def test_predicate():
class Dummy:
def _true(self):
return True
def _false(self):
return False
def _list(self):
return [1, 2, 3]
def _empty(self):
return []
def _identity(self, arg):
return arg
d = Dummy()
assert validate.Predicate("_true")(d) == d
assert validate.Predicate("_list")(d) == d
assert validate.Predicate("_identity", arg=True)(d) == d
assert validate.Predicate("_identity", arg=1)(d) == d
assert validate.Predicate("_identity", arg="abc")(d) == d
with pytest.raises(ValidationError, match="Invalid input."):
validate.Predicate("_false")(d)
with pytest.raises(ValidationError):
validate.Predicate("_empty")(d)
with pytest.raises(ValidationError):
validate.Predicate("_identity", arg=False)(d)
with pytest.raises(ValidationError):
validate.Predicate("_identity", arg=0)(d)
with pytest.raises(ValidationError):
validate.Predicate("_identity", arg="")(d)
def test_predicate_repr():
assert (repr(validate.Predicate(method='foo', error=None)) ==
'<Predicate(method={0!r}, kwargs={1!r}, error={2!r})>'.format(
'foo', {}, 'Invalid input.'))
assert (repr(validate.Predicate(method='foo', error='bar', zoo=1)) ==
'<Predicate(method={0!r}, kwargs={1!r}, error={2!r})>'.format(
'foo', {str('zoo') if PY2 else 'zoo': 1}, 'bar'))
def test_predicate_repr():
assert repr(
validate.Predicate(method="foo", error=None)
) == "<Predicate(method={!r}, kwargs={!r}, error={!r})>".format(
"foo", {}, "Invalid input.")
assert repr(validate.Predicate(
method="foo", error="bar",
zoo=1)) == "<Predicate(method={!r}, kwargs={!r}, error={!r})>".format(
"foo", {"zoo": 1}, "bar")
def test_predicate_custom_message():
class Dummy(object):
def _false(self):
return False
def __str__(self):
return 'Dummy'
d = Dummy()
with pytest.raises(ValidationError) as excinfo:
validate.Predicate('_false', error='{input}.{method} is invalid!')(d)
assert 'Dummy._false is invalid!' in str(excinfo)
def test_predicate_custom_message():
class Dummy:
def _false(self):
return False
def __str__(self):
return "Dummy"
d = Dummy()
with pytest.raises(ValidationError, match="Dummy._false is invalid!"):
validate.Predicate("_false", error="{input}.{method} is invalid!")(d)
class Tensor(PyBioSchema):
name = fields.String(required=True,
validate=validate.Predicate("isidentifier"),
bioimageio_description="Tensor name.")
description = fields.String(missing=None)
axes = fields.Axes(
required=True,
bioimageio_description=
"""Axes identifying characters from: bitczyx. Same length and order as the axes in `shape`.
| character | description |
| --- | --- |
| b | batch (groups multiple samples) |
| i | instance/index/element |
| t | time |
| c | channel |
| z | spatial dimension z |
| y | spatial dimension y |
| x | spatial dimension x |""",
)
data_type = fields.String(
required=True,
bioimageio_description=
"The data type of this tensor. For inputs, only `float32` is allowed and the consumer "
"software needs to ensure that the correct data type is passed here. For outputs can be any of `float32, "
"float64, (u)int8, (u)int16, (u)int32, (u)int64`. The data flow in bioimage.io models is explained "
"[in this diagram.](https://docs.google.com/drawings/d/1FTw8-Rn6a6nXdkZ_SkMumtcjvur9mtIhRqLwnKqZNHM/edit).",
)
data_range = fields.Tuple(
(fields.Float(allow_nan=True), fields.Float(allow_nan=True)),
missing=(None, None),
bioimageio_description=
"Tuple `(minimum, maximum)` specifying the allowed range of the data in this tensor. "
"If not specified, the full data range that can be expressed in `data_type` is allowed.",
)
shape: fields.Union
processing_name: str
@validates_schema
def validate_processing_kwargs(self, data, **kwargs):
axes = data["axes"]
processing_list = data.get(self.processing_name, [])
for processing in processing_list:
name = processing.name
kwargs = processing.kwargs or {}
kwarg_axes = kwargs.get("axes", "")
if any(a not in axes for a in kwarg_axes):
raise PyBioValidationException(
"`kwargs.axes` needs to be subset of axes")
def test_predicate():
class Dummy(object):
def _true(self):
return True
def _false(self):
return False
def _list(self):
return [1, 2, 3]
def _empty(self):
return []
def _identity(self, arg):
return arg
d = Dummy()
assert validate.Predicate('_true')(d) == d
assert validate.Predicate('_list')(d) == d
assert validate.Predicate('_identity', arg=True)(d) == d
assert validate.Predicate('_identity', arg=1)(d) == d
assert validate.Predicate('_identity', arg='abc')(d) == d
with pytest.raises(ValidationError) as excinfo:
validate.Predicate('_false')(d)
assert 'Invalid input.' in str(excinfo)
with pytest.raises(ValidationError):
validate.Predicate('_empty')(d)
with pytest.raises(ValidationError):
validate.Predicate('_identity', arg=False)(d)
with pytest.raises(ValidationError):
validate.Predicate('_identity', arg=0)(d)
with pytest.raises(ValidationError):
validate.Predicate('_identity', arg='')(d)
class ScaleMeanVariance(PyBioSchema):
mode = fields.ProcMode(required=True,
valid_modes=("per_dataset", "per_sample"))
reference_tensor: fields.String(
required=True, validate=validate.Predicate("isidentifier"))
class ScaleRange(Preprocessing.ScaleRange):
reference_tensor: fields.String(
required=True, validate=validate.Predicate("isidentifier"))