Colin Maudry
999 lines
41 KiB
Python
999 lines
41 KiB
Python
import urllib.parse
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from datetime import datetime
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import dash_bootstrap_components as dbc
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import polars as pl
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import polars.selectors as cs
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from dash import (
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ALL,
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Input,
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Output,
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State,
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callback,
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ctx,
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dcc,
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html,
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no_update,
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register_page,
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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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get_duplicate_matrix,
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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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name = "Observatoire"
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register_page(
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__name__,
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path="/observatoire",
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title="Observatoire | decp.info",
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name=name,
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description="Visualisez l'état de la publication des données essentielles des marchés publics en France.",
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image_url=meta_content["image_url"],
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order=3,
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)
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options_years = {}
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for year in reversed(range(2017, datetime.now().year + 1)):
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year = str(year)
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options_years[year] = year
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options_departements = []
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for code in departements.keys():
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departement = {
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"label": f"{departements[code]['departement']} ({code})",
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"value": code,
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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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dbc.Modal(
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[
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dbc.ModalHeader(dbc.ModalTitle("Montants")),
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dbc.ModalBody(
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[
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dcc.Markdown(
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"""
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Les données saisies et publiées par les acheteurs comportent de nombreux montants farfelus qui sabotent les statistiques, au lieu de montants estimés avec rigueur. On parle de montants atteignant parfois les millions de milliards. Certains réutilisateurs des données mettent de côté ces marchés ou bien modifient les montants selon des règles fatalement arbitraires. J'ai fait le choix de ne quasiment pas modifier les données* afin de visibiliser le problème.
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Alors, on fait comment ?
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\\* Les montants composés de plus de 11 chiffres, sans les décimales, [sont ramenés](https://github.com/ColinMaudry/decp-processing/blob/main/src/tasks/clean.py#L63-L71) à 12 311 111 111, un nombre qui reste très élevé et qui est facilement reconnaissable.
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"""
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),
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]
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),
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dbc.ModalFooter(
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dbc.Button("Fermer", id="montant-modal-close", className="ms-auto")
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),
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],
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id="montant-modal",
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is_open=False,
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),
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html.Div(
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className="container-fluid",
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children=[
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html.H2(children=[name], id="page_title"),
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dcc.Loading(
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overlay_style={"visibility": "visible", "filter": "blur(2px)"},
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id="loading-statistques",
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type="default",
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children=[
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dbc.Row(
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[
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dbc.Col(
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xl=3,
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lg=4,
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id="filters",
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children=[
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html.H5("Période d'attribution"),
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dbc.Row(
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dbc.Col(
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dcc.Dropdown(
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id="dashboard_year",
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options=options_years,
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placeholder="12 derniers mois",
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persistence=True,
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persistence_type="local",
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),
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),
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),
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html.H5("Acheteur"),
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dbc.Row(
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dbc.Col(
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dcc.Input(
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id="dashboard_acheteur_id",
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placeholder="SIRET",
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debounce=True,
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style={"width": "100%"},
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persistence=True,
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persistence_type="local",
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),
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),
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),
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dbc.Row(
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dbc.Col(
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dcc.Dropdown(
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id="dashboard_acheteur_categorie",
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options=get_enum_values_as_dict(
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"acheteur_categorie"
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),
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placeholder="Catégorie",
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persistence=True,
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persistence_type="local",
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)
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),
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),
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dbc.Row(
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dbc.Col(
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dcc.Dropdown(
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id="dashboard_acheteur_departement_code",
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searchable=True,
