Mercurial > repos > yating-l > data_manager_fetch_reference_data
comparison fetch_reference_data.py @ 0:099d2508bcc3 draft
planemo upload
| author | yating-l |
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| date | Wed, 03 May 2017 16:18:10 -0400 |
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| -1:000000000000 | 0:099d2508bcc3 |
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| 1 #!/usr/bin/env python | |
| 2 # ref: https://galaxyproject.org/admin/tools/data-managers/how-to/define/ | |
| 3 | |
| 4 import sys | |
| 5 import os | |
| 6 import tempfile | |
| 7 import shutil | |
| 8 import argparse | |
| 9 import urllib2 | |
| 10 import tarfile | |
| 11 | |
| 12 from galaxy.util.json import from_json_string, to_json_string | |
| 13 | |
| 14 CHUNK_SIZE = 2**20 #1mb | |
| 15 | |
| 16 def cleanup_before_exit(tmp_dir): | |
| 17 if tmp_dir and os.path.exists(tmp_dir): | |
| 18 shutil.rmtree(tmp_dir) | |
| 19 | |
| 20 def stop_err(msg): | |
| 21 sys.stderr.write(msg) | |
| 22 sys.exit(1) | |
| 23 | |
| 24 def get_reference_id_name(params): | |
| 25 genome_id = params['param_dict']['genome_id'] | |
| 26 genome_name = params['param_dict']['genome_name'] | |
| 27 return genome_id, genome_name | |
| 28 | |
| 29 def download_from_GlimmerHMM(data_manager_dict, params, target_directory, sequence_id, sequence_name ): | |
| 30 GlimmerHMM_DOWNLOAD_URL = 'ftp://ccb.jhu.edu/pub/software/glimmerhmm/GlimmerHMM-3.0.4.tar.gz' | |
| 31 GlimmerHMM_TRAINED_DIR = os.path.join('GlimmerHMM', 'trained_dir', sequence_id) | |
| 32 with tarfile.open('GlimmerHMM-3.0.4.tar', mode='r:*') as tar: | |
| 33 subdir = [ | |
| 34 tarinfo for tarinfo in tar.getmembers() | |
| 35 if sequence_id in tarinfo.name | |
| 36 ] | |
| 37 tar.extractall(members=subdir) | |
| 38 glimmerhmm_trained_target_dir = os.path.join(target_directory, sequence_id) | |
| 39 shutil.copytree(GlimmerHMM_TRAINED_DIR, glimmerhmm_trained_target_dir) | |
| 40 data_table_entry = dict(value=sequence_id, name=sequence_name, path=glimmerhmm_trained_target_dir) | |
| 41 _add_data_table_entry(data_manager_dict, data_table_entry) | |
| 42 | |
| 43 cleanup_before_exit(GlimmerHMM_TRAINED_DIR) | |
| 44 | |
| 45 def _add_data_table_entry( data_manager_dict, data_table_entry ): | |
| 46 data_manager_dict['data_tables'] = data_manager_dict.get( 'data_tables', {} ) | |
| 47 data_manager_dict['data_tables']['reference_data'] = data_manager_dict['data_tables'].get('reference_data', []) | |
| 48 data_manager_dict['data_tables']['reference_data'].append( data_table_entry ) | |
| 49 return data_manager_dict | |
| 50 | |
| 51 REFERENCE_SOURCE_TO_DOWNLOAD = dict(glimmerhmm=download_from_GlimmerHMM) | |
| 52 | |
| 53 def main(): | |
| 54 #Parse Command Line | |
| 55 parser = argparse.ArgumentParser() | |
| 56 args = parser.parse_args() | |
| 57 | |
| 58 filename = args[0] | |
| 59 | |
| 60 params = from_json_string(open(filename).read()) | |
| 61 target_directory = params['output_data'][0]['extra_files_path'] | |
| 62 os.mkdir(target_directory) | |
| 63 data_manager_dict = {} | |
| 64 | |
| 65 sequence_id, sequence_name = get_reference_id_name(params) | |
| 66 | |
| 67 #Fetch the FASTA | |
| 68 REFERENCE_SOURCE_TO_DOWNLOAD[params['param_dict']['file_path']](data_manager_dict, params, target_directory, sequence_id, sequence_name) | |
| 69 | |
| 70 #save info to json file | |
| 71 open(filename, 'wb').write(to_json_string(data_manager_dict)) | |
| 72 | |
| 73 if __name__ == "__main__": | |
| 74 main() | |
| 75 |
