def func_hook(self, np_func: _NpFn, wrapped: _MyFn[_Arr]) -> _MyFn[_Arr]:
"""This method is called on the wrapped function before returning
Parameters
----------
np_func : Callable[...->ndarray]
The `numpy` function being wrapped.
wrapped : Callable[...->Array]
The wrapped function.
Returns
-------
wrapped : Callable[...->Array]
The wrapped function.
"""
super().func_hook(np_func, wrapped)
old_name = f"{self._mod_name}.{np_func.__name__}"
new_name = f"{np_func.__module__}.{np_func.__name__}"
msg = "This function will be removed in numpy_linalg 0.4.0"
new_wrapped = _np.deprecate(wrapped,
old_name=old_name,
new_name=new_name,
message=msg)
return super().func_hook(np_func, new_wrapped)
else:
raise ValueError('1D option "%s" is strange'
% oned_as)
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(ByteOrder, message="""
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.
""")
class MatVarReader(object):
''' Abstract class defining required interface for var readers'''
def __init__(self, file_reader):
pass
def read_header(self):
''' Returns header '''
pass
def array_from_header(self, header):
''' Reads array given header '''
Partially flattened array.
Raises
------
ValueError
If `start > stop`.
"""
if stop is None:
stop = arr.ndim
newshape = arr.shape[:start] + (-1,) + arr.shape[stop:]
if len(newshape) > arr.ndim + 1:
raise ValueError(f"start={start} > stop={stop}")
return np.reshape(arr, newshape)
flattish = np.deprecate(ravelaxes, old_name='flattish', new_name='ravelaxes',
message="This alias will be removed in v.0.4.0")
@set_module('numpy_linalg')
def unravelaxis(arr: np.ndarray, axis: int, shape: ty.Tuple[int, ...]
) -> np.ndarray:
"""Partial unflattening.
Folds an `axis` into `shape`.
Parameters
----------
arr : np.ndarray (...,L,M*N*...*P*Q,R,...)
Array to be partially folded.
axis : int
Axis to be folded.
loader_path = os.path.abspath(loader_path)
if not os.path.isdir(loader_path):
libdir = os.path.dirname(loader_path)
else:
libdir = loader_path
for ln in libname_ext:
try:
libpath = os.path.join(libdir, ln)
return ctypes.cdll[libpath]
except OSError, e:
pass
raise e
ctypes_load_library = deprecate(load_library, 'ctypes_load_library',
'load_library')
def _num_fromflags(flaglist):
num = 0
for val in flaglist:
num += _flagdict[val]
return num
_flagnames = ['C_CONTIGUOUS', 'F_CONTIGUOUS', 'ALIGNED', 'WRITEABLE',
'OWNDATA', 'UPDATEIFCOPY']
def _flags_fromnum(num):
res = []
for key in _flagnames:
value = _flagdict[key]
if (num & value):
res.append(key)
C[should_be_nan] = np.nan
return C
def maybe_unwrap_results(results):
"""
Gets raw results back from wrapped results.
Can be used in plotting functions or other post-estimation type
routines.
"""
return getattr(results, '_results', results)
class Bunch(dict):
"""
Returns a dict-like object with keys accessible via attribute lookup.
"""
def __init__(self, **kw):
dict.__init__(self, kw)
self.__dict__ = self
webuse = np.deprecate(webuse,
old_name='statsmodels.tools.tools.webuse',
new_name='statsmodels.datasets.webuse',
message='webuse will be removed from the tools '
'namespace in the 0.7.0 release. Please use the'
' new import.')
#Use this to add a new axis to an array
#compatibility only
NewAxis = None
#deprecated
UFuncType = type(um.sin)
UfuncType = type(um.sin)
ArrayType = mu.ndarray
arraytype = mu.ndarray
LittleEndian = (sys.byteorder == 'little')
from numpy import deprecate
# backward compatibility
arrayrange = deprecate(functions.arange, 'arrayrange', 'arange')
# deprecated names
matrixmultiply = deprecate(mu.dot, 'matrixmultiply', 'dot')
def DumpArray(m, fp):
m.dump(fp)
def LoadArray(fp):
import cPickle
return cPickle.load(fp)
def array_constructor(shape, typecode, thestr, Endian=LittleEndian):
if typecode == "O":
x = array(thestr, "O")
else:
from pandas import DatetimeIndex # pylint: disable=E0611
date_range = DatetimeIndex(start=start_date, freq=offset, periods=n)
return data, date_range
def get_logdet(m):
from statsmodels.tools.linalg import logdet_symm
return logdet_symm(m)
get_logdet = np.deprecate(
get_logdet,
"statsmodels.tsa.vector_ar.util.get_logdet",
"statsmodels.tools.linalg.logdet_symm",
"get_logdet is deprecated and will be removed in " "0.8.0",
)
def norm_signif_level(alpha=0.05):
return stats.norm.ppf(1 - alpha / 2)
def acf_to_acorr(acf):
diag = np.diag(acf[0])
# numpy broadcasting sufficient
return acf / np.sqrt(np.outer(diag, diag))
def varsim(coefs, intercept, sig_u, steps=100, initvalues=None, seed=None):
__all__ = ['bicg', 'bicgstab', 'cg', 'cgs', 'gmres', 'qmr']
# Deprecated on January 26, 2008
from scipy.sparse.linalg import isolve
from numpy import deprecate
for name in __all__:
oldfn = getattr(isolve, name)
oldname = 'scipy.linalg.' + name
newname = 'scipy.sparse.linalg.' + name
newfn = deprecate(oldfn, oldname=oldname, newname=newname)
exec(name + ' = newfn')
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
"""
if r is None:
r = np_matrix_rank(X)
V, D, U = L.svd(X, full_matrices=0)
order = np.argsort(D)
order = order[::-1]
value = []
for i in range(r):
value.append(V[:, order[i]])
return np.asarray(np.transpose(value)).astype(np.float64)
StepFunction = np.deprecate(StepFunction,
old_name='statsmodels.tools.tools.StepFunction',
new_name='statsmodels.distributions.StepFunction')
monotone_fn_inverter = np.deprecate(monotone_fn_inverter,
old_name='statsmodels.tools.tools'
'.monotone_fn_inverter',
new_name='statsmodels.distributions'
'.monotone_fn_inverter')
ECDF = np.deprecate(ECDF,
old_name='statsmodels.tools.tools.ECDF',
new_name='statsmodels.distributions.ECDF')
def unsqueeze(data, axis, oldshape):
"""
Unsqueeze a collapsed array
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(_load, message="""
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.
