Merge branch 'dev' of git_opytex:/lafrite/recopytex into dev
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commit
494567cdb5
41
recopytex/dashboard/assets/style.css
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41
recopytex/dashboard/assets/style.css
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body {
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margin: 0px;
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font-family: 'Source Sans Pro','Roboto','Open Sans','Liberation Sans','DejaVu Sans','Verdana','Helvetica','Arial',sans-serif;
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}
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header {
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margin: 0px 0px 20px 0px;
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background-color: #333333;
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color: #ffffff;
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padding: 20px;
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}
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header > h1 {
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margin: 0px;
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}
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main {
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width: 95vw;
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margin: auto;
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}
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#select {
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margin-bottom: 20px;
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}
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#select > div {
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width: 40vw;
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margin: auto;
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}
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#analysis {
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display: flex;
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flex-flow: row wrap;
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}
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#analysis > * {
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display: flex;
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flex-flow: column;
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width: 45vw;
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margin: auto;
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}
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@ -26,29 +26,26 @@ COLORS = {
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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 = 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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html.Header(
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children=[
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dbc.Alert("Dernière sauvegarde", id="lastsave", color="success"),
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html.H1("Analyse des notes"),
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html.P("Dernière sauvegarde", id="lastsave"),
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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.H2("Choix de l'évaluation"),
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dbc.Row(
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html.Main(
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[
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dbc.Col(
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html.Section(
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[
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html.Div(
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[
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"Classe: ",
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dbc.Select(
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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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@ -56,9 +53,30 @@ 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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]
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],
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style={
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"display": "flex",
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"flex-flow": "column",
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},
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),
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dbc.Col(
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html.Div(
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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={
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"display": "flex",
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"flex-flow": "column",
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},
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),
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],
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id="select",
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style={
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"display": "flex",
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"flex-flow": "row wrap",
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},
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),
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html.Div(
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[
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"Evaluation: ",
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dbc.Select(id="csv"),
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@ -95,35 +113,13 @@ app.layout = html.Div(
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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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columns=[{"id": "count", "name": "count"},
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{"id": "mean", "name": "mean"},
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{"id": "std", "name": "std"},
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{"id": "min", "name": "min"},
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{"id": "25%", "name": "25%"},
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{"id": "50%", "name": "50%"},
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{"id": "75%", "name": "75%"},
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{"id": "max", "name": "max"},
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]
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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.H2("Édition des notes"),
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dbc.Row(
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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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columns=[
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{"id": c, "name": c} for c in NO_ST_COLUMNS.values()
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],
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style_cell={
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"whiteSpace": "normal",
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"height": "auto",
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@ -131,12 +127,19 @@ app.layout = html.Div(
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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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html.Button("Ajouter un élément", id="btn_add_element"),
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],
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id="big_table",
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),
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html.H2("Actions"),
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dcc.Store(id="final_score"),
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]
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],
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className="content",
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style={
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"width": "95vw",
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"margin": "auto",
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},
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),
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],
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)
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@ -179,9 +182,7 @@ def update_final_scores(data):
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return [{}]
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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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assessment_scores = scores.groupby(["Eleve"]).agg({"Note": "sum", "Bareme": "sum"})
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return [assessment_scores.reset_index().to_dict("records")]
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@ -223,7 +224,9 @@ def update_final_scores_hist(data):
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return [{'data': [], 'layout':[]}]
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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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-0.5,
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assessment_scores.Bareme.max(),
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int(assessment_scores.Bareme.max() * 2 + 2),
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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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@ -279,11 +282,11 @@ def update_competence_fig(data):
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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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pt.index.get_level_values(0).map(str)
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+ ":"
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+ pt.index.get_level_values(1)
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+ pt.index.get_level_values(1).map(str)
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+ " "
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+ pt.index.get_level_values(2)
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+ pt.index.get_level_values(2).map(str)
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)
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fig = go.Figure()
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@ -312,7 +315,6 @@ def update_competence_fig(data):
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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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@ -324,9 +326,9 @@ def save_scores(data, csv):
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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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return [f"Soucis pour sauvegarder à {datetime.today()} dans {csv}"]
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else:
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return [f"Dernière sauvegarde {datetime.today()} dans {csv}"], "success"
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return [f"Dernière sauvegarde {datetime.today()} dans {csv}"]
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def highlight_value(df):
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@ -359,15 +361,16 @@ def highlight_value(df):
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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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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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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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infos = pd.DataFrame.from_records(
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[{k: stack.iloc[-1][k] for k in NO_ST_COLUMNS.values()}]
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)
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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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@ -87,8 +87,9 @@ def new_exam():
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@cli.command()
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def exam_analysis():
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exam_app.run_server(debug=True)
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@click.option("--debug", default=0, help="Debug mode for dash")
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def exam_analysis(debug):
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exam_app.run_server(debug=bool(debug))
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@cli.command()
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