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multi=True,
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placeholder="Département",
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options=options_departements,
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persistence=True,
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persistence_type="local",
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),
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),
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),
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html.H5("Titulaire"),
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dbc.Row(
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dbc.Col(
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dcc.Input(
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id="dashboard_titulaire_id",
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placeholder="SIRET",
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debounce=True,
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style={"width": "100%"},
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persistence=True,
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persistence_type="local",
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),
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),
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),
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dbc.Row(
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dbc.Col(
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dcc.Dropdown(
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id="dashboard_titulaire_categorie",
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placeholder="Catégorie",
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options=get_enum_values_as_dict(
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"titulaire_categorie"
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),
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persistence=True,
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persistence_type="local",
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),
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),
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),
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dbc.Row(
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dbc.Col(
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dcc.Dropdown(
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id="dashboard_titulaire_departement_code",
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searchable=True,
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multi=True,
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placeholder="Département",
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options=options_departements,
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persistence=True,
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persistence_type="local",
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),
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),
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),
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html.H5("Marché"),
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dbc.Row(
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dbc.Col(
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dcc.Dropdown(
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id="dashboard_marche_type",
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placeholder="Type",
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options=get_enum_values_as_dict("type"),
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persistence=True,
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persistence_type="local",
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),
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),
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),
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dbc.Row(
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dbc.Col(
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dcc.Input(
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id="dashboard_marche_objet",
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placeholder="Objet",
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debounce=True,
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style={"width": "100%"},
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persistence=True,
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persistence_type="local",
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),
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),
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),
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dbc.Row(
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[
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dbc.Col(
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dcc.Input(
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id="dashboard_marche_code_cpv",
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placeholder="Code CPV (début)",
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debounce=True,
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style={"width": "100%"},
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persistence=True,
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persistence_type="local",
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),
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lg=8,
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),
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dbc.Col(
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html.A(
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"liste des codes",
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href="https://cpvcodes.eu/fr",
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target="_blank",
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),
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lg=4,
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),
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]
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),
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dbc.Row(
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[
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dbc.Col(
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dcc.Input(
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id="dashboard_montant_min",
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placeholder="Montant min.",
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type="number",
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min=0,
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debounce=True,
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style={"width": "100%"},
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persistence=True,
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persistence_type="local",
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),
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width=6,
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),
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dbc.Col(
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dcc.Input(
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id="dashboard_montant_max",
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placeholder="Montant max.",
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type="number",
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min=0,
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debounce=True,
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style={"width": "100%"},
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persistence=True,
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persistence_type="local",
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),
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width=6,
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),
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]
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),
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dbc.Row(
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dbc.Col(
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dcc.Dropdown(
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id="dashboard_marche_techniques",
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placeholder="Techniques d'achat",
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options=get_enum_values_as_dict(
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"techniques"
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),
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multi=True,
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persistence=True,
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persistence_type="local",
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),
