Feat: Hist graph and describe
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@ -6,9 +6,11 @@ import dash_html_components as html
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import dash_core_components as dcc
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import dash_table
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from dash.exceptions import PreventUpdate
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import plotly.graph_objects as go
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from pathlib import Path
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from datetime import datetime
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import pandas as pd
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import numpy as np
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from .. import flat_df_students, pp_q_scores
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@ -23,53 +25,92 @@ COLORS = {
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3: "#68D42F",
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}
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app = dash.Dash(__name__)
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external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
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app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
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# app = dash.Dash(__name__)
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app.layout = html.Div([
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app.layout = html.Div(
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[
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html.H1("Analyse des notes"),
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html.Div(["Classe: ", dcc.Dropdown(
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id='tribe',
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options=[{"label": t["name"], "value": t["name"]} for t in config["tribes"]],
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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 config["tribes"]
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],
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value=config["tribes"][0]["name"],
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)]),
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html.Div(["Evaluation: ", dcc.Dropdown(id='csv')]),
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html.Div([dash_table.DataTable(
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),
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"Evaluation: ",
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dcc.Dropdown(id="csv"),
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],
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style={"columnCount": 2},
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),
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html.Div(
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[
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dash_table.DataTable(
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id="final_score_table",
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columns = [{"id": "Élève", "name": "Élève"}, {"id": "Note", "name": "Note"},{"id": "Barème", "name": "Bareme"}],
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columns=[
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{"id": "Élève", "name": "Élève"},
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{"id": "Note", "name": "Note"},
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{"id": "Barème", "name": "Bareme"},
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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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"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_header={
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'backgroundColor': 'rgb(230, 230, 230)',
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'fontWeight': 'bold'
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"backgroundColor": "rgb(230, 230, 230)",
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"fontWeight": "bold",
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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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"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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]),
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html.Div(
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[
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dash_table.DataTable(
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id="final_score_describe",
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),
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dcc.Graph(id="fig_assessment_hist"),
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]
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),
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],
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style={"columnCount": 2},
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),
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html.Br(),
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html.Div([dash_table.DataTable(
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html.Div(
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[
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dash_table.DataTable(
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id="scores_table",
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columns=[{"id": c, "name": c} for c in NO_ST_COLUMNS.values()],
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style_cell={
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'whiteSpace': 'normal',
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'height': 'auto',
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"whiteSpace": "normal",
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"height": "auto",
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},
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style_data_conditional=[],
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editable=True,
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)]),
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)
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]
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),
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html.P(id="lastsave"),
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])
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dcc.Store(id="final_score"),
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]
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)
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@app.callback(
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[dash.dependencies.Output("csv", "options"), dash.dependencies.Output("csv", "value")],
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[
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dash.dependencies.Output("csv", "options"),
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dash.dependencies.Output("csv", "value"),
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],
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[dash.dependencies.Input("tribe", "value")],
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)
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def update_csvs(value):
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@ -82,25 +123,92 @@ def update_csvs(value):
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except IndexError:
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return []
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@app.callback(
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[dash.dependencies.Output("final_score_table", "columns"), dash.dependencies.Output("final_score_table", "data")],
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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_table(data):
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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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try:
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scores = pd.DataFrame.from_records(data)
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scores = flat_df_students(scores).dropna(subset=["Score"])
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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 [{"id": c, "name": c} for c in assessment_scores.reset_index().columns], assessment_scores.reset_index().to_dict('records')
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assessment_scores = scores.groupby(["Eleve"]).agg(
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{"Note": "sum", "Bareme": "sum"}
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)
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return [assessment_scores.reset_index().to_dict("records")]
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except KeyError:
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raise PreventUpdate
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@app.callback(
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[
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dash.dependencies.Output("final_score_table", "columns"),
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dash.dependencies.Output("final_score_table", "data"),
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],
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[dash.dependencies.Input("final_score", "data")],
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)
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def update_final_scores_table(data):
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assessment_scores = pd.DataFrame.from_records(data)
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return [
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{"id": c, "name": c} for c in assessment_scores.columns
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], assessment_scores.to_dict("records")
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@app.callback(
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[
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dash.dependencies.Output("final_score_describe", "columns"),
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dash.dependencies.Output("final_score_describe", "data"),
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],
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[dash.dependencies.Input("final_score", "data")],
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)
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def update_final_scores_descr(data):
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desc = pd.DataFrame.from_records(data)["Note"].describe()
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print(desc.keys())
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return [{"id": c, "name": c} for c in desc.keys()], [desc.to_dict()]
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@app.callback(
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[
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dash.dependencies.Output("fig_assessment_hist", "figure"),
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],
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[dash.dependencies.Input("final_score", "data")],
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)
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def update_final_scores_hist(data):
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assessment_scores = pd.DataFrame.from_records(data)
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ranges = np.linspace(
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0, assessment_scores.Bareme.max(), int(assessment_scores.Bareme.max() * 2 + 1)
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)
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bins = pd.cut(assessment_scores["Note"], ranges)
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assessment_scores["Bin"] = bins
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assessment_grouped = (
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assessment_scores.reset_index()
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.groupby("Bin")
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.agg({"Bareme": "count", "Eleve": lambda x: "\n".join(x)})
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)
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assessment_grouped.index = assessment_grouped.index.map(lambda i: i.right)
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fig = go.Figure()
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fig.add_bar(
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x=assessment_grouped.index,
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y=assessment_grouped.Bareme,
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text=assessment_grouped.Eleve,
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textposition="auto",
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hovertemplate="",
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marker_color="#4E89DE",
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)
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return [fig]
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@app.callback(
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[dash.dependencies.Output("lastsave", "children")],
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[dash.dependencies.Input("scores_table", "data"), dash.dependencies.State("csv", "value")],
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[
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dash.dependencies.Input("scores_table", "data"),
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dash.dependencies.State("csv", "value"),
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],
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)
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def save_scores(data, csv):
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scores = pd.DataFrame.from_records(data)
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@ -115,18 +223,22 @@ def highlight_value(df):
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for v, color in COLORS.items():
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hight += [
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{
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'if': {
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'filter_query': '{{{}}} = {}'.format(col, v),
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'column_id': col
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},
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'backgroundColor': color,
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'color': 'white'
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} for col in df.columns if col not in NO_ST_COLUMNS.values()
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"if": {"filter_query": "{{{}}} = {}".format(col, v), "column_id": col},
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"backgroundColor": color,
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"color": "white",
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}
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for col in df.columns
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if col not in NO_ST_COLUMNS.values()
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]
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return hight
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@app.callback(
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[dash.dependencies.Output("scores_table", "columns"), dash.dependencies.Output("scores_table", "data"), dash.dependencies.Output("scores_table", "style_data_conditional"), ],
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[
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dash.dependencies.Output("scores_table", "columns"),
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dash.dependencies.Output("scores_table", "data"),
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dash.dependencies.Output("scores_table", "style_data_conditional"),
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],
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[dash.dependencies.Input("csv", "value")],
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)
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def update_scores_table(value):
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@ -137,4 +249,8 @@ def update_scores_table(value):
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# stack = stack.drop(columns=["Nom", "Trimestre", "Date", "Competence", "Domaine", "Est_nivele", "Bareme"])
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# except KeyError:
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# stack = stack
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return [{"id": c, "name": c} for c in stack.columns], stack.to_dict('records'), highlight_value(stack)
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return (
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[{"id": c, "name": c} for c in stack.columns],
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stack.to_dict("records"),
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highlight_value(stack),
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)
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