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Fix numpy warnings: MaskedArray partition et Mean of empty slice

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- Convert MaskedArray to ndarray avant np.percentile() via
  np.asarray(data.compressed()) et np.ma.filled(data, np.nan)
- Supprimer RuntimeWarning "Mean of empty slice" dans MSRM et
  ondelette avec warnings.catch_warnings()
- Ajout import warnings dans visualizations.py

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
2026-05-10 03:02:36 +02:00
parent e2845b9e6d
commit 569f2e680c
2 changed files with 15 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
lidar_pipeline/rendering.py
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@ -204,7 +204,7 @@ def _apply_colormap(data, tif_file):
# Find matching colormap
for key, info in COLORMAPS.items():
if key in name:
valid_data = np.asarray(data[~np.isnan(data)] if hasattr(data, 'compressed') else data.flatten())
valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
valid_data = valid_data[~np.isnan(valid_data)]
vmin = vmax = None
@ -239,7 +239,7 @@ def _apply_colormap(data, tif_file):
return data, info['cmap'], info['title'], legend, info['description'], False
# Default: terrain colormap with percentile stretch
valid_data = np.asarray(data[~np.isnan(data)] if hasattr(data, 'compressed') else data.flatten())
valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
valid_data = valid_data[~np.isnan(valid_data)]
p2, p98 = np.percentile(valid_data, (2, 98))
data = np.clip((data - p2) / (p98 - p2), 0, 1)
@ -324,6 +324,10 @@ def tif_to_png(tif_file, vis_dir, resolution):
valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
valid_data = valid_data[~np.isnan(valid_data)]
# Convert MaskedArray to plain ndarray (NaN-filled) to avoid numpy warnings
if isinstance(data, np.ma.MaskedArray):
data = np.ma.filled(data, np.nan)
# Apply colormap
data, cmap, title, legend_label, description, is_rgb_result = _apply_colormap(data, tif_file)

13
lidar_pipeline/visualizations.py
View File

@ -6,6 +6,7 @@ parameters and returns the path to the output GeoTIFF file, or None on error.
import logging
import time
import warnings
from pathlib import Path
import numpy as np
@ -391,8 +392,10 @@ def generate_mslrm(dem_file, basename, vis_dir, resolution):
lrm_stack.append(lrm_norm.astype(np.float32))
lrm_array = np.array(lrm_stack)
with np.errstate(invalid='ignore'):
mslrm = np.sqrt(np.nanmean(lrm_array ** 2, axis=0))
with np.errstate(invalid='ignore', divide='ignore'):
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='Mean of empty slice')
mslrm = np.sqrt(np.nanmean(lrm_array ** 2, axis=0))
mslrm[nan_mask] = np.nan
_save_tif(output, mslrm.astype(np.float32), transform, crs)
logger.info(f" ✓ MSRM terminé ({time.time()-t0:.1f}s){gpu_tag}")
@ -659,8 +662,10 @@ def generate_wavelet(dem_file, basename, vis_dir, resolution):
response = response / std_val
wavelet_stack.append(response)
with np.errstate(invalid='ignore'):
combined = np.sqrt(np.nanmean(np.array(wavelet_stack) ** 2, axis=0))
with np.errstate(invalid='ignore', divide='ignore'):
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='Mean of empty slice')
combined = np.sqrt(np.nanmean(np.array(wavelet_stack) ** 2, axis=0))
combined[nan_mask] = np.nan
_save_tif(output, combined.astype(np.float32), transform, crs)