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0a5a931d01
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894ebc4ec8
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#!/usr/bin/env python
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# encoding: utf-8
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import dash
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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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import dash_bootstrap_components as dbc
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from .. import flat_df_students, pp_q_scores
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from ..config import NO_ST_COLUMNS
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from ..scripts.getconfig import config, CONFIGPATH
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COLORS = {
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".": "black",
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0: "#E7472B",
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1: "#FF712B",
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2: "#F2EC4C",
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3: "#68D42F",
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}
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app = dash.Dash(external_stylesheets=[dbc.themes.SIMPLEX])
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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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children=[
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dbc.NavbarSimple(
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children=[
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dbc.Alert("Dernière sauvegarde", id="lastsave", color="success"),
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],
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brand="Analyse des notes",
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brand_href="#",
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color="success",
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dark=True,
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),
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html.Br(),
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dbc.Row(
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[
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dbc.Col(
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[
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"Classe: ",
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dbc.Select(
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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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]
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),
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dbc.Col(
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[
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"Evaluation: ",
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dbc.Select(id="csv"),
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]
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),
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],
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),
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html.Br(),
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dbc.Row(
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[
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dbc.Col(
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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": "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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}
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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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},
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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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),
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dbc.Col(
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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(
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id="fig_assessment_hist",
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),
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# dcc.Graph(id="fig_competences"),
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]
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),
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],
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),
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html.Br(),
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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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},
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style_data_conditional=[],
