def handle(self, file: str, verbosity: int = 1, **options):
"""Execute the main function."""
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
# Load the ontology file
with open(file) as obo_file:
G = obonet.read_obo(obo_file)
if verbosity > 0:
self.stdout.write("Preprocessing")
cv_name = "relationship"
# Initializing ontology
ontology = OntologyLoader(cv_name)
# Load typedefs as Dbxrefs and Cvterm
if verbosity > 0:
self.stdout.write("Loading typedefs")
for data in tqdm(G.graph["typedefs"],
disable=False if verbosity > 0 else True):
ontology.store_type_def(data)
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done"))
def handle(self, file: str, verbosity: int = 1, cpu: int = 1, **options):
"""Execute the main function."""
if verbosity > 0:
self.stdout.write("Preprocessing")
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
# Load the publication file
with open(file) as tab_file:
for line in tab_file:
organism, doi = line.strip().split("\t")
tasks.append(
pool.submit(OrganismLoader().store_organism_publication,
organism, doi))
if verbosity > 0:
self.stdout.write("Loading organism publications")
for task in tqdm(as_completed(tasks), total=len(tasks)):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done"))
def handle(
self, file: str, soterm: str, verbosity: int = 1, cpu: int = 1, **options
):
"""Execute the main function."""
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
# retrieve only the file name
filename = os.path.basename(file)
try:
sequence_file = SequenceLoader(filename=filename)
except ImportingError as e:
raise CommandError(e)
if verbosity > 0:
self.stdout.write("Processing file: {}".format(filename))
fasta_sequences = SeqIO.parse(open(file), "fasta")
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
for fasta in fasta_sequences:
tasks.append(
pool.submit(sequence_file.add_sequence_to_feature, fasta, soterm)
)
if verbosity > 0:
self.stdout.write("Loading")
for task in tqdm(as_completed(tasks), total=len(tasks)):
if task.result():
raise (task.result())
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done with {}".format(filename)))
def handle(self,
file: str,
cvterm: str,
soterm: str,
doi: str = None,
verbosity: int = 1,
cpu: int = 1,
**options):
"""Execute the main function."""
if verbosity > 0:
self.stdout.write("Preprocessing")
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
# retrieve only the file name
filename = os.path.basename(file)
try:
feature_file = FeatureLoader(filename=filename,
source="GFF_source")
except ImportingError as e:
raise CommandError(e)
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
# Load the annotation file
with open(file) as tab_file:
for line in tab_file:
if line.startswith("#"):
continue
feature, annotation = line.strip().split("\t")
tasks.append(
pool.submit(
feature_file.store_feature_annotation,
feature,
soterm,
cvterm,
annotation,
doi,
))
if verbosity > 0:
self.stdout.write("Loading feature annotations")
for task in tqdm(as_completed(tasks), total=len(tasks)):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done"))
def handle(self,
file: str,
format: str,
cpu: int = 1,
verbosity: int = 1,
**options):
"""Execute the main function."""
# retrieve only the file name
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
if format == "blast-xml":
source = "BLAST_source"
elif format == "interproscan-xml":
source = "InterproScan_source"
else:
raise CommandError("Format allowed options are blast-xml or "
"interproscan-xml only, not {}".format(format))
filename = os.path.basename(file)
try:
feature_file = FeatureLoader(filename=filename, source=source)
except ImportingError as e:
raise CommandError(e)
if verbosity > 0:
self.stdout.write("Processing file: {}".format(filename))
try:
records = SearchIO.parse(file, format)
except ValueError as e:
return CommandError(e)
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
for record in records:
for hit in record.hits:
tasks.append(
pool.submit(feature_file.store_bio_searchio_hit, hit,
record.target))
if verbosity > 0:
self.stdout.write("Loading")
for task in tqdm(as_completed(tasks), total=len(tasks)):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
pool.shutdown()
if len(feature_file.ignored_goterms) > 0:
self.stdout.write(
self.style.WARNING("Ignored GO terms: {}".format(
feature_file.ignored_goterms)))
if verbosity > 0:
self.stdout.write(
self.style.SUCCESS("Done with {}".format(filename)))
def handle(self,
file: str,
organism: str,
soterm: str,
nosequence: bool = False,
cpu: int = 1,
description: str = None,
url: str = None,
doi: str = None,
verbosity: int = 1,
**options) -> None:
"""Execute the main function."""