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),
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),
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dbc.Row(
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[
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dbc.Col("Sous-traitance :", lg=5),
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dbc.Col(
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dbc.RadioItems(
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id="dashboard_marche_sousTraitanceDeclaree",
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options=[
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{
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"label": "Tous",
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"value": "all",
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},
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{
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"label": "Oui",
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"value": "oui",
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},
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{
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"label": "Non",
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"value": "non",
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},
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],
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value="all",
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inline=True,
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persistence=True,
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persistence_type="local",
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),
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lg=7,
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),
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]
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),
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dbc.Row(
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[
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dbc.Col("Marché innovant :", lg=5),
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dbc.Col(
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dbc.RadioItems(
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id="dashboard_marche_innovant",
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options=[
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{
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"label": "Tous",
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"value": "all",
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},
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{
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"label": "Oui",
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"value": "oui",
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},
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{
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"label": "Non",
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"value": "non",
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},
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],
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value="all",
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inline=True,
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persistence=True,
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persistence_type="local",
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),
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lg=7,
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),
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]
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),
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dbc.Row(
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dbc.Col(
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dcc.Dropdown(
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id="dashboard_marche_considerationsSociales",
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placeholder="Considérations sociales",
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options=get_enum_values_as_dict(
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"considerationsSociales"
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),
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multi=True,
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persistence=True,
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persistence_type="local",
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),
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),
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),
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dbc.Row(
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dbc.Col(
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dcc.Dropdown(
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id="dashboard_marche_considerationsEnvironnementales",
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placeholder="Considérations environnementales",
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multi=True,
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options=get_enum_values_as_dict(
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"considerationsEnvironnementales"
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),
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persistence=True,
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persistence_type="local",
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),
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),
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),
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dcc.Download(id="download-observatoire"),
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dbc.Button(
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"Prévisualiser les données",
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id="btn-observatoire-preview",
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className="btn btn-primary",
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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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readOnly=True,
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style={"display": "none"},
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),
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html.Div(id="observatoire-copy-container"),
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],
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),
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dbc.Col(
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width=12,
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lg=8,
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xl=9,
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id="cards",
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children=[],
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),
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]
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)
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],
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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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className="btn btn-primary",
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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="btn btn-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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@callback(
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Output("dashboard_year", "value"),
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Output("dashboard_acheteur_id", "value"),
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Output("dashboard_acheteur_categorie", "value"),
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Output("dashboard_acheteur_departement_code", "value"),
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Output("dashboard_titulaire_id", "value"),
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Output("dashboard_titulaire_categorie", "value"),
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Output("dashboard_titulaire_departement_code", "value"),
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Output("dashboard_marche_type", "value"),
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Output("dashboard_marche_objet", "value"),
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Output("dashboard_marche_code_cpv", "value"),
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Output("dashboard_montant_min", "value"),
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Output("dashboard_montant_max", "value"),
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Output("dashboard_marche_techniques", "value"),
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Output("dashboard_marche_innovant", "value"),
