def prepare_tool(
tool: Tool,
toolversions: List[str],
isorphan: bool,
is_published_pipeline: bool = False,
):
# Stuff to list on the documentation page:
# - Versions of tools
# - Generated command
# - Cool if it grouped the tools by vendor
# -
if not tool:
return None
try:
if is_published_pipeline:
return ""
if tool.type() == ToolType.CommandTool:
return prepare_commandtool_page(tool, toolversions)
elif tool.type() == ToolType.Workflow:
return prepare_workflow_page(tool, toolversions)
elif tool.type() == ToolType.CodeTool:
return prepare_code_tool_page(tool, toolversions)
except Exception as e:
traceback.print_exc()
Logger.critical("Couldn't generate documentation for " + tool.id() +
" " + str(e))
def prepare_quickstart(tool: Tool):
required_python_input_map = "\n".join(" " * 15 + i.id() + "=None,"
for i in tool.tool_inputs()
if not i.intype.optional)
python_step_name = tool.id().lower() + "_step"
output_python_code = "\n".join(
" " * 7 + f'wf.output("{o.id()}", source={python_step_name}.{o.id()})'
for o in tool.tool_outputs())
python_codeblock = f"""\
.. code-block:: python
from {tool.__module__} import {tool.__class__.__name__}
wf = WorkflowBuilder("myworkflow")
wf.step(
"{python_step_name}",
{tool.__class__.__name__}(
{required_python_input_map}
)
)
{output_python_code}
"""
return f"""\
def prepare_run_instructions_input_file(tool: Tool, user_inps: dict,
other_inps: dict,
reference_information: str):
yaml_user_inps = CwlTranslator.stringify_translated_inputs(user_inps)
yaml_other_inps = CwlTranslator.stringify_translated_inputs(other_inps)
indented_user = "".join(" " * 7 + s
for s in yaml_user_inps.splitlines(True))
indented_other = "".join(" " * 7 + s
for s in yaml_other_inps.splitlines(True))
not_localising_secondary_warning = ""
if isinstance(tool, WorkflowBase):
inputs_that_arent_localising_secondary_files = [
t.id() for t in tool.tool_inputs()
if t.doc.skip_sourcing_secondary_files
]
if len(inputs_that_arent_localising_secondary_files) > 0:
not_localising_secondary_warning = f"""\
.. warning::
The secondary files for the inputs '{"', '".join(inputs_that_arent_localising_secondary_files)}' will not automatically \
localise using janis prepare and are built just after download. Please note this can take a few hours to build \
before the pipeline runs.
"""
has_static = len(other_inps) > 0
tb = " " * 4
run_args = ["janis run [...run options]", tb + "--inputs inputs.yaml"]
static_generation = ("" if not has_static else f"""\
# static inputs
janis inputs --static {tool.id()} > static.yaml""")
static_yaml = ("" if not has_static else f"""\
**static.yaml**
.. code-block:: yaml
{indented_other}""")
if has_static:
run_args.append(tb + "--inputs static.yaml")
if isinstance(tool, CommandTool) and not tool.container():
run_args.append(
tb +
f"--container-override '{tool.id()}=<organisation/container:version>'"
)
run_args.append(tb + tool.id())
run_statement = " \\\n".join(" " * 3 + el for el in run_args)
if reference_information:
reference_information = f"The following inputs have a suggested source. Using janis prepare with the relevant \
``--source-hint`` will automatically download these files. See `below <#additional-configuration-inputs>`_ for \
more information about inputs for {tool.id()}.\n{reference_information}"
return f"""\
def validate_inputs(tool: Tool, additional_inputs):
errors = {}
input_values_from_workflow = {}
if isinstance(tool, Workflow):
input_values_from_workflow = {
inpkey: inp.value
for inpkey, inp in tool.input_nodes.items() if inp.value
}
input_values_to_use = {**input_values_from_workflow, **additional_inputs}
for inp in tool.tool_inputs():
