def _get_actual_file_if_queueable(
extracted_subfile: Dict,
original_file: OriginalFile,
samples: List[Sample]) -> OriginalFile:
"""Returns the actual file from the archive if it should be queued.
If the file has been processed or has an unstarted DownloaderJob,
None will be returned.
`extracted_subfile` should be a Dict containing metadata about the
file that was extracted from an archive.
`original_file` should be the file associated with the CURRENT
DownloaderJob.
`samples` are the samples that the actual file should be associated
with if it has to be created.
"""
# Check to see if we've made this original file before:
potential_existing_files = OriginalFile.objects.filter(
source_filename=original_file.source_filename,
filename=extracted_subfile['filename'],
is_archive=False
)
if potential_existing_files.count() > 0:
# We've already created this record, let's see if we actually
# needed to download it or if we just got it because we needed
# a file in the same archive.
actual_file = potential_existing_files[0]
if actual_file.needs_processing():
if not actual_file.is_downloaded:
actual_file.is_downloaded = True
actual_file.save()
return actual_file
else:
return None
else:
actual_file = OriginalFile()
actual_file.is_downloaded = True
actual_file.is_archive = False
actual_file.absolute_file_path = extracted_subfile['absolute_path']
actual_file.filename = extracted_subfile['filename']
actual_file.calculate_size()
actual_file.calculate_sha1()
actual_file.has_raw = True
actual_file.source_url = original_file.source_url
actual_file.source_filename = original_file.source_filename
actual_file.save()
for sample in samples:
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.sample = sample
original_file_sample_association.original_file = actual_file
original_file_sample_association.save()
return actual_file
def test_queue_downloader_jobs_for_original_files(self, mock_send_task):
"""Make sure that queue_downloader_jobs queues all expected Downloader
jobs for a given experiment.
"""
# First, create an experiment with two samples associated with it
# and create two original files for each of those samples.
experiment_object = Experiment()
experiment_object.accession_code = "Experiment1"
experiment_object.save()
sample_object_1 = Sample()
sample_object_1.accession_code = "Sample1"
sample_object_1.platform_accession_code = "Illumina Genome Analyzer"
sample_object_1.platform_accession_name = "Illumina Genome Analyzer"
sample_object_1.technology = "RNA-SEQ"
sample_object_1.manufacturer = "ILLUMINA"
sample_object_1.source_database = "SRA"
sample_object_1.save()
sample_object_2 = Sample()
sample_object_2.accession_code = "Sample2"
sample_object_2.platform_accession_code = "Illumina Genome Analyzer"
sample_object_2.platform_accession_name = "Illumina Genome Analyzer"
sample_object_2.technology = "RNA-SEQ"
sample_object_2.manufacturer = "ILLUMINA"
sample_object_2.source_database = "SRA"
sample_object_2.save()
association = ExperimentSampleAssociation()
association.experiment = experiment_object
association.sample = sample_object_1
association.save()
association = ExperimentSampleAssociation()
association.experiment = experiment_object
association.sample = sample_object_2
association.save()
sample_1_original_files = []
sample_2_original_files = []
original_file = OriginalFile()
original_file.source_url = "first_url"
original_file.source_filename = "first_filename"
original_file.is_downloaded = False
original_file.has_raw = True
original_file.save()
sample_1_original_files.append(original_file)
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file
original_file_sample_association.sample = sample_object_1
original_file_sample_association.save()
original_file = OriginalFile()
original_file.source_url = "second_url"
original_file.source_filename = "second_filename"
original_file.is_downloaded = False
original_file.has_raw = True
original_file.save()
sample_2_original_files.append(original_file)
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file
original_file_sample_association.sample = sample_object_1
original_file_sample_association.save()
original_file = OriginalFile()
original_file.source_url = "third_url"
original_file.source_filename = "third_filename"
original_file.is_downloaded = False
original_file.has_raw = True
original_file.save()
sample_2_original_files.append(original_file)
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file
original_file_sample_association.sample = sample_object_2
original_file_sample_association.save()
original_file = OriginalFile()
original_file.source_url = "fourth_url"
original_file.source_filename = "fourth_filename"
original_file.is_downloaded = False
original_file.has_raw = True
original_file.save()
sample_2_original_files.append(original_file)
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file
original_file_sample_association.sample = sample_object_2
original_file_sample_association.save()
survey_job = SurveyJob(source_type="SRA")
survey_job.save()
surveyor = SraSurveyor(survey_job)
surveyor.queue_downloader_job_for_original_files(
sample_1_original_files, experiment_object.accession_code
)
surveyor.queue_downloader_job_for_original_files(
sample_2_original_files, experiment_object.accession_code
)
self.assertEqual(DownloaderJob.objects.all().count(), 2)
def test_no_repeat_jobs(self):
"""Make sure that queue_downloader_jobs queues all expected Downloader
jobs for a given experiment.
