Colin Maudry
133 lines
4.4 KiB
Python
133 lines
4.4 KiB
Python
from dash import html, dcc, dash_table, register_page, Input, Output, State, callback
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from dotenv import load_dotenv
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import os
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import polars as pl
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from src.utils import split_filter_part
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load_dotenv()
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df = pl.scan_parquet(os.getenv("DATA_FILE_PARQUET_PATH"))
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title = "Tableau"
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register_page(__name__, path="/", title=f"decp.info - {title}", name=title, order=1)
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datatable = dash_table.DataTable(
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cell_selectable=False,
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id="table",
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page_size=20,
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page_current=0,
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page_action="custom",
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filter_action="custom",
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filter_options={"case": "insensitive", "placeholder_text": "Filtrer..."},
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columns=[{"name": i, "id": i} for i in df.collect_schema().names()],
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selected_columns=[],
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selected_rows=[],
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# sort_action="native",
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# sort_mode="multi",
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export_format="xlsx",
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export_columns="visible",
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export_headers="ids",
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style_cell_conditional=[
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{
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"if": {"column_id": "objet"},
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"minWidth": "350px",
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"textAlign": "left",
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"overflow": "hidden",
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"lineHeight": "14px",
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"whiteSpace": "normal",
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},
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],
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data_timestamp=0,
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)
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layout = [
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html.Div(
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html.Details(
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children=[
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html.Summary(html.H3("Utilisation")),
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dcc.Markdown(
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"""
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**Filtres**
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Vous pouvez appliquer un filtre pour chaque colonne en entrant du texte sous le nom de la colonne, puis en tapant sur `Entrée`.
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- Champs textuels : la recherche est insensible à la casse (majuscules/minuscules).
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- Champs numériques : possibilité d'ajouter < ou > devant le chiffre recherché pour chercher des valeurs inférieures ou supérieur.
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**Télécharger le résultat**
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Vous pouvez télécharger le résultat de vos filtres et tris en cliquant sur Télécharger au format Excel.
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Les colonnes supprimées seront absentes du fichier téléchargé.
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Si vous téléchargez un volume important de données, il se peut que vous attendiez quelques minutes avant le début du téléchargement.
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"""
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),
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],
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id="instructions",
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),
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id="header",
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),
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# html.Div(
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# [
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# "Recherche dans objet : ",
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# dcc.Input(id="search", value="", type="text"),
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# ]
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# )]),
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dcc.Loading(
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overlay_style={"visibility": "visible", "filter": "blur(2px)"},
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id="loading-1",
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type="default",
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children=datatable,
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),
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]
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@callback(
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Output("table", "data"),
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Output("table", "data_timestamp"),
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Input("table", "page_current"),
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Input("table", "page_size"),
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Input("table", "filter_query"),
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State("table", "data_timestamp"),
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)
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def update_table(page_current, page_size, filter_query, data_timestamp):
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print(" + + + + + + + + + + + + + + + + + + ")
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print("Filter query:", filter_query)
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# 1. Apply Filters
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dff = df # start from the original data
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if filter_query:
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filtering_expressions = filter_query.split(" && ")
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for filter_part in filtering_expressions:
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col_name, operator, filter_value = split_filter_part(filter_part)
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print("filter_value:", filter_value)
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print("filter_value_type:", type(filter_value))
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if operator in ("<", "<=", ">", ">="):
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filter_value = int(filter_value)
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if operator == "<":
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dff = dff.filter(pl.col(col_name) < filter_value)
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elif operator == ">":
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dff = dff.filter(pl.col(col_name) > filter_value)
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elif operator == ">=":
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dff = dff.filter(pl.col(col_name) >= filter_value)
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elif operator == "<=":
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dff = dff.filter(pl.col(col_name) <= filter_value)
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# these operators match polars series filter operators
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elif operator == "contains":
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dff = dff.filter(pl.col(col_name).str.contains("(?i)" + filter_value))
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# elif operator == 'datestartswith':
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# dff = dff.filter(pl.col(col_name).str.startswith(filter_value)")
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# 2. Paginate Data
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start_row = page_current * page_size
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# end_row = (page_current + 1) * page_size
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dff = dff.slice(start_row, page_size).collect()
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# print("dff_sliced:", dff.select("titulaire.typeId"))
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dff = dff.to_dicts()
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return dff, data_timestamp + 1 # update data, update timestamp
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