inpkey = inp.id()
value = input_values_to_use.get(inpkey)
if inp.intype.validate_value(value, allow_null_if_not_optional=False):
continue
errors[inpkey] = (
inp.intype.invalid_value_hint(value) or
f"An internal error occurred when validating {inpkey} from {inp.intype.id()}"
)
if len(errors) == 0:
return True
raise ValueError(f"There were errors in {len(errors)} inputs: " +
str(errors))
def evaluate_translation(tool: Tool) -> Union[str, bool]:
"""
Evaluate if we can successfully translate to wdl and cwl
# TODO: validate translations (will look into better way to ensure validation tool exists)
:param tool: Janis tool
:type tool: Tool
:return: error message or True if we can successfully translate to wdl and cwl
:rtype: Union[str, bool]
"""
engines = test_helpers.get_available_engines()
output_dir = os.path.join(os.getcwd(), "tests_output", tool.id())
errors = []
for engine in engines:
try:
translator = engines[engine]
translator.translate(
tool, export_path=output_dir, to_console=False, to_disk=True
)
except Exception as e:
errors.append(f"{translator.name}: translation failed {str(e)}")
if errors:
return ", ".join(errors)
return True
def cascade_batchrun_inputs(workflow: Tool, inputs: List[Dict],
options: BatchRunRequirements):
fields_to_group = set(options.fields)
fields_to_group.add(options.groupby)
wfins = workflow.inputs_map()
required_ar_depth_of_groupby_fields = {
f: 1 + count_janisarray_depth(wfins[f].intype)
for f in fields_to_group
}
ins = {}
for inp in inputs:
for k, v in inp.items():
if k in fields_to_group:
if k not in ins:
ins[k] = []
# We'll look at the shape of the data, and decide whether
# we can just use the value, or we need to wrap it in another array
if count_array_depth(
v) < required_ar_depth_of_groupby_fields[k]:
v = [v]
ins[k].extend(v)
else:
# overwrite the previous value
ins[k] = v
# If inputs
return ins
def check_existence_of_files(wf: Tool, inputs: Dict):
doesnt_exist = {}
for inp in wf.tool_inputs():
intype = inp.intype
is_path = isinstance(intype, (File, Directory))
is_array_of_paths = isinstance(intype, Array) and isinstance(
intype.fundamental_type(), (File, Directory))
if not (is_path or is_array_of_paths):
continue
val = inputs.get(inp.id())
if val is None:
if inp.intype.optional:
continue
raise Exception(
f"Expected input '{inp.id()}' was not found or is null")
doesnt_exist.update(
InputChecker.check_base_with_type(inp, intype, val))
if len(doesnt_exist) > 0:
import ruamel.yaml
stringified = ruamel.yaml.dump(doesnt_exist,
default_flow_style=False)
raise Exception("The following inputs were not found:\n" +
stringified)
def inputs_modifier(self, tool: Tool, inputs: Dict,
hints: Dict[str, str]) -> Dict:
new_inputs = {}
for inp in tool.inputs_map().values():
if inp.id() not in inputs:
continue
new_inputs[inp.id()] = self.process_single_input(
inp.id(), inp.intype, inputs[inp.id()])
return {**inputs, **new_inputs}
def prepare_run_instructions(tool: Tool):
metadata = tool.bind_metadata() or tool.metadata
has_array_of_arrays_inps = True
bla = any(
(isinstance(i.intype, Array) and isinstance(i.intype.subtype(), Array))
for i in tool.tool_inputs())
static_input_tuples = [[
i.id(), i.intype.id(),
prepare_source(i.doc.source)
] for i in tool.tool_inputs() if i.doc.quality == InputQualityType.static
and i.doc.source is not None]
reference_information = ""
if len(static_input_tuples) > 0:
static_input_headers = ["Name", "Type", "Source"]
reference_information = tabulate(static_input_tuples,
headers=static_input_headers,
tablefmt="rst")
# overrides = metadata.sample_input_overrides or {}
user_inps = {}
other_inps = {}
for i in tool.tool_inputs():
