3.2.7. effect.testing module¶
Various functions and dispatchers for testing effects.
Usually the best way to test effects is by using perform_sequence()
.
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effect.testing.
perform_sequence
(seq, eff, fallback_dispatcher=None)¶ Perform an Effect by looking up performers for intents in an ordered “plan”.
First, an example:
@do def code_under_test(): r = yield Effect(MyIntent('a')) r2 = yield Effect(OtherIntent('b')) yield do_return((r, r2)) def test_code(): seq = [ (MyIntent('a'), lambda i: 'result1'), (OtherIntent('b'), lambda i: 'result2') ] eff = code_under_test() assert perform_sequence(seq, eff) == ('result1', 'result2')
Every time an intent is to be performed, it is checked against the next item in the sequence, and the associated function is used to calculate its result. Note that the objects used for intents must provide a meaningful
__eq__
implementation, since they will be checked for equality. Using something like attrs or pyrsistent’s PClass is recommended for your intents, since they will auto-generate __eq__ and many other methods useful for immutable objects.If an intent can’t be found in the sequence or the fallback dispatcher, an
AssertionError
is raised with a log of all intents that were performed so far. Each item in the log starts with one of three prefixes:sequence
: this intent was found in the sequencefallback
: a performer for this intent was provided by the fallback dispatcherNOT FOUND
: no performer for this intent was found.NEXT EXPECTED
: the next item in the sequence, if there is one. This will appear immediately after aNOT FOUND
.
Parameters: - sequence (list) – List of
(intent, fn)
tuples, wherefn
is a function that should accept an intent and return a result. - eff (Effect) – The Effect to perform.
- fallback_dispatcher – A dispatcher to use for intents that aren’t
found in the sequence. if None is provided,
base_dispatcher
is used.
Returns: Result of performed sequence
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effect.testing.
parallel_sequence
(parallel_seqs, fallback_dispatcher=None)¶ Convenience for expecting a ParallelEffects in an expected intent sequence, as required by
perform_sequence()
orSequenceDispatcher
.This lets you verify that intents are performed in parallel in the context of
perform_sequence()
. It returns a two-tuple as expected by that function, so you can use it like this:@do def code_under_test(): r = yield Effect(SerialIntent('serial')) r2 = yield parallel([Effect(MyIntent('a')), Effect(OtherIntent('b'))]) yield do_return((r, r2)) def test_code(): seq = [ (SerialIntent('serial'), lambda i: 'result1'), nested_parallel([ [(MyIntent('a'), lambda i: 'a result')], [(OtherIntent('b'), lambda i: 'b result')] ]), ] eff = code_under_test() assert perform_sequence(seq, eff) == ('result1', 'result2')
The argument is expected to be a list of intent sequences, one for each parallel effect expected. Each sequence will be performed with
perform_sequence()
and the respective effect that’s being run in parallel. The order of the sequences must match that of the order of parallel effects.Parameters: - parallel_seqs – list of lists of (intent, performer), like
what
perform_sequence()
accepts. - fallback_dispatcher – an optional dispatcher to compose onto the sequence dispatcher.
Returns: (intent, performer) tuple as expected by
perform_sequence()
where intent is ParallelEffects object- parallel_seqs – list of lists of (intent, performer), like
what
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effect.testing.
nested_sequence
(seq, get_effect=<operator.attrgetter object>, fallback_dispatcher=TypeDispatcher(mapping={<class 'effect._intents.Constant'>: <function perform_constant>, <class 'effect._intents.Error'>: <function perform_error>, <class 'effect._intents.Func'>: <function perform_func>}))¶ Return a function of Intent -> a that performs an effect retrieved from the intent (by accessing its effect attribute, by default) with the given intent-sequence.
