Ausgabe der neuen DB Einträge
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hubobel
parent
bad48e1627
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2399 changed files with 843193 additions and 43 deletions
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"""
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Tests for the public interface of Automat.
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"""
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from functools import reduce
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from unittest import TestCase
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from automat._methodical import ArgSpec, _getArgNames, _getArgSpec, _filterArgs
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from .. import MethodicalMachine, NoTransition
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from .. import _methodical
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class MethodicalTests(TestCase):
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"""
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Tests for L{MethodicalMachine}.
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"""
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def test_oneTransition(self):
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"""
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L{MethodicalMachine} provides a way for you to declare a state machine
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with inputs, outputs, and states as methods. When you have declared an
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input, an output, and a state, calling the input method in that state
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will produce the specified output.
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"""
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class Machination(object):
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machine = MethodicalMachine()
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@machine.input()
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def anInput(self):
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"an input"
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@machine.output()
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def anOutput(self):
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"an output"
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return "an-output-value"
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@machine.output()
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def anotherOutput(self):
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"another output"
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return "another-output-value"
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@machine.state(initial=True)
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def anState(self):
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"a state"
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@machine.state()
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def anotherState(self):
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"another state"
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anState.upon(anInput, enter=anotherState, outputs=[anOutput])
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anotherState.upon(anInput, enter=anotherState,
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outputs=[anotherOutput])
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m = Machination()
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self.assertEqual(m.anInput(), ["an-output-value"])
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self.assertEqual(m.anInput(), ["another-output-value"])
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def test_machineItselfIsPrivate(self):
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"""
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L{MethodicalMachine} is an implementation detail. If you attempt to
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access it on an instance of your class, you will get an exception.
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However, since tools may need to access it for the purposes of, for
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example, visualization, you may access it on the class itself.
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"""
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expectedMachine = MethodicalMachine()
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class Machination(object):
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machine = expectedMachine
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machination = Machination()
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with self.assertRaises(AttributeError) as cm:
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machination.machine
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self.assertIn("MethodicalMachine is an implementation detail",
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str(cm.exception))
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self.assertIs(Machination.machine, expectedMachine)
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def test_outputsArePrivate(self):
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"""
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One of the benefits of using a state machine is that your output method
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implementations don't need to take invalid state transitions into
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account - the methods simply won't be called. This property would be
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broken if client code called output methods directly, so output methods
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are not directly visible under their names.
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"""
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class Machination(object):
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machine = MethodicalMachine()
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counter = 0
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@machine.input()
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def anInput(self):
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"an input"
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@machine.output()
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def anOutput(self):
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self.counter += 1
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@machine.state(initial=True)
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def state(self):
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"a machine state"
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state.upon(anInput, enter=state, outputs=[anOutput])
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mach1 = Machination()
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mach1.anInput()
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self.assertEqual(mach1.counter, 1)
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mach2 = Machination()
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with self.assertRaises(AttributeError) as cm:
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mach2.anOutput
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self.assertEqual(mach2.counter, 0)
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self.assertIn(
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"Machination.anOutput is a state-machine output method; to "
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"produce this output, call an input method instead.",
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str(cm.exception)
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)
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def test_multipleMachines(self):
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"""
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Two machines may co-exist happily on the same instance; they don't
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interfere with each other.
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"""
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class MultiMach(object):
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a = MethodicalMachine()
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b = MethodicalMachine()
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@a.input()
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def inputA(self):
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"input A"
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@b.input()
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def inputB(self):
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"input B"
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@a.state(initial=True)
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def initialA(self):
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"initial A"
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@b.state(initial=True)
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def initialB(self):
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"initial B"
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@a.output()
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def outputA(self):
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return "A"
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@b.output()
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def outputB(self):
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return "B"
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initialA.upon(inputA, initialA, [outputA])
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initialB.upon(inputB, initialB, [outputB])
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mm = MultiMach()
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self.assertEqual(mm.inputA(), ["A"])
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self.assertEqual(mm.inputB(), ["B"])
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def test_collectOutputs(self):
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"""
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Outputs can be combined with the "collector" argument to "upon".
