def get_library_folder(gi, library_folder_name):
galaxy_fs.make_path(gi.libraries, library_folder_name)
data_folder = galaxy_fs.get_path(gi, library_folder_name)
data_folder_id = data_folder["id"]
lib_name = library_folder_name.split("/")[0]
libs = gi.libraries.get_libraries(name=lib_name)
if len(libs) == 0:
fail('''couldn't find library named "%s"''', lib_name)
lib = libs[0]
lib_id = lib["id"]
return lib_id, data_folder_id
def upload_dataset(gi, data_path, file_type, folder_name, link="link_to_files"):
lib_id, folder_id = get_library_folder(gi, folder_name)
data_name = os.path.basename(data_path)
galaxy_dataset = folder_name + "/" + data_name
galaxy_path = galaxy_fs.get_path(gi, galaxy_dataset)
if not galaxy_path:
logging.info("Uploading: %s", galaxy_dataset)
logging.info("File type: %s", file_type)
logging.info("File path: %s", data_path)
if os.path.isabs(data_path):
galaxy_path = gi.libraries.upload_from_galaxy_filesystem(
lib_id, data_path, folder_id, link_data_only=link, file_type=file_type)[0]
else:
galaxy_path=gi.libraries.upload_file_from_server(
lib_id, data_path, folder_id, link_data_only=link, file_type=file_type)[0]
else:
logging.info('''Skipping upload, file exists at "{}"'''.format(galaxy_path))
dataset = {"id": galaxy_path["id"], "name": data_name, "src": "ld"}
return dataset
def collect_inputs(config_path):
# Load YAML config file.
config_fh = open(config_path)
logging.info("Config path: %s", (config_path))
config = dynamic_yaml.load(config_fh)
logging.info("Config: %s", (config))
galaxy_api_key = os.getenv("GALAXY_API_KEY")
if not galaxy_api_key:
fail(
"missing GALAXY_API_KEY; set this in the environment or .env file")
def require_config(key):
v = config.get(key)
if not v:
fail('missing "%s" in the config', key)
return v
# Load config
galaxy_url = require_config("galaxy_url")
common_inputs = require_config("common_inputs")
workflow_name = require_config("workflow")
library_folder_name = require_config("library_folder")
sample_conf = require_config("sample")
workflow_params_config = config.get("workflow_params", {})
replacement_params = config.get("replacement_params", {})
tags = config.get("tags")
publish = config.get("publish")
# Create galaxy object and get workflow descsription
gi = bioblend.galaxy.GalaxyInstance(url=galaxy_url, key=galaxy_api_key)
wfdesc = get_workflow_description(gi, workflow_name)
# Create History
sample = sample_conf["name"]
alphanum = string.ascii_lowercase + string.digits
rand = ''.join(random.choice(alphanum) for x in range(8))
history_name = workflow_name + " " + sample + " " + rand
logging.info("Creating history: %s", history_name)
history = get_or_create_history(gi, history_name)
history_id = history["id"]
create_history_tags(gi, history_id, tags)
gi.histories.update_history(history_id, published=publish)
# Upload common_inputs
logging.info("Uploading Common Inputs")
cm_files = require_config("common_input_files")
for cm_key, cm_val in cm_files.items():
fd_name = os.path.dirname(common_inputs[cm_key])
file_type = os.path.splitext(cm_val)[-1].lstrip(".")
logging.info("Galaxy Common Inputs folder name: %s", fd_name)
logging.info("Common Input file name: %s", cm_val)
upload_dataset(gi, cm_val, file_type, fd_name)
# Find files on filesystem
logging.info("Collecting files from filesystem")
sample_files = sample_conf["files"]
# Upload files to Data Library
logging.info("Uploading sample data")
file_type = sample_conf["filetype"]
sample_ids = []
for sf in sample_files:
sample_ids.append(
upload_dataset(gi, sf, file_type, library_folder_name)["id"])
# Create collection list in history
logging.info("Populating sample data in history")
sample_data = create_dataset_or_collection_in_history(
gi, sample, history_id, sample_ids)
logging.info("sample collection data: %s", sample_data)
logging.info("Preparing to invoke workflow")
common_inputs_library_ids = {}
for k, v in common_inputs.items():
logging.info('''Collecting common inputs from Galaxy: "%s" "%s"''', k,
v)
f = galaxy_fs.get_path(gi, v)
logging.info('''f path: "%s"''', f)
common_inputs_library_ids[k] = f["id"]
logging.info('''Collecting common inputs dict: "%s"''',
common_inputs_library_ids)
steps_by_label = {}
inputs = {}
for step in wfdesc["steps"].values():
label = step.get("label")
logging.info('''Step label is: "%s"''', label)
uuid = step.get("uuid")
steps_by_label[label] = step
if label in common_inputs_library_ids:
lib_id = common_inputs_library_ids[label]
inputs[uuid] = {
"id": lib_id,
"src": "ld",
}
if label == "INPUT":
inputs[uuid] = {
"id": sample_data["id"],
"src": sample_data["src"],
}
params = {}
if workflow_params_config:
for step_label, step_params in workflow_params_config.items():
if step_label not in steps_by_label:
fail('configuring workflow params, missing step with label {}',
step_label)
step_id = steps_by_label[step_label]['id']
if steps_by_label[step_label]['type'] == 'subworkflow':
sub_dict = {}
for sub_label, sub_params in step_params.items():
sub_wfdesc = get_workflow_description(
gi, steps_by_label[step_label]['name'])
sub_wf_step = [
v for k, v in sub_wfdesc['steps'].items()
if v['label'] == sub_label
][0]
param_key, param_value = list(sub_params.items())[0]
sub_id = "|".join([str(sub_wf_step['id']), param_key])
sub_dict[sub_id] = param_value
params[step_id] = sub_dict
else:
step_dict = {}
for step_k, step_v in step_params.items():
step_dict[step_k] = step_v
params[step_id] = step_dict
# Replacement params
replace_dict = {}
for k, v in replacement_params.items():
logging.info("Collecting Replacement params: %s %s", k, v)
replace_dict[k] = v
# Invoke workflow
logging.info("Invoking workflow")
workflow_id = wfdesc["uuid"]
logging.info("Replacement params: %s", replace_dict)
logging.info("Workflow params: %s", params)
logging.info("Inputs: %s", inputs)
res = gi.workflows.invoke_workflow(workflow_id,
inputs,
history_id=history_id,
params=params,
import_inputs_to_history=False,
replacement_params=replace_dict)
print(json.dumps(res, indent=2))