if verbosity > 0:
self.stdout.write("Preprocessing")
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
# retrieve only the file name
filename = os.path.basename(file)
try:
sequence_file = SequenceLoader(filename=filename,
description=description,
url=url,
doi=doi)
except ImportingError as e:
raise CommandError(e)
fasta_sequences = SeqIO.parse(open(file), "fasta")
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
for fasta in fasta_sequences:
tasks.append(
pool.submit(
sequence_file.store_biopython_seq_record,
fasta,
soterm,
organism,
nosequence,
))
if verbosity > 0:
self.stdout.write("Loading")
for task in tqdm(as_completed(tasks), total=len(tasks)):
if task.result():
raise (task.result())
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done"))
def handle(self, file: str, verbosity: int = 1, **options):
"""Execute the main function."""
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
# Load the ontology file
with open(file) as obo_file:
G = obonet.read_obo(obo_file)
if verbosity > 0:
self.stdout.write("Preprocessing")
cv_name = G.graph["default-namespace"][0]
cv_definition = G.graph["data-version"]
# Initializing ontology
ontology = OntologyLoader(cv_name, cv_definition)
if verbosity > 0:
self.stdout.write("Loading typedefs")
# Load typedefs as Dbxrefs and Cvterm
for typedef in tqdm(
G.graph["typedefs"], disable=False if verbosity > 0 else True
):
ontology.store_type_def(typedef)
if verbosity > 0:
self.stdout.write("Loading terms")
for n, data in tqdm(
G.nodes(data=True), disable=False if verbosity > 0 else True
):
ontology.store_term(n, data)
if verbosity > 0:
self.stdout.write("Loading relationships")
for u, v, type in tqdm(
G.edges(keys=True), disable=False if verbosity > 0 else True
):
ontology.store_relationship(u, v, type)
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done"))
def handle(self, file=str, verbosity: int = 1, cpu: int = 1, **options):
"""Execute the main function."""
if verbosity > 0:
self.stdout.write("Preprocessing")
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
# filename = os.path.basename(file)
bib_database = None
try:
bib_database = bibtexparser.load(open(file))
except ValueError as e:
return CommandError(e)
bibtex = PublicationLoader()
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
for entry in bib_database.entries:
# create model object for each entry
if entry["ENTRYTYPE"]:
tasks.append(pool.submit(bibtex.store_bibtex_entry, entry))
if verbosity > 0:
self.stdout.write("Loading")
for task in tqdm(
as_completed(tasks),
total=len(tasks),
disable=False if verbosity > 0 else True,
):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done"))
def handle(self,
file: str,
organism: str,
doi: str = None,
ignore: str = None,
qtl: bool = False,
cpu: int = 1,
verbosity: int = 1,
**options):
"""Execute the main function."""
# retrieve only the file name
filename = os.path.basename(file)
if verbosity > 0:
self.stdout.write("Processing file: {}".format(filename))
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
try:
index_file = "{}.tbi".format(file)
FileValidator().validate(index_file)
except ImportingError:
try:
index_file = "{}.csi".format(file)
FileValidator().validate(index_file)
except ImportingError:
raise CommandError("No index found (.tbi/.csi)")
try:
feature_file = FeatureLoader(filename=filename,
source="GFF_SOURCE",
doi=doi)
except ImportingError as e:
raise CommandError(e)
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
chunk_size = cpu * 2
# Load the GFF3 file
with open(file) as tbx_file:
tbx = pysam.TabixFile(filename=tbx_file.name, index=index_file)
for row in tqdm(tbx.fetch(parser=pysam.asGTF()),
total=get_num_lines(file)):
if ignore is not None and row.feature in ignore:
continue
tasks.append(
pool.submit(feature_file.store_tabix_GFF_feature, row,
organism, qtl))
if len(tasks) >= chunk_size:
for task in as_completed(tasks):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
tasks.clear()
else:
for task in as_completed(tasks):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
tasks.clear()
pool.shutdown()
if verbosity > 0:
self.stdout.write("Loading relationships")
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
for item in feature_file.relationships:
tasks.append(
pool.submit(
feature_file.store_relationship,
organism,
item["subject_id"],
item["object_id"],
))
for task in tqdm(as_completed(tasks), total=len(tasks)):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
pool.shutdown()
if feature_file.ignored_attrs is not None:
self.stdout.write(
self.style.WARNING("Ignored attrs: {}".format(
feature_file.ignored_attrs)))
if verbosity > 0:
self.stdout.write(
self.style.SUCCESS("Done with {}".format(filename)))
def handle(self, file: str, cpu: int = 1, verbosity: int = 1, **options):
"""Execute the main function."""