""")
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()
libdir = os.path.dirname(loader_path)
else:
libdir = loader_path
for ln in libname_ext:
libpath = os.path.join(libdir, ln)
if os.path.exists(libpath):
try:
return ctypes.cdll[libpath]
except OSError:
## defective lib file
raise
## if no successful return in the libname_ext loop:
raise OSError("no file with expected extension")
ctypes_load_library = deprecate(load_library, "ctypes_load_library",
"load_library")
def _num_fromflags(flaglist):
num = 0
for val in flaglist:
num += _flagdict[val]
return num
_flagnames = [
"C_CONTIGUOUS",
"F_CONTIGUOUS",
"ALIGNED",
"WRITEABLE",
"OWNDATA",
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(npfile,
message="""
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.
""")
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(npfile, message="""
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.
""")
'find_edges':ImageFilter.FIND_EDGES,
'smooth':ImageFilter.SMOOTH,
'smooth_more':ImageFilter.SMOOTH_MORE,
'sharpen':ImageFilter.SHARPEN
}
im = toimage(arr)
if ftype not in _tdict.keys():
raise ValueError("Unknown filter type.")
return fromimage(im.filter(_tdict[ftype]))
def radon(arr,theta=None):
"""`radon` is deprecated in scipy 0.11, and will be removed in 0.12
For this functionality, please use the "radon" function in scikits-image.
"""
if theta is None:
theta = mgrid[0:180]
s = zeros((arr.shape[1],len(theta)), float)
k = 0
for th in theta:
im = imrotate(arr,-th)
s[:,k] = sum(im,axis=0)
k += 1
return s
radon = numpy.deprecate(radon)
clapack = _DeprecatedImport("scipy.linalg.blas.clapack", "scipy.linalg.lapack")
flapack = _DeprecatedImport("scipy.linalg.blas.flapack", "scipy.linalg.lapack")
# Expose all functions (only flapack --- clapack is an implementation detail)
empty_module = None
from scipy.linalg._flapack import *
del empty_module
_dep_message = """The `*gegv` family of routines has been deprecated in
LAPACK 3.6.0 in favor of the `*ggev` family of routines.
The corresponding wrappers will be removed from SciPy in
a future release."""
cgegv = _np.deprecate(cgegv, old_name="cgegv", message=_dep_message)
dgegv = _np.deprecate(dgegv, old_name="dgegv", message=_dep_message)
sgegv = _np.deprecate(sgegv, old_name="sgegv", message=_dep_message)
zgegv = _np.deprecate(zgegv, old_name="zgegv", message=_dep_message)
# Modyfy _flapack in this scope so the deprecation warnings apply to
# functions returned by get_lapack_funcs.
_flapack.cgegv = cgegv
_flapack.dgegv = dgegv
_flapack.sgegv = sgegv
_flapack.zgegv = zgegv
# some convenience alias for complex functions
_lapack_alias = {
"corghr": "cunghr",
"zorghr": "zunghr",
from info import __doc__
from numpy import deprecate
# 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(fread, message="""
scipy.io.fread can be replaced with NumPy I/O routines such as
np.load, np.fromfile as well as NumPy's memory-mapping capabilities.
""")
fwrite = deprecate(fwrite, message="""
scipy.io.fwrite can be replaced with NumPy I/O routines such as np.save,
np.savez and x.tofile. Also, files can be directly memory-mapped into NumPy
arrays which is often a better way of reading large files.
