def search_files(config):
# project id
project_id = config['project_id']
# bucket name
bucket_name = config['buckets']['open']
# connect to google cloud storage
gcs = GcsConnector(project_id, bucket_name)
#---------------------
# search for MAF files
#---------------------
maf_file = re.compile("^.*.maf$")
# search only these tumor types defined in config files
search_tumors = ["tcga/" + d.lower() for d in config['all_tumor_types']]
data_library = gcs.search_files(search_patterns=['.maf'],
regex_search_pattern=maf_file,
prefixes=search_tumors)
data_library['basefilename'] = map(
lambda x: os.path.splitext(os.path.basename(x))[0],
data_library['filename'])
data_library['unique_filename'] = format_dupe_values(
data_library['basefilename'])
return data_library
def main(config):
# etl = util.DataETL("isb-cgc", "isb-cgc-open") # this starts a new connectioni
project_id = config['project_id']
bucket_name = config['buckets']['open']
# connect to bucket to get files
gcs = GcsConnector(project_id, bucket_name)
isoform_file = re.compile("^.*.isoform.quantification.txt.json$")
data_library = gcs.search_files(
search_patterns=['.isoform.quantification.txt'],
regex_search_pattern=isoform_file,
prefixes=['tcga/intermediary/mirna/isoform/'])
# we are eliminating bad files - size 0; could be hg18 etc
data_library.loc[:, 'basefilename'] = data_library['filename'].map(
lambda x: os.path.splitext(os.path.basename(x))[0].replace(
'.json', ''))
data_library = data_library.query('size > 0')
conn = sqlite3.connect('../etl-mirna-isoform.db')
sql = 'SELECT * from {0}'.format('task_queue')
all_files_df = pd.read_sql_query(sql, conn)
conn.close()
with open('downloadedfiles.txt') as f:
lines = f.read().splitlines()
all_files_df = all_files_df[(all_files_df.DatafileName.isin(
data_library.basefilename))]
all_files_df = all_files_df[~(all_files_df.DatafileName.isin(lines))]
data_library = all_files_df
print data_library
conn = sqlite3.connect('etl-isoform-download.db')
submit_to_queue(data_library, conn, 'task_queue')
queue_df = data_library
# restart ETL; this gets the diff; also takes care of errors
try:
sql = 'SELECT * from task_queue_status where errors="None"'
queue_df2 = pd.read_sql_query(sql, conn)
print 'completed: ', len(queue_df2)
queue_df = queue_df[~(
queue_df.DatafileNameKey.isin(queue_df2.DatafileNameKey))]
print 'Not completed: ', len(queue_df)
except Exception, e:
pass
def main(config):
# etl = util.DataETL("isb-cgc", "isb-cgc-open") # this starts a new connectioni
project_id = config['project_id']
bucket_name = config['buckets']['open']
# connect to bucket to get files
gcs = GcsConnector(project_id, bucket_name)
isoform_file = re.compile("^.*.isoform.quantification.txt.json$")
data_library = gcs.search_files(search_patterns=['.isoform.quantification.txt'], regex_search_pattern=isoform_file, prefixes=['tcga/intermediary/mirna/isoform/'])
# we are eliminating bad files - size 0; could be hg18 etc
data_library.loc[:, 'basefilename'] = data_library['filename'].map(lambda x: os.path.splitext(os.path.basename(x))[0].replace('.json', ''))
data_library = data_library.query('size > 0')
conn = sqlite3.connect('../etl-mirna-isoform.db')
sql = 'SELECT * from {0}'.format('task_queue')
all_files_df = pd.read_sql_query(sql, conn)
conn.close()
with open('downloadedfiles.txt') as f:
lines = f.read().splitlines()
all_files_df = all_files_df[ (all_files_df.DatafileName.isin(data_library.basefilename))]
all_files_df = all_files_df[ ~ (all_files_df.DatafileName.isin(lines))]
data_library = all_files_df
print data_library
conn = sqlite3.connect('etl-isoform-download.db')
submit_to_queue(data_library, conn, 'task_queue')
queue_df = data_library
# restart ETL; this gets the diff; also takes care of errors
try:
sql = 'SELECT * from task_queue_status where errors="None"'
queue_df2 = pd.read_sql_query(sql, conn)
print 'completed: ', len(queue_df2)
queue_df = queue_df[ ~(queue_df.DatafileNameKey.isin(queue_df2.DatafileNameKey))]
print 'Not completed: ', len(queue_df)
except Exception, e:
pass
def search_files(config):
# project id
project_id = config['project_id']
# bucket name
bucket_name = config['buckets']['open']
# connect to google cloud storage
gcs = GcsConnector(project_id, bucket_name)
