126 lines
4.0 KiB
Python
126 lines
4.0 KiB
Python
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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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from pathlib import Path
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import pandas as pd
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from .. import flat_df_students, pp_q_scores
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from .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(__name__)
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app.layout = html.Div([
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html.H1("Coucou"),
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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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value=config["tribes"][0]["name"],
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)]),
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html.Div(["Evaluation: ", dcc.Dropdown(id='exam')]),
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html.Div([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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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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html.Br(),
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html.Div([dash_table.DataTable(
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id="scores_table",
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columns = [{"id": "plop", "name": "popo"}],
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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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)]),
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])
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@app.callback(
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[dash.dependencies.Output("exam", "options"), dash.dependencies.Output("exam", "value")],
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[dash.dependencies.Input("tribe", "value")],
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)
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def update_exams(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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return [{"label": str(c), "value": str(c)} for c in csvs], str(csvs[0])
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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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[dash.dependencies.Input("exam", "value")],
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)
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def update_scores_table(value):
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if not value:
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raise PreventUpdate
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try:
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scores = pd.read_csv(value, encoding="UTF8")
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comments = scores.iloc[0]
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scores.drop([0], inplace=True)
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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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except KeyError:
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raise PreventUpdate
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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': {
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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 ["Exercice", "Question", "Commentaire"]
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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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[dash.dependencies.Input("exam", "value")],
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)
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def update_scores_table(value):
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if not value:
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raise PreventUpdate
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scores = pd.read_csv(value, encoding="UTF8")
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try:
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stack = scores.drop(columns=["Nom", "Trimestre", "Date", "Competence", "Domaine", "Est_nivele", "Bareme"])
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except KeyError:
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stack = scores
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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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