153 lines
4.2 KiB
Python
153 lines
4.2 KiB
Python
"""NVIDIA Data Center Revenue Chart"""
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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from src.data.ai_infrastructure import nvidia_revenue, nvidia_revenue_meta
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from src.utils.styling import get_theme, EXPORT_DPI, REVENUE, GRAY_DARK, AI_SPEND
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def plot_nvidia_datacenter() -> str:
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plt.rcParams.update(get_theme())
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fig, ax1 = plt.subplots(figsize=(14, 8))
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# Filter data center revenue
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quarters = [d["fiscal_quarter"] for d in nvidia_revenue]
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dc_rev = [
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d.get(
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"data_center_billions",
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d.get("compute_billions", 0) + d.get("networking_billions", 0),
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)
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for d in nvidia_revenue
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]
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# Calculate YoY growth
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growth = []
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for i in range(len(dc_rev)):
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if i >= 4: # Need previous year's same quarter
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prev = dc_rev[i - 4]
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if prev > 0:
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growth.append(((dc_rev[i] - prev) / prev) * 100)
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else:
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growth.append(None)
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else:
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growth.append(None)
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# Plot DC revenue (left axis)
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ax1.fill_between(range(len(dc_rev)), dc_rev, alpha=0.3, color=REVENUE)
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line1 = ax1.plot(
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range(len(dc_rev)), dc_rev, color=REVENUE, linewidth=2, label="Data Center Revenue"
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)
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ax1.set_ylabel("Data Center Revenue ($B)", fontsize=12, color=REVENUE)
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ax1.set_ylim(0, max(dc_rev) * 1.2)
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# Plot YoY growth (right axis)
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ax2 = ax1.twinx()
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growth_values = [g for g in growth if g is not None]
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growth_indices = [i for i, g in enumerate(growth) if g is not None]
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line2 = ax2.plot(
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growth_indices,
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growth_values,
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color=AI_SPEND,
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linewidth=2,
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marker="o",
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markersize=4,
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label="YoY Growth Rate",
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)
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ax2.set_ylabel("YoY Growth Rate (%)", fontsize=12, color=AI_SPEND)
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ax2.set_ylim(-50, max(growth_values) * 1.1)
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# AI infrastructure buildout shading
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ai_start_idx = 17 # FY2024 Q2
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ax1.axvspan(ai_start_idx - 0.5, len(dc_rev) - 0.5, alpha=0.1, color=REVENUE)
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ax1.text(
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ai_start_idx,
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max(dc_rev) * 0.8,
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"AI Infrastructure\nBuildout",
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fontsize=10,
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ha="center",
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style="italic",
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color=REVENUE,
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)
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# Annotations
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ax1.annotate(
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"AI demand surge\n(>$10B DC)",
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xy=(17, dc_rev[17]),
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xytext=(14, dc_rev[17] + 10),
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arrowprops=dict(arrowstyle="->", color="gray", lw=1),
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fontsize=9,
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ha="center",
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)
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# FY2025 Q4 annotation (index 23, ~$39.3B)
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ax1.annotate(
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"$39.3B DC rev",
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xy=(23, dc_rev[23]),
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xytext=(20, dc_rev[23] + 5),
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arrowprops=dict(arrowstyle="->", color="gray", lw=1),
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fontsize=9,
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ha="center",
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)
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# FY2027 Q1 annotation (index 28, ~$75.2B, decelerating)
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ax1.annotate(
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"$75.2B DC rev, 83.4% YoY",
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xy=(28, dc_rev[28]),
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xytext=(25, dc_rev[28] + 5),
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arrowprops=dict(arrowstyle="->", color="gray", lw=1),
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fontsize=9,
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ha="center",
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)
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# Title and subtitle
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ax1.set_title(
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"NVIDIA Data Center Revenue \u2014 Growth and Deceleration",
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fontsize=16,
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fontweight="bold",
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)
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fig.text(
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0.5,
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0.93,
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"Quarterly revenue FY2020\u2013FY2027 | Growth rate: from 364% to ~83%",
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fontsize=11,
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ha="center",
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style="italic",
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color=GRAY_DARK,
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)
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ax1.set_xlabel("Quarter (FY2020-Q1 \u2192 FY2027-Q1)", fontsize=12)
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ax1.grid(True, alpha=0.3)
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# Format x-axis
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ax1.set_xticks(range(0, len(quarters), 4))
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ax1.set_xticklabels(
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[q for i, q in enumerate(quarters) if i % 4 == 0],
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rotation=45,
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ha="right",
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fontsize=8,
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)
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# Combined legend
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left", fontsize=9)
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fig.savefig(
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"output/charts/07_nvidia_datacenter.png",
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dpi=EXPORT_DPI,
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facecolor=fig.get_facecolor(),
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edgecolor="none",
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)
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plt.close(fig)
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return "output/charts/07_nvidia_datacenter.png"
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def main():
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path = plot_nvidia_datacenter()
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print(f"Chart saved: {path}")
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if __name__ == "__main__":
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main()
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