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d76d275 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | #!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Medical Oxygen Supply Dataset."""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
SCENARIOS = ['referral_hospital', 'district_hospital', 'rural_health_centre']
def load_scenarios(data_dir='data'):
dfs = {}
for sc in SCENARIOS:
path = os.path.join(data_dir, f'oxygen_{sc}.csv')
if os.path.exists(path):
dfs[sc] = pd.read_csv(path)
return dfs
def make_report(dfs, output='validation_report.png'):
fig, axes = plt.subplots(4, 2, figsize=(16, 24))
fig.suptitle(
'Medical Oxygen Supply — Validation Report\n'
'(Referral Hospital → District Hospital → Rural Health Centre)',
fontsize=15, fontweight='bold', y=0.99)
colors = ['#2ecc71', '#f39c12', '#e74c3c']
x = np.arange(len(SCENARIOS))
labels = ['Referral Hosp', 'District Hosp', 'Rural HC']
# Panel 1: Oxygen available today
ax = axes[0, 0]
avail = [dfs[sc]['oxygen_available_today'].mean()*100 for sc in SCENARIOS if sc in dfs]
ax.bar(x, avail, color=colors, alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=9)
for i, v in enumerate(avail):
ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
ax.set_ylabel('Availability (%)')
ax.set_title('Oxygen Available on Day of Assessment')
ax.set_ylim(0, 100)
# Panel 2: Oxygen source distribution (district)
ax = axes[0, 1]
df = dfs.get('district_hospital', list(dfs.values())[0])
src = df['primary_oxygen_source'].value_counts()
ax.barh(range(len(src)), src.values, color='#3498db', alpha=0.7)
ax.set_yticks(range(len(src)))
ax.set_yticklabels([s.replace('_', ' ').title() for s in src.index], fontsize=8)
ax.set_xlabel('Count')
ax.set_title('Primary Oxygen Source (District Hospital)')
# Panel 3: Pulse oximetry functional
ax = axes[1, 0]
pox = [dfs[sc]['pulse_oximeter_functional'].mean()*100 for sc in SCENARIOS if sc in dfs]
ax.bar(x, pox, color=colors, alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=9)
for i, v in enumerate(pox):
ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
ax.set_ylabel('Rate (%)')
ax.set_title('Functional Pulse Oximetry')
# Panel 4: Patients needing vs receiving oxygen
ax = axes[1, 1]
w = 0.3
need = [dfs[sc]['patients_needing_oxygen'].mean() for sc in SCENARIOS if sc in dfs]
recv = [dfs[sc]['patients_received_oxygen'].mean() for sc in SCENARIOS if sc in dfs]
ax.bar(x - w/2, need, w, label='Needing O₂', color='#e74c3c', alpha=0.8)
ax.bar(x + w/2, recv, w, label='Received O₂', color='#2ecc71', alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=9)
ax.set_ylabel('Mean Patients/Month')
ax.set_title('Oxygen Need vs Delivery Gap')
ax.legend(fontsize=8)
# Panel 5: Hypoxemia deaths
ax = axes[2, 0]
deaths = [dfs[sc]['deaths_hypoxemia_related'].mean() for sc in SCENARIOS if sc in dfs]
ax.bar(x, deaths, color=colors, alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=9)
for i, v in enumerate(deaths):
ax.text(i, v + 0.1, f'{v:.1f}', ha='center', fontsize=10, fontweight='bold')
ax.set_ylabel('Mean Deaths/Month')
ax.set_title('Hypoxemia-Related Deaths (per observation)')
# Panel 6: Shortage causes (district)
ax = axes[2, 1]
short_df = df[df['shortage_cause'] != 'not_applicable']
if len(short_df) > 0:
causes = short_df['shortage_cause'].value_counts().head(8)
ax.barh(range(len(causes)), causes.values, color='#e74c3c', alpha=0.7)
ax.set_yticks(range(len(causes)))
ax.set_yticklabels([s.replace('_', ' ').title() for s in causes.index], fontsize=7)
ax.set_xlabel('Count')
ax.set_title('Top Oxygen Shortage Causes (District)')
# Panel 7: Concentrator functional rate
ax = axes[3, 0]
conc_func = []
for sc in SCENARIOS:
if sc in dfs:
has_conc = dfs[sc][dfs[sc]['concentrator_count'] > 0]
if len(has_conc) > 0:
rate = (has_conc['concentrator_functional'] / has_conc['concentrator_count']).mean() * 100
else:
rate = 0
conc_func.append(rate)
ax.bar(x, conc_func, color=colors, alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=9)
for i, v in enumerate(conc_func):
ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
ax.set_ylabel('Functional Rate (%)')
ax.set_title('Concentrator Functionality (among facilities with concentrators)')
# Panel 8: Distance to refill & transport
ax = axes[3, 1]
dist = [dfs[sc]['distance_to_refill_km'].mean() for sc in SCENARIOS if sc in dfs]
ax.bar(x, dist, color=colors, alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=9)
for i, v in enumerate(dist):
ax.text(i, v + 2, f'{v:.0f}km', ha='center', fontsize=10, fontweight='bold')
ax.set_ylabel('Distance (km)')
ax.set_title('Mean Distance to Cylinder Refill Point')
plt.tight_layout(rect=[0, 0, 1, 0.97])
plt.savefig(output, dpi=150, bbox_inches='tight')
print(f'Saved validation report to {output}')
plt.close()
if __name__ == '__main__':
dfs = load_scenarios()
if dfs:
make_report(dfs)
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