4da581c168
Instead of having the optional requirements example be a example that is itself a unittest just move the example to be an actual unit test that gets tested using the various engine types and change the example to be something slightly different (but shows the same kind of usage information). Change-Id: Ia03a81a6be636c501a35e7e290f587f7d05f8b30
110 lines
4.3 KiB
Python
110 lines
4.3 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2015 Hewlett-Packard Development Company, L.P.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import collections
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import math
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import os
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import sys
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top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
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os.pardir,
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os.pardir))
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sys.path.insert(0, top_dir)
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from taskflow import engines
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from taskflow.patterns import linear_flow
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from taskflow import task
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# INTRO: This shows how to use a tasks/atoms ability to take requirements from
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# its execute functions default parameters and shows how to provide those
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# via different methods when needed, to influence those parameters to in
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# this case calculate the distance between two points in 2D space.
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# A 2D point.
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Point = collections.namedtuple("Point", "x,y")
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def is_near(val, expected, tolerance=0.001):
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# Floats don't really provide equality...
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if val > (expected + tolerance):
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return False
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if val < (expected - tolerance):
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return False
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return True
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class DistanceTask(task.Task):
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# See: http://en.wikipedia.org/wiki/Distance#Distance_in_Euclidean_space
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default_provides = 'distance'
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def execute(self, a=Point(0, 0), b=Point(0, 0)):
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return math.sqrt(math.pow(b.x - a.x, 2) + math.pow(b.y - a.y, 2))
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if __name__ == '__main__':
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# For these we rely on the execute() methods points by default being
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# at the origin (and we override it with store values when we want) at
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# execution time (which then influences what is calculated).
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any_distance = linear_flow.Flow("origin").add(DistanceTask())
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results = engines.run(any_distance)
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print(results)
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print("%s is near-enough to %s: %s" % (results['distance'],
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0.0,
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is_near(results['distance'], 0.0)))
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results = engines.run(any_distance, store={'a': Point(1, 1)})
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print(results)
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print("%s is near-enough to %s: %s" % (results['distance'],
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1.4142,
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is_near(results['distance'],
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1.4142)))
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results = engines.run(any_distance, store={'a': Point(10, 10)})
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print(results)
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print("%s is near-enough to %s: %s" % (results['distance'],
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14.14199,
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is_near(results['distance'],
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14.14199)))
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results = engines.run(any_distance,
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store={'a': Point(5, 5), 'b': Point(10, 10)})
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print(results)
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print("%s is near-enough to %s: %s" % (results['distance'],
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7.07106,
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is_near(results['distance'],
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7.07106)))
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# For this we use the ability to override at task creation time the
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# optional arguments so that we don't need to continue to send them
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# in via the 'store' argument like in the above (and we fix the new
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# starting point 'a' at (10, 10) instead of (0, 0)...
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ten_distance = linear_flow.Flow("ten")
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ten_distance.add(DistanceTask(inject={'a': Point(10, 10)}))
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results = engines.run(ten_distance, store={'b': Point(10, 10)})
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print(results)
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print("%s is near-enough to %s: %s" % (results['distance'],
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0.0,
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is_near(results['distance'], 0.0)))
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results = engines.run(ten_distance)
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print(results)
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print("%s is near-enough to %s: %s" % (results['distance'],
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14.14199,
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is_near(results['distance'],
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14.14199)))
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