deeplearning/modulus/modulus-core-v021/_modules/modulus/utils/graphcast/icospheres.html
Source code for modulus.utils.graphcast.icospheres
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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import numpy as np
import json
try:
import pymesh
except ImportError:
Warning("pymesh is not installed. Please install it to use icosphere.")
# TODO apply a transformation to make faces parallel to ploes
[docs]def generate_and_save_icospheres(
save_path: str = "icospheres.json", level: int = 6
) -> None: # pragma: no cover
"""enerate icospheres from level 0 to 6 (inclusive) and save them to a json file.
Parameters
----------
path : str
Path to save the json file.
"""
radius = 1
center = np.array((0, 0, 0))
icospheres = {"vertices": [], "faces": []}
# Generate icospheres from level 0 to 6 (inclusive)
for order in range(level + 1):
icosphere = pymesh.generate_icosphere(radius, center, refinement_order=order)
icospheres["order_" + str(order) + "_vertices"] = icosphere.vertices
icospheres["order_" + str(order) + "_faces"] = icosphere.faces
icosphere.add_attribute("face_centroid")
icospheres[
"order_" + str(order) + "_face_centroid"
] = icosphere.get_face_attribute("face_centroid")
# save icosphere vertices and faces to a json file
icospheres_dict = {
key: (value.tolist() if isinstance(value, np.ndarray) else value)
for key, value in icospheres.items()
}
with open(save_path, "w") as f:
json.dump(icospheres_dict, f)if __name__ == "__main__":
generate_and_save_icospheres(level=6)