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Output("dashboard_marche_sousTraitanceDeclaree", "value"),
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Output("dashboard_marche_considerationsSociales", "value"),
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Output("dashboard_marche_considerationsEnvironnementales", "value"),
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Input("dashboard_url", "search"),
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Input("dashboard_url", "pathname"),
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State("observatoire-filters", "data"),
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)
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def restore_filters(search, _pathname, stored_filters):
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if search:
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params = urllib.parse.parse_qs(search.lstrip("?"))
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acheteur_id = (params.get("acheteur_id") or [None])[0] or None
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titulaire_id = (params.get("titulaire_id") or [None])[0] or None
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if acheteur_id or titulaire_id:
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return (
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None,
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acheteur_id,
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None,
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None,
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titulaire_id,
|
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None,
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None,
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None,
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None,
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None,
|
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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)
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return (no_update,) * 17
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@callback(
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Output("observatoire-share-url", "value"),
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Output("observatoire-copy-container", "children"),
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Input("dashboard_acheteur_id", "value"),
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Input("dashboard_titulaire_id", "value"),
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State("dashboard_url", "href"),
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prevent_initial_call=True,
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)
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def sync_observatoire_share_url(acheteur_id, titulaire_id, href):
|
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if not href:
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return no_update, no_update
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|
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base_url = href.split("?")[0]
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|
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params = {}
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if acheteur_id:
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params["acheteur_id"] = acheteur_id
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if titulaire_id:
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params["titulaire_id"] = titulaire_id
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query_string = urllib.parse.urlencode(params)
|
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full_url = f"{base_url}?{query_string}" if query_string else base_url
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|
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copy_button = dcc.Clipboard(
|
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id="btn-copy-observatoire-url",
|
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target_id="observatoire-share-url",
|
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title="Copier l'URL de cette vue",
|
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style={
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"display": "inline-block",
|
||
"fontSize": 20,
|
||
"verticalAlign": "top",
|
||
"cursor": "pointer",
|
||
},
|
||
className="fa fa-link",
|
||
children=[
|
||
dbc.Button(
|
||
"Partager",
|
||
className="btn btn-primary mt-2",
|
||
title="Copier l'adresse de cette vue filtrée pour la partager.",
|
||
style={"display": "none"},
|
||
)
|
||
],
|
||
)
|
||
|
||
return full_url, copy_button
|
||
|
||
|
||
@callback(
|
||
Output("cards", "children"),
|
||
Output("btn-download-observatoire", "disabled"),
|
||
Output("btn-download-observatoire", "children"),
|
||
Input("dashboard_year", "value"),
|
||
Input("dashboard_acheteur_id", "value"),
|
||
Input("dashboard_acheteur_categorie", "value"),
|
||
Input("dashboard_acheteur_departement_code", "value"),
|
||
Input("dashboard_titulaire_id", "value"),
|
||
Input("dashboard_titulaire_categorie", "value"),
|
||
Input("dashboard_titulaire_departement_code", "value"),
|
||
Input("dashboard_marche_type", "value"),
|
||
Input("dashboard_marche_objet", "value"),
|
||
Input("dashboard_marche_code_cpv", "value"),
|
||
Input("dashboard_montant_min", "value"),
|
||
Input("dashboard_montant_max", "value"),
|
||
Input("dashboard_marche_techniques", "value"),
|
||
Input("dashboard_marche_innovant", "value"),
|
||
Input("dashboard_marche_sousTraitanceDeclaree", "value"),
|
||
Input("dashboard_marche_considerationsSociales", "value"),
|
||
Input("dashboard_marche_considerationsEnvironnementales", "value"),
|
||
)
|
||
def udpate_dashboard_cards(
|
||
dashboard_year,
|
||
dashboard_acheteur_id,
|
||
dashboard_acheteur_categorie,
|
||
dashboard_acheteur_departement_code,
|
||
dashboard_titulaire_id,
|
||
dashboard_titulaire_categorie,
|
||
dashboard_titulaire_departement_code,
|
||
dashboard_marche_type,
|
||
dashboard_marche_objet,
|
||
dashboard_marche_code_cpv,
|
||
dashboard_montant_min,
|
||
dashboard_montant_max,
|
||
dashboard_marche_techniques,
|
||
dashboard_marche_innovant,
|
||
dashboard_marche_sous_traitance_declaree,
|
||
dashboard_marche_considerations_sociales,
|
||
dashboard_marche_considerations_environnementales,
|
||
):
|
||
lff: pl.LazyFrame = df.lazy()
|
||
|
||
columns = [
|
||
"uid",
|
||
cs.starts_with("acheteur"),
|
||
cs.starts_with("titulaire"),
|
||
"dateNotification",
|
||
"montant",
|
||
"considerationsSociales",
|
||
"considerationsEnvironnementales",
|
||
"marcheInnovant",
|
||
"sousTraitanceDeclaree",
|
||
"techniques",
|
||
"sourceDataset",
|
||
"type",
|
||
"codeCPV",
|
||
]
|
||
|
||
if dashboard_marche_objet:
|
||
columns.append("objet")
|
||
|
||
lff = lff.select(columns)
|
||
|
||
# Filtrage des données
|
||
lff = prepare_dashboard_data(
|
||
lff=lff,
|
||
year=dashboard_year,
|
||
acheteur_id=dashboard_acheteur_id,
|
||
acheteur_categorie=dashboard_acheteur_categorie,
|
||
acheteur_departement_code=dashboard_acheteur_departement_code,
|
||
titulaire_id=dashboard_titulaire_id,
|
||
titulaire_categorie=dashboard_titulaire_categorie,
|
||
titulaire_departement_code=dashboard_titulaire_departement_code,
|
||
type=dashboard_marche_type,
|
||
objet=dashboard_marche_objet,
|
||
code_cpv=dashboard_marche_code_cpv,
|
||
considerations_sociales=dashboard_marche_considerations_sociales,
|
||
considerations_environnementales=dashboard_marche_considerations_environnementales,
|
||
montant_min=dashboard_montant_min,
|
||
montant_max=dashboard_montant_max,
|
||
techniques=dashboard_marche_techniques,
|
||
marche_innovant=dashboard_marche_innovant,
|
||
sous_traitance_declaree=dashboard_marche_sous_traitance_declaree,
|
||
)
|
||
|
||
# Génération des métriques
|
||
dff = lff.collect(engine="streaming")
|
||
|
||
logger.debug("Filter data: " + str(dff.height))
|
||
|
||
df_per_uid = (
|
||
dff.select("uid", "montant").group_by("uid").agg(pl.col("montant").first())
|
||
)
|
||
nb_marches = df_per_uid.height
|
||
|
||
if nb_marches == 0:
|
||
dl_disabled, dl_text = True, "Pas de données à télécharger"
|
||
elif nb_marches > 65000:
|
||
dl_disabled, dl_text = True, "Téléchargement désactivé au-delà de 65 000 lignes"
|
||
else:
|
||
dl_disabled, dl_text = False, "Télécharger au format Excel"
|
||
|
||
cards = []
|
||
|
||
card_summary_table = get_dashboard_summary_table(dff, df_per_uid, nb_marches)
|
||
|
||
cards.append(make_card(title="Résumé", paragraphs=card_summary_table))
|
||
|
||
donut_acheteur_categorie, nb_acheteur_categories = make_donut(
|
||
lff,
|
||
"acheteur_categorie",
|
||
nulls="Autres",
|
||
per_uid=True,
|
||
potentially_many_names=True,
|
||
)
|
||
cards.append(
|
||
make_card(
|
||
title="Catégorie d'acheteur",
|
||
subtitle="en nombre de marchés attribués",
|
||
fig=donut_acheteur_categorie,
|
||
lg=12 if nb_acheteur_categories > 4 else 6,
|
||
xl=8 if nb_acheteur_categories > 4 else 4,
|
||
)
|
||
)
|
||
|
||
donut_titulaire_categorie = make_donut(
|
||
lff, "titulaire_categorie", per_uid=False, nulls="?"
