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71e5ad9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | #!/usr/bin/env python3
"""
plot_uncertainty.py — Three-panel publication figure for the MC dropout map.
Run after mc_dropout_inference.py. Reads the two GeoTIFFs (or, as fallback,
the .npy/parquet outputs if rasterio wasn't available) and emits:
figure_uncertainty_3panel.png (300 dpi, 18 × 7 in)
figure_uncertainty_3panel.pdf (vector, for journal submission)
"""
from __future__ import annotations
import sys
from pathlib import Path
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
_THIS = Path(__file__).resolve()
SOC_CODE_DIR = _THIS.parent.parent.parent.parent # SOCmapping/
sys.path.insert(0, str(SOC_CODE_DIR))
from _paths import SOC_CODE_DIR as _SOC_CODE_DIR, SOC_REBUTTAL_DIR # noqa: E402
OUT_DIR = SOC_REBUTTAL_DIR / 'gpu_experiments' / 'uncertainty'
MEAN_TIF = OUT_DIR / 'SGT_1mil_2023_mean_mc30.tif'
STD_TIF = OUT_DIR / 'SGT_1mil_2023_std_mc30.tif'
# ASSUMPTION: the existing Figures 14/15 in the paper use the viridis
# colormap (confirmed: SOCmapping/SpatiotemporalGatedTransformer/mapping.py
# uses cmap='viridis'). Panel 1 mirrors that exactly. If the paper actually
# uses a different cmap (e.g. YlOrBr, custom SOC palette), swap CMAP_MEAN.
CMAP_MEAN = 'viridis'
CMAP_STD = 'OrRd'
CMAP_CV = 'RdYlGn_r'
def load_geotiff(path: Path):
try:
import rasterio
except ImportError:
print(f'rasterio missing; cannot read {path}.', file=sys.stderr)
return None, None, None
with rasterio.open(path) as src:
arr = src.read(1, masked=False).astype(np.float32)
extent = (src.bounds.left, src.bounds.right,
src.bounds.bottom, src.bounds.top)
crs = src.crs.to_string()
arr[~np.isfinite(arr)] = np.nan
return arr, extent, crs
def maybe_load_bavaria_outline():
"""Try to load Bavaria boundary for overlay; return None if unavailable."""
try:
import geopandas as gpd
# ASSUMPTION: bavaria.geojson exists in the project dir
for cand in (
_SOC_CODE_DIR / 'SpatiotemporalGatedTransformer' / 'bavaria.geojson',
_SOC_CODE_DIR / 'Maps' / 'bavaria.geojson',
_SOC_CODE_DIR / 'balancedDataset' / 'bavaria.geojson',
):
if cand.exists():
bav = gpd.read_file(cand)
if 'EPSG:32632' not in str(bav.crs):
bav = bav.to_crs('EPSG:32632')
return bav
except Exception as e:
print(f'Bavaria outline load failed: {e}', file=sys.stderr)
return None
def add_decorations(ax, bavaria, extent):
"""Bavaria outline, scale bar, north arrow, nodata-as-white."""
if bavaria is not None:
bavaria.boundary.plot(ax=ax, color='black', linewidth=0.5)
# ASSUMPTION: simple scale bar 50 km long-bar in lower-left.
if extent is not None:
x0 = extent[0] + 0.05 * (extent[1] - extent[0])
y0 = extent[2] + 0.05 * (extent[3] - extent[2])
bar_len = 50_000 # 50 km in metres (UTM)
ax.plot([x0, x0 + bar_len], [y0, y0], color='black', linewidth=2)
ax.text(x0 + bar_len / 2, y0 + (extent[3] - extent[2]) * 0.015,
'50 km', ha='center', va='bottom', fontsize=8)
# North arrow top-right
nx = extent[0] + 0.94 * (extent[1] - extent[0])
ny = extent[2] + 0.88 * (extent[3] - extent[2])
dy = (extent[3] - extent[2]) * 0.05
ax.annotate('N', xy=(nx, ny + dy), xytext=(nx, ny),
ha='center', fontsize=11, fontweight='bold',
arrowprops=dict(arrowstyle='-|>', color='black', lw=1.5))
def main():
mean_arr, extent, _ = load_geotiff(MEAN_TIF)
std_arr, _, _ = load_geotiff(STD_TIF)
if mean_arr is None or std_arr is None:
