from __future__ import annotations import json import textwrap from pathlib import Path import matplotlib.font_manager as fm import matplotlib.patheffects as pe import matplotlib.pyplot as plt import numpy as np import pandas as pd from matplotlib.font_manager import FontProperties from matplotlib.patches import Patch, Rectangle from PIL import Image, ImageDraw try: import bbcstyle except ModuleNotFoundError: bbcstyle = None ROOT = Path(__file__).resolve().parents[2] OUT_DIR = Path(__file__).resolve().parent / "output" READY_ROOT = Path(__file__).resolve().parent FONT_DIR = ROOT / "grafici_rifatti_v01" / "github_bbcstyle_top20" / "assets" / "fonts" GREEN = "#1f642c" GREEN_DARK = "#174f24" OFF_WHITE = "#f7fbf4" SOFT_WHITE = "#dfeee0" MUTED_WHITE = "#bdd8bf" NOTE_FACE = "#fff7f8" NOTE_TEXT = "#243524" GREY_FACE = "#eef1ee" GREY_TEXT = "#4e5851" GRID = (1, 1, 1, 0.20) BAR_MUTED = (1, 1, 1, 0.27) LINE_MUTED = (1, 1, 1, 0.34) S8 = "#f2b8c1" S8_EDGE = "#ffd6de" PUNTA = "#8e3d7a" MORBIDA = "#f2b8c1" STATION_COLORS = { "Airuno": "#ffd1d1", "Osnago": "#ff7faa", "Cernusco-Merate": "#c4447a", "Olgiate-Calco-Brivio": "#f0a4d8", } MERATESE = set(STATION_COLORS) LINE_COLORS = { "S8": S8, "S1/S12": "#e84b5f", "S5": "#ff9a3d", "S6": "#ffd83d", "Totale Trenord": SOFT_WHITE, } SLIDE_SIZE = (16, 9) SLIDE_DPI = 220 SIGNATURE = "Analisi di Simone Ghezzi Colombo" def load_font(filename: str) -> FontProperties: path = FONT_DIR / filename fm.fontManager.addfont(str(path)) return FontProperties(fname=str(path)) TITLE_FONT = load_font("BricolageGrotesque.ttf") TEXT_FONT = load_font("OpenSans-Regular.ttf") BOLD_FONT = load_font("OpenSans-Bold.ttf") def p(*parts: str) -> Path: return ROOT.joinpath(*parts) def next_path(stem: str, suffix: str = ".png") -> Path: OUT_DIR.mkdir(parents=True, exist_ok=True) for idx in range(1, 100): candidate = OUT_DIR / f"{stem}_v{idx:02d}{suffix}" if not candidate.exists(): return candidate raise RuntimeError(f"Troppi output per {stem}.") def setup() -> tuple[plt.Figure, plt.Axes]: if bbcstyle is not None: bbcstyle.use() else: plt.style.use("default") plt.rcParams.update( { "figure.facecolor": "none", "axes.facecolor": "none", "savefig.facecolor": "none", "axes.grid": False, "axes.unicode_minus": False, } ) fig, ax = plt.subplots(figsize=SLIDE_SIZE, constrained_layout=False) fig.patch.set_alpha(0) ax.set_facecolor("none") return fig, ax def wrap(text: str, width: int = 44) -> str: return "\n".join( textwrap.wrap(str(text), width=width, break_long_words=False, break_on_hyphens=False) ) def add_header( fig: plt.Figure, title: str, subtitle: str, *, title_size: int = 32, title_y: float = 0.950, subtitle_y: float = 0.885, ) -> None: fig.text( 0.5, title_y, title, ha="center", va="top", fontsize=title_size, fontproperties=TITLE_FONT, color=OFF_WHITE, linespacing=0.92, path_effects=[pe.withStroke(linewidth=2.0, foreground=GREEN_DARK, alpha=0.30)], ) if subtitle: fig.text( 0.5, subtitle_y, subtitle, ha="center", va="top", fontsize=13.2, fontproperties=TEXT_FONT, color=SOFT_WHITE, ) def add_source(fig: plt.Figure, source: str) -> None: source = "\n".join(textwrap.wrap(source, width=132, break_long_words=False, break_on_hyphens=False)) fig.text( 