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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