def check_type(stochastic):
"""
type, shape = check_type(stochastic)
Checks the type of a stochastic's value. Output value 'type' may be
bool, int, float, or complex. Nonnative numpy dtypes are lumped into
these categories. Output value 'shape' is () if the stochastic's value
is scalar, or a nontrivial tuple otherwise.
"""
val = stochastic.value
if val.__class__ is bool:
return bool, ()
elif val.__class__ in [int, uint, long, byte, short, intc, int_, longlong, intp, ubyte, ushort, uintc, uint, ulonglong, uintp]:
return int, ()
elif val.__class__ in [float, single, float_, longfloat]:
return float, ()
elif val.__class__ in [complex, csingle, complex_, clongfloat]:
return complex, ()
elif isinstance(val, ndarray):
if obj2sctype(val) is bool_:
return bool, val.shape
elif obj2sctype(val) in [byte, short, intc, int_, longlong, intp, ubyte, ushort, uintc, uint, ulonglong, uintp]:
return int, val.shape
elif obj2sctype(val) in [single, float_, longfloat]:
return float, val.shape
elif obj2sctype(val) in [csingle, complex_, clongfloat]:
return complex, val.shape
else:
return 'object', ()
def check_type(stochastic):
"""
type, shape = check_type(stochastic)
Checks the type of a stochastic's value. Output value 'type' may be
bool, int, float, or complex. Nonnative numpy dtypes are lumped into
these categories. Output value 'shape' is () if the stochastic's value
is scalar, or a nontrivial tuple otherwise.
"""
val = stochastic.value
if val.__class__ in bool_dtypes:
return bool, ()
elif val.__class__ in integer_dtypes:
return int, ()
elif val.__class__ in float_dtypes:
return float, ()
elif val.__class__ in complex_dtypes:
return complex, ()
elif isinstance(val, ndarray):
if obj2sctype(val) in bool_dtypes:
return bool, val.shape
elif obj2sctype(val) in integer_dtypes:
return int, val.shape
elif obj2sctype(val) in float_dtypes:
return float, val.shape
elif obj2sctype(val) in complex_dtypes:
return complex, val.shape
else:
return 'object', val.shape
else:
return 'object', ()
def print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray):
print '+',
for char in ntypes: print char,
print
for row in ntypes:
if row == 'O':
rowtype = GenericObject
else:
rowtype = np.obj2sctype(row)
print row,
for col in ntypes:
if col == 'O':
coltype = GenericObject
else:
coltype = np.obj2sctype(col)
try:
if firstarray:
rowvalue = np.array([rowtype(inputfirstvalue)], dtype=rowtype)
else:
rowvalue = rowtype(inputfirstvalue)
colvalue = coltype(inputsecondvalue)
value = np.add(rowvalue,colvalue)
if isinstance(value, np.ndarray):
char = value.dtype.char
else:
char = np.dtype(type(value)).char
except ValueError:
char = '!'
except OverflowError:
char = '@'
except TypeError:
char = '#'
print char,
print
def test_floating_exceptions(self):
"""Test basic arithmetic function errors"""
oldsettings = np.seterr(all='raise')
try:
# Test for all real and complex float types
for typecode in np.typecodes['AllFloat']:
ftype = np.obj2sctype(typecode)
if np.dtype(ftype).kind == 'f':
# Get some extreme values for the type
fi = np.finfo(ftype)
ft_tiny = fi.tiny
ft_max = fi.max
ft_eps = fi.eps
underflow = 'underflow'
divbyzero = 'divide by zero'
else:
# 'c', complex, corresponding real dtype
rtype = type(ftype(0).real)
fi = np.finfo(rtype)
ft_tiny = ftype(fi.tiny)
ft_max = ftype(fi.max)
ft_eps = ftype(fi.eps)
# The complex types raise different exceptions
underflow = ''
divbyzero = ''
overflow = 'overflow'
invalid = 'invalid'
self.assert_raises_fpe(underflow,
lambda a,b:a/b, ft_tiny, ft_max)
self.assert_raises_fpe(underflow,
lambda a,b:a*b, ft_tiny, ft_tiny)
self.assert_raises_fpe(overflow,
lambda a,b:a*b, ft_max, ftype(2))
self.assert_raises_fpe(overflow,
lambda a,b:a/b, ft_max, ftype(0.5))
self.assert_raises_fpe(overflow,
lambda a,b:a+b, ft_max, ft_max*ft_eps)
self.assert_raises_fpe(overflow,
lambda a,b:a-b, -ft_max, ft_max*ft_eps)
self.assert_raises_fpe(overflow,
np.power, ftype(2), ftype(2**fi.nexp))
self.assert_raises_fpe(divbyzero,
lambda a,b:a/b, ftype(1), ftype(0))
self.assert_raises_fpe(invalid,
lambda a,b:a/b, ftype(np.inf), ftype(np.inf))
self.assert_raises_fpe(invalid,
lambda a,b:a/b, ftype(0), ftype(0))
self.assert_raises_fpe(invalid,
lambda a,b:a-b, ftype(np.inf), ftype(np.inf))
self.assert_raises_fpe(invalid,
lambda a,b:a+b, ftype(np.inf), ftype(-np.inf))
self.assert_raises_fpe(invalid,
lambda a,b:a*b, ftype(0), ftype(np.inf))
finally:
np.seterr(**oldsettings)
def update_from_dict(self, level, data_d):
if len(data_d) > 0:
dtype = numpy.dtype(
{'names': list(data_d.keys()),
'formats': list(numpy.obj2sctype(v)
for v in list(data_d.values()))
})
self.update_from_array(level,
numpy.array([tuple(list(data_d.values()))],
dtype=dtype))
def complex_type(scalar_type):
"""
Determine the complex type corresponding to a component scalar type.
