Python TestOneInOneOut示例

示例#1

文件： mpi_process_test.py 项目： davrodgon/dispel4py

def test_pipeline():
    prod = t.TestProducer()
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()

    graph = WorkflowGraph()
    graph.connect(prod, 'output', cons1, 'input')
    graph.connect(cons1, 'output', cons2, 'input')

    process(graph, {prod: [{}, {}, {}]})

示例#2

def testTee():
    graph = WorkflowGraph()
    prod = t.TestProducer()
    prev = prod
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    graph.connect(prod, 'output', cons1, 'input')
    graph.connect(prod, 'output', cons2, 'input')
    args.num = 3
    process(graph, inputs={prod: [{}, {}, {}, {}, {}]}, args=args)

示例#3

文件： simple_process_test.py 项目： davrodgon/dispel4py

def testPipeline():
    prod = t.TestProducer()
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    graph = WorkflowGraph()
    graph.connect(prod, 'output', cons1, 'input')
    graph.connect(cons1, 'output', cons2, 'input')
    results = simple_process.process_and_return(
        graph, inputs={prod: [{}, {}, {}, {}, {}]})
    assert {cons2.id: {'output': [1, 2, 3, 4, 5]}} == results

示例#4

def testPipeline():
    prod = t.TestProducer()
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    graph = WorkflowGraph()
    graph.connect(prod, 'output', cons1, 'input')
    graph.connect(cons1, 'output', cons2, 'input')
    args = argparse.Namespace
    args.num = 5
    args.simple = False
    process(graph, inputs={prod: [{}, {}, {}]}, args=args)

示例#5

def testSquare():
    graph = WorkflowGraph()
    prod = t.TestProducer(2)
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    last = t.TestTwoInOneOut()
    graph.connect(prod, 'output0', cons1, 'input')
    graph.connect(prod, 'output1', cons2, 'input')
    graph.connect(cons1, 'output', last, 'input0')
    graph.connect(cons2, 'output', last, 'input1')
    args.num = 4
    process(graph, inputs={prod: [{}]}, args=args)

示例#6

文件： simple_process_test.py 项目： davrodgon/dispel4py

def testSquare():
    graph = WorkflowGraph()
    prod = t.TestProducer(2)
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    last = t.TestTwoInOneOut()
    graph.connect(prod, 'output0', cons1, 'input')
    graph.connect(prod, 'output1', cons2, 'input')
    graph.connect(cons1, 'output', last, 'input0')
    graph.connect(cons2, 'output', last, 'input1')
    results = simple_process.process_and_return(graph, {prod: [{}]})
    assert {last.id: {'output': ['1', '1']}} == results

示例#7

文件： grouping_alltoone.py 项目： favourjuwe/EcologicalSimulation

def testAlltoOne():

    graph = WorkflowGraph()
    prod = t.TestProducer()
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    cons1.numprocesses = 5
    cons2.numprocesses = 5
    graph.connect(prod, 'output', cons1, 'input')
    cons2.inputconnections['input']['grouping'] = 'global'
    graph.connect(cons1, 'output', cons2, 'input')
    return graph

示例#8

文件： teecopy.py 项目： favourjuwe/EcologicalSimulation

def testTee():
    '''
    Creates a graph with two consumer nodes and a tee connection.

    :rtype: the created graph
    '''
    graph = WorkflowGraph()
    prod = t.TestProducer()
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    graph.connect(prod, 'output', cons1, 'input')
    graph.connect(prod, 'output', cons2, 'input')
    return graph

示例#9

文件： group_by.py 项目： favourjuwe/EcologicalSimulation

def testGrouping():

    words = t.RandomWordProducer()
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    cons3 = t.TestOneInOneOut()
    count = t.WordCounter()
    graph = WorkflowGraph()
    graph.connect(words, 'output', cons1, 'input')
    graph.connect(cons1, 'output', cons2, 'input')
    graph.connect(cons2, 'output', cons3, 'input')
    graph.connect(cons3, 'output', count, 'input')

    graph.partitions = [[words], [cons1, cons2, cons3], [count]]
    return graph

示例#10

def testAlltoOne():
    '''
    Creates a graph with two consumer nodes and a global grouping.
    
    :rtype: the created graph
    '''
    graph = WorkflowGraph()
    prod = t.TestProducer()
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    cons1.numprocesses=5
    cons2.numprocesses=5
    graph.connect(prod, 'output', cons1, 'input')
    cons2.inputconnections['input']['grouping'] = 'global'
    graph.connect(cons1, 'output', cons2, 'input')
    return graph

示例#11

文件： parallel_pipeline.py 项目： favourjuwe/EcologicalSimulation

def testParallelPipeline():
    '''
    Creates a graph with 4 nodes.

