Feat: Final mark for students

recopytex/dashboard/pages/exams_scores/app.py

@@ -7,62 +7,71 @@ from .models import get_tribes, get_exams
 from .callbacks import *
 
 layout = html.Div(
-        children=[
+    children=[
-            html.Header(
+        html.Header(
-                children=[
+            children=[
-                    html.H1("Analyse des notes"),
+                html.H1("Analyse des notes"),
-                    html.P("Dernière sauvegarde", id="lastsave"),
+                html.P("Dernière sauvegarde", id="lastsave"),
+            ],
+        ),
+        html.Main(
+            children=[
+                html.Section(
+                    children=[
+                        html.Div(
+                            children=[
+                                "Classe: ",
+                                dcc.Dropdown(
+                                    id="tribe",
+                                    options=[
+                                        {"label": t["name"], "value": t["name"]}
+                                        for t in get_tribes().values()
+                                    ],
+                                    value=next(iter(get_tribes().values()))["name"],
+                                ),
+                            ],
+                        ),
+                        html.Div(
+                            children=[
+                                "Evaluation: ",
+                                dcc.Dropdown(id="exam_select"),
+                            ],
+                        ),
                     ],
+                    id="selects",
                 ),
-            html.Main(
+                html.Section(
-                children=[
+                    children=[
-                    html.Section(
+                        html.Div(
-                        children=[
+                            children=[
-                            html.Div(
+                                dash_table.DataTable(
-                                children=[
+                                    id="final_score_table",
-                                    "Classe: ",
+                                    columns=[
-                                    dcc.Dropdown(
+                                        {"name": "Étudiant", "id": "student_name"},
-                                        id="tribe",
+                                        {"name": "Note", "id": "mark"},
-                                        options=[
+                                        {"name": "Barème", "id": "score_rate"},
-                                            {"label": t["name"], "value": t["name"]}
-                                            for t in get_tribes().values()
-                                            ],
-                                        value=next(iter(get_tribes().values()))["name"],
-                                        ),
                                     ],
-                                ),
-                            html.Div(
-                                children=[
-                                    "Evaluation: ",
-                                    dcc.Dropdown(id="exam_select"),
-                                    ],
-                                ),
-                            ],
-                        id="selects",
-                        ),
-                    html.Section(
-                        children=[
-                            html.Div(
-                                children=[],
-                                id="final_score_table_container",
-                                ),
-                            ],
-                        id="analysis",
-                        ),
-                    html.Section(
-                        children=[
-                            dash_table.DataTable(
-                                id="scores_table",
-                                columns=[],
-                                style_data_conditional=[],
-                                fixed_columns={},
-                                editable=True,
                                 )
                             ],
-                        id="edit",
+                            id="final_score_table_container",
                         ),
                     ],
+                    id="analysis",
+                ),
+                html.Section(
+                    children=[
+                        dash_table.DataTable(
+                            id="scores_table",
+                            columns=[],
+                            style_data_conditional=[],
+                            fixed_columns={},
+                            editable=True,
+                        )
+                    ],
+                    id="edit",
                 ),
-            dcc.Store(id="scores"),
             ],
-        )
+        ),
+        dcc.Store(id="scores"),
+    ],
+)

recopytex/dashboard/pages/exams_scores/callbacks.py

@@ -17,6 +17,7 @@ from .models import (
     get_unstack_scores,
     get_students_from_exam,
     get_score_colors,
+    score_to_final_mark,
 )
 
 
@@ -77,3 +78,17 @@ def update_scores_store(exam):
         highlight_scores(students, score_color),
         {"headers": True, "data": len(fixed_columns)},
     ]
+
+
+@app.callback(
+    [
+        Output("final_score_table", "data"),
+    ],
+    [
+        Input("scores_table", "data"),
+    ],
+)
+def update_scores_store(scores):
+    scores_df = pd.DataFrame.from_records(scores)
+    # print(scores_df)
+    return score_to_final_mark(scores_df)

recopytex/dashboard/pages/exams_scores/models.py

@@ -1,10 +1,44 @@
-#!/usr/bin/env python
+#!/use/bin/env python
 # encoding: utf-8
 
 from recopytex.database.filesystem.loader import CSVLoader
 from recopytex.datalib.dataframe import column_values_to_column
+import recopytex.datalib.on_score_column as on_column
+import pandas as pd
 
-LOADER = CSVLoader("./test_config.yml")
+LOADER = CSVLoader("./test_confia.ml")
+SCORES_CONFIG = LOADER.get_config()["scores"]
+
+
+def unstack_scores(scores):
+    """Put student_name values to columns
+
+    :param scores: Score dataframe with one line per score
+    :returns: Scrore dataframe with student_name in columns
+
+    """
+    kept_columns = [col for col in LOADER.score_columns if col != "score"]
+    return column_values_to_column("student_name", "score", kept_columns, scores)
+
+
+def stack_scores(scores):
+    """Student columns are melt to rows with student_name column
+
+    :param scores: Score dataframe with student_name in columns
+    :returns: Scrore dataframe with one line per score
+
+    """
+    kept_columns = [
+        c for c in LOADER.score_columns if c not in ["score", "student_name"]
+    ]
+    student_names = [c for c in scores.columns if c not in kept_columns]
+    return pd.melt(
+        scores,
+        id_vars=kept_columns,
+        value_vars=student_names,
+        var_name="student_name",
+        value_name="score",
+    )
 
 
 def get_tribes():
@@ -21,8 +55,7 @@ def get_record_scores(exam):
 
 def get_unstack_scores(exam):
     flat_scores = LOADER.get_exam_scores(exam)
-    kept_columns = [col for col in LOADER.score_columns if col != "score"]
+    return unstack_scores(flat_scores)
-    return column_values_to_column("student_name", "score", kept_columns, flat_scores)
 
 
 def get_students_from_exam(exam):
@@ -31,8 +64,32 @@ def get_students_from_exam(exam):
 
 
 def get_score_colors():
-    scores_config = LOADER.get_config()["valid_scores"]
     score_color = {}
-    for key, score in scores_config.items():
+    for key, score in SCORES_CONFIG.items():
         score_color[score["value"]] = score["color"]
     return score_color
+
+
+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(
+    x, max([v["value"] for v in SCORES_CONFIG.values() if isinstance(v["value"], int)])
+)
+
+
+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 = filtered_scores.assign(
+        score=filtered_scores.apply(score_to_numeric_score, axis=1)
+    )
+    filtered_scores = filtered_scores.assign(
+        mark=filtered_scores.apply(score_to_mark, axis=1)
+    )
+    final_score = filtered_scores.groupby(["student_name"])[
+        ["mark", "score_rate"]
+    ].sum()
+    return [final_score.reset_index().to_dict("records")]
+

recopytex/datalib/on_score_column.py

@@ -2,6 +2,7 @@
 # encoding: utf-8
 
 from math import ceil
+import numpy as np
 
 
 def is_none_score(x, score_config):
@@ -39,7 +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
+    return x["score"] in none_values or x["score"] is None or np.isnan(x["score"])
 
 
 def score_to_numeric_score(x, score_config):