feat(chart): NVIDIA data center revenue with growth deceleration

src/charts/nvidia_revenue.py

@@ -0,0 +1,152 @@
+"""NVIDIA Data Center Revenue Chart"""
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+from src.data.ai_infrastructure import nvidia_revenue, nvidia_revenue_meta
+from src.utils.styling import get_theme, EXPORT_DPI, REVENUE, GRAY_DARK, AI_SPEND
+
+
+def plot_nvidia_datacenter() -> str:
+    plt.rcParams.update(get_theme())
+    fig, ax1 = plt.subplots(figsize=(14, 8))
+
+    # Filter data center revenue
+    quarters = [d["fiscal_quarter"] for d in nvidia_revenue]
+    dc_rev = [
+        d.get(
+            "data_center_billions",
+            d.get("compute_billions", 0) + d.get("networking_billions", 0),
+        )
+        for d in nvidia_revenue
+    ]
+
+    # Calculate YoY growth
+    growth = []
+    for i in range(len(dc_rev)):
+        if i >= 4:  # Need previous year's same quarter
+            prev = dc_rev[i - 4]
+            if prev > 0:
+                growth.append(((dc_rev[i] - prev) / prev) * 100)
+            else:
+                growth.append(None)
+        else:
+            growth.append(None)
+
+    # Plot DC revenue (left axis)
+    ax1.fill_between(range(len(dc_rev)), dc_rev, alpha=0.3, color=REVENUE)
+    line1 = ax1.plot(
+        range(len(dc_rev)), dc_rev, color=REVENUE, linewidth=2, label="Data Center Revenue"
+    )
+    ax1.set_ylabel("Data Center Revenue ($B)", fontsize=12, color=REVENUE)
+    ax1.set_ylim(0, max(dc_rev) * 1.2)
+
+    # Plot YoY growth (right axis)
+    ax2 = ax1.twinx()
+    growth_values = [g for g in growth if g is not None]
+    growth_indices = [i for i, g in enumerate(growth) if g is not None]
+    line2 = ax2.plot(
+        growth_indices,
+        growth_values,
+        color=AI_SPEND,
+        linewidth=2,
+        marker="o",
+        markersize=4,
+        label="YoY Growth Rate",
+    )
+    ax2.set_ylabel("YoY Growth Rate (%)", fontsize=12, color=AI_SPEND)
+    ax2.set_ylim(-50, max(growth_values) * 1.1)
+
+    # AI infrastructure buildout shading
+    ai_start_idx = 17  # FY2024 Q2
+    ax1.axvspan(ai_start_idx - 0.5, len(dc_rev) - 0.5, alpha=0.1, color=REVENUE)
+    ax1.text(
+        ai_start_idx,
+        max(dc_rev) * 0.8,
+        "AI Infrastructure\nBuildout",
+        fontsize=10,
+        ha="center",
+        style="italic",
+        color=REVENUE,
+    )
+
+    # Annotations
+    ax1.annotate(
+        "AI demand surge\n(>$10B DC)",
+        xy=(17, dc_rev[17]),
+        xytext=(14, dc_rev[17] + 10),
+        arrowprops=dict(arrowstyle="->", color="gray", lw=1),
+        fontsize=9,
+        ha="center",
+    )
+
+    # FY2025 Q4 annotation (index 23, ~$39.3B)
+    ax1.annotate(
+        "$39.3B DC rev",
+        xy=(23, dc_rev[23]),
+        xytext=(20, dc_rev[23] + 5),
+        arrowprops=dict(arrowstyle="->", color="gray", lw=1),
+        fontsize=9,
+        ha="center",
+    )
+
+    # FY2027 Q1 annotation (index 28, ~$75.2B, decelerating)
+    ax1.annotate(
+        "$75.2B DC rev, 70.7% YoY",
+        xy=(28, dc_rev[28]),
+        xytext=(25, dc_rev[28] + 5),
+        arrowprops=dict(arrowstyle="->", color="gray", lw=1),
+        fontsize=9,
+        ha="center",
+    )
+
+    # Title and subtitle
+    ax1.set_title(
+        "NVIDIA Data Center Revenue \u2014 Growth and Deceleration",
+        fontsize=16,
+        fontweight="bold",
+    )
+    fig.text(
+        0.5,
+        0.93,
+        "Quarterly revenue FY2020\u2013FY2027 | Growth rate: from 323% to ~70%",
+        fontsize=11,
+        ha="center",
+        style="italic",
+        color=GRAY_DARK,
+    )
+
+    ax1.set_xlabel("Quarter (FY2020-Q1 \u2192 FY2027-Q1)", fontsize=12)
+    ax1.grid(True, alpha=0.3)
+
+    # Format x-axis
+    ax1.set_xticks(range(0, len(quarters), 4))
+    ax1.set_xticklabels(
+        [q for i, q in enumerate(quarters) if i % 4 == 0],
+        rotation=45,
+        ha="right",
+        fontsize=8,
+    )
+
+    # Combined legend
+    lines1, labels1 = ax1.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left", fontsize=9)
+
+    fig.savefig(
+        "output/charts/07_nvidia_datacenter.png",
+        dpi=EXPORT_DPI,
+        facecolor=fig.get_facecolor(),
+        edgecolor="none",
+    )
+    plt.close(fig)
+    return "output/charts/07_nvidia_datacenter.png"
+
+
+def main():
+    path = plot_nvidia_datacenter()
+    print(f"Chart saved: {path}")
+
+
+if __name__ == "__main__":
+    main()