mirror of
https://github.com/galaxyproject/galaxy.git
synced 2026-09-24 16:30:27 +08:00
John Chilton
e0a5e82bda
This means steps that are not connecting an output of one step to the input of another. This could potentially address all sorts of untraditional (in a Galaxy sense) workflows where some sort of data is managed externally. The most important use I think I have heard discussed is that of data managers - this can be used in cases where data managers depend on one another (grab the fasta files in one step, index them in another) or workflows where a downstream analysis depends on index data populated via data managers in earlier steps. Not really sure how to represent these in the workflow editor - but this is a power user feature anyway so hopefully that is not super pressing. The YAML to workflow DSL supports the operation (see test cases) so these power users (a euphemism for Dan I guess) can just use that for now.
301 lines
8.0 KiB
Python
301 lines
8.0 KiB
Python
import sys
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import yaml
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import json
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import uuid
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try:
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from collections import OrderedDict
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except ImportError:
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from galaxy.util.odict import odict as OrderedDict
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STEP_TYPE_ALIASES = {
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'input': 'data_input',
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'input_collection': 'data_collection_input',
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}
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def yaml_to_workflow(has_yaml):
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as_python = yaml.load(has_yaml)
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if isinstance(as_python, list):
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as_python = {"steps": as_python}
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__ensure_defaults(as_python, {
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"a_galaxy_workflow": "true",
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"format-version": "0.1",
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"annotation": "",
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"name": "Workflow",
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"uuid": str(uuid.uuid4()),
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})
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steps = as_python["steps"]
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conversion_context = ConversionContext()
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if isinstance(steps, list):
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steps_as_dict = OrderedDict()
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for i, step in enumerate(steps):
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steps_as_dict[ str(i) ] = step
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if "id" not in step:
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step["id"] = i
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if "label" in step:
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label = step["label"]
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conversion_context.labels[label] = i
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if "position" not in step:
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# TODO: this really should be optional in Galaxy API.
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step["position"] = {
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"left": 10 * i,
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"top": 10 * i
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}
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as_python["steps"] = steps_as_dict
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steps = steps_as_dict
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for i, step in steps.iteritems():
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step_type = step.get("type", "tool")
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step_type = STEP_TYPE_ALIASES.get(step_type, step_type)
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if step_type not in [ "data_input", "data_collection_input", "tool", "pause"]:
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raise Exception("Unknown step type encountered %s" % step_type)
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step["type"] = step_type
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eval("transform_%s" % step_type)(conversion_context, step)
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return as_python
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def transform_data_input(context, step):
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transform_input(context, step, default_name="Input dataset")
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def transform_data_collection_input(context, step):
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transform_input(context, step, default_name="Input dataset collection")
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def transform_input(context, step, default_name):
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default_name = step.get("label", default_name)
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__ensure_defaults( step, {
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"annotation": "",
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})
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__ensure_inputs_connections(step)
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if not "inputs" in step:
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step["inputs"] = [{}]
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step_inputs = step["inputs"][0]
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if "name" in step_inputs:
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name = step_inputs["name"]
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else:
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name = default_name
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__ensure_defaults( step_inputs, {
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"name": name,
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"description": "",
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})
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tool_state = {
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"name": name
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}
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if "collection_type" in step:
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tool_state["collection_type"] = step["collection_type"]
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__populate_tool_state(step, tool_state)
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def transform_pause(context, step, default_name="Pause for dataset review"):
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default_name = step.get("label", default_name)
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__ensure_defaults( step, {
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"annotation": "",
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})
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__ensure_inputs_connections(step)
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if not "inputs" in step:
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step["inputs"] = [{}]
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step_inputs = step["inputs"][0]
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if "name" in step_inputs:
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name = step_inputs["name"]
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else:
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name = default_name
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__ensure_defaults( step_inputs, {
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"name": name,
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})
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tool_state = {
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"name": name
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}
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connect = __init_connect_dict(step)
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__populate_input_connections(context, step, connect)
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__populate_tool_state(step, tool_state)
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def transform_tool(context, step):
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if "tool_id" not in step:
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raise Exception("Tool steps must define a tool_id.")