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
# Load the ontology file
with open(file) as obo_file:
G = read_obo(obo_file)
cv_definition = G.graph["data-version"]
if verbosity > 0:
self.stdout.write("Preprocessing")
# Instantiating Ontology in order to have access to secondary cv, db,
# cvterm, and dbxref, even though the main cv will not be used.
# There will be a ontology for each namespace, plus one called
# gene_ontology for storing type_defs
try:
ontology = OntologyLoader("biological_process", cv_definition)
ontology = OntologyLoader("molecular_function", cv_definition)
ontology = OntologyLoader("cellular_component", cv_definition)
ontology = OntologyLoader("external", cv_definition)
ontology = OntologyLoader("gene_ontology", cv_definition)
except ImportingError as e:
raise CommandError(e)
# Load typedefs as Dbxrefs and Cvterm
if verbosity > 0:
self.stdout.write("Loading typedefs ({} threads)".format(cpu))
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
for typedef in G.graph["typedefs"]:
tasks.append(pool.submit(ontology.store_type_def, typedef))
for task in tqdm(as_completed(tasks), total=len(tasks)):
if task.result():
raise (task.result())
# Load the cvterms
if verbosity > 0:
self.stdout.write("Loading terms ({} threads)".format(cpu))
lock = Lock()
tasks = list()
for n, data in G.nodes(data=True):
tasks.append(pool.submit(ontology.store_term, n, data, lock))
for task in tqdm(as_completed(tasks), total=len(tasks)):
if task.result():
raise (task.result())
# Load the relationship between cvterms
if verbosity > 0:
self.stdout.write("Loading relationships ({} threads)".format(cpu))
tasks = list()
for u, v, type in G.edges(keys=True):
tasks.append(pool.submit(ontology.store_relationship, u, v, type))
for task in tqdm(as_completed(tasks), total=len(tasks)):
if task.result():
raise (task.result())
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done"))
def handle(
self,
file: str,
organism: str,
doi: str = None,
cpu: int = 1,
verbosity: int = 1,
**options
):
"""Execute the main function."""
# retrieve only the file name
filename = os.path.basename(file)
if verbosity > 0:
self.stdout.write("Processing file: {}".format(filename))
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
try:
index_file = "{}.tbi".format(file)
FileValidator().validate(index_file)
except ImportingError:
try:
index_file = "{}.csi".format(file)
FileValidator().validate(index_file)
except ImportingError:
raise CommandError("No index found (.tbi/.csi)")
try:
feature_file = FeatureLoader(
filename=filename, source="VCF_SOURCE", doi=doi
)
except ImportingError as e:
raise CommandError(e)
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
chunk_size = cpu * 2
# Load the GFF3 file
with open(file) as tbx_file:
tbx = pysam.TabixFile(filename=tbx_file.name, index=index_file)
for row in tqdm(tbx.fetch(parser=pysam.asVCF()), total=get_num_lines(file)):
tasks.append(
pool.submit(feature_file.store_tabix_VCF_feature, row, organism)
)
if len(tasks) >= chunk_size:
for task in as_completed(tasks):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
tasks.clear()
else:
for task in as_completed(tasks):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
tasks.clear()
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done with {}".format(filename)))
def handle(self, file: str, cpu: int = 1, verbosity: int = 0, **options):
"""Execute the main function."""