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editable=True,
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),
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dbc.Button("Ajouter un élément", id="btn_add_element"),
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]
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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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[
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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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if not value:
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raise PreventUpdate
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p = Path(value)
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csvs = list(p.glob("*.csv"))
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try:
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return [{"label": str(c), "value": str(c)} for c in csvs], str(csvs[0])
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except IndexError:
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return []
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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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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(
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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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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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fig.update_layout(
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height=300,
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margin=dict(l=5, r=5, b=5, t=5),
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)
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return [fig]
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# @app.callback(
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# [
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# dash.dependencies.Output("fig_competences", "figure"),
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# ],
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# [dash.dependencies.Input("scores_table", "data")],
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# )
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# def update_competence_fig(data):
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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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# pt = pd.pivot_table(
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# scores,
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# index=["Exercice", "Question", "Commentaire"],
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# columns="Score",
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# aggfunc="size",
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# fill_value=0,
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# )
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# for i in {i for i in pt.index.get_level_values(0)}:
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# pt.loc[(str(i), "", ""), :] = ""
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# pt.sort_index(inplace=True)
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# index = (
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# pt.index.get_level_values(0)
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# + ":"
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# + pt.index.get_level_values(1)
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# + " "
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# + pt.index.get_level_values(2)
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# )
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#
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# fig = go.Figure()
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# bars = [
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# {"score": -1, "name": "Pas de réponse", "color": COLORS["."]},
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# {"score": 0, "name": "Faut", "color": COLORS[0]},
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# {"score": 1, "name": "Peu juste", "color": COLORS[1]},
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# {"score": 2, "name": "Presque juste", "color": COLORS[2]},
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# {"score": 3, "name": "Juste", "color": COLORS[3]},
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# ]
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# for b in bars:
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# try:
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# fig.add_bar(
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# x=index, y=pt[b["score"]], name=b["name"], marker_color=b["color"]
