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clairs_to.py
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clairs_to.py
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# BSD 3-Clause License
#
# Copyright 2023 The University of Hong Kong, Department of Computer Science
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import sys
from importlib import import_module
DATA_PREP_SCRIPTS_FOLDER = "src"
DEEP_LEARNING_FOLDER = "clairs"
deep_learning_folder = [
"predict",
"call_variants",
]
REPO_NAME = "clairs_to"
data_preprocess_folder = [
"extract_candidates_calling",
"create_tensor_pileup_calling",
"sort_vcf",
"postprocess_vcf",
'concat_files',
'add_back_missing_variants_in_genotyping',
"nonsomatic_tagging",
'realign_reads',
'realign_variants',
'postfilter_variants',
"haplotype_filtering",
'cal_af_distribution',
"compare_vcf",
"select_hetero_snp_for_phasing",
'cna_germline_tagging',
]
def directory_for(submodule_name):
if submodule_name in deep_learning_folder:
return DEEP_LEARNING_FOLDER
if submodule_name in data_preprocess_folder:
return DATA_PREP_SCRIPTS_FOLDER
return ""
def print_help_messages():
from textwrap import dedent
print(dedent("""\
{0} submodule invocator:
Usage: python clairs_to.py [submodule] [options of the submodule]
Available data preparation submodules:\n{1}
Available clairs_to submodules:\n{2}
""".format(
REPO_NAME,
"\n".join(" - %s" % submodule_name for submodule_name in data_preprocess_folder),
"\n".join(" - %s" % submodule_name for submodule_name in deep_learning_folder),
)
))
def main():
if len(sys.argv) <= 1 or sys.argv[1] == "-h" or sys.argv[1] == "--help":
print_help_messages()
sys.exit(0)
submodule_name = sys.argv[1]
if (
submodule_name not in deep_learning_folder and
submodule_name not in data_preprocess_folder
):
sys.exit("[ERROR] Submodule %s not found." % (submodule_name))
directory = directory_for(submodule_name)
submodule = import_module("%s.%s" % (directory, submodule_name))
sys.argv = sys.argv[1:]
sys.argv[0] += (".py")
# Note: need to make sure every submodule contains main() method
submodule.main()
sys.exit(0)
if __name__ == "__main__":
main()