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Merged
celine-raille merged 4 commits into from
Sep 2, 2026
Merged

Update for compatibility with numpy v1 and numpy v2 #35

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396fb8a
update for compatibility numpy v1 and numpy v2
celine-raille May 27, 2026
48ea2e1
test ci
celine-raille May 27, 2026
4b800c7
CI: fix pip path issue
celine-raille Jun 1, 2026
8bf5eca
Merge branch 'main' into 33-numpy-2
guillaumeeb Sep 1, 2026
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2 changes: 1 addition & 1 deletion .github/workflows/ci.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,7 @@ jobs:
- name: Create test env
shell: bash -l {0}
run: |
conda create -n test_env python=3.12 libgdal=3.13 -c conda-forge -c defaults -y
conda create -n test_env python=3.12 pip libgdal=3.13 -c conda-forge -c defaults -y
conda activate test_env
PIP_NO_BINARY=rasterio pip install .
- name: test
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -37,7 +37,7 @@
'pyscaffold',
'gdal>=3.5.0,<3.14',
'tqdm>=4.66',
'numpy<2'
'numpy'
],
entry_points="""
[rasterio.rio_plugins]
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2 changes: 1 addition & 1 deletion src/eolab/georastertools/processing/algo.py
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Expand Up @@ -797,6 +797,6 @@ def hillshade(input_data : np.ndarray, elevation : float = 0.0, azimuth : float
ratios = np.maximum(ratios, new_ratios)

angles = np.arctan(ratios)
out[0, radius: shape[1] - radius, radius: shape[2] - radius] = angles > np.radians(elevation)
out[0, radius: shape[1] - radius, radius: shape[2] - radius] = angles > np.radians(elevation, dtype=np.float32)

return out
20 changes: 11 additions & 9 deletions src/eolab/georastertools/processing/stats.py
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Expand Up @@ -479,18 +479,20 @@ def _gen_stats(dataset, stats: List[str] = None,
feature_stats = dict()

# compute stats
# we force dtype to float64 to avoid precision errors
functions = {
'min': np.ma.min,
'max': np.ma.max,
'mean': np.ma.mean,
'sum': np.ma.sum,
'std': np.ma.std,
'median': np.ma.median
'min': {'function': np.ma.min, 'options': {}},
'max': {'function': np.ma.max, 'options': {}},
'mean': {'function': np.ma.mean, 'options': {"dtype": np.float64}},
'sum': {'function': np.ma.sum, 'options': {"dtype": np.float64}},
'std': {'function': np.ma.std, 'options': {"dtype": np.float64}},
'median': {'function': np.ma.median, 'options': {}},
}

for key, function in functions.items():
for key, key_content in functions.items():
if key in stats:
feature_stats[f'{prefix_stats}{key}'] = float(function(dataset))
function = key_content["function"]
feature_stats[f'{prefix_stats}{key}'] = float(function(dataset, **key_content["options"]))

if 'range' in stats:
min_key = f'{prefix_stats}min'
Expand All @@ -503,7 +505,7 @@ def _gen_stats(dataset, stats: List[str] = None,
# because np.ma has no percentile computation capabilities
dataset_com = dataset.compressed()
for pctile in [s for s in stats if s.startswith('percentile_')]:
q = float(pctile.replace("percentile_", ''))
q = np.float64(pctile.replace("percentile_", ''))
feature_stats[f'{prefix_stats}{pctile}'] = np.percentile(dataset_com, q)
if 'mad' in stats:
feature_stats[f'{prefix_stats}mad'] = median_abs_deviation(dataset_com.flatten())
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76 changes: 38 additions & 38 deletions tests/test_stats.py
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Expand Up @@ -50,7 +50,7 @@ def test_compute_zonal_default_stats():
[{'count': 64186, 'min': -1.0, 'max': 1.0, 'mean': 0.698426, 'std': 0.210732}],
[{'count': 83063, 'min': -1.0, 'max': 1.0, 'mean': 0.699223, 'std': 0.213408}],
[{'count': 4038, 'min': -0.206738, 'max': 1.0, 'mean': 0.734667, 'std': 0.230044}],
[{'count': 29232, 'min': -0.83908, 'max': 1.0, 'mean': 0.602069, 'std': 0.217282}],
[{'count': 29232, 'min': -0.83908, 'max': 1.0, 'mean': 0.60207, 'std': 0.217282}],
[{'count': 177106, 'min': -1.0, 'max': 1.0, 'mean': 0.548052, 'std': 0.261178}],
[{'count': 17772, 'min': 0.038081, 'max': 1.0, 'mean': 0.61795, 'std': 0.269253}],
[{'count': 169829, 'min': -1.0, 'max': 1.0, 'mean': 0.525915, 'std': 0.258398}],
Expand Down Expand Up @@ -85,79 +85,79 @@ def test_compute_zonal_extra_stats():
for geom_stats in statistics for d in geom_stats for key, val in d.items()]