|
||
)
|
||
cards.append(
|
||
make_card(
|
||
title="Catégorie d'entreprise",
|
||
subtitle="en nombre de titulaires",
|
||
fig=donut_titulaire_categorie,
|
||
)
|
||
)
|
||
|
||
donut_marche_type = make_donut(lff, "type", per_uid=True, nulls="?")
|
||
cards.append(
|
||
make_card(
|
||
title="Type d'achat",
|
||
subtitle="en nombre de marchés attribués",
|
||
fig=donut_marche_type,
|
||
)
|
||
)
|
||
|
||
distance_histogram = get_distance_histogram(lff)
|
||
cards.append(
|
||
make_card(
|
||
title="Distance acheteur–titulaire",
|
||
subtitle="en nombre de marchés, échelle logarithmique",
|
||
fig=distance_histogram,
|
||
)
|
||
)
|
||
|
||
top_acheteurs = get_top_org_table(
|
||
lff, org_type="acheteur", filters=False, extra_columns=[]
|
||
)
|
||
cards.append(make_card(title="Top acheteurs", fig=top_acheteurs, lg=12, xl=8))
|
||
|
||
top_titulaires = get_top_org_table(
|
||
lff, org_type="titulaire", filters=False, extra_columns=[]
|
||
)
|
||
cards.append(make_card(title="Top titulaires", fig=top_titulaires, lg=12, xl=8))
|
||
|
||
geographic_maps: list[dbc.Col] = get_geographic_maps(dff)
|
||
|
||
other_cards = []
|
||
|
||
sources_barchart = get_barchart_sources(lff, type_date="dateNotification")
|
||
other_cards.append(
|
||
make_card(
|
||
title="Sources de données",
|
||
subtitle="Nombre de marchés attribués par mois de notification et source de données",
|
||
fig=sources_barchart,
|
||
lg=12,
|
||
xl=8,
|
||
)
|
||
)
|
||
|
||
duplicate_matrix = get_duplicate_matrix()
|
||
other_cards.append(
|
||
make_card(
|
||
title="Matrice de doublons entre sources de données",
|
||
subtitle="Ce graphique illustre les doublons de marchés publics entre sources, c'est-à-dire la proportion de marchés publiés par plus d'une source.",
|
||
fig=duplicate_matrix,
|
||
lg=12,
|
||
xl=8,
|
||
)
|
||
)
|
||
|
||
return dbc.Row(children=cards + geographic_maps + other_cards), dl_disabled, dl_text
|
||
|
||
|
||
@callback(
|
||
Output("download-observatoire", "data"),
|
||
Input("btn-download-observatoire", "n_clicks"),
|
||
State("dashboard_year", "value"),
|
||
State("dashboard_acheteur_id", "value"),
|
||
State("dashboard_acheteur_categorie", "value"),
|
||
State("dashboard_acheteur_departement_code", "value"),
|
||
State("dashboard_titulaire_id", "value"),
|
||
State("dashboard_titulaire_categorie", "value"),
|
||
State("dashboard_titulaire_departement_code", "value"),
|
||
State("dashboard_marche_type", "value"),
|
||
State("dashboard_marche_objet", "value"),
|
||
State("dashboard_marche_code_cpv", "value"),
|
||
State("dashboard_montant_min", "value"),
|
||
State("dashboard_montant_max", "value"),
|
||
State("dashboard_marche_techniques", "value"),
|
||
State("dashboard_marche_innovant", "value"),
|
||
State("dashboard_marche_sousTraitanceDeclaree", "value"),
|
||
State("dashboard_marche_considerationsSociales", "value"),
|
||
State("dashboard_marche_considerationsEnvironnementales", "value"),
|
||
prevent_initial_call=True,
|
||
)
|
||
def download_observatoire(
|
||
_n_clicks,
|
||
dashboard_year,
|
||
dashboard_acheteur_id,
|
||
dashboard_acheteur_categorie,
|
||
dashboard_acheteur_departement_code,
|
||
dashboard_titulaire_id,
|
