Feat: add row to scores_table!!
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21397272c9
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@ -11,6 +11,7 @@ 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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@ -25,17 +26,29 @@ COLORS = {
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3: "#68D42F",
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}
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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(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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html.H1("Analyse des notes"),
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html.Div(
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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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dcc.Dropdown(
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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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@ -43,13 +56,20 @@ app.layout = html.Div(
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],
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value=config["tribes"][0]["name"],
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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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),
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html.Div(
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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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@ -73,18 +93,20 @@ app.layout = html.Div(
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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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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(id="fig_assessment_hist"),
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dcc.Graph(id="fig_competences"),
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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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style={"columnCount": 2},
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),
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html.Br(),
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html.Div(
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@ -98,10 +120,10 @@ app.layout = html.Div(
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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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dbc.Button("Ajouter un élément", id="btn_add_element"),
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]
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),
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html.P(id="lastsave"),
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dcc.Store(id="final_score"),
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]
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)
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@ -201,74 +223,78 @@ def update_final_scores_hist(data):
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hovertemplate="",
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marker_color="#4E89DE",
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)
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# fig = go.Figure(
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# data=go.Histogram(
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# x=assessment_scores["Note"],
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# xbins={"start": 0, "end": assessment_scores["Bareme"].max(), "size": 0.25},
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# ),
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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("fig_competences", "figure"),
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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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[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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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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[dash.dependencies.Output("lastsave", "children")],
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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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print(f"save at {csv} ({datetime.today()})")
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scores.to_csv(csv, index=False)
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return [datetime.today()]
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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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def highlight_value(df):
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@ -293,18 +319,23 @@ def highlight_value(df):
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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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dash.dependencies.Input("csv", "value"),
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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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def update_scores_table(value):
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if not value:
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raise PreventUpdate
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stack = pd.read_csv(value, encoding="UTF8")
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# try:
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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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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)
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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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