async def on_offloding_event(source, rpcname, code, resourcename,
params):
print("Received from: {} app".format(source))
print("Received RCP name: {}".format(rpcname))
print("Received the source code: {}".format(code))
print("Received params: {}".format(params))
if source == "Android":
self.params = params
# Java file already cached in MAMoC Repository
if path.exists("java_classes/{}.java".format(self.class_name)):
result = self.executor.startExecuting(
self.class_name, "{}.java".format(self.class_name),
params)
else:
# if it is a class, it must start with package keyword
if code.strip().split(' ', 1)[0] == "package":
code, self.class_name = Transformer(
code, resourcename, params).start()
else:
code, self.class_name = Transformer(
code, resourcename, params).start(type="method")
with open("java_classes/{}.java".format(self.class_name),
"w") as java_file:
print("{}".format(code), file=java_file)
result = self.executor.startExecuting(
self.class_name, "{}.java".format(self.class_name),
params)
print(result)
if result: # if building and execution were successful, send back output and duration in seconds
output = result[0]
duration = result[1]
output = self.decode_bytes(output)
self.publish('uk.ac.standrews.cs.mamoc.offloadingresult',
output, duration)
# register the procedure for next time rpc request
try:
re = await self.register(
self.execute_java,
rpcname,
options=RegisterOptions(invoke=u'roundrobin'))
except ApplicationError as e:
print("could not register procedure: {0}".format(e))
else:
print("{} endpoints registered".format(re))
elif source == "iOS":
print("received from iOS app")
else:
print("unrecognized source!")
async def onJoin(self, details):
await self.register(self, options=RegisterOptions(invoke='roundrobin'))
await self.subscribe(self.on_sim_telemetry, 'sim.telemetry')
self.is_running = True
while self.is_running:
await self.pass_outgoing_cmd()
await asyncio.sleep(0.1)
class DockingWampApi(ComponentSession):
def authorize_request(self, uri, claims):
return True
@endpoint('docking', 'docking_request', 'docking_response', options=RegisterOptions(invoke='roundrobin'))
def run_docking(self, request, claims):
"""
Perform a PLANTS (Protein-Ligand ANT System) molecular docking.
For a detail description of the input see the file:
schemas/endpoints/docking-request.v1.json
"""
task_id = self.component_config.session.session_id
self.log.info("Plants Docking ID: {}".format(task_id))
# Docking options are equal to the request
plants_config = request.copy()
# Transfer the files content
plants_config['protein_file'] = request['protein_file']['content']
plants_config['ligand_file'] = request['ligand_file']['content']
# Prepare docking directory
workdir = os.path.abspath(request['workdir'])
plants_config["workdir"] = prepare_work_dir(
workdir, create=True)
# location of the executable
file_path = os.path.realpath(__file__)
root = os.path.split(file_path)[0]
plants_config['exec_path'] = os.path.join(root, 'plants_linux')
# Run docking
docking = PlantsDocking(**plants_config)
success = docking.run(plants_config['protein_file'], plants_config['ligand_file'])
if success:
status = 'completed'
results = docking.results()
output = {key: encode_file(value) for key, value in results.items()}
# Add path to cluster dendrogram
clusterplot = os.path.join(workdir, 'cluster_dendrogram.pdf')
if os.path.isfile(clusterplot):
results['clusterplot'] = {'content': None, 'extension': 'pdf', 'path': clusterplot}
else:
self.log.error('PLANTS docking FAILS!!')
docking.delete()
status = 'failed'
output = None
return {'status': status, 'output': output}
async def onJoin(self, details):
print("Join:", details)
self.register(self.ping,
"com.myapp.ping",
options=RegisterOptions(details_arg="details"))
counter = 0
while True:
print("publish: com.myapp.hello", counter)
self.publish("com.myapp.hello", counter)
counter += 1
await asyncio.sleep(1)
async def _register(self):
try:
options = RegisterOptions(**self._register_options_kwargs)
session = self._manager.session.application_session
print(
f'Registration of {self._procedure} with name {self.name} starting'
)
self._registration = await session.register(
endpoint=self._endpoint_wrapper,
procedure=self._procedure,
options=options,
prefix=self._prefix)
print(
f'Registration of {self._procedure} with name {self.name} succeeded'
)
except Exception as e:
print(
f'Registration of {self._procedure} with name {self.name} failed'
)
self._exception = e
def bootstrap():
wamp = FlaskAutobahnSync(app)
app.wamp = wamp
serve_history = []
@wamp.subscribe(u'com.flask_app.page_served')
def page_served(wid, timestamp):
data = (wid, timestamp)
print('[Worker %s] Received %s' % (worker_id, data))
serve_history.append(data)
# Authorize multiple registers given we can start this app in concurrent workers
register_opt = RegisterOptions(invoke=u'random')
@wamp.register(u'com.flask_app.get_request_history', options=register_opt)
def get_request_history(wid):
print('[Worker %s] Send request history to worker %s' %
(worker_id, wid))
return serve_history
return app
class RoundrobinComponent(ComponentSession):
def authorize_request(self, uri, claims):
# Authorize calls to API endpoints
return True
@endpoint('parallel',
'roundrobin_request',
'roundrobin_response',
options=RegisterOptions(invoke=u'roundrobin'))
def parallel_call(self, request, claims):
"""
Parallel call
Simulate some CPU intensive task by sleeping for 5 seconds
then return number variable to the power of 2.
"""
self.sleep(request['number'])
request['number'] = request['number']**POWER
return request
@endpoint('sequential', 'roundrobin_request', 'roundrobin_response')
def sequential_call(self, request, claims):
"""
Sequential call
Similar to parallel_call but without the roundrobin registration
"""
self.sleep(request['number'])
request['number'] = request['number']**POWER
return request
def sleep(self, number):
self.log.info('Process number {0} after {1} seconds delay'.format(
number, DELAY))
sleep(5)
class WorkflowWampApi(ComponentSession):
"""
Workflow WAMP methods.
Defines `require_config` to retrieve system and database configuration
upon WAMP session setup
"""
def authorize_request(self, uri, claims):
return True
@endpoint('run_workflow',
'run_workflow_request',
'run_workflow_response',
options=RegisterOptions(invoke=u'roundrobin'))
def retrieve_structures(self, workflow, session=None):
"""
Run a LIE Workflow
"""
print(workflow)
print(session)
return session
class MDWampApi(ComponentSession):
"""
Molecular dynamics WAMP methods.
"""
def authorize_request(self, uri, claims):
return True
@endpoint('query_gromacs_results',
'query_gromacs_results_request',
'async_gromacs_response',
options=RegisterOptions(invoke='roundrobin'))
def query_gromacs_results(self, request, claims):
"""
Check the status of the simulation and return the results if available.
The request should at least contain a task_id stored in the cerise job DB.
The response is a typical async_gromacs response, a 'Future' object.
"""
output = yield query_simulation_results(request, self.db)
for key, value in request.items():
if key not in output:
output[key] = value
return_value(output)
@endpoint('async_gromacs_ligand',
'async_gromacs_ligand_request',
'async_gromacs_response',
options=RegisterOptions(invoke='roundrobin'))
def run_async_ligand_solvent_md(self, request, claims):
"""
Run Gromacs MD of ligand in solvent. Invoke a ligand solvent simulation
and returns immediate, returning to the caller information for querying the results.
TODO: Still requires the protein topology and positional restraint
(include) files. Makes no sense for ligand in solvent but
required by gromit somehow.