# ref is the following
ref = [[{'sum': 195899.96875, 'median': 0.636118, 'range': 2.0,
ref = [[{'sum': 195899.928989, 'median': 0.636118, 'range': 2.0,
'percentile_5': 0.15373, 'mad': 0.218207, 'majority': -1.0,
'minority': -0.996727, 'unique': 285995,
'nodata': 0, 'valid': 1.0}],
[{'sum': 31886.951172, 'median': 0.701654, 'range': 2.0,
[{'sum': 31886.95005, 'median': 0.701654, 'range': 2.0,
'percentile_5': 0.280309, 'mad': 0.161672, 'majority': 1.0,
'minority': -0.979167, 'unique': 44398,
'nodata': 367, 'valid': 0.992054}],
[{'sum': 7843.094727, 'median': 0.682968, 'range': 2.0,
[{'sum': 7843.094361, 'median': 0.682968, 'range': 2.0,
'percentile_5': 0.12968, 'mad': 0.146711, 'majority': -1.0,
'minority': -0.993174, 'unique': 12189,
'nodata': 0, 'valid': 1.0}],
[{'sum': 56049.859375, 'median': 0.668857, 'range': 2.0,
[{'sum': 56049.860487, 'median': 0.668857, 'range': 2.0,
'percentile_5': 0.154594, 'mad': 0.148523, 'majority': 1.0,
'minority': -0.977465, 'unique': 83944,
'nodata': 245, 'valid': 0.997214}],
[{'sum': 9305.114258, 'median': 0.713093, 'range': 0.911131,
[{'sum': 9305.114276, 'median': 0.713093, 'range': 0.911131,
'percentile_5': 0.358831, 'mad': 0.143713, 'majority': 0.6,
'minority': 0.088869, 'unique': 13137,
'nodata': 244, 'valid': 0.981971}],
[{'sum': 30402.613281, 'median': 0.753216, 'range': 1.656766,
[{'sum': 30402.614902, 'median': 0.753216, 'range': 1.656766,
'percentile_5': 0.433678, 'mad': 0.124387, 'majority': 1.0,
'minority': -0.656766, 'unique': 40146,
'nodata': 85, 'valid': 0.997948}],
[{'sum': 31683.017578, 'median': 0.471969, 'range': 1.927374,
[{'sum': 31683.020613, 'median': 0.471969, 'range': 1.927374,
'percentile_5': 0.110149, 'mad': 0.168122, 'majority': 0.5,
'minority': -0.927374, 'unique': 63745,
'nodata': 0, 'valid': 1.0}],
[{'sum': 56840.1875, 'median': 0.629144, 'range': 2.0,
[{'sum': 56840.183386, 'median': 0.629144, 'range': 2.0,
'percentile_5': 0.134994, 'mad': 0.188218, 'majority': -1.0,
'minority': -0.993671, 'unique': 89338,
'nodata': 85, 'valid': 0.99909}],
[{'sum': 30468.535156, 'median': 0.619463, 'range': 2.0,
[{'sum': 30468.534019, 'median': 0.619463, 'range': 2.0,
'percentile_5': 0.15824, 'mad': 0.161906, 'majority': -1.0,
'minority': -0.991903, 'unique': 49427,
'nodata': 0, 'valid': 1.0}],
[{'sum': 44829.203125, 'median': 0.704596, 'range': 2.0,
[{'sum': 44829.198908, 'median': 0.704596, 'range': 2.0,
'percentile_5': 0.307304, 'mad': 0.169161, 'majority': 1.0,
'minority': -0.969231, 'unique': 61646,
'nodata': 337, 'valid': 0.994777}],
[{'sum': 58079.574219, 'median': 0.727142, 'range': 2.0,
[{'sum': 58079.578482, 'median': 0.727142, 'range': 2.0,
'percentile_5': 0.211673, 'mad': 0.138837, 'majority': 1.0,
'minority': -0.962406, 'unique': 79181,
'nodata': 0, 'valid': 1.0}],
[{'sum': 2966.584717, 'median': 0.786099, 'range': 1.206738,
[{'sum': 2966.584778, 'median': 0.786099, 'range': 1.206738,
'percentile_5': 0.241052, 'mad': 0.153867, 'majority': 0.941176,
'minority': -0.206738, 'unique': 4007,
'nodata': 62, 'valid': 0.984878}],
[{'sum': 17599.695312, 'median': 0.586743, 'range': 1.83908,
[{'sum': 17599.695646, 'median': 0.586743, 'range': 1.83908,
'percentile_5': 0.255563, 'mad': 0.168826, 'majority': 1.0,
'minority': -0.83908, 'unique': 28731,
'nodata': 290, 'valid': 0.990177}],
[{'sum': 97063.34375, 'median': 0.548321, 'range': 2.0,
[{'sum': 97063.345854, 'median': 0.548321, 'range': 2.0,
'percentile_5': 0.121661, 'mad': 0.177506, 'majority': -1.0,
'minority': -0.996276, 'unique': 165519,
'nodata': 55, 'valid': 0.99969}],
[{'sum': 10982.208008, 'median': 0.647735, 'range': 0.961919,
[{'sum': 10982.207629, 'median': 0.647735, 'range': 0.961919,
'percentile_5': 0.135674, 'mad': 0.24678, 'majority': 1.0,
'minority': 0.038081, 'unique': 17550,
'nodata': 337, 'valid': 0.98139}],
[{'sum': 89315.601562, 'median': 0.491085, 'range': 2.0,
[{'sum': 89315.609434, 'median': 0.491085, 'range': 2.0,
'percentile_5': 0.12513, 'mad': 0.170515, 'majority': -1.0,
'minority': -0.995074, 'unique': 158615,
'nodata': 0, 'valid': 1.0}],
[{'sum': 17259.078125, 'median': 0.589402, 'range': 1.993846,
[{'sum': 17259.079321, 'median': 0.589402, 'range': 1.993846,
'percentile_5': 0.324657, 'mad': 0.142261, 'majority': -1.0,
'minority': -0.974359, 'unique': 28279,
'nodata': 0, 'valid': 1.0}],
[{'sum': 31228.535156, 'median': 0.564933, 'range': 2.0,
[{'sum': 31228.533127, 'median': 0.564933, 'range': 2.0,
'percentile_5': 0.111111, 'mad': 0.227924, 'majority': -1.0,
'minority': -0.962085, 'unique': 54423,
'nodata': 0, 'valid': 1.0}],
[{'sum': 20114.677734, 'median': 0.537082, 'range': 2.0,
[{'sum': 20114.676941, 'median': 0.537082, 'range': 2.0,
'percentile_5': 0.231518, 'mad': 0.16068, 'majority': -1.0,
'minority': -0.991091, 'unique': 34565,
'nodata': 259, 'valid': 0.992713}]]
Expand Down Expand Up @@ -231,17 +231,17 @@ def test_compute_zonal_stats_per_category():
for geom_stats in statistics for d in geom_stats for key, val in d.items()]

