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| """Combined LCZ + LST distribution plot in academic style."""
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| import numpy as np
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| import matplotlib.pyplot as plt
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| from matplotlib.ticker import FixedLocator, FuncFormatter
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| from scipy.interpolate import PchipInterpolator, make_interp_spline
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| plt.rcParams.update({
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| 'font.family': 'serif',
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| 'mathtext.fontset': 'dejavuserif',
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| 'axes.linewidth': 0.6,
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| 'xtick.major.width': 0.5,
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| 'ytick.major.width': 0.5,
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| 'xtick.major.size': 3,
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| 'ytick.major.size': 3,
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| })
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| def human_format(x, pos):
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| if x >= 1e9:
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| return f'{x/1e9:g}B'
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| elif x >= 1e6:
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| return f'{x/1e6:g}M'
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| elif x >= 1e3:
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| return f'{x/1e3:g}K'
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| elif x >= 1:
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| return f'{x:g}'
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| return '0'
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| lst_temps, lst_counts = [], []
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| in_data = False
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| with open('/workspace/storage/lst-earthformer/lst_temperature_stats.txt') as f:
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| for line in f:
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| if line.startswith('Temp'):
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| in_data = True
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| continue
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| if line.startswith('---'):
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| continue
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| if in_data and line.strip():
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| parts = line.split()
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| lst_temps.append(int(parts[0]))
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| lst_counts.append(int(parts[1].replace(',', '')))
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| lst_temps = np.array(lst_temps)
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| lst_counts = np.array(lst_counts, dtype=np.float64)
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| mask = (lst_temps >= -50) & (lst_temps <= 175)
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| lst_temps_trim = lst_temps[mask]
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| lst_counts_trim = lst_counts[mask]
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| bin_width = 5
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| bin_edges = np.arange(-50, 176, bin_width)
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| bin_centers = bin_edges[:-1] + bin_width / 2
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| binned_counts = np.zeros(len(bin_centers), dtype=np.float64)
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| for i, (lo, hi) in enumerate(zip(bin_edges[:-1], bin_edges[1:])):
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| sel = (lst_temps_trim >= lo) & (lst_temps_trim < hi)
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| binned_counts[i] = lst_counts_trim[sel].sum()
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| lcz_classes, lcz_counts = [], []
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| in_data = False
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| with open('/workspace/storage/lst-earthformer/lcz_class_stats.txt') as f:
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| for line in f:
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| if line.startswith('Rank'):
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| in_data = True
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| continue
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| if line.startswith('---') or line.startswith('='):
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| if in_data and line.startswith('='):
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| in_data = False
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| continue
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| if in_data and line.strip():
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| parts = line.split()
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| lcz_classes.append(int(parts[1]))
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| lcz_counts.append(int(parts[2].replace(',', '')))
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| order = np.argsort(lcz_classes)
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| lcz_classes = np.array(lcz_classes)[order]
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| lcz_counts = np.array(lcz_counts, dtype=np.float64)[order]
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| total_lcz = lcz_counts.sum()
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| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5.0))
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| fig.subplots_adjust(wspace=0.22, left=0.08, right=0.99, top=0.97, bottom=0.12)
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| bar_color = '#6BAED6'
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| curve_color = '#2171B5'
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| fmt = FuncFormatter(human_format)
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| x_pos = np.arange(len(lcz_classes))
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| ax1.bar(x_pos, lcz_counts, width=0.75, color=bar_color, edgecolor='white', linewidth=0.3)
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| pchip = PchipInterpolator(x_pos, lcz_counts)
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| x_fine = np.linspace(x_pos[0], x_pos[-1], 300)
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| y_fine = np.maximum(pchip(x_fine), 0)
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| ax1.plot(x_fine, y_fine, color=curve_color, linewidth=1.0)
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| ax1.yaxis.set_major_locator(FixedLocator([0, 1.2e6, 2.4e6]))
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| ax1.yaxis.set_major_formatter(fmt)
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| ax1.set_ylim(0, 3e6)
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| ax1.set_xticks(x_pos)
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| ax1.set_xticklabels([str(c) for c in lcz_classes], fontsize=16)
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| ax1.set_xlabel('LCZ Class', fontsize=16)
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| ax1.set_ylabel('Pixels', fontsize=16)
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| ax1.tick_params(axis='both', labelsize=16)
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| ax1.set_xlim(-0.6, len(lcz_classes) - 0.4)
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| dominant_cls = lcz_classes[np.argmax(lcz_counts)]
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| dominant_pct = lcz_counts.max() / total_lcz * 100
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| txt = f'Total pixels: ~{int(round(total_lcz, -6) // 1e6):.0f}M\nDominant: Class {dominant_cls} ({dominant_pct:.1f}%)'
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| ax1.text(0.97, 0.97, txt, transform=ax1.transAxes, fontsize=16,
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| verticalalignment='top', horizontalalignment='right',
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| bbox=dict(boxstyle='round,pad=0.3', facecolor='white', edgecolor='0.6', linewidth=0.5))
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| ax1.text(0.97, 0.78, 'LCZ Distribution', transform=ax1.transAxes, fontsize=16,
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| color='red', fontweight='bold', verticalalignment='top', horizontalalignment='right')
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| ax2.bar(bin_centers, binned_counts, width=bin_width * 0.85, color=bar_color, edgecolor='white', linewidth=0.3)
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| pchip2 = PchipInterpolator(bin_centers, binned_counts)
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| x_smooth2 = np.linspace(bin_centers[0], bin_centers[-1], 500)
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| y_smooth2 = np.maximum(pchip2(x_smooth2), 0)
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| ax2.plot(x_smooth2, y_smooth2, color=curve_color, linewidth=1.0)
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| ax2.yaxis.set_major_locator(FixedLocator([0, 5e8, 1e9, 1.5e9, 2e9]))
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| ax2.yaxis.set_major_formatter(fmt)
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| ax2.set_ylim(0, 2.2e9)
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| ax2.set_xlabel('Temperature (\u00b0F)', fontsize=16)
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| ax2.set_ylabel('Pixels', fontsize=16)
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| ax2.tick_params(axis='both', labelsize=16)
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| ax2.set_xlim(-55, 180)
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| total_pixels_lst = int(binned_counts.sum())
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| mean_t = np.average(bin_centers, weights=binned_counts)
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| variance = np.average((bin_centers - mean_t)**2, weights=binned_counts)
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| std_t = np.sqrt(variance)
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| txt2 = f'Total pixels: ~{total_pixels_lst / 1e9:.1f}B\nMean: {mean_t:.1f}\u00b0F Std: {std_t:.1f}\u00b0F'
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| ax2.text(0.97, 0.97, txt2, transform=ax2.transAxes, fontsize=16,
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| verticalalignment='top', horizontalalignment='right',
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| bbox=dict(boxstyle='round,pad=0.3', facecolor='white', edgecolor='0.6', linewidth=0.5))
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| ax2.text(0.97, 0.78, 'Temperature Distribution', transform=ax2.transAxes, fontsize=16,
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| color='red', fontweight='bold', verticalalignment='top', horizontalalignment='right')
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| plt.savefig('/workspace/storage/lst-earthformer/combined_distribution.png', dpi=300, facecolor='white')
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| plt.close()
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| print('Saved combined_distribution.png')
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