A demonstration is best:
SequenceDispatcher([ (BoundFields(effect=mock.ANY, fields={...}), nested_sequence([(SomeIntent(), perform_some_intent)])) ])
The point is that sometimes you have an intent that wraps another effect, and you want to ensure that the nested effects follow some sequence in the context of that wrapper intent.
get_effect
defaults toattrgetter('effect')
, so you can override it if your intent stores its nested effect in a different attribute. Or, more interestingly, if it’s something other than a single effect, e.g. for ParallelEffects see theparallel_sequence()
function.Parameters: - seq (list) – sequence of intents like
SequenceDispatcher
takes - get_effect – callable to get the inner effect from the wrapper intent.
- fallback_dispatcher – an optional dispatcher to compose onto the sequence dispatcher.
Returns: callable
that can be used as performer of a wrapped intent- seq (list) – sequence of intents like
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class
effect.testing.
SequenceDispatcher
(sequence)¶ Bases:
object
A dispatcher which steps through a sequence of (intent, func) tuples and runs
func
to perform intents in strict sequence.This is the dispatcher used by
perform_sequence()
. In general that function should be used directly, instead of this dispatcher.It’s important to use with sequence.consume(): to ensure that all of the intents are performed. Otherwise, if your code has a bug that causes it to return before all effects are performed, your test may not fail.
None
is returned if the next intent in the sequence is not equal to the intent being performed, or if there are no more items left in the sequence (this is standard behavior for dispatchers that don’t handle an intent). This lets this dispatcher be composed easily with others.Parameters: sequence (list) – Sequence of (intent, fn). -
consume
(**kwds)¶ Return a context manager that can be used with the with syntax to ensure that all steps are performed by the end.
-
consumed
()¶ Return True if all of the steps were performed.
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effect.testing.
noop
(intent)¶ Return None. This is just a handy way to make your intent sequences (as used by
perform_sequence()
) more concise when the effects you’re expecting in a test don’t return a result (and are instead only performed for their side-effects):seq = [ (Prompt('Enter your name: '), lambda i: 'Chris') (Greet('Chris'), noop), ]
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effect.testing.
const
(value)¶ Return function that takes an argument but always return given value. Useful when creating sequence used by
perform_sequence()
. For example,>>> dt = datetime(1970, 1, 1) >>> seq = [(Func(datetime.now), const(dt))]
Parameters: value – This will be returned when called by returned function Returns: callable
that takes an arg and always returnsvalue
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effect.testing.
conste
(excp)¶ Like
const()
but takes and exception and returns function that raises the exceptionParameters: excp – Exception that will be raised Type: Exception
Returns: callable
that will raise given exception
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effect.testing.
intent_func
(fname)¶ Return function that returns Effect of tuple of fname and its args. Useful in writing tests that expect intent based on args. For example, if you are testing following function:
@do def code_under_test(arg1, arg2, eff_returning_func=eff_returning_func): r = yield Effect(MyIntent('a')) r2 = yield eff_returning_func(arg1, arg2) yield do_return((r, r2))
you will need to know the intents which
eff_returning_func
generates to test this usingperform_sequence()
. You can avoid that by doing:def test_code(): test_eff_func = intent_func("erf") seq = [ (MyIntent('a'), const('result1')), (("erf", 'a1', 'a2'), const('result2')) ] eff = code_under_test('a1', 'a2', eff_returning_func=test_eff_func) assert perform_sequence(seq, eff) == ('result1', 'result2')
Here, the
seq
ensures thateff_returning_func
is called with argumentsa1
anda2
.Parameters: fname (str) – First member of intent tuple returned Returns: callable
with multiple positional arguments
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effect.testing.
resolve_effect
(effect, result, is_error=False)¶ Supply a result for an effect, allowing its callbacks to run.
Note that is a pretty low-level testing utility; it’s much better to use a higher-level tool like
perform_sequence()
in your tests.The return value of the last callback is returned, unless any callback returns another Effect, in which case an Effect representing that operation plus the remaining callbacks will be returned.
