/usr/lib/python2.7/dist-packages/automat/_test/test_methodical.py is in python-automat 0.6.0-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 | """
Tests for the public interface of Automat.
"""
from functools import reduce
from unittest import TestCase
from .. import MethodicalMachine, NoTransition
from .. import _methodical
class MethodicalTests(TestCase):
"""
Tests for L{MethodicalMachine}.
"""
def test_oneTransition(self):
"""
L{MethodicalMachine} provides a way for you to declare a state machine
with inputs, outputs, and states as methods. When you have declared an
input, an output, and a state, calling the input method in that state
will produce the specified output.
"""
class Machination(object):
machine = MethodicalMachine()
@machine.input()
def anInput(self):
"an input"
@machine.output()
def anOutput(self):
"an output"
return "an-output-value"
@machine.output()
def anotherOutput(self):
"another output"
return "another-output-value"
@machine.state(initial=True)
def anState(self):
"a state"
@machine.state()
def anotherState(self):
"another state"
anState.upon(anInput, enter=anotherState, outputs=[anOutput])
anotherState.upon(anInput, enter=anotherState,
outputs=[anotherOutput])
m = Machination()
self.assertEqual(m.anInput(), ["an-output-value"])
self.assertEqual(m.anInput(), ["another-output-value"])
def test_machineItselfIsPrivate(self):
"""
L{MethodicalMachine} is an implementation detail. If you attempt to
access it on an instance of your class, you will get an exception.
However, since tools may need to access it for the purposes of, for
example, visualization, you may access it on the class itself.
"""
expectedMachine = MethodicalMachine()
class Machination(object):
machine = expectedMachine
machination = Machination()
with self.assertRaises(AttributeError) as cm:
machination.machine
self.assertIn("MethodicalMachine is an implementation detail",
str(cm.exception))
self.assertIs(Machination.machine, expectedMachine)
def test_outputsArePrivate(self):
"""
One of the benefits of using a state machine is that your output method
implementations don't need to take invalid state transitions into
account - the methods simply won't be called. This property would be
broken if client code called output methods directly, so output methods
are not directly visible under their names.
"""
class Machination(object):
machine = MethodicalMachine()
counter = 0
@machine.input()
def anInput(self):
"an input"
@machine.output()
def anOutput(self):
self.counter += 1
@machine.state(initial=True)
def state(self):
"a machine state"
state.upon(anInput, enter=state, outputs=[anOutput])
mach1 = Machination()
mach1.anInput()
self.assertEqual(mach1.counter, 1)
mach2 = Machination()
with self.assertRaises(AttributeError) as cm:
mach2.anOutput
self.assertEqual(mach2.counter, 0)
self.assertIn(
"Machination.anOutput is a state-machine output method; to "
"produce this output, call an input method instead.",
str(cm.exception)
)
def test_multipleMachines(self):
"""
Two machines may co-exist happily on the same instance; they don't
interfere with each other.
"""
class MultiMach(object):
a = MethodicalMachine()
b = MethodicalMachine()
@a.input()
def inputA(self):
"input A"
@b.input()
def inputB(self):
"input B"
@a.state(initial=True)
def initialA(self):
"initial A"
@b.state(initial=True)
def initialB(self):
"initial B"
@a.output()
def outputA(self):
return "A"
@b.output()
def outputB(self):
return "B"
initialA.upon(inputA, initialA, [outputA])
initialB.upon(inputB, initialB, [outputB])
mm = MultiMach()
self.assertEqual(mm.inputA(), ["A"])
self.assertEqual(mm.inputB(), ["B"])
def test_collectOutputs(self):
"""
Outputs can be combined with the "collector" argument to "upon".
"""
import operator
class Machine(object):
m = MethodicalMachine()
@m.input()
def input(self):
"an input"
@m.output()
def outputA(self):
return "A"
@m.output()
def outputB(self):
return "B"
@m.state(initial=True)
def state(self):
"a state"
state.upon(input, state, [outputA, outputB],
collector=lambda x: reduce(operator.add, x))
m = Machine()
self.assertEqual(m.input(), "AB")
def test_methodName(self):
"""
Input methods preserve their declared names.
"""
class Mech(object):
m = MethodicalMachine()
@m.input()
def declaredInputName(self):
"an input"
@m.state(initial=True)
def aState(self):
"state"
m = Mech()
with self.assertRaises(TypeError) as cm:
m.declaredInputName("too", "many", "arguments")
self.assertIn("declaredInputName", str(cm.exception))
def test_inputWithArguments(self):
"""
If an input takes an argument, it will pass that along to its output.
"""
class Mechanism(object):
m = MethodicalMachine()
@m.input()
def input(self, x, y=1):
"an input"
@m.state(initial=True)
def state(self):
"a state"
@m.output()
def output(self, x, y=1):
self._x = x
return x + y
state.upon(input, state, [output])
m = Mechanism()
self.assertEqual(m.input(3), [4])
self.assertEqual(m._x, 3)
def test_inputFunctionsMustBeEmpty(self):
"""
The wrapped input function must have an empty body.