if i.intype.optional or i.default is not None:
continue
val = i.doc.example or prepare_default_for_type(i.id(), i.intype)
if i.doc and i.doc.quality and i.doc.quality != InputQualityType.user:
other_inps[i.id()] = val
else:
# catch None and InputQualityType.user
user_inps[i.id()] = val
if has_array_of_arrays_inps:
return prepare_run_instructions_input_file(tool, user_inps, other_inps,
reference_information)
else:
return prepare_run_instructions_cli(tool, user_inps, other_inps,
reference_information)
def evaluate(cls, tool: Tool) -> Union[str, bool]:
"""
Evaluate a Janis tool whether they satisfy certain criteria for them to be publishable
:param tool: Janis tool
:type tool: Tool
:return: error message or True if valid
:rtype: Union[str, bool]
"""
if tool.skip_test():
return cls.STATUS_SKIPPED
if tool.type() == ToolType.Workflow:
return cls.evaluate_workflow(tool)
elif tool.type() == ToolType.CommandTool:
return cls.evaluate_command_tool(tool)
elif tool.type() == ToolType.CodeTool:
return cls.evaluate_code_tool(tool)
raise Exception("Unrecognised tool type: " + str(tool.type()))
def ensure_outputs_are_in_workflow_and_are_compatible(
tool: Tool, outputs: List[str], compatible_type: DataType):
tool_outputs: Dict[str, TOutput] = tool.outputs_map()
failed_outputs = []
untyped_outputs = []
for o in outputs:
if o not in tool_outputs:
failed_outputs.append(o)
elif not compatible_type.can_receive_from(tool_outputs[o].outtype):
untyped_outputs.append(o)
return failed_outputs, untyped_outputs
def evaluate_container(tool: Tool) -> Union[str, bool]:
"""
Evaluate if the container specified for this tool exists in the remote registry
:param tool: Janis tool
:type tool: Tool
:return: error message or True if listed container for this tool exists in the remote registry
:rtype: Union[str, bool]
"""
# If there is no container, we don't need to check if the container exists in the registry
if not tool.containers():
return True
# Some tool might not have container, we only want to check if a container is listed, its digest exists
containers = [v for k, v in tool.containers().items()]
containers = list(filter(None, containers))
if not containers:
return True
test_helpers.verify_janis_assistant_installed()
from janis_assistant.data.container import get_digests_from_containers
cache_location = os.path.join(os.getcwd(), "tests_output", "containers")
digests = get_digests_from_containers(containers, cache_location=cache_location)
errors = []
for c in containers:
# if digest is exactly the same, it means digest is not found (it's just the tag name)
if c not in digests or digests[c] == c:
# if the container nameis already using hash, we don't want to report any issue here
if not "@sha256:" in c:
errors.append(f"container {c} not found")
if errors:
return ", ".join(errors)
return True
def evaluate_unittest_exists(tool: Tool) -> Union[str, bool]:
"""
Evaluate if test suite for this tool is provided
:param tool: Janis tool
:type tool: Tool
:return: error message or True if unit tests for this tool exists
:rtype: Union[str, bool]
"""
if tool.tests():
return True
return "Mising unit tests"
def evaluate_tool_module(tool: Tool) -> Union[str, bool]:
"""
Evaluate if tool module name for documentation is provided
:param tool: Janis tool
:type tool: Tool
:return: error message or True if tool module name for this tool exists
:rtype: Union[str, bool]
"""
if not tool.tool_module():
return "Missing tool module"
return True
def evaluate_friendly_name(tool: Tool) -> Union[str, bool]:
"""
Evaluate if a friendly name for documentation is provided
:param tool: Janis tool
:type tool: Tool
:return: error message or True if a friendly name for this tool exists