This allows you to test your code in a somewhat “channel”-oriented way:
eff = do_thing() next_eff = resolve_effect(eff, first_result) next_eff = resolve_effect(next_eff, second_result) result = resolve_effect(next_eff, third_result)Equivalently, if you don’t care about intermediate results:
result = resolve_effect( resolve_effect( resolve_effect( do_thing(), first_result), second_result), third_result)
NOTE: parallel effects have no special support. They can be resolved with a sequence, and if they’re returned from another effect’s callback they will be returned just like any other effect.
Parameters: - is_error (bool) – Indicate whether the result should be treated as an exception or a regular result.
- result – If
is_error
is False, this can be any object and will be treated as the result of the effect. Ifis_error
is True, this must be a three-tuple in the style ofsys.exc_info
.
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effect.testing.
fail_effect
(effect, exception)¶ Resolve an effect with an exception, so its error handler will be run.
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class
effect.testing.
EQDispatcher
(mapping)¶ Bases:
object
An equality-based (constant) dispatcher.
This dispatcher looks up intents by equality and performs them by returning an associated constant value.
This is sometimes useful, but
perform_sequence()
should be preferred, since it constrains the order of effects, which is usually important.Users provide a mapping of intents to results, where the intents are matched against the intents being performed with a simple equality check (not a type check!).
The mapping must be provided as a sequence of two-tuples. We don’t use a dict because we don’t want to require that the intents be hashable (in practice a lot of them aren’t, and it’s a pain to require it). If you want to construct your mapping as a dict, you can, just pass in the result of
d.items()
.e.g.:
>>> sync_perform(EQDispatcher([(MyIntent(1, 2), 'the-result')]), ... Effect(MyIntent(1, 2))) 'the-result'
assuming MyIntent supports
__eq__
by value.Parameters: mapping (list) – A sequence of tuples of (intent, result).
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class
effect.testing.
EQFDispatcher
(mapping)¶ Bases:
object
An Equality-based function dispatcher.
This dispatcher looks up intents by equality and performs them by invoking an associated function.
This is sometimes useful, but
perform_sequence()
should be preferred, since it constrains the order of effects, which is usually important.Users provide a mapping of intents to functions, where the intents are matched against the intents being performed with a simple equality check (not a type check!). The functions in the mapping will be passed only the intent and are expected to return the result or raise an exception.
The mapping must be provided as a sequence of two-tuples. We don’t use a dict because we don’t want to require that the intents be hashable (in practice a lot of them aren’t, and it’s a pain to require it). If you want to construct your mapping as a dict, you can, just pass in the result of
d.items()
.e.g.:
>>> sync_perform( ... EQFDispatcher([( ... MyIntent(1, 2), lambda i: 'the-result')]), ... Effect(MyIntent(1, 2))) 'the-result'
assuming MyIntent supports
__eq__
by value.Parameters: mapping (list) – A sequence of two-tuples of (intent, function).
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class
effect.testing.
Stub
(intent)¶ Bases:
object
DEPRECATED in favor of using
perform_sequence()
.An intent which wraps another intent, to flag that the intent should be automatically resolved by
resolve_stub()
.Stub
is intentionally not performable by any default mechanism.
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effect.testing.
ESConstant
(x)¶ DEPRECATED. Return Effect(Stub(Constant(x)))
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effect.testing.
ESError
(x)¶ DEPRECATED. Return Effect(Stub(Error(x)))
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effect.testing.
ESFunc
(x)¶ DEPRECATED. Return Effect(Stub(Func(x)))
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effect.testing.
resolve_stubs
(dispatcher, effect)¶ DEPRECATED in favor of using
perform_sequence()
.Successively performs effects with resolve_stub until a non-Effect value, or an Effect with a non-stub intent is returned, and return that value.
Parallel effects are supported by recursively invoking resolve_stubs on the child effects, if all of their children are stubs.
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effect.testing.
resolve_stub
(dispatcher, effect)¶ DEPRECATED in favor of
perform_sequence()
.Automatically perform an effect, if its intent is a
Stub
.Note that resolve_stubs is preferred to this function, since it handles chains of stub effects.