make_figures.py

"""Regenerate the README results figure from the classification engine.

Offline: runs the engine on the bundled sample portfolio and plots (a) how far
each category sits from the 70% floor and (b) the post-exclusion carbon
intensity against the 1.5C pathway target — illustrating that the pathway is a
diagnostic, not a gate.

    pip install matplotlib
    python make_figures.py        # writes docs/sfdr2_classification.png
"""
import os

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

from sfdr2.data import SAMPLE_PORTFOLIO
from sfdr2.alignment import assess
from sfdr2.classify import classify
from sfdr2.config import CATEGORY_FLOOR_PCT

HERE = os.path.dirname(os.path.abspath(__file__))
DOCS = os.path.join(HERE, "docs")
os.makedirs(DOCS, exist_ok=True)

YEAR = 2025


def main():
    a = assess(SAMPLE_PORTFOLIO, year=YEAR)
    c = classify(SAMPLE_PORTFOLIO, year=YEAR)

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4.6))

    # Panel A — category qualifying weight vs the 70% floor
    cats = ["Sustainable", "Transition", "ESG Basics"]
    direct = [a["categories"][k]["direct_pct"] for k in cats]
    meets = [a["categories"][k]["meets"] for k in cats]
    colours = ["#2e6f4e" if m else "#b8bcc0" for m in meets]
    ax1.bar(cats, direct, color=colours)
    ax1.axhline(CATEGORY_FLOOR_PCT, color="#b5462f", ls="--", lw=1.5,
                label=f"{CATEGORY_FLOOR_PCT:.0f}% category floor")
    for i, v in enumerate(direct):
        ax1.text(i, v + 1, f"{v:.0f}%", ha="center", va="bottom", fontsize=9)
    ax1.set_ylabel("Qualifying weight (% of portfolio)")
    ax1.set_ylim(0, max(100, max(direct) + 10))
    ax1.set_title(f"Category test → classified: {c.label}")
    ax1.legend(frameon=False, fontsize=9)
    ax1.grid(alpha=0.25, axis="y")

    # Panel B — carbon intensity vs the 1.5C decarbonisation pathway (diagnostic)
    labels = ["Parent\n(pre-screen)", "Post-exclusion\nWACI", "1.5C pathway\ntarget"]
    vals = [a["parent_intensity"], a["post_excl_waci"], a["pathway_target"]]
    bcols = ["#9bb4c7", "#4a7a96", "#2e6f4e"]
    bars = ax2.bar(labels, vals, color=bcols)
    for b, v in zip(bars, vals):
        ax2.text(b.get_x() + b.get_width() / 2, b.get_height(), f"{v:.0f}",
                 ha="center", va="bottom", fontsize=9)
    status = "on pathway" if a["pathway_aligned"] else "NOT on pathway (reweighting required)"
    ax2.set_ylabel("WACI (tCO2e / EUR m revenue)")
    ax2.set_title(f"Pathway diagnostic — {status}")
    ax2.grid(alpha=0.25, axis="y")

    fig.tight_layout()
    out = os.path.join(DOCS, "sfdr2_classification.png")
    fig.savefig(out, dpi=130, bbox_inches="tight")
    print("wrote", out)


if __name__ == "__main__":
    main()