"""
# Protein structure not needed. Explicitly set to None
request['protein_file'] = None
output = yield self.run_async_gromacs_gromacs(request, claims)
return_value(output)
@endpoint('async_gromacs_protein',
'async_gromacs_protein_request',
'async_gromacs_response',
options=RegisterOptions(invoke='roundrobin'))
def run_async_protein_protein_md(self, request, claims):
"""
Run asynchronous Gromacs MD of a protein-ligand system in solvent
"""
output = yield self.run_async_gromacs_gromacs(request, claims)
return_value(output)
@endpoint('gromacs_ligand',
'gromacs_ligand_request',
'gromacs_response',
options=RegisterOptions(invoke='roundrobin'))
def run_ligand_solvent_md(self, request, claims):
"""
Run Gromacs MD of ligand in solvent
TODO: Still requires the protein topology and positional restraint
(include) files. Makes no sense for ligand in solvent but
required by gromit somehow.
"""
# Protein structure not needed. Explicitly set to None
request['protein_file'] = None
output = yield self.run_gromacs_gromacs(request, claims)
return_value(output)
@endpoint('gromacs_protein',
'gromacs_protein_request',
'gromacs_response',
options=RegisterOptions(invoke='roundrobin'))
def run_ligand_protein_md(self, request, claims):
"""
Run Gromacs MD of a protein-ligand system in solvent
"""
output = yield self.run_gromacs_gromacs(request, claims)
return_value(output)
@chainable
def run_gromacs_gromacs(self, request, claims):
"""
First it calls gromit to compute the Ligand-solute energies, then
calls gromit to calculate the protein-ligand energies.
The Cerise-client infrastructure is used to perform the computations
in a remote server, see:
http://cerise-client.readthedocs.io/en/master/index.html
This function expects the following keywords files to call gromit:
* cerise_file
* protein_file (optional)
* protein_top
* ligand_file
* topology_file
* residues
The cerise_file is the path to the file containing the configuration
information required to start a Cerise service.
Further include files (e.g. *itp files) can be included as a list:
include=[atom_types.itp, another_itp.itp]
To perform the energy decomposition a list of the numerical residues
identifiers is expected, for example:
residues=[1, 5, 7, 8]
Note: the protein_file arguments is optional if you do not provide it
the method will perform a SOLVENT LIGAND MD if you provide the
`protein_file` it will perform a PROTEIN-LIGAND MD.
"""
cerise_config, gromacs_config = self.setup_environment(request)
cerise_config['clean_remote'] = request.get('clean_remote_workdir',
True)
# Run the MD and retrieve the energies
output = yield call_cerise_gromit(gromacs_config, cerise_config,
self.db)
return_value(output)
@chainable
def run_async_gromacs_gromacs(self, request, claims):
"""
async version of the `run_gromacs_gromacs` function.
"""
cerise_config, gromacs_config = self.setup_environment(request)
cerise_config['clean_remote'] = request.get('clean_remote_workdir',
True)
output = yield call_async_cerise_gromit(gromacs_config, cerise_config,
self.db)
return_value(output)
def setup_environment(self, request):
"""
Set all the configuration to perform a simulation.
"""
# Base workdir needs to exist. Might be shared between docker and host
check_workdir(request['workdir'])
task_id = uuid.uuid1().hex
request.update({"task_id": task_id})
self.log.info("starting gromacs task_id: {}".format(task_id))
task_workdir = create_task_workdir(request['workdir'])
request['workdir'] = task_workdir
self.log.info("store output in: {0}".format(task_workdir))
# Copy input files to task workdir
request = copy_file_path_objects_to_workdir(request.copy())
# Build 'include' file list for cerise/CWL
request['include'] = []
for file_type in ('attype_itp', 'protein_posre_itp'):
request['include'].append(request[file_type])
# Load GROMACS configuration
gromacs_config = set_gromacs_input(request)
# Load Cerise configuration
cerise_config = create_cerise_config(request)
cerise_config['task_id'] = task_id
with open(os.path.join(request['workdir'], "cerise.json"), "w") as f:
json.dump(cerise_config, f)
return cerise_config, gromacs_config
class StructuresWampApi(CheminfoDescriptorsWampApi, CheminfoMolhandleWampApi,
CheminfoFingerprintsWampApi, ComponentSession):
"""
Structure database WAMP methods.
"""
def authorize_request(self, uri, claims):
return True
@endpoint('chemical_similarity',
'chemical_similarity_request',
'chemical_similarity_response',
options=RegisterOptions(invoke=u'roundrobin'))
def calculate_chemical_similarity(self, request, claims):
request['workdir'] = os.path.abspath(request['workdir'])
return super(StructuresWampApi,
self).calculate_chemical_similarity(request, claims)
@endpoint('descriptors',
'descriptors_request',
'descriptors_response',
options=RegisterOptions(invoke=u'roundrobin'))
def get_descriptors(self, request, claims):
request['workdir'] = os.path.abspath(request['workdir'])
return super(StructuresWampApi, self).get_descriptors(request, claims)
@endpoint('convert',
'convert_request',
'convert_response',
options=RegisterOptions(invoke=u'roundrobin'))
def convert_structures(self, request, claims):
request['workdir'] = os.path.abspath(request['workdir'])
return super(StructuresWampApi,
self).convert_structures(request, claims)
@endpoint('addh',
'addh_request',
'addh_response',
options=RegisterOptions(invoke=u'roundrobin'))
def addh_structures(self, request, claims):
request['workdir'] = os.path.abspath(request['workdir'])
return super(StructuresWampApi, self).addh_structures(request, claims)
@endpoint('removeh',
'removeh_request',
'removeh_response',
options=RegisterOptions(invoke=u'roundrobin'))
def removeh_structures(self, request, claims):
request['workdir'] = os.path.abspath(request['workdir'])
return super(StructuresWampApi,
self).removeh_structures(request, claims)
@endpoint('make3d',
'make3d_request',
'make3d_response',
options=RegisterOptions(invoke=u'roundrobin'))
def make3d_structures(self, request, claims):
request['workdir'] = os.path.abspath(request['workdir'])
return super(StructuresWampApi,
self).make3d_structures(request, claims)
@endpoint('info',
'info_request',
'info_response',
options=RegisterOptions(invoke=u'roundrobin'))
def structure_attributes(self, request, claims):
return super(StructuresWampApi,
self).structure_attributes(request, claims)
@endpoint('rotate',
'rotate_request',
'rotate_response',
options=RegisterOptions(invoke=u'roundrobin'))
def rotate_structures(self, request, claims):
request['workdir'] = os.path.abspath(request['workdir'])
return super(StructuresWampApi,
self).rotate_structures(request, claims)
@endpoint('supported_toolkits',
'supported_toolkits_request',
'supported_toolkits_response',
options=RegisterOptions(invoke=u'roundrobin'))
def supported_toolkits(self, request, claims):
"""
Query available toolkits.
