def import_sra_from_staging(self, ctx, params):
"""
:param params: instance of type "SRAToReadsParams" (required params:
staging_file_subdir_path: subdirectory file path e.g. for file:
/data/bulk/user_name/file_name staging_file_subdir_path is
file_name for file:
/data/bulk/user_name/subdir_1/subdir_2/file_name
staging_file_subdir_path is subdir_1/subdir_2/file_name
sequencing_tech: sequencing technology name: output reads file
name workspace_name: workspace name/ID of the object Optional
Params: single_genome: whether the reads are from a single genome
or a metagenome. insert_size_mean: mean (average) insert length
insert_size_std_dev: standard deviation of insert lengths
read_orientation_outward: whether reads in a pair point outward)
-> structure: parameter "staging_file_subdir_path" of String,
parameter "sequencing_tech" of type "sequencing_tech", parameter
"name" of type "name", parameter "workspace_name" of type
"workspace_name" (workspace name of the object), parameter
"single_genome" of type "single_genome", parameter
"insert_size_mean" of type "insert_size_mean", parameter
"insert_size_std_dev" of type "insert_size_std_dev", parameter
"read_orientation_outward" of type "read_orientation_outward"
:returns: instance of type "UploadMethodResult" -> structure:
parameter "obj_ref" of type "obj_ref", parameter "report_name" of
type "report_name", parameter "report_ref" of type "report_ref"
"""
# ctx is the context object
# return variables are: returnVal
#BEGIN import_sra_from_staging
print '--->\nRunning uploadmethods.import_sra_from_staging\nparams:'
print json.dumps(params, indent=1)
for key, value in params.iteritems():
if isinstance(value, basestring):
params[key] = value.strip()
importer = ImportSRAUtil(self.config)
returnVal = importer.import_sra_from_staging(params)
reportVal = importer.generate_report(returnVal['obj_ref'], params)
returnVal.update(reportVal)
#END import_sra_from_staging
# At some point might do deeper type checking...
if not isinstance(returnVal, dict):
raise ValueError('Method import_sra_from_staging return value ' +
'returnVal is not type dict as required.')
# return the results
return [returnVal]
def import_sra_from_web(self, ctx, params):
"""
:param params: instance of type "WebSRAToReadsParams" -> structure:
parameter "download_type" of String, parameter "sra_urls_to_add"
of type "sra_urls_to_add" (download_type: download type for web
source fastq file ('Direct Download', 'FTP', 'DropBox', 'Google
Drive') sra_urls_to_add: dict of SRA file URLs required params:
file_url: SRA file URL sequencing_tech: sequencing technology
name: output reads file name workspace_name: workspace name/ID of
the object Optional Params: single_genome: whether the reads are
from a single genome or a metagenome. insert_size_mean: mean
(average) insert length insert_size_std_dev: standard deviation of
insert lengths read_orientation_outward: whether reads in a pair
point outward) -> structure: parameter "file_url" of String,
parameter "sequencing_tech" of type "sequencing_tech", parameter
"name" of type "name", parameter "single_genome" of type
"single_genome", parameter "insert_size_mean" of type
"insert_size_mean", parameter "insert_size_std_dev" of type
"insert_size_std_dev", parameter "read_orientation_outward" of
type "read_orientation_outward", parameter "workspace_name" of
type "workspace_name" (workspace name of the object)
:returns: instance of type "WebSRAToReadsResult" -> structure:
parameter "obj_refs" of list of String, parameter "report_name" of
type "report_name", parameter "report_ref" of type "report_ref"
"""
# ctx is the context object
# return variables are: returnVal
#BEGIN import_sra_from_web
print '--->\nRunning uploadmethods.import_sra_from_web\nparams:'
print json.dumps(params, indent=1)
for key, value in params.iteritems():
if isinstance(value, basestring):
params[key] = value.strip()
importer = ImportSRAUtil(self.config)
returnVal = importer.import_sra_from_web(params)
params['uploaded_files'] = returnVal.get('uploaded_files')
reportVal = importer.generate_report(returnVal['obj_refs'], params)
returnVal.update(reportVal)
#END import_sra_from_web
# At some point might do deeper type checking...
if not isinstance(returnVal, dict):
raise ValueError('Method import_sra_from_web return value ' +
'returnVal is not type dict as required.')
