if minus_ones == 0:
pass
elif minus_ones == 1:
known_dimensions_size = -np.product(shape,axis=0) * dt.itemsize
unknown_dimension_size, illegal = divmod(self.remaining_bytes(),
known_dimensions_size)
if illegal:
raise ValueError("unknown dimension doesn't match filesize")
shape[shape.index(-1)] = unknown_dimension_size
else:
raise ValueError(
"illegal -1 count; can only specify one unknown dimension")
sz = dt.itemsize * np.product(shape)
dt_endian = self._endian_from_dtype(dt)
buf = self.file.read(sz)
arr = np.ndarray(shape=shape,
dtype=dt,
buffer=buf,
order=order)
if (not endian == 'dtype') and (dt_endian != endian):
return arr.byteswap()
return arr.copy()
npfile = np.deprecate_with_doc("""
You can achieve the same effect as using npfile, using ndarray.tofile
and numpy.fromfile.
Even better you can use memory-mapped arrays and data-types to map out a
file format for direct manipulation in NumPy.
""")(npfile)
from info import __doc__
from numpy import deprecate_with_doc
# These are all deprecated (until the end deprecated tag)
from npfile import npfile
from data_store import save, load, create_module, create_shelf
from array_import import read_array, write_array
from pickler import objload, objsave
from numpyio import packbits, unpackbits, bswap, fread, fwrite, \
convert_objectarray
fread = deprecate_with_doc("""
scipy.io.fread is can be replaced with raw reading capabilities of NumPy
including fromfile as well as memory-mapping capabilities.
""")(fread)
fwrite = deprecate_with_doc("""
scipy.io.fwrite can be replaced with raw writing capabilities of
NumPy. Also, remember that files can be directly memory-mapped into NumPy
arrays which is often a better way of reading especially large files.
Look at the tofile methods as well as save and savez for writing arrays into
easily transported files of data.
""")(fwrite)
bswap = deprecate_with_doc("""
scipy.io.bswap is easily replaced with the byteswap method on an array.
out = scipy.io.bswap(arr) --> out = arr.byteswap(True)
""")(bswap)
return ret
else:
raise ValueError('Unknown mat file type, version %s' % ret)
class ByteOrder(object):
''' Namespace for byte ordering '''
little_endian = boc.sys_is_le
native_code = boc.native_code
swapped_code = boc.swapped_code
to_numpy_code = boc.to_numpy_code
ByteOrder = np.deprecate_with_doc("""
We no longer use the ByteOrder class, and deprecate it; we will remove
it in future versions of scipy. Please use the
scipy.io.matlab.byteordercodes module instead.
""")(ByteOrder)
class MatStreamAgent(object):
''' Base object for readers / getters from mat file streams
Attaches to initialized stream
Base class for "getters" - which do store state of what they are
reading on initialization, and therefore need to be initialized
before each read, and "readers" which do not store state, and only
need to be initialized once on object creation
Implements common array reading functions
from io import StringIO
from typing import Any, Dict
import numpy as np
AR: np.ndarray[Any, np.dtype[np.float64]]
AR_DICT: Dict[str, np.ndarray[Any, np.dtype[np.float64]]]
FILE: StringIO
def func(a: int) -> bool: ...
reveal_type(np.deprecate(func)) # E: def (a: builtins.int) -> builtins.bool
reveal_type(np.deprecate()) # E: _Deprecate
reveal_type(np.deprecate_with_doc("test")) # E: _Deprecate
reveal_type(np.deprecate_with_doc(None)) # E: _Deprecate
reveal_type(np.byte_bounds(AR)) # E: Tuple[builtins.int, builtins.int]
reveal_type(np.byte_bounds(np.float64())) # E: Tuple[builtins.int, builtins.int]
reveal_type(np.who(None)) # E: None
reveal_type(np.who(AR_DICT)) # E: None
reveal_type(np.info(1, output=FILE)) # E: None
reveal_type(np.source(np.interp, output=FILE)) # E: None
reveal_type(np.lookfor("binary representation", output=FILE)) # E: None
reveal_type(np.safe_eval("1 + 1")) # E: Any
from info import __doc__
from numpy import deprecate_with_doc
# These are all deprecated (until the end deprecated tag)
from npfile import npfile
from data_store import save, load, create_module, create_shelf
from array_import import read_array, write_array
from pickler import objload, objsave
from numpyio import packbits, unpackbits, bswap, fread, fwrite, \
convert_objectarray
fread = deprecate_with_doc("""
scipy.io.fread is can be replaced with raw reading capabilities of NumPy
including fromfile as well as memory-mapping capabilities.