For a detailed input description see the file:
mdstudio_structures/schemas/endpoints/supported_toolkits_request_v1.json
And for a detailed description of the output see:
mdstudio_structures/schemas/endpoints/supported_toolkits_response_v1.json
"""
return {'status': 'completed', 'toolkits': list(toolkits.keys())}
@endpoint('remove_residues',
'remove_residues_request',
'remove_residues_response',
options=RegisterOptions(invoke=u'roundrobin'))
def remove_residues(self, request, claims):
"""
Remove residues from a PDB structure
For a detailed input description see the file:
mdstudio_structures/schemas/endpoints/removed_residues_request_v1.json
And for a detailed description of the output see:
mdstudio_structures/schemas/endpoints/removed_residues_response_v1.json
"""
request['workdir'] = os.path.abspath(request['workdir'])
# Parse the structure
parser = PDBParser(PERMISSIVE=True)
struc_obj = StringIO(request.get('mol'))
structure = parser.get_structure('mol_object', struc_obj)
struc_obj.close()
to_remove = [r.upper() for r in request.get('residues', [])]
removed = []
for model in structure:
for chain in model:
for residue in chain:
if residue.get_resname() in to_remove:
chain.detach_child(residue.id)
removed.append(residue.get_resname())
if len(chain) == 0:
model.detach_child(chain.id)
self.log.info('Removed residues: {0}'.format(','.join(removed)))
# Save to file or string
pdbio = PDBIO()
pdbio.set_structure(structure)
status = 'completed'
if request.get('workdir'):
result = os.path.join(request.get('workdir'), 'structure.pdb')
pdbio.save(result)
else:
outfile = StringIO()
pdbio.save(outfile)
outfile.seek(0)
result = outfile.read()
return {'status': status, 'mol': result}
@endpoint('retrieve_rcsb_structure',
'retrieve_rcsb_structure_request',
'retrieve_rcsb_structure_response',
options=RegisterOptions(invoke=u'roundrobin'))
def fetch_rcsb_structure(self, request, claims):
"""
Download a structure file from the RCSB database using a PDB ID
For a detailed input description see the file:
mdstudio_structures/schemas/endpoints/retrieve_rcsb_structures_request_v1.json
And for a detailed description of the output see:
mdstudio_structures/schemas/endpoints/retrieve_rcsb_structures_response_v1.json
"""
# Create workdir and save file
request['workdir'] = os.path.abspath(request['workdir'])
workdir = os.path.join(request.get('workdir', tempfile.gettempdir()))
if not os.path.isdir(workdir):
os.makedirs(workdir)
# Retrieve the PDB file
pdb_id = request['pdb_id'].upper()
pdb = PDBList()
dfile = pdb.retrieve_pdb_file(pdb_id,
file_format=request.get(
'rcsb_file_format', 'pdb'),
pdir=workdir,
overwrite=True)
# Change file extension
base, ext = os.path.splitext(dfile)
ext = ext.lstrip('.')
if ext == 'ent':
os.rename(dfile, '{0}.pdb'.format(base))
dfile = '{0}.pdb'.format(base)
# Return file path if workdir in function arguments else return
# file content inline.
if os.path.isfile(dfile):
status = 'completed'
if 'workdir' in request:
molecule = dfile
else:
with open(dfile, 'r') as f:
molecule = f.read()
else:
self.log.error('Unable to download structure: {0}'.format(pdb_id))
status = 'failed'
molecule = None
result = {'path': None, 'content': molecule, 'extension': ext}
return {'status': status, 'mol': result}
id="ltri-csgo-gsi-money", options=options)
yield session.publish(f"{SRC_PREFIX}ltri-csgo-gsi-money-inv",
wamp_component.state.player.state.money_as_color_inv() * LEDS_IN_ARRAY_DEFAULT,
id="ltri-csgo-gsi-money-inv", options=options)
yield None
@wamp_component.on_join
@inlineCallbacks
def joined(session, details):
print("session ready")
wamp_component.loop_gsi_publish = task.LoopingCall(publish_gsi, session=session)
wamp_component.loop_gsi_publish.start(.05)
@wamp_component.register("com.lambentri.edge.la4.machine.list", options=RegisterOptions(invoke="roundrobin"))
def list_active_machine_instances():
"""List all available machines"""
schema = MachineDict(
machines=
{
# these don't quite work yet
"cs-gsi-de": Machine(
name="CSGO-GSI",
iname="DE_",
id="ltri-csgo-gsi-de",
desc="Machine State for CSGO Defusal games"
),
"cs-gsi-cs": Machine(
name="CSGO-GSI",
iname="CS_",
class ATBWampApi(ComponentSession):
"""
Automated Topology Builder API WAMP methods.
"""
def authorize_request(self, uri, claims):
return True
@staticmethod
def get_mol(geometry):
"""
Retrieve molecular geometry
"""
structure = geometry['content']
if structure is None and geometry['path']:
if os.path.isfile(geometry['path']):
with open(geometry['path'], 'r') as molfile:
structure = molfile.read()
else:
raise IOError('Structure file not defined')
return structure
def _parse_server_error(self, error):
"""
Parse ATB server JSON error construct
"""
error_dict = {}
if hasattr(error, 'read'):
try:
error_dict = json.load(error)
self.log.error('ATB server error: {0}'.format(
error_dict.get('error_msg')))
except Exception:
self.log.error('Unknown ATB server error')
return error_dict
def _exceute_api_call(self, call, **kwargs):
"""
Execute ATB server API call
"""
try:
return call(**kwargs)
except URLError as error:
self.log.error('ATB server URL {0} not known/reachable'.format(
SETTINGS['atb_url']))
return self._parse_server_error(error)
except HTTPError as error:
return self._parse_server_error(error)
def _init_atb_api(self, api_token=None):
"""
Start ATB server API interface.
The ATB API is initiated at every WAMP method request using a global
or user specific ATB API token.
:param api_token: valid ATB server API token
:type api_token: :py:str
:rtype: ATBServerApi object
"""
if not api_token:
self.log.error('Using the ATB server requires a valid API token')
return None
api = ATBServerApi(api_token=api_token,
timeout=SETTINGS['atb_api_timeout'],
debug=SETTINGS['atb_api_debug'],
host=SETTINGS['atb_url'],
api_format=u'json')
if not api:
raise IOError('Unable to use the ATB API')
return api
@endpoint('submit',
'atb_submit_request',
'atb_submit_response',
options=RegisterOptions(invoke=u'roundrobin'))
def atb_submit_calculation(self, request, claims):
"""
Submit a new calculation to the ATB server
"""
# Init ATBServerApi
api = self._init_atb_api(api_token=request['atb_api_token'])
# Open file if needed
pdb = self.get_mol(request['pdb'])
response = self._exceute_api_call(api.Molecules.submit,
pdb=pdb,
netcharge=request['netcharge'],
moltype=request['moltype'],
public=request['public'])
if response and response.get(u'status', None) == u'error':
# Check if molecule has been calculated already
if response.get(
u'error_msg',
'').startswith('Your submission matched a previously'):
# Extract molid from response
m = re.search('(?<=molid=)[0-9]*',
response.get(u'error_msg', ''))
molid = None
if m:
molid = m.group()
if molid.isdigit():
molid = int(molid)
# Get the molecule data for molid
response = [
self._exceute_api_call(api.Molecules.molid, molid=molid)
]
return {
'result': [
mol.moldict for mol in response
if isinstance(mol, ATB_Mol)
]
}
@endpoint('get_structure',
'atb_get_structure_request',
'atb_get_structure_response',
options=RegisterOptions(invoke=u'roundrobin'))
def atb_structure_download(self, request, claims):
"""
Retrieve a structure file from the ATB server by molid
Supports download of structures in the following file formats:
- pqr_allatom_optimised: APBS pqr format
- pqr_allatom_unoptimised: APBS pqr format
- pqr_uniatom_optimised: APBS pqr format
- pqr_uniatom_unoptimised: APBS pqr format
- pdb_allatom_optimised: PDB format
- pdb_allatom_unoptimised: PDB format
- pdb_uniatom_optimised: PDB format
- pdb_uniatom_unoptimised: PDB format
- cif_allatom: CIF format
- cif_allatom_extended: CIF format
- cif_uniatom: CIF format
- cif_uniatom_extended: CIF format
"""
if request['fformat'] not in SUPPORTED_STRUCTURE_FILE_FORMATS:
self.log.error('Structure format {0} not supported'.format(
request['fformat']))
return
# Init ATBServerApi
api = self._init_atb_api(api_token=request['atb_api_token'])
# Get the molecule by molid
molecule = self._exceute_api_call(api.Molecules.molid,
molid=request['molid'])
filename = None
if 'workdir' in request:
workdir = os.path.abspath(request['workdir'])
filename = os.path.join(
workdir, '{0}.{1}'.format(
request['fformat'],
SUPPORTED_FILE_EXTENTIONS.get(request['fformat'], 'txt')))
if molecule and isinstance(molecule, ATB_Mol):
structure = self._exceute_api_call(
molecule.download_file,
file=request['fformat'],
outputType=SUPPORTED_STRUCTURE_FILE_FORMATS.get(
request['fformat'], 'cry'),
ffVersion=request['ffversion'],
hash=request['atb_hash'],
fnme=filename)
return structure
else:
self.log.error(
'Unable to retrieve structure file for molid: {0}'.format(
request['molid']))
@endpoint('get_topology',
'atb_get_topology_request',
'atb_get_topology_response',
options=RegisterOptions(invoke=u'roundrobin'))
def atb_topology_download(self, request, claims):
"""
Retrieve a topology and parameter files from the ATB server by molid
Supports download of structures in the following file formats:
- lammps_allatom_optimised: LAMMPS MD
- lammps_allatom_unoptimised: LAMMPS MD
- lammps_uniatom_optimised: LAMMPS MD
- lammps_uniatom_unoptimised: LAMMPS MD
- mtb96_allatom: GROMOS96
- mtb96_uniatom: GROMOS96
- mtb_allatom: GROMOS11
- mtb_uniatom: GROMOS11
- rtp_allatom: GROMACS
- rtp_uniatom: GROMACS
- cns_allatom_top: CNS
- cns_allatom_param: CNS
- cns_uniatom_top: CNS
- cns_uniatom_param: CNS
"""
if request['fformat'] not in SUPPORTED_TOPOLOGY_FILE_FORMATS:
self.log.error('Structure format {0} not supported'.format(
request['fformat']))
return
# Init ATBServerApi
api = self._init_atb_api(api_token=request['atb_api_token'])
# Get the molecule by molid
molecule = self._exceute_api_call(api.Molecules.molid,
molid=request['molid'])
filename = None
if 'workdir' in request:
workdir = os.path.abspath(request['workdir'])
filename = os.path.join(
workdir, '{0}.{1}'.format(
request['fformat'],
SUPPORTED_FILE_EXTENTIONS.get(request['fformat'], 'top')))
if molecule and isinstance(molecule, ATB_Mol):
structure = self._exceute_api_call(
molecule.download_file,
file=SUPPORTED_TOPOLOGY_FILE_FORMATS[request['fformat']],
outputType='top',
ffVersion=request['ffversion'],
hash=request['atb_hash'],
fnme=filename)
return structure
else:
self.log.error(
'Unable to retrieve topology/parameter file for molid: {0}'.