""")
bswap = deprecate(bswap, message="""
scipy.io.bswap can be replaced with the byteswap method on an array.
out = scipy.io.bswap(arr) --> out = arr.byteswap(True)
""")
packbits = deprecate(packbits, message="""
The functionality of scipy.io.packbits is now available as numpy.packbits
def _bessel_diff_formula(v, z, n, L, phase):
# from AMS55.
# L(v,z) = J(v,z), Y(v,z), H1(v,z), H2(v,z), phase = -1
# L(v,z) = I(v,z) or exp(v*pi*i)K(v,z), phase = 1
# For K, you can pull out the exp((v-k)*pi*i) into the caller
p = 1.0
s = L(v - n, z)
for i in xrange(1, n + 1):
p = phase * (p * (n - i + 1)) / i # = choose(k, i)
s += p * L(v - n + i * 2, z)
return s / (2.**n)
bessel_diff_formula = np.deprecate(
_bessel_diff_formula,
message="bessel_diff_formula is a private function, do not use it!")
def jvp(v, z, n=1):
"""Return the nth derivative of Jv(z) with respect to z.
"""
if not isinstance(n, int) or (n < 0):
raise ValueError("n must be a non-negative integer.")
if n == 0:
return jv(v, z)
else:
return _bessel_diff_formula(v, z, n, jv, -1)
# return (jvp(v-1,z,n-1) - jvp(v+1,z,n-1))/2.0
"""
if r is None:
r = np_matrix_rank(X)
V, D, U = L.svd(X, full_matrices=0)
order = np.argsort(D)
order = order[::-1]
value = []
for i in range(r):
value.append(V[:, order[i]])
return np.asarray(np.transpose(value)).astype(np.float64)
StepFunction = np.deprecate(StepFunction,
old_name='statsmodels.tools.tools.StepFunction',
new_name='statsmodels.distributions.StepFunction')
monotone_fn_inverter = np.deprecate(monotone_fn_inverter,
old_name='statsmodels.tools.tools'
'.monotone_fn_inverter',
new_name='statsmodels.distributions'
'.monotone_fn_inverter')
ECDF = np.deprecate(ECDF,
old_name='statsmodels.tools.tools.ECDF',
new_name='statsmodels.distributions.ECDF')
def unsqueeze(data, axis, oldshape):
"""
Unsqueeze a collapsed array
gk *= 1.05
id = _gen_unique_rand(gk)
j = np.floor(id * 1. / m).astype(tp)
i = (id - j * m).astype(tp)
vals = np.random.rand(k).astype(dtype)
return coo_matrix((vals, (i, j)), shape=(m, n)).asformat(format)
#################################
# Deprecated functions
################################
__all__ += ['speye', 'spidentity', 'spkron', 'lil_eye', 'lil_diags']
spkron = np.deprecate(kron, old_name='spkron', new_name='scipy.sparse.kron')
speye = np.deprecate(eye, old_name='speye', new_name='scipy.sparse.eye')
spidentity = np.deprecate(identity,
old_name='spidentity',
new_name='scipy.sparse.identity')
def lil_eye((r, c), k=0, dtype='d'):
"""Generate a lil_matrix of dimensions (r,c) with the k-th
diagonal set to 1.
Parameters
----------
r,c : int
row and column-dimensions of the output.
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy
from filters import *
from fourier import *
from interpolation import *
from measurements import *
from morphology import *
from io import *
# doccer is moved to scipy.misc in scipy 0.8
from scipy.misc import doccer
doccer = numpy.deprecate(doccer, old_name='doccer',
new_name='scipy.misc.doccer')
from info import __doc__
__version__ = '2.0'
from numpy.testing import Tester
test = Tester().test
"""
if r is None:
r = rank(X)
V, D, U = L.svd(X, full_matrices=0)
order = np.argsort(D)
order = order[::-1]
value = []
for i in range(r):
value.append(V[:, order[i]])
return np.asarray(np.transpose(value)).astype(np.float64)
StepFunction = np.deprecate(StepFunction,
old_name='statsmodels.tools.tools.StepFunction',
new_name='statsmodels.distributions.StepFunction')
monotone_fn_inverter = np.deprecate(
monotone_fn_inverter,
old_name='statsmodels.tools.tools.monotone_fn_inverter',
new_name='statsmodels.distributions.monotone_fn_inverter')
ECDF = np.deprecate(ECDF,
old_name='statsmodels.tools.tools.ECDF',
new_name='statsmodels.distributions.ECDF')
def unsqueeze(data, axis, oldshape):
"""
Unsqueeze a collapsed array
>>> from numpy import mean
using HDF5 or .npz files instead.
""")(_create_module)
def _create_shelf(file_name,data):
"""Use this to write the data to a new file
"""
shelf_name = file_name.split('.')[0]
f = dumb_shelve.open(shelf_name,'w')
for i in data.keys():
# print 'saving...',i
f[i] = data[i]
# print 'done'
f.close()
create_shelf = 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.
""")(_create_shelf)
def save_as_module(file_name=None,data=None):
""" Save the dictionary "data" into
a module and shelf named save
"""
_create_module(file_name)
_create_shelf(file_name,data)
save = deprecate(save_as_module, 'save', 'save_as_module')
if obs.ndim != code_book.ndim:
raise ValueError("Observation and code_book should have the same rank")
if obs.ndim == 1:
obs = obs[:, np.newaxis]
code_book = code_book[:, np.newaxis]
dist = cdist(obs, code_book)
code = dist.argmin(axis=1)
min_dist = dist[np.arange(len(code)), code]
return code, min_dist
# py_vq2 was equivalent to py_vq
py_vq2 = np.deprecate(py_vq, old_name='py_vq2', new_name='py_vq')
def _kmeans(obs, guess, thresh=1e-5):
""" "raw" version of k-means.
