def parallelStdDev(index=0):
composite = WorkflowGraph()
parStdDev = StdDevPE(index)
reduceStdDev = StdDevReducePE()
composite.connect(parStdDev, parStdDev.OUTPUT_NAME, reduceStdDev, reduceStdDev.INPUT_NAME)
composite.inputmappings = { 'input' : (parStdDev, parStdDev.INPUT_NAME) }
composite.outputmappings = { 'output' : (reduceStdDev, reduceStdDev.OUTPUT_NAME) }
return composite
def parallel_aggregate(instPE, reducePE):
composite = WorkflowGraph()
reducePE.inputconnections[AggregatePE.INPUT_NAME]['grouping'] = 'global'
reducePE.numprocesses = 1
composite.connect(instPE, AggregatePE.OUTPUT_NAME, reducePE, AggregatePE.INPUT_NAME)
composite.inputmappings = { 'input' : (instPE, AggregatePE.INPUT_NAME) }
composite.outputmappings = { 'output' : (reducePE, AggregatePE.OUTPUT_NAME) }
return composite
def parallelAvg(index=0):
composite = WorkflowGraph()
parAvg = AverageParallelPE(index)
reduceAvg = AverageReducePE()
composite.connect(parAvg, parAvg.OUTPUT_NAME, reduceAvg, reduceAvg.INPUT_NAME)
composite.inputmappings = { 'input' : (parAvg, parAvg.INPUT_NAME) }
composite.outputmappings = { 'output' : (reduceAvg, reduceAvg.OUTPUT_NAME) }
return composite
def parallelStdDev(index=0):
composite = WorkflowGraph()
parStdDev = StdDevPE(index)
reduceStdDev = StdDevReducePE()
composite.connect(parStdDev, parStdDev.OUTPUT_NAME, reduceStdDev,
reduceStdDev.INPUT_NAME)
composite.inputmappings = {'input': (parStdDev, parStdDev.INPUT_NAME)}
composite.outputmappings = {
'output': (reduceStdDev, reduceStdDev.OUTPUT_NAME)
}
return composite
def parallelStdDev(index=0):
'''
Creates a STDDEV composite PE that can be parallelised using a map-reduce pattern.
'''
composite = WorkflowGraph()
parStdDev = StdDevPE(index)
reduceStdDev = StdDevReducePE()
composite.connect(parStdDev, parStdDev.OUTPUT_NAME, reduceStdDev, reduceStdDev.INPUT_NAME)
composite.inputmappings = { 'input' : (parStdDev, parStdDev.INPUT_NAME) }
composite.outputmappings = { 'output' : (reduceStdDev, reduceStdDev.OUTPUT_NAME) }
return composite
def parallelAvg(index=0):
'''
Creates an AVG composite PE that can be parallelised using a map-reduce pattern.
'''
composite = WorkflowGraph()
parAvg = AverageParallelPE(index)
reduceAvg = AverageReducePE()
composite.connect(parAvg, parAvg.OUTPUT_NAME, reduceAvg, reduceAvg.INPUT_NAME)
composite.inputmappings = { 'input' : (parAvg, parAvg.INPUT_NAME) }
composite.outputmappings = { 'output' : (reduceAvg, reduceAvg.OUTPUT_NAME) }
return composite
def parallelAvg(index=0):
'''
Creates an AVG composite PE that can be parallelised using a map-reduce pattern.
'''
composite = WorkflowGraph()
parAvg = AverageParallelPE(index)
reduceAvg = AverageReducePE()
composite.connect(parAvg, parAvg.OUTPUT_NAME, reduceAvg,
reduceAvg.INPUT_NAME)
composite.inputmappings = {'input': (parAvg, parAvg.INPUT_NAME)}
composite.outputmappings = {'output': (reduceAvg, reduceAvg.OUTPUT_NAME)}
return composite
def parallelStdDev(index=0):
'''
Creates a STDDEV composite PE that can be parallelised using a map-reduce pattern.
'''
composite = WorkflowGraph()
parStdDev = StdDevPE(index)
reduceStdDev = StdDevReducePE()
composite.connect(parStdDev, parStdDev.OUTPUT_NAME, reduceStdDev,
reduceStdDev.INPUT_NAME)
composite.inputmappings = {'input': (parStdDev, parStdDev.INPUT_NAME)}
composite.outputmappings = {
'output': (reduceStdDev, reduceStdDev.OUTPUT_NAME)
}
return composite
def create_iterative_chain(functions,
FunctionPE_class=SimpleFunctionPE,
name_prefix='PE_',
name_suffix=''):
'''
Creates a composite PE wrapping a pipeline that processes obspy streams.
:param chain: list of functions that process data iteratively. The function
accepts one input parameter, data, and returns an output data block
(or None).