"""
scalar_type = np.obj2sctype(scalar_type)
if scalar_type == np.single:
return np.csingle
elif scalar_type == np.float_:
return np.complex_
elif scalar_type == np.longfloat:
return np.clongfloat
else:
raise ValueError('Invalid scalar_type: {0}.'.format(scalar_type))
def scalar_type(complex_type):
"""
Determine the type of the real and imaginary parts of a complex type.
"""
complex_type = np.obj2sctype(complex_type)
if complex_type == np.csingle:
return np.single
elif complex_type == np.complex_:
return np.float_
elif complex_type == np.clongfloat:
return np.longfloat
else:
raise ValueError('Invalid complex_type: {0}.'.format(complex_type))
def get_dtype(obj):
'''
Helper function to return the `numpy.dtype` of an arbitrary object.
Parameters
----------
obj : object
Any object (but typically some kind of number or array).
Returns
-------
dtype : `numpy.dtype`
The type of the given object.
'''
if hasattr(obj, 'dtype'):
return obj.dtype
else:
return np.obj2sctype(type(obj))
def init(self, z,y,x,t,d, k, focus=0, bandlimit=0,mask=None, dtype=numpy.complex64, itype=numpy.int32, ss_w=2, ss_v=1, ss_u=2, cutoff=1e-2, ): self.init_empty_arrays() if not isinstance(focus, numpy.ndarray) or focus.size==1: focus = focus*numpy.ones(t.shape, numpy.obj2sctype(focus)) self.z, self.y, self.x = z, y, x self.t, self.d = t, d self.k, self.bandlimit, self.focus, self.mask = k, bandlimit, focus, mask self.dtype, self.itype = dtype, itype self.ss_w, self.ss_v, self.ss_u, self.cutoff = ss_w, ss_v, ss_u, cutoff self._estimate_size_only = False return self
def test_floating_exceptions_power(self):
"""Test basic arithmetic function errors"""
oldsettings = np.seterr(all='raise')
try:
# Test for all real and complex float types
for typecode in np.typecodes['AllFloat']:
ftype = np.obj2sctype(typecode)
if np.dtype(ftype).kind == 'f':
# Get some extreme values for the type
fi = np.finfo(ftype)
else:
# 'c', complex, corresponding real dtype
rtype = type(ftype(0).real)
fi = np.finfo(rtype)
overflow = 'overflow'
self.assert_raises_fpe(overflow,
np.power, ftype(2), ftype(2**fi.nexp))
finally:
np.seterr(**oldsettings)
import numpy as np
np.obj2sctype(np.int32)
np.obj2sctype(np.array([1., 2.]))
np.obj2sctype(np.array([1.j]))
np.obj2sctype(dict)
np.obj2sctype('string')
np.obj2sctype(1, default=list)
def convert(image, dtype, force_copy=False, uniform=False):
"""
Convert an image to the requested data-type.
Warnings are issued in case of precision loss, or when negative values
are clipped during conversion to unsigned integer types (sign loss).
Floating point values are expected to be normalized and will be clipped
to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
signed integers respectively.
Numbers are not shifted to the negative side when converting from
unsigned to signed integer types. Negative values will be clipped when
converting to unsigned integers.
Parameters
----------
image : ndarray
Input image.
dtype : dtype
Target data-type.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
uniform : bool, optional
Uniformly quantize the floating point range to the integer range.
By default (uniform=False) floating point values are scaled and
rounded to the nearest integers, which minimizes back and forth
conversion errors.
