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omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/fcompiler/nv.py | from numpy.distutils.fcompiler import FCompiler
compilers = ['NVHPCFCompiler']
class NVHPCFCompiler(FCompiler):
""" NVIDIA High Performance Computing (HPC) SDK Fortran Compiler
https://developer.nvidia.com/hpc-sdk
Since august 2020 the NVIDIA HPC SDK includes the compilers formerly known as The Portland Group compilers,
https://www.pgroup.com/index.htm.
See also `numpy.distutils.fcompiler.pg`.
"""
compiler_type = 'nv'
description = 'NVIDIA HPC SDK'
version_pattern = r'\s*(nvfortran|(pg(f77|f90|fortran)) \(aka nvfortran\)) (?P<version>[\d.-]+).*'
executables = {
'version_cmd': ["<F90>", "-V"],
'compiler_f77': ["nvfortran"],
'compiler_fix': ["nvfortran", "-Mfixed"],
'compiler_f90': ["nvfortran"],
'linker_so': ["<F90>"],
'archiver': ["ar", "-cr"],
'ranlib': ["ranlib"]
}
pic_flags = ['-fpic']
module_dir_switch = '-module '
module_include_switch = '-I'
def get_flags(self):
opt = ['-Minform=inform', '-Mnosecond_underscore']
return self.pic_flags + opt
def get_flags_opt(self):
return ['-fast']
def get_flags_debug(self):
return ['-g']
def get_flags_linker_so(self):
return ["-shared", '-fpic']
def runtime_library_dir_option(self, dir):
return '-R%s' % dir
if __name__ == '__main__':
from distutils import log
log.set_verbosity(2)
from numpy.distutils import customized_fcompiler
print(customized_fcompiler(compiler='nv').get_version())
| 1,560 | Python | 27.907407 | 111 | 0.603205 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/fcompiler/fujitsu.py | """
fujitsu
Supports Fujitsu compiler function.
This compiler is developed by Fujitsu and is used in A64FX on Fugaku.
"""
from numpy.distutils.fcompiler import FCompiler
compilers = ['FujitsuFCompiler']
class FujitsuFCompiler(FCompiler):
compiler_type = 'fujitsu'
description = 'Fujitsu Fortran Compiler'
possible_executables = ['frt']
version_pattern = r'frt \(FRT\) (?P<version>[a-z\d.]+)'
# $ frt --version
# frt (FRT) x.x.x yyyymmdd
executables = {
'version_cmd' : ["<F77>", "--version"],
'compiler_f77' : ["frt", "-Fixed"],
'compiler_fix' : ["frt", "-Fixed"],
'compiler_f90' : ["frt"],
'linker_so' : ["frt", "-shared"],
'archiver' : ["ar", "-cr"],
'ranlib' : ["ranlib"]
}
pic_flags = ['-KPIC']
module_dir_switch = '-M'
module_include_switch = '-I'
def get_flags_opt(self):
return ['-O3']
def get_flags_debug(self):
return ['-g']
def runtime_library_dir_option(self, dir):
return f'-Wl,-rpath={dir}'
def get_libraries(self):
return ['fj90f', 'fj90i', 'fjsrcinfo']
if __name__ == '__main__':
from distutils import log
from numpy.distutils import customized_fcompiler
log.set_verbosity(2)
print(customized_fcompiler('fujitsu').get_version())
| 1,333 | Python | 27.382978 | 69 | 0.581395 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_ccompiler_opt.py | import re, textwrap, os
from os import sys, path
from distutils.errors import DistutilsError
is_standalone = __name__ == '__main__' and __package__ is None
if is_standalone:
import unittest, contextlib, tempfile, shutil
sys.path.append(path.abspath(path.join(path.dirname(__file__), "..")))
from ccompiler_opt import CCompilerOpt
# from numpy/testing/_private/utils.py
@contextlib.contextmanager
def tempdir(*args, **kwargs):
tmpdir = tempfile.mkdtemp(*args, **kwargs)
try:
yield tmpdir
finally:
shutil.rmtree(tmpdir)
def assert_(expr, msg=''):
if not expr:
raise AssertionError(msg)
else:
from numpy.distutils.ccompiler_opt import CCompilerOpt
from numpy.testing import assert_, tempdir
# architectures and compilers to test
arch_compilers = dict(
x86 = ("gcc", "clang", "icc", "iccw", "msvc"),
x64 = ("gcc", "clang", "icc", "iccw", "msvc"),
ppc64 = ("gcc", "clang"),
ppc64le = ("gcc", "clang"),
armhf = ("gcc", "clang"),
aarch64 = ("gcc", "clang"),
s390x = ("gcc", "clang"),
noarch = ("gcc",)
)
class FakeCCompilerOpt(CCompilerOpt):
fake_info = ""
def __init__(self, trap_files="", trap_flags="", *args, **kwargs):
self.fake_trap_files = trap_files
self.fake_trap_flags = trap_flags
CCompilerOpt.__init__(self, None, **kwargs)
def __repr__(self):
return textwrap.dedent("""\
<<<<
march : {}
compiler : {}
----------------
{}
>>>>
""").format(self.cc_march, self.cc_name, self.report())
def dist_compile(self, sources, flags, **kwargs):
assert(isinstance(sources, list))
assert(isinstance(flags, list))
if self.fake_trap_files:
for src in sources:
if re.match(self.fake_trap_files, src):
self.dist_error("source is trapped by a fake interface")
if self.fake_trap_flags:
for f in flags:
if re.match(self.fake_trap_flags, f):
self.dist_error("flag is trapped by a fake interface")
# fake objects
return zip(sources, [' '.join(flags)] * len(sources))
def dist_info(self):
return FakeCCompilerOpt.fake_info
@staticmethod
def dist_log(*args, stderr=False):
pass
class _Test_CCompilerOpt:
arch = None # x86_64
cc = None # gcc
def setup_class(self):
FakeCCompilerOpt.conf_nocache = True
self._opt = None
def nopt(self, *args, **kwargs):
FakeCCompilerOpt.fake_info = (self.arch, self.cc, "")
return FakeCCompilerOpt(*args, **kwargs)
def opt(self):
if not self._opt:
self._opt = self.nopt()
return self._opt
def march(self):
return self.opt().cc_march
def cc_name(self):
return self.opt().cc_name
def get_targets(self, targets, groups, **kwargs):
FakeCCompilerOpt.conf_target_groups = groups
opt = self.nopt(
cpu_baseline=kwargs.get("baseline", "min"),
cpu_dispatch=kwargs.get("dispatch", "max"),
trap_files=kwargs.get("trap_files", ""),
trap_flags=kwargs.get("trap_flags", "")
)
with tempdir() as tmpdir:
file = os.path.join(tmpdir, "test_targets.c")
with open(file, 'w') as f:
f.write(targets)
gtargets = []
gflags = {}
fake_objects = opt.try_dispatch([file])
for source, flags in fake_objects:
gtar = path.basename(source).split('.')[1:-1]
glen = len(gtar)
if glen == 0:
gtar = "baseline"
elif glen == 1:
gtar = gtar[0].upper()
else:
# converting multi-target into parentheses str format to be equivalent
# to the configuration statements syntax.
gtar = ('('+' '.join(gtar)+')').upper()
gtargets.append(gtar)
gflags[gtar] = flags
has_baseline, targets = opt.sources_status[file]
targets = targets + ["baseline"] if has_baseline else targets
# convert tuple that represent multi-target into parentheses str format
targets = [
'('+' '.join(tar)+')' if isinstance(tar, tuple) else tar
for tar in targets
]
if len(targets) != len(gtargets) or not all(t in gtargets for t in targets):
raise AssertionError(
"'sources_status' returns different targets than the compiled targets\n"
"%s != %s" % (targets, gtargets)
)
# return targets from 'sources_status' since the order is matters
return targets, gflags
def arg_regex(self, **kwargs):
map2origin = dict(
x64 = "x86",
ppc64le = "ppc64",
aarch64 = "armhf",
clang = "gcc",
)
march = self.march(); cc_name = self.cc_name()
map_march = map2origin.get(march, march)
map_cc = map2origin.get(cc_name, cc_name)
for key in (
march, cc_name, map_march, map_cc,
march + '_' + cc_name,
map_march + '_' + cc_name,
march + '_' + map_cc,
map_march + '_' + map_cc,
) :
regex = kwargs.pop(key, None)
if regex is not None:
break
if regex:
if isinstance(regex, dict):
for k, v in regex.items():
if v[-1:] not in ')}$?\\.+*':
regex[k] = v + '$'
else:
assert(isinstance(regex, str))
if regex[-1:] not in ')}$?\\.+*':
regex += '$'
return regex
def expect(self, dispatch, baseline="", **kwargs):
match = self.arg_regex(**kwargs)
if match is None:
return
opt = self.nopt(
cpu_baseline=baseline, cpu_dispatch=dispatch,
trap_files=kwargs.get("trap_files", ""),
trap_flags=kwargs.get("trap_flags", "")
)
features = ' '.join(opt.cpu_dispatch_names())
if not match:
if len(features) != 0:
raise AssertionError(
'expected empty features, not "%s"' % features
)
return
if not re.match(match, features, re.IGNORECASE):
raise AssertionError(
'dispatch features "%s" not match "%s"' % (features, match)
)
def expect_baseline(self, baseline, dispatch="", **kwargs):
match = self.arg_regex(**kwargs)
if match is None:
return
opt = self.nopt(
cpu_baseline=baseline, cpu_dispatch=dispatch,
trap_files=kwargs.get("trap_files", ""),
trap_flags=kwargs.get("trap_flags", "")
)
features = ' '.join(opt.cpu_baseline_names())
if not match:
if len(features) != 0:
raise AssertionError(
'expected empty features, not "%s"' % features
)
return
if not re.match(match, features, re.IGNORECASE):
raise AssertionError(
'baseline features "%s" not match "%s"' % (features, match)
)
def expect_flags(self, baseline, dispatch="", **kwargs):
match = self.arg_regex(**kwargs)
if match is None:
return
opt = self.nopt(
cpu_baseline=baseline, cpu_dispatch=dispatch,
trap_files=kwargs.get("trap_files", ""),
trap_flags=kwargs.get("trap_flags", "")
)
flags = ' '.join(opt.cpu_baseline_flags())
if not match:
if len(flags) != 0:
raise AssertionError(
'expected empty flags not "%s"' % flags
)
return
if not re.match(match, flags):
raise AssertionError(
'flags "%s" not match "%s"' % (flags, match)
)
def expect_targets(self, targets, groups={}, **kwargs):
match = self.arg_regex(**kwargs)
if match is None:
return
targets, _ = self.get_targets(targets=targets, groups=groups, **kwargs)
targets = ' '.join(targets)
if not match:
if len(targets) != 0:
raise AssertionError(
'expected empty targets, not "%s"' % targets
)
return
if not re.match(match, targets, re.IGNORECASE):
raise AssertionError(
'targets "%s" not match "%s"' % (targets, match)
)
def expect_target_flags(self, targets, groups={}, **kwargs):
match_dict = self.arg_regex(**kwargs)
if match_dict is None:
return
assert(isinstance(match_dict, dict))
_, tar_flags = self.get_targets(targets=targets, groups=groups)
for match_tar, match_flags in match_dict.items():
if match_tar not in tar_flags:
raise AssertionError(
'expected to find target "%s"' % match_tar
)
flags = tar_flags[match_tar]
if not match_flags:
if len(flags) != 0:
raise AssertionError(
'expected to find empty flags in target "%s"' % match_tar
)
if not re.match(match_flags, flags):
raise AssertionError(
'"%s" flags "%s" not match "%s"' % (match_tar, flags, match_flags)
)
def test_interface(self):
wrong_arch = "ppc64" if self.arch != "ppc64" else "x86"
wrong_cc = "clang" if self.cc != "clang" else "icc"
opt = self.opt()
assert_(getattr(opt, "cc_on_" + self.arch))
assert_(not getattr(opt, "cc_on_" + wrong_arch))
assert_(getattr(opt, "cc_is_" + self.cc))
assert_(not getattr(opt, "cc_is_" + wrong_cc))
def test_args_empty(self):
for baseline, dispatch in (
("", "none"),
(None, ""),
("none +none", "none - none"),
("none -max", "min - max"),
("+vsx2 -VSX2", "vsx avx2 avx512f -max"),
("max -vsx - avx + avx512f neon -MAX ",
"min -min + max -max -vsx + avx2 -avx2 +NONE")
) :
opt = self.nopt(cpu_baseline=baseline, cpu_dispatch=dispatch)
assert(len(opt.cpu_baseline_names()) == 0)
assert(len(opt.cpu_dispatch_names()) == 0)
def test_args_validation(self):
if self.march() == "unknown":
return
# check sanity of argument's validation
for baseline, dispatch in (
("unkown_feature - max +min", "unknown max min"), # unknowing features
("#avx2", "$vsx") # groups and polices aren't acceptable
) :
try:
self.nopt(cpu_baseline=baseline, cpu_dispatch=dispatch)
raise AssertionError("excepted an exception for invalid arguments")
except DistutilsError:
pass
def test_skip(self):
# only takes what platform supports and skip the others
# without casing exceptions
self.expect(
"sse vsx neon",
x86="sse", ppc64="vsx", armhf="neon", unknown=""
)
self.expect(
"sse41 avx avx2 vsx2 vsx3 neon_vfpv4 asimd",
x86 = "sse41 avx avx2",
ppc64 = "vsx2 vsx3",
armhf = "neon_vfpv4 asimd",
unknown = ""
)
# any features in cpu_dispatch must be ignored if it's part of baseline
self.expect(
"sse neon vsx", baseline="sse neon vsx",
x86="", ppc64="", armhf=""
)
self.expect(
"avx2 vsx3 asimdhp", baseline="avx2 vsx3 asimdhp",
x86="", ppc64="", armhf=""
)
def test_implies(self):
# baseline combining implied features, so we count
# on it instead of testing 'feature_implies()'' directly
self.expect_baseline(
"fma3 avx2 asimd vsx3",
# .* between two spaces can validate features in between
x86 = "sse .* sse41 .* fma3.*avx2",
ppc64 = "vsx vsx2 vsx3",
armhf = "neon neon_fp16 neon_vfpv4 asimd"
)
"""
special cases
"""
# in icc and msvc, FMA3 and AVX2 can't be separated
# both need to implies each other, same for avx512f & cd
for f0, f1 in (
("fma3", "avx2"),
("avx512f", "avx512cd"),
):
diff = ".* sse42 .* %s .*%s$" % (f0, f1)
self.expect_baseline(f0,
x86_gcc=".* sse42 .* %s$" % f0,
x86_icc=diff, x86_iccw=diff
)
self.expect_baseline(f1,
x86_gcc=".* avx .* %s$" % f1,
x86_icc=diff, x86_iccw=diff
)
# in msvc, following features can't be separated too
for f in (("fma3", "avx2"), ("avx512f", "avx512cd", "avx512_skx")):
for ff in f:
self.expect_baseline(ff,
x86_msvc=".*%s" % ' '.join(f)
)
# in ppc64le VSX and VSX2 can't be separated
self.expect_baseline("vsx", ppc64le="vsx vsx2")
# in aarch64 following features can't be separated
for f in ("neon", "neon_fp16", "neon_vfpv4", "asimd"):
self.expect_baseline(f, aarch64="neon neon_fp16 neon_vfpv4 asimd")
def test_args_options(self):
# max & native
for o in ("max", "native"):
if o == "native" and self.cc_name() == "msvc":
continue
self.expect(o,
trap_files=".*cpu_(sse|vsx|neon|vx).c",
x86="", ppc64="", armhf="", s390x=""
)
self.expect(o,
trap_files=".*cpu_(sse3|vsx2|neon_vfpv4|vxe).c",
x86="sse sse2", ppc64="vsx", armhf="neon neon_fp16",
aarch64="", ppc64le="", s390x="vx"
)
self.expect(o,
trap_files=".*cpu_(popcnt|vsx3).c",
x86="sse .* sse41", ppc64="vsx vsx2",
armhf="neon neon_fp16 .* asimd .*",
s390x="vx vxe vxe2"
)
self.expect(o,
x86_gcc=".* xop fma4 .* avx512f .* avx512_knl avx512_knm avx512_skx .*",
# in icc, xop and fam4 aren't supported
x86_icc=".* avx512f .* avx512_knl avx512_knm avx512_skx .*",
x86_iccw=".* avx512f .* avx512_knl avx512_knm avx512_skx .*",
# in msvc, avx512_knl avx512_knm aren't supported
x86_msvc=".* xop fma4 .* avx512f .* avx512_skx .*",
armhf=".* asimd asimdhp asimddp .*",
ppc64="vsx vsx2 vsx3 vsx4.*",
s390x="vx vxe vxe2.*"
)
# min
self.expect("min",
x86="sse sse2", x64="sse sse2 sse3",
armhf="", aarch64="neon neon_fp16 .* asimd",
ppc64="", ppc64le="vsx vsx2", s390x=""
)
self.expect(
"min", trap_files=".*cpu_(sse2|vsx2).c",
x86="", ppc64le=""
)
# an exception must triggered if native flag isn't supported
# when option "native" is activated through the args
try:
self.expect("native",
trap_flags=".*(-march=native|-xHost|/QxHost).*",
x86=".*", ppc64=".*", armhf=".*", s390x=".*"
)
if self.march() != "unknown":
raise AssertionError(
"excepted an exception for %s" % self.march()
)
except DistutilsError:
if self.march() == "unknown":
raise AssertionError("excepted no exceptions")
def test_flags(self):
self.expect_flags(
"sse sse2 vsx vsx2 neon neon_fp16 vx vxe",
x86_gcc="-msse -msse2", x86_icc="-msse -msse2",
x86_iccw="/arch:SSE2",
x86_msvc="/arch:SSE2" if self.march() == "x86" else "",
ppc64_gcc= "-mcpu=power8",
ppc64_clang="-maltivec -mvsx -mpower8-vector",
armhf_gcc="-mfpu=neon-fp16 -mfp16-format=ieee",
aarch64="",
s390x="-mzvector -march=arch12"
)
# testing normalize -march
self.expect_flags(
"asimd",
aarch64="",
armhf_gcc=r"-mfp16-format=ieee -mfpu=neon-fp-armv8 -march=armv8-a\+simd"
)
self.expect_flags(
"asimdhp",
aarch64_gcc=r"-march=armv8.2-a\+fp16",
armhf_gcc=r"-mfp16-format=ieee -mfpu=neon-fp-armv8 -march=armv8.2-a\+fp16"
)
self.expect_flags(
"asimddp", aarch64_gcc=r"-march=armv8.2-a\+dotprod"
)
self.expect_flags(
# asimdfhm implies asimdhp
"asimdfhm", aarch64_gcc=r"-march=armv8.2-a\+fp16\+fp16fml"
)
self.expect_flags(
"asimddp asimdhp asimdfhm",
aarch64_gcc=r"-march=armv8.2-a\+dotprod\+fp16\+fp16fml"
)
self.expect_flags(
"vx vxe vxe2",
s390x=r"-mzvector -march=arch13"
)
def test_targets_exceptions(self):
for targets in (
"bla bla", "/*@targets",
"/*@targets */",
"/*@targets unknown */",
"/*@targets $unknown_policy avx2 */",
"/*@targets #unknown_group avx2 */",
"/*@targets $ */",
"/*@targets # vsx */",
"/*@targets #$ vsx */",
"/*@targets vsx avx2 ) */",
"/*@targets vsx avx2 (avx2 */",
"/*@targets vsx avx2 () */",
"/*@targets vsx avx2 ($autovec) */", # no features
"/*@targets vsx avx2 (xxx) */",
"/*@targets vsx avx2 (baseline) */",
) :
try:
self.expect_targets(
targets,
x86="", armhf="", ppc64="", s390x=""
)
if self.march() != "unknown":
raise AssertionError(
"excepted an exception for %s" % self.march()
)
except DistutilsError:
if self.march() == "unknown":
raise AssertionError("excepted no exceptions")
def test_targets_syntax(self):
for targets in (
"/*@targets $keep_baseline sse vsx neon vx*/",
"/*@targets,$keep_baseline,sse,vsx,neon vx*/",
"/*@targets*$keep_baseline*sse*vsx*neon*vx*/",
"""
/*
** @targets
** $keep_baseline, sse vsx,neon, vx
*/
""",
"""
/*
************@targets****************
** $keep_baseline, sse vsx, neon, vx
************************************
*/
""",
"""
/*
/////////////@targets/////////////////
//$keep_baseline//sse//vsx//neon//vx
/////////////////////////////////////
*/
""",
"""
/*
@targets
$keep_baseline
SSE VSX NEON VX*/
"""
) :
self.expect_targets(targets,
x86="sse", ppc64="vsx", armhf="neon", s390x="vx", unknown=""
)
def test_targets(self):
# test skipping baseline features
self.expect_targets(
"""
/*@targets
sse sse2 sse41 avx avx2 avx512f
vsx vsx2 vsx3 vsx4
neon neon_fp16 asimdhp asimddp
vx vxe vxe2
*/
""",
baseline="avx vsx2 asimd vx vxe",
x86="avx512f avx2", armhf="asimddp asimdhp", ppc64="vsx4 vsx3",
s390x="vxe2"
)
# test skipping non-dispatch features
self.expect_targets(
"""
/*@targets
sse41 avx avx2 avx512f
vsx2 vsx3 vsx4
asimd asimdhp asimddp
vx vxe vxe2
*/
""",
baseline="", dispatch="sse41 avx2 vsx2 asimd asimddp vxe2",
x86="avx2 sse41", armhf="asimddp asimd", ppc64="vsx2", s390x="vxe2"
)
# test skipping features that not supported
self.expect_targets(
"""
/*@targets
sse2 sse41 avx2 avx512f
vsx2 vsx3 vsx4
neon asimdhp asimddp
vx vxe vxe2
*/
""",
baseline="",
trap_files=".*(avx2|avx512f|vsx3|vsx4|asimddp|vxe2).c",
x86="sse41 sse2", ppc64="vsx2", armhf="asimdhp neon",
s390x="vxe vx"
)
# test skipping features that implies each other
self.expect_targets(
"""
/*@targets
sse sse2 avx fma3 avx2 avx512f avx512cd
vsx vsx2 vsx3
neon neon_vfpv4 neon_fp16 neon_fp16 asimd asimdhp
asimddp asimdfhm
*/
""",
baseline="",
x86_gcc="avx512cd avx512f avx2 fma3 avx sse2",
x86_msvc="avx512cd avx2 avx sse2",
x86_icc="avx512cd avx2 avx sse2",
x86_iccw="avx512cd avx2 avx sse2",
ppc64="vsx3 vsx2 vsx",
ppc64le="vsx3 vsx2",
armhf="asimdfhm asimddp asimdhp asimd neon_vfpv4 neon_fp16 neon",
aarch64="asimdfhm asimddp asimdhp asimd"
)
def test_targets_policies(self):
# 'keep_baseline', generate objects for baseline features
self.expect_targets(
"""
/*@targets
$keep_baseline
sse2 sse42 avx2 avx512f
vsx2 vsx3
neon neon_vfpv4 asimd asimddp
vx vxe vxe2
*/
""",
baseline="sse41 avx2 vsx2 asimd vsx3 vxe",
x86="avx512f avx2 sse42 sse2",
ppc64="vsx3 vsx2",
armhf="asimddp asimd neon_vfpv4 neon",
# neon, neon_vfpv4, asimd implies each other
aarch64="asimddp asimd",
s390x="vxe2 vxe vx"
)
# 'keep_sort', leave the sort as-is
self.expect_targets(
"""
/*@targets
$keep_baseline $keep_sort
avx512f sse42 avx2 sse2
vsx2 vsx3
asimd neon neon_vfpv4 asimddp
vxe vxe2
*/
""",
x86="avx512f sse42 avx2 sse2",
ppc64="vsx2 vsx3",
armhf="asimd neon neon_vfpv4 asimddp",
# neon, neon_vfpv4, asimd implies each other
aarch64="asimd asimddp",
s390x="vxe vxe2"
)
# 'autovec', skipping features that can't be
# vectorized by the compiler
self.expect_targets(
"""
/*@targets
$keep_baseline $keep_sort $autovec
avx512f avx2 sse42 sse41 sse2
vsx3 vsx2
asimddp asimd neon_vfpv4 neon
*/
""",
x86_gcc="avx512f avx2 sse42 sse41 sse2",
x86_icc="avx512f avx2 sse42 sse41 sse2",
x86_iccw="avx512f avx2 sse42 sse41 sse2",
x86_msvc="avx512f avx2 sse2"
if self.march() == 'x86' else "avx512f avx2",
ppc64="vsx3 vsx2",
armhf="asimddp asimd neon_vfpv4 neon",
# neon, neon_vfpv4, asimd implies each other
aarch64="asimddp asimd"
)
for policy in ("$maxopt", "$autovec"):
# 'maxopt' and autovec set the max acceptable optimization flags
self.expect_target_flags(
"/*@targets baseline %s */" % policy,
gcc={"baseline":".*-O3.*"}, icc={"baseline":".*-O3.*"},
iccw={"baseline":".*/O3.*"}, msvc={"baseline":".*/O2.*"},
unknown={"baseline":".*"}
)
# 'werror', force compilers to treat warnings as errors
self.expect_target_flags(
"/*@targets baseline $werror */",
gcc={"baseline":".*-Werror.*"}, icc={"baseline":".*-Werror.*"},
iccw={"baseline":".*/Werror.*"}, msvc={"baseline":".*/WX.*"},
unknown={"baseline":".*"}
)
def test_targets_groups(self):
self.expect_targets(
"""
/*@targets $keep_baseline baseline #test_group */
""",
groups=dict(
test_group=("""
$keep_baseline
asimddp sse2 vsx2 avx2 vsx3
avx512f asimdhp
""")
),
x86="avx512f avx2 sse2 baseline",
ppc64="vsx3 vsx2 baseline",
armhf="asimddp asimdhp baseline"
)
# test skip duplicating and sorting
self.expect_targets(
"""
/*@targets
* sse42 avx avx512f
* #test_group_1
* vsx2
* #test_group_2
* asimddp asimdfhm
*/
""",
groups=dict(
test_group_1=("""
VSX2 vsx3 asimd avx2 SSE41
"""),
test_group_2=("""
vsx2 vsx3 asImd aVx2 sse41
""")
),
x86="avx512f avx2 avx sse42 sse41",
ppc64="vsx3 vsx2",
# vsx2 part of the default baseline of ppc64le, option ("min")
ppc64le="vsx3",
armhf="asimdfhm asimddp asimd",
# asimd part of the default baseline of aarch64, option ("min")
aarch64="asimdfhm asimddp"
)
def test_targets_multi(self):
self.expect_targets(
"""
/*@targets
(avx512_clx avx512_cnl) (asimdhp asimddp)
*/
""",
x86=r"\(avx512_clx avx512_cnl\)",
armhf=r"\(asimdhp asimddp\)",
)
# test skipping implied features and auto-sort
self.expect_targets(
"""
/*@targets
f16c (sse41 avx sse42) (sse3 avx2 avx512f)
vsx2 (vsx vsx3 vsx2)
(neon neon_vfpv4 asimd asimdhp asimddp)
*/
""",
x86="avx512f f16c avx",
ppc64="vsx3 vsx2",
ppc64le="vsx3", # vsx2 part of baseline
armhf=r"\(asimdhp asimddp\)",
)
# test skipping implied features and keep sort
self.expect_targets(
"""
/*@targets $keep_sort
(sse41 avx sse42) (sse3 avx2 avx512f)
(vsx vsx3 vsx2)
(asimddp neon neon_vfpv4 asimd asimdhp)
(vx vxe vxe2)
*/
""",
x86="avx avx512f",
ppc64="vsx3",
armhf=r"\(asimdhp asimddp\)",
s390x="vxe2"
)
# test compiler variety and avoiding duplicating
self.expect_targets(
"""
/*@targets $keep_sort
fma3 avx2 (fma3 avx2) (avx2 fma3) avx2 fma3
*/
""",
x86_gcc=r"fma3 avx2 \(fma3 avx2\)",
x86_icc="avx2", x86_iccw="avx2",
x86_msvc="avx2"
)
def new_test(arch, cc):
if is_standalone: return textwrap.dedent("""\
class TestCCompilerOpt_{class_name}(_Test_CCompilerOpt, unittest.TestCase):
arch = '{arch}'
cc = '{cc}'
def __init__(self, methodName="runTest"):
unittest.TestCase.__init__(self, methodName)
self.setup_class()
""").format(
class_name=arch + '_' + cc, arch=arch, cc=cc
)
return textwrap.dedent("""\
class TestCCompilerOpt_{class_name}(_Test_CCompilerOpt):
arch = '{arch}'
cc = '{cc}'
""").format(
class_name=arch + '_' + cc, arch=arch, cc=cc
)
"""
if 1 and is_standalone:
FakeCCompilerOpt.fake_info = "x86_icc"
cco = FakeCCompilerOpt(None, cpu_baseline="avx2")
print(' '.join(cco.cpu_baseline_names()))
print(cco.cpu_baseline_flags())
unittest.main()
sys.exit()
"""
for arch, compilers in arch_compilers.items():
for cc in compilers:
exec(new_test(arch, cc))
if is_standalone:
unittest.main()
| 28,763 | Python | 34.555006 | 90 | 0.481208 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_exec_command.py | import os
import sys
from tempfile import TemporaryFile
from numpy.distutils import exec_command
from numpy.distutils.exec_command import get_pythonexe
from numpy.testing import tempdir, assert_, assert_warns
# In python 3 stdout, stderr are text (unicode compliant) devices, so to
# emulate them import StringIO from the io module.
from io import StringIO
class redirect_stdout:
"""Context manager to redirect stdout for exec_command test."""
def __init__(self, stdout=None):
self._stdout = stdout or sys.stdout
def __enter__(self):
self.old_stdout = sys.stdout
sys.stdout = self._stdout
def __exit__(self, exc_type, exc_value, traceback):
self._stdout.flush()
sys.stdout = self.old_stdout
# note: closing sys.stdout won't close it.
self._stdout.close()
class redirect_stderr:
"""Context manager to redirect stderr for exec_command test."""
def __init__(self, stderr=None):
self._stderr = stderr or sys.stderr
def __enter__(self):
self.old_stderr = sys.stderr
sys.stderr = self._stderr
def __exit__(self, exc_type, exc_value, traceback):
self._stderr.flush()
sys.stderr = self.old_stderr
# note: closing sys.stderr won't close it.
self._stderr.close()
class emulate_nonposix:
"""Context manager to emulate os.name != 'posix' """
def __init__(self, osname='non-posix'):
self._new_name = osname
def __enter__(self):
self._old_name = os.name
os.name = self._new_name
def __exit__(self, exc_type, exc_value, traceback):
os.name = self._old_name
def test_exec_command_stdout():
# Regression test for gh-2999 and gh-2915.
# There are several packages (nose, scipy.weave.inline, Sage inline
# Fortran) that replace stdout, in which case it doesn't have a fileno
# method. This is tested here, with a do-nothing command that fails if the
# presence of fileno() is assumed in exec_command.
# The code has a special case for posix systems, so if we are on posix test
# both that the special case works and that the generic code works.
# Test posix version:
with redirect_stdout(StringIO()):
with redirect_stderr(TemporaryFile()):
with assert_warns(DeprecationWarning):
exec_command.exec_command("cd '.'")
if os.name == 'posix':
# Test general (non-posix) version:
with emulate_nonposix():
with redirect_stdout(StringIO()):
with redirect_stderr(TemporaryFile()):
with assert_warns(DeprecationWarning):
exec_command.exec_command("cd '.'")
def test_exec_command_stderr():
# Test posix version:
with redirect_stdout(TemporaryFile(mode='w+')):
with redirect_stderr(StringIO()):
with assert_warns(DeprecationWarning):
exec_command.exec_command("cd '.'")
if os.name == 'posix':
# Test general (non-posix) version:
with emulate_nonposix():
with redirect_stdout(TemporaryFile()):
with redirect_stderr(StringIO()):
with assert_warns(DeprecationWarning):
exec_command.exec_command("cd '.'")
class TestExecCommand:
def setup_method(self):
self.pyexe = get_pythonexe()
def check_nt(self, **kws):
s, o = exec_command.exec_command('cmd /C echo path=%path%')
assert_(s == 0)
assert_(o != '')
s, o = exec_command.exec_command(
'"%s" -c "import sys;sys.stderr.write(sys.platform)"' % self.pyexe)
assert_(s == 0)
assert_(o == 'win32')
def check_posix(self, **kws):
s, o = exec_command.exec_command("echo Hello", **kws)
assert_(s == 0)
assert_(o == 'Hello')
s, o = exec_command.exec_command('echo $AAA', **kws)
assert_(s == 0)
assert_(o == '')
s, o = exec_command.exec_command('echo "$AAA"', AAA='Tere', **kws)
assert_(s == 0)
assert_(o == 'Tere')
s, o = exec_command.exec_command('echo "$AAA"', **kws)
assert_(s == 0)
assert_(o == '')
if 'BBB' not in os.environ:
os.environ['BBB'] = 'Hi'
s, o = exec_command.exec_command('echo "$BBB"', **kws)
assert_(s == 0)
assert_(o == 'Hi')
s, o = exec_command.exec_command('echo "$BBB"', BBB='Hey', **kws)
assert_(s == 0)
assert_(o == 'Hey')
s, o = exec_command.exec_command('echo "$BBB"', **kws)
assert_(s == 0)
assert_(o == 'Hi')
del os.environ['BBB']
s, o = exec_command.exec_command('echo "$BBB"', **kws)
assert_(s == 0)
assert_(o == '')
s, o = exec_command.exec_command('this_is_not_a_command', **kws)
assert_(s != 0)
assert_(o != '')
s, o = exec_command.exec_command('echo path=$PATH', **kws)
assert_(s == 0)
assert_(o != '')
s, o = exec_command.exec_command(
'"%s" -c "import sys,os;sys.stderr.write(os.name)"' %
self.pyexe, **kws)
assert_(s == 0)
assert_(o == 'posix')
def check_basic(self, *kws):
s, o = exec_command.exec_command(
'"%s" -c "raise \'Ignore me.\'"' % self.pyexe, **kws)
assert_(s != 0)
assert_(o != '')
s, o = exec_command.exec_command(
'"%s" -c "import sys;sys.stderr.write(\'0\');'
'sys.stderr.write(\'1\');sys.stderr.write(\'2\')"' %
self.pyexe, **kws)
assert_(s == 0)
assert_(o == '012')
s, o = exec_command.exec_command(
'"%s" -c "import sys;sys.exit(15)"' % self.pyexe, **kws)
assert_(s == 15)
assert_(o == '')
s, o = exec_command.exec_command(
'"%s" -c "print(\'Heipa\'")' % self.pyexe, **kws)
assert_(s == 0)
assert_(o == 'Heipa')
def check_execute_in(self, **kws):
with tempdir() as tmpdir:
fn = "file"
tmpfile = os.path.join(tmpdir, fn)
with open(tmpfile, 'w') as f:
f.write('Hello')
s, o = exec_command.exec_command(
'"%s" -c "f = open(\'%s\', \'r\'); f.close()"' %
(self.pyexe, fn), **kws)
assert_(s != 0)
assert_(o != '')
s, o = exec_command.exec_command(
'"%s" -c "f = open(\'%s\', \'r\'); print(f.read()); '
'f.close()"' % (self.pyexe, fn), execute_in=tmpdir, **kws)
assert_(s == 0)
assert_(o == 'Hello')
def test_basic(self):
with redirect_stdout(StringIO()):
with redirect_stderr(StringIO()):
with assert_warns(DeprecationWarning):
if os.name == "posix":
self.check_posix(use_tee=0)
self.check_posix(use_tee=1)
elif os.name == "nt":
self.check_nt(use_tee=0)
self.check_nt(use_tee=1)
self.check_execute_in(use_tee=0)
self.check_execute_in(use_tee=1)
| 7,308 | Python | 32.995349 | 79 | 0.52422 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_log.py | import io
import re
from contextlib import redirect_stdout
import pytest
from numpy.distutils import log
def setup_module():
f = io.StringIO() # changing verbosity also logs here, capture that
with redirect_stdout(f):
log.set_verbosity(2, force=True) # i.e. DEBUG
def teardown_module():
log.set_verbosity(0, force=True) # the default
r_ansi = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
@pytest.mark.parametrize("func_name", ["error", "warn", "info", "debug"])
def test_log_prefix(func_name):
func = getattr(log, func_name)
msg = f"{func_name} message"
f = io.StringIO()
with redirect_stdout(f):
func(msg)
out = f.getvalue()
assert out # sanity check
clean_out = r_ansi.sub("", out)
line = next(line for line in clean_out.splitlines())
assert line == f"{func_name.upper()}: {msg}"
| 868 | Python | 23.828571 | 73 | 0.625576 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_from_template.py |
from numpy.distutils.from_template import process_str
from numpy.testing import assert_equal
pyf_src = """
python module foo
<_rd=real,double precision>
interface
subroutine <s,d>foosub(tol)
<_rd>, intent(in,out) :: tol
end subroutine <s,d>foosub
end interface
end python module foo
"""
expected_pyf = """
python module foo
interface
subroutine sfoosub(tol)
real, intent(in,out) :: tol
end subroutine sfoosub
subroutine dfoosub(tol)
double precision, intent(in,out) :: tol
end subroutine dfoosub
end interface
end python module foo
"""
def normalize_whitespace(s):
"""
Remove leading and trailing whitespace, and convert internal
stretches of whitespace to a single space.
"""
return ' '.join(s.split())
def test_from_template():
"""Regression test for gh-10712."""
pyf = process_str(pyf_src)
normalized_pyf = normalize_whitespace(pyf)
normalized_expected_pyf = normalize_whitespace(expected_pyf)
assert_equal(normalized_pyf, normalized_expected_pyf)
| 1,103 | Python | 23.533333 | 64 | 0.660925 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_system_info.py | import os
import shutil
import pytest
from tempfile import mkstemp, mkdtemp
from subprocess import Popen, PIPE
from distutils.errors import DistutilsError
from numpy.testing import assert_, assert_equal, assert_raises
from numpy.distutils import ccompiler, customized_ccompiler
from numpy.distutils.system_info import system_info, ConfigParser, mkl_info
from numpy.distutils.system_info import AliasedOptionError
from numpy.distutils.system_info import default_lib_dirs, default_include_dirs
from numpy.distutils import _shell_utils
def get_class(name, notfound_action=1):
"""
notfound_action:
0 - do nothing
1 - display warning message
2 - raise error
"""
cl = {'temp1': Temp1Info,
'temp2': Temp2Info,
'duplicate_options': DuplicateOptionInfo,
}.get(name.lower(), _system_info)
return cl()
simple_site = """
[ALL]
library_dirs = {dir1:s}{pathsep:s}{dir2:s}
libraries = {lib1:s},{lib2:s}
extra_compile_args = -I/fake/directory -I"/path with/spaces" -Os
runtime_library_dirs = {dir1:s}
[temp1]
library_dirs = {dir1:s}
libraries = {lib1:s}
runtime_library_dirs = {dir1:s}
[temp2]
library_dirs = {dir2:s}
libraries = {lib2:s}
extra_link_args = -Wl,-rpath={lib2_escaped:s}
rpath = {dir2:s}
[duplicate_options]
mylib_libs = {lib1:s}
libraries = {lib2:s}
"""
site_cfg = simple_site
fakelib_c_text = """
/* This file is generated from numpy/distutils/testing/test_system_info.py */
#include<stdio.h>
void foo(void) {
printf("Hello foo");
}
void bar(void) {
printf("Hello bar");
}
"""
def have_compiler():
""" Return True if there appears to be an executable compiler
"""
compiler = customized_ccompiler()
try:
cmd = compiler.compiler # Unix compilers
except AttributeError:
try:
if not compiler.initialized:
compiler.initialize() # MSVC is different
except (DistutilsError, ValueError):
return False
cmd = [compiler.cc]
try:
p = Popen(cmd, stdout=PIPE, stderr=PIPE)
p.stdout.close()
p.stderr.close()
p.wait()
except OSError:
return False
return True
HAVE_COMPILER = have_compiler()
class _system_info(system_info):
def __init__(self,
default_lib_dirs=default_lib_dirs,
default_include_dirs=default_include_dirs,
verbosity=1,
):
self.__class__.info = {}
self.local_prefixes = []
defaults = {'library_dirs': '',
'include_dirs': '',
'runtime_library_dirs': '',
'rpath': '',
'src_dirs': '',
'search_static_first': "0",
'extra_compile_args': '',
'extra_link_args': ''}
self.cp = ConfigParser(defaults)
# We have to parse the config files afterwards
# to have a consistent temporary filepath
def _check_libs(self, lib_dirs, libs, opt_libs, exts):
"""Override _check_libs to return with all dirs """
info = {'libraries': libs, 'library_dirs': lib_dirs}
return info
class Temp1Info(_system_info):
"""For testing purposes"""
section = 'temp1'
class Temp2Info(_system_info):
"""For testing purposes"""
section = 'temp2'
class DuplicateOptionInfo(_system_info):
"""For testing purposes"""
section = 'duplicate_options'
class TestSystemInfoReading:
def setup_method(self):
""" Create the libraries """
# Create 2 sources and 2 libraries
self._dir1 = mkdtemp()
self._src1 = os.path.join(self._dir1, 'foo.c')
self._lib1 = os.path.join(self._dir1, 'libfoo.so')
self._dir2 = mkdtemp()
self._src2 = os.path.join(self._dir2, 'bar.c')
self._lib2 = os.path.join(self._dir2, 'libbar.so')
# Update local site.cfg
global simple_site, site_cfg
site_cfg = simple_site.format(**{
'dir1': self._dir1,
'lib1': self._lib1,
'dir2': self._dir2,
'lib2': self._lib2,
'pathsep': os.pathsep,
'lib2_escaped': _shell_utils.NativeParser.join([self._lib2])
})
# Write site.cfg
fd, self._sitecfg = mkstemp()
os.close(fd)
with open(self._sitecfg, 'w') as fd:
fd.write(site_cfg)
# Write the sources
with open(self._src1, 'w') as fd:
fd.write(fakelib_c_text)
with open(self._src2, 'w') as fd:
fd.write(fakelib_c_text)
# We create all class-instances
def site_and_parse(c, site_cfg):
c.files = [site_cfg]
c.parse_config_files()
return c
self.c_default = site_and_parse(get_class('default'), self._sitecfg)
self.c_temp1 = site_and_parse(get_class('temp1'), self._sitecfg)
self.c_temp2 = site_and_parse(get_class('temp2'), self._sitecfg)
self.c_dup_options = site_and_parse(get_class('duplicate_options'),
self._sitecfg)
def teardown_method(self):
# Do each removal separately
try:
shutil.rmtree(self._dir1)
except Exception:
pass
try:
shutil.rmtree(self._dir2)
except Exception:
pass
try:
os.remove(self._sitecfg)
except Exception:
pass
def test_all(self):
# Read in all information in the ALL block
tsi = self.c_default
assert_equal(tsi.get_lib_dirs(), [self._dir1, self._dir2])
assert_equal(tsi.get_libraries(), [self._lib1, self._lib2])
assert_equal(tsi.get_runtime_lib_dirs(), [self._dir1])
extra = tsi.calc_extra_info()
assert_equal(extra['extra_compile_args'], ['-I/fake/directory', '-I/path with/spaces', '-Os'])
def test_temp1(self):
# Read in all information in the temp1 block
tsi = self.c_temp1
assert_equal(tsi.get_lib_dirs(), [self._dir1])
assert_equal(tsi.get_libraries(), [self._lib1])
assert_equal(tsi.get_runtime_lib_dirs(), [self._dir1])
def test_temp2(self):
# Read in all information in the temp2 block
tsi = self.c_temp2
assert_equal(tsi.get_lib_dirs(), [self._dir2])
assert_equal(tsi.get_libraries(), [self._lib2])
# Now from rpath and not runtime_library_dirs
assert_equal(tsi.get_runtime_lib_dirs(key='rpath'), [self._dir2])
extra = tsi.calc_extra_info()
assert_equal(extra['extra_link_args'], ['-Wl,-rpath=' + self._lib2])
def test_duplicate_options(self):
# Ensure that duplicates are raising an AliasedOptionError
tsi = self.c_dup_options
assert_raises(AliasedOptionError, tsi.get_option_single, "mylib_libs", "libraries")
assert_equal(tsi.get_libs("mylib_libs", [self._lib1]), [self._lib1])
assert_equal(tsi.get_libs("libraries", [self._lib2]), [self._lib2])
@pytest.mark.skipif(not HAVE_COMPILER, reason="Missing compiler")
def test_compile1(self):
# Compile source and link the first source
c = customized_ccompiler()
previousDir = os.getcwd()
try:
# Change directory to not screw up directories
os.chdir(self._dir1)
c.compile([os.path.basename(self._src1)], output_dir=self._dir1)
# Ensure that the object exists
assert_(os.path.isfile(self._src1.replace('.c', '.o')) or
os.path.isfile(self._src1.replace('.c', '.obj')))
finally:
os.chdir(previousDir)
@pytest.mark.skipif(not HAVE_COMPILER, reason="Missing compiler")
@pytest.mark.skipif('msvc' in repr(ccompiler.new_compiler()),
reason="Fails with MSVC compiler ")
def test_compile2(self):
# Compile source and link the second source
tsi = self.c_temp2
c = customized_ccompiler()
extra_link_args = tsi.calc_extra_info()['extra_link_args']
previousDir = os.getcwd()
try:
# Change directory to not screw up directories
os.chdir(self._dir2)
c.compile([os.path.basename(self._src2)], output_dir=self._dir2,
extra_postargs=extra_link_args)
# Ensure that the object exists
assert_(os.path.isfile(self._src2.replace('.c', '.o')))
finally:
os.chdir(previousDir)
HAS_MKL = "mkl_rt" in mkl_info().calc_libraries_info().get("libraries", [])
@pytest.mark.xfail(HAS_MKL, reason=("`[DEFAULT]` override doesn't work if "
"numpy is built with MKL support"))
def test_overrides(self):
previousDir = os.getcwd()
cfg = os.path.join(self._dir1, 'site.cfg')
shutil.copy(self._sitecfg, cfg)
try:
os.chdir(self._dir1)
# Check that the '[ALL]' section does not override
# missing values from other sections
info = mkl_info()
lib_dirs = info.cp['ALL']['library_dirs'].split(os.pathsep)
assert info.get_lib_dirs() != lib_dirs
# But if we copy the values to a '[mkl]' section the value
# is correct
with open(cfg, 'r') as fid:
mkl = fid.read().replace('[ALL]', '[mkl]', 1)
with open(cfg, 'w') as fid:
fid.write(mkl)
info = mkl_info()
assert info.get_lib_dirs() == lib_dirs
# Also, the values will be taken from a section named '[DEFAULT]'
with open(cfg, 'r') as fid:
dflt = fid.read().replace('[mkl]', '[DEFAULT]', 1)
with open(cfg, 'w') as fid:
fid.write(dflt)
info = mkl_info()
assert info.get_lib_dirs() == lib_dirs
finally:
os.chdir(previousDir)
def test_distutils_parse_env_order(monkeypatch):
from numpy.distutils.system_info import _parse_env_order
env = 'NPY_TESTS_DISTUTILS_PARSE_ENV_ORDER'
base_order = list('abcdef')
monkeypatch.setenv(env, 'b,i,e,f')
order, unknown = _parse_env_order(base_order, env)
assert len(order) == 3
assert order == list('bef')
assert len(unknown) == 1
# For when LAPACK/BLAS optimization is disabled
monkeypatch.setenv(env, '')
order, unknown = _parse_env_order(base_order, env)
assert len(order) == 0
assert len(unknown) == 0
for prefix in '^!':
monkeypatch.setenv(env, f'{prefix}b,i,e')
order, unknown = _parse_env_order(base_order, env)
assert len(order) == 4
assert order == list('acdf')
assert len(unknown) == 1
with pytest.raises(ValueError):
monkeypatch.setenv(env, 'b,^e,i')
_parse_env_order(base_order, env)
with pytest.raises(ValueError):
monkeypatch.setenv(env, '!b,^e,i')
_parse_env_order(base_order, env)
| 11,009 | Python | 32.981481 | 102 | 0.578708 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_fcompiler_intel.py | import numpy.distutils.fcompiler
from numpy.testing import assert_
intel_32bit_version_strings = [
("Intel(R) Fortran Intel(R) 32-bit Compiler Professional for applications"
"running on Intel(R) 32, Version 11.1", '11.1'),
]
intel_64bit_version_strings = [
("Intel(R) Fortran IA-64 Compiler Professional for applications"
"running on IA-64, Version 11.0", '11.0'),
("Intel(R) Fortran Intel(R) 64 Compiler Professional for applications"
"running on Intel(R) 64, Version 11.1", '11.1')
]
class TestIntelFCompilerVersions:
def test_32bit_version(self):
fc = numpy.distutils.fcompiler.new_fcompiler(compiler='intel')
for vs, version in intel_32bit_version_strings:
v = fc.version_match(vs)
assert_(v == version)
class TestIntelEM64TFCompilerVersions:
def test_64bit_version(self):
fc = numpy.distutils.fcompiler.new_fcompiler(compiler='intelem')
for vs, version in intel_64bit_version_strings:
v = fc.version_match(vs)
assert_(v == version)
| 1,058 | Python | 33.161289 | 78 | 0.672023 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_fcompiler_nagfor.py | from numpy.testing import assert_
import numpy.distutils.fcompiler
nag_version_strings = [('nagfor', 'NAG Fortran Compiler Release '
'6.2(Chiyoda) Build 6200', '6.2'),
('nagfor', 'NAG Fortran Compiler Release '
'6.1(Tozai) Build 6136', '6.1'),
('nagfor', 'NAG Fortran Compiler Release '
'6.0(Hibiya) Build 1021', '6.0'),
('nagfor', 'NAG Fortran Compiler Release '
'5.3.2(971)', '5.3.2'),
('nag', 'NAGWare Fortran 95 compiler Release 5.1'
'(347,355-367,375,380-383,389,394,399,401-402,407,'
'431,435,437,446,459-460,463,472,494,496,503,508,'
'511,517,529,555,557,565)', '5.1')]
class TestNagFCompilerVersions:
def test_version_match(self):
for comp, vs, version in nag_version_strings:
fc = numpy.distutils.fcompiler.new_fcompiler(compiler=comp)
v = fc.version_match(vs)
assert_(v == version)
| 1,102 | Python | 46.95652 | 75 | 0.50726 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_mingw32ccompiler.py | import shutil
import subprocess
import sys
import pytest
from numpy.distutils import mingw32ccompiler
@pytest.mark.skipif(sys.platform != 'win32', reason='win32 only test')
def test_build_import():
'''Test the mingw32ccompiler.build_import_library, which builds a
`python.a` from the MSVC `python.lib`
'''
# make sure `nm.exe` exists and supports the current python version. This
# can get mixed up when the PATH has a 64-bit nm but the python is 32-bit
try:
out = subprocess.check_output(['nm.exe', '--help'])
except FileNotFoundError:
pytest.skip("'nm.exe' not on path, is mingw installed?")
supported = out[out.find(b'supported targets:'):]
if sys.maxsize < 2**32:
if b'pe-i386' not in supported:
raise ValueError("'nm.exe' found but it does not support 32-bit "
"dlls when using 32-bit python. Supported "
"formats: '%s'" % supported)
elif b'pe-x86-64' not in supported:
raise ValueError("'nm.exe' found but it does not support 64-bit "
"dlls when using 64-bit python. Supported "
"formats: '%s'" % supported)
# Hide the import library to force a build
has_import_lib, fullpath = mingw32ccompiler._check_for_import_lib()
if has_import_lib:
shutil.move(fullpath, fullpath + '.bak')
try:
# Whew, now we can actually test the function
mingw32ccompiler.build_import_library()
finally:
if has_import_lib:
shutil.move(fullpath + '.bak', fullpath)
| 1,609 | Python | 36.44186 | 77 | 0.61964 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_build_ext.py | '''Tests for numpy.distutils.build_ext.'''
import os
import subprocess
import sys
from textwrap import indent, dedent
import pytest
@pytest.mark.slow
def test_multi_fortran_libs_link(tmp_path):
'''
Ensures multiple "fake" static libraries are correctly linked.
see gh-18295
'''
# We need to make sure we actually have an f77 compiler.
# This is nontrivial, so we'll borrow the utilities
# from f2py tests:
from numpy.f2py.tests.util import has_f77_compiler
if not has_f77_compiler():
pytest.skip('No F77 compiler found')
# make some dummy sources
with open(tmp_path / '_dummy1.f', 'w') as fid:
fid.write(indent(dedent('''\
FUNCTION dummy_one()
RETURN
END FUNCTION'''), prefix=' '*6))
with open(tmp_path / '_dummy2.f', 'w') as fid:
fid.write(indent(dedent('''\
FUNCTION dummy_two()
RETURN
END FUNCTION'''), prefix=' '*6))
with open(tmp_path / '_dummy.c', 'w') as fid:
# doesn't need to load - just needs to exist
fid.write('int PyInit_dummyext;')
# make a setup file
with open(tmp_path / 'setup.py', 'w') as fid:
srctree = os.path.join(os.path.dirname(__file__), '..', '..', '..')
fid.write(dedent(f'''\
def configuration(parent_package="", top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration("", parent_package, top_path)
config.add_library("dummy1", sources=["_dummy1.f"])
config.add_library("dummy2", sources=["_dummy2.f"])
config.add_extension("dummyext", sources=["_dummy.c"], libraries=["dummy1", "dummy2"])
return config
if __name__ == "__main__":
import sys
sys.path.insert(0, r"{srctree}")
from numpy.distutils.core import setup
setup(**configuration(top_path="").todict())'''))
# build the test extensino and "install" into a temporary directory
build_dir = tmp_path
subprocess.check_call([sys.executable, 'setup.py', 'build', 'install',
'--prefix', str(tmp_path / 'installdir'),
'--record', str(tmp_path / 'tmp_install_log.txt'),
],
cwd=str(build_dir),
)
# get the path to the so
so = None
with open(tmp_path /'tmp_install_log.txt') as fid:
for line in fid:
if 'dummyext' in line:
so = line.strip()
break
assert so is not None
| 2,664 | Python | 35.506849 | 102 | 0.546547 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_ccompiler_opt_conf.py | import unittest
from os import sys, path
is_standalone = __name__ == '__main__' and __package__ is None
if is_standalone:
sys.path.append(path.abspath(path.join(path.dirname(__file__), "..")))
from ccompiler_opt import CCompilerOpt
else:
from numpy.distutils.ccompiler_opt import CCompilerOpt
arch_compilers = dict(
x86 = ("gcc", "clang", "icc", "iccw", "msvc"),
x64 = ("gcc", "clang", "icc", "iccw", "msvc"),
ppc64 = ("gcc", "clang"),
ppc64le = ("gcc", "clang"),
armhf = ("gcc", "clang"),
aarch64 = ("gcc", "clang"),
narch = ("gcc",)
)
class FakeCCompilerOpt(CCompilerOpt):
fake_info = ("arch", "compiler", "extra_args")
def __init__(self, *args, **kwargs):
CCompilerOpt.__init__(self, None, **kwargs)
def dist_compile(self, sources, flags, **kwargs):
return sources
def dist_info(self):
return FakeCCompilerOpt.fake_info
@staticmethod
def dist_log(*args, stderr=False):
pass
class _TestConfFeatures(FakeCCompilerOpt):
"""A hook to check the sanity of configured features
- before it called by the abstract class '_Feature'
"""
def conf_features_partial(self):
conf_all = self.conf_features
for feature_name, feature in conf_all.items():
self.test_feature(
"attribute conf_features",
conf_all, feature_name, feature
)
conf_partial = FakeCCompilerOpt.conf_features_partial(self)
for feature_name, feature in conf_partial.items():
self.test_feature(
"conf_features_partial()",
conf_partial, feature_name, feature
)
return conf_partial
def test_feature(self, log, search_in, feature_name, feature_dict):
error_msg = (
"during validate '{}' within feature '{}', "
"march '{}' and compiler '{}'\n>> "
).format(log, feature_name, self.cc_march, self.cc_name)
if not feature_name.isupper():
raise AssertionError(error_msg + "feature name must be in uppercase")
for option, val in feature_dict.items():
self.test_option_types(error_msg, option, val)
self.test_duplicates(error_msg, option, val)
self.test_implies(error_msg, search_in, feature_name, feature_dict)
self.test_group(error_msg, search_in, feature_name, feature_dict)
self.test_extra_checks(error_msg, search_in, feature_name, feature_dict)
def test_option_types(self, error_msg, option, val):
for tp, available in (
((str, list), (
"implies", "headers", "flags", "group", "detect", "extra_checks"
)),
((str,), ("disable",)),
((int,), ("interest",)),
((bool,), ("implies_detect",)),
((bool, type(None)), ("autovec",)),
) :
found_it = option in available
if not found_it:
continue
if not isinstance(val, tp):
error_tp = [t.__name__ for t in (*tp,)]
error_tp = ' or '.join(error_tp)
raise AssertionError(error_msg +
"expected '%s' type for option '%s' not '%s'" % (
error_tp, option, type(val).__name__
))
break
if not found_it:
raise AssertionError(error_msg + "invalid option name '%s'" % option)
def test_duplicates(self, error_msg, option, val):
if option not in (
"implies", "headers", "flags", "group", "detect", "extra_checks"
) : return
if isinstance(val, str):
val = val.split()
if len(val) != len(set(val)):
raise AssertionError(error_msg + "duplicated values in option '%s'" % option)
def test_implies(self, error_msg, search_in, feature_name, feature_dict):
if feature_dict.get("disabled") is not None:
return
implies = feature_dict.get("implies", "")
if not implies:
return
if isinstance(implies, str):
implies = implies.split()
if feature_name in implies:
raise AssertionError(error_msg + "feature implies itself")
for impl in implies:
impl_dict = search_in.get(impl)
if impl_dict is not None:
if "disable" in impl_dict:
raise AssertionError(error_msg + "implies disabled feature '%s'" % impl)
continue
raise AssertionError(error_msg + "implies non-exist feature '%s'" % impl)
def test_group(self, error_msg, search_in, feature_name, feature_dict):
if feature_dict.get("disabled") is not None:
return
group = feature_dict.get("group", "")
if not group:
return
if isinstance(group, str):
group = group.split()
for f in group:
impl_dict = search_in.get(f)
if not impl_dict or "disable" in impl_dict:
continue
raise AssertionError(error_msg +
"in option 'group', '%s' already exists as a feature name" % f
)
def test_extra_checks(self, error_msg, search_in, feature_name, feature_dict):
if feature_dict.get("disabled") is not None:
return
extra_checks = feature_dict.get("extra_checks", "")
if not extra_checks:
return
if isinstance(extra_checks, str):
extra_checks = extra_checks.split()
for f in extra_checks:
impl_dict = search_in.get(f)
if not impl_dict or "disable" in impl_dict:
continue
raise AssertionError(error_msg +
"in option 'extra_checks', extra test case '%s' already exists as a feature name" % f
)
class TestConfFeatures(unittest.TestCase):
def __init__(self, methodName="runTest"):
unittest.TestCase.__init__(self, methodName)
self._setup()
def _setup(self):
FakeCCompilerOpt.conf_nocache = True
def test_features(self):
for arch, compilers in arch_compilers.items():
for cc in compilers:
FakeCCompilerOpt.fake_info = (arch, cc, "")
_TestConfFeatures()
if is_standalone:
unittest.main()
| 6,347 | Python | 34.864407 | 101 | 0.557271 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_fcompiler_gnu.py | from numpy.testing import assert_
import numpy.distutils.fcompiler
g77_version_strings = [
('GNU Fortran 0.5.25 20010319 (prerelease)', '0.5.25'),
('GNU Fortran (GCC 3.2) 3.2 20020814 (release)', '3.2'),
('GNU Fortran (GCC) 3.3.3 20040110 (prerelease) (Debian)', '3.3.3'),
('GNU Fortran (GCC) 3.3.3 (Debian 20040401)', '3.3.3'),
('GNU Fortran (GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-5)) 3.2.2'
' 20030222 (Red Hat Linux 3.2.2-5)', '3.2.2'),
]
gfortran_version_strings = [
('GNU Fortran 95 (GCC 4.0.3 20051023 (prerelease) (Debian 4.0.2-3))',
'4.0.3'),
('GNU Fortran 95 (GCC) 4.1.0', '4.1.0'),
('GNU Fortran 95 (GCC) 4.2.0 20060218 (experimental)', '4.2.0'),
('GNU Fortran (GCC) 4.3.0 20070316 (experimental)', '4.3.0'),
('GNU Fortran (rubenvb-4.8.0) 4.8.0', '4.8.0'),
('4.8.0', '4.8.0'),
('4.0.3-7', '4.0.3'),
("gfortran: warning: couldn't understand kern.osversion '14.1.0\n4.9.1",
'4.9.1'),
("gfortran: warning: couldn't understand kern.osversion '14.1.0\n"
"gfortran: warning: yet another warning\n4.9.1",
'4.9.1'),
('GNU Fortran (crosstool-NG 8a21ab48) 7.2.0', '7.2.0')
]
class TestG77Versions:
def test_g77_version(self):
fc = numpy.distutils.fcompiler.new_fcompiler(compiler='gnu')
for vs, version in g77_version_strings:
v = fc.version_match(vs)
assert_(v == version, (vs, v))
def test_not_g77(self):
fc = numpy.distutils.fcompiler.new_fcompiler(compiler='gnu')
for vs, _ in gfortran_version_strings:
v = fc.version_match(vs)
assert_(v is None, (vs, v))
class TestGFortranVersions:
def test_gfortran_version(self):
fc = numpy.distutils.fcompiler.new_fcompiler(compiler='gnu95')
for vs, version in gfortran_version_strings:
v = fc.version_match(vs)
assert_(v == version, (vs, v))
def test_not_gfortran(self):
fc = numpy.distutils.fcompiler.new_fcompiler(compiler='gnu95')
for vs, _ in g77_version_strings:
v = fc.version_match(vs)
assert_(v is None, (vs, v))
| 2,136 | Python | 37.160714 | 76 | 0.589888 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_shell_utils.py | import pytest
import subprocess
import json
import sys
from numpy.distutils import _shell_utils
argv_cases = [
[r'exe'],
[r'path/exe'],
[r'path\exe'],
[r'\\server\path\exe'],
[r'path to/exe'],
[r'path to\exe'],
[r'exe', '--flag'],
[r'path/exe', '--flag'],
[r'path\exe', '--flag'],
[r'path to/exe', '--flag'],
[r'path to\exe', '--flag'],
# flags containing literal quotes in their name
[r'path to/exe', '--flag-"quoted"'],
[r'path to\exe', '--flag-"quoted"'],
[r'path to/exe', '"--flag-quoted"'],
[r'path to\exe', '"--flag-quoted"'],
]
@pytest.fixture(params=[
_shell_utils.WindowsParser,
_shell_utils.PosixParser
])
def Parser(request):
return request.param
@pytest.fixture
def runner(Parser):
if Parser != _shell_utils.NativeParser:
pytest.skip('Unable to run with non-native parser')
if Parser == _shell_utils.WindowsParser:
return lambda cmd: subprocess.check_output(cmd)
elif Parser == _shell_utils.PosixParser:
# posix has no non-shell string parsing
return lambda cmd: subprocess.check_output(cmd, shell=True)
else:
raise NotImplementedError
@pytest.mark.parametrize('argv', argv_cases)
def test_join_matches_subprocess(Parser, runner, argv):
"""
Test that join produces strings understood by subprocess
"""
# invoke python to return its arguments as json
cmd = [
sys.executable, '-c',
'import json, sys; print(json.dumps(sys.argv[1:]))'
]
joined = Parser.join(cmd + argv)
json_out = runner(joined).decode()
assert json.loads(json_out) == argv
@pytest.mark.parametrize('argv', argv_cases)
def test_roundtrip(Parser, argv):
"""
Test that split is the inverse operation of join
"""
try:
joined = Parser.join(argv)
assert argv == Parser.split(joined)
except NotImplementedError:
pytest.skip("Not implemented")
| 1,954 | Python | 24.38961 | 67 | 0.622313 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_misc_util.py | from os.path import join, sep, dirname
from numpy.distutils.misc_util import (
appendpath, minrelpath, gpaths, get_shared_lib_extension, get_info
)
from numpy.testing import (
assert_, assert_equal
)
ajoin = lambda *paths: join(*((sep,)+paths))
class TestAppendpath:
def test_1(self):
assert_equal(appendpath('prefix', 'name'), join('prefix', 'name'))
assert_equal(appendpath('/prefix', 'name'), ajoin('prefix', 'name'))
assert_equal(appendpath('/prefix', '/name'), ajoin('prefix', 'name'))
assert_equal(appendpath('prefix', '/name'), join('prefix', 'name'))
def test_2(self):
assert_equal(appendpath('prefix/sub', 'name'),
join('prefix', 'sub', 'name'))
assert_equal(appendpath('prefix/sub', 'sup/name'),
join('prefix', 'sub', 'sup', 'name'))
assert_equal(appendpath('/prefix/sub', '/prefix/name'),
ajoin('prefix', 'sub', 'name'))
def test_3(self):
assert_equal(appendpath('/prefix/sub', '/prefix/sup/name'),
ajoin('prefix', 'sub', 'sup', 'name'))
assert_equal(appendpath('/prefix/sub/sub2', '/prefix/sup/sup2/name'),
ajoin('prefix', 'sub', 'sub2', 'sup', 'sup2', 'name'))
assert_equal(appendpath('/prefix/sub/sub2', '/prefix/sub/sup/name'),
ajoin('prefix', 'sub', 'sub2', 'sup', 'name'))
class TestMinrelpath:
def test_1(self):
n = lambda path: path.replace('/', sep)
assert_equal(minrelpath(n('aa/bb')), n('aa/bb'))
assert_equal(minrelpath('..'), '..')
assert_equal(minrelpath(n('aa/..')), '')
assert_equal(minrelpath(n('aa/../bb')), 'bb')
assert_equal(minrelpath(n('aa/bb/..')), 'aa')
assert_equal(minrelpath(n('aa/bb/../..')), '')
assert_equal(minrelpath(n('aa/bb/../cc/../dd')), n('aa/dd'))
assert_equal(minrelpath(n('.././..')), n('../..'))
assert_equal(minrelpath(n('aa/bb/.././../dd')), n('dd'))
class TestGpaths:
def test_gpaths(self):
local_path = minrelpath(join(dirname(__file__), '..'))
ls = gpaths('command/*.py', local_path)
assert_(join(local_path, 'command', 'build_src.py') in ls, repr(ls))
f = gpaths('system_info.py', local_path)
assert_(join(local_path, 'system_info.py') == f[0], repr(f))
class TestSharedExtension:
def test_get_shared_lib_extension(self):
import sys
ext = get_shared_lib_extension(is_python_ext=False)
if sys.platform.startswith('linux'):
assert_equal(ext, '.so')
elif sys.platform.startswith('gnukfreebsd'):
assert_equal(ext, '.so')
elif sys.platform.startswith('darwin'):
assert_equal(ext, '.dylib')
elif sys.platform.startswith('win'):
assert_equal(ext, '.dll')
# just check for no crash
assert_(get_shared_lib_extension(is_python_ext=True))
def test_installed_npymath_ini():
# Regression test for gh-7707. If npymath.ini wasn't installed, then this
# will give an error.
info = get_info('npymath')
assert isinstance(info, dict)
assert "define_macros" in info
| 3,218 | Python | 37.783132 | 78 | 0.569608 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_fcompiler.py | from numpy.testing import assert_
import numpy.distutils.fcompiler
customizable_flags = [
('f77', 'F77FLAGS'),
('f90', 'F90FLAGS'),
('free', 'FREEFLAGS'),
('arch', 'FARCH'),
('debug', 'FDEBUG'),
('flags', 'FFLAGS'),
('linker_so', 'LDFLAGS'),
]
def test_fcompiler_flags(monkeypatch):
monkeypatch.setenv('NPY_DISTUTILS_APPEND_FLAGS', '0')
fc = numpy.distutils.fcompiler.new_fcompiler(compiler='none')
flag_vars = fc.flag_vars.clone(lambda *args, **kwargs: None)
for opt, envvar in customizable_flags:
new_flag = '-dummy-{}-flag'.format(opt)
prev_flags = getattr(flag_vars, opt)
monkeypatch.setenv(envvar, new_flag)
new_flags = getattr(flag_vars, opt)
monkeypatch.delenv(envvar)
assert_(new_flags == [new_flag])
monkeypatch.setenv('NPY_DISTUTILS_APPEND_FLAGS', '1')
for opt, envvar in customizable_flags:
new_flag = '-dummy-{}-flag'.format(opt)
prev_flags = getattr(flag_vars, opt)
monkeypatch.setenv(envvar, new_flag)
new_flags = getattr(flag_vars, opt)
monkeypatch.delenv(envvar)
if prev_flags is None:
assert_(new_flags == [new_flag])
else:
assert_(new_flags == prev_flags + [new_flag])
| 1,277 | Python | 28.045454 | 65 | 0.60924 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/distutils/tests/test_npy_pkg_config.py | import os
from numpy.distutils.npy_pkg_config import read_config, parse_flags
from numpy.testing import temppath, assert_
simple = """\
[meta]
Name = foo
Description = foo lib
Version = 0.1
[default]
cflags = -I/usr/include
libs = -L/usr/lib
"""
simple_d = {'cflags': '-I/usr/include', 'libflags': '-L/usr/lib',
'version': '0.1', 'name': 'foo'}
simple_variable = """\
[meta]
Name = foo
Description = foo lib
Version = 0.1
[variables]
prefix = /foo/bar
libdir = ${prefix}/lib
includedir = ${prefix}/include
[default]
cflags = -I${includedir}
libs = -L${libdir}
"""
simple_variable_d = {'cflags': '-I/foo/bar/include', 'libflags': '-L/foo/bar/lib',
'version': '0.1', 'name': 'foo'}
class TestLibraryInfo:
def test_simple(self):
with temppath('foo.ini') as path:
with open(path, 'w') as f:
f.write(simple)
pkg = os.path.splitext(path)[0]
out = read_config(pkg)
assert_(out.cflags() == simple_d['cflags'])
assert_(out.libs() == simple_d['libflags'])
assert_(out.name == simple_d['name'])
assert_(out.version == simple_d['version'])
def test_simple_variable(self):
with temppath('foo.ini') as path:
with open(path, 'w') as f:
f.write(simple_variable)
pkg = os.path.splitext(path)[0]
out = read_config(pkg)
assert_(out.cflags() == simple_variable_d['cflags'])
assert_(out.libs() == simple_variable_d['libflags'])
assert_(out.name == simple_variable_d['name'])
assert_(out.version == simple_variable_d['version'])
out.vars['prefix'] = '/Users/david'
assert_(out.cflags() == '-I/Users/david/include')
class TestParseFlags:
def test_simple_cflags(self):
d = parse_flags("-I/usr/include")
assert_(d['include_dirs'] == ['/usr/include'])
d = parse_flags("-I/usr/include -DFOO")
assert_(d['include_dirs'] == ['/usr/include'])
assert_(d['macros'] == ['FOO'])
d = parse_flags("-I /usr/include -DFOO")
assert_(d['include_dirs'] == ['/usr/include'])
assert_(d['macros'] == ['FOO'])
def test_simple_lflags(self):
d = parse_flags("-L/usr/lib -lfoo -L/usr/lib -lbar")
assert_(d['library_dirs'] == ['/usr/lib', '/usr/lib'])
assert_(d['libraries'] == ['foo', 'bar'])
d = parse_flags("-L /usr/lib -lfoo -L/usr/lib -lbar")
assert_(d['library_dirs'] == ['/usr/lib', '/usr/lib'])
assert_(d['libraries'] == ['foo', 'bar'])
| 2,557 | Python | 29.094117 | 82 | 0.559249 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/doc/constants.py | """
=========
Constants
=========
.. currentmodule:: numpy
NumPy includes several constants:
%(constant_list)s
"""
#
# Note: the docstring is autogenerated.
#
import re
import textwrap
# Maintain same format as in numpy.add_newdocs
constants = []
def add_newdoc(module, name, doc):
constants.append((name, doc))
add_newdoc('numpy', 'pi',
"""
``pi = 3.1415926535897932384626433...``
References
----------
https://en.wikipedia.org/wiki/Pi
""")
add_newdoc('numpy', 'e',
"""
Euler's constant, base of natural logarithms, Napier's constant.
``e = 2.71828182845904523536028747135266249775724709369995...``
See Also
--------
exp : Exponential function
log : Natural logarithm
References
----------
https://en.wikipedia.org/wiki/E_%28mathematical_constant%29
""")
add_newdoc('numpy', 'euler_gamma',
"""
``γ = 0.5772156649015328606065120900824024310421...``
References
----------
https://en.wikipedia.org/wiki/Euler-Mascheroni_constant
""")
add_newdoc('numpy', 'inf',
"""
IEEE 754 floating point representation of (positive) infinity.
Returns
-------
y : float
A floating point representation of positive infinity.
See Also
--------
isinf : Shows which elements are positive or negative infinity
isposinf : Shows which elements are positive infinity
isneginf : Shows which elements are negative infinity
isnan : Shows which elements are Not a Number
isfinite : Shows which elements are finite (not one of Not a Number,
positive infinity and negative infinity)
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Also that positive infinity is not equivalent to negative infinity. But
infinity is equivalent to positive infinity.
`Inf`, `Infinity`, `PINF` and `infty` are aliases for `inf`.
Examples
--------
>>> np.inf
inf
>>> np.array([1]) / 0.
array([ Inf])
""")
add_newdoc('numpy', 'nan',
"""
IEEE 754 floating point representation of Not a Number (NaN).
Returns
-------
y : A floating point representation of Not a Number.
See Also
--------
isnan : Shows which elements are Not a Number.
isfinite : Shows which elements are finite (not one of
Not a Number, positive infinity and negative infinity)
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
`NaN` and `NAN` are aliases of `nan`.
Examples
--------
>>> np.nan
nan
>>> np.log(-1)
nan
>>> np.log([-1, 1, 2])
array([ NaN, 0. , 0.69314718])
""")
add_newdoc('numpy', 'newaxis',
"""
A convenient alias for None, useful for indexing arrays.
Examples
--------
>>> newaxis is None
True
>>> x = np.arange(3)
>>> x
array([0, 1, 2])
>>> x[:, newaxis]
array([[0],
[1],
[2]])
>>> x[:, newaxis, newaxis]
array([[[0]],
[[1]],
[[2]]])
>>> x[:, newaxis] * x
array([[0, 0, 0],
[0, 1, 2],
[0, 2, 4]])
Outer product, same as ``outer(x, y)``:
>>> y = np.arange(3, 6)
>>> x[:, newaxis] * y
array([[ 0, 0, 0],
[ 3, 4, 5],
[ 6, 8, 10]])
``x[newaxis, :]`` is equivalent to ``x[newaxis]`` and ``x[None]``:
>>> x[newaxis, :].shape
(1, 3)
>>> x[newaxis].shape
(1, 3)
>>> x[None].shape
(1, 3)
>>> x[:, newaxis].shape
(3, 1)
""")
add_newdoc('numpy', 'NZERO',
"""
IEEE 754 floating point representation of negative zero.
Returns
-------
y : float
A floating point representation of negative zero.
See Also
--------
PZERO : Defines positive zero.
isinf : Shows which elements are positive or negative infinity.
isposinf : Shows which elements are positive infinity.
isneginf : Shows which elements are negative infinity.
isnan : Shows which elements are Not a Number.
isfinite : Shows which elements are finite - not one of
Not a Number, positive infinity and negative infinity.
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). Negative zero is considered to be a finite number.
Examples
--------
>>> np.NZERO
-0.0
>>> np.PZERO
0.0
>>> np.isfinite([np.NZERO])
array([ True])
>>> np.isnan([np.NZERO])
array([False])
>>> np.isinf([np.NZERO])
array([False])
""")
add_newdoc('numpy', 'PZERO',
"""
IEEE 754 floating point representation of positive zero.
Returns
-------
y : float
A floating point representation of positive zero.
See Also
--------
NZERO : Defines negative zero.
isinf : Shows which elements are positive or negative infinity.
isposinf : Shows which elements are positive infinity.
isneginf : Shows which elements are negative infinity.
isnan : Shows which elements are Not a Number.
isfinite : Shows which elements are finite - not one of
Not a Number, positive infinity and negative infinity.
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). Positive zero is considered to be a finite number.
Examples
--------
>>> np.PZERO
0.0
>>> np.NZERO
-0.0
>>> np.isfinite([np.PZERO])
array([ True])
>>> np.isnan([np.PZERO])
array([False])
>>> np.isinf([np.PZERO])
array([False])
""")
add_newdoc('numpy', 'NAN',
"""
IEEE 754 floating point representation of Not a Number (NaN).
`NaN` and `NAN` are equivalent definitions of `nan`. Please use
`nan` instead of `NAN`.
See Also
--------
nan
""")
add_newdoc('numpy', 'NaN',
"""
IEEE 754 floating point representation of Not a Number (NaN).
`NaN` and `NAN` are equivalent definitions of `nan`. Please use
`nan` instead of `NaN`.
See Also
--------
nan
""")
add_newdoc('numpy', 'NINF',
"""
IEEE 754 floating point representation of negative infinity.
Returns
-------
y : float
A floating point representation of negative infinity.
See Also
--------
isinf : Shows which elements are positive or negative infinity
isposinf : Shows which elements are positive infinity
isneginf : Shows which elements are negative infinity
isnan : Shows which elements are Not a Number
isfinite : Shows which elements are finite (not one of Not a Number,
positive infinity and negative infinity)
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Also that positive infinity is not equivalent to negative infinity. But
infinity is equivalent to positive infinity.
Examples
--------
>>> np.NINF
-inf
>>> np.log(0)
-inf
""")
add_newdoc('numpy', 'PINF',
"""
IEEE 754 floating point representation of (positive) infinity.
Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for
`inf`. For more details, see `inf`.
See Also
--------
inf
""")
add_newdoc('numpy', 'infty',
"""
IEEE 754 floating point representation of (positive) infinity.
Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for
`inf`. For more details, see `inf`.
See Also
--------
inf
""")
add_newdoc('numpy', 'Inf',
"""
IEEE 754 floating point representation of (positive) infinity.
Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for
`inf`. For more details, see `inf`.
See Also
--------
inf
""")
add_newdoc('numpy', 'Infinity',
"""
IEEE 754 floating point representation of (positive) infinity.
Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for
`inf`. For more details, see `inf`.
See Also
--------
inf
""")
if __doc__:
constants_str = []
constants.sort()
for name, doc in constants:
s = textwrap.dedent(doc).replace("\n", "\n ")
# Replace sections by rubrics
lines = s.split("\n")
new_lines = []
for line in lines:
m = re.match(r'^(\s+)[-=]+\s*$', line)
if m and new_lines:
prev = textwrap.dedent(new_lines.pop())
new_lines.append('%s.. rubric:: %s' % (m.group(1), prev))
new_lines.append('')
else:
new_lines.append(line)
s = "\n".join(new_lines)
# Done.
constants_str.append(""".. data:: %s\n %s""" % (name, s))
constants_str = "\n".join(constants_str)
__doc__ = __doc__ % dict(constant_list=constants_str)
del constants_str, name, doc
del line, lines, new_lines, m, s, prev
del constants, add_newdoc
| 9,154 | Python | 21.16707 | 75 | 0.582915 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/doc/__init__.py | import os
ref_dir = os.path.join(os.path.dirname(__file__))
__all__ = sorted(f[:-3] for f in os.listdir(ref_dir) if f.endswith('.py') and
not f.startswith('__'))
for f in __all__:
__import__(__name__ + '.' + f)
del f, ref_dir
__doc__ = """\
Topical documentation
=====================
The following topics are available:
%s
You can view them by
>>> help(np.doc.TOPIC) #doctest: +SKIP
""" % '\n- '.join([''] + __all__)
__all__.extend(['__doc__'])
| 508 | Python | 17.851851 | 77 | 0.488189 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/doc/ufuncs.py | """
===================
Universal Functions
===================
Ufuncs are, generally speaking, mathematical functions or operations that are
applied element-by-element to the contents of an array. That is, the result
in each output array element only depends on the value in the corresponding
input array (or arrays) and on no other array elements. NumPy comes with a
large suite of ufuncs, and scipy extends that suite substantially. The simplest
example is the addition operator: ::
>>> np.array([0,2,3,4]) + np.array([1,1,-1,2])
array([1, 3, 2, 6])
The ufunc module lists all the available ufuncs in numpy. Documentation on
the specific ufuncs may be found in those modules. This documentation is
intended to address the more general aspects of ufuncs common to most of
them. All of the ufuncs that make use of Python operators (e.g., +, -, etc.)
have equivalent functions defined (e.g. add() for +)
Type coercion
=============
What happens when a binary operator (e.g., +,-,\\*,/, etc) deals with arrays of
two different types? What is the type of the result? Typically, the result is
the higher of the two types. For example: ::
float32 + float64 -> float64
int8 + int32 -> int32
int16 + float32 -> float32
float32 + complex64 -> complex64
There are some less obvious cases generally involving mixes of types
(e.g. uints, ints and floats) where equal bit sizes for each are not
capable of saving all the information in a different type of equivalent
bit size. Some examples are int32 vs float32 or uint32 vs int32.
Generally, the result is the higher type of larger size than both
(if available). So: ::
int32 + float32 -> float64
uint32 + int32 -> int64
Finally, the type coercion behavior when expressions involve Python
scalars is different than that seen for arrays. Since Python has a
limited number of types, combining a Python int with a dtype=np.int8
array does not coerce to the higher type but instead, the type of the
array prevails. So the rules for Python scalars combined with arrays is
that the result will be that of the array equivalent the Python scalar
if the Python scalar is of a higher 'kind' than the array (e.g., float
vs. int), otherwise the resultant type will be that of the array.
For example: ::
Python int + int8 -> int8
Python float + int8 -> float64
ufunc methods
=============
Binary ufuncs support 4 methods.
**.reduce(arr)** applies the binary operator to elements of the array in
sequence. For example: ::
>>> np.add.reduce(np.arange(10)) # adds all elements of array
45
For multidimensional arrays, the first dimension is reduced by default: ::
>>> np.add.reduce(np.arange(10).reshape(2,5))
array([ 5, 7, 9, 11, 13])
The axis keyword can be used to specify different axes to reduce: ::
>>> np.add.reduce(np.arange(10).reshape(2,5),axis=1)
array([10, 35])
**.accumulate(arr)** applies the binary operator and generates an
equivalently shaped array that includes the accumulated amount for each
element of the array. A couple examples: ::
>>> np.add.accumulate(np.arange(10))
array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45])
>>> np.multiply.accumulate(np.arange(1,9))
array([ 1, 2, 6, 24, 120, 720, 5040, 40320])
The behavior for multidimensional arrays is the same as for .reduce(),
as is the use of the axis keyword).
**.reduceat(arr,indices)** allows one to apply reduce to selected parts
of an array. It is a difficult method to understand. See the documentation
at:
**.outer(arr1,arr2)** generates an outer operation on the two arrays arr1 and
arr2. It will work on multidimensional arrays (the shape of the result is
the concatenation of the two input shapes.: ::
>>> np.multiply.outer(np.arange(3),np.arange(4))
array([[0, 0, 0, 0],
[0, 1, 2, 3],
[0, 2, 4, 6]])
Output arguments
================
All ufuncs accept an optional output array. The array must be of the expected
output shape. Beware that if the type of the output array is of a different
(and lower) type than the output result, the results may be silently truncated
or otherwise corrupted in the downcast to the lower type. This usage is useful
when one wants to avoid creating large temporary arrays and instead allows one
to reuse the same array memory repeatedly (at the expense of not being able to
use more convenient operator notation in expressions). Note that when the
output argument is used, the ufunc still returns a reference to the result.
>>> x = np.arange(2)
>>> np.add(np.arange(2),np.arange(2.),x)
array([0, 2])
>>> x
array([0, 2])
and & or as ufuncs
==================
Invariably people try to use the python 'and' and 'or' as logical operators
(and quite understandably). But these operators do not behave as normal
operators since Python treats these quite differently. They cannot be
overloaded with array equivalents. Thus using 'and' or 'or' with an array
results in an error. There are two alternatives:
1) use the ufunc functions logical_and() and logical_or().
2) use the bitwise operators & and \\|. The drawback of these is that if
the arguments to these operators are not boolean arrays, the result is
likely incorrect. On the other hand, most usages of logical_and and
logical_or are with boolean arrays. As long as one is careful, this is
a convenient way to apply these operators.
"""
| 5,357 | Python | 37.826087 | 79 | 0.720179 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/shape_base.pyi | from collections.abc import Sequence
from typing import TypeVar, overload, Any, SupportsIndex
from numpy import generic
from numpy._typing import ArrayLike, NDArray, _ArrayLike
_SCT = TypeVar("_SCT", bound=generic)
_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
__all__: list[str]
@overload
def atleast_1d(arys: _ArrayLike[_SCT], /) -> NDArray[_SCT]: ...
@overload
def atleast_1d(arys: ArrayLike, /) -> NDArray[Any]: ...
@overload
def atleast_1d(*arys: ArrayLike) -> list[NDArray[Any]]: ...
@overload
def atleast_2d(arys: _ArrayLike[_SCT], /) -> NDArray[_SCT]: ...
@overload
def atleast_2d(arys: ArrayLike, /) -> NDArray[Any]: ...
@overload
def atleast_2d(*arys: ArrayLike) -> list[NDArray[Any]]: ...
@overload
def atleast_3d(arys: _ArrayLike[_SCT], /) -> NDArray[_SCT]: ...
@overload
def atleast_3d(arys: ArrayLike, /) -> NDArray[Any]: ...
@overload
def atleast_3d(*arys: ArrayLike) -> list[NDArray[Any]]: ...
@overload
def vstack(tup: Sequence[_ArrayLike[_SCT]]) -> NDArray[_SCT]: ...
@overload
def vstack(tup: Sequence[ArrayLike]) -> NDArray[Any]: ...
@overload
def hstack(tup: Sequence[_ArrayLike[_SCT]]) -> NDArray[_SCT]: ...
@overload
def hstack(tup: Sequence[ArrayLike]) -> NDArray[Any]: ...
@overload
def stack(
arrays: Sequence[_ArrayLike[_SCT]],
axis: SupportsIndex = ...,
out: None = ...,
) -> NDArray[_SCT]: ...
@overload
def stack(
arrays: Sequence[ArrayLike],
axis: SupportsIndex = ...,
out: None = ...,
) -> NDArray[Any]: ...
@overload
def stack(
arrays: Sequence[ArrayLike],
axis: SupportsIndex = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def block(arrays: _ArrayLike[_SCT]) -> NDArray[_SCT]: ...
@overload
def block(arrays: ArrayLike) -> NDArray[Any]: ...
| 1,744 | unknown | 25.439394 | 65 | 0.650803 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/multiarray.py | """
Create the numpy.core.multiarray namespace for backward compatibility. In v1.16
the multiarray and umath c-extension modules were merged into a single
_multiarray_umath extension module. So we replicate the old namespace
by importing from the extension module.
"""
import functools
from . import overrides
from . import _multiarray_umath
from ._multiarray_umath import * # noqa: F403
# These imports are needed for backward compatibility,
# do not change them. issue gh-15518
# _get_ndarray_c_version is semi-public, on purpose not added to __all__
from ._multiarray_umath import (
_fastCopyAndTranspose, _flagdict, from_dlpack, _insert, _reconstruct,
_vec_string, _ARRAY_API, _monotonicity, _get_ndarray_c_version,
_get_madvise_hugepage, _set_madvise_hugepage,
)
__all__ = [
'_ARRAY_API', 'ALLOW_THREADS', 'BUFSIZE', 'CLIP', 'DATETIMEUNITS',
'ITEM_HASOBJECT', 'ITEM_IS_POINTER', 'LIST_PICKLE', 'MAXDIMS',
'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'NEEDS_INIT', 'NEEDS_PYAPI',
'RAISE', 'USE_GETITEM', 'USE_SETITEM', 'WRAP', '_fastCopyAndTranspose',
'_flagdict', 'from_dlpack', '_insert', '_reconstruct', '_vec_string',
'_monotonicity', 'add_docstring', 'arange', 'array', 'asarray',
'asanyarray', 'ascontiguousarray', 'asfortranarray', 'bincount',
'broadcast', 'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast',
'compare_chararrays', 'concatenate', 'copyto', 'correlate', 'correlate2',
'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data',
'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype',
'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat',
'frombuffer', 'fromfile', 'fromiter', 'fromstring',
'get_handler_name', 'get_handler_version', 'inner', 'interp',
'interp_complex', 'is_busday', 'lexsort', 'matmul', 'may_share_memory',
'min_scalar_type', 'ndarray', 'nditer', 'nested_iters',
'normalize_axis_index', 'packbits', 'promote_types', 'putmask',
'ravel_multi_index', 'result_type', 'scalar', 'set_datetimeparse_function',
'set_legacy_print_mode', 'set_numeric_ops', 'set_string_function',
'set_typeDict', 'shares_memory', 'tracemalloc_domain', 'typeinfo',
'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros']
# For backward compatibility, make sure pickle imports these functions from here
_reconstruct.__module__ = 'numpy.core.multiarray'
scalar.__module__ = 'numpy.core.multiarray'
from_dlpack.__module__ = 'numpy'
arange.__module__ = 'numpy'
array.__module__ = 'numpy'
asarray.__module__ = 'numpy'
asanyarray.__module__ = 'numpy'
ascontiguousarray.__module__ = 'numpy'
asfortranarray.__module__ = 'numpy'
datetime_data.__module__ = 'numpy'
empty.__module__ = 'numpy'
frombuffer.__module__ = 'numpy'
fromfile.__module__ = 'numpy'
fromiter.__module__ = 'numpy'
frompyfunc.__module__ = 'numpy'
fromstring.__module__ = 'numpy'
geterrobj.__module__ = 'numpy'
may_share_memory.__module__ = 'numpy'
nested_iters.__module__ = 'numpy'
promote_types.__module__ = 'numpy'
set_numeric_ops.__module__ = 'numpy'
seterrobj.__module__ = 'numpy'
zeros.__module__ = 'numpy'
# We can't verify dispatcher signatures because NumPy's C functions don't
# support introspection.
array_function_from_c_func_and_dispatcher = functools.partial(
overrides.array_function_from_dispatcher,
module='numpy', docs_from_dispatcher=True, verify=False)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.empty_like)
def empty_like(prototype, dtype=None, order=None, subok=None, shape=None):
"""
empty_like(prototype, dtype=None, order='K', subok=True, shape=None)
Return a new array with the same shape and type as a given array.
Parameters
----------
prototype : array_like
The shape and data-type of `prototype` define these same attributes
of the returned array.
dtype : data-type, optional
Overrides the data type of the result.
.. versionadded:: 1.6.0
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `prototype` is Fortran
contiguous, 'C' otherwise. 'K' means match the layout of `prototype`
as closely as possible.
.. versionadded:: 1.6.0
subok : bool, optional.
If True, then the newly created array will use the sub-class
type of `prototype`, otherwise it will be a base-class array. Defaults
to True.
shape : int or sequence of ints, optional.
Overrides the shape of the result. If order='K' and the number of
dimensions is unchanged, will try to keep order, otherwise,
order='C' is implied.
.. versionadded:: 1.17.0
Returns
-------
out : ndarray
Array of uninitialized (arbitrary) data with the same
shape and type as `prototype`.
See Also
--------
ones_like : Return an array of ones with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full_like : Return a new array with shape of input filled with value.
empty : Return a new uninitialized array.
Notes
-----
This function does *not* initialize the returned array; to do that use
`zeros_like` or `ones_like` instead. It may be marginally faster than
the functions that do set the array values.
Examples
--------
>>> a = ([1,2,3], [4,5,6]) # a is array-like
>>> np.empty_like(a)
array([[-1073741821, -1073741821, 3], # uninitialized
[ 0, 0, -1073741821]])
>>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
>>> np.empty_like(a)
array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000], # uninitialized
[ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])
"""
return (prototype,)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.concatenate)
def concatenate(arrays, axis=None, out=None, *, dtype=None, casting=None):
"""
concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting="same_kind")
Join a sequence of arrays along an existing axis.
Parameters
----------
a1, a2, ... : sequence of array_like
The arrays must have the same shape, except in the dimension
corresponding to `axis` (the first, by default).
axis : int, optional
The axis along which the arrays will be joined. If axis is None,
arrays are flattened before use. Default is 0.
out : ndarray, optional
If provided, the destination to place the result. The shape must be
correct, matching that of what concatenate would have returned if no
out argument were specified.
dtype : str or dtype
If provided, the destination array will have this dtype. Cannot be
provided together with `out`.
.. versionadded:: 1.20.0
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Defaults to 'same_kind'.
.. versionadded:: 1.20.0
Returns
-------
res : ndarray
The concatenated array.
See Also
--------
ma.concatenate : Concatenate function that preserves input masks.
array_split : Split an array into multiple sub-arrays of equal or
near-equal size.
split : Split array into a list of multiple sub-arrays of equal size.
hsplit : Split array into multiple sub-arrays horizontally (column wise).
vsplit : Split array into multiple sub-arrays vertically (row wise).
dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
stack : Stack a sequence of arrays along a new axis.
block : Assemble arrays from blocks.
hstack : Stack arrays in sequence horizontally (column wise).
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
column_stack : Stack 1-D arrays as columns into a 2-D array.
Notes
-----
When one or more of the arrays to be concatenated is a MaskedArray,
this function will return a MaskedArray object instead of an ndarray,
but the input masks are *not* preserved. In cases where a MaskedArray
is expected as input, use the ma.concatenate function from the masked
array module instead.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
[3, 4, 6]])
>>> np.concatenate((a, b), axis=None)
array([1, 2, 3, 4, 5, 6])
This function will not preserve masking of MaskedArray inputs.
>>> a = np.ma.arange(3)
>>> a[1] = np.ma.masked
>>> b = np.arange(2, 5)
>>> a
masked_array(data=[0, --, 2],
mask=[False, True, False],
fill_value=999999)
>>> b
array([2, 3, 4])
>>> np.concatenate([a, b])
masked_array(data=[0, 1, 2, 2, 3, 4],
mask=False,
fill_value=999999)
>>> np.ma.concatenate([a, b])
masked_array(data=[0, --, 2, 2, 3, 4],
mask=[False, True, False, False, False, False],
fill_value=999999)
"""
if out is not None:
# optimize for the typical case where only arrays is provided
arrays = list(arrays)
arrays.append(out)
return arrays
@array_function_from_c_func_and_dispatcher(_multiarray_umath.inner)
def inner(a, b):
"""
inner(a, b, /)
Inner product of two arrays.
Ordinary inner product of vectors for 1-D arrays (without complex
conjugation), in higher dimensions a sum product over the last axes.
Parameters
----------
a, b : array_like
If `a` and `b` are nonscalar, their last dimensions must match.
Returns
-------
out : ndarray
If `a` and `b` are both
scalars or both 1-D arrays then a scalar is returned; otherwise
an array is returned.
``out.shape = (*a.shape[:-1], *b.shape[:-1])``
Raises
------
ValueError
If both `a` and `b` are nonscalar and their last dimensions have
different sizes.
See Also
--------
tensordot : Sum products over arbitrary axes.
dot : Generalised matrix product, using second last dimension of `b`.
einsum : Einstein summation convention.
Notes
-----
For vectors (1-D arrays) it computes the ordinary inner-product::
np.inner(a, b) = sum(a[:]*b[:])
More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::
np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))
or explicitly::
np.inner(a, b)[i0,...,ir-2,j0,...,js-2]
= sum(a[i0,...,ir-2,:]*b[j0,...,js-2,:])
In addition `a` or `b` may be scalars, in which case::
np.inner(a,b) = a*b
Examples
--------
Ordinary inner product for vectors:
>>> a = np.array([1,2,3])
>>> b = np.array([0,1,0])
>>> np.inner(a, b)
2
Some multidimensional examples:
>>> a = np.arange(24).reshape((2,3,4))
>>> b = np.arange(4)
>>> c = np.inner(a, b)
>>> c.shape
(2, 3)
>>> c
array([[ 14, 38, 62],
[ 86, 110, 134]])
>>> a = np.arange(2).reshape((1,1,2))
>>> b = np.arange(6).reshape((3,2))
>>> c = np.inner(a, b)
>>> c.shape
(1, 1, 3)
>>> c
array([[[1, 3, 5]]])
An example where `b` is a scalar:
>>> np.inner(np.eye(2), 7)
array([[7., 0.],
[0., 7.]])
"""
return (a, b)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.where)
def where(condition, x=None, y=None):
"""
where(condition, [x, y], /)
Return elements chosen from `x` or `y` depending on `condition`.
.. note::
When only `condition` is provided, this function is a shorthand for
``np.asarray(condition).nonzero()``. Using `nonzero` directly should be
preferred, as it behaves correctly for subclasses. The rest of this
documentation covers only the case where all three arguments are
provided.
Parameters
----------
condition : array_like, bool
Where True, yield `x`, otherwise yield `y`.
x, y : array_like
Values from which to choose. `x`, `y` and `condition` need to be
broadcastable to some shape.
Returns
-------
out : ndarray
An array with elements from `x` where `condition` is True, and elements
from `y` elsewhere.
See Also
--------
choose
nonzero : The function that is called when x and y are omitted
Notes
-----
If all the arrays are 1-D, `where` is equivalent to::
[xv if c else yv
for c, xv, yv in zip(condition, x, y)]
Examples
--------
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.where(a < 5, a, 10*a)
array([ 0, 1, 2, 3, 4, 50, 60, 70, 80, 90])
This can be used on multidimensional arrays too:
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
The shapes of x, y, and the condition are broadcast together:
>>> x, y = np.ogrid[:3, :4]
>>> np.where(x < y, x, 10 + y) # both x and 10+y are broadcast
array([[10, 0, 0, 0],
[10, 11, 1, 1],
[10, 11, 12, 2]])
>>> a = np.array([[0, 1, 2],
... [0, 2, 4],
... [0, 3, 6]])
>>> np.where(a < 4, a, -1) # -1 is broadcast
array([[ 0, 1, 2],
[ 0, 2, -1],
[ 0, 3, -1]])
"""
return (condition, x, y)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.lexsort)
def lexsort(keys, axis=None):
"""
lexsort(keys, axis=-1)
Perform an indirect stable sort using a sequence of keys.
Given multiple sorting keys, which can be interpreted as columns in a
spreadsheet, lexsort returns an array of integer indices that describes
the sort order by multiple columns. The last key in the sequence is used
for the primary sort order, the second-to-last key for the secondary sort
order, and so on. The keys argument must be a sequence of objects that
can be converted to arrays of the same shape. If a 2D array is provided
for the keys argument, its rows are interpreted as the sorting keys and
sorting is according to the last row, second last row etc.
Parameters
----------
keys : (k, N) array or tuple containing k (N,)-shaped sequences
The `k` different "columns" to be sorted. The last column (or row if
`keys` is a 2D array) is the primary sort key.
axis : int, optional
Axis to be indirectly sorted. By default, sort over the last axis.
Returns
-------
indices : (N,) ndarray of ints
Array of indices that sort the keys along the specified axis.
See Also
--------
argsort : Indirect sort.
ndarray.sort : In-place sort.
sort : Return a sorted copy of an array.
Examples
--------
Sort names: first by surname, then by name.
>>> surnames = ('Hertz', 'Galilei', 'Hertz')
>>> first_names = ('Heinrich', 'Galileo', 'Gustav')
>>> ind = np.lexsort((first_names, surnames))
>>> ind
array([1, 2, 0])
>>> [surnames[i] + ", " + first_names[i] for i in ind]
['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']
Sort two columns of numbers:
>>> a = [1,5,1,4,3,4,4] # First column
>>> b = [9,4,0,4,0,2,1] # Second column
>>> ind = np.lexsort((b,a)) # Sort by a, then by b
>>> ind
array([2, 0, 4, 6, 5, 3, 1])
>>> [(a[i],b[i]) for i in ind]
[(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]
Note that sorting is first according to the elements of ``a``.
Secondary sorting is according to the elements of ``b``.
A normal ``argsort`` would have yielded:
>>> [(a[i],b[i]) for i in np.argsort(a)]
[(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]
Structured arrays are sorted lexically by ``argsort``:
>>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],
... dtype=np.dtype([('x', int), ('y', int)]))
>>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))
array([2, 0, 4, 6, 5, 3, 1])
"""
if isinstance(keys, tuple):
return keys
else:
return (keys,)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.can_cast)
def can_cast(from_, to, casting=None):
"""
can_cast(from_, to, casting='safe')
Returns True if cast between data types can occur according to the
casting rule. If from is a scalar or array scalar, also returns
True if the scalar value can be cast without overflow or truncation
to an integer.
Parameters
----------
from_ : dtype, dtype specifier, scalar, or array
Data type, scalar, or array to cast from.
to : dtype or dtype specifier
Data type to cast to.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
Returns
-------
out : bool
True if cast can occur according to the casting rule.
Notes
-----
.. versionchanged:: 1.17.0
Casting between a simple data type and a structured one is possible only
for "unsafe" casting. Casting to multiple fields is allowed, but
casting from multiple fields is not.
.. versionchanged:: 1.9.0
Casting from numeric to string types in 'safe' casting mode requires
that the string dtype length is long enough to store the maximum
integer/float value converted.
See also
--------
dtype, result_type
Examples
--------
Basic examples
>>> np.can_cast(np.int32, np.int64)
True
>>> np.can_cast(np.float64, complex)
True
>>> np.can_cast(complex, float)
False
>>> np.can_cast('i8', 'f8')
True
>>> np.can_cast('i8', 'f4')
False
>>> np.can_cast('i4', 'S4')
False
Casting scalars
>>> np.can_cast(100, 'i1')
True
>>> np.can_cast(150, 'i1')
False
>>> np.can_cast(150, 'u1')
True
>>> np.can_cast(3.5e100, np.float32)
False
>>> np.can_cast(1000.0, np.float32)
True
Array scalar checks the value, array does not
>>> np.can_cast(np.array(1000.0), np.float32)
True
>>> np.can_cast(np.array([1000.0]), np.float32)
False
Using the casting rules
>>> np.can_cast('i8', 'i8', 'no')
True
>>> np.can_cast('<i8', '>i8', 'no')
False
>>> np.can_cast('<i8', '>i8', 'equiv')
True
>>> np.can_cast('<i4', '>i8', 'equiv')
False
>>> np.can_cast('<i4', '>i8', 'safe')
True
>>> np.can_cast('<i8', '>i4', 'safe')
False
>>> np.can_cast('<i8', '>i4', 'same_kind')
True
>>> np.can_cast('<i8', '>u4', 'same_kind')
False
>>> np.can_cast('<i8', '>u4', 'unsafe')
True
"""
return (from_,)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.min_scalar_type)
def min_scalar_type(a):
"""
min_scalar_type(a, /)
For scalar ``a``, returns the data type with the smallest size
and smallest scalar kind which can hold its value. For non-scalar
array ``a``, returns the vector's dtype unmodified.
Floating point values are not demoted to integers,
and complex values are not demoted to floats.
Parameters
----------
a : scalar or array_like
The value whose minimal data type is to be found.
Returns
-------
out : dtype
The minimal data type.
Notes
-----
.. versionadded:: 1.6.0
See Also
--------
result_type, promote_types, dtype, can_cast
Examples
--------
>>> np.min_scalar_type(10)
dtype('uint8')
>>> np.min_scalar_type(-260)
dtype('int16')
>>> np.min_scalar_type(3.1)
dtype('float16')
>>> np.min_scalar_type(1e50)
dtype('float64')
>>> np.min_scalar_type(np.arange(4,dtype='f8'))
dtype('float64')
"""
return (a,)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.result_type)
def result_type(*arrays_and_dtypes):
"""
result_type(*arrays_and_dtypes)
Returns the type that results from applying the NumPy
type promotion rules to the arguments.
Type promotion in NumPy works similarly to the rules in languages
like C++, with some slight differences. When both scalars and
arrays are used, the array's type takes precedence and the actual value
of the scalar is taken into account.
For example, calculating 3*a, where a is an array of 32-bit floats,
intuitively should result in a 32-bit float output. If the 3 is a
32-bit integer, the NumPy rules indicate it can't convert losslessly
into a 32-bit float, so a 64-bit float should be the result type.
By examining the value of the constant, '3', we see that it fits in
an 8-bit integer, which can be cast losslessly into the 32-bit float.
Parameters
----------
arrays_and_dtypes : list of arrays and dtypes
The operands of some operation whose result type is needed.
Returns
-------
out : dtype
The result type.
See also
--------
dtype, promote_types, min_scalar_type, can_cast
Notes
-----
.. versionadded:: 1.6.0
The specific algorithm used is as follows.
Categories are determined by first checking which of boolean,
integer (int/uint), or floating point (float/complex) the maximum
kind of all the arrays and the scalars are.
If there are only scalars or the maximum category of the scalars
is higher than the maximum category of the arrays,
the data types are combined with :func:`promote_types`
to produce the return value.
Otherwise, `min_scalar_type` is called on each array, and
the resulting data types are all combined with :func:`promote_types`
to produce the return value.
The set of int values is not a subset of the uint values for types
with the same number of bits, something not reflected in
:func:`min_scalar_type`, but handled as a special case in `result_type`.
Examples
--------
>>> np.result_type(3, np.arange(7, dtype='i1'))
dtype('int8')
>>> np.result_type('i4', 'c8')
dtype('complex128')
>>> np.result_type(3.0, -2)
dtype('float64')
"""
return arrays_and_dtypes
@array_function_from_c_func_and_dispatcher(_multiarray_umath.dot)
def dot(a, b, out=None):
"""
dot(a, b, out=None)
Dot product of two arrays. Specifically,
- If both `a` and `b` are 1-D arrays, it is inner product of vectors
(without complex conjugation).
- If both `a` and `b` are 2-D arrays, it is matrix multiplication,
but using :func:`matmul` or ``a @ b`` is preferred.
- If either `a` or `b` is 0-D (scalar), it is equivalent to :func:`multiply`
and using ``numpy.multiply(a, b)`` or ``a * b`` is preferred.
- If `a` is an N-D array and `b` is a 1-D array, it is a sum product over
the last axis of `a` and `b`.
- If `a` is an N-D array and `b` is an M-D array (where ``M>=2``), it is a
sum product over the last axis of `a` and the second-to-last axis of `b`::
dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
Parameters
----------
a : array_like
First argument.
b : array_like
Second argument.
out : ndarray, optional
Output argument. This must have the exact kind that would be returned
if it was not used. In particular, it must have the right type, must be
C-contiguous, and its dtype must be the dtype that would be returned
for `dot(a,b)`. This is a performance feature. Therefore, if these
conditions are not met, an exception is raised, instead of attempting
to be flexible.
Returns
-------
output : ndarray
Returns the dot product of `a` and `b`. If `a` and `b` are both
scalars or both 1-D arrays then a scalar is returned; otherwise
an array is returned.
If `out` is given, then it is returned.
Raises
------
ValueError
If the last dimension of `a` is not the same size as
the second-to-last dimension of `b`.
See Also
--------
vdot : Complex-conjugating dot product.
tensordot : Sum products over arbitrary axes.
einsum : Einstein summation convention.
matmul : '@' operator as method with out parameter.
linalg.multi_dot : Chained dot product.
Examples
--------
>>> np.dot(3, 4)
12
Neither argument is complex-conjugated:
>>> np.dot([2j, 3j], [2j, 3j])
(-13+0j)
For 2-D arrays it is the matrix product:
>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.dot(a, b)
array([[4, 1],
[2, 2]])
>>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
>>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
>>> np.dot(a, b)[2,3,2,1,2,2]
499128
>>> sum(a[2,3,2,:] * b[1,2,:,2])
499128
"""
return (a, b, out)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.vdot)
def vdot(a, b):
"""
vdot(a, b, /)
Return the dot product of two vectors.
The vdot(`a`, `b`) function handles complex numbers differently than
dot(`a`, `b`). If the first argument is complex the complex conjugate
of the first argument is used for the calculation of the dot product.
Note that `vdot` handles multidimensional arrays differently than `dot`:
it does *not* perform a matrix product, but flattens input arguments
to 1-D vectors first. Consequently, it should only be used for vectors.
Parameters
----------
a : array_like
If `a` is complex the complex conjugate is taken before calculation
of the dot product.
b : array_like
Second argument to the dot product.
Returns
-------
output : ndarray
Dot product of `a` and `b`. Can be an int, float, or
complex depending on the types of `a` and `b`.
See Also
--------
dot : Return the dot product without using the complex conjugate of the
first argument.
Examples
--------
>>> a = np.array([1+2j,3+4j])
>>> b = np.array([5+6j,7+8j])
>>> np.vdot(a, b)
(70-8j)
>>> np.vdot(b, a)
(70+8j)
Note that higher-dimensional arrays are flattened!
>>> a = np.array([[1, 4], [5, 6]])
>>> b = np.array([[4, 1], [2, 2]])
>>> np.vdot(a, b)
30
>>> np.vdot(b, a)
30
>>> 1*4 + 4*1 + 5*2 + 6*2
30
"""
return (a, b)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.bincount)
def bincount(x, weights=None, minlength=None):
"""
bincount(x, /, weights=None, minlength=0)
Count number of occurrences of each value in array of non-negative ints.
The number of bins (of size 1) is one larger than the largest value in
`x`. If `minlength` is specified, there will be at least this number
of bins in the output array (though it will be longer if necessary,
depending on the contents of `x`).
Each bin gives the number of occurrences of its index value in `x`.
If `weights` is specified the input array is weighted by it, i.e. if a
value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead
of ``out[n] += 1``.
Parameters
----------
x : array_like, 1 dimension, nonnegative ints
Input array.
weights : array_like, optional
Weights, array of the same shape as `x`.
minlength : int, optional
A minimum number of bins for the output array.
.. versionadded:: 1.6.0
Returns
-------
out : ndarray of ints
The result of binning the input array.
The length of `out` is equal to ``np.amax(x)+1``.
Raises
------
ValueError
If the input is not 1-dimensional, or contains elements with negative
values, or if `minlength` is negative.
TypeError
If the type of the input is float or complex.
See Also
--------
histogram, digitize, unique
Examples
--------
>>> np.bincount(np.arange(5))
array([1, 1, 1, 1, 1])
>>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
array([1, 3, 1, 1, 0, 0, 0, 1])
>>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
>>> np.bincount(x).size == np.amax(x)+1
True
The input array needs to be of integer dtype, otherwise a
TypeError is raised:
>>> np.bincount(np.arange(5, dtype=float))
Traceback (most recent call last):
...
TypeError: Cannot cast array data from dtype('float64') to dtype('int64')
according to the rule 'safe'
A possible use of ``bincount`` is to perform sums over
variable-size chunks of an array, using the ``weights`` keyword.
>>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
>>> x = np.array([0, 1, 1, 2, 2, 2])
>>> np.bincount(x, weights=w)
array([ 0.3, 0.7, 1.1])
"""
return (x, weights)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.ravel_multi_index)
def ravel_multi_index(multi_index, dims, mode=None, order=None):
"""
ravel_multi_index(multi_index, dims, mode='raise', order='C')
Converts a tuple of index arrays into an array of flat
indices, applying boundary modes to the multi-index.
Parameters
----------
multi_index : tuple of array_like
A tuple of integer arrays, one array for each dimension.
dims : tuple of ints
The shape of array into which the indices from ``multi_index`` apply.
mode : {'raise', 'wrap', 'clip'}, optional
Specifies how out-of-bounds indices are handled. Can specify
either one mode or a tuple of modes, one mode per index.
* 'raise' -- raise an error (default)
* 'wrap' -- wrap around
* 'clip' -- clip to the range
In 'clip' mode, a negative index which would normally
wrap will clip to 0 instead.
order : {'C', 'F'}, optional
Determines whether the multi-index should be viewed as
indexing in row-major (C-style) or column-major
(Fortran-style) order.
Returns
-------
raveled_indices : ndarray
An array of indices into the flattened version of an array
of dimensions ``dims``.
See Also
--------
unravel_index
Notes
-----
.. versionadded:: 1.6.0
Examples
--------
>>> arr = np.array([[3,6,6],[4,5,1]])
>>> np.ravel_multi_index(arr, (7,6))
array([22, 41, 37])
>>> np.ravel_multi_index(arr, (7,6), order='F')
array([31, 41, 13])
>>> np.ravel_multi_index(arr, (4,6), mode='clip')
array([22, 23, 19])
>>> np.ravel_multi_index(arr, (4,4), mode=('clip','wrap'))
array([12, 13, 13])
>>> np.ravel_multi_index((3,1,4,1), (6,7,8,9))
1621
"""
return multi_index
@array_function_from_c_func_and_dispatcher(_multiarray_umath.unravel_index)
def unravel_index(indices, shape=None, order=None):
"""
unravel_index(indices, shape, order='C')
Converts a flat index or array of flat indices into a tuple
of coordinate arrays.
Parameters
----------
indices : array_like
An integer array whose elements are indices into the flattened
version of an array of dimensions ``shape``. Before version 1.6.0,
this function accepted just one index value.
shape : tuple of ints
The shape of the array to use for unraveling ``indices``.
.. versionchanged:: 1.16.0
Renamed from ``dims`` to ``shape``.
order : {'C', 'F'}, optional
Determines whether the indices should be viewed as indexing in
row-major (C-style) or column-major (Fortran-style) order.
.. versionadded:: 1.6.0
Returns
-------
unraveled_coords : tuple of ndarray
Each array in the tuple has the same shape as the ``indices``
array.
See Also
--------
ravel_multi_index
Examples
--------
>>> np.unravel_index([22, 41, 37], (7,6))
(array([3, 6, 6]), array([4, 5, 1]))
>>> np.unravel_index([31, 41, 13], (7,6), order='F')
(array([3, 6, 6]), array([4, 5, 1]))
>>> np.unravel_index(1621, (6,7,8,9))
(3, 1, 4, 1)
"""
return (indices,)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.copyto)
def copyto(dst, src, casting=None, where=None):
"""
copyto(dst, src, casting='same_kind', where=True)
Copies values from one array to another, broadcasting as necessary.
Raises a TypeError if the `casting` rule is violated, and if
`where` is provided, it selects which elements to copy.
.. versionadded:: 1.7.0
Parameters
----------
dst : ndarray
The array into which values are copied.
src : array_like
The array from which values are copied.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur when copying.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
where : array_like of bool, optional
A boolean array which is broadcasted to match the dimensions
of `dst`, and selects elements to copy from `src` to `dst`
wherever it contains the value True.
"""
return (dst, src, where)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.putmask)
def putmask(a, mask, values):
"""
putmask(a, mask, values)
Changes elements of an array based on conditional and input values.
Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``.
If `values` is not the same size as `a` and `mask` then it will repeat.
This gives behavior different from ``a[mask] = values``.
Parameters
----------
a : ndarray
Target array.
mask : array_like
Boolean mask array. It has to be the same shape as `a`.
values : array_like
Values to put into `a` where `mask` is True. If `values` is smaller
than `a` it will be repeated.
See Also
--------
place, put, take, copyto
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> np.putmask(x, x>2, x**2)
>>> x
array([[ 0, 1, 2],
[ 9, 16, 25]])
If `values` is smaller than `a` it is repeated:
>>> x = np.arange(5)
>>> np.putmask(x, x>1, [-33, -44])
>>> x
array([ 0, 1, -33, -44, -33])
"""
return (a, mask, values)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.packbits)
def packbits(a, axis=None, bitorder='big'):
"""
packbits(a, /, axis=None, bitorder='big')
Packs the elements of a binary-valued array into bits in a uint8 array.
The result is padded to full bytes by inserting zero bits at the end.
Parameters
----------
a : array_like
An array of integers or booleans whose elements should be packed to
bits.
axis : int, optional
The dimension over which bit-packing is done.
``None`` implies packing the flattened array.
bitorder : {'big', 'little'}, optional
The order of the input bits. 'big' will mimic bin(val),
``[0, 0, 0, 0, 0, 0, 1, 1] => 3 = 0b00000011``, 'little' will
reverse the order so ``[1, 1, 0, 0, 0, 0, 0, 0] => 3``.
Defaults to 'big'.
.. versionadded:: 1.17.0
Returns
-------
packed : ndarray
Array of type uint8 whose elements represent bits corresponding to the
logical (0 or nonzero) value of the input elements. The shape of
`packed` has the same number of dimensions as the input (unless `axis`
is None, in which case the output is 1-D).
See Also
--------
unpackbits: Unpacks elements of a uint8 array into a binary-valued output
array.
Examples
--------
>>> a = np.array([[[1,0,1],
... [0,1,0]],
... [[1,1,0],
... [0,0,1]]])
>>> b = np.packbits(a, axis=-1)
>>> b
array([[[160],
[ 64]],
[[192],
[ 32]]], dtype=uint8)
Note that in binary 160 = 1010 0000, 64 = 0100 0000, 192 = 1100 0000,
and 32 = 0010 0000.
"""
return (a,)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.unpackbits)
def unpackbits(a, axis=None, count=None, bitorder='big'):
"""
unpackbits(a, /, axis=None, count=None, bitorder='big')
Unpacks elements of a uint8 array into a binary-valued output array.
Each element of `a` represents a bit-field that should be unpacked
into a binary-valued output array. The shape of the output array is
either 1-D (if `axis` is ``None``) or the same shape as the input
array with unpacking done along the axis specified.
Parameters
----------
a : ndarray, uint8 type
Input array.
axis : int, optional
The dimension over which bit-unpacking is done.
``None`` implies unpacking the flattened array.
count : int or None, optional
The number of elements to unpack along `axis`, provided as a way
of undoing the effect of packing a size that is not a multiple
of eight. A non-negative number means to only unpack `count`
bits. A negative number means to trim off that many bits from
the end. ``None`` means to unpack the entire array (the
default). Counts larger than the available number of bits will
add zero padding to the output. Negative counts must not
exceed the available number of bits.
.. versionadded:: 1.17.0
bitorder : {'big', 'little'}, optional
The order of the returned bits. 'big' will mimic bin(val),
``3 = 0b00000011 => [0, 0, 0, 0, 0, 0, 1, 1]``, 'little' will reverse
the order to ``[1, 1, 0, 0, 0, 0, 0, 0]``.
Defaults to 'big'.
.. versionadded:: 1.17.0
Returns
-------
unpacked : ndarray, uint8 type
The elements are binary-valued (0 or 1).
See Also
--------
packbits : Packs the elements of a binary-valued array into bits in
a uint8 array.
Examples
--------
>>> a = np.array([[2], [7], [23]], dtype=np.uint8)
>>> a
array([[ 2],
[ 7],
[23]], dtype=uint8)
>>> b = np.unpackbits(a, axis=1)
>>> b
array([[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 0, 1, 1, 1]], dtype=uint8)
>>> c = np.unpackbits(a, axis=1, count=-3)
>>> c
array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 0]], dtype=uint8)
>>> p = np.packbits(b, axis=0)
>>> np.unpackbits(p, axis=0)
array([[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> np.array_equal(b, np.unpackbits(p, axis=0, count=b.shape[0]))
True
"""
return (a,)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.shares_memory)
def shares_memory(a, b, max_work=None):
"""
shares_memory(a, b, /, max_work=None)
Determine if two arrays share memory.
.. warning::
This function can be exponentially slow for some inputs, unless
`max_work` is set to a finite number or ``MAY_SHARE_BOUNDS``.
If in doubt, use `numpy.may_share_memory` instead.
Parameters
----------
a, b : ndarray
Input arrays
max_work : int, optional
Effort to spend on solving the overlap problem (maximum number
of candidate solutions to consider). The following special
values are recognized:
max_work=MAY_SHARE_EXACT (default)
The problem is solved exactly. In this case, the function returns
True only if there is an element shared between the arrays. Finding
the exact solution may take extremely long in some cases.
max_work=MAY_SHARE_BOUNDS
Only the memory bounds of a and b are checked.
Raises
------
numpy.TooHardError
Exceeded max_work.
Returns
-------
out : bool
See Also
--------
may_share_memory
Examples
--------
>>> x = np.array([1, 2, 3, 4])
>>> np.shares_memory(x, np.array([5, 6, 7]))
False
>>> np.shares_memory(x[::2], x)
True
>>> np.shares_memory(x[::2], x[1::2])
False
Checking whether two arrays share memory is NP-complete, and
runtime may increase exponentially in the number of
dimensions. Hence, `max_work` should generally be set to a finite
number, as it is possible to construct examples that take
extremely long to run:
>>> from numpy.lib.stride_tricks import as_strided
>>> x = np.zeros([192163377], dtype=np.int8)
>>> x1 = as_strided(x, strides=(36674, 61119, 85569), shape=(1049, 1049, 1049))
>>> x2 = as_strided(x[64023025:], strides=(12223, 12224, 1), shape=(1049, 1049, 1))
>>> np.shares_memory(x1, x2, max_work=1000)
Traceback (most recent call last):
...
numpy.TooHardError: Exceeded max_work
Running ``np.shares_memory(x1, x2)`` without `max_work` set takes
around 1 minute for this case. It is possible to find problems
that take still significantly longer.
"""
return (a, b)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.may_share_memory)
def may_share_memory(a, b, max_work=None):
"""
may_share_memory(a, b, /, max_work=None)
Determine if two arrays might share memory
A return of True does not necessarily mean that the two arrays
share any element. It just means that they *might*.
Only the memory bounds of a and b are checked by default.
Parameters
----------
a, b : ndarray
Input arrays
max_work : int, optional
Effort to spend on solving the overlap problem. See
`shares_memory` for details. Default for ``may_share_memory``
is to do a bounds check.
Returns
-------
out : bool
See Also
--------
shares_memory
Examples
--------
>>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
False
>>> x = np.zeros([3, 4])
>>> np.may_share_memory(x[:,0], x[:,1])
True
"""
return (a, b)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.is_busday)
def is_busday(dates, weekmask=None, holidays=None, busdaycal=None, out=None):
"""
is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None)
Calculates which of the given dates are valid days, and which are not.
.. versionadded:: 1.7.0
Parameters
----------
dates : array_like of datetime64[D]
The array of dates to process.
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates. They may be
specified in any order, and NaT (not-a-time) dates are ignored.
This list is saved in a normalized form that is suited for
fast calculations of valid days.
busdaycal : busdaycalendar, optional
A `busdaycalendar` object which specifies the valid days. If this
parameter is provided, neither weekmask nor holidays may be
provided.
out : array of bool, optional
If provided, this array is filled with the result.
Returns
-------
out : array of bool
An array with the same shape as ``dates``, containing True for
each valid day, and False for each invalid day.
See Also
--------
busdaycalendar : An object that specifies a custom set of valid days.
busday_offset : Applies an offset counted in valid days.
busday_count : Counts how many valid days are in a half-open date range.
Examples
--------
>>> # The weekdays are Friday, Saturday, and Monday
... np.is_busday(['2011-07-01', '2011-07-02', '2011-07-18'],
... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
array([False, False, True])
"""
return (dates, weekmask, holidays, out)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.busday_offset)
def busday_offset(dates, offsets, roll=None, weekmask=None, holidays=None,
busdaycal=None, out=None):
"""
busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None)
First adjusts the date to fall on a valid day according to
the ``roll`` rule, then applies offsets to the given dates
counted in valid days.
.. versionadded:: 1.7.0
Parameters
----------
dates : array_like of datetime64[D]
The array of dates to process.
offsets : array_like of int
The array of offsets, which is broadcast with ``dates``.
roll : {'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding'}, optional
How to treat dates that do not fall on a valid day. The default
is 'raise'.
* 'raise' means to raise an exception for an invalid day.
* 'nat' means to return a NaT (not-a-time) for an invalid day.
* 'forward' and 'following' mean to take the first valid day
later in time.
* 'backward' and 'preceding' mean to take the first valid day
earlier in time.
* 'modifiedfollowing' means to take the first valid day
later in time unless it is across a Month boundary, in which
case to take the first valid day earlier in time.
* 'modifiedpreceding' means to take the first valid day
earlier in time unless it is across a Month boundary, in which
case to take the first valid day later in time.
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates. They may be
specified in any order, and NaT (not-a-time) dates are ignored.
This list is saved in a normalized form that is suited for
fast calculations of valid days.
busdaycal : busdaycalendar, optional
A `busdaycalendar` object which specifies the valid days. If this
parameter is provided, neither weekmask nor holidays may be
provided.
out : array of datetime64[D], optional
If provided, this array is filled with the result.
Returns
-------
out : array of datetime64[D]
An array with a shape from broadcasting ``dates`` and ``offsets``
together, containing the dates with offsets applied.
See Also
--------
busdaycalendar : An object that specifies a custom set of valid days.
is_busday : Returns a boolean array indicating valid days.
busday_count : Counts how many valid days are in a half-open date range.
Examples
--------
>>> # First business day in October 2011 (not accounting for holidays)
... np.busday_offset('2011-10', 0, roll='forward')
numpy.datetime64('2011-10-03')
>>> # Last business day in February 2012 (not accounting for holidays)
... np.busday_offset('2012-03', -1, roll='forward')
numpy.datetime64('2012-02-29')
>>> # Third Wednesday in January 2011
... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed')
numpy.datetime64('2011-01-19')
>>> # 2012 Mother's Day in Canada and the U.S.
... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')
numpy.datetime64('2012-05-13')
>>> # First business day on or after a date
... np.busday_offset('2011-03-20', 0, roll='forward')
numpy.datetime64('2011-03-21')
>>> np.busday_offset('2011-03-22', 0, roll='forward')
numpy.datetime64('2011-03-22')
>>> # First business day after a date
... np.busday_offset('2011-03-20', 1, roll='backward')
numpy.datetime64('2011-03-21')
>>> np.busday_offset('2011-03-22', 1, roll='backward')
numpy.datetime64('2011-03-23')
"""
return (dates, offsets, weekmask, holidays, out)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.busday_count)
def busday_count(begindates, enddates, weekmask=None, holidays=None,
busdaycal=None, out=None):
"""
busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None)
Counts the number of valid days between `begindates` and
`enddates`, not including the day of `enddates`.
If ``enddates`` specifies a date value that is earlier than the
corresponding ``begindates`` date value, the count will be negative.
.. versionadded:: 1.7.0
Parameters
----------
begindates : array_like of datetime64[D]
The array of the first dates for counting.
enddates : array_like of datetime64[D]
The array of the end dates for counting, which are excluded
from the count themselves.
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates. They may be
specified in any order, and NaT (not-a-time) dates are ignored.
This list is saved in a normalized form that is suited for
fast calculations of valid days.
busdaycal : busdaycalendar, optional
A `busdaycalendar` object which specifies the valid days. If this
parameter is provided, neither weekmask nor holidays may be
provided.
out : array of int, optional
If provided, this array is filled with the result.
Returns
-------
out : array of int
An array with a shape from broadcasting ``begindates`` and ``enddates``
together, containing the number of valid days between
the begin and end dates.
See Also
--------
busdaycalendar : An object that specifies a custom set of valid days.
is_busday : Returns a boolean array indicating valid days.
busday_offset : Applies an offset counted in valid days.
Examples
--------
>>> # Number of weekdays in January 2011
... np.busday_count('2011-01', '2011-02')
21
>>> # Number of weekdays in 2011
>>> np.busday_count('2011', '2012')
260
>>> # Number of Saturdays in 2011
... np.busday_count('2011', '2012', weekmask='Sat')
53
"""
return (begindates, enddates, weekmask, holidays, out)
@array_function_from_c_func_and_dispatcher(
_multiarray_umath.datetime_as_string)
def datetime_as_string(arr, unit=None, timezone=None, casting=None):
"""
datetime_as_string(arr, unit=None, timezone='naive', casting='same_kind')
Convert an array of datetimes into an array of strings.
Parameters
----------
arr : array_like of datetime64
The array of UTC timestamps to format.
unit : str
One of None, 'auto', or a :ref:`datetime unit <arrays.dtypes.dateunits>`.
timezone : {'naive', 'UTC', 'local'} or tzinfo
Timezone information to use when displaying the datetime. If 'UTC', end
with a Z to indicate UTC time. If 'local', convert to the local timezone
first, and suffix with a +-#### timezone offset. If a tzinfo object,
then do as with 'local', but use the specified timezone.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}
Casting to allow when changing between datetime units.
Returns
-------
str_arr : ndarray
An array of strings the same shape as `arr`.
Examples
--------
>>> import pytz
>>> d = np.arange('2002-10-27T04:30', 4*60, 60, dtype='M8[m]')
>>> d
array(['2002-10-27T04:30', '2002-10-27T05:30', '2002-10-27T06:30',
'2002-10-27T07:30'], dtype='datetime64[m]')
Setting the timezone to UTC shows the same information, but with a Z suffix
>>> np.datetime_as_string(d, timezone='UTC')
array(['2002-10-27T04:30Z', '2002-10-27T05:30Z', '2002-10-27T06:30Z',
'2002-10-27T07:30Z'], dtype='<U35')
Note that we picked datetimes that cross a DST boundary. Passing in a
``pytz`` timezone object will print the appropriate offset
>>> np.datetime_as_string(d, timezone=pytz.timezone('US/Eastern'))
array(['2002-10-27T00:30-0400', '2002-10-27T01:30-0400',
'2002-10-27T01:30-0500', '2002-10-27T02:30-0500'], dtype='<U39')
Passing in a unit will change the precision
>>> np.datetime_as_string(d, unit='h')
array(['2002-10-27T04', '2002-10-27T05', '2002-10-27T06', '2002-10-27T07'],
dtype='<U32')
>>> np.datetime_as_string(d, unit='s')
array(['2002-10-27T04:30:00', '2002-10-27T05:30:00', '2002-10-27T06:30:00',
'2002-10-27T07:30:00'], dtype='<U38')
'casting' can be used to specify whether precision can be changed
>>> np.datetime_as_string(d, unit='h', casting='safe')
Traceback (most recent call last):
...
TypeError: Cannot create a datetime string as units 'h' from a NumPy
datetime with units 'm' according to the rule 'safe'
"""
return (arr,)
| 55,434 | Python | 31.782377 | 128 | 0.604449 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/fromnumeric.pyi | import datetime as dt
from collections.abc import Sequence
from typing import Union, Any, overload, TypeVar, Literal, SupportsIndex
from numpy import (
ndarray,
number,
uint64,
int_,
int64,
intp,
float16,
bool_,
floating,
complexfloating,
object_,
generic,
_OrderKACF,
_OrderACF,
_ModeKind,
_PartitionKind,
_SortKind,
_SortSide,
)
from numpy._typing import (
DTypeLike,
_DTypeLike,
ArrayLike,
_ArrayLike,
NDArray,
_ShapeLike,
_Shape,
_ArrayLikeBool_co,
_ArrayLikeUInt_co,
_ArrayLikeInt_co,
_ArrayLikeFloat_co,
_ArrayLikeComplex_co,
_ArrayLikeObject_co,
_IntLike_co,
_BoolLike_co,
_ComplexLike_co,
_NumberLike_co,
_ScalarLike_co,
)
_SCT = TypeVar("_SCT", bound=generic)
_SCT_uifcO = TypeVar("_SCT_uifcO", bound=number[Any] | object_)
_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
__all__: list[str]
@overload
def take(
a: _ArrayLike[_SCT],
indices: _IntLike_co,
axis: None = ...,
out: None = ...,
mode: _ModeKind = ...,
) -> _SCT: ...
@overload
def take(
a: ArrayLike,
indices: _IntLike_co,
axis: None | SupportsIndex = ...,
out: None = ...,
mode: _ModeKind = ...,
) -> Any: ...
@overload
def take(
a: _ArrayLike[_SCT],
indices: _ArrayLikeInt_co,
axis: None | SupportsIndex = ...,
out: None = ...,
mode: _ModeKind = ...,
) -> NDArray[_SCT]: ...
@overload
def take(
a: ArrayLike,
indices: _ArrayLikeInt_co,
axis: None | SupportsIndex = ...,
out: None = ...,
mode: _ModeKind = ...,
) -> NDArray[Any]: ...
@overload
def take(
a: ArrayLike,
indices: _ArrayLikeInt_co,
axis: None | SupportsIndex = ...,
out: _ArrayType = ...,
mode: _ModeKind = ...,
) -> _ArrayType: ...
@overload
def reshape(
a: _ArrayLike[_SCT],
newshape: _ShapeLike,
order: _OrderACF = ...,
) -> NDArray[_SCT]: ...
@overload
def reshape(
a: ArrayLike,
newshape: _ShapeLike,
order: _OrderACF = ...,
) -> NDArray[Any]: ...
@overload
def choose(
a: _IntLike_co,
choices: ArrayLike,
out: None = ...,
mode: _ModeKind = ...,
) -> Any: ...
@overload
def choose(
a: _ArrayLikeInt_co,
choices: _ArrayLike[_SCT],
out: None = ...,
mode: _ModeKind = ...,
) -> NDArray[_SCT]: ...
@overload
def choose(
a: _ArrayLikeInt_co,
choices: ArrayLike,
out: None = ...,
mode: _ModeKind = ...,
) -> NDArray[Any]: ...
@overload
def choose(
a: _ArrayLikeInt_co,
choices: ArrayLike,
out: _ArrayType = ...,
mode: _ModeKind = ...,
) -> _ArrayType: ...
@overload
def repeat(
a: _ArrayLike[_SCT],
repeats: _ArrayLikeInt_co,
axis: None | SupportsIndex = ...,
) -> NDArray[_SCT]: ...
@overload
def repeat(
a: ArrayLike,
repeats: _ArrayLikeInt_co,
axis: None | SupportsIndex = ...,
) -> NDArray[Any]: ...
def put(
a: NDArray[Any],
ind: _ArrayLikeInt_co,
v: ArrayLike,
mode: _ModeKind = ...,
) -> None: ...
@overload
def swapaxes(
a: _ArrayLike[_SCT],
axis1: SupportsIndex,
axis2: SupportsIndex,
) -> NDArray[_SCT]: ...
@overload
def swapaxes(
a: ArrayLike,
axis1: SupportsIndex,
axis2: SupportsIndex,
) -> NDArray[Any]: ...
@overload
def transpose(
a: _ArrayLike[_SCT],
axes: None | _ShapeLike = ...
) -> NDArray[_SCT]: ...
@overload
def transpose(
a: ArrayLike,
axes: None | _ShapeLike = ...
) -> NDArray[Any]: ...
@overload
def partition(
a: _ArrayLike[_SCT],
kth: _ArrayLikeInt_co,
axis: None | SupportsIndex = ...,
kind: _PartitionKind = ...,
order: None | str | Sequence[str] = ...,
) -> NDArray[_SCT]: ...
@overload
def partition(
a: ArrayLike,
kth: _ArrayLikeInt_co,
axis: None | SupportsIndex = ...,
kind: _PartitionKind = ...,
order: None | str | Sequence[str] = ...,
) -> NDArray[Any]: ...
def argpartition(
a: ArrayLike,
kth: _ArrayLikeInt_co,
axis: None | SupportsIndex = ...,
kind: _PartitionKind = ...,
order: None | str | Sequence[str] = ...,
) -> NDArray[intp]: ...
@overload
def sort(
a: _ArrayLike[_SCT],
axis: None | SupportsIndex = ...,
kind: None | _SortKind = ...,
order: None | str | Sequence[str] = ...,
) -> NDArray[_SCT]: ...
@overload
def sort(
a: ArrayLike,
axis: None | SupportsIndex = ...,
kind: None | _SortKind = ...,
order: None | str | Sequence[str] = ...,
) -> NDArray[Any]: ...
def argsort(
a: ArrayLike,
axis: None | SupportsIndex = ...,
kind: None | _SortKind = ...,
order: None | str | Sequence[str] = ...,
) -> NDArray[intp]: ...
@overload
def argmax(
a: ArrayLike,
axis: None = ...,
out: None = ...,
*,
keepdims: Literal[False] = ...,
) -> intp: ...
@overload
def argmax(
a: ArrayLike,
axis: None | SupportsIndex = ...,
out: None = ...,
*,
keepdims: bool = ...,
) -> Any: ...
@overload
def argmax(
a: ArrayLike,
axis: None | SupportsIndex = ...,
out: _ArrayType = ...,
*,
keepdims: bool = ...,
) -> _ArrayType: ...
@overload
def argmin(
a: ArrayLike,
axis: None = ...,
out: None = ...,
*,
keepdims: Literal[False] = ...,
) -> intp: ...
@overload
def argmin(
a: ArrayLike,
axis: None | SupportsIndex = ...,
out: None = ...,
*,
keepdims: bool = ...,
) -> Any: ...
@overload
def argmin(
a: ArrayLike,
axis: None | SupportsIndex = ...,
out: _ArrayType = ...,
*,
keepdims: bool = ...,
) -> _ArrayType: ...
@overload
def searchsorted(
a: ArrayLike,
v: _ScalarLike_co,
side: _SortSide = ...,
sorter: None | _ArrayLikeInt_co = ..., # 1D int array
) -> intp: ...
@overload
def searchsorted(
a: ArrayLike,
v: ArrayLike,
side: _SortSide = ...,
sorter: None | _ArrayLikeInt_co = ..., # 1D int array
) -> NDArray[intp]: ...
@overload
def resize(
a: _ArrayLike[_SCT],
new_shape: _ShapeLike,
) -> NDArray[_SCT]: ...
@overload
def resize(
a: ArrayLike,
new_shape: _ShapeLike,
) -> NDArray[Any]: ...
@overload
def squeeze(
a: _SCT,
axis: None | _ShapeLike = ...,
) -> _SCT: ...
@overload
def squeeze(
a: _ArrayLike[_SCT],
axis: None | _ShapeLike = ...,
) -> NDArray[_SCT]: ...
@overload
def squeeze(
a: ArrayLike,
axis: None | _ShapeLike = ...,
) -> NDArray[Any]: ...
@overload
def diagonal(
a: _ArrayLike[_SCT],
offset: SupportsIndex = ...,
axis1: SupportsIndex = ...,
axis2: SupportsIndex = ..., # >= 2D array
) -> NDArray[_SCT]: ...
@overload
def diagonal(
a: ArrayLike,
offset: SupportsIndex = ...,
axis1: SupportsIndex = ...,
axis2: SupportsIndex = ..., # >= 2D array
) -> NDArray[Any]: ...
@overload
def trace(
a: ArrayLike, # >= 2D array
offset: SupportsIndex = ...,
axis1: SupportsIndex = ...,
axis2: SupportsIndex = ...,
dtype: DTypeLike = ...,
out: None = ...,
) -> Any: ...
@overload
def trace(
a: ArrayLike, # >= 2D array
offset: SupportsIndex = ...,
axis1: SupportsIndex = ...,
axis2: SupportsIndex = ...,
dtype: DTypeLike = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def ravel(a: _ArrayLike[_SCT], order: _OrderKACF = ...) -> NDArray[_SCT]: ...
@overload
def ravel(a: ArrayLike, order: _OrderKACF = ...) -> NDArray[Any]: ...
def nonzero(a: ArrayLike) -> tuple[NDArray[intp], ...]: ...
def shape(a: ArrayLike) -> _Shape: ...
@overload
def compress(
condition: _ArrayLikeBool_co, # 1D bool array
a: _ArrayLike[_SCT],
axis: None | SupportsIndex = ...,
out: None = ...,
) -> NDArray[_SCT]: ...
@overload
def compress(
condition: _ArrayLikeBool_co, # 1D bool array
a: ArrayLike,
axis: None | SupportsIndex = ...,
out: None = ...,
) -> NDArray[Any]: ...
@overload
def compress(
condition: _ArrayLikeBool_co, # 1D bool array
a: ArrayLike,
axis: None | SupportsIndex = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def clip(
a: _SCT,
a_min: None | ArrayLike,
a_max: None | ArrayLike,
out: None = ...,
*,
dtype: None = ...,
where: None | _ArrayLikeBool_co = ...,
order: _OrderKACF = ...,
subok: bool = ...,
signature: str | tuple[None | str, ...] = ...,
extobj: list[Any] = ...,
) -> _SCT: ...
@overload
def clip(
a: _ScalarLike_co,
a_min: None | ArrayLike,
a_max: None | ArrayLike,
out: None = ...,
*,
dtype: None = ...,
where: None | _ArrayLikeBool_co = ...,
order: _OrderKACF = ...,
subok: bool = ...,
signature: str | tuple[None | str, ...] = ...,
extobj: list[Any] = ...,
) -> Any: ...
@overload
def clip(
a: _ArrayLike[_SCT],
a_min: None | ArrayLike,
a_max: None | ArrayLike,
out: None = ...,
*,
dtype: None = ...,
where: None | _ArrayLikeBool_co = ...,
order: _OrderKACF = ...,
subok: bool = ...,
signature: str | tuple[None | str, ...] = ...,
extobj: list[Any] = ...,
) -> NDArray[_SCT]: ...
@overload
def clip(
a: ArrayLike,
a_min: None | ArrayLike,
a_max: None | ArrayLike,
out: None = ...,
*,
dtype: None = ...,
where: None | _ArrayLikeBool_co = ...,
order: _OrderKACF = ...,
subok: bool = ...,
signature: str | tuple[None | str, ...] = ...,
extobj: list[Any] = ...,
) -> NDArray[Any]: ...
@overload
def clip(
a: ArrayLike,
a_min: None | ArrayLike,
a_max: None | ArrayLike,
out: _ArrayType = ...,
*,
dtype: DTypeLike,
where: None | _ArrayLikeBool_co = ...,
order: _OrderKACF = ...,
subok: bool = ...,
signature: str | tuple[None | str, ...] = ...,
extobj: list[Any] = ...,
) -> Any: ...
@overload
def clip(
a: ArrayLike,
a_min: None | ArrayLike,
a_max: None | ArrayLike,
out: _ArrayType,
*,
dtype: DTypeLike = ...,
where: None | _ArrayLikeBool_co = ...,
order: _OrderKACF = ...,
subok: bool = ...,
signature: str | tuple[None | str, ...] = ...,
extobj: list[Any] = ...,
) -> _ArrayType: ...
@overload
def sum(
a: _ArrayLike[_SCT],
axis: None = ...,
dtype: None = ...,
out: None = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> _SCT: ...
@overload
def sum(
a: ArrayLike,
axis: None | _ShapeLike = ...,
dtype: DTypeLike = ...,
out: None = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def sum(
a: ArrayLike,
axis: None | _ShapeLike = ...,
dtype: DTypeLike = ...,
out: _ArrayType = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> _ArrayType: ...
@overload
def all(
a: ArrayLike,
axis: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> bool_: ...
@overload
def all(
a: ArrayLike,
axis: None | _ShapeLike = ...,
out: None = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def all(
a: ArrayLike,
axis: None | _ShapeLike = ...,
out: _ArrayType = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> _ArrayType: ...
@overload
def any(
a: ArrayLike,
axis: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> bool_: ...
@overload
def any(
a: ArrayLike,
axis: None | _ShapeLike = ...,
out: None = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def any(
a: ArrayLike,
axis: None | _ShapeLike = ...,
out: _ArrayType = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> _ArrayType: ...
@overload
def cumsum(
a: _ArrayLike[_SCT],
axis: None | SupportsIndex = ...,
dtype: None = ...,
out: None = ...,
) -> NDArray[_SCT]: ...
@overload
def cumsum(
a: ArrayLike,
axis: None | SupportsIndex = ...,
dtype: None = ...,
out: None = ...,
) -> NDArray[Any]: ...
@overload
def cumsum(
a: ArrayLike,
axis: None | SupportsIndex = ...,
dtype: _DTypeLike[_SCT] = ...,
out: None = ...,
) -> NDArray[_SCT]: ...
@overload
def cumsum(
a: ArrayLike,
axis: None | SupportsIndex = ...,
dtype: DTypeLike = ...,
out: None = ...,
) -> NDArray[Any]: ...
@overload
def cumsum(
a: ArrayLike,
axis: None | SupportsIndex = ...,
dtype: DTypeLike = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def ptp(
a: _ArrayLike[_SCT],
axis: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
) -> _SCT: ...
@overload
def ptp(
a: ArrayLike,
axis: None | _ShapeLike = ...,
out: None = ...,
keepdims: bool = ...,
) -> Any: ...
@overload
def ptp(
a: ArrayLike,
axis: None | _ShapeLike = ...,
out: _ArrayType = ...,
keepdims: bool = ...,
) -> _ArrayType: ...
@overload
def amax(
a: _ArrayLike[_SCT],
axis: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> _SCT: ...
@overload
def amax(
a: ArrayLike,
axis: None | _ShapeLike = ...,
out: None = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def amax(
a: ArrayLike,
axis: None | _ShapeLike = ...,
out: _ArrayType = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> _ArrayType: ...
@overload
def amin(
a: _ArrayLike[_SCT],
axis: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> _SCT: ...
@overload
def amin(
a: ArrayLike,
axis: None | _ShapeLike = ...,
out: None = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def amin(
a: ArrayLike,
axis: None | _ShapeLike = ...,
out: _ArrayType = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> _ArrayType: ...
# TODO: `np.prod()``: For object arrays `initial` does not necessarily
# have to be a numerical scalar.
# The only requirement is that it is compatible
# with the `.__mul__()` method(s) of the passed array's elements.
# Note that the same situation holds for all wrappers around
# `np.ufunc.reduce`, e.g. `np.sum()` (`.__add__()`).
@overload
def prod(
a: _ArrayLikeBool_co,
axis: None = ...,
dtype: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> int_: ...
@overload
def prod(
a: _ArrayLikeUInt_co,
axis: None = ...,
dtype: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> uint64: ...
@overload
def prod(
a: _ArrayLikeInt_co,
axis: None = ...,
dtype: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> int64: ...
@overload
def prod(
a: _ArrayLikeFloat_co,
axis: None = ...,
dtype: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> floating[Any]: ...
@overload
def prod(
a: _ArrayLikeComplex_co,
axis: None = ...,
dtype: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> complexfloating[Any, Any]: ...
@overload
def prod(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: None = ...,
out: None = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def prod(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None = ...,
dtype: _DTypeLike[_SCT] = ...,
out: None = ...,
keepdims: Literal[False] = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> _SCT: ...
@overload
def prod(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: None | DTypeLike = ...,
out: None = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def prod(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: None | DTypeLike = ...,
out: _ArrayType = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> _ArrayType: ...
@overload
def cumprod(
a: _ArrayLikeBool_co,
axis: None | SupportsIndex = ...,
dtype: None = ...,
out: None = ...,
) -> NDArray[int_]: ...
@overload
def cumprod(
a: _ArrayLikeUInt_co,
axis: None | SupportsIndex = ...,
dtype: None = ...,
out: None = ...,
) -> NDArray[uint64]: ...
@overload
def cumprod(
a: _ArrayLikeInt_co,
axis: None | SupportsIndex = ...,
dtype: None = ...,
out: None = ...,
) -> NDArray[int64]: ...
@overload
def cumprod(
a: _ArrayLikeFloat_co,
axis: None | SupportsIndex = ...,
dtype: None = ...,
out: None = ...,
) -> NDArray[floating[Any]]: ...
@overload
def cumprod(
a: _ArrayLikeComplex_co,
axis: None | SupportsIndex = ...,
dtype: None = ...,
out: None = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def cumprod(
a: _ArrayLikeObject_co,
axis: None | SupportsIndex = ...,
dtype: None = ...,
out: None = ...,
) -> NDArray[object_]: ...
@overload
def cumprod(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | SupportsIndex = ...,
dtype: _DTypeLike[_SCT] = ...,
out: None = ...,
) -> NDArray[_SCT]: ...
@overload
def cumprod(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | SupportsIndex = ...,
dtype: DTypeLike = ...,
out: None = ...,
) -> NDArray[Any]: ...
@overload
def cumprod(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | SupportsIndex = ...,
dtype: DTypeLike = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
def ndim(a: ArrayLike) -> int: ...
def size(a: ArrayLike, axis: None | int = ...) -> int: ...
@overload
def around(
a: _BoolLike_co,
decimals: SupportsIndex = ...,
out: None = ...,
) -> float16: ...
@overload
def around(
a: _SCT_uifcO,
decimals: SupportsIndex = ...,
out: None = ...,
) -> _SCT_uifcO: ...
@overload
def around(
a: _ComplexLike_co | object_,
decimals: SupportsIndex = ...,
out: None = ...,
) -> Any: ...
@overload
def around(
a: _ArrayLikeBool_co,
decimals: SupportsIndex = ...,
out: None = ...,
) -> NDArray[float16]: ...
@overload
def around(
a: _ArrayLike[_SCT_uifcO],
decimals: SupportsIndex = ...,
out: None = ...,
) -> NDArray[_SCT_uifcO]: ...
@overload
def around(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
decimals: SupportsIndex = ...,
out: None = ...,
) -> NDArray[Any]: ...
@overload
def around(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
decimals: SupportsIndex = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def mean(
a: _ArrayLikeFloat_co,
axis: None = ...,
dtype: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> floating[Any]: ...
@overload
def mean(
a: _ArrayLikeComplex_co,
axis: None = ...,
dtype: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> complexfloating[Any, Any]: ...
@overload
def mean(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: None = ...,
out: None = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def mean(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None = ...,
dtype: _DTypeLike[_SCT] = ...,
out: None = ...,
keepdims: Literal[False] = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> _SCT: ...
@overload
def mean(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: DTypeLike = ...,
out: None = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def mean(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: DTypeLike = ...,
out: _ArrayType = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> _ArrayType: ...
@overload
def std(
a: _ArrayLikeComplex_co,
axis: None = ...,
dtype: None = ...,
out: None = ...,
ddof: float = ...,
keepdims: Literal[False] = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> floating[Any]: ...
@overload
def std(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: None = ...,
out: None = ...,
ddof: float = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def std(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None = ...,
dtype: _DTypeLike[_SCT] = ...,
out: None = ...,
ddof: float = ...,
keepdims: Literal[False] = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> _SCT: ...
@overload
def std(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: DTypeLike = ...,
out: None = ...,
ddof: float = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def std(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: DTypeLike = ...,
out: _ArrayType = ...,
ddof: float = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> _ArrayType: ...
@overload
def var(
a: _ArrayLikeComplex_co,
axis: None = ...,
dtype: None = ...,
out: None = ...,
ddof: float = ...,
keepdims: Literal[False] = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> floating[Any]: ...
@overload
def var(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: None = ...,
out: None = ...,
ddof: float = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def var(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None = ...,
dtype: _DTypeLike[_SCT] = ...,
out: None = ...,
ddof: float = ...,
keepdims: Literal[False] = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> _SCT: ...
@overload
def var(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: DTypeLike = ...,
out: None = ...,
ddof: float = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def var(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: None | _ShapeLike = ...,
dtype: DTypeLike = ...,
out: _ArrayType = ...,
ddof: float = ...,
keepdims: bool = ...,
*,
where: _ArrayLikeBool_co = ...,
) -> _ArrayType: ...
| 23,472 | unknown | 21.355238 | 77 | 0.530845 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/records.pyi | import os
from collections.abc import Sequence, Iterable
from typing import (
Any,
TypeVar,
overload,
Protocol,
)
from numpy import (
format_parser as format_parser,
record as record,
recarray as recarray,
dtype,
generic,
void,
_ByteOrder,
_SupportsBuffer,
)
from numpy._typing import (
ArrayLike,
DTypeLike,
NDArray,
_ShapeLike,
_ArrayLikeVoid_co,
_NestedSequence,
)
_SCT = TypeVar("_SCT", bound=generic)
_RecArray = recarray[Any, dtype[_SCT]]
class _SupportsReadInto(Protocol):
def seek(self, offset: int, whence: int, /) -> object: ...
def tell(self, /) -> int: ...
def readinto(self, buffer: memoryview, /) -> int: ...
__all__: list[str]
@overload
def fromarrays(
arrayList: Iterable[ArrayLike],
dtype: DTypeLike = ...,
shape: None | _ShapeLike = ...,
formats: None = ...,
names: None = ...,
titles: None = ...,
aligned: bool = ...,
byteorder: None = ...,
) -> _RecArray[Any]: ...
@overload
def fromarrays(
arrayList: Iterable[ArrayLike],
dtype: None = ...,
shape: None | _ShapeLike = ...,
*,
formats: DTypeLike,
names: None | str | Sequence[str] = ...,
titles: None | str | Sequence[str] = ...,
aligned: bool = ...,
byteorder: None | _ByteOrder = ...,
) -> _RecArray[record]: ...
@overload
def fromrecords(
recList: _ArrayLikeVoid_co | tuple[Any, ...] | _NestedSequence[tuple[Any, ...]],
dtype: DTypeLike = ...,
shape: None | _ShapeLike = ...,
formats: None = ...,
names: None = ...,
titles: None = ...,
aligned: bool = ...,
byteorder: None = ...,
) -> _RecArray[record]: ...
@overload
def fromrecords(
recList: _ArrayLikeVoid_co | tuple[Any, ...] | _NestedSequence[tuple[Any, ...]],
dtype: None = ...,
shape: None | _ShapeLike = ...,
*,
formats: DTypeLike,
names: None | str | Sequence[str] = ...,
titles: None | str | Sequence[str] = ...,
aligned: bool = ...,
byteorder: None | _ByteOrder = ...,
) -> _RecArray[record]: ...
@overload
def fromstring(
datastring: _SupportsBuffer,
dtype: DTypeLike,
shape: None | _ShapeLike = ...,
offset: int = ...,
formats: None = ...,
names: None = ...,
titles: None = ...,
aligned: bool = ...,
byteorder: None = ...,
) -> _RecArray[record]: ...
@overload
def fromstring(
datastring: _SupportsBuffer,
dtype: None = ...,
shape: None | _ShapeLike = ...,
offset: int = ...,
*,
formats: DTypeLike,
names: None | str | Sequence[str] = ...,
titles: None | str | Sequence[str] = ...,
aligned: bool = ...,
byteorder: None | _ByteOrder = ...,
) -> _RecArray[record]: ...
@overload
def fromfile(
fd: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _SupportsReadInto,
dtype: DTypeLike,
shape: None | _ShapeLike = ...,
offset: int = ...,
formats: None = ...,
names: None = ...,
titles: None = ...,
aligned: bool = ...,
byteorder: None = ...,
) -> _RecArray[Any]: ...
@overload
def fromfile(
fd: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _SupportsReadInto,
dtype: None = ...,
shape: None | _ShapeLike = ...,
offset: int = ...,
*,
formats: DTypeLike,
names: None | str | Sequence[str] = ...,
titles: None | str | Sequence[str] = ...,
aligned: bool = ...,
byteorder: None | _ByteOrder = ...,
) -> _RecArray[record]: ...
@overload
def array(
obj: _SCT | NDArray[_SCT],
dtype: None = ...,
shape: None | _ShapeLike = ...,
offset: int = ...,
formats: None = ...,
names: None = ...,
titles: None = ...,
aligned: bool = ...,
byteorder: None = ...,
copy: bool = ...,
) -> _RecArray[_SCT]: ...
@overload
def array(
obj: ArrayLike,
dtype: DTypeLike,
shape: None | _ShapeLike = ...,
offset: int = ...,
formats: None = ...,
names: None = ...,
titles: None = ...,
aligned: bool = ...,
byteorder: None = ...,
copy: bool = ...,
) -> _RecArray[Any]: ...
@overload
def array(
obj: ArrayLike,
dtype: None = ...,
shape: None | _ShapeLike = ...,
offset: int = ...,
*,
formats: DTypeLike,
names: None | str | Sequence[str] = ...,
titles: None | str | Sequence[str] = ...,
aligned: bool = ...,
byteorder: None | _ByteOrder = ...,
copy: bool = ...,
) -> _RecArray[record]: ...
@overload
def array(
obj: None,
dtype: DTypeLike,
shape: _ShapeLike,
offset: int = ...,
formats: None = ...,
names: None = ...,
titles: None = ...,
aligned: bool = ...,
byteorder: None = ...,
copy: bool = ...,
) -> _RecArray[Any]: ...
@overload
def array(
obj: None,
dtype: None = ...,
*,
shape: _ShapeLike,
offset: int = ...,
formats: DTypeLike,
names: None | str | Sequence[str] = ...,
titles: None | str | Sequence[str] = ...,
aligned: bool = ...,
byteorder: None | _ByteOrder = ...,
copy: bool = ...,
) -> _RecArray[record]: ...
@overload
def array(
obj: _SupportsReadInto,
dtype: DTypeLike,
shape: None | _ShapeLike = ...,
offset: int = ...,
formats: None = ...,
names: None = ...,
titles: None = ...,
aligned: bool = ...,
byteorder: None = ...,
copy: bool = ...,
) -> _RecArray[Any]: ...
@overload
def array(
obj: _SupportsReadInto,
dtype: None = ...,
shape: None | _ShapeLike = ...,
offset: int = ...,
*,
formats: DTypeLike,
names: None | str | Sequence[str] = ...,
titles: None | str | Sequence[str] = ...,
aligned: bool = ...,
byteorder: None | _ByteOrder = ...,
copy: bool = ...,
) -> _RecArray[record]: ...
| 5,692 | unknown | 23.225532 | 84 | 0.531448 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/setup_common.py | # Code common to build tools
import copy
import pathlib
import sys
import textwrap
import warnings
from numpy.distutils.misc_util import mingw32
#-------------------
# Versioning support
#-------------------
# How to change C_API_VERSION ?
# - increase C_API_VERSION value
# - record the hash for the new C API with the cversions.py script
# and add the hash to cversions.txt
# The hash values are used to remind developers when the C API number was not
# updated - generates a MismatchCAPIWarning warning which is turned into an
# exception for released version.
# Binary compatibility version number. This number is increased whenever the
# C-API is changed such that binary compatibility is broken, i.e. whenever a
# recompile of extension modules is needed.
C_ABI_VERSION = 0x01000009
# Minor API version. This number is increased whenever a change is made to the
# C-API -- whether it breaks binary compatibility or not. Some changes, such
# as adding a function pointer to the end of the function table, can be made
# without breaking binary compatibility. In this case, only the C_API_VERSION
# (*not* C_ABI_VERSION) would be increased. Whenever binary compatibility is
# broken, both C_API_VERSION and C_ABI_VERSION should be increased.
#
# 0x00000008 - 1.7.x
# 0x00000009 - 1.8.x
# 0x00000009 - 1.9.x
# 0x0000000a - 1.10.x
# 0x0000000a - 1.11.x
# 0x0000000a - 1.12.x
# 0x0000000b - 1.13.x
# 0x0000000c - 1.14.x
# 0x0000000c - 1.15.x
# 0x0000000d - 1.16.x
# 0x0000000d - 1.19.x
# 0x0000000e - 1.20.x
# 0x0000000e - 1.21.x
# 0x0000000f - 1.22.x
# 0x00000010 - 1.23.x
C_API_VERSION = 0x00000010
class MismatchCAPIWarning(Warning):
pass
def get_api_versions(apiversion, codegen_dir):
"""
Return current C API checksum and the recorded checksum.
Return current C API checksum and the recorded checksum for the given
version of the C API version.
"""
# Compute the hash of the current API as defined in the .txt files in
# code_generators
sys.path.insert(0, codegen_dir)
try:
m = __import__('genapi')
numpy_api = __import__('numpy_api')
curapi_hash = m.fullapi_hash(numpy_api.full_api)
apis_hash = m.get_versions_hash()
finally:
del sys.path[0]
return curapi_hash, apis_hash[apiversion]
def check_api_version(apiversion, codegen_dir):
"""Emits a MismatchCAPIWarning if the C API version needs updating."""
curapi_hash, api_hash = get_api_versions(apiversion, codegen_dir)
# If different hash, it means that the api .txt files in
# codegen_dir have been updated without the API version being
# updated. Any modification in those .txt files should be reflected
# in the api and eventually abi versions.
# To compute the checksum of the current API, use numpy/core/cversions.py
if not curapi_hash == api_hash:
msg = ("API mismatch detected, the C API version "
"numbers have to be updated. Current C api version is %d, "
"with checksum %s, but recorded checksum for C API version %d "
"in core/codegen_dir/cversions.txt is %s. If functions were "
"added in the C API, you have to update C_API_VERSION in %s."
)
warnings.warn(msg % (apiversion, curapi_hash, apiversion, api_hash,
__file__),
MismatchCAPIWarning, stacklevel=2)
FUNC_CALL_ARGS = {}
def set_sig(sig):
prefix, _, args = sig.partition("(")
args = args.rpartition(")")[0]
funcname = prefix.rpartition(" ")[-1]
args = [arg.strip() for arg in args.split(",")]
FUNC_CALL_ARGS[funcname] = ", ".join("(%s) 0" % arg for arg in args)
for file in [
"feature_detection_locale.h",
"feature_detection_math.h",
"feature_detection_misc.h",
"feature_detection_stdio.h",
]:
with open(pathlib.Path(__file__).parent / file) as f:
for line in f:
if line.startswith("#"):
continue
if not line.strip():
continue
set_sig(line)
# Mandatory functions: if not found, fail the build
MANDATORY_FUNCS = ["sin", "cos", "tan", "sinh", "cosh", "tanh", "fabs",
"floor", "ceil", "sqrt", "log10", "log", "exp", "asin",
"acos", "atan", "fmod", 'modf', 'frexp', 'ldexp']
# Standard functions which may not be available and for which we have a
# replacement implementation. Note that some of these are C99 functions.
OPTIONAL_STDFUNCS = ["expm1", "log1p", "acosh", "asinh", "atanh",
"rint", "trunc", "exp2", "log2", "hypot", "atan2", "pow",
"copysign", "nextafter", "strtoll", "strtoull", "cbrt"]
OPTIONAL_LOCALE_FUNCS = ["strtold_l"]
OPTIONAL_FILE_FUNCS = ["ftello", "fseeko", "fallocate"]
OPTIONAL_MISC_FUNCS = ["backtrace", "madvise"]
OPTIONAL_HEADERS = [
# sse headers only enabled automatically on amd64/x32 builds
"xmmintrin.h", # SSE
"emmintrin.h", # SSE2
"immintrin.h", # AVX
"features.h", # for glibc version linux
"xlocale.h", # see GH#8367
"dlfcn.h", # dladdr
"execinfo.h", # backtrace
"libunwind.h", # backtrace for LLVM/Clang using libunwind
"sys/mman.h", #madvise
]
# optional gcc compiler builtins and their call arguments and optional a
# required header and definition name (HAVE_ prepended)
# call arguments are required as the compiler will do strict signature checking
OPTIONAL_INTRINSICS = [("__builtin_isnan", '5.'),
("__builtin_isinf", '5.'),
("__builtin_isfinite", '5.'),
("__builtin_bswap32", '5u'),
("__builtin_bswap64", '5u'),
("__builtin_expect", '5, 0'),
("__builtin_mul_overflow", '5, 5, (int*)5'),
# MMX only needed for icc, but some clangs don't have it
("_m_from_int64", '0', "emmintrin.h"),
("_mm_load_ps", '(float*)0', "xmmintrin.h"), # SSE
("_mm_prefetch", '(float*)0, _MM_HINT_NTA',
"xmmintrin.h"), # SSE
("_mm_load_pd", '(double*)0', "emmintrin.h"), # SSE2
("__builtin_prefetch", "(float*)0, 0, 3"),
# check that the linker can handle avx
("__asm__ volatile", '"vpand %xmm1, %xmm2, %xmm3"',
"stdio.h", "LINK_AVX"),
("__asm__ volatile", '"vpand %ymm1, %ymm2, %ymm3"',
"stdio.h", "LINK_AVX2"),
("__asm__ volatile", '"vpaddd %zmm1, %zmm2, %zmm3"',
"stdio.h", "LINK_AVX512F"),
("__asm__ volatile", '"vfpclasspd $0x40, %zmm15, %k6\\n"\
"vmovdqu8 %xmm0, %xmm1\\n"\
"vpbroadcastmb2q %k0, %xmm0\\n"',
"stdio.h", "LINK_AVX512_SKX"),
("__asm__ volatile", '"xgetbv"', "stdio.h", "XGETBV"),
]
# function attributes
# tested via "int %s %s(void *);" % (attribute, name)
# function name will be converted to HAVE_<upper-case-name> preprocessor macro
OPTIONAL_FUNCTION_ATTRIBUTES = [('__attribute__((optimize("unroll-loops")))',
'attribute_optimize_unroll_loops'),
('__attribute__((optimize("O3")))',
'attribute_optimize_opt_3'),
('__attribute__((optimize("O2")))',
'attribute_optimize_opt_2'),
('__attribute__((nonnull (1)))',
'attribute_nonnull'),
('__attribute__((target ("avx")))',
'attribute_target_avx'),
('__attribute__((target ("avx2")))',
'attribute_target_avx2'),
('__attribute__((target ("avx512f")))',
'attribute_target_avx512f'),
('__attribute__((target ("avx512f,avx512dq,avx512bw,avx512vl,avx512cd")))',
'attribute_target_avx512_skx'),
]
# function attributes with intrinsics
# To ensure your compiler can compile avx intrinsics with just the attributes
# gcc 4.8.4 support attributes but not with intrisics
# tested via "#include<%s> int %s %s(void *){code; return 0;};" % (header, attribute, name, code)
# function name will be converted to HAVE_<upper-case-name> preprocessor macro
# The _mm512_castps_si512 instruction is specific check for AVX-512F support
# in gcc-4.9 which is missing a subset of intrinsics. See
# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=61878
OPTIONAL_FUNCTION_ATTRIBUTES_WITH_INTRINSICS = [('__attribute__((target("avx2,fma")))',
'attribute_target_avx2_with_intrinsics',
'__m256 temp = _mm256_set1_ps(1.0); temp = \
_mm256_fmadd_ps(temp, temp, temp)',
'immintrin.h'),
('__attribute__((target("avx512f")))',
'attribute_target_avx512f_with_intrinsics',
'__m512i temp = _mm512_castps_si512(_mm512_set1_ps(1.0))',
'immintrin.h'),
('__attribute__((target ("avx512f,avx512dq,avx512bw,avx512vl,avx512cd")))',
'attribute_target_avx512_skx_with_intrinsics',
'__mmask8 temp = _mm512_fpclass_pd_mask(_mm512_set1_pd(1.0), 0x01);\
__m512i unused_temp = \
_mm512_castps_si512(_mm512_set1_ps(1.0));\
_mm_mask_storeu_epi8(NULL, 0xFF, _mm_broadcastmb_epi64(temp))',
'immintrin.h'),
]
# variable attributes tested via "int %s a" % attribute
OPTIONAL_VARIABLE_ATTRIBUTES = ["__thread", "__declspec(thread)"]
# Subset of OPTIONAL_STDFUNCS which may already have HAVE_* defined by Python.h
OPTIONAL_STDFUNCS_MAYBE = [
"expm1", "log1p", "acosh", "atanh", "asinh", "hypot", "copysign",
"ftello", "fseeko"
]
# C99 functions: float and long double versions
C99_FUNCS = [
"sin", "cos", "tan", "sinh", "cosh", "tanh", "fabs", "floor", "ceil",
"rint", "trunc", "sqrt", "log10", "log", "log1p", "exp", "expm1",
"asin", "acos", "atan", "asinh", "acosh", "atanh", "hypot", "atan2",
"pow", "fmod", "modf", 'frexp', 'ldexp', "exp2", "log2", "copysign",
"nextafter", "cbrt"
]
C99_FUNCS_SINGLE = [f + 'f' for f in C99_FUNCS]
C99_FUNCS_EXTENDED = [f + 'l' for f in C99_FUNCS]
C99_COMPLEX_TYPES = [
'complex double', 'complex float', 'complex long double'
]
C99_COMPLEX_FUNCS = [
"cabs", "cacos", "cacosh", "carg", "casin", "casinh", "catan",
"catanh", "ccos", "ccosh", "cexp", "cimag", "clog", "conj", "cpow",
"cproj", "creal", "csin", "csinh", "csqrt", "ctan", "ctanh"
]
def fname2def(name):
return "HAVE_%s" % name.upper()
def sym2def(symbol):
define = symbol.replace(' ', '')
return define.upper()
def type2def(symbol):
define = symbol.replace(' ', '_')
return define.upper()
# Code to detect long double representation taken from MPFR m4 macro
def check_long_double_representation(cmd):
cmd._check_compiler()
body = LONG_DOUBLE_REPRESENTATION_SRC % {'type': 'long double'}
# Disable whole program optimization (the default on vs2015, with python 3.5+)
# which generates intermediary object files and prevents checking the
# float representation.
if sys.platform == "win32" and not mingw32():
try:
cmd.compiler.compile_options.remove("/GL")
except (AttributeError, ValueError):
pass
# Disable multi-file interprocedural optimization in the Intel compiler on Linux
# which generates intermediary object files and prevents checking the
# float representation.
elif (sys.platform != "win32"
and cmd.compiler.compiler_type.startswith('intel')
and '-ipo' in cmd.compiler.cc_exe):
newcompiler = cmd.compiler.cc_exe.replace(' -ipo', '')
cmd.compiler.set_executables(
compiler=newcompiler,
compiler_so=newcompiler,
compiler_cxx=newcompiler,
linker_exe=newcompiler,
linker_so=newcompiler + ' -shared'
)
# We need to use _compile because we need the object filename
src, obj = cmd._compile(body, None, None, 'c')
try:
ltype = long_double_representation(pyod(obj))
return ltype
except ValueError:
# try linking to support CC="gcc -flto" or icc -ipo
# struct needs to be volatile so it isn't optimized away
# additionally "clang -flto" requires the foo struct to be used
body = body.replace('struct', 'volatile struct')
body += "int main(void) { return foo.before[0]; }\n"
src, obj = cmd._compile(body, None, None, 'c')
cmd.temp_files.append("_configtest")
cmd.compiler.link_executable([obj], "_configtest")
ltype = long_double_representation(pyod("_configtest"))
return ltype
finally:
cmd._clean()
LONG_DOUBLE_REPRESENTATION_SRC = r"""
/* "before" is 16 bytes to ensure there's no padding between it and "x".
* We're not expecting any "long double" bigger than 16 bytes or with
* alignment requirements stricter than 16 bytes. */
typedef %(type)s test_type;
struct {
char before[16];
test_type x;
char after[8];
} foo = {
{ '\0', '\0', '\0', '\0', '\0', '\0', '\0', '\0',
'\001', '\043', '\105', '\147', '\211', '\253', '\315', '\357' },
-123456789.0,
{ '\376', '\334', '\272', '\230', '\166', '\124', '\062', '\020' }
};
"""
def pyod(filename):
"""Python implementation of the od UNIX utility (od -b, more exactly).
Parameters
----------
filename : str
name of the file to get the dump from.
Returns
-------
out : seq
list of lines of od output
Notes
-----
We only implement enough to get the necessary information for long double
representation, this is not intended as a compatible replacement for od.
"""
out = []
with open(filename, 'rb') as fid:
yo2 = [oct(o)[2:] for o in fid.read()]
for i in range(0, len(yo2), 16):
line = ['%07d' % int(oct(i)[2:])]
line.extend(['%03d' % int(c) for c in yo2[i:i+16]])
out.append(" ".join(line))
return out
_BEFORE_SEQ = ['000', '000', '000', '000', '000', '000', '000', '000',
'001', '043', '105', '147', '211', '253', '315', '357']
_AFTER_SEQ = ['376', '334', '272', '230', '166', '124', '062', '020']
_IEEE_DOUBLE_BE = ['301', '235', '157', '064', '124', '000', '000', '000']
_IEEE_DOUBLE_LE = _IEEE_DOUBLE_BE[::-1]
_INTEL_EXTENDED_12B = ['000', '000', '000', '000', '240', '242', '171', '353',
'031', '300', '000', '000']
_INTEL_EXTENDED_16B = ['000', '000', '000', '000', '240', '242', '171', '353',
'031', '300', '000', '000', '000', '000', '000', '000']
_MOTOROLA_EXTENDED_12B = ['300', '031', '000', '000', '353', '171',
'242', '240', '000', '000', '000', '000']
_IEEE_QUAD_PREC_BE = ['300', '031', '326', '363', '105', '100', '000', '000',
'000', '000', '000', '000', '000', '000', '000', '000']
_IEEE_QUAD_PREC_LE = _IEEE_QUAD_PREC_BE[::-1]
_IBM_DOUBLE_DOUBLE_BE = (['301', '235', '157', '064', '124', '000', '000', '000'] +
['000'] * 8)
_IBM_DOUBLE_DOUBLE_LE = (['000', '000', '000', '124', '064', '157', '235', '301'] +
['000'] * 8)
def long_double_representation(lines):
"""Given a binary dump as given by GNU od -b, look for long double
representation."""
# Read contains a list of 32 items, each item is a byte (in octal
# representation, as a string). We 'slide' over the output until read is of
# the form before_seq + content + after_sequence, where content is the long double
# representation:
# - content is 12 bytes: 80 bits Intel representation
# - content is 16 bytes: 80 bits Intel representation (64 bits) or quad precision
# - content is 8 bytes: same as double (not implemented yet)
read = [''] * 32
saw = None
for line in lines:
# we skip the first word, as od -b output an index at the beginning of
# each line
for w in line.split()[1:]:
read.pop(0)
read.append(w)
# If the end of read is equal to the after_sequence, read contains
# the long double
if read[-8:] == _AFTER_SEQ:
saw = copy.copy(read)
# if the content was 12 bytes, we only have 32 - 8 - 12 = 12
# "before" bytes. In other words the first 4 "before" bytes went
# past the sliding window.
if read[:12] == _BEFORE_SEQ[4:]:
if read[12:-8] == _INTEL_EXTENDED_12B:
return 'INTEL_EXTENDED_12_BYTES_LE'
if read[12:-8] == _MOTOROLA_EXTENDED_12B:
return 'MOTOROLA_EXTENDED_12_BYTES_BE'
# if the content was 16 bytes, we are left with 32-8-16 = 16
# "before" bytes, so 8 went past the sliding window.
elif read[:8] == _BEFORE_SEQ[8:]:
if read[8:-8] == _INTEL_EXTENDED_16B:
return 'INTEL_EXTENDED_16_BYTES_LE'
elif read[8:-8] == _IEEE_QUAD_PREC_BE:
return 'IEEE_QUAD_BE'
elif read[8:-8] == _IEEE_QUAD_PREC_LE:
return 'IEEE_QUAD_LE'
elif read[8:-8] == _IBM_DOUBLE_DOUBLE_LE:
return 'IBM_DOUBLE_DOUBLE_LE'
elif read[8:-8] == _IBM_DOUBLE_DOUBLE_BE:
return 'IBM_DOUBLE_DOUBLE_BE'
# if the content was 8 bytes, left with 32-8-8 = 16 bytes
elif read[:16] == _BEFORE_SEQ:
if read[16:-8] == _IEEE_DOUBLE_LE:
return 'IEEE_DOUBLE_LE'
elif read[16:-8] == _IEEE_DOUBLE_BE:
return 'IEEE_DOUBLE_BE'
if saw is not None:
raise ValueError("Unrecognized format (%s)" % saw)
else:
# We never detected the after_sequence
raise ValueError("Could not lock sequences (%s)" % saw)
def check_for_right_shift_internal_compiler_error(cmd):
"""
On our arm CI, this fails with an internal compilation error
The failure looks like the following, and can be reproduced on ARM64 GCC 5.4:
<source>: In function 'right_shift':
<source>:4:20: internal compiler error: in expand_shift_1, at expmed.c:2349
ip1[i] = ip1[i] >> in2;
^
Please submit a full bug report,
with preprocessed source if appropriate.
See <http://gcc.gnu.org/bugs.html> for instructions.
Compiler returned: 1
This function returns True if this compiler bug is present, and we need to
turn off optimization for the function
"""
cmd._check_compiler()
has_optimize = cmd.try_compile(textwrap.dedent("""\
__attribute__((optimize("O3"))) void right_shift() {}
"""), None, None)
if not has_optimize:
return False
no_err = cmd.try_compile(textwrap.dedent("""\
typedef long the_type; /* fails also for unsigned and long long */
__attribute__((optimize("O3"))) void right_shift(the_type in2, the_type *ip1, int n) {
for (int i = 0; i < n; i++) {
if (in2 < (the_type)sizeof(the_type) * 8) {
ip1[i] = ip1[i] >> in2;
}
}
}
"""), None, None)
return not no_err
| 20,393 | Python | 41.844538 | 107 | 0.544402 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/generate_numpy_api.py | import os
import genapi
from genapi import \
TypeApi, GlobalVarApi, FunctionApi, BoolValuesApi
import numpy_api
# use annotated api when running under cpychecker
h_template = r"""
#if defined(_MULTIARRAYMODULE) || defined(WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE)
typedef struct {
PyObject_HEAD
npy_bool obval;
} PyBoolScalarObject;
extern NPY_NO_EXPORT PyTypeObject PyArrayMapIter_Type;
extern NPY_NO_EXPORT PyTypeObject PyArrayNeighborhoodIter_Type;
extern NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2];
%s
#else
#if defined(PY_ARRAY_UNIQUE_SYMBOL)
#define PyArray_API PY_ARRAY_UNIQUE_SYMBOL
#endif
#if defined(NO_IMPORT) || defined(NO_IMPORT_ARRAY)
extern void **PyArray_API;
#else
#if defined(PY_ARRAY_UNIQUE_SYMBOL)
void **PyArray_API;
#else
static void **PyArray_API=NULL;
#endif
#endif
%s
#if !defined(NO_IMPORT_ARRAY) && !defined(NO_IMPORT)
static int
_import_array(void)
{
int st;
PyObject *numpy = PyImport_ImportModule("numpy.core._multiarray_umath");
PyObject *c_api = NULL;
if (numpy == NULL) {
return -1;
}
c_api = PyObject_GetAttrString(numpy, "_ARRAY_API");
Py_DECREF(numpy);
if (c_api == NULL) {
PyErr_SetString(PyExc_AttributeError, "_ARRAY_API not found");
return -1;
}
if (!PyCapsule_CheckExact(c_api)) {
PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is not PyCapsule object");
Py_DECREF(c_api);
return -1;
}
PyArray_API = (void **)PyCapsule_GetPointer(c_api, NULL);
Py_DECREF(c_api);
if (PyArray_API == NULL) {
PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is NULL pointer");
return -1;
}
/* Perform runtime check of C API version */
if (NPY_VERSION != PyArray_GetNDArrayCVersion()) {
PyErr_Format(PyExc_RuntimeError, "module compiled against "\
"ABI version 0x%%x but this version of numpy is 0x%%x", \
(int) NPY_VERSION, (int) PyArray_GetNDArrayCVersion());
return -1;
}
if (NPY_FEATURE_VERSION > PyArray_GetNDArrayCFeatureVersion()) {
PyErr_Format(PyExc_RuntimeError, "module compiled against "\
"API version 0x%%x but this version of numpy is 0x%%x", \
(int) NPY_FEATURE_VERSION, (int) PyArray_GetNDArrayCFeatureVersion());
return -1;
}
/*
* Perform runtime check of endianness and check it matches the one set by
* the headers (npy_endian.h) as a safeguard
*/
st = PyArray_GetEndianness();
if (st == NPY_CPU_UNKNOWN_ENDIAN) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as unknown endian");
return -1;
}
#if NPY_BYTE_ORDER == NPY_BIG_ENDIAN
if (st != NPY_CPU_BIG) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as "\
"big endian, but detected different endianness at runtime");
return -1;
}
#elif NPY_BYTE_ORDER == NPY_LITTLE_ENDIAN
if (st != NPY_CPU_LITTLE) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as "\
"little endian, but detected different endianness at runtime");
return -1;
}
#endif
return 0;
}
#define import_array() {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return NULL; } }
#define import_array1(ret) {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return ret; } }
#define import_array2(msg, ret) {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, msg); return ret; } }
#endif
#endif
"""
c_template = r"""
/* These pointers will be stored in the C-object for use in other
extension modules
*/
void *PyArray_API[] = {
%s
};
"""
c_api_header = """
===========
NumPy C-API
===========
"""
def generate_api(output_dir, force=False):
basename = 'multiarray_api'
h_file = os.path.join(output_dir, '__%s.h' % basename)
c_file = os.path.join(output_dir, '__%s.c' % basename)
d_file = os.path.join(output_dir, '%s.txt' % basename)
targets = (h_file, c_file, d_file)
sources = numpy_api.multiarray_api
if (not force and not genapi.should_rebuild(targets, [numpy_api.__file__, __file__])):
return targets
else:
do_generate_api(targets, sources)
return targets
def do_generate_api(targets, sources):
header_file = targets[0]
c_file = targets[1]
doc_file = targets[2]
global_vars = sources[0]
scalar_bool_values = sources[1]
types_api = sources[2]
multiarray_funcs = sources[3]
multiarray_api = sources[:]
module_list = []
extension_list = []
init_list = []
# Check multiarray api indexes
multiarray_api_index = genapi.merge_api_dicts(multiarray_api)
genapi.check_api_dict(multiarray_api_index)
numpyapi_list = genapi.get_api_functions('NUMPY_API',
multiarray_funcs)
# Create dict name -> *Api instance
api_name = 'PyArray_API'
multiarray_api_dict = {}
for f in numpyapi_list:
name = f.name
index = multiarray_funcs[name][0]
annotations = multiarray_funcs[name][1:]
multiarray_api_dict[f.name] = FunctionApi(f.name, index, annotations,
f.return_type,
f.args, api_name)
for name, val in global_vars.items():
index, type = val
multiarray_api_dict[name] = GlobalVarApi(name, index, type, api_name)
for name, val in scalar_bool_values.items():
index = val[0]
multiarray_api_dict[name] = BoolValuesApi(name, index, api_name)
for name, val in types_api.items():
index = val[0]
internal_type = None if len(val) == 1 else val[1]
multiarray_api_dict[name] = TypeApi(
name, index, 'PyTypeObject', api_name, internal_type)
if len(multiarray_api_dict) != len(multiarray_api_index):
keys_dict = set(multiarray_api_dict.keys())
keys_index = set(multiarray_api_index.keys())
raise AssertionError(
"Multiarray API size mismatch - "
"index has extra keys {}, dict has extra keys {}"
.format(keys_index - keys_dict, keys_dict - keys_index)
)
extension_list = []
for name, index in genapi.order_dict(multiarray_api_index):
api_item = multiarray_api_dict[name]
extension_list.append(api_item.define_from_array_api_string())
init_list.append(api_item.array_api_define())
module_list.append(api_item.internal_define())
# Write to header
s = h_template % ('\n'.join(module_list), '\n'.join(extension_list))
genapi.write_file(header_file, s)
# Write to c-code
s = c_template % ',\n'.join(init_list)
genapi.write_file(c_file, s)
# write to documentation
s = c_api_header
for func in numpyapi_list:
s += func.to_ReST()
s += '\n\n'
genapi.write_file(doc_file, s)
return targets
| 7,007 | Python | 28.56962 | 162 | 0.632082 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/records.py | """
Record Arrays
=============
Record arrays expose the fields of structured arrays as properties.
Most commonly, ndarrays contain elements of a single type, e.g. floats,
integers, bools etc. However, it is possible for elements to be combinations
of these using structured types, such as::
>>> a = np.array([(1, 2.0), (1, 2.0)], dtype=[('x', np.int64), ('y', np.float64)])
>>> a
array([(1, 2.), (1, 2.)], dtype=[('x', '<i8'), ('y', '<f8')])
Here, each element consists of two fields: x (and int), and y (a float).
This is known as a structured array. The different fields are analogous
to columns in a spread-sheet. The different fields can be accessed as
one would a dictionary::
>>> a['x']
array([1, 1])
>>> a['y']
array([2., 2.])
Record arrays allow us to access fields as properties::
>>> ar = np.rec.array(a)
>>> ar.x
array([1, 1])
>>> ar.y
array([2., 2.])
"""
import warnings
from collections import Counter
from contextlib import nullcontext
from . import numeric as sb
from . import numerictypes as nt
from numpy.compat import os_fspath
from numpy.core.overrides import set_module
from .arrayprint import _get_legacy_print_mode
# All of the functions allow formats to be a dtype
__all__ = [
'record', 'recarray', 'format_parser',
'fromarrays', 'fromrecords', 'fromstring', 'fromfile', 'array',
]
ndarray = sb.ndarray
_byteorderconv = {'b':'>',
'l':'<',
'n':'=',
'B':'>',
'L':'<',
'N':'=',
'S':'s',
's':'s',
'>':'>',
'<':'<',
'=':'=',
'|':'|',
'I':'|',
'i':'|'}
# formats regular expression
# allows multidimensional spec with a tuple syntax in front
# of the letter code '(2,3)f4' and ' ( 2 , 3 ) f4 '
# are equally allowed
numfmt = nt.sctypeDict
def find_duplicate(list):
"""Find duplication in a list, return a list of duplicated elements"""
return [
item
for item, counts in Counter(list).items()
if counts > 1
]
@set_module('numpy')
class format_parser:
"""
Class to convert formats, names, titles description to a dtype.
After constructing the format_parser object, the dtype attribute is
the converted data-type:
``dtype = format_parser(formats, names, titles).dtype``
Attributes
----------
dtype : dtype
The converted data-type.
Parameters
----------
formats : str or list of str
The format description, either specified as a string with
comma-separated format descriptions in the form ``'f8, i4, a5'``, or
a list of format description strings in the form
``['f8', 'i4', 'a5']``.
names : str or list/tuple of str
The field names, either specified as a comma-separated string in the
form ``'col1, col2, col3'``, or as a list or tuple of strings in the
form ``['col1', 'col2', 'col3']``.
An empty list can be used, in that case default field names
('f0', 'f1', ...) are used.
titles : sequence
Sequence of title strings. An empty list can be used to leave titles
out.
aligned : bool, optional
If True, align the fields by padding as the C-compiler would.
Default is False.
byteorder : str, optional
If specified, all the fields will be changed to the
provided byte-order. Otherwise, the default byte-order is
used. For all available string specifiers, see `dtype.newbyteorder`.
See Also
--------
dtype, typename, sctype2char
Examples
--------
>>> np.format_parser(['<f8', '<i4', '<a5'], ['col1', 'col2', 'col3'],
... ['T1', 'T2', 'T3']).dtype
dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'), (('T3', 'col3'), 'S5')])
`names` and/or `titles` can be empty lists. If `titles` is an empty list,
titles will simply not appear. If `names` is empty, default field names
will be used.
>>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
... []).dtype
dtype([('col1', '<f8'), ('col2', '<i4'), ('col3', '<S5')])
>>> np.format_parser(['<f8', '<i4', '<a5'], [], []).dtype
dtype([('f0', '<f8'), ('f1', '<i4'), ('f2', 'S5')])
"""
def __init__(self, formats, names, titles, aligned=False, byteorder=None):
self._parseFormats(formats, aligned)
self._setfieldnames(names, titles)
self._createdtype(byteorder)
def _parseFormats(self, formats, aligned=False):
""" Parse the field formats """
if formats is None:
raise ValueError("Need formats argument")
if isinstance(formats, list):
dtype = sb.dtype(
[('f{}'.format(i), format_) for i, format_ in enumerate(formats)],
aligned,
)
else:
dtype = sb.dtype(formats, aligned)
fields = dtype.fields
if fields is None:
dtype = sb.dtype([('f1', dtype)], aligned)
fields = dtype.fields
keys = dtype.names
self._f_formats = [fields[key][0] for key in keys]
self._offsets = [fields[key][1] for key in keys]
self._nfields = len(keys)
def _setfieldnames(self, names, titles):
"""convert input field names into a list and assign to the _names
attribute """
if names:
if type(names) in [list, tuple]:
pass
elif isinstance(names, str):
names = names.split(',')
else:
raise NameError("illegal input names %s" % repr(names))
self._names = [n.strip() for n in names[:self._nfields]]
else:
self._names = []
# if the names are not specified, they will be assigned as
# "f0, f1, f2,..."
# if not enough names are specified, they will be assigned as "f[n],
# f[n+1],..." etc. where n is the number of specified names..."
self._names += ['f%d' % i for i in range(len(self._names),
self._nfields)]
# check for redundant names
_dup = find_duplicate(self._names)
if _dup:
raise ValueError("Duplicate field names: %s" % _dup)
if titles:
self._titles = [n.strip() for n in titles[:self._nfields]]
else:
self._titles = []
titles = []
if self._nfields > len(titles):
self._titles += [None] * (self._nfields - len(titles))
def _createdtype(self, byteorder):
dtype = sb.dtype({
'names': self._names,
'formats': self._f_formats,
'offsets': self._offsets,
'titles': self._titles,
})
if byteorder is not None:
byteorder = _byteorderconv[byteorder[0]]
dtype = dtype.newbyteorder(byteorder)
self.dtype = dtype
class record(nt.void):
"""A data-type scalar that allows field access as attribute lookup.
"""
# manually set name and module so that this class's type shows up
# as numpy.record when printed
__name__ = 'record'
__module__ = 'numpy'
def __repr__(self):
if _get_legacy_print_mode() <= 113:
return self.__str__()
return super().__repr__()
def __str__(self):
if _get_legacy_print_mode() <= 113:
return str(self.item())
return super().__str__()
def __getattribute__(self, attr):
if attr in ('setfield', 'getfield', 'dtype'):
return nt.void.__getattribute__(self, attr)
try:
return nt.void.__getattribute__(self, attr)
except AttributeError:
pass
fielddict = nt.void.__getattribute__(self, 'dtype').fields
res = fielddict.get(attr, None)
if res:
obj = self.getfield(*res[:2])
# if it has fields return a record,
# otherwise return the object
try:
dt = obj.dtype
except AttributeError:
#happens if field is Object type
return obj
if dt.names is not None:
return obj.view((self.__class__, obj.dtype))
return obj
else:
raise AttributeError("'record' object has no "
"attribute '%s'" % attr)
def __setattr__(self, attr, val):
if attr in ('setfield', 'getfield', 'dtype'):
raise AttributeError("Cannot set '%s' attribute" % attr)
fielddict = nt.void.__getattribute__(self, 'dtype').fields
res = fielddict.get(attr, None)
if res:
return self.setfield(val, *res[:2])
else:
if getattr(self, attr, None):
return nt.void.__setattr__(self, attr, val)
else:
raise AttributeError("'record' object has no "
"attribute '%s'" % attr)
def __getitem__(self, indx):
obj = nt.void.__getitem__(self, indx)
# copy behavior of record.__getattribute__,
if isinstance(obj, nt.void) and obj.dtype.names is not None:
return obj.view((self.__class__, obj.dtype))
else:
# return a single element
return obj
def pprint(self):
"""Pretty-print all fields."""
# pretty-print all fields
names = self.dtype.names
maxlen = max(len(name) for name in names)
fmt = '%% %ds: %%s' % maxlen
rows = [fmt % (name, getattr(self, name)) for name in names]
return "\n".join(rows)
# The recarray is almost identical to a standard array (which supports
# named fields already) The biggest difference is that it can use
# attribute-lookup to find the fields and it is constructed using
# a record.
# If byteorder is given it forces a particular byteorder on all
# the fields (and any subfields)
class recarray(ndarray):
"""Construct an ndarray that allows field access using attributes.
Arrays may have a data-types containing fields, analogous
to columns in a spread sheet. An example is ``[(x, int), (y, float)]``,
where each entry in the array is a pair of ``(int, float)``. Normally,
these attributes are accessed using dictionary lookups such as ``arr['x']``
and ``arr['y']``. Record arrays allow the fields to be accessed as members
of the array, using ``arr.x`` and ``arr.y``.
Parameters
----------
shape : tuple
Shape of output array.
dtype : data-type, optional
The desired data-type. By default, the data-type is determined
from `formats`, `names`, `titles`, `aligned` and `byteorder`.
formats : list of data-types, optional
A list containing the data-types for the different columns, e.g.
``['i4', 'f8', 'i4']``. `formats` does *not* support the new
convention of using types directly, i.e. ``(int, float, int)``.
Note that `formats` must be a list, not a tuple.
Given that `formats` is somewhat limited, we recommend specifying
`dtype` instead.
names : tuple of str, optional
The name of each column, e.g. ``('x', 'y', 'z')``.
buf : buffer, optional
By default, a new array is created of the given shape and data-type.
If `buf` is specified and is an object exposing the buffer interface,
the array will use the memory from the existing buffer. In this case,
the `offset` and `strides` keywords are available.
Other Parameters
----------------
titles : tuple of str, optional
Aliases for column names. For example, if `names` were
``('x', 'y', 'z')`` and `titles` is
``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then
``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``.
byteorder : {'<', '>', '='}, optional
Byte-order for all fields.
aligned : bool, optional
Align the fields in memory as the C-compiler would.
strides : tuple of ints, optional
Buffer (`buf`) is interpreted according to these strides (strides
define how many bytes each array element, row, column, etc.
occupy in memory).
offset : int, optional
Start reading buffer (`buf`) from this offset onwards.
order : {'C', 'F'}, optional
Row-major (C-style) or column-major (Fortran-style) order.
Returns
-------
rec : recarray
Empty array of the given shape and type.
See Also
--------
core.records.fromrecords : Construct a record array from data.
record : fundamental data-type for `recarray`.
format_parser : determine a data-type from formats, names, titles.
Notes
-----
This constructor can be compared to ``empty``: it creates a new record
array but does not fill it with data. To create a record array from data,
use one of the following methods:
1. Create a standard ndarray and convert it to a record array,
using ``arr.view(np.recarray)``
2. Use the `buf` keyword.
3. Use `np.rec.fromrecords`.
Examples
--------
Create an array with two fields, ``x`` and ``y``:
>>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', '<f8'), ('y', '<i8')])
>>> x
array([(1., 2), (3., 4)], dtype=[('x', '<f8'), ('y', '<i8')])
>>> x['x']
array([1., 3.])
View the array as a record array:
>>> x = x.view(np.recarray)
>>> x.x
array([1., 3.])
>>> x.y
array([2, 4])
Create a new, empty record array:
>>> np.recarray((2,),
... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP
rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),
(3471280, 1.2134086255804012e-316, 0)],
dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])
"""
# manually set name and module so that this class's type shows
# up as "numpy.recarray" when printed
__name__ = 'recarray'
__module__ = 'numpy'
def __new__(subtype, shape, dtype=None, buf=None, offset=0, strides=None,
formats=None, names=None, titles=None,
byteorder=None, aligned=False, order='C'):
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder).dtype
if buf is None:
self = ndarray.__new__(subtype, shape, (record, descr), order=order)
else:
self = ndarray.__new__(subtype, shape, (record, descr),
buffer=buf, offset=offset,
strides=strides, order=order)
return self
def __array_finalize__(self, obj):
if self.dtype.type is not record and self.dtype.names is not None:
# if self.dtype is not np.record, invoke __setattr__ which will
# convert it to a record if it is a void dtype.
self.dtype = self.dtype
def __getattribute__(self, attr):
# See if ndarray has this attr, and return it if so. (note that this
# means a field with the same name as an ndarray attr cannot be
# accessed by attribute).
try:
return object.__getattribute__(self, attr)
except AttributeError: # attr must be a fieldname
pass
# look for a field with this name
fielddict = ndarray.__getattribute__(self, 'dtype').fields
try:
res = fielddict[attr][:2]
except (TypeError, KeyError) as e:
raise AttributeError("recarray has no attribute %s" % attr) from e
obj = self.getfield(*res)
# At this point obj will always be a recarray, since (see
# PyArray_GetField) the type of obj is inherited. Next, if obj.dtype is
# non-structured, convert it to an ndarray. Then if obj is structured
# with void type convert it to the same dtype.type (eg to preserve
# numpy.record type if present), since nested structured fields do not
# inherit type. Don't do this for non-void structures though.
if obj.dtype.names is not None:
if issubclass(obj.dtype.type, nt.void):
return obj.view(dtype=(self.dtype.type, obj.dtype))
return obj
else:
return obj.view(ndarray)
# Save the dictionary.
# If the attr is a field name and not in the saved dictionary
# Undo any "setting" of the attribute and do a setfield
# Thus, you can't create attributes on-the-fly that are field names.
def __setattr__(self, attr, val):
# Automatically convert (void) structured types to records
# (but not non-void structures, subarrays, or non-structured voids)
if attr == 'dtype' and issubclass(val.type, nt.void) and val.names is not None:
val = sb.dtype((record, val))
newattr = attr not in self.__dict__
try:
ret = object.__setattr__(self, attr, val)
except Exception:
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
if attr not in fielddict:
raise
else:
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
if attr not in fielddict:
return ret
if newattr:
# We just added this one or this setattr worked on an
# internal attribute.
try:
object.__delattr__(self, attr)
except Exception:
return ret
try:
res = fielddict[attr][:2]
except (TypeError, KeyError) as e:
raise AttributeError(
"record array has no attribute %s" % attr
) from e
return self.setfield(val, *res)
def __getitem__(self, indx):
obj = super().__getitem__(indx)
# copy behavior of getattr, except that here
# we might also be returning a single element
if isinstance(obj, ndarray):
if obj.dtype.names is not None:
obj = obj.view(type(self))
if issubclass(obj.dtype.type, nt.void):
return obj.view(dtype=(self.dtype.type, obj.dtype))
return obj
else:
return obj.view(type=ndarray)
else:
# return a single element
return obj
def __repr__(self):
repr_dtype = self.dtype
if self.dtype.type is record or not issubclass(self.dtype.type, nt.void):
# If this is a full record array (has numpy.record dtype),
# or if it has a scalar (non-void) dtype with no records,
# represent it using the rec.array function. Since rec.array
# converts dtype to a numpy.record for us, convert back
# to non-record before printing
if repr_dtype.type is record:
repr_dtype = sb.dtype((nt.void, repr_dtype))
prefix = "rec.array("
fmt = 'rec.array(%s,%sdtype=%s)'
else:
# otherwise represent it using np.array plus a view
# This should only happen if the user is playing
# strange games with dtypes.
prefix = "array("
fmt = 'array(%s,%sdtype=%s).view(numpy.recarray)'
# get data/shape string. logic taken from numeric.array_repr
if self.size > 0 or self.shape == (0,):
lst = sb.array2string(
self, separator=', ', prefix=prefix, suffix=',')
else:
# show zero-length shape unless it is (0,)
lst = "[], shape=%s" % (repr(self.shape),)
lf = '\n'+' '*len(prefix)
if _get_legacy_print_mode() <= 113:
lf = ' ' + lf # trailing space
return fmt % (lst, lf, repr_dtype)
def field(self, attr, val=None):
if isinstance(attr, int):
names = ndarray.__getattribute__(self, 'dtype').names
attr = names[attr]
fielddict = ndarray.__getattribute__(self, 'dtype').fields
res = fielddict[attr][:2]
if val is None:
obj = self.getfield(*res)
if obj.dtype.names is not None:
return obj
return obj.view(ndarray)
else:
return self.setfield(val, *res)
def _deprecate_shape_0_as_None(shape):
if shape == 0:
warnings.warn(
"Passing `shape=0` to have the shape be inferred is deprecated, "
"and in future will be equivalent to `shape=(0,)`. To infer "
"the shape and suppress this warning, pass `shape=None` instead.",
FutureWarning, stacklevel=3)
return None
else:
return shape
@set_module("numpy.rec")
def fromarrays(arrayList, dtype=None, shape=None, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
"""Create a record array from a (flat) list of arrays
Parameters
----------
arrayList : list or tuple
List of array-like objects (such as lists, tuples,
and ndarrays).
dtype : data-type, optional
valid dtype for all arrays
shape : int or tuple of ints, optional
Shape of the resulting array. If not provided, inferred from
``arrayList[0]``.
formats, names, titles, aligned, byteorder :
If `dtype` is ``None``, these arguments are passed to
`numpy.format_parser` to construct a dtype. See that function for
detailed documentation.
Returns
-------
np.recarray
Record array consisting of given arrayList columns.
Examples
--------
>>> x1=np.array([1,2,3,4])
>>> x2=np.array(['a','dd','xyz','12'])
>>> x3=np.array([1.1,2,3,4])
>>> r = np.core.records.fromarrays([x1,x2,x3],names='a,b,c')
>>> print(r[1])
(2, 'dd', 2.0) # may vary
>>> x1[1]=34
>>> r.a
array([1, 2, 3, 4])
>>> x1 = np.array([1, 2, 3, 4])
>>> x2 = np.array(['a', 'dd', 'xyz', '12'])
>>> x3 = np.array([1.1, 2, 3,4])
>>> r = np.core.records.fromarrays(
... [x1, x2, x3],
... dtype=np.dtype([('a', np.int32), ('b', 'S3'), ('c', np.float32)]))
>>> r
rec.array([(1, b'a', 1.1), (2, b'dd', 2. ), (3, b'xyz', 3. ),
(4, b'12', 4. )],
dtype=[('a', '<i4'), ('b', 'S3'), ('c', '<f4')])
"""
arrayList = [sb.asarray(x) for x in arrayList]
# NumPy 1.19.0, 2020-01-01
shape = _deprecate_shape_0_as_None(shape)
if shape is None:
shape = arrayList[0].shape
elif isinstance(shape, int):
shape = (shape,)
if formats is None and dtype is None:
# go through each object in the list to see if it is an ndarray
# and determine the formats.
formats = [obj.dtype for obj in arrayList]
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder).dtype
_names = descr.names
# Determine shape from data-type.
if len(descr) != len(arrayList):
raise ValueError("mismatch between the number of fields "
"and the number of arrays")
d0 = descr[0].shape
nn = len(d0)
if nn > 0:
shape = shape[:-nn]
_array = recarray(shape, descr)
# populate the record array (makes a copy)
for k, obj in enumerate(arrayList):
nn = descr[k].ndim
testshape = obj.shape[:obj.ndim - nn]
name = _names[k]
if testshape != shape:
raise ValueError(f'array-shape mismatch in array {k} ("{name}")')
_array[name] = obj
return _array
@set_module("numpy.rec")
def fromrecords(recList, dtype=None, shape=None, formats=None, names=None,
titles=None, aligned=False, byteorder=None):
"""Create a recarray from a list of records in text form.
Parameters
----------
recList : sequence
data in the same field may be heterogeneous - they will be promoted
to the highest data type.
dtype : data-type, optional
valid dtype for all arrays
shape : int or tuple of ints, optional
shape of each array.
formats, names, titles, aligned, byteorder :
If `dtype` is ``None``, these arguments are passed to
`numpy.format_parser` to construct a dtype. See that function for
detailed documentation.
If both `formats` and `dtype` are None, then this will auto-detect
formats. Use list of tuples rather than list of lists for faster
processing.
Returns
-------
np.recarray
record array consisting of given recList rows.
Examples
--------
>>> r=np.core.records.fromrecords([(456,'dbe',1.2),(2,'de',1.3)],
... names='col1,col2,col3')
>>> print(r[0])
(456, 'dbe', 1.2)
>>> r.col1
array([456, 2])
>>> r.col2
array(['dbe', 'de'], dtype='<U3')
>>> import pickle
>>> pickle.loads(pickle.dumps(r))
rec.array([(456, 'dbe', 1.2), ( 2, 'de', 1.3)],
dtype=[('col1', '<i8'), ('col2', '<U3'), ('col3', '<f8')])
"""
if formats is None and dtype is None: # slower
obj = sb.array(recList, dtype=object)
arrlist = [sb.array(obj[..., i].tolist()) for i in range(obj.shape[-1])]
return fromarrays(arrlist, formats=formats, shape=shape, names=names,
titles=titles, aligned=aligned, byteorder=byteorder)
if dtype is not None:
descr = sb.dtype((record, dtype))
else:
descr = format_parser(formats, names, titles, aligned, byteorder).dtype
try:
retval = sb.array(recList, dtype=descr)
except (TypeError, ValueError):
# NumPy 1.19.0, 2020-01-01
shape = _deprecate_shape_0_as_None(shape)
if shape is None:
shape = len(recList)
if isinstance(shape, int):
shape = (shape,)
if len(shape) > 1:
raise ValueError("Can only deal with 1-d array.")
_array = recarray(shape, descr)
for k in range(_array.size):
_array[k] = tuple(recList[k])
# list of lists instead of list of tuples ?
# 2018-02-07, 1.14.1
warnings.warn(
"fromrecords expected a list of tuples, may have received a list "
"of lists instead. In the future that will raise an error",
FutureWarning, stacklevel=2)
return _array
else:
if shape is not None and retval.shape != shape:
retval.shape = shape
res = retval.view(recarray)
return res
@set_module("numpy.rec")
def fromstring(datastring, dtype=None, shape=None, offset=0, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
r"""Create a record array from binary data
Note that despite the name of this function it does not accept `str`
instances.
Parameters
----------
datastring : bytes-like
Buffer of binary data
dtype : data-type, optional
Valid dtype for all arrays
shape : int or tuple of ints, optional
Shape of each array.
offset : int, optional
Position in the buffer to start reading from.
formats, names, titles, aligned, byteorder :
If `dtype` is ``None``, these arguments are passed to
`numpy.format_parser` to construct a dtype. See that function for
detailed documentation.
Returns
-------
np.recarray
Record array view into the data in datastring. This will be readonly
if `datastring` is readonly.
See Also
--------
numpy.frombuffer
Examples
--------
>>> a = b'\x01\x02\x03abc'
>>> np.core.records.fromstring(a, dtype='u1,u1,u1,S3')
rec.array([(1, 2, 3, b'abc')],
dtype=[('f0', 'u1'), ('f1', 'u1'), ('f2', 'u1'), ('f3', 'S3')])
>>> grades_dtype = [('Name', (np.str_, 10)), ('Marks', np.float64),
... ('GradeLevel', np.int32)]
>>> grades_array = np.array([('Sam', 33.3, 3), ('Mike', 44.4, 5),
... ('Aadi', 66.6, 6)], dtype=grades_dtype)
>>> np.core.records.fromstring(grades_array.tobytes(), dtype=grades_dtype)
rec.array([('Sam', 33.3, 3), ('Mike', 44.4, 5), ('Aadi', 66.6, 6)],
dtype=[('Name', '<U10'), ('Marks', '<f8'), ('GradeLevel', '<i4')])
>>> s = '\x01\x02\x03abc'
>>> np.core.records.fromstring(s, dtype='u1,u1,u1,S3')
Traceback (most recent call last)
...
TypeError: a bytes-like object is required, not 'str'
"""
if dtype is None and formats is None:
raise TypeError("fromstring() needs a 'dtype' or 'formats' argument")
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder).dtype
itemsize = descr.itemsize
# NumPy 1.19.0, 2020-01-01
shape = _deprecate_shape_0_as_None(shape)
if shape in (None, -1):
shape = (len(datastring) - offset) // itemsize
_array = recarray(shape, descr, buf=datastring, offset=offset)
return _array
def get_remaining_size(fd):
pos = fd.tell()
try:
fd.seek(0, 2)
return fd.tell() - pos
finally:
fd.seek(pos, 0)
@set_module("numpy.rec")
def fromfile(fd, dtype=None, shape=None, offset=0, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
"""Create an array from binary file data
Parameters
----------
fd : str or file type
If file is a string or a path-like object then that file is opened,
else it is assumed to be a file object. The file object must
support random access (i.e. it must have tell and seek methods).
dtype : data-type, optional
valid dtype for all arrays
shape : int or tuple of ints, optional
shape of each array.
offset : int, optional
Position in the file to start reading from.
formats, names, titles, aligned, byteorder :
If `dtype` is ``None``, these arguments are passed to
`numpy.format_parser` to construct a dtype. See that function for
detailed documentation
Returns
-------
np.recarray
record array consisting of data enclosed in file.
Examples
--------
>>> from tempfile import TemporaryFile
>>> a = np.empty(10,dtype='f8,i4,a5')
>>> a[5] = (0.5,10,'abcde')
>>>
>>> fd=TemporaryFile()
>>> a = a.newbyteorder('<')
>>> a.tofile(fd)
>>>
>>> _ = fd.seek(0)
>>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
... byteorder='<')
>>> print(r[5])
(0.5, 10, 'abcde')
>>> r.shape
(10,)
"""
if dtype is None and formats is None:
raise TypeError("fromfile() needs a 'dtype' or 'formats' argument")
# NumPy 1.19.0, 2020-01-01
shape = _deprecate_shape_0_as_None(shape)
if shape is None:
shape = (-1,)
elif isinstance(shape, int):
shape = (shape,)
if hasattr(fd, 'readinto'):
# GH issue 2504. fd supports io.RawIOBase or io.BufferedIOBase interface.
# Example of fd: gzip, BytesIO, BufferedReader
# file already opened
ctx = nullcontext(fd)
else:
# open file
ctx = open(os_fspath(fd), 'rb')
with ctx as fd:
if offset > 0:
fd.seek(offset, 1)
size = get_remaining_size(fd)
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder).dtype
itemsize = descr.itemsize
shapeprod = sb.array(shape).prod(dtype=nt.intp)
shapesize = shapeprod * itemsize
if shapesize < 0:
shape = list(shape)
shape[shape.index(-1)] = size // -shapesize
shape = tuple(shape)
shapeprod = sb.array(shape).prod(dtype=nt.intp)
nbytes = shapeprod * itemsize
if nbytes > size:
raise ValueError(
"Not enough bytes left in file for specified shape and type")
# create the array
_array = recarray(shape, descr)
nbytesread = fd.readinto(_array.data)
if nbytesread != nbytes:
raise OSError("Didn't read as many bytes as expected")
return _array
@set_module("numpy.rec")
def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None,
names=None, titles=None, aligned=False, byteorder=None, copy=True):
"""
Construct a record array from a wide-variety of objects.
A general-purpose record array constructor that dispatches to the
appropriate `recarray` creation function based on the inputs (see Notes).
Parameters
----------
obj : any
Input object. See Notes for details on how various input types are
treated.
dtype : data-type, optional
Valid dtype for array.
shape : int or tuple of ints, optional
Shape of each array.
offset : int, optional
Position in the file or buffer to start reading from.
strides : tuple of ints, optional
Buffer (`buf`) is interpreted according to these strides (strides
define how many bytes each array element, row, column, etc.
occupy in memory).
formats, names, titles, aligned, byteorder :
If `dtype` is ``None``, these arguments are passed to
`numpy.format_parser` to construct a dtype. See that function for
detailed documentation.
copy : bool, optional
Whether to copy the input object (True), or to use a reference instead.
This option only applies when the input is an ndarray or recarray.
Defaults to True.
Returns
-------
np.recarray
Record array created from the specified object.
Notes
-----
If `obj` is ``None``, then call the `~numpy.recarray` constructor. If
`obj` is a string, then call the `fromstring` constructor. If `obj` is a
list or a tuple, then if the first object is an `~numpy.ndarray`, call
`fromarrays`, otherwise call `fromrecords`. If `obj` is a
`~numpy.recarray`, then make a copy of the data in the recarray
(if ``copy=True``) and use the new formats, names, and titles. If `obj`
is a file, then call `fromfile`. Finally, if obj is an `ndarray`, then
return ``obj.view(recarray)``, making a copy of the data if ``copy=True``.
Examples
--------
>>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> np.core.records.array(a)
rec.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]],
dtype=int32)
>>> b = [(1, 1), (2, 4), (3, 9)]
>>> c = np.core.records.array(b, formats = ['i2', 'f2'], names = ('x', 'y'))
>>> c
rec.array([(1, 1.0), (2, 4.0), (3, 9.0)],
dtype=[('x', '<i2'), ('y', '<f2')])
>>> c.x
rec.array([1, 2, 3], dtype=int16)
>>> c.y
rec.array([ 1.0, 4.0, 9.0], dtype=float16)
>>> r = np.rec.array(['abc','def'], names=['col1','col2'])
>>> print(r.col1)
abc
>>> r.col1
array('abc', dtype='<U3')
>>> r.col2
array('def', dtype='<U3')
"""
if ((isinstance(obj, (type(None), str)) or hasattr(obj, 'readinto')) and
formats is None and dtype is None):
raise ValueError("Must define formats (or dtype) if object is "
"None, string, or an open file")
kwds = {}
if dtype is not None:
dtype = sb.dtype(dtype)
elif formats is not None:
dtype = format_parser(formats, names, titles,
aligned, byteorder).dtype
else:
kwds = {'formats': formats,
'names': names,
'titles': titles,
'aligned': aligned,
'byteorder': byteorder
}
if obj is None:
if shape is None:
raise ValueError("Must define a shape if obj is None")
return recarray(shape, dtype, buf=obj, offset=offset, strides=strides)
elif isinstance(obj, bytes):
return fromstring(obj, dtype, shape=shape, offset=offset, **kwds)
elif isinstance(obj, (list, tuple)):
if isinstance(obj[0], (tuple, list)):
return fromrecords(obj, dtype=dtype, shape=shape, **kwds)
else:
return fromarrays(obj, dtype=dtype, shape=shape, **kwds)
elif isinstance(obj, recarray):
if dtype is not None and (obj.dtype != dtype):
new = obj.view(dtype)
else:
new = obj
if copy:
new = new.copy()
return new
elif hasattr(obj, 'readinto'):
return fromfile(obj, dtype=dtype, shape=shape, offset=offset)
elif isinstance(obj, ndarray):
if dtype is not None and (obj.dtype != dtype):
new = obj.view(dtype)
else:
new = obj
if copy:
new = new.copy()
return new.view(recarray)
else:
interface = getattr(obj, "__array_interface__", None)
if interface is None or not isinstance(interface, dict):
raise ValueError("Unknown input type")
obj = sb.array(obj)
if dtype is not None and (obj.dtype != dtype):
obj = obj.view(dtype)
return obj.view(recarray)
| 37,545 | Python | 33.132727 | 87 | 0.562285 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/numeric.pyi | from collections.abc import Callable, Sequence
from typing import (
Any,
overload,
TypeVar,
Literal,
SupportsAbs,
SupportsIndex,
NoReturn,
)
from typing_extensions import TypeGuard
from numpy import (
ComplexWarning as ComplexWarning,
generic,
unsignedinteger,
signedinteger,
floating,
complexfloating,
bool_,
int_,
intp,
float64,
timedelta64,
object_,
_OrderKACF,
_OrderCF,
)
from numpy._typing import (
ArrayLike,
NDArray,
DTypeLike,
_ShapeLike,
_DTypeLike,
_ArrayLike,
_SupportsArrayFunc,
_ScalarLike_co,
_ArrayLikeBool_co,
_ArrayLikeUInt_co,
_ArrayLikeInt_co,
_ArrayLikeFloat_co,
_ArrayLikeComplex_co,
_ArrayLikeTD64_co,
_ArrayLikeObject_co,
_ArrayLikeUnknown,
)
_T = TypeVar("_T")
_SCT = TypeVar("_SCT", bound=generic)
_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
_CorrelateMode = Literal["valid", "same", "full"]
__all__: list[str]
@overload
def zeros_like(
a: _ArrayType,
dtype: None = ...,
order: _OrderKACF = ...,
subok: Literal[True] = ...,
shape: None = ...,
) -> _ArrayType: ...
@overload
def zeros_like(
a: _ArrayLike[_SCT],
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike = ...,
) -> NDArray[_SCT]: ...
@overload
def zeros_like(
a: object,
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike= ...,
) -> NDArray[Any]: ...
@overload
def zeros_like(
a: Any,
dtype: _DTypeLike[_SCT],
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike= ...,
) -> NDArray[_SCT]: ...
@overload
def zeros_like(
a: Any,
dtype: DTypeLike,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike= ...,
) -> NDArray[Any]: ...
@overload
def ones(
shape: _ShapeLike,
dtype: None = ...,
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[float64]: ...
@overload
def ones(
shape: _ShapeLike,
dtype: _DTypeLike[_SCT],
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def ones(
shape: _ShapeLike,
dtype: DTypeLike,
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def ones_like(
a: _ArrayType,
dtype: None = ...,
order: _OrderKACF = ...,
subok: Literal[True] = ...,
shape: None = ...,
) -> _ArrayType: ...
@overload
def ones_like(
a: _ArrayLike[_SCT],
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike = ...,
) -> NDArray[_SCT]: ...
@overload
def ones_like(
a: object,
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike= ...,
) -> NDArray[Any]: ...
@overload
def ones_like(
a: Any,
dtype: _DTypeLike[_SCT],
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike= ...,
) -> NDArray[_SCT]: ...
@overload
def ones_like(
a: Any,
dtype: DTypeLike,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike= ...,
) -> NDArray[Any]: ...
@overload
def full(
shape: _ShapeLike,
fill_value: Any,
dtype: None = ...,
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def full(
shape: _ShapeLike,
fill_value: Any,
dtype: _DTypeLike[_SCT],
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def full(
shape: _ShapeLike,
fill_value: Any,
dtype: DTypeLike,
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def full_like(
a: _ArrayType,
fill_value: Any,
dtype: None = ...,
order: _OrderKACF = ...,
subok: Literal[True] = ...,
shape: None = ...,
) -> _ArrayType: ...
@overload
def full_like(
a: _ArrayLike[_SCT],
fill_value: Any,
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike = ...,
) -> NDArray[_SCT]: ...
@overload
def full_like(
a: object,
fill_value: Any,
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike= ...,
) -> NDArray[Any]: ...
@overload
def full_like(
a: Any,
fill_value: Any,
dtype: _DTypeLike[_SCT],
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike= ...,
) -> NDArray[_SCT]: ...
@overload
def full_like(
a: Any,
fill_value: Any,
dtype: DTypeLike,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike= ...,
) -> NDArray[Any]: ...
@overload
def count_nonzero(
a: ArrayLike,
axis: None = ...,
*,
keepdims: Literal[False] = ...,
) -> int: ...
@overload
def count_nonzero(
a: ArrayLike,
axis: _ShapeLike = ...,
*,
keepdims: bool = ...,
) -> Any: ... # TODO: np.intp or ndarray[np.intp]
def isfortran(a: NDArray[Any] | generic) -> bool: ...
def argwhere(a: ArrayLike) -> NDArray[intp]: ...
def flatnonzero(a: ArrayLike) -> NDArray[intp]: ...
@overload
def correlate(
a: _ArrayLikeUnknown,
v: _ArrayLikeUnknown,
mode: _CorrelateMode = ...,
) -> NDArray[Any]: ...
@overload
def correlate(
a: _ArrayLikeBool_co,
v: _ArrayLikeBool_co,
mode: _CorrelateMode = ...,
) -> NDArray[bool_]: ...
@overload
def correlate(
a: _ArrayLikeUInt_co,
v: _ArrayLikeUInt_co,
mode: _CorrelateMode = ...,
) -> NDArray[unsignedinteger[Any]]: ...
@overload
def correlate(
a: _ArrayLikeInt_co,
v: _ArrayLikeInt_co,
mode: _CorrelateMode = ...,
) -> NDArray[signedinteger[Any]]: ...
@overload
def correlate(
a: _ArrayLikeFloat_co,
v: _ArrayLikeFloat_co,
mode: _CorrelateMode = ...,
) -> NDArray[floating[Any]]: ...
@overload
def correlate(
a: _ArrayLikeComplex_co,
v: _ArrayLikeComplex_co,
mode: _CorrelateMode = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def correlate(
a: _ArrayLikeTD64_co,
v: _ArrayLikeTD64_co,
mode: _CorrelateMode = ...,
) -> NDArray[timedelta64]: ...
@overload
def correlate(
a: _ArrayLikeObject_co,
v: _ArrayLikeObject_co,
mode: _CorrelateMode = ...,
) -> NDArray[object_]: ...
@overload
def convolve(
a: _ArrayLikeUnknown,
v: _ArrayLikeUnknown,
mode: _CorrelateMode = ...,
) -> NDArray[Any]: ...
@overload
def convolve(
a: _ArrayLikeBool_co,
v: _ArrayLikeBool_co,
mode: _CorrelateMode = ...,
) -> NDArray[bool_]: ...
@overload
def convolve(
a: _ArrayLikeUInt_co,
v: _ArrayLikeUInt_co,
mode: _CorrelateMode = ...,
) -> NDArray[unsignedinteger[Any]]: ...
@overload
def convolve(
a: _ArrayLikeInt_co,
v: _ArrayLikeInt_co,
mode: _CorrelateMode = ...,
) -> NDArray[signedinteger[Any]]: ...
@overload
def convolve(
a: _ArrayLikeFloat_co,
v: _ArrayLikeFloat_co,
mode: _CorrelateMode = ...,
) -> NDArray[floating[Any]]: ...
@overload
def convolve(
a: _ArrayLikeComplex_co,
v: _ArrayLikeComplex_co,
mode: _CorrelateMode = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def convolve(
a: _ArrayLikeTD64_co,
v: _ArrayLikeTD64_co,
mode: _CorrelateMode = ...,
) -> NDArray[timedelta64]: ...
@overload
def convolve(
a: _ArrayLikeObject_co,
v: _ArrayLikeObject_co,
mode: _CorrelateMode = ...,
) -> NDArray[object_]: ...
@overload
def outer(
a: _ArrayLikeUnknown,
b: _ArrayLikeUnknown,
out: None = ...,
) -> NDArray[Any]: ...
@overload
def outer(
a: _ArrayLikeBool_co,
b: _ArrayLikeBool_co,
out: None = ...,
) -> NDArray[bool_]: ...
@overload
def outer(
a: _ArrayLikeUInt_co,
b: _ArrayLikeUInt_co,
out: None = ...,
) -> NDArray[unsignedinteger[Any]]: ...
@overload
def outer(
a: _ArrayLikeInt_co,
b: _ArrayLikeInt_co,
out: None = ...,
) -> NDArray[signedinteger[Any]]: ...
@overload
def outer(
a: _ArrayLikeFloat_co,
b: _ArrayLikeFloat_co,
out: None = ...,
) -> NDArray[floating[Any]]: ...
@overload
def outer(
a: _ArrayLikeComplex_co,
b: _ArrayLikeComplex_co,
out: None = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def outer(
a: _ArrayLikeTD64_co,
b: _ArrayLikeTD64_co,
out: None = ...,
) -> NDArray[timedelta64]: ...
@overload
def outer(
a: _ArrayLikeObject_co,
b: _ArrayLikeObject_co,
out: None = ...,
) -> NDArray[object_]: ...
@overload
def outer(
a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
b: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
out: _ArrayType,
) -> _ArrayType: ...
@overload
def tensordot(
a: _ArrayLikeUnknown,
b: _ArrayLikeUnknown,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[Any]: ...
@overload
def tensordot(
a: _ArrayLikeBool_co,
b: _ArrayLikeBool_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[bool_]: ...
@overload
def tensordot(
a: _ArrayLikeUInt_co,
b: _ArrayLikeUInt_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[unsignedinteger[Any]]: ...
@overload
def tensordot(
a: _ArrayLikeInt_co,
b: _ArrayLikeInt_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[signedinteger[Any]]: ...
@overload
def tensordot(
a: _ArrayLikeFloat_co,
b: _ArrayLikeFloat_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[floating[Any]]: ...
@overload
def tensordot(
a: _ArrayLikeComplex_co,
b: _ArrayLikeComplex_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def tensordot(
a: _ArrayLikeTD64_co,
b: _ArrayLikeTD64_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[timedelta64]: ...
@overload
def tensordot(
a: _ArrayLikeObject_co,
b: _ArrayLikeObject_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[object_]: ...
@overload
def roll(
a: _ArrayLike[_SCT],
shift: _ShapeLike,
axis: None | _ShapeLike = ...,
) -> NDArray[_SCT]: ...
@overload
def roll(
a: ArrayLike,
shift: _ShapeLike,
axis: None | _ShapeLike = ...,
) -> NDArray[Any]: ...
def rollaxis(
a: NDArray[_SCT],
axis: int,
start: int = ...,
) -> NDArray[_SCT]: ...
def moveaxis(
a: NDArray[_SCT],
source: _ShapeLike,
destination: _ShapeLike,
) -> NDArray[_SCT]: ...
@overload
def cross(
a: _ArrayLikeUnknown,
b: _ArrayLikeUnknown,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: None | int = ...,
) -> NDArray[Any]: ...
@overload
def cross(
a: _ArrayLikeBool_co,
b: _ArrayLikeBool_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: None | int = ...,
) -> NoReturn: ...
@overload
def cross(
a: _ArrayLikeUInt_co,
b: _ArrayLikeUInt_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: None | int = ...,
) -> NDArray[unsignedinteger[Any]]: ...
@overload
def cross(
a: _ArrayLikeInt_co,
b: _ArrayLikeInt_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: None | int = ...,
) -> NDArray[signedinteger[Any]]: ...
@overload
def cross(
a: _ArrayLikeFloat_co,
b: _ArrayLikeFloat_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: None | int = ...,
) -> NDArray[floating[Any]]: ...
@overload
def cross(
a: _ArrayLikeComplex_co,
b: _ArrayLikeComplex_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: None | int = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def cross(
a: _ArrayLikeObject_co,
b: _ArrayLikeObject_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: None | int = ...,
) -> NDArray[object_]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: type[int] = ...,
sparse: Literal[False] = ...,
) -> NDArray[int_]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: type[int] = ...,
sparse: Literal[True] = ...,
) -> tuple[NDArray[int_], ...]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: _DTypeLike[_SCT],
sparse: Literal[False] = ...,
) -> NDArray[_SCT]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: _DTypeLike[_SCT],
sparse: Literal[True],
) -> tuple[NDArray[_SCT], ...]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: DTypeLike,
sparse: Literal[False] = ...,
) -> NDArray[Any]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: DTypeLike,
sparse: Literal[True],
) -> tuple[NDArray[Any], ...]: ...
def fromfunction(
function: Callable[..., _T],
shape: Sequence[int],
*,
dtype: DTypeLike = ...,
like: _SupportsArrayFunc = ...,
**kwargs: Any,
) -> _T: ...
def isscalar(element: object) -> TypeGuard[
generic | bool | int | float | complex | str | bytes | memoryview
]: ...
def binary_repr(num: int, width: None | int = ...) -> str: ...
def base_repr(
number: SupportsAbs[float],
base: float = ...,
padding: SupportsIndex = ...,
) -> str: ...
@overload
def identity(
n: int,
dtype: None = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[float64]: ...
@overload
def identity(
n: int,
dtype: _DTypeLike[_SCT],
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def identity(
n: int,
dtype: DTypeLike,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
def allclose(
a: ArrayLike,
b: ArrayLike,
rtol: float = ...,
atol: float = ...,
equal_nan: bool = ...,
) -> bool: ...
@overload
def isclose(
a: _ScalarLike_co,
b: _ScalarLike_co,
rtol: float = ...,
atol: float = ...,
equal_nan: bool = ...,
) -> bool_: ...
@overload
def isclose(
a: ArrayLike,
b: ArrayLike,
rtol: float = ...,
atol: float = ...,
equal_nan: bool = ...,
) -> NDArray[bool_]: ...
def array_equal(a1: ArrayLike, a2: ArrayLike, equal_nan: bool = ...) -> bool: ...
def array_equiv(a1: ArrayLike, a2: ArrayLike) -> bool: ...
| 14,230 | unknown | 20.62766 | 81 | 0.566479 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_internal.py | """
A place for internal code
Some things are more easily handled Python.