# Fallback to the parquet (point scatter rendering instead of raster)
# ASSUMPTION: at least one of GeoTIFF or parquet is present after
# running mc_dropout_inference.py.
import pandas as pd
df = pd.read_parquet(OUT_DIR / 'mc_dropout_points.parquet')
# Fall back to a scatter-render at low DPI; this branch only fires
# if rasterio failed at write time, which would be a setup bug.
print('GeoTIFF missing — falling back to scatter; result may be sparse.',
file=sys.stderr)
mean_arr = std_arr = None
cv_arr = None
# We'll just plot scatter points in original lon/lat space.
scatter_mode = True
else:
scatter_mode = False
with np.errstate(invalid='ignore', divide='ignore'):
cv_arr = np.where(mean_arr > 1e-6,
np.clip(100 * std_arr / mean_arr, 0, 100),
np.nan)
bavaria = maybe_load_bavaria_outline() if not scatter_mode else None
fig, axes = plt.subplots(1, 3, figsize=(18, 7), dpi=300)
plt.subplots_adjust(wspace=0.15)
if scatter_mode:
# --- scatter fallback (rasterio missing) ---
import pandas as pd
df = pd.read_parquet(OUT_DIR / 'mc_dropout_points.parquet')
for ax, key, cmap, label, title in [
(axes[0], 'mean_pred_g_per_kg', CMAP_MEAN,
'Mean predicted SOC (g/kg)',
'SGT 1.1M — 2023 SOC prediction (MC mean, n=30)'),
(axes[1], 'std_pred_g_per_kg', CMAP_STD,
'Prediction uncertainty — std (g/kg)',
'MC Dropout uncertainty (n=30 passes)'),
(axes[2], 'cv_pct', CMAP_CV,
'Coefficient of variation (%)',
'Relative uncertainty (CV = std/mean × 100%)'),
]:
sc = ax.scatter(df['longitude'], df['latitude'],
c=df[key], cmap=cmap, s=1, marker='s')
ax.set_title(title, fontsize=11, fontweight='bold')
ax.set_xlabel('Longitude (°E)')
ax.set_ylabel('Latitude (°N)')
cbar = fig.colorbar(sc, ax=ax, fraction=0.046, pad=0.04)
cbar.set_label(label, fontsize=9)
ax.set_aspect('equal', adjustable='box')
else:
panel_specs = [
(axes[0], mean_arr, CMAP_MEAN,
'Mean predicted SOC (g/kg)',
'SGT 1.1M — 2023 SOC prediction (MC mean, n=30)',
dict(vmin=0, vmax=float(np.nanpercentile(mean_arr, 99)))),
(axes[1], std_arr, CMAP_STD,
'Prediction uncertainty — std (g/kg)',
'MC Dropout uncertainty (n=30 passes)',
dict(vmin=0, vmax=float(np.nanpercentile(std_arr, 99)))),
(axes[2], cv_arr, CMAP_CV,
'Coefficient of variation (%)',
'Relative uncertainty (CV = std/mean × 100%)',
dict(vmin=0, vmax=100)),
]
for ax, arr, cmap, label, title, kwargs in panel_specs:
im = ax.imshow(arr, extent=extent, cmap=cmap, origin='upper',
interpolation='nearest', **kwargs)
ax.set_title(title, fontsize=11, fontweight='bold')
ax.set_xlabel('UTM Easting (m)')
ax.set_ylabel('UTM Northing (m)')
cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
cbar.set_label(label, fontsize=9)
ax.set_facecolor('white')
add_decorations(ax, bavaria, extent)
out_png = OUT_DIR / 'figure_uncertainty_3panel.png'
out_pdf = OUT_DIR / 'figure_uncertainty_3panel.pdf'
fig.savefig(out_png, dpi=300, bbox_inches='tight')
fig.savefig(out_pdf, bbox_inches='tight')
print(f'Wrote {out_png}', flush=True)
print(f'Wrote {out_pdf}', flush=True)
plt.close(fig)
if __name__ == '__main__':
main()
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