"""
image = np.asarray(image)
dtypeobj_in = image.dtype
dtypeobj_out = np.dtype(dtype)
dtype_in = dtypeobj_in.type
dtype_out = dtypeobj_out.type
kind_in = dtypeobj_in.kind
kind_out = dtypeobj_out.kind
itemsize_in = dtypeobj_in.itemsize
itemsize_out = dtypeobj_out.itemsize
# Below, we do an `issubdtype` check. Its purpose is to find out
# whether we can get away without doing any image conversion. This happens
# when:
#
# - the output and input dtypes are the same or
# - when the output is specified as a type, and the input dtype
# is a subclass of that type (e.g. `np.floating` will allow
# `float32` and `float64` arrays through)
if np.issubdtype(dtype_in, np.obj2sctype(dtype)):
if force_copy:
image = image.copy()
return image
if not (dtype_in in _supported_types and dtype_out in _supported_types):
raise ValueError("Can not convert from {} to {}.".format(
dtypeobj_in, dtypeobj_out))
if kind_in in 'ui':
imin_in = np.iinfo(dtype_in).min
imax_in = np.iinfo(dtype_in).max
if kind_out in 'ui':
imin_out = np.iinfo(dtype_out).min
imax_out = np.iinfo(dtype_out).max
# any -> binary
if kind_out == 'b':
return image > dtype_in(dtype_range[dtype_in][1] / 2)
# binary -> any
if kind_in == 'b':
result = image.astype(dtype_out)
if kind_out != 'f':
result *= dtype_out(dtype_range[dtype_out][1])
return result
# float -> any
if kind_in == 'f':
if kind_out == 'f':
# float -> float
return image.astype(dtype_out)
if np.min(image) < -1.0 or np.max(image) > 1.0:
raise ValueError("Images of type float must be between -1 and 1.")
# floating point -> integer
# use float type that can represent output integer type
computation_type = _dtype_itemsize(itemsize_out, dtype_in, np.float32,
np.float64)
if not uniform:
if kind_out == 'u':
image_out = np.multiply(image,
imax_out,
dtype=computation_type)
else:
image_out = np.multiply(image, (imax_out - imin_out) / 2,
dtype=computation_type)
image_out -= 1.0 / 2.
np.rint(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
elif kind_out == 'u':
image_out = np.multiply(image,
imax_out + 1,
dtype=computation_type)
np.clip(image_out, 0, imax_out, out=image_out)
else:
image_out = np.multiply(image, (imax_out - imin_out + 1.0) / 2.0,
dtype=computation_type)
np.floor(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
return image_out.astype(dtype_out)
# signed/unsigned int -> float
if kind_out == 'f':
# use float type that can exactly represent input integers
computation_type = _dtype_itemsize(itemsize_in, dtype_out, np.float32,
np.float64)
if kind_in == 'u':
# using np.divide or np.multiply doesn't copy the data
# until the computation time
image = np.multiply(image, 1. / imax_in, dtype=computation_type)
# DirectX uses this conversion also for signed ints
# if imin_in:
# np.maximum(image, -1.0, out=image)
else:
image = np.add(image, 0.5, dtype=computation_type)
image *= 2 / (imax_in - imin_in)
return np.asarray(image, dtype_out)
# unsigned int -> signed/unsigned int
if kind_in == 'u':
if kind_out == 'i':
# unsigned int -> signed int
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1)
return image.view(dtype_out)
else:
# unsigned int -> unsigned int
return _scale(image, 8 * itemsize_in, 8 * itemsize_out)
# signed int -> unsigned int
if kind_out == 'u':
image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out)
result = np.empty(image.shape, dtype_out)
np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
return result
# signed int -> signed int
if itemsize_in > itemsize_out:
return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1)
image = image.astype(_dtype_bits('i', itemsize_out * 8))
image -= imin_in
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
image += imin_out
return image.astype(dtype_out)
def _convert(image, dtype, force_copy=False, uniform=False):
"""
Adapted from: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/dtype.py#L510-L531
Convert an image to the requested data-type.
Warnings are issued in case of precision loss, or when negative values
are clipped during conversion to unsigned integer types (sign loss).
Floating point values are expected to be normalized and will be clipped
to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
signed integers respectively.
Numbers are not shifted to the negative side when converting from
unsigned to signed integer types. Negative values will be clipped when
converting to unsigned integers.
Parameters
----------
image : ndarray
Input image.
dtype : dtype
Target data-type.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
uniform : bool, optional
Uniformly quantize the floating point range to the integer range.
By default (uniform=False) floating point values are scaled and
rounded to the nearest integers, which minimizes back and forth
conversion errors.
.. versionchanged :: 0.15
``_convert`` no longer warns about possible precision or sign
information loss. See discussions on these warnings at:
https://github.com/scikit-image/scikit-image/issues/2602
https://github.com/scikit-image/scikit-image/issues/543#issuecomment-208202228
https://github.com/scikit-image/scikit-image/pull/3575
References
----------
.. [1] DirectX data conversion rules.
https://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
.. [2] Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25",
pp 7-8. Khronos Group, 2010.
.. [3] Proper treatment of pixels as integers. A.W. Paeth.
In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
.. [4] Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels",
pp 47-57. Morgan Kaufmann, 1998.