"""
import ast
import re
import sys
import platform
import warnings
from .multiarray import dtype, array, ndarray, promote_types
try:
import ctypes
except ImportError:
ctypes = None
IS_PYPY = platform.python_implementation() == 'PyPy'
if sys.byteorder == 'little':
_nbo = '<'
else:
_nbo = '>'
def _makenames_list(adict, align):
allfields = []
for fname, obj in adict.items():
n = len(obj)
if not isinstance(obj, tuple) or n not in (2, 3):
raise ValueError("entry not a 2- or 3- tuple")
if n > 2 and obj[2] == fname:
continue
num = int(obj[1])
if num < 0:
raise ValueError("invalid offset.")
format = dtype(obj[0], align=align)
if n > 2:
title = obj[2]
else:
title = None
allfields.append((fname, format, num, title))
# sort by offsets
allfields.sort(key=lambda x: x[2])
names = [x[0] for x in allfields]
formats = [x[1] for x in allfields]
offsets = [x[2] for x in allfields]
titles = [x[3] for x in allfields]
return names, formats, offsets, titles
# Called in PyArray_DescrConverter function when
# a dictionary without "names" and "formats"
# fields is used as a data-type descriptor.
def _usefields(adict, align):
try:
names = adict[-1]
except KeyError:
names = None
if names is None:
names, formats, offsets, titles = _makenames_list(adict, align)
else:
formats = []
offsets = []
titles = []
for name in names:
res = adict[name]
formats.append(res[0])
offsets.append(res[1])
if len(res) > 2:
titles.append(res[2])
else:
titles.append(None)
return dtype({"names": names,
"formats": formats,
"offsets": offsets,
"titles": titles}, align)
# construct an array_protocol descriptor list
# from the fields attribute of a descriptor
# This calls itself recursively but should eventually hit
# a descriptor that has no fields and then return
# a simple typestring
def _array_descr(descriptor):
fields = descriptor.fields
if fields is None:
subdtype = descriptor.subdtype
if subdtype is None:
if descriptor.metadata is None:
return descriptor.str
else:
new = descriptor.metadata.copy()
if new:
return (descriptor.str, new)
else:
return descriptor.str
else:
return (_array_descr(subdtype[0]), subdtype[1])
names = descriptor.names
ordered_fields = [fields[x] + (x,) for x in names]
result = []
offset = 0
for field in ordered_fields:
if field[1] > offset:
num = field[1] - offset
result.append(('', f'|V{num}'))
offset += num
elif field[1] < offset:
raise ValueError(
"dtype.descr is not defined for types with overlapping or "
"out-of-order fields")
if len(field) > 3:
name = (field[2], field[3])
else:
name = field[2]
if field[0].subdtype:
tup = (name, _array_descr(field[0].subdtype[0]),
field[0].subdtype[1])
else:
tup = (name, _array_descr(field[0]))
offset += field[0].itemsize
result.append(tup)
if descriptor.itemsize > offset:
num = descriptor.itemsize - offset
result.append(('', f'|V{num}'))
return result
# Build a new array from the information in a pickle.
# Note that the name numpy.core._internal._reconstruct is embedded in
# pickles of ndarrays made with NumPy before release 1.0
# so don't remove the name here, or you'll
# break backward compatibility.
def _reconstruct(subtype, shape, dtype):
return ndarray.__new__(subtype, shape, dtype)
# format_re was originally from numarray by J. Todd Miller
format_re = re.compile(r'(?P<order1>[<>|=]?)'
r'(?P<repeats> *[(]?[ ,0-9]*[)]? *)'
r'(?P<order2>[<>|=]?)'
r'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
sep_re = re.compile(r'\s*,\s*')
space_re = re.compile(r'\s+$')
# astr is a string (perhaps comma separated)
_convorder = {'=': _nbo}
def _commastring(astr):
startindex = 0
result = []
while startindex < len(astr):
mo = format_re.match(astr, pos=startindex)
try:
(order1, repeats, order2, dtype) = mo.groups()
except (TypeError, AttributeError):
raise ValueError(
f'format number {len(result)+1} of "{astr}" is not recognized'
) from None
startindex = mo.end()
# Separator or ending padding
if startindex < len(astr):
if space_re.match(astr, pos=startindex):
startindex = len(astr)
else:
mo = sep_re.match(astr, pos=startindex)
if not mo:
raise ValueError(
'format number %d of "%s" is not recognized' %
(len(result)+1, astr))
startindex = mo.end()
if order2 == '':
order = order1
elif order1 == '':
order = order2
else:
order1 = _convorder.get(order1, order1)
order2 = _convorder.get(order2, order2)
if (order1 != order2):
raise ValueError(
'inconsistent byte-order specification %s and %s' %
(order1, order2))
order = order1
if order in ('|', '=', _nbo):
order = ''
dtype = order + dtype
if (repeats == ''):
newitem = dtype
else:
newitem = (dtype, ast.literal_eval(repeats))
result.append(newitem)
return result
class dummy_ctype:
def __init__(self, cls):
self._cls = cls
def __mul__(self, other):
return self
def __call__(self, *other):
return self._cls(other)
def __eq__(self, other):
return self._cls == other._cls
def __ne__(self, other):
return self._cls != other._cls
def _getintp_ctype():
val = _getintp_ctype.cache
if val is not None:
return val
if ctypes is None:
import numpy as np
val = dummy_ctype(np.intp)
else:
char = dtype('p').char
if char == 'i':
val = ctypes.c_int
elif char == 'l':
val = ctypes.c_long
elif char == 'q':
val = ctypes.c_longlong
else:
val = ctypes.c_long
_getintp_ctype.cache = val
return val
_getintp_ctype.cache = None
# Used for .ctypes attribute of ndarray
class _missing_ctypes:
def cast(self, num, obj):
return num.value
class c_void_p:
def __init__(self, ptr):
self.value = ptr
class _ctypes:
def __init__(self, array, ptr=None):
self._arr = array
if ctypes:
self._ctypes = ctypes
self._data = self._ctypes.c_void_p(ptr)
else:
# fake a pointer-like object that holds onto the reference
self._ctypes = _missing_ctypes()
self._data = self._ctypes.c_void_p(ptr)
self._data._objects = array
if self._arr.ndim == 0:
self._zerod = True
else:
self._zerod = False
def data_as(self, obj):
"""
Return the data pointer cast to a particular c-types object.
For example, calling ``self._as_parameter_`` is equivalent to
``self.data_as(ctypes.c_void_p)``. Perhaps you want to use the data as a
pointer to a ctypes array of floating-point data:
``self.data_as(ctypes.POINTER(ctypes.c_double))``.
The returned pointer will keep a reference to the array.
"""
# _ctypes.cast function causes a circular reference of self._data in
# self._data._objects. Attributes of self._data cannot be released
# until gc.collect is called. Make a copy of the pointer first then let
# it hold the array reference. This is a workaround to circumvent the
# CPython bug https://bugs.python.org/issue12836
ptr = self._ctypes.cast(self._data, obj)
ptr._arr = self._arr
return ptr
def shape_as(self, obj):
"""
Return the shape tuple as an array of some other c-types
type. For example: ``self.shape_as(ctypes.c_short)``.
"""
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.shape)
def strides_as(self, obj):
"""
Return the strides tuple as an array of some other
c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
"""
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.strides)
@property
def data(self):
"""
A pointer to the memory area of the array as a Python integer.
This memory area may contain data that is not aligned, or not in correct
byte-order. The memory area may not even be writeable. The array
flags and data-type of this array should be respected when passing this
attribute to arbitrary C-code to avoid trouble that can include Python
crashing. User Beware! The value of this attribute is exactly the same
as ``self._array_interface_['data'][0]``.
Note that unlike ``data_as``, a reference will not be kept to the array:
code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
pointer to a deallocated array, and should be spelt
``(a + b).ctypes.data_as(ctypes.c_void_p)``
"""
return self._data.value
@property
def shape(self):
"""
(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the C-integer corresponding to ``dtype('p')`` on this
platform (see `~numpy.ctypeslib.c_intp`). This base-type could be
`ctypes.c_int`, `ctypes.c_long`, or `ctypes.c_longlong` depending on
the platform. The ctypes array contains the shape of
the underlying array.
"""
return self.shape_as(_getintp_ctype())
@property
def strides(self):
"""
(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the same as for the shape attribute. This ctypes array
contains the strides information from the underlying array. This strides
information is important for showing how many bytes must be jumped to
get to the next element in the array.
"""
return self.strides_as(_getintp_ctype())
@property
def _as_parameter_(self):
"""
Overrides the ctypes semi-magic method
Enables `c_func(some_array.ctypes)`
"""
return self.data_as(ctypes.c_void_p)
# Numpy 1.21.0, 2021-05-18
def get_data(self):
"""Deprecated getter for the `_ctypes.data` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_data" is deprecated. Use "data" instead',
DeprecationWarning, stacklevel=2)
return self.data
def get_shape(self):
"""Deprecated getter for the `_ctypes.shape` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_shape" is deprecated. Use "shape" instead',
DeprecationWarning, stacklevel=2)
return self.shape
def get_strides(self):
"""Deprecated getter for the `_ctypes.strides` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_strides" is deprecated. Use "strides" instead',
DeprecationWarning, stacklevel=2)
return self.strides
def get_as_parameter(self):
"""Deprecated getter for the `_ctypes._as_parameter_` property.
.. deprecated:: 1.21
"""
warnings.warn(
'"get_as_parameter" is deprecated. Use "_as_parameter_" instead',
DeprecationWarning, stacklevel=2,
)
return self._as_parameter_
def _newnames(datatype, order):
"""
Given a datatype and an order object, return a new names tuple, with the
order indicated
"""
oldnames = datatype.names
nameslist = list(oldnames)
if isinstance(order, str):
order = [order]
seen = set()
if isinstance(order, (list, tuple)):
for name in order:
try:
nameslist.remove(name)
except ValueError:
if name in seen:
raise ValueError(f"duplicate field name: {name}") from None
else:
raise ValueError(f"unknown field name: {name}") from None
seen.add(name)
return tuple(list(order) + nameslist)
raise ValueError(f"unsupported order value: {order}")
def _copy_fields(ary):
"""Return copy of structured array with padding between fields removed.
Parameters
----------
ary : ndarray
Structured array from which to remove padding bytes
Returns
-------
ary_copy : ndarray
Copy of ary with padding bytes removed
"""
dt = ary.dtype
copy_dtype = {'names': dt.names,
'formats': [dt.fields[name][0] for name in dt.names]}
return array(ary, dtype=copy_dtype, copy=True)
def _promote_fields(dt1, dt2):
""" Perform type promotion for two structured dtypes.
Parameters
----------
dt1 : structured dtype
First dtype.
dt2 : structured dtype
Second dtype.
Returns
-------
out : dtype
The promoted dtype
Notes
-----
If one of the inputs is aligned, the result will be. The titles of
both descriptors must match (point to the same field).
"""
# Both must be structured and have the same names in the same order
if (dt1.names is None or dt2.names is None) or dt1.names != dt2.names:
raise TypeError("invalid type promotion")
# if both are identical, we can (maybe!) just return the same dtype.
identical = dt1 is dt2
new_fields = []
for name in dt1.names:
field1 = dt1.fields[name]
field2 = dt2.fields[name]
new_descr = promote_types(field1[0], field2[0])
identical = identical and new_descr is field1[0]
# Check that the titles match (if given):
if field1[2:] != field2[2:]:
raise TypeError("invalid type promotion")
if len(field1) == 2:
new_fields.append((name, new_descr))
else:
new_fields.append(((field1[2], name), new_descr))
res = dtype(new_fields, align=dt1.isalignedstruct or dt2.isalignedstruct)
# Might as well preserve identity (and metadata) if the dtype is identical
# and the itemsize, offsets are also unmodified. This could probably be
# sped up, but also probably just be removed entirely.
if identical and res.itemsize == dt1.itemsize:
for name in dt1.names:
if dt1.fields[name][1] != res.fields[name][1]:
return res # the dtype changed.
return dt1
return res
def _getfield_is_safe(oldtype, newtype, offset):
""" Checks safety of getfield for object arrays.
As in _view_is_safe, we need to check that memory containing objects is not
reinterpreted as a non-object datatype and vice versa.
Parameters
----------
oldtype : data-type
Data type of the original ndarray.
newtype : data-type
Data type of the field being accessed by ndarray.getfield
offset : int
Offset of the field being accessed by ndarray.getfield
Raises
------
TypeError
If the field access is invalid
"""
if newtype.hasobject or oldtype.hasobject:
if offset == 0 and newtype == oldtype:
return
if oldtype.names is not None:
for name in oldtype.names:
if (oldtype.fields[name][1] == offset and
oldtype.fields[name][0] == newtype):
return
raise TypeError("Cannot get/set field of an object array")
return
def _view_is_safe(oldtype, newtype):
""" Checks safety of a view involving object arrays, for example when
doing::
np.zeros(10, dtype=oldtype).view(newtype)
Parameters
----------
oldtype : data-type
Data type of original ndarray
newtype : data-type
Data type of the view
Raises
------
TypeError
If the new type is incompatible with the old type.
"""
# if the types are equivalent, there is no problem.
# for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
if oldtype == newtype:
return
if newtype.hasobject or oldtype.hasobject:
raise TypeError("Cannot change data-type for object array.")
return
# Given a string containing a PEP 3118 format specifier,
# construct a NumPy dtype
_pep3118_native_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'h',
'H': 'H',
'i': 'i',
'I': 'I',
'l': 'l',
'L': 'L',
'q': 'q',
'Q': 'Q',
'e': 'e',
'f': 'f',
'd': 'd',
'g': 'g',
'Zf': 'F',
'Zd': 'D',
'Zg': 'G',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
_pep3118_standard_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'i2',
'H': 'u2',
'i': 'i4',
'I': 'u4',
'l': 'i4',
'L': 'u4',
'q': 'i8',
'Q': 'u8',
'e': 'f2',
'f': 'f',
'd': 'd',
'Zf': 'F',
'Zd': 'D',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
_pep3118_unsupported_map = {
'u': 'UCS-2 strings',
'&': 'pointers',
't': 'bitfields',
'X': 'function pointers',
}
class _Stream:
def __init__(self, s):
self.s = s
self.byteorder = '@'
def advance(self, n):
res = self.s[:n]
self.s = self.s[n:]
return res
def consume(self, c):
if self.s[:len(c)] == c:
self.advance(len(c))
return True
return False
def consume_until(self, c):
if callable(c):
i = 0
while i < len(self.s) and not c(self.s[i]):
i = i + 1
return self.advance(i)
else:
i = self.s.index(c)
res = self.advance(i)
self.advance(len(c))
return res
@property
def next(self):
return self.s[0]
def __bool__(self):
return bool(self.s)
def _dtype_from_pep3118(spec):
stream = _Stream(spec)
dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
return dtype
def __dtype_from_pep3118(stream, is_subdtype):
field_spec = dict(
names=[],
formats=[],
offsets=[],
itemsize=0
)
offset = 0
common_alignment = 1
is_padding = False
# Parse spec
while stream:
value = None
# End of structure, bail out to upper level
if stream.consume('}'):
break
# Sub-arrays (1)
shape = None
if stream.consume('('):
shape = stream.consume_until(')')
shape = tuple(map(int, shape.split(',')))
# Byte order
if stream.next in ('@', '=', '<', '>', '^', '!'):
byteorder = stream.advance(1)
if byteorder == '!':
byteorder = '>'
stream.byteorder = byteorder
# Byte order characters also control native vs. standard type sizes
if stream.byteorder in ('@', '^'):
type_map = _pep3118_native_map
type_map_chars = _pep3118_native_typechars
else:
type_map = _pep3118_standard_map
type_map_chars = _pep3118_standard_typechars
# Item sizes
itemsize_str = stream.consume_until(lambda c: not c.isdigit())
if itemsize_str:
itemsize = int(itemsize_str)
else:
itemsize = 1
# Data types
is_padding = False
if stream.consume('T{'):
value, align = __dtype_from_pep3118(
stream, is_subdtype=True)
elif stream.next in type_map_chars:
if stream.next == 'Z':
typechar = stream.advance(2)
else:
typechar = stream.advance(1)
is_padding = (typechar == 'x')
dtypechar = type_map[typechar]
if dtypechar in 'USV':
dtypechar += '%d' % itemsize
itemsize = 1
numpy_byteorder = {'@': '=', '^': '='}.get(
stream.byteorder, stream.byteorder)
value = dtype(numpy_byteorder + dtypechar)
align = value.alignment
elif stream.next in _pep3118_unsupported_map:
desc = _pep3118_unsupported_map[stream.next]
raise NotImplementedError(
"Unrepresentable PEP 3118 data type {!r} ({})"
.format(stream.next, desc))
else:
raise ValueError("Unknown PEP 3118 data type specifier %r" % stream.s)
#
# Native alignment may require padding
#
# Here we assume that the presence of a '@' character implicitly implies
# that the start of the array is *already* aligned.
#
extra_offset = 0
if stream.byteorder == '@':
start_padding = (-offset) % align
intra_padding = (-value.itemsize) % align
offset += start_padding
if intra_padding != 0:
if itemsize > 1 or (shape is not None and _prod(shape) > 1):
# Inject internal padding to the end of the sub-item
value = _add_trailing_padding(value, intra_padding)
else:
# We can postpone the injection of internal padding,
# as the item appears at most once
extra_offset += intra_padding
# Update common alignment
common_alignment = _lcm(align, common_alignment)
# Convert itemsize to sub-array
if itemsize != 1:
value = dtype((value, (itemsize,)))
# Sub-arrays (2)
if shape is not None:
value = dtype((value, shape))
# Field name
if stream.consume(':'):
name = stream.consume_until(':')
else:
name = None
if not (is_padding and name is None):
if name is not None and name in field_spec['names']:
raise RuntimeError(f"Duplicate field name '{name}' in PEP3118 format")
field_spec['names'].append(name)
field_spec['formats'].append(value)
field_spec['offsets'].append(offset)
offset += value.itemsize
offset += extra_offset
field_spec['itemsize'] = offset
# extra final padding for aligned types
if stream.byteorder == '@':
field_spec['itemsize'] += (-offset) % common_alignment
# Check if this was a simple 1-item type, and unwrap it
if (field_spec['names'] == [None]
and field_spec['offsets'][0] == 0
and field_spec['itemsize'] == field_spec['formats'][0].itemsize
and not is_subdtype):
ret = field_spec['formats'][0]
else:
_fix_names(field_spec)
ret = dtype(field_spec)
# Finished
return ret, common_alignment
def _fix_names(field_spec):
""" Replace names which are None with the next unused f%d name """
names = field_spec['names']
for i, name in enumerate(names):
if name is not None:
continue
j = 0
while True:
name = f'f{j}'
if name not in names:
break
j = j + 1
names[i] = name
def _add_trailing_padding(value, padding):
"""Inject the specified number of padding bytes at the end of a dtype"""
if value.fields is None:
field_spec = dict(
names=['f0'],
formats=[value],
offsets=[0],
itemsize=value.itemsize
)
else:
fields = value.fields
names = value.names
field_spec = dict(
names=names,
formats=[fields[name][0] for name in names],
offsets=[fields[name][1] for name in names],
itemsize=value.itemsize
)
field_spec['itemsize'] += padding
return dtype(field_spec)
def _prod(a):
p = 1
for x in a:
p *= x
return p
def _gcd(a, b):
"""Calculate the greatest common divisor of a and b"""
while b:
a, b = b, a % b
return a
def _lcm(a, b):
return a // _gcd(a, b) * b
def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
""" Format the error message for when __array_ufunc__ gives up. """
args_string = ', '.join(['{!r}'.format(arg) for arg in inputs] +
['{}={!r}'.format(k, v)
for k, v in kwargs.items()])
args = inputs + kwargs.get('out', ())
types_string = ', '.join(repr(type(arg).__name__) for arg in args)
return ('operand type(s) all returned NotImplemented from '
'__array_ufunc__({!r}, {!r}, {}): {}'
.format(ufunc, method, args_string, types_string))
def array_function_errmsg_formatter(public_api, types):
""" Format the error message for when __array_ufunc__ gives up. """
func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
return ("no implementation found for '{}' on types that implement "
'__array_function__: {}'.format(func_name, list(types)))
def _ufunc_doc_signature_formatter(ufunc):
"""
Builds a signature string which resembles PEP 457
This is used to construct the first line of the docstring
"""
# input arguments are simple
if ufunc.nin == 1:
in_args = 'x'
else:
in_args = ', '.join(f'x{i+1}' for i in range(ufunc.nin))
# output arguments are both keyword or positional
if ufunc.nout == 0:
out_args = ', /, out=()'
elif ufunc.nout == 1:
out_args = ', /, out=None'
else:
out_args = '[, {positional}], / [, out={default}]'.format(
positional=', '.join(
'out{}'.format(i+1) for i in range(ufunc.nout)),
default=repr((None,)*ufunc.nout)
)
# keyword only args depend on whether this is a gufunc
kwargs = (
", casting='same_kind'"
", order='K'"
", dtype=None"
", subok=True"
)
# NOTE: gufuncs may or may not support the `axis` parameter
if ufunc.signature is None:
kwargs = f", where=True{kwargs}[, signature, extobj]"
else:
kwargs += "[, signature, extobj, axes, axis]"
# join all the parts together
return '{name}({in_args}{out_args}, *{kwargs})'.format(
name=ufunc.__name__,
in_args=in_args,
out_args=out_args,
kwargs=kwargs
)
def npy_ctypes_check(cls):
# determine if a class comes from ctypes, in order to work around
# a bug in the buffer protocol for those objects, bpo-10746
try:
# ctypes class are new-style, so have an __mro__. This probably fails
# for ctypes classes with multiple inheritance.
if IS_PYPY:
# (..., _ctypes.basics._CData, Bufferable, object)
ctype_base = cls.__mro__[-3]
else:
# # (..., _ctypes._CData, object)
ctype_base = cls.__mro__[-2]
# right now, they're part of the _ctypes module
return '_ctypes' in ctype_base.__module__
except Exception:
return False
| 28,220 | Python | 29.215203 | 86 | 0.548689 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/umath_tests.py | """
Shim for _umath_tests to allow a deprecation period for the new name.
"""
import warnings
# 2018-04-04, numpy 1.15.0
warnings.warn(("numpy.core.umath_tests is an internal NumPy "
"module and should not be imported. It will "
"be removed in a future NumPy release."),
category=DeprecationWarning, stacklevel=2)
from ._umath_tests import *
| 389 | Python | 26.857141 | 69 | 0.655527 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/umath.py | """
Create the numpy.core.umath namespace for backward compatibility. In v1.16
the multiarray and umath c-extension modules were merged into a single
_multiarray_umath extension module. So we replicate the old namespace
by importing from the extension module.
"""
from . import _multiarray_umath
from ._multiarray_umath import * # noqa: F403
# These imports are needed for backward compatibility,
# do not change them. issue gh-11862
# _ones_like is semi-public, on purpose not added to __all__
from ._multiarray_umath import _UFUNC_API, _add_newdoc_ufunc, _ones_like
__all__ = [
'_UFUNC_API', 'ERR_CALL', 'ERR_DEFAULT', 'ERR_IGNORE', 'ERR_LOG',
'ERR_PRINT', 'ERR_RAISE', 'ERR_WARN', 'FLOATING_POINT_SUPPORT',
'FPE_DIVIDEBYZERO', 'FPE_INVALID', 'FPE_OVERFLOW', 'FPE_UNDERFLOW', 'NAN',
'NINF', 'NZERO', 'PINF', 'PZERO', 'SHIFT_DIVIDEBYZERO', 'SHIFT_INVALID',
'SHIFT_OVERFLOW', 'SHIFT_UNDERFLOW', 'UFUNC_BUFSIZE_DEFAULT',
'UFUNC_PYVALS_NAME', '_add_newdoc_ufunc', 'absolute', 'add',
'arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctan2', 'arctanh',
'bitwise_and', 'bitwise_or', 'bitwise_xor', 'cbrt', 'ceil', 'conj',
'conjugate', 'copysign', 'cos', 'cosh', 'deg2rad', 'degrees', 'divide',
'divmod', 'e', 'equal', 'euler_gamma', 'exp', 'exp2', 'expm1', 'fabs',
'floor', 'floor_divide', 'float_power', 'fmax', 'fmin', 'fmod', 'frexp',
'frompyfunc', 'gcd', 'geterrobj', 'greater', 'greater_equal', 'heaviside',
'hypot', 'invert', 'isfinite', 'isinf', 'isnan', 'isnat', 'lcm', 'ldexp',
'left_shift', 'less', 'less_equal', 'log', 'log10', 'log1p', 'log2',
'logaddexp', 'logaddexp2', 'logical_and', 'logical_not', 'logical_or',
'logical_xor', 'maximum', 'minimum', 'mod', 'modf', 'multiply', 'negative',
'nextafter', 'not_equal', 'pi', 'positive', 'power', 'rad2deg', 'radians',
'reciprocal', 'remainder', 'right_shift', 'rint', 'seterrobj', 'sign',
'signbit', 'sin', 'sinh', 'spacing', 'sqrt', 'square', 'subtract', 'tan',
'tanh', 'true_divide', 'trunc']
| 2,040 | Python | 54.162161 | 79 | 0.637255 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/overrides.py | """Implementation of __array_function__ overrides from NEP-18."""
import collections
import functools
import os
from numpy.core._multiarray_umath import (
add_docstring, implement_array_function, _get_implementing_args)
from numpy.compat._inspect import getargspec
ARRAY_FUNCTION_ENABLED = bool(
int(os.environ.get('NUMPY_EXPERIMENTAL_ARRAY_FUNCTION', 1)))
array_function_like_doc = (
"""like : array_like, optional
Reference object to allow the creation of arrays which are not
NumPy arrays. If an array-like passed in as ``like`` supports
the ``__array_function__`` protocol, the result will be defined
by it. In this case, it ensures the creation of an array object
compatible with that passed in via this argument."""
)
def set_array_function_like_doc(public_api):
if public_api.__doc__ is not None:
public_api.__doc__ = public_api.__doc__.replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
)
return public_api
add_docstring(
implement_array_function,
"""
Implement a function with checks for __array_function__ overrides.
All arguments are required, and can only be passed by position.
Parameters
----------
implementation : function
Function that implements the operation on NumPy array without
overrides when called like ``implementation(*args, **kwargs)``.
public_api : function
Function exposed by NumPy's public API originally called like
``public_api(*args, **kwargs)`` on which arguments are now being
checked.
relevant_args : iterable
Iterable of arguments to check for __array_function__ methods.
args : tuple
Arbitrary positional arguments originally passed into ``public_api``.
kwargs : dict
Arbitrary keyword arguments originally passed into ``public_api``.
Returns
-------
Result from calling ``implementation()`` or an ``__array_function__``
method, as appropriate.
Raises
------
TypeError : if no implementation is found.
""")
# exposed for testing purposes; used internally by implement_array_function
add_docstring(
_get_implementing_args,
"""
Collect arguments on which to call __array_function__.
Parameters
----------
relevant_args : iterable of array-like
Iterable of possibly array-like arguments to check for
__array_function__ methods.
Returns
-------
Sequence of arguments with __array_function__ methods, in the order in
which they should be called.
""")
ArgSpec = collections.namedtuple('ArgSpec', 'args varargs keywords defaults')
def verify_matching_signatures(implementation, dispatcher):
"""Verify that a dispatcher function has the right signature."""
implementation_spec = ArgSpec(*getargspec(implementation))
dispatcher_spec = ArgSpec(*getargspec(dispatcher))
if (implementation_spec.args != dispatcher_spec.args or
implementation_spec.varargs != dispatcher_spec.varargs or
implementation_spec.keywords != dispatcher_spec.keywords or
(bool(implementation_spec.defaults) !=
bool(dispatcher_spec.defaults)) or
(implementation_spec.defaults is not None and
len(implementation_spec.defaults) !=
len(dispatcher_spec.defaults))):
raise RuntimeError('implementation and dispatcher for %s have '
'different function signatures' % implementation)
if implementation_spec.defaults is not None:
if dispatcher_spec.defaults != (None,) * len(dispatcher_spec.defaults):
raise RuntimeError('dispatcher functions can only use None for '
'default argument values')
def set_module(module):
"""Decorator for overriding __module__ on a function or class.
Example usage::
@set_module('numpy')
def example():
pass
assert example.__module__ == 'numpy'
"""
def decorator(func):
if module is not None:
func.__module__ = module
return func
return decorator
def array_function_dispatch(dispatcher, module=None, verify=True,
docs_from_dispatcher=False):
"""Decorator for adding dispatch with the __array_function__ protocol.
See NEP-18 for example usage.
Parameters
----------
dispatcher : callable
Function that when called like ``dispatcher(*args, **kwargs)`` with
arguments from the NumPy function call returns an iterable of
array-like arguments to check for ``__array_function__``.
module : str, optional
__module__ attribute to set on new function, e.g., ``module='numpy'``.
By default, module is copied from the decorated function.
verify : bool, optional
If True, verify the that the signature of the dispatcher and decorated
function signatures match exactly: all required and optional arguments
should appear in order with the same names, but the default values for
all optional arguments should be ``None``. Only disable verification
if the dispatcher's signature needs to deviate for some particular
reason, e.g., because the function has a signature like
``func(*args, **kwargs)``.
docs_from_dispatcher : bool, optional
If True, copy docs from the dispatcher function onto the dispatched
function, rather than from the implementation. This is useful for
functions defined in C, which otherwise don't have docstrings.
Returns
-------
Function suitable for decorating the implementation of a NumPy function.
"""
if not ARRAY_FUNCTION_ENABLED:
def decorator(implementation):
if docs_from_dispatcher:
add_docstring(implementation, dispatcher.__doc__)
if module is not None:
implementation.__module__ = module
return implementation
return decorator
def decorator(implementation):
if verify:
verify_matching_signatures(implementation, dispatcher)
if docs_from_dispatcher:
add_docstring(implementation, dispatcher.__doc__)
@functools.wraps(implementation)
def public_api(*args, **kwargs):
relevant_args = dispatcher(*args, **kwargs)
return implement_array_function(
implementation, public_api, relevant_args, args, kwargs)
public_api.__code__ = public_api.__code__.replace(
co_name=implementation.__name__,
co_filename='<__array_function__ internals>')
if module is not None:
public_api.__module__ = module
public_api._implementation = implementation
return public_api
return decorator
def array_function_from_dispatcher(
implementation, module=None, verify=True, docs_from_dispatcher=True):
"""Like array_function_dispatcher, but with function arguments flipped."""
def decorator(dispatcher):
return array_function_dispatch(
dispatcher, module, verify=verify,
docs_from_dispatcher=docs_from_dispatcher)(implementation)
return decorator
| 7,297 | Python | 34.6 | 79 | 0.651638 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_ufunc_config.py | """
Functions for changing global ufunc configuration
This provides helpers which wrap `umath.geterrobj` and `umath.seterrobj`
"""
import collections.abc
import contextlib
from .overrides import set_module
from .umath import (
UFUNC_BUFSIZE_DEFAULT,
ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,
SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,
)
from . import umath
__all__ = [
"seterr", "geterr", "setbufsize", "getbufsize", "seterrcall", "geterrcall",
"errstate",
]
_errdict = {"ignore": ERR_IGNORE,
"warn": ERR_WARN,
"raise": ERR_RAISE,
"call": ERR_CALL,
"print": ERR_PRINT,
"log": ERR_LOG}
_errdict_rev = {value: key for key, value in _errdict.items()}
@set_module('numpy')
def seterr(all=None, divide=None, over=None, under=None, invalid=None):
"""
Set how floating-point errors are handled.
Note that operations on integer scalar types (such as `int16`) are
handled like floating point, and are affected by these settings.
Parameters
----------
all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Set treatment for all types of floating-point errors at once:
- ignore: Take no action when the exception occurs.
- warn: Print a `RuntimeWarning` (via the Python `warnings` module).
- raise: Raise a `FloatingPointError`.
- call: Call a function specified using the `seterrcall` function.
- print: Print a warning directly to ``stdout``.
- log: Record error in a Log object specified by `seterrcall`.
The default is not to change the current behavior.
divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for division by zero.
over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point overflow.
under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point underflow.
invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for invalid floating-point operation.
Returns
-------
old_settings : dict
Dictionary containing the old settings.
See also
--------
seterrcall : Set a callback function for the 'call' mode.
geterr, geterrcall, errstate
Notes
-----
The floating-point exceptions are defined in the IEEE 754 standard [1]_:
- Division by zero: infinite result obtained from finite numbers.
- Overflow: result too large to be expressed.
- Underflow: result so close to zero that some precision
was lost.
- Invalid operation: result is not an expressible number, typically
indicates that a NaN was produced.
.. [1] https://en.wikipedia.org/wiki/IEEE_754
Examples
--------
>>> old_settings = np.seterr(all='ignore') #seterr to known value
>>> np.seterr(over='raise')
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.seterr(**old_settings) # reset to default
{'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.int16(32000) * np.int16(3)
30464
>>> old_settings = np.seterr(all='warn', over='raise')
>>> np.int16(32000) * np.int16(3)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
FloatingPointError: overflow encountered in short_scalars
>>> old_settings = np.seterr(all='print')
>>> np.geterr()
{'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
>>> np.int16(32000) * np.int16(3)
30464
"""
pyvals = umath.geterrobj()
old = geterr()
if divide is None:
divide = all or old['divide']
if over is None:
over = all or old['over']
if under is None:
under = all or old['under']
if invalid is None:
invalid = all or old['invalid']
maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) +
(_errdict[over] << SHIFT_OVERFLOW) +
(_errdict[under] << SHIFT_UNDERFLOW) +
(_errdict[invalid] << SHIFT_INVALID))
pyvals[1] = maskvalue
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def geterr():
"""
Get the current way of handling floating-point errors.
Returns
-------
res : dict
A dictionary with keys "divide", "over", "under", and "invalid",
whose values are from the strings "ignore", "print", "log", "warn",
"raise", and "call". The keys represent possible floating-point
exceptions, and the values define how these exceptions are handled.
See Also
--------
geterrcall, seterr, seterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterr()
{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}
>>> np.arange(3.) / np.arange(3.)
array([nan, 1., 1.])
>>> oldsettings = np.seterr(all='warn', over='raise')
>>> np.geterr()
{'divide': 'warn', 'over': 'raise', 'under': 'warn', 'invalid': 'warn'}
>>> np.arange(3.) / np.arange(3.)
array([nan, 1., 1.])
"""
maskvalue = umath.geterrobj()[1]
mask = 7
res = {}
val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask
res['divide'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_OVERFLOW) & mask
res['over'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_UNDERFLOW) & mask
res['under'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_INVALID) & mask
res['invalid'] = _errdict_rev[val]
return res
@set_module('numpy')
def setbufsize(size):
"""
Set the size of the buffer used in ufuncs.
Parameters
----------
size : int
Size of buffer.
"""
if size > 10e6:
raise ValueError("Buffer size, %s, is too big." % size)
if size < 5:
raise ValueError("Buffer size, %s, is too small." % size)
if size % 16 != 0:
raise ValueError("Buffer size, %s, is not a multiple of 16." % size)
pyvals = umath.geterrobj()
old = getbufsize()
pyvals[0] = size
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def getbufsize():
"""
Return the size of the buffer used in ufuncs.
Returns
-------
getbufsize : int
Size of ufunc buffer in bytes.
"""
return umath.geterrobj()[0]
@set_module('numpy')
def seterrcall(func):
"""
Set the floating-point error callback function or log object.
There are two ways to capture floating-point error messages. The first
is to set the error-handler to 'call', using `seterr`. Then, set
the function to call using this function.
The second is to set the error-handler to 'log', using `seterr`.
Floating-point errors then trigger a call to the 'write' method of
the provided object.
Parameters
----------
func : callable f(err, flag) or object with write method
Function to call upon floating-point errors ('call'-mode) or
object whose 'write' method is used to log such message ('log'-mode).
The call function takes two arguments. The first is a string describing
the type of error (such as "divide by zero", "overflow", "underflow",
or "invalid value"), and the second is the status flag. The flag is a
byte, whose four least-significant bits indicate the type of error, one
of "divide", "over", "under", "invalid"::
[0 0 0 0 divide over under invalid]
In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.
If an object is provided, its write method should take one argument,
a string.
Returns
-------
h : callable, log instance or None
The old error handler.
See Also
--------
seterr, geterr, geterrcall
Examples
--------
Callback upon error:
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> saved_handler = np.seterrcall(err_handler)
>>> save_err = np.seterr(all='call')
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])
>>> np.seterrcall(saved_handler)
<function err_handler at 0x...>
>>> np.seterr(**save_err)
{'divide': 'call', 'over': 'call', 'under': 'call', 'invalid': 'call'}
Log error message:
>>> class Log:
... def write(self, msg):
... print("LOG: %s" % msg)
...
>>> log = Log()
>>> saved_handler = np.seterrcall(log)
>>> save_err = np.seterr(all='log')
>>> np.array([1, 2, 3]) / 0.0
LOG: Warning: divide by zero encountered in divide
array([inf, inf, inf])
>>> np.seterrcall(saved_handler)
<numpy.core.numeric.Log object at 0x...>
>>> np.seterr(**save_err)
{'divide': 'log', 'over': 'log', 'under': 'log', 'invalid': 'log'}
"""
if func is not None and not isinstance(func, collections.abc.Callable):
if (not hasattr(func, 'write') or
not isinstance(func.write, collections.abc.Callable)):
raise ValueError("Only callable can be used as callback")
pyvals = umath.geterrobj()
old = geterrcall()
pyvals[2] = func
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def geterrcall():
"""
Return the current callback function used on floating-point errors.
When the error handling for a floating-point error (one of "divide",
"over", "under", or "invalid") is set to 'call' or 'log', the function
that is called or the log instance that is written to is returned by
`geterrcall`. This function or log instance has been set with
`seterrcall`.
Returns
-------
errobj : callable, log instance or None
The current error handler. If no handler was set through `seterrcall`,
``None`` is returned.
See Also
--------
seterrcall, seterr, geterr
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterrcall() # we did not yet set a handler, returns None
>>> oldsettings = np.seterr(all='call')
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
>>> oldhandler = np.seterrcall(err_handler)
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])
>>> cur_handler = np.geterrcall()
>>> cur_handler is err_handler
True
"""
return umath.geterrobj()[2]
class _unspecified:
pass
_Unspecified = _unspecified()
@set_module('numpy')
class errstate(contextlib.ContextDecorator):
"""
errstate(**kwargs)
Context manager for floating-point error handling.
Using an instance of `errstate` as a context manager allows statements in
that context to execute with a known error handling behavior. Upon entering
the context the error handling is set with `seterr` and `seterrcall`, and
upon exiting it is reset to what it was before.
.. versionchanged:: 1.17.0
`errstate` is also usable as a function decorator, saving
a level of indentation if an entire function is wrapped.
See :py:class:`contextlib.ContextDecorator` for more information.
Parameters
----------
kwargs : {divide, over, under, invalid}
Keyword arguments. The valid keywords are the possible floating-point
exceptions. Each keyword should have a string value that defines the
treatment for the particular error. Possible values are
{'ignore', 'warn', 'raise', 'call', 'print', 'log'}.
See Also
--------
seterr, geterr, seterrcall, geterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> olderr = np.seterr(all='ignore') # Set error handling to known state.
>>> np.arange(3) / 0.
array([nan, inf, inf])
>>> with np.errstate(divide='warn'):
... np.arange(3) / 0.
array([nan, inf, inf])
>>> np.sqrt(-1)
nan
>>> with np.errstate(invalid='raise'):
... np.sqrt(-1)
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
FloatingPointError: invalid value encountered in sqrt
Outside the context the error handling behavior has not changed:
>>> np.geterr()
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
"""
def __init__(self, *, call=_Unspecified, **kwargs):
self.call = call
self.kwargs = kwargs
def __enter__(self):
self.oldstate = seterr(**self.kwargs)
if self.call is not _Unspecified:
self.oldcall = seterrcall(self.call)
def __exit__(self, *exc_info):
seterr(**self.oldstate)
if self.call is not _Unspecified:
seterrcall(self.oldcall)
def _setdef():
defval = [UFUNC_BUFSIZE_DEFAULT, ERR_DEFAULT, None]
umath.seterrobj(defval)
# set the default values
_setdef()
| 13,382 | Python | 28.939597 | 82 | 0.607831 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/fromnumeric.py | """Module containing non-deprecated functions borrowed from Numeric.
"""
import functools
import types
import warnings
import numpy as np
from . import multiarray as mu
from . import overrides
from . import umath as um
from . import numerictypes as nt
from .multiarray import asarray, array, asanyarray, concatenate
from . import _methods
_dt_ = nt.sctype2char
# functions that are methods
__all__ = [
'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',
'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',
'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',
'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',
'ravel', 'repeat', 'reshape', 'resize', 'round_',
'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',
'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',
]
_gentype = types.GeneratorType
# save away Python sum
_sum_ = sum
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
# functions that are now methods
def _wrapit(obj, method, *args, **kwds):
try:
wrap = obj.__array_wrap__
except AttributeError:
wrap = None
result = getattr(asarray(obj), method)(*args, **kwds)
if wrap:
if not isinstance(result, mu.ndarray):
result = asarray(result)
result = wrap(result)
return result
def _wrapfunc(obj, method, *args, **kwds):
bound = getattr(obj, method, None)
if bound is None:
return _wrapit(obj, method, *args, **kwds)
try:
return bound(*args, **kwds)
except TypeError:
# A TypeError occurs if the object does have such a method in its
# class, but its signature is not identical to that of NumPy's. This
# situation has occurred in the case of a downstream library like
# 'pandas'.
#
# Call _wrapit from within the except clause to ensure a potential
# exception has a traceback chain.
return _wrapit(obj, method, *args, **kwds)
def _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs):
passkwargs = {k: v for k, v in kwargs.items()
if v is not np._NoValue}
if type(obj) is not mu.ndarray:
try:
reduction = getattr(obj, method)
except AttributeError:
pass
else:
# This branch is needed for reductions like any which don't
# support a dtype.
if dtype is not None:
return reduction(axis=axis, dtype=dtype, out=out, **passkwargs)
else:
return reduction(axis=axis, out=out, **passkwargs)
return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
def _take_dispatcher(a, indices, axis=None, out=None, mode=None):
return (a, out)
@array_function_dispatch(_take_dispatcher)
def take(a, indices, axis=None, out=None, mode='raise'):
"""
Take elements from an array along an axis.
When axis is not None, this function does the same thing as "fancy"
indexing (indexing arrays using arrays); however, it can be easier to use
if you need elements along a given axis. A call such as
``np.take(arr, indices, axis=3)`` is equivalent to
``arr[:,:,:,indices,...]``.
Explained without fancy indexing, this is equivalent to the following use
of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of
indices::
Ni, Nk = a.shape[:axis], a.shape[axis+1:]
Nj = indices.shape
for ii in ndindex(Ni):
for jj in ndindex(Nj):
for kk in ndindex(Nk):
out[ii + jj + kk] = a[ii + (indices[jj],) + kk]
Parameters
----------
a : array_like (Ni..., M, Nk...)
The source array.
indices : array_like (Nj...)
The indices of the values to extract.
.. versionadded:: 1.8.0
Also allow scalars for indices.
axis : int, optional
The axis over which to select values. By default, the flattened
input array is used.
out : ndarray, optional (Ni..., Nj..., Nk...)
If provided, the result will be placed in this array. It should
be of the appropriate shape and dtype. Note that `out` is always
buffered if `mode='raise'`; use other modes for better performance.
mode : {'raise', 'wrap', 'clip'}, optional
Specifies how out-of-bounds indices will behave.
* 'raise' -- raise an error (default)
* 'wrap' -- wrap around
* 'clip' -- clip to the range
'clip' mode means that all indices that are too large are replaced
by the index that addresses the last element along that axis. Note
that this disables indexing with negative numbers.
Returns
-------
out : ndarray (Ni..., Nj..., Nk...)
The returned array has the same type as `a`.
See Also
--------
compress : Take elements using a boolean mask
ndarray.take : equivalent method
take_along_axis : Take elements by matching the array and the index arrays
Notes
-----
By eliminating the inner loop in the description above, and using `s_` to
build simple slice objects, `take` can be expressed in terms of applying
fancy indexing to each 1-d slice::
Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
for kk in ndindex(Nj):
out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]
For this reason, it is equivalent to (but faster than) the following use
of `apply_along_axis`::
out = np.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)
Examples
--------
>>> a = [4, 3, 5, 7, 6, 8]
>>> indices = [0, 1, 4]
>>> np.take(a, indices)
array([4, 3, 6])
In this example if `a` is an ndarray, "fancy" indexing can be used.
>>> a = np.array(a)
>>> a[indices]
array([4, 3, 6])
If `indices` is not one dimensional, the output also has these dimensions.
>>> np.take(a, [[0, 1], [2, 3]])
array([[4, 3],
[5, 7]])
"""
return _wrapfunc(a, 'take', indices, axis=axis, out=out, mode=mode)
def _reshape_dispatcher(a, newshape, order=None):
return (a,)
# not deprecated --- copy if necessary, view otherwise
@array_function_dispatch(_reshape_dispatcher)
def reshape(a, newshape, order='C'):
"""
Gives a new shape to an array without changing its data.
Parameters
----------
a : array_like
Array to be reshaped.
newshape : int or tuple of ints
The new shape should be compatible with the original shape. If
an integer, then the result will be a 1-D array of that length.
One shape dimension can be -1. In this case, the value is
inferred from the length of the array and remaining dimensions.
order : {'C', 'F', 'A'}, optional
Read the elements of `a` using this index order, and place the
elements into the reshaped array using this index order. 'C'
means to read / write the elements using C-like index order,
with the last axis index changing fastest, back to the first
axis index changing slowest. 'F' means to read / write the
elements using Fortran-like index order, with the first index
changing fastest, and the last index changing slowest. Note that
the 'C' and 'F' options take no account of the memory layout of
the underlying array, and only refer to the order of indexing.
'A' means to read / write the elements in Fortran-like index
order if `a` is Fortran *contiguous* in memory, C-like order
otherwise.
Returns
-------
reshaped_array : ndarray
This will be a new view object if possible; otherwise, it will
be a copy. Note there is no guarantee of the *memory layout* (C- or
Fortran- contiguous) of the returned array.
See Also
--------
ndarray.reshape : Equivalent method.
Notes
-----
It is not always possible to change the shape of an array without
copying the data. If you want an error to be raised when the data is copied,
you should assign the new shape to the shape attribute of the array::
>>> a = np.zeros((10, 2))
# A transpose makes the array non-contiguous
>>> b = a.T
# Taking a view makes it possible to modify the shape without modifying
# the initial object.
>>> c = b.view()
>>> c.shape = (20)
Traceback (most recent call last):
...
AttributeError: Incompatible shape for in-place modification. Use
`.reshape()` to make a copy with the desired shape.
The `order` keyword gives the index ordering both for *fetching* the values
from `a`, and then *placing* the values into the output array.
For example, let's say you have an array:
>>> a = np.arange(6).reshape((3, 2))
>>> a
array([[0, 1],
[2, 3],
[4, 5]])
You can think of reshaping as first raveling the array (using the given
index order), then inserting the elements from the raveled array into the
new array using the same kind of index ordering as was used for the
raveling.
>>> np.reshape(a, (2, 3)) # C-like index ordering
array([[0, 1, 2],
[3, 4, 5]])
>>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
array([[0, 1, 2],
[3, 4, 5]])
>>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
array([[0, 4, 3],
[2, 1, 5]])
>>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
array([[0, 4, 3],
[2, 1, 5]])
Examples
--------
>>> a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])
>>> np.reshape(a, 6, order='F')
array([1, 4, 2, 5, 3, 6])
>>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2
array([[1, 2],
[3, 4],
[5, 6]])
"""
return _wrapfunc(a, 'reshape', newshape, order=order)
def _choose_dispatcher(a, choices, out=None, mode=None):
yield a
yield from choices
yield out
@array_function_dispatch(_choose_dispatcher)
def choose(a, choices, out=None, mode='raise'):
"""
Construct an array from an index array and a list of arrays to choose from.
First of all, if confused or uncertain, definitely look at the Examples -
in its full generality, this function is less simple than it might
seem from the following code description (below ndi =
`numpy.lib.index_tricks`):
``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.
But this omits some subtleties. Here is a fully general summary:
Given an "index" array (`a`) of integers and a sequence of ``n`` arrays
(`choices`), `a` and each choice array are first broadcast, as necessary,
to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =
0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``
for each ``i``. Then, a new array with shape ``Ba.shape`` is created as
follows:
* if ``mode='raise'`` (the default), then, first of all, each element of
``a`` (and thus ``Ba``) must be in the range ``[0, n-1]``; now, suppose
that ``i`` (in that range) is the value at the ``(j0, j1, ..., jm)``
position in ``Ba`` - then the value at the same position in the new array
is the value in ``Bchoices[i]`` at that same position;
* if ``mode='wrap'``, values in `a` (and thus `Ba`) may be any (signed)
integer; modular arithmetic is used to map integers outside the range
`[0, n-1]` back into that range; and then the new array is constructed
as above;
* if ``mode='clip'``, values in `a` (and thus ``Ba``) may be any (signed)
integer; negative integers are mapped to 0; values greater than ``n-1``
are mapped to ``n-1``; and then the new array is constructed as above.
Parameters
----------
a : int array
This array must contain integers in ``[0, n-1]``, where ``n`` is the
number of choices, unless ``mode=wrap`` or ``mode=clip``, in which
cases any integers are permissible.
choices : sequence of arrays
Choice arrays. `a` and all of the choices must be broadcastable to the
same shape. If `choices` is itself an array (not recommended), then
its outermost dimension (i.e., the one corresponding to
``choices.shape[0]``) is taken as defining the "sequence".
out : array, optional
If provided, the result will be inserted into this array. It should
be of the appropriate shape and dtype. Note that `out` is always
buffered if ``mode='raise'``; use other modes for better performance.
mode : {'raise' (default), 'wrap', 'clip'}, optional
Specifies how indices outside ``[0, n-1]`` will be treated:
* 'raise' : an exception is raised
* 'wrap' : value becomes value mod ``n``
* 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1
Returns
-------
merged_array : array
The merged result.
Raises
------
ValueError: shape mismatch
If `a` and each choice array are not all broadcastable to the same
shape.
See Also
--------
ndarray.choose : equivalent method
numpy.take_along_axis : Preferable if `choices` is an array
Notes
-----
To reduce the chance of misinterpretation, even though the following
"abuse" is nominally supported, `choices` should neither be, nor be
thought of as, a single array, i.e., the outermost sequence-like container
should be either a list or a tuple.
Examples
--------
>>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
... [20, 21, 22, 23], [30, 31, 32, 33]]
>>> np.choose([2, 3, 1, 0], choices
... # the first element of the result will be the first element of the
... # third (2+1) "array" in choices, namely, 20; the second element
... # will be the second element of the fourth (3+1) choice array, i.e.,
... # 31, etc.
... )
array([20, 31, 12, 3])
>>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
array([20, 31, 12, 3])
>>> # because there are 4 choice arrays
>>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
array([20, 1, 12, 3])
>>> # i.e., 0
A couple examples illustrating how choose broadcasts:
>>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
>>> choices = [-10, 10]
>>> np.choose(a, choices)
array([[ 10, -10, 10],
[-10, 10, -10],
[ 10, -10, 10]])
>>> # With thanks to Anne Archibald
>>> a = np.array([0, 1]).reshape((2,1,1))
>>> c1 = np.array([1, 2, 3]).reshape((1,3,1))
>>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
>>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
array([[[ 1, 1, 1, 1, 1],
[ 2, 2, 2, 2, 2],
[ 3, 3, 3, 3, 3]],
[[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5]]])
"""
return _wrapfunc(a, 'choose', choices, out=out, mode=mode)
def _repeat_dispatcher(a, repeats, axis=None):
return (a,)
@array_function_dispatch(_repeat_dispatcher)
def repeat(a, repeats, axis=None):
"""
Repeat elements of an array.
Parameters
----------
a : array_like
Input array.
repeats : int or array of ints
The number of repetitions for each element. `repeats` is broadcasted
to fit the shape of the given axis.
axis : int, optional
The axis along which to repeat values. By default, use the
flattened input array, and return a flat output array.
Returns
-------
repeated_array : ndarray
Output array which has the same shape as `a`, except along
the given axis.
See Also
--------
tile : Tile an array.
unique : Find the unique elements of an array.
Examples
--------
>>> np.repeat(3, 4)
array([3, 3, 3, 3])
>>> x = np.array([[1,2],[3,4]])
>>> np.repeat(x, 2)
array([1, 1, 2, 2, 3, 3, 4, 4])
>>> np.repeat(x, 3, axis=1)
array([[1, 1, 1, 2, 2, 2],
[3, 3, 3, 4, 4, 4]])
>>> np.repeat(x, [1, 2], axis=0)
array([[1, 2],
[3, 4],
[3, 4]])
"""
return _wrapfunc(a, 'repeat', repeats, axis=axis)
def _put_dispatcher(a, ind, v, mode=None):
return (a, ind, v)
@array_function_dispatch(_put_dispatcher)
def put(a, ind, v, mode='raise'):
"""
Replaces specified elements of an array with given values.
The indexing works on the flattened target array. `put` is roughly
equivalent to:
::
a.flat[ind] = v
Parameters
----------
a : ndarray
Target array.
ind : array_like
Target indices, interpreted as integers.
v : array_like
Values to place in `a` at target indices. If `v` is shorter than
`ind` it will be repeated as necessary.
mode : {'raise', 'wrap', 'clip'}, optional
Specifies how out-of-bounds indices will behave.
* 'raise' -- raise an error (default)
* 'wrap' -- wrap around
* 'clip' -- clip to the range
'clip' mode means that all indices that are too large are replaced
by the index that addresses the last element along that axis. Note
that this disables indexing with negative numbers. In 'raise' mode,
if an exception occurs the target array may still be modified.
See Also
--------
putmask, place
put_along_axis : Put elements by matching the array and the index arrays
Examples
--------
>>> a = np.arange(5)
>>> np.put(a, [0, 2], [-44, -55])
>>> a
array([-44, 1, -55, 3, 4])
>>> a = np.arange(5)
>>> np.put(a, 22, -5, mode='clip')
>>> a
array([ 0, 1, 2, 3, -5])
"""
try:
put = a.put
except AttributeError as e:
raise TypeError("argument 1 must be numpy.ndarray, "
"not {name}".format(name=type(a).__name__)) from e
return put(ind, v, mode=mode)
def _swapaxes_dispatcher(a, axis1, axis2):
return (a,)
@array_function_dispatch(_swapaxes_dispatcher)
def swapaxes(a, axis1, axis2):
"""
Interchange two axes of an array.
Parameters
----------
a : array_like
Input array.
axis1 : int
First axis.
axis2 : int
Second axis.
Returns
-------
a_swapped : ndarray
For NumPy >= 1.10.0, if `a` is an ndarray, then a view of `a` is
returned; otherwise a new array is created. For earlier NumPy
versions a view of `a` is returned only if the order of the
axes is changed, otherwise the input array is returned.
Examples
--------
>>> x = np.array([[1,2,3]])
>>> np.swapaxes(x,0,1)
array([[1],
[2],
[3]])
>>> x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])
>>> x
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> np.swapaxes(x,0,2)
array([[[0, 4],
[2, 6]],
[[1, 5],
[3, 7]]])
"""
return _wrapfunc(a, 'swapaxes', axis1, axis2)
def _transpose_dispatcher(a, axes=None):
return (a,)
@array_function_dispatch(_transpose_dispatcher)
def transpose(a, axes=None):
"""
Reverse or permute the axes of an array; returns the modified array.
For an array a with two axes, transpose(a) gives the matrix transpose.
Refer to `numpy.ndarray.transpose` for full documentation.
Parameters
----------
a : array_like
Input array.
axes : tuple or list of ints, optional
If specified, it must be a tuple or list which contains a permutation of
[0,1,..,N-1] where N is the number of axes of a. The i'th axis of the
returned array will correspond to the axis numbered ``axes[i]`` of the
input. If not specified, defaults to ``range(a.ndim)[::-1]``, which
reverses the order of the axes.
Returns
-------
p : ndarray
`a` with its axes permuted. A view is returned whenever
possible.
See Also
--------
ndarray.transpose : Equivalent method
moveaxis
argsort
Notes
-----
Use `transpose(a, argsort(axes))` to invert the transposition of tensors
when using the `axes` keyword argument.
Transposing a 1-D array returns an unchanged view of the original array.
Examples
--------
>>> x = np.arange(4).reshape((2,2))
>>> x
array([[0, 1],
[2, 3]])
>>> np.transpose(x)
array([[0, 2],
[1, 3]])
>>> x = np.ones((1, 2, 3))
>>> np.transpose(x, (1, 0, 2)).shape
(2, 1, 3)
>>> x = np.ones((2, 3, 4, 5))
>>> np.transpose(x).shape
(5, 4, 3, 2)
"""
return _wrapfunc(a, 'transpose', axes)
def _partition_dispatcher(a, kth, axis=None, kind=None, order=None):
return (a,)
@array_function_dispatch(_partition_dispatcher)
def partition(a, kth, axis=-1, kind='introselect', order=None):
"""
Return a partitioned copy of an array.
Creates a copy of the array with its elements rearranged in such a
way that the value of the element in k-th position is in the
position it would be in a sorted array. All elements smaller than
the k-th element are moved before this element and all equal or
greater are moved behind it. The ordering of the elements in the two
partitions is undefined.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
Array to be sorted.
kth : int or sequence of ints
Element index to partition by. The k-th value of the element
will be in its final sorted position and all smaller elements
will be moved before it and all equal or greater elements behind
it. The order of all elements in the partitions is undefined. If
provided with a sequence of k-th it will partition all elements
indexed by k-th of them into their sorted position at once.
.. deprecated:: 1.22.0
Passing booleans as index is deprecated.
axis : int or None, optional
Axis along which to sort. If None, the array is flattened before
sorting. The default is -1, which sorts along the last axis.
kind : {'introselect'}, optional
Selection algorithm. Default is 'introselect'.
order : str or list of str, optional
When `a` is an array with fields defined, this argument
specifies which fields to compare first, second, etc. A single
field can be specified as a string. Not all fields need be
specified, but unspecified fields will still be used, in the
order in which they come up in the dtype, to break ties.
Returns
-------
partitioned_array : ndarray
Array of the same type and shape as `a`.
See Also
--------
ndarray.partition : Method to sort an array in-place.
argpartition : Indirect partition.
sort : Full sorting
Notes
-----
The various selection algorithms are characterized by their average
speed, worst case performance, work space size, and whether they are
stable. A stable sort keeps items with the same key in the same
relative order. The available algorithms have the following
properties:
================= ======= ============= ============ =======
kind speed worst case work space stable
================= ======= ============= ============ =======
'introselect' 1 O(n) 0 no
================= ======= ============= ============ =======
All the partition algorithms make temporary copies of the data when
partitioning along any but the last axis. Consequently,
partitioning along the last axis is faster and uses less space than
partitioning along any other axis.
The sort order for complex numbers is lexicographic. If both the
real and imaginary parts are non-nan then the order is determined by
the real parts except when they are equal, in which case the order
is determined by the imaginary parts.
Examples
--------
>>> a = np.array([3, 4, 2, 1])
>>> np.partition(a, 3)
array([2, 1, 3, 4])
>>> np.partition(a, (1, 3))
array([1, 2, 3, 4])
"""
if axis is None:
# flatten returns (1, N) for np.matrix, so always use the last axis
a = asanyarray(a).flatten()
axis = -1
else:
a = asanyarray(a).copy(order="K")
a.partition(kth, axis=axis, kind=kind, order=order)
return a
def _argpartition_dispatcher(a, kth, axis=None, kind=None, order=None):
return (a,)
@array_function_dispatch(_argpartition_dispatcher)
def argpartition(a, kth, axis=-1, kind='introselect', order=None):
"""
Perform an indirect partition along the given axis using the
algorithm specified by the `kind` keyword. It returns an array of
indices of the same shape as `a` that index data along the given
axis in partitioned order.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
Array to sort.
kth : int or sequence of ints
Element index to partition by. The k-th element will be in its
final sorted position and all smaller elements will be moved
before it and all larger elements behind it. The order all
elements in the partitions is undefined. If provided with a
sequence of k-th it will partition all of them into their sorted
position at once.
.. deprecated:: 1.22.0
Passing booleans as index is deprecated.
axis : int or None, optional
Axis along which to sort. The default is -1 (the last axis). If
None, the flattened array is used.
kind : {'introselect'}, optional
Selection algorithm. Default is 'introselect'
order : str or list of str, optional
When `a` is an array with fields defined, this argument
specifies which fields to compare first, second, etc. A single
field can be specified as a string, and not all fields need be
specified, but unspecified fields will still be used, in the
order in which they come up in the dtype, to break ties.
Returns
-------
index_array : ndarray, int
Array of indices that partition `a` along the specified axis.
If `a` is one-dimensional, ``a[index_array]`` yields a partitioned `a`.
More generally, ``np.take_along_axis(a, index_array, axis)``
always yields the partitioned `a`, irrespective of dimensionality.
See Also
--------
partition : Describes partition algorithms used.
ndarray.partition : Inplace partition.
argsort : Full indirect sort.
take_along_axis : Apply ``index_array`` from argpartition
to an array as if by calling partition.
Notes
-----
See `partition` for notes on the different selection algorithms.
Examples
--------
One dimensional array:
>>> x = np.array([3, 4, 2, 1])
>>> x[np.argpartition(x, 3)]
array([2, 1, 3, 4])
>>> x[np.argpartition(x, (1, 3))]
array([1, 2, 3, 4])
>>> x = [3, 4, 2, 1]
>>> np.array(x)[np.argpartition(x, 3)]
array([2, 1, 3, 4])
Multi-dimensional array:
>>> x = np.array([[3, 4, 2], [1, 3, 1]])
>>> index_array = np.argpartition(x, kth=1, axis=-1)
>>> np.take_along_axis(x, index_array, axis=-1) # same as np.partition(x, kth=1)
array([[2, 3, 4],
[1, 1, 3]])
"""
return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order)
def _sort_dispatcher(a, axis=None, kind=None, order=None):
return (a,)
@array_function_dispatch(_sort_dispatcher)
def sort(a, axis=-1, kind=None, order=None):
"""
Return a sorted copy of an array.
Parameters
----------
a : array_like
Array to be sorted.
axis : int or None, optional
Axis along which to sort. If None, the array is flattened before
sorting. The default is -1, which sorts along the last axis.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
Sorting algorithm. The default is 'quicksort'. Note that both 'stable'
and 'mergesort' use timsort or radix sort under the covers and, in general,
the actual implementation will vary with data type. The 'mergesort' option
is retained for backwards compatibility.
.. versionchanged:: 1.15.0.
The 'stable' option was added.
order : str or list of str, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
Returns
-------
sorted_array : ndarray
Array of the same type and shape as `a`.
See Also
--------
ndarray.sort : Method to sort an array in-place.
argsort : Indirect sort.
lexsort : Indirect stable sort on multiple keys.
searchsorted : Find elements in a sorted array.
partition : Partial sort.
Notes
-----
The various sorting algorithms are characterized by their average speed,
worst case performance, work space size, and whether they are stable. A
stable sort keeps items with the same key in the same relative
order. The four algorithms implemented in NumPy have the following
properties:
=========== ======= ============= ============ ========
kind speed worst case work space stable
=========== ======= ============= ============ ========
'quicksort' 1 O(n^2) 0 no
'heapsort' 3 O(n*log(n)) 0 no
'mergesort' 2 O(n*log(n)) ~n/2 yes
'timsort' 2 O(n*log(n)) ~n/2 yes
=========== ======= ============= ============ ========
.. note:: The datatype determines which of 'mergesort' or 'timsort'
is actually used, even if 'mergesort' is specified. User selection
at a finer scale is not currently available.
All the sort algorithms make temporary copies of the data when
sorting along any but the last axis. Consequently, sorting along
the last axis is faster and uses less space than sorting along
any other axis.
The sort order for complex numbers is lexicographic. If both the real
and imaginary parts are non-nan then the order is determined by the
real parts except when they are equal, in which case the order is
determined by the imaginary parts.
Previous to numpy 1.4.0 sorting real and complex arrays containing nan
values led to undefined behaviour. In numpy versions >= 1.4.0 nan
values are sorted to the end. The extended sort order is:
* Real: [R, nan]
* Complex: [R + Rj, R + nanj, nan + Rj, nan + nanj]
where R is a non-nan real value. Complex values with the same nan
placements are sorted according to the non-nan part if it exists.
Non-nan values are sorted as before.
.. versionadded:: 1.12.0
quicksort has been changed to `introsort <https://en.wikipedia.org/wiki/Introsort>`_.
When sorting does not make enough progress it switches to
`heapsort <https://en.wikipedia.org/wiki/Heapsort>`_.
This implementation makes quicksort O(n*log(n)) in the worst case.
'stable' automatically chooses the best stable sorting algorithm
for the data type being sorted.
It, along with 'mergesort' is currently mapped to
`timsort <https://en.wikipedia.org/wiki/Timsort>`_
or `radix sort <https://en.wikipedia.org/wiki/Radix_sort>`_
depending on the data type.
API forward compatibility currently limits the
ability to select the implementation and it is hardwired for the different
data types.
.. versionadded:: 1.17.0
Timsort is added for better performance on already or nearly
sorted data. On random data timsort is almost identical to
mergesort. It is now used for stable sort while quicksort is still the
default sort if none is chosen. For timsort details, refer to
`CPython listsort.txt <https://github.com/python/cpython/blob/3.7/Objects/listsort.txt>`_.
'mergesort' and 'stable' are mapped to radix sort for integer data types. Radix sort is an
O(n) sort instead of O(n log n).
.. versionchanged:: 1.18.0
NaT now sorts to the end of arrays for consistency with NaN.
Examples
--------
>>> a = np.array([[1,4],[3,1]])
>>> np.sort(a) # sort along the last axis
array([[1, 4],
[1, 3]])
>>> np.sort(a, axis=None) # sort the flattened array
array([1, 1, 3, 4])
>>> np.sort(a, axis=0) # sort along the first axis
array([[1, 1],
[3, 4]])
Use the `order` keyword to specify a field to use when sorting a
structured array:
>>> dtype = [('name', 'S10'), ('height', float), ('age', int)]
>>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),
... ('Galahad', 1.7, 38)]
>>> a = np.array(values, dtype=dtype) # create a structured array
>>> np.sort(a, order='height') # doctest: +SKIP
array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
('Lancelot', 1.8999999999999999, 38)],
dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
Sort by age, then height if ages are equal:
>>> np.sort(a, order=['age', 'height']) # doctest: +SKIP
array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38),
('Arthur', 1.8, 41)],
dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
"""
if axis is None:
# flatten returns (1, N) for np.matrix, so always use the last axis
a = asanyarray(a).flatten()
axis = -1
else:
a = asanyarray(a).copy(order="K")
a.sort(axis=axis, kind=kind, order=order)
return a
def _argsort_dispatcher(a, axis=None, kind=None, order=None):
return (a,)
@array_function_dispatch(_argsort_dispatcher)
def argsort(a, axis=-1, kind=None, order=None):
"""
Returns the indices that would sort an array.
Perform an indirect sort along the given axis using the algorithm specified
by the `kind` keyword. It returns an array of indices of the same shape as
`a` that index data along the given axis in sorted order.
Parameters
----------
a : array_like
Array to sort.
axis : int or None, optional
Axis along which to sort. The default is -1 (the last axis). If None,
the flattened array is used.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
Sorting algorithm. The default is 'quicksort'. Note that both 'stable'
and 'mergesort' use timsort under the covers and, in general, the
actual implementation will vary with data type. The 'mergesort' option
is retained for backwards compatibility.
.. versionchanged:: 1.15.0.
The 'stable' option was added.
order : str or list of str, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
Returns
-------
index_array : ndarray, int
Array of indices that sort `a` along the specified `axis`.
If `a` is one-dimensional, ``a[index_array]`` yields a sorted `a`.
More generally, ``np.take_along_axis(a, index_array, axis=axis)``
always yields the sorted `a`, irrespective of dimensionality.
See Also
--------
sort : Describes sorting algorithms used.
lexsort : Indirect stable sort with multiple keys.
ndarray.sort : Inplace sort.
argpartition : Indirect partial sort.
take_along_axis : Apply ``index_array`` from argsort
to an array as if by calling sort.
Notes
-----
See `sort` for notes on the different sorting algorithms.
As of NumPy 1.4.0 `argsort` works with real/complex arrays containing
nan values. The enhanced sort order is documented in `sort`.
Examples
--------
One dimensional array:
>>> x = np.array([3, 1, 2])
>>> np.argsort(x)
array([1, 2, 0])
Two-dimensional array:
>>> x = np.array([[0, 3], [2, 2]])
>>> x
array([[0, 3],
[2, 2]])
>>> ind = np.argsort(x, axis=0) # sorts along first axis (down)
>>> ind
array([[0, 1],
[1, 0]])
>>> np.take_along_axis(x, ind, axis=0) # same as np.sort(x, axis=0)
array([[0, 2],
[2, 3]])
>>> ind = np.argsort(x, axis=1) # sorts along last axis (across)
>>> ind
array([[0, 1],
[0, 1]])
>>> np.take_along_axis(x, ind, axis=1) # same as np.sort(x, axis=1)
array([[0, 3],
[2, 2]])
Indices of the sorted elements of a N-dimensional array:
>>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape)
>>> ind
(array([0, 1, 1, 0]), array([0, 0, 1, 1]))
>>> x[ind] # same as np.sort(x, axis=None)
array([0, 2, 2, 3])
Sorting with keys:
>>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
>>> x
array([(1, 0), (0, 1)],
dtype=[('x', '<i4'), ('y', '<i4')])
>>> np.argsort(x, order=('x','y'))
array([1, 0])
>>> np.argsort(x, order=('y','x'))
array([0, 1])
"""
return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order)
def _argmax_dispatcher(a, axis=None, out=None, *, keepdims=np._NoValue):
return (a, out)
@array_function_dispatch(_argmax_dispatcher)
def argmax(a, axis=None, out=None, *, keepdims=np._NoValue):
"""
Returns the indices of the maximum values along an axis.
Parameters
----------
a : array_like
Input array.
axis : int, optional
By default, the index is into the flattened array, otherwise
along the specified axis.
out : array, optional
If provided, the result will be inserted into this array. It should
be of the appropriate shape and dtype.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the array.
.. versionadded:: 1.22.0
Returns
-------
index_array : ndarray of ints
Array of indices into the array. It has the same shape as `a.shape`
with the dimension along `axis` removed. If `keepdims` is set to True,
then the size of `axis` will be 1 with the resulting array having same
shape as `a.shape`.
See Also
--------
ndarray.argmax, argmin
amax : The maximum value along a given axis.
unravel_index : Convert a flat index into an index tuple.
take_along_axis : Apply ``np.expand_dims(index_array, axis)``
from argmax to an array as if by calling max.
Notes
-----
In case of multiple occurrences of the maximum values, the indices
corresponding to the first occurrence are returned.
Examples
--------
>>> a = np.arange(6).reshape(2,3) + 10
>>> a
array([[10, 11, 12],
[13, 14, 15]])
>>> np.argmax(a)
5
>>> np.argmax(a, axis=0)
array([1, 1, 1])
>>> np.argmax(a, axis=1)
array([2, 2])
Indexes of the maximal elements of a N-dimensional array:
>>> ind = np.unravel_index(np.argmax(a, axis=None), a.shape)
>>> ind
(1, 2)
>>> a[ind]
15
>>> b = np.arange(6)
>>> b[1] = 5
>>> b
array([0, 5, 2, 3, 4, 5])
>>> np.argmax(b) # Only the first occurrence is returned.
1
>>> x = np.array([[4,2,3], [1,0,3]])
>>> index_array = np.argmax(x, axis=-1)
>>> # Same as np.amax(x, axis=-1, keepdims=True)
>>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)
array([[4],
[3]])
>>> # Same as np.amax(x, axis=-1)
>>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1)
array([4, 3])
Setting `keepdims` to `True`,
>>> x = np.arange(24).reshape((2, 3, 4))
>>> res = np.argmax(x, axis=1, keepdims=True)
>>> res.shape
(2, 1, 4)
"""
kwds = {'keepdims': keepdims} if keepdims is not np._NoValue else {}
return _wrapfunc(a, 'argmax', axis=axis, out=out, **kwds)
def _argmin_dispatcher(a, axis=None, out=None, *, keepdims=np._NoValue):
return (a, out)
@array_function_dispatch(_argmin_dispatcher)
def argmin(a, axis=None, out=None, *, keepdims=np._NoValue):
"""
Returns the indices of the minimum values along an axis.
Parameters
----------
a : array_like
Input array.
axis : int, optional
By default, the index is into the flattened array, otherwise
along the specified axis.
out : array, optional
If provided, the result will be inserted into this array. It should
be of the appropriate shape and dtype.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the array.
.. versionadded:: 1.22.0
Returns
-------
index_array : ndarray of ints
Array of indices into the array. It has the same shape as `a.shape`
with the dimension along `axis` removed. If `keepdims` is set to True,
then the size of `axis` will be 1 with the resulting array having same
shape as `a.shape`.
See Also
--------
ndarray.argmin, argmax
amin : The minimum value along a given axis.
unravel_index : Convert a flat index into an index tuple.
take_along_axis : Apply ``np.expand_dims(index_array, axis)``
from argmin to an array as if by calling min.
Notes
-----
In case of multiple occurrences of the minimum values, the indices
corresponding to the first occurrence are returned.
Examples
--------
>>> a = np.arange(6).reshape(2,3) + 10
>>> a
array([[10, 11, 12],
[13, 14, 15]])
>>> np.argmin(a)
0
>>> np.argmin(a, axis=0)
array([0, 0, 0])
>>> np.argmin(a, axis=1)
array([0, 0])
Indices of the minimum elements of a N-dimensional array:
>>> ind = np.unravel_index(np.argmin(a, axis=None), a.shape)
>>> ind
(0, 0)
>>> a[ind]
10
>>> b = np.arange(6) + 10
>>> b[4] = 10
>>> b
array([10, 11, 12, 13, 10, 15])
>>> np.argmin(b) # Only the first occurrence is returned.
0
>>> x = np.array([[4,2,3], [1,0,3]])
>>> index_array = np.argmin(x, axis=-1)
>>> # Same as np.amin(x, axis=-1, keepdims=True)
>>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)
array([[2],
[0]])
>>> # Same as np.amax(x, axis=-1)
>>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1)
array([2, 0])
Setting `keepdims` to `True`,
>>> x = np.arange(24).reshape((2, 3, 4))
>>> res = np.argmin(x, axis=1, keepdims=True)
>>> res.shape
(2, 1, 4)
"""
kwds = {'keepdims': keepdims} if keepdims is not np._NoValue else {}
return _wrapfunc(a, 'argmin', axis=axis, out=out, **kwds)
def _searchsorted_dispatcher(a, v, side=None, sorter=None):
return (a, v, sorter)
@array_function_dispatch(_searchsorted_dispatcher)
def searchsorted(a, v, side='left', sorter=None):
"""
Find indices where elements should be inserted to maintain order.
Find the indices into a sorted array `a` such that, if the
corresponding elements in `v` were inserted before the indices, the
order of `a` would be preserved.
Assuming that `a` is sorted:
====== ============================
`side` returned index `i` satisfies
====== ============================
left ``a[i-1] < v <= a[i]``
right ``a[i-1] <= v < a[i]``
====== ============================
Parameters
----------
a : 1-D array_like
Input array. If `sorter` is None, then it must be sorted in
ascending order, otherwise `sorter` must be an array of indices
that sort it.
v : array_like
Values to insert into `a`.
side : {'left', 'right'}, optional
If 'left', the index of the first suitable location found is given.
If 'right', return the last such index. If there is no suitable
index, return either 0 or N (where N is the length of `a`).
sorter : 1-D array_like, optional
Optional array of integer indices that sort array a into ascending
order. They are typically the result of argsort.
.. versionadded:: 1.7.0
Returns
-------
indices : int or array of ints
Array of insertion points with the same shape as `v`,
or an integer if `v` is a scalar.
See Also
--------
sort : Return a sorted copy of an array.
histogram : Produce histogram from 1-D data.
Notes
-----
Binary search is used to find the required insertion points.
As of NumPy 1.4.0 `searchsorted` works with real/complex arrays containing
`nan` values. The enhanced sort order is documented in `sort`.
This function uses the same algorithm as the builtin python `bisect.bisect_left`
(``side='left'``) and `bisect.bisect_right` (``side='right'``) functions,
which is also vectorized in the `v` argument.
Examples
--------
>>> np.searchsorted([1,2,3,4,5], 3)
2
>>> np.searchsorted([1,2,3,4,5], 3, side='right')
3
>>> np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])
array([0, 5, 1, 2])
"""
return _wrapfunc(a, 'searchsorted', v, side=side, sorter=sorter)
def _resize_dispatcher(a, new_shape):
return (a,)
@array_function_dispatch(_resize_dispatcher)
def resize(a, new_shape):
"""
Return a new array with the specified shape.
If the new array is larger than the original array, then the new
array is filled with repeated copies of `a`. Note that this behavior
is different from a.resize(new_shape) which fills with zeros instead
of repeated copies of `a`.
Parameters
----------
a : array_like
Array to be resized.
new_shape : int or tuple of int
Shape of resized array.
Returns
-------
reshaped_array : ndarray
The new array is formed from the data in the old array, repeated
if necessary to fill out the required number of elements. The
data are repeated iterating over the array in C-order.
See Also
--------
numpy.reshape : Reshape an array without changing the total size.
numpy.pad : Enlarge and pad an array.
numpy.repeat : Repeat elements of an array.
ndarray.resize : resize an array in-place.
Notes
-----
When the total size of the array does not change `~numpy.reshape` should
be used. In most other cases either indexing (to reduce the size)
or padding (to increase the size) may be a more appropriate solution.
Warning: This functionality does **not** consider axes separately,
i.e. it does not apply interpolation/extrapolation.
It fills the return array with the required number of elements, iterating
over `a` in C-order, disregarding axes (and cycling back from the start if
the new shape is larger). This functionality is therefore not suitable to
resize images, or data where each axis represents a separate and distinct
entity.
Examples
--------
>>> a=np.array([[0,1],[2,3]])
>>> np.resize(a,(2,3))
array([[0, 1, 2],
[3, 0, 1]])
>>> np.resize(a,(1,4))
array([[0, 1, 2, 3]])
>>> np.resize(a,(2,4))
array([[0, 1, 2, 3],
[0, 1, 2, 3]])
"""
if isinstance(new_shape, (int, nt.integer)):
new_shape = (new_shape,)
a = ravel(a)
new_size = 1
for dim_length in new_shape:
new_size *= dim_length
if dim_length < 0:
raise ValueError('all elements of `new_shape` must be non-negative')
if a.size == 0 or new_size == 0:
# First case must zero fill. The second would have repeats == 0.
return np.zeros_like(a, shape=new_shape)
repeats = -(-new_size // a.size) # ceil division
a = concatenate((a,) * repeats)[:new_size]
return reshape(a, new_shape)
def _squeeze_dispatcher(a, axis=None):
return (a,)
@array_function_dispatch(_squeeze_dispatcher)
def squeeze(a, axis=None):
"""
Remove axes of length one from `a`.
Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
.. versionadded:: 1.7.0
Selects a subset of the entries of length one in the
shape. If an axis is selected with shape entry greater than
one, an error is raised.
Returns
-------
squeezed : ndarray
The input array, but with all or a subset of the
dimensions of length 1 removed. This is always `a` itself
or a view into `a`. Note that if all axes are squeezed,
the result is a 0d array and not a scalar.
Raises
------
ValueError
If `axis` is not None, and an axis being squeezed is not of length 1
See Also
--------
expand_dims : The inverse operation, adding entries of length one
reshape : Insert, remove, and combine dimensions, and resize existing ones
Examples
--------
>>> x = np.array([[[0], [1], [2]]])
>>> x.shape
(1, 3, 1)
>>> np.squeeze(x).shape
(3,)
>>> np.squeeze(x, axis=0).shape
(3, 1)
>>> np.squeeze(x, axis=1).shape
Traceback (most recent call last):
...
ValueError: cannot select an axis to squeeze out which has size not equal to one
>>> np.squeeze(x, axis=2).shape
(1, 3)
>>> x = np.array([[1234]])
>>> x.shape
(1, 1)
>>> np.squeeze(x)
array(1234) # 0d array
>>> np.squeeze(x).shape
()
>>> np.squeeze(x)[()]
1234
"""
try:
squeeze = a.squeeze
except AttributeError:
return _wrapit(a, 'squeeze', axis=axis)
if axis is None:
return squeeze()
else:
return squeeze(axis=axis)
def _diagonal_dispatcher(a, offset=None, axis1=None, axis2=None):
return (a,)
@array_function_dispatch(_diagonal_dispatcher)
def diagonal(a, offset=0, axis1=0, axis2=1):
"""
Return specified diagonals.
If `a` is 2-D, returns the diagonal of `a` with the given offset,
i.e., the collection of elements of the form ``a[i, i+offset]``. If
`a` has more than two dimensions, then the axes specified by `axis1`
and `axis2` are used to determine the 2-D sub-array whose diagonal is
returned. The shape of the resulting array can be determined by
removing `axis1` and `axis2` and appending an index to the right equal
to the size of the resulting diagonals.
In versions of NumPy prior to 1.7, this function always returned a new,
independent array containing a copy of the values in the diagonal.
In NumPy 1.7 and 1.8, it continues to return a copy of the diagonal,
but depending on this fact is deprecated. Writing to the resulting
array continues to work as it used to, but a FutureWarning is issued.
Starting in NumPy 1.9 it returns a read-only view on the original array.
Attempting to write to the resulting array will produce an error.
In some future release, it will return a read/write view and writing to
the returned array will alter your original array. The returned array
will have the same type as the input array.
If you don't write to the array returned by this function, then you can
just ignore all of the above.
If you depend on the current behavior, then we suggest copying the
returned array explicitly, i.e., use ``np.diagonal(a).copy()`` instead
of just ``np.diagonal(a)``. This will work with both past and future
versions of NumPy.
Parameters
----------
a : array_like
Array from which the diagonals are taken.
offset : int, optional
Offset of the diagonal from the main diagonal. Can be positive or
negative. Defaults to main diagonal (0).
axis1 : int, optional
Axis to be used as the first axis of the 2-D sub-arrays from which
the diagonals should be taken. Defaults to first axis (0).
axis2 : int, optional
Axis to be used as the second axis of the 2-D sub-arrays from
which the diagonals should be taken. Defaults to second axis (1).
Returns
-------
array_of_diagonals : ndarray
If `a` is 2-D, then a 1-D array containing the diagonal and of the
same type as `a` is returned unless `a` is a `matrix`, in which case
a 1-D array rather than a (2-D) `matrix` is returned in order to
maintain backward compatibility.
If ``a.ndim > 2``, then the dimensions specified by `axis1` and `axis2`
are removed, and a new axis inserted at the end corresponding to the
diagonal.
Raises
------
ValueError
If the dimension of `a` is less than 2.
See Also
--------
diag : MATLAB work-a-like for 1-D and 2-D arrays.
diagflat : Create diagonal arrays.
trace : Sum along diagonals.
Examples
--------
>>> a = np.arange(4).reshape(2,2)
>>> a
array([[0, 1],
[2, 3]])
>>> a.diagonal()
array([0, 3])
>>> a.diagonal(1)
array([1])
A 3-D example:
>>> a = np.arange(8).reshape(2,2,2); a
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> a.diagonal(0, # Main diagonals of two arrays created by skipping
... 0, # across the outer(left)-most axis last and
... 1) # the "middle" (row) axis first.
array([[0, 6],
[1, 7]])
The sub-arrays whose main diagonals we just obtained; note that each
corresponds to fixing the right-most (column) axis, and that the
diagonals are "packed" in rows.
>>> a[:,:,0] # main diagonal is [0 6]
array([[0, 2],
[4, 6]])
>>> a[:,:,1] # main diagonal is [1 7]
array([[1, 3],
[5, 7]])
The anti-diagonal can be obtained by reversing the order of elements
using either `numpy.flipud` or `numpy.fliplr`.
>>> a = np.arange(9).reshape(3, 3)
>>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> np.fliplr(a).diagonal() # Horizontal flip
array([2, 4, 6])
>>> np.flipud(a).diagonal() # Vertical flip
array([6, 4, 2])
Note that the order in which the diagonal is retrieved varies depending
on the flip function.
"""
if isinstance(a, np.matrix):
# Make diagonal of matrix 1-D to preserve backward compatibility.
return asarray(a).diagonal(offset=offset, axis1=axis1, axis2=axis2)
else:
return asanyarray(a).diagonal(offset=offset, axis1=axis1, axis2=axis2)
def _trace_dispatcher(
a, offset=None, axis1=None, axis2=None, dtype=None, out=None):
return (a, out)
@array_function_dispatch(_trace_dispatcher)
def trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None):
"""
Return the sum along diagonals of the array.
If `a` is 2-D, the sum along its diagonal with the given offset
is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.
If `a` has more than two dimensions, then the axes specified by axis1 and
axis2 are used to determine the 2-D sub-arrays whose traces are returned.
The shape of the resulting array is the same as that of `a` with `axis1`
and `axis2` removed.
Parameters
----------
a : array_like
Input array, from which the diagonals are taken.
offset : int, optional
Offset of the diagonal from the main diagonal. Can be both positive
and negative. Defaults to 0.
axis1, axis2 : int, optional
Axes to be used as the first and second axis of the 2-D sub-arrays
from which the diagonals should be taken. Defaults are the first two
axes of `a`.
dtype : dtype, optional
Determines the data-type of the returned array and of the accumulator
where the elements are summed. If dtype has the value None and `a` is
of integer type of precision less than the default integer
precision, then the default integer precision is used. Otherwise,
the precision is the same as that of `a`.
out : ndarray, optional
Array into which the output is placed. Its type is preserved and
it must be of the right shape to hold the output.
Returns
-------
sum_along_diagonals : ndarray
If `a` is 2-D, the sum along the diagonal is returned. If `a` has
larger dimensions, then an array of sums along diagonals is returned.
See Also
--------
diag, diagonal, diagflat
Examples
--------
>>> np.trace(np.eye(3))
3.0
>>> a = np.arange(8).reshape((2,2,2))
>>> np.trace(a)
array([6, 8])
>>> a = np.arange(24).reshape((2,2,2,3))
>>> np.trace(a).shape
(2, 3)
"""
if isinstance(a, np.matrix):
# Get trace of matrix via an array to preserve backward compatibility.
return asarray(a).trace(offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out)
else:
return asanyarray(a).trace(offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out)
def _ravel_dispatcher(a, order=None):
return (a,)
@array_function_dispatch(_ravel_dispatcher)
def ravel(a, order='C'):
"""Return a contiguous flattened array.
A 1-D array, containing the elements of the input, is returned. A copy is
made only if needed.
As of NumPy 1.10, the returned array will have the same type as the input
array. (for example, a masked array will be returned for a masked array
input)
Parameters
----------
a : array_like
Input array. The elements in `a` are read in the order specified by
`order`, and packed as a 1-D array.
order : {'C','F', 'A', 'K'}, optional
The elements of `a` are read using this index order. 'C' means
to index the elements in row-major, C-style order,
with the last axis index changing fastest, back to the first
axis index changing slowest. 'F' means to index the elements
in column-major, Fortran-style order, with the
first index changing fastest, and the last index changing
slowest. Note that the 'C' and 'F' options take no account of
the memory layout of the underlying array, and only refer to
the order of axis indexing. 'A' means to read the elements in
Fortran-like index order if `a` is Fortran *contiguous* in
memory, C-like order otherwise. 'K' means to read the
elements in the order they occur in memory, except for
reversing the data when strides are negative. By default, 'C'
index order is used.
Returns
-------
y : array_like
y is an array of the same subtype as `a`, with shape ``(a.size,)``.
Note that matrices are special cased for backward compatibility, if `a`
is a matrix, then y is a 1-D ndarray.
See Also
--------
ndarray.flat : 1-D iterator over an array.
ndarray.flatten : 1-D array copy of the elements of an array
in row-major order.
ndarray.reshape : Change the shape of an array without changing its data.
Notes
-----
In row-major, C-style order, in two dimensions, the row index
varies the slowest, and the column index the quickest. This can
be generalized to multiple dimensions, where row-major order
implies that the index along the first axis varies slowest, and
the index along the last quickest. The opposite holds for
column-major, Fortran-style index ordering.
When a view is desired in as many cases as possible, ``arr.reshape(-1)``
may be preferable.
Examples
--------
It is equivalent to ``reshape(-1, order=order)``.
>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> np.ravel(x)
array([1, 2, 3, 4, 5, 6])
>>> x.reshape(-1)
array([1, 2, 3, 4, 5, 6])
>>> np.ravel(x, order='F')
array([1, 4, 2, 5, 3, 6])
When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:
>>> np.ravel(x.T)
array([1, 4, 2, 5, 3, 6])
>>> np.ravel(x.T, order='A')
array([1, 2, 3, 4, 5, 6])
When ``order`` is 'K', it will preserve orderings that are neither 'C'
nor 'F', but won't reverse axes:
>>> a = np.arange(3)[::-1]; a
array([2, 1, 0])
>>> a.ravel(order='C')
array([2, 1, 0])
>>> a.ravel(order='K')
array([2, 1, 0])
>>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
array([[[ 0, 2, 4],
[ 1, 3, 5]],
[[ 6, 8, 10],
[ 7, 9, 11]]])
>>> a.ravel(order='C')
array([ 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11])
>>> a.ravel(order='K')
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
"""
if isinstance(a, np.matrix):
return asarray(a).ravel(order=order)
else:
return asanyarray(a).ravel(order=order)
def _nonzero_dispatcher(a):
return (a,)
@array_function_dispatch(_nonzero_dispatcher)
def nonzero(a):
"""
Return the indices of the elements that are non-zero.
Returns a tuple of arrays, one for each dimension of `a`,
containing the indices of the non-zero elements in that
dimension. The values in `a` are always tested and returned in
row-major, C-style order.
To group the indices by element, rather than dimension, use `argwhere`,
which returns a row for each non-zero element.
.. note::
When called on a zero-d array or scalar, ``nonzero(a)`` is treated
as ``nonzero(atleast_1d(a))``.
.. deprecated:: 1.17.0
Use `atleast_1d` explicitly if this behavior is deliberate.
Parameters
----------
a : array_like
Input array.
Returns
-------
tuple_of_arrays : tuple
Indices of elements that are non-zero.
See Also
--------
flatnonzero :
Return indices that are non-zero in the flattened version of the input
array.
ndarray.nonzero :
Equivalent ndarray method.
count_nonzero :
Counts the number of non-zero elements in the input array.
Notes
-----
While the nonzero values can be obtained with ``a[nonzero(a)]``, it is
recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which
will correctly handle 0-d arrays.
Examples
--------
>>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])
>>> x
array([[3, 0, 0],
[0, 4, 0],
[5, 6, 0]])
>>> np.nonzero(x)
(array([0, 1, 2, 2]), array([0, 1, 0, 1]))
>>> x[np.nonzero(x)]
array([3, 4, 5, 6])
>>> np.transpose(np.nonzero(x))
array([[0, 0],
[1, 1],
[2, 0],
[2, 1]])
A common use for ``nonzero`` is to find the indices of an array, where
a condition is True. Given an array `a`, the condition `a` > 3 is a
boolean array and since False is interpreted as 0, np.nonzero(a > 3)
yields the indices of the `a` where the condition is true.
>>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> a > 3
array([[False, False, False],
[ True, True, True],
[ True, True, True]])
>>> np.nonzero(a > 3)
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
Using this result to index `a` is equivalent to using the mask directly:
>>> a[np.nonzero(a > 3)]
array([4, 5, 6, 7, 8, 9])
>>> a[a > 3] # prefer this spelling
array([4, 5, 6, 7, 8, 9])
``nonzero`` can also be called as a method of the array.
>>> (a > 3).nonzero()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
"""
return _wrapfunc(a, 'nonzero')
def _shape_dispatcher(a):
return (a,)
@array_function_dispatch(_shape_dispatcher)
def shape(a):
"""
Return the shape of an array.
Parameters
----------
a : array_like
Input array.
Returns
-------
shape : tuple of ints
The elements of the shape tuple give the lengths of the
corresponding array dimensions.
See Also
--------
len : ``len(a)`` is equivalent to ``np.shape(a)[0]`` for N-D arrays with
``N>=1``.
ndarray.shape : Equivalent array method.
Examples
--------
>>> np.shape(np.eye(3))
(3, 3)
>>> np.shape([[1, 3]])
(1, 2)
>>> np.shape([0])
(1,)
>>> np.shape(0)
()
>>> a = np.array([(1, 2), (3, 4), (5, 6)],
... dtype=[('x', 'i4'), ('y', 'i4')])
>>> np.shape(a)
(3,)
>>> a.shape
(3,)
"""
try:
result = a.shape
except AttributeError:
result = asarray(a).shape
return result
def _compress_dispatcher(condition, a, axis=None, out=None):
return (condition, a, out)
@array_function_dispatch(_compress_dispatcher)
def compress(condition, a, axis=None, out=None):
"""
Return selected slices of an array along given axis.
When working along a given axis, a slice along that axis is returned in
`output` for each index where `condition` evaluates to True. When
working on a 1-D array, `compress` is equivalent to `extract`.
Parameters
----------
condition : 1-D array of bools
Array that selects which entries to return. If len(condition)
is less than the size of `a` along the given axis, then output is
truncated to the length of the condition array.
a : array_like
Array from which to extract a part.
axis : int, optional
Axis along which to take slices. If None (default), work on the
flattened array.
out : ndarray, optional
Output array. Its type is preserved and it must be of the right
shape to hold the output.
Returns
-------
compressed_array : ndarray
A copy of `a` without the slices along axis for which `condition`
is false.
See Also
--------
take, choose, diag, diagonal, select
ndarray.compress : Equivalent method in ndarray
extract : Equivalent method when working on 1-D arrays
:ref:`ufuncs-output-type`
Examples
--------
>>> a = np.array([[1, 2], [3, 4], [5, 6]])
>>> a
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.compress([0, 1], a, axis=0)
array([[3, 4]])
>>> np.compress([False, True, True], a, axis=0)
array([[3, 4],
[5, 6]])
>>> np.compress([False, True], a, axis=1)
array([[2],
[4],
[6]])
Working on the flattened array does not return slices along an axis but
selects elements.
>>> np.compress([False, True], a)
array([2])
"""
return _wrapfunc(a, 'compress', condition, axis=axis, out=out)
def _clip_dispatcher(a, a_min, a_max, out=None, **kwargs):
return (a, a_min, a_max)
@array_function_dispatch(_clip_dispatcher)
def clip(a, a_min, a_max, out=None, **kwargs):
"""
Clip (limit) the values in an array.
Given an interval, values outside the interval are clipped to
the interval edges. For example, if an interval of ``[0, 1]``
is specified, values smaller than 0 become 0, and values larger
than 1 become 1.
Equivalent to but faster than ``np.minimum(a_max, np.maximum(a, a_min))``.
No check is performed to ensure ``a_min < a_max``.
Parameters
----------
a : array_like
Array containing elements to clip.
a_min, a_max : array_like or None
Minimum and maximum value. If ``None``, clipping is not performed on
the corresponding edge. Only one of `a_min` and `a_max` may be
``None``. Both are broadcast against `a`.
out : ndarray, optional
The results will be placed in this array. It may be the input
array for in-place clipping. `out` must be of the right shape
to hold the output. Its type is preserved.
**kwargs
For other keyword-only arguments, see the
:ref:`ufunc docs <ufuncs.kwargs>`.
.. versionadded:: 1.17.0
Returns
-------
clipped_array : ndarray
An array with the elements of `a`, but where values
< `a_min` are replaced with `a_min`, and those > `a_max`
with `a_max`.
See Also
--------
:ref:`ufuncs-output-type`
Notes
-----
When `a_min` is greater than `a_max`, `clip` returns an
array in which all values are equal to `a_max`,
as shown in the second example.
Examples
--------
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.clip(a, 1, 8)
array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])
>>> np.clip(a, 8, 1)
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
>>> np.clip(a, 3, 6, out=a)
array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
>>> a
array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])
"""
return _wrapfunc(a, 'clip', a_min, a_max, out=out, **kwargs)
def _sum_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
initial=None, where=None):
return (a, out)
@array_function_dispatch(_sum_dispatcher)
def sum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,
initial=np._NoValue, where=np._NoValue):
"""
Sum of array elements over a given axis.
Parameters
----------
a : array_like
Elements to sum.
axis : None or int or tuple of ints, optional
Axis or axes along which a sum is performed. The default,
axis=None, will sum all of the elements of the input array. If
axis is negative it counts from the last to the first axis.
.. versionadded:: 1.7.0
If axis is a tuple of ints, a sum is performed on all of the axes
specified in the tuple instead of a single axis or all the axes as
before.
dtype : dtype, optional
The type of the returned array and of the accumulator in which the
elements are summed. The dtype of `a` is used by default unless `a`
has an integer dtype of less precision than the default platform
integer. In that case, if `a` is signed then the platform integer
is used while if `a` is unsigned then an unsigned integer of the
same precision as the platform integer is used.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `sum` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
initial : scalar, optional
Starting value for the sum. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.15.0
where : array_like of bool, optional
Elements to include in the sum. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.17.0
Returns
-------
sum_along_axis : ndarray
An array with the same shape as `a`, with the specified
axis removed. If `a` is a 0-d array, or if `axis` is None, a scalar
is returned. If an output array is specified, a reference to
`out` is returned.
See Also
--------
ndarray.sum : Equivalent method.
add.reduce : Equivalent functionality of `add`.
cumsum : Cumulative sum of array elements.
trapz : Integration of array values using the composite trapezoidal rule.
mean, average
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
The sum of an empty array is the neutral element 0:
>>> np.sum([])
0.0
For floating point numbers the numerical precision of sum (and
``np.add.reduce``) is in general limited by directly adding each number
individually to the result causing rounding errors in every step.
However, often numpy will use a numerically better approach (partial
pairwise summation) leading to improved precision in many use-cases.
This improved precision is always provided when no ``axis`` is given.
When ``axis`` is given, it will depend on which axis is summed.
Technically, to provide the best speed possible, the improved precision
is only used when the summation is along the fast axis in memory.
Note that the exact precision may vary depending on other parameters.
In contrast to NumPy, Python's ``math.fsum`` function uses a slower but
more precise approach to summation.
Especially when summing a large number of lower precision floating point
numbers, such as ``float32``, numerical errors can become significant.
In such cases it can be advisable to use `dtype="float64"` to use a higher
precision for the output.
Examples
--------
>>> np.sum([0.5, 1.5])
2.0
>>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
1
>>> np.sum([[0, 1], [0, 5]])
6
>>> np.sum([[0, 1], [0, 5]], axis=0)
array([0, 6])
>>> np.sum([[0, 1], [0, 5]], axis=1)
array([1, 5])
>>> np.sum([[0, 1], [np.nan, 5]], where=[False, True], axis=1)
array([1., 5.])
If the accumulator is too small, overflow occurs:
>>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
-128
You can also start the sum with a value other than zero:
>>> np.sum([10], initial=5)
15
"""
if isinstance(a, _gentype):
# 2018-02-25, 1.15.0
warnings.warn(
"Calling np.sum(generator) is deprecated, and in the future will give a different result. "
"Use np.sum(np.fromiter(generator)) or the python sum builtin instead.",
DeprecationWarning, stacklevel=3)
res = _sum_(a)
if out is not None:
out[...] = res
return out
return res
return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
initial=initial, where=where)
def _any_dispatcher(a, axis=None, out=None, keepdims=None, *,
where=np._NoValue):
return (a, where, out)
@array_function_dispatch(_any_dispatcher)
def any(a, axis=None, out=None, keepdims=np._NoValue, *, where=np._NoValue):
"""
Test whether any array element along a given axis evaluates to True.
Returns single boolean if `axis` is ``None``
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical OR reduction is performed.
The default (``axis=None``) is to perform a logical OR over all
the dimensions of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
.. versionadded:: 1.7.0
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
out : ndarray, optional
Alternate output array in which to place the result. It must have
the same shape as the expected output and its type is preserved
(e.g., if it is of type float, then it will remain so, returning
1.0 for True and 0.0 for False, regardless of the type of `a`).
See :ref:`ufuncs-output-type` for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `any` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
where : array_like of bool, optional
Elements to include in checking for any `True` values.
See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.20.0
Returns
-------
any : bool or ndarray
A new boolean or `ndarray` is returned unless `out` is specified,
in which case a reference to `out` is returned.
See Also
--------
ndarray.any : equivalent method
all : Test whether all elements along a given axis evaluate to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity evaluate
to `True` because these are not equal to zero.
Examples
--------
>>> np.any([[True, False], [True, True]])
True
>>> np.any([[True, False], [False, False]], axis=0)
array([ True, False])
>>> np.any([-1, 0, 5])
True
>>> np.any(np.nan)
True
>>> np.any([[True, False], [False, False]], where=[[False], [True]])
False
>>> o=np.array(False)
>>> z=np.any([-1, 4, 5], out=o)
>>> z, o
(array(True), array(True))
>>> # Check now that z is a reference to o
>>> z is o
True
>>> id(z), id(o) # identity of z and o # doctest: +SKIP
(191614240, 191614240)
"""
return _wrapreduction(a, np.logical_or, 'any', axis, None, out,
keepdims=keepdims, where=where)
def _all_dispatcher(a, axis=None, out=None, keepdims=None, *,
where=None):
return (a, where, out)
@array_function_dispatch(_all_dispatcher)
def all(a, axis=None, out=None, keepdims=np._NoValue, *, where=np._NoValue):
"""
Test whether all array elements along a given axis evaluate to True.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical AND reduction is performed.
The default (``axis=None``) is to perform a logical AND over all
the dimensions of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
.. versionadded:: 1.7.0
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
out : ndarray, optional
Alternate output array in which to place the result.
It must have the same shape as the expected output and its
type is preserved (e.g., if ``dtype(out)`` is float, the result
will consist of 0.0's and 1.0's). See :ref:`ufuncs-output-type` for more
details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `all` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
where : array_like of bool, optional
Elements to include in checking for all `True` values.
See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.20.0
Returns
-------
all : ndarray, bool
A new boolean or array is returned unless `out` is specified,
in which case a reference to `out` is returned.
See Also
--------
ndarray.all : equivalent method
any : Test whether any element along a given axis evaluates to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity
evaluate to `True` because these are not equal to zero.
Examples
--------
>>> np.all([[True,False],[True,True]])
False
>>> np.all([[True,False],[True,True]], axis=0)
array([ True, False])
>>> np.all([-1, 4, 5])
True
>>> np.all([1.0, np.nan])
True
>>> np.all([[True, True], [False, True]], where=[[True], [False]])
True
>>> o=np.array(False)
>>> z=np.all([-1, 4, 5], out=o)
>>> id(z), id(o), z
(28293632, 28293632, array(True)) # may vary
"""
return _wrapreduction(a, np.logical_and, 'all', axis, None, out,
keepdims=keepdims, where=where)
def _cumsum_dispatcher(a, axis=None, dtype=None, out=None):
return (a, out)
@array_function_dispatch(_cumsum_dispatcher)
def cumsum(a, axis=None, dtype=None, out=None):
"""
Return the cumulative sum of the elements along a given axis.
Parameters
----------
a : array_like
Input array.
axis : int, optional
Axis along which the cumulative sum is computed. The default
(None) is to compute the cumsum over the flattened array.
dtype : dtype, optional
Type of the returned array and of the accumulator in which the
elements are summed. If `dtype` is not specified, it defaults
to the dtype of `a`, unless `a` has an integer dtype with a
precision less than that of the default platform integer. In
that case, the default platform integer is used.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output
but the type will be cast if necessary. See :ref:`ufuncs-output-type` for
more details.
Returns
-------
cumsum_along_axis : ndarray.
A new array holding the result is returned unless `out` is
specified, in which case a reference to `out` is returned. The
result has the same size as `a`, and the same shape as `a` if
`axis` is not None or `a` is a 1-d array.
See Also
--------
sum : Sum array elements.
trapz : Integration of array values using the composite trapezoidal rule.
diff : Calculate the n-th discrete difference along given axis.
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
``cumsum(a)[-1]`` may not be equal to ``sum(a)`` for floating-point
values since ``sum`` may use a pairwise summation routine, reducing
the roundoff-error. See `sum` for more information.
Examples
--------
>>> a = np.array([[1,2,3], [4,5,6]])
>>> a
array([[1, 2, 3],
[4, 5, 6]])
>>> np.cumsum(a)
array([ 1, 3, 6, 10, 15, 21])
>>> np.cumsum(a, dtype=float) # specifies type of output value(s)
array([ 1., 3., 6., 10., 15., 21.])
>>> np.cumsum(a,axis=0) # sum over rows for each of the 3 columns
array([[1, 2, 3],
[5, 7, 9]])
>>> np.cumsum(a,axis=1) # sum over columns for each of the 2 rows
array([[ 1, 3, 6],
[ 4, 9, 15]])
``cumsum(b)[-1]`` may not be equal to ``sum(b)``
>>> b = np.array([1, 2e-9, 3e-9] * 1000000)
>>> b.cumsum()[-1]
1000000.0050045159
>>> b.sum()
1000000.0050000029
"""
return _wrapfunc(a, 'cumsum', axis=axis, dtype=dtype, out=out)
def _ptp_dispatcher(a, axis=None, out=None, keepdims=None):
return (a, out)
@array_function_dispatch(_ptp_dispatcher)
def ptp(a, axis=None, out=None, keepdims=np._NoValue):
"""
Range of values (maximum - minimum) along an axis.
The name of the function comes from the acronym for 'peak to peak'.
.. warning::
`ptp` preserves the data type of the array. This means the
return value for an input of signed integers with n bits
(e.g. `np.int8`, `np.int16`, etc) is also a signed integer
with n bits. In that case, peak-to-peak values greater than
``2**(n-1)-1`` will be returned as negative values. An example
with a work-around is shown below.
Parameters
----------
a : array_like
Input values.
axis : None or int or tuple of ints, optional
Axis along which to find the peaks. By default, flatten the
array. `axis` may be negative, in
which case it counts from the last to the first axis.
.. versionadded:: 1.15.0
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
out : array_like
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type of the output values will be cast if necessary.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `ptp` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
ptp : ndarray
A new array holding the result, unless `out` was
specified, in which case a reference to `out` is returned.
Examples
--------
>>> x = np.array([[4, 9, 2, 10],
... [6, 9, 7, 12]])
>>> np.ptp(x, axis=1)
array([8, 6])
>>> np.ptp(x, axis=0)
array([2, 0, 5, 2])
>>> np.ptp(x)
10
This example shows that a negative value can be returned when
the input is an array of signed integers.
>>> y = np.array([[1, 127],
... [0, 127],
... [-1, 127],
... [-2, 127]], dtype=np.int8)
>>> np.ptp(y, axis=1)
array([ 126, 127, -128, -127], dtype=int8)
A work-around is to use the `view()` method to view the result as
unsigned integers with the same bit width:
>>> np.ptp(y, axis=1).view(np.uint8)
array([126, 127, 128, 129], dtype=uint8)
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if type(a) is not mu.ndarray:
try:
ptp = a.ptp
except AttributeError:
pass
else:
return ptp(axis=axis, out=out, **kwargs)
return _methods._ptp(a, axis=axis, out=out, **kwargs)
def _amax_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,
where=None):
return (a, out)
@array_function_dispatch(_amax_dispatcher)
def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
where=np._NoValue):
"""
Return the maximum of an array or maximum along an axis.
Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
Axis or axes along which to operate. By default, flattened input is
used.
.. versionadded:: 1.7.0
If this is a tuple of ints, the maximum is selected over multiple axes,
instead of a single axis or all the axes as before.
out : ndarray, optional
Alternative output array in which to place the result. Must
be of the same shape and buffer length as the expected output.
See :ref:`ufuncs-output-type` for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `amax` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
initial : scalar, optional
The minimum value of an output element. Must be present to allow
computation on empty slice. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.15.0
where : array_like of bool, optional
Elements to compare for the maximum. See `~numpy.ufunc.reduce`
for details.
.. versionadded:: 1.17.0
Returns
-------
amax : ndarray or scalar
Maximum of `a`. If `axis` is None, the result is a scalar value.
If `axis` is given, the result is an array of dimension
``a.ndim - 1``.
See Also
--------
amin :
The minimum value of an array along a given axis, propagating any NaNs.
nanmax :
The maximum value of an array along a given axis, ignoring any NaNs.
maximum :
Element-wise maximum of two arrays, propagating any NaNs.
fmax :
Element-wise maximum of two arrays, ignoring any NaNs.
argmax :
Return the indices of the maximum values.
nanmin, minimum, fmin
Notes
-----
NaN values are propagated, that is if at least one item is NaN, the
corresponding max value will be NaN as well. To ignore NaN values
(MATLAB behavior), please use nanmax.
Don't use `amax` for element-wise comparison of 2 arrays; when
``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than
``amax(a, axis=0)``.
Examples
--------
>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
[2, 3]])
>>> np.amax(a) # Maximum of the flattened array
3
>>> np.amax(a, axis=0) # Maxima along the first axis
array([2, 3])
>>> np.amax(a, axis=1) # Maxima along the second axis
array([1, 3])
>>> np.amax(a, where=[False, True], initial=-1, axis=0)
array([-1, 3])
>>> b = np.arange(5, dtype=float)
>>> b[2] = np.NaN
>>> np.amax(b)
nan
>>> np.amax(b, where=~np.isnan(b), initial=-1)
4.0
>>> np.nanmax(b)
4.0
You can use an initial value to compute the maximum of an empty slice, or
to initialize it to a different value:
>>> np.amax([[-50], [10]], axis=-1, initial=0)
array([ 0, 10])
Notice that the initial value is used as one of the elements for which the
maximum is determined, unlike for the default argument Python's max
function, which is only used for empty iterables.
>>> np.amax([5], initial=6)
6
>>> max([5], default=6)
5
"""
return _wrapreduction(a, np.maximum, 'max', axis, None, out,
keepdims=keepdims, initial=initial, where=where)
def _amin_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,
where=None):
return (a, out)
@array_function_dispatch(_amin_dispatcher)
def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
where=np._NoValue):
"""
Return the minimum of an array or minimum along an axis.
Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
Axis or axes along which to operate. By default, flattened input is
used.
.. versionadded:: 1.7.0
If this is a tuple of ints, the minimum is selected over multiple axes,
instead of a single axis or all the axes as before.
out : ndarray, optional
Alternative output array in which to place the result. Must
be of the same shape and buffer length as the expected output.
See :ref:`ufuncs-output-type` for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `amin` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
initial : scalar, optional
The maximum value of an output element. Must be present to allow
computation on empty slice. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.15.0
where : array_like of bool, optional
Elements to compare for the minimum. See `~numpy.ufunc.reduce`
for details.
.. versionadded:: 1.17.0
Returns
-------
amin : ndarray or scalar
Minimum of `a`. If `axis` is None, the result is a scalar value.
If `axis` is given, the result is an array of dimension
``a.ndim - 1``.
See Also
--------
amax :
The maximum value of an array along a given axis, propagating any NaNs.
nanmin :
The minimum value of an array along a given axis, ignoring any NaNs.
minimum :
Element-wise minimum of two arrays, propagating any NaNs.
fmin :
Element-wise minimum of two arrays, ignoring any NaNs.
argmin :
Return the indices of the minimum values.
nanmax, maximum, fmax
Notes
-----
NaN values are propagated, that is if at least one item is NaN, the
corresponding min value will be NaN as well. To ignore NaN values
(MATLAB behavior), please use nanmin.
Don't use `amin` for element-wise comparison of 2 arrays; when
``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
``amin(a, axis=0)``.
Examples
--------
>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
[2, 3]])
>>> np.amin(a) # Minimum of the flattened array
0
>>> np.amin(a, axis=0) # Minima along the first axis
array([0, 1])
>>> np.amin(a, axis=1) # Minima along the second axis
array([0, 2])
>>> np.amin(a, where=[False, True], initial=10, axis=0)
array([10, 1])
>>> b = np.arange(5, dtype=float)
>>> b[2] = np.NaN
>>> np.amin(b)
nan
>>> np.amin(b, where=~np.isnan(b), initial=10)
0.0
>>> np.nanmin(b)
0.0
>>> np.amin([[-50], [10]], axis=-1, initial=0)
array([-50, 0])
Notice that the initial value is used as one of the elements for which the
minimum is determined, unlike for the default argument Python's max
function, which is only used for empty iterables.
Notice that this isn't the same as Python's ``default`` argument.
>>> np.amin([6], initial=5)
5
>>> min([6], default=5)
6
"""
return _wrapreduction(a, np.minimum, 'min', axis, None, out,
keepdims=keepdims, initial=initial, where=where)
def _prod_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
initial=None, where=None):
return (a, out)
@array_function_dispatch(_prod_dispatcher)
def prod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,
initial=np._NoValue, where=np._NoValue):
"""
Return the product of array elements over a given axis.
Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
Axis or axes along which a product is performed. The default,
axis=None, will calculate the product of all the elements in the
input array. If axis is negative it counts from the last to the
first axis.
.. versionadded:: 1.7.0
If axis is a tuple of ints, a product is performed on all of the
axes specified in the tuple instead of a single axis or all the
axes as before.
dtype : dtype, optional
The type of the returned array, as well as of the accumulator in
which the elements are multiplied. The dtype of `a` is used by
default unless `a` has an integer dtype of less precision than the
default platform integer. In that case, if `a` is signed then the
platform integer is used while if `a` is unsigned then an unsigned
integer of the same precision as the platform integer is used.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left in the
result as dimensions with size one. With this option, the result
will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `prod` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
initial : scalar, optional
The starting value for this product. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.15.0
where : array_like of bool, optional
Elements to include in the product. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.17.0
Returns
-------
product_along_axis : ndarray, see `dtype` parameter above.
An array shaped as `a` but with the specified axis removed.
Returns a reference to `out` if specified.
See Also
--------
ndarray.prod : equivalent method
:ref:`ufuncs-output-type`
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow. That means that, on a 32-bit platform:
>>> x = np.array([536870910, 536870910, 536870910, 536870910])
>>> np.prod(x)
16 # may vary
The product of an empty array is the neutral element 1:
>>> np.prod([])
1.0
Examples
--------
By default, calculate the product of all elements:
>>> np.prod([1.,2.])
2.0
Even when the input array is two-dimensional:
>>> np.prod([[1.,2.],[3.,4.]])
24.0
But we can also specify the axis over which to multiply:
>>> np.prod([[1.,2.],[3.,4.]], axis=1)
array([ 2., 12.])
Or select specific elements to include:
>>> np.prod([1., np.nan, 3.], where=[True, False, True])
3.0
If the type of `x` is unsigned, then the output type is
the unsigned platform integer:
>>> x = np.array([1, 2, 3], dtype=np.uint8)
>>> np.prod(x).dtype == np.uint
True
If `x` is of a signed integer type, then the output type
is the default platform integer:
>>> x = np.array([1, 2, 3], dtype=np.int8)
>>> np.prod(x).dtype == int
True
You can also start the product with a value other than one:
>>> np.prod([1, 2], initial=5)
10
"""
return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,
keepdims=keepdims, initial=initial, where=where)
def _cumprod_dispatcher(a, axis=None, dtype=None, out=None):
return (a, out)
@array_function_dispatch(_cumprod_dispatcher)
def cumprod(a, axis=None, dtype=None, out=None):
"""
Return the cumulative product of elements along a given axis.
Parameters
----------
a : array_like
Input array.
axis : int, optional
Axis along which the cumulative product is computed. By default
the input is flattened.
dtype : dtype, optional
Type of the returned array, as well as of the accumulator in which
the elements are multiplied. If *dtype* is not specified, it
defaults to the dtype of `a`, unless `a` has an integer dtype with
a precision less than that of the default platform integer. In
that case, the default platform integer is used instead.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output
but the type of the resulting values will be cast if necessary.
Returns
-------
cumprod : ndarray
A new array holding the result is returned unless `out` is
specified, in which case a reference to out is returned.
See Also
--------
:ref:`ufuncs-output-type`
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
Examples
--------
>>> a = np.array([1,2,3])
>>> np.cumprod(a) # intermediate results 1, 1*2
... # total product 1*2*3 = 6
array([1, 2, 6])
>>> a = np.array([[1, 2, 3], [4, 5, 6]])
>>> np.cumprod(a, dtype=float) # specify type of output
array([ 1., 2., 6., 24., 120., 720.])
The cumulative product for each column (i.e., over the rows) of `a`:
>>> np.cumprod(a, axis=0)
array([[ 1, 2, 3],
[ 4, 10, 18]])
The cumulative product for each row (i.e. over the columns) of `a`:
>>> np.cumprod(a,axis=1)
array([[ 1, 2, 6],
[ 4, 20, 120]])
"""
return _wrapfunc(a, 'cumprod', axis=axis, dtype=dtype, out=out)
def _ndim_dispatcher(a):
return (a,)
@array_function_dispatch(_ndim_dispatcher)
def ndim(a):
"""
Return the number of dimensions of an array.