format(request['molid']))
@endpoint('structure_query',
'atb_structure_query_request',
'atb_structure_query_response',
options=RegisterOptions(invoke=u'roundrobin'))
def atb_structure_query(self, request, claims):
"""
Query the ATB server database for molecules based on a structure
16-11-2017: Method only works with HTTP GET
"""
# Get structure
structure = self.get_mol(request['mol'])
# Adjust structure format if needed
if request['mol']['extension'] and request['mol'][
'extension'] != request['structure_format']:
if not request['mol']['extension'] in ('pdb', 'mol', 'mol2', 'sdf',
'inchi'):
raise IOError('File format not supported: {0}'.format(
request['mol']['extension']))
request['structure_format'] = request['mol']['extension']
# Init ATBServerApi
api = self._init_atb_api(api_token=request['atb_api_token'])
result = self._exceute_api_call(
api.Molecules.structure_search,
structure_format=request['structure_format'],
structure=structure,
netcharge=request.get('netcharge', '*'),
method=u'GET')
output = {'matches': result.get('matches', [])}
if 'search_molecule' in result:
for key in ('inchi', 'inchi_key'):
output['search_{0}'.format(
key)] = result['search_molecule'][key]
return output
@endpoint('molecule_query',
'atb_molecule_query_request',
'atb_molecule_query_response',
options=RegisterOptions(invoke=u'roundrobin'))
def atb_molecule_query(self, request, claims):
"""
Query the ATB server database for molecules based on molecule meta-data
The ATB server molecules database can be queried using the
./api/v0.1/molecules/search.py endpoint using any of the following query
attributes or 'any' for a whildcard search. Multiple query attributes
will be chained using the AND logical operator.
iupac: the official IUPAC name of the molecule (str)
inchi_key: the unique InChI code of the molecule (str)
smiles: canonical SMILES of the molecule (str)
common_name: the common name of the molecule (str)
formula: the molecular formula (e.a. C2H6O) (str)
maximum_qm_level: string of comma seperated integers.
curation_trust: level of expected accuracy of the molecule parameters
as string of comma seperated integers. 0 by default:
-1 = unfinished ATB molecule
0 = finished ATB molecule
1 = manual parameters from reliable users
2 = manual parameters from official source
is_finished: are calculations for the molecule still running
user_label: any user specific label that may have been given to
the molecule.
is_finished: query for finished molecules only
max_atoms: maximum number of atoms (int)
min_atoms: minimum number of atoms (int)
has_pdb_hetId: molecule has PDB heteroatom ID (bool)
match_partial: enable partial matching of string attributes (bool)
False by default.
Query values are case insensitive but the attributes (keys) are.
"""
# Init ATBServerApi
api = self._init_atb_api(api_token=request['atb_api_token'])
# Get molecule directly using ATB molid
if 'molid' in request:
response = [
self._exceute_api_call(api.Molecules.molid,
molid=request['molid'])
]
else:
# Execute ATB molecule search query
del request['atb_api_token']
response = [
mol.moldict for mol in self._exceute_api_call(
api.Molecules.search, **request)
if isinstance(mol, ATB_Mol)
]
if isinstance(response, list):
if not len(response):
self.log.info('ATB molecule query did not yield any results')
return {'result': response}
else:
self.log.error('Unable to execute ATB molecule query')
class LambentMachine(DocMixin, ApplicationSession):
regs = {}
subs = {}
grp = "machine"
tickers = {}
machines = {
# "SlowFakeMachine-a.b": SlowFakeMachine(),
# "FastFakeMachine-c.d": FastFakeMachine()
}
machine_library = [
SlowFakeMachine,
FastFakeMachine,
"lambents.solids.SolidStep",
"lambents.solids.SolidStepHSV",
"lambents.chasers.SimpleColorChaser",
"lambents.chasers.MultiColorChaser",
"lambents.chasers.MultiNoSpaceChaser",
"lambents.chasers.MultiNoSpaceChaserRGB",
"lambents.rainbows.RainbowChaser",
"lambents.scapes.BounceScape",
"lambents.rocker.SolidRocker",
"lambents.rocker.ChasingRocker",
"lambents.growth.FireflyHSV",
"lambents.growth.GMFireflyRandomHSV",
]
brightness_tgt = BrightnessEnum(255)
brightness_act = 255
def _handle_loading_config_from_file(self, path):
try:
f = open(path)
loaded = load(f)
print(loaded)
except FileNotFoundError:
self.log.info("config file: attempted to load but file not found")
return
except YAMLError:
self.log.info("config file: failed to parse YAML")
return
self.log.info("config file: loaded from path")
if "machines" not in loaded:
self.log.info("config file: no machines segment in config!")