0.045, 0.045, source, ha="left", va="bottom", fontsize=8.6, fontproperties=TEXT_FONT, color=MUTED_WHITE, ) fig.text( 0.955, 0.045, SIGNATURE, ha="right", va="bottom", fontsize=8.6, fontproperties=TEXT_FONT, color=MUTED_WHITE, ) def clean(ax: plt.Axes) -> None: for spine in ax.spines.values(): spine.set_visible(False) ax.tick_params(length=0) def axis_style(ax: plt.Axes, xlabel: str | None = None, ylabel: str | None = None) -> None: ax.grid(True, color=GRID, linewidth=1.15) ax.tick_params(axis="both", colors=MUTED_WHITE, labelsize=12.2) for tick in ax.get_xticklabels() + ax.get_yticklabels(): tick.set_fontproperties(TEXT_FONT) if xlabel: ax.set_xlabel(xlabel, fontsize=12.4, fontproperties=TEXT_FONT, color=SOFT_WHITE, labelpad=9) if ylabel: ax.set_ylabel(ylabel, fontsize=12.4, fontproperties=TEXT_FONT, color=SOFT_WHITE, labelpad=9) def save(fig: plt.Figure, stem: str) -> tuple[Path, Path]: transparent = next_path(f"{stem}_trasparente") fig.savefig(transparent, dpi=SLIDE_DPI, transparent=True) plt.close(fig) preview = transparent.with_name(transparent.name.replace("_trasparente_", "_preview_verde_")) image = Image.open(transparent).convert("RGBA") bg = Image.new("RGBA", image.size, GREEN) bg.alpha_composite(image) bg.convert("RGB").save(preview, quality=95) return transparent, preview def fmt_int(value: float) -> str: return f"{int(round(value)):,.0f}".replace(",", ".") def fmt_plus(value: float) -> str: return ("+" if value >= 0 else "") + fmt_int(value) def shorten(text: str, max_chars: int = 42) -> str: replacements = { "Fiumicino Aeroporto": "Fiumicino", "Milano Passante": "Milano Pass.", "Milano Porta Garibaldi": "Milano P. Garibaldi", "Civita Castellana": "Civita Cast.", "San Cristoforo": "S. Cristoforo", } out = str(text).replace("–", "-").replace(" / ", "/") for k, v in replacements.items(): out = out.replace(k, v) if len(out) <= max_chars: return out words: list[str] = [] for word in out.split(): candidate = " ".join(words + [word]) if len(candidate) > max_chars - 1: break words.append(word) return " ".join(words).rstrip(" -/") + "..." def label_right(ax: plt.Axes, x: float, y: float, text: str, color: str | tuple, bold: bool = False, size: float = 10.8) -> None: ax.text( x, y, text, ha="left", va="center", fontsize=size, fontproperties=BOLD_FONT if bold else TEXT_FONT, color=color, path_effects=[pe.withStroke(linewidth=2.0, foreground=GREEN_DARK, alpha=0.55)], ) def label_box( ax: plt.Axes, x: float, y: float, text: str, color: str, *, ha: str = "left", size: float = 8.5, facecolor: str = NOTE_FACE, text_color: str = NOTE_TEXT, ) -> None: ax.text( x, y, text, ha=ha, va="center", fontsize=size, fontproperties=BOLD_FONT, color=text_color, bbox={ "boxstyle": "round,pad=0.20", "facecolor": facecolor, "edgecolor": color, "linewidth": 1.15, "alpha": 0.97, }, ) def chart_linee_s_indice() -> tuple[Path, Path]: df = pd.read_csv(p("ANALISI INIZIALE", "s8", "Linee_S1S12_S5_S6_S8_2019-2025_dataset.csv")) fig, ax = setup() add_header( fig, "Linee suburbane Trenord: indice 2019=100", "La S8 è l'unica tra le linee considerate nettamente sopra il livello 2019.", title_size=31, ) order = ["S1/S12", "S5", "S6", "S8", "Totale Trenord"] for serie in order: sub = df[df["Serie"].eq(serie)].sort_values("Anno") is_s8 = serie == "S8" is_total = serie == "Totale Trenord" ax.plot( sub["Anno"], sub["Indice_2019_100"], color=LINE_COLORS[serie], linewidth=4.4 if is_s8 else (3.0 if not is_total else 2.3), linestyle="--" if is_total else "-", marker="o", markersize=6.4 if is_s8 else 4.7, alpha=1 if is_s8 else 0.88, ) last = sub.iloc[-1] label_right(ax, last["Anno"] + 0.12, last["Indice_2019_100"], serie, LINE_COLORS[serie], is_s8, 12.4 if is_s8 else 10.8) ax.axhline(100, color=MUTED_WHITE, linewidth=1.0, linestyle=":", alpha=0.65) ax.set_xlim(2018.7, 2025.9) ax.set_ylim(0, 170) ax.set_xticks([2019, 2020, 2021, 2022, 2023, 2024, 2025]) ax.set_yticks([0, 40, 80, 120, 160]) axis_style(ax, "Anno", "Indice (2019=100)") clean(ax) add_source(fig, "Fonti: Regione Lombardia, Trenord, relazioni e comunicati 2019-2025. Elaborazione su indice 2019=100.") fig.subplots_adjust(left=0.085, right=0.90, top=0.845, bottom=0.135) return save(fig, "01_linee_suburbane_trenord_indice_2019_100") def chart_delta_linee() -> tuple[Path, Path]: df = pd.read_csv(p("linee", "2024 2025", "plotly_linee_suburbane_S1S12_summary_CORRETTO.csv")) df = df.sort_values("Delta").reset_index(drop=True) fig, ax = setup() add_header( fig, "Variazione assoluta passeggeri", "Linee suburbane Trenord: 2025 vs 2024, passeggeri/giorno feriale.", title_size=32, ) extra_colors = { "S1/S12": "#e84b5f", "S5": "#ff9a3d", "S6": "#ffd83d", } colors = [S8 if x == "S8" else extra_colors.get(x, BAR_MUTED) for x in df["N. linea"]] alphas = [1.0 if x == "S8" else (0.78 if x in extra_colors else 0.55) for x in df["N. linea"]] edges = [S8_EDGE if x == "S8" else (1, 1, 1, 0.0) for x in df["N. linea"]] bars = ax.barh(range(len(df)), df["Delta"], color=colors, edgecolor=edges, linewidth=[2.2 if x == "S8" else 0 for x in df["N. linea"]], height=0.62) for bar, alpha in zip(bars, alphas): bar.set_alpha(alpha) ax.axvline(0, color=SOFT_WHITE, linewidth=1.0, alpha=0.85) ax.set_yticks([]) ax.set_ylim(-0.85, len(df) - 0.15) for i, row in df.iterrows(): is_s8 = row["N. linea"] == "S8" is_colored = row["N. linea"] in extra_colors ax.text(-8600, i, row["N. linea"], ha="right", va="center", fontsize=13.4, fontproperties=BOLD_FONT if is_s8 else TEXT_FONT, color=OFF_WHITE if (is_s8 or is_colored) else SOFT_WHITE) val = row["Delta"] ax.text(val + (620 if val >= 0 else -620), i, fmt_plus(val), ha="left" if val >= 0 else "right", va="center", fontsize=11.4, fontproperties=BOLD_FONT if is_s8 else TEXT_FONT, color=OFF_WHITE if (is_s8 or is_colored) else SOFT_WHITE) s112_y = int(df.index[df["N. linea"].eq("S1/S12")][0]) ax.text( 14600, s112_y + 0.46, "da 70 a 130 corse/giorno grazie a S12", ha="center", va="center", fontsize=10.8, fontproperties=BOLD_FONT, color=NOTE_TEXT, bbox={ "boxstyle": "round,pad=0.22", "facecolor": NOTE_FACE, "edgecolor": S8_EDGE, "linewidth": 1.0, "alpha": 0.98, }, ) ax.set_xlim(-9300, 25000) ax.set_xticks([-5000, 0, 5000, 10000, 15000, 20000]) ax.set_xticklabels(["-5.000", "0", "5.000", "10.000", "15.000", "20.000"]) axis_style(ax, "Passeggeri/giorno feriale (2025 - 