
+                        dcc.Graph(id="fig_competences"),
                     ]
                 ),
             ],
@@ -156,42 +166,47 @@ def update_csvs(value):
 def update_final_scores(data):
     if not data:
         raise PreventUpdate
-    try:
+
     scores = pd.DataFrame.from_records(data)
+    try:
+        if scores.iloc[0]["Commentaire"] == "commentaire":
+            scores.drop([0], inplace=True)
+    except KeyError:
+        pass
     scores = flat_df_students(scores).dropna(subset=["Score"])
+    if scores.empty:
+        return [{}]
+
     scores = pp_q_scores(scores)
     assessment_scores = scores.groupby(["Eleve"]).agg(
         {"Note": "sum", "Bareme": "sum"}
     )
     return [assessment_scores.reset_index().to_dict("records")]
-    except KeyError:
-        raise PreventUpdate
 
 
 @app.callback(
     [
-        dash.dependencies.Output("final_score_table", "columns"),
         dash.dependencies.Output("final_score_table", "data"),
     ],
     [dash.dependencies.Input("final_score", "data")],
 )
 def update_final_scores_table(data):
     assessment_scores = pd.DataFrame.from_records(data)
-    return [
+    return [assessment_scores.to_dict("records")]
-        {"id": c, "name": c} for c in assessment_scores.columns
-    ], assessment_scores.to_dict("records")
 
 
 @app.callback(
     [
-        dash.dependencies.Output("final_score_describe", "columns"),
         dash.dependencies.Output("final_score_describe", "data"),
     ],
     [dash.dependencies.Input("final_score", "data")],
 )
 def update_final_scores_descr(data):
-    desc = pd.DataFrame.from_records(data)["Note"].describe()
+    scores = pd.DataFrame.from_records(data) 
-    return [{"id": c, "name": c} for c in desc.keys()], [desc.to_dict()]
+    if scores.empty:
+        return [[{}]]
+    desc = scores["Note"].describe().T
+    return [[desc.to_dict()]]
 
 
 @app.callback(
@@ -203,6 +218,9 @@ def update_final_scores_descr(data):
 def update_final_scores_hist(data):
     assessment_scores = pd.DataFrame.from_records(data)
 
+    if assessment_scores.empty:
+        return [{}]
+
     ranges = np.linspace(
         0, assessment_scores.Bareme.max(), int(assessment_scores.Bareme.max() * 2 + 1)
     )
@@ -230,51 +248,64 @@ def update_final_scores_hist(data):
     return [fig]
 
 
-# @app.callback(
+@app.callback(
-#     [
+    [
-#         dash.dependencies.Output("fig_competences", "figure"),
+        dash.dependencies.Output("fig_competences", "figure"),
-#     ],
+    ],
-#     [dash.dependencies.Input("scores_table", "data")],
+    [dash.dependencies.Input("scores_table", "data")],
-# )
+)
-# def update_competence_fig(data):
+def update_competence_fig(data):
-#     scores = pd.DataFrame.from_records(data)
+    scores = pd.DataFrame.from_records(data)
-#     scores = flat_df_students(scores).dropna(subset=["Score"])
+    try:
-#     scores = pp_q_scores(scores)
+        if scores.iloc[0]["Commentaire"] == "commentaire":
-#     pt = pd.pivot_table(
+            scores.drop([0], inplace=True)
-#         scores,
+    except KeyError:
-#         index=["Exercice", "Question", "Commentaire"],
+        pass
-#         columns="Score",
+    scores = flat_df_students(scores).dropna(subset=["Score"])
-#         aggfunc="size",
+
-#         fill_value=0,
+    if scores.empty:
-#     )
+        return [{}]
-#     for i in {i for i in pt.index.get_level_values(0)}:
+
-#         pt.loc[(str(i), "", ""), :] = ""
+    scores = pp_q_scores(scores)
-#     pt.sort_index(inplace=True)
+    pt = pd.pivot_table(
-#     index = (
+        scores,
-#         pt.index.get_level_values(0)
+        index=["Exercice", "Question", "Commentaire"],
-#         + ":"
+        columns="Score",
-#         + pt.index.get_level_values(1)
+        aggfunc="size",
-#         + " "
+        fill_value=0,
-#         + pt.index.get_level_values(2)
+    )
-#     )
+    for i in {i for i in pt.index.get_level_values(0)}:
-#
+        pt.loc[(str(i), "", ""), :] = ""
-#     fig = go.Figure()
+    pt.sort_index(inplace=True)
-#     bars = [
+    index = (
-#         {"score": -1, "name": "Pas de réponse", "color": COLORS["."]},
+        pt.index.get_level_values(0)
-#         {"score": 0, "name": "Faut", "color": COLORS[0]},
+        + ":"
-#         {"score": 1, "name": "Peu juste", "color": COLORS[1]},
+        + pt.index.get_level_values(1)
-#         {"score": 2, "name": "Presque juste", "color": COLORS[2]},
+        + " "
-#         {"score": 3, "name": "Juste", "color": COLORS[3]},
+        + pt.index.get_level_values(2)
-#     ]
+    )
-#     for b in bars:
+
-#         try:
+    fig = go.Figure()
-#             fig.add_bar(
+    bars = [
-#                 x=index, y=pt[b["score"]], name=b["name"], marker_color=b["color"]
+        {"score": -1, "name": "Pas de réponse", "color": COLORS["."]},
-#             )
+        {"score": 0, "name": "Faut", "color": COLORS[0]},
-#         except KeyError:
+        {"score": 1, "name": "Peu juste", "color": COLORS[1]},
-#             pass
+        {"score": 2, "name": "Presque juste", "color": COLORS[2]},
-#     fig.update_layout(barmode="relative")
+        {"score": 3, "name": "Juste", "color": COLORS[3]},
-#     return [fig]
+    ]
+    for b in bars:
+        try:
+            fig.add_bar(
+                x=index, y=pt[b["score"]], name=b["name"], marker_color=b["color"]
+            )
+        except KeyError:
+            pass
+    fig.update_layout(barmode="relative")
+    fig.update_layout(
+        height=500,
+        margin=dict(l=5, r=5, b=5, t=5),
+    )
+    return [fig]
 
 
 @app.callback(