Feat: filter dans store scores

recopytex/dashboard/student_analysis/app.py

@@ -10,8 +10,10 @@ from datetime import date, datetime
 import uuid
 import pandas as pd
 import yaml
+from pathlib import Path
 
 from ...scripts.getconfig import config
+from ... import flat_df_students, pp_q_scores
 from ...config import NO_ST_COLUMNS
 from ..app import app
 from ...scripts.exam import Exam
@@ -117,9 +119,11 @@ layout = html.Div(
             ]
         ),
         dcc.Store(id="student-scores"),
+        html.P(id="test"),
     ]
 )
 
+
 @app.callback(
         [
             dash.dependencies.Output("student", "options"),
@@ -138,15 +142,54 @@ def update_students_list(tribe):
     value = students[0]["Nom"]
     return options, value
 
-# @app.callback(
+
-#     [
+@app.callback(
-#         dash.dependencies.Output("student-scores", "data"),
+    [
-#     ],
+        dash.dependencies.Output("student-scores", "data"),
-#     [
+    ],
-#         dash.dependencies.Input("tribe", "value"),
+    [
-#         dash.dependencies.Input("student", "value"),
+        dash.dependencies.Input("tribe", "value"),
-#         dash.dependencies.Input("term", "value"),
+        dash.dependencies.Input("student", "value"),
-#         ],
+        dash.dependencies.Input("term", "value"),
-#         )
+        ],
-# def update_student_scores(tribe, student, term):
+        )
-#     return []
+def update_student_scores(tribe, student, term):
+    tribe_config = [t for t in config["tribes"] if t["name"] == tribe][0]
+
+    p = Path(tribe_config["name"])
+    csvs = list(p.glob("*.csv"))
+
+    dfs = []
+    for csv in csvs:
+        try:
+            scores = pd.read_csv(csv)
+        except pd.errors.ParserError:
+            pass
+        else:
+            try:
+                if scores.iloc[0]["Commentaire"] == "commentaire":
+                    scores.drop([0], inplace=True)
+            except KeyError:
+                pass
+            scores = flat_df_students(scores).dropna(subset=["Score"])
+            scores = scores[scores["Eleve"] == student]
+            scores = scores[scores["Trimestre"] == term]
+            dfs.append(scores)
+
+    df = pd.concat(dfs)
+
+    return [df.to_dict("records")]
+
+
+@app.callback(
+    [
+        dash.dependencies.Output("test", "children"),
+    ],
+    [
+        dash.dependencies.Input("student-scores", "data"),
+        ],
+)
+def update_test(data):
+    return [str(data)]
+
+