"""
# First, create an experiment with two samples associated with it
# and create two original files for each of those samples.
experiment_object = Experiment()
experiment_object.accession_code = "Experiment1"
experiment_object.save()
sample_object = Sample()
sample_object.accession_code = "Sample1"
sample_object.platform_accession_code = "Illumina Genome Analyzer"
sample_object.platform_accession_name = "Illumina Genome Analyzer"
sample_object.technology = "RNA-SEQ"
sample_object.manufacturer = "ILLUMINA"
sample_object.source_database = "SRA"
sample_object.save()
original_file_1 = OriginalFile()
original_file_1.source_url = "first_url"
original_file_1.source_filename = "first_filename"
original_file_1.is_downloaded = False
original_file_1.has_raw = True
original_file_1.save()
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file_1
original_file_sample_association.sample = sample_object
original_file_sample_association.save()
original_file_2 = OriginalFile()
original_file_2.source_url = "second_url"
original_file_2.source_filename = "second_filename"
original_file_2.is_downloaded = False
original_file_2.has_raw = True
original_file_2.save()
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file_2
original_file_sample_association.sample = sample_object
original_file_sample_association.save()
dlj = DownloaderJob()
dlj.save()
DownloaderJobOriginalFileAssociation(
downloader_job=dlj, original_file=original_file_1
).save()
DownloaderJobOriginalFileAssociation(
downloader_job=dlj, original_file=original_file_2
).save()
survey_job = SurveyJob(source_type="SRA")
survey_job.save()
surveyor = SraSurveyor(survey_job)
surveyor.queue_downloader_job_for_original_files(
[original_file_1, original_file_2], experiment_object.accession_code
)
# We made one DownloaderJob in this test, so
# queue_downloader_job_for_original_files didn't have anything
# to do, so there should still be only one:
self.assertEqual(1, DownloaderJob.objects.all().count())
def create_samples_from_api(self, experiment: Experiment,
platform_dict: Dict) -> List[Sample]:
"""Generates a Sample item for each sample in an AE experiment.
There are many possible data situations for a sample:
- If the sample only has raw data available:
- If it is on a platform that we support:
Download this raw data and process it
- If it is not on a platform we support:
Don't download anything, don't process anything
- If the sample has both raw and derived data:
- If the raw data is on a platform we support:
Download the raw data and process it, abandon the derived data
- If the raw data is not on a platform we support
Download the derived data and no-op it, abandon the raw data
- If the sample only has derived data:
Download the derived data and no-op it.
See an example at: https://www.ebi.ac.uk/arrayexpress/json/v3/experiments/E-MTAB-3050/samples
"""
created_samples = []
samples_endpoint = SAMPLES_URL.format(experiment.accession_code)
r = utils.requests_retry_session().get(samples_endpoint, timeout=60)
samples = r.json()["experiment"]["sample"]
# The SDRF is the complete metadata record on a sample/property basis.
# We run this through our harmonizer and then attach the properties
# to our created samples.
SDRF_URL_TEMPLATE = "https://www.ebi.ac.uk/arrayexpress/files/{code}/{code}.sdrf.txt"
sdrf_url = SDRF_URL_TEMPLATE.format(code=experiment.accession_code)
sdrf_samples = harmony.parse_sdrf(sdrf_url)
harmonized_samples = harmony.harmonize(sdrf_samples)
# An experiment can have many samples
for sample_data in samples:
# For some reason, this sample has no files associated with it.
if "file" not in sample_data or len(sample_data['file']) == 0:
continue
# Each sample is given an experimenatlly-unique title.
flat_sample = utils.flatten(sample_data)
title = harmony.extract_title(flat_sample)