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
filename = os.path.basename(file)
if verbosity > 0:
self.stdout.write("Processing file: {}".format(filename))
try:
groups = open(file, "r")
# retrieve only the file name
except ImportingError as e:
raise CommandError(e)
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
cv, created = Cv.objects.get_or_create(name="feature_property")
ortho_db, created = Db.objects.get_or_create(name="ORTHOMCL_SOURCE")
ortho_dbxref, created = Dbxref.objects.get_or_create(
accession="ORTHOMCL_SOURCE", db=ortho_db)
cvterm_cluster, created = Cvterm.objects.get_or_create(
name="orthologous group",
cv=cv,
dbxref=ortho_dbxref,
is_obsolete=0,
is_relationshiptype=0,
)
# hardcoded as orthomcl uses protein input
soterm = "polypeptide"
source = "null"
featureloader = FeatureLoader(source=source, filename=filename)
# each line is an orthologous group
for line in groups:
members = []
name = ""
fields = re.split(r"\s+", line.strip())
if re.search(r"^(\w+)\:", fields[0]):
group_field = re.match(r"^(\w+)\:", fields[0])
name = group_field.group(1)
fields.pop(0)
for field in fields:
if re.search(r"^(\w+)\|(\S+)", field):
member_field = re.match(r"^(\w+)\|(\S+)", field)
ident = member_field.group(2)
members.append(ident)
else:
raise CommandError("Cluster has no identification, check.")
# only orthologous groups with 2 or more members allowed
if len(members) > 1:
tasks.append(
pool.submit(
featureloader.store_feature_groups,
soterm=soterm,
group=members,
term=cvterm_cluster.cvterm_id,
value=name,
))
if verbosity > 0:
self.stdout.write("Loading")
for task in tqdm(as_completed(tasks), total=len(tasks)):
if task.result():
raise (task.result())
pool.shutdown()
if verbosity > 0:
self.stdout.write(
self.style.SUCCESS("Done with {}".format(filename)))
def test_validate_file(self):
"""Tests - validate file."""
# test file not exists
file_path = "/tmp/machado.test.file"
v = FileValidator()
with self.assertRaisesMessage(ImportingError,
"{} does not exist".format(file_path)):
v.validate(file_path=file_path)
# test wrong file type
file_path = "/tmp/machado.test.dir"
os.mkdir(file_path)
v = FileValidator()
with self.assertRaisesMessage(ImportingError,
"{} is not a file".format(file_path)):
v.validate(file_path=file_path)
os.rmdir(file_path)
# test file not readable
file_path = "/tmp/machado.test.file"
os.mknod(file_path, mode=0o200)
v = FileValidator()
with self.assertRaisesMessage(ImportingError,
"{} is not readable".format(file_path)):
v.validate(file_path=file_path)
os.remove(file_path)
def handle(self,
file: str,
organism: str,
program: str,
programversion: str,
name: str = None,
description: str = None,
algorithm: str = None,
assaydb: str = "SRA",
timeexecuted: str = None,
norm: int = 1,
cpu: int = 1,
verbosity: int = 0,
**options):
"""Execute the main function."""
filename = os.path.basename(file)
if verbosity > 0:
self.stdout.write("Processing file: {}".format(filename))
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
# start reading file
try:
rnaseq_data = open(file, "r")
# retrieve only the file name
except ImportingError as e:
raise CommandError(e)
header = 1
# analysis_list = defaultdict(list)
analysis_list = list()
# instantiate Loader
analysis_file = AnalysisLoader()
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
for line in rnaseq_data:
fields = re.split("\t", line.rstrip())
nfields = len(fields)
# validate fields within line
try:
FieldsValidator().validate(nfields, fields)
except ImportingError as e:
raise CommandError(e)
# read header and instantiate analysis object for each assay
# e.g. SRR12345.
if header:
# first element is the string "gene" - need to be removed
fields.pop(0)
for i in range(len(fields)):
# parse field to get SRA ID. e.g.: SRR5167848.htseq
# try to remove ".htseq" part of string
string = re.match(r"(\w+)\.(\w+)", fields[i])
try:
assay = string.group(1)
except IntegrityError as e:
raise CommandError(e)
# store analysis
try:
analysis = analysis_file.store_analysis(
program=program,
sourcename=fields[i],
programversion=programversion,
timeexecuted=timeexecuted,
algorithm=algorithm,
name=assay,
description=description,
filename=filename,
)
except ImportingError as e:
raise CommandError(e)
# store quantification
try:
analysis_file.store_quantification(analysis=analysis,
assayacc=assay)
except ImportingError as e:
raise CommandError(e)
