185 lines
6.1 KiB
Python
185 lines
6.1 KiB
Python
"""Agent Adoption Survey Comparison Chart
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Grouped horizontal bar chart comparing key enterprise AI adoption metrics
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across three major 2025 surveys: LangChain, McKinsey, and PwC.
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"""
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import matplotlib
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matplotlib.use("Agg")
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# Patch matplotlib Path.__deepcopy__ to break Python 3.14 recursion loop
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try:
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from matplotlib.path import Path
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_original_path_deepcopy = Path.__deepcopy__
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def _safe_path_deepcopy(self, memo):
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if id(self) in memo:
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return memo[id(self)]
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memo[id(self)] = self
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return self
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Path.__deepcopy__ = _safe_path_deepcopy
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except Exception:
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pass
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import matplotlib.pyplot as plt
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import matplotlib.colors as mcolors
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import matplotlib.patches as mpatches
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from src.data.agent_adoption import agent_survey_data
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from src.utils.styling import (
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get_theme, EXPORT_DPI, AGENT_GROWTH, GRAY_DARK, BLACK, WHITE, GRAY_LIGHT,
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)
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def _shade(base_hex: str, factor: float) -> str:
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"""Lighten or darken a hex color by a given factor (0–1)."""
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r, g, b = mcolors.to_rgb(base_hex)
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# Blend toward white to lighten
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r2 = r + (1.0 - r) * factor
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g2 = g + (1.0 - g) * factor
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b2 = b + (1.0 - b) * factor
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return mcolors.to_hex((r2, g2, b2))
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def plot_agent_adoption() -> str:
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"""Generate grouped horizontal bar chart of survey comparisons."""
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plt.rcParams.update(get_theme())
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fig, ax = plt.subplots(figsize=(14, 8))
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# ------------------------------------------------------------------
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# Data
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# ------------------------------------------------------------------
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lc = agent_survey_data["langchain_2025"]
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mc = agent_survey_data["mckinsey_2025"]
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pc = agent_survey_data["pwc_2025"]
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# Each row is a comparable category; values are [LangChain, McKinsey, PwC]
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# Where a survey has no direct comparable metric, we use None.
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categories = [
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"Production\nDeployment",
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"Overall\nAI Adoption",
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"Budget\nIncrease",
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"Scaling\nAgentic AI",
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"Productivity\nValue",
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]
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# Values mapped to closest comparable metrics
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values = [
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# Production / Deployment
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[lc["production"], None, pc["ai_agents_already_adopted"]],
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# Overall AI Adoption / Maturity
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[lc["observability_implemented"], mc["overall_ai_adoption"], None],
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# Budget / Investment Intent
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[lc["multi_model_deployments"], None, pc["plan_increase_ai_budgets"]],
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# Scaling / Experimentation
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[None, mc["agentic_ai_scaling"], None],
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# Measurable Value / Productivity
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[None, None, pc["measurable_productivity_value"]],
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]
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# Survey identifiers
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surveys = [
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"LangChain\n(n=1,340)",
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"McKinsey\n(n=1,993)",
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"PwC\n(n=308)",
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]
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# Colors: base AGENT_GROWTH with increasing lightness
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colors = [
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AGENT_GROWTH, # LangChain — full purple
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_shade(AGENT_GROWTH, 0.25), # McKinsey — lighter
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_shade(AGENT_GROWTH, 0.50), # PwC — lightest
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]
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# ------------------------------------------------------------------
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# Plotting
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# ------------------------------------------------------------------
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n_cats = len(categories)
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bar_height = 0.22
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x_positions = [0, 1, 2] # offset within each group
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y_positions = []
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for i in range(n_cats):
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base_y = i * 3 # three bars per category
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y_positions.append([base_y + off for off in [0.0, 0.22, 0.44]])
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# Plot bars
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for row_idx, (cat, row_vals) in enumerate(zip(categories, values)):
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for col_idx, val in enumerate(row_vals):
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if val is None:
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continue
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y = y_positions[row_idx][col_idx]
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ax.barh(y, val, height=bar_height,
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color=colors[col_idx],
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edgecolor=WHITE, linewidth=0.8,
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label=surveys[col_idx] if row_idx == 0 else None)
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# Value label on bar
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ax.text(val + 1.0, y, f"{val:.1f}%",
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va="center", fontsize=9, color=GRAY_DARK,
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fontweight="bold")
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# Y-axis: category labels centered on each group
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group_centers = [i * 3 + 0.22 for i in range(n_cats)]
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ax.set_yticks(group_centers)
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ax.set_yticklabels(categories, fontsize=11, fontweight="bold")
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# Inset legend-like labels inside each group
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legend_y_offset = 0.55
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for col_idx in range(3):
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ax.text(-0.5, group_centers[0] + legend_y_offset - col_idx * 0.22,
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surveys[col_idx], fontsize=8, color=colors[col_idx],
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ha="left", va="center", fontweight="bold")
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# Axis config
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ax.set_xlim(0, 105)
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ax.set_xlabel("Percentage (%)", fontsize=11, color=GRAY_DARK)
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ax.set_xticks(range(0, 106, 10))
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ax.tick_params(axis="x", labelsize=9)
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# Grid
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ax.xaxis.grid(True, alpha=0.3, color=GRAY_LIGHT)
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ax.yaxis.grid(False)
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# Spine cleanup
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.spines["left"].set_color("#cccccc")
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ax.spines["bottom"].set_color("#cccccc")
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# Title
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ax.set_title(
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"Enterprise Agent Adoption — Survey Comparison",
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fontsize=18, fontweight="bold", pad=16, color=BLACK,
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)
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ax.text(
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0.5, -0.18,
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"LangChain (n=1,340) | McKinsey (n=1,993) | PwC (n=308)",
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transform=ax.transAxes,
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fontsize=11, color=GRAY_DARK, ha="center",
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)
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# Legend
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handles = []
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for col_idx in range(3):
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handles.append(
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mpatches.Rectangle((0, 0), 1, 1, color=colors[col_idx], alpha=1)
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)
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ax.legend(handles, surveys, loc="lower right", fontsize=9,
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frameon=True, edgecolor="#cccccc")
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# Adjust layout
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fig.subplots_adjust(left=0.28, right=0.95, top=0.85, bottom=0.10)
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# Save
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out_path = "output/charts/10_agent_adoption.png"
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fig.savefig(out_path, dpi=EXPORT_DPI,
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facecolor=fig.get_facecolor(), edgecolor="none")
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plt.close(fig)
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return out_path
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def main():
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path = plot_agent_adoption()
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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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