2024)") clean(ax) add_source(fig, "Fonte: Dati frequentazione linee regionali Lombardia, 2024-2025. Metodo: solo campagne disponibili in entrambi gli anni; escluse S10, S30, S31, S40, S50.") fig.subplots_adjust(left=0.22, right=0.945, top=0.84, bottom=0.135) return save(fig, "02_variazione_assoluta_linee_suburbane_2025_2024") def chart_metro() -> tuple[Path, Path]: df = pd.read_csv(p("linee", "comparazione metro", "numeri_da_metropolitana.csv")) genova = df["servizio"].eq("Genova Metro") df.loc[genova, "passeggeri_annui"] = 12_600_000 df.loc[genova, "passeggeri_annui_label"] = "12,6 mln" df = df.sort_values("passeggeri_annui", ascending=True).reset_index(drop=True) fig, ax = setup() add_header( fig, "Passeggeri annui per linea", "La S8 ha numeri da metropolitana.", title_size=32, ) vals = df["passeggeri_annui"] / 1_000_000 colors = [S8 if "S8" in s else BAR_MUTED for s in df["servizio"]] bars = ax.barh(range(len(df)), vals, color=colors, edgecolor=[S8_EDGE if "S8" in s else (1, 1, 1, 0) for s in df["servizio"]], linewidth=[2.2 if "S8" in s else 0 for s in df["servizio"]], height=0.66) for bar, servizio in zip(bars, df["servizio"]): if "S8" not in servizio: bar.set_alpha(0.58) ax.set_yticks([]) for i, row in df.iterrows(): is_s8 = "S8" in row["servizio"] ax.text(-0.36, i, shorten(row["servizio"], 35), ha="right", va="center", fontsize=12.1, fontproperties=BOLD_FONT if is_s8 else TEXT_FONT, color=OFF_WHITE if is_s8 else SOFT_WHITE) ax.text(vals.iloc[i] + 0.20, i, row["passeggeri_annui_label"], ha="left", va="center", fontsize=11.8, fontproperties=BOLD_FONT if is_s8 else TEXT_FONT, color=OFF_WHITE if is_s8 else SOFT_WHITE) if pd.notna(row["freq_punta"]): freq = str(row["freq_punta"]).replace(".0", "").replace(".", ",") ax.text( vals.iloc[i] + 0.18, i - 0.22, f"freq. {freq} min", ha="left", va="center", fontsize=9.7, fontproperties=BOLD_FONT if is_s8 else TEXT_FONT, color=NOTE_TEXT, bbox={ "boxstyle": "round,pad=0.17", "facecolor": NOTE_FACE, "edgecolor": S8_EDGE if is_s8 else MUTED_WHITE, "linewidth": 0.9, "alpha": 0.96, }, ) ax.set_xlim(0, 18.7) ax.set_xticks([0, 5, 10, 15]) axis_style(ax, "Passeggeri annui, milioni") clean(ax) add_source(fig, "Fonti: dati passeggeri e frequenze da schede/operatori indicati nel dataset; S8 benchmark con dati utente/Trenord.") fig.subplots_adjust(left=0.28, right=0.955, top=0.84, bottom=0.135) return save(fig, "03_comparazione_metro_s8_benchmark") def chart_top20() -> tuple[Path, Path]: df = pd.read_excel( p("linee", "top 20 italia", "base_dati_top20_linee_ferroviarie_nonAV_italia_colori_finali_tilo_blu_ticino.xlsx"), sheet_name="Top20", ).sort_values("rank", ascending=False).reset_index(drop=True) fig, ax = setup() add_header( fig, "Le 20 linee ferroviarie locali più usate in Italia", "La S8 Lecco-Milano è al 4° posto, con 14,8 milioni di passeggeri annui.", title_size=31, ) vals = df["central_mln"].astype(float) ax.barh(range(len(df)), vals, color=[S8 if x == "S8" else BAR_MUTED for x in df["line_code"]], edgecolor=[S8_EDGE if x == "S8" else (1, 1, 1, 0) for x in df["line_code"]], linewidth=[2.1 if x == "S8" else 0 for x in df["line_code"]], height=0.63) ax.set_yticks([]) for i, row in