||
dashboard_titulaire_categorie,
|
||
dashboard_titulaire_departement_code,
|
||
dashboard_marche_type,
|
||
dashboard_marche_objet,
|
||
dashboard_marche_code_cpv,
|
||
dashboard_montant_min,
|
||
dashboard_montant_max,
|
||
dashboard_marche_techniques,
|
||
dashboard_marche_innovant,
|
||
dashboard_marche_sous_traitance_declaree,
|
||
dashboard_considerations_sociales,
|
||
dashboard_considerations_environnementales,
|
||
):
|
||
lff = prepare_dashboard_data(
|
||
lff=df.lazy(),
|
||
year=dashboard_year,
|
||
acheteur_id=dashboard_acheteur_id,
|
||
acheteur_categorie=dashboard_acheteur_categorie,
|
||
acheteur_departement_code=dashboard_acheteur_departement_code,
|
||
titulaire_id=dashboard_titulaire_id,
|
||
titulaire_categorie=dashboard_titulaire_categorie,
|
||
titulaire_departement_code=dashboard_titulaire_departement_code,
|
||
type=dashboard_marche_type,
|
||
objet=dashboard_marche_objet,
|
||
code_cpv=dashboard_marche_code_cpv,
|
||
considerations_sociales=dashboard_considerations_sociales,
|
||
considerations_environnementales=dashboard_considerations_environnementales,
|
||
montant_min=dashboard_montant_min,
|
||
montant_max=dashboard_montant_max,
|
||
techniques=dashboard_marche_techniques,
|
||
marche_innovant=dashboard_marche_innovant,
|
||
sous_traitance_declaree=dashboard_marche_sous_traitance_declaree,
|
||
)
|
||
|
||
def to_bytes(buffer):
|
||
lff.collect(engine="streaming").write_excel(buffer, worksheet="DECP")
|
||
|
||
date = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
|
||
return dcc.send_bytes(to_bytes, filename=f"decp_observatoire_{date}.xlsx")
|
||
|
||
|
||
@callback(
|
||
Output("montant-modal", "is_open"),
|
||
Input({"type": "modal-trigger", "index": ALL}, "n_clicks"),
|
||
Input("montant-modal-close", "n_clicks"),
|
||
prevent_initial_call=True,
|
||
)
|
||
def toggle_montant_modal(n_triggers, _close):
|
||
return isinstance(ctx.triggered_id, dict) and any(n_triggers)
|
||
|
||
|
||
@callback(
|
||
Output("page_title", "children"),
|
||
Input("dashboard_acheteur_id", "value"),
|
||
Input("dashboard_titulaire_id", "value"),
|
||
prevent_initial_call=False,
|
||
)
|
||
def add_organization_name_in_title(acheteur_id, titulaire_id):
|
||
def lookup_nom(df_org, id_col, nom_col, org_id):
|
||
match = df_org.filter(pl.col(id_col) == org_id)
|
||
return match[nom_col].item(0) if match.height >= 1 else None
|
||
|
||
if acheteur_id and len(acheteur_id) == 14:
|
||
if nom := lookup_nom(df_acheteurs, "acheteur_id", "acheteur_nom", acheteur_id):
|
||
return [
|
||
name,
|
||
html.Small(nom, className="text-muted d-block fw-normal fs-5"),
|
||
]
|
||
elif titulaire_id and len(titulaire_id) == 14:
|
||
if nom := lookup_nom(
|
||
df_titulaires, "titulaire_id", "titulaire_nom", titulaire_id
|
||
):
|
||
return [
|
||
name,
|
||
html.Small(nom, className="text-muted d-block fw-normal fs-5"),
|
||
]
|
||
return name
|
||
|
||
|
||
@callback(
|
||
Output("observatoire-preview", "is_open"),
|
||
Input("btn-observatoire-preview", "n_clicks"),
|
||
State("observatoire-preview", "is_open"),
|
||
prevent_initial_call=True,
|
||
)
|
||
def toggle_observatoire_preview(n_clicks, is_open):
|
||
return not is_open
|