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# )
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# except KeyError:
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# pass
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# fig.update_layout(barmode="relative")
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# return [fig]
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@app.callback(
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[
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dash.dependencies.Output("lastsave", "children"),
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dash.dependencies.Output("lastsave", "color"),
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],
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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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try:
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scores = pd.DataFrame.from_records(data)
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scores.to_csv(csv, index=False)
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except:
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return [f"Soucis pour sauvegarder à {datetime.today()} dans {csv}"], "warning"
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else:
|
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return [f"Dernière sauvegarde {datetime.today()} dans {csv}"], "success"
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|
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def highlight_value(df):
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""" Cells style """
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hight = []
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for v, color in COLORS.items():
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hight += [
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{
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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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|
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|
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@app.callback(
|
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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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[
|
|
||||||
dash.dependencies.Input("csv", "value"),
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|
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dash.dependencies.Input("btn_add_element", "n_clicks"),
|
|
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dash.dependencies.State("scores_table", "data"),
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|
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],
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)
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def update_scores_table(csv, add_element, data):
|
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ctx = dash.callback_context
|
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if ctx.triggered[0]['prop_id'] == "csv.value":
|
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stack = pd.read_csv(csv, encoding="UTF8")
|
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elif ctx.triggered[0]['prop_id'] == "btn_add_element.n_clicks":
|
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stack = pd.DataFrame.from_records(data)
|
|
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infos = pd.DataFrame.from_records([{k: stack.iloc[-1][k] for k in NO_ST_COLUMNS.values()}])
|
|
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stack = stack.append(infos)
|
|
||||||
return (
|
|
||||||
[{"id": c, "name": c} for c in stack.columns],
|
|
||||||
stack.to_dict("records"),
|
|
||||||
highlight_value(stack),
|
|
||||||
)
|
|
||||||
|
|
||||||
299
recopytex/scripts/exam_dash.py
Normal file
299
recopytex/scripts/exam_dash.py
Normal file
@ -0,0 +1,299 @@
|
|||||||
|
#!/usr/bin/env python
|
||||||
|
# encoding: utf-8
|
||||||
|
|
||||||
|
import dash
|
||||||
|
import dash_html_components as html
|
||||||
|
import dash_core_components as dcc
|
||||||
|
import dash_table
|
||||||
|
from dash.exceptions import PreventUpdate
|
||||||
|
import plotly.graph_objects as go
|
||||||
|
from pathlib import Path
|
||||||
|
from datetime import datetime
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
from .. import flat_df_students, pp_q_scores
|
||||||
|
from ..config import NO_ST_COLUMNS
|
||||||
|
from .getconfig import config, CONFIGPATH
|
||||||
|
|
||||||
|
COLORS = {
|
||||||
|
".": "black",
|
||||||
|
0: "#E7472B",
|
||||||
|
1: "#FF712B",
|
||||||
|
2: "#F2EC4C",
|
||||||
|
3: "#68D42F",
|
||||||
|
}
|
||||||
|
|
||||||
|
external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
|
||||||
|
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
|
||||||
|
# app = dash.Dash(__name__)
|
||||||
|
|
||||||
|
app.layout = html.Div(
|
||||||
|
children=[
|
||||||
|
html.H1("Analyse des notes"),