df.iterrows(): code = row["line_code"] name = str(row["line_name"]).replace("–", "-") if name.startswith(code): name = name[len(code):].strip() label = f"{int(row['rank'])}. {code} · {shorten(name, 36)}" is_s8 = code == "S8" ax.text(-0.45, i, label, ha="right", va="center", fontsize=9.5, fontproperties=BOLD_FONT if is_s8 else TEXT_FONT, color=OFF_WHITE if is_s8 else SOFT_WHITE) ax.add_patch(Rectangle((-0.25, i - 0.18), 0.060, 0.36, transform=ax.transData, clip_on=False, color=str(row["color_hex"]), linewidth=0)) ax.text(vals.iloc[i] + 0.13, i, f"{vals.iloc[i]:.1f}", ha="left", va="center", fontsize=10.0 if not is_s8 else 12.1, fontproperties=BOLD_FONT if is_s8 else TEXT_FONT, color=OFF_WHITE if is_s8 else SOFT_WHITE) ax.text(16.4, int(np.where(df["line_code"].to_numpy() == "S8")[0][0]), "4° in Italia", ha="left", va="center", fontsize=12.0, fontproperties=BOLD_FONT, color=OFF_WHITE) ax.set_xlim(0, 22.2) ax.set_xticks([0, 5, 10, 15, 20]) axis_style(ax, "Milioni di passeggeri annui") clean(ax) add_source(fig, "Fonte: elaborazione su dati Trenord, Cotral, Trenitalia e Regioni. Lombardia: campagne di frequentazione 2025.") fig.subplots_adjust(left=0.32, right=0.945, top=0.84, bottom=0.135) return save(fig, "04_top20_linee_locali_italia_s8") def chart_stazioni_indice() -> tuple[Path, Path]: df = pd.read_csv( p("ANALISI INIZIALE", "stazioni", "update", "S8_stazioni_Saliti24H_Novembre_2015_2025_no_Maggianico_indice2019_100.csv") ) df = df[~df["Stazione"].eq("Vercurago-S. Girolamo")].copy() fig, ax = setup() add_header( fig, "Passeggeri nelle stazioni S8: 2015-2025", "Indice base 2019=100: nel Meratese la crescita accelera dopo il 2022.", title_size=31, ) for station, sub in df.groupby("Stazione"): sub = sub.sort_values("Anno") is_mer = station in MERATESE color = STATION_COLORS.get(station, LINE_MUTED) ax.plot(sub["Anno"], sub["Indice_2019_100"], color=color, linewidth=3.9 if is_mer else 1.7, marker="o", markersize=5.2 if is_mer else 3.1, alpha=1 if is_mer else 0.60) last = sub.iloc[-1] if is_mer or last["Indice_2019_100"] > 115 or last["Indice_2019_100"] < 75: label_right(ax, 2025.12, last["Indice_2019_100"], station, color if is_mer else MUTED_WHITE, is_mer, 10.0 if is_mer else 8.9) ax.axhline(100, color=MUTED_WHITE, linewidth=1.0, linestyle=":", alpha=0.65) ax.set_xlim(2014.7, 2026.3) ax.set_ylim(0, 275) ax.set_xticks([2015, 2017, 2019, 2021, 2023, 2025]) ax.set_yticks([0, 50, 100, 150, 200, 250]) axis_style(ax, "Anno", "Indice (2019=100)") clean(ax) add_source(fig, "Fonti: Regione Lombardia, Flussi Stazioni Ferroviarie 2015-2023; campagne Lombardia 2024-2025; elaborazione su novembre feriale.") fig.subplots_adjust(left=0.085, right=0.86, top=0.84, bottom=0.135) return save(fig, "05_stazioni_s8_indice_2019_100") def chart_saliti_per_corsa() -> tuple[Path, Path]: df = pd.read_csv( p( "stazioni", "altro", "Scatter_precedenti_filtrati_base700", "Dataset_scatter_precedenti_filtrati_base2019_almeno700.csv", ) ) fig, ax = setup() add_header( fig, "Crescita passeggeri nelle stazioni lombarde", "Le stazioni meratesi sono un caso raro nel confronto lombardo.", title_size=31, ) x = df["Growth_pct_Saliti24H"] y = df["Growth_pct_Saliti_per_corsa"] size = np.clip(np.sqrt(df["Delta_abs_Saliti24H"].clip(lower=1)) * 3.2, 12, 170) others = df[~df["IsMeratese"]] ax.scatter( others["Growth_pct_Saliti24H"], others["Growth_pct_Saliti_per_corsa"], s=np.clip(np.sqrt(others["Delta_abs_Saliti24H"].clip(lower=1)) * 3.4, 14, 160), color=(1, 1, 1, 0.22), linewidth=0, ) mer = df[df["IsMeratese"]] for _, row in mer.iterrows(): label = str(row["Label"]) ax.scatter( row["Growth_pct_Saliti24H"], row["Growth_pct_Saliti_per_corsa"], s=190, color=STATION_COLORS.get(label, S8), edgecolor=OFF_WHITE, linewidth=1.1, zorder=4, ) ax.axhline(0, color=MUTED_WHITE, linestyle=":", linewidth=1.1, alpha=0.75) ax.axvline(0, color=MUTED_WHITE, linestyle=":", linewidth=1.1, alpha=0.75) label_offsets = { "Airuno": (4, 2), "Osnago": (4, 1), "Cernusco-Merate": (4, 3), "Olgiate-Calco-Brivio": (4, -5), } for _, row in mer.iterrows(): label = str(row["Label"]) dx, dy = label_offsets[label] label_box( ax, row["Growth_pct_Saliti24H"] + dx, row["Growth_pct_Saliti_per_corsa"] + dy, label, STATION_COLORS.get(label, S8), size=11.3, text_color="#5b2438", ) selected = { "MILANOSCRISTOFORO": (2, 3, "Milano S. Cristoforo"), "TREVIGLIOOVEST": (2, 3, "Treviglio Ovest"), "CARNATEUSMATE": (2, -4, "Carnate Usmate"), "ARCORE": (2, 2, "Arcore"), "CALOLZIOCORTEOLGINATE": (2, -5, "Calolziocorte-Olginate"), } for key, (dx, dy, label) in selected.items(): sub = df[df["StationKey"].eq(key)] if not sub.empty: row = sub.iloc[0] label_box( ax, row["Growth_pct_Saliti24H"] + dx, row["Growth_pct_Saliti_per_corsa"] + dy, label, "#c9d0ca", size=8.8, facecolor=GREY_FACE, text_color=GREY_TEXT, ) ax.set_xlim(-75, 168) ax.set_ylim(-75, 145) ax.set_xticks([-50, 0, 50, 100, 150]) ax.set_yticks([-50, 0, 50, 100]) axis_style(ax, "Crescita % passeggeri 2019-2025", "Cambiamento % passeggeri per corsa 2019-2025") clean(ax) add_source(fig, "Filtro: stazioni lombarde con almeno 700 saliti24H nel 2019. Dimensione punto = crescita assoluta passeggeri.") fig.subplots_adjust(left=0.09, right=0.945, top=0.84, bottom=0.135) return save(fig, "06_saliti_per_corsa_stazioni_s8") def chart_peso_punta() -> tuple[Path, Path]: df = pd.read_csv( p("stazioni", "cambio rapporto", "orari", "Grafico_scientifico_S8_peso_punta_2015_2025_dataset_v2.csv") ) fig, ax = setup() add_header( fig, "Peso della punta nelle stazioni S8", "Quota di saliti 7-9 sul totale giornaliero, novembre feriale 2015-2025.", title_size=31, ) mer_label_offsets = { "Airuno": 2.2, "Osnago": -0.2, "Cernusco-Merate": 3.4, "Olgiate-Calco-Brivio": 1.1, } other_label_y = { "Carnate Usmate": 23.6, "Arcore": 22.0, "Calolziocorte-Olginate": 20.4, "Monza": 18.5, "Sesto S. Giovanni": 14.2, "Lecco": 12.4, "Milano Greco Pirelli": 10.8, "Milano Porta Garibaldi": 9.6, } for station, sub in df.groupby("Stazione"): sub = sub.sort_values("Anno") is_mer = station in MERATESE color = STATION_COLORS.get(station, LINE_MUTED) ax.plot( sub["Anno"], sub["Peso_punta_pct"], color=color, linewidth=4.0 if is_mer else 1.45, marker="o", markersize=5.2 if is_mer else 2.8, alpha=1 if is_mer else 0.46, ) last = sub.iloc[-1] if is_mer: label_box( ax, 