    :rtype: the created graph
    '''
    graph = WorkflowGraph()
    prod = t.TestProducer()
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    cons3 = t.TestOneInOneOut()

    graph.connect(prod, 'output', cons1, 'input')
    graph.connect(cons1, 'output', cons2, 'input')
    graph.connect(cons1, 'output', cons3, 'input')

    return graph

示例#12

文件： grouping_onetoall.py 项目： rayson1223/dispel4py

def testOnetoAll():
    graph = WorkflowGraph()
    prod = t.TestProducer()
    cons = t.TestOneInOneOut()
    cons.numprocesses = 2
    cons.inputconnections['input']['grouping'] = 'all'
    graph.connect(prod, 'output', cons, 'input')
    return graph

示例#13

def testParallelPipeline():
    '''
    Creates the parallel pipeline graph with partitioning information.

    :rtype: the created graph
    '''
    graph = WorkflowGraph()
    prod = t.TestProducer()
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    cons3 = t.TestOneInOneOut()

    graph.connect(prod, 'output', cons1, 'input')
    graph.connect(cons1, 'output', cons2, 'input')
    graph.connect(cons1, 'output', cons3, 'input')

    graph.partitions = [[prod, cons1, cons2], [cons3]]

    return graph

示例#14

文件： large_teecopy.py 项目： favourjuwe/EcologicalSimulation

def testTee():
    '''
    Creates a graph with two consumer nodes and a tee connection.

    :rtype: the created graph
    '''
    graph = WorkflowGraph()
    try:
        numIterations = int(sys.argv[4])
    except:
        numIterations = 1
    prod = t.NumberProducer(numIterations)
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOut()
    cons3 = t.TestOneInOneOut()
    graph.connect(prod, 'output', cons1, 'input')
    graph.connect(cons1, 'output', cons2, 'input')
    graph.connect(cons1, 'output', cons3, 'input')
    return graph

示例#15

def testPipeline(graph):
    '''
    Adds a pipeline to the given graph.

    :rtype: the created graph
    '''
    prod = t.TestProducer()
    prev = prod
    for i in range(5):
        cons = t.TestOneInOneOut()
        graph.connect(prev, 'output', cons, 'input')
        prev = cons
    return graph

示例#16

文件： unconnected_pipeline.py 项目： rayson1223/dispel4py

def testPipeline(graph):
    '''
    Creates a pipeline and adds it to the given graph.
    
    :rtype: the modified graph
    '''
    prod = t.TestProducer()
    prev = prod
    part1 = [prod]
    part2 = []
    for i in range(5):
        cons = t.TestOneInOneOut()
        part2.append(cons)
        graph.connect(prev, 'output', cons, 'input')
        prev = cons
    return graph

示例#17

def testSplitMerge():
    '''
    Creates the split/merge graph with 4 nodes.
    
    :rtype: the created graph
    '''
    graph = WorkflowGraph()
    prod = t.TestProducer(2)
    cons1 = t.TestOneInOneOut()
    cons2 = t.TestOneInOneOutWriter()
    last = t.TestTwoInOneOut()
    graph.connect(prod, 'output0', cons1, 'input')
    graph.connect(prod, 'output1', cons2, 'input')
    graph.connect(cons1, 'output', last, 'input0')
    graph.connect(cons2, 'output', last, 'input1')
    return graph

示例#18

文件： delayed_pipeline.py 项目： xpivan/dispel4py

        Each node in the graph is executed as a separate MPI process.
        This graph has 3 nodes. For this reason we need at least 3 MPI
        processes to execute it.

    Output::

        Processes: {'TestDelayOneInOneOut2': [2, 3], 'TestProducer0': [4], \
'TestOneInOneOut1': [0, 1]}
        TestProducer0 (rank 4): Processed 10 iterations.
        TestOneInOneOut1 (rank 1): Processed 5 iterations.
        TestOneInOneOut1 (rank 0): Processed 5 iterations.
        TestDelayOneInOneOut2 (rank 3): Average processing time: 1.00058307648
        TestDelayOneInOneOut2 (rank 3): Processed 5 iterations.
        TestDelayOneInOneOut2 (rank 2): Average processing time: 1.00079641342
        TestDelayOneInOneOut2 (rank 2): Processed 5 iterations.

'''

from dispel4py.examples.graph_testing import testing_PEs as t
from dispel4py.workflow_graph import WorkflowGraph
from dispel4py.new.monitoring import ProcessTimingPE

prod = t.TestProducer()
cons1 = t.TestOneInOneOut()
''' adding a processing timer '''
cons2 = ProcessTimingPE(t.TestDelayOneInOneOut())
''' important: this is the graph_variable '''
graph = WorkflowGraph()
graph.connect(prod, 'output', cons1, 'input')
graph.connect(cons1, 'output', cons2, 'input')