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6 Commits
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9e0ea14d05
| Author | SHA1 | Date |
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 Bertrand Benjamin
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9e0ea14d05 | |
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Bertrand Benjamin
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2031ade1ab | |
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Bertrand Benjamin
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6ed55c07d4 | |
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Bertrand Benjamin
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1d234ea5fc | |
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Bertrand Benjamin
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646314ad88 | |
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Bertrand Benjamin
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0739cfdae7 |
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@ -7,70 +7,62 @@ from .models import get_tribes, get_exams
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from .callbacks import *
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layout = html.Div(
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children=[
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html.Header(
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children=[
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html.H1("Analyse des notes"),
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html.P("Dernière sauvegarde", id="lastsave"),
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],
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),
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html.Main(
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html.Section(
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[
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html.Div(
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[
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"Classe: ",
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dcc.Dropdown(
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id="tribe",
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options=[
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{"label": t["name"], "value": t["name"]}
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for t in get_tribes().values()
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],
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value=next(iter(get_tribes().values()))["name"],
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),
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],
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),
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html.Div(
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[
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"Evaluation: ",
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dcc.Dropdown(id="exam_select"),
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],
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html.P(id="test"),
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],
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id="select",
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),
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html.Section(
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[
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html.Div(
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dash_table.DataTable(
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id="final_score_table",
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columns=[
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{"id": "Eleve", "name": "Élève"},
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{"id": "Note", "name": "Note"},
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{"id": "Bareme", "name": "Barème"},
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],
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data=[],
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style_data_conditional=[
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{
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"if": {"row_index": "odd"},
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"backgroundColor": "rgb(248, 248, 248)",
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}
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],
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style_data={
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"width": "100px",
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"maxWidth": "100px",
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"minWidth": "100px",
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},
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),
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id="final_score_table_container",
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children=[
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html.Header(
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children=[
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html.H1("Analyse des notes"),
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html.P("Dernière sauvegarde", id="lastsave"),
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],
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),
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html.Main(
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children=[
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html.Section(
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children=[
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html.Div(
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children=[
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"Classe: ",
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dcc.Dropdown(
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id="tribe",
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options=[
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{"label": t["name"], "value": t["name"]}
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for t in get_tribes().values()
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],
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value=next(iter(get_tribes().values()))["name"],
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),
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],
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),
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html.Div(
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children=[
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"Evaluation: ",
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dcc.Dropdown(id="exam_select"),
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],
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),
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],
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id="selects",
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),
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html.Section(
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children=[
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html.Div(
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children=[],
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id="final_score_table_container",
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),
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],
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id="analysis",
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),
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html.Section(
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children=[
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dash_table.DataTable(
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id="scores_table",
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columns=[],
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style_data_conditional=[],
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fixed_columns={},
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editable=True,
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)
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],
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id="edit",
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),
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],
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id="analysis",
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),
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html.Section(
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id="scores_table",
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),
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),
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dcc.Store(id="scores"),
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],
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)
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),
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dcc.Store(id="scores"),
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],
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)
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@ -1,11 +1,15 @@
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#!/usr/bin/env python
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# encoding: utf-8
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from dash.dependencies import Input, Output
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from dash.dependencies import Input, Output, State
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import dash
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from dash.exceptions import PreventUpdate
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import dash_table
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import json
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import pandas as pd
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from ...app import app
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from .models import get_tribes, get_exams
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from .models import get_tribes, get_exams, get_unstack_scores, get_students_from_exam
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@app.callback(
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@ -15,12 +19,11 @@ from .models import get_tribes, get_exams
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],
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[Input("tribe", "value")],
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)
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def update_csvs(value):
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if not value:
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def update_exams_choices(tribe):
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if not tribe:
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raise PreventUpdate
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exams = get_exams(value)
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exams = get_exams(tribe)
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exams.reset_index(inplace=True)
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print(exams.loc[0, "name"])
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if not exams.empty:
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return [
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{"label": e["name"], "value": e.to_json()} for i, e in exams.iterrows()
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@ -30,24 +33,35 @@ def update_csvs(value):
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@app.callback(
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[
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dash.dependencies.Output("final_score", "data"),
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Output("scores_table", "columns"),
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Output("scores_table", "data"),
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Output("scores_table", "style_data_conditional"),
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Output("scores_table", "fixed_columns"),
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],
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[
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Input("exam_select", "value"),
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],
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[dash.dependencies.Input("scores_table", "data")],
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)