Returns
-------
code_book
The lowest distortion codebook found.
avg_dist
The average distance a observation is from a code in the book.
Lower means the code_book matches the data better.
See Also
--------
from .sf_error import SpecialFunctionWarning, SpecialFunctionError
from ._ufuncs import *
from .basic import *
from ._logsumexp import logsumexp, softmax
from . import specfun
from . import orthogonal
from .orthogonal import *
from .spfun_stats import multigammaln
from ._ellip_harm import ellip_harm, ellip_harm_2, ellip_normal
from .lambertw import lambertw
from ._spherical_bessel import (spherical_jn, spherical_yn, spherical_in,
spherical_kn)
from numpy import deprecate
hyp2f0 = deprecate(hyp2f0, message="hyp2f0 is deprecated in SciPy 1.2")
hyp1f2 = deprecate(hyp1f2, message="hyp1f2 is deprecated in SciPy 1.2")
hyp3f0 = deprecate(hyp3f0, message="hyp3f0 is deprecated in SciPy 1.2")
del deprecate
__all__ = [s for s in dir() if not s.startswith('_')]
from numpy.dual import register_func
register_func('i0',i0)
del register_func
from scipy._lib._testutils import PytestTester
test = PytestTester(__name__)
del PytestTester
loader_path = os.path.abspath(loader_path)
if not os.path.isdir(loader_path):
libdir = os.path.dirname(loader_path)
else:
libdir = loader_path
for ln in libname_ext:
try:
libpath = os.path.join(libdir, ln)
return ctypes.cdll[libpath]
except OSError, e:
pass
raise e
ctypes_load_library = deprecate(load_library, 'ctypes_load_library',
'load_library')
def _num_fromflags(flaglist):
num = 0
for val in flaglist:
num += _flagdict[val]
return num
_flagnames = [
'C_CONTIGUOUS', 'F_CONTIGUOUS', 'ALIGNED', 'WRITEABLE', 'OWNDATA',
'UPDATEIFCOPY'
]
def _create_shelf(file_name, data):
"""Use this to write the data to a new file
"""
shelf_name = file_name.split('.')[0]
f = dumb_shelve.open(shelf_name, 'w')
for i in data.keys():
# print 'saving...',i
f[i] = data[i]
# print 'done'
f.close()
create_shelf = 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.
""")(_create_shelf)
def save_as_module(file_name=None, data=None):
""" Save the dictionary "data" into
a module and shelf named save
"""
_create_module(file_name)
_create_shelf(file_name, data)
save = deprecate(save_as_module, 'save', 'save_as_module')
d_ks = ksstat(z, stats.norm.cdf, alternative='two_sided')
if pvalmethod == 'approx':
pval = pval_lf(d_ks, nobs)
elif pvalmethod == 'table':
#pval = pval_lftable(d_ks, nobs)
pval = lilliefors_table.prob(d_ks, nobs)
return d_ks, pval
lilliefors = kstest_normal
lillifors = np.deprecate(lilliefors, 'lillifors', 'lilliefors',
"Use lilliefors, lillifors will be "
"removed in 0.9 \n(Note: misspelling missing 'e')")
#old version:
#------------
tble = '''\
00 20 15 10 05 01 .1
4 .303 .321 .346 .376 .413 .433
5 .289 .303 .319 .343 .397 .439
6 .269 .281 .297 .323 .371 .424
7 .252 .264 .280 .304 .351 .402
8 .239 .250 .265 .288 .333 .384
9 .227 .238 .252 .274 .317 .365
10 .217 .228 .241 .262 .304 .352
dz = get_slice_spacing(series)
return dx, dy, dz
@csv_series
def get_image_size(series: Series):
rows = get_common_tag(series, 'Rows')
columns = get_common_tag(series, 'Columns')
slices = len(series)
return rows, columns, slices
# ------------------ DEPRECATED ------------------------
get_image_plane = np.deprecate(get_slices_plane, old_name='get_image_plane')
get_xyz_spacing = np.deprecate(get_voxel_spacing, old_name='get_xyz_spacing')
@np.deprecate
def get_axes_permutation(row: pd.Series):
return np.abs(get_orientation_matrix(row)).argmax(axis=0)
@np.deprecate
@csv_series
def get_image_position_patient(series: Series):
"""Returns ImagePositionPatient stacked into array."""
