def testComposite():
comp = CompositePE()
cons1 = TestOneInOneOut()
cons2 = TestOneInOneOut()
comp.connect(cons1, 'output', cons2, 'input')
comp._map_input('comp_input', cons1, 'input')
comp._map_output('comp_output', cons2, 'output')
prod = TestProducer()
cons = TestOneInOneOut()
graph = WorkflowGraph()
graph.connect(prod, 'output', comp, 'comp_input')
graph.connect(comp, 'comp_output', cons, 'input')
graph.flatten()
results = simple_process.process_and_return(graph, {prod: 10})
tools.eq_({cons.id: {'output': list(range(1, 11))}}, results)
def testComposite():
comp = CompositePE()
cons1 = TestOneInOneOut()
cons2 = TestOneInOneOut()
comp.connect(cons1, "output", cons2, "input")
comp._map_input("comp_input", cons1, "input")
comp._map_output("comp_output", cons2, "output")
prod = TestProducer()
cons = TestOneInOneOut()
graph = WorkflowGraph()
graph.connect(prod, "output", comp, "comp_input")
graph.connect(comp, "comp_output", cons, "input")
graph.flatten()
results = simple_process.process_and_return(graph, {prod: 10})
tools.eq_({cons.id: {"output": list(range(1, 11))}}, results)
def testCreateChain():
def add(a, b):
return a + b
def mult(a, b):
return a * b
def is_odd(a):
return a % 2 == 1
c = [(add, {"b": 1}), (mult, {"b": 3}), is_odd]
chain = create_iterative_chain(c)
prod = TestProducer()
graph = WorkflowGraph()
graph.connect(prod, "output", chain, "input")
graph.flatten()
results = simple_process.process_and_return(graph, {prod: 2})
for key, value in results.items():
tools.eq_({"output": [False, True]}, value)
def testCreateChain():
def add(a, b):
return a + b
def mult(a, b):
return a * b
def is_odd(a):
return a % 2 == 1
c = [(add, {'b': 1}), (mult, {'b': 3}), is_odd]
chain = create_iterative_chain(c)
prod = TestProducer()
graph = WorkflowGraph()
graph.connect(prod, 'output', chain, 'input')
graph.flatten()
results = simple_process.process_and_return(graph, {prod: 2})
for key, value in results.items():
tools.eq_({'output': [False, True]}, value)
def testCompositeWithCreateParams():
cons1 = TestOneInOneOut()
cons2 = TestOneInOneOut()
def create_graph(graph, connections):
for i in range(connections):
graph.connect(cons1, "output", cons2, "input")
comp = CompositePE(create_graph, {"connections": 2})
comp._map_input("comp_input", cons1, "input")
comp._map_output("comp_output", cons2, "output")
prod = TestProducer()
cons = TestOneInOneOut()
graph = WorkflowGraph()
graph.connect(prod, "output", comp, "comp_input")
graph.connect(comp, "comp_output", cons, "input")
graph.flatten()
results = simple_process.process_and_return(graph, {prod: 10})
expected = []
for i in range(1, 11):
expected += [i, i]
tools.eq_({cons.id: {"output": expected}}, results)
def testCompositeWithCreateParams():
cons1 = TestOneInOneOut()
cons2 = TestOneInOneOut()
def create_graph(graph, connections):
for i in range(connections):
graph.connect(cons1, 'output', cons2, 'input')
comp = CompositePE(create_graph, {'connections': 2})
comp._map_input('comp_input', cons1, 'input')
comp._map_output('comp_output', cons2, 'output')
prod = TestProducer()
cons = TestOneInOneOut()
graph = WorkflowGraph()
graph.connect(prod, 'output', comp, 'comp_input')
graph.connect(comp, 'comp_output', cons, 'input')
graph.flatten()
results = simple_process.process_and_return(graph, {prod: 10})
expected = []
for i in range(1, 11):
expected += [i, i]
tools.eq_({cons.id: {'output': expected}}, results)
sc = Source()
sc.name = 'PE_source'
squaref = SimpleFunctionPE(square, {'prov_cluster': 'mycluster'})
#squaref=SimpleFunctionPE(square)
divf = Div()
divf.name = 'PE_div'
#processes=[squaref,divf]
#chain = create_iterative_chain(processes, FunctionPE_class=SimpleFunctionPE)
#Initialise the graph
graph = WorkflowGraph()
#Common way of composing the graph
graph.connect(sc, 'output', squaref, 'input')
graph.connect(squaref, 'output', divf, 'input')
#graph.connect(divf,'output',squaref,'input')
# Alternatively with pipeline array
#Create pipelines from functions
#graph.connect(sc,'output',chain,'input')
graph.flatten()
#Prepare Input
input_data = {"PE_source": [{"input": [25]}]}
sc = Source()
sc.name='PE_source'
squaref=SimpleFunctionPE(square,{'prov_cluster':'mycluster'})
#squaref=SimpleFunctionPE(square)
divf=Div()
divf.name='PE_div'
#processes=[squaref,divf]
#chain = create_iterative_chain(processes, FunctionPE_class=SimpleFunctionPE)
#Initialise the graph
graph = WorkflowGraph()
#Common way of composing the graph
graph.connect(sc,'output',squaref,'input')
graph.connect(squaref,'output',divf,'input')
#graph.connect(divf,'output',squaref,'input')
# Alternatively with pipeline array
#Create pipelines from functions
#graph.connect(sc,'output',chain,'input')
graph.flatten()
#Prepare Input
input_data = {"PE_source": [{"input": [25]}]}