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def update_final_scores(data):
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if not data:
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raise PreventUpdate
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def update_scores_store(exam):
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ctx = dash.callback_context
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if not exam:
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return [[], [], [], {}]
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exam = pd.DataFrame.from_dict([json.loads(exam)])
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scores = get_unstack_scores(exam)
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fixed_columns = [
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"exercise",
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"question",
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"competence",
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"theme",
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"comment",
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"score_rate",
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"is_leveled",
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]
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columns = fixed_columns + list(get_students_from_exam(exam))
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scores = pd.DataFrame.from_records(data)
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try:
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if scores.iloc[0]["Commentaire"] == "commentaire":
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scores.drop([0], inplace=True)
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except KeyError:
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pass
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scores = flat_df_students(scores).dropna(subset=["Score"])
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if scores.empty:
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return [{}]
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scores = pp_q_scores(scores)
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assessment_scores = scores.groupby(["Eleve"]).agg({"Note": "sum", "Bareme": "sum"})
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return [assessment_scores.reset_index().to_dict("records")]
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return [
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[{"id": c, "name": c} for c in columns],
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scores.to_dict("records"),
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[],
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{"headers": True, "data": len(fixed_columns)},
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]
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@ -1,7 +1,8 @@
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#!/usr/bin/env python
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# encoding: utf-8
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from ....database.filesystem.loader import CSVLoader
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from recopytex.database.filesystem.loader import CSVLoader
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from recopytex.lib.dataframe import column_values_to_column
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LOADER = CSVLoader("./test_config.yml")
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@ -13,3 +14,17 @@ def get_tribes():
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def get_exams(tribe):
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return LOADER.get_exams([tribe])
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def get_record_scores(exam):
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return LOADER.get_exam_scores(exam)
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def get_unstack_scores(exam):
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flat_scores = LOADER.get_exam_scores(exam)
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kept_columns = [col for col in LOADER.score_columns if col != "score"]
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return column_values_to_column(flat_scores, "student_name", "score", kept_columns)
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def get_students_from_exam(exam):
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flat_scores = LOADER.get_exam_scores(exam)
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return flat_scores["student_name"].unique()
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@ -43,6 +43,49 @@ class CSVLoader(Loader):
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""" Get config """
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return self._config
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@property
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def exam_columns(self):
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return pd.Index(["name", "date", "term", "origin", "tribe", "id"])
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@property
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def question_columns(self):
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return pd.Index(
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[
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"exercise",
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"question",
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"competence",
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"theme",
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"comment",
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"score_rate",
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"is_leveled",
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"origin",
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"exam_id",
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"id",
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]
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)
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@property
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def score_columns(self):
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return pd.Index(
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[
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"term",
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"exam",
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"date",
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"exercise",
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"question",
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"competence",
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"theme",
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"comment",
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"score_rate",
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"is_leveled",
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"origin",
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"exam_id",
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"question_id",
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"student_name",
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"score",
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]
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)
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def rename_columns(self, dataframe):
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"""Rename dataframe column to match with `csv_fields`
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@ -84,8 +127,8 @@ class CSVLoader(Loader):
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:example:
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>>> loader = CSVLoader("./test_config.yml")
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>>> exams = loader.get_exams(["Tribe1"])
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>>> exams.columns
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Index(['name', 'date', 'term', 'origin', 'tribe', 'id'], dtype='object')
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>>> all(exams.columns == loader.exam_columns)
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True
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>>> exams
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name date term origin tribe id
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0 DS 12/01/2021 1 example/Tribe1/210112_DS.csv Tribe1 DS_Tribe1
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@ -118,10 +161,7 @@ class CSVLoader(Loader):
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:example:
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>>> loader = CSVLoader("./test_config.yml")
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>>> exams = loader.get_exams(["Tribe1"])
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>>> loader.get_exam_questions([exams.iloc[0]]).columns
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Index(['exercise', 'question', 'competence', 'theme', 'comment', 'score_rate',
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'is_leveled', 'origin', 'exam_id', 'id'],
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dtype='object')
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>>> all(loader.get_exam_questions([exams.iloc[0]]).columns == loader.score_columns)
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>>> questions = loader.get_exam_questions(exams)
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>>> questions.iloc[0]
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exercise Exercice 1
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@ -172,11 +212,8 @@ class CSVLoader(Loader):
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>>> exams = loader.get_exams(["Tribe1"])
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>>> questions = loader.get_exam_questions(exams)
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>>> scores = loader.get_questions_scores(questions)
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>>> scores.columns
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Index(['term', 'exam', 'date', 'exercise', 'question', 'competence', 'theme',
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'comment', 'score_rate', 'is_leveled', 'origin', 'exam_id',
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'question_id', 'student_name', 'score'],
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dtype='object')
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>>> all(scores.columns == loader.score_columns)
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True
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>>> scores["student_name"].unique()
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array(['Star Tice', 'Umberto Dingate', 'Starlin Crangle',
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'Humbert Bourcq', 'Gabriella Handyside', 'Stewart Eaves',
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@ -214,6 +251,24 @@ class CSVLoader(Loader):
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return pd.concat(scores)
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def get_exam_scores(self, exams=[]):
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"""Get scores for all question of the exam
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:param exams: list or dataframe of exams metadatas (need origin field to find the csv)
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:example:
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>>> loader = CSVLoader("./test_config.yml")
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>>> exams = loader.get_exams(["Tribe1"])
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>>> scores = loader.get_exam_scores(exams)
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>>> scores.columns
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Index(['term', 'exam', 'date', 'exercise', 'question', 'competence', 'theme',
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'comment', 'score_rate', 'is_leveled', 'origin', 'exam_id',
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'question_id', 'student_name', 'score'],
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dtype='object')
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"""
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questions = self.get_exam_questions(exams)
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return self.get_questions_scores(questions)
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def get_students(self, tribes=[]):
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"""Get student list
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