def _validate(self):
"""
Assert that this term is well-formed. This should be called exactly
once, at the end of Term._init().
"""
if self.dtype is NotSpecified:
raise DTypeNotSpecified(termname=type(self).__name__)
def _validate_dtype(cls, passed_dtype):
"""
Validate a `dtype` passed to Term.__new__.
If passed_dtype is NotSpecified, then we try to fall back to a
class-level attribute. If a value is found at that point, we pass it
to np.dtype so that users can pass `float` or `bool` and have them
coerce to the appropriate numpy types.
Returns
-------
validated : np.dtype
The dtype to use for the new term.
Raises
------
DTypeNotSpecified
When no dtype was passed to the instance, and the class doesn't
provide a default.
InvalidDType
When either the class or the instance provides a value not
coercible to a numpy dtype.
"""
dtype = passed_dtype
if dtype is NotSpecified:
dtype = cls.dtype
if dtype is NotSpecified:
raise DTypeNotSpecified(termname=cls.__name__)
try:
dtype = dtype_class(dtype)
except TypeError:
raise InvalidDType(dtype=dtype, termname=cls.__name__)
return dtype
def validate_dtype(termname, dtype, missing_value):
"""
Validate a `dtype` and `missing_value` passed to Term.__new__.
Ensures that we know how to represent ``dtype``, and that missing_value
is specified for types without default missing values.
Returns
-------
validated_dtype, validated_missing_value : np.dtype, any
The dtype and missing_value to use for the new term.
Raises
------
DTypeNotSpecified
When no dtype was passed to the instance, and the class doesn't
provide a default.
NotDType
When either the class or the instance provides a value not
coercible to a numpy dtype.
NoDefaultMissingValue
When dtype requires an explicit missing_value, but
``missing_value`` is NotSpecified.
"""
if dtype is NotSpecified:
raise DTypeNotSpecified(termname=termname)
try:
dtype = dtype_class(dtype)
except TypeError:
raise NotDType(dtype=dtype, termname=termname)
if not can_represent_dtype(dtype):
raise UnsupportedDType(dtype=dtype, termname=termname)
if missing_value is NotSpecified:
missing_value = default_missing_value_for_dtype(dtype)
try:
_coerce_to_dtype(missing_value, dtype)
except TypeError as e:
raise TypeError(
"Missing value {value!r} is not a valid choice "
"for term {termname} with dtype {dtype}.\n\n"
"Coercion attempt failed with: {error}".format(
termname=termname,
value=missing_value,
dtype=dtype,
error=e,
)
)
return dtype, missing_value
def _validate(self):
"""
Assert that this term is well-formed. This should be called exactly
once, at the end of Term._init().
"""
if self.inputs is NotSpecified:
raise TermInputsNotSpecified(termname=type(self).__name__)
if self.window_length is NotSpecified:
raise WindowLengthNotSpecified(termname=type(self).__name__)
if self.dtype is NotSpecified:
raise DTypeNotSpecified(termname=type(self).__name__)
if self.mask is NotSpecified and not self.atomic:
# This isn't user error, this is a bug in our code.
raise AssertionError("{term} has no mask".format(term=self))
if self.window_length:
for child in self.inputs:
if not child.atomic:
raise InputTermNotAtomic(parent=self, child=child)
def validate_dtype(termname, dtype, missing_value):
"""
Validate a `dtype` and `missing_value` passed to Term.__new__.
Ensures that we know how to represent ``dtype``, and that missing_value
is specified for types without default missing values.
Returns
-------
validated_dtype, validated_missing_value : np.dtype, any
The dtype and missing_value to use for the new term.
Raises
------
DTypeNotSpecified
When no dtype was passed to the instance, and the class doesn't
provide a default.
NotDType
When either the class or the instance provides a value not
coercible to a numpy dtype.
NoDefaultMissingValue
When dtype requires an explicit missing_value, but
``missing_value`` is NotSpecified.
"""
if dtype is NotSpecified:
raise DTypeNotSpecified(termname=termname)
try:
dtype = dtype_class(dtype)
except TypeError:
raise NotDType(dtype=dtype, termname=termname)
if not can_represent_dtype(dtype):
raise UnsupportedDType(dtype=dtype, termname=termname)
if missing_value is NotSpecified:
missing_value = default_missing_value_for_dtype(dtype)
return dtype, missing_value
def validate_dtype(termname, dtype, missing_value):
"""
Validate a `dtype` and `missing_value` passed to Term.__new__.
Ensures that we know how to represent ``dtype``, and that missing_value
is specified for types without default missing values.
Returns
-------
validated_dtype, validated_missing_value : np.dtype, any
The dtype and missing_value to use for the new term.
Raises
------
DTypeNotSpecified
When no dtype was passed to the instance, and the class doesn't
provide a default.
NotDType
When either the class or the instance provides a value not
coercible to a numpy dtype.
NoDefaultMissingValue
When dtype requires an explicit missing_value, but
``missing_value`` is NotSpecified.
"""
if dtype is NotSpecified:
raise DTypeNotSpecified(termname=termname)
try:
dtype = dtype_class(dtype)
except TypeError:
raise NotDType(dtype=dtype, termname=termname)
if not can_represent_dtype(dtype):
raise UnsupportedDType(dtype=dtype, termname=termname)
if missing_value is NotSpecified:
missing_value = default_missing_value_for_dtype(dtype)
try:
if (dtype == categorical_dtype):
# This check is necessary because we use object dtype for
# categoricals, and numpy will allow us to promote numerical
# values to object even though we don't support them.
_assert_valid_categorical_missing_value(missing_value)
# For any other type, we can check if the missing_value is safe by
# making an array of that value and trying to safely convert it to
# the desired type.
# 'same_kind' allows casting between things like float32 and
# float64, but not str and int.
array([missing_value]).astype(dtype=dtype, casting='same_kind')
except TypeError as e:
raise TypeError(
"Missing value {value!r} is not a valid choice "
"for term {termname} with dtype {dtype}.\n\n"
"Coercion attempt failed with: {error}".format(
termname=termname,
value=missing_value,
dtype=dtype,
error=e,
)
)
return dtype, missing_value