#---------------------
# search for MAF files
#---------------------
maf_file = re.compile("^.*.maf$")
# search only these tumor types defined in config files
search_tumors = ["tcga/" + d.lower() for d in config['all_tumor_types']]
data_library = gcs.search_files(search_patterns=['.maf'], regex_search_pattern=maf_file, prefixes=search_tumors)
data_library['basefilename'] = map(lambda x: os.path.splitext(os.path.basename(x))[0], data_library['filename'])
data_library['unique_filename'] = format_dupe_values(data_library['basefilename'])
return data_library
def main(config):
log_filename = 'etl_download_isoform.log'
log_name = 'etl_download_isoform'
log = configure_logging(log_name, log_filename)
log.info('begin downloading isoform files')
# etl = util.DataETL("isb-cgc", "isb-cgc-open") # this starts a new connection
project_id = config['project_id']
bucket_name = config['buckets']['open']
# connect to bucket to get files
gcs = GcsConnector(project_id, bucket_name)
isoform_file = re.compile("^.*.isoform.quantification.txt.json$")
data_library = gcs.search_files(
search_patterns=['.isoform.quantification.txt'],
regex_search_pattern=isoform_file,
prefixes=[config['mirna_isoform_matrix']['isoform_gcs_dir']])
# we are eliminating bad files - size 0; could be hg18 etc
data_library.loc[:, 'basefilename'] = data_library['filename'].map(
lambda x: os.path.splitext(os.path.basename(x))[0].replace(
'.json', ''))
data_library = data_library.query('size > 0')
log.info('\tbegin selecting isoform files from sql-lite isoform db')
conn = sqlite3.connect(config['mirna_isoform_matrix']['isoform_file_db'])
sql = 'SELECT * from {0}'.format('task_queue')
all_files_df = pd.read_sql_query(sql, conn)
conn.close()
log.info('\tfinished selecting isoform files')
log.info('\tbegin reading from down loaded files')
with open(config['mirna_isoform_matrix']
['isoform_download_prev_files']) as f:
lines = f.read().splitlines()
log.info('\tfinished reading from down loaded files')
log.info(
'filter files.\n\tfiles in cloud storage: %s\n\tfiles previously marked to download: %s\n%s\n'
% (len(data_library), len(all_files_df), data_library))
all_files_df = all_files_df[(all_files_df.DatafileName.isin(
data_library.basefilename))]
all_files_df = all_files_df[~(all_files_df.DatafileName.isin(lines))]
data_library = all_files_df
log.info('finished filter files: %s\n%s\n' %
(len(data_library), data_library))
conn = sqlite3.connect(config['mirna_isoform_matrix']['isoform_file_db'])
submit_to_queue(data_library, conn, 'task_queue', log)
queue_df = data_library
# restart ETL; this gets the diff; also takes care of errors
try:
conn = sqlite3.connect('isoform_download.db')
sql = 'SELECT * from task_queue_status where errors="None"'
queue_df2 = pd.read_sql_query(sql, conn)
log.info('\tso far completed: ' % (len(queue_df2)))
queue_df = queue_df[~(
queue_df.DatafileNameKey.isin(queue_df2.DatafileNameKey))]
log.info('\tso far not completed: ' % (len(queue_df)))
except Exception:
log.exception(
'\n++++++++++++++++++++++\n\tproblem filtering completed jobs, ignoring\n++++++++++++++++++++++\n'
)
# -----------------------------------------------------
# thread this with concurrent futures
#------------------------------------------------------
log.info('\tsubmit jobs to process manager')
pm = process_manager.ProcessManager(max_workers=200,
db='isoform_download.db',
table='task_queue_status',
log=log)
for count, df in data_library.iterrows():
row = df.to_dict()
if 0 == count % 512:
time.sleep(10)
if 0 == count % 2048:
log.info('\t\tsubmitting %s file: %s' %
(count, row['DatafileName']))
if not os.path.isdir(
config['mirna_isoform_matrix']['isoform_download_dir'] +
row['Platform']):
os.makedirs(
config['mirna_isoform_matrix']['isoform_download_dir'] +
row['Platform'])
outfilename = config['mirna_isoform_matrix'][
'isoform_download_dir'] + row['Platform'] + "/" + row[
'DatafileName']
pm.submit(download_file, project_id, bucket_name,
row['DatafileNameKey'], outfilename, '')
time.sleep(0.2)
log.info('\tsubmitted %s total jobs to process manager' % (count))
log.info('\tstart process manager completion check')
pm.start()
log.info('finished downloading isoform files')