return np.stack(list(map(_get_image_position_patient, series)))
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 .sf_error import SpecialFunctionWarning, SpecialFunctionError
from ._ufuncs import *
from .basic import *
from ._logsumexp import logsumexp, softmax
from . import specfun
from . import orthogonal
from .orthogonal import *
from .spfun_stats import multigammaln
from ._ellip_harm import ellip_harm, ellip_harm_2, ellip_normal
from .lambertw import lambertw
from ._spherical_bessel import (spherical_jn, spherical_yn, spherical_in,
spherical_kn)
from numpy import deprecate
hyp2f0 = deprecate(hyp2f0, message="hyp2f0 is deprecated in SciPy 1.2")
hyp1f2 = deprecate(hyp1f2, message="hyp1f2 is deprecated in SciPy 1.2")
hyp3f0 = deprecate(hyp3f0, message="hyp3f0 is deprecated in SciPy 1.2")
del deprecate
__all__ = [s for s in dir() if not s.startswith('_')]
from numpy.dual import register_func
register_func('i0', i0)
del register_func
from scipy._lib._testutils import PytestTester
test = PytestTester(__name__)
del PytestTester
'find_edges': ImageFilter.FIND_EDGES,
'smooth': ImageFilter.SMOOTH,
'smooth_more': ImageFilter.SMOOTH_MORE,
'sharpen': ImageFilter.SHARPEN
}
im = toimage(arr)
if ftype not in _tdict.keys():
raise ValueError("Unknown filter type.")
return fromimage(im.filter(_tdict[ftype]))
def radon(arr, theta=None):
"""`radon` is deprecated in scipy 0.11, and will be removed in 0.12
For this functionality, please use the "radon" function in scikits-image.
"""
if theta is None:
theta = mgrid[0:180]
s = zeros((arr.shape[1], len(theta)), float)
k = 0
for th in theta:
im = imrotate(arr, -th)
s[:, k] = sum(im, axis=0)
k += 1
return s
radon = numpy.deprecate(radon)
"""
return getattr(results, '_results', results)
class Bunch(dict):
"""
Returns a dict-like object with keys accessible via attribute lookup.
"""
def __init__(self, **kw):
dict.__init__(self, kw)
self.__dict__ = self
webuse = np.deprecate(webuse,
old_name='statsmodels.tools.tools.webuse',
new_name='statsmodels.datasets.webuse',
message='webuse will be removed from the tools '
'namespace in the 0.7.0 release. Please use the'
' new import.')
def _ensure_2d(x, ndarray=False):
"""
Parameters
----------
x : array, Series, DataFrame or None
Input to verify dimensions, and to transform as necesary
ndarray : bool
Flag indicating whether to always return a NumPy array. Setting False
will return an pandas DataFrame when the input is a Series or a
DataFrame.
#Use this to add a new axis to an array
#compatibility only
NewAxis = None
#deprecated
UFuncType = type(um.sin)
UfuncType = type(um.sin)
ArrayType = mu.ndarray
arraytype = mu.ndarray
LittleEndian = (sys.byteorder == 'little')
from numpy import deprecate
# backward compatibility
arrayrange = deprecate(functions.arange, 'arrayrange', 'arange')
# deprecated names
matrixmultiply = deprecate(mu.dot, 'matrixmultiply', 'dot')
def DumpArray(m, fp):
m.dump(fp)
def LoadArray(fp):
import cPickle
return cPickle.load(fp)
def array_constructor(shape, typecode, thestr, Endian=LittleEndian):
# Use this to add a new axis to an array
# compatibility only
NewAxis = None
# deprecated
UFuncType = type(um.sin)
UfuncType = type(um.sin)
ArrayType = mu.ndarray
arraytype = mu.ndarray
LittleEndian = sys.byteorder == "little"
from numpy import deprecate
# backward compatibility
arrayrange = deprecate(functions.arange, "arrayrange", "arange")
# deprecated names
matrixmultiply = deprecate(mu.dot, "matrixmultiply", "dot")
def DumpArray(m, fp):
m.dump(fp)
def LoadArray(fp):
import cPickle
return cPickle.load(fp)
def _bessel_diff_formula(v, z, n, L, phase):
# from AMS55.
# L(v,z) = J(v,z), Y(v,z), H1(v,z), H2(v,z), phase = -1
# L(v,z) = I(v,z) or exp(v*pi*i)K(v,z), phase = 1
# For K, you can pull out the exp((v-k)*pi*i) into the caller
p = 1.0
s = L(v-n, z)
for i in xrange(1, n+1):
p = phase * (p * (n-i+1)) / i # = choose(k, i)
s += p*L(v-n + i*2, z)
return s / (2.**n)
bessel_diff_formula = np.deprecate(_bessel_diff_formula,
message="bessel_diff_formula is a private function, do not use it!")
def jvp(v,z,n=1):
"""Return the nth derivative of Jv(z) with respect to z.
"""
if not isinstance(n,int) or (n < 0):
raise ValueError("n must be a non-negative integer.")
if n == 0:
return jv(v,z)
else:
return _bessel_diff_formula(v, z, n, jv, -1)
# return (jvp(v-1,z,n-1) - jvp(v+1,z,n-1))/2.0
def yvp(v,z,n=1):
if obs.ndim != code_book.ndim:
raise ValueError("Observation and code_book should have the same rank")
if obs.ndim == 1:
obs = obs[:, np.newaxis]
code_book = code_book[:, np.newaxis]
dist = cdist(obs, code_book)
code = dist.argmin(axis=1)
min_dist = dist[np.arange(len(code)), code]
return code, min_dist
# py_vq2 was equivalent to py_vq
py_vq2 = np.deprecate(py_vq, old_name='py_vq2', new_name='py_vq')
def _kmeans(obs, guess, thresh=1e-5):
""" "raw" version of k-means.