Parameters
----------
a : array_like
Input array. If it is not already an ndarray, a conversion is
attempted.
Returns
-------
number_of_dimensions : int
The number of dimensions in `a`. Scalars are zero-dimensional.
See Also
--------
ndarray.ndim : equivalent method
shape : dimensions of array
ndarray.shape : dimensions of array
Examples
--------
>>> np.ndim([[1,2,3],[4,5,6]])
2
>>> np.ndim(np.array([[1,2,3],[4,5,6]]))
2
>>> np.ndim(1)
0
"""
try:
return a.ndim
except AttributeError:
return asarray(a).ndim
def _size_dispatcher(a, axis=None):
return (a,)
@array_function_dispatch(_size_dispatcher)
def size(a, axis=None):
"""
Return the number of elements along a given axis.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which the elements are counted. By default, give
the total number of elements.
Returns
-------
element_count : int
Number of elements along the specified axis.
See Also
--------
shape : dimensions of array
ndarray.shape : dimensions of array
ndarray.size : number of elements in array
Examples
--------
>>> a = np.array([[1,2,3],[4,5,6]])
>>> np.size(a)
6
>>> np.size(a,1)
3
>>> np.size(a,0)
2
"""
if axis is None:
try:
return a.size
except AttributeError:
return asarray(a).size
else:
try:
return a.shape[axis]
except AttributeError:
return asarray(a).shape[axis]
def _around_dispatcher(a, decimals=None, out=None):
return (a, out)
@array_function_dispatch(_around_dispatcher)
def around(a, decimals=0, out=None):
"""
Evenly round to the given number of decimals.
Parameters
----------
a : array_like
Input data.
decimals : int, optional
Number of decimal places to round to (default: 0). If
decimals is negative, it specifies the number of positions to
the left of the decimal point.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary. See :ref:`ufuncs-output-type` for more
details.
Returns
-------
rounded_array : ndarray
An array of the same type as `a`, containing the rounded values.
Unless `out` was specified, a new array is created. A reference to
the result is returned.
The real and imaginary parts of complex numbers are rounded
separately. The result of rounding a float is a float.
See Also
--------
ndarray.round : equivalent method
ceil, fix, floor, rint, trunc
Notes
-----
For values exactly halfway between rounded decimal values, NumPy
rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
-0.5 and 0.5 round to 0.0, etc.
``np.around`` uses a fast but sometimes inexact algorithm to round
floating-point datatypes. For positive `decimals` it is equivalent to
``np.true_divide(np.rint(a * 10**decimals), 10**decimals)``, which has
error due to the inexact representation of decimal fractions in the IEEE
floating point standard [1]_ and errors introduced when scaling by powers
of ten. For instance, note the extra "1" in the following:
>>> np.round(56294995342131.5, 3)
56294995342131.51
If your goal is to print such values with a fixed number of decimals, it is
preferable to use numpy's float printing routines to limit the number of
printed decimals:
>>> np.format_float_positional(56294995342131.5, precision=3)
'56294995342131.5'
The float printing routines use an accurate but much more computationally
demanding algorithm to compute the number of digits after the decimal
point.
Alternatively, Python's builtin `round` function uses a more accurate
but slower algorithm for 64-bit floating point values:
>>> round(56294995342131.5, 3)
56294995342131.5
>>> np.round(16.055, 2), round(16.055, 2) # equals 16.0549999999999997
(16.06, 16.05)
References
----------
.. [1] "Lecture Notes on the Status of IEEE 754", William Kahan,
https://people.eecs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF
Examples
--------
>>> np.around([0.37, 1.64])
array([0., 2.])
>>> np.around([0.37, 1.64], decimals=1)
array([0.4, 1.6])
>>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
array([0., 2., 2., 4., 4.])
>>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
array([ 1, 2, 3, 11])
>>> np.around([1,2,3,11], decimals=-1)
array([ 0, 0, 0, 10])
"""
return _wrapfunc(a, 'round', decimals=decimals, out=out)
def _mean_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None, *,
where=None):
return (a, where, out)
@array_function_dispatch(_mean_dispatcher)
def mean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, *,
where=np._NoValue):
"""
Compute the arithmetic mean along the specified axis.
Returns the average of the array elements. The average is taken over
the flattened array by default, otherwise over the specified axis.
`float64` intermediate and return values are used for integer inputs.
Parameters
----------
a : array_like
Array containing numbers whose mean is desired. If `a` is not an
array, a conversion is attempted.
axis : None or int or tuple of ints, optional
Axis or axes along which the means are computed. The default is to
compute the mean of the flattened array.
.. versionadded:: 1.7.0
If this is a tuple of ints, a mean is performed over multiple axes,
instead of a single axis or all the axes as before.
dtype : data-type, optional
Type to use in computing the mean. For integer inputs, the default
is `float64`; for floating point inputs, it is the same as the
input dtype.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
See :ref:`ufuncs-output-type` for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `mean` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
where : array_like of bool, optional
Elements to include in the mean. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.20.0
Returns
-------
m : ndarray, see dtype parameter above
If `out=None`, returns a new array containing the mean values,
otherwise a reference to the output array is returned.
See Also
--------
average : Weighted average
std, var, nanmean, nanstd, nanvar
Notes
-----
The arithmetic mean is the sum of the elements along the axis divided
by the number of elements.
Note that for floating-point input, the mean is computed using the
same precision the input has. Depending on the input data, this can
cause the results to be inaccurate, especially for `float32` (see
example below). Specifying a higher-precision accumulator using the
`dtype` keyword can alleviate this issue.
By default, `float16` results are computed using `float32` intermediates
for extra precision.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> np.mean(a)
2.5
>>> np.mean(a, axis=0)
array([2., 3.])
>>> np.mean(a, axis=1)
array([1.5, 3.5])
In single precision, `mean` can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.mean(a)
0.54999924
Computing the mean in float64 is more accurate:
>>> np.mean(a, dtype=np.float64)
0.55000000074505806 # may vary
Specifying a where argument:
>>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]])
>>> np.mean(a)
12.0
>>> np.mean(a, where=[[True], [False], [False]])
9.0
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if where is not np._NoValue:
kwargs['where'] = where
if type(a) is not mu.ndarray:
try:
mean = a.mean
except AttributeError:
pass
else:
return mean(axis=axis, dtype=dtype, out=out, **kwargs)
return _methods._mean(a, axis=axis, dtype=dtype,
out=out, **kwargs)
def _std_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,
keepdims=None, *, where=None):
return (a, where, out)
@array_function_dispatch(_std_dispatcher)
def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue, *,
where=np._NoValue):
"""
Compute the standard deviation along the specified axis.
Returns the standard deviation, a measure of the spread of a distribution,
of the array elements. The standard deviation is computed for the
flattened array by default, otherwise over the specified axis.
Parameters
----------
a : array_like
Calculate the standard deviation of these values.
axis : None or int or tuple of ints, optional
Axis or axes along which the standard deviation is computed. The
default is to compute the standard deviation of the flattened array.
.. versionadded:: 1.7.0
If this is a tuple of ints, a standard deviation is performed over
multiple axes, instead of a single axis or all the axes as before.
dtype : dtype, optional
Type to use in computing the standard deviation. For arrays of
integer type the default is float64, for arrays of float types it is
the same as the array type.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output but the type (of the calculated
values) will be cast if necessary.
ddof : int, optional
Means Delta Degrees of Freedom. The divisor used in calculations
is ``N - ddof``, where ``N`` represents the number of elements.
By default `ddof` is zero.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `std` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
where : array_like of bool, optional
Elements to include in the standard deviation.
See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.20.0
Returns
-------
standard_deviation : ndarray, see dtype parameter above.
If `out` is None, return a new array containing the standard deviation,
otherwise return a reference to the output array.
See Also
--------
var, mean, nanmean, nanstd, nanvar
:ref:`ufuncs-output-type`
Notes
-----
The standard deviation is the square root of the average of the squared
deviations from the mean, i.e., ``std = sqrt(mean(x))``, where
``x = abs(a - a.mean())**2``.
The average squared deviation is typically calculated as ``x.sum() / N``,
where ``N = len(x)``. If, however, `ddof` is specified, the divisor
``N - ddof`` is used instead. In standard statistical practice, ``ddof=1``
provides an unbiased estimator of the variance of the infinite population.
``ddof=0`` provides a maximum likelihood estimate of the variance for
normally distributed variables. The standard deviation computed in this
function is the square root of the estimated variance, so even with
``ddof=1``, it will not be an unbiased estimate of the standard deviation
per se.
Note that, for complex numbers, `std` takes the absolute
value before squaring, so that the result is always real and nonnegative.
For floating-point input, the *std* is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for float32 (see example below).
Specifying a higher-accuracy accumulator using the `dtype` keyword can
alleviate this issue.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> np.std(a)
1.1180339887498949 # may vary
>>> np.std(a, axis=0)
array([1., 1.])
>>> np.std(a, axis=1)
array([0.5, 0.5])
In single precision, std() can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.std(a)
0.45000005
Computing the standard deviation in float64 is more accurate:
>>> np.std(a, dtype=np.float64)
0.44999999925494177 # may vary
Specifying a where argument:
>>> a = np.array([[14, 8, 11, 10], [7, 9, 10, 11], [10, 15, 5, 10]])
>>> np.std(a)
2.614064523559687 # may vary
>>> np.std(a, where=[[True], [True], [False]])
2.0
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if where is not np._NoValue:
kwargs['where'] = where
if type(a) is not mu.ndarray:
try:
std = a.std
except AttributeError:
pass
else:
return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)
return _methods._std(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
**kwargs)
def _var_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,
keepdims=None, *, where=None):
return (a, where, out)
@array_function_dispatch(_var_dispatcher)
def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue, *,
where=np._NoValue):
"""
Compute the variance along the specified axis.
Returns the variance of the array elements, a measure of the spread of a
distribution. The variance is computed for the flattened array by
default, otherwise over the specified axis.
Parameters
----------
a : array_like
Array containing numbers whose variance is desired. If `a` is not an
array, a conversion is attempted.
axis : None or int or tuple of ints, optional
Axis or axes along which the variance is computed. The default is to
compute the variance of the flattened array.
.. versionadded:: 1.7.0
If this is a tuple of ints, a variance is performed over multiple axes,
instead of a single axis or all the axes as before.
dtype : data-type, optional
Type to use in computing the variance. For arrays of integer type
the default is `float64`; for arrays of float types it is the same as
the array type.
out : ndarray, optional
Alternate output array in which to place the result. It must have
the same shape as the expected output, but the type is cast if
necessary.
ddof : int, optional
"Delta Degrees of Freedom": the divisor used in the calculation is
``N - ddof``, where ``N`` represents the number of elements. By
default `ddof` is zero.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `var` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
where : array_like of bool, optional
Elements to include in the variance. See `~numpy.ufunc.reduce` for
details.
.. versionadded:: 1.20.0
Returns
-------
variance : ndarray, see dtype parameter above
If ``out=None``, returns a new array containing the variance;
otherwise, a reference to the output array is returned.
See Also
--------
std, mean, nanmean, nanstd, nanvar
:ref:`ufuncs-output-type`
Notes
-----
The variance is the average of the squared deviations from the mean,
i.e., ``var = mean(x)``, where ``x = abs(a - a.mean())**2``.
The mean is typically calculated as ``x.sum() / N``, where ``N = len(x)``.
If, however, `ddof` is specified, the divisor ``N - ddof`` is used
instead. In standard statistical practice, ``ddof=1`` provides an
unbiased estimator of the variance of a hypothetical infinite population.
``ddof=0`` provides a maximum likelihood estimate of the variance for
normally distributed variables.
Note that for complex numbers, the absolute value is taken before
squaring, so that the result is always real and nonnegative.
For floating-point input, the variance is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for `float32` (see example
below). Specifying a higher-accuracy accumulator using the ``dtype``
keyword can alleviate this issue.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> np.var(a)
1.25
>>> np.var(a, axis=0)
array([1., 1.])
>>> np.var(a, axis=1)
array([0.25, 0.25])
In single precision, var() can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.var(a)
0.20250003
Computing the variance in float64 is more accurate:
>>> np.var(a, dtype=np.float64)
0.20249999932944759 # may vary
>>> ((1-0.55)**2 + (0.1-0.55)**2)/2
0.2025
Specifying a where argument:
>>> a = np.array([[14, 8, 11, 10], [7, 9, 10, 11], [10, 15, 5, 10]])
>>> np.var(a)
6.833333333333333 # may vary
>>> np.var(a, where=[[True], [True], [False]])
4.0
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if where is not np._NoValue:
kwargs['where'] = where
if type(a) is not mu.ndarray:
try:
var = a.var
except AttributeError:
pass
else:
return var(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)
return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
**kwargs)
# Aliases of other functions. These have their own definitions only so that
# they can have unique docstrings.
@array_function_dispatch(_around_dispatcher)
def round_(a, decimals=0, out=None):
"""
Round an array to the given number of decimals.
See Also
--------
around : equivalent function; see for details.
"""
return around(a, decimals=decimals, out=out)
@array_function_dispatch(_prod_dispatcher, verify=False)
def product(*args, **kwargs):
"""
Return the product of array elements over a given axis.
See Also
--------
prod : equivalent function; see for details.
"""
return prod(*args, **kwargs)
@array_function_dispatch(_cumprod_dispatcher, verify=False)
def cumproduct(*args, **kwargs):
"""
Return the cumulative product over the given axis.
See Also
--------
cumprod : equivalent function; see for details.
"""
return cumprod(*args, **kwargs)
@array_function_dispatch(_any_dispatcher, verify=False)
def sometrue(*args, **kwargs):
"""
Check whether some values are true.
Refer to `any` for full documentation.
See Also
--------
any : equivalent function; see for details.
"""
return any(*args, **kwargs)
@array_function_dispatch(_all_dispatcher, verify=False)
def alltrue(*args, **kwargs):
"""
Check if all elements of input array are true.
See Also
--------
numpy.all : Equivalent function; see for details.
"""
return all(*args, **kwargs)
| 123,483 | Python | 31.650449 | 103 | 0.603306 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/defchararray.pyi | from typing import (
Literal as L,
overload,
TypeVar,
Any,
)
from numpy import (
chararray as chararray,
dtype,
str_,
bytes_,
int_,
bool_,
object_,
_OrderKACF,
)
from numpy._typing import (
NDArray,
_ArrayLikeStr_co as U_co,
_ArrayLikeBytes_co as S_co,
_ArrayLikeInt_co as i_co,
_ArrayLikeBool_co as b_co,
)
from numpy.core.multiarray import compare_chararrays as compare_chararrays
_SCT = TypeVar("_SCT", str_, bytes_)
_CharArray = chararray[Any, dtype[_SCT]]
__all__: list[str]
# Comparison
@overload
def equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
@overload
def not_equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def not_equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
@overload
def greater_equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def greater_equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
@overload
def less_equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def less_equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
@overload
def greater(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def greater(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
@overload
def less(x1: U_co, x2: U_co) -> NDArray[bool_]: ...
@overload
def less(x1: S_co, x2: S_co) -> NDArray[bool_]: ...
# String operations
@overload
def add(x1: U_co, x2: U_co) -> NDArray[str_]: ...
@overload
def add(x1: S_co, x2: S_co) -> NDArray[bytes_]: ...
@overload
def multiply(a: U_co, i: i_co) -> NDArray[str_]: ...
@overload
def multiply(a: S_co, i: i_co) -> NDArray[bytes_]: ...
@overload
def mod(a: U_co, value: Any) -> NDArray[str_]: ...
@overload
def mod(a: S_co, value: Any) -> NDArray[bytes_]: ...
@overload
def capitalize(a: U_co) -> NDArray[str_]: ...
@overload
def capitalize(a: S_co) -> NDArray[bytes_]: ...
@overload
def center(a: U_co, width: i_co, fillchar: U_co = ...) -> NDArray[str_]: ...
@overload
def center(a: S_co, width: i_co, fillchar: S_co = ...) -> NDArray[bytes_]: ...
def decode(
a: S_co,
encoding: None | str = ...,
errors: None | str = ...,
) -> NDArray[str_]: ...
def encode(
a: U_co,
encoding: None | str = ...,
errors: None | str = ...,
) -> NDArray[bytes_]: ...
@overload
def expandtabs(a: U_co, tabsize: i_co = ...) -> NDArray[str_]: ...
@overload
def expandtabs(a: S_co, tabsize: i_co = ...) -> NDArray[bytes_]: ...
@overload
def join(sep: U_co, seq: U_co) -> NDArray[str_]: ...
@overload
def join(sep: S_co, seq: S_co) -> NDArray[bytes_]: ...
@overload
def ljust(a: U_co, width: i_co, fillchar: U_co = ...) -> NDArray[str_]: ...
@overload
def ljust(a: S_co, width: i_co, fillchar: S_co = ...) -> NDArray[bytes_]: ...
@overload
def lower(a: U_co) -> NDArray[str_]: ...
@overload
def lower(a: S_co) -> NDArray[bytes_]: ...
@overload
def lstrip(a: U_co, chars: None | U_co = ...) -> NDArray[str_]: ...
@overload
def lstrip(a: S_co, chars: None | S_co = ...) -> NDArray[bytes_]: ...
@overload
def partition(a: U_co, sep: U_co) -> NDArray[str_]: ...
@overload
def partition(a: S_co, sep: S_co) -> NDArray[bytes_]: ...
@overload
def replace(
a: U_co,
old: U_co,
new: U_co,
count: None | i_co = ...,
) -> NDArray[str_]: ...
@overload
def replace(
a: S_co,
old: S_co,
new: S_co,
count: None | i_co = ...,
) -> NDArray[bytes_]: ...
@overload
def rjust(
a: U_co,
width: i_co,
fillchar: U_co = ...,
) -> NDArray[str_]: ...
@overload
def rjust(
a: S_co,
width: i_co,
fillchar: S_co = ...,
) -> NDArray[bytes_]: ...
@overload
def rpartition(a: U_co, sep: U_co) -> NDArray[str_]: ...
@overload
def rpartition(a: S_co, sep: S_co) -> NDArray[bytes_]: ...
@overload
def rsplit(
a: U_co,
sep: None | U_co = ...,
maxsplit: None | i_co = ...,
) -> NDArray[object_]: ...
@overload
def rsplit(
a: S_co,
sep: None | S_co = ...,
maxsplit: None | i_co = ...,
) -> NDArray[object_]: ...
@overload
def rstrip(a: U_co, chars: None | U_co = ...) -> NDArray[str_]: ...
@overload
def rstrip(a: S_co, chars: None | S_co = ...) -> NDArray[bytes_]: ...
@overload
def split(
a: U_co,
sep: None | U_co = ...,
maxsplit: None | i_co = ...,
) -> NDArray[object_]: ...
@overload
def split(
a: S_co,
sep: None | S_co = ...,
maxsplit: None | i_co = ...,
) -> NDArray[object_]: ...
@overload
def splitlines(a: U_co, keepends: None | b_co = ...) -> NDArray[object_]: ...
@overload
def splitlines(a: S_co, keepends: None | b_co = ...) -> NDArray[object_]: ...
@overload
def strip(a: U_co, chars: None | U_co = ...) -> NDArray[str_]: ...
@overload
def strip(a: S_co, chars: None | S_co = ...) -> NDArray[bytes_]: ...
@overload
def swapcase(a: U_co) -> NDArray[str_]: ...
@overload
def swapcase(a: S_co) -> NDArray[bytes_]: ...
@overload
def title(a: U_co) -> NDArray[str_]: ...
@overload
def title(a: S_co) -> NDArray[bytes_]: ...
@overload
def translate(
a: U_co,
table: U_co,
deletechars: None | U_co = ...,
) -> NDArray[str_]: ...
@overload
def translate(
a: S_co,
table: S_co,
deletechars: None | S_co = ...,
) -> NDArray[bytes_]: ...
@overload
def upper(a: U_co) -> NDArray[str_]: ...
@overload
def upper(a: S_co) -> NDArray[bytes_]: ...
@overload
def zfill(a: U_co, width: i_co) -> NDArray[str_]: ...
@overload
def zfill(a: S_co, width: i_co) -> NDArray[bytes_]: ...
# String information
@overload
def count(
a: U_co,
sub: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def count(
a: S_co,
sub: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def endswith(
a: U_co,
suffix: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[bool_]: ...
@overload
def endswith(
a: S_co,
suffix: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[bool_]: ...
@overload
def find(
a: U_co,
sub: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def find(
a: S_co,
sub: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def index(
a: U_co,
sub: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def index(
a: S_co,
sub: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
def isalpha(a: U_co | S_co) -> NDArray[bool_]: ...
def isalnum(a: U_co | S_co) -> NDArray[bool_]: ...
def isdecimal(a: U_co | S_co) -> NDArray[bool_]: ...
def isdigit(a: U_co | S_co) -> NDArray[bool_]: ...
def islower(a: U_co | S_co) -> NDArray[bool_]: ...
def isnumeric(a: U_co | S_co) -> NDArray[bool_]: ...
def isspace(a: U_co | S_co) -> NDArray[bool_]: ...
def istitle(a: U_co | S_co) -> NDArray[bool_]: ...
def isupper(a: U_co | S_co) -> NDArray[bool_]: ...
@overload
def rfind(
a: U_co,
sub: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def rfind(
a: S_co,
sub: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def rindex(
a: U_co,
sub: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def rindex(
a: S_co,
sub: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[int_]: ...
@overload
def startswith(
a: U_co,
prefix: U_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[bool_]: ...
@overload
def startswith(
a: S_co,
prefix: S_co,
start: i_co = ...,
end: None | i_co = ...,
) -> NDArray[bool_]: ...
def str_len(A: U_co | S_co) -> NDArray[int_]: ...
# Overload 1 and 2: str- or bytes-based array-likes
# overload 3: arbitrary object with unicode=False (-> bytes_)
# overload 4: arbitrary object with unicode=True (-> str_)
@overload
def array(
obj: U_co,
itemsize: None | int = ...,
copy: bool = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[str_]: ...
@overload
def array(
obj: S_co,
itemsize: None | int = ...,
copy: bool = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[bytes_]: ...
@overload
def array(
obj: object,
itemsize: None | int = ...,
copy: bool = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[bytes_]: ...
@overload
def array(
obj: object,
itemsize: None | int = ...,
copy: bool = ...,
unicode: L[True] = ...,
order: _OrderKACF = ...,
) -> _CharArray[str_]: ...
@overload
def asarray(
obj: U_co,
itemsize: None | int = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[str_]: ...
@overload
def asarray(
obj: S_co,
itemsize: None | int = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[bytes_]: ...
@overload
def asarray(
obj: object,
itemsize: None | int = ...,
unicode: L[False] = ...,
order: _OrderKACF = ...,
) -> _CharArray[bytes_]: ...
@overload
def asarray(
obj: object,
itemsize: None | int = ...,
unicode: L[True] = ...,
order: _OrderKACF = ...,
) -> _CharArray[str_]: ...
| 9,216 | unknown | 20.841232 | 78 | 0.534505 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/arrayprint.py | """Array printing function
$Id: arrayprint.py,v 1.9 2005/09/13 13:58:44 teoliphant Exp $
"""
__all__ = ["array2string", "array_str", "array_repr", "set_string_function",
"set_printoptions", "get_printoptions", "printoptions",
"format_float_positional", "format_float_scientific"]
__docformat__ = 'restructuredtext'
#
# Written by Konrad Hinsen <[email protected]>
# last revision: 1996-3-13
# modified by Jim Hugunin 1997-3-3 for repr's and str's (and other details)
# and by Perry Greenfield 2000-4-1 for numarray
# and by Travis Oliphant 2005-8-22 for numpy
# Note: Both scalartypes.c.src and arrayprint.py implement strs for numpy
# scalars but for different purposes. scalartypes.c.src has str/reprs for when
# the scalar is printed on its own, while arrayprint.py has strs for when
# scalars are printed inside an ndarray. Only the latter strs are currently
# user-customizable.
import functools
import numbers
import sys
try:
from _thread import get_ident
except ImportError:
from _dummy_thread import get_ident
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
_format_options = {
'edgeitems': 3, # repr N leading and trailing items of each dimension
'threshold': 1000, # total items > triggers array summarization
'floatmode': 'maxprec',
'precision': 8, # precision of floating point representations
'suppress': False, # suppress printing small floating values in exp format
'linewidth': 75,
'nanstr': 'nan',
'infstr': 'inf',
'sign': '-',
'formatter': None,
# Internally stored as an int to simplify comparisons; converted from/to
# str/False on the way in/out.
'legacy': sys.maxsize}
def _make_options_dict(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None, infstr=None,
sign=None, formatter=None, floatmode=None, legacy=None):
"""
Make a dictionary out of the non-None arguments, plus conversion of
*legacy* and sanity checks.
"""
options = {k: v for k, v in locals().items() if v is not None}
if suppress is not None:
options['suppress'] = bool(suppress)
modes = ['fixed', 'unique', 'maxprec', 'maxprec_equal']
if floatmode not in modes + [None]:
raise ValueError("floatmode option must be one of " +
", ".join('"{}"'.format(m) for m in modes))
if sign not in [None, '-', '+', ' ']:
raise ValueError("sign option must be one of ' ', '+', or '-'")
if legacy == False:
options['legacy'] = sys.maxsize
elif legacy == '1.13':
options['legacy'] = 113
elif legacy == '1.21':
options['legacy'] = 121
elif legacy is None:
pass # OK, do nothing.
else:
warnings.warn(
"legacy printing option can currently only be '1.13', '1.21', or "
"`False`", stacklevel=3)
if threshold is not None:
# forbid the bad threshold arg suggested by stack overflow, gh-12351
if not isinstance(threshold, numbers.Number):
raise TypeError("threshold must be numeric")
if np.isnan(threshold):
raise ValueError("threshold must be non-NAN, try "
"sys.maxsize for untruncated representation")
if precision is not None:
# forbid the bad precision arg as suggested by issue #18254
try:
options['precision'] = operator.index(precision)
except TypeError as e:
raise TypeError('precision must be an integer') from e
return options
@set_module('numpy')
def set_printoptions(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None, infstr=None,
formatter=None, sign=None, floatmode=None, *, legacy=None):
"""
Set printing options.
These options determine the way floating point numbers, arrays and
other NumPy objects are displayed.
Parameters
----------
precision : int or None, optional
Number of digits of precision for floating point output (default 8).
May be None if `floatmode` is not `fixed`, to print as many digits as
necessary to uniquely specify the value.
threshold : int, optional
Total number of array elements which trigger summarization
rather than full repr (default 1000).
To always use the full repr without summarization, pass `sys.maxsize`.
edgeitems : int, optional
Number of array items in summary at beginning and end of
each dimension (default 3).
linewidth : int, optional
The number of characters per line for the purpose of inserting
line breaks (default 75).
suppress : bool, optional
If True, always print floating point numbers using fixed point
notation, in which case numbers equal to zero in the current precision
will print as zero. If False, then scientific notation is used when
absolute value of the smallest number is < 1e-4 or the ratio of the
maximum absolute value to the minimum is > 1e3. The default is False.
nanstr : str, optional
String representation of floating point not-a-number (default nan).
infstr : str, optional
String representation of floating point infinity (default inf).
sign : string, either '-', '+', or ' ', optional
Controls printing of the sign of floating-point types. If '+', always
print the sign of positive values. If ' ', always prints a space
(whitespace character) in the sign position of positive values. If
'-', omit the sign character of positive values. (default '-')
formatter : dict of callables, optional
If not None, the keys should indicate the type(s) that the respective
formatting function applies to. Callables should return a string.
Types that are not specified (by their corresponding keys) are handled
by the default formatters. Individual types for which a formatter
can be set are:
- 'bool'
- 'int'
- 'timedelta' : a `numpy.timedelta64`
- 'datetime' : a `numpy.datetime64`
- 'float'
- 'longfloat' : 128-bit floats
- 'complexfloat'
- 'longcomplexfloat' : composed of two 128-bit floats
- 'numpystr' : types `numpy.string_` and `numpy.unicode_`
- 'object' : `np.object_` arrays
Other keys that can be used to set a group of types at once are:
- 'all' : sets all types
- 'int_kind' : sets 'int'
- 'float_kind' : sets 'float' and 'longfloat'
- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
- 'str_kind' : sets 'numpystr'
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types. Can take the following values
(default maxprec_equal):
* 'fixed': Always print exactly `precision` fractional digits,
even if this would print more or fewer digits than
necessary to specify the value uniquely.
* 'unique': Print the minimum number of fractional digits necessary
to represent each value uniquely. Different elements may
have a different number of digits. The value of the
`precision` option is ignored.
* 'maxprec': Print at most `precision` fractional digits, but if
an element can be uniquely represented with fewer digits
only print it with that many.
* 'maxprec_equal': Print at most `precision` fractional digits,
but if every element in the array can be uniquely
represented with an equal number of fewer digits, use that
many digits for all elements.
legacy : string or `False`, optional
If set to the string `'1.13'` enables 1.13 legacy printing mode. This
approximates numpy 1.13 print output by including a space in the sign
position of floats and different behavior for 0d arrays. This also
enables 1.21 legacy printing mode (described below).
If set to the string `'1.21'` enables 1.21 legacy printing mode. This
approximates numpy 1.21 print output of complex structured dtypes
by not inserting spaces after commas that separate fields and after
colons.
If set to `False`, disables legacy mode.
Unrecognized strings will be ignored with a warning for forward
compatibility.
.. versionadded:: 1.14.0
.. versionchanged:: 1.22.0
See Also
--------
get_printoptions, printoptions, set_string_function, array2string
Notes
-----
`formatter` is always reset with a call to `set_printoptions`.
Use `printoptions` as a context manager to set the values temporarily.
Examples
--------
Floating point precision can be set:
>>> np.set_printoptions(precision=4)
>>> np.array([1.123456789])
[1.1235]
Long arrays can be summarised:
>>> np.set_printoptions(threshold=5)
>>> np.arange(10)
array([0, 1, 2, ..., 7, 8, 9])
Small results can be suppressed:
>>> eps = np.finfo(float).eps
>>> x = np.arange(4.)
>>> x**2 - (x + eps)**2
array([-4.9304e-32, -4.4409e-16, 0.0000e+00, 0.0000e+00])
>>> np.set_printoptions(suppress=True)
>>> x**2 - (x + eps)**2
array([-0., -0., 0., 0.])
A custom formatter can be used to display array elements as desired:
>>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
>>> x = np.arange(3)
>>> x
array([int: 0, int: -1, int: -2])
>>> np.set_printoptions() # formatter gets reset
>>> x
array([0, 1, 2])
To put back the default options, you can use:
>>> np.set_printoptions(edgeitems=3, infstr='inf',
... linewidth=75, nanstr='nan', precision=8,
... suppress=False, threshold=1000, formatter=None)
Also to temporarily override options, use `printoptions` as a context manager:
>>> with np.printoptions(precision=2, suppress=True, threshold=5):
... np.linspace(0, 10, 10)
array([ 0. , 1.11, 2.22, ..., 7.78, 8.89, 10. ])
"""
opt = _make_options_dict(precision, threshold, edgeitems, linewidth,
suppress, nanstr, infstr, sign, formatter,
floatmode, legacy)
# formatter is always reset
opt['formatter'] = formatter
_format_options.update(opt)
# set the C variable for legacy mode
if _format_options['legacy'] == 113:
set_legacy_print_mode(113)
# reset the sign option in legacy mode to avoid confusion
_format_options['sign'] = '-'
elif _format_options['legacy'] == 121:
set_legacy_print_mode(121)
elif _format_options['legacy'] == sys.maxsize:
set_legacy_print_mode(0)
@set_module('numpy')
def get_printoptions():
"""
Return the current print options.
Returns
-------
print_opts : dict
Dictionary of current print options with keys
- precision : int
- threshold : int
- edgeitems : int
- linewidth : int
- suppress : bool
- nanstr : str
- infstr : str
- formatter : dict of callables
- sign : str
For a full description of these options, see `set_printoptions`.
See Also
--------
set_printoptions, printoptions, set_string_function
"""
opts = _format_options.copy()
opts['legacy'] = {
113: '1.13', 121: '1.21', sys.maxsize: False,
}[opts['legacy']]
return opts
def _get_legacy_print_mode():
"""Return the legacy print mode as an int."""
return _format_options['legacy']
@set_module('numpy')
@contextlib.contextmanager
def printoptions(*args, **kwargs):
"""Context manager for setting print options.
Set print options for the scope of the `with` block, and restore the old
options at the end. See `set_printoptions` for the full description of
available options.
Examples
--------
>>> from numpy.testing import assert_equal
>>> with np.printoptions(precision=2):
... np.array([2.0]) / 3
array([0.67])
The `as`-clause of the `with`-statement gives the current print options:
>>> with np.printoptions(precision=2) as opts:
... assert_equal(opts, np.get_printoptions())
See Also
--------
set_printoptions, get_printoptions
"""
opts = np.get_printoptions()
try:
np.set_printoptions(*args, **kwargs)
yield np.get_printoptions()
finally:
np.set_printoptions(**opts)
def _leading_trailing(a, edgeitems, index=()):
"""
Keep only the N-D corners (leading and trailing edges) of an array.
Should be passed a base-class ndarray, since it makes no guarantees about
preserving subclasses.
"""
axis = len(index)
if axis == a.ndim:
return a[index]
if a.shape[axis] > 2*edgeitems:
return concatenate((
_leading_trailing(a, edgeitems, index + np.index_exp[ :edgeitems]),
_leading_trailing(a, edgeitems, index + np.index_exp[-edgeitems:])
), axis=axis)
else:
return _leading_trailing(a, edgeitems, index + np.index_exp[:])
def _object_format(o):
""" Object arrays containing lists should be printed unambiguously """
if type(o) is list:
fmt = 'list({!r})'
else:
fmt = '{!r}'
return fmt.format(o)
def repr_format(x):
return repr(x)
def str_format(x):
return str(x)
def _get_formatdict(data, *, precision, floatmode, suppress, sign, legacy,
formatter, **kwargs):
# note: extra arguments in kwargs are ignored
# wrapped in lambdas to avoid taking a code path with the wrong type of data
formatdict = {
'bool': lambda: BoolFormat(data),
'int': lambda: IntegerFormat(data),
'float': lambda: FloatingFormat(
data, precision, floatmode, suppress, sign, legacy=legacy),
'longfloat': lambda: FloatingFormat(
data, precision, floatmode, suppress, sign, legacy=legacy),
'complexfloat': lambda: ComplexFloatingFormat(
data, precision, floatmode, suppress, sign, legacy=legacy),
'longcomplexfloat': lambda: ComplexFloatingFormat(
data, precision, floatmode, suppress, sign, legacy=legacy),
'datetime': lambda: DatetimeFormat(data, legacy=legacy),
'timedelta': lambda: TimedeltaFormat(data),
'object': lambda: _object_format,
'void': lambda: str_format,
'numpystr': lambda: repr_format}
# we need to wrap values in `formatter` in a lambda, so that the interface
# is the same as the above values.
def indirect(x):
return lambda: x
if formatter is not None:
fkeys = [k for k in formatter.keys() if formatter[k] is not None]
if 'all' in fkeys:
for key in formatdict.keys():
formatdict[key] = indirect(formatter['all'])
if 'int_kind' in fkeys:
for key in ['int']:
formatdict[key] = indirect(formatter['int_kind'])
if 'float_kind' in fkeys:
for key in ['float', 'longfloat']:
formatdict[key] = indirect(formatter['float_kind'])
if 'complex_kind' in fkeys:
for key in ['complexfloat', 'longcomplexfloat']:
formatdict[key] = indirect(formatter['complex_kind'])
if 'str_kind' in fkeys:
formatdict['numpystr'] = indirect(formatter['str_kind'])
for key in formatdict.keys():
if key in fkeys:
formatdict[key] = indirect(formatter[key])
return formatdict
def _get_format_function(data, **options):
"""
find the right formatting function for the dtype_
"""
dtype_ = data.dtype
dtypeobj = dtype_.type
formatdict = _get_formatdict(data, **options)
if dtypeobj is None:
return formatdict["numpystr"]()
elif issubclass(dtypeobj, _nt.bool_):
return formatdict['bool']()
elif issubclass(dtypeobj, _nt.integer):
if issubclass(dtypeobj, _nt.timedelta64):
return formatdict['timedelta']()
else:
return formatdict['int']()
elif issubclass(dtypeobj, _nt.floating):
if issubclass(dtypeobj, _nt.longfloat):
return formatdict['longfloat']()
else:
return formatdict['float']()
elif issubclass(dtypeobj, _nt.complexfloating):
if issubclass(dtypeobj, _nt.clongfloat):
return formatdict['longcomplexfloat']()
else:
return formatdict['complexfloat']()
elif issubclass(dtypeobj, (_nt.unicode_, _nt.string_)):
return formatdict['numpystr']()
elif issubclass(dtypeobj, _nt.datetime64):
return formatdict['datetime']()
elif issubclass(dtypeobj, _nt.object_):
return formatdict['object']()
elif issubclass(dtypeobj, _nt.void):
if dtype_.names is not None:
return StructuredVoidFormat.from_data(data, **options)
else:
return formatdict['void']()
else:
return formatdict['numpystr']()
def _recursive_guard(fillvalue='...'):
"""
Like the python 3.2 reprlib.recursive_repr, but forwards *args and **kwargs
Decorates a function such that if it calls itself with the same first
argument, it returns `fillvalue` instead of recursing.
Largely copied from reprlib.recursive_repr
"""
def decorating_function(f):
repr_running = set()
@functools.wraps(f)
def wrapper(self, *args, **kwargs):
key = id(self), get_ident()
if key in repr_running:
return fillvalue
repr_running.add(key)
try:
return f(self, *args, **kwargs)
finally:
repr_running.discard(key)
return wrapper
return decorating_function
# gracefully handle recursive calls, when object arrays contain themselves
@_recursive_guard()
def _array2string(a, options, separator=' ', prefix=""):
# The formatter __init__s in _get_format_function cannot deal with
# subclasses yet, and we also need to avoid recursion issues in
# _formatArray with subclasses which return 0d arrays in place of scalars
data = asarray(a)
if a.shape == ():
a = data
if a.size > options['threshold']:
summary_insert = "..."
data = _leading_trailing(data, options['edgeitems'])
else:
summary_insert = ""
# find the right formatting function for the array
format_function = _get_format_function(data, **options)
# skip over "["
next_line_prefix = " "
# skip over array(
next_line_prefix += " "*len(prefix)
lst = _formatArray(a, format_function, options['linewidth'],
next_line_prefix, separator, options['edgeitems'],
summary_insert, options['legacy'])
return lst
def _array2string_dispatcher(
a, max_line_width=None, precision=None,
suppress_small=None, separator=None, prefix=None,
style=None, formatter=None, threshold=None,
edgeitems=None, sign=None, floatmode=None, suffix=None,
*, legacy=None):
return (a,)
@array_function_dispatch(_array2string_dispatcher, module='numpy')
def array2string(a, max_line_width=None, precision=None,
suppress_small=None, separator=' ', prefix="",
style=np._NoValue, formatter=None, threshold=None,
edgeitems=None, sign=None, floatmode=None, suffix="",
*, legacy=None):
"""
Return a string representation of an array.
Parameters
----------
a : ndarray
Input array.
max_line_width : int, optional
Inserts newlines if text is longer than `max_line_width`.
Defaults to ``numpy.get_printoptions()['linewidth']``.
precision : int or None, optional
Floating point precision.
Defaults to ``numpy.get_printoptions()['precision']``.
suppress_small : bool, optional
Represent numbers "very close" to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smaller (in absolute value) than 5e-9 are represented as
zero.
Defaults to ``numpy.get_printoptions()['suppress']``.
separator : str, optional
Inserted between elements.
prefix : str, optional
suffix : str, optional
The length of the prefix and suffix strings are used to respectively
align and wrap the output. An array is typically printed as::
prefix + array2string(a) + suffix
The output is left-padded by the length of the prefix string, and
wrapping is forced at the column ``max_line_width - len(suffix)``.
It should be noted that the content of prefix and suffix strings are
not included in the output.
style : _NoValue, optional
Has no effect, do not use.
.. deprecated:: 1.14.0
formatter : dict of callables, optional
If not None, the keys should indicate the type(s) that the respective
formatting function applies to. Callables should return a string.
Types that are not specified (by their corresponding keys) are handled
by the default formatters. Individual types for which a formatter
can be set are:
- 'bool'
- 'int'
- 'timedelta' : a `numpy.timedelta64`
- 'datetime' : a `numpy.datetime64`
- 'float'
- 'longfloat' : 128-bit floats
- 'complexfloat'
- 'longcomplexfloat' : composed of two 128-bit floats
- 'void' : type `numpy.void`
- 'numpystr' : types `numpy.string_` and `numpy.unicode_`
Other keys that can be used to set a group of types at once are:
- 'all' : sets all types
- 'int_kind' : sets 'int'
- 'float_kind' : sets 'float' and 'longfloat'
- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
- 'str_kind' : sets 'numpystr'
threshold : int, optional
Total number of array elements which trigger summarization
rather than full repr.
Defaults to ``numpy.get_printoptions()['threshold']``.
edgeitems : int, optional
Number of array items in summary at beginning and end of
each dimension.
Defaults to ``numpy.get_printoptions()['edgeitems']``.
sign : string, either '-', '+', or ' ', optional
Controls printing of the sign of floating-point types. If '+', always
print the sign of positive values. If ' ', always prints a space
(whitespace character) in the sign position of positive values. If
'-', omit the sign character of positive values.
Defaults to ``numpy.get_printoptions()['sign']``.
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types.
Defaults to ``numpy.get_printoptions()['floatmode']``.
Can take the following values:
- 'fixed': Always print exactly `precision` fractional digits,
even if this would print more or fewer digits than
necessary to specify the value uniquely.
- 'unique': Print the minimum number of fractional digits necessary
to represent each value uniquely. Different elements may
have a different number of digits. The value of the
`precision` option is ignored.
- 'maxprec': Print at most `precision` fractional digits, but if
an element can be uniquely represented with fewer digits
only print it with that many.
- 'maxprec_equal': Print at most `precision` fractional digits,
but if every element in the array can be uniquely
represented with an equal number of fewer digits, use that
many digits for all elements.
legacy : string or `False`, optional
If set to the string `'1.13'` enables 1.13 legacy printing mode. This
approximates numpy 1.13 print output by including a space in the sign
position of floats and different behavior for 0d arrays. If set to
`False`, disables legacy mode. Unrecognized strings will be ignored
with a warning for forward compatibility.
.. versionadded:: 1.14.0
Returns
-------
array_str : str
String representation of the array.
Raises
------
TypeError
if a callable in `formatter` does not return a string.
See Also
--------
array_str, array_repr, set_printoptions, get_printoptions
Notes
-----
If a formatter is specified for a certain type, the `precision` keyword is
ignored for that type.
This is a very flexible function; `array_repr` and `array_str` are using
`array2string` internally so keywords with the same name should work
identically in all three functions.
Examples
--------
>>> x = np.array([1e-16,1,2,3])
>>> np.array2string(x, precision=2, separator=',',
... suppress_small=True)
'[0.,1.,2.,3.]'
>>> x = np.arange(3.)
>>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x})
'[0.00 1.00 2.00]'
>>> x = np.arange(3)
>>> np.array2string(x, formatter={'int':lambda x: hex(x)})
'[0x0 0x1 0x2]'
"""
overrides = _make_options_dict(precision, threshold, edgeitems,
max_line_width, suppress_small, None, None,
sign, formatter, floatmode, legacy)
options = _format_options.copy()
options.update(overrides)
if options['legacy'] <= 113:
if style is np._NoValue:
style = repr
if a.shape == () and a.dtype.names is None:
return style(a.item())
elif style is not np._NoValue:
# Deprecation 11-9-2017 v1.14
warnings.warn("'style' argument is deprecated and no longer functional"
" except in 1.13 'legacy' mode",
DeprecationWarning, stacklevel=3)
if options['legacy'] > 113:
options['linewidth'] -= len(suffix)
# treat as a null array if any of shape elements == 0
if a.size == 0:
return "[]"
return _array2string(a, options, separator, prefix)
def _extendLine(s, line, word, line_width, next_line_prefix, legacy):
needs_wrap = len(line) + len(word) > line_width
if legacy > 113:
# don't wrap lines if it won't help
if len(line) <= len(next_line_prefix):
needs_wrap = False
if needs_wrap:
s += line.rstrip() + "\n"
line = next_line_prefix
line += word
return s, line
def _extendLine_pretty(s, line, word, line_width, next_line_prefix, legacy):
"""
Extends line with nicely formatted (possibly multi-line) string ``word``.
"""
words = word.splitlines()
if len(words) == 1 or legacy <= 113:
return _extendLine(s, line, word, line_width, next_line_prefix, legacy)
max_word_length = max(len(word) for word in words)
if (len(line) + max_word_length > line_width and
len(line) > len(next_line_prefix)):
s += line.rstrip() + '\n'
line = next_line_prefix + words[0]
indent = next_line_prefix
else:
indent = len(line)*' '
line += words[0]
for word in words[1::]:
s += line.rstrip() + '\n'
line = indent + word
suffix_length = max_word_length - len(words[-1])
line += suffix_length*' '
return s, line
def _formatArray(a, format_function, line_width, next_line_prefix,
separator, edge_items, summary_insert, legacy):
"""formatArray is designed for two modes of operation:
1. Full output
2. Summarized output
"""
def recurser(index, hanging_indent, curr_width):
"""
By using this local function, we don't need to recurse with all the
arguments. Since this function is not created recursively, the cost is
not significant
"""
axis = len(index)
axes_left = a.ndim - axis
if axes_left == 0:
return format_function(a[index])
# when recursing, add a space to align with the [ added, and reduce the
# length of the line by 1
next_hanging_indent = hanging_indent + ' '
if legacy <= 113:
next_width = curr_width
else:
next_width = curr_width - len(']')
a_len = a.shape[axis]
show_summary = summary_insert and 2*edge_items < a_len
if show_summary:
leading_items = edge_items
trailing_items = edge_items
else:
leading_items = 0
trailing_items = a_len
# stringify the array with the hanging indent on the first line too
s = ''
# last axis (rows) - wrap elements if they would not fit on one line
if axes_left == 1:
# the length up until the beginning of the separator / bracket
if legacy <= 113:
elem_width = curr_width - len(separator.rstrip())
else:
elem_width = curr_width - max(len(separator.rstrip()), len(']'))
line = hanging_indent
for i in range(leading_items):
word = recurser(index + (i,), next_hanging_indent, next_width)
s, line = _extendLine_pretty(
s, line, word, elem_width, hanging_indent, legacy)
line += separator
if show_summary:
s, line = _extendLine(
s, line, summary_insert, elem_width, hanging_indent, legacy)
if legacy <= 113:
line += ", "
else:
line += separator
for i in range(trailing_items, 1, -1):
word = recurser(index + (-i,), next_hanging_indent, next_width)
s, line = _extendLine_pretty(
s, line, word, elem_width, hanging_indent, legacy)
line += separator
if legacy <= 113:
# width of the separator is not considered on 1.13
elem_width = curr_width
word = recurser(index + (-1,), next_hanging_indent, next_width)
s, line = _extendLine_pretty(
s, line, word, elem_width, hanging_indent, legacy)
s += line
# other axes - insert newlines between rows
else:
s = ''
line_sep = separator.rstrip() + '\n'*(axes_left - 1)
for i in range(leading_items):
nested = recurser(index + (i,), next_hanging_indent, next_width)
s += hanging_indent + nested + line_sep
if show_summary:
if legacy <= 113:
# trailing space, fixed nbr of newlines, and fixed separator
s += hanging_indent + summary_insert + ", \n"
else:
s += hanging_indent + summary_insert + line_sep
for i in range(trailing_items, 1, -1):
nested = recurser(index + (-i,), next_hanging_indent,
next_width)
s += hanging_indent + nested + line_sep
nested = recurser(index + (-1,), next_hanging_indent, next_width)
s += hanging_indent + nested
# remove the hanging indent, and wrap in []
s = '[' + s[len(hanging_indent):] + ']'
return s
try:
# invoke the recursive part with an initial index and prefix
return recurser(index=(),
hanging_indent=next_line_prefix,
curr_width=line_width)
finally:
# recursive closures have a cyclic reference to themselves, which
# requires gc to collect (gh-10620). To avoid this problem, for
# performance and PyPy friendliness, we break the cycle:
recurser = None
def _none_or_positive_arg(x, name):
if x is None:
return -1
if x < 0:
raise ValueError("{} must be >= 0".format(name))
return x
class FloatingFormat:
""" Formatter for subtypes of np.floating """
def __init__(self, data, precision, floatmode, suppress_small, sign=False,
*, legacy=None):
# for backcompatibility, accept bools
if isinstance(sign, bool):
sign = '+' if sign else '-'
self._legacy = legacy
if self._legacy <= 113:
# when not 0d, legacy does not support '-'
if data.shape != () and sign == '-':
sign = ' '
self.floatmode = floatmode
if floatmode == 'unique':
self.precision = None
else:
self.precision = precision
self.precision = _none_or_positive_arg(self.precision, 'precision')
self.suppress_small = suppress_small
self.sign = sign
self.exp_format = False
self.large_exponent = False
self.fillFormat(data)
def fillFormat(self, data):
# only the finite values are used to compute the number of digits
finite_vals = data[isfinite(data)]
# choose exponential mode based on the non-zero finite values:
abs_non_zero = absolute(finite_vals[finite_vals != 0])
if len(abs_non_zero) != 0:
max_val = np.max(abs_non_zero)
min_val = np.min(abs_non_zero)
with errstate(over='ignore'): # division can overflow
if max_val >= 1.e8 or (not self.suppress_small and
(min_val < 0.0001 or max_val/min_val > 1000.)):
self.exp_format = True
# do a first pass of printing all the numbers, to determine sizes
if len(finite_vals) == 0:
self.pad_left = 0
self.pad_right = 0
self.trim = '.'
self.exp_size = -1
self.unique = True
self.min_digits = None
elif self.exp_format:
trim, unique = '.', True
if self.floatmode == 'fixed' or self._legacy <= 113:
trim, unique = 'k', False
strs = (dragon4_scientific(x, precision=self.precision,
unique=unique, trim=trim, sign=self.sign == '+')
for x in finite_vals)
frac_strs, _, exp_strs = zip(*(s.partition('e') for s in strs))
int_part, frac_part = zip(*(s.split('.') for s in frac_strs))
self.exp_size = max(len(s) for s in exp_strs) - 1
self.trim = 'k'
self.precision = max(len(s) for s in frac_part)
self.min_digits = self.precision
self.unique = unique
# for back-compat with np 1.13, use 2 spaces & sign and full prec
if self._legacy <= 113:
self.pad_left = 3
else:
# this should be only 1 or 2. Can be calculated from sign.
self.pad_left = max(len(s) for s in int_part)
# pad_right is only needed for nan length calculation
self.pad_right = self.exp_size + 2 + self.precision
else:
trim, unique = '.', True
if self.floatmode == 'fixed':
trim, unique = 'k', False
strs = (dragon4_positional(x, precision=self.precision,
fractional=True,
unique=unique, trim=trim,
sign=self.sign == '+')
for x in finite_vals)
int_part, frac_part = zip(*(s.split('.') for s in strs))
if self._legacy <= 113:
self.pad_left = 1 + max(len(s.lstrip('-+')) for s in int_part)
else:
self.pad_left = max(len(s) for s in int_part)
self.pad_right = max(len(s) for s in frac_part)
self.exp_size = -1
self.unique = unique
if self.floatmode in ['fixed', 'maxprec_equal']:
self.precision = self.min_digits = self.pad_right
self.trim = 'k'
else:
self.trim = '.'
self.min_digits = 0
if self._legacy > 113:
# account for sign = ' ' by adding one to pad_left
if self.sign == ' ' and not any(np.signbit(finite_vals)):
self.pad_left += 1
# if there are non-finite values, may need to increase pad_left
if data.size != finite_vals.size:
neginf = self.sign != '-' or any(data[isinf(data)] < 0)
nanlen = len(_format_options['nanstr'])
inflen = len(_format_options['infstr']) + neginf
offset = self.pad_right + 1 # +1 for decimal pt
self.pad_left = max(self.pad_left, nanlen - offset, inflen - offset)
def __call__(self, x):
if not np.isfinite(x):
with errstate(invalid='ignore'):
if np.isnan(x):
sign = '+' if self.sign == '+' else ''
ret = sign + _format_options['nanstr']
else: # isinf
sign = '-' if x < 0 else '+' if self.sign == '+' else ''
ret = sign + _format_options['infstr']
return ' '*(self.pad_left + self.pad_right + 1 - len(ret)) + ret
if self.exp_format:
return dragon4_scientific(x,
precision=self.precision,
min_digits=self.min_digits,
unique=self.unique,
trim=self.trim,
sign=self.sign == '+',
pad_left=self.pad_left,
exp_digits=self.exp_size)
else:
return dragon4_positional(x,
precision=self.precision,
min_digits=self.min_digits,
unique=self.unique,
fractional=True,
trim=self.trim,
sign=self.sign == '+',
pad_left=self.pad_left,
pad_right=self.pad_right)
@set_module('numpy')
def format_float_scientific(x, precision=None, unique=True, trim='k',
sign=False, pad_left=None, exp_digits=None,
min_digits=None):
"""
Format a floating-point scalar as a decimal string in scientific notation.
Provides control over rounding, trimming and padding. Uses and assumes
IEEE unbiased rounding. Uses the "Dragon4" algorithm.
Parameters
----------
x : python float or numpy floating scalar
Value to format.
precision : non-negative integer or None, optional
Maximum number of digits to print. May be None if `unique` is
`True`, but must be an integer if unique is `False`.
unique : boolean, optional
If `True`, use a digit-generation strategy which gives the shortest
representation which uniquely identifies the floating-point number from
other values of the same type, by judicious rounding. If `precision`
is given fewer digits than necessary can be printed. If `min_digits`
is given more can be printed, in which cases the last digit is rounded
with unbiased rounding.
If `False`, digits are generated as if printing an infinite-precision
value and stopping after `precision` digits, rounding the remaining
value with unbiased rounding
trim : one of 'k', '.', '0', '-', optional
Controls post-processing trimming of trailing digits, as follows:
* 'k' : keep trailing zeros, keep decimal point (no trimming)
* '.' : trim all trailing zeros, leave decimal point
* '0' : trim all but the zero before the decimal point. Insert the
zero if it is missing.
* '-' : trim trailing zeros and any trailing decimal point
sign : boolean, optional
Whether to show the sign for positive values.
pad_left : non-negative integer, optional
Pad the left side of the string with whitespace until at least that
many characters are to the left of the decimal point.
exp_digits : non-negative integer, optional
Pad the exponent with zeros until it contains at least this many digits.
If omitted, the exponent will be at least 2 digits.
min_digits : non-negative integer or None, optional
Minimum number of digits to print. This only has an effect for
`unique=True`. In that case more digits than necessary to uniquely
identify the value may be printed and rounded unbiased.
-- versionadded:: 1.21.0
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_positional
Examples
--------
>>> np.format_float_scientific(np.float32(np.pi))
'3.1415927e+00'
>>> s = np.float32(1.23e24)
>>> np.format_float_scientific(s, unique=False, precision=15)
'1.230000071797338e+24'
>>> np.format_float_scientific(s, exp_digits=4)
'1.23e+0024'
"""
precision = _none_or_positive_arg(precision, 'precision')
pad_left = _none_or_positive_arg(pad_left, 'pad_left')
exp_digits = _none_or_positive_arg(exp_digits, 'exp_digits')
min_digits = _none_or_positive_arg(min_digits, 'min_digits')
if min_digits > 0 and precision > 0 and min_digits > precision:
raise ValueError("min_digits must be less than or equal to precision")
return dragon4_scientific(x, precision=precision, unique=unique,
trim=trim, sign=sign, pad_left=pad_left,
exp_digits=exp_digits, min_digits=min_digits)
@set_module('numpy')
def format_float_positional(x, precision=None, unique=True,
fractional=True, trim='k', sign=False,
pad_left=None, pad_right=None, min_digits=None):
"""
Format a floating-point scalar as a decimal string in positional notation.
Provides control over rounding, trimming and padding. Uses and assumes
IEEE unbiased rounding. Uses the "Dragon4" algorithm.
Parameters
----------
x : python float or numpy floating scalar
Value to format.
precision : non-negative integer or None, optional
Maximum number of digits to print. May be None if `unique` is
`True`, but must be an integer if unique is `False`.
unique : boolean, optional
If `True`, use a digit-generation strategy which gives the shortest
representation which uniquely identifies the floating-point number from
other values of the same type, by judicious rounding. If `precision`
is given fewer digits than necessary can be printed, or if `min_digits`
is given more can be printed, in which cases the last digit is rounded
with unbiased rounding.
If `False`, digits are generated as if printing an infinite-precision
value and stopping after `precision` digits, rounding the remaining
value with unbiased rounding
fractional : boolean, optional
If `True`, the cutoffs of `precision` and `min_digits` refer to the
total number of digits after the decimal point, including leading
zeros.
If `False`, `precision` and `min_digits` refer to the total number of
significant digits, before or after the decimal point, ignoring leading
zeros.
trim : one of 'k', '.', '0', '-', optional
Controls post-processing trimming of trailing digits, as follows:
* 'k' : keep trailing zeros, keep decimal point (no trimming)
* '.' : trim all trailing zeros, leave decimal point
* '0' : trim all but the zero before the decimal point. Insert the
zero if it is missing.
* '-' : trim trailing zeros and any trailing decimal point
sign : boolean, optional
Whether to show the sign for positive values.
pad_left : non-negative integer, optional
Pad the left side of the string with whitespace until at least that
many characters are to the left of the decimal point.
pad_right : non-negative integer, optional
Pad the right side of the string with whitespace until at least that
many characters are to the right of the decimal point.
min_digits : non-negative integer or None, optional
Minimum number of digits to print. Only has an effect if `unique=True`
in which case additional digits past those necessary to uniquely
identify the value may be printed, rounding the last additional digit.
-- versionadded:: 1.21.0
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_scientific
Examples
--------
>>> np.format_float_positional(np.float32(np.pi))
'3.1415927'
>>> np.format_float_positional(np.float16(np.pi))
'3.14'
>>> np.format_float_positional(np.float16(0.3))
'0.3'
>>> np.format_float_positional(np.float16(0.3), unique=False, precision=10)
'0.3000488281'
"""
precision = _none_or_positive_arg(precision, 'precision')
pad_left = _none_or_positive_arg(pad_left, 'pad_left')
pad_right = _none_or_positive_arg(pad_right, 'pad_right')
min_digits = _none_or_positive_arg(min_digits, 'min_digits')
if not fractional and precision == 0:
raise ValueError("precision must be greater than 0 if "
"fractional=False")
if min_digits > 0 and precision > 0 and min_digits > precision:
raise ValueError("min_digits must be less than or equal to precision")
return dragon4_positional(x, precision=precision, unique=unique,
fractional=fractional, trim=trim,
sign=sign, pad_left=pad_left,
pad_right=pad_right, min_digits=min_digits)
class IntegerFormat:
def __init__(self, data):
if data.size > 0:
max_str_len = max(len(str(np.max(data))),
len(str(np.min(data))))
else:
max_str_len = 0
self.format = '%{}d'.format(max_str_len)
def __call__(self, x):
return self.format % x
class BoolFormat:
def __init__(self, data, **kwargs):
# add an extra space so " True" and "False" have the same length and
# array elements align nicely when printed, except in 0d arrays
self.truestr = ' True' if data.shape != () else 'True'
def __call__(self, x):
return self.truestr if x else "False"
class ComplexFloatingFormat:
""" Formatter for subtypes of np.complexfloating """
def __init__(self, x, precision, floatmode, suppress_small,
sign=False, *, legacy=None):
# for backcompatibility, accept bools
if isinstance(sign, bool):
sign = '+' if sign else '-'
floatmode_real = floatmode_imag = floatmode
if legacy <= 113:
floatmode_real = 'maxprec_equal'
floatmode_imag = 'maxprec'
self.real_format = FloatingFormat(
x.real, precision, floatmode_real, suppress_small,
sign=sign, legacy=legacy
)
self.imag_format = FloatingFormat(
x.imag, precision, floatmode_imag, suppress_small,
sign='+', legacy=legacy
)
def __call__(self, x):
r = self.real_format(x.real)
i = self.imag_format(x.imag)
# add the 'j' before the terminal whitespace in i
sp = len(i.rstrip())
i = i[:sp] + 'j' + i[sp:]
return r + i
class _TimelikeFormat:
def __init__(self, data):
non_nat = data[~isnat(data)]
if len(non_nat) > 0:
# Max str length of non-NaT elements
max_str_len = max(len(self._format_non_nat(np.max(non_nat))),
len(self._format_non_nat(np.min(non_nat))))
else:
max_str_len = 0
if len(non_nat) < data.size:
# data contains a NaT
max_str_len = max(max_str_len, 5)
self._format = '%{}s'.format(max_str_len)
self._nat = "'NaT'".rjust(max_str_len)
def _format_non_nat(self, x):
# override in subclass
raise NotImplementedError
def __call__(self, x):
if isnat(x):
return self._nat
else:
return self._format % self._format_non_nat(x)
class DatetimeFormat(_TimelikeFormat):
def __init__(self, x, unit=None, timezone=None, casting='same_kind',
legacy=False):
# Get the unit from the dtype
if unit is None:
if x.dtype.kind == 'M':
unit = datetime_data(x.dtype)[0]
else:
unit = 's'
if timezone is None:
timezone = 'naive'
self.timezone = timezone
self.unit = unit
self.casting = casting
self.legacy = legacy
# must be called after the above are configured
super().__init__(x)
def __call__(self, x):
if self.legacy <= 113:
return self._format_non_nat(x)
return super().__call__(x)
def _format_non_nat(self, x):
return "'%s'" % datetime_as_string(x,
unit=self.unit,
timezone=self.timezone,
casting=self.casting)
class TimedeltaFormat(_TimelikeFormat):
def _format_non_nat(self, x):
return str(x.astype('i8'))
class SubArrayFormat:
def __init__(self, format_function):
self.format_function = format_function
def __call__(self, arr):
if arr.ndim <= 1:
return "[" + ", ".join(self.format_function(a) for a in arr) + "]"
return "[" + ", ".join(self.__call__(a) for a in arr) + "]"
class StructuredVoidFormat:
"""
Formatter for structured np.void objects.
This does not work on structured alias types like np.dtype(('i4', 'i2,i2')),
as alias scalars lose their field information, and the implementation
relies upon np.void.__getitem__.
"""
def __init__(self, format_functions):
self.format_functions = format_functions
@classmethod
def from_data(cls, data, **options):
"""
This is a second way to initialize StructuredVoidFormat, using the raw data
as input. Added to avoid changing the signature of __init__.
"""
format_functions = []
for field_name in data.dtype.names:
format_function = _get_format_function(data[field_name], **options)
if data.dtype[field_name].shape != ():
format_function = SubArrayFormat(format_function)
format_functions.append(format_function)
return cls(format_functions)
def __call__(self, x):
str_fields = [
format_function(field)
for field, format_function in zip(x, self.format_functions)
]
if len(str_fields) == 1:
return "({},)".format(str_fields[0])
else:
return "({})".format(", ".join(str_fields))
def _void_scalar_repr(x):
"""
Implements the repr for structured-void scalars. It is called from the
scalartypes.c.src code, and is placed here because it uses the elementwise
formatters defined above.
"""
return StructuredVoidFormat.from_data(array(x), **_format_options)(x)
_typelessdata = [int_, float_, complex_, bool_]
if issubclass(intc, int):
_typelessdata.append(intc)
if issubclass(longlong, int):
_typelessdata.append(longlong)
def dtype_is_implied(dtype):
"""
Determine if the given dtype is implied by the representation of its values.
Parameters
----------
dtype : dtype
Data type
Returns
-------
implied : bool
True if the dtype is implied by the representation of its values.
Examples
--------
>>> np.core.arrayprint.dtype_is_implied(int)
True
>>> np.array([1, 2, 3], int)
array([1, 2, 3])
>>> np.core.arrayprint.dtype_is_implied(np.int8)
False
>>> np.array([1, 2, 3], np.int8)
array([1, 2, 3], dtype=int8)
"""
dtype = np.dtype(dtype)
if _format_options['legacy'] <= 113 and dtype.type == bool_:
return False
# not just void types can be structured, and names are not part of the repr
if dtype.names is not None:
return False
return dtype.type in _typelessdata
def dtype_short_repr(dtype):
"""
Convert a dtype to a short form which evaluates to the same dtype.
The intent is roughly that the following holds
>>> from numpy import *
>>> dt = np.int64([1, 2]).dtype
>>> assert eval(dtype_short_repr(dt)) == dt
"""
if type(dtype).__repr__ != np.dtype.__repr__:
# TODO: Custom repr for user DTypes, logic should likely move.
return repr(dtype)
if dtype.names is not None:
# structured dtypes give a list or tuple repr
return str(dtype)
elif issubclass(dtype.type, flexible):
# handle these separately so they don't give garbage like str256
return "'%s'" % str(dtype)
typename = dtype.name
# quote typenames which can't be represented as python variable names
if typename and not (typename[0].isalpha() and typename.isalnum()):
typename = repr(typename)
return typename
def _array_repr_implementation(
arr, max_line_width=None, precision=None, suppress_small=None,
array2string=array2string):
"""Internal version of array_repr() that allows overriding array2string."""
if max_line_width is None:
max_line_width = _format_options['linewidth']
if type(arr) is not ndarray:
class_name = type(arr).__name__
else:
class_name = "array"
skipdtype = dtype_is_implied(arr.dtype) and arr.size > 0
prefix = class_name + "("
suffix = ")" if skipdtype else ","
if (_format_options['legacy'] <= 113 and
arr.shape == () and not arr.dtype.names):
lst = repr(arr.item())
elif arr.size > 0 or arr.shape == (0,):
lst = array2string(arr, max_line_width, precision, suppress_small,
', ', prefix, suffix=suffix)
else: # show zero-length shape unless it is (0,)
lst = "[], shape=%s" % (repr(arr.shape),)
arr_str = prefix + lst + suffix
if skipdtype:
return arr_str
dtype_str = "dtype={})".format(dtype_short_repr(arr.dtype))
# compute whether we should put dtype on a new line: Do so if adding the
# dtype would extend the last line past max_line_width.
# Note: This line gives the correct result even when rfind returns -1.
last_line_len = len(arr_str) - (arr_str.rfind('\n') + 1)
spacer = " "
if _format_options['legacy'] <= 113:
if issubclass(arr.dtype.type, flexible):
spacer = '\n' + ' '*len(class_name + "(")
elif last_line_len + len(dtype_str) + 1 > max_line_width:
spacer = '\n' + ' '*len(class_name + "(")
return arr_str + spacer + dtype_str
def _array_repr_dispatcher(
arr, max_line_width=None, precision=None, suppress_small=None):
return (arr,)
@array_function_dispatch(_array_repr_dispatcher, module='numpy')
def array_repr(arr, max_line_width=None, precision=None, suppress_small=None):
"""
Return the string representation of an array.
Parameters
----------
arr : ndarray
Input array.
max_line_width : int, optional
Inserts newlines if text is longer than `max_line_width`.
Defaults to ``numpy.get_printoptions()['linewidth']``.
precision : int, optional
Floating point precision.
Defaults to ``numpy.get_printoptions()['precision']``.
suppress_small : bool, optional
Represent numbers "very close" to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smaller (in absolute value) than 5e-9 are represented as
zero.
Defaults to ``numpy.get_printoptions()['suppress']``.
Returns
-------
string : str
The string representation of an array.
See Also
--------
array_str, array2string, set_printoptions
Examples
--------
>>> np.array_repr(np.array([1,2]))
'array([1, 2])'
>>> np.array_repr(np.ma.array([0.]))
'MaskedArray([0.])'
>>> np.array_repr(np.array([], np.int32))
'array([], dtype=int32)'
>>> x = np.array([1e-6, 4e-7, 2, 3])
>>> np.array_repr(x, precision=6, suppress_small=True)
'array([0.000001, 0. , 2. , 3. ])'
"""
return _array_repr_implementation(
arr, max_line_width, precision, suppress_small)
@_recursive_guard()
def _guarded_repr_or_str(v):
if isinstance(v, bytes):
return repr(v)
return str(v)
def _array_str_implementation(
a, max_line_width=None, precision=None, suppress_small=None,
array2string=array2string):
"""Internal version of array_str() that allows overriding array2string."""
if (_format_options['legacy'] <= 113 and
a.shape == () and not a.dtype.names):
return str(a.item())
# the str of 0d arrays is a special case: It should appear like a scalar,
# so floats are not truncated by `precision`, and strings are not wrapped
# in quotes. So we return the str of the scalar value.
if a.shape == ():
# obtain a scalar and call str on it, avoiding problems for subclasses
# for which indexing with () returns a 0d instead of a scalar by using
# ndarray's getindex. Also guard against recursive 0d object arrays.
return _guarded_repr_or_str(np.ndarray.__getitem__(a, ()))
return array2string(a, max_line_width, precision, suppress_small, ' ', "")
def _array_str_dispatcher(
a, max_line_width=None, precision=None, suppress_small=None):
return (a,)
@array_function_dispatch(_array_str_dispatcher, module='numpy')
def array_str(a, max_line_width=None, precision=None, suppress_small=None):
"""
Return a string representation of the data in an array.
The data in the array is returned as a single string. This function is
similar to `array_repr`, the difference being that `array_repr` also
returns information on the kind of array and its data type.
Parameters
----------
a : ndarray
Input array.
max_line_width : int, optional
Inserts newlines if text is longer than `max_line_width`.
Defaults to ``numpy.get_printoptions()['linewidth']``.
precision : int, optional
Floating point precision.
Defaults to ``numpy.get_printoptions()['precision']``.
suppress_small : bool, optional
Represent numbers "very close" to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smaller (in absolute value) than 5e-9 are represented as
zero.
Defaults to ``numpy.get_printoptions()['suppress']``.
See Also
--------
array2string, array_repr, set_printoptions
Examples
--------
>>> np.array_str(np.arange(3))
'[0 1 2]'
"""
return _array_str_implementation(
a, max_line_width, precision, suppress_small)
# needed if __array_function__ is disabled
_array2string_impl = getattr(array2string, '__wrapped__', array2string)
_default_array_str = functools.partial(_array_str_implementation,
array2string=_array2string_impl)
_default_array_repr = functools.partial(_array_repr_implementation,
array2string=_array2string_impl)
def set_string_function(f, repr=True):
"""
Set a Python function to be used when pretty printing arrays.
Parameters
----------
f : function or None
Function to be used to pretty print arrays. The function should expect
a single array argument and return a string of the representation of
the array. If None, the function is reset to the default NumPy function
to print arrays.
repr : bool, optional
If True (default), the function for pretty printing (``__repr__``)
is set, if False the function that returns the default string
representation (``__str__``) is set.
See Also
--------
set_printoptions, get_printoptions
Examples
--------
>>> def pprint(arr):
... return 'HA! - What are you going to do now?'
...
>>> np.set_string_function(pprint)
>>> a = np.arange(10)
>>> a
HA! - What are you going to do now?
>>> _ = a
>>> # [0 1 2 3 4 5 6 7 8 9]
We can reset the function to the default:
>>> np.set_string_function(None)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
`repr` affects either pretty printing or normal string representation.
Note that ``__repr__`` is still affected by setting ``__str__``
because the width of each array element in the returned string becomes
equal to the length of the result of ``__str__()``.
>>> x = np.arange(4)
>>> np.set_string_function(lambda x:'random', repr=False)
>>> x.__str__()
'random'
>>> x.__repr__()
'array([0, 1, 2, 3])'
"""
if f is None:
if repr:
return multiarray.set_string_function(_default_array_repr, 1)
else:
return multiarray.set_string_function(_default_array_str, 0)
else:
return multiarray.set_string_function(f, repr)
| 62,839 | Python | 35.834701 | 83 | 0.593899 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_internal.pyi | from typing import Any, TypeVar, overload, Generic
import ctypes as ct
from numpy import ndarray
from numpy.ctypeslib import c_intp
_CastT = TypeVar("_CastT", bound=ct._CanCastTo) # Copied from `ctypes.cast`
_CT = TypeVar("_CT", bound=ct._CData)
_PT = TypeVar("_PT", bound=None | int)
# TODO: Let the likes of `shape_as` and `strides_as` return `None`
# for 0D arrays once we've got shape-support
class _ctypes(Generic[_PT]):
@overload
def __new__(cls, array: ndarray[Any, Any], ptr: None = ...) -> _ctypes[None]: ...
@overload
def __new__(cls, array: ndarray[Any, Any], ptr: _PT) -> _ctypes[_PT]: ...
@property
def data(self) -> _PT: ...
@property
def shape(self) -> ct.Array[c_intp]: ...
@property
def strides(self) -> ct.Array[c_intp]: ...
@property
def _as_parameter_(self) -> ct.c_void_p: ...
def data_as(self, obj: type[_CastT]) -> _CastT: ...
def shape_as(self, obj: type[_CT]) -> ct.Array[_CT]: ...
def strides_as(self, obj: type[_CT]) -> ct.Array[_CT]: ...
| 1,032 | unknown | 32.32258 | 85 | 0.604651 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/numeric.py | import functools
import itertools
import operator
import sys
import warnings
import numbers
import numpy as np
from . import multiarray
from .multiarray import (
_fastCopyAndTranspose as fastCopyAndTranspose, ALLOW_THREADS,
BUFSIZE, CLIP, MAXDIMS, MAY_SHARE_BOUNDS, MAY_SHARE_EXACT, RAISE,
WRAP, arange, array, asarray, asanyarray, ascontiguousarray,
asfortranarray, broadcast, can_cast, compare_chararrays,
concatenate, copyto, dot, dtype, empty,
empty_like, flatiter, frombuffer, from_dlpack, fromfile, fromiter,
fromstring, inner, lexsort, matmul, may_share_memory,
min_scalar_type, ndarray, nditer, nested_iters, promote_types,
putmask, result_type, set_numeric_ops, shares_memory, vdot, where,
zeros, normalize_axis_index)
from . import overrides
from . import umath
from . import shape_base
from .overrides import set_array_function_like_doc, set_module
from .umath import (multiply, invert, sin, PINF, NAN)
from . import numerictypes
from .numerictypes import longlong, intc, int_, float_, complex_, bool_
from ._exceptions import TooHardError, AxisError
from ._ufunc_config import errstate
bitwise_not = invert
ufunc = type(sin)
newaxis = None
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
__all__ = [
'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc',
'arange', 'array', 'asarray', 'asanyarray', 'ascontiguousarray',
'asfortranarray', 'zeros', 'count_nonzero', 'empty', 'broadcast', 'dtype',
'fromstring', 'fromfile', 'frombuffer', 'from_dlpack', 'where',
'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', 'lexsort',
'set_numeric_ops', 'can_cast', 'promote_types', 'min_scalar_type',
'result_type', 'isfortran', 'empty_like', 'zeros_like', 'ones_like',
'correlate', 'convolve', 'inner', 'dot', 'outer', 'vdot', 'roll',
'rollaxis', 'moveaxis', 'cross', 'tensordot', 'little_endian',
'fromiter', 'array_equal', 'array_equiv', 'indices', 'fromfunction',
'isclose', 'isscalar', 'binary_repr', 'base_repr', 'ones',
'identity', 'allclose', 'compare_chararrays', 'putmask',
'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN',
'False_', 'True_', 'bitwise_not', 'CLIP', 'RAISE', 'WRAP', 'MAXDIMS',
'BUFSIZE', 'ALLOW_THREADS', 'ComplexWarning', 'full', 'full_like',
'matmul', 'shares_memory', 'may_share_memory', 'MAY_SHARE_BOUNDS',
'MAY_SHARE_EXACT', 'TooHardError', 'AxisError']
@set_module('numpy')
class ComplexWarning(RuntimeWarning):
"""
The warning raised when casting a complex dtype to a real dtype.
As implemented, casting a complex number to a real discards its imaginary
part, but this behavior may not be what the user actually wants.
"""
pass
def _zeros_like_dispatcher(a, dtype=None, order=None, subok=None, shape=None):
return (a,)
@array_function_dispatch(_zeros_like_dispatcher)
def zeros_like(a, dtype=None, order='K', subok=True, shape=None):
"""
Return an array of zeros with the same shape and type as a given array.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of
the returned array.
dtype : data-type, optional
Overrides the data type of the result.
.. versionadded:: 1.6.0
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible.
.. versionadded:: 1.6.0
subok : bool, optional.
If True, then the newly created array will use the sub-class
type of `a`, otherwise it will be a base-class array. Defaults
to True.
shape : int or sequence of ints, optional.
Overrides the shape of the result. If order='K' and the number of
dimensions is unchanged, will try to keep order, otherwise,
order='C' is implied.
.. versionadded:: 1.17.0
Returns
-------
out : ndarray
Array of zeros with the same shape and type as `a`.
See Also
--------
empty_like : Return an empty array with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
full_like : Return a new array with shape of input filled with value.
zeros : Return a new array setting values to zero.
Examples
--------
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.zeros_like(x)
array([[0, 0, 0],
[0, 0, 0]])
>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.])
>>> np.zeros_like(y)
array([0., 0., 0.])
"""
res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)
# needed instead of a 0 to get same result as zeros for string dtypes
z = zeros(1, dtype=res.dtype)
multiarray.copyto(res, z, casting='unsafe')
return res
def _ones_dispatcher(shape, dtype=None, order=None, *, like=None):
return(like,)
@set_array_function_like_doc
@set_module('numpy')
def ones(shape, dtype=None, order='C', *, like=None):
"""
Return a new array of given shape and type, filled with ones.
Parameters
----------
shape : int or sequence of ints
Shape of the new array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
order : {'C', 'F'}, optional, default: C
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array of ones with the given shape, dtype, and order.
See Also
--------
ones_like : Return an array of ones with shape and type of input.
empty : Return a new uninitialized array.
zeros : Return a new array setting values to zero.
full : Return a new array of given shape filled with value.
Examples
--------
>>> np.ones(5)
array([1., 1., 1., 1., 1.])
>>> np.ones((5,), dtype=int)
array([1, 1, 1, 1, 1])
>>> np.ones((2, 1))
array([[1.],
[1.]])
>>> s = (2,2)
>>> np.ones(s)
array([[1., 1.],
[1., 1.]])
"""
if like is not None:
return _ones_with_like(shape, dtype=dtype, order=order, like=like)
a = empty(shape, dtype, order)
multiarray.copyto(a, 1, casting='unsafe')
return a
_ones_with_like = array_function_dispatch(
_ones_dispatcher
)(ones)
def _ones_like_dispatcher(a, dtype=None, order=None, subok=None, shape=None):
return (a,)
@array_function_dispatch(_ones_like_dispatcher)
def ones_like(a, dtype=None, order='K', subok=True, shape=None):
"""
Return an array of ones with the same shape and type as a given array.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of
the returned array.
dtype : data-type, optional
Overrides the data type of the result.
.. versionadded:: 1.6.0
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible.
.. versionadded:: 1.6.0
subok : bool, optional.
If True, then the newly created array will use the sub-class
type of `a`, otherwise it will be a base-class array. Defaults
to True.
shape : int or sequence of ints, optional.
Overrides the shape of the result. If order='K' and the number of
dimensions is unchanged, will try to keep order, otherwise,
order='C' is implied.
.. versionadded:: 1.17.0
Returns
-------
out : ndarray
Array of ones with the same shape and type as `a`.
See Also
--------
empty_like : Return an empty array with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full_like : Return a new array with shape of input filled with value.
ones : Return a new array setting values to one.
Examples
--------
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.ones_like(x)
array([[1, 1, 1],
[1, 1, 1]])
>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.])
>>> np.ones_like(y)
array([1., 1., 1.])
"""
res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)
multiarray.copyto(res, 1, casting='unsafe')
return res
def _full_dispatcher(shape, fill_value, dtype=None, order=None, *, like=None):
return(like,)
@set_array_function_like_doc
@set_module('numpy')
def full(shape, fill_value, dtype=None, order='C', *, like=None):
"""
Return a new array of given shape and type, filled with `fill_value`.
Parameters
----------
shape : int or sequence of ints
Shape of the new array, e.g., ``(2, 3)`` or ``2``.
fill_value : scalar or array_like
Fill value.
dtype : data-type, optional
The desired data-type for the array The default, None, means
``np.array(fill_value).dtype``.
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array of `fill_value` with the given shape, dtype, and order.
See Also
--------
full_like : Return a new array with shape of input filled with value.
empty : Return a new uninitialized array.
ones : Return a new array setting values to one.
zeros : Return a new array setting values to zero.
Examples
--------
>>> np.full((2, 2), np.inf)
array([[inf, inf],
[inf, inf]])
>>> np.full((2, 2), 10)
array([[10, 10],
[10, 10]])
>>> np.full((2, 2), [1, 2])
array([[1, 2],
[1, 2]])
"""
if like is not None:
return _full_with_like(shape, fill_value, dtype=dtype, order=order, like=like)
if dtype is None:
fill_value = asarray(fill_value)
dtype = fill_value.dtype
a = empty(shape, dtype, order)
multiarray.copyto(a, fill_value, casting='unsafe')
return a
_full_with_like = array_function_dispatch(
_full_dispatcher
)(full)
def _full_like_dispatcher(a, fill_value, dtype=None, order=None, subok=None, shape=None):
return (a,)
@array_function_dispatch(_full_like_dispatcher)
def full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None):
"""
Return a full array with the same shape and type as a given array.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of
the returned array.
fill_value : array_like
Fill value.
dtype : data-type, optional
Overrides the data type of the result.
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible.
subok : bool, optional.
If True, then the newly created array will use the sub-class
type of `a`, otherwise it will be a base-class array. Defaults
to True.
shape : int or sequence of ints, optional.
Overrides the shape of the result. If order='K' and the number of
dimensions is unchanged, will try to keep order, otherwise,
order='C' is implied.
.. versionadded:: 1.17.0
Returns
-------
out : ndarray
Array of `fill_value` with the same shape and type as `a`.
See Also
--------
empty_like : Return an empty array with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full : Return a new array of given shape filled with value.
Examples
--------
>>> x = np.arange(6, dtype=int)
>>> np.full_like(x, 1)
array([1, 1, 1, 1, 1, 1])
>>> np.full_like(x, 0.1)
array([0, 0, 0, 0, 0, 0])
>>> np.full_like(x, 0.1, dtype=np.double)
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
>>> np.full_like(x, np.nan, dtype=np.double)
array([nan, nan, nan, nan, nan, nan])
>>> y = np.arange(6, dtype=np.double)
>>> np.full_like(y, 0.1)
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
>>> y = np.zeros([2, 2, 3], dtype=int)
>>> np.full_like(y, [0, 0, 255])
array([[[ 0, 0, 255],
[ 0, 0, 255]],
[[ 0, 0, 255],
[ 0, 0, 255]]])
"""
res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)
multiarray.copyto(res, fill_value, casting='unsafe')
return res
def _count_nonzero_dispatcher(a, axis=None, *, keepdims=None):
return (a,)
@array_function_dispatch(_count_nonzero_dispatcher)
def count_nonzero(a, axis=None, *, keepdims=False):
"""
Counts the number of non-zero values in the array ``a``.
The word "non-zero" is in reference to the Python 2.x
built-in method ``__nonzero__()`` (renamed ``__bool__()``
in Python 3.x) of Python objects that tests an object's
"truthfulness". For example, any number is considered
truthful if it is nonzero, whereas any string is considered
truthful if it is not the empty string. Thus, this function
(recursively) counts how many elements in ``a`` (and in
sub-arrays thereof) have their ``__nonzero__()`` or ``__bool__()``
method evaluated to ``True``.
Parameters
----------
a : array_like
The array for which to count non-zeros.
axis : int or tuple, optional
Axis or tuple of axes along which to count non-zeros.
Default is None, meaning that non-zeros will be counted
along a flattened version of ``a``.
.. versionadded:: 1.12.0
keepdims : bool, optional
If this is set to True, the axes that are counted are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
.. versionadded:: 1.19.0
Returns
-------
count : int or array of int
Number of non-zero values in the array along a given axis.
Otherwise, the total number of non-zero values in the array
is returned.
See Also
--------
nonzero : Return the coordinates of all the non-zero values.
Examples
--------
>>> np.count_nonzero(np.eye(4))
4
>>> a = np.array([[0, 1, 7, 0],
... [3, 0, 2, 19]])
>>> np.count_nonzero(a)
5
>>> np.count_nonzero(a, axis=0)
array([1, 1, 2, 1])
>>> np.count_nonzero(a, axis=1)
array([2, 3])
>>> np.count_nonzero(a, axis=1, keepdims=True)
array([[2],
[3]])
"""
if axis is None and not keepdims:
return multiarray.count_nonzero(a)
a = asanyarray(a)
# TODO: this works around .astype(bool) not working properly (gh-9847)
if np.issubdtype(a.dtype, np.character):
a_bool = a != a.dtype.type()
else:
a_bool = a.astype(np.bool_, copy=False)
return a_bool.sum(axis=axis, dtype=np.intp, keepdims=keepdims)
@set_module('numpy')
def isfortran(a):
"""
Check if the array is Fortran contiguous but *not* C contiguous.
This function is obsolete and, because of changes due to relaxed stride
checking, its return value for the same array may differ for versions
of NumPy >= 1.10.0 and previous versions. If you only want to check if an
array is Fortran contiguous use ``a.flags.f_contiguous`` instead.
Parameters
----------
a : ndarray
Input array.
Returns
-------
isfortran : bool
Returns True if the array is Fortran contiguous but *not* C contiguous.
Examples
--------
np.array allows to specify whether the array is written in C-contiguous
order (last index varies the fastest), or FORTRAN-contiguous order in
memory (first index varies the fastest).
>>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
>>> a
array([[1, 2, 3],
[4, 5, 6]])
>>> np.isfortran(a)
False
>>> b = np.array([[1, 2, 3], [4, 5, 6]], order='F')
>>> b
array([[1, 2, 3],
[4, 5, 6]])
>>> np.isfortran(b)
True
The transpose of a C-ordered array is a FORTRAN-ordered array.
>>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
>>> a
array([[1, 2, 3],
[4, 5, 6]])
>>> np.isfortran(a)
False
>>> b = a.T
>>> b
array([[1, 4],
[2, 5],
[3, 6]])
>>> np.isfortran(b)
True
C-ordered arrays evaluate as False even if they are also FORTRAN-ordered.
>>> np.isfortran(np.array([1, 2], order='F'))
False
"""
return a.flags.fnc
def _argwhere_dispatcher(a):
return (a,)
@array_function_dispatch(_argwhere_dispatcher)
def argwhere(a):
"""
Find the indices of array elements that are non-zero, grouped by element.
Parameters
----------
a : array_like
Input data.
Returns
-------
index_array : (N, a.ndim) ndarray
Indices of elements that are non-zero. Indices are grouped by element.
This array will have shape ``(N, a.ndim)`` where ``N`` is the number of
non-zero items.
See Also
--------
where, nonzero
Notes
-----
``np.argwhere(a)`` is almost the same as ``np.transpose(np.nonzero(a))``,
but produces a result of the correct shape for a 0D array.
The output of ``argwhere`` is not suitable for indexing arrays.
For this purpose use ``nonzero(a)`` instead.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.argwhere(x>1)
array([[0, 2],
[1, 0],
[1, 1],
[1, 2]])
"""
# nonzero does not behave well on 0d, so promote to 1d
if np.ndim(a) == 0:
a = shape_base.atleast_1d(a)
# then remove the added dimension
return argwhere(a)[:,:0]
return transpose(nonzero(a))
def _flatnonzero_dispatcher(a):
return (a,)
@array_function_dispatch(_flatnonzero_dispatcher)
def flatnonzero(a):
"""
Return indices that are non-zero in the flattened version of a.
This is equivalent to ``np.nonzero(np.ravel(a))[0]``.
Parameters
----------
a : array_like
Input data.
Returns
-------
res : ndarray
Output array, containing the indices of the elements of ``a.ravel()``
that are non-zero.
See Also
--------
nonzero : Return the indices of the non-zero elements of the input array.
ravel : Return a 1-D array containing the elements of the input array.
Examples
--------
>>> x = np.arange(-2, 3)
>>> x
array([-2, -1, 0, 1, 2])
>>> np.flatnonzero(x)
array([0, 1, 3, 4])
Use the indices of the non-zero elements as an index array to extract
these elements:
>>> x.ravel()[np.flatnonzero(x)]
array([-2, -1, 1, 2])
"""
return np.nonzero(np.ravel(a))[0]
def _correlate_dispatcher(a, v, mode=None):
return (a, v)
@array_function_dispatch(_correlate_dispatcher)
def correlate(a, v, mode='valid'):
r"""
Cross-correlation of two 1-dimensional sequences.
This function computes the correlation as generally defined in signal
processing texts:
.. math:: c_k = \sum_n a_{n+k} \cdot \overline{v_n}
with a and v sequences being zero-padded where necessary and
:math:`\overline x` denoting complex conjugation.
Parameters
----------
a, v : array_like
Input sequences.
mode : {'valid', 'same', 'full'}, optional
Refer to the `convolve` docstring. Note that the default
is 'valid', unlike `convolve`, which uses 'full'.
old_behavior : bool
`old_behavior` was removed in NumPy 1.10. If you need the old
behavior, use `multiarray.correlate`.
Returns
-------
out : ndarray
Discrete cross-correlation of `a` and `v`.
See Also
--------
convolve : Discrete, linear convolution of two one-dimensional sequences.
multiarray.correlate : Old, no conjugate, version of correlate.
scipy.signal.correlate : uses FFT which has superior performance on large arrays.
Notes
-----
The definition of correlation above is not unique and sometimes correlation
may be defined differently. Another common definition is:
.. math:: c'_k = \sum_n a_{n} \cdot \overline{v_{n+k}}
which is related to :math:`c_k` by :math:`c'_k = c_{-k}`.
`numpy.correlate` may perform slowly in large arrays (i.e. n = 1e5) because it does
not use the FFT to compute the convolution; in that case, `scipy.signal.correlate` might
be preferable.
Examples
--------
>>> np.correlate([1, 2, 3], [0, 1, 0.5])
array([3.5])
>>> np.correlate([1, 2, 3], [0, 1, 0.5], "same")
array([2. , 3.5, 3. ])
>>> np.correlate([1, 2, 3], [0, 1, 0.5], "full")
array([0.5, 2. , 3.5, 3. , 0. ])
Using complex sequences:
>>> np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full')
array([ 0.5-0.5j, 1.0+0.j , 1.5-1.5j, 3.0-1.j , 0.0+0.j ])
Note that you get the time reversed, complex conjugated result
(:math:`\overline{c_{-k}}`) when the two input sequences a and v change
places:
>>> np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full')
array([ 0.0+0.j , 3.0+1.j , 1.5+1.5j, 1.0+0.j , 0.5+0.5j])
"""
return multiarray.correlate2(a, v, mode)
def _convolve_dispatcher(a, v, mode=None):
return (a, v)
@array_function_dispatch(_convolve_dispatcher)
def convolve(a, v, mode='full'):
"""
Returns the discrete, linear convolution of two one-dimensional sequences.
The convolution operator is often seen in signal processing, where it
models the effect of a linear time-invariant system on a signal [1]_. In
probability theory, the sum of two independent random variables is
distributed according to the convolution of their individual
distributions.
If `v` is longer than `a`, the arrays are swapped before computation.
Parameters
----------
a : (N,) array_like
First one-dimensional input array.
v : (M,) array_like
Second one-dimensional input array.
mode : {'full', 'valid', 'same'}, optional
'full':
By default, mode is 'full'. This returns the convolution
at each point of overlap, with an output shape of (N+M-1,). At
the end-points of the convolution, the signals do not overlap
completely, and boundary effects may be seen.
'same':
Mode 'same' returns output of length ``max(M, N)``. Boundary
effects are still visible.
'valid':
Mode 'valid' returns output of length
``max(M, N) - min(M, N) + 1``. The convolution product is only given
for points where the signals overlap completely. Values outside
the signal boundary have no effect.
Returns
-------
out : ndarray
Discrete, linear convolution of `a` and `v`.
See Also
--------
scipy.signal.fftconvolve : Convolve two arrays using the Fast Fourier
Transform.
scipy.linalg.toeplitz : Used to construct the convolution operator.
polymul : Polynomial multiplication. Same output as convolve, but also
accepts poly1d objects as input.
Notes
-----
The discrete convolution operation is defined as
.. math:: (a * v)_n = \\sum_{m = -\\infty}^{\\infty} a_m v_{n - m}
It can be shown that a convolution :math:`x(t) * y(t)` in time/space
is equivalent to the multiplication :math:`X(f) Y(f)` in the Fourier
domain, after appropriate padding (padding is necessary to prevent
circular convolution). Since multiplication is more efficient (faster)
than convolution, the function `scipy.signal.fftconvolve` exploits the
FFT to calculate the convolution of large data-sets.
References
----------
.. [1] Wikipedia, "Convolution",
https://en.wikipedia.org/wiki/Convolution
Examples
--------
Note how the convolution operator flips the second array
before "sliding" the two across one another:
>>> np.convolve([1, 2, 3], [0, 1, 0.5])
array([0. , 1. , 2.5, 4. , 1.5])
Only return the middle values of the convolution.
Contains boundary effects, where zeros are taken
into account:
>>> np.convolve([1,2,3],[0,1,0.5], 'same')
array([1. , 2.5, 4. ])
The two arrays are of the same length, so there
is only one position where they completely overlap:
>>> np.convolve([1,2,3],[0,1,0.5], 'valid')
array([2.5])
"""
a, v = array(a, copy=False, ndmin=1), array(v, copy=False, ndmin=1)
if (len(v) > len(a)):
a, v = v, a
if len(a) == 0:
raise ValueError('a cannot be empty')
if len(v) == 0:
raise ValueError('v cannot be empty')
return multiarray.correlate(a, v[::-1], mode)
def _outer_dispatcher(a, b, out=None):
return (a, b, out)
@array_function_dispatch(_outer_dispatcher)
def outer(a, b, out=None):
"""
Compute the outer product of two vectors.
Given two vectors, ``a = [a0, a1, ..., aM]`` and
``b = [b0, b1, ..., bN]``,
the outer product [1]_ is::
[[a0*b0 a0*b1 ... a0*bN ]
[a1*b0 .
[ ... .
[aM*b0 aM*bN ]]
Parameters
----------
a : (M,) array_like
First input vector. Input is flattened if
not already 1-dimensional.
b : (N,) array_like
Second input vector. Input is flattened if
not already 1-dimensional.
out : (M, N) ndarray, optional
A location where the result is stored
.. versionadded:: 1.9.0
Returns
-------
out : (M, N) ndarray
``out[i, j] = a[i] * b[j]``
See also
--------
inner
einsum : ``einsum('i,j->ij', a.ravel(), b.ravel())`` is the equivalent.
ufunc.outer : A generalization to dimensions other than 1D and other
operations. ``np.multiply.outer(a.ravel(), b.ravel())``
is the equivalent.
tensordot : ``np.tensordot(a.ravel(), b.ravel(), axes=((), ()))``
is the equivalent.
References
----------
.. [1] : G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd
ed., Baltimore, MD, Johns Hopkins University Press, 1996,
pg. 8.
Examples
--------
Make a (*very* coarse) grid for computing a Mandelbrot set:
>>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
>>> rl
array([[-2., -1., 0., 1., 2.],
[-2., -1., 0., 1., 2.],
[-2., -1., 0., 1., 2.],
[-2., -1., 0., 1., 2.],
[-2., -1., 0., 1., 2.]])
>>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))
>>> im
array([[0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j],
[0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j],
[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
[0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j],
[0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]])
>>> grid = rl + im
>>> grid
array([[-2.+2.j, -1.+2.j, 0.+2.j, 1.+2.j, 2.+2.j],
[-2.+1.j, -1.+1.j, 0.+1.j, 1.+1.j, 2.+1.j],
[-2.+0.j, -1.+0.j, 0.+0.j, 1.+0.j, 2.+0.j],
[-2.-1.j, -1.-1.j, 0.-1.j, 1.-1.j, 2.-1.j],
[-2.-2.j, -1.-2.j, 0.-2.j, 1.-2.j, 2.-2.j]])
An example using a "vector" of letters:
>>> x = np.array(['a', 'b', 'c'], dtype=object)
>>> np.outer(x, [1, 2, 3])
array([['a', 'aa', 'aaa'],
['b', 'bb', 'bbb'],
['c', 'cc', 'ccc']], dtype=object)
"""
a = asarray(a)
b = asarray(b)
return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis, :], out)
def _tensordot_dispatcher(a, b, axes=None):
return (a, b)
@array_function_dispatch(_tensordot_dispatcher)
def tensordot(a, b, axes=2):
"""
Compute tensor dot product along specified axes.
Given two tensors, `a` and `b`, and an array_like object containing
two array_like objects, ``(a_axes, b_axes)``, sum the products of
`a`'s and `b`'s elements (components) over the axes specified by
``a_axes`` and ``b_axes``. The third argument can be a single non-negative
integer_like scalar, ``N``; if it is such, then the last ``N`` dimensions
of `a` and the first ``N`` dimensions of `b` are summed over.
Parameters
----------
a, b : array_like
Tensors to "dot".
axes : int or (2,) array_like
* integer_like
If an int N, sum over the last N axes of `a` and the first N axes
of `b` in order. The sizes of the corresponding axes must match.
* (2,) array_like
Or, a list of axes to be summed over, first sequence applying to `a`,
second to `b`. Both elements array_like must be of the same length.
Returns
-------
output : ndarray
The tensor dot product of the input.
See Also
--------
dot, einsum
Notes
-----
Three common use cases are:
* ``axes = 0`` : tensor product :math:`a\\otimes b`
* ``axes = 1`` : tensor dot product :math:`a\\cdot b`
* ``axes = 2`` : (default) tensor double contraction :math:`a:b`
When `axes` is integer_like, the sequence for evaluation will be: first
the -Nth axis in `a` and 0th axis in `b`, and the -1th axis in `a` and
Nth axis in `b` last.
When there is more than one axis to sum over - and they are not the last
(first) axes of `a` (`b`) - the argument `axes` should consist of
two sequences of the same length, with the first axis to sum over given
first in both sequences, the second axis second, and so forth.
The shape of the result consists of the non-contracted axes of the
first tensor, followed by the non-contracted axes of the second.
Examples
--------
A "traditional" example:
>>> a = np.arange(60.).reshape(3,4,5)
>>> b = np.arange(24.).reshape(4,3,2)
>>> c = np.tensordot(a,b, axes=([1,0],[0,1]))
>>> c.shape
(5, 2)
>>> c
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
>>> # A slower but equivalent way of computing the same...
>>> d = np.zeros((5,2))
>>> for i in range(5):
... for j in range(2):
... for k in range(3):
... for n in range(4):
... d[i,j] += a[k,n,i] * b[n,k,j]
>>> c == d
array([[ True, True],
[ True, True],
[ True, True],
[ True, True],
[ True, True]])
An extended example taking advantage of the overloading of + and \\*:
>>> a = np.array(range(1, 9))
>>> a.shape = (2, 2, 2)
>>> A = np.array(('a', 'b', 'c', 'd'), dtype=object)
>>> A.shape = (2, 2)
>>> a; A
array([[[1, 2],
[3, 4]],
[[5, 6],
[7, 8]]])
array([['a', 'b'],
['c', 'd']], dtype=object)
>>> np.tensordot(a, A) # third argument default is 2 for double-contraction
array(['abbcccdddd', 'aaaaabbbbbbcccccccdddddddd'], dtype=object)
>>> np.tensordot(a, A, 1)
array([[['acc', 'bdd'],
['aaacccc', 'bbbdddd']],
[['aaaaacccccc', 'bbbbbdddddd'],
['aaaaaaacccccccc', 'bbbbbbbdddddddd']]], dtype=object)
>>> np.tensordot(a, A, 0) # tensor product (result too long to incl.)
array([[[[['a', 'b'],
['c', 'd']],
...
>>> np.tensordot(a, A, (0, 1))
array([[['abbbbb', 'cddddd'],
['aabbbbbb', 'ccdddddd']],
[['aaabbbbbbb', 'cccddddddd'],
['aaaabbbbbbbb', 'ccccdddddddd']]], dtype=object)
>>> np.tensordot(a, A, (2, 1))
array([[['abb', 'cdd'],
['aaabbbb', 'cccdddd']],
[['aaaaabbbbbb', 'cccccdddddd'],
['aaaaaaabbbbbbbb', 'cccccccdddddddd']]], dtype=object)
>>> np.tensordot(a, A, ((0, 1), (0, 1)))
array(['abbbcccccddddddd', 'aabbbbccccccdddddddd'], dtype=object)
>>> np.tensordot(a, A, ((2, 1), (1, 0)))
array(['acccbbdddd', 'aaaaacccccccbbbbbbdddddddd'], dtype=object)
"""
try:
iter(axes)
except Exception:
axes_a = list(range(-axes, 0))
axes_b = list(range(0, axes))
else:
axes_a, axes_b = axes
try:
na = len(axes_a)
axes_a = list(axes_a)
except TypeError:
axes_a = [axes_a]
na = 1
try:
nb = len(axes_b)
axes_b = list(axes_b)
except TypeError:
axes_b = [axes_b]
nb = 1
a, b = asarray(a), asarray(b)
as_ = a.shape
nda = a.ndim
bs = b.shape
ndb = b.ndim
equal = True
if na != nb:
equal = False
else:
for k in range(na):
if as_[axes_a[k]] != bs[axes_b[k]]:
equal = False
break
if axes_a[k] < 0:
axes_a[k] += nda
if axes_b[k] < 0:
axes_b[k] += ndb
if not equal:
raise ValueError("shape-mismatch for sum")
# Move the axes to sum over to the end of "a"
# and to the front of "b"
notin = [k for k in range(nda) if k not in axes_a]
newaxes_a = notin + axes_a
N2 = 1
for axis in axes_a:
N2 *= as_[axis]
newshape_a = (int(multiply.reduce([as_[ax] for ax in notin])), N2)
olda = [as_[axis] for axis in notin]
notin = [k for k in range(ndb) if k not in axes_b]
newaxes_b = axes_b + notin
N2 = 1
for axis in axes_b:
N2 *= bs[axis]
newshape_b = (N2, int(multiply.reduce([bs[ax] for ax in notin])))
oldb = [bs[axis] for axis in notin]
at = a.transpose(newaxes_a).reshape(newshape_a)
bt = b.transpose(newaxes_b).reshape(newshape_b)
res = dot(at, bt)
return res.reshape(olda + oldb)
def _roll_dispatcher(a, shift, axis=None):
return (a,)
@array_function_dispatch(_roll_dispatcher)
def roll(a, shift, axis=None):
"""
Roll array elements along a given axis.
Elements that roll beyond the last position are re-introduced at
the first.
Parameters
----------
a : array_like
Input array.
shift : int or tuple of ints
The number of places by which elements are shifted. If a tuple,
then `axis` must be a tuple of the same size, and each of the
given axes is shifted by the corresponding number. If an int
while `axis` is a tuple of ints, then the same value is used for
all given axes.
axis : int or tuple of ints, optional
Axis or axes along which elements are shifted. By default, the
array is flattened before shifting, after which the original
shape is restored.
Returns
-------
res : ndarray
Output array, with the same shape as `a`.
See Also
--------
rollaxis : Roll the specified axis backwards, until it lies in a
given position.
Notes
-----
.. versionadded:: 1.12.0
Supports rolling over multiple dimensions simultaneously.
Examples
--------
>>> x = np.arange(10)
>>> np.roll(x, 2)
array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])
>>> np.roll(x, -2)
array([2, 3, 4, 5, 6, 7, 8, 9, 0, 1])
>>> x2 = np.reshape(x, (2, 5))
>>> x2
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> np.roll(x2, 1)
array([[9, 0, 1, 2, 3],
[4, 5, 6, 7, 8]])
>>> np.roll(x2, -1)
array([[1, 2, 3, 4, 5],
[6, 7, 8, 9, 0]])
>>> np.roll(x2, 1, axis=0)
array([[5, 6, 7, 8, 9],
[0, 1, 2, 3, 4]])
>>> np.roll(x2, -1, axis=0)
array([[5, 6, 7, 8, 9],
[0, 1, 2, 3, 4]])
>>> np.roll(x2, 1, axis=1)
array([[4, 0, 1, 2, 3],
[9, 5, 6, 7, 8]])
>>> np.roll(x2, -1, axis=1)
array([[1, 2, 3, 4, 0],
[6, 7, 8, 9, 5]])
>>> np.roll(x2, (1, 1), axis=(1, 0))
array([[9, 5, 6, 7, 8],
[4, 0, 1, 2, 3]])
>>> np.roll(x2, (2, 1), axis=(1, 0))
array([[8, 9, 5, 6, 7],
[3, 4, 0, 1, 2]])
"""
a = asanyarray(a)
if axis is None:
return roll(a.ravel(), shift, 0).reshape(a.shape)
else:
axis = normalize_axis_tuple(axis, a.ndim, allow_duplicate=True)
broadcasted = broadcast(shift, axis)
if broadcasted.ndim > 1:
raise ValueError(
"'shift' and 'axis' should be scalars or 1D sequences")
shifts = {ax: 0 for ax in range(a.ndim)}
for sh, ax in broadcasted:
shifts[ax] += sh
rolls = [((slice(None), slice(None)),)] * a.ndim
for ax, offset in shifts.items():
offset %= a.shape[ax] or 1 # If `a` is empty, nothing matters.
if offset:
# (original, result), (original, result)
rolls[ax] = ((slice(None, -offset), slice(offset, None)),
(slice(-offset, None), slice(None, offset)))
result = empty_like(a)
for indices in itertools.product(*rolls):
arr_index, res_index = zip(*indices)
result[res_index] = a[arr_index]
return result
def _rollaxis_dispatcher(a, axis, start=None):
return (a,)
@array_function_dispatch(_rollaxis_dispatcher)
def rollaxis(a, axis, start=0):
"""
Roll the specified axis backwards, until it lies in a given position.
This function continues to be supported for backward compatibility, but you
should prefer `moveaxis`. The `moveaxis` function was added in NumPy
1.11.
Parameters
----------
a : ndarray
Input array.
axis : int
The axis to be rolled. The positions of the other axes do not
change relative to one another.
start : int, optional
When ``start <= axis``, the axis is rolled back until it lies in
this position. When ``start > axis``, the axis is rolled until it
lies before this position. The default, 0, results in a "complete"
roll. The following table describes how negative values of ``start``
are interpreted:
.. table::
:align: left
+-------------------+----------------------+
| ``start`` | Normalized ``start`` |
+===================+======================+
| ``-(arr.ndim+1)`` | raise ``AxisError`` |
+-------------------+----------------------+
| ``-arr.ndim`` | 0 |
+-------------------+----------------------+
| |vdots| | |vdots| |
+-------------------+----------------------+
| ``-1`` | ``arr.ndim-1`` |
+-------------------+----------------------+
| ``0`` | ``0`` |
+-------------------+----------------------+
| |vdots| | |vdots| |
+-------------------+----------------------+
| ``arr.ndim`` | ``arr.ndim`` |
+-------------------+----------------------+
| ``arr.ndim + 1`` | raise ``AxisError`` |
+-------------------+----------------------+
.. |vdots| unicode:: U+22EE .. Vertical Ellipsis
Returns
-------
res : ndarray
For NumPy >= 1.10.0 a view of `a` is always returned. For earlier
NumPy versions a view of `a` is returned only if the order of the
axes is changed, otherwise the input array is returned.
See Also
--------
moveaxis : Move array axes to new positions.
roll : Roll the elements of an array by a number of positions along a
given axis.
Examples
--------
>>> a = np.ones((3,4,5,6))
>>> np.rollaxis(a, 3, 1).shape
(3, 6, 4, 5)
>>> np.rollaxis(a, 2).shape
(5, 3, 4, 6)
>>> np.rollaxis(a, 1, 4).shape
(3, 5, 6, 4)
"""
n = a.ndim
axis = normalize_axis_index(axis, n)
if start < 0:
start += n
msg = "'%s' arg requires %d <= %s < %d, but %d was passed in"
if not (0 <= start < n + 1):
raise AxisError(msg % ('start', -n, 'start', n + 1, start))
if axis < start:
# it's been removed
start -= 1
if axis == start:
return a[...]
axes = list(range(0, n))
axes.remove(axis)
axes.insert(start, axis)
return a.transpose(axes)
def normalize_axis_tuple(axis, ndim, argname=None, allow_duplicate=False):
"""
Normalizes an axis argument into a tuple of non-negative integer axes.
This handles shorthands such as ``1`` and converts them to ``(1,)``,
as well as performing the handling of negative indices covered by
`normalize_axis_index`.
By default, this forbids axes from being specified multiple times.
Used internally by multi-axis-checking logic.
.. versionadded:: 1.13.0
Parameters
----------
axis : int, iterable of int
The un-normalized index or indices of the axis.
ndim : int
The number of dimensions of the array that `axis` should be normalized
against.
argname : str, optional
A prefix to put before the error message, typically the name of the
argument.
allow_duplicate : bool, optional
If False, the default, disallow an axis from being specified twice.
Returns
-------
normalized_axes : tuple of int
The normalized axis index, such that `0 <= normalized_axis < ndim`
Raises
------
AxisError
If any axis provided is out of range
ValueError
If an axis is repeated
See also
--------
normalize_axis_index : normalizing a single scalar axis
"""
# Optimization to speed-up the most common cases.
if type(axis) not in (tuple, list):
try:
axis = [operator.index(axis)]
except TypeError:
pass
# Going via an iterator directly is slower than via list comprehension.
axis = tuple([normalize_axis_index(ax, ndim, argname) for ax in axis])
if not allow_duplicate and len(set(axis)) != len(axis):
if argname:
raise ValueError('repeated axis in `{}` argument'.format(argname))
else:
raise ValueError('repeated axis')
return axis
def _moveaxis_dispatcher(a, source, destination):
return (a,)
@array_function_dispatch(_moveaxis_dispatcher)
def moveaxis(a, source, destination):
"""
Move axes of an array to new positions.
Other axes remain in their original order.
.. versionadded:: 1.11.0
Parameters
----------
a : np.ndarray
The array whose axes should be reordered.
source : int or sequence of int
Original positions of the axes to move. These must be unique.
destination : int or sequence of int
Destination positions for each of the original axes. These must also be
unique.
Returns
-------
result : np.ndarray
Array with moved axes. This array is a view of the input array.
See Also
--------
transpose : Permute the dimensions of an array.
swapaxes : Interchange two axes of an array.
Examples
--------
>>> x = np.zeros((3, 4, 5))
>>> np.moveaxis(x, 0, -1).shape
(4, 5, 3)
>>> np.moveaxis(x, -1, 0).shape
(5, 3, 4)
These all achieve the same result:
>>> np.transpose(x).shape
(5, 4, 3)
>>> np.swapaxes(x, 0, -1).shape
(5, 4, 3)
>>> np.moveaxis(x, [0, 1], [-1, -2]).shape
(5, 4, 3)
>>> np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape
(5, 4, 3)
"""
try:
# allow duck-array types if they define transpose
transpose = a.transpose
except AttributeError:
a = asarray(a)
transpose = a.transpose
source = normalize_axis_tuple(source, a.ndim, 'source')
destination = normalize_axis_tuple(destination, a.ndim, 'destination')
if len(source) != len(destination):
raise ValueError('`source` and `destination` arguments must have '
'the same number of elements')
order = [n for n in range(a.ndim) if n not in source]
for dest, src in sorted(zip(destination, source)):
order.insert(dest, src)
result = transpose(order)
return result
def _cross_dispatcher(a, b, axisa=None, axisb=None, axisc=None, axis=None):
return (a, b)
@array_function_dispatch(_cross_dispatcher)
def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None):
"""
Return the cross product of two (arrays of) vectors.
The cross product of `a` and `b` in :math:`R^3` is a vector perpendicular
to both `a` and `b`. If `a` and `b` are arrays of vectors, the vectors
are defined by the last axis of `a` and `b` by default, and these axes
can have dimensions 2 or 3. Where the dimension of either `a` or `b` is
2, the third component of the input vector is assumed to be zero and the
cross product calculated accordingly. In cases where both input vectors
have dimension 2, the z-component of the cross product is returned.
Parameters
----------
a : array_like
Components of the first vector(s).
b : array_like
Components of the second vector(s).
axisa : int, optional
Axis of `a` that defines the vector(s). By default, the last axis.
axisb : int, optional
Axis of `b` that defines the vector(s). By default, the last axis.
axisc : int, optional
Axis of `c` containing the cross product vector(s). Ignored if
both input vectors have dimension 2, as the return is scalar.
By default, the last axis.
axis : int, optional
If defined, the axis of `a`, `b` and `c` that defines the vector(s)
and cross product(s). Overrides `axisa`, `axisb` and `axisc`.
Returns
-------
c : ndarray
Vector cross product(s).
Raises
------
ValueError
When the dimension of the vector(s) in `a` and/or `b` does not
equal 2 or 3.
See Also
--------
inner : Inner product
outer : Outer product.
ix_ : Construct index arrays.
Notes
-----
.. versionadded:: 1.9.0
Supports full broadcasting of the inputs.
Examples
--------
Vector cross-product.
>>> x = [1, 2, 3]
>>> y = [4, 5, 6]
>>> np.cross(x, y)
array([-3, 6, -3])
One vector with dimension 2.
>>> x = [1, 2]
>>> y = [4, 5, 6]
>>> np.cross(x, y)
array([12, -6, -3])
Equivalently:
>>> x = [1, 2, 0]
>>> y = [4, 5, 6]
>>> np.cross(x, y)
array([12, -6, -3])
Both vectors with dimension 2.
>>> x = [1,2]
>>> y = [4,5]
>>> np.cross(x, y)
array(-3)
Multiple vector cross-products. Note that the direction of the cross
product vector is defined by the *right-hand rule*.
>>> x = np.array([[1,2,3], [4,5,6]])
>>> y = np.array([[4,5,6], [1,2,3]])
>>> np.cross(x, y)
array([[-3, 6, -3],
[ 3, -6, 3]])
The orientation of `c` can be changed using the `axisc` keyword.
>>> np.cross(x, y, axisc=0)
array([[-3, 3],
[ 6, -6],
[-3, 3]])
Change the vector definition of `x` and `y` using `axisa` and `axisb`.
