def ensure_type(val,
dtype,
ndim,
name,
length=None,
can_be_none=False,
shape=None,
warn_on_cast=True,
add_newaxis_on_deficient_ndim=False):
"""Typecheck the size, shape and dtype of a numpy array, with optional
casting.
Parameters
----------
val : {np.ndaraay, None}
The array to check
dtype : {nd.dtype, str}
The dtype you'd like the array to have
ndim : int
The number of dimensions you'd like the array to have
name : str
name of the array. This is used when throwing exceptions, so that
we can describe to the user which array is messed up.
length : int, optional
How long should the array be?
can_be_none : bool
Is ``val == None`` acceptable?
shape : tuple, optional
What should be shape of the array be? If the provided tuple has
Nones in it, those will be semantically interpreted as matching
any length in that dimension. So, for example, using the shape
spec ``(None, None, 3)`` will ensure that the last dimension is of
length three without constraining the first two dimensions
warn_on_cast : bool, default=True
Raise a warning when the dtypes don't match and a cast is done.
add_newaxis_on_deficient_ndim : bool, default=True
Add a new axis to the beginining of the array if the number of
dimensions is deficient by one compared to your specification. For
instance, if you're trying to get out an array of ``ndim == 3``,
but the user provides an array of ``shape == (10, 10)``, a new axis will
be created with length 1 in front, so that the return value is of
shape ``(1, 10, 10)``.
Notes
-----
The returned value will always be C-contiguous.
Returns
-------
typechecked_val : np.ndarray, None
If `val=None` and `can_be_none=True`, then this will return None.
Otherwise, it will return val (or a copy of val). If the dtype wasn't right,
it'll be casted to the right shape. If the array was not C-contiguous, it'll
be copied as well.
"""
if can_be_none and val is None:
return None
if not isinstance(val, np.ndarray):
if isinstance(val, collections.Iterable):
# If they give us an iterator, let's try...
if isinstance(val, collections.Sequence):
# sequences are easy. these are like lists and stuff
val = np.array(val, dtype=dtype)
else:
# this is a generator...
val = np.array(list(val), dtype=dtype)
elif np.isscalar(val) and add_newaxis_on_deficient_ndim and ndim == 1:
# special case: if the user is looking for a 1d array, and
# they request newaxis upconversion, and provided a scalar
# then we should reshape the scalar to be a 1d length-1 array
val = np.array([val])
else:
raise TypeError(("%s must be numpy array. "
" You supplied type %s" % (name, type(val))))
if warn_on_cast and val.dtype != dtype:
warnings.warn("Casting %s dtype=%s to %s " % (name, val.dtype, dtype),
TypeCastPerformanceWarning)
if not val.ndim == ndim:
if add_newaxis_on_deficient_ndim and val.ndim + 1 == ndim:
val = val[np.newaxis, ...]
else:
raise ValueError(("%s must be ndim %s. "
"You supplied %s" % (name, ndim, val.ndim)))
val = np.ascontiguousarray(val, dtype=dtype)
if length is not None and len(val) != length:
raise ValueError(("%s must be length %s. "
"You supplied %s" % (name, length, len(val))))
if shape is not None:
# the shape specified given by the user can look like (None, None 3)
# which indicates that ANY length is accepted in dimension 0 or
# dimension 1
sentenel = object()
error = ValueError(
("%s must be shape %s. You supplied "
"%s" % (name, str(shape).replace('None', 'Any'), val.shape)))
for a, b in zip_longest(val.shape, shape, fillvalue=sentenel):
if a is sentenel or b is sentenel:
# if the sentenel was reached, it means that the ndim didn't
# match or something. this really shouldn't happen
raise error
if b is None:
# if the user's shape spec has a None in it, it matches anything
continue
if a != b:
# check for equality
raise error
return val
def ensure_type(val, dtype, ndim, name, length=None, can_be_none=False, shape=None,
warn_on_cast=True, add_newaxis_on_deficient_ndim=False):
"""Typecheck the size, shape and dtype of a numpy array, with optional
casting.
Parameters
----------
val : {np.ndaraay, None}
The array to check
dtype : {nd.dtype, str}
The dtype you'd like the array to have
ndim : int
The number of dimensions you'd like the array to have
name : str
name of the array. This is used when throwing exceptions, so that
we can describe to the user which array is messed up.
length : int, optional
How long should the array be?
can_be_none : bool
Is ``val == None`` acceptable?
shape : tuple, optional
What should be shape of the array be? If the provided tuple has
Nones in it, those will be semantically interpreted as matching
any length in that dimension. So, for example, using the shape
spec ``(None, None, 3)`` will ensure that the last dimension is of
length three without constraining the first two dimensions
warn_on_cast : bool, default=True
Raise a warning when the dtypes don't match and a cast is done.
add_newaxis_on_deficient_ndim : bool, default=True
Add a new axis to the beginining of the array if the number of
dimensions is deficient by one compared to your specification. For
instance, if you're trying to get out an array of ``ndim == 3``,
but the user provides an array of ``shape == (10, 10)``, a new axis will
be created with length 1 in front, so that the return value is of
shape ``(1, 10, 10)``.
Notes
-----
The returned value will always be C-contiguous.
Returns
-------
typechecked_val : np.ndarray, None
If `val=None` and `can_be_none=True`, then this will return None.
Otherwise, it will return val (or a copy of val). If the dtype wasn't right,
it'll be casted to the right shape. If the array was not C-contiguous, it'll
be copied as well.
"""
if can_be_none and val is None:
return None
if not isinstance(val, np.ndarray):
if isinstance(val, collections.Iterable):
# If they give us an iterator, let's try...
if isinstance(val, collections.Sequence):
# sequences are easy. these are like lists and stuff
val = np.array(val, dtype=dtype)
else:
# this is a generator...
val = np.array(list(val), dtype=dtype)
elif np.isscalar(val) and add_newaxis_on_deficient_ndim and ndim == 1:
# special case: if the user is looking for a 1d array, and
# they request newaxis upconversion, and provided a scalar
# then we should reshape the scalar to be a 1d length-1 array
val = np.array([val])
else:
raise TypeError(("%s must be numpy array. "
" You supplied type %s" % (name, type(val))))
if warn_on_cast and val.dtype != dtype:
warnings.warn("Casting %s dtype=%s to %s " % (name, val.dtype, dtype),
TypeCastPerformanceWarning)
if not val.ndim == ndim:
if add_newaxis_on_deficient_ndim and val.ndim + 1 == ndim:
val = val[np.newaxis, ...]
else:
raise ValueError(("%s must be ndim %s. "
"You supplied %s" % (name, ndim, val.ndim)))
val = np.ascontiguousarray(val, dtype=dtype)
if length is not None and len(val) != length:
raise ValueError(("%s must be length %s. "
"You supplied %s" % (name, length, len(val))))
if shape is not None:
# the shape specified given by the user can look like (None, None 3)
# which indicates that ANY length is accepted in dimension 0 or
# dimension 1
sentenel = object()
error = ValueError(("%s must be shape %s. You supplied "
"%s" % (name, str(shape).replace('None', 'Any'), val.shape)))
for a, b in zip_longest(val.shape, shape, fillvalue=sentenel):
if a is sentenel or b is sentenel:
# if the sentenel was reached, it means that the ndim didn't
# match or something. this really shouldn't happen
raise error
if b is None:
# if the user's shape spec has a None in it, it matches anything
continue
if a != b:
# check for equality
raise error
return val