Feat: Final mark for students

recopytex/dashboard/pages/exams_scores/app.py

@@ -43,7 +43,16 @@ layout = html.Div(
                 html.Section(
                     children=[
                         html.Div(
-                                children=[],
+                            children=[
+                                dash_table.DataTable(
+                                    id="final_score_table",
+                                    columns=[
+                                        {"name": "Étudiant", "id": "student_name"},
+                                        {"name": "Note", "id": "mark"},
+                                        {"name": "Barème", "id": "score_rate"},
+                                    ],
+                                )
+                            ],
                             id="final_score_table_container",
                         ),
                     ],

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 column_values_to_column("student_name", "score", kept_columns, flat_scores)
+    return unstack_scores(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):