>>> x = np.array([[1,2,3], [4,5,6], [7, 8, 9]])
>>> y = np.array([[7, 8, 9], [4,5,6], [1,2,3]])
>>> np.cross(x, y)
array([[ -6, 12, -6],
[ 0, 0, 0],
[ 6, -12, 6]])
>>> np.cross(x, y, axisa=0, axisb=0)
array([[-24, 48, -24],
[-30, 60, -30],
[-36, 72, -36]])
"""
if axis is not None:
axisa, axisb, axisc = (axis,) * 3
a = asarray(a)
b = asarray(b)
# Check axisa and axisb are within bounds
axisa = normalize_axis_index(axisa, a.ndim, msg_prefix='axisa')
axisb = normalize_axis_index(axisb, b.ndim, msg_prefix='axisb')
# Move working axis to the end of the shape
a = moveaxis(a, axisa, -1)
b = moveaxis(b, axisb, -1)
msg = ("incompatible dimensions for cross product\n"
"(dimension must be 2 or 3)")
if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3):
raise ValueError(msg)
# Create the output array
shape = broadcast(a[..., 0], b[..., 0]).shape
if a.shape[-1] == 3 or b.shape[-1] == 3:
shape += (3,)
# Check axisc is within bounds
axisc = normalize_axis_index(axisc, len(shape), msg_prefix='axisc')
dtype = promote_types(a.dtype, b.dtype)
cp = empty(shape, dtype)
# create local aliases for readability
a0 = a[..., 0]
a1 = a[..., 1]
if a.shape[-1] == 3:
a2 = a[..., 2]
b0 = b[..., 0]
b1 = b[..., 1]
if b.shape[-1] == 3:
b2 = b[..., 2]
if cp.ndim != 0 and cp.shape[-1] == 3:
cp0 = cp[..., 0]
cp1 = cp[..., 1]
cp2 = cp[..., 2]
if a.shape[-1] == 2:
if b.shape[-1] == 2:
# a0 * b1 - a1 * b0
multiply(a0, b1, out=cp)
cp -= a1 * b0
return cp
else:
assert b.shape[-1] == 3
# cp0 = a1 * b2 - 0 (a2 = 0)
# cp1 = 0 - a0 * b2 (a2 = 0)
# cp2 = a0 * b1 - a1 * b0
multiply(a1, b2, out=cp0)
multiply(a0, b2, out=cp1)
negative(cp1, out=cp1)
multiply(a0, b1, out=cp2)
cp2 -= a1 * b0
else:
assert a.shape[-1] == 3
if b.shape[-1] == 3:
# cp0 = a1 * b2 - a2 * b1
# cp1 = a2 * b0 - a0 * b2
# cp2 = a0 * b1 - a1 * b0
multiply(a1, b2, out=cp0)
tmp = array(a2 * b1)
cp0 -= tmp
multiply(a2, b0, out=cp1)
multiply(a0, b2, out=tmp)
cp1 -= tmp
multiply(a0, b1, out=cp2)
multiply(a1, b0, out=tmp)
cp2 -= tmp
else:
assert b.shape[-1] == 2
# cp0 = 0 - a2 * b1 (b2 = 0)
# cp1 = a2 * b0 - 0 (b2 = 0)
# cp2 = a0 * b1 - a1 * b0
multiply(a2, b1, out=cp0)
negative(cp0, out=cp0)
multiply(a2, b0, out=cp1)
multiply(a0, b1, out=cp2)
cp2 -= a1 * b0
return moveaxis(cp, -1, axisc)
little_endian = (sys.byteorder == 'little')
@set_module('numpy')
def indices(dimensions, dtype=int, sparse=False):
"""
Return an array representing the indices of a grid.
Compute an array where the subarrays contain index values 0, 1, ...
varying only along the corresponding axis.
Parameters
----------
dimensions : sequence of ints
The shape of the grid.
dtype : dtype, optional
Data type of the result.
sparse : boolean, optional
Return a sparse representation of the grid instead of a dense
representation. Default is False.
.. versionadded:: 1.17
Returns
-------
grid : one ndarray or tuple of ndarrays
If sparse is False:
Returns one array of grid indices,
``grid.shape = (len(dimensions),) + tuple(dimensions)``.
If sparse is True:
Returns a tuple of arrays, with
``grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1)`` with
dimensions[i] in the ith place
See Also
--------
mgrid, ogrid, meshgrid
Notes
-----
The output shape in the dense case is obtained by prepending the number
of dimensions in front of the tuple of dimensions, i.e. if `dimensions`
is a tuple ``(r0, ..., rN-1)`` of length ``N``, the output shape is
``(N, r0, ..., rN-1)``.
The subarrays ``grid[k]`` contains the N-D array of indices along the
``k-th`` axis. Explicitly::
grid[k, i0, i1, ..., iN-1] = ik
Examples
--------
>>> grid = np.indices((2, 3))
>>> grid.shape
(2, 2, 3)
>>> grid[0] # row indices
array([[0, 0, 0],
[1, 1, 1]])
>>> grid[1] # column indices
array([[0, 1, 2],
[0, 1, 2]])
The indices can be used as an index into an array.
>>> x = np.arange(20).reshape(5, 4)
>>> row, col = np.indices((2, 3))
>>> x[row, col]
array([[0, 1, 2],
[4, 5, 6]])
Note that it would be more straightforward in the above example to
extract the required elements directly with ``x[:2, :3]``.
If sparse is set to true, the grid will be returned in a sparse
representation.
>>> i, j = np.indices((2, 3), sparse=True)
>>> i.shape
(2, 1)
>>> j.shape
(1, 3)
>>> i # row indices
array([[0],
[1]])
>>> j # column indices
array([[0, 1, 2]])
"""
dimensions = tuple(dimensions)
N = len(dimensions)
shape = (1,)*N
if sparse:
res = tuple()
else:
res = empty((N,)+dimensions, dtype=dtype)
for i, dim in enumerate(dimensions):
idx = arange(dim, dtype=dtype).reshape(
shape[:i] + (dim,) + shape[i+1:]
)
if sparse:
res = res + (idx,)
else:
res[i] = idx
return res
def _fromfunction_dispatcher(function, shape, *, dtype=None, like=None, **kwargs):
return (like,)
@set_array_function_like_doc
@set_module('numpy')
def fromfunction(function, shape, *, dtype=float, like=None, **kwargs):
"""
Construct an array by executing a function over each coordinate.
The resulting array therefore has a value ``fn(x, y, z)`` at
coordinate ``(x, y, z)``.
Parameters
----------
function : callable
The function is called with N parameters, where N is the rank of
`shape`. Each parameter represents the coordinates of the array
varying along a specific axis. For example, if `shape`
were ``(2, 2)``, then the parameters would be
``array([[0, 0], [1, 1]])`` and ``array([[0, 1], [0, 1]])``
shape : (N,) tuple of ints
Shape of the output array, which also determines the shape of
the coordinate arrays passed to `function`.
dtype : data-type, optional
Data-type of the coordinate arrays passed to `function`.
By default, `dtype` is float.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
fromfunction : any
The result of the call to `function` is passed back directly.
Therefore the shape of `fromfunction` is completely determined by
`function`. If `function` returns a scalar value, the shape of
`fromfunction` would not match the `shape` parameter.
See Also
--------
indices, meshgrid
Notes
-----
Keywords other than `dtype` are passed to `function`.
Examples
--------
>>> np.fromfunction(lambda i, j: i, (2, 2), dtype=float)
array([[0., 0.],
[1., 1.]])
>>> np.fromfunction(lambda i, j: j, (2, 2), dtype=float)
array([[0., 1.],
[0., 1.]])
>>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
array([[ True, False, False],
[False, True, False],
[False, False, True]])
>>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)
array([[0, 1, 2],
[1, 2, 3],
[2, 3, 4]])
"""
if like is not None:
return _fromfunction_with_like(function, shape, dtype=dtype, like=like, **kwargs)
args = indices(shape, dtype=dtype)
return function(*args, **kwargs)
_fromfunction_with_like = array_function_dispatch(
_fromfunction_dispatcher
)(fromfunction)
def _frombuffer(buf, dtype, shape, order):
return frombuffer(buf, dtype=dtype).reshape(shape, order=order)
@set_module('numpy')
def isscalar(element):
"""
Returns True if the type of `element` is a scalar type.
Parameters
----------
element : any
Input argument, can be of any type and shape.
Returns
-------
val : bool
True if `element` is a scalar type, False if it is not.
See Also
--------
ndim : Get the number of dimensions of an array
Notes
-----
If you need a stricter way to identify a *numerical* scalar, use
``isinstance(x, numbers.Number)``, as that returns ``False`` for most
non-numerical elements such as strings.
In most cases ``np.ndim(x) == 0`` should be used instead of this function,
as that will also return true for 0d arrays. This is how numpy overloads
functions in the style of the ``dx`` arguments to `gradient` and the ``bins``
argument to `histogram`. Some key differences:
+--------------------------------------+---------------+-------------------+
| x |``isscalar(x)``|``np.ndim(x) == 0``|
+======================================+===============+===================+
| PEP 3141 numeric objects (including | ``True`` | ``True`` |
| builtins) | | |
+--------------------------------------+---------------+-------------------+
| builtin string and buffer objects | ``True`` | ``True`` |
+--------------------------------------+---------------+-------------------+
| other builtin objects, like | ``False`` | ``True`` |
| `pathlib.Path`, `Exception`, | | |
| the result of `re.compile` | | |
+--------------------------------------+---------------+-------------------+
| third-party objects like | ``False`` | ``True`` |
| `matplotlib.figure.Figure` | | |
+--------------------------------------+---------------+-------------------+
| zero-dimensional numpy arrays | ``False`` | ``True`` |
+--------------------------------------+---------------+-------------------+
| other numpy arrays | ``False`` | ``False`` |
+--------------------------------------+---------------+-------------------+
| `list`, `tuple`, and other sequence | ``False`` | ``False`` |
| objects | | |
+--------------------------------------+---------------+-------------------+
Examples
--------
>>> np.isscalar(3.1)
True
>>> np.isscalar(np.array(3.1))
False
>>> np.isscalar([3.1])
False
>>> np.isscalar(False)
True
>>> np.isscalar('numpy')
True
NumPy supports PEP 3141 numbers:
>>> from fractions import Fraction
>>> np.isscalar(Fraction(5, 17))
True
>>> from numbers import Number
>>> np.isscalar(Number())
True
"""
return (isinstance(element, generic)
or type(element) in ScalarType
or isinstance(element, numbers.Number))
@set_module('numpy')
def binary_repr(num, width=None):
"""
Return the binary representation of the input number as a string.
For negative numbers, if width is not given, a minus sign is added to the
front. If width is given, the two's complement of the number is
returned, with respect to that width.
In a two's-complement system negative numbers are represented by the two's
complement of the absolute value. This is the most common method of
representing signed integers on computers [1]_. A N-bit two's-complement
system can represent every integer in the range
:math:`-2^{N-1}` to :math:`+2^{N-1}-1`.
Parameters
----------
num : int
Only an integer decimal number can be used.
width : int, optional
The length of the returned string if `num` is positive, or the length
of the two's complement if `num` is negative, provided that `width` is
at least a sufficient number of bits for `num` to be represented in the
designated form.
If the `width` value is insufficient, it will be ignored, and `num` will
be returned in binary (`num` > 0) or two's complement (`num` < 0) form
with its width equal to the minimum number of bits needed to represent
the number in the designated form. This behavior is deprecated and will
later raise an error.
.. deprecated:: 1.12.0
Returns
-------
bin : str
Binary representation of `num` or two's complement of `num`.
See Also
--------
base_repr: Return a string representation of a number in the given base
system.
bin: Python's built-in binary representation generator of an integer.
Notes
-----
`binary_repr` is equivalent to using `base_repr` with base 2, but about 25x
faster.
References
----------
.. [1] Wikipedia, "Two's complement",
https://en.wikipedia.org/wiki/Two's_complement
Examples
--------
>>> np.binary_repr(3)
'11'
>>> np.binary_repr(-3)
'-11'
>>> np.binary_repr(3, width=4)
'0011'
The two's complement is returned when the input number is negative and
width is specified:
>>> np.binary_repr(-3, width=3)
'101'
>>> np.binary_repr(-3, width=5)
'11101'
"""
def warn_if_insufficient(width, binwidth):
if width is not None and width < binwidth:
warnings.warn(
"Insufficient bit width provided. This behavior "
"will raise an error in the future.", DeprecationWarning,
stacklevel=3)
# Ensure that num is a Python integer to avoid overflow or unwanted
# casts to floating point.
num = operator.index(num)
if num == 0:
return '0' * (width or 1)
elif num > 0:
binary = bin(num)[2:]
binwidth = len(binary)
outwidth = (binwidth if width is None
else max(binwidth, width))
warn_if_insufficient(width, binwidth)
return binary.zfill(outwidth)
else:
if width is None:
return '-' + bin(-num)[2:]
else:
poswidth = len(bin(-num)[2:])
# See gh-8679: remove extra digit
# for numbers at boundaries.
if 2**(poswidth - 1) == -num:
poswidth -= 1
twocomp = 2**(poswidth + 1) + num
binary = bin(twocomp)[2:]
binwidth = len(binary)
outwidth = max(binwidth, width)
warn_if_insufficient(width, binwidth)
return '1' * (outwidth - binwidth) + binary
@set_module('numpy')
def base_repr(number, base=2, padding=0):
"""
Return a string representation of a number in the given base system.
Parameters
----------
number : int
The value to convert. Positive and negative values are handled.
base : int, optional
Convert `number` to the `base` number system. The valid range is 2-36,
the default value is 2.
padding : int, optional
Number of zeros padded on the left. Default is 0 (no padding).
Returns
-------
out : str
String representation of `number` in `base` system.
See Also
--------
binary_repr : Faster version of `base_repr` for base 2.
Examples
--------
>>> np.base_repr(5)
'101'
>>> np.base_repr(6, 5)
'11'
>>> np.base_repr(7, base=5, padding=3)
'00012'
>>> np.base_repr(10, base=16)
'A'
>>> np.base_repr(32, base=16)
'20'
"""
digits = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'
if base > len(digits):
raise ValueError("Bases greater than 36 not handled in base_repr.")
elif base < 2:
raise ValueError("Bases less than 2 not handled in base_repr.")
num = abs(number)
res = []
while num:
res.append(digits[num % base])
num //= base
if padding:
res.append('0' * padding)
if number < 0:
res.append('-')
return ''.join(reversed(res or '0'))
# These are all essentially abbreviations
# These might wind up in a special abbreviations module
def _maketup(descr, val):
dt = dtype(descr)
# Place val in all scalar tuples:
fields = dt.fields
if fields is None:
return val
else:
res = [_maketup(fields[name][0], val) for name in dt.names]
return tuple(res)
def _identity_dispatcher(n, dtype=None, *, like=None):
return (like,)
@set_array_function_like_doc
@set_module('numpy')
def identity(n, dtype=None, *, like=None):
"""
Return the identity array.
The identity array is a square array with ones on
the main diagonal.
Parameters
----------
n : int
Number of rows (and columns) in `n` x `n` output.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
`n` x `n` array with its main diagonal set to one,
and all other elements 0.
Examples
--------
>>> np.identity(3)
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
"""
if like is not None:
return _identity_with_like(n, dtype=dtype, like=like)
from numpy import eye
return eye(n, dtype=dtype, like=like)
_identity_with_like = array_function_dispatch(
_identity_dispatcher
)(identity)
def _allclose_dispatcher(a, b, rtol=None, atol=None, equal_nan=None):
return (a, b)
@array_function_dispatch(_allclose_dispatcher)
def allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
"""
Returns True if two arrays are element-wise equal within a tolerance.
The tolerance values are positive, typically very small numbers. The
relative difference (`rtol` * abs(`b`)) and the absolute difference
`atol` are added together to compare against the absolute difference
between `a` and `b`.
NaNs are treated as equal if they are in the same place and if
``equal_nan=True``. Infs are treated as equal if they are in the same
place and of the same sign in both arrays.
Parameters
----------
a, b : array_like
Input arrays to compare.
rtol : float
The relative tolerance parameter (see Notes).
atol : float
The absolute tolerance parameter (see Notes).
equal_nan : bool
Whether to compare NaN's as equal. If True, NaN's in `a` will be
considered equal to NaN's in `b` in the output array.
.. versionadded:: 1.10.0
Returns
-------
allclose : bool
Returns True if the two arrays are equal within the given
tolerance; False otherwise.
See Also
--------
isclose, all, any, equal
Notes
-----
If the following equation is element-wise True, then allclose returns
True.
absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
The above equation is not symmetric in `a` and `b`, so that
``allclose(a, b)`` might be different from ``allclose(b, a)`` in
some rare cases.
The comparison of `a` and `b` uses standard broadcasting, which
means that `a` and `b` need not have the same shape in order for
``allclose(a, b)`` to evaluate to True. The same is true for
`equal` but not `array_equal`.
`allclose` is not defined for non-numeric data types.
`bool` is considered a numeric data-type for this purpose.
Examples
--------
>>> np.allclose([1e10,1e-7], [1.00001e10,1e-8])
False
>>> np.allclose([1e10,1e-8], [1.00001e10,1e-9])
True
>>> np.allclose([1e10,1e-8], [1.0001e10,1e-9])
False
>>> np.allclose([1.0, np.nan], [1.0, np.nan])
False
>>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
True
"""
res = all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))
return bool(res)
def _isclose_dispatcher(a, b, rtol=None, atol=None, equal_nan=None):
return (a, b)
@array_function_dispatch(_isclose_dispatcher)
def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
"""
Returns a boolean array where two arrays are element-wise equal within a
tolerance.
The tolerance values are positive, typically very small numbers. The
relative difference (`rtol` * abs(`b`)) and the absolute difference
`atol` are added together to compare against the absolute difference
between `a` and `b`.
.. warning:: The default `atol` is not appropriate for comparing numbers
that are much smaller than one (see Notes).
Parameters
----------
a, b : array_like
Input arrays to compare.
rtol : float
The relative tolerance parameter (see Notes).
atol : float
The absolute tolerance parameter (see Notes).
equal_nan : bool
Whether to compare NaN's as equal. If True, NaN's in `a` will be
considered equal to NaN's in `b` in the output array.
Returns
-------
y : array_like
Returns a boolean array of where `a` and `b` are equal within the
given tolerance. If both `a` and `b` are scalars, returns a single
boolean value.
See Also
--------
allclose
math.isclose
Notes
-----
.. versionadded:: 1.7.0
For finite values, isclose uses the following equation to test whether
two floating point values are equivalent.
absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
Unlike the built-in `math.isclose`, the above equation is not symmetric
in `a` and `b` -- it assumes `b` is the reference value -- so that
`isclose(a, b)` might be different from `isclose(b, a)`. Furthermore,
the default value of atol is not zero, and is used to determine what
small values should be considered close to zero. The default value is
appropriate for expected values of order unity: if the expected values
are significantly smaller than one, it can result in false positives.
`atol` should be carefully selected for the use case at hand. A zero value
for `atol` will result in `False` if either `a` or `b` is zero.
`isclose` is not defined for non-numeric data types.
`bool` is considered a numeric data-type for this purpose.
Examples
--------
>>> np.isclose([1e10,1e-7], [1.00001e10,1e-8])
array([ True, False])
>>> np.isclose([1e10,1e-8], [1.00001e10,1e-9])
array([ True, True])
>>> np.isclose([1e10,1e-8], [1.0001e10,1e-9])
array([False, True])
>>> np.isclose([1.0, np.nan], [1.0, np.nan])
array([ True, False])
>>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
array([ True, True])
>>> np.isclose([1e-8, 1e-7], [0.0, 0.0])
array([ True, False])
>>> np.isclose([1e-100, 1e-7], [0.0, 0.0], atol=0.0)
array([False, False])
>>> np.isclose([1e-10, 1e-10], [1e-20, 0.0])
array([ True, True])
>>> np.isclose([1e-10, 1e-10], [1e-20, 0.999999e-10], atol=0.0)
array([False, True])
"""
def within_tol(x, y, atol, rtol):
with errstate(invalid='ignore'):
return less_equal(abs(x-y), atol + rtol * abs(y))
x = asanyarray(a)
y = asanyarray(b)
# Make sure y is an inexact type to avoid bad behavior on abs(MIN_INT).
# This will cause casting of x later. Also, make sure to allow subclasses
# (e.g., for numpy.ma).
# NOTE: We explicitly allow timedelta, which used to work. This could
# possibly be deprecated. See also gh-18286.
# timedelta works if `atol` is an integer or also a timedelta.
# Although, the default tolerances are unlikely to be useful
if y.dtype.kind != "m":
dt = multiarray.result_type(y, 1.)
y = asanyarray(y, dtype=dt)
xfin = isfinite(x)
yfin = isfinite(y)
if all(xfin) and all(yfin):
return within_tol(x, y, atol, rtol)
else:
finite = xfin & yfin
cond = zeros_like(finite, subok=True)
# Because we're using boolean indexing, x & y must be the same shape.
# Ideally, we'd just do x, y = broadcast_arrays(x, y). It's in
# lib.stride_tricks, though, so we can't import it here.
x = x * ones_like(cond)
y = y * ones_like(cond)
# Avoid subtraction with infinite/nan values...
cond[finite] = within_tol(x[finite], y[finite], atol, rtol)
# Check for equality of infinite values...
cond[~finite] = (x[~finite] == y[~finite])
if equal_nan:
# Make NaN == NaN
both_nan = isnan(x) & isnan(y)
# Needed to treat masked arrays correctly. = True would not work.
cond[both_nan] = both_nan[both_nan]
return cond[()] # Flatten 0d arrays to scalars
def _array_equal_dispatcher(a1, a2, equal_nan=None):
return (a1, a2)
@array_function_dispatch(_array_equal_dispatcher)
def array_equal(a1, a2, equal_nan=False):
"""
True if two arrays have the same shape and elements, False otherwise.
Parameters
----------
a1, a2 : array_like
Input arrays.
equal_nan : bool
Whether to compare NaN's as equal. If the dtype of a1 and a2 is
complex, values will be considered equal if either the real or the
imaginary component of a given value is ``nan``.
.. versionadded:: 1.19.0
Returns
-------
b : bool
Returns True if the arrays are equal.
See Also
--------
allclose: Returns True if two arrays are element-wise equal within a
tolerance.
array_equiv: Returns True if input arrays are shape consistent and all
elements equal.
Examples
--------
>>> np.array_equal([1, 2], [1, 2])
True
>>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
True
>>> np.array_equal([1, 2], [1, 2, 3])
False
>>> np.array_equal([1, 2], [1, 4])
False
>>> a = np.array([1, np.nan])
>>> np.array_equal(a, a)
False
>>> np.array_equal(a, a, equal_nan=True)
True
When ``equal_nan`` is True, complex values with nan components are
considered equal if either the real *or* the imaginary components are nan.
>>> a = np.array([1 + 1j])
>>> b = a.copy()
>>> a.real = np.nan
>>> b.imag = np.nan
>>> np.array_equal(a, b, equal_nan=True)
True
"""
try:
a1, a2 = asarray(a1), asarray(a2)
except Exception:
return False
if a1.shape != a2.shape:
return False
if not equal_nan:
return bool(asarray(a1 == a2).all())
# Handling NaN values if equal_nan is True
a1nan, a2nan = isnan(a1), isnan(a2)
# NaN's occur at different locations
if not (a1nan == a2nan).all():
return False
# Shapes of a1, a2 and masks are guaranteed to be consistent by this point
return bool(asarray(a1[~a1nan] == a2[~a1nan]).all())
def _array_equiv_dispatcher(a1, a2):
return (a1, a2)
@array_function_dispatch(_array_equiv_dispatcher)
def array_equiv(a1, a2):
"""
Returns True if input arrays are shape consistent and all elements equal.
Shape consistent means they are either the same shape, or one input array
can be broadcasted to create the same shape as the other one.
Parameters
----------
a1, a2 : array_like
Input arrays.
Returns
-------
out : bool
True if equivalent, False otherwise.
Examples
--------
>>> np.array_equiv([1, 2], [1, 2])
True
>>> np.array_equiv([1, 2], [1, 3])
False
Showing the shape equivalence:
>>> np.array_equiv([1, 2], [[1, 2], [1, 2]])
True
>>> np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]])
False
>>> np.array_equiv([1, 2], [[1, 2], [1, 3]])
False
"""
try:
a1, a2 = asarray(a1), asarray(a2)
except Exception:
return False
try:
multiarray.broadcast(a1, a2)
except Exception:
return False
return bool(asarray(a1 == a2).all())
Inf = inf = infty = Infinity = PINF
nan = NaN = NAN
False_ = bool_(False)
True_ = bool_(True)
def extend_all(module):
existing = set(__all__)
mall = getattr(module, '__all__')
for a in mall:
if a not in existing:
__all__.append(a)
from .umath import *
from .numerictypes import *
from . import fromnumeric
from .fromnumeric import *
from . import arrayprint
from .arrayprint import *
from . import _asarray
from ._asarray import *
from . import _ufunc_config
from ._ufunc_config import *
extend_all(fromnumeric)
extend_all(umath)
extend_all(numerictypes)
extend_all(arrayprint)
extend_all(_asarray)
extend_all(_ufunc_config)
| 77,444 | Python | 29.311155 | 92 | 0.55617 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/getlimits.pyi | from numpy import (
finfo as finfo,
iinfo as iinfo,
)
__all__: list[str]
| 82 | unknown | 10.857141 | 19 | 0.597561 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/function_base.pyi | from typing import (
Literal as L,
overload,
Any,
SupportsIndex,
TypeVar,
)
from numpy import floating, complexfloating, generic
from numpy._typing import (
NDArray,
DTypeLike,
_DTypeLike,
_ArrayLikeFloat_co,
_ArrayLikeComplex_co,
)
_SCT = TypeVar("_SCT", bound=generic)
__all__: list[str]
@overload
def linspace(
start: _ArrayLikeFloat_co,
stop: _ArrayLikeFloat_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
retstep: L[False] = ...,
dtype: None = ...,
axis: SupportsIndex = ...,
) -> NDArray[floating[Any]]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
retstep: L[False] = ...,
dtype: None = ...,
axis: SupportsIndex = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
retstep: L[False] = ...,
dtype: _DTypeLike[_SCT] = ...,
axis: SupportsIndex = ...,
) -> NDArray[_SCT]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
retstep: L[False] = ...,
dtype: DTypeLike = ...,
axis: SupportsIndex = ...,
) -> NDArray[Any]: ...
@overload
def linspace(
start: _ArrayLikeFloat_co,
stop: _ArrayLikeFloat_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
retstep: L[True] = ...,
dtype: None = ...,
axis: SupportsIndex = ...,
) -> tuple[NDArray[floating[Any]], floating[Any]]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
retstep: L[True] = ...,
dtype: None = ...,
axis: SupportsIndex = ...,
) -> tuple[NDArray[complexfloating[Any, Any]], complexfloating[Any, Any]]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
retstep: L[True] = ...,
dtype: _DTypeLike[_SCT] = ...,
axis: SupportsIndex = ...,
) -> tuple[NDArray[_SCT], _SCT]: ...
@overload
def linspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
retstep: L[True] = ...,
dtype: DTypeLike = ...,
axis: SupportsIndex = ...,
) -> tuple[NDArray[Any], Any]: ...
@overload
def logspace(
start: _ArrayLikeFloat_co,
stop: _ArrayLikeFloat_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
base: _ArrayLikeFloat_co = ...,
dtype: None = ...,
axis: SupportsIndex = ...,
) -> NDArray[floating[Any]]: ...
@overload
def logspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
base: _ArrayLikeComplex_co = ...,
dtype: None = ...,
axis: SupportsIndex = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def logspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
base: _ArrayLikeComplex_co = ...,
dtype: _DTypeLike[_SCT] = ...,
axis: SupportsIndex = ...,
) -> NDArray[_SCT]: ...
@overload
def logspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
base: _ArrayLikeComplex_co = ...,
dtype: DTypeLike = ...,
axis: SupportsIndex = ...,
) -> NDArray[Any]: ...
@overload
def geomspace(
start: _ArrayLikeFloat_co,
stop: _ArrayLikeFloat_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
dtype: None = ...,
axis: SupportsIndex = ...,
) -> NDArray[floating[Any]]: ...
@overload
def geomspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
dtype: None = ...,
axis: SupportsIndex = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def geomspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
dtype: _DTypeLike[_SCT] = ...,
axis: SupportsIndex = ...,
) -> NDArray[_SCT]: ...
@overload
def geomspace(
start: _ArrayLikeComplex_co,
stop: _ArrayLikeComplex_co,
num: SupportsIndex = ...,
endpoint: bool = ...,
dtype: DTypeLike = ...,
axis: SupportsIndex = ...,
) -> NDArray[Any]: ...
# Re-exported to `np.lib.function_base`
def add_newdoc(
place: str,
obj: str,
doc: str | tuple[str, str] | list[tuple[str, str]],
warn_on_python: bool = ...,
) -> None: ...
| 4,725 | unknown | 24.138298 | 78 | 0.584127 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_asarray.pyi | from collections.abc import Iterable
from typing import TypeVar, Union, overload, Literal
from numpy import ndarray
from numpy._typing import DTypeLike, _SupportsArrayFunc
_ArrayType = TypeVar("_ArrayType", bound=ndarray)
_Requirements = Literal[
"C", "C_CONTIGUOUS", "CONTIGUOUS",
"F", "F_CONTIGUOUS", "FORTRAN",
"A", "ALIGNED",
"W", "WRITEABLE",
"O", "OWNDATA"
]
_E = Literal["E", "ENSUREARRAY"]
_RequirementsWithE = Union[_Requirements, _E]
@overload
def require(
a: _ArrayType,
dtype: None = ...,
requirements: None | _Requirements | Iterable[_Requirements] = ...,
*,
like: _SupportsArrayFunc = ...
) -> _ArrayType: ...
@overload
def require(
a: object,
dtype: DTypeLike = ...,
requirements: _E | Iterable[_RequirementsWithE] = ...,
*,
like: _SupportsArrayFunc = ...
) -> ndarray: ...
@overload
def require(
a: object,
dtype: DTypeLike = ...,
requirements: None | _Requirements | Iterable[_Requirements] = ...,
*,
like: _SupportsArrayFunc = ...
) -> ndarray: ...
| 1,051 | unknown | 23.465116 | 71 | 0.625119 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/getlimits.py | """Machine limits for Float32 and Float64 and (long double) if available...
"""
__all__ = ['finfo', 'iinfo']
import warnings
from ._machar import MachAr
from .overrides import set_module
from . import numeric
from . import numerictypes as ntypes
from .numeric import array, inf, NaN
from .umath import log10, exp2, nextafter, isnan
def _fr0(a):
"""fix rank-0 --> rank-1"""
if a.ndim == 0:
a = a.copy()
a.shape = (1,)
return a
def _fr1(a):
"""fix rank > 0 --> rank-0"""
if a.size == 1:
a = a.copy()
a.shape = ()
return a
class MachArLike:
""" Object to simulate MachAr instance """
def __init__(self, ftype, *, eps, epsneg, huge, tiny,
ibeta, smallest_subnormal=None, **kwargs):
self.params = _MACHAR_PARAMS[ftype]
self.ftype = ftype
self.title = self.params['title']
# Parameter types same as for discovered MachAr object.
if not smallest_subnormal:
self._smallest_subnormal = nextafter(
self.ftype(0), self.ftype(1), dtype=self.ftype)
else:
self._smallest_subnormal = smallest_subnormal
self.epsilon = self.eps = self._float_to_float(eps)
self.epsneg = self._float_to_float(epsneg)
self.xmax = self.huge = self._float_to_float(huge)
self.xmin = self._float_to_float(tiny)
self.smallest_normal = self.tiny = self._float_to_float(tiny)
self.ibeta = self.params['itype'](ibeta)
self.__dict__.update(kwargs)
self.precision = int(-log10(self.eps))
self.resolution = self._float_to_float(
self._float_conv(10) ** (-self.precision))
self._str_eps = self._float_to_str(self.eps)
self._str_epsneg = self._float_to_str(self.epsneg)
self._str_xmin = self._float_to_str(self.xmin)
self._str_xmax = self._float_to_str(self.xmax)
self._str_resolution = self._float_to_str(self.resolution)
self._str_smallest_normal = self._float_to_str(self.xmin)
@property
def smallest_subnormal(self):
"""Return the value for the smallest subnormal.
Returns
-------
smallest_subnormal : float
value for the smallest subnormal.
Warns
-----
UserWarning
If the calculated value for the smallest subnormal is zero.
"""
# Check that the calculated value is not zero, in case it raises a
# warning.
value = self._smallest_subnormal
if self.ftype(0) == value:
warnings.warn(
'The value of the smallest subnormal for {} type '
'is zero.'.format(self.ftype), UserWarning, stacklevel=2)
return self._float_to_float(value)
@property
def _str_smallest_subnormal(self):
"""Return the string representation of the smallest subnormal."""
return self._float_to_str(self.smallest_subnormal)
def _float_to_float(self, value):
"""Converts float to float.
Parameters
----------
value : float
value to be converted.
"""
return _fr1(self._float_conv(value))
def _float_conv(self, value):
"""Converts float to conv.
Parameters
----------
value : float
value to be converted.
"""
return array([value], self.ftype)
def _float_to_str(self, value):
"""Converts float to str.
Parameters
----------
value : float
value to be converted.
"""
return self.params['fmt'] % array(_fr0(value)[0], self.ftype)
_convert_to_float = {
ntypes.csingle: ntypes.single,
ntypes.complex_: ntypes.float_,
ntypes.clongfloat: ntypes.longfloat
}
# Parameters for creating MachAr / MachAr-like objects
_title_fmt = 'numpy {} precision floating point number'
_MACHAR_PARAMS = {
ntypes.double: dict(
itype = ntypes.int64,
fmt = '%24.16e',
title = _title_fmt.format('double')),
ntypes.single: dict(
itype = ntypes.int32,
fmt = '%15.7e',
title = _title_fmt.format('single')),
ntypes.longdouble: dict(
itype = ntypes.longlong,
fmt = '%s',
title = _title_fmt.format('long double')),
ntypes.half: dict(
itype = ntypes.int16,
fmt = '%12.5e',
title = _title_fmt.format('half'))}
# Key to identify the floating point type. Key is result of
# ftype('-0.1').newbyteorder('<').tobytes()
# See:
# https://perl5.git.perl.org/perl.git/blob/3118d7d684b56cbeb702af874f4326683c45f045:/Configure
_KNOWN_TYPES = {}
def _register_type(machar, bytepat):
_KNOWN_TYPES[bytepat] = machar
_float_ma = {}
def _register_known_types():
# Known parameters for float16
# See docstring of MachAr class for description of parameters.
f16 = ntypes.float16
float16_ma = MachArLike(f16,
machep=-10,
negep=-11,
minexp=-14,
maxexp=16,
it=10,
iexp=5,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(f16(-10)),
epsneg=exp2(f16(-11)),
huge=f16(65504),
tiny=f16(2 ** -14))
_register_type(float16_ma, b'f\xae')
_float_ma[16] = float16_ma
# Known parameters for float32
f32 = ntypes.float32
float32_ma = MachArLike(f32,
machep=-23,
negep=-24,
minexp=-126,
maxexp=128,
it=23,
iexp=8,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(f32(-23)),
epsneg=exp2(f32(-24)),
huge=f32((1 - 2 ** -24) * 2**128),
tiny=exp2(f32(-126)))
_register_type(float32_ma, b'\xcd\xcc\xcc\xbd')
_float_ma[32] = float32_ma
# Known parameters for float64
f64 = ntypes.float64
epsneg_f64 = 2.0 ** -53.0
tiny_f64 = 2.0 ** -1022.0
float64_ma = MachArLike(f64,
machep=-52,
negep=-53,
minexp=-1022,
maxexp=1024,
it=52,
iexp=11,
ibeta=2,
irnd=5,
ngrd=0,
eps=2.0 ** -52.0,
epsneg=epsneg_f64,
huge=(1.0 - epsneg_f64) / tiny_f64 * f64(4),
tiny=tiny_f64)
_register_type(float64_ma, b'\x9a\x99\x99\x99\x99\x99\xb9\xbf')
_float_ma[64] = float64_ma
# Known parameters for IEEE 754 128-bit binary float
ld = ntypes.longdouble
epsneg_f128 = exp2(ld(-113))
tiny_f128 = exp2(ld(-16382))
# Ignore runtime error when this is not f128
with numeric.errstate(all='ignore'):
huge_f128 = (ld(1) - epsneg_f128) / tiny_f128 * ld(4)
float128_ma = MachArLike(ld,
machep=-112,
negep=-113,
minexp=-16382,
maxexp=16384,
it=112,
iexp=15,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(ld(-112)),
epsneg=epsneg_f128,
huge=huge_f128,
tiny=tiny_f128)
# IEEE 754 128-bit binary float
_register_type(float128_ma,
b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
_register_type(float128_ma,
b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
_float_ma[128] = float128_ma
# Known parameters for float80 (Intel 80-bit extended precision)
epsneg_f80 = exp2(ld(-64))
tiny_f80 = exp2(ld(-16382))
# Ignore runtime error when this is not f80
with numeric.errstate(all='ignore'):
huge_f80 = (ld(1) - epsneg_f80) / tiny_f80 * ld(4)
float80_ma = MachArLike(ld,
machep=-63,
negep=-64,
minexp=-16382,
maxexp=16384,
it=63,
iexp=15,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(ld(-63)),
epsneg=epsneg_f80,
huge=huge_f80,
tiny=tiny_f80)
# float80, first 10 bytes containing actual storage
_register_type(float80_ma, b'\xcd\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf')
_float_ma[80] = float80_ma
# Guessed / known parameters for double double; see:
# https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format#Double-double_arithmetic
# These numbers have the same exponent range as float64, but extended number of
# digits in the significand.
huge_dd = nextafter(ld(inf), ld(0), dtype=ld)
# As the smallest_normal in double double is so hard to calculate we set
# it to NaN.
smallest_normal_dd = NaN
# Leave the same value for the smallest subnormal as double
smallest_subnormal_dd = ld(nextafter(0., 1.))
float_dd_ma = MachArLike(ld,
machep=-105,
negep=-106,
minexp=-1022,
maxexp=1024,
it=105,
iexp=11,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(ld(-105)),
epsneg=exp2(ld(-106)),
huge=huge_dd,
tiny=smallest_normal_dd,
smallest_subnormal=smallest_subnormal_dd)
# double double; low, high order (e.g. PPC 64)
_register_type(float_dd_ma,
b'\x9a\x99\x99\x99\x99\x99Y<\x9a\x99\x99\x99\x99\x99\xb9\xbf')
# double double; high, low order (e.g. PPC 64 le)
_register_type(float_dd_ma,
b'\x9a\x99\x99\x99\x99\x99\xb9\xbf\x9a\x99\x99\x99\x99\x99Y<')
_float_ma['dd'] = float_dd_ma
def _get_machar(ftype):
""" Get MachAr instance or MachAr-like instance
Get parameters for floating point type, by first trying signatures of
various known floating point types, then, if none match, attempting to
identify parameters by analysis.
Parameters
----------
ftype : class
Numpy floating point type class (e.g. ``np.float64``)
Returns
-------
ma_like : instance of :class:`MachAr` or :class:`MachArLike`
Object giving floating point parameters for `ftype`.
Warns
-----
UserWarning
If the binary signature of the float type is not in the dictionary of
known float types.
"""
params = _MACHAR_PARAMS.get(ftype)
if params is None:
raise ValueError(repr(ftype))
# Detect known / suspected types
key = ftype('-0.1').newbyteorder('<').tobytes()
ma_like = None
if ftype == ntypes.longdouble:
# Could be 80 bit == 10 byte extended precision, where last bytes can
# be random garbage.
# Comparing first 10 bytes to pattern first to avoid branching on the
# random garbage.
ma_like = _KNOWN_TYPES.get(key[:10])
if ma_like is None:
ma_like = _KNOWN_TYPES.get(key)
if ma_like is not None:
return ma_like
# Fall back to parameter discovery
warnings.warn(
f'Signature {key} for {ftype} does not match any known type: '
'falling back to type probe function.\n'
'This warnings indicates broken support for the dtype!',
UserWarning, stacklevel=2)
return _discovered_machar(ftype)
def _discovered_machar(ftype):
""" Create MachAr instance with found information on float types
"""
params = _MACHAR_PARAMS[ftype]
return MachAr(lambda v: array([v], ftype),
lambda v:_fr0(v.astype(params['itype']))[0],
lambda v:array(_fr0(v)[0], ftype),
lambda v: params['fmt'] % array(_fr0(v)[0], ftype),
params['title'])
@set_module('numpy')
class finfo:
"""
finfo(dtype)
Machine limits for floating point types.
Attributes
----------
bits : int
The number of bits occupied by the type.
eps : float
The difference between 1.0 and the next smallest representable float
larger than 1.0. For example, for 64-bit binary floats in the IEEE-754
standard, ``eps = 2**-52``, approximately 2.22e-16.
epsneg : float
The difference between 1.0 and the next smallest representable float
less than 1.0. For example, for 64-bit binary floats in the IEEE-754
standard, ``epsneg = 2**-53``, approximately 1.11e-16.
iexp : int
The number of bits in the exponent portion of the floating point
representation.
machar : MachAr
The object which calculated these parameters and holds more
detailed information.
.. deprecated:: 1.22
machep : int
The exponent that yields `eps`.
max : floating point number of the appropriate type
The largest representable number.
maxexp : int
The smallest positive power of the base (2) that causes overflow.
min : floating point number of the appropriate type
The smallest representable number, typically ``-max``.
minexp : int
The most negative power of the base (2) consistent with there
being no leading 0's in the mantissa.
negep : int
The exponent that yields `epsneg`.
nexp : int
The number of bits in the exponent including its sign and bias.
nmant : int
The number of bits in the mantissa.
precision : int
The approximate number of decimal digits to which this kind of
float is precise.
resolution : floating point number of the appropriate type
The approximate decimal resolution of this type, i.e.,
``10**-precision``.
tiny : float
An alias for `smallest_normal`, kept for backwards compatibility.
smallest_normal : float
The smallest positive floating point number with 1 as leading bit in
the mantissa following IEEE-754 (see Notes).
smallest_subnormal : float
The smallest positive floating point number with 0 as leading bit in
the mantissa following IEEE-754.
Parameters
----------
dtype : float, dtype, or instance
Kind of floating point data-type about which to get information.
See Also
--------
MachAr : The implementation of the tests that produce this information.
iinfo : The equivalent for integer data types.
spacing : The distance between a value and the nearest adjacent number
nextafter : The next floating point value after x1 towards x2
Notes
-----
For developers of NumPy: do not instantiate this at the module level.
The initial calculation of these parameters is expensive and negatively
impacts import times. These objects are cached, so calling ``finfo()``
repeatedly inside your functions is not a problem.
Note that ``smallest_normal`` is not actually the smallest positive
representable value in a NumPy floating point type. As in the IEEE-754
standard [1]_, NumPy floating point types make use of subnormal numbers to
fill the gap between 0 and ``smallest_normal``. However, subnormal numbers
may have significantly reduced precision [2]_.
References
----------
.. [1] IEEE Standard for Floating-Point Arithmetic, IEEE Std 754-2008,
pp.1-70, 2008, http://www.doi.org/10.1109/IEEESTD.2008.4610935
.. [2] Wikipedia, "Denormal Numbers",
https://en.wikipedia.org/wiki/Denormal_number
"""
_finfo_cache = {}
def __new__(cls, dtype):
try:
dtype = numeric.dtype(dtype)
except TypeError:
# In case a float instance was given
dtype = numeric.dtype(type(dtype))
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
dtypes = [dtype]
newdtype = numeric.obj2sctype(dtype)
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
if not issubclass(dtype, numeric.inexact):
raise ValueError("data type %r not inexact" % (dtype))
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
if not issubclass(dtype, numeric.floating):
newdtype = _convert_to_float[dtype]
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
obj = object.__new__(cls)._init(dtype)
for dt in dtypes:
cls._finfo_cache[dt] = obj
return obj
def _init(self, dtype):
self.dtype = numeric.dtype(dtype)
machar = _get_machar(dtype)
for word in ['precision', 'iexp',
'maxexp', 'minexp', 'negep',
'machep']:
setattr(self, word, getattr(machar, word))
for word in ['resolution', 'epsneg', 'smallest_subnormal']:
setattr(self, word, getattr(machar, word).flat[0])
self.bits = self.dtype.itemsize * 8
self.max = machar.huge.flat[0]
self.min = -self.max
self.eps = machar.eps.flat[0]
self.nexp = machar.iexp
self.nmant = machar.it
self._machar = machar
self._str_tiny = machar._str_xmin.strip()
self._str_max = machar._str_xmax.strip()
self._str_epsneg = machar._str_epsneg.strip()
self._str_eps = machar._str_eps.strip()
self._str_resolution = machar._str_resolution.strip()
self._str_smallest_normal = machar._str_smallest_normal.strip()
self._str_smallest_subnormal = machar._str_smallest_subnormal.strip()
return self
def __str__(self):
fmt = (
'Machine parameters for %(dtype)s\n'
'---------------------------------------------------------------\n'
'precision = %(precision)3s resolution = %(_str_resolution)s\n'
'machep = %(machep)6s eps = %(_str_eps)s\n'
'negep = %(negep)6s epsneg = %(_str_epsneg)s\n'
'minexp = %(minexp)6s tiny = %(_str_tiny)s\n'
'maxexp = %(maxexp)6s max = %(_str_max)s\n'
'nexp = %(nexp)6s min = -max\n'
'smallest_normal = %(_str_smallest_normal)s '
'smallest_subnormal = %(_str_smallest_subnormal)s\n'
'---------------------------------------------------------------\n'
)
return fmt % self.__dict__
def __repr__(self):
c = self.__class__.__name__
d = self.__dict__.copy()
d['klass'] = c
return (("%(klass)s(resolution=%(resolution)s, min=-%(_str_max)s,"
" max=%(_str_max)s, dtype=%(dtype)s)") % d)
@property
def smallest_normal(self):
"""Return the value for the smallest normal.
Returns
-------
smallest_normal : float
Value for the smallest normal.
Warns
-----
UserWarning
If the calculated value for the smallest normal is requested for
double-double.
"""
# This check is necessary because the value for smallest_normal is
# platform dependent for longdouble types.
if isnan(self._machar.smallest_normal.flat[0]):
warnings.warn(
'The value of smallest normal is undefined for double double',
UserWarning, stacklevel=2)
return self._machar.smallest_normal.flat[0]
@property
def tiny(self):
"""Return the value for tiny, alias of smallest_normal.
Returns
-------
tiny : float
Value for the smallest normal, alias of smallest_normal.
Warns
-----
UserWarning
If the calculated value for the smallest normal is requested for
double-double.
"""
return self.smallest_normal
@property
def machar(self):
"""The object which calculated these parameters and holds more
detailed information.
.. deprecated:: 1.22
"""
# Deprecated 2021-10-27, NumPy 1.22
warnings.warn(
"`finfo.machar` is deprecated (NumPy 1.22)",
DeprecationWarning, stacklevel=2,
)
return self._machar
@set_module('numpy')
class iinfo:
"""
iinfo(type)
Machine limits for integer types.
Attributes
----------
bits : int
The number of bits occupied by the type.
min : int
The smallest integer expressible by the type.
max : int
The largest integer expressible by the type.
Parameters
----------
int_type : integer type, dtype, or instance
The kind of integer data type to get information about.
See Also
--------
finfo : The equivalent for floating point data types.
Examples
--------
With types:
>>> ii16 = np.iinfo(np.int16)
>>> ii16.min
-32768
>>> ii16.max
32767
>>> ii32 = np.iinfo(np.int32)
>>> ii32.min
-2147483648
>>> ii32.max
2147483647
With instances:
>>> ii32 = np.iinfo(np.int32(10))
>>> ii32.min
-2147483648
>>> ii32.max
2147483647
"""
_min_vals = {}
_max_vals = {}
def __init__(self, int_type):
try:
self.dtype = numeric.dtype(int_type)
except TypeError:
self.dtype = numeric.dtype(type(int_type))
self.kind = self.dtype.kind
self.bits = self.dtype.itemsize * 8
self.key = "%s%d" % (self.kind, self.bits)
if self.kind not in 'iu':
raise ValueError("Invalid integer data type %r." % (self.kind,))
@property
def min(self):
"""Minimum value of given dtype."""
if self.kind == 'u':
return 0
else:
try:
val = iinfo._min_vals[self.key]
except KeyError:
val = int(-(1 << (self.bits-1)))
iinfo._min_vals[self.key] = val
return val
@property
def max(self):
"""Maximum value of given dtype."""
try:
val = iinfo._max_vals[self.key]
except KeyError:
if self.kind == 'u':
val = int((1 << self.bits) - 1)
else:
val = int((1 << (self.bits-1)) - 1)
iinfo._max_vals[self.key] = val
return val
def __str__(self):
"""String representation."""
fmt = (
'Machine parameters for %(dtype)s\n'
'---------------------------------------------------------------\n'
'min = %(min)s\n'
'max = %(max)s\n'
'---------------------------------------------------------------\n'
)
return fmt % {'dtype': self.dtype, 'min': self.min, 'max': self.max}
def __repr__(self):
return "%s(min=%s, max=%s, dtype=%s)" % (self.__class__.__name__,
self.min, self.max, self.dtype)
| 24,124 | Python | 33.513591 | 102 | 0.530716 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/einsumfunc.pyi | from collections.abc import Sequence
from typing import TypeVar, Any, overload, Union, Literal
from numpy import (
ndarray,
dtype,
bool_,
unsignedinteger,
signedinteger,
floating,
complexfloating,
number,
_OrderKACF,
)
from numpy._typing import (
_ArrayLikeBool_co,
_ArrayLikeUInt_co,
_ArrayLikeInt_co,
_ArrayLikeFloat_co,
_ArrayLikeComplex_co,
_DTypeLikeBool,
_DTypeLikeUInt,
_DTypeLikeInt,
_DTypeLikeFloat,
_DTypeLikeComplex,
_DTypeLikeComplex_co,
)
_ArrayType = TypeVar(
"_ArrayType",
bound=ndarray[Any, dtype[Union[bool_, number[Any]]]],
)
_OptimizeKind = None | bool | Literal["greedy", "optimal"] | Sequence[Any]
_CastingSafe = Literal["no", "equiv", "safe", "same_kind"]
_CastingUnsafe = Literal["unsafe"]
__all__: list[str]
# TODO: Properly handle the `casting`-based combinatorics
# TODO: We need to evaluate the content `__subscripts` in order
# to identify whether or an array or scalar is returned. At a cursory
# glance this seems like something that can quite easily be done with
# a mypy plugin.
# Something like `is_scalar = bool(__subscripts.partition("->")[-1])`
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeBool_co,
out: None = ...,
dtype: None | _DTypeLikeBool = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeUInt_co,
out: None = ...,
dtype: None | _DTypeLikeUInt = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeInt_co,
out: None = ...,
dtype: None | _DTypeLikeInt = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeFloat_co,
out: None = ...,
dtype: None | _DTypeLikeFloat = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeComplex_co,
out: None = ...,
dtype: None | _DTypeLikeComplex = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: Any,
casting: _CastingUnsafe,
dtype: None | _DTypeLikeComplex_co = ...,
out: None = ...,
order: _OrderKACF = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeComplex_co,
out: _ArrayType,
dtype: None | _DTypeLikeComplex_co = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> _ArrayType: ...
@overload
def einsum(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: Any,
out: _ArrayType,
casting: _CastingUnsafe,
dtype: None | _DTypeLikeComplex_co = ...,
order: _OrderKACF = ...,
optimize: _OptimizeKind = ...,
) -> _ArrayType: ...
# NOTE: `einsum_call` is a hidden kwarg unavailable for public use.
# It is therefore excluded from the signatures below.
# NOTE: In practice the list consists of a `str` (first element)
# and a variable number of integer tuples.
def einsum_path(
subscripts: str | _ArrayLikeInt_co,
/,
*operands: _ArrayLikeComplex_co,
optimize: _OptimizeKind = ...,
) -> tuple[list[Any], str]: ...
| 3,778 | unknown | 25.062069 | 74 | 0.612758 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_asarray.py | """
Functions in the ``as*array`` family that promote array-likes into arrays.
`require` fits this category despite its name not matching this pattern.
"""
from .overrides import (
array_function_dispatch,
set_array_function_like_doc,
set_module,
)
from .multiarray import array, asanyarray
__all__ = ["require"]
def _require_dispatcher(a, dtype=None, requirements=None, *, like=None):
return (like,)
@set_array_function_like_doc
@set_module('numpy')
def require(a, dtype=None, requirements=None, *, like=None):
"""
Return an ndarray of the provided type that satisfies requirements.
This function is useful to be sure that an array with the correct flags
is returned for passing to compiled code (perhaps through ctypes).
Parameters
----------
a : array_like
The object to be converted to a type-and-requirement-satisfying array.
dtype : data-type
The required data-type. If None preserve the current dtype. If your
application requires the data to be in native byteorder, include
a byteorder specification as a part of the dtype specification.
requirements : str or list of str
The requirements list can be any of the following
* 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
* 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
* 'ALIGNED' ('A') - ensure a data-type aligned array
* 'WRITEABLE' ('W') - ensure a writable array
* 'OWNDATA' ('O') - ensure an array that owns its own data
* 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array with specified requirements and type if given.
See Also
--------
asarray : Convert input to an ndarray.
asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfortranarray : Convert input to an ndarray with column-major
memory order.
ndarray.flags : Information about the memory layout of the array.
Notes
-----
The returned array will be guaranteed to have the listed requirements
by making a copy if needed.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> x.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
>>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
>>> y.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
"""
if like is not None:
return _require_with_like(
a,
dtype=dtype,
requirements=requirements,
like=like,
)
possible_flags = {'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
'A': 'A', 'ALIGNED': 'A',
'W': 'W', 'WRITEABLE': 'W',
'O': 'O', 'OWNDATA': 'O',
'E': 'E', 'ENSUREARRAY': 'E'}
if not requirements:
return asanyarray(a, dtype=dtype)
else:
requirements = {possible_flags[x.upper()] for x in requirements}
if 'E' in requirements:
requirements.remove('E')
subok = False
else:
subok = True
order = 'A'
if requirements >= {'C', 'F'}:
raise ValueError('Cannot specify both "C" and "F" order')
elif 'F' in requirements:
order = 'F'
requirements.remove('F')
elif 'C' in requirements:
order = 'C'
requirements.remove('C')
arr = array(a, dtype=dtype, order=order, copy=False, subok=subok)
for prop in requirements:
if not arr.flags[prop]:
arr = arr.copy(order)
break
return arr
_require_with_like = array_function_dispatch(
_require_dispatcher
)(require)
| 4,121 | Python | 28.654676 | 78 | 0.595001 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/__init__.py | """
Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
Please note that this module is private. All functions and objects
are available in the main ``numpy`` namespace - use that instead.
"""
from numpy.version import version as __version__
import os
import warnings
# disables OpenBLAS affinity setting of the main thread that limits
# python threads or processes to one core
env_added = []
for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
if envkey not in os.environ:
os.environ[envkey] = '1'
env_added.append(envkey)
try:
from . import multiarray
except ImportError as exc:
import sys
msg = """
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html
Please note and check the following:
* The Python version is: Python%d.%d from "%s"
* The NumPy version is: "%s"
and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: %s
""" % (sys.version_info[0], sys.version_info[1], sys.executable,
__version__, exc)
raise ImportError(msg)
finally:
for envkey in env_added:
del os.environ[envkey]
del envkey
del env_added
del os
from . import umath
# Check that multiarray,umath are pure python modules wrapping
# _multiarray_umath and not either of the old c-extension modules
if not (hasattr(multiarray, '_multiarray_umath') and
hasattr(umath, '_multiarray_umath')):
import sys
path = sys.modules['numpy'].__path__
msg = ("Something is wrong with the numpy installation. "
"While importing we detected an older version of "
"numpy in {}. One method of fixing this is to repeatedly uninstall "
"numpy until none is found, then reinstall this version.")
raise ImportError(msg.format(path))
from . import numerictypes as nt
multiarray.set_typeDict(nt.sctypeDict)
from . import numeric
from .numeric import *
from . import fromnumeric
from .fromnumeric import *
from . import defchararray as char
from . import records as rec
from .records import record, recarray, format_parser
from .memmap import *
from .defchararray import chararray
from . import function_base
from .function_base import *
from . import _machar
from ._machar import *
from . import getlimits
from .getlimits import *
from . import shape_base
from .shape_base import *
from . import einsumfunc
from .einsumfunc import *
del nt
from .fromnumeric import amax as max, amin as min, round_ as round
from .numeric import absolute as abs
# do this after everything else, to minimize the chance of this misleadingly
# appearing in an import-time traceback
from . import _add_newdocs
from . import _add_newdocs_scalars
# add these for module-freeze analysis (like PyInstaller)
from . import _dtype_ctypes
from . import _internal
from . import _dtype
from . import _methods
__all__ = ['char', 'rec', 'memmap']
__all__ += numeric.__all__
__all__ += ['record', 'recarray', 'format_parser']
__all__ += ['chararray']
__all__ += function_base.__all__
__all__ += getlimits.__all__
__all__ += shape_base.__all__
__all__ += einsumfunc.__all__
# We used to use `np.core._ufunc_reconstruct` to unpickle. This is unnecessary,
# but old pickles saved before 1.20 will be using it, and there is no reason
# to break loading them.
def _ufunc_reconstruct(module, name):
# The `fromlist` kwarg is required to ensure that `mod` points to the
# inner-most module rather than the parent package when module name is
# nested. This makes it possible to pickle non-toplevel ufuncs such as
# scipy.special.expit for instance.
mod = __import__(module, fromlist=[name])
return getattr(mod, name)
def _ufunc_reduce(func):
# Report the `__name__`. pickle will try to find the module. Note that
# pickle supports for this `__name__` to be a `__qualname__`. It may
# make sense to add a `__qualname__` to ufuncs, to allow this more
# explicitly (Numba has ufuncs as attributes).
# See also: https://github.com/dask/distributed/issues/3450
return func.__name__
def _DType_reconstruct(scalar_type):
# This is a work-around to pickle type(np.dtype(np.float64)), etc.
# and it should eventually be replaced with a better solution, e.g. when
# DTypes become HeapTypes.
return type(dtype(scalar_type))
def _DType_reduce(DType):
# To pickle a DType without having to add top-level names, pickle the
# scalar type for now (and assume that reconstruction will be possible).
if DType is dtype:
return "dtype" # must pickle `np.dtype` as a singleton.
scalar_type = DType.type # pickle the scalar type for reconstruction
return _DType_reconstruct, (scalar_type,)
def __getattr__(name):
# Deprecated 2021-10-20, NumPy 1.22
if name == "machar":
warnings.warn(
"The `np.core.machar` module is deprecated (NumPy 1.22)",
DeprecationWarning, stacklevel=2,
)
return _machar
raise AttributeError(f"Module {__name__!r} has no attribute {name!r}")
import copyreg
copyreg.pickle(ufunc, _ufunc_reduce)
copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
# Unclutter namespace (must keep _*_reconstruct for unpickling)
del copyreg
del _ufunc_reduce
del _DType_reduce
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
| 5,660 | Python | 30.983051 | 79 | 0.708304 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_exceptions.py | """
Various richly-typed exceptions, that also help us deal with string formatting
in python where it's easier.
By putting the formatting in `__str__`, we also avoid paying the cost for
users who silence the exceptions.
"""
from numpy.core.overrides import set_module
def _unpack_tuple(tup):
if len(tup) == 1:
return tup[0]
else:
return tup
def _display_as_base(cls):
"""
A decorator that makes an exception class look like its base.
We use this to hide subclasses that are implementation details - the user
should catch the base type, which is what the traceback will show them.
Classes decorated with this decorator are subject to removal without a
deprecation warning.
"""
assert issubclass(cls, Exception)
cls.__name__ = cls.__base__.__name__
return cls
class UFuncTypeError(TypeError):
""" Base class for all ufunc exceptions """
def __init__(self, ufunc):
self.ufunc = ufunc
@_display_as_base
class _UFuncBinaryResolutionError(UFuncTypeError):
""" Thrown when a binary resolution fails """
def __init__(self, ufunc, dtypes):
super().__init__(ufunc)
self.dtypes = tuple(dtypes)
assert len(self.dtypes) == 2
def __str__(self):
return (
"ufunc {!r} cannot use operands with types {!r} and {!r}"
).format(
self.ufunc.__name__, *self.dtypes
)
@_display_as_base
class _UFuncNoLoopError(UFuncTypeError):
""" Thrown when a ufunc loop cannot be found """
def __init__(self, ufunc, dtypes):
super().__init__(ufunc)
self.dtypes = tuple(dtypes)
def __str__(self):
return (
"ufunc {!r} did not contain a loop with signature matching types "
"{!r} -> {!r}"
).format(
self.ufunc.__name__,
_unpack_tuple(self.dtypes[:self.ufunc.nin]),
_unpack_tuple(self.dtypes[self.ufunc.nin:])
)
@_display_as_base
class _UFuncCastingError(UFuncTypeError):
def __init__(self, ufunc, casting, from_, to):
super().__init__(ufunc)
self.casting = casting
self.from_ = from_
self.to = to
@_display_as_base
class _UFuncInputCastingError(_UFuncCastingError):
""" Thrown when a ufunc input cannot be casted """
def __init__(self, ufunc, casting, from_, to, i):
super().__init__(ufunc, casting, from_, to)
self.in_i = i
def __str__(self):
# only show the number if more than one input exists
i_str = "{} ".format(self.in_i) if self.ufunc.nin != 1 else ""
return (
"Cannot cast ufunc {!r} input {}from {!r} to {!r} with casting "
"rule {!r}"
).format(
self.ufunc.__name__, i_str, self.from_, self.to, self.casting
)
@_display_as_base
class _UFuncOutputCastingError(_UFuncCastingError):
""" Thrown when a ufunc output cannot be casted """
def __init__(self, ufunc, casting, from_, to, i):
super().__init__(ufunc, casting, from_, to)
self.out_i = i
def __str__(self):
# only show the number if more than one output exists
i_str = "{} ".format(self.out_i) if self.ufunc.nout != 1 else ""
return (
"Cannot cast ufunc {!r} output {}from {!r} to {!r} with casting "
"rule {!r}"
).format(
self.ufunc.__name__, i_str, self.from_, self.to, self.casting
)
# Exception used in shares_memory()
@set_module('numpy')
class TooHardError(RuntimeError):
pass
@set_module('numpy')
class AxisError(ValueError, IndexError):
"""Axis supplied was invalid.
This is raised whenever an ``axis`` parameter is specified that is larger
than the number of array dimensions.
For compatibility with code written against older numpy versions, which
raised a mixture of `ValueError` and `IndexError` for this situation, this
exception subclasses both to ensure that ``except ValueError`` and
``except IndexError`` statements continue to catch `AxisError`.
.. versionadded:: 1.13
Parameters
----------
axis : int or str
The out of bounds axis or a custom exception message.
If an axis is provided, then `ndim` should be specified as well.
ndim : int, optional
The number of array dimensions.
msg_prefix : str, optional
A prefix for the exception message.
Attributes
----------
axis : int, optional
The out of bounds axis or ``None`` if a custom exception
message was provided. This should be the axis as passed by
the user, before any normalization to resolve negative indices.
.. versionadded:: 1.22
ndim : int, optional
The number of array dimensions or ``None`` if a custom exception
message was provided.
.. versionadded:: 1.22
Examples
--------
>>> array_1d = np.arange(10)
>>> np.cumsum(array_1d, axis=1)
Traceback (most recent call last):
...
numpy.AxisError: axis 1 is out of bounds for array of dimension 1
Negative axes are preserved:
>>> np.cumsum(array_1d, axis=-2)
Traceback (most recent call last):
...
numpy.AxisError: axis -2 is out of bounds for array of dimension 1
The class constructor generally takes the axis and arrays'
dimensionality as arguments:
>>> print(np.AxisError(2, 1, msg_prefix='error'))
error: axis 2 is out of bounds for array of dimension 1
Alternatively, a custom exception message can be passed:
>>> print(np.AxisError('Custom error message'))
Custom error message
"""
__slots__ = ("axis", "ndim", "_msg")
def __init__(self, axis, ndim=None, msg_prefix=None):
if ndim is msg_prefix is None:
# single-argument form: directly set the error message
self._msg = axis
self.axis = None
self.ndim = None
else:
self._msg = msg_prefix
self.axis = axis
self.ndim = ndim
def __str__(self):
axis = self.axis
ndim = self.ndim
if axis is ndim is None:
return self._msg
else:
msg = f"axis {axis} is out of bounds for array of dimension {ndim}"
if self._msg is not None:
msg = f"{self._msg}: {msg}"
return msg
@_display_as_base
class _ArrayMemoryError(MemoryError):
""" Thrown when an array cannot be allocated"""
def __init__(self, shape, dtype):
self.shape = shape
self.dtype = dtype
@property
def _total_size(self):
num_bytes = self.dtype.itemsize
for dim in self.shape:
num_bytes *= dim
return num_bytes
@staticmethod
def _size_to_string(num_bytes):
""" Convert a number of bytes into a binary size string """
# https://en.wikipedia.org/wiki/Binary_prefix
LOG2_STEP = 10
STEP = 1024
units = ['bytes', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB']
unit_i = max(num_bytes.bit_length() - 1, 1) // LOG2_STEP
unit_val = 1 << (unit_i * LOG2_STEP)
n_units = num_bytes / unit_val
del unit_val
# ensure we pick a unit that is correct after rounding
if round(n_units) == STEP:
unit_i += 1
n_units /= STEP
# deal with sizes so large that we don't have units for them
if unit_i >= len(units):
new_unit_i = len(units) - 1
n_units *= 1 << ((unit_i - new_unit_i) * LOG2_STEP)
unit_i = new_unit_i
unit_name = units[unit_i]
# format with a sensible number of digits
if unit_i == 0:
# no decimal point on bytes
return '{:.0f} {}'.format(n_units, unit_name)
elif round(n_units) < 1000:
# 3 significant figures, if none are dropped to the left of the .
return '{:#.3g} {}'.format(n_units, unit_name)
else:
# just give all the digits otherwise
return '{:#.0f} {}'.format(n_units, unit_name)
def __str__(self):
size_str = self._size_to_string(self._total_size)
return (
"Unable to allocate {} for an array with shape {} and data type {}"
.format(size_str, self.shape, self.dtype)
)
| 8,344 | Python | 29.680147 | 79 | 0.585451 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/einsumfunc.py | """
Implementation of optimized einsum.
"""
import itertools
import operator
from numpy.core.multiarray import c_einsum
from numpy.core.numeric import asanyarray, tensordot
from numpy.core.overrides import array_function_dispatch
__all__ = ['einsum', 'einsum_path']
einsum_symbols = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
einsum_symbols_set = set(einsum_symbols)
def _flop_count(idx_contraction, inner, num_terms, size_dictionary):
"""
Computes the number of FLOPS in the contraction.
Parameters
----------
idx_contraction : iterable
The indices involved in the contraction
inner : bool
Does this contraction require an inner product?
num_terms : int
The number of terms in a contraction
size_dictionary : dict
The size of each of the indices in idx_contraction
Returns
-------
flop_count : int
The total number of FLOPS required for the contraction.
Examples
--------
>>> _flop_count('abc', False, 1, {'a': 2, 'b':3, 'c':5})
30
>>> _flop_count('abc', True, 2, {'a': 2, 'b':3, 'c':5})
60
"""
overall_size = _compute_size_by_dict(idx_contraction, size_dictionary)
op_factor = max(1, num_terms - 1)
if inner:
op_factor += 1
return overall_size * op_factor
def _compute_size_by_dict(indices, idx_dict):
"""
Computes the product of the elements in indices based on the dictionary
idx_dict.
Parameters
----------
indices : iterable
Indices to base the product on.
idx_dict : dictionary
Dictionary of index sizes
Returns
-------
ret : int
The resulting product.
Examples
--------
>>> _compute_size_by_dict('abbc', {'a': 2, 'b':3, 'c':5})
90
"""
ret = 1
for i in indices:
ret *= idx_dict[i]
return ret
def _find_contraction(positions, input_sets, output_set):
"""
Finds the contraction for a given set of input and output sets.
Parameters
----------
positions : iterable
Integer positions of terms used in the contraction.
input_sets : list
List of sets that represent the lhs side of the einsum subscript
output_set : set
Set that represents the rhs side of the overall einsum subscript
Returns
-------
new_result : set
The indices of the resulting contraction
remaining : list
List of sets that have not been contracted, the new set is appended to
the end of this list
idx_removed : set
Indices removed from the entire contraction
idx_contraction : set
The indices used in the current contraction
Examples
--------
# A simple dot product test case
>>> pos = (0, 1)
>>> isets = [set('ab'), set('bc')]
>>> oset = set('ac')
>>> _find_contraction(pos, isets, oset)
({'a', 'c'}, [{'a', 'c'}], {'b'}, {'a', 'b', 'c'})
# A more complex case with additional terms in the contraction
>>> pos = (0, 2)
>>> isets = [set('abd'), set('ac'), set('bdc')]
>>> oset = set('ac')
>>> _find_contraction(pos, isets, oset)
({'a', 'c'}, [{'a', 'c'}, {'a', 'c'}], {'b', 'd'}, {'a', 'b', 'c', 'd'})
"""
idx_contract = set()
idx_remain = output_set.copy()
remaining = []
for ind, value in enumerate(input_sets):
if ind in positions:
idx_contract |= value
else:
remaining.append(value)
idx_remain |= value
new_result = idx_remain & idx_contract
idx_removed = (idx_contract - new_result)
remaining.append(new_result)
return (new_result, remaining, idx_removed, idx_contract)
def _optimal_path(input_sets, output_set, idx_dict, memory_limit):
"""
Computes all possible pair contractions, sieves the results based
on ``memory_limit`` and returns the lowest cost path. This algorithm
scales factorial with respect to the elements in the list ``input_sets``.
Parameters
----------
input_sets : list
List of sets that represent the lhs side of the einsum subscript
output_set : set
Set that represents the rhs side of the overall einsum subscript
idx_dict : dictionary
Dictionary of index sizes
memory_limit : int
The maximum number of elements in a temporary array
Returns
-------
path : list
The optimal contraction order within the memory limit constraint.
Examples
--------
>>> isets = [set('abd'), set('ac'), set('bdc')]
>>> oset = set()
>>> idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4}
>>> _optimal_path(isets, oset, idx_sizes, 5000)
[(0, 2), (0, 1)]
"""
full_results = [(0, [], input_sets)]
for iteration in range(len(input_sets) - 1):
iter_results = []
# Compute all unique pairs
for curr in full_results:
cost, positions, remaining = curr
for con in itertools.combinations(range(len(input_sets) - iteration), 2):
# Find the contraction
cont = _find_contraction(con, remaining, output_set)
new_result, new_input_sets, idx_removed, idx_contract = cont
# Sieve the results based on memory_limit
new_size = _compute_size_by_dict(new_result, idx_dict)
if new_size > memory_limit:
continue
# Build (total_cost, positions, indices_remaining)
total_cost = cost + _flop_count(idx_contract, idx_removed, len(con), idx_dict)
new_pos = positions + [con]
iter_results.append((total_cost, new_pos, new_input_sets))
# Update combinatorial list, if we did not find anything return best
# path + remaining contractions
if iter_results:
full_results = iter_results
else:
path = min(full_results, key=lambda x: x[0])[1]
path += [tuple(range(len(input_sets) - iteration))]
return path
# If we have not found anything return single einsum contraction
if len(full_results) == 0:
return [tuple(range(len(input_sets)))]
path = min(full_results, key=lambda x: x[0])[1]
return path
def _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit, path_cost, naive_cost):
"""Compute the cost (removed size + flops) and resultant indices for
performing the contraction specified by ``positions``.
Parameters
----------
positions : tuple of int
The locations of the proposed tensors to contract.
input_sets : list of sets
The indices found on each tensors.
output_set : set
The output indices of the expression.
idx_dict : dict
Mapping of each index to its size.
memory_limit : int
The total allowed size for an intermediary tensor.
path_cost : int
The contraction cost so far.
naive_cost : int
The cost of the unoptimized expression.
Returns
-------
cost : (int, int)
A tuple containing the size of any indices removed, and the flop cost.
positions : tuple of int
The locations of the proposed tensors to contract.
new_input_sets : list of sets
The resulting new list of indices if this proposed contraction is performed.
"""
# Find the contraction
contract = _find_contraction(positions, input_sets, output_set)
idx_result, new_input_sets, idx_removed, idx_contract = contract
# Sieve the results based on memory_limit
new_size = _compute_size_by_dict(idx_result, idx_dict)
if new_size > memory_limit:
return None
# Build sort tuple
old_sizes = (_compute_size_by_dict(input_sets[p], idx_dict) for p in positions)
removed_size = sum(old_sizes) - new_size
# NB: removed_size used to be just the size of any removed indices i.e.:
# helpers.compute_size_by_dict(idx_removed, idx_dict)
cost = _flop_count(idx_contract, idx_removed, len(positions), idx_dict)
sort = (-removed_size, cost)
# Sieve based on total cost as well
if (path_cost + cost) > naive_cost:
return None
# Add contraction to possible choices
return [sort, positions, new_input_sets]
def _update_other_results(results, best):
"""Update the positions and provisional input_sets of ``results`` based on
performing the contraction result ``best``. Remove any involving the tensors
contracted.
Parameters
----------
results : list
List of contraction results produced by ``_parse_possible_contraction``.
best : list
The best contraction of ``results`` i.e. the one that will be performed.
Returns
-------
mod_results : list
The list of modified results, updated with outcome of ``best`` contraction.
"""
best_con = best[1]
bx, by = best_con
mod_results = []
for cost, (x, y), con_sets in results:
# Ignore results involving tensors just contracted
if x in best_con or y in best_con:
continue
# Update the input_sets
del con_sets[by - int(by > x) - int(by > y)]
del con_sets[bx - int(bx > x) - int(bx > y)]
con_sets.insert(-1, best[2][-1])
# Update the position indices
mod_con = x - int(x > bx) - int(x > by), y - int(y > bx) - int(y > by)
mod_results.append((cost, mod_con, con_sets))
return mod_results
def _greedy_path(input_sets, output_set, idx_dict, memory_limit):
"""
Finds the path by contracting the best pair until the input list is
exhausted. The best pair is found by minimizing the tuple
``(-prod(indices_removed), cost)``. What this amounts to is prioritizing
matrix multiplication or inner product operations, then Hadamard like
operations, and finally outer operations. Outer products are limited by
``memory_limit``. This algorithm scales cubically with respect to the
number of elements in the list ``input_sets``.
Parameters
----------
input_sets : list
List of sets that represent the lhs side of the einsum subscript
output_set : set
Set that represents the rhs side of the overall einsum subscript
idx_dict : dictionary
Dictionary of index sizes
memory_limit : int
The maximum number of elements in a temporary array
Returns
-------
path : list
The greedy contraction order within the memory limit constraint.
Examples
--------
>>> isets = [set('abd'), set('ac'), set('bdc')]
>>> oset = set()
>>> idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4}
>>> _greedy_path(isets, oset, idx_sizes, 5000)
[(0, 2), (0, 1)]
"""
# Handle trivial cases that leaked through
if len(input_sets) == 1:
return [(0,)]
elif len(input_sets) == 2:
return [(0, 1)]
# Build up a naive cost
contract = _find_contraction(range(len(input_sets)), input_sets, output_set)
idx_result, new_input_sets, idx_removed, idx_contract = contract
naive_cost = _flop_count(idx_contract, idx_removed, len(input_sets), idx_dict)
# Initially iterate over all pairs
comb_iter = itertools.combinations(range(len(input_sets)), 2)
known_contractions = []
path_cost = 0
path = []
for iteration in range(len(input_sets) - 1):
# Iterate over all pairs on first step, only previously found pairs on subsequent steps
for positions in comb_iter:
# Always initially ignore outer products
if input_sets[positions[0]].isdisjoint(input_sets[positions[1]]):
continue
result = _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit, path_cost,
naive_cost)
if result is not None:
known_contractions.append(result)
# If we do not have a inner contraction, rescan pairs including outer products
if len(known_contractions) == 0:
# Then check the outer products
for positions in itertools.combinations(range(len(input_sets)), 2):
result = _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit,
path_cost, naive_cost)
if result is not None:
known_contractions.append(result)
# If we still did not find any remaining contractions, default back to einsum like behavior
if len(known_contractions) == 0:
path.append(tuple(range(len(input_sets))))
break
# Sort based on first index
best = min(known_contractions, key=lambda x: x[0])
# Now propagate as many unused contractions as possible to next iteration
known_contractions = _update_other_results(known_contractions, best)
# Next iteration only compute contractions with the new tensor
# All other contractions have been accounted for
input_sets = best[2]
new_tensor_pos = len(input_sets) - 1
comb_iter = ((i, new_tensor_pos) for i in range(new_tensor_pos))
# Update path and total cost
path.append(best[1])
path_cost += best[0][1]
return path
def _can_dot(inputs, result, idx_removed):
"""
Checks if we can use BLAS (np.tensordot) call and its beneficial to do so.
Parameters
----------
inputs : list of str
Specifies the subscripts for summation.
result : str
Resulting summation.
idx_removed : set
Indices that are removed in the summation
Returns
-------
type : bool
Returns true if BLAS should and can be used, else False
Notes
-----
If the operations is BLAS level 1 or 2 and is not already aligned
we default back to einsum as the memory movement to copy is more
costly than the operation itself.
Examples
--------
# Standard GEMM operation
>>> _can_dot(['ij', 'jk'], 'ik', set('j'))
True
# Can use the standard BLAS, but requires odd data movement
>>> _can_dot(['ijj', 'jk'], 'ik', set('j'))
False
# DDOT where the memory is not aligned
>>> _can_dot(['ijk', 'ikj'], '', set('ijk'))
False
"""
# All `dot` calls remove indices
if len(idx_removed) == 0:
return False
# BLAS can only handle two operands
if len(inputs) != 2:
return False
input_left, input_right = inputs
for c in set(input_left + input_right):
# can't deal with repeated indices on same input or more than 2 total
nl, nr = input_left.count(c), input_right.count(c)
if (nl > 1) or (nr > 1) or (nl + nr > 2):
return False
# can't do implicit summation or dimension collapse e.g.
# "ab,bc->c" (implicitly sum over 'a')
# "ab,ca->ca" (take diagonal of 'a')
if nl + nr - 1 == int(c in result):
return False
# Build a few temporaries
set_left = set(input_left)
set_right = set(input_right)
keep_left = set_left - idx_removed
keep_right = set_right - idx_removed
rs = len(idx_removed)
# At this point we are a DOT, GEMV, or GEMM operation
# Handle inner products
# DDOT with aligned data
if input_left == input_right:
return True
# DDOT without aligned data (better to use einsum)
if set_left == set_right:
return False
# Handle the 4 possible (aligned) GEMV or GEMM cases
# GEMM or GEMV no transpose
if input_left[-rs:] == input_right[:rs]:
return True
# GEMM or GEMV transpose both
if input_left[:rs] == input_right[-rs:]:
return True
# GEMM or GEMV transpose right
if input_left[-rs:] == input_right[-rs:]:
return True
# GEMM or GEMV transpose left
if input_left[:rs] == input_right[:rs]:
return True
# Einsum is faster than GEMV if we have to copy data
if not keep_left or not keep_right:
return False
# We are a matrix-matrix product, but we need to copy data
return True
def _parse_einsum_input(operands):
"""
A reproduction of einsum c side einsum parsing in python.
Returns
-------
input_strings : str
Parsed input strings
output_string : str
Parsed output string
operands : list of array_like
The operands to use in the numpy contraction
Examples
--------
The operand list is simplified to reduce printing:
>>> np.random.seed(123)
>>> a = np.random.rand(4, 4)
>>> b = np.random.rand(4, 4, 4)
>>> _parse_einsum_input(('...a,...a->...', a, b))
('za,xza', 'xz', [a, b]) # may vary
>>> _parse_einsum_input((a, [Ellipsis, 0], b, [Ellipsis, 0]))
('za,xza', 'xz', [a, b]) # may vary
"""
if len(operands) == 0:
raise ValueError("No input operands")
if isinstance(operands[0], str):
subscripts = operands[0].replace(" ", "")
operands = [asanyarray(v) for v in operands[1:]]
# Ensure all characters are valid
for s in subscripts:
if s in '.,->':
continue
if s not in einsum_symbols:
raise ValueError("Character %s is not a valid symbol." % s)
else:
tmp_operands = list(operands)
operand_list = []
subscript_list = []
for p in range(len(operands) // 2):
operand_list.append(tmp_operands.pop(0))
subscript_list.append(tmp_operands.pop(0))
output_list = tmp_operands[-1] if len(tmp_operands) else None
operands = [asanyarray(v) for v in operand_list]
subscripts = ""
last = len(subscript_list) - 1
for num, sub in enumerate(subscript_list):
for s in sub:
if s is Ellipsis:
subscripts += "..."
else:
try:
s = operator.index(s)
except TypeError as e:
raise TypeError("For this input type lists must contain "
"either int or Ellipsis") from e
subscripts += einsum_symbols[s]
if num != last:
subscripts += ","
if output_list is not None:
subscripts += "->"
for s in output_list:
if s is Ellipsis:
subscripts += "..."
else:
try:
s = operator.index(s)
except TypeError as e:
raise TypeError("For this input type lists must contain "
"either int or Ellipsis") from e
subscripts += einsum_symbols[s]
# Check for proper "->"
if ("-" in subscripts) or (">" in subscripts):
invalid = (subscripts.count("-") > 1) or (subscripts.count(">") > 1)
if invalid or (subscripts.count("->") != 1):
raise ValueError("Subscripts can only contain one '->'.")
# Parse ellipses
if "." in subscripts:
used = subscripts.replace(".", "").replace(",", "").replace("->", "")
unused = list(einsum_symbols_set - set(used))
ellipse_inds = "".join(unused)
longest = 0
if "->" in subscripts:
input_tmp, output_sub = subscripts.split("->")
split_subscripts = input_tmp.split(",")
out_sub = True
else:
split_subscripts = subscripts.split(',')
out_sub = False
for num, sub in enumerate(split_subscripts):
if "." in sub:
if (sub.count(".") != 3) or (sub.count("...") != 1):
raise ValueError("Invalid Ellipses.")
# Take into account numerical values
if operands[num].shape == ():
ellipse_count = 0
else:
ellipse_count = max(operands[num].ndim, 1)
ellipse_count -= (len(sub) - 3)
if ellipse_count > longest:
longest = ellipse_count
if ellipse_count < 0:
raise ValueError("Ellipses lengths do not match.")
elif ellipse_count == 0:
split_subscripts[num] = sub.replace('...', '')
else:
rep_inds = ellipse_inds[-ellipse_count:]
split_subscripts[num] = sub.replace('...', rep_inds)
subscripts = ",".join(split_subscripts)
if longest == 0:
out_ellipse = ""
else:
out_ellipse = ellipse_inds[-longest:]
if out_sub:
subscripts += "->" + output_sub.replace("...", out_ellipse)
else:
# Special care for outputless ellipses
output_subscript = ""
tmp_subscripts = subscripts.replace(",", "")
for s in sorted(set(tmp_subscripts)):
if s not in (einsum_symbols):
raise ValueError("Character %s is not a valid symbol." % s)
if tmp_subscripts.count(s) == 1:
output_subscript += s
normal_inds = ''.join(sorted(set(output_subscript) -
set(out_ellipse)))
subscripts += "->" + out_ellipse + normal_inds
# Build output string if does not exist
if "->" in subscripts:
input_subscripts, output_subscript = subscripts.split("->")
else:
input_subscripts = subscripts
# Build output subscripts
tmp_subscripts = subscripts.replace(",", "")
output_subscript = ""
for s in sorted(set(tmp_subscripts)):
if s not in einsum_symbols:
raise ValueError("Character %s is not a valid symbol." % s)
if tmp_subscripts.count(s) == 1:
output_subscript += s
# Make sure output subscripts are in the input
for char in output_subscript:
if char not in input_subscripts:
raise ValueError("Output character %s did not appear in the input"
% char)
# Make sure number operands is equivalent to the number of terms
if len(input_subscripts.split(',')) != len(operands):
raise ValueError("Number of einsum subscripts must be equal to the "
"number of operands.")
return (input_subscripts, output_subscript, operands)
def _einsum_path_dispatcher(*operands, optimize=None, einsum_call=None):
# NOTE: technically, we should only dispatch on array-like arguments, not
# subscripts (given as strings). But separating operands into
# arrays/subscripts is a little tricky/slow (given einsum's two supported
# signatures), so as a practical shortcut we dispatch on everything.
# Strings will be ignored for dispatching since they don't define
# __array_function__.
return operands
@array_function_dispatch(_einsum_path_dispatcher, module='numpy')
def einsum_path(*operands, optimize='greedy', einsum_call=False):
"""
einsum_path(subscripts, *operands, optimize='greedy')
Evaluates the lowest cost contraction order for an einsum expression by
considering the creation of intermediate arrays.
Parameters
----------
subscripts : str
Specifies the subscripts for summation.
*operands : list of array_like
These are the arrays for the operation.
optimize : {bool, list, tuple, 'greedy', 'optimal'}
Choose the type of path. If a tuple is provided, the second argument is
assumed to be the maximum intermediate size created. If only a single
argument is provided the largest input or output array size is used
as a maximum intermediate size.
* if a list is given that starts with ``einsum_path``, uses this as the
contraction path
* if False no optimization is taken
* if True defaults to the 'greedy' algorithm
* 'optimal' An algorithm that combinatorially explores all possible
ways of contracting the listed tensors and choosest the least costly
path. Scales exponentially with the number of terms in the
contraction.
* 'greedy' An algorithm that chooses the best pair contraction
at each step. Effectively, this algorithm searches the largest inner,
Hadamard, and then outer products at each step. Scales cubically with
the number of terms in the contraction. Equivalent to the 'optimal'
path for most contractions.
Default is 'greedy'.
Returns
-------
path : list of tuples
A list representation of the einsum path.
string_repr : str
A printable representation of the einsum path.
Notes
-----
The resulting path indicates which terms of the input contraction should be
contracted first, the result of this contraction is then appended to the
end of the contraction list. This list can then be iterated over until all
intermediate contractions are complete.
See Also
--------
einsum, linalg.multi_dot
Examples
--------
We can begin with a chain dot example. In this case, it is optimal to
contract the ``b`` and ``c`` tensors first as represented by the first
element of the path ``(1, 2)``. The resulting tensor is added to the end
of the contraction and the remaining contraction ``(0, 1)`` is then
completed.
>>> np.random.seed(123)
>>> a = np.random.rand(2, 2)
>>> b = np.random.rand(2, 5)
>>> c = np.random.rand(5, 2)
>>> path_info = np.einsum_path('ij,jk,kl->il', a, b, c, optimize='greedy')
>>> print(path_info[0])
['einsum_path', (1, 2), (0, 1)]
>>> print(path_info[1])
Complete contraction: ij,jk,kl->il # may vary
Naive scaling: 4
Optimized scaling: 3
Naive FLOP count: 1.600e+02
Optimized FLOP count: 5.600e+01
Theoretical speedup: 2.857
Largest intermediate: 4.000e+00 elements
-------------------------------------------------------------------------
scaling current remaining
-------------------------------------------------------------------------
3 kl,jk->jl ij,jl->il
3 jl,ij->il il->il
A more complex index transformation example.
>>> I = np.random.rand(10, 10, 10, 10)
>>> C = np.random.rand(10, 10)
>>> path_info = np.einsum_path('ea,fb,abcd,gc,hd->efgh', C, C, I, C, C,
... optimize='greedy')
>>> print(path_info[0])
['einsum_path', (0, 2), (0, 3), (0, 2), (0, 1)]
>>> print(path_info[1])
Complete contraction: ea,fb,abcd,gc,hd->efgh # may vary
Naive scaling: 8
Optimized scaling: 5
Naive FLOP count: 8.000e+08
Optimized FLOP count: 8.000e+05
Theoretical speedup: 1000.000
Largest intermediate: 1.000e+04 elements
--------------------------------------------------------------------------
scaling current remaining
--------------------------------------------------------------------------
5 abcd,ea->bcde fb,gc,hd,bcde->efgh
5 bcde,fb->cdef gc,hd,cdef->efgh
5 cdef,gc->defg hd,defg->efgh
5 defg,hd->efgh efgh->efgh
"""
# Figure out what the path really is
path_type = optimize
if path_type is True:
path_type = 'greedy'
if path_type is None:
path_type = False
explicit_einsum_path = False
memory_limit = None
# No optimization or a named path algorithm
if (path_type is False) or isinstance(path_type, str):
pass
# Given an explicit path
elif len(path_type) and (path_type[0] == 'einsum_path'):
explicit_einsum_path = True
# Path tuple with memory limit
elif ((len(path_type) == 2) and isinstance(path_type[0], str) and
isinstance(path_type[1], (int, float))):
memory_limit = int(path_type[1])
path_type = path_type[0]
else:
raise TypeError("Did not understand the path: %s" % str(path_type))
# Hidden option, only einsum should call this
einsum_call_arg = einsum_call
# Python side parsing
input_subscripts, output_subscript, operands = _parse_einsum_input(operands)
# Build a few useful list and sets
input_list = input_subscripts.split(',')
input_sets = [set(x) for x in input_list]
output_set = set(output_subscript)
indices = set(input_subscripts.replace(',', ''))
# Get length of each unique dimension and ensure all dimensions are correct
dimension_dict = {}
broadcast_indices = [[] for x in range(len(input_list))]
for tnum, term in enumerate(input_list):
sh = operands[tnum].shape
if len(sh) != len(term):
raise ValueError("Einstein sum subscript %s does not contain the "
"correct number of indices for operand %d."
% (input_subscripts[tnum], tnum))
for cnum, char in enumerate(term):
dim = sh[cnum]
# Build out broadcast indices
if dim == 1:
broadcast_indices[tnum].append(char)
if char in dimension_dict.keys():
# For broadcasting cases we always want the largest dim size
if dimension_dict[char] == 1:
dimension_dict[char] = dim
elif dim not in (1, dimension_dict[char]):
raise ValueError("Size of label '%s' for operand %d (%d) "
"does not match previous terms (%d)."
% (char, tnum, dimension_dict[char], dim))
else:
dimension_dict[char] = dim
# Convert broadcast inds to sets
broadcast_indices = [set(x) for x in broadcast_indices]
# Compute size of each input array plus the output array
size_list = [_compute_size_by_dict(term, dimension_dict)
for term in input_list + [output_subscript]]
max_size = max(size_list)
if memory_limit is None:
memory_arg = max_size
else:
memory_arg = memory_limit
# Compute naive cost
# This isn't quite right, need to look into exactly how einsum does this
inner_product = (sum(len(x) for x in input_sets) - len(indices)) > 0
naive_cost = _flop_count(indices, inner_product, len(input_list), dimension_dict)
# Compute the path
if explicit_einsum_path:
path = path_type[1:]
elif (
(path_type is False)
or (len(input_list) in [1, 2])
or (indices == output_set)
):
# Nothing to be optimized, leave it to einsum
path = [tuple(range(len(input_list)))]
elif path_type == "greedy":
path = _greedy_path(input_sets, output_set, dimension_dict, memory_arg)
elif path_type == "optimal":
path = _optimal_path(input_sets, output_set, dimension_dict, memory_arg)
else:
raise KeyError("Path name %s not found", path_type)
cost_list, scale_list, size_list, contraction_list = [], [], [], []
# Build contraction tuple (positions, gemm, einsum_str, remaining)
for cnum, contract_inds in enumerate(path):
# Make sure we remove inds from right to left
contract_inds = tuple(sorted(list(contract_inds), reverse=True))
contract = _find_contraction(contract_inds, input_sets, output_set)
out_inds, input_sets, idx_removed, idx_contract = contract
cost = _flop_count(idx_contract, idx_removed, len(contract_inds), dimension_dict)
cost_list.append(cost)
scale_list.append(len(idx_contract))
size_list.append(_compute_size_by_dict(out_inds, dimension_dict))
bcast = set()
tmp_inputs = []
for x in contract_inds:
tmp_inputs.append(input_list.pop(x))
bcast |= broadcast_indices.pop(x)
new_bcast_inds = bcast - idx_removed
# If we're broadcasting, nix blas
if not len(idx_removed & bcast):
do_blas = _can_dot(tmp_inputs, out_inds, idx_removed)
else:
do_blas = False
# Last contraction
if (cnum - len(path)) == -1:
idx_result = output_subscript
else:
sort_result = [(dimension_dict[ind], ind) for ind in out_inds]
idx_result = "".join([x[1] for x in sorted(sort_result)])
input_list.append(idx_result)
broadcast_indices.append(new_bcast_inds)
einsum_str = ",".join(tmp_inputs) + "->" + idx_result
contraction = (contract_inds, idx_removed, einsum_str, input_list[:], do_blas)
contraction_list.append(contraction)
opt_cost = sum(cost_list) + 1
if len(input_list) != 1:
# Explicit "einsum_path" is usually trusted, but we detect this kind of
# mistake in order to prevent from returning an intermediate value.
raise RuntimeError(
"Invalid einsum_path is specified: {} more operands has to be "
"contracted.".format(len(input_list) - 1))
if einsum_call_arg:
return (operands, contraction_list)
# Return the path along with a nice string representation
overall_contraction = input_subscripts + "->" + output_subscript
header = ("scaling", "current", "remaining")
speedup = naive_cost / opt_cost
max_i = max(size_list)
path_print = " Complete contraction: %s\n" % overall_contraction
path_print += " Naive scaling: %d\n" % len(indices)
path_print += " Optimized scaling: %d\n" % max(scale_list)
path_print += " Naive FLOP count: %.3e\n" % naive_cost
path_print += " Optimized FLOP count: %.3e\n" % opt_cost
path_print += " Theoretical speedup: %3.3f\n" % speedup
path_print += " Largest intermediate: %.3e elements\n" % max_i
path_print += "-" * 74 + "\n"
path_print += "%6s %24s %40s\n" % header
path_print += "-" * 74
for n, contraction in enumerate(contraction_list):
inds, idx_rm, einsum_str, remaining, blas = contraction
remaining_str = ",".join(remaining) + "->" + output_subscript
path_run = (scale_list[n], einsum_str, remaining_str)
path_print += "\n%4d %24s %40s" % path_run
path = ['einsum_path'] + path
return (path, path_print)
def _einsum_dispatcher(*operands, out=None, optimize=None, **kwargs):
# Arguably we dispatch on more arguments than we really should; see note in
# _einsum_path_dispatcher for why.
yield from operands
yield out
# Rewrite einsum to handle different cases
@array_function_dispatch(_einsum_dispatcher, module='numpy')
def einsum(*operands, out=None, optimize=False, **kwargs):
"""
einsum(subscripts, *operands, out=None, dtype=None, order='K',
casting='safe', optimize=False)
Evaluates the Einstein summation convention on the operands.
Using the Einstein summation convention, many common multi-dimensional,
linear algebraic array operations can be represented in a simple fashion.
In *implicit* mode `einsum` computes these values.
In *explicit* mode, `einsum` provides further flexibility to compute
other array operations that might not be considered classical Einstein
summation operations, by disabling, or forcing summation over specified
subscript labels.
See the notes and examples for clarification.
Parameters
----------
subscripts : str
Specifies the subscripts for summation as comma separated list of
subscript labels. An implicit (classical Einstein summation)
calculation is performed unless the explicit indicator '->' is
included as well as subscript labels of the precise output form.
operands : list of array_like
These are the arrays for the operation.
out : ndarray, optional
If provided, the calculation is done into this array.
dtype : {data-type, None}, optional
If provided, forces the calculation to use the data type specified.
Note that you may have to also give a more liberal `casting`
parameter to allow the conversions. Default is None.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the output. 'C' means it should
be C contiguous. 'F' means it should be Fortran contiguous,
'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
'K' means it should be as close to the layout as the inputs as
is possible, including arbitrarily permuted axes.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Setting this to
'unsafe' is not recommended, as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
Default is 'safe'.
optimize : {False, True, 'greedy', 'optimal'}, optional
Controls if intermediate optimization should occur. No optimization
will occur if False and True will default to the 'greedy' algorithm.
Also accepts an explicit contraction list from the ``np.einsum_path``
function. See ``np.einsum_path`` for more details. Defaults to False.
Returns
-------
output : ndarray
The calculation based on the Einstein summation convention.
See Also
--------
einsum_path, dot, inner, outer, tensordot, linalg.multi_dot
einops :
similar verbose interface is provided by
`einops <https://github.com/arogozhnikov/einops>`_ package to cover
additional operations: transpose, reshape/flatten, repeat/tile,
squeeze/unsqueeze and reductions.
opt_einsum :
`opt_einsum <https://optimized-einsum.readthedocs.io/en/stable/>`_
optimizes contraction order for einsum-like expressions
in backend-agnostic manner.
Notes
-----
.. versionadded:: 1.6.0
The Einstein summation convention can be used to compute
many multi-dimensional, linear algebraic array operations. `einsum`
provides a succinct way of representing these.
A non-exhaustive list of these operations,
which can be computed by `einsum`, is shown below along with examples:
* Trace of an array, :py:func:`numpy.trace`.
* Return a diagonal, :py:func:`numpy.diag`.
* Array axis summations, :py:func:`numpy.sum`.
* Transpositions and permutations, :py:func:`numpy.transpose`.
* Matrix multiplication and dot product, :py:func:`numpy.matmul` :py:func:`numpy.dot`.
* Vector inner and outer products, :py:func:`numpy.inner` :py:func:`numpy.outer`.
* Broadcasting, element-wise and scalar multiplication, :py:func:`numpy.multiply`.
* Tensor contractions, :py:func:`numpy.tensordot`.
* Chained array operations, in efficient calculation order, :py:func:`numpy.einsum_path`.
The subscripts string is a comma-separated list of subscript labels,
where each label refers to a dimension of the corresponding operand.
Whenever a label is repeated it is summed, so ``np.einsum('i,i', a, b)``
is equivalent to :py:func:`np.inner(a,b) <numpy.inner>`. If a label
appears only once, it is not summed, so ``np.einsum('i', a)`` produces a
view of ``a`` with no changes. A further example ``np.einsum('ij,jk', a, b)``
describes traditional matrix multiplication and is equivalent to
:py:func:`np.matmul(a,b) <numpy.matmul>`. Repeated subscript labels in one
operand take the diagonal. For example, ``np.einsum('ii', a)`` is equivalent
to :py:func:`np.trace(a) <numpy.trace>`.
In *implicit mode*, the chosen subscripts are important
since the axes of the output are reordered alphabetically. This
means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
``np.einsum('ji', a)`` takes its transpose. Additionally,
``np.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
``np.einsum('ij,jh', a, b)`` returns the transpose of the
multiplication since subscript 'h' precedes subscript 'i'.
In *explicit mode* the output can be directly controlled by
specifying output subscript labels. This requires the
identifier '->' as well as the list of output subscript labels.
This feature increases the flexibility of the function since
summing can be disabled or forced when required. The call
``np.einsum('i->', a)`` is like :py:func:`np.sum(a, axis=-1) <numpy.sum>`,
and ``np.einsum('ii->i', a)`` is like :py:func:`np.diag(a) <numpy.diag>`.
The difference is that `einsum` does not allow broadcasting by default.
Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the
order of the output subscript labels and therefore returns matrix
multiplication, unlike the example above in implicit mode.
To enable and control broadcasting, use an ellipsis. Default
NumPy-style broadcasting is done by adding an ellipsis
to the left of each term, like ``np.einsum('...ii->...i', a)``.
To take the trace along the first and last axes,
you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
product with the left-most indices instead of rightmost, one can do
``np.einsum('ij...,jk...->ik...', a, b)``.
When there is only one operand, no axes are summed, and no output
parameter is provided, a view into the operand is returned instead
of a new array. Thus, taking the diagonal as ``np.einsum('ii->i', a)``
produces a view (changed in version 1.10.0).
`einsum` also provides an alternative way to provide the subscripts
and operands as ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``.
If the output shape is not provided in this format `einsum` will be
calculated in implicit mode, otherwise it will be performed explicitly.
The examples below have corresponding `einsum` calls with the two
parameter methods.
.. versionadded:: 1.10.0
Views returned from einsum are now writeable whenever the input array
is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now
have the same effect as :py:func:`np.swapaxes(a, 0, 2) <numpy.swapaxes>`
and ``np.einsum('ii->i', a)`` will return a writeable view of the diagonal
of a 2D array.
.. versionadded:: 1.12.0
Added the ``optimize`` argument which will optimize the contraction order
of an einsum expression. For a contraction with three or more operands this
can greatly increase the computational efficiency at the cost of a larger
memory footprint during computation.
Typically a 'greedy' algorithm is applied which empirical tests have shown
returns the optimal path in the majority of cases. In some cases 'optimal'
will return the superlative path through a more expensive, exhaustive search.
For iterative calculations it may be advisable to calculate the optimal path
once and reuse that path by supplying it as an argument. An example is given
below.
See :py:func:`numpy.einsum_path` for more details.
Examples
--------
>>> a = np.arange(25).reshape(5,5)
>>> b = np.arange(5)
>>> c = np.arange(6).reshape(2,3)
Trace of a matrix:
>>> np.einsum('ii', a)
60
>>> np.einsum(a, [0,0])
60
>>> np.trace(a)
60
Extract the diagonal (requires explicit form):
>>> np.einsum('ii->i', a)
array([ 0, 6, 12, 18, 24])
>>> np.einsum(a, [0,0], [0])
array([ 0, 6, 12, 18, 24])
>>> np.diag(a)
array([ 0, 6, 12, 18, 24])
Sum over an axis (requires explicit form):
>>> np.einsum('ij->i', a)
array([ 10, 35, 60, 85, 110])
>>> np.einsum(a, [0,1], [0])
array([ 10, 35, 60, 85, 110])
>>> np.sum(a, axis=1)
array([ 10, 35, 60, 85, 110])
For higher dimensional arrays summing a single axis can be done with ellipsis:
>>> np.einsum('...j->...', a)
array([ 10, 35, 60, 85, 110])
>>> np.einsum(a, [Ellipsis,1], [Ellipsis])
array([ 10, 35, 60, 85, 110])
Compute a matrix transpose, or reorder any number of axes:
>>> np.einsum('ji', c)
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum('ij->ji', c)
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum(c, [1,0])
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.transpose(c)
array([[0, 3],
[1, 4],
[2, 5]])
Vector inner products:
>>> np.einsum('i,i', b, b)
30
>>> np.einsum(b, [0], b, [0])
30
>>> np.inner(b,b)
30
Matrix vector multiplication:
>>> np.einsum('ij,j', a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum(a, [0,1], b, [1])
array([ 30, 80, 130, 180, 230])
>>> np.dot(a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum('...j,j', a, b)
array([ 30, 80, 130, 180, 230])
Broadcasting and scalar multiplication:
>>> np.einsum('..., ...', 3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum(',ij', 3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum(3, [Ellipsis], c, [Ellipsis])
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.multiply(3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
Vector outer product:
>>> np.einsum('i,j', np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.einsum(np.arange(2)+1, [0], b, [1])
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.outer(np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
Tensor contraction:
>>> a = np.arange(60.).reshape(3,4,5)
>>> b = np.arange(24.).reshape(4,3,2)
>>> np.einsum('ijk,jil->kl', a, b)
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
>>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
>>> np.tensordot(a,b, axes=([1,0],[0,1]))
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
Writeable returned arrays (since version 1.10.0):
>>> a = np.zeros((3, 3))
>>> np.einsum('ii->i', a)[:] = 1
>>> a
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
Example of ellipsis use:
>>> a = np.arange(6).reshape((3,2))
>>> b = np.arange(12).reshape((4,3))
>>> np.einsum('ki,jk->ij', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('ki,...k->i...', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('k...,jk', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
Chained array operations. For more complicated contractions, speed ups
might be achieved by repeatedly computing a 'greedy' path or pre-computing the
'optimal' path and repeatedly applying it, using an
`einsum_path` insertion (since version 1.12.0). Performance improvements can be
particularly significant with larger arrays:
>>> a = np.ones(64).reshape(2,4,8)
Basic `einsum`: ~1520ms (benchmarked on 3.1GHz Intel i5.)
>>> for iteration in range(500):
... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a)
Sub-optimal `einsum` (due to repeated path calculation time): ~330ms
>>> for iteration in range(500):
... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')
Greedy `einsum` (faster optimal path approximation): ~160ms
>>> for iteration in range(500):
... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='greedy')
Optimal `einsum` (best usage pattern in some use cases): ~110ms
>>> path = np.einsum_path('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')[0]
>>> for iteration in range(500):
... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=path)
"""
# Special handling if out is specified
specified_out = out is not None
# If no optimization, run pure einsum
if optimize is False:
if specified_out:
kwargs['out'] = out
return c_einsum(*operands, **kwargs)
# Check the kwargs to avoid a more cryptic error later, without having to
# repeat default values here
valid_einsum_kwargs = ['dtype', 'order', 'casting']
unknown_kwargs = [k for (k, v) in kwargs.items() if
k not in valid_einsum_kwargs]
if len(unknown_kwargs):
raise TypeError("Did not understand the following kwargs: %s"
% unknown_kwargs)
# Build the contraction list and operand
operands, contraction_list = einsum_path(*operands, optimize=optimize,
einsum_call=True)
# Handle order kwarg for output array, c_einsum allows mixed case
output_order = kwargs.pop('order', 'K')
if output_order.upper() == 'A':
if all(arr.flags.f_contiguous for arr in operands):
output_order = 'F'
else:
output_order = 'C'
# Start contraction loop
for num, contraction in enumerate(contraction_list):
inds, idx_rm, einsum_str, remaining, blas = contraction
tmp_operands = [operands.pop(x) for x in inds]
# Do we need to deal with the output?
handle_out = specified_out and ((num + 1) == len(contraction_list))
# Call tensordot if still possible
if blas:
# Checks have already been handled
input_str, results_index = einsum_str.split('->')
input_left, input_right = input_str.split(',')
tensor_result = input_left + input_right
for s in idx_rm:
tensor_result = tensor_result.replace(s, "")
# Find indices to contract over
left_pos, right_pos = [], []
for s in sorted(idx_rm):
left_pos.append(input_left.find(s))
right_pos.append(input_right.find(s))
# Contract!
new_view = tensordot(*tmp_operands, axes=(tuple(left_pos), tuple(right_pos)))
# Build a new view if needed
if (tensor_result != results_index) or handle_out:
if handle_out:
kwargs["out"] = out
new_view = c_einsum(tensor_result + '->' + results_index, new_view, **kwargs)
# Call einsum
else:
# If out was specified
if handle_out:
kwargs["out"] = out
# Do the contraction
new_view = c_einsum(einsum_str, *tmp_operands, **kwargs)
# Append new items and dereference what we can
operands.append(new_view)
del tmp_operands, new_view
if specified_out:
return out
else:
return asanyarray(operands[0], order=output_order)
| 51,869 | Python | 34.921053 | 118 | 0.585899 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_dtype_ctypes.py | """
Conversion from ctypes to dtype.
In an ideal world, we could achieve this through the PEP3118 buffer protocol,
something like::
def dtype_from_ctypes_type(t):
# needed to ensure that the shape of `t` is within memoryview.format
class DummyStruct(ctypes.Structure):
_fields_ = [('a', t)]
# empty to avoid memory allocation
ctype_0 = (DummyStruct * 0)()
mv = memoryview(ctype_0)
# convert the struct, and slice back out the field
return _dtype_from_pep3118(mv.format)['a']
Unfortunately, this fails because:
* ctypes cannot handle length-0 arrays with PEP3118 (bpo-32782)
* PEP3118 cannot represent unions, but both numpy and ctypes can
* ctypes cannot handle big-endian structs with PEP3118 (bpo-32780)
"""
# We delay-import ctypes for distributions that do not include it.
# While this module is not used unless the user passes in ctypes
# members, it is eagerly imported from numpy/core/__init__.py.
import numpy as np
def _from_ctypes_array(t):
return np.dtype((dtype_from_ctypes_type(t._type_), (t._length_,)))
def _from_ctypes_structure(t):
for item in t._fields_:
if len(item) > 2:
raise TypeError(
"ctypes bitfields have no dtype equivalent")
if hasattr(t, "_pack_"):
import ctypes
formats = []
offsets = []
names = []
current_offset = 0
for fname, ftyp in t._fields_:
names.append(fname)
formats.append(dtype_from_ctypes_type(ftyp))
# Each type has a default offset, this is platform dependent for some types.
effective_pack = min(t._pack_, ctypes.alignment(ftyp))
current_offset = ((current_offset + effective_pack - 1) // effective_pack) * effective_pack
offsets.append(current_offset)
current_offset += ctypes.sizeof(ftyp)
return np.dtype(dict(
formats=formats,
offsets=offsets,
names=names,
itemsize=ctypes.sizeof(t)))
else:
fields = []
for fname, ftyp in t._fields_:
fields.append((fname, dtype_from_ctypes_type(ftyp)))
# by default, ctypes structs are aligned
return np.dtype(fields, align=True)
def _from_ctypes_scalar(t):
"""
Return the dtype type with endianness included if it's the case
"""
if getattr(t, '__ctype_be__', None) is t:
return np.dtype('>' + t._type_)
elif getattr(t, '__ctype_le__', None) is t:
return np.dtype('<' + t._type_)
else:
return np.dtype(t._type_)
def _from_ctypes_union(t):
import ctypes
formats = []
offsets = []
names = []
for fname, ftyp in t._fields_:
names.append(fname)
formats.append(dtype_from_ctypes_type(ftyp))
offsets.append(0) # Union fields are offset to 0
return np.dtype(dict(
formats=formats,
offsets=offsets,
names=names,
itemsize=ctypes.sizeof(t)))
def dtype_from_ctypes_type(t):
"""
Construct a dtype object from a ctypes type
"""
import _ctypes
if issubclass(t, _ctypes.Array):
return _from_ctypes_array(t)
elif issubclass(t, _ctypes._Pointer):
raise TypeError("ctypes pointers have no dtype equivalent")
elif issubclass(t, _ctypes.Structure):
return _from_ctypes_structure(t)
elif issubclass(t, _ctypes.Union):
return _from_ctypes_union(t)
elif isinstance(getattr(t, '_type_', None), str):
return _from_ctypes_scalar(t)
else:
raise NotImplementedError(
"Unknown ctypes type {}".format(t.__name__))
| 3,673 | Python | 30.135593 | 103 | 0.612034 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_add_newdocs.py | """
This is only meant to add docs to objects defined in C-extension modules.
The purpose is to allow easier editing of the docstrings without
requiring a re-compile.
NOTE: Many of the methods of ndarray have corresponding functions.
If you update these docstrings, please keep also the ones in
core/fromnumeric.py, core/defmatrix.py up-to-date.
"""
from numpy.core.function_base import add_newdoc
from numpy.core.overrides import array_function_like_doc
###############################################################################
#
# flatiter
#
# flatiter needs a toplevel description
#
###############################################################################
add_newdoc('numpy.core', 'flatiter',
"""
Flat iterator object to iterate over arrays.
A `flatiter` iterator is returned by ``x.flat`` for any array `x`.
It allows iterating over the array as if it were a 1-D array,
either in a for-loop or by calling its `next` method.
Iteration is done in row-major, C-style order (the last
index varying the fastest). The iterator can also be indexed using
basic slicing or advanced indexing.
See Also
--------
ndarray.flat : Return a flat iterator over an array.
ndarray.flatten : Returns a flattened copy of an array.
Notes
-----
A `flatiter` iterator can not be constructed directly from Python code
by calling the `flatiter` constructor.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> type(fl)
<class 'numpy.flatiter'>
>>> for item in fl:
... print(item)
...
0
1
2
3
4
5
>>> fl[2:4]
array([2, 3])
""")
# flatiter attributes
add_newdoc('numpy.core', 'flatiter', ('base',
"""
A reference to the array that is iterated over.
Examples
--------
>>> x = np.arange(5)
>>> fl = x.flat
>>> fl.base is x
True
"""))
add_newdoc('numpy.core', 'flatiter', ('coords',
"""
An N-dimensional tuple of current coordinates.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> fl.coords
(0, 0)
>>> next(fl)
0
>>> fl.coords
(0, 1)
"""))
add_newdoc('numpy.core', 'flatiter', ('index',
"""
Current flat index into the array.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> fl.index
0
>>> next(fl)
0
>>> fl.index
1
"""))
# flatiter functions
add_newdoc('numpy.core', 'flatiter', ('__array__',
"""__array__(type=None) Get array from iterator
"""))
add_newdoc('numpy.core', 'flatiter', ('copy',
"""
copy()
Get a copy of the iterator as a 1-D array.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> fl = x.flat
>>> fl.copy()
array([0, 1, 2, 3, 4, 5])
"""))
###############################################################################
#
# nditer
#
###############################################################################
add_newdoc('numpy.core', 'nditer',
"""
nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0)
Efficient multi-dimensional iterator object to iterate over arrays.
To get started using this object, see the
:ref:`introductory guide to array iteration <arrays.nditer>`.
Parameters
----------
op : ndarray or sequence of array_like
The array(s) to iterate over.
flags : sequence of str, optional
Flags to control the behavior of the iterator.
* ``buffered`` enables buffering when required.
* ``c_index`` causes a C-order index to be tracked.
* ``f_index`` causes a Fortran-order index to be tracked.
* ``multi_index`` causes a multi-index, or a tuple of indices
with one per iteration dimension, to be tracked.
* ``common_dtype`` causes all the operands to be converted to
a common data type, with copying or buffering as necessary.
* ``copy_if_overlap`` causes the iterator to determine if read
operands have overlap with write operands, and make temporary
copies as necessary to avoid overlap. False positives (needless
copying) are possible in some cases.
* ``delay_bufalloc`` delays allocation of the buffers until
a reset() call is made. Allows ``allocate`` operands to
be initialized before their values are copied into the buffers.
* ``external_loop`` causes the ``values`` given to be
one-dimensional arrays with multiple values instead of
zero-dimensional arrays.
* ``grow_inner`` allows the ``value`` array sizes to be made
larger than the buffer size when both ``buffered`` and
``external_loop`` is used.
* ``ranged`` allows the iterator to be restricted to a sub-range
of the iterindex values.
* ``refs_ok`` enables iteration of reference types, such as
object arrays.
* ``reduce_ok`` enables iteration of ``readwrite`` operands
which are broadcasted, also known as reduction operands.
* ``zerosize_ok`` allows `itersize` to be zero.
op_flags : list of list of str, optional
This is a list of flags for each operand. At minimum, one of
``readonly``, ``readwrite``, or ``writeonly`` must be specified.
* ``readonly`` indicates the operand will only be read from.
* ``readwrite`` indicates the operand will be read from and written to.
* ``writeonly`` indicates the operand will only be written to.
* ``no_broadcast`` prevents the operand from being broadcasted.
* ``contig`` forces the operand data to be contiguous.
* ``aligned`` forces the operand data to be aligned.