# A sample may actually have many sub files.
# If there is raw data, take that.
# If not, take the derived.
has_raw = False
for sub_file in sample_data['file']:
# For ex: E-GEOD-15645
if isinstance(sub_file['comment'], list):
sub_file_mod = sub_file
sub_file_mod['comment'] = sub_file['comment'][0]
else:
sub_file_mod = sub_file
# Some have the 'data' field, but not the actual data
# Ex: E-GEOD-9656
if sub_file_mod['type'] == "data" and sub_file_mod[
'comment'].get('value', None) != None:
has_raw = True
if 'raw' in sub_file_mod['comment'].get('value', ''):
has_raw = True
skip_sample = False
for sub_file in sample_data['file']:
# Don't get the raw data if it's only a 1-color sample.
if 'Cy3' in str(sample_data) and 'Cy5' not in str(sample_data):
has_raw = False
# Skip derived data if we have it raw.
if has_raw and "derived data" in sub_file['type']:
continue
download_url = None
filename = sub_file["name"]
# sub_file["comment"] is only a list if there's
# more than one comment...
comments = sub_file["comment"]
if isinstance(comments, list):
# Could be: "Derived ArrayExpress Data Matrix FTP
# file" or: "ArrayExpress FTP file". If there is
# no comment with a name including "FTP file" then
# we don't know where to download it so we need to
# mark this job as an error. Therefore don't catch
# the potential exception where download_url
# doesn't get defined.
for comment in comments:
if "FTP file" in comment["name"]:
download_url = comment["value"]
break
else:
download_url = comments["value"]
if not download_url:
logger.error(
"Sample %s did not specify a download url, skipping.",
sample_accession_code,
experiment_accession_code=experiment.accession_code,
survey_job=self.survey_job.id,
sub_file=sub_file)
skip_sample = True
continue
if not filename:
logger.error(
"Sample %s did not specify a filename, skipping.",
sample_accession_code,
experiment_accession_code=experiment.accession_code,
survey_job=self.survey_job.id,
sub_file=sub_file)
skip_sample = True
continue
if skip_sample:
continue
# The accession code is not a simple matter to determine.
sample_source_name = sample_data["source"].get("name", "")
sample_assay_name = sample_data["assay"].get("name", "")
sample_accession_code = self.determine_sample_accession(
experiment.accession_code, sample_source_name,
sample_assay_name, filename)
# Figure out the Organism for this sample
organism_name = UNKNOWN
for characteristic in sample_data["characteristic"]:
if characteristic["category"].upper() == "ORGANISM":
organism_name = characteristic["value"].upper()
if organism_name == UNKNOWN:
logger.error(
"Sample %s did not specify the organism name.",
sample_accession_code,
experiment_accession_code=experiment.accession_code,
survey_job=self.survey_job.id)
organism = None
continue
else:
organism = Organism.get_object_for_name(organism_name)
# Create the sample object
try:
# Associate it with the experiment, but since it
# already exists it already has original files
# associated with it and it's already been downloaded,
# so don't add it to created_samples.
sample_object = Sample.objects.get(
accession_code=sample_accession_code)
# If input experiment includes new protocol information,
# update sample's protocol_info.
existing_protocols = sample_object.protocol_info
protocol_info, is_updated = self.update_sample_protocol_info(
existing_protocols, experiment.protocol_description,
experiment.source_url + '/protocols')
if is_updated:
sample_object.protocol_info = protocol_info
sample_obejct.save()
logger.debug(
"Sample %s already exists, skipping object creation.",
sample_accession_code,
experiment_accession_code=experiment.accession_code,
survey_job=self.survey_job.id)
except Sample.DoesNotExist:
sample_object = Sample()
# The basics
sample_object.source_database = "ARRAY_EXPRESS"
sample_object.title = title
sample_object.accession_code = sample_accession_code
sample_object.source_archive_url = samples_endpoint
sample_object.organism = organism
sample_object.platform_name = platform_dict[
"platform_accession_name"]
sample_object.platform_accession_code = platform_dict[
"platform_accession_code"]
sample_object.manufacturer = platform_dict["manufacturer"]
sample_object.technology = "MICROARRAY"
protocol_info, is_updated = self.update_sample_protocol_info(
existing_protocols=[],
experiment_protocol=experiment.protocol_description,
protocol_url=experiment.source_url + '/protocols')
