def validate_sequencing_run(sequencing_run):
"""
Validate a SequencingRun object for upload to irida
If the parser is working as intended (and has proper validation) this should never return
a ValidationResult with a list of errors. This function should be used when building a model
from scratch, building a new parser, and as a final redundancy on parsers.
:param sequencing_run: SequencingRun object to validate
:return: ValidationResult object with list of errors if any
"""
validation_result = model.ValidationResult()
# Validation objects
v_sequencing_run = Validator(model.SequencingRun.uploadable_schema,
allow_unknown=True)
v_project = Validator(model.Project.uploadable_schema, allow_unknown=True)
v_sample = Validator(model.Sample.uploadable_schema, allow_unknown=True)
v_sequence_file = Validator(model.SequenceFile.uploadable_schema,
allow_unknown=True)
# validation is nested so we can catch multiple levels of project/sample/file errors
# Validate base SequencingRun Object
try:
_validate_object(v_sequencing_run, sequencing_run)
# Validate projects in sequencing run
for p in sequencing_run.project_list:
try:
_validate_object(v_project, p)
# Validate samples in project
for s in p.sample_list:
try:
_validate_object(v_sample, s)
# Validate SequenceFile on Sample
_validate_object(v_sequence_file, s.sequence_file)
# Validate tricky sequence_file rule
_validate_sequence_file_names(s.sequence_file)
except model.exceptions.ModelValidationError as e:
validation_result.add_error(e)
except model.exceptions.ModelValidationError as e:
validation_result.add_error(e)
except model.exceptions.ModelValidationError as e:
validation_result.add_error(e)
return validation_result
def prepare_and_validate_for_upload(sequencing_run):
"""
Prepares IRIDA to accept the sequencing run
Validates that projects exist,
Creates Samples on Projects on Irida if they do not exist yet
Collects all errors during prep/validation in ValidationResult
:param sequencing_run: SequencingRun object
:return: ValidationResult object with all errors that raised while prepping
"""
# get api
api_instance = _get_api_instance()
validation_result = model.ValidationResult()
# Start online validation
logging.debug("Checking existence of projects")
for project in sequencing_run.project_list:
logging.debug("Checking existence of project: {}".format(project.id))
if not api_instance.project_exists(project.id):
# No project, add error to validation result and continue
logging.debug("Could not find project: {}".format(project.id))
err = api.exceptions.IridaResourceError("Project does not exist", project.id)
validation_result.add_error(err)
continue
logging.debug("Project {} exists".format(project.id))
logging.debug("Checking existence of samples")
for sample in project.sample_list:
logging.debug("Checking existence of Sample {} on Project {}".format(sample.sample_name, project.id))
if api_instance.sample_exists(sample.sample_name, project.id):
logging.debug("Sample {} exists on Project {}".format(sample.sample_name, project.id))
else:
logging.debug("Sample not found, creating new Sample")
try:
api_instance.send_sample(sample, project.id)
except api.exceptions.IridaResourceError as e:
logging.debug("Sample could not be created")
validation_result.add_error(e)
continue
logging.debug("Verifying sample was created")
if not api_instance.sample_exists(sample.sample_name, project.id):
logging.debug("Sample was not created")
err = api.exceptions.IridaResourceError("Could not create new Sample on Project {}", project.id)
validation_result.add_error(err)
continue
logging.debug("Sample Created")
return validation_result
def get_sequencing_run(sample_sheet):
"""
Does local validation on the integrety of the run directory / sample sheet
Throws a ValidationError with a valadation result attached if it cannot make a sequencing run
:param sample_sheet:
:return: SequencingRun
"""
# Try to get the sample sheet, validate that the sample sheet is valid
validation_result = validation.validate_sample_sheet(sample_sheet)
if not validation_result.is_valid():
logging.error("Errors occurred while getting sample sheet")
raise exceptions.ValidationError(
"Errors occurred while getting sample sheet",
validation_result)
# Try to parse the meta data from the sample sheet, throw validation error if errors occur
validation_result = model.ValidationResult()
try:
run_metadata = sample_parser.parse_metadata(sample_sheet)
except exceptions.SampleSheetError as error:
validation_result.add_error(error)
logging.error("Errors occurred while parsing metadata")
raise exceptions.ValidationError(
"Errors occurred while parsing metadata", validation_result)
# Try to build sequencing run from sample sheet & meta data, raise validation error if errors occur
try:
sequencing_run = sample_parser.build_sequencing_run_from_samples(
sample_sheet, run_metadata)
except exceptions.SequenceFileError as error:
validation_result.add_error(error)
logging.error(
"Errors occurred while building sequence run from sample sheet"
)
raise exceptions.ValidationError(
"Errors occurred while building sequence run from sample sheet",
validation_result)
return sequencing_run
def validate_sample_sheet(sample_sheet_file):
"""
Checks if the given sample_sheet_file can be parsed
Requires [Header] because it contains Workflow
Requires [Data] for creating Sample objects and requires
Sample_ID, Sample_Name, Sample_Project and Description table headers
arguments:
sample_sheet_file -- path to SampleSheet.csv
returns ValidationResult object - stores list of string error messages
"""
csv_reader = get_csv_reader(sample_sheet_file)
v_res = model.ValidationResult()
all_data_headers_found = False
data_sect_found = False
check_data_headers = False
# status of required data headers
found_data_headers = {
"Sample_Name": False,
"Project_ID": False,
"File_Forward": False,
"File_Reverse": False}
for line in csv_reader:
if "[Data]" in line:
data_sect_found = True
check_data_headers = True # next line contains data headers
elif check_data_headers:
for data_header in found_data_headers.keys():
if data_header in line:
found_data_headers[data_header] = True
# if all required dataHeaders are found
if all(found_data_headers.values()):
all_data_headers_found = True
check_data_headers = False
if not all([data_sect_found, all_data_headers_found]):
if data_sect_found is False:
v_res.add_error(exceptions.SampleSheetError("[Data] section not found in SampleSheet", sample_sheet_file))
if all_data_headers_found is False:
missing_str = ""
for data_header in found_data_headers:
if found_data_headers[data_header] is False:
missing_str = missing_str + data_header + ", "
missing_str = missing_str[:-2] # remove last ", "
v_res.add_error(exceptions.SampleSheetError("Missing required data header(s): " +
missing_str, sample_sheet_file))
return v_res