Feat: question levels bar figure

recopytex/dashboard/pages/exams_scores/app.py

@@ -72,6 +72,14 @@ layout = html.Div(
                             ],
                             id="fig_exam_histo_container",
                         ),
+                        html.Div(
+                            children=[
+                                dcc.Graph(
+                                    id="fig_questions_bar",
+                                )
+                            ],
+                            id="fig_questions_bar_container",
+                        ),
                     ],
                     id="analysis",
                 ),

recopytex/dashboard/pages/exams_scores/callbacks.py

@@ -18,7 +18,10 @@ from .models import (
     get_unstack_scores,
     get_students_from_exam,
     get_score_colors,
+    get_level_color_bar,
     score_to_final_mark,
+    stack_scores,
+    pivot_score_on,
 )
 
 
@@ -90,8 +93,8 @@ def update_scores_store(exam):
 )
 def update_finale_score_table(scores):
     scores_df = pd.DataFrame.from_records(scores)
-    # print(scores_df)
-    return score_to_final_mark(scores_df)
+    stacked_scores = stack_scores(scores_df)
+    return score_to_final_mark(stacked_scores)
 
 
 @app.callback(
@@ -106,7 +109,6 @@ def update_finale_score_table(scores):
 def update_statictics_table(finale_score):
     df = pd.DataFrame.from_records(finale_score)
     statistics = df["mark"].describe().to_frame().T
-    print(statistics)
     return [
         [{"id": c, "name": c} for c in statistics.columns],
         statistics.to_dict("records"),
@@ -155,3 +157,54 @@ def update_exam_histo(finale_scores):
         margin=dict(l=5, r=5, b=5, t=5),
     )
     return [fig]
+
+
+@app.callback(
+    [
+        Output("fig_questions_bar", "figure"),
+    ],
+    [
+        Input("scores_table", "data"),
+    ],
+)
+def update_questions_bar(finale_scores):
+    scores = pd.DataFrame.from_records(finale_scores)
+    scores = stack_scores(scores)
+
+    if scores.empty:
+        return [go.Figure(data=[go.Scatter(x=[], y=[])])]
+
+    pt = pivot_score_on(scores, ["exercise", "question", "comment"], "score")
+    print(pt)
+
+    # separation  between exercises
+    for i in {i for i in pt.index.get_level_values(0)}:
+        pt.loc[(str(i), "", ""), :] = ""
+    pt.sort_index(inplace=True)
+
+    # Bar label
+    index = (
+        pt.index.get_level_values(0).map(str)
+        + ":"
+        + pt.index.get_level_values(1).map(str)
+        + " "
+        + pt.index.get_level_values(2).map(str)
+    )
+
+    fig = go.Figure()
+
+    bars = get_level_color_bar()
+
+    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]

recopytex/dashboard/pages/exams_scores/models.py

@@ -70,6 +70,13 @@ def get_score_colors():
     return score_color
 
 
+def get_level_color_bar():
+    return [
+        {"score": s["value"], "name": s["comment"], "color": s["color"]}
+        for s in SCORES_CONFIG.values()
+    ]
+
+
 is_none_score = lambda x: on_column.is_none_score(x, SCORES_CONFIG)
 score_to_numeric_score = lambda x: on_column.score_to_numeric_score(x, SCORES_CONFIG)
 score_to_mark = lambda x: on_column.score_to_mark(
@@ -80,8 +87,7 @@ score_to_mark = lambda x: on_column.score_to_mark(
 def score_to_final_mark(scores):
     """ Compute marks then reduce to final mark per student """
 
-    melted_scores = stack_scores(scores)
-    filtered_scores = melted_scores[~melted_scores.apply(is_none_score, axis=1)]
+    filtered_scores = scores[~scores.apply(is_none_score, axis=1)]
     filtered_scores = filtered_scores.assign(
         score=filtered_scores.apply(score_to_numeric_score, axis=1)
     )
@@ -93,3 +99,20 @@ def score_to_final_mark(scores):
     ].sum()
     return [final_score.reset_index().to_dict("records")]
 
+
+def pivot_score_on(scores, index, columns, aggfunc="size"):
+    """Pivot scores on index, columns with aggfunc
+
+    It assumes thant scores are levels
+
+    """
+    filtered_scores = scores[~scores.apply(is_none_score, axis=1)]
+    pt = pd.pivot_table(
+        scores,
+        index=index,
+        columns=columns,
+        aggfunc=aggfunc,
+        fill_value=0,
+    )
+    return pt
+