# Do not check is_updated the first time because we must
# save a list so we can append to it later.
sample_object.protocol_info = protocol_info
sample_object.save()
# Directly assign the harmonized properties
harmonized_sample = harmonized_samples[title]
for key, value in harmonized_sample.items():
setattr(sample_object, key, value)
sample_object.save()
sample_annotation = SampleAnnotation()
sample_annotation.data = sample_data
sample_annotation.sample = sample_object
sample_annotation.is_ccdl = False
sample_annotation.save()
original_file = OriginalFile()
original_file.filename = filename
original_file.source_filename = filename
original_file.source_url = download_url
original_file.is_downloaded = False
original_file.is_archive = True
original_file.has_raw = has_raw
original_file.save()
original_file_sample_association = OriginalFileSampleAssociation(
)
original_file_sample_association.original_file = original_file
original_file_sample_association.sample = sample_object
original_file_sample_association.save()
created_samples.append(sample_object)
logger.debug(
"Created " + str(sample_object),
experiment_accession_code=experiment.accession_code,
survey_job=self.survey_job.id,
sample=sample_object.id)
# Create associations if they don't already exist
ExperimentSampleAssociation.objects.get_or_create(
experiment=experiment, sample=sample_object)
ExperimentOrganismAssociation.objects.get_or_create(
experiment=experiment, organism=organism)
return created_samples
def download_geo(job_id: int) -> None:
"""The main function for the GEO Downloader.
Downloads a single tar file containing the files representing
samples relating to a single experiment stored in
GEO.
"""
job = utils.start_job(job_id)
accession_code = job.accession_code
original_file = job.original_files.first()
if not original_file:
job.failure_reason = "No files associated with the job."
logger.error("No files associated with the job.", downloader_job=job_id)
utils.end_downloader_job(job, success=False)
return
url = original_file.source_url
related_samples = original_file.samples.exclude(technology="RNA-SEQ")
# First, download the sample archive URL.
# Then, unpack all the ones downloaded.
# Then create processor jobs!
# The files for all of the samples are contained within the same zip file. Therefore only
# download the one.
os.makedirs(LOCAL_ROOT_DIR + "/" + accession_code, exist_ok=True)
dl_file_path = LOCAL_ROOT_DIR + "/" + accession_code + "/" + url.split("/")[-1]
logger.debug("Starting to download: " + url, job_id=job_id, accession_code=accession_code)
_download_file(url, dl_file_path, job)
original_file.absolute_file_path = dl_file_path
original_file.is_downloaded = True
original_file.save()
unpacked_sample_files = []
try:
# enumerate all files inside the archive
archived_files = list(ArchivedFile(dl_file_path).get_files())
except FileExtractionError as e:
job.failure_reason = e
logger.exception(
"Error occurred while extracting file.", path=dl_file_path, exception=str(e)
)
utils.end_downloader_job(job, success=False)
return
for og_file in archived_files:
sample = og_file.get_sample()
# We don't want RNA-Seq data from GEO:
# https://github.com/AlexsLemonade/refinebio/issues/966
if sample and sample.technology == "RNA-SEQ":
logger.warn("RNA-Seq sample found in GEO downloader job.", sample=sample)
continue
if not sample and (
not og_file.is_processable() or og_file.experiment_accession_code() != accession_code
):
# skip the files that we know are not processable and can't be associated with a sample
# also skip the files were we couldn't find a sample and they don't mention the current experiment
continue
potential_existing_file = OriginalFile.objects.filter(
source_filename=original_file.source_filename,
filename=og_file.filename,
is_archive=False,
).first()
if potential_existing_file:
# We've already created this record, let's see if we actually
# needed to download it or if we just got it because we needed
# a file in the same archive.
if potential_existing_file.needs_processing():
if not potential_existing_file.is_downloaded:
potential_existing_file.is_downloaded = True
potential_existing_file.save()
unpacked_sample_files.append(potential_existing_file)
continue
# Then this is a new file and we should create an original file for it
actual_file = OriginalFile()
actual_file.is_downloaded = True
actual_file.is_archive = False
actual_file.absolute_file_path = og_file.file_path
actual_file.filename = og_file.filename
actual_file.calculate_size()
actual_file.calculate_sha1()
actual_file.has_raw = True
actual_file.source_url = original_file.source_url
actual_file.source_filename = original_file.source_filename
actual_file.save()
# try to see if the file should be associated with a sample
if sample:
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.sample = sample
original_file_sample_association.original_file = actual_file
original_file_sample_association.save()
else:
# if not, we can associate this file with all samples in the experiment
for sample in related_samples:
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.sample = sample
original_file_sample_association.original_file = actual_file
original_file_sample_association.save()
unpacked_sample_files.append(actual_file)
if len(unpacked_sample_files) > 0:
success = True
logger.debug(
"File downloaded and extracted successfully.",
url=url,
dl_file_path=dl_file_path,
downloader_job=job_id,
)
else:
success = False
logger.info(
"Unable to extract any files.",
url=url,
dl_file_path=dl_file_path,
downloader_job=job_id,
)
job.failure_reason = "Failed to extract any downloaded files."