# finally, store each analysis in a list.
analysis_list.insert(i, analysis)
header = 0
else:
# first element is the feature acc. "e.g.: AT2G44195.1.TAIR10"
feature_name = fields.pop(0)
for i in range(len(fields)):
if norm:
normscore = fields[i]
rawscore = None
else:
normscore = None
rawscore = fields[i]
# store analysis feature for each value
tasks.append(
pool.submit(
analysis_file.store_analysisfeature,
analysis_list[i],
feature_name,
organism,
rawscore,
normscore,
))
if verbosity > 0:
self.stdout.write("Loading")
for task in tqdm(as_completed(tasks), total=len(tasks)):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
pool.shutdown()
if verbosity > 0:
self.stdout.write(
self.style.SUCCESS("Done with {}".format(filename)))
def handle(
self,
file: str,
format: str,
so_query: str,
so_subject: str,
organism_query: str,
organism_subject: str,
program: str,
programversion: str,
name: str = None,
description: str = None,
algorithm: str = None,
cpu: int = 1,
verbosity: int = 1,
**options
):
"""Execute the main function."""
filename = os.path.basename(file)
if organism_query == "mutispecies multispecies":
raise CommandError("Query's organism cannot be multispecies")
if format not in VALID_FORMAT:
raise CommandError(
"The format is not valid. Please choose: " "{}".format(VALID_FORMAT)
)
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
try:
similarity_file = SimilarityLoader(
filename=filename,
so_query=so_query,
so_subject=so_subject,
org_query=organism_query,
org_subject=organism_subject,
algorithm=algorithm,
name=name,
description=description,
program=program,
programversion=programversion,
input_format=format,
)
except ImportingError as e:
raise CommandError(e)
try:
similarity_records = SearchIO.parse(file, format)
except ValueError as e:
return CommandError(e)
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
if verbosity > 0:
self.stdout.write("Processing file: {}".format(filename))
for record in similarity_records:
if len(record.hsps) > 0:
tasks.append(
pool.submit(similarity_file.store_bio_searchio_query_result, record)
)
if verbosity > 0:
self.stdout.write("Loading")
for task in tqdm(as_completed(tasks), total=len(tasks)):
try:
task.result()
except ImportingError as e:
raise CommandError(e)
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done with {}".format(filename)))
def handle(self,
file: str,
biomaterialdb: str,
assaydb: str,
cpu: int = 1,
verbosity: int = 0,
**options):
"""Execute the main function."""
filename = os.path.basename(file)
nfields = 8
if verbosity > 0:
self.stdout.write("Processing file: {}".format(filename))
# instantiate project, biomaterial and assay
try:
project_file = ProjectLoader()
biomaterial_file = BiomaterialLoader()
assay_file = AssayLoader()
treatment_file = TreatmentLoader()
except ImportingError as e:
raise CommandError(e)
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
try:
rnaseq_data = open(file, "r")
# retrieve only the file name
except ImportingError as e:
raise CommandError(e)
# each line is an RNA-seq experiment
# e.g:
# Oryza sativa,GSE112368,GSM3068810,SRR6902930,heat leaf,Heat stress,Leaf,Jul-20-2018
for line in rnaseq_data:
fields = re.split(",", line.strip())
organism_name = fields[0]
try:
FieldsValidator().validate(nfields, fields)
except ImportingError as e:
raise CommandError(e)
# get organism - mandatory
try:
organism = retrieve_organism(organism=organism_name)
except ObjectDoesNotExist as e:
raise ImportingError(e)
# store project
try:
# e.g: "GSExxx" from GEO
project_model = project_file.store_project(name=fields[1],
filename=filename)
except ObjectDoesNotExist as e:
raise ImportingError(e)
# store biomaterial (sample)
try:
# e.g: "GSMxxxx" from GEO
biomaterial_model = biomaterial_file.store_biomaterial(
db=biomaterialdb,
acc=fields[2],
organism=organism,
name=fields[2],
filename=filename,
description=fields[6],
)
except ImportingError as e:
raise CommandError(e)
# store treatment
try:
# e.g. "Heat"
treatment_model = treatment_file.store_treatment(
name=fields[5], biomaterial=biomaterial_model)
except ImportingError as e:
raise CommandError(e)
try:
biomaterial_file.store_biomaterial_treatment(
biomaterial=biomaterial_model, treatment=treatment_model)
except ImportingError as e:
raise CommandError(e)
# store assay (experiment)
try:
# e.g. "SRRxxxx" from GEO
assay_model = assay_file.store_assay(
db=assaydb,
acc=fields[3],
assaydate=fields[7],
name=fields[3],
filename=filename,
description=fields[4],
)
assay_file.store_assay_project(assay=assay_model,
project=project_model)
assay_file.store_assay_biomaterial(
assay=assay_model, biomaterial=biomaterial_model)
except ImportingError as e:
raise CommandError(e)
if verbosity > 0:
self.stdout.write(
self.style.SUCCESS("Done with {}".format(filename)))
def handle(self,
file: str,
name: str,
verbosity: int = 1,
cpu: int = 1,
**options):
"""Execute the main function."""