return
machines = loaded['machines']
for mach in machines:
try:
self.init_machine_instance(
machine_cls=mach.get("cls"),
machine_name=mach.get("name"),
machine_kwargs=mach.get("kwargs"),
direct=True,
)
except MachineLoadException as e:
print("failed to load from config", e)
def __init__(self, config=None):
ApplicationSession.__init__(self, config)
txaio.start_logging()
if os.environ.get("LA4_CONFIG_PATH"):
path = os.environ.get("LA4_CONFIG_PATH")
self.log.info("config file: specified in environ @ %s" % path)
self._handle_loading_config_from_file(path)
else:
parser = argparse.ArgumentParser()
parser.add_argument("--config", help="A config file to read from")
args = parser.parse_args()
self.log.info("config file: passed in via args @ %s" % args.config)
self._handle_loading_config_from_file(args.config)
# pull built-in machines from a config file if specified
# get redis from config
# check for passed in config file
# if no redis/config, try and find the default config file
# start the tick runner(s). There is one for each of the speeds
# The tick runner will iterate through all machines and find machines set to the specific enum
# and step them along ever X seconds
self._init_timers()
# In all config cases, then check for machine instances to spin up and spin them out
# make sure to handle machine funk and log effectively:
# One crashing machine should not kill the component.
# Code changes that break configs should not kill the component
# in the redis config mode, changes to machines are stored in redis for persistence automatically
# in the file config mode, the persistence save calls are made by function call and (maybe have history?
def _init_timers(self):
for enum in TickEnum:
self.tickers[enum] = task.LoopingCall(self.do_tick, enum=enum)
self.tickers[enum].start(enum.value)
print("alll timers initd")
@docupub(topics=["com.lambentri.edge.la4.machine.link.src."],
shapes={
"com.lambentri.edge.la4.machine.link.src.": {
"args.res": ["int*450+"],
"id": "str"
}
})
@inlineCallbacks
def do_tick(self, enum: TickEnum):
"""A function that gets called hundreds of times per second. Depending on the TickEnum may emit stuff"""
# TODO rewrite this to use n/100 fractions instead of this *gestures* insanity
if not self.is_connected():
print("TICK, not connected, passings")
return
if enum == TickEnum.FHUNDREDTHS and self.brightness_act != self.brightness_tgt.value:
# if self.brightness_tgt == BrightnessEnum.OFF:
# if self.brightness_act > 40:
# self.brightness_act = 0
# else:
# self.brightness_act += .15
# elif abs(self.brightness_tgt.value - self.brightness_act) < .3:
# self.brightness_act = self.brightness_tgt.value
# else:
# fract = abs(self.brightness_tgt.value - self.brightness_act) / 10.
# # avoid steps too big at the start however, fades may need more tuning of the divisor values
# if fract > .5:
# fract = fract/3
# if self.brightness_act < self.brightness_tgt.value:
# self.brightness_act = self.brightness_act + fract
# elif self.brightness_act > self.brightness_tgt.value:
# self.brightness_act = self.brightness_act - fract
if self.brightness_act < self.brightness_tgt.value:
self.brightness_act += 1
else:
self.brightness_act -= 1
# print(self.machines.values())
operating_machines = filter(
lambda x: x.speed.value == enum.value and x.running.value ==
RunningEnum.RUNNING.value, self.machines.values())
# print(operating_machines)
for mach in operating_machines:
res = mach.step()
if self.brightness_act == 0:
res = [0] * len(res)
elif self.brightness_act != 255:
res = [int(i * self.brightness_act / 255.) for i in res]
# print(mach.speed.value == TickEnum.TENS.value)
# if mach.speed.value == TickEnum.TENS.value:
# # print(res)
# print(vars)
# print(vars(mach))
#
# # yield self.publish(f"com.lambentri.edge.la4.machine.link.srx.{mach.id}", res)
# # yield self.publish(f"com.lambentri.edge.la4.device.82667777.esp_0602a5", res)
# print(res[0:12])
# sparkle publishy test
# chunked = chunks(res, 3)
# res = list(itertools.chain.from_iterable([[int(v*random.choice([.20, .40, .60, .80, 1.00])) for v in i] for i in chunked]))
options = PublishOptions(retain=True)
yield self.publish(
f"com.lambentri.edge.la4.machine.link.src.{mach.id}",
res,
id=mach.id,
options=options)
def change_machine_ticks(self, machine_name: str, machine_tick: TickEnum):
pass # changes the value of the tick enum on the machine
def _find_class_in_library(self, name):
for item in self.machine_library:
if isinstance(item, str):
try:
mod = pydoc.locate("components." + item)
if mod.__name__ == name:
return mod
except:
pass
elif issubclass(item, FakeMachine):
if item.__name__ == name:
return item
else:
raise MachineLoadException(
f"Unable to locate a lighting class named '{name}'")
@wamp.register("com.lambentri.edge.la4.machine.tick_up")
def machine_tick_up(self, machine_name: str):
"""Ticks up a machine's tick index (slower)"""
mach = self.machines.get(machine_name)
mach.speed = mach.speed.next_up(mach.speed)
return {"speed": mach.speed.name}
@wamp.register("com.lambentri.edge.la4.machine.tick_dn")
def machine_tick_dn(self, machine_name: str):
"""Ticks down a machine's tick indes (faster)"""
mach = self.machines.get(machine_name)
mach.speed = mach.speed.next_dn(mach.speed)
return {"speed": mach.speed.name}
@wamp.register("com.lambentri.edge.la4.machine.library",
options=RegisterOptions(invoke="roundrobin"))
def machine_library_retrieve(self):
"""Lists available machine templates in the library"""
machine_ret = {}
for item in self.machine_library:
if isinstance(item, str):
# print(item)
# TODO cache this
try:
component_name = f"components.{item}"
# print(component_name)
mod = pydoc.locate(component_name)
machine_ret[mod.name] = {
"desc": mod.desc,
"cls": mod.__name__,
"grp": mod.grps,
"conf": mod.get_config()
}
except AttributeError as e:
pass
#print(f"Unable to find machine of class '{item}'")
# print(e)
pass # import inplace and inspect to get k:v
elif issubclass(item, FakeMachine):
machine_ret[item.name] = {
"desc": item.desc,
"cls": item.__name__,
"grp": item.grps,
"conf": {}
}
else:
print("unknown")
print(type(item))
return machine_ret
# @inlineCallbacks
@wamp.register("com.lambentri.edge.la4.machine.init")
def init_machine_instance(self,
machine_cls: str,
machine_name: str,
machine_kwargs: dict,
direct: bool = False):
# TODO FIGURE OUT HOW TO SPECIFY WHICH MACHINE PROVIDER TO USE WHEN CALLING INIT
# PERHAPS EACH IMPL SHOULD SELF IDENTIFY AND CHECK? FOR NOW THE OTHER MACHINE PROVIDERS ARE UNCONFIGURABLE
"""Starts a machine. NOTE passing in the same machine_name will overwrite whatever was there. This can be leveraged to edit machine details!!"""