"""
dtype_range = {bool: (False, True),
np.bool_: (False, True),
np.bool8: (False, True),
float: (-1, 1),
np.float_: (-1, 1),
np.float16: (-1, 1),
np.float32: (-1, 1),
np.float64: (-1, 1)}
def _dtype_itemsize(itemsize, *dtypes):
"""Return first of `dtypes` with itemsize greater than `itemsize`
Parameters
----------
itemsize: int
The data type object element size.
Other Parameters
----------------
*dtypes:
Any Object accepted by `np.dtype` to be converted to a data
type object
Returns
-------
dtype: data type object
First of `dtypes` with itemsize greater than `itemsize`.
"""
return next(dt for dt in dtypes if np.dtype(dt).itemsize >= itemsize)
def _dtype_bits(kind, bits, itemsize=1):
"""Return dtype of `kind` that can store a `bits` wide unsigned int
Parameters:
kind: str
Data type kind.
bits: int
Desired number of bits.
itemsize: int
The data type object element size.
Returns
-------
dtype: data type object
Data type of `kind` that can store a `bits` wide unsigned int
"""
s = next(i for i in (itemsize, ) + (2, 4, 8) if
bits < (i * 8) or (bits == (i * 8) and kind == 'u'))
return np.dtype(kind + str(s))
def _scale(a, n, m, copy=True):
"""Scale an array of unsigned/positive integers from `n` to `m` bits.
Numbers can be represented exactly only if `m` is a multiple of `n`.
Parameters
----------
a : ndarray
Input image array.
n : int
Number of bits currently used to encode the values in `a`.
m : int
Desired number of bits to encode the values in `out`.
copy : bool, optional
If True, allocates and returns new array. Otherwise, modifies
`a` in place.
Returns
-------
out : array
Output image array. Has the same kind as `a`.
"""
kind = a.dtype.kind
if n > m and a.max() < 2 ** m:
mnew = int(np.ceil(m / 2) * 2)
if mnew > m:
dtype = "int{}".format(mnew)
else:
dtype = "uint{}".format(mnew)
n = int(np.ceil(n / 2) * 2)
return a.astype(_dtype_bits(kind, m))
elif n == m:
return a.copy() if copy else a
elif n > m:
# downscale with precision loss
if copy:
b = np.empty(a.shape, _dtype_bits(kind, m))
np.floor_divide(a, 2**(n - m), out=b, dtype=a.dtype,
casting='unsafe')
return b
else:
a //= 2**(n - m)
return a
elif m % n == 0:
# exact upscale to a multiple of `n` bits
if copy:
b = np.empty(a.shape, _dtype_bits(kind, m))
np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype)
return b
else:
a = a.astype(_dtype_bits(kind, m, a.dtype.itemsize), copy=False)
a *= (2**m - 1) // (2**n - 1)
return a
else:
# upscale to a multiple of `n` bits,
# then downscale with precision loss
o = (m // n + 1) * n
if copy:
b = np.empty(a.shape, _dtype_bits(kind, o))
np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype)
b //= 2**(o - m)
return b
else:
a = a.astype(_dtype_bits(kind, o, a.dtype.itemsize), copy=False)
a *= (2**o - 1) // (2**n - 1)
a //= 2**(o - m)
return a
image = np.asarray(image)
dtypeobj_in = image.dtype
if dtype is np.floating:
dtypeobj_out = np.dtype('float64')
else:
dtypeobj_out = np.dtype(dtype)
dtype_in = dtypeobj_in.type
dtype_out = dtypeobj_out.type
kind_in = dtypeobj_in.kind
kind_out = dtypeobj_out.kind
itemsize_in = dtypeobj_in.itemsize
itemsize_out = dtypeobj_out.itemsize
# Below, we do an `issubdtype` check. Its purpose is to find out
# whether we can get away without doing any image conversion. This happens
# when:
#
# - the output and input dtypes are the same or
# - when the output is specified as a type, and the input dtype
# is a subclass of that type (e.g. `np.floating` will allow
# `float32` and `float64` arrays through)
if np.issubdtype(dtype_in, np.obj2sctype(dtype)):
if force_copy:
image = image.copy()
return image
if kind_in in 'ui':
imin_in = np.iinfo(dtype_in).min
imax_in = np.iinfo(dtype_in).max
if kind_out in 'ui':
imin_out = np.iinfo(dtype_out).min
imax_out = np.iinfo(dtype_out).max
# any -> binary
if kind_out == 'b':
return image > dtype_in(dtype_range[dtype_in][1] / 2)
# binary -> any
if kind_in == 'b':
result = image.astype(dtype_out)
if kind_out != 'f':
result *= dtype_out(dtype_range[dtype_out][1])
return result
# float -> any
if kind_in == 'f':
if kind_out == 'f':
# float -> float
return image.astype(dtype_out)
if np.min(image) < -1.0 or np.max(image) > 1.0:
raise ValueError("Images of type float must be between -1 and 1.")