||
|
||
|
||
@callback(
|
||
Output("observatoire-preview-table", "data"),
|
||
Output("observatoire-preview-table", "columns"),
|
||
Input("observatoire-preview", "is_open"),
|
||
State("dashboard_year", "value"),
|
||
State("dashboard_acheteur_id", "value"),
|
||
State("dashboard_acheteur_categorie", "value"),
|
||
State("dashboard_acheteur_departement_code", "value"),
|
||
State("dashboard_titulaire_id", "value"),
|
||
State("dashboard_titulaire_categorie", "value"),
|
||
State("dashboard_titulaire_departement_code", "value"),
|
||
State("dashboard_marche_type", "value"),
|
||
State("dashboard_marche_objet", "value"),
|
||
State("dashboard_marche_code_cpv", "value"),
|
||
State("dashboard_montant_min", "value"),
|
||
State("dashboard_montant_max", "value"),
|
||
State("dashboard_marche_techniques", "value"),
|
||
State("dashboard_marche_innovant", "value"),
|
||
State("dashboard_marche_sousTraitanceDeclaree", "value"),
|
||
State("dashboard_marche_considerationsSociales", "value"),
|
||
State("dashboard_marche_considerationsEnvironnementales", "value"),
|
||
prevent_initial_call=True,
|
||
)
|
||
def populate_preview_table(
|
||
is_open,
|
||
dashboard_year,
|
||
dashboard_acheteur_id,
|
||
dashboard_acheteur_categorie,
|
||
dashboard_acheteur_departement_code,
|
||
dashboard_titulaire_id,
|
||
dashboard_titulaire_categorie,
|
||
dashboard_titulaire_departement_code,
|
||
dashboard_marche_type,
|
||
dashboard_marche_objet,
|
||
dashboard_marche_code_cpv,
|
||
dashboard_montant_min,
|
||
dashboard_montant_max,
|
||
dashboard_marche_techniques,
|
||
dashboard_marche_innovant,
|
||
dashboard_marche_sous_traitance_declaree,
|
||
dashboard_marche_considerations_sociales,
|
||
dashboard_marche_considerations_environnementales,
|
||
):
|
||
if not is_open:
|
||
return no_update, no_update
|
||
|
||
available_in_df = [col for col in OBSERVATOIRE_COLUMNS if col in df.columns]
|
||
lff = prepare_dashboard_data(
|
||
lff=df.lazy().select(available_in_df),
|
||
year=dashboard_year,
|
||
acheteur_id=dashboard_acheteur_id,
|
||
acheteur_categorie=dashboard_acheteur_categorie,
|
||
acheteur_departement_code=dashboard_acheteur_departement_code,
|
||
titulaire_id=dashboard_titulaire_id,
|
||
titulaire_categorie=dashboard_titulaire_categorie,
|
||
titulaire_departement_code=dashboard_titulaire_departement_code,
|
||
type=dashboard_marche_type,
|
||
objet=dashboard_marche_objet,
|
||
code_cpv=dashboard_marche_code_cpv,
|
||
considerations_sociales=dashboard_marche_considerations_sociales,
|
||
considerations_environnementales=dashboard_marche_considerations_environnementales,
|
||
montant_min=dashboard_montant_min,
|
||
montant_max=dashboard_montant_max,
|
||
techniques=dashboard_marche_techniques,
|
||
marche_innovant=dashboard_marche_innovant,
|
||
sous_traitance_declaree=dashboard_marche_sous_traitance_declaree,
|
||
)
|
||
|
||
dff = lff.collect(engine="streaming")
|
||
|
||
table_data = dff.to_dicts()
|
||
table_columns = [
|
||
{
|
||
"name": data_schema.get(col, {}).get("title", col),
|
||
"id": col,
|
||
"type": "text",
|
||
"format": {"nully": "N/A"},
|
||
}
|
||
for col in available_in_df
|
||
]
|
||
return table_data, table_columns
|