Returns
-------
code_book
the lowest distortion codebook found.
avg_dist
the average distance a observation is from a code in the book.
Lower means the code_book matches the data better.
See Also
--------
__all__ = ['who', 'source', 'info', 'doccer', 'pade',
'comb', 'factorial', 'factorial2', 'factorialk', 'logsumexp']
from . import doccer
from .common import *
from numpy import who as _who, source as _source, info as _info
import numpy as np
from scipy.interpolate._pade import pade as _pade
from scipy.special import (comb as _comb, logsumexp as _lsm,
factorial as _fact, factorial2 as _fact2, factorialk as _factk)
import sys
_msg = ("Importing `%(name)s` from scipy.misc is deprecated in scipy 1.0.0. Use "
"`scipy.special.%(name)s` instead.")
comb = np.deprecate(_comb, message=_msg % {"name": _comb.__name__})
logsumexp = np.deprecate(_lsm, message=_msg % {"name": _lsm.__name__})
factorial = np.deprecate(_fact, message=_msg % {"name": _fact.__name__})
factorial2 = np.deprecate(_fact2, message=_msg % {"name": _fact2.__name__})
factorialk = np.deprecate(_factk, message=_msg % {"name": _factk.__name__})
_msg = ("Importing `pade` from scipy.misc is deprecated in scipy 1.0.0. Use "
"`scipy.interpolate.pade` instead.")
pade = np.deprecate(_pade, message=_msg)
_msg = ("Importing `%(name)s` from scipy.misc is deprecated in scipy 1.0.0. Use "
"`numpy.%(name)s` instead.")
who = np.deprecate(_who, message=_msg % {"name": "who"})
source = np.deprecate(_source, message=_msg % {"name": "source"})
@np.deprecate(message=_msg % {"name": "info.(..., toplevel='scipy')"})
libdir = os.path.dirname(loader_path)
else:
libdir = loader_path
for ln in libname_ext:
libpath = os.path.join(libdir, ln)
if os.path.exists(libpath):
try:
return ctypes.cdll[libpath]
except OSError:
## defective lib file
raise
## if no successful return in the libname_ext loop:
raise OSError("no file with expected extension")
ctypes_load_library = deprecate(load_library, "ctypes_load_library", "load_library")
def _num_fromflags(flaglist):
num = 0
for val in flaglist:
num += _flagdict[val]
return num
_flagnames = ["C_CONTIGUOUS", "F_CONTIGUOUS", "ALIGNED", "WRITEABLE", "OWNDATA", "UPDATEIFCOPY"]
def _flags_fromnum(num):
res = []
for key in _flagnames:
while id.size < k:
gk *= 1.05
id = _gen_unique_rand(gk)
j = np.floor(id * 1. / m).astype(tp)
i = (id - j * m).astype(tp)
vals = np.random.rand(k).astype(dtype)
return coo_matrix((vals, (i, j)), shape=(m, n)).asformat(format)
#################################
# Deprecated functions
################################
__all__ += [ 'speye','spidentity', 'spkron', 'lil_eye', 'lil_diags' ]
spkron = np.deprecate(kron, old_name='spkron', new_name='scipy.sparse.kron')
speye = np.deprecate(eye, old_name='speye', new_name='scipy.sparse.eye')
spidentity = np.deprecate(identity, old_name='spidentity',
new_name='scipy.sparse.identity')
def lil_eye((r,c), k=0, dtype='d'):
"""Generate a lil_matrix of dimensions (r,c) with the k-th
diagonal set to 1.
Parameters
----------
r,c : int
row and column-dimensions of the output.
k : int
# Backward compatibility
from scipy._lib._util import DeprecatedImport as _DeprecatedImport
clapack = _DeprecatedImport("scipy.linalg.blas.clapack", "scipy.linalg.lapack")
flapack = _DeprecatedImport("scipy.linalg.blas.flapack", "scipy.linalg.lapack")
# Expose all functions (only flapack --- clapack is an implementation detail)
empty_module = None
from scipy.linalg._flapack import *
del empty_module
_dep_message = """The `*gegv` family of routines has been deprecated in
LAPACK 3.6.0 in favor of the `*ggev` family of routines.
The corresponding wrappers will be removed from SciPy in
a future release."""
cgegv = _np.deprecate(cgegv, old_name='cgegv', message=_dep_message)
dgegv = _np.deprecate(dgegv, old_name='dgegv', message=_dep_message)
sgegv = _np.deprecate(sgegv, old_name='sgegv', message=_dep_message)
zgegv = _np.deprecate(zgegv, old_name='zgegv', message=_dep_message)