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"""
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import operator
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class Machine(object):
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m = MethodicalMachine()
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@m.input()
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def input(self):
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"an input"
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@m.output()
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def outputA(self):
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return "A"
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@m.output()
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def outputB(self):
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return "B"
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@m.state(initial=True)
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def state(self):
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"a state"
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state.upon(input, state, [outputA, outputB],
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collector=lambda x: reduce(operator.add, x))
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m = Machine()
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self.assertEqual(m.input(), "AB")
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def test_methodName(self):
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"""
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Input methods preserve their declared names.
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"""
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class Mech(object):
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m = MethodicalMachine()
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@m.input()
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def declaredInputName(self):
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"an input"
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@m.state(initial=True)
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def aState(self):
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"state"
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m = Mech()
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with self.assertRaises(TypeError) as cm:
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m.declaredInputName("too", "many", "arguments")
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self.assertIn("declaredInputName", str(cm.exception))
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def test_inputWithArguments(self):
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"""
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If an input takes an argument, it will pass that along to its output.
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"""
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class Mechanism(object):
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m = MethodicalMachine()
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@m.input()
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def input(self, x, y=1):
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"an input"
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@m.state(initial=True)
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def state(self):
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"a state"
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@m.output()
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def output(self, x, y=1):
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self._x = x
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return x + y
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state.upon(input, state, [output])
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m = Mechanism()
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self.assertEqual(m.input(3), [4])
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self.assertEqual(m._x, 3)
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def test_outputWithSubsetOfArguments(self):
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"""
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Inputs pass arguments that output will accept.
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"""
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class Mechanism(object):
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m = MethodicalMachine()
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@m.input()
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def input(self, x, y=1):
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"an input"
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@m.state(initial=True)
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def state(self):
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"a state"
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@m.output()
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def outputX(self, x):
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self._x = x
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return x
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@m.output()
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def outputY(self, y):
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self._y = y
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return y
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@m.output()
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def outputNoArgs(self):
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return None
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state.upon(input, state, [outputX, outputY, outputNoArgs])
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m = Mechanism()
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# Pass x as positional argument.
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self.assertEqual(m.input(3), [3, 1, None])
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self.assertEqual(m._x, 3)
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self.assertEqual(m._y, 1)
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# Pass x as key word argument.
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self.assertEqual(m.input(x=4), [4, 1, None])
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self.assertEqual(m._x, 4)
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self.assertEqual(m._y, 1)
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# Pass y as positional argument.
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self.assertEqual(m.input(6, 3), [6, 3, None])
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self.assertEqual(m._x, 6)
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self.assertEqual(m._y, 3)
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# Pass y as key word argument.
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self.assertEqual(m.input(5, y=2), [5, 2, None])
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self.assertEqual(m._x, 5)
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self.assertEqual(m._y, 2)
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def test_inputFunctionsMustBeEmpty(self):
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"""
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The wrapped input function must have an empty body.
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"""
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# input functions are executed to assert that the signature matches,
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# but their body must be empty
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_methodical._empty() # chase coverage
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_methodical._docstring()
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class Mechanism(object):
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m = MethodicalMachine()
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with self.assertRaises(ValueError) as cm:
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@m.input()
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def input(self):
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"an input"
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list() # pragma: no cover
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self.assertEqual(str(cm.exception), "function body must be empty")
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# all three of these cases should be valid. Functions/methods with
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# docstrings produce slightly different bytecode than ones without.
