"""
Chart 3: Hyperscaler capex trajectory 2023-2026

Sources (all from 10-K filings, earnings transcripts, and consolidated reporting):
  - 2023 actuals: Platformonomics 2024 retrospective (implied from 62% YoY growth)
  - 2024 actuals: Platformonomics Cloud Table Stakes 2024 Retrospective (Feb 2025)
  - 2025 actuals: Companies' Q4 2025 earnings reports (Jan-Feb 2026)
  - 2026 guidance: Companies' Q1 2026 earnings reports (Apr-May 2026)

Notes / data gaps:
  - Microsoft reports fiscal-year (July-June); we use calendar-year approximation
  - Apple and Oracle excluded (different infrastructure model, smaller scale)
  - 2026 numbers are guidance, not actuals; revisable each quarter
  - Numbers represent total company capex, ~75% of which is AI infrastructure
    per Q1 2026 commentary (CNBC, Feb 2026)
"""

from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np

HERE = Path(__file__).parent
OUT = HERE / "chart_03_hyperscaler_capex.png"

years = [2023, 2024, 2025, 2026]
amazon =   [48,  83.9, 125, 200]
microsoft = [41, 75.6, 95,  190]
alphabet =  [32, 52.5, 92,  185]
meta =      [28, 39.2, 72,  135]

companies = [
    ("Amazon",   amazon,    "#FF9900"),
    ("Microsoft", microsoft, "#0078D4"),
    ("Alphabet", alphabet,  "#34A853"),
    ("Meta",     meta,      "#1877F2"),
]

fig, ax = plt.subplots(figsize=(11, 6), dpi=200)
x = np.arange(len(years))
width = 0.20

for i, (name, vals, color) in enumerate(companies):
    bars = ax.bar(x + (i - 1.5) * width, vals, width, label=name, color=color, edgecolor="white")
    for bar, v in zip(bars, vals):
        ax.text(bar.get_x() + bar.get_width()/2, v + 4, f"${v:.0f}B",
                ha="center", va="bottom", fontsize=8, color="#333")

totals = [a+b+c+d for a,b,c,d in zip(amazon, microsoft, alphabet, meta)]
for xi, t in zip(x, totals):
    ax.text(xi, max(amazon[x.tolist().index(xi)], microsoft[x.tolist().index(xi)],
                    alphabet[x.tolist().index(xi)], meta[x.tolist().index(xi)]) + 22,
            f"Total: ${t:.0f}B", ha="center", va="bottom", fontsize=10,
            fontweight="bold", color="#333",
            bbox=dict(boxstyle="round,pad=0.3", fc="#f5f5f5", ec="#aaa", lw=0.5))

# Mark 2026 as guidance
ax.annotate("Guidance\n(forward)", xy=(x[3], 215), xytext=(x[3], 252),
            fontsize=9, color="#a33", ha="center", style="italic")

# Title and labels
ax.set_title("Hyperscaler capex 2023–2026: tripled in two years",
             fontsize=14, fontweight="bold", pad=14)
ax.set_xticks(x)
ax.set_xticklabels([f"{y}{' E' if y == 2026 else ''}" for y in years])
ax.set_ylabel("Capital expenditure ($B, calendar year)", fontsize=10)
ax.set_ylim(0, 230)
ax.legend(loc="upper left", frameon=False, ncol=4, fontsize=10)
ax.grid(axis="y", alpha=0.25)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

caption = (
    "Sources: Platformonomics 2024 Retrospective (Feb 2025), company Q4 2025 + Q1 2026 earnings releases.\n"
    "2026 figures are forward guidance (Microsoft $190B incl. ~$25B component price inflation; Alphabet revised Apr 2026).\n"
    "Microsoft reported on a fiscal year ending June; figures approximate calendar year. ~75% of capex is AI infrastructure."
)
fig.text(0.02, 0.005, caption, fontsize=7, color="#555")

plt.tight_layout(rect=[0, 0.05, 1, 1])
plt.savefig(OUT, dpi=200, bbox_inches="tight")
print(f"wrote {OUT}")
print("year | total")
for y, t in zip(years, totals):
    print(f"{y} | ${t:.0f}B")
print(f"growth 2023->2026: {totals[-1]/totals[0]:.1f}x")
print(f"growth 2024->2026: {totals[-1]/totals[1]:.1f}x")