# Modyfy _flapack in this scope so the deprecation warnings apply to
# functions returned by get_lapack_funcs.
_flapack.cgegv = cgegv
_flapack.dgegv = dgegv
_flapack.sgegv = sgegv
_flapack.zgegv = zgegv
# Patching EVR Methods to handle value ranges
_evr_doc = """
w,z,info = {name}(a,[jobz,range,uplo,il,iu,lwork,overwrite_a,vl,vu])
d_ks = ksstat(z, test_d, alternative='two_sided')
if pvalmethod == 'approx':
pval = pval_lf(d_ks, nobs)
# check pval is in desired range
if pval > 0.1:
pval = lilliefors_table.prob(d_ks, nobs)
elif pvalmethod == 'table':
pval = lilliefors_table.prob(d_ks, nobs)
return d_ks, pval
lilliefors = kstest_fit
lillifors = np.deprecate(
lilliefors, 'lillifors', 'lilliefors', "Use lilliefors, lillifors will be "
"removed in 0.9 \n(Note: misspelling missing 'e')")
# namespace aliases
from functools import partial
kstest_normal = kstest_fit
kstest_exponential = partial(kstest_fit, dist='exp')
# old version:
# ------------
'''
tble = \
00 20 15 10 05 01 .1
4 .303 .321 .346 .376 .413 .433
5 .289 .303 .319 .343 .397 .439
6 .269 .281 .297 .323 .371 .424
__all__ = ['bicg','bicgstab','cg','cgs','gmres','qmr']
# Deprecated on January 26, 2008
from scipy.sparse.linalg import isolve
from numpy import deprecate
for name in __all__:
oldfn = getattr(isolve, name)
oldname='scipy.linalg.' + name
newname='scipy.sparse.linalg.' + name
newfn = deprecate(oldfn, old_name=oldname, new_name=newname)
exec(name + ' = newfn')
This function is deprecated in scipy 0.11 and will be removed for 0.12
Parameters
----------
file_name : str, optional
File name of the module to save.
data : dict, optional
The dictionary to store in the module.
"""
_create_module(file_name)
_create_shelf(file_name,data)
save_as_module = np.deprecate(save_as_module)
def _load(module):
""" Load data into module from a shelf with
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 + '"]')
"""
if r is None:
r = rank(X)
V, D, U = L.svd(X, full_matrices=0)
order = np.argsort(D)
order = order[::-1]
value = []
for i in range(r):
value.append(V[:,order[i]])
return np.asarray(np.transpose(value)).astype(np.float64)
StepFunction = np.deprecate(StepFunction,
old_name = 'statsmodels.tools.tools.StepFunction',
new_name = 'statsmodels.distributions.StepFunction')
monotone_fn_inverter = np.deprecate(monotone_fn_inverter,
old_name = 'statsmodels.tools.tools.monotone_fn_inverter',
new_name = 'statsmodels.distributions.monotone_fn_inverter')
ECDF = np.deprecate(ECDF,
old_name = 'statsmodels.tools.tools.ECDF',
new_name = 'statsmodels.distributions.ECDF')
def unsqueeze(data, axis, oldshape):
"""
Unsqueeze a collapsed array
>>> from numpy import mean
>>> from numpy.random import standard_normal
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)
def irfft2(a, s=None, axes=(-2, -1)):
"""
Compute the 2-dimensional inverse fft of a real array.
Parameters
----------
a : array (real)
input array
s : sequence (int)
shape of the inverse fft
axis : int
axis over which to compute the inverse fft
Notes
-----
This is really irfftn with different default.
"""
return irfftn(a, s, axes)
# Deprecated names
from numpy import deprecate
refft = deprecate(rfft, 'refft', 'rfft')
irefft = deprecate(irfft, 'irefft', 'irfft')
refft2 = deprecate(rfft2, 'refft2', 'rfft2')
irefft2 = deprecate(irfft2, 'irefft2', 'irfft2')
refftn = deprecate(rfftn, 'refftn', 'rfftn')
irefftn = deprecate(irfftn, 'irefftn', 'irfftn')
]
from . import doccer
from .common import *
from numpy import who as _who, source as _source, info as _info
import numpy as np
from scipy.interpolate._pade import pade as _pade
from scipy.special import (comb as _comb, logsumexp as _lsm, factorial as
_fact, factorial2 as _fact2, factorialk as _factk)
import sys
_msg = (
"Importing `%(name)s` from scipy.misc is deprecated in scipy 1.0.0. Use "
"`scipy.special.%(name)s` instead.")
comb = np.deprecate(_comb, message=_msg % {"name": _comb.__name__})
logsumexp = np.deprecate(_lsm, message=_msg % {"name": _lsm.__name__})
factorial = np.deprecate(_fact, message=_msg % {"name": _fact.__name__})
factorial2 = np.deprecate(_fact2, message=_msg % {"name": _fact2.__name__})
factorialk = np.deprecate(_factk, message=_msg % {"name": _factk.__name__})
_msg = ("Importing `pade` from scipy.misc is deprecated in scipy 1.0.0. Use "
"`scipy.interpolate.pade` instead.")
pade = np.deprecate(_pade, message=_msg)
_msg = (
"Importing `%(name)s` from scipy.misc is deprecated in scipy 1.0.0. Use "
"`numpy.%(name)s` instead.")
who = np.deprecate(_who, message=_msg % {"name": "who"})
source = np.deprecate(_source, message=_msg % {"name": "source"})
from info import __doc__
from numpy import deprecate
# 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(fread,
message="""
scipy.io.fread can be replaced with NumPy I/O routines such as
np.load, np.fromfile as well as NumPy's memory-mapping capabilities.