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class MechanismWithDocstring(object):
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m = MethodicalMachine()
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@m.input()
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def input(self):
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"an input"
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@m.state(initial=True)
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def start(self):
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"starting state"
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start.upon(input, enter=start, outputs=[])
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MechanismWithDocstring().input()
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class MechanismWithPass(object):
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m = MethodicalMachine()
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@m.input()
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def input(self):
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pass
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@m.state(initial=True)
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def start(self):
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"starting state"
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start.upon(input, enter=start, outputs=[])
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MechanismWithPass().input()
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class MechanismWithDocstringAndPass(object):
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m = MethodicalMachine()
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@m.input()
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def input(self):
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"an input"
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pass
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@m.state(initial=True)
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def start(self):
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"starting state"
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start.upon(input, enter=start, outputs=[])
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MechanismWithDocstringAndPass().input()
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class MechanismReturnsNone(object):
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m = MethodicalMachine()
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@m.input()
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def input(self):
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return None
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@m.state(initial=True)
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def start(self):
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"starting state"
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start.upon(input, enter=start, outputs=[])
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MechanismReturnsNone().input()
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class MechanismWithDocstringAndReturnsNone(object):
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m = MethodicalMachine()
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@m.input()
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def input(self):
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"an input"
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return None
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@m.state(initial=True)
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def start(self):
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"starting state"
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start.upon(input, enter=start, outputs=[])
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MechanismWithDocstringAndReturnsNone().input()
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def test_inputOutputMismatch(self):
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"""
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All the argument lists of the outputs for a given input must match; if
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one does not the call to C{upon} will raise a C{TypeError}.
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"""
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class Mechanism(object):
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m = MethodicalMachine()
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@m.input()
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def nameOfInput(self, a):
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"an input"
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@m.output()
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def outputThatMatches(self, a):
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"an output that matches"
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@m.output()
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def outputThatDoesntMatch(self, b):
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"an output that doesn't match"
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@m.state()
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def state(self):
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"a state"
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with self.assertRaises(TypeError) as cm:
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state.upon(nameOfInput, state, [outputThatMatches,
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outputThatDoesntMatch])
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self.assertIn("nameOfInput", str(cm.exception))
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self.assertIn("outputThatDoesntMatch", str(cm.exception))
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def test_getArgNames(self):
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"""
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Type annotations should be included in the set of
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"""
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spec = ArgSpec(
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args=('a', 'b'),
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varargs=None,
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varkw=None,
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defaults=None,
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kwonlyargs=(),
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kwonlydefaults=None,
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annotations=(('a', int), ('b', str)),
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)
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self.assertEqual(
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_getArgNames(spec),
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{'a', 'b', ('a', int), ('b', str)},
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)
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def test_filterArgs(self):
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"""
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filterArgs() should not filter the `args` parameter
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if outputSpec accepts `*args`.
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"""
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inputSpec = _getArgSpec(lambda *args, **kwargs: None)
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outputSpec = _getArgSpec(lambda *args, **kwargs: None)
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argsIn = ()
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argsOut, _ = _filterArgs(argsIn, {}, inputSpec, outputSpec)
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self.assertIs(argsIn, argsOut)
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def test_multipleInitialStatesFailure(self):
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"""
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A L{MethodicalMachine} can only have one initial state.
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"""
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class WillFail(object):
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m = MethodicalMachine()
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@m.state(initial=True)
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def firstInitialState(self):
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"The first initial state -- this is OK."
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with self.assertRaises(ValueError):
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@m.state(initial=True)
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def secondInitialState(self):
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"The second initial state -- results in a ValueError."
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def test_multipleTransitionsFailure(self):
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"""
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A L{MethodicalMachine} can only have one transition per start/event
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pair.
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"""
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class WillFail(object):
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m = MethodicalMachine()
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@m.state(initial=True)
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def start(self):
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"We start here."
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@m.state()
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def end(self):
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"Rainbows end."
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@m.input()
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def event(self):
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"An event."
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start.upon(event, enter=end, outputs=[])
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with self.assertRaises(ValueError):
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start.upon(event, enter=end, outputs=[])
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def test_badTransitionForCurrentState(self):
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"""
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Calling any input method that lacks a transition for the machine's
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current state raises an informative L{NoTransition}.
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"""
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class OnlyOnePath(object):
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m = MethodicalMachine()
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@m.state(initial=True)
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def start(self):
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"Start state."
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@m.state()
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def end(self):
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"End state."
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@m.input()
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def advance(self):
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"Move from start to end."