# this is called on startup as well as when adding new configs
# startup uses direct=True, since we're not trying to de-chunk our chunked frontend UI
# print(machine_cls)
cls = self._find_class_in_library(machine_cls)
# print(machine_kwargs)
# print(machine_name)
# print(cls.__name__)
# build kwargs for cls
if not direct:
built_kwargs = {}
for k, v in cls.meta.config.items():
if v['cls'].serialize()['name'] == "TupleConfig":
matching_config = [
vi for ki, vi in machine_kwargs.items()
if ki.startswith(k + "-")
]
built_kwargs[k] = tuple(matching_config)
else:
built_kwargs = machine_kwargs
if hasattr(cls.meta, "state_status"):
set_status = cls.meta.state_status
else:
set_status = False
id = f"{cls.__name__}-x-{machine_name}"
mach = cls(config_params=built_kwargs, set_status=set_status)
mach.set_id(id)
mach.set_instance_name(machine_name)
if hasattr(cls.meta, "state_status"):
if cls.meta.state_status:
pass
self.machines[id] = mach
res = self.machines[id].step()
# yield self.publish(f"com.lambentri.edge.la4.device.82667777.esp_0602a5", res)
# @wamp.register("com.lambentri.edge.la4.machine.edit")
# def modify_machine_instance(self):
# # this allows you to change execution parameters on a machine and restart it
# pass
@wamp.register("com.lambentri.edge.la4.machine.pause")
def pause_machine(self, machine_name: str):
""" Toggles a machine's RunningEnum"""
mach = self.machines.get(machine_name)
if mach.running == RunningEnum.RUNNING:
mach.running = RunningEnum.NOTRUNNING
else:
mach.running = RunningEnum.RUNNING
return {"running": mach.running.name}
@wamp.register("com.lambentri.edge.la4.machine.rm")
def destroy_machine(self, machine_name: str):
"""Deletes a machine instance"""
try:
del self.machines[machine_name]
except:
print(
f"Someone attempted to delete a machine that doesn't exist : {machine_name}"
)
# deletes machine (from live/redis instance)
schema = MachineDictSerializer()
serialized = schema.dump({
"machines": self.machines,
"speed_enum":
{k: v.value
for k, v in TickEnum.__members__.items()}
})
return serialized.data
# pass
@wamp.register("com.lambentri.edge.la4.machine.list",
options=RegisterOptions(invoke="roundrobin"))
def list_active_machine_instances(self):
"""List all available machines"""
schema = MachineDictSerializer()
# print(self.machines)
serialized = schema.dump({
"machines": self.machines,
"speed_enum":
{k: v.value
for k, v in TickEnum.__members__.items()}
})
return serialized.data
@inlineCallbacks
def global_brightness_publish(self):
yield self.publish("com.lambentri.edge.la4.machine.gb",
brightness=self.brightness_tgt.value)
@wamp.register("com.lambentri.edge.la4.machine.gb.up")
def global_brightness_value_up(self):
"""Move the global brightness up a single tick"""
self.brightness_tgt = self.brightness_tgt.next_up(self.brightness_tgt)
self.global_brightness_publish()
return {"brightness": self.brightness_tgt.value}
@wamp.register("com.lambentri.edge.la4.machine.gb.dn")
def global_brightness_value_dn(self):
"""Move the global brightness down a single tick"""
self.brightness_tgt = self.brightness_tgt.next_dn(self.brightness_tgt)
self.global_brightness_publish()
return {"brightness": self.brightness_tgt.value}
@wamp.register("com.lambentri.edge.la4.machine.gb.set")
def global_brightness_value_set(self, value: int):
"""Set the global brightness"""
self.brightness_tgt = BrightnessEnum(value)
self.global_brightness_publish()
return {"brightness": self.brightness_tgt.value}
@wamp.register("com.lambentri.edge.la4.machine.gb.get")
def global_brightness_value_get(self):
"""get the global brightness"""
return {"brightness": self.brightness_tgt.value}
@inlineCallbacks
def onJoin(self, details):
print("joined")
self.regs = yield self.register(self)
self.subs = yield self.subscribe(self)
self.document()
def onJoin(self, details):
print('Joined session={}'.format(details.realm))
reactor.suggestThreadPoolSize(MAX_CONCURRENT_TASKS)
options = RegisterOptions(concurrency=MAX_CONCURRENT_TASKS, invoke='roundrobin')
yield self.register(self.get_faces_coordinates, "io.crossbar.demo.cvengine.detect_faces", options)
def __init__(self, config=None):
super().__init__(config)
self.machine_id = get_machine_id()
self.display = display.Display()
self.register_options = RegisterOptions(match='exact', invoke='roundrobin')
id="ltri-dota-gsi-money-inv",
options=options)
yield None
@wamp_component.on_join
@inlineCallbacks
def joined(session, details):
print("session ready")
wamp_component.loop_gsi_publish = task.LoopingCall(publish_gsi,
session=session)
wamp_component.loop_gsi_publish.start(.05)
@wamp_component.register("com.lambentri.edge.la4.machine.list",
options=RegisterOptions(invoke="roundrobin"))
def list_active_machine_instances():
"""List all available machines"""
schema = MachineDict(
machines={
# these don't quite work yet
# these work
"dota-gsi-health":
Machine(name="dota-GSI",
iname="HEALTH",
id="ltri-dota-gsi-health",
desc="Machine State for DOTA Health"),
"dota-gsi-health-inv":
Machine(name="dota-GSI",
iname="HEALTH_INV",
class PylieWampApi(ComponentSession):
"""
Pylie WAMP methods.
Defines `require_config` to retrieve system and database configuration
upon WAMP session setup
"""
def authorize_request(self, uri, claims):
return True
@staticmethod
def get_file_content(path_file):
"""
Get path_file content, inline or from file
"""
file_content = path_file['content']
if file_content is None and path_file['path']:
if os.path.isfile(path_file['path']):
with open(path_file['path'], 'r') as fc:
file_content = fc.read()
else:
raise IOError('Structure file not defined')
return file_content
def _get_config(self, config, name):
ref_config = pylie_config.get(name).dict()
ref_config.update(config)
return ref_config
def _import_to_dataframe(self, infile, ext='csv'):
df = PANDAS_IMPORTERS[ext](infile)
if 'Unnamed: 0' in df:
del df['Unnamed: 0']
return df
def _export_dataframe(self, df, outfile, file_format='csv'):
if file_format not in PANDAS_EXPORTERS:
self.log.error('Unsupported file format: {0}'.format(file_format))
return False
if hasattr(df, PANDAS_EXPORTERS[file_format]):
method = getattr(df, PANDAS_EXPORTERS[file_format])
# Export to file
with open(outfile, 'w') as outf:
method(outf)
return True
return False
def _check_file_status(self, filepath):
if os.path.isfile(filepath):
return 'completed'
else:
self.log.error("File: {} does not exist!".format(filepath))
return 'failed'
@endpoint('liedeltag',
'liedeltag_request',
'liedeltag_response',
options=RegisterOptions(invoke='roundrobin'))
def calculate_lie_deltag(self, request, claims):
"""
For a detailed input description see:
pylie/schemas/endpoints/liedeltag_request.json
For a detailed output description see:
pylie/schemas/endpoints/liedeltag_response.json
"""
alpha_beta_gamma = request[u'alpha_beta_gamma']
# Filter DataFrame
file_string = StringIO(self.get_file_content(request[u'dataframe']))
dfobject = LIEDataFrame(self._import_to_dataframe(file_string))
dg_calc = dfobject.liedeltag(params=alpha_beta_gamma,
kBt=request[u'kBt'])
# Create workdir to save file
workdir = os.path.abspath(request[u'workdir'])
# Save dataframe
file_format = request[u'fileformat']
filepath = os.path.join(workdir, 'liedeltag.{0}'.format(file_format))
if self._export_dataframe(dg_calc, filepath, file_format=file_format):
results = dg_calc.to_dict()
else:
return None
return {'liedeltag_file': encoder(filepath), 'liedeltag': results}
@endpoint('concat_dataframes',
'concat_dataframes_request',
'concat_dataframes_response',
options=RegisterOptions(invoke='roundrobin'))
def concat_dataframes(self, request, claims):
"""
Combine multiple tabular DataFrames into one new DataFrame using
the Python Pandas library.
For a detailed input description see:
pylie/schemas/endpoints/concat_dataframes_request.json
For a detailed output description see:
pylie/schemas/endpoints/concat_dataframes_response.json
"""
# Import all files
dfs = []
for dataframe in request[u'dataframes']:
file_string = StringIO(self.get_file_content(dataframe))
dfobject = self._import_to_dataframe(file_string)
if isinstance(dfobject, DataFrame):
dfs.append(dfobject)
# Concatenate dataframes
if len(dfs) > 1:
concat_df = concat(dfs,
ignore_index=request[u'ignore_index'],
axis=request[u'axis'],
join=request[u'join'])
# Create workdir to save file
workdir = os.path.abspath(request[u'workdir'])
file_format = request[u'file_format']
filepath = os.path.join(workdir, 'joined.{0}'.format(file_format))
if self._export_dataframe(concat_df,
filepath,
file_format=file_format):
concat_mdframe = filepath
else:
return None
return {'concat_mdframe': encoder(concat_mdframe)}
@endpoint('calculate_lie_average',
'calculate_lie_average_request',
'calculate_lie_average_response',
options=RegisterOptions(invoke='roundrobin'))
def calculate_lie_average(self, request, claims):
"""
Calculate LIE electrostatic and Van der Waals energy averages from
a MDFrame.