* ``nbo`` forces the operand data to be in native byte order.
* ``copy`` allows a temporary read-only copy if required.
* ``updateifcopy`` allows a temporary read-write copy if required.
* ``allocate`` causes the array to be allocated if it is None
in the ``op`` parameter.
* ``no_subtype`` prevents an ``allocate`` operand from using a subtype.
* ``arraymask`` indicates that this operand is the mask to use
for selecting elements when writing to operands with the
'writemasked' flag set. The iterator does not enforce this,
but when writing from a buffer back to the array, it only
copies those elements indicated by this mask.
* ``writemasked`` indicates that only elements where the chosen
``arraymask`` operand is True will be written to.
* ``overlap_assume_elementwise`` can be used to mark operands that are
accessed only in the iterator order, to allow less conservative
copying when ``copy_if_overlap`` is present.
op_dtypes : dtype or tuple of dtype(s), optional
The required data type(s) of the operands. If copying or buffering
is enabled, the data will be converted to/from their original types.
order : {'C', 'F', 'A', 'K'}, optional
Controls the iteration order. 'C' means C order, 'F' means
Fortran order, 'A' means 'F' order if all the arrays are Fortran
contiguous, 'C' order otherwise, and 'K' means as close to the
order the array elements appear in memory as possible. This also
affects the element memory order of ``allocate`` operands, as they
are allocated to be compatible with iteration order.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur when making a copy
or buffering. Setting this to 'unsafe' is not recommended,
as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
op_axes : list of list of ints, optional
If provided, is a list of ints or None for each operands.
The list of axes for an operand is a mapping from the dimensions
of the iterator to the dimensions of the operand. A value of
-1 can be placed for entries, causing that dimension to be
treated as `newaxis`.
itershape : tuple of ints, optional
The desired shape of the iterator. This allows ``allocate`` operands
with a dimension mapped by op_axes not corresponding to a dimension
of a different operand to get a value not equal to 1 for that
dimension.
buffersize : int, optional
When buffering is enabled, controls the size of the temporary
buffers. Set to 0 for the default value.
Attributes
----------
dtypes : tuple of dtype(s)
The data types of the values provided in `value`. This may be
different from the operand data types if buffering is enabled.
Valid only before the iterator is closed.
finished : bool
Whether the iteration over the operands is finished or not.
has_delayed_bufalloc : bool
If True, the iterator was created with the ``delay_bufalloc`` flag,
and no reset() function was called on it yet.
has_index : bool
If True, the iterator was created with either the ``c_index`` or
the ``f_index`` flag, and the property `index` can be used to
retrieve it.
has_multi_index : bool
If True, the iterator was created with the ``multi_index`` flag,
and the property `multi_index` can be used to retrieve it.
index
When the ``c_index`` or ``f_index`` flag was used, this property
provides access to the index. Raises a ValueError if accessed
and ``has_index`` is False.
iterationneedsapi : bool
Whether iteration requires access to the Python API, for example
if one of the operands is an object array.
iterindex : int
An index which matches the order of iteration.
itersize : int
Size of the iterator.
itviews
Structured view(s) of `operands` in memory, matching the reordered
and optimized iterator access pattern. Valid only before the iterator
is closed.
multi_index
When the ``multi_index`` flag was used, this property
provides access to the index. Raises a ValueError if accessed
accessed and ``has_multi_index`` is False.
ndim : int
The dimensions of the iterator.
nop : int
The number of iterator operands.
operands : tuple of operand(s)
The array(s) to be iterated over. Valid only before the iterator is
closed.
shape : tuple of ints
Shape tuple, the shape of the iterator.
value
Value of ``operands`` at current iteration. Normally, this is a
tuple of array scalars, but if the flag ``external_loop`` is used,
it is a tuple of one dimensional arrays.
Notes
-----
`nditer` supersedes `flatiter`. The iterator implementation behind
`nditer` is also exposed by the NumPy C API.
The Python exposure supplies two iteration interfaces, one which follows
the Python iterator protocol, and another which mirrors the C-style
do-while pattern. The native Python approach is better in most cases, but
if you need the coordinates or index of an iterator, use the C-style pattern.
Examples
--------
Here is how we might write an ``iter_add`` function, using the
Python iterator protocol:
>>> def iter_add_py(x, y, out=None):
... addop = np.add
... it = np.nditer([x, y, out], [],
... [['readonly'], ['readonly'], ['writeonly','allocate']])
... with it:
... for (a, b, c) in it:
... addop(a, b, out=c)
... return it.operands[2]
Here is the same function, but following the C-style pattern:
>>> def iter_add(x, y, out=None):
... addop = np.add
... it = np.nditer([x, y, out], [],
... [['readonly'], ['readonly'], ['writeonly','allocate']])
... with it:
... while not it.finished:
... addop(it[0], it[1], out=it[2])
... it.iternext()
... return it.operands[2]
Here is an example outer product function:
>>> def outer_it(x, y, out=None):
... mulop = np.multiply
... it = np.nditer([x, y, out], ['external_loop'],
... [['readonly'], ['readonly'], ['writeonly', 'allocate']],
... op_axes=[list(range(x.ndim)) + [-1] * y.ndim,
... [-1] * x.ndim + list(range(y.ndim)),
... None])
... with it:
... for (a, b, c) in it:
... mulop(a, b, out=c)
... return it.operands[2]
>>> a = np.arange(2)+1
>>> b = np.arange(3)+1
>>> outer_it(a,b)
array([[1, 2, 3],
[2, 4, 6]])
Here is an example function which operates like a "lambda" ufunc:
>>> def luf(lamdaexpr, *args, **kwargs):
... '''luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)'''
... nargs = len(args)
... op = (kwargs.get('out',None),) + args
... it = np.nditer(op, ['buffered','external_loop'],
... [['writeonly','allocate','no_broadcast']] +
... [['readonly','nbo','aligned']]*nargs,
... order=kwargs.get('order','K'),
... casting=kwargs.get('casting','safe'),
... buffersize=kwargs.get('buffersize',0))
... while not it.finished:
... it[0] = lamdaexpr(*it[1:])
... it.iternext()
... return it.operands[0]
>>> a = np.arange(5)
>>> b = np.ones(5)
>>> luf(lambda i,j:i*i + j/2, a, b)
array([ 0.5, 1.5, 4.5, 9.5, 16.5])
If operand flags ``"writeonly"`` or ``"readwrite"`` are used the
operands may be views into the original data with the
`WRITEBACKIFCOPY` flag. In this case `nditer` must be used as a
context manager or the `nditer.close` method must be called before
using the result. The temporary data will be written back to the
original data when the `__exit__` function is called but not before:
>>> a = np.arange(6, dtype='i4')[::-2]
>>> with np.nditer(a, [],
... [['writeonly', 'updateifcopy']],
... casting='unsafe',
... op_dtypes=[np.dtype('f4')]) as i:
... x = i.operands[0]
... x[:] = [-1, -2, -3]
... # a still unchanged here
>>> a, x
(array([-1, -2, -3], dtype=int32), array([-1., -2., -3.], dtype=float32))
It is important to note that once the iterator is exited, dangling
references (like `x` in the example) may or may not share data with
the original data `a`. If writeback semantics were active, i.e. if
`x.base.flags.writebackifcopy` is `True`, then exiting the iterator
will sever the connection between `x` and `a`, writing to `x` will
no longer write to `a`. If writeback semantics are not active, then
`x.data` will still point at some part of `a.data`, and writing to
one will affect the other.
Context management and the `close` method appeared in version 1.15.0.
""")
# nditer methods
add_newdoc('numpy.core', 'nditer', ('copy',
"""
copy()
Get a copy of the iterator in its current state.
Examples
--------
>>> x = np.arange(10)
>>> y = x + 1
>>> it = np.nditer([x, y])
>>> next(it)
(array(0), array(1))
>>> it2 = it.copy()
>>> next(it2)
(array(1), array(2))
"""))
add_newdoc('numpy.core', 'nditer', ('operands',
"""
operands[`Slice`]
The array(s) to be iterated over. Valid only before the iterator is closed.
"""))
add_newdoc('numpy.core', 'nditer', ('debug_print',
"""
debug_print()
Print the current state of the `nditer` instance and debug info to stdout.
"""))
add_newdoc('numpy.core', 'nditer', ('enable_external_loop',
"""
enable_external_loop()
When the "external_loop" was not used during construction, but
is desired, this modifies the iterator to behave as if the flag
was specified.
"""))
add_newdoc('numpy.core', 'nditer', ('iternext',
"""
iternext()
Check whether iterations are left, and perform a single internal iteration
without returning the result. Used in the C-style pattern do-while
pattern. For an example, see `nditer`.
Returns
-------
iternext : bool
Whether or not there are iterations left.
"""))
add_newdoc('numpy.core', 'nditer', ('remove_axis',
"""
remove_axis(i, /)
Removes axis `i` from the iterator. Requires that the flag "multi_index"
be enabled.
"""))
add_newdoc('numpy.core', 'nditer', ('remove_multi_index',
"""
remove_multi_index()
When the "multi_index" flag was specified, this removes it, allowing
the internal iteration structure to be optimized further.
"""))
add_newdoc('numpy.core', 'nditer', ('reset',
"""
reset()
Reset the iterator to its initial state.
"""))
add_newdoc('numpy.core', 'nested_iters',
"""
nested_iters(op, axes, flags=None, op_flags=None, op_dtypes=None, \
order="K", casting="safe", buffersize=0)
Create nditers for use in nested loops
Create a tuple of `nditer` objects which iterate in nested loops over
different axes of the op argument. The first iterator is used in the
outermost loop, the last in the innermost loop. Advancing one will change
the subsequent iterators to point at its new element.
Parameters
----------
op : ndarray or sequence of array_like
The array(s) to iterate over.
axes : list of list of int
Each item is used as an "op_axes" argument to an nditer
flags, op_flags, op_dtypes, order, casting, buffersize (optional)
See `nditer` parameters of the same name
Returns
-------
iters : tuple of nditer
An nditer for each item in `axes`, outermost first
See Also
--------
nditer
Examples
--------
Basic usage. Note how y is the "flattened" version of
[a[:, 0, :], a[:, 1, 0], a[:, 2, :]] since we specified
the first iter's axes as [1]
>>> a = np.arange(12).reshape(2, 3, 2)
>>> i, j = np.nested_iters(a, [[1], [0, 2]], flags=["multi_index"])
>>> for x in i:
... print(i.multi_index)
... for y in j:
... print('', j.multi_index, y)
(0,)
(0, 0) 0
(0, 1) 1
(1, 0) 6
(1, 1) 7
(1,)
(0, 0) 2
(0, 1) 3
(1, 0) 8
(1, 1) 9
(2,)
(0, 0) 4
(0, 1) 5
(1, 0) 10
(1, 1) 11
""")
add_newdoc('numpy.core', 'nditer', ('close',
"""
close()
Resolve all writeback semantics in writeable operands.
.. versionadded:: 1.15.0
See Also
--------
:ref:`nditer-context-manager`
"""))
###############################################################################
#
# broadcast
#
###############################################################################
add_newdoc('numpy.core', 'broadcast',
"""
Produce an object that mimics broadcasting.
Parameters
----------
in1, in2, ... : array_like
Input parameters.
Returns
-------
b : broadcast object
Broadcast the input parameters against one another, and
return an object that encapsulates the result.
Amongst others, it has ``shape`` and ``nd`` properties, and
may be used as an iterator.
See Also
--------
broadcast_arrays
broadcast_to
broadcast_shapes
Examples
--------
Manually adding two vectors, using broadcasting:
>>> x = np.array([[1], [2], [3]])
>>> y = np.array([4, 5, 6])
>>> b = np.broadcast(x, y)
>>> out = np.empty(b.shape)
>>> out.flat = [u+v for (u,v) in b]
>>> out
array([[5., 6., 7.],
[6., 7., 8.],
[7., 8., 9.]])
Compare against built-in broadcasting:
>>> x + y
array([[5, 6, 7],
[6, 7, 8],
[7, 8, 9]])
""")
# attributes
add_newdoc('numpy.core', 'broadcast', ('index',
"""
current index in broadcasted result
Examples
--------
>>> x = np.array([[1], [2], [3]])
>>> y = np.array([4, 5, 6])
>>> b = np.broadcast(x, y)
>>> b.index
0
>>> next(b), next(b), next(b)
((1, 4), (1, 5), (1, 6))
>>> b.index
3
"""))
add_newdoc('numpy.core', 'broadcast', ('iters',
"""
tuple of iterators along ``self``'s "components."
Returns a tuple of `numpy.flatiter` objects, one for each "component"
of ``self``.
See Also
--------
numpy.flatiter
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> row, col = b.iters
>>> next(row), next(col)
(1, 4)
"""))
add_newdoc('numpy.core', 'broadcast', ('ndim',
"""
Number of dimensions of broadcasted result. Alias for `nd`.
.. versionadded:: 1.12.0
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.ndim
2
"""))
add_newdoc('numpy.core', 'broadcast', ('nd',
"""
Number of dimensions of broadcasted result. For code intended for NumPy
1.12.0 and later the more consistent `ndim` is preferred.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.nd
2
"""))
add_newdoc('numpy.core', 'broadcast', ('numiter',
"""
Number of iterators possessed by the broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.numiter
2
"""))
add_newdoc('numpy.core', 'broadcast', ('shape',
"""
Shape of broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.shape
(3, 3)
"""))
add_newdoc('numpy.core', 'broadcast', ('size',
"""
Total size of broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.size
9
"""))
add_newdoc('numpy.core', 'broadcast', ('reset',
"""
reset()
Reset the broadcasted result's iterator(s).
Parameters
----------
None
Returns
-------
None
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.index
0
>>> next(b), next(b), next(b)
((1, 4), (2, 4), (3, 4))
>>> b.index
3
>>> b.reset()
>>> b.index
0
"""))
###############################################################################
#
# numpy functions
#
###############################################################################
add_newdoc('numpy.core.multiarray', 'array',
"""
array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0,
like=None)
Create an array.
Parameters
----------
object : array_like
An array, any object exposing the array interface, an object whose
__array__ method returns an array, or any (nested) sequence.
If object is a scalar, a 0-dimensional array containing object is
returned.
dtype : data-type, optional
The desired data-type for the array. If not given, then the type will
be determined as the minimum type required to hold the objects in the
sequence.
copy : bool, optional
If true (default), then the object is copied. Otherwise, a copy will
only be made if __array__ returns a copy, if obj is a nested sequence,
or if a copy is needed to satisfy any of the other requirements
(`dtype`, `order`, etc.).
order : {'K', 'A', 'C', 'F'}, optional
Specify the memory layout of the array. If object is not an array, the
newly created array will be in C order (row major) unless 'F' is
specified, in which case it will be in Fortran order (column major).
If object is an array the following holds.
===== ========= ===================================================
order no copy copy=True
===== ========= ===================================================
'K' unchanged F & C order preserved, otherwise most similar order
'A' unchanged F order if input is F and not C, otherwise C order
'C' C order C order
'F' F order F order
===== ========= ===================================================
When ``copy=False`` and a copy is made for other reasons, the result is
the same as if ``copy=True``, with some exceptions for 'A', see the
Notes section. The default order is 'K'.
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise
the returned array will be forced to be a base-class array (default).
ndmin : int, optional
Specifies the minimum number of dimensions that the resulting
array should have. Ones will be prepended to the shape as
needed to meet this requirement.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
An array object satisfying the specified requirements.
See Also
--------
empty_like : Return an empty array with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full_like : Return a new array with shape of input filled with value.
empty : Return a new uninitialized array.
ones : Return a new array setting values to one.
zeros : Return a new array setting values to zero.
full : Return a new array of given shape filled with value.
Notes
-----
When order is 'A' and `object` is an array in neither 'C' nor 'F' order,
and a copy is forced by a change in dtype, then the order of the result is
not necessarily 'C' as expected. This is likely a bug.
Examples
--------
>>> np.array([1, 2, 3])
array([1, 2, 3])
Upcasting:
>>> np.array([1, 2, 3.0])
array([ 1., 2., 3.])
More than one dimension:
>>> np.array([[1, 2], [3, 4]])
array([[1, 2],
[3, 4]])
Minimum dimensions 2:
>>> np.array([1, 2, 3], ndmin=2)
array([[1, 2, 3]])
Type provided:
>>> np.array([1, 2, 3], dtype=complex)
array([ 1.+0.j, 2.+0.j, 3.+0.j])
Data-type consisting of more than one element:
>>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
>>> x['a']
array([1, 3])
Creating an array from sub-classes:
>>> np.array(np.mat('1 2; 3 4'))
array([[1, 2],
[3, 4]])
>>> np.array(np.mat('1 2; 3 4'), subok=True)
matrix([[1, 2],
[3, 4]])
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'asarray',
"""
asarray(a, dtype=None, order=None, *, like=None)
Convert the input to an array.
Parameters
----------
a : array_like
Input data, in any form that can be converted to an array. This
includes lists, lists of tuples, tuples, tuples of tuples, tuples
of lists and ndarrays.
dtype : data-type, optional
By default, the data-type is inferred from the input data.
order : {'C', 'F', 'A', 'K'}, optional
Memory layout. 'A' and 'K' depend on the order of input array a.
'C' row-major (C-style),
'F' column-major (Fortran-style) memory representation.
'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise
'K' (keep) preserve input order
Defaults to 'K'.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array interpretation of `a`. No copy is performed if the input
is already an ndarray with matching dtype and order. If `a` is a
subclass of ndarray, a base class ndarray is returned.
See Also
--------
asanyarray : Similar function which passes through subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfarray : Convert input to a floating point ndarray.
asfortranarray : Convert input to an ndarray with column-major
memory order.
asarray_chkfinite : Similar function which checks input for NaNs and Infs.
fromiter : Create an array from an iterator.
fromfunction : Construct an array by executing a function on grid
positions.
Examples
--------
Convert a list into an array:
>>> a = [1, 2]
>>> np.asarray(a)
array([1, 2])
Existing arrays are not copied:
>>> a = np.array([1, 2])
>>> np.asarray(a) is a
True
If `dtype` is set, array is copied only if dtype does not match:
>>> a = np.array([1, 2], dtype=np.float32)
>>> np.asarray(a, dtype=np.float32) is a
True
>>> np.asarray(a, dtype=np.float64) is a
False
Contrary to `asanyarray`, ndarray subclasses are not passed through:
>>> issubclass(np.recarray, np.ndarray)
True
>>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
>>> np.asarray(a) is a
False
>>> np.asanyarray(a) is a
True
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'asanyarray',
"""
asanyarray(a, dtype=None, order=None, *, like=None)
Convert the input to an ndarray, but pass ndarray subclasses through.
Parameters
----------
a : array_like
Input data, in any form that can be converted to an array. This
includes scalars, lists, lists of tuples, tuples, tuples of tuples,
tuples of lists, and ndarrays.
dtype : data-type, optional
By default, the data-type is inferred from the input data.
order : {'C', 'F', 'A', 'K'}, optional
Memory layout. 'A' and 'K' depend on the order of input array a.
'C' row-major (C-style),
'F' column-major (Fortran-style) memory representation.
'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise
'K' (keep) preserve input order
Defaults to 'C'.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray or an ndarray subclass
Array interpretation of `a`. If `a` is an ndarray or a subclass
of ndarray, it is returned as-is and no copy is performed.
See Also
--------
asarray : Similar function which always returns ndarrays.
ascontiguousarray : Convert input to a contiguous array.
asfarray : Convert input to a floating point ndarray.
asfortranarray : Convert input to an ndarray with column-major
memory order.
asarray_chkfinite : Similar function which checks input for NaNs and
Infs.
fromiter : Create an array from an iterator.
fromfunction : Construct an array by executing a function on grid
positions.
Examples
--------
Convert a list into an array:
>>> a = [1, 2]
>>> np.asanyarray(a)
array([1, 2])
Instances of `ndarray` subclasses are passed through as-is:
>>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
>>> np.asanyarray(a) is a
True
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'ascontiguousarray',
"""
ascontiguousarray(a, dtype=None, *, like=None)
Return a contiguous array (ndim >= 1) in memory (C order).
Parameters
----------
a : array_like
Input array.
dtype : str or dtype object, optional
Data-type of returned array.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Contiguous array of same shape and content as `a`, with type `dtype`
if specified.
See Also
--------
asfortranarray : Convert input to an ndarray with column-major
memory order.
require : Return an ndarray that satisfies requirements.
ndarray.flags : Information about the memory layout of the array.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> np.ascontiguousarray(x, dtype=np.float32)
array([[0., 1., 2.],
[3., 4., 5.]], dtype=float32)
>>> x.flags['C_CONTIGUOUS']
True
Note: This function returns an array with at least one-dimension (1-d)
so it will not preserve 0-d arrays.
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'asfortranarray',
"""
asfortranarray(a, dtype=None, *, like=None)
Return an array (ndim >= 1) laid out in Fortran order in memory.
Parameters
----------
a : array_like
Input array.
dtype : str or dtype object, optional
By default, the data-type is inferred from the input data.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
The input `a` in Fortran, or column-major, order.
See Also
--------
ascontiguousarray : Convert input to a contiguous (C order) array.
asanyarray : Convert input to an ndarray with either row or
column-major memory order.
require : Return an ndarray that satisfies requirements.
ndarray.flags : Information about the memory layout of the array.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> y = np.asfortranarray(x)
>>> x.flags['F_CONTIGUOUS']
False
>>> y.flags['F_CONTIGUOUS']
True
Note: This function returns an array with at least one-dimension (1-d)
so it will not preserve 0-d arrays.
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'empty',
"""
empty(shape, dtype=float, order='C', *, like=None)
Return a new array of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
Shape of the empty array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
Desired output data-type for the array, e.g, `numpy.int8`. Default is
`numpy.float64`.
order : {'C', 'F'}, optional, default: 'C'
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array of uninitialized (arbitrary) data of the given shape, dtype, and
order. Object arrays will be initialized to None.
See Also
--------
empty_like : Return an empty array with shape and type of input.
ones : Return a new array setting values to one.
zeros : Return a new array setting values to zero.
full : Return a new array of given shape filled with value.
Notes
-----
`empty`, unlike `zeros`, does not set the array values to zero,
and may therefore be marginally faster. On the other hand, it requires
the user to manually set all the values in the array, and should be
used with caution.
Examples
--------
>>> np.empty([2, 2])
array([[ -9.74499359e+001, 6.69583040e-309],
[ 2.13182611e-314, 3.06959433e-309]]) #uninitialized
>>> np.empty([2, 2], dtype=int)
array([[-1073741821, -1067949133],
[ 496041986, 19249760]]) #uninitialized
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'scalar',
"""
scalar(dtype, obj)
Return a new scalar array of the given type initialized with obj.
This function is meant mainly for pickle support. `dtype` must be a
valid data-type descriptor. If `dtype` corresponds to an object
descriptor, then `obj` can be any object, otherwise `obj` must be a
string. If `obj` is not given, it will be interpreted as None for object
type and as zeros for all other types.
""")
add_newdoc('numpy.core.multiarray', 'zeros',
"""
zeros(shape, dtype=float, order='C', *, like=None)
Return a new array of given shape and type, filled with zeros.
Parameters
----------
shape : int or tuple of ints
Shape of the new array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
order : {'C', 'F'}, optional, default: 'C'
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array of zeros with the given shape, dtype, and order.
See Also
--------
zeros_like : Return an array of zeros with shape and type of input.
empty : Return a new uninitialized array.
ones : Return a new array setting values to one.
full : Return a new array of given shape filled with value.
Examples
--------
>>> np.zeros(5)
array([ 0., 0., 0., 0., 0.])
>>> np.zeros((5,), dtype=int)
array([0, 0, 0, 0, 0])
>>> np.zeros((2, 1))
array([[ 0.],
[ 0.]])
>>> s = (2,2)
>>> np.zeros(s)
array([[ 0., 0.],
[ 0., 0.]])
>>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
array([(0, 0), (0, 0)],
dtype=[('x', '<i4'), ('y', '<i4')])
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'set_typeDict',
"""set_typeDict(dict)
Set the internal dictionary that can look up an array type using a
registered code.
""")
add_newdoc('numpy.core.multiarray', 'fromstring',
"""
fromstring(string, dtype=float, count=-1, *, sep, like=None)
A new 1-D array initialized from text data in a string.
Parameters
----------
string : str
A string containing the data.
dtype : data-type, optional
The data type of the array; default: float. For binary input data,
the data must be in exactly this format. Most builtin numeric types are
supported and extension types may be supported.
.. versionadded:: 1.18.0
Complex dtypes.
count : int, optional
Read this number of `dtype` elements from the data. If this is
negative (the default), the count will be determined from the
length of the data.
sep : str, optional
The string separating numbers in the data; extra whitespace between
elements is also ignored.
.. deprecated:: 1.14
Passing ``sep=''``, the default, is deprecated since it will
trigger the deprecated binary mode of this function. This mode
interprets `string` as binary bytes, rather than ASCII text with
decimal numbers, an operation which is better spelt
``frombuffer(string, dtype, count)``. If `string` contains unicode
text, the binary mode of `fromstring` will first encode it into
bytes using either utf-8 (python 3) or the default encoding
(python 2), neither of which produce sane results.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
arr : ndarray
The constructed array.
Raises
------
ValueError
If the string is not the correct size to satisfy the requested
`dtype` and `count`.
See Also
--------
frombuffer, fromfile, fromiter
Examples
--------
>>> np.fromstring('1 2', dtype=int, sep=' ')
array([1, 2])
>>> np.fromstring('1, 2', dtype=int, sep=',')
array([1, 2])
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'compare_chararrays',
"""
compare_chararrays(a1, a2, cmp, rstrip)
Performs element-wise comparison of two string arrays using the
comparison operator specified by `cmp_op`.
Parameters
----------
a1, a2 : array_like
Arrays to be compared.
cmp : {"<", "<=", "==", ">=", ">", "!="}
Type of comparison.
rstrip : Boolean
If True, the spaces at the end of Strings are removed before the comparison.
Returns
-------
out : ndarray
The output array of type Boolean with the same shape as a and b.
Raises
------
ValueError
If `cmp_op` is not valid.
TypeError
If at least one of `a` or `b` is a non-string array
Examples
--------
>>> a = np.array(["a", "b", "cde"])
>>> b = np.array(["a", "a", "dec"])
>>> np.compare_chararrays(a, b, ">", True)
array([False, True, False])
""")
add_newdoc('numpy.core.multiarray', 'fromiter',
"""
fromiter(iter, dtype, count=-1, *, like=None)
Create a new 1-dimensional array from an iterable object.
Parameters
----------
iter : iterable object
An iterable object providing data for the array.
dtype : data-type
The data-type of the returned array.
.. versionchanged:: 1.23
Object and subarray dtypes are now supported (note that the final
result is not 1-D for a subarray dtype).
count : int, optional
The number of items to read from *iterable*. The default is -1,
which means all data is read.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
The output array.
Notes
-----
Specify `count` to improve performance. It allows ``fromiter`` to
pre-allocate the output array, instead of resizing it on demand.
Examples
--------
>>> iterable = (x*x for x in range(5))
>>> np.fromiter(iterable, float)
array([ 0., 1., 4., 9., 16.])
A carefully constructed subarray dtype will lead to higher dimensional
results:
>>> iterable = ((x+1, x+2) for x in range(5))
>>> np.fromiter(iterable, dtype=np.dtype((int, 2)))
array([[1, 2],
[2, 3],
[3, 4],
[4, 5],
[5, 6]])
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'fromfile',
"""
fromfile(file, dtype=float, count=-1, sep='', offset=0, *, like=None)
Construct an array from data in a text or binary file.
A highly efficient way of reading binary data with a known data-type,
as well as parsing simply formatted text files. Data written using the
`tofile` method can be read using this function.
Parameters
----------
file : file or str or Path
Open file object or filename.
.. versionchanged:: 1.17.0
`pathlib.Path` objects are now accepted.
dtype : data-type
Data type of the returned array.
For binary files, it is used to determine the size and byte-order
of the items in the file.
Most builtin numeric types are supported and extension types may be supported.
.. versionadded:: 1.18.0
Complex dtypes.
count : int
Number of items to read. ``-1`` means all items (i.e., the complete
file).
sep : str
Separator between items if file is a text file.
Empty ("") separator means the file should be treated as binary.
Spaces (" ") in the separator match zero or more whitespace characters.
A separator consisting only of spaces must match at least one
whitespace.
offset : int
The offset (in bytes) from the file's current position. Defaults to 0.
Only permitted for binary files.
.. versionadded:: 1.17.0
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
See also
--------
load, save
ndarray.tofile
loadtxt : More flexible way of loading data from a text file.
Notes
-----
Do not rely on the combination of `tofile` and `fromfile` for
data storage, as the binary files generated are not platform
independent. In particular, no byte-order or data-type information is
saved. Data can be stored in the platform independent ``.npy`` format
using `save` and `load` instead.
Examples
--------
Construct an ndarray:
>>> dt = np.dtype([('time', [('min', np.int64), ('sec', np.int64)]),
... ('temp', float)])
>>> x = np.zeros((1,), dtype=dt)
>>> x['time']['min'] = 10; x['temp'] = 98.25
>>> x
array([((10, 0), 98.25)],
dtype=[('time', [('min', '<i8'), ('sec', '<i8')]), ('temp', '<f8')])
Save the raw data to disk:
>>> import tempfile
>>> fname = tempfile.mkstemp()[1]
>>> x.tofile(fname)
Read the raw data from disk:
>>> np.fromfile(fname, dtype=dt)
array([((10, 0), 98.25)],
dtype=[('time', [('min', '<i8'), ('sec', '<i8')]), ('temp', '<f8')])
The recommended way to store and load data:
>>> np.save(fname, x)
>>> np.load(fname + '.npy')
array([((10, 0), 98.25)],
dtype=[('time', [('min', '<i8'), ('sec', '<i8')]), ('temp', '<f8')])
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'frombuffer',
"""
frombuffer(buffer, dtype=float, count=-1, offset=0, *, like=None)
Interpret a buffer as a 1-dimensional array.
Parameters
----------
buffer : buffer_like
An object that exposes the buffer interface.
dtype : data-type, optional
Data-type of the returned array; default: float.
count : int, optional
Number of items to read. ``-1`` means all data in the buffer.
offset : int, optional
Start reading the buffer from this offset (in bytes); default: 0.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Notes
-----
If the buffer has data that is not in machine byte-order, this should
be specified as part of the data-type, e.g.::
>>> dt = np.dtype(int)
>>> dt = dt.newbyteorder('>')
>>> np.frombuffer(buf, dtype=dt) # doctest: +SKIP
The data of the resulting array will not be byteswapped, but will be
interpreted correctly.
This function creates a view into the original object. This should be safe
in general, but it may make sense to copy the result when the original
object is mutable or untrusted.
Examples
--------
>>> s = b'hello world'
>>> np.frombuffer(s, dtype='S1', count=5, offset=6)
array([b'w', b'o', b'r', b'l', b'd'], dtype='|S1')
>>> np.frombuffer(b'\\x01\\x02', dtype=np.uint8)
array([1, 2], dtype=uint8)
>>> np.frombuffer(b'\\x01\\x02\\x03\\x04\\x05', dtype=np.uint8, count=3)
array([1, 2, 3], dtype=uint8)
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', 'from_dlpack',
"""
from_dlpack(x, /)
Create a NumPy array from an object implementing the ``__dlpack__``
protocol. Generally, the returned NumPy array is a read-only view
of the input object. See [1]_ and [2]_ for more details.
Parameters
----------
x : object
A Python object that implements the ``__dlpack__`` and
``__dlpack_device__`` methods.
Returns
-------
out : ndarray
References
----------
.. [1] Array API documentation,
https://data-apis.org/array-api/latest/design_topics/data_interchange.html#syntax-for-data-interchange-with-dlpack
.. [2] Python specification for DLPack,
https://dmlc.github.io/dlpack/latest/python_spec.html
Examples
--------
>>> import torch
>>> x = torch.arange(10)
>>> # create a view of the torch tensor "x" in NumPy
>>> y = np.from_dlpack(x)
""")
add_newdoc('numpy.core', 'fastCopyAndTranspose',
"""_fastCopyAndTranspose(a)""")
add_newdoc('numpy.core.multiarray', 'correlate',
"""cross_correlate(a,v, mode=0)""")
add_newdoc('numpy.core.multiarray', 'arange',
"""
arange([start,] stop[, step,], dtype=None, *, like=None)
Return evenly spaced values within a given interval.
``arange`` can be called with a varying number of positional arguments:
* ``arange(stop)``: Values are generated within the half-open interval
``[0, stop)`` (in other words, the interval including `start` but
excluding `stop`).
* ``arange(start, stop)``: Values are generated within the half-open
interval ``[start, stop)``.
* ``arange(start, stop, step)`` Values are generated within the half-open
interval ``[start, stop)``, with spacing between values given by
``step``.
For integer arguments the function is roughly equivalent to the Python
built-in :py:class:`range`, but returns an ndarray rather than a ``range``
instance.
When using a non-integer step, such as 0.1, it is often better to use
`numpy.linspace`.
See the Warning sections below for more information.
Parameters
----------
start : integer or real, optional
Start of interval. The interval includes this value. The default
start value is 0.
stop : integer or real
End of interval. The interval does not include this value, except
in some cases where `step` is not an integer and floating point
round-off affects the length of `out`.
step : integer or real, optional
Spacing between values. For any output `out`, this is the distance
between two adjacent values, ``out[i+1] - out[i]``. The default
step size is 1. If `step` is specified as a position argument,
`start` must also be given.
dtype : dtype, optional
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
arange : ndarray
Array of evenly spaced values.
For floating point arguments, the length of the result is
``ceil((stop - start)/step)``. Because of floating point overflow,
this rule may result in the last element of `out` being greater
than `stop`.
Warnings
--------
The length of the output might not be numerically stable.
Another stability issue is due to the internal implementation of
`numpy.arange`.
The actual step value used to populate the array is
``dtype(start + step) - dtype(start)`` and not `step`. Precision loss
can occur here, due to casting or due to using floating points when
`start` is much larger than `step`. This can lead to unexpected
behaviour. For example::
>>> np.arange(0, 5, 0.5, dtype=int)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
>>> np.arange(-3, 3, 0.5, dtype=int)
array([-3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8])
In such cases, the use of `numpy.linspace` should be preferred.
The built-in :py:class:`range` generates :std:doc:`Python built-in integers
that have arbitrary size <c-api/long>`, while `numpy.arange` produces
`numpy.int32` or `numpy.int64` numbers. This may result in incorrect
results for large integer values::
>>> power = 40
>>> modulo = 10000
>>> x1 = [(n ** power) % modulo for n in range(8)]
>>> x2 = [(n ** power) % modulo for n in np.arange(8)]
>>> print(x1)
[0, 1, 7776, 8801, 6176, 625, 6576, 4001] # correct
>>> print(x2)
[0, 1, 7776, 7185, 0, 5969, 4816, 3361] # incorrect
See Also
--------
numpy.linspace : Evenly spaced numbers with careful handling of endpoints.
numpy.ogrid: Arrays of evenly spaced numbers in N-dimensions.
numpy.mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.
Examples
--------
>>> np.arange(3)
array([0, 1, 2])
>>> np.arange(3.0)
array([ 0., 1., 2.])
>>> np.arange(3,7)
array([3, 4, 5, 6])
>>> np.arange(3,7,2)
array([3, 5])
""".replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
))
add_newdoc('numpy.core.multiarray', '_get_ndarray_c_version',
"""_get_ndarray_c_version()
Return the compile time NPY_VERSION (formerly called NDARRAY_VERSION) number.
""")
add_newdoc('numpy.core.multiarray', '_reconstruct',
"""_reconstruct(subtype, shape, dtype)
Construct an empty array. Used by Pickles.
""")
add_newdoc('numpy.core.multiarray', 'set_string_function',
"""
set_string_function(f, repr=1)
Internal method to set a function to be used when pretty printing arrays.
""")
add_newdoc('numpy.core.multiarray', 'set_numeric_ops',
"""
set_numeric_ops(op1=func1, op2=func2, ...)
Set numerical operators for array objects.
.. deprecated:: 1.16
For the general case, use :c:func:`PyUFunc_ReplaceLoopBySignature`.
For ndarray subclasses, define the ``__array_ufunc__`` method and
override the relevant ufunc.
Parameters
----------
op1, op2, ... : callable
Each ``op = func`` pair describes an operator to be replaced.
For example, ``add = lambda x, y: np.add(x, y) % 5`` would replace
addition by modulus 5 addition.
Returns
-------
saved_ops : list of callables
A list of all operators, stored before making replacements.
Notes
-----
.. warning::
Use with care! Incorrect usage may lead to memory errors.
A function replacing an operator cannot make use of that operator.
For example, when replacing add, you may not use ``+``. Instead,
directly call ufuncs.
Examples
--------
>>> def add_mod5(x, y):
... return np.add(x, y) % 5
...
>>> old_funcs = np.set_numeric_ops(add=add_mod5)
>>> x = np.arange(12).reshape((3, 4))
>>> x + x
array([[0, 2, 4, 1],
[3, 0, 2, 4],
[1, 3, 0, 2]])
>>> ignore = np.set_numeric_ops(**old_funcs) # restore operators
""")
add_newdoc('numpy.core.multiarray', 'promote_types',
"""
promote_types(type1, type2)
Returns the data type with the smallest size and smallest scalar
kind to which both ``type1`` and ``type2`` may be safely cast.
The returned data type is always considered "canonical", this mainly
means that the promoted dtype will always be in native byte order.
This function is symmetric, but rarely associative.
Parameters
----------
type1 : dtype or dtype specifier
First data type.
type2 : dtype or dtype specifier
Second data type.
Returns
-------
out : dtype
The promoted data type.
Notes
-----
Please see `numpy.result_type` for additional information about promotion.
.. versionadded:: 1.6.0
Starting in NumPy 1.9, promote_types function now returns a valid string
length when given an integer or float dtype as one argument and a string
dtype as another argument. Previously it always returned the input string
dtype, even if it wasn't long enough to store the max integer/float value
converted to a string.
.. versionchanged:: 1.23.0
NumPy now supports promotion for more structured dtypes. It will now
remove unnecessary padding from a structure dtype and promote included
fields individually.
See Also
--------
result_type, dtype, can_cast
Examples
--------
>>> np.promote_types('f4', 'f8')
dtype('float64')
>>> np.promote_types('i8', 'f4')
dtype('float64')
>>> np.promote_types('>i8', '<c8')
dtype('complex128')
>>> np.promote_types('i4', 'S8')
dtype('S11')
An example of a non-associative case:
>>> p = np.promote_types
>>> p('S', p('i1', 'u1'))
dtype('S6')
>>> p(p('S', 'i1'), 'u1')
dtype('S4')
""")
add_newdoc('numpy.core.multiarray', 'c_einsum',
"""
c_einsum(subscripts, *operands, out=None, dtype=None, order='K',
casting='safe')
*This documentation shadows that of the native python implementation of the `einsum` function,
except all references and examples related to the `optimize` argument (v 0.12.0) have been removed.*
Evaluates the Einstein summation convention on the operands.
Using the Einstein summation convention, many common multi-dimensional,
linear algebraic array operations can be represented in a simple fashion.
In *implicit* mode `einsum` computes these values.
In *explicit* mode, `einsum` provides further flexibility to compute
other array operations that might not be considered classical Einstein
summation operations, by disabling, or forcing summation over specified
subscript labels.
See the notes and examples for clarification.
Parameters
----------
subscripts : str
Specifies the subscripts for summation as comma separated list of
subscript labels. An implicit (classical Einstein summation)
calculation is performed unless the explicit indicator '->' is
included as well as subscript labels of the precise output form.
operands : list of array_like
These are the arrays for the operation.
out : ndarray, optional
If provided, the calculation is done into this array.
dtype : {data-type, None}, optional
If provided, forces the calculation to use the data type specified.
Note that you may have to also give a more liberal `casting`
parameter to allow the conversions. Default is None.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the output. 'C' means it should
be C contiguous. 'F' means it should be Fortran contiguous,
'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
'K' means it should be as close to the layout of the inputs as
is possible, including arbitrarily permuted axes.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Setting this to
'unsafe' is not recommended, as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
Default is 'safe'.
optimize : {False, True, 'greedy', 'optimal'}, optional
Controls if intermediate optimization should occur. No optimization
will occur if False and True will default to the 'greedy' algorithm.
Also accepts an explicit contraction list from the ``np.einsum_path``
function. See ``np.einsum_path`` for more details. Defaults to False.
Returns
-------
output : ndarray
The calculation based on the Einstein summation convention.
See Also
--------
einsum_path, dot, inner, outer, tensordot, linalg.multi_dot
Notes
-----
.. versionadded:: 1.6.0
The Einstein summation convention can be used to compute
many multi-dimensional, linear algebraic array operations. `einsum`
provides a succinct way of representing these.
A non-exhaustive list of these operations,
which can be computed by `einsum`, is shown below along with examples:
* Trace of an array, :py:func:`numpy.trace`.
* Return a diagonal, :py:func:`numpy.diag`.
* Array axis summations, :py:func:`numpy.sum`.
* Transpositions and permutations, :py:func:`numpy.transpose`.
* Matrix multiplication and dot product, :py:func:`numpy.matmul` :py:func:`numpy.dot`.
* Vector inner and outer products, :py:func:`numpy.inner` :py:func:`numpy.outer`.
* Broadcasting, element-wise and scalar multiplication, :py:func:`numpy.multiply`.
* Tensor contractions, :py:func:`numpy.tensordot`.
* Chained array operations, in efficient calculation order, :py:func:`numpy.einsum_path`.
The subscripts string is a comma-separated list of subscript labels,
where each label refers to a dimension of the corresponding operand.
Whenever a label is repeated it is summed, so ``np.einsum('i,i', a, b)``
is equivalent to :py:func:`np.inner(a,b) <numpy.inner>`. If a label
appears only once, it is not summed, so ``np.einsum('i', a)`` produces a
view of ``a`` with no changes. A further example ``np.einsum('ij,jk', a, b)``
describes traditional matrix multiplication and is equivalent to
:py:func:`np.matmul(a,b) <numpy.matmul>`. Repeated subscript labels in one
operand take the diagonal. For example, ``np.einsum('ii', a)`` is equivalent
to :py:func:`np.trace(a) <numpy.trace>`.
In *implicit mode*, the chosen subscripts are important
since the axes of the output are reordered alphabetically. This
means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
``np.einsum('ji', a)`` takes its transpose. Additionally,
``np.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
``np.einsum('ij,jh', a, b)`` returns the transpose of the
multiplication since subscript 'h' precedes subscript 'i'.
In *explicit mode* the output can be directly controlled by
specifying output subscript labels. This requires the
identifier '->' as well as the list of output subscript labels.
This feature increases the flexibility of the function since
summing can be disabled or forced when required. The call
``np.einsum('i->', a)`` is like :py:func:`np.sum(a, axis=-1) <numpy.sum>`,
and ``np.einsum('ii->i', a)`` is like :py:func:`np.diag(a) <numpy.diag>`.
The difference is that `einsum` does not allow broadcasting by default.
Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the
order of the output subscript labels and therefore returns matrix
multiplication, unlike the example above in implicit mode.
To enable and control broadcasting, use an ellipsis. Default
NumPy-style broadcasting is done by adding an ellipsis
to the left of each term, like ``np.einsum('...ii->...i', a)``.
To take the trace along the first and last axes,
you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
product with the left-most indices instead of rightmost, one can do
``np.einsum('ij...,jk...->ik...', a, b)``.
When there is only one operand, no axes are summed, and no output
parameter is provided, a view into the operand is returned instead
of a new array. Thus, taking the diagonal as ``np.einsum('ii->i', a)``
produces a view (changed in version 1.10.0).
`einsum` also provides an alternative way to provide the subscripts
and operands as ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``.
If the output shape is not provided in this format `einsum` will be
calculated in implicit mode, otherwise it will be performed explicitly.
The examples below have corresponding `einsum` calls with the two
parameter methods.
.. versionadded:: 1.10.0
Views returned from einsum are now writeable whenever the input array
is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now
have the same effect as :py:func:`np.swapaxes(a, 0, 2) <numpy.swapaxes>`
and ``np.einsum('ii->i', a)`` will return a writeable view of the diagonal
of a 2D array.
Examples
--------
>>> a = np.arange(25).reshape(5,5)
>>> b = np.arange(5)
>>> c = np.arange(6).reshape(2,3)
Trace of a matrix:
>>> np.einsum('ii', a)
60
>>> np.einsum(a, [0,0])
60
>>> np.trace(a)
60
Extract the diagonal (requires explicit form):
>>> np.einsum('ii->i', a)
array([ 0, 6, 12, 18, 24])
>>> np.einsum(a, [0,0], [0])
array([ 0, 6, 12, 18, 24])
>>> np.diag(a)
array([ 0, 6, 12, 18, 24])
Sum over an axis (requires explicit form):
>>> np.einsum('ij->i', a)
array([ 10, 35, 60, 85, 110])
>>> np.einsum(a, [0,1], [0])
array([ 10, 35, 60, 85, 110])
>>> np.sum(a, axis=1)
array([ 10, 35, 60, 85, 110])
For higher dimensional arrays summing a single axis can be done with ellipsis:
>>> np.einsum('...j->...', a)
array([ 10, 35, 60, 85, 110])
>>> np.einsum(a, [Ellipsis,1], [Ellipsis])
array([ 10, 35, 60, 85, 110])
Compute a matrix transpose, or reorder any number of axes:
>>> np.einsum('ji', c)
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum('ij->ji', c)
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum(c, [1,0])
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.transpose(c)
array([[0, 3],
[1, 4],
[2, 5]])
Vector inner products:
>>> np.einsum('i,i', b, b)
30
>>> np.einsum(b, [0], b, [0])
30
>>> np.inner(b,b)
30
Matrix vector multiplication:
>>> np.einsum('ij,j', a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum(a, [0,1], b, [1])
array([ 30, 80, 130, 180, 230])
>>> np.dot(a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum('...j,j', a, b)
array([ 30, 80, 130, 180, 230])
Broadcasting and scalar multiplication:
>>> np.einsum('..., ...', 3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum(',ij', 3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum(3, [Ellipsis], c, [Ellipsis])
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.multiply(3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
Vector outer product:
>>> np.einsum('i,j', np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.einsum(np.arange(2)+1, [0], b, [1])
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.outer(np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
Tensor contraction:
>>> a = np.arange(60.).reshape(3,4,5)
>>> b = np.arange(24.).reshape(4,3,2)
>>> np.einsum('ijk,jil->kl', a, b)
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> np.tensordot(a,b, axes=([1,0],[0,1]))
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
Writeable returned arrays (since version 1.10.0):
>>> a = np.zeros((3, 3))
>>> np.einsum('ii->i', a)[:] = 1
>>> a
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
Example of ellipsis use:
>>> a = np.arange(6).reshape((3,2))
>>> b = np.arange(12).reshape((4,3))
>>> np.einsum('ki,jk->ij', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('ki,...k->i...', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('k...,jk', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
""")
##############################################################################
#
# Documentation for ndarray attributes and methods
#
##############################################################################
##############################################################################
#
# ndarray object
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'ndarray',
"""
ndarray(shape, dtype=float, buffer=None, offset=0,
strides=None, order=None)
An array object represents a multidimensional, homogeneous array
of fixed-size items. An associated data-type object describes the
format of each element in the array (its byte-order, how many bytes it
occupies in memory, whether it is an integer, a floating point number,
or something else, etc.)
Arrays should be constructed using `array`, `zeros` or `empty` (refer
to the See Also section below). The parameters given here refer to
a low-level method (`ndarray(...)`) for instantiating an array.
For more information, refer to the `numpy` module and examine the
methods and attributes of an array.
Parameters
----------
(for the __new__ method; see Notes below)
shape : tuple of ints
Shape of created array.
dtype : data-type, optional
Any object that can be interpreted as a numpy data type.
buffer : object exposing buffer interface, optional
Used to fill the array with data.
offset : int, optional
Offset of array data in buffer.
strides : tuple of ints, optional
Strides of data in memory.
order : {'C', 'F'}, optional
Row-major (C-style) or column-major (Fortran-style) order.
Attributes
----------
T : ndarray
Transpose of the array.
data : buffer
The array's elements, in memory.
dtype : dtype object
Describes the format of the elements in the array.
flags : dict
Dictionary containing information related to memory use, e.g.,
'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
flat : numpy.flatiter object
Flattened version of the array as an iterator. The iterator
allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for
assignment examples; TODO).
imag : ndarray
Imaginary part of the array.
real : ndarray
Real part of the array.
size : int
Number of elements in the array.
itemsize : int
The memory use of each array element in bytes.
nbytes : int
The total number of bytes required to store the array data,
i.e., ``itemsize * size``.
ndim : int
The array's number of dimensions.
shape : tuple of ints
Shape of the array.
strides : tuple of ints
The step-size required to move from one element to the next in
memory. For example, a contiguous ``(3, 4)`` array of type
``int16`` in C-order has strides ``(8, 2)``. This implies that
to move from element to element in memory requires jumps of 2 bytes.
To move from row-to-row, one needs to jump 8 bytes at a time
(``2 * 4``).
ctypes : ctypes object
Class containing properties of the array needed for interaction
with ctypes.
base : ndarray
If the array is a view into another array, that array is its `base`
(unless that array is also a view). The `base` array is where the
array data is actually stored.
See Also
--------
array : Construct an array.
zeros : Create an array, each element of which is zero.
empty : Create an array, but leave its allocated memory unchanged (i.e.,
it contains "garbage").
dtype : Create a data-type.
numpy.typing.NDArray : An ndarray alias :term:`generic <generic type>`
w.r.t. its `dtype.type <numpy.dtype.type>`.
Notes
-----
There are two modes of creating an array using ``__new__``:
1. If `buffer` is None, then only `shape`, `dtype`, and `order`
are used.
2. If `buffer` is an object exposing the buffer interface, then
all keywords are interpreted.
No ``__init__`` method is needed because the array is fully initialized
after the ``__new__`` method.
Examples
--------
These examples illustrate the low-level `ndarray` constructor. Refer
to the `See Also` section above for easier ways of constructing an
ndarray.
First mode, `buffer` is None:
>>> np.ndarray(shape=(2,2), dtype=float, order='F')
array([[0.0e+000, 0.0e+000], # random
[ nan, 2.5e-323]])
Second mode:
>>> np.ndarray((2,), buffer=np.array([1,2,3]),
... offset=np.int_().itemsize,
... dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])
""")
##############################################################################
#
# ndarray attributes
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_interface__',
"""Array protocol: Python side."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_priority__',
"""Array priority."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_struct__',
"""Array protocol: C-struct side."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__dlpack__',
"""a.__dlpack__(*, stream=None)
DLPack Protocol: Part of the Array API."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__dlpack_device__',
"""a.__dlpack_device__()
DLPack Protocol: Part of the Array API."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('base',
"""
Base object if memory is from some other object.
Examples
--------
The base of an array that owns its memory is None:
>>> x = np.array([1,2,3,4])
>>> x.base is None
True
Slicing creates a view, whose memory is shared with x:
>>> y = x[2:]
>>> y.base is x
True
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('ctypes',
"""
An object to simplify the interaction of the array with the ctypes
module.
This attribute creates an object that makes it easier to use arrays
when calling shared libraries with the ctypes module. The returned
object has, among others, data, shape, and strides attributes (see
Notes below) which themselves return ctypes objects that can be used
as arguments to a shared library.
Parameters
----------
None
Returns
-------
c : Python object
Possessing attributes data, shape, strides, etc.
See Also
--------
numpy.ctypeslib
Notes
-----
Below are the public attributes of this object which were documented
in "Guide to NumPy" (we have omitted undocumented public attributes,
as well as documented private attributes):
.. autoattribute:: numpy.core._internal._ctypes.data
:noindex:
.. autoattribute:: numpy.core._internal._ctypes.shape
:noindex:
.. autoattribute:: numpy.core._internal._ctypes.strides
:noindex:
.. automethod:: numpy.core._internal._ctypes.data_as
:noindex:
.. automethod:: numpy.core._internal._ctypes.shape_as
:noindex:
.. automethod:: numpy.core._internal._ctypes.strides_as
:noindex:
If the ctypes module is not available, then the ctypes attribute
of array objects still returns something useful, but ctypes objects
are not returned and errors may be raised instead. In particular,
the object will still have the ``as_parameter`` attribute which will
return an integer equal to the data attribute.
Examples
--------
>>> import ctypes
>>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)
>>> x
array([[0, 1],
[2, 3]], dtype=int32)
>>> x.ctypes.data
31962608 # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))
<__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents
c_uint(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents
c_ulong(4294967296)
>>> x.ctypes.shape
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary
>>> x.ctypes.strides
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('data',
"""Python buffer object pointing to the start of the array's data."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dtype',
"""
Data-type of the array's elements.
.. warning::
Setting ``arr.dtype`` is discouraged and may be deprecated in the
future. Setting will replace the ``dtype`` without modifying the
memory (see also `ndarray.view` and `ndarray.astype`).
Parameters
----------
None
Returns
-------
d : numpy dtype object
See Also
--------
ndarray.astype : Cast the values contained in the array to a new data-type.
ndarray.view : Create a view of the same data but a different data-type.
numpy.dtype
Examples
--------
>>> x
array([[0, 1],
[2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('imag',
"""
The imaginary part of the array.
Examples
--------
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0. , 0.70710678])
>>> x.imag.dtype
dtype('float64')
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('itemsize',
"""
Length of one array element in bytes.
Examples
--------
>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('flags',
"""
Information about the memory layout of the array.
Attributes
----------
C_CONTIGUOUS (C)
The data is in a single, C-style contiguous segment.
F_CONTIGUOUS (F)
The data is in a single, Fortran-style contiguous segment.
OWNDATA (O)
The array owns the memory it uses or borrows it from another object.
WRITEABLE (W)
The data area can be written to. Setting this to False locks
the data, making it read-only. A view (slice, etc.) inherits WRITEABLE
from its base array at creation time, but a view of a writeable
array may be subsequently locked while the base array remains writeable.
(The opposite is not true, in that a view of a locked array may not
be made writeable. However, currently, locking a base object does not
lock any views that already reference it, so under that circumstance it
is possible to alter the contents of a locked array via a previously
created writeable view onto it.) Attempting to change a non-writeable
array raises a RuntimeError exception.
ALIGNED (A)
The data and all elements are aligned appropriately for the hardware.
WRITEBACKIFCOPY (X)
This array is a copy of some other array. The C-API function
PyArray_ResolveWritebackIfCopy must be called before deallocating
to the base array will be updated with the contents of this array.
FNC
F_CONTIGUOUS and not C_CONTIGUOUS.
FORC
F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).
BEHAVED (B)
ALIGNED and WRITEABLE.
CARRAY (CA)
BEHAVED and C_CONTIGUOUS.
FARRAY (FA)
BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.
Notes
-----
The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),
or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag
names are only supported in dictionary access.
Only the WRITEBACKIFCOPY, WRITEABLE, and ALIGNED flags can be
changed by the user, via direct assignment to the attribute or dictionary
entry, or by calling `ndarray.setflags`.
The array flags cannot be set arbitrarily:
- WRITEBACKIFCOPY can only be set ``False``.
- ALIGNED can only be set ``True`` if the data is truly aligned.
- WRITEABLE can only be set ``True`` if the array owns its own memory
or the ultimate owner of the memory exposes a writeable buffer
interface or is a string.
Arrays can be both C-style and Fortran-style contiguous simultaneously.
This is clear for 1-dimensional arrays, but can also be true for higher
dimensional arrays.
Even for contiguous arrays a stride for a given dimension
``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``
or the array has no elements.
It does *not* generally hold that ``self.strides[-1] == self.itemsize``
for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for
Fortran-style contiguous arrays is true.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('flat',
"""
A 1-D iterator over the array.
This is a `numpy.flatiter` instance, which acts similarly to, but is not
a subclass of, Python's built-in iterator object.
See Also
--------
flatten : Return a copy of the array collapsed into one dimension.
flatiter
Examples
--------
>>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
[4, 5, 6]])
>>> x.flat[3]
4
>>> x.T
array([[1, 4],
[2, 5],
[3, 6]])
>>> x.T.flat[3]
5
>>> type(x.flat)
<class 'numpy.flatiter'>
An assignment example:
>>> x.flat = 3; x
array([[3, 3, 3],
[3, 3, 3]])
>>> x.flat[[1,4]] = 1; x
array([[3, 1, 3],
[3, 1, 3]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('nbytes',
"""
Total bytes consumed by the elements of the array.
Notes
-----
Does not include memory consumed by non-element attributes of the
array object.
Examples
--------
>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('ndim',
"""
Number of array dimensions.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('real',
"""
The real part of the array.
Examples
--------
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1. , 0.70710678])
>>> x.real.dtype
dtype('float64')
See Also
--------
numpy.real : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('shape',
"""
Tuple of array dimensions.
The shape property is usually used to get the current shape of an array,
but may also be used to reshape the array in-place by assigning a tuple of
array dimensions to it. As with `numpy.reshape`, one of the new shape
dimensions can be -1, in which case its value is inferred from the size of
the array and the remaining dimensions. Reshaping an array in-place will
fail if a copy is required.
.. warning::
Setting ``arr.shape`` is discouraged and may be deprecated in the
future. Using `ndarray.reshape` is the preferred approach.
Examples
--------
>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: Incompatible shape for in-place modification. Use
`.reshape()` to make a copy with the desired shape.
See Also
--------
numpy.shape : Equivalent getter function.
numpy.reshape : Function similar to setting ``shape``.
ndarray.reshape : Method similar to setting ``shape``.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('size',
"""
Number of elements in the array.
Equal to ``np.prod(a.shape)``, i.e., the product of the array's
dimensions.
Notes
-----
`a.size` returns a standard arbitrary precision Python integer. This
may not be the case with other methods of obtaining the same value
(like the suggested ``np.prod(a.shape)``, which returns an instance
of ``np.int_``), and may be relevant if the value is used further in
calculations that may overflow a fixed size integer type.
Examples
--------
>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('strides',
"""
Tuple of bytes to step in each dimension when traversing an array.
The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`
is::
offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in the
"ndarray.rst" file in the NumPy reference guide.
.. warning::
Setting ``arr.strides`` is discouraged and may be deprecated in the
future. `numpy.lib.stride_tricks.as_strided` should be preferred
to create a new view of the same data in a safer way.
Notes
-----
Imagine an array of 32-bit integers (each 4 bytes)::
x = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other
(known as a contiguous block of memory). The strides of an array tell
us how many bytes we have to skip in memory to move to the next position
along a certain axis. For example, we have to skip 4 bytes (1 value) to
move to the next column, but 20 bytes (5 values) to get to the same
position in the next row. As such, the strides for the array `x` will be
``(20, 4)``.
See Also
--------
numpy.lib.stride_tricks.as_strided
Examples
--------
>>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('T',
"""
The transposed array.
Same as ``self.transpose()``.
Examples
--------
>>> x = np.array([[1.,2.],[3.,4.]])
>>> x
array([[ 1., 2.],
[ 3., 4.]])
>>> x.T
array([[ 1., 3.],
[ 2., 4.]])
>>> x = np.array([1.,2.,3.,4.])
>>> x
array([ 1., 2., 3., 4.])
>>> x.T
array([ 1., 2., 3., 4.])
See Also
--------
transpose
"""))
##############################################################################
#
# ndarray methods
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array__',
""" a.__array__([dtype], /) -> reference if type unchanged, copy otherwise.
Returns either a new reference to self if dtype is not given or a new array
of provided data type if dtype is different from the current dtype of the
array.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_finalize__',
"""a.__array_finalize__(obj, /)
Present so subclasses can call super. Does nothing.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_prepare__',
"""a.__array_prepare__(array[, context], /)
Returns a view of `array` with the same type as self.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_wrap__',
"""a.__array_wrap__(array[, context], /)
Returns a view of `array` with the same type as self.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__copy__',
"""a.__copy__()
Used if :func:`copy.copy` is called on an array. Returns a copy of the array.
Equivalent to ``a.copy(order='K')``.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__class_getitem__',
"""a.__class_getitem__(item, /)
Return a parametrized wrapper around the `~numpy.ndarray` type.
.. versionadded:: 1.22
Returns
-------
alias : types.GenericAlias
A parametrized `~numpy.ndarray` type.
Examples
--------
>>> from typing import Any
>>> import numpy as np
>>> np.ndarray[Any, np.dtype[Any]]
numpy.ndarray[typing.Any, numpy.dtype[typing.Any]]
Notes
-----
This method is only available for python 3.9 and later.
See Also
--------
:pep:`585` : Type hinting generics in standard collections.
numpy.typing.NDArray : An ndarray alias :term:`generic <generic type>`
w.r.t. its `dtype.type <numpy.dtype.type>`.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__deepcopy__',
"""a.__deepcopy__(memo, /) -> Deep copy of array.
Used if :func:`copy.deepcopy` is called on an array.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__reduce__',
"""a.__reduce__()
For pickling.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__setstate__',
"""a.__setstate__(state, /)
For unpickling.
The `state` argument must be a sequence that contains the following
elements:
Parameters
----------
version : int
optional pickle version. If omitted defaults to 0.
shape : tuple
dtype : data-type
isFortran : bool
rawdata : string or list
a binary string with the data (or a list if 'a' is an object array)
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('all',
"""
a.all(axis=None, out=None, keepdims=False, *, where=True)
Returns True if all elements evaluate to True.
Refer to `numpy.all` for full documentation.
See Also
--------
numpy.all : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('any',
"""
a.any(axis=None, out=None, keepdims=False, *, where=True)
Returns True if any of the elements of `a` evaluate to True.
Refer to `numpy.any` for full documentation.
See Also
--------
numpy.any : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argmax',
"""
a.argmax(axis=None, out=None, *, keepdims=False)
Return indices of the maximum values along the given axis.
Refer to `numpy.argmax` for full documentation.
See Also
--------
numpy.argmax : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argmin',
"""
a.argmin(axis=None, out=None, *, keepdims=False)
Return indices of the minimum values along the given axis.
Refer to `numpy.argmin` for detailed documentation.
See Also
--------
numpy.argmin : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argsort',
"""
a.argsort(axis=-1, kind=None, order=None)
Returns the indices that would sort this array.
Refer to `numpy.argsort` for full documentation.
See Also
--------
numpy.argsort : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argpartition',
"""
a.argpartition(kth, axis=-1, kind='introselect', order=None)
Returns the indices that would partition this array.
Refer to `numpy.argpartition` for full documentation.
.. versionadded:: 1.8.0
See Also
--------
numpy.argpartition : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('astype',
"""
a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
Copy of the array, cast to a specified type.
Parameters
----------
dtype : str or dtype
Typecode or data-type to which the array is cast.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout order of the result.
'C' means C order, 'F' means Fortran order, 'A'
means 'F' order if all the arrays are Fortran contiguous,
'C' order otherwise, and 'K' means as close to the
order the array elements appear in memory as possible.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Defaults to 'unsafe'
for backwards compatibility.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
subok : bool, optional
If True, then sub-classes will be passed-through (default), otherwise
the returned array will be forced to be a base-class array.
copy : bool, optional
By default, astype always returns a newly allocated array. If this
is set to false, and the `dtype`, `order`, and `subok`
requirements are satisfied, the input array is returned instead
of a copy.
Returns
-------
arr_t : ndarray
Unless `copy` is False and the other conditions for returning the input
array are satisfied (see description for `copy` input parameter), `arr_t`
is a new array of the same shape as the input array, with dtype, order
given by `dtype`, `order`.
Notes
-----
.. versionchanged:: 1.17.0
Casting between a simple data type and a structured one is possible only
for "unsafe" casting. Casting to multiple fields is allowed, but
casting from multiple fields is not.
.. versionchanged:: 1.9.0
Casting from numeric to string types in 'safe' casting mode requires
that the string dtype length is long enough to store the max
integer/float value converted.
Raises
------
ComplexWarning
When casting from complex to float or int. To avoid this,
one should use ``a.real.astype(t)``.
Examples
--------
>>> x = np.array([1, 2, 2.5])
>>> x
array([1. , 2. , 2.5])
>>> x.astype(int)
array([1, 2, 2])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('byteswap',
"""
a.byteswap(inplace=False)
Swap the bytes of the array elements
Toggle between low-endian and big-endian data representation by
returning a byteswapped array, optionally swapped in-place.
Arrays of byte-strings are not swapped. The real and imaginary
parts of a complex number are swapped individually.
Parameters
----------
inplace : bool, optional
If ``True``, swap bytes in-place, default is ``False``.
Returns
-------
out : ndarray
The byteswapped array. If `inplace` is ``True``, this is
a view to self.
Examples
--------
>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> list(map(hex, A))
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([ 256, 1, 13090], dtype=int16)
>>> list(map(hex, A))
['0x100', '0x1', '0x3322']
Arrays of byte-strings are not swapped
>>> A = np.array([b'ceg', b'fac'])
>>> A.byteswap()
array([b'ceg', b'fac'], dtype='|S3')
``A.newbyteorder().byteswap()`` produces an array with the same values
but different representation in memory
>>> A = np.array([1, 2, 3])
>>> A.view(np.uint8)
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
0, 0], dtype=uint8)
>>> A.newbyteorder().byteswap(inplace=True)
array([1, 2, 3])
>>> A.view(np.uint8)
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
0, 3], dtype=uint8)
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('choose',
"""
a.choose(choices, out=None, mode='raise')
Use an index array to construct a new array from a set of choices.
Refer to `numpy.choose` for full documentation.
See Also
--------
numpy.choose : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('clip',
"""
a.clip(min=None, max=None, out=None, **kwargs)
Return an array whose values are limited to ``[min, max]``.
One of max or min must be given.
Refer to `numpy.clip` for full documentation.
See Also
--------
numpy.clip : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('compress',
"""
a.compress(condition, axis=None, out=None)
Return selected slices of this array along given axis.
Refer to `numpy.compress` for full documentation.
See Also
--------
numpy.compress : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('conj',
"""
a.conj()
Complex-conjugate all elements.
Refer to `numpy.conjugate` for full documentation.
See Also
--------
numpy.conjugate : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('conjugate',
"""
a.conjugate()
Return the complex conjugate, element-wise.
Refer to `numpy.conjugate` for full documentation.
See Also
--------
numpy.conjugate : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('copy',
"""
a.copy(order='C')
Return a copy of the array.
Parameters
----------
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the copy. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible. (Note that this function and :func:`numpy.copy` are very
similar but have different default values for their order=
arguments, and this function always passes sub-classes through.)
See also
--------
numpy.copy : Similar function with different default behavior
numpy.copyto
Notes
-----
This function is the preferred method for creating an array copy. The
function :func:`numpy.copy` is similar, but it defaults to using order 'K',
and will not pass sub-classes through by default.
Examples
--------
>>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x
array([[0, 0, 0],
[0, 0, 0]])
>>> y
array([[1, 2, 3],
[4, 5, 6]])
>>> y.flags['C_CONTIGUOUS']
True
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('cumprod',
"""
a.cumprod(axis=None, dtype=None, out=None)
Return the cumulative product of the elements along the given axis.
Refer to `numpy.cumprod` for full documentation.
See Also
--------
numpy.cumprod : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('cumsum',
"""
a.cumsum(axis=None, dtype=None, out=None)
Return the cumulative sum of the elements along the given axis.
Refer to `numpy.cumsum` for full documentation.
See Also
--------
numpy.cumsum : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('diagonal',
"""
a.diagonal(offset=0, axis1=0, axis2=1)
Return specified diagonals. In NumPy 1.9 the returned array is a
read-only view instead of a copy as in previous NumPy versions. In
a future version the read-only restriction will be removed.
Refer to :func:`numpy.diagonal` for full documentation.
See Also
--------
numpy.diagonal : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dot'))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dump',
"""a.dump(file)
Dump a pickle of the array to the specified file.
The array can be read back with pickle.load or numpy.load.
Parameters
----------
file : str or Path
A string naming the dump file.
.. versionchanged:: 1.17.0
`pathlib.Path` objects are now accepted.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dumps',
"""
a.dumps()
Returns the pickle of the array as a string.
pickle.loads will convert the string back to an array.
Parameters
----------
None
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('fill',
"""
a.fill(value)
Fill the array with a scalar value.
Parameters
----------
value : scalar
All elements of `a` will be assigned this value.
Examples
--------
>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([1., 1.])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('flatten',
"""
a.flatten(order='C')
Return a copy of the array collapsed into one dimension.
Parameters
----------
order : {'C', 'F', 'A', 'K'}, optional
'C' means to flatten in row-major (C-style) order.
'F' means to flatten in column-major (Fortran-
style) order. 'A' means to flatten in column-major
order if `a` is Fortran *contiguous* in memory,
row-major order otherwise. 'K' means to flatten
`a` in the order the elements occur in memory.
The default is 'C'.
Returns
-------
y : ndarray
A copy of the input array, flattened to one dimension.
See Also
--------
ravel : Return a flattened array.
flat : A 1-D flat iterator over the array.
Examples
--------
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('getfield',
"""
a.getfield(dtype, offset=0)
Returns a field of the given array as a certain type.
A field is a view of the array data with a given data-type. The values in
the view are determined by the given type and the offset into the current
array in bytes. The offset needs to be such that the view dtype fits in the
array dtype; for example an array of dtype complex128 has 16-byte elements.
If taking a view with a 32-bit integer (4 bytes), the offset needs to be
between 0 and 12 bytes.
Parameters
----------
dtype : str or dtype
The data type of the view. The dtype size of the view can not be larger
than that of the array itself.
offset : int
Number of bytes to skip before beginning the element view.
Examples
--------
>>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[1.+1.j, 0.+0.j],
[0.+0.j, 2.+4.j]])
>>> x.getfield(np.float64)
array([[1., 0.],
[0., 2.]])
By choosing an offset of 8 bytes we can select the complex part of the
array for our view:
>>> x.getfield(np.float64, offset=8)
array([[1., 0.],
[0., 4.]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('item',
"""
a.item(*args)
Copy an element of an array to a standard Python scalar and return it.
Parameters
----------
\\*args : Arguments (variable number and type)
* none: in this case, the method only works for arrays
with one element (`a.size == 1`), which element is
copied into a standard Python scalar object and returned.
* int_type: this argument is interpreted as a flat index into
the array, specifying which element to copy and return.
* tuple of int_types: functions as does a single int_type argument,
except that the argument is interpreted as an nd-index into the
array.
Returns
-------
z : Standard Python scalar object
A copy of the specified element of the array as a suitable
Python scalar
Notes
-----
When the data type of `a` is longdouble or clongdouble, item() returns
a scalar array object because there is no available Python scalar that
would not lose information. Void arrays return a buffer object for item(),
unless fields are defined, in which case a tuple is returned.
`item` is very similar to a[args], except, instead of an array scalar,
a standard Python scalar is returned. This can be useful for speeding up
access to elements of the array and doing arithmetic on elements of the
array using Python's optimized math.
Examples
--------
>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
[1, 3, 6],
[1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('itemset',
"""
a.itemset(*args)
Insert scalar into an array (scalar is cast to array's dtype, if possible)
There must be at least 1 argument, and define the last argument
as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster
than ``a[args] = item``. The item should be a scalar value and `args`
must select a single item in the array `a`.
Parameters
----------
\\*args : Arguments
If one argument: a scalar, only used in case `a` is of size 1.
If two arguments: the last argument is the value to be set
and must be a scalar, the first argument specifies a single array
element location. It is either an int or a tuple.
Notes
-----
Compared to indexing syntax, `itemset` provides some speed increase
for placing a scalar into a particular location in an `ndarray`,
if you must do this. However, generally this is discouraged:
among other problems, it complicates the appearance of the code.
Also, when using `itemset` (and `item`) inside a loop, be sure
to assign the methods to a local variable to avoid the attribute
look-up at each loop iteration.
Examples
--------
>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
[1, 3, 6],
[1, 0, 1]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[2, 2, 6],
[1, 0, 6],
[1, 0, 9]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('max',
"""
a.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
Return the maximum along a given axis.
Refer to `numpy.amax` for full documentation.
See Also
--------
numpy.amax : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('mean',
"""
a.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)
Returns the average of the array elements along given axis.
Refer to `numpy.mean` for full documentation.
See Also
--------
numpy.mean : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('min',
"""
a.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
Return the minimum along a given axis.
Refer to `numpy.amin` for full documentation.
See Also
--------
numpy.amin : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('newbyteorder',
"""
arr.newbyteorder(new_order='S', /)
Return the array with the same data viewed with a different byte order.
Equivalent to::
arr.view(arr.dtype.newbytorder(new_order))
Changes are also made in all fields and sub-arrays of the array data
type.
Parameters
----------
new_order : string, optional
Byte order to force; a value from the byte order specifications
below. `new_order` codes can be any of:
* 'S' - swap dtype from current to opposite endian
* {'<', 'little'} - little endian
* {'>', 'big'} - big endian
* {'=', 'native'} - native order, equivalent to `sys.byteorder`
* {'|', 'I'} - ignore (no change to byte order)
The default value ('S') results in swapping the current
byte order.
Returns
-------
new_arr : array
New array object with the dtype reflecting given change to the
byte order.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('nonzero',
"""
a.nonzero()
Return the indices of the elements that are non-zero.
Refer to `numpy.nonzero` for full documentation.
See Also
--------
numpy.nonzero : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('prod',
"""
a.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)
Return the product of the array elements over the given axis
Refer to `numpy.prod` for full documentation.
See Also
--------
numpy.prod : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('ptp',
"""
a.ptp(axis=None, out=None, keepdims=False)
Peak to peak (maximum - minimum) value along a given axis.
Refer to `numpy.ptp` for full documentation.
See Also
--------
numpy.ptp : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('put',
"""
a.put(indices, values, mode='raise')
Set ``a.flat[n] = values[n]`` for all `n` in indices.
Refer to `numpy.put` for full documentation.
See Also
--------
numpy.put : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('ravel',
"""
a.ravel([order])
Return a flattened array.
Refer to `numpy.ravel` for full documentation.
See Also
--------
numpy.ravel : equivalent function
ndarray.flat : a flat iterator on the array.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('repeat',
"""
a.repeat(repeats, axis=None)
Repeat elements of an array.
Refer to `numpy.repeat` for full documentation.
See Also
--------
numpy.repeat : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('reshape',
"""
a.reshape(shape, order='C')
Returns an array containing the same data with a new shape.
Refer to `numpy.reshape` for full documentation.
See Also
--------
numpy.reshape : equivalent function
Notes
-----
Unlike the free function `numpy.reshape`, this method on `ndarray` allows
the elements of the shape parameter to be passed in as separate arguments.
For example, ``a.reshape(10, 11)`` is equivalent to
``a.reshape((10, 11))``.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('resize',
"""
a.resize(new_shape, refcheck=True)
Change shape and size of array in-place.
Parameters
----------
new_shape : tuple of ints, or `n` ints
Shape of resized array.
refcheck : bool, optional
If False, reference count will not be checked. Default is True.
Returns
-------
None
Raises
------
ValueError
If `a` does not own its own data or references or views to it exist,
and the data memory must be changed.
PyPy only: will always raise if the data memory must be changed, since
there is no reliable way to determine if references or views to it
exist.
SystemError
If the `order` keyword argument is specified. This behaviour is a
bug in NumPy.
See Also
--------
resize : Return a new array with the specified shape.
Notes
-----
This reallocates space for the data area if necessary.
Only contiguous arrays (data elements consecutive in memory) can be
resized.
The purpose of the reference count check is to make sure you
do not use this array as a buffer for another Python object and then
reallocate the memory. However, reference counts can increase in
other ways so if you are sure that you have not shared the memory
for this array with another Python object, then you may safely set
`refcheck` to False.
Examples
--------
Shrinking an array: array is flattened (in the order that the data are
stored in memory), resized, and reshaped:
>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
[1]])
>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
[2]])
Enlarging an array: as above, but missing entries are filled with zeros:
>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
[3, 0, 0]])
Referencing an array prevents resizing...
>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that references or is referenced ...
Unless `refcheck` is False:
>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('round',
"""
a.round(decimals=0, out=None)
Return `a` with each element rounded to the given number of decimals.
Refer to `numpy.around` for full documentation.
See Also
--------
numpy.around : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('searchsorted',
"""
a.searchsorted(v, side='left', sorter=None)
Find indices where elements of v should be inserted in a to maintain order.
For full documentation, see `numpy.searchsorted`
See Also
--------
numpy.searchsorted : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('setfield',
"""
a.setfield(val, dtype, offset=0)
Put a value into a specified place in a field defined by a data-type.
Place `val` into `a`'s field defined by `dtype` and beginning `offset`
bytes into the field.
Parameters
----------
val : object
Value to be placed in field.
dtype : dtype object
Data-type of the field in which to place `val`.
offset : int, optional
The number of bytes into the field at which to place `val`.
Returns
-------
None
See Also
--------
getfield
Examples
--------
>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
[3, 3, 3],
[3, 3, 3]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
[1.5e-323, 1.0e+000, 1.5e-323],
[1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('setflags',
"""
a.setflags(write=None, align=None, uic=None)
Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY,
respectively.
These Boolean-valued flags affect how numpy interprets the memory
area used by `a` (see Notes below). The ALIGNED flag can only
be set to True if the data is actually aligned according to the type.
The WRITEBACKIFCOPY and flag can never be set
to True. The flag WRITEABLE can only be set to True if the array owns its
own memory, or the ultimate owner of the memory exposes a writeable buffer
interface, or is a string. (The exception for string is made so that
unpickling can be done without copying memory.)
Parameters
----------
write : bool, optional
Describes whether or not `a` can be written to.
align : bool, optional
Describes whether or not `a` is aligned properly for its type.
uic : bool, optional
Describes whether or not `a` is a copy of another "base" array.
Notes
-----
Array flags provide information about how the memory area used
for the array is to be interpreted. There are 7 Boolean flags
in use, only four of which can be changed by the user:
WRITEBACKIFCOPY, WRITEABLE, and ALIGNED.
WRITEABLE (W) the data area can be written to;
ALIGNED (A) the data and strides are aligned appropriately for the hardware
(as determined by the compiler);
WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced
by .base). When the C-API function PyArray_ResolveWritebackIfCopy is
called, the base array will be updated with the contents of this array.
All flags can be accessed using the single (upper case) letter as well
as the full name.
Examples
--------
>>> y = np.array([[3, 1, 7],
... [2, 0, 0],
... [8, 5, 9]])
>>> y
array([[3, 1, 7],
[2, 0, 0],
[8, 5, 9]])
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : False
ALIGNED : False
WRITEBACKIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('sort',
"""
a.sort(axis=-1, kind=None, order=None)
Sort an array in-place. Refer to `numpy.sort` for full documentation.
Parameters
----------
axis : int, optional
Axis along which to sort. Default is -1, which means sort along the
last axis.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
Sorting algorithm. The default is 'quicksort'. Note that both 'stable'
and 'mergesort' use timsort under the covers and, in general, the
actual implementation will vary with datatype. The 'mergesort' option
is retained for backwards compatibility.
.. versionchanged:: 1.15.0
The 'stable' option was added.
order : str or list of str, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
See Also
--------
numpy.sort : Return a sorted copy of an array.
numpy.argsort : Indirect sort.
numpy.lexsort : Indirect stable sort on multiple keys.
numpy.searchsorted : Find elements in sorted array.
numpy.partition: Partial sort.
Notes
-----
See `numpy.sort` for notes on the different sorting algorithms.
Examples
--------
>>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
[1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
[1, 4]])
Use the `order` keyword to specify a field to use when sorting a
structured array:
>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([(b'c', 1), (b'a', 2)],
dtype=[('x', 'S1'), ('y', '<i8')])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('partition',
"""
a.partition(kth, axis=-1, kind='introselect', order=None)
Rearranges the elements in the array in such a way that the value of the
element in kth position is in the position it would be in a sorted array.
All elements smaller than the kth element are moved before this element and
all equal or greater are moved behind it. The ordering of the elements in
the two partitions is undefined.
.. versionadded:: 1.8.0
Parameters
----------
kth : int or sequence of ints
Element index to partition by. The kth element value will be in its
final sorted position and all smaller elements will be moved before it
and all equal or greater elements behind it.
The order of all elements in the partitions is undefined.
If provided with a sequence of kth it will partition all elements
indexed by kth of them into their sorted position at once.
.. deprecated:: 1.22.0
Passing booleans as index is deprecated.
axis : int, optional
Axis along which to sort. Default is -1, which means sort along the
last axis.
kind : {'introselect'}, optional
Selection algorithm. Default is 'introselect'.
order : str or list of str, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need to be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
See Also
--------
numpy.partition : Return a partitioned copy of an array.
argpartition : Indirect partition.
sort : Full sort.
Notes
-----
See ``np.partition`` for notes on the different algorithms.
Examples
--------
>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
>>> a.partition((1, 3))
>>> a
array([1, 2, 3, 4])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('squeeze',
"""
a.squeeze(axis=None)
Remove axes of length one from `a`.
Refer to `numpy.squeeze` for full documentation.
See Also
--------
numpy.squeeze : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('std',
"""
a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)
Returns the standard deviation of the array elements along given axis.
Refer to `numpy.std` for full documentation.
See Also
--------
numpy.std : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('sum',
"""
a.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)
Return the sum of the array elements over the given axis.
Refer to `numpy.sum` for full documentation.
See Also
--------
numpy.sum : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('swapaxes',
"""
a.swapaxes(axis1, axis2)
Return a view of the array with `axis1` and `axis2` interchanged.
Refer to `numpy.swapaxes` for full documentation.
See Also
--------
numpy.swapaxes : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('take',
"""
a.take(indices, axis=None, out=None, mode='raise')
Return an array formed from the elements of `a` at the given indices.
Refer to `numpy.take` for full documentation.
See Also
--------
numpy.take : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('tofile',
"""
a.tofile(fid, sep="", format="%s")
Write array to a file as text or binary (default).
Data is always written in 'C' order, independent of the order of `a`.
The data produced by this method can be recovered using the function
fromfile().
Parameters
----------
fid : file or str or Path
An open file object, or a string containing a filename.
.. versionchanged:: 1.17.0
`pathlib.Path` objects are now accepted.
sep : str
Separator between array items for text output.
If "" (empty), a binary file is written, equivalent to
``file.write(a.tobytes())``.
format : str
Format string for text file output.
Each entry in the array is formatted to text by first converting
it to the closest Python type, and then using "format" % item.
Notes
-----
This is a convenience function for quick storage of array data.
Information on endianness and precision is lost, so this method is not a
good choice for files intended to archive data or transport data between
machines with different endianness. Some of these problems can be overcome
by outputting the data as text files, at the expense of speed and file
size.
When fid is a file object, array contents are directly written to the
file, bypassing the file object's ``write`` method. As a result, tofile
cannot be used with files objects supporting compression (e.g., GzipFile)
or file-like objects that do not support ``fileno()`` (e.g., BytesIO).
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('tolist',
"""
a.tolist()
Return the array as an ``a.ndim``-levels deep nested list of Python scalars.
Return a copy of the array data as a (nested) Python list.
Data items are converted to the nearest compatible builtin Python type, via
the `~numpy.ndarray.item` function.
If ``a.ndim`` is 0, then since the depth of the nested list is 0, it will
not be a list at all, but a simple Python scalar.
Parameters
----------
none
Returns
-------
y : object, or list of object, or list of list of object, or ...
The possibly nested list of array elements.
Notes
-----
The array may be recreated via ``a = np.array(a.tolist())``, although this
may sometimes lose precision.
Examples
--------
For a 1D array, ``a.tolist()`` is almost the same as ``list(a)``,
except that ``tolist`` changes numpy scalars to Python scalars:
>>> a = np.uint32([1, 2])
>>> a_list = list(a)
>>> a_list
[1, 2]
>>> type(a_list[0])
<class 'numpy.uint32'>
>>> a_tolist = a.tolist()
>>> a_tolist
[1, 2]
>>> type(a_tolist[0])
<class 'int'>
Additionally, for a 2D array, ``tolist`` applies recursively:
>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]
The base case for this recursion is a 0D array:
>>> a = np.array(1)
>>> list(a)
Traceback (most recent call last):
...
TypeError: iteration over a 0-d array
>>> a.tolist()
1
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('tobytes', """
a.tobytes(order='C')
Construct Python bytes containing the raw data bytes in the array.
Constructs Python bytes showing a copy of the raw contents of
data memory. The bytes object is produced in C-order by default.
This behavior is controlled by the ``order`` parameter.
.. versionadded:: 1.9.0
Parameters
----------
order : {'C', 'F', 'A'}, optional
Controls the memory layout of the bytes object. 'C' means C-order,
'F' means F-order, 'A' (short for *Any*) means 'F' if `a` is
Fortran contiguous, 'C' otherwise. Default is 'C'.
Returns
-------
s : bytes
Python bytes exhibiting a copy of `a`'s raw data.
Examples
--------
>>> x = np.array([[0, 1], [2, 3]], dtype='<u2')
>>> x.tobytes()
b'\\x00\\x00\\x01\\x00\\x02\\x00\\x03\\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\\x00\\x00\\x02\\x00\\x01\\x00\\x03\\x00'
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('tostring', r"""
a.tostring(order='C')
A compatibility alias for `tobytes`, with exactly the same behavior.
Despite its name, it returns `bytes` not `str`\ s.
.. deprecated:: 1.19.0
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('trace',
"""
a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
Return the sum along diagonals of the array.
Refer to `numpy.trace` for full documentation.
See Also
--------
numpy.trace : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('transpose',
"""
a.transpose(*axes)
Returns a view of the array with axes transposed.
For a 1-D array this has no effect, as a transposed vector is simply the
same vector. To convert a 1-D array into a 2D column vector, an additional
dimension must be added. `np.atleast2d(a).T` achieves this, as does
`a[:, np.newaxis]`.
For a 2-D array, this is a standard matrix transpose.
For an n-D array, if axes are given, their order indicates how the
axes are permuted (see Examples). If axes are not provided and
``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
Parameters
----------
axes : None, tuple of ints, or `n` ints
* None or no argument: reverses the order of the axes.
* tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
`i`-th axis becomes `a.transpose()`'s `j`-th axis.
* `n` ints: same as an n-tuple of the same ints (this form is
intended simply as a "convenience" alternative to the tuple form)
Returns
-------
out : ndarray
View of `a`, with axes suitably permuted.
See Also
--------
transpose : Equivalent function
ndarray.T : Array property returning the array transposed.
ndarray.reshape : Give a new shape to an array without changing its data.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
[3, 4]])
>>> a.transpose()
array([[1, 3],
[2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
[2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
[2, 4]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('var',
"""
a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)
Returns the variance of the array elements, along given axis.
Refer to `numpy.var` for full documentation.
See Also
--------
numpy.var : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('view',
"""
a.view([dtype][, type])
New view of array with the same data.
.. note::
Passing None for ``dtype`` is different from omitting the parameter,
since the former invokes ``dtype(None)`` which is an alias for
``dtype('float_')``.
Parameters
----------
dtype : data-type or ndarray sub-class, optional
Data-type descriptor of the returned view, e.g., float32 or int16.
Omitting it results in the view having the same data-type as `a`.
This argument can also be specified as an ndarray sub-class, which
then specifies the type of the returned object (this is equivalent to
setting the ``type`` parameter).
type : Python type, optional
Type of the returned view, e.g., ndarray or matrix. Again, omission
of the parameter results in type preservation.
Notes
-----
``a.view()`` is used two different ways:
``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
of the array's memory with a different data-type. This can cause a
reinterpretation of the bytes of memory.
``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
returns an instance of `ndarray_subclass` that looks at the same array
(same shape, dtype, etc.) This does not cause a reinterpretation of the
memory.
For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
bytes per entry than the previous dtype (for example, converting a regular
array to a structured array), then the last axis of ``a`` must be
contiguous. This axis will be resized in the result.
.. versionchanged:: 1.23.0
Only the last axis needs to be contiguous. Previously, the entire array
had to be C-contiguous.
Examples
--------
>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
Viewing array data using a different type and dtype:
>>> y = x.view(dtype=np.int16, type=np.matrix)
>>> y
matrix([[513]], dtype=int16)
>>> print(type(y))
<class 'numpy.matrix'>
Creating a view on a structured array so it can be used in calculations
>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(-1,2)
>>> xv
array([[1, 2],
[3, 4]], dtype=int8)
>>> xv.mean(0)
array([2., 3.])
Making changes to the view changes the underlying array
>>> xv[0,1] = 20
>>> x
array([(1, 20), (3, 4)], dtype=[('a', 'i1'), ('b', 'i1')])
Using a view to convert an array to a recarray:
>>> z = x.view(np.recarray)
>>> z.a
array([1, 3], dtype=int8)
Views share data:
>>> x[0] = (9, 10)
>>> z[0]
(9, 10)
Views that change the dtype size (bytes per entry) should normally be
avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
>>> x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16)
>>> y = x[:, ::2]
>>> y
array([[1, 3],
[4, 6]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
...
ValueError: To change to a dtype of a different size, the last axis must be contiguous
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 3)],
[(4, 6)]], dtype=[('width', '<i2'), ('length', '<i2')])
However, views that change dtype are totally fine for arrays with a
contiguous last axis, even if the rest of the axes are not C-contiguous:
>>> x = np.arange(2 * 3 * 4, dtype=np.int8).reshape(2, 3, 4)
>>> x.transpose(1, 0, 2).view(np.int16)
array([[[ 256, 770],
[3340, 3854]],
<BLANKLINE>
[[1284, 1798],
[4368, 4882]],
<BLANKLINE>
[[2312, 2826],
[5396, 5910]]], dtype=int16)
"""))
##############################################################################
#
# umath functions
#
##############################################################################
add_newdoc('numpy.core.umath', 'frompyfunc',
"""
frompyfunc(func, /, nin, nout, *[, identity])
Takes an arbitrary Python function and returns a NumPy ufunc.
Can be used, for example, to add broadcasting to a built-in Python
function (see Examples section).
Parameters
----------
func : Python function object
An arbitrary Python function.
nin : int
The number of input arguments.
nout : int
The number of objects returned by `func`.
identity : object, optional
The value to use for the `~numpy.ufunc.identity` attribute of the resulting
object. If specified, this is equivalent to setting the underlying
C ``identity`` field to ``PyUFunc_IdentityValue``.
If omitted, the identity is set to ``PyUFunc_None``. Note that this is
_not_ equivalent to setting the identity to ``None``, which implies the
operation is reorderable.
Returns
-------
out : ufunc
Returns a NumPy universal function (``ufunc``) object.
See Also
--------
vectorize : Evaluates pyfunc over input arrays using broadcasting rules of numpy.
Notes
-----
The returned ufunc always returns PyObject arrays.
Examples
--------
Use frompyfunc to add broadcasting to the Python function ``oct``:
>>> oct_array = np.frompyfunc(oct, 1, 1)
>>> oct_array(np.array((10, 30, 100)))
array(['0o12', '0o36', '0o144'], dtype=object)
>>> np.array((oct(10), oct(30), oct(100))) # for comparison
array(['0o12', '0o36', '0o144'], dtype='<U5')
""")
add_newdoc('numpy.core.umath', 'geterrobj',
"""
geterrobj()
Return the current object that defines floating-point error handling.
The error object contains all information that defines the error handling
behavior in NumPy. `geterrobj` is used internally by the other
functions that get and set error handling behavior (`geterr`, `seterr`,
`geterrcall`, `seterrcall`).
Returns
-------
errobj : list
The error object, a list containing three elements:
[internal numpy buffer size, error mask, error callback function].
The error mask is a single integer that holds the treatment information
on all four floating point errors. The information for each error type
is contained in three bits of the integer. If we print it in base 8, we
can see what treatment is set for "invalid", "under", "over", and
"divide" (in that order). The printed string can be interpreted with
* 0 : 'ignore'
* 1 : 'warn'
* 2 : 'raise'
* 3 : 'call'
* 4 : 'print'
* 5 : 'log'
See Also
--------
seterrobj, seterr, geterr, seterrcall, geterrcall
getbufsize, setbufsize
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterrobj() # first get the defaults
[8192, 521, None]
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> old_bufsize = np.setbufsize(20000)
>>> old_err = np.seterr(divide='raise')
>>> old_handler = np.seterrcall(err_handler)
>>> np.geterrobj()
[8192, 521, <function err_handler at 0x91dcaac>]
>>> old_err = np.seterr(all='ignore')
>>> np.base_repr(np.geterrobj()[1], 8)
'0'
>>> old_err = np.seterr(divide='warn', over='log', under='call',
... invalid='print')
>>> np.base_repr(np.geterrobj()[1], 8)
'4351'
""")
add_newdoc('numpy.core.umath', 'seterrobj',
"""
seterrobj(errobj, /)
Set the object that defines floating-point error handling.
The error object contains all information that defines the error handling
behavior in NumPy. `seterrobj` is used internally by the other
functions that set error handling behavior (`seterr`, `seterrcall`).
Parameters
----------
errobj : list
The error object, a list containing three elements:
[internal numpy buffer size, error mask, error callback function].
The error mask is a single integer that holds the treatment information
on all four floating point errors. The information for each error type
is contained in three bits of the integer. If we print it in base 8, we
can see what treatment is set for "invalid", "under", "over", and
"divide" (in that order). The printed string can be interpreted with
* 0 : 'ignore'
* 1 : 'warn'
* 2 : 'raise'
* 3 : 'call'
* 4 : 'print'
* 5 : 'log'
See Also
--------
geterrobj, seterr, geterr, seterrcall, geterrcall
getbufsize, setbufsize
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> old_errobj = np.geterrobj() # first get the defaults
>>> old_errobj
[8192, 521, None]
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> new_errobj = [20000, 12, err_handler]
>>> np.seterrobj(new_errobj)
>>> np.base_repr(12, 8) # int for divide=4 ('print') and over=1 ('warn')
'14'
>>> np.geterr()
{'over': 'warn', 'divide': 'print', 'invalid': 'ignore', 'under': 'ignore'}
>>> np.geterrcall() is err_handler
True
""")
##############################################################################
#
# compiled_base functions
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'add_docstring',
"""
add_docstring(obj, docstring)
Add a docstring to a built-in obj if possible.
If the obj already has a docstring raise a RuntimeError
If this routine does not know how to add a docstring to the object
raise a TypeError
""")
add_newdoc('numpy.core.umath', '_add_newdoc_ufunc',
"""
add_ufunc_docstring(ufunc, new_docstring)
Replace the docstring for a ufunc with new_docstring.
This method will only work if the current docstring for
the ufunc is NULL. (At the C level, i.e. when ufunc->doc is NULL.)
Parameters
----------
ufunc : numpy.ufunc
A ufunc whose current doc is NULL.
new_docstring : string
The new docstring for the ufunc.
Notes
-----
This method allocates memory for new_docstring on
the heap. Technically this creates a mempory leak, since this
memory will not be reclaimed until the end of the program
even if the ufunc itself is removed. However this will only
be a problem if the user is repeatedly creating ufuncs with
no documentation, adding documentation via add_newdoc_ufunc,
and then throwing away the ufunc.
""")
add_newdoc('numpy.core.multiarray', 'get_handler_name',
"""
get_handler_name(a: ndarray) -> str,None
Return the name of the memory handler used by `a`. If not provided, return
the name of the memory handler that will be used to allocate data for the
next `ndarray` in this context. May return None if `a` does not own its
memory, in which case you can traverse ``a.base`` for a memory handler.
""")
add_newdoc('numpy.core.multiarray', 'get_handler_version',
"""
get_handler_version(a: ndarray) -> int,None
Return the version of the memory handler used by `a`. If not provided,
return the version of the memory handler that will be used to allocate data
for the next `ndarray` in this context. May return None if `a` does not own
its memory, in which case you can traverse ``a.base`` for a memory handler.
""")
add_newdoc('numpy.core.multiarray', '_get_madvise_hugepage',
"""
_get_madvise_hugepage() -> bool
Get use of ``madvise (2)`` MADV_HUGEPAGE support when
allocating the array data. Returns the currently set value.
See `global_state` for more information.
""")
add_newdoc('numpy.core.multiarray', '_set_madvise_hugepage',
"""
_set_madvise_hugepage(enabled: bool) -> bool
Set or unset use of ``madvise (2)`` MADV_HUGEPAGE support when
allocating the array data. Returns the previously set value.
See `global_state` for more information.
""")
add_newdoc('numpy.core._multiarray_tests', 'format_float_OSprintf_g',
"""
format_float_OSprintf_g(val, precision)
Print a floating point scalar using the system's printf function,
equivalent to:
printf("%.*g", precision, val);
for half/float/double, or replacing 'g' by 'Lg' for longdouble. This
method is designed to help cross-validate the format_float_* methods.
Parameters
----------
val : python float or numpy floating scalar
Value to format.
precision : non-negative integer, optional
Precision given to printf.
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_scientific
format_float_positional
""")
##############################################################################
#
# Documentation for ufunc attributes and methods
#
##############################################################################
##############################################################################
#
# ufunc object
#
##############################################################################
add_newdoc('numpy.core', 'ufunc',
"""
Functions that operate element by element on whole arrays.
To see the documentation for a specific ufunc, use `info`. For
example, ``np.info(np.sin)``. Because ufuncs are written in C
(for speed) and linked into Python with NumPy's ufunc facility,
Python's help() function finds this page whenever help() is called
on a ufunc.
A detailed explanation of ufuncs can be found in the docs for :ref:`ufuncs`.
**Calling ufuncs:** ``op(*x[, out], where=True, **kwargs)``
Apply `op` to the arguments `*x` elementwise, broadcasting the arguments.
The broadcasting rules are:
* Dimensions of length 1 may be prepended to either array.
* Arrays may be repeated along dimensions of length 1.
Parameters
----------
*x : array_like
Input arrays.
out : ndarray, None, or tuple of ndarray and None, optional
Alternate array object(s) in which to put the result; if provided, it
must have a shape that the inputs broadcast to. A tuple of arrays
(possible only as a keyword argument) must have length equal to the
number of outputs; use None for uninitialized outputs to be
allocated by the ufunc.
where : array_like, optional
This condition is broadcast over the input. At locations where the
condition is True, the `out` array will be set to the ufunc result.
Elsewhere, the `out` array will retain its original value.
Note that if an uninitialized `out` array is created via the default
``out=None``, locations within it where the condition is False will
remain uninitialized.
**kwargs
For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.
Returns
-------
r : ndarray or tuple of ndarray
`r` will have the shape that the arrays in `x` broadcast to; if `out` is
provided, it will be returned. If not, `r` will be allocated and
may contain uninitialized values. If the function has more than one
output, then the result will be a tuple of arrays.
""")
##############################################################################
#
# ufunc attributes
#
##############################################################################
add_newdoc('numpy.core', 'ufunc', ('identity',
"""
The identity value.
Data attribute containing the identity element for the ufunc, if it has one.
If it does not, the attribute value is None.
Examples
--------
>>> np.add.identity
0
>>> np.multiply.identity
1
>>> np.power.identity
1
>>> print(np.exp.identity)
None
"""))
add_newdoc('numpy.core', 'ufunc', ('nargs',
"""
The number of arguments.
Data attribute containing the number of arguments the ufunc takes, including
optional ones.
Notes
-----
Typically this value will be one more than what you might expect because all
ufuncs take the optional "out" argument.
Examples
--------
>>> np.add.nargs
3
>>> np.multiply.nargs
3
>>> np.power.nargs
3
>>> np.exp.nargs
2
"""))
add_newdoc('numpy.core', 'ufunc', ('nin',
"""
The number of inputs.
Data attribute containing the number of arguments the ufunc treats as input.
Examples
--------
>>> np.add.nin
2
>>> np.multiply.nin
2
>>> np.power.nin
2
>>> np.exp.nin
1
"""))
add_newdoc('numpy.core', 'ufunc', ('nout',
"""
The number of outputs.
Data attribute containing the number of arguments the ufunc treats as output.
Notes
-----
Since all ufuncs can take output arguments, this will always be (at least) 1.
Examples
--------
>>> np.add.nout
1
>>> np.multiply.nout
1
>>> np.power.nout
1
>>> np.exp.nout
1
"""))
add_newdoc('numpy.core', 'ufunc', ('ntypes',
"""
The number of types.
The number of numerical NumPy types - of which there are 18 total - on which
the ufunc can operate.
See Also
--------
numpy.ufunc.types
Examples
--------
>>> np.add.ntypes
18
>>> np.multiply.ntypes
18
>>> np.power.ntypes
17
>>> np.exp.ntypes
7
>>> np.remainder.ntypes
14
"""))
add_newdoc('numpy.core', 'ufunc', ('types',
"""
Returns a list with types grouped input->output.
Data attribute listing the data-type "Domain-Range" groupings the ufunc can
deliver. The data-types are given using the character codes.
See Also
--------
numpy.ufunc.ntypes
Examples
--------
>>> np.add.types
['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
'GG->G', 'OO->O']
>>> np.multiply.types
['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
'GG->G', 'OO->O']
>>> np.power.types
['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',
'OO->O']
>>> np.exp.types
['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']
>>> np.remainder.types
['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']
"""))
add_newdoc('numpy.core', 'ufunc', ('signature',
"""
Definition of the core elements a generalized ufunc operates on.
The signature determines how the dimensions of each input/output array
are split into core and loop dimensions:
1. Each dimension in the signature is matched to a dimension of the
corresponding passed-in array, starting from the end of the shape tuple.
2. Core dimensions assigned to the same label in the signature must have
exactly matching sizes, no broadcasting is performed.
3. The core dimensions are removed from all inputs and the remaining
dimensions are broadcast together, defining the loop dimensions.
Notes
-----
Generalized ufuncs are used internally in many linalg functions, and in
the testing suite; the examples below are taken from these.
For ufuncs that operate on scalars, the signature is None, which is
equivalent to '()' for every argument.
Examples
--------
>>> np.core.umath_tests.matrix_multiply.signature
'(m,n),(n,p)->(m,p)'
>>> np.linalg._umath_linalg.det.signature
'(m,m)->()'
>>> np.add.signature is None
True # equivalent to '(),()->()'
"""))
##############################################################################
#
# ufunc methods
#
##############################################################################
add_newdoc('numpy.core', 'ufunc', ('reduce',
"""
reduce(array, axis=0, dtype=None, out=None, keepdims=False, initial=<no value>, where=True)
Reduces `array`'s dimension by one, by applying ufunc along one axis.
Let :math:`array.shape = (N_0, ..., N_i, ..., N_{M-1})`. Then
:math:`ufunc.reduce(array, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
ufunc to each :math:`array[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
For a one-dimensional array, reduce produces results equivalent to:
::
r = op.identity # op = ufunc
for i in range(len(A)):
r = op(r, A[i])
return r
For example, add.reduce() is equivalent to sum().
Parameters
----------
array : array_like
The array to act on.
axis : None or int or tuple of ints, optional
Axis or axes along which a reduction is performed.
The default (`axis` = 0) is perform a reduction over the first
dimension of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
.. versionadded:: 1.7.0
If this is None, a reduction is performed over all the axes.
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
For operations which are either not commutative or not associative,
doing a reduction over multiple axes is not well-defined. The
ufuncs do not currently raise an exception in this case, but will
likely do so in the future.
dtype : data-type code, optional
The type used to represent the intermediate results. Defaults
to the data-type of the output array if this is provided, or
the data-type of the input array if no output array is provided.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If not provided or None,
a freshly-allocated array is returned. For consistency with
``ufunc.__call__``, if given as a keyword, this may be wrapped in a
1-element tuple.
.. versionchanged:: 1.13.0
Tuples are allowed for keyword argument.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `array`.
.. versionadded:: 1.7.0
initial : scalar, optional
The value with which to start the reduction.
If the ufunc has no identity or the dtype is object, this defaults
to None - otherwise it defaults to ufunc.identity.
If ``None`` is given, the first element of the reduction is used,
and an error is thrown if the reduction is empty.
.. versionadded:: 1.15.0
where : array_like of bool, optional
A boolean array which is broadcasted to match the dimensions
of `array`, and selects elements to include in the reduction. Note
that for ufuncs like ``minimum`` that do not have an identity
defined, one has to pass in also ``initial``.
.. versionadded:: 1.17.0
Returns
-------
r : ndarray
The reduced array. If `out` was supplied, `r` is a reference to it.
Examples
--------
>>> np.multiply.reduce([2,3,5])
30
A multi-dimensional array example:
>>> X = np.arange(8).reshape((2,2,2))
>>> X
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> np.add.reduce(X, 0)
array([[ 4, 6],
[ 8, 10]])
>>> np.add.reduce(X) # confirm: default axis value is 0
array([[ 4, 6],
[ 8, 10]])
>>> np.add.reduce(X, 1)
array([[ 2, 4],
[10, 12]])
>>> np.add.reduce(X, 2)
array([[ 1, 5],
[ 9, 13]])
You can use the ``initial`` keyword argument to initialize the reduction
with a different value, and ``where`` to select specific elements to include:
>>> np.add.reduce([10], initial=5)
15
>>> np.add.reduce(np.ones((2, 2, 2)), axis=(0, 2), initial=10)
array([14., 14.])
>>> a = np.array([10., np.nan, 10])
>>> np.add.reduce(a, where=~np.isnan(a))
20.0
Allows reductions of empty arrays where they would normally fail, i.e.
for ufuncs without an identity.
>>> np.minimum.reduce([], initial=np.inf)
inf
>>> np.minimum.reduce([[1., 2.], [3., 4.]], initial=10., where=[True, False])
array([ 1., 10.])
>>> np.minimum.reduce([])
Traceback (most recent call last):
...
ValueError: zero-size array to reduction operation minimum which has no identity
"""))
add_newdoc('numpy.core', 'ufunc', ('accumulate',
"""
accumulate(array, axis=0, dtype=None, out=None)
Accumulate the result of applying the operator to all elements.
For a one-dimensional array, accumulate produces results equivalent to::
r = np.empty(len(A))
t = op.identity # op = the ufunc being applied to A's elements
for i in range(len(A)):
t = op(t, A[i])
r[i] = t
return r
For example, add.accumulate() is equivalent to np.cumsum().
For a multi-dimensional array, accumulate is applied along only one
axis (axis zero by default; see Examples below) so repeated use is
necessary if one wants to accumulate over multiple axes.
Parameters
----------
array : array_like
The array to act on.
axis : int, optional
The axis along which to apply the accumulation; default is zero.
dtype : data-type code, optional
The data-type used to represent the intermediate results. Defaults
to the data-type of the output array if such is provided, or the
data-type of the input array if no output array is provided.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If not provided or None,
a freshly-allocated array is returned. For consistency with
``ufunc.__call__``, if given as a keyword, this may be wrapped in a
1-element tuple.
.. versionchanged:: 1.13.0
Tuples are allowed for keyword argument.
Returns
-------
r : ndarray
The accumulated values. If `out` was supplied, `r` is a reference to
`out`.
Examples
--------
1-D array examples:
>>> np.add.accumulate([2, 3, 5])
array([ 2, 5, 10])
>>> np.multiply.accumulate([2, 3, 5])
array([ 2, 6, 30])
2-D array examples:
>>> I = np.eye(2)
>>> I
array([[1., 0.],
[0., 1.]])
Accumulate along axis 0 (rows), down columns:
>>> np.add.accumulate(I, 0)
array([[1., 0.],
[1., 1.]])
>>> np.add.accumulate(I) # no axis specified = axis zero
array([[1., 0.],
[1., 1.]])
Accumulate along axis 1 (columns), through rows:
>>> np.add.accumulate(I, 1)
array([[1., 1.],
[0., 1.]])
"""))
add_newdoc('numpy.core', 'ufunc', ('reduceat',
"""
reduceat(array, indices, axis=0, dtype=None, out=None)
Performs a (local) reduce with specified slices over a single axis.
For i in ``range(len(indices))``, `reduceat` computes
``ufunc.reduce(array[indices[i]:indices[i+1]])``, which becomes the i-th
generalized "row" parallel to `axis` in the final result (i.e., in a
2-D array, for example, if `axis = 0`, it becomes the i-th row, but if
`axis = 1`, it becomes the i-th column). There are three exceptions to this:
* when ``i = len(indices) - 1`` (so for the last index),
``indices[i+1] = array.shape[axis]``.
* if ``indices[i] >= indices[i + 1]``, the i-th generalized "row" is
simply ``array[indices[i]]``.
* if ``indices[i] >= len(array)`` or ``indices[i] < 0``, an error is raised.
The shape of the output depends on the size of `indices`, and may be
larger than `array` (this happens if ``len(indices) > array.shape[axis]``).
Parameters
----------
array : array_like
The array to act on.
indices : array_like
Paired indices, comma separated (not colon), specifying slices to
reduce.
axis : int, optional
The axis along which to apply the reduceat.
dtype : data-type code, optional
The type used to represent the intermediate results. Defaults
to the data type of the output array if this is provided, or
the data type of the input array if no output array is provided.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If not provided or None,
a freshly-allocated array is returned. For consistency with
``ufunc.__call__``, if given as a keyword, this may be wrapped in a
1-element tuple.
.. versionchanged:: 1.13.0
Tuples are allowed for keyword argument.
Returns
-------
r : ndarray
The reduced values. If `out` was supplied, `r` is a reference to
`out`.
Notes
-----
A descriptive example:
If `array` is 1-D, the function `ufunc.accumulate(array)` is the same as
``ufunc.reduceat(array, indices)[::2]`` where `indices` is
``range(len(array) - 1)`` with a zero placed
in every other element:
``indices = zeros(2 * len(array) - 1)``,
``indices[1::2] = range(1, len(array))``.
Don't be fooled by this attribute's name: `reduceat(array)` is not
necessarily smaller than `array`.
Examples
--------
To take the running sum of four successive values:
>>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]
array([ 6, 10, 14, 18])
A 2-D example:
>>> x = np.linspace(0, 15, 16).reshape(4,4)
>>> x
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[12., 13., 14., 15.]])
::
# reduce such that the result has the following five rows:
# [row1 + row2 + row3]
# [row4]
# [row2]
# [row3]
# [row1 + row2 + row3 + row4]
>>> np.add.reduceat(x, [0, 3, 1, 2, 0])
array([[12., 15., 18., 21.],
[12., 13., 14., 15.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[24., 28., 32., 36.]])
::
# reduce such that result has the following two columns:
# [col1 * col2 * col3, col4]
>>> np.multiply.reduceat(x, [0, 3], 1)
array([[ 0., 3.],
[ 120., 7.],
[ 720., 11.],
[2184., 15.]])
"""))
add_newdoc('numpy.core', 'ufunc', ('outer',
r"""
outer(A, B, /, **kwargs)
Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.
Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of
``op.outer(A, B)`` is an array of dimension M + N such that:
.. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =
op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])
For `A` and `B` one-dimensional, this is equivalent to::
r = empty(len(A),len(B))
for i in range(len(A)):
for j in range(len(B)):
r[i,j] = op(A[i], B[j]) # op = ufunc in question
Parameters
----------
A : array_like
First array
B : array_like
Second array
kwargs : any
Arguments to pass on to the ufunc. Typically `dtype` or `out`.
See `ufunc` for a comprehensive overview of all available arguments.
Returns
-------
r : ndarray
Output array
See Also
--------
numpy.outer : A less powerful version of ``np.multiply.outer``
that `ravel`\ s all inputs to 1D. This exists
primarily for compatibility with old code.
tensordot : ``np.tensordot(a, b, axes=((), ()))`` and
``np.multiply.outer(a, b)`` behave same for all
dimensions of a and b.
Examples
--------
>>> np.multiply.outer([1, 2, 3], [4, 5, 6])
array([[ 4, 5, 6],
[ 8, 10, 12],
[12, 15, 18]])
A multi-dimensional example:
>>> A = np.array([[1, 2, 3], [4, 5, 6]])
>>> A.shape
(2, 3)
>>> B = np.array([[1, 2, 3, 4]])
>>> B.shape
(1, 4)
>>> C = np.multiply.outer(A, B)
>>> C.shape; C
(2, 3, 1, 4)
array([[[[ 1, 2, 3, 4]],
[[ 2, 4, 6, 8]],
[[ 3, 6, 9, 12]]],
[[[ 4, 8, 12, 16]],
[[ 5, 10, 15, 20]],
[[ 6, 12, 18, 24]]]])
"""))
add_newdoc('numpy.core', 'ufunc', ('at',
"""
at(a, indices, b=None, /)
Performs unbuffered in place operation on operand 'a' for elements
specified by 'indices'. For addition ufunc, this method is equivalent to
``a[indices] += b``, except that results are accumulated for elements that
are indexed more than once. For example, ``a[[0,0]] += 1`` will only
increment the first element once because of buffering, whereas
``add.at(a, [0,0], 1)`` will increment the first element twice.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
The array to perform in place operation on.
indices : array_like or tuple
Array like index object or slice object for indexing into first
operand. If first operand has multiple dimensions, indices can be a
tuple of array like index objects or slice objects.
b : array_like
Second operand for ufuncs requiring two operands. Operand must be
broadcastable over first operand after indexing or slicing.
Examples
--------
Set items 0 and 1 to their negative values:
>>> a = np.array([1, 2, 3, 4])
>>> np.negative.at(a, [0, 1])
>>> a
array([-1, -2, 3, 4])
Increment items 0 and 1, and increment item 2 twice:
>>> a = np.array([1, 2, 3, 4])
>>> np.add.at(a, [0, 1, 2, 2], 1)
>>> a
array([2, 3, 5, 4])
Add items 0 and 1 in first array to second array,
and store results in first array:
>>> a = np.array([1, 2, 3, 4])
>>> b = np.array([1, 2])
>>> np.add.at(a, [0, 1], b)
>>> a
array([2, 4, 3, 4])
"""))
##############################################################################
#
# Documentation for dtype attributes and methods
#
##############################################################################
##############################################################################
#
# dtype object
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'dtype',
"""
dtype(dtype, align=False, copy=False)
Create a data type object.
A numpy array is homogeneous, and contains elements described by a
dtype object. A dtype object can be constructed from different
combinations of fundamental numeric types.
Parameters
----------
dtype
Object to be converted to a data type object.
align : bool, optional
Add padding to the fields to match what a C compiler would output
for a similar C-struct. Can be ``True`` only if `obj` is a dictionary
or a comma-separated string. If a struct dtype is being created,
this also sets a sticky alignment flag ``isalignedstruct``.
copy : bool, optional
Make a new copy of the data-type object. If ``False``, the result
may just be a reference to a built-in data-type object.