:rtype: Union[str, bool]
"""
if not tool.friendly_name():
return "Missing friendly name"
return True
def prepare_run_instructions_input_file(tool: Tool, user_inps: dict,
other_inps: dict,
reference_information: str):
yaml_user_inps = CwlTranslator.stringify_translated_inputs(user_inps)
yaml_other_inps = CwlTranslator.stringify_translated_inputs(other_inps)
indented_user = "".join(" " * 7 + s
for s in yaml_user_inps.splitlines(True))
indented_other = "".join(" " * 7 + s
for s in yaml_other_inps.splitlines(True))
has_static = len(other_inps) > 0
tb = " " * 4
run_args = ["janis run [...run options]", tb + "--inputs inputs.yaml"]
static_generation = ("" if not has_static else f"""\
# static inputs
janis inputs --static {tool.id()} > static.yaml""")
static_yaml = ("" if not has_static else f"""\
**static.yaml**
.. code-block:: yaml
{indented_other}""")
if has_static:
run_args.append(tb + "--inputs static.yaml")
if isinstance(tool, CommandTool) and not tool.container():
run_args.append(
tb +
f"--container-override '{tool.id()}=<organisation/container:version>'"
)
run_args.append(tb + tool.id())
run_statement = " \\\n".join(" " * 3 + el for el in run_args)
return f"""\
def inputs_modifier(self, tool: Tool, inputs: Dict, hints: Dict[str, str]) -> Dict:
new_inputs = {}
for inp in tool.tool_inputs():
if not isinstance(inp.intype, File):
continue
value = inputs.get(inp.id())
if value is not None:
processed_value = self.check_input_for_correctness(
inp.id(), inp.intype, value
)
if processed_value is not None:
new_inputs[inp.id()] = processed_value
return {**inputs, **new_inputs}
def evaluate_output_params(self, wf: Tool, additional_inputs: dict):
mapped_inps = CwlTranslator().build_inputs_file(
wf, recursive=False, additional_inputs=additional_inputs
)
output_names: Dict[str, any] = {}
output_folders: Dict[str, any] = {}
if isinstance(wf, Workflow):
for o in wf.output_nodes.values():
output_names[o.id()] = self.evaluate_output_selector(
o.output_name, mapped_inps
)
output_folders[o.id()] = self.evaluate_output_selector(
o.output_folder, mapped_inps
)
outputs: List[WorkflowOutputModel] = []
for o in wf.tool_outputs():
# We'll
ext = None
innertype = o.outtype
iscopyable = isinstance(o.outtype, (File, Directory)) or (
isinstance(o.outtype, Array)
and isinstance(o.outtype.fundamental_type(), (File, Directory))
)
while isinstance(innertype, Array):
innertype = innertype.subtype()
if isinstance(o.outtype, File):
ext = o.outtype.extension
outputs.append(
WorkflowOutputModel(
tag=o.id(),
iscopyable=iscopyable,
original_path=None,
new_path=None,
timestamp=None,
output_name=output_names.get(o.id()),
output_folder=output_folders.get(o.id()),
secondaries=o.outtype.secondary_files(),
extension=ext,
)
)
return self.database.outputsDB.insert_many(outputs)
def inputs_modifier(self, wf: Tool, inputs: Dict, hints: Dict[str, str]):
nin = {**inputs}
inmap = wf.inputs_map()
# Change the 'cwd' just for the scope of this block
with Path(self.cwd):
for tag, value in nin.items():
if tag not in inmap:
# Only localise tags within the inputsdict that are in the tool inputs
# This might be a problem later for passthrough inputs (Janis doesn't support yet)
continue
intype = inmap[tag].intype
# If the type of the input is:
# (a) A File / Directory
isfiletype = isinstance(intype.received_type(),
(File, Directory))
# (b) Array with a fundamental type (recursively searches if nested array) of File or Directory
isbasefiletype = isinstance(
intype.received_type(), Array) and isinstance(
intype.fundamental_type(), (File, Directory))
# (Skip if not one of these conditions)
if not (isfiletype or isbasefiletype):