:param requestId: id of the request that the stream is associated with
:param controlParameters: environment parameters for the processing
elements
:rtype: dictionary inputs and outputs of the composite PE that was created
'''
prev = None
first = None
graph = WorkflowGraph()
for fn_desc in functions:
try:
fn = fn_desc[0]
params = fn_desc[1]
except TypeError:
fn = fn_desc
params = {}
# print 'adding %s to chain' % fn.__name__
pe = FunctionPE_class()
pe.compute_fn = fn
pe.params = params
pe.name = name_prefix + fn.__name__ + name_suffix
if prev:
graph.connect(prev, IterativePE.OUTPUT_NAME,
pe, IterativePE.INPUT_NAME)
else:
first = pe
prev = pe
# Map inputs and outputs of the wrapper to the nodes in the subgraph
graph.inputmappings = {'input': (first, IterativePE.INPUT_NAME)}
graph.outputmappings = {'output': (prev, IterativePE.OUTPUT_NAME)}
return graph
def createProcessingComposite(chain,
suffix='',
controlParameters={},
provRecorder=None):
'''
Creates a composite PE wrapping a pipeline that processes obspy streams.
:param chain: list of functions that process obspy streams. The function takes one input parameter, stream, and returns an output stream.
:param requestId: id of the request that the stream is associated with
:param controlParameters: environment parameters for the processing elements
:rtype: dictionary inputs and outputs of the composite PE that was created
'''
prev = None
first = None
graph = WorkflowGraph()
for fn_desc in chain:
pe = ObspyStreamPE()
try:
fn = fn_desc[0]
params = fn_desc[1]
except TypeError:
fn = fn_desc
params = {}
pe.compute_fn = fn
pe.name = 'ObspyStreamPE_' + fn.__name__ + suffix
pe.controlParameters = controlParameters
pe.appParameters = dict(params)
pe.setCompute(fn, params)
# connect the metadata output to the provenance recorder PE if there is one
if provRecorder:
graph.connect(pe, 'metadata', provRecorder, 'metadata')
if prev:
graph.connect(prev, OUTPUT_NAME, pe, INPUT_NAME)
else:
first = pe
prev = pe
# Map inputs and outputs of the wrapper to the nodes in the subgraph
graph.inputmappings = {'input': (first, INPUT_NAME)}
graph.outputmappings = {'output': (prev, OUTPUT_NAME)}
return graph
def create_iterative_chain(functions,
FunctionPE_class=SimpleFunctionPE,
name_prefix='PE_',
name_suffix=''):
'''
Creates a composite PE wrapping a pipeline that processes obspy streams.
:param chain: list of functions that process data iteratively. The function
accepts one input parameter, data, and returns an output data block
(or None).
:param requestId: id of the request that the stream is associated with
:param controlParameters: environment parameters for the processing
elements
:rtype: dictionary inputs and outputs of the composite PE that was created
'''
prev = None
first = None
graph = WorkflowGraph()
for fn_desc in functions:
try:
fn = fn_desc[0]
params = fn_desc[1]
except TypeError:
fn = fn_desc
params = {}
# print 'adding %s to chain' % fn.__name__
pe = FunctionPE_class()
pe.compute_fn = fn
pe.params = params
pe.name = name_prefix + fn.__name__ + name_suffix
if prev:
graph.connect(prev, IterativePE.OUTPUT_NAME, pe,
IterativePE.INPUT_NAME)
else:
first = pe
prev = pe
# Map inputs and outputs of the wrapper to the nodes in the subgraph
graph.inputmappings = {'input': (first, IterativePE.INPUT_NAME)}
graph.outputmappings = {'output': (prev, IterativePE.OUTPUT_NAME)}
return graph
def createProcessingComposite(chain, suffix='', controlParameters={}, provRecorder=None):
'''
Creates a composite PE wrapping a pipeline that processes obspy streams.
:param chain: list of functions that process obspy streams. The function takes one input parameter, stream, and returns an output stream.
:param requestId: id of the request that the stream is associated with
:param controlParameters: environment parameters for the processing elements
:rtype: dictionary inputs and outputs of the composite PE that was created
'''
prev = None
first = None
graph = WorkflowGraph()
for fn_desc in chain:
pe = ObspyStreamPE()
try:
fn = fn_desc[0]
params = fn_desc[1]
except TypeError:
fn = fn_desc
params = {}
pe.compute_fn = fn
pe.name = 'ObspyStreamPE_' + fn.__name__ + suffix
pe.controlParameters = controlParameters
pe.appParameters = dict(params)
pe.setCompute(fn, params)
# connect the metadata output to the provenance recorder PE if there is one
if provRecorder:
graph.connect(pe, 'metadata', provRecorder, 'metadata')
if prev:
graph.connect(prev, OUTPUT_NAME, pe, INPUT_NAME)
else:
first = pe
prev = pe
# Map inputs and outputs of the wrapper to the nodes in the subgraph
graph.inputmappings = { 'input' : (first, INPUT_NAME) }
graph.outputmappings = { 'output' : (prev, OUTPUT_NAME) }
return graph
