def mount_gatk_gvcf_inputs(inputs_param="inputs"):
# Get input gVCFs for this task
print "Mounting task input collection"
inputs_dir = ""
if inputs_param in arvados.current_task()['parameters']:
inputs_dir = arvados.get_task_param_mount(inputs_param)
else:
inputs_dir = arvados.get_job_param_mount(inputs_param)
# Sanity check input gVCFs
input_gvcf_files = []
for f in arvados.util.listdir_recursive(inputs_dir):
if re.search(r'\.vcf\.gz$', f):
input_gvcf_files.append(os.path.join(inputs_dir, f))
elif re.search(r'\.tbi$', f):
pass
elif re.search(r'\.interval_list$', f):
pass
else:
print "WARNING: collection contains unexpected file %s" % f
if len(input_gvcf_files) == 0:
raise errors.InvalidArgumentError(
"Expected one or more .vcf.gz files in collection (found 0 while recursively searching %s)"
% inputs_dir)
# Ensure we can read the gVCF files and that they each have an index
for gvcf_file in input_gvcf_files:
if not os.access(gvcf_file, os.R_OK):
raise errors.FileAccessError("gVCF file not readable: %s" %
gvcf_file)
# Ensure we have corresponding .tbi index and can read it as well
(gvcf_file_base, gvcf_file_ext) = os.path.splitext(gvcf_file)
assert (gvcf_file_ext == ".gz")
tbi_file = gvcf_file_base + ".gz.tbi"
if not os.access(tbi_file, os.R_OK):
tbi_file = gvcf_file_base + ".tbi"
if not os.access(tbi_file, os.R_OK):
raise errors.FileAccessError(
"No readable gVCF index file for gVCF file: %s" %
gvcf_file)
return input_gvcf_files
def get_file_path(parameter,regex):
"""
Return the path to a file with (name) set in script parameters (parameter), using regex (regex):
Basically to avoid:
ref_collection_id = this_job['script_parameters']['reference_index']
ref_collection = coll(ref_collection_id)
for file in ref_collection:
if not re.search('.*f(ast)?a(.gz)?$',file):
continue
ref_file = file
ref_path = os.path.join(arvados.get_job_param_mount("reference_index"),ref_file)
"""
collection_id = arvados.current_job()['script_parameters'][parameter]
collection_handle = Collection(collection_id)
for file in collection_handle:
if not re.search(regex,file):
continue
out_file = file
out_path = os.path.join(arvados.get_job_param_mount(parameter),out_file)
return out_path
def spawn_new_task_per_bed_line(script_parameter, regex, if_sequence=0, and_end_task=True):
"""
Generalized form of one_task_per_pair_input_file from
https://github.com/curoverse/arvados/blob/master/crunch_scripts/arvados_bwa.py
Creates a new task if the file in the collection matches the regex
"""
if if_sequence != arvados.current_task()['sequence']:
return
job_input = arvados.current_job()['script_parameters'][script_parameter]
input_collection = Collection(job_input)
for name in input_collection:
if not re.search(regex,name):
continue
name_path = os.path.join(arvados.get_job_param_mount(script_parameter),name)
bed_lines = (line.split() for line in open(name_path, 'r'))
# Start the biggest regions first
def cmp_desc_region_size(a, b):
return ((int(b[2]) - int(b[1])) -
(int(a[2]) - int(a[1])))
for bed_line in sorted(bed_lines, cmp=cmp_desc_region_size):
print bed_line
new_task_attrs = {
'job_uuid': arvados.current_job()['uuid'],
'created_by_job_task_uuid': arvados.current_task()['uuid'],
'sequence': if_sequence + 1,
'parameters': {
'chrom': bed_line[0],
'start': bed_line[1],
'end': bed_line[2]
}
}
arvados.api().job_tasks().create(body=new_task_attrs).execute()
if and_end_task:
arvados.api().job_tasks().update(uuid=arvados.current_task()['uuid'],
body={'success':True}
).execute()
exit()
import numpy as np
########################################################################################################################
# Read constants
NUM_RETRIES = int(arvados.getjobparam('num-retries'))
assert NUM_RETRIES > 0, "'num-retries' must be strictly positive"
antigen_type = str(arvados.getjobparam('antigen-type'))
########################################################################################################################
#Set-up collection and logging file to write out to
out = arvados.collection.Collection(num_retries=NUM_RETRIES)
time_logging_fh = out.open('time_log.txt', 'w')
########################################################################################################################
# Load settings
t0 = time.time()
settings = imp.load_source('settings', arvados.get_job_param_mount('settings'))
t1 = time.time()
time_logging_fh.write('Loading settings %fs\n' % (t1 - t0))
########################################################################################################################
#Get path lengths and path integers
