| """Validate synthetic climate & maternal-neonatal outcomes dataset.""" |
|
|
| from __future__ import annotations |
|
|
| from pathlib import Path |
|
|
| import matplotlib.pyplot as plt |
| import pandas as pd |
|
|
| SCENARIO_FILES = { |
| "heat_exposed_pregnancy": "maternal_heat_exposed.csv", |
| "drought_food_insecure_pregnancy": "maternal_drought_food_insecure.csv", |
| "flood_disrupted_services": "maternal_flood_disrupted.csv", |
| } |
|
|
| COLORS = {"heat_exposed_pregnancy": "#e6550d", "drought_food_insecure_pregnancy": "#d95f02", "flood_disrupted_services": "#3182bd"} |
|
|
|
|
| def load_data() -> pd.DataFrame: |
| frames = [] |
| for scenario, filename in SCENARIO_FILES.items(): |
| df = pd.read_csv(Path("data") / filename) |
| frames.append(df) |
| return pd.concat(frames, ignore_index=True) |
|
|
|
|
| def plot_validation(df: pd.DataFrame, output_path: Path) -> None: |
| fig, axes = plt.subplots(4, 2, figsize=(14, 16)) |
| axes = axes.flatten() |
|
|
| outcome_cols = ["preterm", "low_birth_weight", "stillbirth", "preeclampsia"] |
| rates = df.groupby("scenario")[outcome_cols].mean() * 100 |
| rates.plot(kind="bar", ax=axes[0]) |
| axes[0].set_title("Adverse Pregnancy Outcomes (%)") |
| axes[0].legend(fontsize=7) |
|
|
| for s in SCENARIO_FILES: |
| subset = df[df["scenario"] == s] |
| axes[1].hist(subset["birth_weight_g"], bins=30, alpha=0.5, color=COLORS[s], label=s) |
| axes[1].set_title("Birth Weight Distribution") |
| axes[1].set_xlabel("Birth weight (g)") |
| axes[1].legend(fontsize=7) |
|
|
| svc_cols = ["anc_4plus", "skilled_birth_attendant", "facility_delivery", "facility_accessible"] |
| svc = df.groupby("scenario")[svc_cols].mean() * 100 |
| svc.plot(kind="bar", ax=axes[2]) |
| axes[2].set_title("Service Access (%)") |
| axes[2].legend(fontsize=7) |
|
|
| for s in SCENARIO_FILES: |
| subset = df[df["scenario"] == s] |
| axes[3].scatter(subset["heat_index"], subset["preterm"], s=6, alpha=0.1, color=COLORS[s], label=s) |
| axes[3].set_title("Heat Index vs Preterm Birth") |
| axes[3].set_xlabel("Heat index") |
| axes[3].legend(fontsize=7) |
|
|
| risk_cols = ["anaemia", "malaria_in_pregnancy", "food_insecure", "dehydration"] |
| risk = df.groupby("scenario")[risk_cols].mean() * 100 |
| risk.plot(kind="bar", ax=axes[4]) |
| axes[4].set_title("Risk Factors (%)") |
| axes[4].legend(fontsize=7) |
|
|
| mort_cols = ["neonatal_death", "maternal_death"] |
| mort = df.groupby("scenario")[mort_cols].mean() * 1000 |
| mort.plot(kind="bar", ax=axes[5]) |
| axes[5].set_title("Mortality (per 1000)") |
| axes[5].legend(fontsize=7) |
|
|
| for s in SCENARIO_FILES: |
| subset = df[df["scenario"] == s] |
| axes[6].hist(subset["gestational_age_weeks"], bins=25, alpha=0.5, color=COLORS[s], label=s) |
| axes[6].set_title("Gestational Age Distribution") |
| axes[6].set_xlabel("Weeks") |
| axes[6].legend(fontsize=7) |
|
|
| neo_cols = ["neonatal_sepsis", "birth_asphyxia", "pph"] |
| neo = df.groupby("scenario")[neo_cols].mean() * 100 |
| neo.plot(kind="bar", ax=axes[7]) |
| axes[7].set_title("Neonatal/Maternal Complications (%)") |
| axes[7].legend(fontsize=7) |
|
|
| plt.tight_layout() |
| fig.savefig(output_path, dpi=200) |
| plt.close(fig) |
|
|
|
|
| def main() -> None: |
| df = load_data() |
| plot_validation(df, Path("validation_report.png")) |
| print("Saved validation_report.png") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