if success:
create_processor_jobs_for_original_files(unpacked_sample_files, job)
if original_file.is_archive:
original_file.delete_local_file()
utils.end_downloader_job(job, success)
return success
def download_geo(job_id: int) -> None:
"""The main function for the GEO Downloader.
Downloads a single tar file containing the files representing
samples relating to a single experiement stored in
GEO.
"""
job = utils.start_job(job_id)
file_assocs = DownloaderJobOriginalFileAssociation.objects.filter(
downloader_job=job)
original_file = file_assocs[0].original_file
url = original_file.source_url
accession_code = job.accession_code
sample_assocs = OriginalFileSampleAssociation.objects.filter(
original_file=original_file)
related_samples = Sample.objects.filter(
id__in=sample_assocs.values('sample_id'))
# First, download the sample archive URL.
# Then, unpack all the ones downloaded.
# Then create processor jobs!
# The files for all of the samples are
# contained within the same zip file. Therefore only
# download the one.
os.makedirs(LOCAL_ROOT_DIR + '/' + accession_code, exist_ok=True)
dl_file_path = LOCAL_ROOT_DIR + '/' + accession_code + '/' + url.split(
'/')[-1]
logger.debug("Starting to download: " + url,
job_id=job_id,
accession_code=accession_code)
_download_file(url, dl_file_path, job)
original_file.absolute_file_path = dl_file_path
original_file.is_downloaded = True
original_file.save()
has_raw = True
unpacked_sample_files = []
# These files are tarred, and also subsequently gzipped
if '.tar' in dl_file_path:
try:
extracted_files = _extract_tar(dl_file_path, accession_code)
except Exception as e:
job.failure_reason = e
logger.exception("Error occured while extracting tar file.",
path=dl_file_path,
exception=str(e))
utils.end_downloader_job(job, success=False)
return
for og_file in extracted_files:
filename = og_file['filename']
if '_' in filename:
sample_id = filename.split('_')[0]
else:
sample_id = filename.split('.')[0]
try:
sample = Sample.objects.get(accession_code=sample_id)
except Exception as e:
# We don't have this sample, but it's not a total failure. This happens.
continue
try:
# Files from the GEO supplemental file are gzipped inside of the tarball. Great!
archive_file = OriginalFile.objects.get(
source_filename__contains=sample_id)
archive_file.is_downloaded = True
archive_file.is_archive = True
archive_file.absolute_file_path = og_file['absolute_path']
archive_file.calculate_size()
archive_file.calculate_sha1()
archive_file.save()
if '.gz' in og_file['filename']:
extracted_subfile = _extract_gz(og_file['absolute_path'],
accession_code)
else:
extracted_subfile = [og_file]
actual_file = OriginalFile()
actual_file.is_downloaded = True
actual_file.is_archive = False
actual_file.absolute_file_path = extracted_subfile[0][
'absolute_path']
actual_file.filename = extracted_subfile[0]['filename']
actual_file.calculate_size()
actual_file.calculate_sha1()
actual_file.has_raw = True
actual_file.source_url = original_file.source_url
actual_file.source_filename = original_file.source_filename
actual_file.save()
original_file_sample_association = OriginalFileSampleAssociation(
)
original_file_sample_association.sample = sample
original_file_sample_association.original_file = actual_file
original_file_sample_association.save()
archive_file.delete_local_file()
archive_file.is_downloaded = False
archive_file.save()
unpacked_sample_files.append(actual_file)
except Exception as e:
# TODO - is this worth failing a job for?
logger.debug(
"Found a file we didn't have an OriginalFile for! Why did this happen?: "
+ og_file['filename'],
exc_info=1,
file=og_file['filename'],
sample_id=sample_id,
accession_code=accession_code)
# If we don't know why we have it, get rid of it.
os.remove(og_file["absolute_path"])
