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gliegard
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Mar 9, 2026
| num_filtered = len(filtered_arrays[0]) | ||
| print(f"Nombre de points filtrés: {num_filtered}") | ||
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| assert num_filtered <= num_points |
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L'algo de calcul des jours, la distribution des jours et la fenetre est assez compliqué.
Et le test ne vérifie pas que l'algo a bien fonctionné.
Pour être plus précis, tu pourrais faire
assert num_points == le_nombre_de_point_total
assert num_filtered == le_nombre_de_points_filtré
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| las_out = laspy.read(OUTPUT_LAS) | ||
| assert len(las_out.points) > 0, "Points preserved after processing" | ||
| assert "Z_ref" in las_out.point_format.dimension_names, "Z_ref dimension added" |
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pareil ici tu pourrais vérifier plus de choses
assert len(las_out.points) == le_nombre_points
| assert isinstance(pipeline, pdal.Pipeline) | ||
| arrays = pipeline.arrays | ||
| assert len(arrays) == 1 | ||
| assert len(arrays[0]) > 0 # Fichier test a des points |
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au lieu de mettre
assert len(arrays[0]) > 0 # Fichier test a des points
autant mettre directement le nb de points du fichier de test
assert len(arrays[0]) ==le_nombre_de_points
refacto function with test
mdupaysign
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… computation - normalize_height_in_pointcloud: build TIN from all points with dtm_marker==1 (no longer restricted to Classification==2), apply filter_dimension/filter_values after HAG computation, remove use_dtm_marker parameter - validate_lidar_file: check that dtm_marker extra dimension is present - main_pretreatment: remove use_dtm_marker call argument - configs/config.yaml: remove use_dtm_marker parameter - tests: update synthetic data with dtm_marker field, use real file without dtm_marker to test the new validation error >
gliegard
suggested changes
Mar 18, 2026
lidar_for_fuel/_version.py
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| @@ -1,4 +1,4 @@ | |||
| __version__ = "0.0.2" | |||
| __version__ = "1.0.0" | |||
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Pas besoin d'incrémenter le n° de version majeur. Le n° de version mineur est suffisant. Passe plutôt en 0.1.0.
Et il faut que ce soit dans un commit à part, car c'est un moment important et unitaire un changement de version.
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Fonction de pré-traitement de LIdarForFuel :