See also
--------
result_type
Examples
--------
Using array-scalar type:
>>> np.dtype(np.int16)
dtype('int16')
Structured type, one field name 'f1', containing int16:
>>> np.dtype([('f1', np.int16)])
dtype([('f1', '<i2')])
Structured type, one field named 'f1', in itself containing a structured
type with one field:
>>> np.dtype([('f1', [('f1', np.int16)])])
dtype([('f1', [('f1', '<i2')])])
Structured type, two fields: the first field contains an unsigned int, the
second an int32:
>>> np.dtype([('f1', np.uint64), ('f2', np.int32)])
dtype([('f1', '<u8'), ('f2', '<i4')])
Using array-protocol type strings:
>>> np.dtype([('a','f8'),('b','S10')])
dtype([('a', '<f8'), ('b', 'S10')])
Using comma-separated field formats. The shape is (2,3):
>>> np.dtype("i4, (2,3)f8")
dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])
Using tuples. ``int`` is a fixed type, 3 the field's shape. ``void``
is a flexible type, here of size 10:
>>> np.dtype([('hello',(np.int64,3)),('world',np.void,10)])
dtype([('hello', '<i8', (3,)), ('world', 'V10')])
Subdivide ``int16`` into 2 ``int8``'s, called x and y. 0 and 1 are
the offsets in bytes:
>>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)}))
dtype((numpy.int16, [('x', 'i1'), ('y', 'i1')]))
Using dictionaries. Two fields named 'gender' and 'age':
>>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]})
dtype([('gender', 'S1'), ('age', 'u1')])
Offsets in bytes, here 0 and 25:
>>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)})
dtype([('surname', 'S25'), ('age', 'u1')])
""")
##############################################################################
#
# dtype attributes
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'dtype', ('alignment',
"""
The required alignment (bytes) of this data-type according to the compiler.
More information is available in the C-API section of the manual.
Examples
--------
>>> x = np.dtype('i4')
>>> x.alignment
4
>>> x = np.dtype(float)
>>> x.alignment
8
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('byteorder',
"""
A character indicating the byte-order of this data-type object.
One of:
=== ==============
'=' native
'<' little-endian
'>' big-endian
'|' not applicable
=== ==============
All built-in data-type objects have byteorder either '=' or '|'.
Examples
--------
>>> dt = np.dtype('i2')
>>> dt.byteorder
'='
>>> # endian is not relevant for 8 bit numbers
>>> np.dtype('i1').byteorder
'|'
>>> # or ASCII strings
>>> np.dtype('S2').byteorder
'|'
>>> # Even if specific code is given, and it is native
>>> # '=' is the byteorder
>>> import sys
>>> sys_is_le = sys.byteorder == 'little'
>>> native_code = sys_is_le and '<' or '>'
>>> swapped_code = sys_is_le and '>' or '<'
>>> dt = np.dtype(native_code + 'i2')
>>> dt.byteorder
'='
>>> # Swapped code shows up as itself
>>> dt = np.dtype(swapped_code + 'i2')
>>> dt.byteorder == swapped_code
True
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('char',
"""A unique character code for each of the 21 different built-in types.
Examples
--------
>>> x = np.dtype(float)
>>> x.char
'd'
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('descr',
"""
`__array_interface__` description of the data-type.
The format is that required by the 'descr' key in the
`__array_interface__` attribute.
Warning: This attribute exists specifically for `__array_interface__`,
and passing it directly to `np.dtype` will not accurately reconstruct
some dtypes (e.g., scalar and subarray dtypes).
Examples
--------
>>> x = np.dtype(float)
>>> x.descr
[('', '<f8')]
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> dt.descr
[('name', '<U16'), ('grades', '<f8', (2,))]
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('fields',
"""
Dictionary of named fields defined for this data type, or ``None``.
The dictionary is indexed by keys that are the names of the fields.
Each entry in the dictionary is a tuple fully describing the field::
(dtype, offset[, title])
Offset is limited to C int, which is signed and usually 32 bits.
If present, the optional title can be any object (if it is a string
or unicode then it will also be a key in the fields dictionary,
otherwise it's meta-data). Notice also that the first two elements
of the tuple can be passed directly as arguments to the ``ndarray.getfield``
and ``ndarray.setfield`` methods.
See Also
--------
ndarray.getfield, ndarray.setfield
Examples
--------
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> print(dt.fields)
{'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)}
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('flags',
"""
Bit-flags describing how this data type is to be interpreted.
Bit-masks are in `numpy.core.multiarray` as the constants
`ITEM_HASOBJECT`, `LIST_PICKLE`, `ITEM_IS_POINTER`, `NEEDS_INIT`,
`NEEDS_PYAPI`, `USE_GETITEM`, `USE_SETITEM`. A full explanation
of these flags is in C-API documentation; they are largely useful
for user-defined data-types.
The following example demonstrates that operations on this particular
dtype requires Python C-API.
Examples
--------
>>> x = np.dtype([('a', np.int32, 8), ('b', np.float64, 6)])
>>> x.flags
16
>>> np.core.multiarray.NEEDS_PYAPI
16
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('hasobject',
"""
Boolean indicating whether this dtype contains any reference-counted
objects in any fields or sub-dtypes.
Recall that what is actually in the ndarray memory representing
the Python object is the memory address of that object (a pointer).
Special handling may be required, and this attribute is useful for
distinguishing data types that may contain arbitrary Python objects
and data-types that won't.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('isbuiltin',
"""
Integer indicating how this dtype relates to the built-in dtypes.
Read-only.
= ========================================================================
0 if this is a structured array type, with fields
1 if this is a dtype compiled into numpy (such as ints, floats etc)
2 if the dtype is for a user-defined numpy type
A user-defined type uses the numpy C-API machinery to extend
numpy to handle a new array type. See
:ref:`user.user-defined-data-types` in the NumPy manual.
= ========================================================================
Examples
--------
>>> dt = np.dtype('i2')
>>> dt.isbuiltin
1
>>> dt = np.dtype('f8')
>>> dt.isbuiltin
1
>>> dt = np.dtype([('field1', 'f8')])
>>> dt.isbuiltin
0
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('isnative',
"""
Boolean indicating whether the byte order of this dtype is native
to the platform.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('isalignedstruct',
"""
Boolean indicating whether the dtype is a struct which maintains
field alignment. This flag is sticky, so when combining multiple
structs together, it is preserved and produces new dtypes which
are also aligned.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('itemsize',
"""
The element size of this data-type object.
For 18 of the 21 types this number is fixed by the data-type.
For the flexible data-types, this number can be anything.
Examples
--------
>>> arr = np.array([[1, 2], [3, 4]])
>>> arr.dtype
dtype('int64')
>>> arr.itemsize
8
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> dt.itemsize
80
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('kind',
"""
A character code (one of 'biufcmMOSUV') identifying the general kind of data.
= ======================
b boolean
i signed integer
u unsigned integer
f floating-point
c complex floating-point
m timedelta
M datetime
O object
S (byte-)string
U Unicode
V void
= ======================
Examples
--------
>>> dt = np.dtype('i4')
>>> dt.kind
'i'
>>> dt = np.dtype('f8')
>>> dt.kind
'f'
>>> dt = np.dtype([('field1', 'f8')])
>>> dt.kind
'V'
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('metadata',
"""
Either ``None`` or a readonly dictionary of metadata (mappingproxy).
The metadata field can be set using any dictionary at data-type
creation. NumPy currently has no uniform approach to propagating
metadata; although some array operations preserve it, there is no
guarantee that others will.
.. warning::
Although used in certain projects, this feature was long undocumented
and is not well supported. Some aspects of metadata propagation
are expected to change in the future.
Examples
--------
>>> dt = np.dtype(float, metadata={"key": "value"})
>>> dt.metadata["key"]
'value'
>>> arr = np.array([1, 2, 3], dtype=dt)
>>> arr.dtype.metadata
mappingproxy({'key': 'value'})
Adding arrays with identical datatypes currently preserves the metadata:
>>> (arr + arr).dtype.metadata
mappingproxy({'key': 'value'})
But if the arrays have different dtype metadata, the metadata may be
dropped:
>>> dt2 = np.dtype(float, metadata={"key2": "value2"})
>>> arr2 = np.array([3, 2, 1], dtype=dt2)
>>> (arr + arr2).dtype.metadata is None
True # The metadata field is cleared so None is returned
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('name',
"""
A bit-width name for this data-type.
Un-sized flexible data-type objects do not have this attribute.
Examples
--------
>>> x = np.dtype(float)
>>> x.name
'float64'
>>> x = np.dtype([('a', np.int32, 8), ('b', np.float64, 6)])
>>> x.name
'void640'
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('names',
"""
Ordered list of field names, or ``None`` if there are no fields.
The names are ordered according to increasing byte offset. This can be
used, for example, to walk through all of the named fields in offset order.
Examples
--------
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> dt.names
('name', 'grades')
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('num',
"""
A unique number for each of the 21 different built-in types.
These are roughly ordered from least-to-most precision.
Examples
--------
>>> dt = np.dtype(str)
>>> dt.num
19
>>> dt = np.dtype(float)
>>> dt.num
12
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('shape',
"""
Shape tuple of the sub-array if this data type describes a sub-array,
and ``()`` otherwise.
Examples
--------
>>> dt = np.dtype(('i4', 4))
>>> dt.shape
(4,)
>>> dt = np.dtype(('i4', (2, 3)))
>>> dt.shape
(2, 3)
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('ndim',
"""
Number of dimensions of the sub-array if this data type describes a
sub-array, and ``0`` otherwise.
.. versionadded:: 1.13.0
Examples
--------
>>> x = np.dtype(float)
>>> x.ndim
0
>>> x = np.dtype((float, 8))
>>> x.ndim
1
>>> x = np.dtype(('i4', (3, 4)))
>>> x.ndim
2
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('str',
"""The array-protocol typestring of this data-type object."""))
add_newdoc('numpy.core.multiarray', 'dtype', ('subdtype',
"""
Tuple ``(item_dtype, shape)`` if this `dtype` describes a sub-array, and
None otherwise.
The *shape* is the fixed shape of the sub-array described by this
data type, and *item_dtype* the data type of the array.
If a field whose dtype object has this attribute is retrieved,
then the extra dimensions implied by *shape* are tacked on to
the end of the retrieved array.
See Also
--------
dtype.base
Examples
--------
>>> x = numpy.dtype('8f')
>>> x.subdtype
(dtype('float32'), (8,))
>>> x = numpy.dtype('i2')
>>> x.subdtype
>>>
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('base',
"""
Returns dtype for the base element of the subarrays,
regardless of their dimension or shape.
See Also
--------
dtype.subdtype
Examples
--------
>>> x = numpy.dtype('8f')
>>> x.base
dtype('float32')
>>> x = numpy.dtype('i2')
>>> x.base
dtype('int16')
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('type',
"""The type object used to instantiate a scalar of this data-type."""))
##############################################################################
#
# dtype methods
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'dtype', ('newbyteorder',
"""
newbyteorder(new_order='S', /)
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
Parameters
----------
new_order : string, optional
Byte order to force; a value from the byte order specifications
below. The default value ('S') results in swapping the current
byte order. `new_order` codes can be any of:
* 'S' - swap dtype from current to opposite endian
* {'<', 'little'} - little endian
* {'>', 'big'} - big endian
* {'=', 'native'} - native order
* {'|', 'I'} - ignore (no change to byte order)
Returns
-------
new_dtype : dtype
New dtype object with the given change to the byte order.
Notes
-----
Changes are also made in all fields and sub-arrays of the data type.
Examples
--------
>>> import sys
>>> sys_is_le = sys.byteorder == 'little'
>>> native_code = sys_is_le and '<' or '>'
>>> swapped_code = sys_is_le and '>' or '<'
>>> native_dt = np.dtype(native_code+'i2')
>>> swapped_dt = np.dtype(swapped_code+'i2')
>>> native_dt.newbyteorder('S') == swapped_dt
True
>>> native_dt.newbyteorder() == swapped_dt
True
>>> native_dt == swapped_dt.newbyteorder('S')
True
>>> native_dt == swapped_dt.newbyteorder('=')
True
>>> native_dt == swapped_dt.newbyteorder('N')
True
>>> native_dt == native_dt.newbyteorder('|')
True
>>> np.dtype('<i2') == native_dt.newbyteorder('<')
True
>>> np.dtype('<i2') == native_dt.newbyteorder('L')
True
>>> np.dtype('>i2') == native_dt.newbyteorder('>')
True
>>> np.dtype('>i2') == native_dt.newbyteorder('B')
True
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('__class_getitem__',
"""
__class_getitem__(item, /)
Return a parametrized wrapper around the `~numpy.dtype` type.
.. versionadded:: 1.22
Returns
-------
alias : types.GenericAlias
A parametrized `~numpy.dtype` type.
Examples
--------
>>> import numpy as np
>>> np.dtype[np.int64]
numpy.dtype[numpy.int64]
Notes
-----
This method is only available for python 3.9 and later.
See Also
--------
:pep:`585` : Type hinting generics in standard collections.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('__ge__',
"""
__ge__(value, /)
Return ``self >= value``.
Equivalent to ``np.can_cast(value, self, casting="safe")``.
See Also
--------
can_cast : Returns True if cast between data types can occur according to
the casting rule.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('__le__',
"""
__le__(value, /)
Return ``self <= value``.
Equivalent to ``np.can_cast(self, value, casting="safe")``.
See Also
--------
can_cast : Returns True if cast between data types can occur according to
the casting rule.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('__gt__',
"""
__ge__(value, /)
Return ``self > value``.
Equivalent to
``self != value and np.can_cast(value, self, casting="safe")``.
See Also
--------
can_cast : Returns True if cast between data types can occur according to
the casting rule.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('__lt__',
"""
__lt__(value, /)
Return ``self < value``.
Equivalent to
``self != value and np.can_cast(self, value, casting="safe")``.
See Also
--------
can_cast : Returns True if cast between data types can occur according to
the casting rule.
"""))
##############################################################################
#
# Datetime-related Methods
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'busdaycalendar',
"""
busdaycalendar(weekmask='1111100', holidays=None)
A business day calendar object that efficiently stores information
defining valid days for the busday family of functions.
The default valid days are Monday through Friday ("business days").
A busdaycalendar object can be specified with any set of weekly
valid days, plus an optional "holiday" dates that always will be invalid.
Once a busdaycalendar object is created, the weekmask and holidays
cannot be modified.
.. versionadded:: 1.7.0
Parameters
----------
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates, no matter which
weekday they fall upon. Holiday dates may be specified in any
order, and NaT (not-a-time) dates are ignored. This list is
saved in a normalized form that is suited for fast calculations
of valid days.
Returns
-------
out : busdaycalendar
A business day calendar object containing the specified
weekmask and holidays values.
See Also
--------
is_busday : Returns a boolean array indicating valid days.
busday_offset : Applies an offset counted in valid days.
busday_count : Counts how many valid days are in a half-open date range.
Attributes
----------
Note: once a busdaycalendar object is created, you cannot modify the
weekmask or holidays. The attributes return copies of internal data.
weekmask : (copy) seven-element array of bool
holidays : (copy) sorted array of datetime64[D]
Examples
--------
>>> # Some important days in July
... bdd = np.busdaycalendar(
... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
>>> # Default is Monday to Friday weekdays
... bdd.weekmask
array([ True, True, True, True, True, False, False])
>>> # Any holidays already on the weekend are removed
... bdd.holidays
array(['2011-07-01', '2011-07-04'], dtype='datetime64[D]')
""")
add_newdoc('numpy.core.multiarray', 'busdaycalendar', ('weekmask',
"""A copy of the seven-element boolean mask indicating valid days."""))
add_newdoc('numpy.core.multiarray', 'busdaycalendar', ('holidays',
"""A copy of the holiday array indicating additional invalid days."""))
add_newdoc('numpy.core.multiarray', 'normalize_axis_index',
"""
normalize_axis_index(axis, ndim, msg_prefix=None)
Normalizes an axis index, `axis`, such that is a valid positive index into
the shape of array with `ndim` dimensions. Raises an AxisError with an
appropriate message if this is not possible.
Used internally by all axis-checking logic.
.. versionadded:: 1.13.0
Parameters
----------
axis : int
The un-normalized index of the axis. Can be negative
ndim : int
The number of dimensions of the array that `axis` should be normalized
against
msg_prefix : str
A prefix to put before the message, typically the name of the argument
Returns
-------
normalized_axis : int
The normalized axis index, such that `0 <= normalized_axis < ndim`
Raises
------
AxisError
If the axis index is invalid, when `-ndim <= axis < ndim` is false.
Examples
--------
>>> normalize_axis_index(0, ndim=3)
0
>>> normalize_axis_index(1, ndim=3)
1
>>> normalize_axis_index(-1, ndim=3)
2
>>> normalize_axis_index(3, ndim=3)
Traceback (most recent call last):
...
AxisError: axis 3 is out of bounds for array of dimension 3
>>> normalize_axis_index(-4, ndim=3, msg_prefix='axes_arg')
Traceback (most recent call last):
...
AxisError: axes_arg: axis -4 is out of bounds for array of dimension 3
""")
add_newdoc('numpy.core.multiarray', 'datetime_data',
"""
datetime_data(dtype, /)
Get information about the step size of a date or time type.
The returned tuple can be passed as the second argument of `numpy.datetime64` and
`numpy.timedelta64`.
Parameters
----------
dtype : dtype
The dtype object, which must be a `datetime64` or `timedelta64` type.
Returns
-------
unit : str
The :ref:`datetime unit <arrays.dtypes.dateunits>` on which this dtype
is based.
count : int
The number of base units in a step.
Examples
--------
>>> dt_25s = np.dtype('timedelta64[25s]')
>>> np.datetime_data(dt_25s)
('s', 25)
>>> np.array(10, dt_25s).astype('timedelta64[s]')
array(250, dtype='timedelta64[s]')
The result can be used to construct a datetime that uses the same units
as a timedelta
>>> np.datetime64('2010', np.datetime_data(dt_25s))
numpy.datetime64('2010-01-01T00:00:00','25s')
""")
##############################################################################
#
# Documentation for `generic` attributes and methods
#
##############################################################################
add_newdoc('numpy.core.numerictypes', 'generic',
"""
Base class for numpy scalar types.
Class from which most (all?) numpy scalar types are derived. For
consistency, exposes the same API as `ndarray`, despite many
consequent attributes being either "get-only," or completely irrelevant.
This is the class from which it is strongly suggested users should derive
custom scalar types.
""")
# Attributes
def refer_to_array_attribute(attr, method=True):
docstring = """
Scalar {} identical to the corresponding array attribute.
Please see `ndarray.{}`.
"""
return attr, docstring.format("method" if method else "attribute", attr)
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('T', method=False))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('base', method=False))
add_newdoc('numpy.core.numerictypes', 'generic', ('data',
"""Pointer to start of data."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('dtype',
"""Get array data-descriptor."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('flags',
"""The integer value of flags."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('flat',
"""A 1-D view of the scalar."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('imag',
"""The imaginary part of the scalar."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('itemsize',
"""The length of one element in bytes."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('nbytes',
"""The length of the scalar in bytes."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('ndim',
"""The number of array dimensions."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('real',
"""The real part of the scalar."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('shape',
"""Tuple of array dimensions."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('size',
"""The number of elements in the gentype."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('strides',
"""Tuple of bytes steps in each dimension."""))
# Methods
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('all'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('any'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('argmax'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('argmin'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('argsort'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('astype'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('byteswap'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('choose'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('clip'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('compress'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('conjugate'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('copy'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('cumprod'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('cumsum'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('diagonal'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('dump'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('dumps'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('fill'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('flatten'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('getfield'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('item'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('itemset'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('max'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('mean'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('min'))
add_newdoc('numpy.core.numerictypes', 'generic', ('newbyteorder',
"""
newbyteorder(new_order='S', /)
Return a new `dtype` with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The `new_order` code can be any from the following:
* 'S' - swap dtype from current to opposite endian
* {'<', 'little'} - little endian
* {'>', 'big'} - big endian
* {'=', 'native'} - native order
* {'|', 'I'} - ignore (no change to byte order)
Parameters
----------
new_order : str, optional
Byte order to force; a value from the byte order specifications
above. The default value ('S') results in swapping the current
byte order.
Returns
-------
new_dtype : dtype
New `dtype` object with the given change to the byte order.
"""))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('nonzero'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('prod'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('ptp'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('put'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('ravel'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('repeat'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('reshape'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('resize'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('round'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('searchsorted'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('setfield'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('setflags'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('sort'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('squeeze'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('std'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('sum'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('swapaxes'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('take'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('tofile'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('tolist'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('tostring'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('trace'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('transpose'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('var'))
add_newdoc('numpy.core.numerictypes', 'generic',
refer_to_array_attribute('view'))
add_newdoc('numpy.core.numerictypes', 'number', ('__class_getitem__',
"""
__class_getitem__(item, /)
Return a parametrized wrapper around the `~numpy.number` type.
.. versionadded:: 1.22
Returns
-------
alias : types.GenericAlias
A parametrized `~numpy.number` type.
Examples
--------
>>> from typing import Any
>>> import numpy as np
>>> np.signedinteger[Any]
numpy.signedinteger[typing.Any]
Notes
-----
This method is only available for python 3.9 and later.
See Also
--------
:pep:`585` : Type hinting generics in standard collections.
"""))
##############################################################################
#
# Documentation for scalar type abstract base classes in type hierarchy
#
##############################################################################
add_newdoc('numpy.core.numerictypes', 'number',
"""
Abstract base class of all numeric scalar types.
""")
add_newdoc('numpy.core.numerictypes', 'integer',
"""
Abstract base class of all integer scalar types.
""")
add_newdoc('numpy.core.numerictypes', 'signedinteger',
"""
Abstract base class of all signed integer scalar types.
""")
add_newdoc('numpy.core.numerictypes', 'unsignedinteger',
"""
Abstract base class of all unsigned integer scalar types.
""")
add_newdoc('numpy.core.numerictypes', 'inexact',
"""
Abstract base class of all numeric scalar types with a (potentially)
inexact representation of the values in its range, such as
floating-point numbers.
""")
add_newdoc('numpy.core.numerictypes', 'floating',
"""
Abstract base class of all floating-point scalar types.
""")
add_newdoc('numpy.core.numerictypes', 'complexfloating',
"""
Abstract base class of all complex number scalar types that are made up of
floating-point numbers.
""")
add_newdoc('numpy.core.numerictypes', 'flexible',
"""
Abstract base class of all scalar types without predefined length.
The actual size of these types depends on the specific `np.dtype`
instantiation.
""")
add_newdoc('numpy.core.numerictypes', 'character',
"""
Abstract base class of all character string scalar types.
""")
| 201,399 | Python | 28.397168 | 128 | 0.584407 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/function_base.py | import functools
import warnings
import operator
import types
from . import numeric as _nx
from .numeric import result_type, NaN, asanyarray, ndim
from numpy.core.multiarray import add_docstring
from numpy.core import overrides
__all__ = ['logspace', 'linspace', 'geomspace']
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
def _linspace_dispatcher(start, stop, num=None, endpoint=None, retstep=None,
dtype=None, axis=None):
return (start, stop)
@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
axis=0):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
.. versionchanged:: 1.20.0
Values are rounded towards ``-inf`` instead of ``0`` when an
integer ``dtype`` is specified. The old behavior can
still be obtained with ``np.linspace(start, stop, num).astype(int)``
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless `endpoint` is set to False.
In that case, the sequence consists of all but the last of ``num + 1``
evenly spaced samples, so that `stop` is excluded. Note that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given, the data type
is inferred from `start` and `stop`. The inferred dtype will never be
an integer; `float` is chosen even if the arguments would produce an
array of integers.
.. versionadded:: 1.9.0
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead of the
number of samples).
geomspace : Similar to `linspace`, but with numbers spaced evenly on a log
scale (a geometric progression).
logspace : Similar to `geomspace`, but with the end points specified as
logarithms.
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array([2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
num = operator.index(num)
if num < 0:
raise ValueError("Number of samples, %s, must be non-negative." % num)
div = (num - 1) if endpoint else num
# Convert float/complex array scalars to float, gh-3504
# and make sure one can use variables that have an __array_interface__, gh-6634
start = asanyarray(start) * 1.0
stop = asanyarray(stop) * 1.0
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
delta = stop - start
y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) * ndim(delta))
# In-place multiplication y *= delta/div is faster, but prevents the multiplicant
# from overriding what class is produced, and thus prevents, e.g. use of Quantities,
# see gh-7142. Hence, we multiply in place only for standard scalar types.
_mult_inplace = _nx.isscalar(delta)
if div > 0:
step = delta / div
if _nx.any(step == 0):
# Special handling for denormal numbers, gh-5437
y /= div
if _mult_inplace:
y *= delta
else:
y = y * delta
else:
if _mult_inplace:
y *= step
else:
y = y * step
else:
# sequences with 0 items or 1 item with endpoint=True (i.e. div <= 0)
# have an undefined step
step = NaN
# Multiply with delta to allow possible override of output class.
y = y * delta
y += start
if endpoint and num > 1:
y[-1] = stop
if axis != 0:
y = _nx.moveaxis(y, 0, axis)
if _nx.issubdtype(dtype, _nx.integer):
_nx.floor(y, out=y)
if retstep:
return y.astype(dtype, copy=False), step
else:
return y.astype(dtype, copy=False)
def _logspace_dispatcher(start, stop, num=None, endpoint=None, base=None,
dtype=None, axis=None):
return (start, stop)
@array_function_dispatch(_logspace_dispatcher)
def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
axis=0):
"""
Return numbers spaced evenly on a log scale.
In linear space, the sequence starts at ``base ** start``
(`base` to the power of `start`) and ends with ``base ** stop``
(see `endpoint` below).
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
``base ** start`` is the starting value of the sequence.
stop : array_like
``base ** stop`` is the final value of the sequence, unless `endpoint`
is False. In that case, ``num + 1`` values are spaced over the
interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
num : integer, optional
Number of samples to generate. Default is 50.
endpoint : boolean, optional
If true, `stop` is the last sample. Otherwise, it is not included.
Default is True.
base : array_like, optional
The base of the log space. The step size between the elements in
``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
Default is 10.0.
dtype : dtype
The type of the output array. If `dtype` is not given, the data type
is inferred from `start` and `stop`. The inferred type will never be
an integer; `float` is chosen even if the arguments would produce an
array of integers.
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
See Also
--------
arange : Similar to linspace, with the step size specified instead of the
number of samples. Note that, when used with a float endpoint, the
endpoint may or may not be included.
linspace : Similar to logspace, but with the samples uniformly distributed
in linear space, instead of log space.
geomspace : Similar to logspace, but with endpoints specified directly.
Notes
-----
Logspace is equivalent to the code
>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
... # doctest: +SKIP
>>> power(base, y).astype(dtype)
... # doctest: +SKIP
Examples
--------
>>> np.logspace(2.0, 3.0, num=4)
array([ 100. , 215.443469 , 464.15888336, 1000. ])
>>> np.logspace(2.0, 3.0, num=4, endpoint=False)
array([100. , 177.827941 , 316.22776602, 562.34132519])
>>> np.logspace(2.0, 3.0, num=4, base=2.0)
array([4. , 5.0396842 , 6.34960421, 8. ])
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 10
>>> x1 = np.logspace(0.1, 1, N, endpoint=True)
>>> x2 = np.logspace(0.1, 1, N, endpoint=False)
>>> y = np.zeros(N)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
y = linspace(start, stop, num=num, endpoint=endpoint, axis=axis)
if dtype is None:
return _nx.power(base, y)
return _nx.power(base, y).astype(dtype, copy=False)
def _geomspace_dispatcher(start, stop, num=None, endpoint=None, dtype=None,
axis=None):
return (start, stop)
@array_function_dispatch(_geomspace_dispatcher)
def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0):
"""
Return numbers spaced evenly on a log scale (a geometric progression).
This is similar to `logspace`, but with endpoints specified directly.
Each output sample is a constant multiple of the previous.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The final value of the sequence, unless `endpoint` is False.
In that case, ``num + 1`` values are spaced over the
interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
num : integer, optional
Number of samples to generate. Default is 50.
endpoint : boolean, optional
If true, `stop` is the last sample. Otherwise, it is not included.
Default is True.
dtype : dtype
The type of the output array. If `dtype` is not given, the data type
is inferred from `start` and `stop`. The inferred dtype will never be
an integer; `float` is chosen even if the arguments would produce an
array of integers.
axis : int, optional
The axis in the result to store the samples. Relevant only if start
or stop are array-like. By default (0), the samples will be along a
new axis inserted at the beginning. Use -1 to get an axis at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
See Also
--------
logspace : Similar to geomspace, but with endpoints specified using log
and base.
linspace : Similar to geomspace, but with arithmetic instead of geometric
progression.
arange : Similar to linspace, with the step size specified instead of the
number of samples.
Notes
-----
If the inputs or dtype are complex, the output will follow a logarithmic
spiral in the complex plane. (There are an infinite number of spirals
passing through two points; the output will follow the shortest such path.)
Examples
--------
>>> np.geomspace(1, 1000, num=4)
array([ 1., 10., 100., 1000.])
>>> np.geomspace(1, 1000, num=3, endpoint=False)
array([ 1., 10., 100.])
>>> np.geomspace(1, 1000, num=4, endpoint=False)
array([ 1. , 5.62341325, 31.6227766 , 177.827941 ])
>>> np.geomspace(1, 256, num=9)
array([ 1., 2., 4., 8., 16., 32., 64., 128., 256.])
Note that the above may not produce exact integers:
>>> np.geomspace(1, 256, num=9, dtype=int)
array([ 1, 2, 4, 7, 16, 32, 63, 127, 256])
>>> np.around(np.geomspace(1, 256, num=9)).astype(int)
array([ 1, 2, 4, 8, 16, 32, 64, 128, 256])
Negative, decreasing, and complex inputs are allowed:
>>> np.geomspace(1000, 1, num=4)
array([1000., 100., 10., 1.])
>>> np.geomspace(-1000, -1, num=4)
array([-1000., -100., -10., -1.])
>>> np.geomspace(1j, 1000j, num=4) # Straight line
array([0. +1.j, 0. +10.j, 0. +100.j, 0.+1000.j])
>>> np.geomspace(-1+0j, 1+0j, num=5) # Circle
array([-1.00000000e+00+1.22464680e-16j, -7.07106781e-01+7.07106781e-01j,
6.12323400e-17+1.00000000e+00j, 7.07106781e-01+7.07106781e-01j,
1.00000000e+00+0.00000000e+00j])
Graphical illustration of `endpoint` parameter:
>>> import matplotlib.pyplot as plt
>>> N = 10
>>> y = np.zeros(N)
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=True), y + 1, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.axis([0.5, 2000, 0, 3])
[0.5, 2000, 0, 3]
>>> plt.grid(True, color='0.7', linestyle='-', which='both', axis='both')
>>> plt.show()
"""
start = asanyarray(start)
stop = asanyarray(stop)
if _nx.any(start == 0) or _nx.any(stop == 0):
raise ValueError('Geometric sequence cannot include zero')
dt = result_type(start, stop, float(num), _nx.zeros((), dtype))
if dtype is None:
dtype = dt
else:
# complex to dtype('complex128'), for instance
dtype = _nx.dtype(dtype)
# Promote both arguments to the same dtype in case, for instance, one is
# complex and another is negative and log would produce NaN otherwise.
# Copy since we may change things in-place further down.
start = start.astype(dt, copy=True)
stop = stop.astype(dt, copy=True)
out_sign = _nx.ones(_nx.broadcast(start, stop).shape, dt)
# Avoid negligible real or imaginary parts in output by rotating to
# positive real, calculating, then undoing rotation
if _nx.issubdtype(dt, _nx.complexfloating):
all_imag = (start.real == 0.) & (stop.real == 0.)
if _nx.any(all_imag):
start[all_imag] = start[all_imag].imag
stop[all_imag] = stop[all_imag].imag
out_sign[all_imag] = 1j
both_negative = (_nx.sign(start) == -1) & (_nx.sign(stop) == -1)
if _nx.any(both_negative):
_nx.negative(start, out=start, where=both_negative)
_nx.negative(stop, out=stop, where=both_negative)
_nx.negative(out_sign, out=out_sign, where=both_negative)
log_start = _nx.log10(start)
log_stop = _nx.log10(stop)
result = logspace(log_start, log_stop, num=num,
endpoint=endpoint, base=10.0, dtype=dtype)
# Make sure the endpoints match the start and stop arguments. This is
# necessary because np.exp(np.log(x)) is not necessarily equal to x.
if num > 0:
result[0] = start
if num > 1 and endpoint:
result[-1] = stop
result = out_sign * result
if axis != 0:
result = _nx.moveaxis(result, 0, axis)
return result.astype(dtype, copy=False)
def _needs_add_docstring(obj):
"""
Returns true if the only way to set the docstring of `obj` from python is
via add_docstring.
This function errs on the side of being overly conservative.
"""
Py_TPFLAGS_HEAPTYPE = 1 << 9
if isinstance(obj, (types.FunctionType, types.MethodType, property)):
return False
if isinstance(obj, type) and obj.__flags__ & Py_TPFLAGS_HEAPTYPE:
return False
return True
def _add_docstring(obj, doc, warn_on_python):
if warn_on_python and not _needs_add_docstring(obj):
warnings.warn(
"add_newdoc was used on a pure-python object {}. "
"Prefer to attach it directly to the source."
.format(obj),
UserWarning,
stacklevel=3)
try:
add_docstring(obj, doc)
except Exception:
pass
def add_newdoc(place, obj, doc, warn_on_python=True):
"""
Add documentation to an existing object, typically one defined in C
The purpose is to allow easier editing of the docstrings without requiring
a re-compile. This exists primarily for internal use within numpy itself.
Parameters
----------
place : str
The absolute name of the module to import from
obj : str
The name of the object to add documentation to, typically a class or
function name
doc : {str, Tuple[str, str], List[Tuple[str, str]]}
If a string, the documentation to apply to `obj`
If a tuple, then the first element is interpreted as an attribute of
`obj` and the second as the docstring to apply - ``(method, docstring)``
If a list, then each element of the list should be a tuple of length
two - ``[(method1, docstring1), (method2, docstring2), ...]``
warn_on_python : bool
If True, the default, emit `UserWarning` if this is used to attach
documentation to a pure-python object.
Notes
-----
This routine never raises an error if the docstring can't be written, but
will raise an error if the object being documented does not exist.
This routine cannot modify read-only docstrings, as appear
in new-style classes or built-in functions. Because this
routine never raises an error the caller must check manually
that the docstrings were changed.
Since this function grabs the ``char *`` from a c-level str object and puts
it into the ``tp_doc`` slot of the type of `obj`, it violates a number of
C-API best-practices, by:
- modifying a `PyTypeObject` after calling `PyType_Ready`
- calling `Py_INCREF` on the str and losing the reference, so the str
will never be released
If possible it should be avoided.
"""
new = getattr(__import__(place, globals(), {}, [obj]), obj)
if isinstance(doc, str):
_add_docstring(new, doc.strip(), warn_on_python)
elif isinstance(doc, tuple):
attr, docstring = doc
_add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)
elif isinstance(doc, list):
for attr, docstring in doc:
_add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)
| 19,019 | Python | 34.886792 | 88 | 0.60918 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/memmap.py | from contextlib import nullcontext
import numpy as np
from .numeric import uint8, ndarray, dtype
from numpy.compat import os_fspath, is_pathlib_path
from numpy.core.overrides import set_module
__all__ = ['memmap']
dtypedescr = dtype
valid_filemodes = ["r", "c", "r+", "w+"]
writeable_filemodes = ["r+", "w+"]
mode_equivalents = {
"readonly":"r",
"copyonwrite":"c",
"readwrite":"r+",
"write":"w+"
}
@set_module('numpy')
class memmap(ndarray):
"""Create a memory-map to an array stored in a *binary* file on disk.
Memory-mapped files are used for accessing small segments of large files
on disk, without reading the entire file into memory. NumPy's
memmap's are array-like objects. This differs from Python's ``mmap``
module, which uses file-like objects.
This subclass of ndarray has some unpleasant interactions with
some operations, because it doesn't quite fit properly as a subclass.
An alternative to using this subclass is to create the ``mmap``
object yourself, then create an ndarray with ndarray.__new__ directly,
passing the object created in its 'buffer=' parameter.
This class may at some point be turned into a factory function
which returns a view into an mmap buffer.
Flush the memmap instance to write the changes to the file. Currently there
is no API to close the underlying ``mmap``. It is tricky to ensure the
resource is actually closed, since it may be shared between different
memmap instances.
Parameters
----------
filename : str, file-like object, or pathlib.Path instance
The file name or file object to be used as the array data buffer.
dtype : data-type, optional
The data-type used to interpret the file contents.
Default is `uint8`.
mode : {'r+', 'r', 'w+', 'c'}, optional
The file is opened in this mode:
+------+-------------------------------------------------------------+
| 'r' | Open existing file for reading only. |
+------+-------------------------------------------------------------+
| 'r+' | Open existing file for reading and writing. |
+------+-------------------------------------------------------------+
| 'w+' | Create or overwrite existing file for reading and writing. |
+------+-------------------------------------------------------------+
| 'c' | Copy-on-write: assignments affect data in memory, but |
| | changes are not saved to disk. The file on disk is |
| | read-only. |
+------+-------------------------------------------------------------+
Default is 'r+'.
offset : int, optional
In the file, array data starts at this offset. Since `offset` is
measured in bytes, it should normally be a multiple of the byte-size
of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of
file are valid; The file will be extended to accommodate the
additional data. By default, ``memmap`` will start at the beginning of
the file, even if ``filename`` is a file pointer ``fp`` and
``fp.tell() != 0``.
shape : tuple, optional
The desired shape of the array. If ``mode == 'r'`` and the number
of remaining bytes after `offset` is not a multiple of the byte-size
of `dtype`, you must specify `shape`. By default, the returned array
will be 1-D with the number of elements determined by file size
and data-type.
order : {'C', 'F'}, optional
Specify the order of the ndarray memory layout:
:term:`row-major`, C-style or :term:`column-major`,
Fortran-style. This only has an effect if the shape is
greater than 1-D. The default order is 'C'.
Attributes
----------
filename : str or pathlib.Path instance
Path to the mapped file.
offset : int
Offset position in the file.
mode : str
File mode.
Methods
-------
flush
Flush any changes in memory to file on disk.
When you delete a memmap object, flush is called first to write
changes to disk.
See also
--------
lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
Notes
-----
The memmap object can be used anywhere an ndarray is accepted.
Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns
``True``.
Memory-mapped files cannot be larger than 2GB on 32-bit systems.
When a memmap causes a file to be created or extended beyond its
current size in the filesystem, the contents of the new part are
unspecified. On systems with POSIX filesystem semantics, the extended
part will be filled with zero bytes.
Examples
--------
>>> data = np.arange(12, dtype='float32')
>>> data.resize((3,4))
This example uses a temporary file so that doctest doesn't write
files to your directory. You would use a 'normal' filename.
>>> from tempfile import mkdtemp
>>> import os.path as path
>>> filename = path.join(mkdtemp(), 'newfile.dat')
Create a memmap with dtype and shape that matches our data:
>>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))
>>> fp
memmap([[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]], dtype=float32)
Write data to memmap array:
>>> fp[:] = data[:]
>>> fp
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
>>> fp.filename == path.abspath(filename)
True
Flushes memory changes to disk in order to read them back
>>> fp.flush()
Load the memmap and verify data was stored:
>>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
>>> newfp
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
Read-only memmap:
>>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
>>> fpr.flags.writeable
False
Copy-on-write memmap:
>>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))
>>> fpc.flags.writeable
True
It's possible to assign to copy-on-write array, but values are only
written into the memory copy of the array, and not written to disk:
>>> fpc
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
>>> fpc[0,:] = 0
>>> fpc
memmap([[ 0., 0., 0., 0.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
File on disk is unchanged:
>>> fpr
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
Offset into a memmap:
>>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)
>>> fpo
memmap([ 4., 5., 6., 7., 8., 9., 10., 11.], dtype=float32)
"""
__array_priority__ = -100.0
def __new__(subtype, filename, dtype=uint8, mode='r+', offset=0,
shape=None, order='C'):
# Import here to minimize 'import numpy' overhead
import mmap
import os.path
try:
mode = mode_equivalents[mode]
except KeyError as e:
if mode not in valid_filemodes:
raise ValueError(
"mode must be one of {!r} (got {!r})"
.format(valid_filemodes + list(mode_equivalents.keys()), mode)
) from None
if mode == 'w+' and shape is None:
raise ValueError("shape must be given")
if hasattr(filename, 'read'):
f_ctx = nullcontext(filename)
else:
f_ctx = open(os_fspath(filename), ('r' if mode == 'c' else mode)+'b')
with f_ctx as fid:
fid.seek(0, 2)
flen = fid.tell()
descr = dtypedescr(dtype)
_dbytes = descr.itemsize
if shape is None:
bytes = flen - offset
if bytes % _dbytes:
raise ValueError("Size of available data is not a "
"multiple of the data-type size.")
size = bytes // _dbytes
shape = (size,)
else:
if not isinstance(shape, tuple):
shape = (shape,)
size = np.intp(1) # avoid default choice of np.int_, which might overflow
for k in shape:
size *= k
bytes = int(offset + size*_dbytes)
if mode in ('w+', 'r+') and flen < bytes:
fid.seek(bytes - 1, 0)
fid.write(b'\0')
fid.flush()
if mode == 'c':
acc = mmap.ACCESS_COPY
elif mode == 'r':
acc = mmap.ACCESS_READ
else:
acc = mmap.ACCESS_WRITE
start = offset - offset % mmap.ALLOCATIONGRANULARITY
bytes -= start
array_offset = offset - start
mm = mmap.mmap(fid.fileno(), bytes, access=acc, offset=start)
self = ndarray.__new__(subtype, shape, dtype=descr, buffer=mm,
offset=array_offset, order=order)
self._mmap = mm
self.offset = offset
self.mode = mode
if is_pathlib_path(filename):
# special case - if we were constructed with a pathlib.path,
# then filename is a path object, not a string
self.filename = filename.resolve()
elif hasattr(fid, "name") and isinstance(fid.name, str):
# py3 returns int for TemporaryFile().name
self.filename = os.path.abspath(fid.name)
# same as memmap copies (e.g. memmap + 1)
else:
self.filename = None
return self
def __array_finalize__(self, obj):
if hasattr(obj, '_mmap') and np.may_share_memory(self, obj):
self._mmap = obj._mmap
self.filename = obj.filename
self.offset = obj.offset
self.mode = obj.mode
else:
self._mmap = None
self.filename = None
self.offset = None
self.mode = None
def flush(self):
"""
Write any changes in the array to the file on disk.
For further information, see `memmap`.
Parameters
----------
None
See Also
--------
memmap
"""
if self.base is not None and hasattr(self.base, 'flush'):
self.base.flush()
def __array_wrap__(self, arr, context=None):
arr = super().__array_wrap__(arr, context)
# Return a memmap if a memmap was given as the output of the
# ufunc. Leave the arr class unchanged if self is not a memmap
# to keep original memmap subclasses behavior
if self is arr or type(self) is not memmap:
return arr
# Return scalar instead of 0d memmap, e.g. for np.sum with
# axis=None
if arr.shape == ():
return arr[()]
# Return ndarray otherwise
return arr.view(np.ndarray)
def __getitem__(self, index):
res = super().__getitem__(index)
if type(res) is memmap and res._mmap is None:
return res.view(type=ndarray)
return res
| 11,688 | Python | 33.58284 | 90 | 0.528405 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/setup.py | import os
import sys
import sysconfig
import pickle
import copy
import warnings
import textwrap
import glob
from os.path import join
from numpy.distutils import log
from distutils.dep_util import newer
from sysconfig import get_config_var
from numpy.compat import npy_load_module
from setup_common import * # noqa: F403
# Set to True to enable relaxed strides checking. This (mostly) means
# that `strides[dim]` is ignored if `shape[dim] == 1` when setting flags.
NPY_RELAXED_STRIDES_CHECKING = (os.environ.get('NPY_RELAXED_STRIDES_CHECKING', "1") != "0")
if not NPY_RELAXED_STRIDES_CHECKING:
raise SystemError(
"Support for NPY_RELAXED_STRIDES_CHECKING=0 has been remove as of "
"NumPy 1.23. This error will eventually be removed entirely.")
# Put NPY_RELAXED_STRIDES_DEBUG=1 in the environment if you want numpy to use a
# bogus value for affected strides in order to help smoke out bad stride usage
# when relaxed stride checking is enabled.
NPY_RELAXED_STRIDES_DEBUG = (os.environ.get('NPY_RELAXED_STRIDES_DEBUG', "0") != "0")
NPY_RELAXED_STRIDES_DEBUG = NPY_RELAXED_STRIDES_DEBUG and NPY_RELAXED_STRIDES_CHECKING
# Set NPY_DISABLE_SVML=1 in the environment to disable the vendored SVML
# library. This option only has significance on a Linux x86_64 host and is most
# useful to avoid improperly requiring SVML when cross compiling.
NPY_DISABLE_SVML = (os.environ.get('NPY_DISABLE_SVML', "0") == "1")
# XXX: ugly, we use a class to avoid calling twice some expensive functions in
# config.h/numpyconfig.h. I don't see a better way because distutils force
# config.h generation inside an Extension class, and as such sharing
# configuration information between extensions is not easy.
# Using a pickled-based memoize does not work because config_cmd is an instance
# method, which cPickle does not like.
#
# Use pickle in all cases, as cPickle is gone in python3 and the difference
# in time is only in build. -- Charles Harris, 2013-03-30
class CallOnceOnly:
def __init__(self):
self._check_types = None
self._check_ieee_macros = None
self._check_complex = None
def check_types(self, *a, **kw):
if self._check_types is None:
out = check_types(*a, **kw)
self._check_types = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_types))
return out
def check_ieee_macros(self, *a, **kw):
if self._check_ieee_macros is None:
out = check_ieee_macros(*a, **kw)
self._check_ieee_macros = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_ieee_macros))
return out
def check_complex(self, *a, **kw):
if self._check_complex is None:
out = check_complex(*a, **kw)
self._check_complex = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_complex))
return out
def can_link_svml():
"""SVML library is supported only on x86_64 architecture and currently
only on linux
"""
if NPY_DISABLE_SVML:
return False
platform = sysconfig.get_platform()
return ("x86_64" in platform
and "linux" in platform
and sys.maxsize > 2**31)
def check_svml_submodule(svmlpath):
if not os.path.exists(svmlpath + "/README.md"):
raise RuntimeError("Missing `SVML` submodule! Run `git submodule "
"update --init` to fix this.")
return True
def pythonlib_dir():
"""return path where libpython* is."""
if sys.platform == 'win32':
return os.path.join(sys.prefix, "libs")
else:
return get_config_var('LIBDIR')
def is_npy_no_signal():
"""Return True if the NPY_NO_SIGNAL symbol must be defined in configuration
header."""
return sys.platform == 'win32'
def is_npy_no_smp():
"""Return True if the NPY_NO_SMP symbol must be defined in public
header (when SMP support cannot be reliably enabled)."""
# Perhaps a fancier check is in order here.
# so that threads are only enabled if there
# are actually multiple CPUS? -- but
# threaded code can be nice even on a single
# CPU so that long-calculating code doesn't
# block.
return 'NPY_NOSMP' in os.environ
def win32_checks(deflist):
from numpy.distutils.misc_util import get_build_architecture
a = get_build_architecture()
# Distutils hack on AMD64 on windows
print('BUILD_ARCHITECTURE: %r, os.name=%r, sys.platform=%r' %
(a, os.name, sys.platform))
if a == 'AMD64':
deflist.append('DISTUTILS_USE_SDK')
# On win32, force long double format string to be 'g', not
# 'Lg', since the MS runtime does not support long double whose
# size is > sizeof(double)
if a == "Intel" or a == "AMD64":
deflist.append('FORCE_NO_LONG_DOUBLE_FORMATTING')
def check_math_capabilities(config, ext, moredefs, mathlibs):
def check_func(
func_name,
decl=False,
headers=["feature_detection_math.h"],
):
return config.check_func(
func_name,
libraries=mathlibs,
decl=decl,
call=True,
call_args=FUNC_CALL_ARGS[func_name],
headers=headers,
)
def check_funcs_once(funcs_name, headers=["feature_detection_math.h"]):
call = dict([(f, True) for f in funcs_name])
call_args = dict([(f, FUNC_CALL_ARGS[f]) for f in funcs_name])
st = config.check_funcs_once(
funcs_name,
libraries=mathlibs,
decl=False,
call=call,
call_args=call_args,
headers=headers,
)
if st:
moredefs.extend([(fname2def(f), 1) for f in funcs_name])
return st
def check_funcs(funcs_name, headers=["feature_detection_math.h"]):
# Use check_funcs_once first, and if it does not work, test func per
# func. Return success only if all the functions are available
if not check_funcs_once(funcs_name, headers=headers):
# Global check failed, check func per func
for f in funcs_name:
if check_func(f, headers=headers):
moredefs.append((fname2def(f), 1))
return 0
else:
return 1
#use_msvc = config.check_decl("_MSC_VER")
if not check_funcs_once(MANDATORY_FUNCS):
raise SystemError("One of the required function to build numpy is not"
" available (the list is %s)." % str(MANDATORY_FUNCS))
# Standard functions which may not be available and for which we have a
# replacement implementation. Note that some of these are C99 functions.
# XXX: hack to circumvent cpp pollution from python: python put its
# config.h in the public namespace, so we have a clash for the common
# functions we test. We remove every function tested by python's
# autoconf, hoping their own test are correct
for f in OPTIONAL_STDFUNCS_MAYBE:
if config.check_decl(fname2def(f),
headers=["Python.h", "math.h"]):
if f in OPTIONAL_STDFUNCS:
OPTIONAL_STDFUNCS.remove(f)
else:
OPTIONAL_FILE_FUNCS.remove(f)
check_funcs(OPTIONAL_STDFUNCS)
check_funcs(OPTIONAL_FILE_FUNCS, headers=["feature_detection_stdio.h"])
check_funcs(OPTIONAL_MISC_FUNCS, headers=["feature_detection_misc.h"])
for h in OPTIONAL_HEADERS:
if config.check_func("", decl=False, call=False, headers=[h]):
h = h.replace(".", "_").replace(os.path.sep, "_")
moredefs.append((fname2def(h), 1))
# Try with both "locale.h" and "xlocale.h"
locale_headers = [
"stdlib.h",
"xlocale.h",
"feature_detection_locale.h",
]
if not check_funcs(OPTIONAL_LOCALE_FUNCS, headers=locale_headers):
# It didn't work with xlocale.h, maybe it will work with locale.h?
locale_headers[1] = "locale.h"
check_funcs(OPTIONAL_LOCALE_FUNCS, headers=locale_headers)
for tup in OPTIONAL_INTRINSICS:
headers = None
if len(tup) == 2:
f, args, m = tup[0], tup[1], fname2def(tup[0])
elif len(tup) == 3:
f, args, headers, m = tup[0], tup[1], [tup[2]], fname2def(tup[0])
else:
f, args, headers, m = tup[0], tup[1], [tup[2]], fname2def(tup[3])
if config.check_func(f, decl=False, call=True, call_args=args,
headers=headers):
moredefs.append((m, 1))
for dec, fn in OPTIONAL_FUNCTION_ATTRIBUTES:
if config.check_gcc_function_attribute(dec, fn):
moredefs.append((fname2def(fn), 1))
if fn == 'attribute_target_avx512f':
# GH-14787: Work around GCC<8.4 bug when compiling with AVX512
# support on Windows-based platforms
if (sys.platform in ('win32', 'cygwin') and
config.check_compiler_gcc() and
not config.check_gcc_version_at_least(8, 4)):
ext.extra_compile_args.extend(
['-ffixed-xmm%s' % n for n in range(16, 32)])
for dec, fn, code, header in OPTIONAL_FUNCTION_ATTRIBUTES_WITH_INTRINSICS:
if config.check_gcc_function_attribute_with_intrinsics(dec, fn, code,
header):
moredefs.append((fname2def(fn), 1))
for fn in OPTIONAL_VARIABLE_ATTRIBUTES:
if config.check_gcc_variable_attribute(fn):
m = fn.replace("(", "_").replace(")", "_")
moredefs.append((fname2def(m), 1))
# C99 functions: float and long double versions
check_funcs(C99_FUNCS_SINGLE)
check_funcs(C99_FUNCS_EXTENDED)
def check_complex(config, mathlibs):
priv = []
pub = []
try:
if os.uname()[0] == "Interix":
warnings.warn("Disabling broken complex support. See #1365", stacklevel=2)
return priv, pub
except Exception:
# os.uname not available on all platforms. blanket except ugly but safe
pass
# Check for complex support
st = config.check_header('complex.h')
if st:
priv.append(('HAVE_COMPLEX_H', 1))
pub.append(('NPY_USE_C99_COMPLEX', 1))
for t in C99_COMPLEX_TYPES:
st = config.check_type(t, headers=["complex.h"])
if st:
pub.append(('NPY_HAVE_%s' % type2def(t), 1))
def check_prec(prec):
flist = [f + prec for f in C99_COMPLEX_FUNCS]
decl = dict([(f, True) for f in flist])
if not config.check_funcs_once(flist, call=decl, decl=decl,
libraries=mathlibs):
for f in flist:
if config.check_func(f, call=True, decl=True,
libraries=mathlibs):
priv.append((fname2def(f), 1))
else:
priv.extend([(fname2def(f), 1) for f in flist])
check_prec('')
check_prec('f')
check_prec('l')
return priv, pub
def check_ieee_macros(config):
priv = []
pub = []
macros = []
def _add_decl(f):
priv.append(fname2def("decl_%s" % f))
pub.append('NPY_%s' % fname2def("decl_%s" % f))
# XXX: hack to circumvent cpp pollution from python: python put its
# config.h in the public namespace, so we have a clash for the common
# functions we test. We remove every function tested by python's
# autoconf, hoping their own test are correct
_macros = ["isnan", "isinf", "signbit", "isfinite"]
for f in _macros:
py_symbol = fname2def("decl_%s" % f)
already_declared = config.check_decl(py_symbol,
headers=["Python.h", "math.h"])
if already_declared:
if config.check_macro_true(py_symbol,
headers=["Python.h", "math.h"]):
pub.append('NPY_%s' % fname2def("decl_%s" % f))
else:
macros.append(f)
# Normally, isnan and isinf are macro (C99), but some platforms only have
# func, or both func and macro version. Check for macro only, and define
# replacement ones if not found.
# Note: including Python.h is necessary because it modifies some math.h
# definitions
for f in macros:
st = config.check_decl(f, headers=["Python.h", "math.h"])
if st:
_add_decl(f)
return priv, pub
def check_types(config_cmd, ext, build_dir):
private_defines = []
public_defines = []
# Expected size (in number of bytes) for each type. This is an
# optimization: those are only hints, and an exhaustive search for the size
# is done if the hints are wrong.
expected = {'short': [2], 'int': [4], 'long': [8, 4],
'float': [4], 'double': [8], 'long double': [16, 12, 8],
'Py_intptr_t': [8, 4], 'PY_LONG_LONG': [8], 'long long': [8],
'off_t': [8, 4]}
# Check we have the python header (-dev* packages on Linux)
result = config_cmd.check_header('Python.h')
if not result:
python = 'python'
if '__pypy__' in sys.builtin_module_names:
python = 'pypy'
raise SystemError(
"Cannot compile 'Python.h'. Perhaps you need to "
"install {0}-dev|{0}-devel.".format(python))
res = config_cmd.check_header("endian.h")
if res:
private_defines.append(('HAVE_ENDIAN_H', 1))
public_defines.append(('NPY_HAVE_ENDIAN_H', 1))
res = config_cmd.check_header("sys/endian.h")
if res:
private_defines.append(('HAVE_SYS_ENDIAN_H', 1))
public_defines.append(('NPY_HAVE_SYS_ENDIAN_H', 1))
# Check basic types sizes
for type in ('short', 'int', 'long'):
res = config_cmd.check_decl("SIZEOF_%s" % sym2def(type), headers=["Python.h"])
if res:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), "SIZEOF_%s" % sym2def(type)))
else:
res = config_cmd.check_type_size(type, expected=expected[type])
if res >= 0:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
for type in ('float', 'double', 'long double'):
already_declared = config_cmd.check_decl("SIZEOF_%s" % sym2def(type),
headers=["Python.h"])
res = config_cmd.check_type_size(type, expected=expected[type])
if res >= 0:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
if not already_declared and not type == 'long double':
private_defines.append(('SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
# Compute size of corresponding complex type: used to check that our
# definition is binary compatible with C99 complex type (check done at
# build time in npy_common.h)
complex_def = "struct {%s __x; %s __y;}" % (type, type)
res = config_cmd.check_type_size(complex_def,
expected=[2 * x for x in expected[type]])
if res >= 0:
public_defines.append(('NPY_SIZEOF_COMPLEX_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % complex_def)
for type in ('Py_intptr_t', 'off_t'):
res = config_cmd.check_type_size(type, headers=["Python.h"],
library_dirs=[pythonlib_dir()],
expected=expected[type])
if res >= 0:
private_defines.append(('SIZEOF_%s' % sym2def(type), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
# We check declaration AND type because that's how distutils does it.
if config_cmd.check_decl('PY_LONG_LONG', headers=['Python.h']):
res = config_cmd.check_type_size('PY_LONG_LONG', headers=['Python.h'],
library_dirs=[pythonlib_dir()],
expected=expected['PY_LONG_LONG'])
if res >= 0:
private_defines.append(('SIZEOF_%s' % sym2def('PY_LONG_LONG'), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def('PY_LONG_LONG'), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % 'PY_LONG_LONG')
res = config_cmd.check_type_size('long long',
expected=expected['long long'])
if res >= 0:
#private_defines.append(('SIZEOF_%s' % sym2def('long long'), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def('long long'), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % 'long long')
if not config_cmd.check_decl('CHAR_BIT', headers=['Python.h']):
raise RuntimeError(
"Config wo CHAR_BIT is not supported"
", please contact the maintainers")
return private_defines, public_defines
def check_mathlib(config_cmd):
# Testing the C math library
mathlibs = []
mathlibs_choices = [[], ["m"], ["cpml"]]
mathlib = os.environ.get("MATHLIB")
if mathlib:
mathlibs_choices.insert(0, mathlib.split(","))
for libs in mathlibs_choices:
if config_cmd.check_func(
"log",
libraries=libs,
call_args="0",
decl="double log(double);",
call=True
):
mathlibs = libs
break
else:
raise RuntimeError(
"math library missing; rerun setup.py after setting the "
"MATHLIB env variable"
)
return mathlibs
def visibility_define(config):
"""Return the define value to use for NPY_VISIBILITY_HIDDEN (may be empty
string)."""
hide = '__attribute__((visibility("hidden")))'
if config.check_gcc_function_attribute(hide, 'hideme'):
return hide
else:
return ''
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import (Configuration, dot_join,
exec_mod_from_location)
from numpy.distutils.system_info import (get_info, blas_opt_info,
lapack_opt_info)
from numpy.distutils.ccompiler_opt import NPY_CXX_FLAGS
from numpy.version import release as is_released
config = Configuration('core', parent_package, top_path)
local_dir = config.local_path
codegen_dir = join(local_dir, 'code_generators')
if is_released:
warnings.simplefilter('error', MismatchCAPIWarning)
# Check whether we have a mismatch between the set C API VERSION and the
# actual C API VERSION
check_api_version(C_API_VERSION, codegen_dir)
generate_umath_py = join(codegen_dir, 'generate_umath.py')
n = dot_join(config.name, 'generate_umath')
generate_umath = exec_mod_from_location('_'.join(n.split('.')),
generate_umath_py)
header_dir = 'include/numpy' # this is relative to config.path_in_package
cocache = CallOnceOnly()
def generate_config_h(ext, build_dir):
target = join(build_dir, header_dir, 'config.h')
d = os.path.dirname(target)
if not os.path.exists(d):
os.makedirs(d)
if newer(__file__, target):
config_cmd = config.get_config_cmd()
log.info('Generating %s', target)
# Check sizeof
moredefs, ignored = cocache.check_types(config_cmd, ext, build_dir)
# Check math library and C99 math funcs availability
mathlibs = check_mathlib(config_cmd)
moredefs.append(('MATHLIB', ','.join(mathlibs)))
check_math_capabilities(config_cmd, ext, moredefs, mathlibs)
moredefs.extend(cocache.check_ieee_macros(config_cmd)[0])
moredefs.extend(cocache.check_complex(config_cmd, mathlibs)[0])
# Signal check
if is_npy_no_signal():
moredefs.append('__NPY_PRIVATE_NO_SIGNAL')
# Windows checks
if sys.platform == 'win32' or os.name == 'nt':
win32_checks(moredefs)
# C99 restrict keyword
moredefs.append(('NPY_RESTRICT', config_cmd.check_restrict()))
# Inline check
inline = config_cmd.check_inline()
if can_link_svml():
moredefs.append(('NPY_CAN_LINK_SVML', 1))
# Use bogus stride debug aid to flush out bugs where users use
# strides of dimensions with length 1 to index a full contiguous
# array.
if NPY_RELAXED_STRIDES_DEBUG:
moredefs.append(('NPY_RELAXED_STRIDES_DEBUG', 1))
else:
moredefs.append(('NPY_RELAXED_STRIDES_DEBUG', 0))
# Get long double representation
rep = check_long_double_representation(config_cmd)
moredefs.append(('HAVE_LDOUBLE_%s' % rep, 1))
if check_for_right_shift_internal_compiler_error(config_cmd):
moredefs.append('NPY_DO_NOT_OPTIMIZE_LONG_right_shift')
moredefs.append('NPY_DO_NOT_OPTIMIZE_ULONG_right_shift')
moredefs.append('NPY_DO_NOT_OPTIMIZE_LONGLONG_right_shift')
moredefs.append('NPY_DO_NOT_OPTIMIZE_ULONGLONG_right_shift')
# Generate the config.h file from moredefs
with open(target, 'w') as target_f:
for d in moredefs:
if isinstance(d, str):
target_f.write('#define %s\n' % (d))
else:
target_f.write('#define %s %s\n' % (d[0], d[1]))
# define inline to our keyword, or nothing
target_f.write('#ifndef __cplusplus\n')
if inline == 'inline':
target_f.write('/* #undef inline */\n')
else:
target_f.write('#define inline %s\n' % inline)
target_f.write('#endif\n')
# add the guard to make sure config.h is never included directly,
# but always through npy_config.h
target_f.write(textwrap.dedent("""
#ifndef NUMPY_CORE_SRC_COMMON_NPY_CONFIG_H_
#error config.h should never be included directly, include npy_config.h instead
#endif
"""))
log.info('File: %s' % target)
with open(target) as target_f:
log.info(target_f.read())
log.info('EOF')
else:
mathlibs = []
with open(target) as target_f:
for line in target_f:
s = '#define MATHLIB'
if line.startswith(s):
value = line[len(s):].strip()
if value:
mathlibs.extend(value.split(','))
# Ugly: this can be called within a library and not an extension,
# in which case there is no libraries attributes (and none is
# needed).
if hasattr(ext, 'libraries'):
ext.libraries.extend(mathlibs)
incl_dir = os.path.dirname(target)
if incl_dir not in config.numpy_include_dirs:
config.numpy_include_dirs.append(incl_dir)
return target
def generate_numpyconfig_h(ext, build_dir):
"""Depends on config.h: generate_config_h has to be called before !"""
# put common include directory in build_dir on search path
# allows using code generation in headers
config.add_include_dirs(join(build_dir, "src", "common"))
config.add_include_dirs(join(build_dir, "src", "npymath"))
target = join(build_dir, header_dir, '_numpyconfig.h')
d = os.path.dirname(target)
if not os.path.exists(d):
os.makedirs(d)
if newer(__file__, target):
config_cmd = config.get_config_cmd()
log.info('Generating %s', target)
# Check sizeof
ignored, moredefs = cocache.check_types(config_cmd, ext, build_dir)
if is_npy_no_signal():
moredefs.append(('NPY_NO_SIGNAL', 1))
if is_npy_no_smp():
moredefs.append(('NPY_NO_SMP', 1))
else:
moredefs.append(('NPY_NO_SMP', 0))
mathlibs = check_mathlib(config_cmd)
moredefs.extend(cocache.check_ieee_macros(config_cmd)[1])
moredefs.extend(cocache.check_complex(config_cmd, mathlibs)[1])
if NPY_RELAXED_STRIDES_DEBUG:
moredefs.append(('NPY_RELAXED_STRIDES_DEBUG', 1))
# Check whether we can use inttypes (C99) formats
if config_cmd.check_decl('PRIdPTR', headers=['inttypes.h']):
moredefs.append(('NPY_USE_C99_FORMATS', 1))
# visibility check
hidden_visibility = visibility_define(config_cmd)
moredefs.append(('NPY_VISIBILITY_HIDDEN', hidden_visibility))
# Add the C API/ABI versions
moredefs.append(('NPY_ABI_VERSION', '0x%.8X' % C_ABI_VERSION))
moredefs.append(('NPY_API_VERSION', '0x%.8X' % C_API_VERSION))
# Add moredefs to header
with open(target, 'w') as target_f:
for d in moredefs:
if isinstance(d, str):
target_f.write('#define %s\n' % (d))
else:
target_f.write('#define %s %s\n' % (d[0], d[1]))
# Define __STDC_FORMAT_MACROS
target_f.write(textwrap.dedent("""
#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS 1
#endif
"""))
# Dump the numpyconfig.h header to stdout
log.info('File: %s' % target)
with open(target) as target_f:
log.info(target_f.read())
log.info('EOF')
config.add_data_files((header_dir, target))
return target
def generate_api_func(module_name):
def generate_api(ext, build_dir):
script = join(codegen_dir, module_name + '.py')
sys.path.insert(0, codegen_dir)
try:
m = __import__(module_name)
log.info('executing %s', script)
h_file, c_file, doc_file = m.generate_api(os.path.join(build_dir, header_dir))
finally:
del sys.path[0]
config.add_data_files((header_dir, h_file),
(header_dir, doc_file))
return (h_file,)
return generate_api
generate_numpy_api = generate_api_func('generate_numpy_api')
generate_ufunc_api = generate_api_func('generate_ufunc_api')
config.add_include_dirs(join(local_dir, "src", "common"))
config.add_include_dirs(join(local_dir, "src"))
config.add_include_dirs(join(local_dir))
config.add_data_dir('include/numpy')
config.add_include_dirs(join('src', 'npymath'))
config.add_include_dirs(join('src', 'multiarray'))
config.add_include_dirs(join('src', 'umath'))
config.add_include_dirs(join('src', 'npysort'))
config.add_include_dirs(join('src', '_simd'))
config.add_define_macros([("NPY_INTERNAL_BUILD", "1")]) # this macro indicates that Numpy build is in process
config.add_define_macros([("HAVE_NPY_CONFIG_H", "1")])
if sys.platform[:3] == "aix":
config.add_define_macros([("_LARGE_FILES", None)])
else:
config.add_define_macros([("_FILE_OFFSET_BITS", "64")])
config.add_define_macros([('_LARGEFILE_SOURCE', '1')])
config.add_define_macros([('_LARGEFILE64_SOURCE', '1')])
config.numpy_include_dirs.extend(config.paths('include'))
deps = [join('src', 'npymath', '_signbit.c'),
join('include', 'numpy', '*object.h'),
join(codegen_dir, 'genapi.py'),
]
#######################################################################
# npymath library #
#######################################################################
subst_dict = dict([("sep", os.path.sep), ("pkgname", "numpy.core")])
def get_mathlib_info(*args):
# Another ugly hack: the mathlib info is known once build_src is run,
# but we cannot use add_installed_pkg_config here either, so we only
# update the substitution dictionary during npymath build
config_cmd = config.get_config_cmd()
# Check that the toolchain works, to fail early if it doesn't
# (avoid late errors with MATHLIB which are confusing if the
# compiler does not work).
for lang, test_code, note in (
('c', 'int main(void) { return 0;}', ''),
('c++', (
'int main(void)'
'{ auto x = 0.0; return static_cast<int>(x); }'
), (
'note: A compiler with support for C++11 language '
'features is required.'
)
),
):
is_cpp = lang == 'c++'
if is_cpp:
# this a workaround to get rid of invalid c++ flags
# without doing big changes to config.
# c tested first, compiler should be here
bk_c = config_cmd.compiler
config_cmd.compiler = bk_c.cxx_compiler()
# Check that Linux compiler actually support the default flags
if hasattr(config_cmd.compiler, 'compiler'):
config_cmd.compiler.compiler.extend(NPY_CXX_FLAGS)
config_cmd.compiler.compiler_so.extend(NPY_CXX_FLAGS)
st = config_cmd.try_link(test_code, lang=lang)
if not st:
# rerun the failing command in verbose mode
config_cmd.compiler.verbose = True
config_cmd.try_link(test_code, lang=lang)
raise RuntimeError(
f"Broken toolchain: cannot link a simple {lang.upper()} "
f"program. {note}"
)
if is_cpp:
config_cmd.compiler = bk_c
mlibs = check_mathlib(config_cmd)
posix_mlib = ' '.join(['-l%s' % l for l in mlibs])
msvc_mlib = ' '.join(['%s.lib' % l for l in mlibs])
subst_dict["posix_mathlib"] = posix_mlib
subst_dict["msvc_mathlib"] = msvc_mlib
npymath_sources = [join('src', 'npymath', 'npy_math_internal.h.src'),
join('src', 'npymath', 'npy_math.c'),
# join('src', 'npymath', 'ieee754.cpp'),
join('src', 'npymath', 'ieee754.c.src'),
join('src', 'npymath', 'npy_math_complex.c.src'),
join('src', 'npymath', 'halffloat.c')
]
def opts_if_msvc(build_cmd):
""" Add flags if we are using MSVC compiler
We can't see `build_cmd` in our scope, because we have not initialized
the distutils build command, so use this deferred calculation to run
when we are building the library.
"""
if build_cmd.compiler.compiler_type != 'msvc':
return []
# Explicitly disable whole-program optimization.
flags = ['/GL-']
# Disable voltbl section for vc142 to allow link using mingw-w64; see:
# https://github.com/matthew-brett/dll_investigation/issues/1#issuecomment-1100468171
if build_cmd.compiler_opt.cc_test_flags(['-d2VolatileMetadata-']):
flags.append('-d2VolatileMetadata-')
return flags
config.add_installed_library('npymath',
sources=npymath_sources + [get_mathlib_info],
install_dir='lib',
build_info={
'include_dirs' : [], # empty list required for creating npy_math_internal.h
'extra_compiler_args': [opts_if_msvc],
})
config.add_npy_pkg_config("npymath.ini.in", "lib/npy-pkg-config",
subst_dict)
config.add_npy_pkg_config("mlib.ini.in", "lib/npy-pkg-config",
subst_dict)
#######################################################################
# multiarray_tests module #
#######################################################################
config.add_extension('_multiarray_tests',
sources=[join('src', 'multiarray', '_multiarray_tests.c.src'),
join('src', 'common', 'mem_overlap.c'),
join('src', 'common', 'npy_argparse.c'),
join('src', 'common', 'npy_hashtable.c')],
depends=[join('src', 'common', 'mem_overlap.h'),
join('src', 'common', 'npy_argparse.h'),
join('src', 'common', 'npy_hashtable.h'),
join('src', 'common', 'npy_extint128.h')],
libraries=['npymath'])
#######################################################################
# _multiarray_umath module - common part #
#######################################################################
common_deps = [
join('src', 'common', 'dlpack', 'dlpack.h'),
join('src', 'common', 'array_assign.h'),
join('src', 'common', 'binop_override.h'),
join('src', 'common', 'cblasfuncs.h'),
join('src', 'common', 'lowlevel_strided_loops.h'),
join('src', 'common', 'mem_overlap.h'),
join('src', 'common', 'npy_argparse.h'),
join('src', 'common', 'npy_cblas.h'),
join('src', 'common', 'npy_config.h'),
join('src', 'common', 'npy_ctypes.h'),
join('src', 'common', 'npy_dlpack.h'),
join('src', 'common', 'npy_extint128.h'),
join('src', 'common', 'npy_import.h'),
join('src', 'common', 'npy_hashtable.h'),
join('src', 'common', 'npy_longdouble.h'),
join('src', 'common', 'npy_svml.h'),
join('src', 'common', 'templ_common.h.src'),
join('src', 'common', 'ucsnarrow.h'),
join('src', 'common', 'ufunc_override.h'),
join('src', 'common', 'umathmodule.h'),
join('src', 'common', 'numpyos.h'),
join('src', 'common', 'npy_cpu_dispatch.h'),
join('src', 'common', 'simd', 'simd.h'),
]
common_src = [
join('src', 'common', 'array_assign.c'),
join('src', 'common', 'mem_overlap.c'),
join('src', 'common', 'npy_argparse.c'),
join('src', 'common', 'npy_hashtable.c'),
join('src', 'common', 'npy_longdouble.c'),
join('src', 'common', 'templ_common.h.src'),
join('src', 'common', 'ucsnarrow.c'),
join('src', 'common', 'ufunc_override.c'),
join('src', 'common', 'numpyos.c'),
join('src', 'common', 'npy_cpu_features.c'),
]
if os.environ.get('NPY_USE_BLAS_ILP64', "0") != "0":
blas_info = get_info('blas_ilp64_opt', 2)
else:
blas_info = get_info('blas_opt', 0)
have_blas = blas_info and ('HAVE_CBLAS', None) in blas_info.get('define_macros', [])
if have_blas:
extra_info = blas_info
# These files are also in MANIFEST.in so that they are always in
# the source distribution independently of HAVE_CBLAS.
common_src.extend([join('src', 'common', 'cblasfuncs.c'),
join('src', 'common', 'python_xerbla.c'),
])
else:
extra_info = {}
#######################################################################
# _multiarray_umath module - multiarray part #
#######################################################################
multiarray_deps = [
join('src', 'multiarray', 'abstractdtypes.h'),
join('src', 'multiarray', 'arrayobject.h'),
join('src', 'multiarray', 'arraytypes.h.src'),
join('src', 'multiarray', 'arrayfunction_override.h'),
join('src', 'multiarray', 'array_coercion.h'),
join('src', 'multiarray', 'array_method.h'),
join('src', 'multiarray', 'npy_buffer.h'),
join('src', 'multiarray', 'calculation.h'),
join('src', 'multiarray', 'common.h'),
join('src', 'multiarray', 'common_dtype.h'),
join('src', 'multiarray', 'convert_datatype.h'),
join('src', 'multiarray', 'convert.h'),
join('src', 'multiarray', 'conversion_utils.h'),
join('src', 'multiarray', 'ctors.h'),
join('src', 'multiarray', 'descriptor.h'),
join('src', 'multiarray', 'dtypemeta.h'),
join('src', 'multiarray', 'dtype_transfer.h'),
join('src', 'multiarray', 'dragon4.h'),
join('src', 'multiarray', 'einsum_debug.h'),
join('src', 'multiarray', 'einsum_sumprod.h'),
join('src', 'multiarray', 'experimental_public_dtype_api.h'),
join('src', 'multiarray', 'getset.h'),
join('src', 'multiarray', 'hashdescr.h'),
join('src', 'multiarray', 'iterators.h'),
join('src', 'multiarray', 'legacy_dtype_implementation.h'),
join('src', 'multiarray', 'mapping.h'),
join('src', 'multiarray', 'methods.h'),
join('src', 'multiarray', 'multiarraymodule.h'),
join('src', 'multiarray', 'nditer_impl.h'),
join('src', 'multiarray', 'number.h'),
join('src', 'multiarray', 'refcount.h'),
join('src', 'multiarray', 'scalartypes.h'),
join('src', 'multiarray', 'sequence.h'),
join('src', 'multiarray', 'shape.h'),
join('src', 'multiarray', 'strfuncs.h'),
join('src', 'multiarray', 'typeinfo.h'),
join('src', 'multiarray', 'usertypes.h'),
join('src', 'multiarray', 'vdot.h'),
join('src', 'multiarray', 'textreading', 'readtext.h'),
join('include', 'numpy', 'arrayobject.h'),
join('include', 'numpy', '_neighborhood_iterator_imp.h'),
join('include', 'numpy', 'npy_endian.h'),
join('include', 'numpy', 'arrayscalars.h'),
join('include', 'numpy', 'noprefix.h'),
join('include', 'numpy', 'npy_interrupt.h'),
join('include', 'numpy', 'npy_3kcompat.h'),
join('include', 'numpy', 'npy_math.h'),
join('include', 'numpy', 'halffloat.h'),
join('include', 'numpy', 'npy_common.h'),
join('include', 'numpy', 'npy_os.h'),
join('include', 'numpy', 'utils.h'),
join('include', 'numpy', 'ndarrayobject.h'),
join('include', 'numpy', 'npy_cpu.h'),
join('include', 'numpy', 'numpyconfig.h'),
join('include', 'numpy', 'ndarraytypes.h'),
join('include', 'numpy', 'npy_1_7_deprecated_api.h'),
# add library sources as distuils does not consider libraries
# dependencies
] + npymath_sources
multiarray_src = [
join('src', 'multiarray', 'abstractdtypes.c'),
join('src', 'multiarray', 'alloc.c'),
join('src', 'multiarray', 'arrayobject.c'),
join('src', 'multiarray', 'arraytypes.h.src'),
join('src', 'multiarray', 'arraytypes.c.src'),
join('src', 'multiarray', 'argfunc.dispatch.c.src'),
join('src', 'multiarray', 'array_coercion.c'),
join('src', 'multiarray', 'array_method.c'),
join('src', 'multiarray', 'array_assign_scalar.c'),
join('src', 'multiarray', 'array_assign_array.c'),
join('src', 'multiarray', 'arrayfunction_override.c'),
join('src', 'multiarray', 'buffer.c'),
join('src', 'multiarray', 'calculation.c'),
join('src', 'multiarray', 'compiled_base.c'),
join('src', 'multiarray', 'common.c'),
join('src', 'multiarray', 'common_dtype.c'),
join('src', 'multiarray', 'convert.c'),
join('src', 'multiarray', 'convert_datatype.c'),
join('src', 'multiarray', 'conversion_utils.c'),
join('src', 'multiarray', 'ctors.c'),
join('src', 'multiarray', 'datetime.c'),
join('src', 'multiarray', 'datetime_strings.c'),
join('src', 'multiarray', 'datetime_busday.c'),
join('src', 'multiarray', 'datetime_busdaycal.c'),
join('src', 'multiarray', 'descriptor.c'),
join('src', 'multiarray', 'dlpack.c'),
join('src', 'multiarray', 'dtypemeta.c'),
join('src', 'multiarray', 'dragon4.c'),
join('src', 'multiarray', 'dtype_transfer.c'),
join('src', 'multiarray', 'einsum.c.src'),
join('src', 'multiarray', 'einsum_sumprod.c.src'),
join('src', 'multiarray', 'experimental_public_dtype_api.c'),
join('src', 'multiarray', 'flagsobject.c'),
join('src', 'multiarray', 'getset.c'),
join('src', 'multiarray', 'hashdescr.c'),
join('src', 'multiarray', 'item_selection.c'),
join('src', 'multiarray', 'iterators.c'),
join('src', 'multiarray', 'legacy_dtype_implementation.c'),
join('src', 'multiarray', 'lowlevel_strided_loops.c.src'),
join('src', 'multiarray', 'mapping.c'),
join('src', 'multiarray', 'methods.c'),
join('src', 'multiarray', 'multiarraymodule.c'),
join('src', 'multiarray', 'nditer_templ.c.src'),
join('src', 'multiarray', 'nditer_api.c'),
join('src', 'multiarray', 'nditer_constr.c'),
join('src', 'multiarray', 'nditer_pywrap.c'),
join('src', 'multiarray', 'number.c'),
join('src', 'multiarray', 'refcount.c'),
join('src', 'multiarray', 'sequence.c'),
join('src', 'multiarray', 'shape.c'),
join('src', 'multiarray', 'scalarapi.c'),
join('src', 'multiarray', 'scalartypes.c.src'),
join('src', 'multiarray', 'strfuncs.c'),
join('src', 'multiarray', 'temp_elide.c'),
join('src', 'multiarray', 'typeinfo.c'),
join('src', 'multiarray', 'usertypes.c'),
join('src', 'multiarray', 'vdot.c'),
join('src', 'common', 'npy_sort.h.src'),
join('src', 'npysort', 'x86-qsort.dispatch.cpp'),
join('src', 'npysort', 'quicksort.cpp'),
join('src', 'npysort', 'mergesort.cpp'),
join('src', 'npysort', 'timsort.cpp'),
join('src', 'npysort', 'heapsort.cpp'),
join('src', 'npysort', 'radixsort.cpp'),
join('src', 'common', 'npy_partition.h'),
join('src', 'npysort', 'selection.cpp'),
join('src', 'common', 'npy_binsearch.h'),
join('src', 'npysort', 'binsearch.cpp'),
join('src', 'multiarray', 'textreading', 'conversions.c'),
join('src', 'multiarray', 'textreading', 'field_types.c'),
join('src', 'multiarray', 'textreading', 'growth.c'),
join('src', 'multiarray', 'textreading', 'readtext.c'),
join('src', 'multiarray', 'textreading', 'rows.c'),
join('src', 'multiarray', 'textreading', 'stream_pyobject.c'),
join('src', 'multiarray', 'textreading', 'str_to_int.c'),
join('src', 'multiarray', 'textreading', 'tokenize.cpp'),
]
#######################################################################
# _multiarray_umath module - umath part #
#######################################################################
def generate_umath_c(ext, build_dir):
target = join(build_dir, header_dir, '__umath_generated.c')
dir = os.path.dirname(target)
if not os.path.exists(dir):
os.makedirs(dir)
script = generate_umath_py
if newer(script, target):
with open(target, 'w') as f:
f.write(generate_umath.make_code(generate_umath.defdict,
generate_umath.__file__))
return []
def generate_umath_doc_header(ext, build_dir):
from numpy.distutils.misc_util import exec_mod_from_location
target = join(build_dir, header_dir, '_umath_doc_generated.h')
dir = os.path.dirname(target)
if not os.path.exists(dir):
os.makedirs(dir)
generate_umath_doc_py = join(codegen_dir, 'generate_umath_doc.py')
if newer(generate_umath_doc_py, target):
n = dot_join(config.name, 'generate_umath_doc')
generate_umath_doc = exec_mod_from_location(
'_'.join(n.split('.')), generate_umath_doc_py)
generate_umath_doc.write_code(target)
umath_src = [
join('src', 'umath', 'umathmodule.c'),
join('src', 'umath', 'reduction.c'),
join('src', 'umath', 'funcs.inc.src'),
join('src', 'umath', 'simd.inc.src'),
join('src', 'umath', 'loops.h.src'),
join('src', 'umath', 'loops_utils.h.src'),
join('src', 'umath', 'loops.c.src'),
join('src', 'umath', 'loops_unary_fp.dispatch.c.src'),
join('src', 'umath', 'loops_arithm_fp.dispatch.c.src'),
join('src', 'umath', 'loops_arithmetic.dispatch.c.src'),
join('src', 'umath', 'loops_minmax.dispatch.c.src'),
join('src', 'umath', 'loops_trigonometric.dispatch.c.src'),
join('src', 'umath', 'loops_umath_fp.dispatch.c.src'),
join('src', 'umath', 'loops_exponent_log.dispatch.c.src'),
join('src', 'umath', 'loops_hyperbolic.dispatch.c.src'),
join('src', 'umath', 'loops_modulo.dispatch.c.src'),
join('src', 'umath', 'matmul.h.src'),
join('src', 'umath', 'matmul.c.src'),
join('src', 'umath', 'clip.h'),
join('src', 'umath', 'clip.cpp'),
join('src', 'umath', 'dispatching.c'),
join('src', 'umath', 'legacy_array_method.c'),
join('src', 'umath', 'wrapping_array_method.c'),
join('src', 'umath', 'ufunc_object.c'),
join('src', 'umath', 'extobj.c'),
join('src', 'umath', 'scalarmath.c.src'),
join('src', 'umath', 'ufunc_type_resolution.c'),
join('src', 'umath', 'override.c'),
# For testing. Eventually, should use public API and be separate:
join('src', 'umath', '_scaled_float_dtype.c'),
]
umath_deps = [
generate_umath_py,
join('include', 'numpy', 'npy_math.h'),
join('include', 'numpy', 'halffloat.h'),
join('src', 'multiarray', 'common.h'),
join('src', 'multiarray', 'number.h'),
join('src', 'common', 'templ_common.h.src'),
join('src', 'umath', 'simd.inc.src'),
join('src', 'umath', 'override.h'),
join(codegen_dir, 'generate_ufunc_api.py'),
join(codegen_dir, 'ufunc_docstrings.py'),
]
svml_path = join('numpy', 'core', 'src', 'umath', 'svml')
svml_objs = []
# we have converted the following into universal intrinsics
# so we can bring the benefits of performance for all platforms
# not just for avx512 on linux without performance/accuracy regression,
# actually the other way around, better performance and
# after all maintainable code.
svml_filter = (
'svml_z0_tanh_d_la.s', 'svml_z0_tanh_s_la.s'
)
if can_link_svml() and check_svml_submodule(svml_path):
svml_objs = glob.glob(svml_path + '/**/*.s', recursive=True)
svml_objs = [o for o in svml_objs if not o.endswith(svml_filter)]
# The ordering of names returned by glob is undefined, so we sort
# to make builds reproducible.
svml_objs.sort()
config.add_extension('_multiarray_umath',
# Forcing C language even though we have C++ sources.
# It forces the C linker and don't link C++ runtime.
language = 'c',
sources=multiarray_src + umath_src +
common_src +
[generate_config_h,
generate_numpyconfig_h,
generate_numpy_api,
join(codegen_dir, 'generate_numpy_api.py'),
join('*.py'),
generate_umath_c,
generate_umath_doc_header,
generate_ufunc_api,
],
depends=deps + multiarray_deps + umath_deps +
common_deps,
libraries=['npymath'],
extra_objects=svml_objs,
extra_info=extra_info,
extra_cxx_compile_args=NPY_CXX_FLAGS)
#######################################################################
# umath_tests module #
#######################################################################
config.add_extension('_umath_tests', sources=[
join('src', 'umath', '_umath_tests.c.src'),
join('src', 'umath', '_umath_tests.dispatch.c'),
join('src', 'common', 'npy_cpu_features.c'),
])
#######################################################################
# custom rational dtype module #
#######################################################################
config.add_extension('_rational_tests',
sources=[join('src', 'umath', '_rational_tests.c')])
#######################################################################
# struct_ufunc_test module #
#######################################################################
config.add_extension('_struct_ufunc_tests',
sources=[join('src', 'umath', '_struct_ufunc_tests.c')])
#######################################################################
# operand_flag_tests module #
#######################################################################
config.add_extension('_operand_flag_tests',
sources=[join('src', 'umath', '_operand_flag_tests.c')])
#######################################################################
# SIMD module #
#######################################################################
config.add_extension('_simd', sources=[
join('src', 'common', 'npy_cpu_features.c'),
join('src', '_simd', '_simd.c'),
join('src', '_simd', '_simd_inc.h.src'),
join('src', '_simd', '_simd_data.inc.src'),
join('src', '_simd', '_simd.dispatch.c.src'),
], depends=[
join('src', 'common', 'npy_cpu_dispatch.h'),
join('src', 'common', 'simd', 'simd.h'),
join('src', '_simd', '_simd.h'),
join('src', '_simd', '_simd_inc.h.src'),
join('src', '_simd', '_simd_data.inc.src'),
join('src', '_simd', '_simd_arg.inc'),
join('src', '_simd', '_simd_convert.inc'),
join('src', '_simd', '_simd_easyintrin.inc'),
join('src', '_simd', '_simd_vector.inc'),
])
config.add_subpackage('tests')
config.add_data_dir('tests/data')
config.add_data_dir('tests/examples')
config.add_data_files('*.pyi')
config.make_svn_version_py()
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)
| 52,651 | Python | 42.51405 | 113 | 0.526809 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/arrayprint.pyi | from types import TracebackType
from collections.abc import Callable
from typing import Any, Literal, TypedDict, SupportsIndex
# Using a private class is by no means ideal, but it is simply a consequence
# of a `contextlib.context` returning an instance of aforementioned class
from contextlib import _GeneratorContextManager
from numpy import (
ndarray,
generic,
bool_,
integer,
timedelta64,
datetime64,
floating,
complexfloating,
void,
str_,
bytes_,
longdouble,
clongdouble,
)
from numpy._typing import ArrayLike, _CharLike_co, _FloatLike_co
_FloatMode = Literal["fixed", "unique", "maxprec", "maxprec_equal"]
class _FormatDict(TypedDict, total=False):
bool: Callable[[bool_], str]
int: Callable[[integer[Any]], str]
timedelta: Callable[[timedelta64], str]
datetime: Callable[[datetime64], str]
float: Callable[[floating[Any]], str]
longfloat: Callable[[longdouble], str]
complexfloat: Callable[[complexfloating[Any, Any]], str]
longcomplexfloat: Callable[[clongdouble], str]
void: Callable[[void], str]
numpystr: Callable[[_CharLike_co], str]
object: Callable[[object], str]
all: Callable[[object], str]
int_kind: Callable[[integer[Any]], str]
float_kind: Callable[[floating[Any]], str]
complex_kind: Callable[[complexfloating[Any, Any]], str]
str_kind: Callable[[_CharLike_co], str]
class _FormatOptions(TypedDict):
precision: int
threshold: int
edgeitems: int
linewidth: int
suppress: bool
nanstr: str
infstr: str
formatter: None | _FormatDict
sign: Literal["-", "+", " "]
floatmode: _FloatMode
legacy: Literal[False, "1.13", "1.21"]
def set_printoptions(
precision: None | SupportsIndex = ...,
threshold: None | int = ...,
edgeitems: None | int = ...,
linewidth: None | int = ...,
suppress: None | bool = ...,
nanstr: None | str = ...,
infstr: None | str = ...,
formatter: None | _FormatDict = ...,
sign: Literal[None, "-", "+", " "] = ...,
floatmode: None | _FloatMode = ...,
*,
legacy: Literal[None, False, "1.13", "1.21"] = ...
) -> None: ...
def get_printoptions() -> _FormatOptions: ...
def array2string(
a: ndarray[Any, Any],
max_line_width: None | int = ...,
precision: None | SupportsIndex = ...,
suppress_small: None | bool = ...,
separator: str = ...,
prefix: str = ...,
# NOTE: With the `style` argument being deprecated,
# all arguments between `formatter` and `suffix` are de facto
# keyworld-only arguments
*,
formatter: None | _FormatDict = ...,
threshold: None | int = ...,
edgeitems: None | int = ...,
sign: Literal[None, "-", "+", " "] = ...,
floatmode: None | _FloatMode = ...,
suffix: str = ...,
legacy: Literal[None, False, "1.13", "1.21"] = ...,
) -> str: ...
def format_float_scientific(
x: _FloatLike_co,
precision: None | int = ...,
unique: bool = ...,
trim: Literal["k", ".", "0", "-"] = ...,
sign: bool = ...,
pad_left: None | int = ...,
exp_digits: None | int = ...,
min_digits: None | int = ...,
) -> str: ...
def format_float_positional(
x: _FloatLike_co,
precision: None | int = ...,
unique: bool = ...,
fractional: bool = ...,
trim: Literal["k", ".", "0", "-"] = ...,
sign: bool = ...,
pad_left: None | int = ...,
pad_right: None | int = ...,
min_digits: None | int = ...,
) -> str: ...
def array_repr(
arr: ndarray[Any, Any],
max_line_width: None | int = ...,
precision: None | SupportsIndex = ...,
suppress_small: None | bool = ...,
) -> str: ...
def array_str(
a: ndarray[Any, Any],
max_line_width: None | int = ...,
precision: None | SupportsIndex = ...,
suppress_small: None | bool = ...,
) -> str: ...
def set_string_function(
f: None | Callable[[ndarray[Any, Any]], str], repr: bool = ...
) -> None: ...
def printoptions(
precision: None | SupportsIndex = ...,
threshold: None | int = ...,
edgeitems: None | int = ...,
linewidth: None | int = ...,
suppress: None | bool = ...,
nanstr: None | str = ...,
infstr: None | str = ...,
formatter: None | _FormatDict = ...,
sign: Literal[None, "-", "+", " "] = ...,
floatmode: None | _FloatMode = ...,
*,
legacy: Literal[None, False, "1.13", "1.21"] = ...
) -> _GeneratorContextManager[_FormatOptions]: ...
| 4,428 | unknown | 29.972028 | 76 | 0.577687 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_add_newdocs_scalars.py | """
This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
our sphinx ``conf.py`` during doc builds, where we want to avoid showing
platform-dependent information.
"""
from numpy.core import dtype
from numpy.core import numerictypes as _numerictypes
from numpy.core.function_base import add_newdoc
import platform
##############################################################################
#
# Documentation for concrete scalar classes
#
##############################################################################
def numeric_type_aliases(aliases):
def type_aliases_gen():
for alias, doc in aliases:
try:
alias_type = getattr(_numerictypes, alias)
except AttributeError:
# The set of aliases that actually exist varies between platforms
pass
else:
yield (alias_type, alias, doc)
return list(type_aliases_gen())
possible_aliases = numeric_type_aliases([
('int8', '8-bit signed integer (``-128`` to ``127``)'),
('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
('float96', '96-bit extended-precision floating-point number type'),
('float128', '128-bit extended-precision floating-point number type'),
('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
])
def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
# note: `:field: value` is rST syntax which renders as field lists.
o = getattr(_numerictypes, obj)
character_code = dtype(o).char
canonical_name_doc = "" if obj == o.__name__ else ":Canonical name: `numpy.{}`\n ".format(obj)
alias_doc = ''.join(":Alias: `numpy.{}`\n ".format(alias) for alias in fixed_aliases)
alias_doc += ''.join(":Alias on this platform ({} {}): `numpy.{}`: {}.\n ".format(platform.system(), platform.machine(), alias, doc)
for (alias_type, alias, doc) in possible_aliases if alias_type is o)
docstring = """
{doc}
:Character code: ``'{character_code}'``
{canonical_name_doc}{alias_doc}
""".format(doc=doc.strip(), character_code=character_code,
canonical_name_doc=canonical_name_doc, alias_doc=alias_doc)
add_newdoc('numpy.core.numerictypes', obj, docstring)
add_newdoc_for_scalar_type('bool_', ['bool8'],
"""
Boolean type (True or False), stored as a byte.
.. warning::
The :class:`bool_` type is not a subclass of the :class:`int_` type
(the :class:`bool_` is not even a number type). This is different
than Python's default implementation of :class:`bool` as a
sub-class of :class:`int`.
""")
add_newdoc_for_scalar_type('byte', [],
"""
Signed integer type, compatible with C ``char``.
""")
add_newdoc_for_scalar_type('short', [],
"""
Signed integer type, compatible with C ``short``.
""")
add_newdoc_for_scalar_type('intc', [],
"""
Signed integer type, compatible with C ``int``.
""")
add_newdoc_for_scalar_type('int_', [],
"""
Signed integer type, compatible with Python `int` and C ``long``.
""")
add_newdoc_for_scalar_type('longlong', [],
"""
Signed integer type, compatible with C ``long long``.
""")
add_newdoc_for_scalar_type('ubyte', [],
"""
Unsigned integer type, compatible with C ``unsigned char``.
""")
add_newdoc_for_scalar_type('ushort', [],
"""
Unsigned integer type, compatible with C ``unsigned short``.
""")
add_newdoc_for_scalar_type('uintc', [],
"""
Unsigned integer type, compatible with C ``unsigned int``.
""")
add_newdoc_for_scalar_type('uint', [],
"""
Unsigned integer type, compatible with C ``unsigned long``.
""")
add_newdoc_for_scalar_type('ulonglong', [],
"""
Signed integer type, compatible with C ``unsigned long long``.
""")
add_newdoc_for_scalar_type('half', [],
"""
Half-precision floating-point number type.
""")
add_newdoc_for_scalar_type('single', [],
"""
Single-precision floating-point number type, compatible with C ``float``.
""")
add_newdoc_for_scalar_type('double', ['float_'],
"""
Double-precision floating-point number type, compatible with Python `float`
and C ``double``.
""")
add_newdoc_for_scalar_type('longdouble', ['longfloat'],
"""
Extended-precision floating-point number type, compatible with C
``long double`` but not necessarily with IEEE 754 quadruple-precision.
""")
add_newdoc_for_scalar_type('csingle', ['singlecomplex'],
"""
Complex number type composed of two single-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('cdouble', ['cfloat', 'complex_'],
"""
Complex number type composed of two double-precision floating-point
numbers, compatible with Python `complex`.
""")
add_newdoc_for_scalar_type('clongdouble', ['clongfloat', 'longcomplex'],
"""
Complex number type composed of two extended-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('object_', [],
"""
Any Python object.
""")
add_newdoc_for_scalar_type('str_', ['unicode_'],
r"""
A unicode string.
When used in arrays, this type strips trailing null codepoints.
Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its
contents as UCS4:
>>> m = memoryview(np.str_("abc"))
>>> m.format
'3w'
>>> m.tobytes()
b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
""")
add_newdoc_for_scalar_type('bytes_', ['string_'],
r"""
A byte string.
When used in arrays, this type strips trailing null bytes.
""")
add_newdoc_for_scalar_type('void', [],
r"""
Either an opaque sequence of bytes, or a structure.
>>> np.void(b'abcd')
void(b'\x61\x62\x63\x64')
Structured `void` scalars can only be constructed via extraction from :ref:`structured_arrays`:
>>> arr = np.array((1, 2), dtype=[('x', np.int8), ('y', np.int8)])
>>> arr[()]
(1, 2) # looks like a tuple, but is `np.void`
""")
add_newdoc_for_scalar_type('datetime64', [],
"""
If created from a 64-bit integer, it represents an offset from
``1970-01-01T00:00:00``.
If created from string, the string can be in ISO 8601 date
or datetime format.
>>> np.datetime64(10, 'Y')
numpy.datetime64('1980')
>>> np.datetime64('1980', 'Y')
numpy.datetime64('1980')
>>> np.datetime64(10, 'D')
numpy.datetime64('1970-01-11')
See :ref:`arrays.datetime` for more information.
""")
add_newdoc_for_scalar_type('timedelta64', [],
"""
A timedelta stored as a 64-bit integer.
See :ref:`arrays.datetime` for more information.
""")
add_newdoc('numpy.core.numerictypes', "integer", ('is_integer',
"""
integer.is_integer() -> bool
Return ``True`` if the number is finite with integral value.
.. versionadded:: 1.22
Examples
--------
>>> np.int64(-2).is_integer()
True
>>> np.uint32(5).is_integer()
True
"""))
# TODO: work out how to put this on the base class, np.floating
for float_name in ('half', 'single', 'double', 'longdouble'):
add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio',
"""
{ftype}.as_integer_ratio() -> (int, int)
Return a pair of integers, whose ratio is exactly equal to the original
floating point number, and with a positive denominator.
Raise `OverflowError` on infinities and a `ValueError` on NaNs.
>>> np.{ftype}(10.0).as_integer_ratio()
(10, 1)
>>> np.{ftype}(0.0).as_integer_ratio()
(0, 1)
>>> np.{ftype}(-.25).as_integer_ratio()
(-1, 4)
""".format(ftype=float_name)))
add_newdoc('numpy.core.numerictypes', float_name, ('is_integer',
f"""
{float_name}.is_integer() -> bool
Return ``True`` if the floating point number is finite with integral
value, and ``False`` otherwise.
.. versionadded:: 1.22
Examples
--------
>>> np.{float_name}(-2.0).is_integer()
True
>>> np.{float_name}(3.2).is_integer()
False
"""))
for int_name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
'int64', 'uint64', 'int64', 'uint64', 'int64', 'uint64'):
# Add negative examples for signed cases by checking typecode
add_newdoc('numpy.core.numerictypes', int_name, ('bit_count',
f"""
{int_name}.bit_count() -> int
Computes the number of 1-bits in the absolute value of the input.
Analogous to the builtin `int.bit_count` or ``popcount`` in C++.
Examples
--------
>>> np.{int_name}(127).bit_count()
7""" +
(f"""
>>> np.{int_name}(-127).bit_count()
7
""" if dtype(int_name).char.islower() else "")))
| 10,232 | Python | 31.798077 | 139 | 0.600762 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/numerictypes.pyi | import sys
import types
from collections.abc import Iterable
from typing import (
Literal as L,
Union,
overload,
Any,
TypeVar,
Protocol,
TypedDict,
)
from numpy import (
ndarray,
dtype,
generic,
bool_,
ubyte,
ushort,
uintc,
uint,
ulonglong,
byte,
short,
intc,
int_,
longlong,
half,
single,
double,
longdouble,
csingle,
cdouble,
clongdouble,
datetime64,
timedelta64,
object_,
str_,
bytes_,
void,
)
from numpy.core._type_aliases import (
sctypeDict as sctypeDict,
sctypes as sctypes,
)
from numpy._typing import DTypeLike, ArrayLike, _DTypeLike
_T = TypeVar("_T")
_SCT = TypeVar("_SCT", bound=generic)
class _CastFunc(Protocol):
def __call__(
self, x: ArrayLike, k: DTypeLike = ...
) -> ndarray[Any, dtype[Any]]: ...
class _TypeCodes(TypedDict):
Character: L['c']
Integer: L['bhilqp']
UnsignedInteger: L['BHILQP']
Float: L['efdg']
Complex: L['FDG']
AllInteger: L['bBhHiIlLqQpP']
AllFloat: L['efdgFDG']
Datetime: L['Mm']
All: L['?bhilqpBHILQPefdgFDGSUVOMm']
class _typedict(dict[type[generic], _T]):
def __getitem__(self, key: DTypeLike) -> _T: ...
if sys.version_info >= (3, 10):
_TypeTuple = Union[
type[Any],
types.UnionType,
tuple[Union[type[Any], types.UnionType, tuple[Any, ...]], ...],
]
else:
_TypeTuple = Union[
type[Any],
tuple[Union[type[Any], tuple[Any, ...]], ...],
]
__all__: list[str]
@overload
def maximum_sctype(t: _DTypeLike[_SCT]) -> type[_SCT]: ...
@overload
def maximum_sctype(t: DTypeLike) -> type[Any]: ...
@overload
def issctype(rep: dtype[Any] | type[Any]) -> bool: ...
@overload
def issctype(rep: object) -> L[False]: ...
@overload
def obj2sctype(rep: _DTypeLike[_SCT], default: None = ...) -> None | type[_SCT]: ...
@overload
def obj2sctype(rep: _DTypeLike[_SCT], default: _T) -> _T | type[_SCT]: ...
@overload
def obj2sctype(rep: DTypeLike, default: None = ...) -> None | type[Any]: ...
@overload
def obj2sctype(rep: DTypeLike, default: _T) -> _T | type[Any]: ...
@overload
def obj2sctype(rep: object, default: None = ...) -> None: ...
@overload
def obj2sctype(rep: object, default: _T) -> _T: ...
@overload
def issubclass_(arg1: type[Any], arg2: _TypeTuple) -> bool: ...
@overload
def issubclass_(arg1: object, arg2: object) -> L[False]: ...
def issubsctype(arg1: DTypeLike, arg2: DTypeLike) -> bool: ...
def issubdtype(arg1: DTypeLike, arg2: DTypeLike) -> bool: ...
def sctype2char(sctype: DTypeLike) -> str: ...
def find_common_type(
array_types: Iterable[DTypeLike],
scalar_types: Iterable[DTypeLike],
) -> dtype[Any]: ...
cast: _typedict[_CastFunc]
nbytes: _typedict[int]
typecodes: _TypeCodes
ScalarType: tuple[
type[int],
type[float],
type[complex],
type[bool],
type[bytes],
type[str],
type[memoryview],
type[bool_],
type[csingle],
type[cdouble],
type[clongdouble],
type[half],
type[single],
type[double],
type[longdouble],
type[byte],
type[short],
type[intc],
type[int_],
type[longlong],
type[timedelta64],
type[datetime64],
type[object_],
type[bytes_],
type[str_],
type[ubyte],
type[ushort],
type[uintc],
type[uint],
type[ulonglong],
type[void],
]
| 3,388 | unknown | 19.919753 | 84 | 0.598878 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/defchararray.py | """
This module contains a set of functions for vectorized string
operations and methods.
.. note::
The `chararray` class exists for backwards compatibility with
Numarray, it is not recommended for new development. Starting from numpy
1.4, if one needs arrays of strings, it is recommended to use arrays of
`dtype` `object_`, `string_` or `unicode_`, and use the free functions
in the `numpy.char` module for fast vectorized string operations.
Some methods will only be available if the corresponding string method is
available in your version of Python.
The preferred alias for `defchararray` is `numpy.char`.
"""
import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
__all__ = [
'equal', 'not_equal', 'greater_equal', 'less_equal',
'greater', 'less', 'str_len', 'add', 'multiply', 'mod', 'capitalize',
'center', 'count', 'decode', 'encode', 'endswith', 'expandtabs',
'find', 'index', 'isalnum', 'isalpha', 'isdigit', 'islower', 'isspace',
'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'partition',
'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit',
'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase',
'title', 'translate', 'upper', 'zfill', 'isnumeric', 'isdecimal',
'array', 'asarray'
]
_globalvar = 0
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy.char')
def _use_unicode(*args):
"""
Helper function for determining the output type of some string
operations.
For an operation on two ndarrays, if at least one is unicode, the
result should be unicode.
"""
for x in args:
if (isinstance(x, str) or
issubclass(numpy.asarray(x).dtype.type, unicode_)):
return unicode_
return string_
def _to_string_or_unicode_array(result):
"""
Helper function to cast a result back into a string or unicode array
if an object array must be used as an intermediary.
"""
return numpy.asarray(result.tolist())
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
def _get_num_chars(a):
"""
Helper function that returns the number of characters per field in
a string or unicode array. This is to abstract out the fact that
for a unicode array this is itemsize / 4.
"""
if issubclass(a.dtype.type, unicode_):
return a.itemsize // 4
return a.itemsize
def _binary_op_dispatcher(x1, x2):
return (x1, x2)
@array_function_dispatch(_binary_op_dispatcher)
def equal(x1, x2):
"""
Return (x1 == x2) element-wise.
Unlike `numpy.equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
not_equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '==', True)
@array_function_dispatch(_binary_op_dispatcher)
def not_equal(x1, x2):
"""
Return (x1 != x2) element-wise.
Unlike `numpy.not_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '!=', True)
@array_function_dispatch(_binary_op_dispatcher)
def greater_equal(x1, x2):
"""
Return (x1 >= x2) element-wise.
Unlike `numpy.greater_equal`, this comparison is performed by
first stripping whitespace characters from the end of the string.
This behavior is provided for backward-compatibility with
numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
equal, not_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '>=', True)
@array_function_dispatch(_binary_op_dispatcher)
def less_equal(x1, x2):
"""
Return (x1 <= x2) element-wise.
Unlike `numpy.less_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
equal, not_equal, greater_equal, greater, less
"""
return compare_chararrays(x1, x2, '<=', True)
@array_function_dispatch(_binary_op_dispatcher)
def greater(x1, x2):
"""
Return (x1 > x2) element-wise.
Unlike `numpy.greater`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
equal, not_equal, greater_equal, less_equal, less
"""
return compare_chararrays(x1, x2, '>', True)
@array_function_dispatch(_binary_op_dispatcher)
def less(x1, x2):
"""
Return (x1 < x2) element-wise.
Unlike `numpy.greater`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
equal, not_equal, greater_equal, less_equal, greater
"""
return compare_chararrays(x1, x2, '<', True)
def _unary_op_dispatcher(a):
return (a,)
@array_function_dispatch(_unary_op_dispatcher)
def str_len(a):
"""
Return len(a) element-wise.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of integers
See Also
--------
builtins.len
"""
# Note: __len__, etc. currently return ints, which are not C-integers.
# Generally intp would be expected for lengths, although int is sufficient
# due to the dtype itemsize limitation.
return _vec_string(a, int_, '__len__')
@array_function_dispatch(_binary_op_dispatcher)
def add(x1, x2):
"""
Return element-wise string concatenation for two arrays of str or unicode.
Arrays `x1` and `x2` must have the same shape.
Parameters
----------
x1 : array_like of str or unicode
Input array.
x2 : array_like of str or unicode
Input array.
Returns
-------
add : ndarray
Output array of `string_` or `unicode_`, depending on input types
of the same shape as `x1` and `x2`.
"""
arr1 = numpy.asarray(x1)
arr2 = numpy.asarray(x2)
out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
dtype = _use_unicode(arr1, arr2)
return _vec_string(arr1, (dtype, out_size), '__add__', (arr2,))
def _multiply_dispatcher(a, i):
return (a,)
@array_function_dispatch(_multiply_dispatcher)
def multiply(a, i):
"""
Return (a * i), that is string multiple concatenation,
element-wise.
Values in `i` of less than 0 are treated as 0 (which yields an
empty string).
Parameters
----------
a : array_like of str or unicode
i : array_like of ints
Returns
-------
out : ndarray
Output array of str or unicode, depending on input types
"""
a_arr = numpy.asarray(a)
i_arr = numpy.asarray(i)
if not issubclass(i_arr.dtype.type, integer):
raise ValueError("Can only multiply by integers")
out_size = _get_num_chars(a_arr) * max(int(i_arr.max()), 0)
return _vec_string(
a_arr, (a_arr.dtype.type, out_size), '__mul__', (i_arr,))
def _mod_dispatcher(a, values):
return (a, values)
@array_function_dispatch(_mod_dispatcher)
def mod(a, values):
"""
Return (a % i), that is pre-Python 2.6 string formatting
(interpolation), element-wise for a pair of array_likes of str
or unicode.
Parameters
----------
a : array_like of str or unicode
values : array_like of values
These values will be element-wise interpolated into the string.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input types
See Also
--------
str.__mod__
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, '__mod__', (values,)))
@array_function_dispatch(_unary_op_dispatcher)
def capitalize(a):
"""
Return a copy of `a` with only the first character of each element
capitalized.
Calls `str.capitalize` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Input array of strings to capitalize.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input
types
See Also
--------
str.capitalize
Examples
--------
>>> c = np.array(['a1b2','1b2a','b2a1','2a1b'],'S4'); c
array(['a1b2', '1b2a', 'b2a1', '2a1b'],
dtype='|S4')
>>> np.char.capitalize(c)
array(['A1b2', '1b2a', 'B2a1', '2a1b'],
dtype='|S4')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'capitalize')
def _center_dispatcher(a, width, fillchar=None):
return (a,)
@array_function_dispatch(_center_dispatcher)
def center(a, width, fillchar=' '):
"""
Return a copy of `a` with its elements centered in a string of
length `width`.
Calls `str.center` element-wise.
Parameters
----------
a : array_like of str or unicode
width : int
The length of the resulting strings
fillchar : str or unicode, optional
The padding character to use (default is space).
Returns
-------
out : ndarray
Output array of str or unicode, depending on input
types
See Also
--------
str.center
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = int(numpy.max(width_arr.flat))
if numpy.issubdtype(a_arr.dtype, numpy.string_):
fillchar = asbytes(fillchar)
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'center', (width_arr, fillchar))
def _count_dispatcher(a, sub, start=None, end=None):
return (a,)
@array_function_dispatch(_count_dispatcher)
def count(a, sub, start=0, end=None):
"""
Returns an array with the number of non-overlapping occurrences of
substring `sub` in the range [`start`, `end`].
Calls `str.count` element-wise.
Parameters
----------
a : array_like of str or unicode
sub : str or unicode
The substring to search for.
start, end : int, optional
Optional arguments `start` and `end` are interpreted as slice
notation to specify the range in which to count.
Returns
-------
out : ndarray
Output array of ints.
See Also
--------
str.count
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
>>> np.char.count(c, 'A')
array([3, 1, 1])
>>> np.char.count(c, 'aA')
array([3, 1, 0])
>>> np.char.count(c, 'A', start=1, end=4)
array([2, 1, 1])
>>> np.char.count(c, 'A', start=1, end=3)
array([1, 0, 0])
"""
return _vec_string(a, int_, 'count', [sub, start] + _clean_args(end))
def _code_dispatcher(a, encoding=None, errors=None):
return (a,)
@array_function_dispatch(_code_dispatcher)
def decode(a, encoding=None, errors=None):
"""
Calls `str.decode` element-wise.
The set of available codecs comes from the Python standard library,
and may be extended at runtime. For more information, see the
:mod:`codecs` module.
Parameters
----------
a : array_like of str or unicode
encoding : str, optional
The name of an encoding
errors : str, optional
Specifies how to handle encoding errors
Returns
-------
out : ndarray
See Also
--------
str.decode
Notes
-----
The type of the result will depend on the encoding specified.
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
>>> np.char.encode(c, encoding='cp037')
array(['\\x81\\xc1\\x81\\xc1\\x81\\xc1', '@@\\x81\\xc1@@',
'\\x81\\x82\\xc2\\xc1\\xc2\\x82\\x81'],
dtype='|S7')
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'decode', _clean_args(encoding, errors)))
@array_function_dispatch(_code_dispatcher)
def encode(a, encoding=None, errors=None):
"""
Calls `str.encode` element-wise.
The set of available codecs comes from the Python standard library,
and may be extended at runtime. For more information, see the codecs
module.
Parameters
----------
a : array_like of str or unicode
encoding : str, optional
The name of an encoding
errors : str, optional
Specifies how to handle encoding errors
Returns
-------
out : ndarray
See Also
--------
str.encode
Notes
-----
The type of the result will depend on the encoding specified.
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'encode', _clean_args(encoding, errors)))
def _endswith_dispatcher(a, suffix, start=None, end=None):
return (a,)
@array_function_dispatch(_endswith_dispatcher)
def endswith(a, suffix, start=0, end=None):
"""
Returns a boolean array which is `True` where the string element
in `a` ends with `suffix`, otherwise `False`.
Calls `str.endswith` element-wise.
Parameters
----------
a : array_like of str or unicode
suffix : str
start, end : int, optional
With optional `start`, test beginning at that position. With
optional `end`, stop comparing at that position.
Returns
-------
out : ndarray
Outputs an array of bools.
See Also
--------
str.endswith
Examples
--------
>>> s = np.array(['foo', 'bar'])
>>> s[0] = 'foo'
>>> s[1] = 'bar'
>>> s
array(['foo', 'bar'], dtype='<U3')
>>> np.char.endswith(s, 'ar')
array([False, True])
>>> np.char.endswith(s, 'a', start=1, end=2)
array([False, True])
"""
return _vec_string(
a, bool_, 'endswith', [suffix, start] + _clean_args(end))
def _expandtabs_dispatcher(a, tabsize=None):
return (a,)
@array_function_dispatch(_expandtabs_dispatcher)
def expandtabs(a, tabsize=8):
"""
Return a copy of each string element where all tab characters are
replaced by one or more spaces.
Calls `str.expandtabs` element-wise.
Return a copy of each string element where all tab characters are
replaced by one or more spaces, depending on the current column
and the given `tabsize`. The column number is reset to zero after
each newline occurring in the string. This doesn't understand other
non-printing characters or escape sequences.