For a detailed input description see:
pylie/schemas/endpoints/calculate_lie_average_request.v1.json
For a detailed output description see:
pydlie/schemas/endpoints/calculate_lie_average_response.v1.json
"""
mdframe = request[u'mdframe']
# Create workdir to save file
workdir = os.path.abspath(request[u'workdir'])
# Import CSV file and run spline fitting filter
file_string = StringIO(self.get_file_content(mdframe))
liemdframe = LIEMDFrame(read_csv(file_string))
if 'Unnamed: 0' in liemdframe.columns:
del liemdframe['Unnamed: 0']
ave = liemdframe.inliers(
method=request[u'inlierFilterMethod']).get_average()
filepath = os.path.join(workdir, 'averaged.csv')
ave.to_csv(filepath)
return {'averaged': encoder(filepath)}
@endpoint('gaussian_filter',
'gaussian_filter_request',
'gaussian_filter_response',
options=RegisterOptions(invoke='roundrobin'))
def filter_gaussian(self, request, claims):
"""
Use multivariate Gaussian Distribution analysis to
filter VdW/Elec values
For a detailed input description see:
pylie/schemas/endpoints/gaussian_filter_request.v1.json
For a detailed output description see:
pydlie/schemas/endpoints/gaussian_filter_response.v1.json
"""
# Filter DataFrame
file_string = StringIO(self.get_file_content(request[u'dataframe']))
dfobject = LIEDataFrame(self._import_to_dataframe(file_string))
gaussian = FilterGaussian(dfobject, confidence=request[u'confidence'])
filtered = gaussian.filter()
self.log.info("Filter detected {0} outliers.".format(
len(filtered.outliers.cases)))
# Create workdir to save file
workdir = os.path.abspath(request[u'workdir'])
# Plot results
if request[u'plot']:
outp = os.path.join(workdir, 'gauss_filter.pdf')
p = gaussian.plot()
p.savefig(outp)
# Save filtered dataframe
file_format = request[u'file_format']
filepath = os.path.join(workdir,
'gauss_filter.{0}'.format(file_format))
if not self._export_dataframe(
filtered, filepath, file_format=file_format):
return None
return {'gauss_filter': encoder(filepath)}
@endpoint('filter_stable_trajectory',
'filter_stable_trajectory_request',
'filter_stable_trajectory_response',
options=RegisterOptions(invoke='roundrobin'))
def filter_stable_trajectory(self, request, claims):
"""
Use FFT and spline-based filtering to detect and extract stable regions
in the MD energy trajectory
For a detailed input description see:
pylie/schemas/endpoints/filter_stable_request.v1.json
For a detailed output description see:
pydlie/schemas/endpoints/filter_stable_response.v1.json
"""
mdframe = request[u'mdframe']
# Create workdir to save file
workdir = os.path.abspath(request[u'workdir'])
# Import CSV file and run spline fitting filter
file_string = StringIO(self.get_file_content(mdframe))
liemdframe = LIEMDFrame(read_csv(file_string))
if 'Unnamed: 0' in liemdframe.columns:
del liemdframe['Unnamed: 0']
splines = FilterSplines(liemdframe, **request[u'FilterSplines'])
liemdframe = splines.filter()
output = {}
# Report the selected stable regions
filtered = liemdframe.inliers()
for pose in filtered.poses:
stable = filtered.get_stable(pose)
if stable:
output['stable_pose_{0}'.format(pose)] = stable
# Create plots
if request[u'do_plot']:
if os.path.exists(workdir):
currpath = os.getcwd()
os.chdir(workdir)
paths = splines.plot(tofile=True,
filetype=request[u'plotFileType'])
for i, image_paths in enumerate(paths, start=1):
fid = '{0}-{1}'.format(os.path.basename(image_paths), i)
output[fid] = encoder(image_paths, inline_content=False)
os.chdir(currpath)
else:
self.log.error(
'Working directory does not exist: {0}'.format(workdir))
# Filter the mdframe
if request[u'do_filter']:
filepath = os.path.join(workdir, 'mdframe_splinefiltered.csv')
filtered.to_csv(filepath)
output['filtered_mdframe'] = encoder(filepath)
return output
@endpoint('collect_energy_trajectories',
'collect_energy_trajectories_request',
'collect_energy_trajectories_response',
options=RegisterOptions(invoke='roundrobin'))
def import_mdene_files(self, request, claims):
"""
Import GROMACS MD trajectory energy files into a LIEMDFrame.
The constructed LIEMDFrame should represents simulations for the same
system with one simulation for the unbound state of the ligand and one
or more simulations for the bound system with the ligand in potentially
multiple binding poses.
For a detailed input description see:
pylie/schemas/endpoints/collect_energy_trajectories_request.v1.json
For a detailed output description see:
pylie/schemas/endpoints/collect_energy_trajectories_response.v1.json
"""
# Use absolute path to save file
workdir = os.path.abspath(request[u'workdir'])
# Collect trajectories
mdframe = LIEMDFrame()
vdw_header = request[u'lie_vdw_header']
ele_header = request[u'lie_ele_header']
for pose, trj in enumerate(request[u'bound_trajectory']):
mdframe.from_file(self.get_file_content(trj), {
vdw_header: 'vdw_bound_{0}'.format(pose + 1),
ele_header: 'coul_bound_{0}'.format(pose + 1)
},
filetype=request[u'filetype'])
self.log.debug('Import file: {0}, pose: {1}'.format(trj, pose))
mdframe.from_file(self.get_file_content(
request[u'unbound_trajectory']), {
vdw_header: 'vdw_unbound',
ele_header: 'coul_unbound'
},
filetype=request[u'filetype'])
self.log.debug('Import unbound file: {0}'.format(
request[u'unbound_trajectory'][u'path']))
# Set the case ID
mdframe.case = request[u'case']
# Store to file
filepath = os.path.join(workdir, 'mdframe.csv')
mdframe.to_csv(filepath)
return {'mdframe': encoder(filepath)}
@endpoint('adan_residue_decomp',
'adan_residue_decomp_request',
'adan_residue_decomp_response',
options=RegisterOptions(invoke='roundrobin'))
def adan_residue_decomp(self, request, claims):
"""
For a detailed input description see:
pylie/schemas/endpoints/adan_residue_decomp_request.v1.json
For a detailed output description see:
pydlie/schemas/endpoints/adan_residue_decomp_response.v1.json
"""
# Load the model
binary = self.get_file_content(request[u'model_pkl'])
model = dill.loads(binary)
# Parse gromacs residue decomposition energy files to DataFrame
decomp_dfs = []
for dcfileobj in request[u'decompose_files']:
dcfile = StringIO(self.get_file_content(dcfileobj))
decomp_dfs.append(parse_gromacs_decomp(dcfile))
# Run AD test
ene = ad_residue_decomp(decomp_dfs,
model['AD']['decVdw'],
model['AD']['decEle'],
cases=request[u'cases'])
# Use absolute path to save file
workdir = os.path.abspath(request[u'workdir'])
filepath = os.path.join(workdir, 'adan_residue_decomp.csv')
ene.to_csv(filepath)
return {'decomp': ene.to_dict()}
@endpoint('adan_dene',
'adan_dene_request',
'adan_dene_response',
options=RegisterOptions(invoke='roundrobin'))
def adan_dene(self, request, claims):
"""
For a detailed input description see:
pylie/schemas/endpoints/adan_dene_request.v1.json
For a detailed output description see:
pydlie/schemas/endpoints/adan_dene_response.v1.json
"""
# Load the model
binary = self.get_file_content(request[u'model_pkl'])
model = dill.loads(binary)
# Parse gromacs residue decomposition energy files to DataFrame
file_string = StringIO(self.get_file_content(request[u'dataframe']))
dfobject = self._import_to_dataframe(file_string)
# Run AD test
ene = ad_dene(dfobject,
model['AD']['Dene']['CovMatrix'],
center=request[u'center'],
ci_cutoff=request[u'ci_cutoff'])
# Use absolute path to save file
workdir = os.path.abspath(request[u'workdir'])
filepath = os.path.join(workdir, 'adan_dene.csv')
ene.to_csv(filepath)
return {'decomp': ene.to_dict()}
@endpoint('adan_dene_yrange',
'adan_dene_yrange_request',
'adan_dene_yrange_response',
options=RegisterOptions(invoke='roundrobin'))
def adan_dene_yrange(self, request, claims):
"""
For a detailed input description see:
pylie/schemas/endpoints/adan_dene_yrange_request.v1.json
For a detailed output description see:
pydlie/schemas/endpoints/adan_dene_yrange_response.v1.json
"""
# Parse gromacs residue decomposition energy files to DataFrame
file_string = StringIO(self.get_file_content(request[u'dataframe']))
dfobject = self._import_to_dataframe(file_string)
# Run AD test
ene = ad_dene_yrange(dfobject, request[u'ymin'], request[u'ymax'])
# Use absolute path to save file
workdir = os.path.abspath(request[u'workdir'])
filepath = os.path.join(workdir, 'adan_dene_yrange.csv')
ene.to_csv(filepath)
return {'decomp': ene.to_dict()}
class SmartCypWampApi(ComponentSession):
"""
WAMP SMARTCyp endpoint methods
"""
def authorize_request(self, uri, claims):
return True
@endpoint('smartcyp_info', 'smartcyp_info_request', 'smartcyp_info_response',
options=RegisterOptions(invoke=u'roundrobin'))
def smartcyp_info(self, request, claims):
"""
Returns an informative summary of the supported SMARTCyp version
and configuration.
