Feat: store exams_scores in store
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@ -7,70 +7,52 @@ 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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),
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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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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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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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),
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dcc.Store(id="scores"),
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],
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)
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@ -3,9 +3,12 @@
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from dash.dependencies import Input, Output
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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_scores
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@app.callback(
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@ -15,39 +18,50 @@ 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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def update_exams_choices(value):
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if not value:
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raise PreventUpdate
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exams = get_exams(value)
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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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], exams.loc[0].to_json()
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return [], None
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@app.callback(
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[
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dash.dependencies.Output("final_score", "data"),
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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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[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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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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# @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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@ -13,3 +13,6 @@ 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_scores(exam):
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return LOADER.get_exam_scores(exam).to_dict('records')
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