mirror of
https://github.com/aqlaboratory/openfold.git
synced 2026-06-04 12:44:26 +08:00
121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
# Copyright 2021 AlQuraishi Laboratory
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import pickle
|
|
import shutil
|
|
|
|
import numpy as np
|
|
import unittest
|
|
|
|
from openfold.data.data_pipeline import DataPipeline
|
|
from openfold.data.templates import HhsearchHitFeaturizer, HmmsearchHitFeaturizer
|
|
import tests.compare_utils as compare_utils
|
|
from tests.config import consts
|
|
|
|
if compare_utils.alphafold_is_installed():
|
|
alphafold = compare_utils.import_alphafold()
|
|
import jax
|
|
import haiku as hk
|
|
|
|
|
|
class TestDataPipeline(unittest.TestCase):
|
|
@compare_utils.skip_unless_alphafold_installed()
|
|
def test_fasta_compare(self):
|
|
# AlphaFold runs the alignments and feature processing at the same
|
|
# time, taking forever. As such, we precompute AlphaFold's features
|
|
# using scripts/generate_alphafold_feature_dict.py and the default
|
|
# databases.
|
|
with open("tests/test_data/alphafold_feature_dict.pickle", "rb") as fp:
|
|
alphafold_feature_dict = pickle.load(fp)
|
|
|
|
if consts.is_multimer:
|
|
# template_featurizer = HmmsearchHitFeaturizer(
|
|
# mmcif_dir="tests/test_data/mmcifs",
|
|
# max_template_date="2021-12-20",
|
|
# max_hits=20,
|
|
# kalign_binary_path=shutil.which("kalign"),
|
|
# _zero_center_positions=False,
|
|
# )
|
|
template_featurizer = HhsearchHitFeaturizer(
|
|
mmcif_dir="tests/test_data/mmcifs",
|
|
max_template_date="2021-12-20",
|
|
max_hits=20,
|
|
kalign_binary_path=shutil.which("kalign"),
|
|
_zero_center_positions=False,
|
|
)
|
|
else:
|
|
template_featurizer = HhsearchHitFeaturizer(
|
|
mmcif_dir="tests/test_data/mmcifs",
|
|
max_template_date="2021-12-20",
|
|
max_hits=20,
|
|
kalign_binary_path=shutil.which("kalign"),
|
|
_zero_center_positions=False,
|
|
)
|
|
|
|
data_pipeline = DataPipeline(
|
|
template_featurizer=template_featurizer,
|
|
)
|
|
|
|
openfold_feature_dict = data_pipeline.process_fasta(
|
|
"tests/test_data/short.fasta",
|
|
"tests/test_data/alignments"
|
|
)
|
|
|
|
openfold_feature_dict["template_all_atom_masks"] = openfold_feature_dict["template_all_atom_mask"]
|
|
|
|
checked = []
|
|
|
|
# AlphaFold and OpenFold process their MSAs in slightly different
|
|
# orders, which we compensate for below.
|
|
m_a = alphafold_feature_dict["msa"]
|
|
m_o = openfold_feature_dict["msa"]
|
|
|
|
# The first row of both MSAs should be the same, no matter what
|
|
self.assertTrue(np.all(m_a[0, :] == m_o[0, :]))
|
|
|
|
# Each row of each MSA should appear exactly once somewhere in its
|
|
# counterpart
|
|
matching_rows = np.all((m_a[:, None, ...] == m_o[None, :, ...]), axis=-1)
|
|
self.assertTrue(
|
|
np.all(
|
|
np.sum(matching_rows, axis=-1) == 1
|
|
)
|
|
)
|
|
|
|
checked.append("msa")
|
|
|
|
# The corresponding rows of the deletion matrix should also be equal
|
|
matching_idx = np.argmax(matching_rows, axis=-1)
|
|
rearranged_o_dmi = openfold_feature_dict["deletion_matrix_int"]
|
|
rearranged_o_dmi = rearranged_o_dmi[matching_idx, :]
|
|
self.assertTrue(
|
|
np.all(
|
|
alphafold_feature_dict["deletion_matrix_int"] ==
|
|
rearranged_o_dmi
|
|
)
|
|
)
|
|
|
|
checked.append("deletion_matrix_int")
|
|
|
|
# Remaining features have to be precisely equal
|
|
for k, v in alphafold_feature_dict.items():
|
|
self.assertTrue(
|
|
k in checked or np.all(v == openfold_feature_dict[k])
|
|
)
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|