try:
nin[tag] = intype.coerce_value_if_possible(value)
except Exception as e:
raise Exception(
f"Couldn't coerce the input for '{tag}' ({value}) to type '{intype}': {e}"
)
continue
# Qualify the fully qualify the filepath
try:
nin[tag] = self.fully_qualify_filename_array_or_single(
value)
except Exception as e:
raise Exception(
f"Couldn't qualify the filename for the input '{tag}' ({value}) to type '{intype}': {e}"
)
return nin
def inputs_modifier(self, tool: Tool, inputs: Dict,
hints: Dict[str, str]) -> Dict:
"""
Download remote files and reploce input with the local files
:param tool: an instance of janis tool
:type tool: janis_core.Tool
:param inputs: a dictionary of tool inputs
:type inputs: dict
:param hints:
:type hints: dict
:return: modified input
:rtype: dict
"""
new_inputs = {}
for inp in tool.tool_inputs():
modification_required = False
if isinstance(inp.intype, File) or (
isinstance(inp.intype, Array)
and isinstance(inp.intype.fundamental_type(), File)):
if inp.id() in inputs and inputs[inp.id()] is not None:
modification_required = True
if modification_required:
source = inputs[inp.id()]
basedir = self.cache_dir
os.makedirs(basedir, exist_ok=True)
new_inputs[inp.id()] = self.localise_inputs(
inp.id(),
inp.intype,
basedir,
source,
# mfranklin 2021-01-08:
# if we specify a remote input, and we're localising files, we should localise secondary files
localise_secondary_files=True,
)
return {**inputs, **new_inputs}
def evaluate_metadata(tool: Tool) -> Union[str, bool]:
"""
Evaluate if important metadata for documentation is provided
:param tool: Janis tool
:type tool: Tool
:return: error message or True if all required metadata for this tool exists
:rtype: Union[str, bool]
"""
METADATA_KEY_CONTRIBUTORS = "contributors"
METADATA_KEY_CREATED_DATE = "created date"
METADATA_KEY_INSTITUTION = "institution"
if isinstance(tool.metadata, Metadata):
required = {
METADATA_KEY_CONTRIBUTORS: tool.metadata.contributors,
METADATA_KEY_CREATED_DATE: tool.metadata.dateCreated,
METADATA_KEY_INSTITUTION: tool.metadata.institution,
}
missing = []
for key, field in required.items():
if field is None or not field:
missing.append(key)
# special case, tool_provider() value overwrites contributors in the documentation
if METADATA_KEY_INSTITUTION in missing:
if tool.tool_provider():
missing.remove(METADATA_KEY_INSTITUTION)
if missing:
return f"Missing metadata: {', '.join(missing)}"
# elif isinstance(self.metadata, ...):
else:
return "Incorrect metadata class"
return True
def tool_modifier(self, tool: Tool, inputs: Dict,
hints: Dict[str, str]) -> Tool:
# Build custom pipeline
w = WorkflowBuilder(tool.id(),
friendly_name=tool.friendly_name(),
version=tool.version())
ins = tool.tool_inputs()
insdict = {i.id(): i for i in ins}
fields = set(self.batch.fields)
inkeys = set(i.id() for i in ins)
invalid_keys = fields - inkeys
if len(invalid_keys) > 0:
raise Exception(
f"Couldn't create batchtool from fields {', '.join(invalid_keys)} "
f"as they do not exist on '{tool.id()}'")
if self.batch.groupby not in inputs:
raise Exception(
f"the group_by field '{self.batch.groupby}' was not found in the inputs"
)
innode_base = {}
for i in ins:
if i.id() in fields:
continue
default = i.default
if isinstance(default, Selector):
default = None
innode_base[i.id()] = w.input(i.id(),
i.intype,
default=default,
doc=i.doc)
raw_groupby_values = inputs[self.batch.groupby]
duplicate_keys = find_duplicates(raw_groupby_values)
if len(duplicate_keys) > 0:
raise Exception(
f"There are duplicate group_by ({self.batch.groupby}) keys in the input: "
+ ", ".join(duplicate_keys))
groupby_values = [