# floating point -> integer
# use float type that can represent output integer type
computation_type = _dtype_itemsize(itemsize_out, dtype_in,
np.float32, np.float64)
if not uniform:
if kind_out == 'u':
image_out = np.multiply(image, imax_out,
dtype=computation_type)
else:
image_out = np.multiply(image, (imax_out - imin_out) / 2,
dtype=computation_type)
image_out -= 1.0 / 2.
np.rint(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
elif kind_out == 'u':
image_out = np.multiply(image, imax_out + 1,
dtype=computation_type)
np.clip(image_out, 0, imax_out, out=image_out)
else:
image_out = np.multiply(image, (imax_out - imin_out + 1.0) / 2.0,
dtype=computation_type)
np.floor(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
return image_out.astype(dtype_out)
# signed/unsigned int -> float
if kind_out == 'f':
# use float type that can exactly represent input integers
computation_type = _dtype_itemsize(itemsize_in, dtype_out,
np.float32, np.float64)
if kind_in == 'u':
# using np.divide or np.multiply doesn't copy the data
# until the computation time
image = np.multiply(image, 1. / imax_in,
dtype=computation_type)
# DirectX uses this conversion also for signed ints
# if imin_in:
# np.maximum(image, -1.0, out=image)
else:
image = np.add(image, 0.5, dtype=computation_type)
image *= 2 / (imax_in - imin_in)
return np.asarray(image, dtype_out)
# unsigned int -> signed/unsigned int
if kind_in == 'u':
if kind_out == 'i':
# unsigned int -> signed int
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1)
return image.view(dtype_out)
else:
# unsigned int -> unsigned int
return _scale(image, 8 * itemsize_in, 8 * itemsize_out)
# signed int -> unsigned int
if kind_out == 'u':
image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out)
result = np.empty(image.shape, dtype_out)
np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
return result
# signed int -> signed int
if itemsize_in > itemsize_out:
return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1)
image = image.astype(_dtype_bits('i', itemsize_out * 8))
image -= imin_in
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
image += imin_out
return image.astype(dtype_out)
def _convert(image, dtype, force_copy=False, uniform=False):
"""
Convert an image to the requested data-type.
Warnings are issued in case of precision loss, or when negative values
are clipped during conversion to unsigned integer types (sign loss).
Floating point values are expected to be normalized and will be clipped
to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
signed integers respectively.
Numbers are not shifted to the negative side when converting from
unsigned to signed integer types. Negative values will be clipped when
converting to unsigned integers.
Parameters
----------
image : ndarray
Input image.
dtype : dtype
Target data-type.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
uniform : bool, optional
Uniformly quantize the floating point range to the integer range.
By default (uniform=False) floating point values are scaled and
rounded to the nearest integers, which minimizes back and forth
conversion errors.
.. versionchanged:: 0.15
``_convert`` no longer warns about possible precision or sign
information loss. See discussions on these warnings at:
https://github.com/scikit-image/scikit-image/issues/2602
https://github.com/scikit-image/scikit-image/issues/543#issuecomment-208202228
https://github.com/scikit-image/scikit-image/pull/3575
References
----------
.. [1] DirectX data conversion rules.
https://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
.. [2] Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25",
pp 7-8. Khronos Group, 2010.
.. [3] Proper treatment of pixels as integers. A.W. Paeth.
In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
.. [4] Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels",
pp 47-57. Morgan Kaufmann, 1998.
"""
image = np.asarray(image)
dtypeobj_in = image.dtype
if dtype is np.floating:
dtypeobj_out = np.dtype('float64')
else:
dtypeobj_out = np.dtype(dtype)
dtype_in = dtypeobj_in.type
dtype_out = dtypeobj_out.type
kind_in = dtypeobj_in.kind
kind_out = dtypeobj_out.kind
itemsize_in = dtypeobj_in.itemsize
itemsize_out = dtypeobj_out.itemsize
# Below, we do an `issubdtype` check. Its purpose is to find out
# whether we can get away without doing any image conversion. This happens
# when:
#
# - the output and input dtypes are the same or
# - when the output is specified as a type, and the input dtype
# is a subclass of that type (e.g. `np.floating` will allow
# `float32` and `float64` arrays through)
if np.issubdtype(dtype_in, np.obj2sctype(dtype)):
if force_copy:
image = image.copy()
return image
if not (dtype_in in _supported_types and dtype_out in _supported_types):
raise ValueError(f'Cannot convert from {dtypeobj_in} to '
f'{dtypeobj_out}.')
if kind_in in 'ui':
imin_in = np.iinfo(dtype_in).min
imax_in = np.iinfo(dtype_in).max
if kind_out in 'ui':
imin_out = np.iinfo(dtype_out).min
imax_out = np.iinfo(dtype_out).max
# any -> binary
if kind_out == 'b':
return image > dtype_in(dtype_range[dtype_in][1] / 2)
# binary -> any
if kind_in == 'b':
result = image.astype(dtype_out)
if kind_out != 'f':
result *= dtype_out(dtype_range[dtype_out][1])
return result
# float -> any
if kind_in == 'f':
if kind_out == 'f':
# float -> float
return image.astype(dtype_out)
if np.min(image) < -1.0 or np.max(image) > 1.0:
raise ValueError("Images of type float must be between -1 and 1.")
