recopytex/recopytex/scripts/exam_dash.py

#!/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
from pathlib import Path
from datetime import datetime
import pandas as pd


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",
}

app = dash.Dash(__name__)

app.layout = html.Div([
    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"],
        )]),
    html.Div(["Evaluation: ", dcc.Dropdown(id='csv')]),
    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.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"),
    ])

@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_table", "columns"), dash.dependencies.Output("final_score_table", "data")],
        [dash.dependencies.Input("scores_table", "data")],
        )
def update_final_scores_table(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 [{"id": c, "name": c} for c in assessment_scores.reset_index().columns], assessment_scores.reset_index().to_dict('records')
    except KeyError:
        raise PreventUpdate

@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)