Validators.transform_identifier_to_be_valid(ident)
for ident in raw_groupby_values
]
duplicate_keys = find_duplicates(groupby_values)
if len(duplicate_keys) > 0:
raise Exception(
f"Janis transformed values in the group_by field ({self.batch.groupby}) to be a valid identifiers, "
f"after this transformation, there were duplicates keys: " +
", ".join(duplicate_keys))
w.input(self.GROUPBY_FIELDNAME, Array(str), value=groupby_values)
steps_created = []
stepid_from_gb = lambda gb: f"{gb}_{tool.id()}"
for gbvalue in groupby_values:
extra_ins = {}
for f in fields:
newkey = f"{f}_{gbvalue}"
extra_ins[f] = w.input(newkey, insdict[f].intype)
steps_created.append(
w.step(stepid_from_gb(gbvalue), tool(**innode_base,
**extra_ins)))
for out in tool.tool_outputs():
output_folders = []
output_name = out.id()
if isinstance(tool, WorkflowBase):
outnode = tool.output_nodes[out.id()]
output_folders = outnode.output_folder or []
if outnode.output_name is not None:
output_name = outnode.output_name
for idx, gbvalue, raw_gbvalue in zip(range(len(groupby_values)),
groupby_values,
raw_groupby_values):
transformed_inputs = {
**inputs,
**{f: inputs[f][idx]
for f in fields}
}
output_folders_transformed = Operator.evaluate_arg(
output_folders, transformed_inputs)
output_name_transformed = Operator.evaluate_arg(
output_name, transformed_inputs)
w.output(
f"{gbvalue}_{out.id()}",
source=w[stepid_from_gb(gbvalue)][out.id()],
output_name=output_name_transformed,
output_folder=[
raw_gbvalue, *(output_folders_transformed or [])
],
)
return w
def from_janis(
wid: str,
outdir: str,
tool: Tool,
environment: Environment,
hints: Dict[str, str],
validation_requirements: Optional[ValidationRequirements],
batchrun_requirements: Optional[BatchRunRequirements],
inputs_dict: dict = None,
dryrun=False,
watch=True,
max_cores=None,
max_memory=None,
keep_intermediate_files=False,
run_in_background=True,
dbconfig=None,
allow_empty_container=False,
container_override: dict = None,
check_files=True,
):
jc = JanisConfiguration.manager()
# output directory has been created
environment.identifier += "_" + wid
tm = WorkflowManager(wid=wid, outdir=outdir, environment=environment)
tm.database.runs.insert(wid)
tm.database.workflowmetadata.wid = wid
tm.database.workflowmetadata.engine = environment.engine
tm.database.workflowmetadata.filescheme = environment.filescheme
tm.database.workflowmetadata.environment = environment.id()
tm.database.workflowmetadata.name = tool.id()
tm.database.workflowmetadata.start = DateUtil.now()
tm.database.workflowmetadata.executiondir = None
tm.database.workflowmetadata.keepexecutiondir = keep_intermediate_files
tm.database.workflowmetadata.configuration = jc
tm.database.workflowmetadata.dbconfig = dbconfig
# This is the only time we're allowed to skip the tm.set_status
# This is a temporary stop gap until "notification on status" is implemented.
# tm.set_status(TaskStatus.PROCESSING)
tm.database.workflowmetadata.status = TaskStatus.PROCESSING
tm.database.commit()
spec = get_ideal_specification_for_engine(environment.engine)
spec_translator = get_translator(spec)
tool_evaluate = tm.prepare_and_output_workflow_to_evaluate_if_required(
tool=tool,
translator=spec_translator,
validation=validation_requirements,
batchrun=batchrun_requirements,
hints=hints,
additional_inputs=inputs_dict,
max_cores=max_cores or jc.environment.max_cores,
max_memory=max_memory or jc.environment.max_ram,
allow_empty_container=allow_empty_container,
container_override=container_override,
check_files=check_files,
)
outdir_workflow = tm.get_path_for_component(
WorkflowManager.WorkflowManagerPath.workflow
)
tm.database.workflowmetadata.submission_workflow = os.path.join(
