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51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
# pylint: disable=g-bad-file-header
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# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Commonly used data structures and functions."""
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import enum
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import tensorflow.compat.v1 as tf
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class NodeType(enum.IntEnum):
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NORMAL = 0
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OBSTACLE = 1
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AIRFOIL = 2
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HANDLE = 3
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INFLOW = 4
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OUTFLOW = 5
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WALL_BOUNDARY = 6
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SIZE = 9
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def triangles_to_edges(faces):
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"""Computes mesh edges from triangles."""
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# collect edges from triangles
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edges = tf.concat([faces[:, 0:2],
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faces[:, 1:3],
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tf.stack([faces[:, 2], faces[:, 0]], axis=1)], axis=0)
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# those edges are sometimes duplicated (within the mesh) and sometimes
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# single (at the mesh boundary).
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# sort & pack edges as single tf.int64
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receivers = tf.reduce_min(edges, axis=1)
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senders = tf.reduce_max(edges, axis=1)
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packed_edges = tf.bitcast(tf.stack([senders, receivers], axis=1), tf.int64)
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# remove duplicates and unpack
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unique_edges = tf.bitcast(tf.unique(packed_edges)[0], tf.int32)
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senders, receivers = tf.unstack(unique_edges, axis=1)
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# create two-way connectivity
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return (tf.concat([senders, receivers], axis=0),
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tf.concat([receivers, senders], axis=0))
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