# ref is the following
ref = [[{'11min': 40.407368, '11max': 44.083961, '11mean': 42.293809, '11count': 244, '11std': 0.849256,
'31min': 40.224247, '31max': 68.915146, '31mean': 46.435981, '31count': 12347, '31std': 4.744863,
'32min': 38.825829, '32max': 46.798634, '32mean': 42.545211, '32count': 6657, '32std': 1.33232,
'42min': 38.214043, '42max': 59.93272, '42mean': 43.375167, '42count': 2716, '42std': 1.595453,
'43min': 38.214043, '43max': 45.618088, '43mean': 42.273938, '43count': 875, '43std': 1.241663}],
[{'11min': 38.475033, '11max': 45.613518, '11mean': 42.386634, '11count': 73437, '11std': 1.090365,
ref = [[{'11min': 40.407368, '11max': 44.083961, '11mean': 42.293813, '11count': 244, '11std': 0.849256,
'31min': 40.224247, '31max': 68.915146, '31mean': 46.435982, '31count': 12347, '31std': 4.744863,
'32min': 38.825829, '32max': 46.798634, '32mean': 42.54521, '32count': 6657, '32std': 1.33232,
'42min': 38.214043, '42max': 59.93272, '42mean': 43.375171, '42count': 2716, '42std': 1.595453,
'43min': 38.214043, '43max': 45.618088, '43mean': 42.273934, '43count': 875, '43std': 1.241663}],
[{'11min': 38.475033, '11max': 45.613518, '11mean': 42.386635, '11count': 73437, '11std': 1.090365,
'12min': 39.241253, '12max': 43.433277, '12mean': 41.991684, '12count': 17339, '12std': 0.313978,
'31min': 33.781662, '31max': 60.81406, '31mean': 44.562402, '31count': 12743, '31std': 3.051407,
'32min': 37.670204, '32max': 64.120644, '32mean': 44.396163, '32count': 18284, '32std': 2.749989,
'42min': 40.806831, '42max': 65.240021, '42mean': 45.256736, '42count': 45240, '42std': 3.348138,
'43min': 42.136879, '43max': 65.507011, '43mean': 48.296517, '43count': 59113, '43std': 3.671298}]]
'32min': 37.670204, '32max': 64.120644, '32mean': 44.396165, '32count': 18284, '32std': 2.749989,
'42min': 40.806831, '42max': 65.240021, '42mean': 45.256735, '42count': 45240, '42std': 3.348138,
'43min': 42.136879, '43max': 65.507011, '43mean': 48.296514, '43count': 59113, '43std': 3.671298}]]