if verbosity > 0:
self.stdout.write("Preprocessing")
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
try:
organism_db = OrganismLoader(organism_db=name)
except ImportingError as e:
raise CommandError(e)
file_names = open(file)
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
current_id = None
taxid, scname = "", ""
synonyms, common_names = [], []
for line in file_names:
columns = re.split(r"\s\|\s", line)
if current_id is not None and current_id != columns[0]:
# store if new record
tasks.append(
pool.submit(
organism_db.store_organism_record,
taxid,
scname,
synonyms,
common_names,
))
taxid, scname = "", ""
synonyms, common_names = [], []
current_id = columns[0]
# get data while current_id remains unchanged
if columns[3] == "scientific name":
taxid = columns[0]
if columns[2] == "" or columns[1] == columns[2]:
scname = columns[1]
else:
scname = "{} {}".format(columns[1], columns[2])
elif columns[3] == "synonym":
synonyms.append(columns[1])
elif columns[3] == "common name":
common_names.append(columns[1])
else:
# insert the last record
tasks.append(
pool.submit(
organism_db.store_organism_record,
taxid,
scname,
synonyms,
common_names,
))
if verbosity > 0:
self.stdout.write("Loading names file")
for task in tqdm(as_completed(tasks), total=len(tasks)):
if task.result():
raise (task.result())
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done"))
def handle(
self,
file: str,
cpu: int = 1,
soterm: str = "mRNA",
verbosity: int = 0,
**options
):
"""Execute the main function."""
filename = os.path.basename(file)
if verbosity > 0:
self.stdout.write("Processing file: {}".format(filename))
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
try:
pairs = open(file, "r")
# retrieve only the file name
except ImportingError as e:
raise CommandError(e)
cvterm_corel = Cvterm.objects.get(
name="correlated with", cv__name="relationship"
).cvterm_id
# feature source is not needed here
source = "null"
featureloader = FeatureLoader(source=source, filename=filename)
size = get_num_lines(file)
# every cpu should be able to handle 5 tasks
chunk = cpu * 5
with ThreadPoolExecutor(max_workers=cpu) as pool:
tasks = list()
for line in tqdm(pairs, total=size):
nfields = 3
fields = re.split(r"\s+", line.rstrip())
try:
FieldsValidator().validate(nfields, fields)
except ImportingError as e:
raise CommandError(e)
# get corrected PCC value (last item from fields list)
value = float(fields.pop()) + 0.7
tasks.append(
pool.submit(
featureloader.store_feature_pairs,
pair=fields,
soterm=soterm,
term=cvterm_corel,
value=value,
)
)
if len(tasks) >= chunk:
for task in as_completed(tasks):
if task.result():
raise (task.result())
tasks.clear()
else:
for task in as_completed(tasks):
if task.result():
raise (task.result())
tasks.clear()
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done with {}".format(filename)))
def handle(self,
file: str,
name: str,
organismdb: str,
verbosity: int = 1,
cpu: int = 1,
**options):
"""Execute the main function."""