""")(fread)
fwrite = deprecate_with_doc("""
scipy.io.fwrite can be replaced with raw writing capabilities of
NumPy. Also, remember that files can be directly memory-mapped into NumPy
arrays which is often a better way of reading especially large files.
Look at the tofile methods as well as save and savez for writing arrays into
easily transported files of data.
""")(fwrite)
bswap = deprecate_with_doc("""
scipy.io.bswap is easily replaced with the byteswap method on an array.
out = scipy.io.bswap(arr) --> out = arr.byteswap(True)
""")(bswap)
>>> from sympy.abc import x, y, z
>>> from nipy.fixes.sympy.utilities.lambdify import lambdify, implemented_function
>>> from sympy import Function
>>> f = implemented_function(Function('f'), lambda x : x+1)
>>> lam_f = lambdify(x, f(x))
>>> lam_f(4)
5
"""
#NIPY-EDIT: compatibility between sympy 0.6.x and 0.7.0. UndefinedFunction
# is, er, not defined in sympy 0.6.x
try:
# Delayed import to avoid circular imports
from sympy.core.function import UndefinedFunction as funcmaker
except ImportError:
from sympy import Function, FunctionClass
funcmaker = lambda name: FunctionClass(Function, name)
# if name, create anonymous function to hold implementation
if isinstance(symfunc, basestring):
symfunc = funcmaker(symfunc)
#NIPY-EDIT: ends
# We need to attach as a method because symfunc will be a class
symfunc._imp_ = staticmethod(implementation)
return symfunc
# This is a nipy compatability wrapper
import numpy as np
aliased_function = np.deprecate_with_doc(
'Please use sympy.utilities.implemented_function')(implemented_function)
return shape
class ByteOrder(object):
""" Namespace for byte ordering """
little_endian = boc.sys_is_le
native_code = boc.native_code
swapped_code = boc.swapped_code
to_numpy_code = boc.to_numpy_code
ByteOrder = np.deprecate_with_doc(
"""
We no longer use the ByteOrder class, and deprecate it; we will remove
it in future versions of scipy. Please use the
scipy.io.matlab.byteordercodes module instead.
"""
)(ByteOrder)
class MatStreamAgent(object):
""" Base object for readers / getters from mat file streams
Attaches to initialized stream
Base class for "getters" - which do store state of what they are
reading on initialization, and therefore need to be initialized
before each read, and "readers" which do not store state, and only
need to be initialized once on object creation
import numpy as np np.deprecate(1) # E: No overload variant np.deprecate_with_doc(1) # E: incompatible type np.byte_bounds(1) # E: incompatible type np.who(1) # E: incompatible type np.lookfor(None) # E: incompatible type np.safe_eval(None) # E: incompatible type
dir, filename = os.path.split(module.__file__)
filebase = filename.split('.')[0]
fn = os.path.join(dir, filebase)
f = dumb_shelve.open(fn, "r")
#exec( 'import ' + module.__name__)
for i in f.keys():
exec('import ' + module.__name__ + ';' + module.__name__ + '.' + i +
'=' + 'f["' + i + '"]')
# print i, 'loaded...'
# print 'done'
load = deprecate_with_doc("""
This is an internal function used with scipy.io.save_as_module
If you are saving arrays into a module, you should think about using
HDF5 or .npz files instead.
""")(_load)
def _create_module(file_name):
""" Create the module file.