cr = arvados.CollectionReader(arvados.getjobparam('path-lengths'),
num_retries=NUM_RETRIES)
t0 = time.time()
with cr.open("path_integers.npy", 'r') as f:
path_integers = np.load(f)
t1 = time.time()
with cr.open("path_lengths.npy", 'r') as f:
path_lengths = np.load(f)
t2 = time.time()
time_logging_fh.write('Loading path integers took %fs\n' % (t1 - t0))
time_logging_fh.write('Loading path lengths took %fs\n' % (t2 - t1))
# metadata:
# batch: your-arbitrary-batch-name
algorithm:
aligner: bwa
mark_duplicates: true
recalibrate: false
realign: false
variantcaller: freebayes
platform: illumina
quality_format: Standard
# for targetted projects, set the region
# variant_regions: /path/to/your.bed
''')
os.unlink("/usr/local/share/bcbio-nextgen/gemini_data")
os.symlink(arvados.get_job_param_mount("gemini_data"),
"/usr/local/share/bcbio-nextgen/gemini_data")
os.chdir(arvados.current_task().tmpdir)
rcode = subprocess.call([
"bcbio_nextgen.py", "--workflow", "template",
"/tmp/crunch-job/freebayes-variant.yaml", "project1",
subst.do_substitution(p, "$(file $(R1))"),
subst.do_substitution(p, "$(file $(R2))")
])
os.chdir("project1/work")
os.symlink("/usr/local/share/bcbio-nextgen/galaxy/tool-data", "tool-data")
NUM_PHASES_TMP += 1
elif not QUALITY:
NUM_PHASES_TMP += 1
NUM_CALLSETS += 1
t1 = time.time()
NUM_PHASES = NUM_PHASES_TMP/NUM_CALLSETS
assert float(NUM_PHASES) == NUM_PHASES_TMP/float(NUM_CALLSETS), "Unequal number of phases per callset"
time_logging_fh.write("Cursory reading of 'pythonic-tiling-callset-files' took %fs\n" % (t1-t0))
#Get callset phenotype files
#Unable to use collections due to csv/json read functions
t0 = time.time()
if arvados.getjobparam('callset-phenotypes') == None:
phenotype_file_paths = []
else:
phenotype_path = arvados.get_job_param_mount('callset-phenotypes')
for root, dirs, files in os.walk(phenotype_path):
assert len(dirs) == 0, "Expects 'callset-phenotypes' to be a flat directory"
phenotype_file_paths = [os.path.join(root, f) for f in files]
t1 = time.time()
time_logging_fh.write("Getting job param mount (and file paths) of 'callset-phenotypes' took %fs\n" % (t1-t0))
########################################################################################################################
population, subjects, callset_names, size = fns.get_population(
ACCEPTED_PATHS,
path_integers,
path_lengths,
NUM_CALLSETS,
NUM_PHASES,
phenotype_file_paths,
callset_collection_reader,
CALLSET_NAME_REGEX,
# batch: your-arbitrary-batch-name
algorithm:
aligner: bwa
mark_duplicates: true
recalibrate: false
realign: false
variantcaller: freebayes
platform: illumina
quality_format: Standard
# for targetted projects, set the region
# variant_regions: /path/to/your.bed
"""
)
os.unlink("/usr/local/share/bcbio-nextgen/gemini_data")
os.symlink(arvados.get_job_param_mount("gemini_data"), "/usr/local/share/bcbio-nextgen/gemini_data")
os.chdir(arvados.current_task().tmpdir)
rcode = subprocess.call(
[
"bcbio_nextgen.py",
"--workflow",
"template",
"/tmp/crunch-job/freebayes-variant.yaml",
"project1",
subst.do_substitution(p, "$(file $(R1))"),
subst.do_substitution(p, "$(file $(R2))"),
]
)
########################################################################################################################
# Read constants
NUM_RETRIES = int(arvados.getjobparam('num-retries'))
assert NUM_RETRIES > 0, "'num-retries' must be strictly positive"
antigen_type = str(arvados.getjobparam('antigen-type'))
########################################################################################################################
#Set-up collection and logging file to write out to
out = arvados.collection.Collection(num_retries=NUM_RETRIES)
time_logging_fh = out.open('time_log.txt', 'w')
info_fh = out.open('log.txt', 'w')
########################################################################################################################
# Load settings
t0 = time.time()
settings = imp.load_source('settings', arvados.get_job_param_mount('settings'))
t1 = time.time()
time_logging_fh.write('Loading settings %fs\n' %(t1-t0))
########################################################################################################################
#Parallelize based on settings
def one_task_per_classifier(num_classifiers_to_parameterize, if_sequence=0, and_end_task=True):
if if_sequence != arvados.current_task()['sequence']:
return
api_client = arvados.api('v1')
for i in range(num_classifiers_to_parameterize):
new_task_attrs = {
'job_uuid': arvados.current_job()['uuid'],
'created_by_job_task_uuid': arvados.current_task()['uuid'],
'sequence': if_sequence + 1,
'parameters': {
'classifier_index':i,