""")
fwrite = deprecate(fwrite,
message="""
scipy.io.fwrite can be replaced with NumPy I/O routines such as np.save,
np.savez and x.tofile. Also, files can be directly memory-mapped into NumPy
arrays which is often a better way of reading large files.
""")
bswap = deprecate(bswap,
message="""
scipy.io.bswap can be replaced with the byteswap method on an array.
out = scipy.io.bswap(arr) --> out = arr.byteswap(True)
""")
def irfft2(a, s=None, axes=(-2,-1)):
"""
Compute the 2-dimensional inverse fft of a real array.
Parameters
----------
a : array (real)
input array
s : sequence (int)
shape of the inverse fft
axis : int
axis over which to compute the inverse fft
Notes
-----
This is really irfftn with different default.
"""
return irfftn(a, s, axes)
# Deprecated names
from numpy import deprecate
refft = deprecate(rfft, 'refft', 'rfft')
irefft = deprecate(irfft, 'irefft', 'irfft')
refft2 = deprecate(rfft2, 'refft2', 'rfft2')
irefft2 = deprecate(irfft2, 'irefft2', 'irfft2')
refftn = deprecate(rfftn, 'refftn', 'rfftn')
irefftn = deprecate(irfftn, 'irefftn', 'irfftn')
True
>>> wgs72.f == 0.003352779454167505
True
>>> wgs72.name
'WGS 72'
>>> wgs72 == (6378135.0, 0.003352779454167505, 'WGS 72')
True
"""
if isinstance(name, str):
name = name.lower().replace(' ', '').partition('/')[0]
ellipsoid_id = ELLIPSOID_IX.get(name, name)
return ELLIPSOID[ellipsoid_id]
select_ellipsoid = deprecate(get_ellipsoid, old_name='select_ellipsoid', new_name='get_ellipsoid')
def unit(vector, norm_zero_vector=1, norm_zero_axis=0):
"""
Convert input vector to a vector of unit length.
Parameters
----------
vector : 3 x m array
m column vectors
norm_zero_vector : one or NaN
Defines the fill value used for zero length vectors.
norm_zero_axis : 0, 1 or 2
Defines the direction that zero length vectors will point after
the normalization is done.
# Backward compatibility
from scipy._lib._util import DeprecatedImport as _DeprecatedImport
clapack = _DeprecatedImport("scipy.linalg.blas.clapack", "scipy.linalg.lapack")
flapack = _DeprecatedImport("scipy.linalg.blas.flapack", "scipy.linalg.lapack")
# Expose all functions (only flapack --- clapack is an implementation detail)
empty_module = None
from scipy.linalg._flapack import *
del empty_module
_dep_message = """The `*gegv` family of routines has been deprecated in
LAPACK 3.6.0 in favor of the `*ggev` family of routines.
The corresponding wrappers will be removed from SciPy in
a future release."""
cgegv = _np.deprecate(cgegv, old_name='cgegv', message=_dep_message)
dgegv = _np.deprecate(dgegv, old_name='dgegv', message=_dep_message)
sgegv = _np.deprecate(sgegv, old_name='sgegv', message=_dep_message)
zgegv = _np.deprecate(zgegv, old_name='zgegv', message=_dep_message)
# Modyfy _flapack in this scope so the deprecation warnings apply to
# functions returned by get_lapack_funcs.
_flapack.cgegv = cgegv
_flapack.dgegv = dgegv
_flapack.sgegv = sgegv
_flapack.zgegv = zgegv
# some convenience alias for complex functions
_lapack_alias = {
'corghr': 'cunghr',
'zorghr': 'zunghr',
"""
Compute the 2-dimensional inverse fft of a real array.
Parameters
----------
a : array (real)
input array
s : sequence (int)
shape of the inverse fft
axis : int
axis over which to compute the inverse fft
Notes
-----
This is really irfftn with different default.
"""
return irfftn(a, s, axes)
# Deprecated names
from numpy import deprecate
refft = deprecate(rfft, "refft", "rfft")
irefft = deprecate(irfft, "irefft", "irfft")
refft2 = deprecate(rfft2, "refft2", "rfft2")
irefft2 = deprecate(irfft2, "irefft2", "irfft2")
refftn = deprecate(rfftn, "refftn", "rfftn")
irefftn = deprecate(irfftn, "irefftn", "irfftn")
offset = offsets[freq]
from pandas import DatetimeIndex # pylint: disable=E0611
date_range = DatetimeIndex(start=start_date, freq=offset, periods=n)
return data, date_range
def get_logdet(m):
from statsmodels.tools.linalg import logdet_symm
return logdet_symm(m)
get_logdet = np.deprecate(
get_logdet, "statsmodels.tsa.vector_ar.util.get_logdet",
"statsmodels.tools.linalg.logdet_symm",
"get_logdet is deprecated and will be removed in "
"0.8.0")
def norm_signif_level(alpha=0.05):
return stats.norm.ppf(1 - alpha / 2)
def acf_to_acorr(acf):
diag = np.diag(acf[0])
# numpy broadcasting sufficient
return acf / np.sqrt(np.outer(diag, diag))
def varsim(coefs, intercept, sig_u, steps=100, initvalues=None, seed=None):
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(_load,
message="""
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.
""")
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()