"""
if not os.path.exists(file_name + '.py'): # don't clobber existing files
module_name = os.path.split(file_name)[-1]
f = open(file_name + '.py', 'w')
f.write('import scipy.io.data_store as data_store\n')
f.write('import %s\n' % module_name)
f.write('data_store._load(%s)' % module_name)
f.close()
if type(img) == Nifti1Image and ext in (".img", ".hdr"):
klass = Nifti1Pair
elif type(img) == Nifti2Image and ext in (".img", ".hdr"):
klass = Nifti2Pair
elif type(img) == Nifti1Pair and ext == ".nii":
klass = Nifti1Image
elif type(img) == Nifti2Pair and ext == ".nii":
klass = Nifti2Image
else:
img_type = ext_map[ext]
klass = class_map[img_type]["class"]
converted = klass.from_image(img)
converted.to_filename(filename)
np.deprecate_with_doc("Please use ``img.dataobj.get_unscaled()`` " "instead")
def read_img_data(img, prefer="scaled"):
""" Read data from image associated with files
We've deprecated this function and will remove it soon. If you want
unscaled data, please use ``img.dataobj.get_unscaled()`` instead. If you
want scaled data, use ``img.get_data()`` (which will cache the loaded
array) or ``np.array(img.dataobj)`` (which won't cache the array). If you
want to load the data as for a modified header, save the image with the
modified header, and reload.
Parameters
----------
img : ``SpatialImage``
the same name as the module.
"""
dir,filename = os.path.split(module.__file__)
filebase = filename.split('.')[0]
fn = os.path.join(dir, filebase)
f = dumb_shelve.open(fn, "r")
#exec( 'import ' + module.__name__)
for i in f.keys():
exec( 'import ' + module.__name__+ ';' +
module.__name__+'.'+i + '=' + 'f["' + i + '"]')
# print i, 'loaded...'
# print 'done'
load = deprecate_with_doc("""
This is an internal function used with scipy.io.save_as_module
If you are saving arrays into a module, you should think about using
HDF5 or .npz files instead.
""")(_load)
def _create_module(file_name):
""" Create the module file.
"""
if not os.path.exists(file_name+'.py'): # don't clobber existing files
module_name = os.path.split(file_name)[-1]
f = open(file_name+'.py','w')
f.write('import scipy.io.data_store as data_store\n')
f.write('import %s\n' % module_name)
f.write('data_store._load(%s)' % module_name)
f.close()
if type(img) == Nifti1Image and ext in ('.img', '.hdr'):
klass = Nifti1Pair
elif type(img) == Nifti2Image and ext in ('.img', '.hdr'):
klass = Nifti2Pair
elif type(img) == Nifti1Pair and ext == '.nii':
klass = Nifti1Image
elif type(img) == Nifti2Pair and ext == '.nii':
klass = Nifti2Image
else:
img_type = ext_map[ext]
klass = class_map[img_type]['class']
converted = klass.from_image(img)
converted.to_filename(filename)
np.deprecate_with_doc('Please use ``img.dataobj.get_unscaled()`` ' 'instead')
def read_img_data(img, prefer='scaled'):
""" Read data from image associated with files
We've deprecated this function and will remove it soon. If you want
unscaled data, please use ``img.dataobj.get_unscaled()`` instead. If you
want scaled data, use ``img.get_data()`` (which will cache the loaded
array) or ``np.array(img.dataobj)`` (which won't cache the array). If you
want to load the data as for a modified header, save the image with the
modified header, and reload.
Parameters
----------
img : ``SpatialImage``
>>> from sympy.abc import x, y, z
>>> from nipy.fixes.sympy.utilities.lambdify import lambdify, implemented_function
>>> from sympy import Function
>>> f = implemented_function(Function('f'), lambda x : x+1)
>>> lam_f = lambdify(x, f(x))
>>> lam_f(4)
5
"""
# NIPY-EDIT: compatibility between sympy 0.6.x and 0.7.0. UndefinedFunction
# is, er, not defined in sympy 0.6.x
try:
# Delayed import to avoid circular imports
from sympy.core.function import UndefinedFunction as funcmaker
except ImportError:
from sympy import Function, FunctionClass
funcmaker = lambda name: FunctionClass(Function, name)
# if name, create anonymous function to hold implementation
if isinstance(symfunc, basestring):
symfunc = funcmaker(symfunc)
# NIPY-EDIT: ends
# We need to attach as a method because symfunc will be a class
symfunc._imp_ = staticmethod(implementation)
return symfunc
# This is a nipy compatability wrapper
import numpy as np
aliased_function = np.deprecate_with_doc("Please use sympy.utilities.implemented_function")(implemented_function)
from __future__ import annotations
from io import StringIO
from typing import Any
import numpy as np
FILE = StringIO()
AR: np.ndarray[Any, np.dtype[np.float64]] = np.arange(10).astype(np.float64)
def func(a: int) -> bool:
...
