Prévisualisation basique des données #65
This commit is contained in:
Colin Maudry
+207
-3
@@ -18,6 +18,7 @@ from dash import (
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)
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from src.figures import (
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DataTable,
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get_barchart_sources,
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get_dashboard_summary_table,
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get_distance_histogram,
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@@ -25,14 +26,17 @@ from src.figures import (
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get_geographic_maps,
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get_top_org_table,
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make_card,
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make_column_picker,
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make_donut,
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)
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from src.utils import (
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data_schema,
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departements,
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df,
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df_acheteurs,
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df_titulaires,
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get_enum_values_as_dict,
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logger,
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meta_content,
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prepare_dashboard_data,
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)
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@@ -61,6 +65,27 @@ for code in departements.keys():
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}
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options_departements.append(departement)
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OBSERVATOIRE_COLUMNS = [
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col
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for col in df.columns
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if col.startswith("acheteur")
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or col.startswith("titulaire")
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or col
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in [
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"uid",
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"dateNotification",
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"montant",
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"considerationsSociales",
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"considerationsEnvironnementales",
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"marcheInnovant",
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"sousTraitanceDeclaree",
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"techniques",
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"sourceDataset",
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"type",
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"codeCPV",
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]
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]
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layout = [
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dcc.Location(id="dashboard_url", refresh="callback-nav"),
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dcc.Store(id="observatoire-filters", storage_type="local"),
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@@ -372,10 +397,16 @@ Alors, on fait comment ?
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),
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dcc.Download(id="download-observatoire"),
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dbc.Button(
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"Télécharger au format Excel",
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id="btn-download-observatoire",
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disabled=True,
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"Prévisualiser les données",
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id="btn-observatoire-preview",
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className="mt-2",
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color="primary",
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outline=True,
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),
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dcc.Input(
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id="observatoire-share-url",
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readOnly=True,
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style={"display": "none"},
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),
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dcc.Input(
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id="observatoire-share-url",
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@@ -398,6 +429,83 @@ Alors, on fait comment ?
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),
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],
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),
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dbc.Offcanvas(
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id="observatoire-preview",
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title="Prévisualisation des données",
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placement="bottom",
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is_open=False,
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scrollable=True,
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style={"height": "75vh"},
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children=[
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# Header row: title + "Colonnes affichées" button
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dbc.Row(
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[
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dbc.Col(
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html.Div(
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className="table-menu",
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children=[
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dbc.Button(
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"Colonnes affichées",
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id="observatoire-preview-columns-open",
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size="sm",
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color="secondary",
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outline=True,
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),
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dbc.Button(
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"Télécharger au format Excel",
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id="btn-download-observatoire",
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disabled=True,
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className="mt-2",
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color="primary",
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outline=True,
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),
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],
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),
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width="auto",
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),
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],
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className="mb-2 align-items-center",
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),
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# Column picker modal
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dbc.Modal(
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[
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dbc.ModalHeader(
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dbc.ModalTitle("Colonnes affichées dans la prévisualisation")
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),
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dbc.ModalBody(
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id="observatoire-preview-columns-body",
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children=make_column_picker("observatoire_preview"),
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),
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dbc.ModalFooter(
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dbc.Button(
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"Fermer",
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id="observatoire-preview-columns-close",
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className="ms-auto",
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n_clicks=0,
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)
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),
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],
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id="observatoire-preview-columns",
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is_open=False,
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fullscreen="md-down",
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scrollable=True,
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size="xl",