|
||||||
|
html.Div(
|
||||||
|
[
|
||||||
|
"Classe: ",
|
||||||
|
dcc.Dropdown(
|
||||||
|
id="tribe",
|
||||||
|
options=[
|
||||||
|
{"label": t["name"], "value": t["name"]}
|
||||||
|
for t in config["tribes"]
|
||||||
|
],
|
||||||
|
value=config["tribes"][0]["name"],
|
||||||
|
),
|
||||||
|
"Evaluation: ",
|
||||||
|
dcc.Dropdown(id="csv"),
|
||||||
|
],
|
||||||
|
style={"columnCount": 2},
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
[
|
||||||
|
dash_table.DataTable(
|
||||||
|
id="final_score_table",
|
||||||
|
columns=[
|
||||||
|
{"id": "Élève", "name": "Élève"},
|
||||||
|
{"id": "Note", "name": "Note"},
|
||||||
|
{"id": "Barème", "name": "Bareme"},
|
||||||
|
],
|
||||||
|
data=[],
|
||||||
|
style_data_conditional=[
|
||||||
|
{
|
||||||
|
"if": {"row_index": "odd"},
|
||||||
|
"backgroundColor": "rgb(248, 248, 248)",
|
||||||
|
}
|
||||||
|
],
|
||||||
|
style_header={
|
||||||
|
"backgroundColor": "rgb(230, 230, 230)",
|
||||||
|
"fontWeight": "bold",
|
||||||
|
},
|
||||||
|
style_data={
|
||||||
|
"width": "100px",
|
||||||
|
"maxWidth": "100px",
|
||||||
|
"minWidth": "100px",
|
||||||
|
},
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
[
|
||||||
|
dash_table.DataTable(
|
||||||
|
id="final_score_describe",
|
||||||
|
),
|
||||||
|
dcc.Graph(id="fig_assessment_hist"),
|
||||||
|
dcc.Graph(id="fig_competences"),
|
||||||
|
]
|
||||||
|
),
|
||||||
|
],
|
||||||
|
style={"columnCount": 2},
|
||||||
|
),
|
||||||
|
html.Br(),
|
||||||
|
html.Div(
|
||||||
|
[
|
||||||
|
dash_table.DataTable(
|
||||||
|
id="scores_table",
|
||||||
|
columns=[{"id": c, "name": c} for c in NO_ST_COLUMNS.values()],
|
||||||
|
style_cell={
|
||||||
|
"whiteSpace": "normal",
|
||||||
|
"height": "auto",
|
||||||
|
},
|
||||||
|
style_data_conditional=[],
|
||||||
|
editable=True,
|
||||||
|
)
|
||||||
|
]
|
||||||
|
),
|
||||||
|
html.P(id="lastsave"),
|
||||||
|
dcc.Store(id="final_score"),
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@app.callback(
|
||||||
|
[
|
||||||
|
dash.dependencies.Output("csv", "options"),
|
||||||
|
dash.dependencies.Output("csv", "value"),
|
||||||
|
],
|
||||||
|
[dash.dependencies.Input("tribe", "value")],
|
||||||
|
)
|
||||||
|
def update_csvs(value):
|
||||||
|
if not value:
|
||||||
|
raise PreventUpdate
|
||||||
|
p = Path(value)
|
||||||
|
csvs = list(p.glob("*.csv"))
|
||||||
|
try:
|
||||||
|
return [{"label": str(c), "value": str(c)} for c in csvs], str(csvs[0])
|
||||||
|
except IndexError:
|
||||||
|
return []
|
||||||
|
|
||||||
|
|
||||||
|
@app.callback(
|
||||||
|
[
|
||||||
|
dash.dependencies.Output("final_score", "data"),
|
||||||
|
],
|
||||||
|
[dash.dependencies.Input("scores_table", "data")],
|
||||||
|
)
|
||||||
|
def update_final_scores(data):
|
||||||
|
if not data:
|
||||||
|
raise PreventUpdate
|
||||||
|
try:
|
||||||
|
scores = pd.DataFrame.from_records(data)
|
||||||
|
scores = flat_df_students(scores).dropna(subset=["Score"])
|
||||||
|
scores = pp_q_scores(scores)
|
||||||
|
assessment_scores = scores.groupby(["Eleve"]).agg(
|
||||||
|
{"Note": "sum", "Bareme": "sum"}
|
||||||
|
)
|
||||||
|
return [assessment_scores.reset_index().to_dict("records")]
|
||||||
|
except KeyError:
|
||||||
|
raise PreventUpdate
|
||||||
|
|
||||||
|
|
||||||
|
@app.callback(
|
||||||
|
[
|
||||||
|
dash.dependencies.Output("final_score_table", "columns"),
|
||||||
|
dash.dependencies.Output("final_score_table", "data"),
|
||||||
|
],
|
||||||
|
[dash.dependencies.Input("final_score", "data")],
|
||||||
|
)
|
||||||
|
def update_final_scores_table(data):
|
||||||
|
assessment_scores = pd.DataFrame.from_records(data)
|
||||||
|
return [
|
||||||
|
{"id": c, "name": c} for c in assessment_scores.columns
|
||||||
|
], assessment_scores.to_dict("records")
|
||||||
|
|
||||||
|
|
||||||
|
@app.callback(
|
||||||
|
[
|
||||||
|
dash.dependencies.Output("final_score_describe", "columns"),
|
||||||
|
dash.dependencies.Output("final_score_describe", "data"),
|
||||||
|
],
|
||||||
|
[dash.dependencies.Input("final_score", "data")],
|
||||||
|
)
|
||||||
|
def update_final_scores_descr(data):
|
||||||
|
desc = pd.DataFrame.from_records(data)["Note"].describe()
|
||||||
|
return [{"id": c, "name": c} for c in desc.keys()], [desc.to_dict()]
|
||||||
|
|
||||||
|
|
||||||
|
@app.callback(
|
||||||
|
[
|
||||||
|
dash.dependencies.Output("fig_assessment_hist", "figure"),
|
||||||
|
],
|
||||||
|
[dash.dependencies.Input("final_score", "data")],
|
||||||
|
)
|
||||||
|
def update_final_scores_hist(data):
|
||||||
|
assessment_scores = pd.DataFrame.from_records(data)
|
||||||
|
ranges = np.linspace(
|
||||||
|
0, assessment_scores.Bareme.max(), int(assessment_scores.Bareme.max() * 2 + 1)
|
||||||
|
)
|
||||||
|
bins = pd.cut(assessment_scores["Note"], ranges)
|
||||||
|
assessment_scores["Bin"] = bins
|
||||||
|
assessment_grouped = (
|
||||||
|
assessment_scores.reset_index()
|
||||||
|
.groupby("Bin")
|
||||||
|
.agg({"Bareme": "count", "Eleve": lambda x: "\n".join(x)})
|
||||||
|
)
|