np.deprecate(func)
np.deprecate()
np.deprecate_with_doc("test")
np.deprecate_with_doc(None)
np.byte_bounds(AR)
np.byte_bounds(np.float64())
np.info(1, output=FILE)
np.source(np.interp, output=FILE)
np.lookfor("binary representation", output=FILE)
if type(img) == Nifti1Image and ext in ('.img', '.hdr'):
klass = Nifti1Pair
elif type(img) == Nifti2Image and ext in ('.img', '.hdr'):
klass = Nifti2Pair
elif type(img) == Nifti1Pair and ext == '.nii':
klass = Nifti1Image
elif type(img) == Nifti2Pair and ext == '.nii':
klass = Nifti2Image
else:
img_type = ext_map[ext]
klass = class_map[img_type]['class']
converted = klass.from_image(img)
converted.to_filename(filename)
np.deprecate_with_doc('Please use ``img.dataobj.get_unscaled()`` '
'instead')
def read_img_data(img, prefer='scaled'):
""" Read data from image associated with files
We've deprecated this function and will remove it soon. If you want
unscaled data, please use ``img.dataobj.get_unscaled()`` instead. If you
want scaled data, use ``img.get_data()`` (which will cache the loaded
array) or ``np.array(img.dataobj)`` (which won't cache the array). If you
want to load the data as for a modified header, save the image with the
modified header, and reload.
Parameters
----------
img : ``SpatialImage``
Image with valid image file in ``img.file_map``. Unlike the
``img.get_data()`` method, this function returns the data read
if minus_ones == 0:
pass
elif minus_ones == 1:
known_dimensions_size = -np.product(shape,axis=0) * dt.itemsize
unknown_dimension_size, illegal = divmod(self.remaining_bytes(),
known_dimensions_size)
if illegal:
raise ValueError("unknown dimension doesn't match filesize")
shape[shape.index(-1)] = unknown_dimension_size
else:
raise ValueError(
"illegal -1 count; can only specify one unknown dimension")
sz = dt.itemsize * np.product(shape)
dt_endian = self._endian_from_dtype(dt)
buf = self.file.read(sz)
arr = np.ndarray(shape=shape,
dtype=dt,
buffer=buf,
order=order)
if (not endian == 'dtype') and (dt_endian != endian):
return arr.byteswap()
return arr.copy()
npfile = np.deprecate_with_doc("""
You can achieve the same effect as using npfile using numpy.save and
numpy.load.
You can use memory-mapped arrays and data-types to map out a
file format for direct manipulation in NumPy.
""")(npfile)
shape = [shape]
minus_ones = shape.count(-1)
if minus_ones == 0:
pass
elif minus_ones == 1:
known_dimensions_size = -np.product(shape, axis=0) * dt.itemsize
unknown_dimension_size, illegal = divmod(self.remaining_bytes(),
known_dimensions_size)
if illegal:
raise ValueError("unknown dimension doesn't match filesize")
shape[shape.index(-1)] = unknown_dimension_size
else:
raise ValueError(
"illegal -1 count; can only specify one unknown dimension")
sz = dt.itemsize * np.product(shape)
dt_endian = self._endian_from_dtype(dt)
buf = self.file.read(sz)
arr = np.ndarray(shape=shape, dtype=dt, buffer=buf, order=order)
if (not endian == 'dtype') and (dt_endian != endian):
return arr.byteswap()
return arr.copy()
npfile = np.deprecate_with_doc("""
You can achieve the same effect as using npfile using numpy.save and
numpy.load.
You can use memory-mapped arrays and data-types to map out a
file format for direct manipulation in NumPy.
""")(npfile)