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@m.input()
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def deadEnd(self):
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"A transition from nowhere to nowhere."
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start.upon(advance, end, [])
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machine = OnlyOnePath()
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with self.assertRaises(NoTransition) as cm:
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machine.deadEnd()
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self.assertIn("deadEnd", str(cm.exception))
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self.assertIn("start", str(cm.exception))
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machine.advance()
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with self.assertRaises(NoTransition) as cm:
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machine.deadEnd()
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self.assertIn("deadEnd", str(cm.exception))
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self.assertIn("end", str(cm.exception))
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def test_saveState(self):
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"""
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L{MethodicalMachine.serializer} is a decorator that modifies its
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decoratee's signature to take a "state" object as its first argument,
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which is the "serialized" argument to the L{MethodicalMachine.state}
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decorator.
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"""
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class Mechanism(object):
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m = MethodicalMachine()
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def __init__(self):
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self.value = 1
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@m.state(serialized="first-state", initial=True)
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def first(self):
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"First state."
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@m.state(serialized="second-state")
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def second(self):
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"Second state."
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@m.serializer()
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def save(self, state):
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return {
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'machine-state': state,
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'some-value': self.value,
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}
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self.assertEqual(
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Mechanism().save(),
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{
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"machine-state": "first-state",
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"some-value": 1,
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}
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)
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def test_restoreState(self):
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"""
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L{MethodicalMachine.unserializer} decorates a function that becomes a
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machine-state unserializer; its return value is mapped to the
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C{serialized} parameter to C{state}, and the L{MethodicalMachine}
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associated with that instance's state is updated to that state.
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"""
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class Mechanism(object):
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m = MethodicalMachine()
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def __init__(self):
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self.value = 1
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self.ranOutput = False
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@m.state(serialized="first-state", initial=True)
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def first(self):
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"First state."
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@m.state(serialized="second-state")
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def second(self):
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"Second state."
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@m.input()
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def input(self):
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"an input"
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@m.output()
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def output(self):
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self.value = 2
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self.ranOutput = True
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return 1
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@m.output()
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||||
def output2(self):
|
||||
return 2
|
||||
first.upon(input, second, [output],
|
||||
collector=lambda x: list(x)[0])
|
||||
second.upon(input, second, [output2],
|
||||
collector=lambda x: list(x)[0])
|
||||
@m.serializer()
|
||||
def save(self, state):
|
||||
return {
|
||||
'machine-state': state,
|
||||
'some-value': self.value,
|
||||
}
|
||||
|
||||
@m.unserializer()
|
||||
def _restore(self, blob):
|
||||
self.value = blob['some-value']
|
||||
return blob['machine-state']
|
||||
|
||||
@classmethod
|
||||
def fromBlob(cls, blob):
|
||||
self = cls()
|
||||
self._restore(blob)
|
||||
return self
|
||||
|
||||
m1 = Mechanism()
|
||||
m1.input()
|
||||
blob = m1.save()
|
||||
m2 = Mechanism.fromBlob(blob)
|
||||
self.assertEqual(m2.ranOutput, False)
|
||||
self.assertEqual(m2.input(), 2)
|
||||
self.assertEqual(
|
||||
m2.save(),
|
||||
{
|
||||
'machine-state': 'second-state',
|
||||
'some-value': 2,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
|
||||
# FIXME: error for wrong types on any call to _oneTransition
|
||||
# FIXME: better public API for .upon; maybe a context manager?
|
||||
# FIXME: when transitions are defined, validate that we can always get to
|
||||
# terminal? do we care about this?
|
||||
# FIXME: implementation (and use-case/example) for passing args from in to out
|
||||
|
||||
# FIXME: possibly these need some kind of support from core
|
||||
# FIXME: wildcard state (in all states, when input X, emit Y and go to Z)
|
||||
# FIXME: wildcard input (in state X, when any input, emit Y and go to Z)
|
||||
# FIXME: combined wildcards (in any state for any input, emit Y go to Z)
|
||||
Loading…
Add table
Add a link
Reference in a new issue