"""
info = smartcyp_version_info()
if info:
return {'status': 'completed', 'info': info}
return {'status': 'failed'}
@endpoint('smartcyp', 'smartcyp_request', 'smartcyp_response', options=RegisterOptions(invoke=u'roundrobin'))
def smartcyp_prediction(self, request, claims):
"""
Run a SMARTCyp prediction for a molecule
:param request:
:param claims:
:return:
"""
# Validate input path_file object for mol
ligand_file = mol_validate_file_object(request['ligand_file'])
# Run smartcyp
base_dir = os.environ.get('BASE_WORK_DIR', request.get('base_work_dir'))
smartcyp = SmartCypRunner(log=self.log, base_work_dir=base_dir)
result_dict = smartcyp.run(ligand_file['content'],
is_smiles=ligand_file['extension'] == 'smi',
output_format=request['output_format'],
noempcorr=request['noempcorr'])
# Remove temporary working directory
smartcyp.delete()
result_dict['status'] = 'completed' if result_dict['result'] is not None else 'failed'
return result_dict
@endpoint('docking_info', 'docking_info_request', 'docking_info_response',
options=RegisterOptions(invoke=u'roundrobin'))
def plants_info(self, request, claims):
"""
Returns an informative summary of the supported PLANTS version
and configuration.
"""
info = plants_version_info()
if info:
return {'status': 'completed', 'info': info}
return {'status': 'failed'}
@endpoint('docking', 'docking_request', 'docking_response', options=RegisterOptions(invoke='roundrobin'))
def plants_docking(self, request, claims):
"""
Perform a PLANTS (Protein-Ligand ANT System) molecular docking.
For a detail description of the input see the file:
schemas/endpoints/docking-request.v1.json
"""
# Validate input path_file object for protein and ligand file
protein_file = mol_validate_file_object(request['protein_file'])
ligand_file = mol_validate_file_object(request['ligand_file'])
# Run docking
base_dir = os.environ.get('BASE_WORK_DIR', request.get('base_work_dir'))
for drop_key in ('protein_file', 'ligand_file', 'base_work_dir'):
if drop_key in request:
del request[drop_key]
docking = PlantsDocking(log=self.log, base_work_dir=base_dir, **request)
if docking.run(protein_file['content'], ligand_file['content']):
return {'status': 'completed', 'result': docking.get_results()}
self.log.error('PLANTS docking failed')
return {'status': 'failed'}
@endpoint('docking_statistics', 'docking_statistics_request', 'docking_statistics_response',
options=RegisterOptions(invoke='roundrobin'))
def plants_docking_statistics(self, request, claims):
"""
Return PLANTS docking statistics for particular docking solutions run previously.
Clustering will also be redone and optionally adjusted.
"""
base_dir = os.environ.get('BASE_WORK_DIR', request.get('base_work_dir'))
if 'base_work_dir' in request:
del request['base_work_dir']
docking = PlantsDocking(log=self.log, base_work_dir=base_dir, **request)
try:
results = docking.get_results(structures=request.get('paths'))
except MDStudioException as error:
self.log.error(repr(error))
return {'status': 'failed'}
if results:
return {'status': 'completed', 'result': results}
self.log.error('PLANTS docking failed')
return {'status': 'failed'}
@endpoint('docking_structures', 'docking_structures_request', 'docking_structures_response',
options=RegisterOptions(invoke='roundrobin'))
def plants_docking_structures(self, request, claims):
"""
Return PLANTS docked structures
"""
base_dir = os.environ.get('BASE_WORK_DIR', request.get('base_work_dir'))
docking = PlantsDocking(log=self.log, base_work_dir=base_dir)
try:
results = docking.get_structures(structures=request.get('paths'),
output_format=request.get('output_format', 'mol2'),
include_protein=request.get('include_protein', False),
create_ensemble=request.get('create_ensemble', True))
except MDStudioException as error:
self.log.error(repr(error))
return {'status': 'failed'}
# Encode files
if not isinstance(results, list):
results = [results]
output = [{"path": None, "extension": request.get('output_format', 'mol2'), "content": mol, "encoding": "utf8"}
for mol in results]
return {'status': 'completed', 'result': output}
@endpoint('spores_info', 'spores_info_request', 'spores_info_response',
options=RegisterOptions(invoke=u'roundrobin'))
def spores_info(self, request, claims):
"""
Returns an informative summary of the supported SPORES version
and configuration.
"""
info = spores_version_info()
if info:
return {'status': 'completed', 'info': info}
return {'status': 'failed'}
@endpoint('spores', 'spores_request', 'spores_response', options=RegisterOptions(invoke='roundrobin'))
def spores_run(self, request, claims):
"""
Perform a SPORES (Structure PrOtonation and REcognition System) structure preparation.
For a detail description of the input see the file:
schemas/endpoints/spores-request.v1.json
"""
# Validate input path_file object for mol
mol = mol_validate_file_object(request['mol'])
spores = SporesRunner(log=self.log, base_work_dir=os.environ.get('BASE_WORK_DIR', request.get('base_work_dir')))
try:
result_dict = spores.run(mol['content'], mode=request['spores_mode'], input_format=request['input_format'])
except Exception as e:
self.log.error(str(e))
return {'status': 'failed'}
finally:
spores.delete()
return {'status': 'completed', 'result': result_dict}
@endpoint('som_prediction', 'som_prediction_request', 'som_prediction_response',
options=RegisterOptions(invoke='roundrobin'))
def som_prediction(self, request, claims):
"""
Run a REST based SOM prediction run
"""
# Validate input path_file object for protein and ligand file
ligand_file = mol_validate_file_object(request['ligand_file'])
# Run combined structure/reactivity prediction
base_dir = os.environ.get('BASE_WORK_DIR', request.get('base_work_dir'))
sompred = CombinedPrediction(base_work_dir=base_dir, **request)
prediction = sompred.run(ligand_file, filter_clusters=request['filter_clusters'])
if prediction:
return {'status': 'completed', 'output': prediction}
self.log.error('SOM prediction failed')
return {'status': 'failed'}