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__ensure_defaults( step, {
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"annotation": "",
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"post_job_actions": {},
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} )
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post_job_actions = step["post_job_actions"]
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tool_state = {
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# TODO: Galaxy should not require tool state actually specify a __page__.
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"__page__": 0,
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}
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connect = __init_connect_dict(step)
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def append_link(key, value):
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if key not in connect:
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connect[key] = []
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connect[key].append(value["$link"])
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def replace_links(value, key=""):
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if __is_link(value):
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append_link(key, value)
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return None
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if isinstance(value, dict):
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new_values = {}
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for k, v in value.iteritems():
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new_key = __join_prefix(key, k)
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new_values[k] = replace_links(v, new_key)
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return new_values
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elif isinstance(value, list):
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new_values = []
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for i, v in enumerate(value):
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# If we are a repeat we need to modify the key
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# but not if values are actually $links.
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if __is_link(v):
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append_link(key, v)
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new_values.append(None)
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else:
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new_key = "%s_%d" % ( key, i )
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new_values.append(replace_links(v, new_key))
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return new_values
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else:
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return value
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if "state" in step:
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step_state = step["state"]
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step_state = replace_links(step_state)
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for key, value in step_state.iteritems():
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tool_state[key] = json.dumps(value)
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del step["state"]
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# Fill in input connections
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__populate_input_connections(context, step, connect)
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__populate_tool_state(step, tool_state)
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# Handle outputs.
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if "outputs" in step:
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for name, output in step.get("outputs", {}).items():
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if output.get("hide", False):
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action_name = "HideDatasetAction%s" % name
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action = __action(
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"HideDatasetAction",
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name,
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)
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post_job_actions[action_name] = action
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if output.get("rename", None):
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new_name = output.get("rename")
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action_name = "RenameDatasetAction%s" % name
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arguments = dict(newname=new_name)
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action = __action(
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"RenameDatasetAction",
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name,
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arguments,
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)
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post_job_actions[action_name] = action
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del step["outputs"]
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class ConversionContext(object):
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def __init__(self):
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self.labels = {}
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def __action(type, name, arguments={}):
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return {
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"action_arguments": arguments,
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"action_type": type,
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"output_name": name,
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}
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def __is_link(value):
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return isinstance(value, dict) and "$link" in value
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def __join_prefix(prefix, key):
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if prefix:
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new_key = "%s|%s" % (prefix, key)
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else:
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new_key = key
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return new_key
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def __init_connect_dict(step):
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if "connect" not in step:
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step["connect"] = {}
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connect = step["connect"]
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del step["connect"]
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return connect
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def __populate_input_connections(context, step, connect):
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__ensure_inputs_connections(step)
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input_connections = step["input_connections"]
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for key, values in connect.iteritems():
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input_connection_value = []
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if not isinstance(values, list):
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values = [ values ]
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for value in values:
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if not isinstance(value, dict):
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if key == "$step":
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value += "#__NO_INPUT_OUTPUT_NAME__"
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value_parts = str(value).split("#")
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if len(value_parts) == 1:
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value_parts.append("output")
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id = value_parts[0]
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if id in context.labels:
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id = context.labels[id]
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value = {"id": int(id), "output_name": value_parts[1]}
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input_connection_value.append(value)
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if key == "$step":
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key = "__NO_INPUT_OUTPUT_NAME__"
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input_connections[key] = input_connection_value
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def __ensure_inputs_connections(step):
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if "input_connections" not in step:
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step["input_connections"] = {}
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def __ensure_defaults(in_dict, defaults):
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for key, value in defaults.items():
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if key not in in_dict:
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in_dict[ key ] = value
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def __populate_tool_state(step, tool_state):
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step["tool_state"] = json.dumps(tool_state)
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def main(argv):
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print json.dumps(yaml_to_workflow(argv[0]))
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if __name__ == "__main__":
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main(sys.argv)
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