# This is a .tgz file.
elif '.tgz' in dl_file_path:
# If this is the MINiML file, it has been preprocessed
if '_family.xml.tgz' in dl_file_path:
has_raw = False
try:
extracted_files = _extract_tgz(dl_file_path, accession_code)
except Exception as e:
job.failure_reason = e
logger.exception("Error occured while extracting tgz file.",
path=dl_file_path,
exception=str(e))
utils.end_downloader_job(job, success=False)
return
for og_file in extracted_files:
if '.txt' in og_file['filename']:
try:
gsm_id = og_file['filename'].split('-')[0]
sample = Sample.objects.get(accession_code=gsm_id)
except Exception as e:
os.remove(og_file["absolute_path"])
continue
actual_file = OriginalFile()
actual_file.is_downloaded = True
actual_file.is_archive = False
actual_file.absolute_file_path = og_file['absolute_path']
actual_file.filename = og_file['filename']
actual_file.calculate_size()
actual_file.calculate_sha1()
actual_file.has_raw = has_raw
actual_file.source_url = original_file.source_url
actual_file.source_filename = original_file.source_filename
actual_file.save()
original_file_sample_association = OriginalFileSampleAssociation(
)
original_file_sample_association.sample = sample
original_file_sample_association.original_file = actual_file
original_file_sample_association.save()
unpacked_sample_files.append(actual_file)
# These files are only gzipped.
# These are generally the _actually_ raw (rather than the non-raw data in a RAW file) data
elif '.gz' in dl_file_path:
try:
extracted_files = _extract_gz(dl_file_path, accession_code)
except Exception as e:
job.failure_reason = e
logger.exception("Error occured while extracting gz file.",
path=dl_file_path,
exception=str(e))
utils.end_downloader_job(job, success=False)
return
for og_file in extracted_files:
filename = og_file['filename']
sample_id = filename.split('.')[0]
try:
# The archive we downloaded
archive_file = OriginalFile.objects.get(
source_filename__contains=filename)
archive_file.is_downloaded = True
archive_file.is_archive = True
archive_file.absolute_file_path = dl_file_path
archive_file.calculate_size()
archive_file.calculate_sha1()
archive_file.save()
actual_file = OriginalFile()
actual_file.is_downloaded = True
actual_file.is_archive = False
actual_file.absolute_file_path = og_file['absolute_path']
actual_file.filename = og_file['filename']
actual_file.calculate_size()
actual_file.calculate_sha1()
actual_file.has_raw = True
actual_file.source_url = original_file.source_url
actual_file.source_filename = original_file.source_filename
actual_file.save()
for sample in related_samples:
new_association = OriginalFileSampleAssociation()
new_association.original_file = actual_file
new_association.sample = sample
new_association.save()
archive_file.delete_local_file()
archive_file.is_downloaded = False
archive_file.save()
unpacked_sample_files.append(actual_file)
except Exception as e:
logger.debug(
"Found a file we didn't have an OriginalFile for! Why did this happen?: "
+ og_file['filename'],
exc_info=1,
file=og_file['filename'],
sample_id=sample_id,
accession_code=accession_code)
os.remove(og_file["absolute_path"])
# This is probably just a .txt file
else:
filename = dl_file_path.split('/')[-1]
sample_id = filename.split('_')[0]
actual_file = OriginalFile()
actual_file.is_downloaded = True
actual_file.is_archive = False
actual_file.absolute_file_path = dl_file_path
actual_file.filename = filename
actual_file.calculate_size()
actual_file.calculate_sha1()
actual_file.has_raw = True
actual_file.source_url = original_file.source_url
actual_file.source_filename = original_file.source_filename
actual_file.save()
for sample in related_samples:
new_association = OriginalFileSampleAssociation()
new_association.original_file = actual_file
new_association.sample = sample
new_association.save()
unpacked_sample_files.append(actual_file)
if len(unpacked_sample_files) > 0:
success = True
logger.debug("File downloaded and extracted successfully.",
url=url,
dl_file_path=dl_file_path,
downloader_job=job_id)
else:
success = False
logger.info("Unable to extract any files.",
url=url,
dl_file_path=dl_file_path,
downloader_job=job_id)
job.failure_reason = "Failed to extract any downloaded files."
if success:
utils.create_processor_jobs_for_original_files(unpacked_sample_files,
job)
if original_file.is_archive:
original_file.delete_local_file()
utils.end_downloader_job(job, success)
return success
