196 lines
8.6 KiB
Python
196 lines
8.6 KiB
Python
import streamlit as st
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import altair as alt
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import numpy as np
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from collections import Counter
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import pandas as pd
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from utils.translations import _
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def afficher_graphique_altair(df):
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# Définir les catégories originales (en français) et leur ordre
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categories_fr = ["Assemblage", "Fabrication", "Traitement", "Extraction"]
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# Créer un dictionnaire de mappage entre les catégories originales et leurs traductions
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mappage_categories = {
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"Assemblage": str(_("pages.visualisations.categories.assembly", "Assemblage")),
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"Fabrication": str(_("pages.visualisations.categories.manufacturing", "Fabrication")),
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"Traitement": str(_("pages.visualisations.categories.processing", "Traitement")),
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"Extraction": str(_("pages.visualisations.categories.extraction", "Extraction"))
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}
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# Filtrer les catégories qui existent dans les données
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categories_fr_filtrees = [cat for cat in categories_fr if cat in df['categorie'].unique()]
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# Parcourir les catégories dans l'ordre défini
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for cat_fr in categories_fr_filtrees:
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# Obtenir le nom traduit de la catégorie pour l'affichage
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cat_traduit = mappage_categories[cat_fr]
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st.markdown(f"### {cat_traduit}")
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# Mais filtrer sur le nom original dans les données
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df_cat = df[df['categorie'] == cat_fr].copy()
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coord_pairs = list(zip(df_cat['ihh_pays'].round(1), df_cat['ihh_acteurs'].round(1)))
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counts = Counter(coord_pairs)
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offset_x = []
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offset_y = {}
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seen = Counter()
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for pair in coord_pairs:
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rank = seen[pair]
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seen[pair] += 1
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if counts[pair] > 1:
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angle = rank * 1.5
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radius = 0.8 + 0.4 * rank
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offset_x.append(radius * np.cos(angle))
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offset_y[pair] = radius * np.sin(angle)
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else:
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offset_x.append(0)
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offset_y[pair] = 0
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df_cat['ihh_pays'] += offset_x
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df_cat['ihh_acteurs'] += [offset_y[p] for p in coord_pairs]
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df_cat['ihh_pays_text'] = df_cat['ihh_pays'] + 0.5
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df_cat['ihh_acteurs_text'] = df_cat['ihh_acteurs'] + 0.5
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base = alt.Chart(df_cat).encode(
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x=alt.X('ihh_pays:Q', title=str(_("pages.visualisations.axis_titles.ihh_countries", "IHH Pays (%)"))),
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y=alt.Y('ihh_acteurs:Q', title=str(_("pages.visualisations.axis_titles.ihh_actors", "IHH Acteurs (%)"))),
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size=alt.Size('criticite_cat:Q', scale=alt.Scale(domain=[1, 2, 3], range=[50, 500, 1000]), legend=None),
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color=alt.Color('criticite_cat:N', scale=alt.Scale(domain=[1, 2, 3], range=['darkgreen', 'orange', 'darkred']))
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)
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points = base.mark_circle(opacity=0.6)
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lines = alt.Chart(df_cat).mark_rule(strokeWidth=0.5, color='gray').encode(
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x='ihh_pays:Q', x2='ihh_pays_text:Q',
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y='ihh_acteurs:Q', y2='ihh_acteurs_text:Q'
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)
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labels = alt.Chart(df_cat).mark_text(
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align='left', dx=3, dy=-3, fontSize=8, font='Arial', angle=335
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).encode(
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x='ihh_pays_text:Q',
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y='ihh_acteurs_text:Q',
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text='nom:N'
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)
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hline_15 = alt.Chart(df_cat).mark_rule(strokeDash=[2,2], color='green').encode(y=alt.datum(15))
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hline_25 = alt.Chart(df_cat).mark_rule(strokeDash=[2,2], color='red').encode(y=alt.datum(25))
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hline_100 = alt.Chart(df_cat).mark_rule(strokeDash=[2,2], color='white').encode(y=alt.datum(100))
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vline_15 = alt.Chart(df_cat).mark_rule(strokeDash=[2,2], color='green').encode(x=alt.datum(15))
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vline_25 = alt.Chart(df_cat).mark_rule(strokeDash=[2,2], color='red').encode(x=alt.datum(25))
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vline_100 = alt.Chart(df_cat).mark_rule(strokeDash=[2,2], color='white').encode(x=alt.datum(100))
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chart = (points + lines + labels + hline_15 + hline_25 + hline_100 + vline_15 + vline_25 + vline_100).properties(
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width=500,
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height=400,
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title=str(_("pages.visualisations.chart_titles.concentration_criticality", "Concentration et criticité – {0}")).format(cat_traduit)
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).interactive()
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st.altair_chart(chart, use_container_width=True)
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def creer_graphes(donnees):
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if not donnees:
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st.warning(str(_("pages.visualisations.no_data", "Aucune donnée à afficher.")))