outdir_workflow, spec_translator.filename(tool_evaluate)
)
tm.database.workflowmetadata.submission_inputs = os.path.join(
outdir_workflow, spec_translator.inputs_filename(tool_evaluate)
)
tm.database.workflowmetadata.submission_resources = os.path.join(
outdir_workflow, spec_translator.dependencies_filename(tool_evaluate)
)
tm.database.commit()
if not dryrun:
if (
not run_in_background
and jc.template
and jc.template.template
and jc.template.template.can_run_in_foreground is False
):
raise Exception(
f"Your template '{jc.template.template.__class__.__name__}' is not allowed to run "
f"in the foreground, try adding the '--background' argument"
)
tm.start_or_submit(run_in_background=run_in_background, watch=watch)
else:
tm.set_status(TaskStatus.DRY_RUN)
tm.database.commit()
return tm
def tool_modifier(self, tool: Tool, inputs: Dict,
hints: Dict[str, str]) -> Tool:
from janis_bioinformatics.data_types import FastaWithDict, Vcf, Bed
from janis_bioinformatics.tools.illumina import HapPyValidator_0_3_9
failed_outputs, untyped_outputs = ensure_outputs_are_in_workflow_and_are_compatible(
tool, self.validation.fields, Vcf())
if len(failed_outputs) > 0:
raise Exception(
f"Some outputs for validation were not found in the tool '{tool.id()}': "
f"{', '.join(failed_outputs)}")
if len(untyped_outputs) > 0:
Logger.critical(
f"Some outputs for validation from the tool '{tool.id()}' were not "
f"compatible with VCF: {', '.join(untyped_outputs)}")
w = WorkflowBuilder(tool.id() + "_validated")
w.input("validatorReference",
FastaWithDict,
value=self.validation.reference)
w.input("validatorTruthVCF", Vcf, value=self.validation.truthVCF)
w.input("validatorIntervals",
Bed(optional=True),
value=self.validation.intervals)
inpdict = {
i.id(): w.input(i.id(), i.intype)
for i in tool.tool_inputs()
}
toolstp = w.step(tool.id(), tool(**inpdict))
if isinstance(tool, Workflow):
wf: Workflow = tool
for o in wf.output_nodes.values():
w.output(
identifier=o.id(),
source=toolstp[o.id()],
output_folder=o.output_folder,
output_name=o.output_name,
)
else:
for o in tool.tool_outputs():
w.output(identifier=o.id(), source=toolstp[o.id()])
for o in self.validation.fields:
sid = "validator_" + o
valstp = w.step(
sid,
HapPyValidator_0_3_9(
compareVCF=toolstp[o],
reportPrefix=
o, # this will generate an input node with format validator_{o}_reportPrefix
reference=w.validatorReference,
truthVCF=w.validatorTruthVCF,
intervals=w.validatorIntervals,
),
)
# Connect all the outputs of the validator to an output
for vo in valstp.tool.outputs():
w.output(
f"validated_{o}_{vo.id()}",
source=valstp[vo.id()],
output_folder="validated",
)
return w
def do_bed_fasta_contig_check(tool: Tool, inputs: Dict[str, any]):
from janis_bioinformatics.data_types import Fasta, Bed, BedTabix
supported_bed_types = (Bed, BedTabix)
beds_inputs = []
refs = []
for i in tool.tool_inputs():
if isinstance(i.intype, supported_bed_types) or (
isinstance(i.intype, Array)
and isinstance(i.intype.subtype(), supported_bed_types)):
beds_inputs.append(i)
if (isinstance(i.intype, Fasta) and i.intype.secondary_files()
and ".fai" in i.intype.secondary_files()):
refs.append(i)
if len(refs) == 0:
return
if len(refs) > 1:
Logger.info(
"Skipping bioinformatics FASTA-BED file checks as there were more than 1 reference"
)
for inp_ref in refs:
value_ref = inputs[inp_ref.id()]
if not value_ref:
Logger.warn(
f"Skipping '{inp_ref.id()}' as no value was provided")
continue
ref_contigs = ContigChecker.get_list_of_contigs_from_fastafai(
value_ref + ".fai")
if not ref_contigs:
Logger.debug(
f"Didn't get any contigs from ref {value_ref}.fai, skipping..."