# floating point -> integer
# use float type that can represent output integer type
computation_type = _dtype_itemsize(itemsize_out, dtype_in,
np.float32, np.float64)
if not uniform:
if kind_out == 'u':
image_out = np.multiply(image, imax_out,
dtype=computation_type)
else:
image_out = np.multiply(image, (imax_out - imin_out) / 2,
dtype=computation_type)
image_out -= 1.0 / 2.
np.rint(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
elif kind_out == 'u':
image_out = np.multiply(image, imax_out + 1,
dtype=computation_type)
np.clip(image_out, 0, imax_out, out=image_out)
else:
image_out = np.multiply(image, (imax_out - imin_out + 1.0) / 2.0,
dtype=computation_type)
np.floor(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
return image_out.astype(dtype_out)
# signed/unsigned int -> float
if kind_out == 'f':
# use float type that can exactly represent input integers
computation_type = _dtype_itemsize(itemsize_in, dtype_out,
np.float32, np.float64)
if kind_in == 'u':
# using np.divide or np.multiply doesn't copy the data
# until the computation time
image = np.multiply(image, 1. / imax_in,
dtype=computation_type)
# DirectX uses this conversion also for signed ints
# if imin_in:
# np.maximum(image, -1.0, out=image)
elif kind_in == 'i':
# From DirectX conversions:
# The most negative value maps to -1.0f
# Every other value is converted to a float (call it c)
# and then result = c * (1.0f / (2⁽ⁿ⁻¹⁾-1)).
image = np.multiply(image, 1. / imax_in,
dtype=computation_type)
np.maximum(image, -1.0, out=image)
else:
image = np.add(image, 0.5, dtype=computation_type)
image *= 2 / (imax_in - imin_in)
return np.asarray(image, dtype_out)
# unsigned int -> signed/unsigned int
if kind_in == 'u':
if kind_out == 'i':
# unsigned int -> signed int
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1)
return image.view(dtype_out)
else:
# unsigned int -> unsigned int
return _scale(image, 8 * itemsize_in, 8 * itemsize_out)
# signed int -> unsigned int
if kind_out == 'u':
image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out)
result = np.empty(image.shape, dtype_out)
np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
return result
# signed int -> signed int
if itemsize_in > itemsize_out:
return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1)
image = image.astype(_dtype_bits('i', itemsize_out * 8))
image -= imin_in
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
image += imin_out
return image.astype(dtype_out)
def _convert(image, dtype, force_copy=False, uniform=False):
""" Convert an image to the requested data-type.
"""
image = np.asarray(image)
dtypeobj_in = image.dtype
dtypeobj_out = np.dtype(dtype)
dtype_in = dtypeobj_in.type
dtype_out = dtypeobj_out.type
kind_in = dtypeobj_in.kind
kind_out = dtypeobj_out.kind
itemsize_in = dtypeobj_in.itemsize
itemsize_out = dtypeobj_out.itemsize
# Below, we do an `issubdtype` check. Its purpose is to find out
# whether we can get away without doing any image conversion. This happens
# when:
#
# - the output and input dtypes are the same or
# - when the output is specified as a type, and the input dtype
# is a subclass of that type (e.g. `np.floating` will allow
# `float32` and `float64` arrays through)
if np.issubdtype(dtype_in, np.obj2sctype(dtype)):
if force_copy:
image = image.copy()
return image
if not (dtype_in in _supported_types and dtype_out in _supported_types):
raise ValueError("Can not convert from {} to {}.".format(
dtypeobj_in, dtypeobj_out))
if kind_in in 'ui':
imin_in = np.iinfo(dtype_in).min
imax_in = np.iinfo(dtype_in).max
if kind_out in 'ui':
imin_out = np.iinfo(dtype_out).min
imax_out = np.iinfo(dtype_out).max
# any -> binary
if kind_out == 'b':
return image > dtype_in(dtype_range[dtype_in][1] / 2)
# binary -> any
if kind_in == 'b':
result = image.astype(dtype_out)
if kind_out != 'f':
result *= dtype_out(dtype_range[dtype_out][1])
return result
# float -> any
if kind_in == 'f':
if kind_out == 'f':
# float -> float
return image.astype(dtype_out)
if np.min(image) < -1.0 or np.max(image) > 1.0:
raise ValueError("Images of type float must be between -1 and 1.")