2025.18, last["Peso_punta_pct"] + mer_label_offsets[station], station, color, size=9.2, text_color="#5b2438", ) elif station in other_label_y: label_right(ax, 2025.14, other_label_y[station], station, MUTED_WHITE, False, 8.6) ax.set_xlim(2014.7, 2026.5) ax.set_ylim(0, 62) ax.set_xticks([2015, 2017, 2019, 2021, 2023, 2025]) ax.set_yticks([0, 10, 20, 30, 40, 50, 60]) axis_style(ax, "Anno", "Saliti 7-9 / saliti 24H (%)") clean(ax) add_source(fig, "Fonti: Regione Lombardia, Flussi Stazioni Ferroviarie; frequentazione stazioni del servizio ferroviario regionale.") fig.subplots_adjust(left=0.095, right=0.86, top=0.84, bottom=0.135) return save(fig, "07_peso_punta_stazioni_s8") def chart_punta_morbida() -> tuple[Path, Path]: series = pd.read_csv( p("stazioni", "cambio rapporto", "orari", "Meratese_series_Saliti7-9_Saliti24H_2015_2025.csv") ) rows: list[dict[str, float | str]] = [] current = series[series["Anno"].isin([2019, 2025])].copy() for station, sub in current.groupby("Stazione"): wide = sub.set_index("Anno") delta_tot = float(wide.loc[2025, "Saliti24H"] - wide.loc[2019, "Saliti24H"]) delta_punta = float(wide.loc[2025, "Saliti7-9"] - wide.loc[2019, "Saliti7-9"]) rows.append( { "Stazione": station, "punta": delta_punta, "morbida": delta_tot - delta_punta, "delta_tot": delta_tot, } ) totals = current.groupby("Anno")[["Saliti7-9", "Saliti24H"]].sum() delta_tot = float(totals.loc[2025, "Saliti24H"] - totals.loc[2019, "Saliti24H"]) delta_punta = float(totals.loc[2025, "Saliti7-9"] - totals.loc[2019, "Saliti7-9"]) rows.append( { "Stazione": "Meratese totale", "punta": delta_punta, "morbida": delta_tot - delta_punta, "delta_tot": delta_tot, } ) df = pd.DataFrame(rows) order = ["Meratese totale", "Airuno", "Osnago", "Cernusco-Merate", "Olgiate-Calco-Brivio"] df["order"] = df["Stazione"].map({k: i for i, k in enumerate(order)}) df = df.sort_values("order", ascending=False).reset_index(drop=True) fig, ax = setup() add_header( fig, "Crescita meratese: punta e morbida", "Variazione saliti/giorno 2025 vs 2019: quasi tutta la crescita è fuori dalla fascia 7-9.", title_size=31, ) y = np.arange(len(df)) ax.barh(y, df["punta"], color=PUNTA, height=0.64, label="Punta 7-9") ax.barh(y, df["morbida"], left=df["punta"], color=MORBIDA, height=0.64, label="Morbida / fuori 7-9") ax.axvline(0, color=SOFT_WHITE, linewidth=1.0, alpha=0.85) ax.set_yticks([]) for i, row in df.iterrows(): station = row["Stazione"] color = STATION_COLORS.get(station, OFF_WHITE) is_agg = station == "Meratese totale" ax.text(-250, i, station, ha="right", va="center", fontsize=12.1, fontproperties=BOLD_FONT if is_agg else TEXT_FONT, color=OFF_WHITE if is_agg else color) ax.text(row["delta_tot"] + 90, i, fmt_plus(row["delta_tot"]), ha="left", va="center", fontsize=11.5, fontproperties=BOLD_FONT if is_agg else TEXT_FONT, color=OFF_WHITE) if row["delta_tot"] > 0: pct_morbida = row["morbida"] / row["delta_tot"] * 100 ax.text(row["punta"] + row["morbida"] / 2, i, f"{pct_morbida:.0f}%", ha="center", va="center", fontsize=10.4, fontproperties=BOLD_FONT, color=GREEN_DARK) handles = [Patch(facecolor=PUNTA, label="Punta 7-9"), Patch(facecolor=MORBIDA, label="Morbida / fuori 7-9")] leg = ax.legend(handles=handles, loc="lower