)
continue
for inp_bed in beds_inputs:
value_bed = inputs[inp_bed.id()]
is_array = isinstance(value_bed, list)
beds = value_bed if is_array else [value_bed]
for b_idx in range(len(beds)):
bed = beds[b_idx]
bed_contigs = ContigChecker.get_list_of_contigs_from_bed(
bed)
missing_contigs = bed_contigs - ref_contigs
if missing_contigs:
inpname = (f"{inp_bed.id()}.{b_idx}"
if is_array else inp_bed.id())
contiglist = (", ".join(missing_contigs)
if len(missing_contigs) < 5 else
(", ".join(list(missing_contigs)[:3]) +
"..."))
Logger.warn(
f"The BED file '{inpname}' contained {len(missing_contigs)} contigs ({contiglist}) that were missing from the reference: {value_ref}"
)
def tool_modifier(self, tool: Tool, inputs: Dict,
hints: Dict[str, str]) -> Tool:
# Build custom pipeline
w = WorkflowBuilder(tool.id(),
friendly_name=tool.friendly_name(),
version=tool.version())
ins = tool.tool_inputs()
insdict = {i.id(): i for i in ins}
fields = set(self.batch.fields)
inkeys = set(i.id() for i in ins)
invalid_keys = fields - inkeys
if len(invalid_keys) > 0:
raise Exception(
f"Couldn't create batchtool from fields {', '.join(invalid_keys)} "
f"as they do not exist on '{tool.id()}'")
if self.batch.groupby not in inputs:
raise Exception(
f"the group_by field '{self.batch.groupby}' was not found in the inputs"
)
innode_base = {}
for i in ins:
if i.id() in fields:
continue
innode_base[i.id()] = w.input(i.id(),
i.intype,
default=i.default,
doc=i.doc)
raw_groupby_values = inputs[self.batch.groupby]
duplicate_keys = find_duplicates(raw_groupby_values)
if len(duplicate_keys) > 0:
raise Exception(
f"There are duplicate group_by ({self.batch.groupby}) keys in the input: "
+ ", ".join(duplicate_keys))
groupby_values = [
Validators.transform_identifier_to_be_valid(ident)
for ident in raw_groupby_values
]
duplicate_keys = find_duplicates(groupby_values)
if len(duplicate_keys) > 0:
raise Exception(
f"Janis transformed values in the group_by field ({self.batch.groupby}) to be a valid identifiers, "
f"after this transformation, there were duplicates keys: " +
", ".join(duplicate_keys))
w.input(self.GROUPBY_FIELDNAME, Array(str), value=groupby_values)
steps_created = []
stepid_from_gb = lambda gb: f"{gbvalue}_{tool.id()}"
for gbvalue in groupby_values:
extra_ins = {}
for f in fields:
newkey = f"{f}_{gbvalue}"
extra_ins[f] = w.input(newkey, insdict[f].intype)
steps_created.append(
w.step(stepid_from_gb(gbvalue), tool(**innode_base,
**extra_ins)))
def transform_token_in_output_namers(token, outputid):
if token is None:
return token
if isinstance(token, list):
return [
transform_token_in_output_namers(t, outputid)
for t in token
]
if isinstance(token, InputSelector):
if token.input_to_select in fields:
# need to transform it
return InputSelector(f"{token.input_to_select}_{outputid}")
else:
return token
elif isinstance(token, (str, int, float, bool)):
return token
else:
raise Exception(
f"Unsure how to translate token of type {token.__class__.__name__} "
)
for out in tool.tool_outputs():
output_folders = []
output_name = out.id()
if isinstance(tool, Workflow):
outnode = tool.output_nodes[out.id()]
output_folders = outnode.output_folder or []
if outnode.output_name:
output_name = outnode.output_name
for gbvalue, raw_gbvalue in zip(groupby_values,
raw_groupby_values):
# This is pretty hacky, we're relying on the output_folder and output_name to be InputSelectors
# or a literal value, otherwise this will probably break (this will probably break for expressions)
output_folders_transformed = transform_token_in_output_namers(
output_folders, gbvalue)
output_name_transformed = transform_token_in_output_namers(
output_name, gbvalue)
w.output(
f"{gbvalue}_{out.id()}",
source=w[stepid_from_gb(gbvalue)][out.id()],
output_name=output_name_transformed,
output_folder=[
raw_gbvalue, *(output_folders_transformed or [])
],
)
return w