# floating point -> integer
# use float type that can represent output integer type
computation_type = _dtype_itemsize(itemsize_out, dtype_in, np.float32,
np.float64)
if not uniform:
if kind_out == 'u':
image_out = np.multiply(image,
imax_out,
dtype=computation_type)
else:
image_out = np.multiply(image, (imax_out - imin_out) / 2,
dtype=computation_type)
image_out -= 1.0 / 2.
np.rint(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
elif kind_out == 'u':
image_out = np.multiply(image,
imax_out + 1,
dtype=computation_type)
np.clip(image_out, 0, imax_out, out=image_out)
else:
image_out = np.multiply(image, (imax_out - imin_out + 1.0) / 2.0,
dtype=computation_type)
np.floor(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
return image_out.astype(dtype_out)
# signed/unsigned int -> float
if kind_out == 'f':
# use float type that can exactly represent input integers
computation_type = _dtype_itemsize(itemsize_in, dtype_out, np.float32,
np.float64)
if kind_in == 'u':
# using np.divide or np.multiply doesn't copy the data
# until the computation time
image = np.multiply(image, 1. / imax_in, dtype=computation_type)
# DirectX uses this conversion also for signed ints
# if imin_in:
# np.maximum(image, -1.0, out=image)
else:
image = np.add(image, 0.5, dtype=computation_type)
image *= 2 / (imax_in - imin_in)
return np.asarray(image, dtype_out)
# unsigned int -> signed/unsigned int
if kind_in == 'u':
if kind_out == 'i':
# unsigned int -> signed int
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1)
return image.view(dtype_out)
else:
# unsigned int -> unsigned int
return _scale(image, 8 * itemsize_in, 8 * itemsize_out)
# signed int -> unsigned int
if kind_out == 'u':
image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out)
result = np.empty(image.shape, dtype_out)
np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
return result
# signed int -> signed int
if itemsize_in > itemsize_out:
return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1)
image = image.astype(_dtype_bits('i', itemsize_out * 8))
image -= imin_in
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
image += imin_out
return image.astype(dtype_out)
def __init__(self, shape, dtype_in=None, data_in=None,
overwrite=True, inverse=True, packed=True, use_pyfftw=True):
try:
nx, ny, nz = shape
except (TypeError, ValueError):
raise ValueError('Expected 3D shape.')
if nx % 2 or ny % 2 or nz % 2:
raise ValueError('All shape dimensions must be even.')
if data_in is not None:
if not isinstance(data_in, np.ndarray):
raise ValueError(
'Invalid type for data_in: {0}.'.format(type(data_in)))
dtype_in = data_in.dtype
# Convert dtype_in to an object in the numpy scalar type hierarchy.
dtype_in = np.obj2sctype(dtype_in)
if dtype_in is None:
raise ValueError('Invalid dtype_in: {0}.'.format(dtype_in))
# Determine the input and output array type and shape.
if packed:
if inverse:
shape_in = (nx, ny, nz//2 + 1)
if not issubclass(dtype_in, np.complexfloating):
raise ValueError(
'Invalid dtype_in for inverse packed transform ' +
'(should be complex): {0}.'.format(dtype_in))
dtype_out = scalar_type(dtype_in)
shape_out = (nx, ny, nz + 2) if overwrite else shape
else:
shape_in = (nx, ny, nz + 2) if overwrite else shape
if not issubclass(dtype_in, np.floating):
raise ValueError(
'Invalid dtype_in for forward packed transform ' +
'(should be floating): {0}.'.format(dtype_in))
dtype_out = complex_type(dtype_in)
shape_out = (nx, ny, nz//2 + 1)
else:
if not issubclass(dtype_in, np.complexfloating):
raise ValueError(
'Expected complex dtype_in for transform: {0}.'
.format(dtype_in))
shape_in = shape_out = shape
dtype_out = dtype_in
if data_in is not None:
if data_in.shape != shape_in:
raise ValueError(
'data_in has wrong shape {0}, expected {1}.'
.format(data_in.shape, shape_in))
self.data_in = data_in
self.nbytes_allocated = 0
else:
# Allocate the input and output data buffers.
self.data_in = allocate(
shape_in, dtype_in, use_pyfftw=use_pyfftw)
self.nbytes_allocated = self.data_in.nbytes
if overwrite:
if packed:
# See https://github.com/hgomersall/pyFFTW/issues/29
self.data_out = self.data_in.view(dtype_out).reshape(shape_out)
# Hide the padding without copying. See http://www.fftw.org/doc/
# Multi_002dDimensional-DFTs-of-Real-Data.html.
if inverse:
self.data_out_padded = self.data_out
self.data_out = self.data_out[:, :, :nz]
else:
self.data_in_padded = self.data_in
self.data_in = self.data_in[:, :, :nz]
else:
self.data_out = self.data_in
else:
self.data_out = allocate(
shape_out, dtype_out, use_pyfftw=use_pyfftw)
self.nbytes_allocated += self.data_out.nbytes
# Try to use pyFFTW to configure the transform, if requested.
self.use_pyfftw = use_pyfftw
if self.use_pyfftw:
try:
import pyfftw
if not pyfftw.is_n_byte_aligned(self.data_in,
pyfftw.simd_alignment):
raise ValueError('data_in is not SIMD aligned.')
