feat(chart): agentic AI market size forecasts

src/charts/market_forecasts.py

@@ -0,0 +1,177 @@
+"""Agent Market Forecasts Chart"""
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.lines as mlines
+import matplotlib.pyplot as plt
+import numpy as np
+from src.data.agent_adoption import agent_market_forecasts
+from src.utils.styling import get_theme, EXPORT_DPI, AGENT_GROWTH, AI_SPEND, GRAY_DARK
+
+
+def _interpolate_forecast(forecast: dict) -> dict:
+    """Interpolate yearly values from start/end using the stated CAGR."""
+    start_val = forecast["year_2025_billions"]
+    cagr = forecast["cagr_percent"]
+
+    # Determine end year key
+    if "year_2033_billions" in forecast:
+        end_key = "year_2033_billions"
+        end_year = 2033
+    else:
+        end_key = "year_2030_billions"
+        end_year = 2030
+
+    end_val = forecast[end_key]
+
+    # Interpolate yearly values using CAGR
+    values = {}
+    for year in range(2025, 2035):
+        if year <= end_year:
+            values[year] = start_val * ((1 + cagr / 100) ** (year - 2025))
+        else:
+            # No forecast beyond end year — leave as None
+            values[year] = None
+
+    return {
+        "source": forecast["source"],
+        "category": forecast.get("category", ""),
+        "cagr": cagr,
+        "start": start_val,
+        "end_val": end_val,
+        "end_year": end_year,
+        "values": values,
+    }
+
+
+def plot_market_forecasts() -> str:
+    plt.rcParams.update(get_theme())
+    fig, ax = plt.subplots(figsize=(14, 8))
+
+    # Define colors per source
+    colors = {
+        "Omdia": "#e74c3c",
+        "BCC Research": "#2980b9",
+        "MarketsandMarkets": "#27ae60",
+        "Grand View Research": "#8e44ad",
+    }
+
+    # Process forecasts
+    processed = [_interpolate_forecast(f) for f in agent_market_forecasts]
+
+    all_years = list(range(2025, 2035))
+
+    # Collect per-year min/max for shaded band (only where forecasts exist)
+    min_vals = []
+    max_vals = []
+    for year in all_years:
+        vals = [p["values"][year] for p in processed if p["values"][year] is not None]
+        if vals:
+            min_vals.append(min(vals))
+            max_vals.append(max(vals))
+        else:
+            min_vals.append(None)
+            max_vals.append(None)
+
+    # Plot each forecast line
+    handles = []
+    labels = []
+    for p in processed:
+        pts_x = []
+        pts_y = []
+        for year in all_years:
+            v = p["values"][year]
+            if v is not None:
+                pts_x.append(year)
+                pts_y.append(v)
+
+        if pts_x:
+            label = f'{p["source"]} ({p["cagr"]}% CAGR)'
+            color = colors.get(p["source"], AI_SPEND)
+            line, = ax.plot(
+                pts_x, pts_y,
+                color=color,
+                linewidth=2.5,
+                label=label,
+                marker="o",
+                markersize=5,
+            )
+            handles.append(line)
+            labels.append(label)
+
+            # Annotate endpoint
+            ax.annotate(
+                f"${p['end_val']:.1f}B",
+                xy=(pts_x[-1], pts_y[-1]),
+                xytext=(5, 8),
+                textcoords="offset points",
+                fontsize=9,
+                fontweight="bold",
+                color=color,
+            )
+
+    # Shaded confidence band between min and max
+    band_x = []
+    band_min = []
+    band_max = []
+    for i, year in enumerate(all_years):
+        if min_vals[i] is not None:
+            band_x.append(year)
+            band_min.append(min_vals[i])
+            band_max.append(max_vals[i])
+
+    if band_x:
+        ax.fill_between(
+            band_x, band_min, band_max,
+            alpha=0.12,
+            color=AGENT_GROWTH,
+            label="Forecast Range",
+        )
+        handles.append(
+            mlines.Line2D([], [], color=AGENT_GROWTH, alpha=0.3, linewidth=2)
+        )
+        labels.append("Forecast Range")
+
+    # Axes configuration
+    ax.set_yscale("log")
+    ax.set_ylim(0.8, 250)
+    ax.set_xlim(2024.5, 2034.5)
+    ax.set_xticks(all_years)
+    ax.set_xlabel("Year", fontsize=12)
+    ax.set_ylabel("Market Size ($ Billions, log scale)", fontsize=12)
+    ax.set_title(
+        "Agentic AI Market Size Forecasts",
+        fontsize=16,
+        fontweight="bold",
+    )
+
+    # Subtitle
+    fig.text(
+        0.5,
+        0.93,
+        "Multiple analyst projections 2025\u20132034",
+        fontsize=11,
+        ha="center",
+        style="italic",
+        color=GRAY_DARK,
+    )
+
+    ax.legend(handles=handles, labels=labels, loc="upper left", fontsize=9)
+    ax.grid(True, alpha=0.3)
+
+    fig.savefig(
+        "output/charts/11_agent_market_forecasts.png",
+        dpi=EXPORT_DPI,
+        facecolor=fig.get_facecolor(),
+        edgecolor="none",
+    )
+    plt.close(fig)
+    return "output/charts/11_agent_market_forecasts.png"
+
+
+def main():
+    path = plot_market_forecasts()
+    print(f"Chart saved: {path}")
+
+
+if __name__ == "__main__":
+    main()