right", bbox_to_anchor=(0.985, 0.035), frameon=True, fontsize=10.4) leg.get_frame().set_facecolor(NOTE_FACE) leg.get_frame().set_edgecolor(SOFT_WHITE) leg.get_frame().set_alpha(0.96) for txt in leg.get_texts(): txt.set_color(NOTE_TEXT) txt.set_fontproperties(TEXT_FONT) ax.set_xlim(-300, 4800) ax.set_xticks([0, 1000, 2000, 3000, 4000]) ax.set_xticklabels(["0", "1.000", "2.000", "3.000", "4.000"]) axis_style(ax, "Passeggeri/giorno (2025 - 2019)") clean(ax) add_source(fig, "Definizioni: punta = variazione saliti 7-9; morbida = variazione saliti 24H al netto della punta. Fonte: Regione Lombardia/Trenord.") fig.subplots_adjust(left=0.235, right=0.92, top=0.84, bottom=0.135) return save(fig, "08_crescita_meratese_punta_morbida") def generate() -> list[dict[str, str]]: charts = [ ("01 Linee suburbane indice 2019=100", chart_linee_s_indice), ("02 Variazione assoluta linee suburbane", chart_delta_linee), ("03 Comparazione metro", chart_metro), ("04 Top20 linee locali Italia", chart_top20), ("05 Stazioni S8 indice 2019=100", chart_stazioni_indice), ("06 Saliti per corsa", chart_saliti_per_corsa), ("07 Peso punta stazioni S8", chart_peso_punta), ("08 Crescita punta/morbida", chart_punta_morbida), ] manifest: list[dict[str, str]] = [] for title, fn in charts: transparent, preview = fn() manifest.append({"grafico": title, "trasparente": str(transparent), "preview_verde": str(preview)}) print(title) print(transparent) print(preview) manifest_path = next_path("manifest_grafici_fedeli_canva", ".json") manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") print(manifest_path) return manifest def copy_ready(manifest: list[dict[str, str]]) -> None: for idx in range(1, 100): ready_dir = READY_ROOT / f"pronti_canva_v{idx:02d}" if not ready_dir.exists(): break else: raise RuntimeError("Troppi pacchetti pronti Canva gia' presenti.") ready_dir.mkdir(parents=True, exist_ok=False) names = [ "01_linee_suburbane_indice_2019_100.png", "02_variazione_assoluta_linee_suburbane.png", "03_comparazione_metro_s8.png", "04_top20_linee_locali_italia.png", "05_stazioni_s8_indice_2019_100.png", "06_saliti_per_corsa_stazioni_s8.png", "07_peso_punta_stazioni_s8.png", "08_crescita_punta_morbida_meratese.png", ] for row, name in zip(manifest, names): Image.open(row["trasparente"]).save(ready_dir / name) previews = [Image.open(row["preview_verde"]).convert("RGB") for row in manifest] thumbs: list[Image.Image] = [] for img, name in zip(previews, names): img.thumbnail((640, 360)) canvas = Image.new("RGB", (640, 400), "white") canvas.paste(img, (0, 0)) ImageDraw.Draw(canvas).text((10, 368), name, fill=(0, 0, 0)) thumbs.append(canvas) sheet = Image.new("RGB", (1280, 1600), (235, 235, 235)) for idx, thumb in enumerate(thumbs): sheet.paste(thumb, ((idx % 2) * 640, (idx // 2) * 400)) sheet.save(ready_dir / "00_anteprima_tutti_grafici.jpg", quality=92) (ready_dir / "README_USO_CANVA.md").write_text( "PNG trasparenti 16:9 da importare in Canva sopra sfondo #1f642c.\n" "La preview JPG serve solo per controllo rapido su verde.\n" "Firma fissa: Analisi di Simone Ghezzi Colombo.\n", encoding="utf-8", ) print(ready_dir) if __name__ == "__main__": copy_ready(generate())