# return the results
return [returnVal]
def __init__(self, config):
self.uploader_utils = UploaderUtil(config)
self.sra_importer = ImportSRAUtil(config)
class ImportReadsUtil:
def __init__(self, config):
self.uploader_utils = UploaderUtil(config)
self.sra_importer = ImportSRAUtil(config)
def import_reads_from_staging(self, params):
self._validate_import_reads_from_staging_params(params)
if params.get('import_type') == 'FASTQ/FASTA':
fastq_importer_params = params
fastq_importer_params['fwd_staging_file_name'] = params.get(
'fastq_fwd_staging_file_name')
fastq_importer_params['rev_staging_file_name'] = params.get(
'fastq_rev_staging_file_name')
returnVal = self.uploader_utils.upload_fastq_file(
fastq_importer_params)
uploaded_file = params.get('fastq_fwd_staging_file_name')
if params.get('fastq_rev_staging_file_name') is not None:
uploaded_file += '\n' + params.get(
'fastq_rev_staging_file_name')
fastq_importer_params['uploaded_files'] = [uploaded_file]
"""
Update the workspace object related meta-data for staged file
"""
self.uploader_utils.update_staging_service(
params.get('fastq_fwd_staging_file_name'),
returnVal['obj_ref'])
if params.get('fastq_rev_staging_file_name') is not None:
self.uploader_utils.update_staging_service(
params.get('fastq_rev_staging_file_name'),
returnVal['obj_ref'])
reportVal = self.sra_importer.generate_report(
[returnVal['obj_ref']], fastq_importer_params)
returnVal.update(reportVal)
elif params.get('import_type') == 'SRA':
sra_importer_params = params
sra_importer_params['staging_file_subdir_path'] = params.get(
'sra_staging_file_name')
returnVal = self.sra_importer.import_sra_from_staging(
sra_importer_params)
sra_importer_params['uploaded_files'] = [
params.get('sra_staging_file_name')
]
"""
Update the workspace object related meta-data for staged file
"""
self.uploader_utils.update_staging_service(
params.get('sra_staging_file_name'), returnVal['obj_ref'])
reportVal = self.sra_importer.generate_report(
[returnVal['obj_ref']], sra_importer_params)
returnVal.update(reportVal)
return returnVal
def _validate_import_reads_from_staging_params(self, params):
"""
_validate_import_reads_from_staging_params:
validates params passed to import_reads_from_staging method
"""
# check for required parameters
for p in ['import_type', 'sequencing_tech', 'name', 'workspace_name']:
if p not in params:
raise ValueError(
'"{}" parameter is required, but missing'.format(p))
valide_import_type = ['FASTQ/FASTA', 'SRA']
if params.get('import_type') not in valide_import_type:
error_msg = 'Import file type [{}] is not supported. '.format(
params.get('import_type'))
error_msg += 'Please selet one of {}'.format(valide_import_type)
raise ValueError(error_msg)
if (params.get('import_type') == 'FASTQ/FASTA'
and not params.get('fastq_fwd_staging_file_name')):
error_msg = 'FASTQ/FASTA input file type selected. But missing FASTQ/FASTA file.'
raise ValueError(error_msg)
if (params.get('import_type') == 'SRA'
and not params.get('sra_staging_file_name')):
error_msg = 'SRA input file type selected. But missing SRA file.'
raise ValueError(error_msg)
if ((params.get('fastq_fwd_staging_file_name')
and params.get('sra_staging_file_name'))
or (params.get('fastq_rev_staging_file_name')
and params.get('sra_staging_file_name'))):
error_msg = 'Both SRA and FASTQ/FASTA file given. Please provide one file type only.'
raise ValueError(error_msg)