for geom_stats, ref_stats in zip(statistics, ref):
for i, band in enumerate(bands):
Expand Down Expand Up @@ -269,17 +269,17 @@ def test_compute_zonal_stats_per_category():
for geom_stats in statistics for d in geom_stats for key, val in d.items()]

# ref is the following
ref = [[{'cetemin': 40.407368, 'cetemax': 44.083961, 'cetemean': 42.293809, 'cetecount': 244, 'cetestd': 0.849256,
'feumin': 40.224247, 'feumax': 68.915146, 'feumean': 46.435981, 'feucount': 12347, 'feustd': 4.744863,
'conmin': 38.825829, 'conmax': 46.798634, 'conmean': 42.545211, 'concount': 6657, 'constd': 1.33232,
'udimin': 38.214043, 'udimax': 59.93272, 'udimean': 43.375167, 'udicount': 2716, 'udistd': 1.595453,
'zicmin': 38.214043, 'zicmax': 45.618088, 'zicmean': 42.273938, 'ziccount': 875, 'zicstd': 1.241663}],
[{'cetemin': 38.475033, 'cetemax': 45.613518, 'cetemean': 42.386634, 'cetecount': 73437, 'cetestd': 1.090365,
ref = [[{'cetemin': 40.407368, 'cetemax': 44.083961, 'cetemean': 42.293813, 'cetecount': 244, 'cetestd': 0.849256,
'feumin': 40.224247, 'feumax': 68.915146, 'feumean': 46.435982, 'feucount': 12347, 'feustd': 4.744863,
'conmin': 38.825829, 'conmax': 46.798634, 'conmean': 42.54521, 'concount': 6657, 'constd': 1.33232,
'udimin': 38.214043, 'udimax': 59.93272, 'udimean': 43.375171, 'udicount': 2716, 'udistd': 1.595453,
'zicmin': 38.214043, 'zicmax': 45.618088, 'zicmean': 42.273934, 'ziccount': 875, 'zicstd': 1.241663}],
[{'cetemin': 38.475033, 'cetemax': 45.613518, 'cetemean': 42.386635, 'cetecount': 73437, 'cetestd': 1.090365,
'chivmin': 39.241253, 'chivmax': 43.433277, 'chivmean': 41.991684, 'chivcount': 17339, 'chivstd': 0.313978,
'feumin': 33.781662, 'feumax': 60.81406, 'feumean': 44.562402, 'feucount': 12743, 'feustd': 3.051407,
'conmin': 37.670204, 'conmax': 64.120644, 'conmean': 44.396163, 'concount': 18284, 'constd': 2.749989,
'udimin': 40.806831, 'udimax': 65.240021, 'udimean': 45.256736, 'udicount': 45240, 'udistd': 3.348138,
'zicmin': 42.136879, 'zicmax': 65.507011, 'zicmean': 48.296517, 'ziccount': 59113, 'zicstd': 3.671298}]]
'conmin': 37.670204, 'conmax': 64.120644, 'conmean': 44.396165, 'concount': 18284, 'constd': 2.749989,
'udimin': 40.806831, 'udimax': 65.240021, 'udimean': 45.256735, 'udicount': 45240, 'udistd': 3.348138,
'zicmin': 42.136879, 'zicmax': 65.507011, 'zicmean': 48.296514, 'ziccount': 59113, 'zicstd': 3.671298}]]

for geom_stats, ref_stats in zip(statistics, ref):
for i, band in enumerate(bands):
Expand Down
Binary file modified ..._refs/test_zonalstats/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-stats-outliers.tif
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