recopytex/dashboard/pages/exams_scores/app.py

@@ -72,14 +72,6 @@ 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

@@ -18,10 +18,7 @@ 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,
 )
 
 
@@ -93,8 +90,8 @@ def update_scores_store(exam):
 )
 def update_finale_score_table(scores):
     scores_df = pd.DataFrame.from_records(scores)
-    stacked_scores = stack_scores(scores_df)
+    # print(scores_df)
-    return score_to_final_mark(stacked_scores)
+    return score_to_final_mark(scores_df)
 
 
 @app.callback(
@@ -109,6 +106,7 @@ 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"),
@@ -157,53 +155,3 @@ 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")
-
-    # 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

@@ -70,13 +70,6 @@ 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(
@@ -87,7 +80,8 @@ 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 """
 
-    filtered_scores = scores[~scores.apply(is_none_score, axis=1)]
+    melted_scores = stack_scores(scores)
+    filtered_scores = melted_scores[~melted_scores.apply(is_none_score, axis=1)]
     filtered_scores = filtered_scores.assign(
         score=filtered_scores.apply(score_to_numeric_score, axis=1)
     )
@@ -99,20 +93,3 @@ 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
-

recopytex/database/filesystem/default_config.yml

@@ -26,37 +26,30 @@ scores: #
     value: 0 
     numeric_value: 0 
     color: "#E7472B"
-    comment: Faux
   FEW: # Few good things
     value: 1 
     numeric_value: 1 
     color: "#FF712B"
-    comment: Peu juste
   NEARLY: # Nearly good but things are missing
     value: 2
     numeric_value: 2
     color: "#F2EC4C"
-    comment: Presque juste
   GOOD: # Everything is good
     value: 3
     numeric_value: 3
     color: "#68D42F"
-    comment: Juste
   NOTFILLED: # The item is not scored yet
     value: ""
     numeric_value: None
     color: white
-    comment: En attente
   NOANSWER: # Student gives no answer (count as 0)
     value: "."
     numeric_value: 0
     color: black
-    comment: Pas de réponse
   ABS: # Student has absent (this score won't be impact the final mark)
     value: a
     numeric_value: None
     color: lightgray
-    comment: Non noté
 
 csv_fields: # dataframe_field: csv_field 
   term: Trimestre

recopytex/database/filesystem/loader.py

@@ -31,11 +31,10 @@ class CSVLoader(Loader):
     :example:
     >>> loader = CSVLoader()
     >>> loader.get_config()
-    {'source': './', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B', 'comment': 'Faux'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B', 'comment': 'Peu juste'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C', 'comment': 'Presque juste'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F', 'comment': 'Juste'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white', 'comment': 'En attente'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black', 'comment': 'Pas de réponse'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray', 'comment': 'Non noté'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}}
+    {'source': './', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}}
-
     >>> loader = CSVLoader("./test_config.yml")
     >>> loader.get_config()
-    {'source': './example', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B', 'comment': 'Faux'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B', 'comment': 'Peu juste'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C', 'comment': 'Presque juste'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F', 'comment': 'Juste'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white', 'comment': 'En attente'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black', 'comment': 'Pas de réponse'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray', 'comment': 'Non noté'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}, 'output': './output', 'templates': 'templates/', 'tribes': {'Tribe1': {'name': 'Tribe1', 'type': 'Type1', 'students': 'tribe1.csv'}, 'Tribe2': {'name': 'Tribe2', 'students': 'tribe2.csv'}}}
+    {'source': './example', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}, 'output': './output', 'templates': 'templates/', 'tribes': {'Tribe1': {'name': 'Tribe1', 'type': 'Type1', 'students': 'tribe1.csv'}, 'Tribe2': {'name': 'Tribe2', 'students': 'tribe2.csv'}}}
     """
 
     CONFIG = DEFAULT_CONFIG

recopytex/datalib/on_score_column.py

@@ -2,7 +2,7 @@
 # encoding: utf-8
 
 from math import ceil
-import pandas as pd
+import numpy as np
 
 
 def is_none_score(x, score_config):
@@ -40,7 +40,7 @@ def is_none_score(x, score_config):
         for v in score_config.values()
         if str(v["numeric_value"]).lower() == "none"
     ]
-    return x["score"] in none_values or pd.isnull(x["score"])
+    return x["score"] in none_values or x["score"] is None or np.isnan(x["score"])
 
 
 def score_to_numeric_score(x, score_config):