if not pyfftw.is_n_byte_aligned(self.data_out,
pyfftw.simd_alignment):
raise ValueError('data_out is not SIMD aligned.')
direction = 'FFTW_BACKWARD' if inverse else 'FFTW_FORWARD'
self.fftw_plan = pyfftw.FFTW(
self.data_in, self.data_out, direction=direction,
flags=('FFTW_ESTIMATE',), axes=(0, 1, 2))
self.fftw_norm = np.float(nx * ny * nz if inverse else 1)
except ImportError:
self.use_pyfftw = False
# Fall back to numpy.fft if we are not using pyFFTW.
if not self.use_pyfftw:
if inverse:
self.transformer = np.fft.irfftn if packed else np.fft.ifftn
else:
self.transformer = np.fft.rfftn if packed else np.fft.fftn
# Remember our options so we can create a reverse plan.
self.shape = shape
self.inverse = inverse
self.packed = packed
self.overwrite = overwrite
import numpy as np
reveal_type(np.issctype(np.generic)) # E: bool
reveal_type(np.issctype("foo")) # E: bool
reveal_type(np.obj2sctype("S8")) # E: Union[numpy.generic, None]
reveal_type(np.obj2sctype("S8", default=None)) # E: Union[numpy.generic, None]
reveal_type(
np.obj2sctype("foo",
default=int) # E: Union[numpy.generic, Type[builtins.int*]]
)
reveal_type(np.issubclass_(np.float64, float)) # E: bool
reveal_type(np.issubclass_(np.float64, (int, float))) # E: bool
reveal_type(np.sctype2char("S8")) # E: str
reveal_type(np.sctype2char(list)) # E: str
reveal_type(np.find_common_type([np.int64], [np.int64])) # E: numpy.dtype
import numpy as np
np.maximum_sctype("S8")
np.maximum_sctype(object)
np.issctype(object)
np.issctype("S8")
np.obj2sctype(list)
np.obj2sctype(list, default=None)
np.obj2sctype(list, default=np.string_)
np.issubclass_(np.int32, int)
np.issubclass_(np.float64, float)
np.issubclass_(np.float64, (int, float))
np.issubsctype("int64", int)
np.issubsctype(np.array([1]), np.array([1]))
np.issubdtype("S1", np.string_)
np.issubdtype(np.float64, np.float32)
np.sctype2char("S1")
np.sctype2char(list)
np.find_common_type([], [np.int64, np.float32, complex])
np.find_common_type((), (np.int64, np.float32, complex))
np.find_common_type([np.int64, np.float32], [])
np.find_common_type([np.float32], [np.int64, np.float64])
np.cast[int]
def get_dtype(obj):
if hasattr(obj, 'dtype'):
return obj.dtype
else:
return np.obj2sctype(obj)
import numpy as np
reveal_type(np.maximum_sctype(np.float64)) # E: Type[{float64}]
reveal_type(np.maximum_sctype("f8")) # E: Type[Any]
reveal_type(np.issctype(np.float64)) # E: bool
reveal_type(np.issctype("foo")) # E: Literal[False]
reveal_type(np.obj2sctype(np.float64)) # E: Union[None, Type[{float64}]]
reveal_type(np.obj2sctype(
np.float64, default=False)) # E: Union[builtins.bool, Type[{float64}]]
reveal_type(np.obj2sctype("S8")) # E: Union[None, Type[Any]]
reveal_type(np.obj2sctype("S8", default=None)) # E: Union[None, Type[Any]]
reveal_type(np.obj2sctype("foo",
default=False)) # E: Union[builtins.bool, Type[Any]]
reveal_type(np.obj2sctype(1)) # E: None
reveal_type(np.obj2sctype(1, default=False)) # E: bool
reveal_type(np.issubclass_(np.float64, float)) # E: bool
reveal_type(np.issubclass_(np.float64, (int, float))) # E: bool
reveal_type(np.issubclass_(1, 1)) # E: Literal[False]
reveal_type(np.sctype2char("S8")) # E: str
reveal_type(np.sctype2char(list)) # E: str
reveal_type(np.find_common_type([np.int64], [np.int64])) # E: numpy.dtype[Any]
reveal_type(np.cast[int]) # E: _CastFunc
reveal_type(np.cast["i8"]) # E: _CastFunc
reveal_type(np.cast[np.int64]) # E: _CastFunc