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),
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# DataTable
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html.Div(
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className="marches_table",
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children=DataTable(
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dtid="observatoire-preview-table",
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page_size=10,
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page_action="native",
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sort_action="native",
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filter_action="native",
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hidden_columns=[],
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columns=[{"id": col, "name": col} for col in OBSERVATOIRE_COLUMNS],
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),
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),
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],
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),
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]
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@@ -586,6 +694,9 @@ def udpate_dashboard_cards(
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# Génération des métriques
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dff = lff.collect(engine="streaming")
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logger.debug("Filter data: " + str(dff.height))
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df_per_uid = (
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dff.select("uid", "montant").group_by("uid").agg(pl.col("montant").first())
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)
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@@ -795,3 +906,96 @@ def add_organization_name_in_title(acheteur_id, titulaire_id):
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html.Small(nom, className="text-muted d-block fw-normal fs-5"),
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]
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return name
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@callback(
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Output("observatoire-preview", "is_open"),
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Input("btn-observatoire-preview", "n_clicks"),
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State("observatoire-preview", "is_open"),
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prevent_initial_call=True,
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)
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def toggle_observatoire_preview(n_clicks, is_open):
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return not is_open
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@callback(
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Output("observatoire-preview-table", "data"),
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Output("observatoire-preview-table", "columns"),
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Input("observatoire-preview", "is_open"),
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State("dashboard_year", "value"),
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State("dashboard_acheteur_id", "value"),
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State("dashboard_acheteur_categorie", "value"),
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State("dashboard_acheteur_departement_code", "value"),
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State("dashboard_titulaire_id", "value"),
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State("dashboard_titulaire_categorie", "value"),
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State("dashboard_titulaire_departement_code", "value"),
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State("dashboard_marche_type", "value"),
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State("dashboard_marche_objet", "value"),
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State("dashboard_marche_code_cpv", "value"),
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State("dashboard_montant_min", "value"),
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State("dashboard_montant_max", "value"),
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State("dashboard_marche_techniques", "value"),
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State("dashboard_marche_innovant", "value"),
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State("dashboard_marche_sousTraitanceDeclaree", "value"),
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State("dashboard_marche_considerationsSociales", "value"),
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State("dashboard_marche_considerationsEnvironnementales", "value"),
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prevent_initial_call=True,
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)
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def populate_preview_table(
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is_open,
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dashboard_year,
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dashboard_acheteur_id,
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dashboard_acheteur_categorie,
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dashboard_acheteur_departement_code,
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dashboard_titulaire_id,
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dashboard_titulaire_categorie,
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dashboard_titulaire_departement_code,
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dashboard_marche_type,
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dashboard_marche_objet,
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dashboard_marche_code_cpv,
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dashboard_montant_min,
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dashboard_montant_max,
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dashboard_marche_techniques,
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dashboard_marche_innovant,
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dashboard_marche_sous_traitance_declaree,
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dashboard_marche_considerations_sociales,
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dashboard_marche_considerations_environnementales,
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):
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if not is_open:
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return no_update, no_update
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available_in_df = [col for col in OBSERVATOIRE_COLUMNS if col in df.columns]
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lff = prepare_dashboard_data(
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lff=df.lazy().select(available_in_df),
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year=dashboard_year,
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acheteur_id=dashboard_acheteur_id,
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acheteur_categorie=dashboard_acheteur_categorie,
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acheteur_departement_code=dashboard_acheteur_departement_code,
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titulaire_id=dashboard_titulaire_id,
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titulaire_categorie=dashboard_titulaire_categorie,
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titulaire_departement_code=dashboard_titulaire_departement_code,
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type=dashboard_marche_type,
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objet=dashboard_marche_objet,
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code_cpv=dashboard_marche_code_cpv,
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considerations_sociales=dashboard_marche_considerations_sociales,
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considerations_environnementales=dashboard_marche_considerations_environnementales,
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montant_min=dashboard_montant_min,
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montant_max=dashboard_montant_max,
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techniques=dashboard_marche_techniques,
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marche_innovant=dashboard_marche_innovant,
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sous_traitance_declaree=dashboard_marche_sous_traitance_declaree,
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)
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dff = lff.collect(engine="streaming")
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table_data = dff.to_dicts()
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table_columns = [
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{
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"name": data_schema.get(col, {}).get("title", col),
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"id": col,
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"type": "text",
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"format": {"nully": "N/A"},
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}
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for col in available_in_df
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]
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return table_data, table_columns
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