Feat: basic score_table
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parent
6ed55c07d4
commit
2031ade1ab
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@ -15,43 +15,53 @@ layout = html.Div(
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],
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),
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html.Main(
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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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# id="scores_table",
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# ),
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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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),
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dcc.Store(id="scores"),
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],
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@ -1,14 +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, get_scores
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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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@ -18,10 +19,10 @@ from .models import get_tribes, get_exams, get_scores
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],
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[Input("tribe", "value")],
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)
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def update_exams_choices(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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if not exams.empty:
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return [
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@ -29,39 +30,38 @@ def update_exams_choices(value):
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], exams.loc[0].to_json()
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return [], None
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@app.callback(
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[Output("scores", "data")],
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[Input("exam_select", "value")]
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)
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def update_scores_store(value):
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if not value:
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return [[]]
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exam = pd.DataFrame.from_dict([json.loads(value)])
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return [get_scores(exam)]
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[
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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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)
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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, "score", "student_name")
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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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# @app.callback(
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# [
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# Output("final_score", "data"),
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# ],
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# [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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#
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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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#
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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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@ -14,5 +14,21 @@ def get_exams(tribe):
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return LOADER.get_exams([tribe])
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def get_scores(exam):
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return LOADER.get_exam_scores(exam).to_dict('records')
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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, value_column="score", unstack_column="student_name"):
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flat_scores = LOADER.get_exam_scores(exam)
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pivot_columns = [col for col in LOADER.score_columns if col != value_column]
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unstacked_df = (
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flat_scores.set_index(pivot_columns)[value_column]
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.unstack(unstack_column)
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.reset_index()
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)
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return unstacked_df
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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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