||||||
|
assessment_grouped.index = assessment_grouped.index.map(lambda i: i.right)
|
||||||
|
fig = go.Figure()
|
||||||
|
fig.add_bar(
|
||||||
|
x=assessment_grouped.index,
|
||||||
|
y=assessment_grouped.Bareme,
|
||||||
|
text=assessment_grouped.Eleve,
|
||||||
|
textposition="auto",
|
||||||
|
hovertemplate="",
|
||||||
|
marker_color="#4E89DE",
|
||||||
|
)
|
||||||
|
return [fig]
|
||||||
|
|
||||||
|
@app.callback(
|
||||||
|
[
|
||||||
|
dash.dependencies.Output("fig_competences", "figure"),
|
||||||
|
],
|
||||||
|
[dash.dependencies.Input("scores_table", "data")],
|
||||||
|
)
|
||||||
|
def update_competence_fig(data):
|
||||||
|
scores = pd.DataFrame.from_records(data)
|
||||||
|
scores = flat_df_students(scores).dropna(subset=["Score"])
|
||||||
|
scores = pp_q_scores(scores)
|
||||||
|
pt = pd.pivot_table(
|
||||||
|
scores,
|
||||||
|
index=["Exercice", "Question", "Commentaire"],
|
||||||
|
columns="Score",
|
||||||
|
aggfunc="size",
|
||||||
|
fill_value=0,
|
||||||
|
)
|
||||||
|
for i in {i for i in pt.index.get_level_values(0)}:
|
||||||
|
pt.loc[(str(i), "", ""), :] = ""
|
||||||
|
pt.sort_index(inplace=True)
|
||||||
|
index = (
|
||||||
|
pt.index.get_level_values(0)
|
||||||
|
+ ":"
|
||||||
|
+ pt.index.get_level_values(1)
|
||||||
|
+ " "
|
||||||
|
+ pt.index.get_level_values(2)
|
||||||
|
)
|
||||||
|
|
||||||
|
fig = go.Figure()
|
||||||
|
bars = [
|
||||||
|
{"score": -1, "name":"Pas de réponse", "color": COLORS["."]},
|
||||||
|
{"score": 0, "name":"Faut", "color": COLORS[0]},
|
||||||
|
{"score": 1, "name":"Peu juste", "color": COLORS[1]},
|
||||||
|
{"score": 2, "name":"Presque juste", "color": COLORS[2]},
|
||||||
|
{"score": 3, "name":"Juste", "color": COLORS[3]},
|
||||||
|
]
|
||||||
|
for b in bars:
|
||||||
|
try:
|
||||||
|
fig.add_bar(x=index, y=pt[b["score"]], name=b["name"], marker_color=b["color"])
|
||||||
|
except KeyError:
|
||||||
|
pass
|
||||||
|
fig.update_layout(barmode="relative")
|
||||||
|
return [fig]
|
||||||
|
|
||||||
|
@app.callback(
|
||||||
|
[dash.dependencies.Output("lastsave", "children")],
|
||||||
|
[
|
||||||
|
dash.dependencies.Input("scores_table", "data"),
|
||||||
|
dash.dependencies.State("csv", "value"),
|
||||||
|
],
|
||||||
|
)
|
||||||
|
def save_scores(data, csv):
|
||||||
|
scores = pd.DataFrame.from_records(data)
|
||||||
|
print(f"save at {csv} ({datetime.today()})")
|
||||||
|
scores.to_csv(csv, index=False)
|
||||||
|
return [datetime.today()]
|
||||||
|
|
||||||
|
|
||||||
|
def highlight_value(df):
|
||||||
|
""" Cells style """
|
||||||
|
hight = []
|
||||||
|
for v, color in COLORS.items():
|
||||||
|
hight += [
|
||||||
|
{
|
||||||
|
"if": {"filter_query": "{{{}}} = {}".format(col, v), "column_id": col},
|
||||||
|
"backgroundColor": color,
|
||||||
|
"color": "white",
|
||||||
|
}
|
||||||
|
for col in df.columns
|
||||||
|
if col not in NO_ST_COLUMNS.values()
|
||||||
|
]
|
||||||
|
return hight
|
||||||
|
|
||||||
|
|
||||||
|
@app.callback(
|
||||||
|
[
|
||||||
|
dash.dependencies.Output("scores_table", "columns"),
|
||||||
|
dash.dependencies.Output("scores_table", "data"),
|
||||||
|
dash.dependencies.Output("scores_table", "style_data_conditional"),
|
||||||
|
],
|
||||||
|
[dash.dependencies.Input("csv", "value")],
|
||||||
|
)
|
||||||
|
def update_scores_table(value):
|
||||||
|
if not value:
|
||||||
|
raise PreventUpdate
|
||||||
|
stack = pd.read_csv(value, encoding="UTF8")
|
||||||
|
# try:
|
||||||
|
# stack = stack.drop(columns=["Nom", "Trimestre", "Date", "Competence", "Domaine", "Est_nivele", "Bareme"])
|
||||||
|
# except KeyError:
|
||||||
|
# stack = stack
|
||||||
|
return (
|
||||||
|
[{"id": c, "name": c} for c in stack.columns],
|
||||||
|
stack.to_dict("records"),
|
||||||
|
highlight_value(stack),
|
||||||
|
)
|
||||||
@ -13,7 +13,7 @@ from .getconfig import config, CONFIGPATH
|
|||||||
from .prompts import prompt_exam, prompt_exercise, prompt_validate
|
from .prompts import prompt_exam, prompt_exercise, prompt_validate
|
||||||
from ..config import NO_ST_COLUMNS
|
from ..config import NO_ST_COLUMNS
|
||||||
from .exam import Exam
|
from .exam import Exam
|
||||||
from ..dashboard.exam import app as exam_app
|
from .exam_dash import app as exam_app
|
||||||
|
|
||||||
|
|
||||||
@click.group()
|
@click.group()
|
||||||
@ -85,12 +85,10 @@ def new_exam():
|
|||||||
base_df.to_csv(exam.path(".csv"), index=False)
|
base_df.to_csv(exam.path(".csv"), index=False)
|
||||||
print(f"Le fichier note a été enregistré à {exam.path('.csv')}")
|
print(f"Le fichier note a été enregistré à {exam.path('.csv')}")
|
||||||
|
|
||||||
|
|
||||||
@cli.command()
|
@cli.command()
|
||||||
def exam_analysis():
|
def exam_analysis():
|
||||||
exam_app.run_server(debug=True)
|
exam_app.run_server(debug=True)
|
||||||
|
|
||||||
|
|
||||||
@cli.command()
|
@cli.command()
|
||||||
@click.argument("csv_file")
|
@click.argument("csv_file")
|
||||||
def report(csv_file):
|
def report(csv_file):
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user