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return
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try:
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df = pd.DataFrame(donnees)
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df['ivc_cat'] = df['ivc'].apply(lambda x: 1 if x <= 15 else (2 if x <= 30 else 3))
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from collections import Counter
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coord_pairs = list(zip(df['ihh_extraction'].round(1), df['ihh_reserves'].round(1)))
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counts = Counter(coord_pairs)
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offset_x, offset_y = [], {}
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seen = Counter()
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for pair in coord_pairs:
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rank = seen[pair]
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seen[pair] += 1
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if counts[pair] > 1:
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angle = rank * 1.5
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radius = 0.8 + 0.4 * rank
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offset_x.append(radius * np.cos(angle))
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offset_y[pair] = radius * np.sin(angle)
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else:
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offset_x.append(0)
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offset_y[pair] = 0
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df['ihh_extraction'] += offset_x
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df['ihh_reserves'] += [offset_y[p] for p in coord_pairs]
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df['ihh_extraction_text'] = df['ihh_extraction'] + 0.5
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df['ihh_reserves_text'] = df['ihh_reserves'] + 0.5
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base = alt.Chart(df).encode(
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x=alt.X('ihh_extraction:Q', title=str(_("pages.visualisations.axis_titles.ihh_extraction", "IHH Extraction (%)"))),
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y=alt.Y('ihh_reserves:Q', title=str(_("pages.visualisations.axis_titles.ihh_reserves", "IHH Réserves (%)"))),
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size=alt.Size('ivc_cat:Q', scale=alt.Scale(domain=[1, 2, 3], range=[50, 500, 1000]), legend=None),
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color=alt.Color('ivc_cat:N', scale=alt.Scale(domain=[1, 2, 3], range=['darkgreen', 'orange', 'darkred'])),
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tooltip=['nom:N', 'ivc:Q', 'ihh_extraction:Q', 'ihh_reserves:Q']
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)
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points = base.mark_circle(opacity=0.6)
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lines = alt.Chart(df).mark_rule(strokeWidth=0.5, color='gray').encode(
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x='ihh_extraction:Q', x2='ihh_extraction_text:Q',
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y='ihh_reserves:Q', y2='ihh_reserves_text:Q'
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)
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labels = alt.Chart(df).mark_text(
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align='left', dx=10, dy=-10, fontSize=10, font='Arial', angle=335
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).encode(
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x='ihh_extraction_text:Q',
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y='ihh_reserves_text:Q',
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text='nom:N'
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)
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hline_15 = alt.Chart(df).mark_rule(strokeDash=[2,2], color='green').encode(y=alt.datum(15))
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hline_25 = alt.Chart(df).mark_rule(strokeDash=[2,2], color='red').encode(y=alt.datum(25))
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hline_100 = alt.Chart(df).mark_rule(strokeDash=[2,2], color='red').encode(y=alt.datum(100))
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vline_15 = alt.Chart(df).mark_rule(strokeDash=[2,2], color='green').encode(x=alt.datum(15))
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vline_25 = alt.Chart(df).mark_rule(strokeDash=[2,2], color='red').encode(x=alt.datum(25))
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vline_100 = alt.Chart(df).mark_rule(strokeDash=[2,2], color='red').encode(x=alt.datum(100))
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chart = (points + lines + labels + hline_15 + hline_25 + hline_100 + vline_15 + vline_25 + vline_100).properties(
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width=600,
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height=500,
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title=str(_("pages.visualisations.chart_titles.concentration_resources", "Concentration des ressources critiques vs vulnérabilité IVC"))
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).interactive()
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st.altair_chart(chart, use_container_width=True)
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except Exception as e:
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st.error(f"{str(_('errors.graph_creation_error', 'Erreur lors de la création du graphique :'))} {e}")
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def lancer_visualisation_ihh_criticite(graph):
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try:
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import networkx as nx
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from utils.graph_utils import recuperer_donnees
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niveaux = nx.get_node_attributes(graph, "niveau")
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noeuds = [n for n, v in niveaux.items() if v == "10" and "Reserves" not in n]
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noeuds.sort()
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df = recuperer_donnees(graph, noeuds)
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if df.empty:
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st.warning(str(_("pages.visualisations.no_data", "Aucune donnée à visualiser.")))
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else:
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afficher_graphique_altair(df)
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except Exception as e:
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st.error(f"{str(_('errors.ihh_criticality_error', 'Erreur dans la visualisation IHH vs Criticité :'))} {e}")
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def lancer_visualisation_ihh_ivc(graph):
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try:
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from utils.graph_utils import recuperer_donnees_2
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noeuds_niveau_2 = [
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n for n, data in graph.nodes(data=True)
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if data.get("niveau") == "2" and "ivc" in data
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]
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if not noeuds_niveau_2:
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return
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data = recuperer_donnees_2(graph, noeuds_niveau_2)
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creer_graphes(data)
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except Exception as e:
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st.error(f"{str(_('errors.ihh_ivc_error', 'Erreur dans la visualisation IHH vs IVC :'))} {e}")
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