if verbosity > 0:
self.stdout.write("Preprocessing")
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
try:
phylotree = PhylotreeLoader(phylotree_name=name,
organism_db=organismdb)
except ImportingError as e:
raise CommandError(e)
file_nodes = open(file)
self.nodes: Dict[int, Dict[str, Any]] = dict()
self.ctr = 0
for line in file_nodes:
columns = re.split(r"\s\|\s", line)
tax_id = int(columns[0])
parent_id = int(columns[1])
level = columns[2]
if self.nodes.get(tax_id) is None:
self.nodes[tax_id] = {
"parent_id": parent_id,
"level": level,
"children": [],
}
else:
self.nodes[tax_id]["parent_id"] = parent_id
self.nodes[tax_id]["level"] = level
if self.nodes.get(parent_id) is None:
self.nodes[parent_id] = {
"parent_id": None,
"level": None,
"children": [tax_id],
}
else:
self.nodes[parent_id]["children"].append(tax_id)
self.walktree(node_id=1)
if verbosity > 0:
self.stdout.write("Loading")
pool = ThreadPoolExecutor(max_workers=cpu)
tasks = list()
# By setting the parent_id to None it's possible to load the
# nodes randomly and using threads.
try:
for key, data in self.nodes.items():
tasks.append(
pool.submit(
phylotree.store_phylonode_record,
tax_id=key,
parent_id=None,
level=data["level"],
left_idx=data["left_idx"],
right_idx=data["right_idx"],
))
for task in tqdm(as_completed(tasks), total=len(tasks)):
if task.result():
tax_id, phylonode = task.result()
self.nodes[tax_id]["phylonode_id"] = phylonode.phylonode_id
except KeyError as e:
raise CommandError("Could not calculate {}. Make it sure it is "
"possible to walk the entire tree "
"structure.".format(e))
if verbosity > 0:
self.stdout.write("Loading nodes relationships")
tasks = list()
# Load the nodes relationship info
for key, data in self.nodes.items():
if data.get("parent_id") is None:
continue
tasks.append(
pool.submit(
phylotree.update_parent_phylonode_id,
data["phylonode_id"],
data["parent_id"],
))
for task in tqdm(as_completed(tasks), total=len(tasks)):
if task.result():
raise (task.result())
pool.shutdown()
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done"))
def handle(self,
file: str,
organism: str,
soterm: str = "mRNA",
cpu: int = 1,
verbosity: int = 0,
**options):
"""Execute the main function."""
filename = os.path.basename(file)
if verbosity > 0:
self.stdout.write("Processing file: {}".format(filename))
try:
organism = retrieve_organism(organism)
except IntegrityError as e:
raise ImportingError(e)
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
try:
clusters = open(file, "r")
# retrieve only the file name
except ImportingError as e:
raise CommandError(e)
tasks = list()
cv, created = Cv.objects.get_or_create(name="feature_property")
coexp_db, created = Db.objects.get_or_create(name="LSTRAP_SOURCE")
coexp_dbxref, created = Dbxref.objects.get_or_create(
accession="LSTRAP_SOURCE", db=coexp_db)
cvterm_cluster, created = Cvterm.objects.get_or_create(
name="coexpression group",
cv=cv,
dbxref=coexp_dbxref,
is_obsolete=0,
is_relationshiptype=0,
)
# feature source is not needed here
source = "null"
featureloader = FeatureLoader(source=source, filename=filename)
pool = ThreadPoolExecutor(max_workers=cpu)
# each line is an coexpression cluster group
for line in tqdm(clusters, total=get_num_lines(file)):
name = ""
fields = re.split(r"\s+", line.strip())
nfields = len(fields)
try:
FieldsValidator().validate(nfields, fields)
except ImportingError as e:
raise CommandError(e)
if re.search(r"^(\w+)\:", fields[0]):
group_field = re.match(r"^(\w+)\:", fields[0])
name = group_field.group(1)
else:
raise CommandError("Cluster identification has problems.")
# remove cluster name before loading
fields.pop(0)
# get cvterm for correlation
tasks.append(
pool.submit(
featureloader.store_feature_groups,
group=fields,
soterm=soterm,
term=cvterm_cluster.cvterm_id,
value=name,
))
if verbosity > 0:
self.stdout.write("Loading")
for task in tqdm(as_completed(tasks), total=len(tasks)):
if task.result():
raise (task.result())
pool.shutdown()
if verbosity > 0:
self.stdout.write(
self.style.SUCCESS("Done with {}".format(filename)))
