def on_run(self):
# Build Workflow
wf = Workflow(project_dir='./loop_workflow')
wf.task_runner = self
# Task 1: Task that will provide an array of values that will be
# iterated over. Setting output_format=mol2 directly as input
# to this task is to demonstrate that parameters passed to the
# LoopTask will be forwarded to the mapped workflows created by
# the LoopTask. Equally so for steps=100 illustrating that input
# of steps in the workflow can obtained from tasks outside of
# the loop
t1 = wf.add_task('Array provider')
t1.set_input(output_format='mol2', steps=100)
# Task 2: Add loop task. The 'mapper_arg' defines the parameter name in
# the input that holds an iterable of input values to iterate
# over. The 'loop_end_task' is required and defines the task
# that 'closes' the loop and collects all results.
t2 = wf.add_task('Loop',
task_type='LoopTask',
mapper_arg='smiles',
loop_end_task='Collector')
wf.connect_task(t1.nid, t2.nid)
# Task 3: Convert SMILES to mol2
# Convert ligand to mol2 format irrespective of input format.
t3 = wf.add_task('Ligand conversion',
task_type='WampTask',
uri='mdgroup.mdstudio_structures.endpoint.convert')
wf.connect_task(t2.nid, t3.nid, smiles='mol')
# Task 4: Convert mol2 to 3D mol2 irrespective if input is 1D/2D or 3D
# mol2 If 'output_format' is not specified it is deduced from
# the input wich is mol2 in this case. There are circumstances
# where conversion to 3D fails, retry upto 3 times.
t4 = wf.add_task('Make_3D',
task_type='WampTask',
uri='mdgroup.mdstudio_structures.endpoint.make3d',
retry_count=3)
wf.connect_task(t3.nid, t4.nid, 'mol')
wf.connect_task(t1.nid, t4.nid, 'steps')
# Task 5: Empty task that server as a collector for all results
# obtained during iteration.
t5 = wf.add_task('Collector')
wf.connect_task(t4.nid, t5.nid, 'mol')
# Set the array of input SMILES string to task 1
wf.input(t1.nid,
smiles=[
'O1[C@@H](CCC1=O)CCC',
'C[C@]12CC[C@H]3[C@@H](CC=C4CCCC[C@]34CO)[C@@H]1CCC2=O',
'CC12CCC3C(CC=C4C=CCCC34C)C1CCC2=O'
])
wf.run()
while wf.is_running:
yield sleep(1)
def on_run(self):
"""
When the microservice has successfully registered with the broker the
on_run method is the first method to be called.
We are using this method now to run our example workflow.
"""
# Workflow constants, these will be saved as part of the workflow
# specification
ligand_format = 'smi'
pH = 7.4
protein_file = os.path.abspath('protein.mol2')
protein_binding_center = [4.9264, 19.0796, 21.9892]
# Build Workflow
wf = Workflow(description='MDStudio WAMP workflow')
# Task 1: convert the SMILES string to mol2 format (2D).
# Add a task using the 'add_task' method always defining:
# an administrative title of the task and the task type here a WampTask
# because we are calling an microservice endpoint defined by uri.
# 'store_output' is True by default and stores the task input/output to disk.
t1 = wf.add_task('Format_conversion',
task_type='WampTask',
uri='mdgroup.mdstudio_structures.endpoint.convert')
# Use 'set_input' do define the input to a task. As we are now building
# a workflow specification these will be task constants but the same
# method will be used later on to define specific input when using the
# workflow specification for a ligand.
t1.set_input(output_format='mol2')
# Task 2: Covert mol2 to 3D mol2 irrespective if input is 1D/2D or 3D mol2
# This particular 3D conversion routine is known to fail sometimes but by
# setting retry_count to 3 the workflow manager will retry 3 times before
# failing.
t2 = wf.add_task('Make_3D',
task_type='WampTask',
uri='mdgroup.mdstudio_structures.endpoint.make3d',
retry_count=3)
t2.set_input(output_format='mol2')