"""
# input functions are executed to assert that the signature matches,
# but their body must be empty
_methodical._empty() # chase coverage
_methodical._docstring()
class Mechanism(object):
m = MethodicalMachine()
with self.assertRaises(ValueError) as cm:
@m.input()
def input(self):
"an input"
list() # pragma: no cover
self.assertEqual(str(cm.exception), "function body must be empty")
# all three of these cases should be valid. Functions/methods with
# docstrings produce slightly different bytecode than ones without.
class MechanismWithDocstring(object):
m = MethodicalMachine()
@m.input()
def input(self):
"an input"
@m.state(initial=True)
def start(self):
"starting state"
start.upon(input, enter=start, outputs=[])
MechanismWithDocstring().input()
class MechanismWithPass(object):
m = MethodicalMachine()
@m.input()
def input(self):
pass
@m.state(initial=True)
def start(self):
"starting state"
start.upon(input, enter=start, outputs=[])
MechanismWithPass().input()
class MechanismWithDocstringAndPass(object):
m = MethodicalMachine()
@m.input()
def input(self):
"an input"
pass
@m.state(initial=True)
def start(self):
"starting state"
start.upon(input, enter=start, outputs=[])
MechanismWithDocstringAndPass().input()
class MechanismReturnsNone(object):
m = MethodicalMachine()
@m.input()
def input(self):
return None
@m.state(initial=True)
def start(self):
"starting state"
start.upon(input, enter=start, outputs=[])
MechanismReturnsNone().input()
class MechanismWithDocstringAndReturnsNone(object):
m = MethodicalMachine()
@m.input()
def input(self):
"an input"
return None
@m.state(initial=True)
def start(self):
"starting state"
start.upon(input, enter=start, outputs=[])
MechanismWithDocstringAndReturnsNone().input()
def test_inputOutputMismatch(self):
"""
All the argument lists of the outputs for a given input must match; if
one does not the call to C{upon} will raise a C{TypeError}.
"""
class Mechanism(object):
m = MethodicalMachine()
@m.input()
def nameOfInput(self, a):
"an input"
@m.output()
def outputThatMatches(self, a):
"an output that matches"
@m.output()
def outputThatDoesntMatch(self, b):
"an output that doesn't match"
@m.state()
def state(self):
"a state"
with self.assertRaises(TypeError) as cm:
state.upon(nameOfInput, state, [outputThatMatches,
outputThatDoesntMatch])
self.assertIn("nameOfInput", str(cm.exception))
self.assertIn("outputThatDoesntMatch", str(cm.exception))
def test_multipleInitialStatesFailure(self):
"""
A L{MethodicalMachine} can only have one initial state.
"""
class WillFail(object):
m = MethodicalMachine()
@m.state(initial=True)
def firstInitialState(self):
"The first initial state -- this is OK."
with self.assertRaises(ValueError):
@m.state(initial=True)
def secondInitialState(self):
"The second initial state -- results in a ValueError."
def test_multipleTransitionsFailure(self):
"""
A L{MethodicalMachine} can only have one transition per start/event
pair.
"""
class WillFail(object):
m = MethodicalMachine()
@m.state(initial=True)
def start(self):
"We start here."
@m.state()
def end(self):
"Rainbows end."
@m.input()
def event(self):
"An event."
start.upon(event, enter=end, outputs=[])
with self.assertRaises(ValueError):
start.upon(event, enter=end, outputs=[])
def test_badTransitionForCurrentState(self):
"""
Calling any input method that lacks a transition for the machine's
current state raises an informative L{NoTransition}.
"""
class OnlyOnePath(object):
m = MethodicalMachine()
@m.state(initial=True)
def start(self):
"Start state."
@m.state()
def end(self):
"End state."
@m.input()
def advance(self):
"Move from start to end."
@m.input()
def deadEnd(self):
"A transition from nowhere to nowhere."
start.upon(advance, end, [])
machine = OnlyOnePath()
with self.assertRaises(NoTransition) as cm:
machine.deadEnd()
self.assertIn("deadEnd", str(cm.exception))
self.assertIn("start", str(cm.exception))
machine.advance()
with self.assertRaises(NoTransition) as cm:
machine.deadEnd()
self.assertIn("deadEnd", str(cm.exception))
self.assertIn("end", str(cm.exception))
def test_saveState(self):
"""
L{MethodicalMachine.serializer} is a decorator that modifies its
decoratee's signature to take a "state" object as its first argument,
which is the "serialized" argument to the L{MethodicalMachine.state}
decorator.
"""
class Mechanism(object):
m = MethodicalMachine()
def __init__(self):
self.value = 1
@m.state(serialized="first-state", initial=True)
def first(self):
"First state."
@m.state(serialized="second-state")
def second(self):
"Second state."
@m.serializer()
def save(self, state):
return {
'machine-state': state,
'some-value': self.value,
}
self.assertEqual(
Mechanism().save(),
{
"machine-state": "first-state",
"some-value": 1,
}
)
def test_restoreState(self):
"""
L{MethodicalMachine.unserializer} decorates a function that becomes a
machine-state unserializer; its return value is mapped to the
C{serialized} parameter to C{state}, and the L{MethodicalMachine}
associated with that instance's state is updated to that state.
"""
class Mechanism(object):
m = MethodicalMachine()
def __init__(self):
self.value = 1
self.ranOutput = False
@m.state(serialized="first-state", initial=True)
def first(self):
"First state."
@m.state(serialized="second-state")
def second(self):
"Second state."
@m.input()
def input(self):
"an input"
@m.output()
def output(self):
self.value = 2
self.ranOutput = True
return 1
@m.output()
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)
|