# Use 'connect_task' to connect t1 to t2 using their unique identifiers
# (nid). In addition we can specify the parameters for task 1 we wish to
# use as input to task 2 as additional argument or keyword arguments to
# the functions. A keyword argument defines a parameter name mapping
# between the two tasks.
wf.connect_task(t1.nid, t2.nid, 'mol')
# Task 3: Adjust ligand protonation state to a given pH if applicable
t3 = wf.add_task('Add hydrogens',
task_type='WampTask',
uri='mdgroup.mdstudio_structures.endpoint.addh')
t3.set_input(output_format='mol2', correctForPH=True, pH=pH)
wf.connect_task(t2.nid, t3.nid, 'mol')
# Task 4: Get the formal charge for the protonated mol2 to use as input
# for ACPYPE or ATB
# Here store_output equals False which will keep all output in memory and
# finally as part of the stored workflow file (*.jgf)
t4 = wf.add_task('Get charge',
task_type='WampTask',
uri='mdgroup.mdstudio_structures.endpoint.info',
store_output=False)
t4.set_input(input_format='mol2')
wf.connect_task(t3.nid, t4.nid, 'mol')
# Task 5: Create rotations of the molecule for better sampling
t5 = wf.add_task('Create 3D rotations',
task_type='WampTask',
uri='mdgroup.mdstudio_structures.endpoint.rotate')
t5.set_input(rotations=[[1, 0, 0, 90], [1, 0, 0, -90], [0, 1, 0, 90],
[0, 1, 0, -90], [0, 0, 1, 90], [0, 0, 1, -90]])
wf.connect_task(t3.nid, t5.nid, 'mol')
# Task 6: Run PLANTS on ligand and protein
# The 'workdir' argument points to a tmp directory that is shared between
# the microservice docker image and the host system to store results.
t6 = wf.add_task('Plants docking',
task_type='WampTask',
uri='mdgroup.mdstudio_smartcyp.endpoint.docking')
t6.set_input(cluster_structures=100,
bindingsite_center=protein_binding_center,
bindingsite_radius=12,
protein_file=protein_file,
threshold=3.0,
base_work_dir='/tmp/mdstudio/mdstudio_smartcyp')
# Here we pass only the 'mol' parameter from task 5 to task 6 where it is
# named 'ligand_file'
wf.connect_task(t5.nid, t6.nid, 'mol', mol='ligand_file')
# Task 7: Extract cluster medians from output using a custom function.
# A task of type 'PythonTask' allows to add custom python functions
# or classes to the workflow. They are defined using the 'custom_func'
# parameter according to the Python import syntax. The package or file
# containing the function should be available as part of the PYTHONPATH.
t7 = wf.add_task('Get cluster medians',
task_type='PythonTask',
custom_func='workflow_helpers.get_docking_medians')
wf.connect_task(t6.nid, t7.nid, 'result')
# Task 8: retrieve median structures
t8 = wf.add_task(
'Retrieve median structures',
task_type='WampTask',
uri='mdgroup.mdstudio_smartcyp.endpoint.docking_structures')
t8.set_input(create_ensemble=False)
wf.connect_task(t7.nid, t8.nid, medians='paths')
# Save the workflow specification
wf.save('workflow_spec.jgf')
# Lets run the workflow specification for a number of ligand SMILES
# The current microservice instance (self) is passed as task_runner to the workflow
# it will be used to make calls to other microservice endpoints when task_type equals WampTask.
wf.task_runner = self
currdir = os.getcwd()
for i, ligand in enumerate([
'O1[C@@H](CCC1=O)CCC',
'C[C@]12CC[C@H]3[C@@H](CC=C4CCCC[C@]34CO)[C@@H]1CCC2=O',
'CC12CCC3C(CC=C4C=CCCC34C)C1CCC2=O'
],
start=1):
wf.load('workflow_spec.jgf')
wf.input(t1.nid,
mol={
'content': ligand,
'path': None,
'extension': ligand_format
})
wf.run(project_dir='./ligand-{0}'.format(i))
while wf.is_running:
yield sleep(1)
os.chdir(currdir)