Parameters
----------
a : array_like of str or unicode
Input array
tabsize : int, optional
Replace tabs with `tabsize` number of spaces. If not given defaults
to 8 spaces.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.expandtabs
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'expandtabs', (tabsize,)))
@array_function_dispatch(_count_dispatcher)
def find(a, sub, start=0, end=None):
"""
For each element, return the lowest index in the string where
substring `sub` is found.
Calls `str.find` element-wise.
For each element, return the lowest index in the string where
substring `sub` is found, such that `sub` is contained in the
range [`start`, `end`].
Parameters
----------
a : array_like of str or unicode
sub : str or unicode
start, end : int, optional
Optional arguments `start` and `end` are interpreted as in
slice notation.
Returns
-------
out : ndarray or int
Output array of ints. Returns -1 if `sub` is not found.
See Also
--------
str.find
"""
return _vec_string(
a, int_, 'find', [sub, start] + _clean_args(end))
@array_function_dispatch(_count_dispatcher)
def index(a, sub, start=0, end=None):
"""
Like `find`, but raises `ValueError` when the substring is not found.
Calls `str.index` element-wise.
Parameters
----------
a : array_like of str or unicode
sub : str or unicode
start, end : int, optional
Returns
-------
out : ndarray
Output array of ints. Returns -1 if `sub` is not found.
See Also
--------
find, str.find
"""
return _vec_string(
a, int_, 'index', [sub, start] + _clean_args(end))
@array_function_dispatch(_unary_op_dispatcher)
def isalnum(a):
"""
Returns true for each element if all characters in the string are
alphanumeric and there is at least one character, false otherwise.
Calls `str.isalnum` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.isalnum
"""
return _vec_string(a, bool_, 'isalnum')
@array_function_dispatch(_unary_op_dispatcher)
def isalpha(a):
"""
Returns true for each element if all characters in the string are
alphabetic and there is at least one character, false otherwise.
Calls `str.isalpha` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.isalpha
"""
return _vec_string(a, bool_, 'isalpha')
@array_function_dispatch(_unary_op_dispatcher)
def isdigit(a):
"""
Returns true for each element if all characters in the string are
digits and there is at least one character, false otherwise.
Calls `str.isdigit` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.isdigit
"""
return _vec_string(a, bool_, 'isdigit')
@array_function_dispatch(_unary_op_dispatcher)
def islower(a):
"""
Returns true for each element if all cased characters in the
string are lowercase and there is at least one cased character,
false otherwise.
Calls `str.islower` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.islower
"""
return _vec_string(a, bool_, 'islower')
@array_function_dispatch(_unary_op_dispatcher)
def isspace(a):
"""
Returns true for each element if there are only whitespace
characters in the string and there is at least one character,
false otherwise.
Calls `str.isspace` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.isspace
"""
return _vec_string(a, bool_, 'isspace')
@array_function_dispatch(_unary_op_dispatcher)
def istitle(a):
"""
Returns true for each element if the element is a titlecased
string and there is at least one character, false otherwise.
Call `str.istitle` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.istitle
"""
return _vec_string(a, bool_, 'istitle')
@array_function_dispatch(_unary_op_dispatcher)
def isupper(a):
"""
Returns true for each element if all cased characters in the
string are uppercase and there is at least one character, false
otherwise.
Call `str.isupper` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.isupper
"""
return _vec_string(a, bool_, 'isupper')
def _join_dispatcher(sep, seq):
return (sep, seq)
@array_function_dispatch(_join_dispatcher)
def join(sep, seq):
"""
Return a string which is the concatenation of the strings in the
sequence `seq`.
Calls `str.join` element-wise.
Parameters
----------
sep : array_like of str or unicode
seq : array_like of str or unicode
Returns
-------
out : ndarray
Output array of str or unicode, depending on input types
See Also
--------
str.join
"""
return _to_string_or_unicode_array(
_vec_string(sep, object_, 'join', (seq,)))
def _just_dispatcher(a, width, fillchar=None):
return (a,)
@array_function_dispatch(_just_dispatcher)
def ljust(a, width, fillchar=' '):
"""
Return an array with the elements of `a` left-justified in a
string of length `width`.
Calls `str.ljust` element-wise.
Parameters
----------
a : array_like of str or unicode
width : int
The length of the resulting strings
fillchar : str or unicode, optional
The character to use for padding
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.ljust
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = int(numpy.max(width_arr.flat))
if numpy.issubdtype(a_arr.dtype, numpy.string_):
fillchar = asbytes(fillchar)
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'ljust', (width_arr, fillchar))
@array_function_dispatch(_unary_op_dispatcher)
def lower(a):
"""
Return an array with the elements converted to lowercase.
Call `str.lower` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See Also
--------
str.lower
Examples
--------
>>> c = np.array(['A1B C', '1BCA', 'BCA1']); c
array(['A1B C', '1BCA', 'BCA1'], dtype='<U5')
>>> np.char.lower(c)
array(['a1b c', '1bca', 'bca1'], dtype='<U5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'lower')
def _strip_dispatcher(a, chars=None):
return (a,)
@array_function_dispatch(_strip_dispatcher)
def lstrip(a, chars=None):
"""
For each element in `a`, return a copy with the leading characters
removed.
Calls `str.lstrip` element-wise.
Parameters
----------
a : array-like, {str, unicode}
Input array.
chars : {str, unicode}, optional
The `chars` argument is a string specifying the set of
characters to be removed. If omitted or None, the `chars`
argument defaults to removing whitespace. The `chars` argument
is not a prefix; rather, all combinations of its values are
stripped.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See Also
--------
str.lstrip
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
The 'a' variable is unstripped from c[1] because whitespace leading.
>>> np.char.lstrip(c, 'a')
array(['AaAaA', ' aA ', 'bBABba'], dtype='<U7')
>>> np.char.lstrip(c, 'A') # leaves c unchanged
array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
>>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, '')).all()
... # XXX: is this a regression? This used to return True
... # np.char.lstrip(c,'') does not modify c at all.
False
>>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, None)).all()
True
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'lstrip', (chars,))
def _partition_dispatcher(a, sep):
return (a,)
@array_function_dispatch(_partition_dispatcher)
def partition(a, sep):
"""
Partition each element in `a` around `sep`.
Calls `str.partition` element-wise.
For each element in `a`, split the element as the first
occurrence of `sep`, and return 3 strings containing the part
before the separator, the separator itself, and the part after
the separator. If the separator is not found, return 3 strings
containing the string itself, followed by two empty strings.
Parameters
----------
a : array_like, {str, unicode}
Input array
sep : {str, unicode}
Separator to split each string element in `a`.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type.
The output array will have an extra dimension with 3
elements per input element.
See Also
--------
str.partition
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'partition', (sep,)))
def _replace_dispatcher(a, old, new, count=None):
return (a,)
@array_function_dispatch(_replace_dispatcher)
def replace(a, old, new, count=None):
"""
For each element in `a`, return a copy of the string with all
occurrences of substring `old` replaced by `new`.
Calls `str.replace` element-wise.
Parameters
----------
a : array-like of str or unicode
old, new : str or unicode
count : int, optional
If the optional argument `count` is given, only the first
`count` occurrences are replaced.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.replace
"""
return _to_string_or_unicode_array(
_vec_string(
a, object_, 'replace', [old, new] + _clean_args(count)))
@array_function_dispatch(_count_dispatcher)
def rfind(a, sub, start=0, end=None):
"""
For each element in `a`, return the highest index in the string
where substring `sub` is found, such that `sub` is contained
within [`start`, `end`].
Calls `str.rfind` element-wise.
Parameters
----------
a : array-like of str or unicode
sub : str or unicode
start, end : int, optional
Optional arguments `start` and `end` are interpreted as in
slice notation.
Returns
-------
out : ndarray
Output array of ints. Return -1 on failure.
See Also
--------
str.rfind
"""
return _vec_string(
a, int_, 'rfind', [sub, start] + _clean_args(end))
@array_function_dispatch(_count_dispatcher)
def rindex(a, sub, start=0, end=None):
"""
Like `rfind`, but raises `ValueError` when the substring `sub` is
not found.
Calls `str.rindex` element-wise.
Parameters
----------
a : array-like of str or unicode
sub : str or unicode
start, end : int, optional
Returns
-------
out : ndarray
Output array of ints.
See Also
--------
rfind, str.rindex
"""
return _vec_string(
a, int_, 'rindex', [sub, start] + _clean_args(end))
@array_function_dispatch(_just_dispatcher)
def rjust(a, width, fillchar=' '):
"""
Return an array with the elements of `a` right-justified in a
string of length `width`.
Calls `str.rjust` element-wise.
Parameters
----------
a : array_like of str or unicode
width : int
The length of the resulting strings
fillchar : str or unicode, optional
The character to use for padding
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.rjust
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = int(numpy.max(width_arr.flat))
if numpy.issubdtype(a_arr.dtype, numpy.string_):
fillchar = asbytes(fillchar)
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'rjust', (width_arr, fillchar))
@array_function_dispatch(_partition_dispatcher)
def rpartition(a, sep):
"""
Partition (split) each element around the right-most separator.
Calls `str.rpartition` element-wise.
For each element in `a`, split the element as the last
occurrence of `sep`, and return 3 strings containing the part
before the separator, the separator itself, and the part after
the separator. If the separator is not found, return 3 strings
containing the string itself, followed by two empty strings.
Parameters
----------
a : array_like of str or unicode
Input array
sep : str or unicode
Right-most separator to split each element in array.
Returns
-------
out : ndarray
Output array of string or unicode, depending on input
type. The output array will have an extra dimension with
3 elements per input element.
See Also
--------
str.rpartition
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'rpartition', (sep,)))
def _split_dispatcher(a, sep=None, maxsplit=None):
return (a,)
@array_function_dispatch(_split_dispatcher)
def rsplit(a, sep=None, maxsplit=None):
"""
For each element in `a`, return a list of the words in the
string, using `sep` as the delimiter string.
Calls `str.rsplit` element-wise.
Except for splitting from the right, `rsplit`
behaves like `split`.
Parameters
----------
a : array_like of str or unicode
sep : str or unicode, optional
If `sep` is not specified or None, any whitespace string
is a separator.
maxsplit : int, optional
If `maxsplit` is given, at most `maxsplit` splits are done,
the rightmost ones.
Returns
-------
out : ndarray
Array of list objects
See Also
--------
str.rsplit, split
"""
# This will return an array of lists of different sizes, so we
# leave it as an object array
return _vec_string(
a, object_, 'rsplit', [sep] + _clean_args(maxsplit))
def _strip_dispatcher(a, chars=None):
return (a,)
@array_function_dispatch(_strip_dispatcher)
def rstrip(a, chars=None):
"""
For each element in `a`, return a copy with the trailing
characters removed.
Calls `str.rstrip` element-wise.
Parameters
----------
a : array-like of str or unicode
chars : str or unicode, optional
The `chars` argument is a string specifying the set of
characters to be removed. If omitted or None, the `chars`
argument defaults to removing whitespace. The `chars` argument
is not a suffix; rather, all combinations of its values are
stripped.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.rstrip
Examples
--------
>>> c = np.array(['aAaAaA', 'abBABba'], dtype='S7'); c
array(['aAaAaA', 'abBABba'],
dtype='|S7')
>>> np.char.rstrip(c, b'a')
array(['aAaAaA', 'abBABb'],
dtype='|S7')
>>> np.char.rstrip(c, b'A')
array(['aAaAa', 'abBABba'],
dtype='|S7')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'rstrip', (chars,))
@array_function_dispatch(_split_dispatcher)
def split(a, sep=None, maxsplit=None):
"""
For each element in `a`, return a list of the words in the
string, using `sep` as the delimiter string.
Calls `str.split` element-wise.
Parameters
----------
a : array_like of str or unicode
sep : str or unicode, optional
If `sep` is not specified or None, any whitespace string is a
separator.
maxsplit : int, optional
If `maxsplit` is given, at most `maxsplit` splits are done.
Returns
-------
out : ndarray
Array of list objects
See Also
--------
str.split, rsplit
"""
# This will return an array of lists of different sizes, so we
# leave it as an object array
return _vec_string(
a, object_, 'split', [sep] + _clean_args(maxsplit))
def _splitlines_dispatcher(a, keepends=None):
return (a,)
@array_function_dispatch(_splitlines_dispatcher)
def splitlines(a, keepends=None):
"""
For each element in `a`, return a list of the lines in the
element, breaking at line boundaries.
Calls `str.splitlines` element-wise.
Parameters
----------
a : array_like of str or unicode
keepends : bool, optional
Line breaks are not included in the resulting list unless
keepends is given and true.
Returns
-------
out : ndarray
Array of list objects
See Also
--------
str.splitlines
"""
return _vec_string(
a, object_, 'splitlines', _clean_args(keepends))
def _startswith_dispatcher(a, prefix, start=None, end=None):
return (a,)
@array_function_dispatch(_startswith_dispatcher)
def startswith(a, prefix, start=0, end=None):
"""
Returns a boolean array which is `True` where the string element
in `a` starts with `prefix`, otherwise `False`.
Calls `str.startswith` element-wise.
Parameters
----------
a : array_like of str or unicode
prefix : str
start, end : int, optional
With optional `start`, test beginning at that position. With
optional `end`, stop comparing at that position.
Returns
-------
out : ndarray
Array of booleans
See Also
--------
str.startswith
"""
return _vec_string(
a, bool_, 'startswith', [prefix, start] + _clean_args(end))
@array_function_dispatch(_strip_dispatcher)
def strip(a, chars=None):
"""
For each element in `a`, return a copy with the leading and
trailing characters removed.
Calls `str.strip` element-wise.
Parameters
----------
a : array-like of str or unicode
chars : str or unicode, optional
The `chars` argument is a string specifying the set of
characters to be removed. If omitted or None, the `chars`
argument defaults to removing whitespace. The `chars` argument
is not a prefix or suffix; rather, all combinations of its
values are stripped.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.strip
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
>>> np.char.strip(c)
array(['aAaAaA', 'aA', 'abBABba'], dtype='<U7')
>>> np.char.strip(c, 'a') # 'a' unstripped from c[1] because whitespace leads
array(['AaAaA', ' aA ', 'bBABb'], dtype='<U7')
>>> np.char.strip(c, 'A') # 'A' unstripped from c[1] because (unprinted) ws trails
array(['aAaAa', ' aA ', 'abBABba'], dtype='<U7')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'strip', _clean_args(chars))
@array_function_dispatch(_unary_op_dispatcher)
def swapcase(a):
"""
Return element-wise a copy of the string with
uppercase characters converted to lowercase and vice versa.
Calls `str.swapcase` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See Also
--------
str.swapcase
Examples
--------
>>> c=np.array(['a1B c','1b Ca','b Ca1','cA1b'],'S5'); c
array(['a1B c', '1b Ca', 'b Ca1', 'cA1b'],
dtype='|S5')
>>> np.char.swapcase(c)
array(['A1b C', '1B cA', 'B cA1', 'Ca1B'],
dtype='|S5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'swapcase')
@array_function_dispatch(_unary_op_dispatcher)
def title(a):
"""
Return element-wise title cased version of string or unicode.
Title case words start with uppercase characters, all remaining cased
characters are lowercase.
Calls `str.title` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.title
Examples
--------
>>> c=np.array(['a1b c','1b ca','b ca1','ca1b'],'S5'); c
array(['a1b c', '1b ca', 'b ca1', 'ca1b'],
dtype='|S5')
>>> np.char.title(c)
array(['A1B C', '1B Ca', 'B Ca1', 'Ca1B'],
dtype='|S5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'title')
def _translate_dispatcher(a, table, deletechars=None):
return (a,)
@array_function_dispatch(_translate_dispatcher)
def translate(a, table, deletechars=None):
"""
For each element in `a`, return a copy of the string where all
characters occurring in the optional argument `deletechars` are
removed, and the remaining characters have been mapped through the
given translation table.
Calls `str.translate` element-wise.
Parameters
----------
a : array-like of str or unicode
table : str of length 256
deletechars : str
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.translate
"""
a_arr = numpy.asarray(a)
if issubclass(a_arr.dtype.type, unicode_):
return _vec_string(
a_arr, a_arr.dtype, 'translate', (table,))
else:
return _vec_string(
a_arr, a_arr.dtype, 'translate', [table] + _clean_args(deletechars))
@array_function_dispatch(_unary_op_dispatcher)
def upper(a):
"""
Return an array with the elements converted to uppercase.
Calls `str.upper` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See Also
--------
str.upper
Examples
--------
>>> c = np.array(['a1b c', '1bca', 'bca1']); c
array(['a1b c', '1bca', 'bca1'], dtype='<U5')
>>> np.char.upper(c)
array(['A1B C', '1BCA', 'BCA1'], dtype='<U5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'upper')
def _zfill_dispatcher(a, width):
return (a,)
@array_function_dispatch(_zfill_dispatcher)
def zfill(a, width):
"""
Return the numeric string left-filled with zeros
Calls `str.zfill` element-wise.
Parameters
----------
a : array_like, {str, unicode}
Input array.
width : int
Width of string to left-fill elements in `a`.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See Also
--------
str.zfill
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = int(numpy.max(width_arr.flat))
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'zfill', (width_arr,))
@array_function_dispatch(_unary_op_dispatcher)
def isnumeric(a):
"""
For each element, return True if there are only numeric
characters in the element.
Calls `unicode.isnumeric` element-wise.
Numeric characters include digit characters, and all characters
that have the Unicode numeric value property, e.g. ``U+2155,
VULGAR FRACTION ONE FIFTH``.
Parameters
----------
a : array_like, unicode
Input array.
Returns
-------
out : ndarray, bool
Array of booleans of same shape as `a`.
See Also
--------
unicode.isnumeric
"""
if _use_unicode(a) != unicode_:
raise TypeError("isnumeric is only available for Unicode strings and arrays")
return _vec_string(a, bool_, 'isnumeric')
@array_function_dispatch(_unary_op_dispatcher)
def isdecimal(a):
"""
For each element, return True if there are only decimal
characters in the element.
Calls `unicode.isdecimal` element-wise.
Decimal characters include digit characters, and all characters
that can be used to form decimal-radix numbers,
e.g. ``U+0660, ARABIC-INDIC DIGIT ZERO``.
Parameters
----------
a : array_like, unicode
Input array.
Returns
-------
out : ndarray, bool
Array of booleans identical in shape to `a`.
See Also
--------
unicode.isdecimal
"""
if _use_unicode(a) != unicode_:
raise TypeError("isnumeric is only available for Unicode strings and arrays")
return _vec_string(a, bool_, 'isdecimal')
@set_module('numpy')
class chararray(ndarray):
"""
chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,
strides=None, order=None)
Provides a convenient view on arrays of string and unicode values.
.. note::
The `chararray` class exists for backwards compatibility with
Numarray, it is not recommended for new development. Starting from numpy
1.4, if one needs arrays of strings, it is recommended to use arrays of
`dtype` `object_`, `string_` or `unicode_`, and use the free functions
in the `numpy.char` module for fast vectorized string operations.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `.endswith`) and infix operators (e.g. ``"+", "*", "%"``)
chararrays should be created using `numpy.char.array` or
`numpy.char.asarray`, rather than this constructor directly.
This constructor creates the array, using `buffer` (with `offset`
and `strides`) if it is not ``None``. If `buffer` is ``None``, then
constructs a new array with `strides` in "C order", unless both
``len(shape) >= 2`` and ``order='F'``, in which case `strides`
is in "Fortran order".
Methods
-------
astype
argsort
copy
count
decode
dump
dumps
encode
endswith
expandtabs
fill
find
flatten
getfield
index
isalnum
isalpha
isdecimal
isdigit
islower
isnumeric
isspace
istitle
isupper
item
join
ljust
lower
lstrip
nonzero
put
ravel
repeat
replace
reshape
resize
rfind
rindex
rjust
rsplit
rstrip
searchsorted
setfield
setflags
sort
split
splitlines
squeeze
startswith
strip
swapaxes
swapcase
take
title
tofile
tolist
tostring
translate
transpose
upper
view
zfill
Parameters
----------
shape : tuple
Shape of the array.
itemsize : int, optional
Length of each array element, in number of characters. Default is 1.
unicode : bool, optional
Are the array elements of type unicode (True) or string (False).
Default is False.
buffer : object exposing the buffer interface or str, optional
Memory address of the start of the array data. Default is None,
in which case a new array is created.
offset : int, optional
Fixed stride displacement from the beginning of an axis?
Default is 0. Needs to be >=0.
strides : array_like of ints, optional
Strides for the array (see `ndarray.strides` for full description).
Default is None.
order : {'C', 'F'}, optional
The order in which the array data is stored in memory: 'C' ->
"row major" order (the default), 'F' -> "column major"
(Fortran) order.
Examples
--------
>>> charar = np.chararray((3, 3))
>>> charar[:] = 'a'
>>> charar
chararray([[b'a', b'a', b'a'],
[b'a', b'a', b'a'],
[b'a', b'a', b'a']], dtype='|S1')
>>> charar = np.chararray(charar.shape, itemsize=5)
>>> charar[:] = 'abc'
>>> charar
chararray([[b'abc', b'abc', b'abc'],
[b'abc', b'abc', b'abc'],
[b'abc', b'abc', b'abc']], dtype='|S5')
"""
def __new__(subtype, shape, itemsize=1, unicode=False, buffer=None,
offset=0, strides=None, order='C'):
global _globalvar
if unicode:
dtype = unicode_
else:
dtype = string_
# force itemsize to be a Python int, since using NumPy integer
# types results in itemsize.itemsize being used as the size of
# strings in the new array.
itemsize = int(itemsize)
if isinstance(buffer, str):
# unicode objects do not have the buffer interface
filler = buffer
buffer = None
else:
filler = None
_globalvar = 1
if buffer is None:
self = ndarray.__new__(subtype, shape, (dtype, itemsize),
order=order)
else:
self = ndarray.__new__(subtype, shape, (dtype, itemsize),
buffer=buffer,
offset=offset, strides=strides,
order=order)
if filler is not None:
self[...] = filler
_globalvar = 0
return self
def __array_finalize__(self, obj):
# The b is a special case because it is used for reconstructing.
if not _globalvar and self.dtype.char not in 'SUbc':
raise ValueError("Can only create a chararray from string data.")
def __getitem__(self, obj):
val = ndarray.__getitem__(self, obj)
if isinstance(val, character):
temp = val.rstrip()
if len(temp) == 0:
val = ''
else:
val = temp
return val
# IMPLEMENTATION NOTE: Most of the methods of this class are
# direct delegations to the free functions in this module.
# However, those that return an array of strings should instead
# return a chararray, so some extra wrapping is required.
def __eq__(self, other):
"""
Return (self == other) element-wise.
See Also
--------
equal
"""
return equal(self, other)
def __ne__(self, other):
"""
Return (self != other) element-wise.
See Also
--------
not_equal
"""
return not_equal(self, other)
def __ge__(self, other):
"""
Return (self >= other) element-wise.
See Also
--------
greater_equal
"""
return greater_equal(self, other)
def __le__(self, other):
"""
Return (self <= other) element-wise.
See Also
--------
less_equal
"""
return less_equal(self, other)
def __gt__(self, other):
"""
Return (self > other) element-wise.
See Also
--------
greater
"""
return greater(self, other)
def __lt__(self, other):
"""
Return (self < other) element-wise.
See Also
--------
less
"""
return less(self, other)
def __add__(self, other):
"""
Return (self + other), that is string concatenation,
element-wise for a pair of array_likes of str or unicode.
See Also
--------
add
"""
return asarray(add(self, other))
def __radd__(self, other):
"""
Return (other + self), that is string concatenation,
element-wise for a pair of array_likes of `string_` or `unicode_`.
See Also
--------
add
"""
return asarray(add(numpy.asarray(other), self))
def __mul__(self, i):
"""
Return (self * i), that is string multiple concatenation,
element-wise.
See Also
--------
multiply
"""
return asarray(multiply(self, i))
def __rmul__(self, i):
"""
Return (self * i), that is string multiple concatenation,
element-wise.
See Also
--------
multiply
"""
return asarray(multiply(self, i))
def __mod__(self, i):
"""
Return (self % i), that is pre-Python 2.6 string formatting
(interpolation), element-wise for a pair of array_likes of `string_`
or `unicode_`.
See Also
--------
mod
"""
return asarray(mod(self, i))
def __rmod__(self, other):
return NotImplemented
def argsort(self, axis=-1, kind=None, order=None):
"""
Return the indices that sort the array lexicographically.
For full documentation see `numpy.argsort`, for which this method is
in fact merely a "thin wrapper."
Examples
--------
>>> c = np.array(['a1b c', '1b ca', 'b ca1', 'Ca1b'], 'S5')
>>> c = c.view(np.chararray); c
chararray(['a1b c', '1b ca', 'b ca1', 'Ca1b'],
dtype='|S5')
>>> c[c.argsort()]
chararray(['1b ca', 'Ca1b', 'a1b c', 'b ca1'],
dtype='|S5')
"""
return self.__array__().argsort(axis, kind, order)
argsort.__doc__ = ndarray.argsort.__doc__
def capitalize(self):
"""
Return a copy of `self` with only the first character of each element
capitalized.
See Also
--------
char.capitalize
"""
return asarray(capitalize(self))
def center(self, width, fillchar=' '):
"""
Return a copy of `self` with its elements centered in a
string of length `width`.
See Also
--------
center
"""
return asarray(center(self, width, fillchar))
def count(self, sub, start=0, end=None):
"""
Returns an array with the number of non-overlapping occurrences of
substring `sub` in the range [`start`, `end`].
See Also
--------
char.count
"""
return count(self, sub, start, end)
def decode(self, encoding=None, errors=None):
"""
Calls `str.decode` element-wise.
See Also
--------
char.decode
"""
return decode(self, encoding, errors)
def encode(self, encoding=None, errors=None):
"""
Calls `str.encode` element-wise.
See Also
--------
char.encode
"""
return encode(self, encoding, errors)
def endswith(self, suffix, start=0, end=None):
"""
Returns a boolean array which is `True` where the string element
in `self` ends with `suffix`, otherwise `False`.
See Also
--------
char.endswith
"""
return endswith(self, suffix, start, end)
def expandtabs(self, tabsize=8):
"""
Return a copy of each string element where all tab characters are
replaced by one or more spaces.
See Also
--------
char.expandtabs
"""
return asarray(expandtabs(self, tabsize))
def find(self, sub, start=0, end=None):
"""
For each element, return the lowest index in the string where
substring `sub` is found.
See Also
--------
char.find
"""
return find(self, sub, start, end)
def index(self, sub, start=0, end=None):
"""
Like `find`, but raises `ValueError` when the substring is not found.
See Also
--------
char.index
"""
return index(self, sub, start, end)
def isalnum(self):
"""
Returns true for each element if all characters in the string
are alphanumeric and there is at least one character, false
otherwise.
See Also
--------
char.isalnum
"""
return isalnum(self)
def isalpha(self):
"""
Returns true for each element if all characters in the string
are alphabetic and there is at least one character, false
otherwise.
See Also
--------
char.isalpha
"""
return isalpha(self)
def isdigit(self):
"""
Returns true for each element if all characters in the string are
digits and there is at least one character, false otherwise.
See Also
--------
char.isdigit
"""
return isdigit(self)
def islower(self):
"""
Returns true for each element if all cased characters in the
string are lowercase and there is at least one cased character,
false otherwise.
See Also
--------
char.islower
"""
return islower(self)
def isspace(self):
"""
Returns true for each element if there are only whitespace
characters in the string and there is at least one character,
false otherwise.
See Also
--------
char.isspace
"""
return isspace(self)
def istitle(self):
"""
Returns true for each element if the element is a titlecased
string and there is at least one character, false otherwise.
See Also
--------
char.istitle
"""
return istitle(self)
def isupper(self):
"""
Returns true for each element if all cased characters in the
string are uppercase and there is at least one character, false
otherwise.
See Also
--------
char.isupper
"""
return isupper(self)
def join(self, seq):
"""
Return a string which is the concatenation of the strings in the
sequence `seq`.
See Also
--------
char.join
"""
return join(self, seq)
def ljust(self, width, fillchar=' '):
"""
Return an array with the elements of `self` left-justified in a
string of length `width`.
See Also
--------
char.ljust
"""
return asarray(ljust(self, width, fillchar))
def lower(self):
"""
Return an array with the elements of `self` converted to
lowercase.
See Also
--------
char.lower
"""
return asarray(lower(self))
def lstrip(self, chars=None):
"""
For each element in `self`, return a copy with the leading characters
removed.
See Also
--------
char.lstrip
"""
return asarray(lstrip(self, chars))
def partition(self, sep):
"""
Partition each element in `self` around `sep`.
See Also
--------
partition
"""
return asarray(partition(self, sep))
def replace(self, old, new, count=None):
"""
For each element in `self`, return a copy of the string with all
occurrences of substring `old` replaced by `new`.
See Also
--------
char.replace
"""
return asarray(replace(self, old, new, count))
def rfind(self, sub, start=0, end=None):
"""
For each element in `self`, return the highest index in the string
where substring `sub` is found, such that `sub` is contained
within [`start`, `end`].
See Also
--------
char.rfind
"""
return rfind(self, sub, start, end)
def rindex(self, sub, start=0, end=None):
"""
Like `rfind`, but raises `ValueError` when the substring `sub` is
not found.
See Also
--------
char.rindex
"""
return rindex(self, sub, start, end)
def rjust(self, width, fillchar=' '):
"""
Return an array with the elements of `self`
right-justified in a string of length `width`.
See Also
--------
char.rjust
"""
return asarray(rjust(self, width, fillchar))
def rpartition(self, sep):
"""
Partition each element in `self` around `sep`.
See Also
--------
rpartition
"""
return asarray(rpartition(self, sep))
def rsplit(self, sep=None, maxsplit=None):
"""
For each element in `self`, return a list of the words in
the string, using `sep` as the delimiter string.
See Also
--------
char.rsplit
"""
return rsplit(self, sep, maxsplit)
def rstrip(self, chars=None):
"""
For each element in `self`, return a copy with the trailing
characters removed.
See Also
--------
char.rstrip
"""
return asarray(rstrip(self, chars))
def split(self, sep=None, maxsplit=None):
"""
For each element in `self`, return a list of the words in the
string, using `sep` as the delimiter string.
See Also
--------
char.split
"""
return split(self, sep, maxsplit)
def splitlines(self, keepends=None):
"""
For each element in `self`, return a list of the lines in the
element, breaking at line boundaries.
See Also
--------
char.splitlines
"""
return splitlines(self, keepends)
def startswith(self, prefix, start=0, end=None):
"""
Returns a boolean array which is `True` where the string element
in `self` starts with `prefix`, otherwise `False`.
See Also
--------
char.startswith
"""
return startswith(self, prefix, start, end)
def strip(self, chars=None):
"""
For each element in `self`, return a copy with the leading and
trailing characters removed.
See Also
--------
char.strip
"""
return asarray(strip(self, chars))
def swapcase(self):
"""
For each element in `self`, return a copy of the string with
uppercase characters converted to lowercase and vice versa.
See Also
--------
char.swapcase
"""
return asarray(swapcase(self))
def title(self):
"""
For each element in `self`, return a titlecased version of the
string: words start with uppercase characters, all remaining cased
characters are lowercase.
See Also
--------
char.title
"""
return asarray(title(self))
def translate(self, table, deletechars=None):
"""
For each element in `self`, return a copy of the string where
all characters occurring in the optional argument
`deletechars` are removed, and the remaining characters have
been mapped through the given translation table.
See Also
--------
char.translate
"""
return asarray(translate(self, table, deletechars))
def upper(self):
"""
Return an array with the elements of `self` converted to
uppercase.
See Also
--------
char.upper
"""
return asarray(upper(self))
def zfill(self, width):
"""
Return the numeric string left-filled with zeros in a string of
length `width`.
See Also
--------
char.zfill
"""
return asarray(zfill(self, width))
def isnumeric(self):
"""
For each element in `self`, return True if there are only
numeric characters in the element.
See Also
--------
char.isnumeric
"""
return isnumeric(self)
def isdecimal(self):
"""
For each element in `self`, return True if there are only
decimal characters in the element.
See Also
--------
char.isdecimal
"""
return isdecimal(self)
@set_module("numpy.char")
def array(obj, itemsize=None, copy=True, unicode=None, order=None):
"""
Create a `chararray`.
.. note::
This class is provided for numarray backward-compatibility.
New code (not concerned with numarray compatibility) should use
arrays of type `string_` or `unicode_` and use the free functions
in :mod:`numpy.char <numpy.core.defchararray>` for fast
vectorized string operations instead.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+, *, %``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
copy : bool, optional
If true (default), then the object is copied. Otherwise, a copy
will only be made if __array__ returns a copy, if obj is a
nested sequence, or if a copy is needed to satisfy any of the other
requirements (`itemsize`, unicode, `order`, etc.).
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or `unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F', 'A'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest). If order is 'A', then the returned array may
be in any order (either C-, Fortran-contiguous, or even
discontiguous).
"""
if isinstance(obj, (bytes, str)):
if unicode is None:
if isinstance(obj, str):
unicode = True
else:
unicode = False
if itemsize is None:
itemsize = len(obj)
shape = len(obj) // itemsize
return chararray(shape, itemsize=itemsize, unicode=unicode,
buffer=obj, order=order)
if isinstance(obj, (list, tuple)):
obj = numpy.asarray(obj)
if isinstance(obj, ndarray) and issubclass(obj.dtype.type, character):
# If we just have a vanilla chararray, create a chararray
# view around it.
if not isinstance(obj, chararray):
obj = obj.view(chararray)
if itemsize is None:
itemsize = obj.itemsize
# itemsize is in 8-bit chars, so for Unicode, we need
# to divide by the size of a single Unicode character,
# which for NumPy is always 4
if issubclass(obj.dtype.type, unicode_):
itemsize //= 4
if unicode is None:
if issubclass(obj.dtype.type, unicode_):
unicode = True
else:
unicode = False
if unicode:
dtype = unicode_
else:
dtype = string_
if order is not None:
obj = numpy.asarray(obj, order=order)
if (copy or
(itemsize != obj.itemsize) or
(not unicode and isinstance(obj, unicode_)) or
(unicode and isinstance(obj, string_))):
obj = obj.astype((dtype, int(itemsize)))
return obj
if isinstance(obj, ndarray) and issubclass(obj.dtype.type, object):
if itemsize is None:
# Since no itemsize was specified, convert the input array to
# a list so the ndarray constructor will automatically
# determine the itemsize for us.
obj = obj.tolist()
# Fall through to the default case
if unicode:
dtype = unicode_
else:
dtype = string_
if itemsize is None:
val = narray(obj, dtype=dtype, order=order, subok=True)
else:
val = narray(obj, dtype=(dtype, itemsize), order=order, subok=True)
return val.view(chararray)
@set_module("numpy.char")
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
| 69,776 | Python | 23.947086 | 86 | 0.585774 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/multiarray.pyi | # TODO: Sort out any and all missing functions in this namespace
import os
import datetime as dt
from collections.abc import Sequence, Callable, Iterable
from typing import (
Literal as L,
Any,
overload,
TypeVar,
SupportsIndex,
final,
Final,
Protocol,
)
from numpy import (
# Re-exports
busdaycalendar as busdaycalendar,
broadcast as broadcast,
dtype as dtype,
ndarray as ndarray,
nditer as nditer,
# The rest
ufunc,
str_,
bool_,
uint8,
intp,
int_,
float64,
timedelta64,
datetime64,
generic,
unsignedinteger,
signedinteger,
floating,
complexfloating,
_OrderKACF,
_OrderCF,
_CastingKind,
_ModeKind,
_SupportsBuffer,
_IOProtocol,
_CopyMode,
_NDIterFlagsKind,
_NDIterOpFlagsKind,
)
from numpy._typing import (
# Shapes
_ShapeLike,
# DTypes
DTypeLike,
_DTypeLike,
# Arrays
NDArray,
ArrayLike,
_ArrayLike,
_SupportsArrayFunc,
_NestedSequence,
_ArrayLikeBool_co,
_ArrayLikeUInt_co,
_ArrayLikeInt_co,
_ArrayLikeFloat_co,
_ArrayLikeComplex_co,
_ArrayLikeTD64_co,
_ArrayLikeDT64_co,
_ArrayLikeObject_co,
_ArrayLikeStr_co,
_ArrayLikeBytes_co,
_ScalarLike_co,
_IntLike_co,
_FloatLike_co,
_TD64Like_co,
)
_T_co = TypeVar("_T_co", covariant=True)
_T_contra = TypeVar("_T_contra", contravariant=True)
_SCT = TypeVar("_SCT", bound=generic)
_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
# Valid time units
_UnitKind = L[
"Y",
"M",
"D",
"h",
"m",
"s",
"ms",
"us", "μs",
"ns",
"ps",
"fs",
"as",
]
_RollKind = L[ # `raise` is deliberately excluded
"nat",
"forward",
"following",
"backward",
"preceding",
"modifiedfollowing",
"modifiedpreceding",
]
class _SupportsLenAndGetItem(Protocol[_T_contra, _T_co]):
def __len__(self) -> int: ...
def __getitem__(self, key: _T_contra, /) -> _T_co: ...
__all__: list[str]
ALLOW_THREADS: Final[int] # 0 or 1 (system-specific)
BUFSIZE: L[8192]
CLIP: L[0]
WRAP: L[1]
RAISE: L[2]
MAXDIMS: L[32]
MAY_SHARE_BOUNDS: L[0]
MAY_SHARE_EXACT: L[-1]
tracemalloc_domain: L[389047]
@overload
def empty_like(
prototype: _ArrayType,
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike = ...,
) -> _ArrayType: ...
@overload
def empty_like(
prototype: _ArrayLike[_SCT],
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike = ...,
) -> NDArray[_SCT]: ...
@overload
def empty_like(
prototype: object,
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike = ...,
) -> NDArray[Any]: ...
@overload
def empty_like(
prototype: Any,
dtype: _DTypeLike[_SCT],
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike = ...,
) -> NDArray[_SCT]: ...
@overload
def empty_like(
prototype: Any,
dtype: DTypeLike,
order: _OrderKACF = ...,
subok: bool = ...,
shape: None | _ShapeLike = ...,
) -> NDArray[Any]: ...
@overload
def array(
object: _ArrayType,
dtype: None = ...,
*,
copy: bool | _CopyMode = ...,
order: _OrderKACF = ...,
subok: L[True],
ndmin: int = ...,
like: _SupportsArrayFunc = ...,
) -> _ArrayType: ...
@overload
def array(
object: _ArrayLike[_SCT],
dtype: None = ...,
*,
copy: bool | _CopyMode = ...,
order: _OrderKACF = ...,
subok: bool = ...,
ndmin: int = ...,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def array(
object: object,
dtype: None = ...,
*,
copy: bool | _CopyMode = ...,
order: _OrderKACF = ...,
subok: bool = ...,
ndmin: int = ...,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def array(
object: Any,
dtype: _DTypeLike[_SCT],
*,
copy: bool | _CopyMode = ...,
order: _OrderKACF = ...,
subok: bool = ...,
ndmin: int = ...,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def array(
object: Any,
dtype: DTypeLike,
*,
copy: bool | _CopyMode = ...,
order: _OrderKACF = ...,
subok: bool = ...,
ndmin: int = ...,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def zeros(
shape: _ShapeLike,
dtype: None = ...,
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[float64]: ...
@overload
def zeros(
shape: _ShapeLike,
dtype: _DTypeLike[_SCT],
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def zeros(
shape: _ShapeLike,
dtype: DTypeLike,
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def empty(
shape: _ShapeLike,
dtype: None = ...,
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[float64]: ...
@overload
def empty(
shape: _ShapeLike,
dtype: _DTypeLike[_SCT],
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def empty(
shape: _ShapeLike,
dtype: DTypeLike,
order: _OrderCF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def unravel_index( # type: ignore[misc]
indices: _IntLike_co,
shape: _ShapeLike,
order: _OrderCF = ...,
) -> tuple[intp, ...]: ...
@overload
def unravel_index(
indices: _ArrayLikeInt_co,
shape: _ShapeLike,
order: _OrderCF = ...,
) -> tuple[NDArray[intp], ...]: ...
@overload
def ravel_multi_index( # type: ignore[misc]
multi_index: Sequence[_IntLike_co],
dims: Sequence[SupportsIndex],
mode: _ModeKind | tuple[_ModeKind, ...] = ...,
order: _OrderCF = ...,
) -> intp: ...
@overload
def ravel_multi_index(
multi_index: Sequence[_ArrayLikeInt_co],
dims: Sequence[SupportsIndex],
mode: _ModeKind | tuple[_ModeKind, ...] = ...,
order: _OrderCF = ...,
) -> NDArray[intp]: ...
# NOTE: Allow any sequence of array-like objects
@overload
def concatenate( # type: ignore[misc]
arrays: _ArrayLike[_SCT],
/,
axis: None | SupportsIndex = ...,
out: None = ...,
*,
dtype: None = ...,
casting: None | _CastingKind = ...
) -> NDArray[_SCT]: ...
@overload
def concatenate( # type: ignore[misc]
arrays: _SupportsLenAndGetItem[int, ArrayLike],
/,
axis: None | SupportsIndex = ...,
out: None = ...,
*,
dtype: None = ...,
casting: None | _CastingKind = ...
) -> NDArray[Any]: ...
@overload
def concatenate( # type: ignore[misc]
arrays: _SupportsLenAndGetItem[int, ArrayLike],
/,
axis: None | SupportsIndex = ...,
out: None = ...,
*,
dtype: _DTypeLike[_SCT],
casting: None | _CastingKind = ...
) -> NDArray[_SCT]: ...
@overload
def concatenate( # type: ignore[misc]
arrays: _SupportsLenAndGetItem[int, ArrayLike],
/,
axis: None | SupportsIndex = ...,
out: None = ...,
*,
dtype: DTypeLike,
casting: None | _CastingKind = ...
) -> NDArray[Any]: ...
@overload
def concatenate(
arrays: _SupportsLenAndGetItem[int, ArrayLike],
/,
axis: None | SupportsIndex = ...,
out: _ArrayType = ...,
*,
dtype: DTypeLike = ...,
casting: None | _CastingKind = ...
) -> _ArrayType: ...
def inner(
a: ArrayLike,
b: ArrayLike,
/,
) -> Any: ...
@overload
def where(
condition: ArrayLike,
/,
) -> tuple[NDArray[intp], ...]: ...
@overload
def where(
condition: ArrayLike,
x: ArrayLike,
y: ArrayLike,
/,
) -> NDArray[Any]: ...
def lexsort(
keys: ArrayLike,
axis: None | SupportsIndex = ...,
) -> Any: ...
def can_cast(
from_: ArrayLike | DTypeLike,
to: DTypeLike,
casting: None | _CastingKind = ...,
) -> bool: ...
def min_scalar_type(
a: ArrayLike, /,
) -> dtype[Any]: ...
def result_type(
*arrays_and_dtypes: ArrayLike | DTypeLike,
) -> dtype[Any]: ...
@overload
def dot(a: ArrayLike, b: ArrayLike, out: None = ...) -> Any: ...
@overload
def dot(a: ArrayLike, b: ArrayLike, out: _ArrayType) -> _ArrayType: ...
@overload
def vdot(a: _ArrayLikeBool_co, b: _ArrayLikeBool_co, /) -> bool_: ... # type: ignore[misc]
@overload
def vdot(a: _ArrayLikeUInt_co, b: _ArrayLikeUInt_co, /) -> unsignedinteger[Any]: ... # type: ignore[misc]
@overload
def vdot(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, /) -> signedinteger[Any]: ... # type: ignore[misc]
@overload
def vdot(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, /) -> floating[Any]: ... # type: ignore[misc]
@overload
def vdot(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, /) -> complexfloating[Any, Any]: ... # type: ignore[misc]
@overload
def vdot(a: _ArrayLikeTD64_co, b: _ArrayLikeTD64_co, /) -> timedelta64: ...
@overload
def vdot(a: _ArrayLikeObject_co, b: Any, /) -> Any: ...
@overload
def vdot(a: Any, b: _ArrayLikeObject_co, /) -> Any: ...
def bincount(
x: ArrayLike,
/,
weights: None | ArrayLike = ...,
minlength: SupportsIndex = ...,
) -> NDArray[intp]: ...
def copyto(
dst: NDArray[Any],
src: ArrayLike,
casting: None | _CastingKind = ...,
where: None | _ArrayLikeBool_co = ...,
) -> None: ...
def putmask(
a: NDArray[Any],
mask: _ArrayLikeBool_co,
values: ArrayLike,
) -> None: ...
def packbits(
a: _ArrayLikeInt_co,
/,
axis: None | SupportsIndex = ...,
bitorder: L["big", "little"] = ...,
) -> NDArray[uint8]: ...
def unpackbits(
a: _ArrayLike[uint8],
/,
axis: None | SupportsIndex = ...,
count: None | SupportsIndex = ...,
bitorder: L["big", "little"] = ...,
) -> NDArray[uint8]: ...
def shares_memory(
a: object,
b: object,
/,
max_work: None | int = ...,
) -> bool: ...
def may_share_memory(
a: object,
b: object,
/,
max_work: None | int = ...,
) -> bool: ...
@overload
def asarray(
a: _ArrayLike[_SCT],
dtype: None = ...,
order: _OrderKACF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def asarray(
a: object,
dtype: None = ...,
order: _OrderKACF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def asarray(
a: Any,
dtype: _DTypeLike[_SCT],
order: _OrderKACF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def asarray(
a: Any,
dtype: DTypeLike,
order: _OrderKACF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def asanyarray(
a: _ArrayType, # Preserve subclass-information
dtype: None = ...,
order: _OrderKACF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> _ArrayType: ...
@overload
def asanyarray(
a: _ArrayLike[_SCT],
dtype: None = ...,
order: _OrderKACF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def asanyarray(
a: object,
dtype: None = ...,
order: _OrderKACF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def asanyarray(
a: Any,
dtype: _DTypeLike[_SCT],
order: _OrderKACF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def asanyarray(
a: Any,
dtype: DTypeLike,
order: _OrderKACF = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def ascontiguousarray(
a: _ArrayLike[_SCT],
dtype: None = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def ascontiguousarray(
a: object,
dtype: None = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def ascontiguousarray(
a: Any,
dtype: _DTypeLike[_SCT],
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def ascontiguousarray(
a: Any,
dtype: DTypeLike,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def asfortranarray(
a: _ArrayLike[_SCT],
dtype: None = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def asfortranarray(
a: object,
dtype: None = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def asfortranarray(
a: Any,
dtype: _DTypeLike[_SCT],
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def asfortranarray(
a: Any,
dtype: DTypeLike,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
# In practice `list[Any]` is list with an int, int and a valid
# `np.seterrcall()` object
def geterrobj() -> list[Any]: ...
def seterrobj(errobj: list[Any], /) -> None: ...
def promote_types(__type1: DTypeLike, __type2: DTypeLike) -> dtype[Any]: ...
# `sep` is a de facto mandatory argument, as its default value is deprecated
@overload
def fromstring(
string: str | bytes,
dtype: None = ...,
count: SupportsIndex = ...,
*,
sep: str,
like: _SupportsArrayFunc = ...,
) -> NDArray[float64]: ...
@overload
def fromstring(
string: str | bytes,
dtype: _DTypeLike[_SCT],
count: SupportsIndex = ...,
*,
sep: str,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def fromstring(
string: str | bytes,
dtype: DTypeLike,
count: SupportsIndex = ...,
*,
sep: str,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
def frompyfunc(
func: Callable[..., Any], /,
nin: SupportsIndex,
nout: SupportsIndex,
*,
identity: Any = ...,
) -> ufunc: ...
@overload
def fromfile(
file: str | bytes | os.PathLike[Any] | _IOProtocol,
dtype: None = ...,
count: SupportsIndex = ...,
sep: str = ...,
offset: SupportsIndex = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[float64]: ...
@overload
def fromfile(
file: str | bytes | os.PathLike[Any] | _IOProtocol,
dtype: _DTypeLike[_SCT],
count: SupportsIndex = ...,
sep: str = ...,
offset: SupportsIndex = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def fromfile(
file: str | bytes | os.PathLike[Any] | _IOProtocol,
dtype: DTypeLike,
count: SupportsIndex = ...,
sep: str = ...,
offset: SupportsIndex = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def fromiter(
iter: Iterable[Any],
dtype: _DTypeLike[_SCT],
count: SupportsIndex = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def fromiter(
iter: Iterable[Any],
dtype: DTypeLike,
count: SupportsIndex = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def frombuffer(
buffer: _SupportsBuffer,
dtype: None = ...,
count: SupportsIndex = ...,
offset: SupportsIndex = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[float64]: ...
@overload
def frombuffer(
buffer: _SupportsBuffer,
dtype: _DTypeLike[_SCT],
count: SupportsIndex = ...,
offset: SupportsIndex = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def frombuffer(
buffer: _SupportsBuffer,
dtype: DTypeLike,
count: SupportsIndex = ...,
offset: SupportsIndex = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def arange( # type: ignore[misc]
stop: _IntLike_co,
/, *,
dtype: None = ...,
like: _SupportsArrayFunc = ...,
) -> NDArray[signedinteger[Any]]: ...
@overload
def arange( # type: ignore[misc]
start: _IntLike_co,
stop: _IntLike_co,
step: _IntLike_co = ...,
dtype: None = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[signedinteger[Any]]: ...
@overload
def arange( # type: ignore[misc]
stop: _FloatLike_co,
/, *,
dtype: None = ...,
like: _SupportsArrayFunc = ...,
) -> NDArray[floating[Any]]: ...
@overload
def arange( # type: ignore[misc]
start: _FloatLike_co,
stop: _FloatLike_co,
step: _FloatLike_co = ...,
dtype: None = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[floating[Any]]: ...
@overload
def arange(
stop: _TD64Like_co,
/, *,
dtype: None = ...,
like: _SupportsArrayFunc = ...,
) -> NDArray[timedelta64]: ...
@overload
def arange(
start: _TD64Like_co,
stop: _TD64Like_co,
step: _TD64Like_co = ...,
dtype: None = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[timedelta64]: ...
@overload
def arange( # both start and stop must always be specified for datetime64
start: datetime64,
stop: datetime64,
step: datetime64 = ...,
dtype: None = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[datetime64]: ...
@overload
def arange(
stop: Any,
/, *,
dtype: _DTypeLike[_SCT],
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def arange(
start: Any,
stop: Any,
step: Any = ...,
dtype: _DTypeLike[_SCT] = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[_SCT]: ...
@overload
def arange(
stop: Any, /,
*,
dtype: DTypeLike,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
@overload
def arange(
start: Any,
stop: Any,
step: Any = ...,
dtype: DTypeLike = ...,
*,
like: _SupportsArrayFunc = ...,
) -> NDArray[Any]: ...
def datetime_data(
dtype: str | _DTypeLike[datetime64] | _DTypeLike[timedelta64], /,
) -> tuple[str, int]: ...
# The datetime functions perform unsafe casts to `datetime64[D]`,
# so a lot of different argument types are allowed here
@overload
def busday_count( # type: ignore[misc]
begindates: _ScalarLike_co | dt.date,
enddates: _ScalarLike_co | dt.date,
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> int_: ...
@overload
def busday_count( # type: ignore[misc]
begindates: ArrayLike | dt.date | _NestedSequence[dt.date],
enddates: ArrayLike | dt.date | _NestedSequence[dt.date],
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> NDArray[int_]: ...
@overload
def busday_count(
begindates: ArrayLike | dt.date | _NestedSequence[dt.date],
enddates: ArrayLike | dt.date | _NestedSequence[dt.date],
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
# `roll="raise"` is (more or less?) equivalent to `casting="safe"`
@overload
def busday_offset( # type: ignore[misc]
dates: datetime64 | dt.date,
offsets: _TD64Like_co | dt.timedelta,
roll: L["raise"] = ...,
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> datetime64: ...
@overload
def busday_offset( # type: ignore[misc]
dates: _ArrayLike[datetime64] | dt.date | _NestedSequence[dt.date],
offsets: _ArrayLikeTD64_co | dt.timedelta | _NestedSequence[dt.timedelta],
roll: L["raise"] = ...,
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> NDArray[datetime64]: ...
@overload
def busday_offset( # type: ignore[misc]
dates: _ArrayLike[datetime64] | dt.date | _NestedSequence[dt.date],
offsets: _ArrayLikeTD64_co | dt.timedelta | _NestedSequence[dt.timedelta],
roll: L["raise"] = ...,
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def busday_offset( # type: ignore[misc]
dates: _ScalarLike_co | dt.date,
offsets: _ScalarLike_co | dt.timedelta,
roll: _RollKind,
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> datetime64: ...
@overload
def busday_offset( # type: ignore[misc]
dates: ArrayLike | dt.date | _NestedSequence[dt.date],
offsets: ArrayLike | dt.timedelta | _NestedSequence[dt.timedelta],
roll: _RollKind,
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> NDArray[datetime64]: ...
@overload
def busday_offset(
dates: ArrayLike | dt.date | _NestedSequence[dt.date],
offsets: ArrayLike | dt.timedelta | _NestedSequence[dt.timedelta],
roll: _RollKind,
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def is_busday( # type: ignore[misc]
dates: _ScalarLike_co | dt.date,
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> bool_: ...
@overload
def is_busday( # type: ignore[misc]
dates: ArrayLike | _NestedSequence[dt.date],
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: None = ...,
) -> NDArray[bool_]: ...
@overload
def is_busday(
dates: ArrayLike | _NestedSequence[dt.date],
weekmask: ArrayLike = ...,
holidays: None | ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
busdaycal: None | busdaycalendar = ...,
out: _ArrayType = ...,
) -> _ArrayType: ...
@overload
def datetime_as_string( # type: ignore[misc]
arr: datetime64 | dt.date,
unit: None | L["auto"] | _UnitKind = ...,
timezone: L["naive", "UTC", "local"] | dt.tzinfo = ...,
casting: _CastingKind = ...,
) -> str_: ...
@overload
def datetime_as_string(
arr: _ArrayLikeDT64_co | _NestedSequence[dt.date],
unit: None | L["auto"] | _UnitKind = ...,
timezone: L["naive", "UTC", "local"] | dt.tzinfo = ...,
casting: _CastingKind = ...,
) -> NDArray[str_]: ...
@overload
def compare_chararrays(
a1: _ArrayLikeStr_co,
a2: _ArrayLikeStr_co,
cmp: L["<", "<=", "==", ">=", ">", "!="],
rstrip: bool,
) -> NDArray[bool_]: ...
@overload
def compare_chararrays(
a1: _ArrayLikeBytes_co,
a2: _ArrayLikeBytes_co,
cmp: L["<", "<=", "==", ">=", ">", "!="],
rstrip: bool,
) -> NDArray[bool_]: ...
def add_docstring(obj: Callable[..., Any], docstring: str, /) -> None: ...
_GetItemKeys = L[
"C", "CONTIGUOUS", "C_CONTIGUOUS",
"F", "FORTRAN", "F_CONTIGUOUS",
"W", "WRITEABLE",
"B", "BEHAVED",
"O", "OWNDATA",
"A", "ALIGNED",
"X", "WRITEBACKIFCOPY",
"CA", "CARRAY",
"FA", "FARRAY",
"FNC",
"FORC",
]
_SetItemKeys = L[
"A", "ALIGNED",
"W", "WRITEABLE",
"X", "WRITEBACKIFCOPY",
]
@final
class flagsobj:
__hash__: None # type: ignore[assignment]
aligned: bool
# NOTE: deprecated
# updateifcopy: bool
writeable: bool
writebackifcopy: bool
@property
def behaved(self) -> bool: ...
@property
def c_contiguous(self) -> bool: ...
@property
def carray(self) -> bool: ...
@property
def contiguous(self) -> bool: ...
@property
def f_contiguous(self) -> bool: ...
@property
def farray(self) -> bool: ...
@property
def fnc(self) -> bool: ...
@property
def forc(self) -> bool: ...
@property
def fortran(self) -> bool: ...
@property
def num(self) -> int: ...
@property
def owndata(self) -> bool: ...
def __getitem__(self, key: _GetItemKeys) -> bool: ...
def __setitem__(self, key: _SetItemKeys, value: bool) -> None: ...
def nested_iters(
op: ArrayLike | Sequence[ArrayLike],
axes: Sequence[Sequence[SupportsIndex]],
flags: None | Sequence[_NDIterFlagsKind] = ...,
op_flags: None | Sequence[Sequence[_NDIterOpFlagsKind]] = ...,
op_dtypes: DTypeLike | Sequence[DTypeLike] = ...,
order: _OrderKACF = ...,
casting: _CastingKind = ...,
buffersize: SupportsIndex = ...,
) -> tuple[nditer, ...]: ...
| 24,386 | unknown | 22.885406 | 117 | 0.569917 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/cversions.py | """Simple script to compute the api hash of the current API.
The API has is defined by numpy_api_order and ufunc_api_order.
"""
from os.path import dirname
from code_generators.genapi import fullapi_hash
from code_generators.numpy_api import full_api
if __name__ == '__main__':
curdir = dirname(__file__)
print(fullapi_hash(full_api))
| 347 | Python | 23.857141 | 62 | 0.714697 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_type_aliases.pyi | from typing import TypedDict
from numpy import generic, signedinteger, unsignedinteger, floating, complexfloating
class _SCTypes(TypedDict):
int: list[type[signedinteger]]
uint: list[type[unsignedinteger]]
float: list[type[floating]]
complex: list[type[complexfloating]]
others: list[type]
sctypeDict: dict[int | str, type[generic]]
sctypes: _SCTypes
| 374 | unknown | 25.785712 | 84 | 0.748663 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/memmap.pyi | from numpy import memmap as memmap
__all__: list[str]
| 55 | unknown | 12.999997 | 34 | 0.709091 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_ufunc_config.pyi | from collections.abc import Callable
from typing import Any, Literal, TypedDict
from numpy import _SupportsWrite
_ErrKind = Literal["ignore", "warn", "raise", "call", "print", "log"]
_ErrFunc = Callable[[str, int], Any]
class _ErrDict(TypedDict):
divide: _ErrKind
over: _ErrKind
under: _ErrKind
invalid: _ErrKind
class _ErrDictOptional(TypedDict, total=False):
all: None | _ErrKind
divide: None | _ErrKind
over: None | _ErrKind
under: None | _ErrKind
invalid: None | _ErrKind
def seterr(
all: None | _ErrKind = ...,
divide: None | _ErrKind = ...,
over: None | _ErrKind = ...,
under: None | _ErrKind = ...,
invalid: None | _ErrKind = ...,
) -> _ErrDict: ...
def geterr() -> _ErrDict: ...
def setbufsize(size: int) -> int: ...
def getbufsize() -> int: ...
def seterrcall(
func: None | _ErrFunc | _SupportsWrite[str]
) -> None | _ErrFunc | _SupportsWrite[str]: ...
def geterrcall() -> None | _ErrFunc | _SupportsWrite[str]: ...
# See `numpy/__init__.pyi` for the `errstate` class
| 1,043 | unknown | 26.473684 | 69 | 0.619367 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_dtype.py | """
A place for code to be called from the implementation of np.dtype
String handling is much easier to do correctly in python.
"""
import numpy as np
_kind_to_stem = {
'u': 'uint',
'i': 'int',
'c': 'complex',
'f': 'float',
'b': 'bool',
'V': 'void',
'O': 'object',
'M': 'datetime',
'm': 'timedelta',
'S': 'bytes',
'U': 'str',
}
def _kind_name(dtype):
try:
return _kind_to_stem[dtype.kind]
except KeyError as e:
raise RuntimeError(
"internal dtype error, unknown kind {!r}"
.format(dtype.kind)
) from None
def __str__(dtype):
if dtype.fields is not None:
return _struct_str(dtype, include_align=True)
elif dtype.subdtype:
return _subarray_str(dtype)
elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
return dtype.str
else:
return dtype.name
def __repr__(dtype):
arg_str = _construction_repr(dtype, include_align=False)
if dtype.isalignedstruct:
arg_str = arg_str + ", align=True"
return "dtype({})".format(arg_str)
def _unpack_field(dtype, offset, title=None):
"""
Helper function to normalize the items in dtype.fields.
Call as:
dtype, offset, title = _unpack_field(*dtype.fields[name])
"""
return dtype, offset, title
def _isunsized(dtype):
# PyDataType_ISUNSIZED
return dtype.itemsize == 0
def _construction_repr(dtype, include_align=False, short=False):
"""
Creates a string repr of the dtype, excluding the 'dtype()' part
surrounding the object. This object may be a string, a list, or
a dict depending on the nature of the dtype. This
is the object passed as the first parameter to the dtype
constructor, and if no additional constructor parameters are
given, will reproduce the exact memory layout.
Parameters
----------
short : bool
If true, this creates a shorter repr using 'kind' and 'itemsize', instead
of the longer type name.
include_align : bool
If true, this includes the 'align=True' parameter
inside the struct dtype construction dict when needed. Use this flag
if you want a proper repr string without the 'dtype()' part around it.
If false, this does not preserve the
'align=True' parameter or sticky NPY_ALIGNED_STRUCT flag for
struct arrays like the regular repr does, because the 'align'
flag is not part of first dtype constructor parameter. This
mode is intended for a full 'repr', where the 'align=True' is
provided as the second parameter.
"""
if dtype.fields is not None:
return _struct_str(dtype, include_align=include_align)
elif dtype.subdtype:
return _subarray_str(dtype)
else:
return _scalar_str(dtype, short=short)
def _scalar_str(dtype, short):
byteorder = _byte_order_str(dtype)
if dtype.type == np.bool_:
if short:
return "'?'"
else:
return "'bool'"
elif dtype.type == np.object_:
# The object reference may be different sizes on different
# platforms, so it should never include the itemsize here.
return "'O'"
elif dtype.type == np.string_:
if _isunsized(dtype):
return "'S'"
else:
return "'S%d'" % dtype.itemsize
elif dtype.type == np.unicode_:
if _isunsized(dtype):
return "'%sU'" % byteorder
else:
return "'%sU%d'" % (byteorder, dtype.itemsize / 4)
# unlike the other types, subclasses of void are preserved - but
# historically the repr does not actually reveal the subclass
elif issubclass(dtype.type, np.void):
if _isunsized(dtype):
return "'V'"
else:
return "'V%d'" % dtype.itemsize
elif dtype.type == np.datetime64:
return "'%sM8%s'" % (byteorder, _datetime_metadata_str(dtype))
elif dtype.type == np.timedelta64:
return "'%sm8%s'" % (byteorder, _datetime_metadata_str(dtype))
elif np.issubdtype(dtype, np.number):
# Short repr with endianness, like '<f8'
if short or dtype.byteorder not in ('=', '|'):
return "'%s%c%d'" % (byteorder, dtype.kind, dtype.itemsize)
# Longer repr, like 'float64'
else:
return "'%s%d'" % (_kind_name(dtype), 8*dtype.itemsize)
elif dtype.isbuiltin == 2:
return dtype.type.__name__
else:
raise RuntimeError(
"Internal error: NumPy dtype unrecognized type number")
def _byte_order_str(dtype):
""" Normalize byteorder to '<' or '>' """
# hack to obtain the native and swapped byte order characters
swapped = np.dtype(int).newbyteorder('S')
native = swapped.newbyteorder('S')
byteorder = dtype.byteorder
if byteorder == '=':
return native.byteorder
if byteorder == 'S':
# TODO: this path can never be reached
return swapped.byteorder
elif byteorder == '|':
return ''
else:
return byteorder
def _datetime_metadata_str(dtype):
# TODO: this duplicates the C metastr_to_unicode functionality
unit, count = np.datetime_data(dtype)
if unit == 'generic':
return ''
elif count == 1:
return '[{}]'.format(unit)
else:
return '[{}{}]'.format(count, unit)
def _struct_dict_str(dtype, includealignedflag):
# unpack the fields dictionary into ls
names = dtype.names
fld_dtypes = []
offsets = []
titles = []
for name in names:
fld_dtype, offset, title = _unpack_field(*dtype.fields[name])
fld_dtypes.append(fld_dtype)
offsets.append(offset)
titles.append(title)
# Build up a string to make the dictionary
if np.core.arrayprint._get_legacy_print_mode() <= 121:
colon = ":"
fieldsep = ","
else:
colon = ": "
fieldsep = ", "
# First, the names
ret = "{'names'%s[" % colon
ret += fieldsep.join(repr(name) for name in names)
# Second, the formats
ret += "], 'formats'%s[" % colon
ret += fieldsep.join(
_construction_repr(fld_dtype, short=True) for fld_dtype in fld_dtypes)
# Third, the offsets
ret += "], 'offsets'%s[" % colon
ret += fieldsep.join("%d" % offset for offset in offsets)
# Fourth, the titles
if any(title is not None for title in titles):
ret += "], 'titles'%s[" % colon
ret += fieldsep.join(repr(title) for title in titles)
# Fifth, the itemsize
ret += "], 'itemsize'%s%d" % (colon, dtype.itemsize)
if (includealignedflag and dtype.isalignedstruct):
# Finally, the aligned flag
ret += ", 'aligned'%sTrue}" % colon
else:
ret += "}"
return ret
def _aligned_offset(offset, alignment):
# round up offset:
return - (-offset // alignment) * alignment
def _is_packed(dtype):
"""
Checks whether the structured data type in 'dtype'
has a simple layout, where all the fields are in order,
and follow each other with no alignment padding.
When this returns true, the dtype can be reconstructed
from a list of the field names and dtypes with no additional
dtype parameters.
Duplicates the C `is_dtype_struct_simple_unaligned_layout` function.
"""
align = dtype.isalignedstruct
max_alignment = 1
total_offset = 0
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
if align:
total_offset = _aligned_offset(total_offset, fld_dtype.alignment)
max_alignment = max(max_alignment, fld_dtype.alignment)
if fld_offset != total_offset:
return False
total_offset += fld_dtype.itemsize
if align:
total_offset = _aligned_offset(total_offset, max_alignment)
if total_offset != dtype.itemsize:
return False
return True
def _struct_list_str(dtype):
items = []
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
item = "("
if title is not None:
item += "({!r}, {!r}), ".format(title, name)
else:
item += "{!r}, ".format(name)
# Special case subarray handling here
if fld_dtype.subdtype is not None:
base, shape = fld_dtype.subdtype
item += "{}, {}".format(
_construction_repr(base, short=True),
shape
)
else:
item += _construction_repr(fld_dtype, short=True)
item += ")"
items.append(item)
return "[" + ", ".join(items) + "]"
def _struct_str(dtype, include_align):
# The list str representation can't include the 'align=' flag,
# so if it is requested and the struct has the aligned flag set,
# we must use the dict str instead.
if not (include_align and dtype.isalignedstruct) and _is_packed(dtype):
sub = _struct_list_str(dtype)
else:
sub = _struct_dict_str(dtype, include_align)
# If the data type isn't the default, void, show it
if dtype.type != np.void:
return "({t.__module__}.{t.__name__}, {f})".format(t=dtype.type, f=sub)
else:
return sub
def _subarray_str(dtype):
base, shape = dtype.subdtype
return "({}, {})".format(
_construction_repr(base, short=True),
shape
)
def _name_includes_bit_suffix(dtype):
if dtype.type == np.object_:
# pointer size varies by system, best to omit it
return False
elif dtype.type == np.bool_:
# implied
return False
elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):
# unspecified
return False
else:
return True
def _name_get(dtype):
# provides dtype.name.__get__, documented as returning a "bit name"
if dtype.isbuiltin == 2:
# user dtypes don't promise to do anything special
return dtype.type.__name__
if issubclass(dtype.type, np.void):
# historically, void subclasses preserve their name, eg `record64`
name = dtype.type.__name__
else:
name = _kind_name(dtype)
# append bit counts
if _name_includes_bit_suffix(dtype):
name += "{}".format(dtype.itemsize * 8)
# append metadata to datetimes
if dtype.type in (np.datetime64, np.timedelta64):
name += _datetime_metadata_str(dtype)
return name
| 10,495 | Python | 27.677596 | 81 | 0.600953 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/__init__.pyi | # NOTE: The `np.core` namespace is deliberately kept empty due to it
# being private (despite the lack of leading underscore)
| 126 | unknown | 41.33332 | 68 | 0.769841 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_machar.py | """
Machine arithmetic - determine the parameters of the
floating-point arithmetic system
Author: Pearu Peterson, September 2003
"""
__all__ = ['MachAr']
from numpy.core.fromnumeric import any
from numpy.core._ufunc_config import errstate
from numpy.core.overrides import set_module
# Need to speed this up...especially for longfloat
# Deprecated 2021-10-20, NumPy 1.22
@set_module('numpy')
class MachAr:
"""
Diagnosing machine parameters.
Attributes
----------
ibeta : int
Radix in which numbers are represented.
it : int
Number of base-`ibeta` digits in the floating point mantissa M.
machep : int
Exponent of the smallest (most negative) power of `ibeta` that,
added to 1.0, gives something different from 1.0
eps : float
Floating-point number ``beta**machep`` (floating point precision)
negep : int
Exponent of the smallest power of `ibeta` that, subtracted
from 1.0, gives something different from 1.0.
epsneg : float
Floating-point number ``beta**negep``.
iexp : int
Number of bits in the exponent (including its sign and bias).
minexp : int
Smallest (most negative) power of `ibeta` consistent with there
being no leading zeros in the mantissa.
xmin : float
Floating-point number ``beta**minexp`` (the smallest [in
magnitude] positive floating point number with full precision).
maxexp : int
Smallest (positive) power of `ibeta` that causes overflow.
xmax : float
``(1-epsneg) * beta**maxexp`` (the largest [in magnitude]
usable floating value).
irnd : int
In ``range(6)``, information on what kind of rounding is done
in addition, and on how underflow is handled.
ngrd : int
Number of 'guard digits' used when truncating the product
of two mantissas to fit the representation.
epsilon : float
Same as `eps`.
tiny : float
An alias for `smallest_normal`, kept for backwards compatibility.
huge : float
Same as `xmax`.
precision : float
``- int(-log10(eps))``
resolution : float
``- 10**(-precision)``
smallest_normal : float
The smallest positive floating point number with 1 as leading bit in
the mantissa following IEEE-754. Same as `xmin`.
smallest_subnormal : float
The smallest positive floating point number with 0 as leading bit in
the mantissa following IEEE-754.
Parameters
----------
float_conv : function, optional
Function that converts an integer or integer array to a float
or float array. Default is `float`.
int_conv : function, optional
Function that converts a float or float array to an integer or
integer array. Default is `int`.
float_to_float : function, optional
Function that converts a float array to float. Default is `float`.
Note that this does not seem to do anything useful in the current
implementation.
float_to_str : function, optional
Function that converts a single float to a string. Default is
``lambda v:'%24.16e' %v``.
title : str, optional
Title that is printed in the string representation of `MachAr`.
See Also
--------
finfo : Machine limits for floating point types.
iinfo : Machine limits for integer types.
References
----------
.. [1] Press, Teukolsky, Vetterling and Flannery,
"Numerical Recipes in C++," 2nd ed,
Cambridge University Press, 2002, p. 31.
"""
def __init__(self, float_conv=float,int_conv=int,
float_to_float=float,
float_to_str=lambda v:'%24.16e' % v,
title='Python floating point number'):
"""
float_conv - convert integer to float (array)
int_conv - convert float (array) to integer
float_to_float - convert float array to float
float_to_str - convert array float to str
title - description of used floating point numbers
"""
# We ignore all errors here because we are purposely triggering
# underflow to detect the properties of the runninng arch.
with errstate(under='ignore'):
self._do_init(float_conv, int_conv, float_to_float, float_to_str, title)
def _do_init(self, float_conv, int_conv, float_to_float, float_to_str, title):
max_iterN = 10000
msg = "Did not converge after %d tries with %s"
one = float_conv(1)
two = one + one
zero = one - one
# Do we really need to do this? Aren't they 2 and 2.0?
# Determine ibeta and beta
a = one
for _ in range(max_iterN):
a = a + a
temp = a + one
temp1 = temp - a
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
b = one
for _ in range(max_iterN):
b = b + b
temp = a + b
itemp = int_conv(temp-a)
if any(itemp != 0):
break
else:
raise RuntimeError(msg % (_, one.dtype))
ibeta = itemp
beta = float_conv(ibeta)
# Determine it and irnd
it = -1
b = one
for _ in range(max_iterN):
it = it + 1
b = b * beta
temp = b + one
temp1 = temp - b
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
betah = beta / two
a = one
for _ in range(max_iterN):
a = a + a
temp = a + one
temp1 = temp - a
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
temp = a + betah
irnd = 0
if any(temp-a != zero):
irnd = 1
tempa = a + beta
temp = tempa + betah
if irnd == 0 and any(temp-tempa != zero):
irnd = 2
# Determine negep and epsneg
negep = it + 3
betain = one / beta
a = one
for i in range(negep):
a = a * betain
b = a
for _ in range(max_iterN):
temp = one - a
if any(temp-one != zero):
break
a = a * beta
negep = negep - 1
# Prevent infinite loop on PPC with gcc 4.0:
if negep < 0:
raise RuntimeError("could not determine machine tolerance "
"for 'negep', locals() -> %s" % (locals()))
else:
raise RuntimeError(msg % (_, one.dtype))
negep = -negep
epsneg = a
# Determine machep and eps
machep = - it - 3
a = b
for _ in range(max_iterN):
temp = one + a
if any(temp-one != zero):
break
a = a * beta
machep = machep + 1
else:
raise RuntimeError(msg % (_, one.dtype))
eps = a
# Determine ngrd
ngrd = 0
temp = one + eps
if irnd == 0 and any(temp*one - one != zero):
ngrd = 1
# Determine iexp
i = 0
k = 1
z = betain
t = one + eps
nxres = 0
for _ in range(max_iterN):
y = z
z = y*y
a = z*one # Check here for underflow
temp = z*t
if any(a+a == zero) or any(abs(z) >= y):
break
temp1 = temp * betain
if any(temp1*beta == z):
break
i = i + 1
k = k + k
else:
raise RuntimeError(msg % (_, one.dtype))
if ibeta != 10:
iexp = i + 1
mx = k + k
else:
iexp = 2
iz = ibeta
while k >= iz:
iz = iz * ibeta
iexp = iexp + 1
mx = iz + iz - 1
# Determine minexp and xmin
for _ in range(max_iterN):
xmin = y
y = y * betain
a = y * one
temp = y * t
if any((a + a) != zero) and any(abs(y) < xmin):
k = k + 1
temp1 = temp * betain
if any(temp1*beta == y) and any(temp != y):
nxres = 3
xmin = y
break
else:
break
else:
raise RuntimeError(msg % (_, one.dtype))
minexp = -k
# Determine maxexp, xmax
if mx <= k + k - 3 and ibeta != 10:
mx = mx + mx
iexp = iexp + 1
maxexp = mx + minexp
irnd = irnd + nxres
if irnd >= 2:
maxexp = maxexp - 2
i = maxexp + minexp
if ibeta == 2 and not i:
maxexp = maxexp - 1
if i > 20:
maxexp = maxexp - 1
if any(a != y):
maxexp = maxexp - 2
xmax = one - epsneg
if any(xmax*one != xmax):
xmax = one - beta*epsneg
xmax = xmax / (xmin*beta*beta*beta)
i = maxexp + minexp + 3
for j in range(i):
if ibeta == 2:
xmax = xmax + xmax
else:
xmax = xmax * beta
smallest_subnormal = abs(xmin / beta ** (it))
self.ibeta = ibeta
self.it = it
self.negep = negep
self.epsneg = float_to_float(epsneg)
self._str_epsneg = float_to_str(epsneg)
self.machep = machep
self.eps = float_to_float(eps)
self._str_eps = float_to_str(eps)
self.ngrd = ngrd
self.iexp = iexp
self.minexp = minexp
self.xmin = float_to_float(xmin)
self._str_xmin = float_to_str(xmin)
self.maxexp = maxexp
self.xmax = float_to_float(xmax)
self._str_xmax = float_to_str(xmax)
self.irnd = irnd
self.title = title
# Commonly used parameters
self.epsilon = self.eps
self.tiny = self.xmin
self.huge = self.xmax
self.smallest_normal = self.xmin
self._str_smallest_normal = float_to_str(self.xmin)
self.smallest_subnormal = float_to_float(smallest_subnormal)
self._str_smallest_subnormal = float_to_str(smallest_subnormal)
import math
self.precision = int(-math.log10(float_to_float(self.eps)))
ten = two + two + two + two + two
resolution = ten ** (-self.precision)
self.resolution = float_to_float(resolution)
self._str_resolution = float_to_str(resolution)
def __str__(self):
fmt = (
'Machine parameters for %(title)s\n'
'---------------------------------------------------------------------\n'
'ibeta=%(ibeta)s it=%(it)s iexp=%(iexp)s ngrd=%(ngrd)s irnd=%(irnd)s\n'
'machep=%(machep)s eps=%(_str_eps)s (beta**machep == epsilon)\n'
'negep =%(negep)s epsneg=%(_str_epsneg)s (beta**epsneg)\n'
'minexp=%(minexp)s xmin=%(_str_xmin)s (beta**minexp == tiny)\n'
'maxexp=%(maxexp)s xmax=%(_str_xmax)s ((1-epsneg)*beta**maxexp == huge)\n'
'smallest_normal=%(smallest_normal)s '
'smallest_subnormal=%(smallest_subnormal)s\n'
'---------------------------------------------------------------------\n'
)
return fmt % self.__dict__
if __name__ == '__main__':
print(MachAr())
| 11,618 | Python | 31.455307 | 88 | 0.509985 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/shape_base.py | __all__ = ['atleast_1d', 'atleast_2d', 'atleast_3d', 'block', 'hstack',
'stack', 'vstack']
import functools
import itertools
import operator
import warnings
from . import numeric as _nx
from . import overrides
from .multiarray import array, asanyarray, normalize_axis_index
from . import fromnumeric as _from_nx
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
def _atleast_1d_dispatcher(*arys):
return arys
@array_function_dispatch(_atleast_1d_dispatcher)
def atleast_1d(*arys):
"""
Convert inputs to arrays with at least one dimension.
Scalar inputs are converted to 1-dimensional arrays, whilst
higher-dimensional inputs are preserved.
Parameters
----------
arys1, arys2, ... : array_like
One or more input arrays.
Returns
-------
ret : ndarray
An array, or list of arrays, each with ``a.ndim >= 1``.
Copies are made only if necessary.
See Also
--------
atleast_2d, atleast_3d
Examples
--------
>>> np.atleast_1d(1.0)
array([1.])
>>> x = np.arange(9.0).reshape(3,3)
>>> np.atleast_1d(x)
array([[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]])
>>> np.atleast_1d(x) is x
True
>>> np.atleast_1d(1, [3, 4])
[array([1]), array([3, 4])]
"""
res = []
for ary in arys:
ary = asanyarray(ary)
if ary.ndim == 0:
result = ary.reshape(1)
else:
result = ary
res.append(result)
if len(res) == 1:
return res[0]
else:
return res
def _atleast_2d_dispatcher(*arys):
return arys
@array_function_dispatch(_atleast_2d_dispatcher)
def atleast_2d(*arys):
"""
View inputs as arrays with at least two dimensions.
Parameters
----------
arys1, arys2, ... : array_like
One or more array-like sequences. Non-array inputs are converted
to arrays. Arrays that already have two or more dimensions are
preserved.
Returns
-------
res, res2, ... : ndarray
An array, or list of arrays, each with ``a.ndim >= 2``.
Copies are avoided where possible, and views with two or more
dimensions are returned.
See Also
--------
atleast_1d, atleast_3d
Examples
--------
>>> np.atleast_2d(3.0)
array([[3.]])
>>> x = np.arange(3.0)
>>> np.atleast_2d(x)
array([[0., 1., 2.]])
>>> np.atleast_2d(x).base is x
True
>>> np.atleast_2d(1, [1, 2], [[1, 2]])
[array([[1]]), array([[1, 2]]), array([[1, 2]])]
"""
res = []
for ary in arys:
ary = asanyarray(ary)
if ary.ndim == 0:
result = ary.reshape(1, 1)
elif ary.ndim == 1:
result = ary[_nx.newaxis, :]
else:
result = ary
res.append(result)
if len(res) == 1:
return res[0]
else:
return res
def _atleast_3d_dispatcher(*arys):
return arys
@array_function_dispatch(_atleast_3d_dispatcher)
def atleast_3d(*arys):
"""
View inputs as arrays with at least three dimensions.
Parameters
----------
arys1, arys2, ... : array_like
One or more array-like sequences. Non-array inputs are converted to
arrays. Arrays that already have three or more dimensions are
preserved.
Returns
-------
res1, res2, ... : ndarray
An array, or list of arrays, each with ``a.ndim >= 3``. Copies are
avoided where possible, and views with three or more dimensions are
returned. For example, a 1-D array of shape ``(N,)`` becomes a view
of shape ``(1, N, 1)``, and a 2-D array of shape ``(M, N)`` becomes a
view of shape ``(M, N, 1)``.
See Also
--------
atleast_1d, atleast_2d
Examples
--------
>>> np.atleast_3d(3.0)
array([[[3.]]])
>>> x = np.arange(3.0)
>>> np.atleast_3d(x).shape
(1, 3, 1)
>>> x = np.arange(12.0).reshape(4,3)
>>> np.atleast_3d(x).shape
(4, 3, 1)
>>> np.atleast_3d(x).base is x.base # x is a reshape, so not base itself
True
>>> for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]):
... print(arr, arr.shape) # doctest: +SKIP
...
[[[1]
[2]]] (1, 2, 1)
[[[1]
[2]]] (1, 2, 1)
[[[1 2]]] (1, 1, 2)
"""
res = []
for ary in arys:
ary = asanyarray(ary)
if ary.ndim == 0:
result = ary.reshape(1, 1, 1)
elif ary.ndim == 1:
result = ary[_nx.newaxis, :, _nx.newaxis]
elif ary.ndim == 2:
result = ary[:, :, _nx.newaxis]
else:
result = ary
res.append(result)
if len(res) == 1:
return res[0]
else:
return res
def _arrays_for_stack_dispatcher(arrays, stacklevel=4):
if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
warnings.warn('arrays to stack must be passed as a "sequence" type '
'such as list or tuple. Support for non-sequence '
'iterables such as generators is deprecated as of '
'NumPy 1.16 and will raise an error in the future.',
FutureWarning, stacklevel=stacklevel)
return ()
return arrays
def _vhstack_dispatcher(tup):
return _arrays_for_stack_dispatcher(tup)
@array_function_dispatch(_vhstack_dispatcher)
def vstack(tup):
"""
Stack arrays in sequence vertically (row wise).
This is equivalent to concatenation along the first axis after 1-D arrays
of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
`vsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of ndarrays
The arrays must have the same shape along all but the first axis.
1-D arrays must have the same length.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays, will be at least 2-D.
See Also
--------
concatenate : Join a sequence of arrays along an existing axis.
stack : Join a sequence of arrays along a new axis.
block : Assemble an nd-array from nested lists of blocks.
hstack : Stack arrays in sequence horizontally (column wise).
dstack : Stack arrays in sequence depth wise (along third axis).
column_stack : Stack 1-D arrays as columns into a 2-D array.
vsplit : Split an array into multiple sub-arrays vertically (row-wise).
Examples
--------
>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.vstack((a,b))
array([[1, 2, 3],
[4, 5, 6]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[4], [5], [6]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[4],
[5],
[6]])
"""
if not overrides.ARRAY_FUNCTION_ENABLED:
# raise warning if necessary
_arrays_for_stack_dispatcher(tup, stacklevel=2)
arrs = atleast_2d(*tup)
if not isinstance(arrs, list):
arrs = [arrs]
return _nx.concatenate(arrs, 0)
@array_function_dispatch(_vhstack_dispatcher)
def hstack(tup):
"""
Stack arrays in sequence horizontally (column wise).
This is equivalent to concatenation along the second axis, except for 1-D
arrays where it concatenates along the first axis. Rebuilds arrays divided
by `hsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of ndarrays
The arrays must have the same shape along all but the second axis,
except 1-D arrays which can be any length.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
concatenate : Join a sequence of arrays along an existing axis.
stack : Join a sequence of arrays along a new axis.
block : Assemble an nd-array from nested lists of blocks.
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third axis).
column_stack : Stack 1-D arrays as columns into a 2-D array.
hsplit : Split an array into multiple sub-arrays horizontally (column-wise).
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((4,5,6))
>>> np.hstack((a,b))
array([1, 2, 3, 4, 5, 6])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[4],[5],[6]])
>>> np.hstack((a,b))
array([[1, 4],
[2, 5],
[3, 6]])
"""
if not overrides.ARRAY_FUNCTION_ENABLED:
# raise warning if necessary
_arrays_for_stack_dispatcher(tup, stacklevel=2)
arrs = atleast_1d(*tup)
if not isinstance(arrs, list):
arrs = [arrs]
# As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
if arrs and arrs[0].ndim == 1:
return _nx.concatenate(arrs, 0)
else:
return _nx.concatenate(arrs, 1)
def _stack_dispatcher(arrays, axis=None, out=None):
arrays = _arrays_for_stack_dispatcher(arrays, stacklevel=6)
if out is not None:
# optimize for the typical case where only arrays is provided
arrays = list(arrays)
arrays.append(out)
return arrays
@array_function_dispatch(_stack_dispatcher)
def stack(arrays, axis=0, out=None):
"""
Join a sequence of arrays along a new axis.
The ``axis`` parameter specifies the index of the new axis in the
dimensions of the result. For example, if ``axis=0`` it will be the first
dimension and if ``axis=-1`` it will be the last dimension.
.. versionadded:: 1.10.0
Parameters
----------
arrays : sequence of array_like
Each array must have the same shape.
axis : int, optional
The axis in the result array along which the input arrays are stacked.
out : ndarray, optional
If provided, the destination to place the result. The shape must be
correct, matching that of what stack would have returned if no
out argument were specified.
Returns
-------
stacked : ndarray
The stacked array has one more dimension than the input arrays.
See Also
--------
concatenate : Join a sequence of arrays along an existing axis.
block : Assemble an nd-array from nested lists of blocks.
split : Split array into a list of multiple sub-arrays of equal size.
Examples
--------
>>> arrays = [np.random.randn(3, 4) for _ in range(10)]
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)
>>> np.stack(arrays, axis=1).shape
(3, 10, 4)
>>> np.stack(arrays, axis=2).shape
(3, 4, 10)
>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.stack((a, b))
array([[1, 2, 3],
[4, 5, 6]])
>>> np.stack((a, b), axis=-1)
array([[1, 4],
[2, 5],
[3, 6]])
"""
if not overrides.ARRAY_FUNCTION_ENABLED:
# raise warning if necessary
_arrays_for_stack_dispatcher(arrays, stacklevel=2)
arrays = [asanyarray(arr) for arr in arrays]
if not arrays:
raise ValueError('need at least one array to stack')
shapes = {arr.shape for arr in arrays}
if len(shapes) != 1:
raise ValueError('all input arrays must have the same shape')
result_ndim = arrays[0].ndim + 1
axis = normalize_axis_index(axis, result_ndim)
sl = (slice(None),) * axis + (_nx.newaxis,)
expanded_arrays = [arr[sl] for arr in arrays]
return _nx.concatenate(expanded_arrays, axis=axis, out=out)
# Internal functions to eliminate the overhead of repeated dispatch in one of
# the two possible paths inside np.block.
# Use getattr to protect against __array_function__ being disabled.
_size = getattr(_from_nx.size, '__wrapped__', _from_nx.size)
_ndim = getattr(_from_nx.ndim, '__wrapped__', _from_nx.ndim)
_concatenate = getattr(_from_nx.concatenate, '__wrapped__', _from_nx.concatenate)
def _block_format_index(index):
"""
Convert a list of indices ``[0, 1, 2]`` into ``"arrays[0][1][2]"``.
"""
idx_str = ''.join('[{}]'.format(i) for i in index if i is not None)
return 'arrays' + idx_str
def _block_check_depths_match(arrays, parent_index=[]):
"""
Recursive function checking that the depths of nested lists in `arrays`
all match. Mismatch raises a ValueError as described in the block
docstring below.
The entire index (rather than just the depth) needs to be calculated
for each innermost list, in case an error needs to be raised, so that
the index of the offending list can be printed as part of the error.
Parameters
----------
arrays : nested list of arrays
The arrays to check
parent_index : list of int
The full index of `arrays` within the nested lists passed to
`_block_check_depths_match` at the top of the recursion.
Returns
-------
first_index : list of int
The full index of an element from the bottom of the nesting in
`arrays`. If any element at the bottom is an empty list, this will
refer to it, and the last index along the empty axis will be None.
max_arr_ndim : int
The maximum of the ndims of the arrays nested in `arrays`.
final_size: int
The number of elements in the final array. This is used the motivate
the choice of algorithm used using benchmarking wisdom.
"""
if type(arrays) is tuple:
# not strictly necessary, but saves us from:
# - more than one way to do things - no point treating tuples like
# lists
# - horribly confusing behaviour that results when tuples are
# treated like ndarray
raise TypeError(
'{} is a tuple. '
'Only lists can be used to arrange blocks, and np.block does '
'not allow implicit conversion from tuple to ndarray.'.format(
_block_format_index(parent_index)
)
)
elif type(arrays) is list and len(arrays) > 0:
idxs_ndims = (_block_check_depths_match(arr, parent_index + [i])
for i, arr in enumerate(arrays))
first_index, max_arr_ndim, final_size = next(idxs_ndims)
for index, ndim, size in idxs_ndims:
final_size += size
if ndim > max_arr_ndim:
max_arr_ndim = ndim
if len(index) != len(first_index):
raise ValueError(
"List depths are mismatched. First element was at depth "
"{}, but there is an element at depth {} ({})".format(
len(first_index),
len(index),
_block_format_index(index)
)
)
# propagate our flag that indicates an empty list at the bottom
if index[-1] is None:
first_index = index
return first_index, max_arr_ndim, final_size
elif type(arrays) is list and len(arrays) == 0:
# We've 'bottomed out' on an empty list
return parent_index + [None], 0, 0
else:
# We've 'bottomed out' - arrays is either a scalar or an array
size = _size(arrays)
return parent_index, _ndim(arrays), size
def _atleast_nd(a, ndim):
# Ensures `a` has at least `ndim` dimensions by prepending
# ones to `a.shape` as necessary
return array(a, ndmin=ndim, copy=False, subok=True)
def _accumulate(values):
return list(itertools.accumulate(values))
def _concatenate_shapes(shapes, axis):
"""Given array shapes, return the resulting shape and slices prefixes.
These help in nested concatenation.
Returns
-------
shape: tuple of int
This tuple satisfies::
shape, _ = _concatenate_shapes([arr.shape for shape in arrs], axis)
shape == concatenate(arrs, axis).shape
slice_prefixes: tuple of (slice(start, end), )
For a list of arrays being concatenated, this returns the slice
in the larger array at axis that needs to be sliced into.
For example, the following holds::
ret = concatenate([a, b, c], axis)
_, (sl_a, sl_b, sl_c) = concatenate_slices([a, b, c], axis)
ret[(slice(None),) * axis + sl_a] == a
ret[(slice(None),) * axis + sl_b] == b
ret[(slice(None),) * axis + sl_c] == c
These are called slice prefixes since they are used in the recursive
blocking algorithm to compute the left-most slices during the
recursion. Therefore, they must be prepended to rest of the slice
that was computed deeper in the recursion.
These are returned as tuples to ensure that they can quickly be added
to existing slice tuple without creating a new tuple every time.
"""
# Cache a result that will be reused.
shape_at_axis = [shape[axis] for shape in shapes]
# Take a shape, any shape
first_shape = shapes[0]
first_shape_pre = first_shape[:axis]
first_shape_post = first_shape[axis+1:]
if any(shape[:axis] != first_shape_pre or
shape[axis+1:] != first_shape_post for shape in shapes):
raise ValueError(
'Mismatched array shapes in block along axis {}.'.format(axis))
shape = (first_shape_pre + (sum(shape_at_axis),) + first_shape[axis+1:])
offsets_at_axis = _accumulate(shape_at_axis)
slice_prefixes = [(slice(start, end),)
for start, end in zip([0] + offsets_at_axis,
offsets_at_axis)]
return shape, slice_prefixes
def _block_info_recursion(arrays, max_depth, result_ndim, depth=0):
"""
Returns the shape of the final array, along with a list
of slices and a list of arrays that can be used for assignment inside the
new array
Parameters
----------
arrays : nested list of arrays
The arrays to check
max_depth : list of int
The number of nested lists
result_ndim : int
The number of dimensions in thefinal array.
Returns
-------
shape : tuple of int
The shape that the final array will take on.
slices: list of tuple of slices
The slices into the full array required for assignment. These are
required to be prepended with ``(Ellipsis, )`` to obtain to correct
final index.
arrays: list of ndarray
The data to assign to each slice of the full array
"""
if depth < max_depth:
shapes, slices, arrays = zip(
*[_block_info_recursion(arr, max_depth, result_ndim, depth+1)
for arr in arrays])
axis = result_ndim - max_depth + depth
shape, slice_prefixes = _concatenate_shapes(shapes, axis)
# Prepend the slice prefix and flatten the slices
slices = [slice_prefix + the_slice
for slice_prefix, inner_slices in zip(slice_prefixes, slices)
for the_slice in inner_slices]
# Flatten the array list
arrays = functools.reduce(operator.add, arrays)
return shape, slices, arrays
else:
# We've 'bottomed out' - arrays is either a scalar or an array
# type(arrays) is not list
# Return the slice and the array inside a list to be consistent with
# the recursive case.
arr = _atleast_nd(arrays, result_ndim)
return arr.shape, [()], [arr]
def _block(arrays, max_depth, result_ndim, depth=0):
"""
Internal implementation of block based on repeated concatenation.
`arrays` is the argument passed to
block. `max_depth` is the depth of nested lists within `arrays` and
`result_ndim` is the greatest of the dimensions of the arrays in
`arrays` and the depth of the lists in `arrays` (see block docstring
for details).
"""
if depth < max_depth:
arrs = [_block(arr, max_depth, result_ndim, depth+1)
for arr in arrays]
return _concatenate(arrs, axis=-(max_depth-depth))
else:
# We've 'bottomed out' - arrays is either a scalar or an array
# type(arrays) is not list
return _atleast_nd(arrays, result_ndim)
def _block_dispatcher(arrays):
# Use type(...) is list to match the behavior of np.block(), which special
# cases list specifically rather than allowing for generic iterables or
# tuple. Also, we know that list.__array_function__ will never exist.
if type(arrays) is list:
for subarrays in arrays:
yield from _block_dispatcher(subarrays)
else:
yield arrays
@array_function_dispatch(_block_dispatcher)
def block(arrays):
"""
Assemble an nd-array from nested lists of blocks.
Blocks in the innermost lists are concatenated (see `concatenate`) along
the last dimension (-1), then these are concatenated along the
second-last dimension (-2), and so on until the outermost list is reached.
Blocks can be of any dimension, but will not be broadcasted using the normal
rules. Instead, leading axes of size 1 are inserted, to make ``block.ndim``
the same for all blocks. This is primarily useful for working with scalars,
and means that code like ``np.block([v, 1])`` is valid, where
``v.ndim == 1``.
When the nested list is two levels deep, this allows block matrices to be
constructed from their components.
.. versionadded:: 1.13.0
Parameters
----------
arrays : nested list of array_like or scalars (but not tuples)
If passed a single ndarray or scalar (a nested list of depth 0), this
is returned unmodified (and not copied).
Elements shapes must match along the appropriate axes (without
broadcasting), but leading 1s will be prepended to the shape as
necessary to make the dimensions match.
Returns
-------
block_array : ndarray
The array assembled from the given blocks.
The dimensionality of the output is equal to the greatest of:
* the dimensionality of all the inputs
* the depth to which the input list is nested
Raises
------
ValueError
* If list depths are mismatched - for instance, ``[[a, b], c]`` is
illegal, and should be spelt ``[[a, b], [c]]``
* If lists are empty - for instance, ``[[a, b], []]``
See Also
--------
concatenate : Join a sequence of arrays along an existing axis.
stack : Join a sequence of arrays along a new axis.
vstack : Stack arrays in sequence vertically (row wise).
hstack : Stack arrays in sequence horizontally (column wise).
dstack : Stack arrays in sequence depth wise (along third axis).
column_stack : Stack 1-D arrays as columns into a 2-D array.
vsplit : Split an array into multiple sub-arrays vertically (row-wise).
Notes
-----
When called with only scalars, ``np.block`` is equivalent to an ndarray
call. So ``np.block([[1, 2], [3, 4]])`` is equivalent to
``np.array([[1, 2], [3, 4]])``.
This function does not enforce that the blocks lie on a fixed grid.
``np.block([[a, b], [c, d]])`` is not restricted to arrays of the form::
AAAbb
AAAbb
cccDD
But is also allowed to produce, for some ``a, b, c, d``::
AAAbb
AAAbb
cDDDD
Since concatenation happens along the last axis first, `block` is _not_
capable of producing the following directly::
AAAbb
cccbb
cccDD
Matlab's "square bracket stacking", ``[A, B, ...; p, q, ...]``, is
equivalent to ``np.block([[A, B, ...], [p, q, ...]])``.
Examples
--------
The most common use of this function is to build a block matrix
>>> A = np.eye(2) * 2
>>> B = np.eye(3) * 3
>>> np.block([
... [A, np.zeros((2, 3))],
... [np.ones((3, 2)), B ]
... ])
array([[2., 0., 0., 0., 0.],
[0., 2., 0., 0., 0.],
[1., 1., 3., 0., 0.],
[1., 1., 0., 3., 0.],
[1., 1., 0., 0., 3.]])
With a list of depth 1, `block` can be used as `hstack`
>>> np.block([1, 2, 3]) # hstack([1, 2, 3])
array([1, 2, 3])
>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.block([a, b, 10]) # hstack([a, b, 10])
array([ 1, 2, 3, 4, 5, 6, 10])
>>> A = np.ones((2, 2), int)
>>> B = 2 * A
>>> np.block([A, B]) # hstack([A, B])
array([[1, 1, 2, 2],
[1, 1, 2, 2]])
With a list of depth 2, `block` can be used in place of `vstack`:
>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.block([[a], [b]]) # vstack([a, b])
array([[1, 2, 3],
[4, 5, 6]])
>>> A = np.ones((2, 2), int)
>>> B = 2 * A
>>> np.block([[A], [B]]) # vstack([A, B])
array([[1, 1],
[1, 1],
[2, 2],
[2, 2]])
It can also be used in places of `atleast_1d` and `atleast_2d`
>>> a = np.array(0)
>>> b = np.array([1])
>>> np.block([a]) # atleast_1d(a)
array([0])
>>> np.block([b]) # atleast_1d(b)
array([1])
>>> np.block([[a]]) # atleast_2d(a)
array([[0]])
>>> np.block([[b]]) # atleast_2d(b)
array([[1]])
"""
arrays, list_ndim, result_ndim, final_size = _block_setup(arrays)
# It was found through benchmarking that making an array of final size
# around 256x256 was faster by straight concatenation on a
# i7-7700HQ processor and dual channel ram 2400MHz.
# It didn't seem to matter heavily on the dtype used.
#
# A 2D array using repeated concatenation requires 2 copies of the array.
#
# The fastest algorithm will depend on the ratio of CPU power to memory
# speed.
# One can monitor the results of the benchmark
# https://pv.github.io/numpy-bench/#bench_shape_base.Block2D.time_block2d
# to tune this parameter until a C version of the `_block_info_recursion`
# algorithm is implemented which would likely be faster than the python
# version.
if list_ndim * final_size > (2 * 512 * 512):
return _block_slicing(arrays, list_ndim, result_ndim)
else:
return _block_concatenate(arrays, list_ndim, result_ndim)
# These helper functions are mostly used for testing.
# They allow us to write tests that directly call `_block_slicing`
# or `_block_concatenate` without blocking large arrays to force the wisdom
# to trigger the desired path.
def _block_setup(arrays):
"""
Returns
(`arrays`, list_ndim, result_ndim, final_size)
"""
bottom_index, arr_ndim, final_size = _block_check_depths_match(arrays)
list_ndim = len(bottom_index)
if bottom_index and bottom_index[-1] is None:
raise ValueError(
'List at {} cannot be empty'.format(
_block_format_index(bottom_index)
)
)
result_ndim = max(arr_ndim, list_ndim)
return arrays, list_ndim, result_ndim, final_size
def _block_slicing(arrays, list_ndim, result_ndim):
shape, slices, arrays = _block_info_recursion(
arrays, list_ndim, result_ndim)
dtype = _nx.result_type(*[arr.dtype for arr in arrays])
# Test preferring F only in the case that all input arrays are F
F_order = all(arr.flags['F_CONTIGUOUS'] for arr in arrays)
C_order = all(arr.flags['C_CONTIGUOUS'] for arr in arrays)
order = 'F' if F_order and not C_order else 'C'
result = _nx.empty(shape=shape, dtype=dtype, order=order)
# Note: In a c implementation, the function
# PyArray_CreateMultiSortedStridePerm could be used for more advanced
# guessing of the desired order.
for the_slice, arr in zip(slices, arrays):
result[(Ellipsis,) + the_slice] = arr
return result
def _block_concatenate(arrays, list_ndim, result_ndim):
result = _block(arrays, list_ndim, result_ndim)
if list_ndim == 0:
# Catch an edge case where _block returns a view because
# `arrays` is a single numpy array and not a list of numpy arrays.
# This might copy scalars or lists twice, but this isn't a likely
# usecase for those interested in performance
result = result.copy()
return result
| 28,985 | Python | 31.242492 | 81 | 0.590926 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_type_aliases.py | """
Due to compatibility, numpy has a very large number of different naming
conventions for the scalar types (those subclassing from `numpy.generic`).
This file produces a convoluted set of dictionaries mapping names to types,
and sometimes other mappings too.
.. data:: allTypes
A dictionary of names to types that will be exposed as attributes through
``np.core.numerictypes.*``
.. data:: sctypeDict
Similar to `allTypes`, but maps a broader set of aliases to their types.
.. data:: sctypes
A dictionary keyed by a "type group" string, providing a list of types
under that group.
"""
from numpy.compat import unicode
from numpy.core._string_helpers import english_lower
from numpy.core.multiarray import typeinfo, dtype
from numpy.core._dtype import _kind_name
sctypeDict = {} # Contains all leaf-node scalar types with aliases
allTypes = {} # Collect the types we will add to the module
# separate the actual type info from the abstract base classes
_abstract_types = {}
_concrete_typeinfo = {}
for k, v in typeinfo.items():
# make all the keys lowercase too
k = english_lower(k)
if isinstance(v, type):
_abstract_types[k] = v
else:
_concrete_typeinfo[k] = v
_concrete_types = {v.type for k, v in _concrete_typeinfo.items()}
def _bits_of(obj):
try:
info = next(v for v in _concrete_typeinfo.values() if v.type is obj)
except StopIteration:
if obj in _abstract_types.values():
msg = "Cannot count the bits of an abstract type"
raise ValueError(msg) from None
# some third-party type - make a best-guess
return dtype(obj).itemsize * 8
else:
return info.bits
def bitname(obj):
"""Return a bit-width name for a given type object"""
bits = _bits_of(obj)
dt = dtype(obj)
char = dt.kind
base = _kind_name(dt)
if base == 'object':
bits = 0
if bits != 0:
char = "%s%d" % (char, bits // 8)
return base, bits, char
def _add_types():
for name, info in _concrete_typeinfo.items():
# define C-name and insert typenum and typechar references also
allTypes[name] = info.type
sctypeDict[name] = info.type
sctypeDict[info.char] = info.type
sctypeDict[info.num] = info.type
for name, cls in _abstract_types.items():
allTypes[name] = cls
_add_types()
# This is the priority order used to assign the bit-sized NPY_INTxx names, which
# must match the order in npy_common.h in order for NPY_INTxx and np.intxx to be
# consistent.
# If two C types have the same size, then the earliest one in this list is used
# as the sized name.
_int_ctypes = ['long', 'longlong', 'int', 'short', 'byte']
_uint_ctypes = list('u' + t for t in _int_ctypes)
def _add_aliases():
for name, info in _concrete_typeinfo.items():
# these are handled by _add_integer_aliases
if name in _int_ctypes or name in _uint_ctypes:
continue
# insert bit-width version for this class (if relevant)
base, bit, char = bitname(info.type)
myname = "%s%d" % (base, bit)
# ensure that (c)longdouble does not overwrite the aliases assigned to
# (c)double
if name in ('longdouble', 'clongdouble') and myname in allTypes:
continue
allTypes[myname] = info.type
# add mapping for both the bit name and the numarray name
sctypeDict[myname] = info.type
# add forward, reverse, and string mapping to numarray
sctypeDict[char] = info.type
_add_aliases()
def _add_integer_aliases():
seen_bits = set()
for i_ctype, u_ctype in zip(_int_ctypes, _uint_ctypes):
i_info = _concrete_typeinfo[i_ctype]
u_info = _concrete_typeinfo[u_ctype]
bits = i_info.bits # same for both
for info, charname, intname in [
(i_info,'i%d' % (bits//8,), 'int%d' % bits),
(u_info,'u%d' % (bits//8,), 'uint%d' % bits)]:
if bits not in seen_bits:
# sometimes two different types have the same number of bits
# if so, the one iterated over first takes precedence
allTypes[intname] = info.type
sctypeDict[intname] = info.type
sctypeDict[charname] = info.type
seen_bits.add(bits)
_add_integer_aliases()
# We use these later
void = allTypes['void']
#
# Rework the Python names (so that float and complex and int are consistent
# with Python usage)
#
def _set_up_aliases():
type_pairs = [('complex_', 'cdouble'),
('int0', 'intp'),
('uint0', 'uintp'),
('single', 'float'),
('csingle', 'cfloat'),
('singlecomplex', 'cfloat'),
('float_', 'double'),
('intc', 'int'),
('uintc', 'uint'),
('int_', 'long'),
('uint', 'ulong'),
('cfloat', 'cdouble'),
('longfloat', 'longdouble'),
('clongfloat', 'clongdouble'),
('longcomplex', 'clongdouble'),
('bool_', 'bool'),
('bytes_', 'string'),
('string_', 'string'),
('str_', 'unicode'),
('unicode_', 'unicode'),
('object_', 'object')]
for alias, t in type_pairs:
allTypes[alias] = allTypes[t]
sctypeDict[alias] = sctypeDict[t]
# Remove aliases overriding python types and modules
to_remove = ['object', 'int', 'float',
'complex', 'bool', 'string', 'datetime', 'timedelta',
'bytes', 'str']
for t in to_remove:
try:
del allTypes[t]
del sctypeDict[t]
except KeyError:
pass
# Additional aliases in sctypeDict that should not be exposed as attributes
attrs_to_remove = ['ulong']
for t in attrs_to_remove:
try:
del allTypes[t]
except KeyError:
pass
_set_up_aliases()
sctypes = {'int': [],
'uint':[],
'float':[],
'complex':[],
'others':[bool, object, bytes, unicode, void]}
def _add_array_type(typename, bits):
try:
t = allTypes['%s%d' % (typename, bits)]
except KeyError:
pass
else:
sctypes[typename].append(t)
def _set_array_types():
ibytes = [1, 2, 4, 8, 16, 32, 64]
fbytes = [2, 4, 8, 10, 12, 16, 32, 64]
for bytes in ibytes:
bits = 8*bytes
_add_array_type('int', bits)
_add_array_type('uint', bits)
for bytes in fbytes:
bits = 8*bytes
_add_array_type('float', bits)
_add_array_type('complex', 2*bits)
_gi = dtype('p')
if _gi.type not in sctypes['int']:
indx = 0
sz = _gi.itemsize
_lst = sctypes['int']
while (indx < len(_lst) and sz >= _lst[indx](0).itemsize):
indx += 1
sctypes['int'].insert(indx, _gi.type)
sctypes['uint'].insert(indx, dtype('P').type)
_set_array_types()
# Add additional strings to the sctypeDict
_toadd = ['int', 'float', 'complex', 'bool', 'object',
'str', 'bytes', ('a', 'bytes_')]
for name in _toadd:
if isinstance(name, tuple):
sctypeDict[name[0]] = allTypes[name[1]]
else:
sctypeDict[name] = allTypes['%s_' % name]
del _toadd, name
| 7,490 | Python | 29.57551 | 80 | 0.566088 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/numerictypes.py | """
numerictypes: Define the numeric type objects
This module is designed so "from numerictypes import \\*" is safe.
Exported symbols include:
Dictionary with all registered number types (including aliases):
sctypeDict
Type objects (not all will be available, depends on platform):
see variable sctypes for which ones you have
Bit-width names
int8 int16 int32 int64 int128
uint8 uint16 uint32 uint64 uint128
float16 float32 float64 float96 float128 float256
complex32 complex64 complex128 complex192 complex256 complex512
datetime64 timedelta64
c-based names
bool_
object_
void, str_, unicode_
byte, ubyte,
short, ushort
intc, uintc,
intp, uintp,
int_, uint,
longlong, ulonglong,
single, csingle,
float_, complex_,
longfloat, clongfloat,
As part of the type-hierarchy: xx -- is bit-width
generic
+-> bool_ (kind=b)
+-> number
| +-> integer
| | +-> signedinteger (intxx) (kind=i)
| | | byte
| | | short
| | | intc
| | | intp int0
| | | int_
| | | longlong
| | \\-> unsignedinteger (uintxx) (kind=u)
| | ubyte
| | ushort
| | uintc
| | uintp uint0
| | uint_
| | ulonglong
| +-> inexact
| +-> floating (floatxx) (kind=f)
| | half
| | single
| | float_ (double)
| | longfloat
| \\-> complexfloating (complexxx) (kind=c)
| csingle (singlecomplex)
| complex_ (cfloat, cdouble)
| clongfloat (longcomplex)
+-> flexible
| +-> character
| | str_ (string_, bytes_) (kind=S) [Python 2]
| | unicode_ (kind=U) [Python 2]
| |
| | bytes_ (string_) (kind=S) [Python 3]
| | str_ (unicode_) (kind=U) [Python 3]
| |
| \\-> void (kind=V)
\\-> object_ (not used much) (kind=O)
"""
import numbers
from numpy.core.multiarray import (
ndarray, array, dtype, datetime_data, datetime_as_string,
busday_offset, busday_count, is_busday, busdaycalendar
)
from numpy.core.overrides import set_module
# we add more at the bottom
__all__ = ['sctypeDict', 'sctypes',
'ScalarType', 'obj2sctype', 'cast', 'nbytes', 'sctype2char',
'maximum_sctype', 'issctype', 'typecodes', 'find_common_type',
'issubdtype', 'datetime_data', 'datetime_as_string',
'busday_offset', 'busday_count', 'is_busday', 'busdaycalendar',
]
# we don't need all these imports, but we need to keep them for compatibility
# for users using np.core.numerictypes.UPPER_TABLE
from ._string_helpers import (
english_lower, english_upper, english_capitalize, LOWER_TABLE, UPPER_TABLE
)
from ._type_aliases import (
sctypeDict,
allTypes,
bitname,
sctypes,
_concrete_types,
_concrete_typeinfo,
_bits_of,
)
from ._dtype import _kind_name
# we don't export these for import *, but we do want them accessible
# as numerictypes.bool, etc.
from builtins import bool, int, float, complex, object, str, bytes
from numpy.compat import long, unicode
# We use this later
generic = allTypes['generic']
genericTypeRank = ['bool', 'int8', 'uint8', 'int16', 'uint16',
'int32', 'uint32', 'int64', 'uint64', 'int128',
'uint128', 'float16',
'float32', 'float64', 'float80', 'float96', 'float128',
'float256',
'complex32', 'complex64', 'complex128', 'complex160',
'complex192', 'complex256', 'complex512', 'object']
@set_module('numpy')
def maximum_sctype(t):
"""
Return the scalar type of highest precision of the same kind as the input.
Parameters
----------
t : dtype or dtype specifier
The input data type. This can be a `dtype` object or an object that
is convertible to a `dtype`.
Returns
-------
out : dtype
The highest precision data type of the same kind (`dtype.kind`) as `t`.
See Also
--------
obj2sctype, mintypecode, sctype2char
dtype
Examples
--------
>>> np.maximum_sctype(int)
<class 'numpy.int64'>
>>> np.maximum_sctype(np.uint8)
<class 'numpy.uint64'>
>>> np.maximum_sctype(complex)
<class 'numpy.complex256'> # may vary
>>> np.maximum_sctype(str)
<class 'numpy.str_'>
>>> np.maximum_sctype('i2')
<class 'numpy.int64'>
>>> np.maximum_sctype('f4')
<class 'numpy.float128'> # may vary
"""
g = obj2sctype(t)
if g is None:
return t
t = g
base = _kind_name(dtype(t))
if base in sctypes:
return sctypes[base][-1]
else:
return t
@set_module('numpy')
def issctype(rep):
"""
Determines whether the given object represents a scalar data-type.
Parameters
----------
rep : any
If `rep` is an instance of a scalar dtype, True is returned. If not,
False is returned.
Returns
-------
out : bool
Boolean result of check whether `rep` is a scalar dtype.
See Also
--------
issubsctype, issubdtype, obj2sctype, sctype2char
Examples
--------
>>> np.issctype(np.int32)
True
>>> np.issctype(list)
False
>>> np.issctype(1.1)
False
Strings are also a scalar type:
>>> np.issctype(np.dtype('str'))
True
"""
if not isinstance(rep, (type, dtype)):
return False
try:
res = obj2sctype(rep)
if res and res != object_:
return True
return False
except Exception:
return False
@set_module('numpy')
def obj2sctype(rep, default=None):
"""
Return the scalar dtype or NumPy equivalent of Python type of an object.
Parameters
----------
rep : any
The object of which the type is returned.
default : any, optional
If given, this is returned for objects whose types can not be
determined. If not given, None is returned for those objects.
Returns
-------
dtype : dtype or Python type
The data type of `rep`.
See Also
--------
sctype2char, issctype, issubsctype, issubdtype, maximum_sctype
Examples
--------
>>> np.obj2sctype(np.int32)
<class 'numpy.int32'>
>>> np.obj2sctype(np.array([1., 2.]))
<class 'numpy.float64'>
>>> np.obj2sctype(np.array([1.j]))
<class 'numpy.complex128'>
>>> np.obj2sctype(dict)
<class 'numpy.object_'>
>>> np.obj2sctype('string')
>>> np.obj2sctype(1, default=list)
<class 'list'>
"""
# prevent abstract classes being upcast
if isinstance(rep, type) and issubclass(rep, generic):
return rep
# extract dtype from arrays
if isinstance(rep, ndarray):
return rep.dtype.type
# fall back on dtype to convert
try:
res = dtype(rep)
except Exception:
return default
else:
return res.type
@set_module('numpy')
def issubclass_(arg1, arg2):
"""
Determine if a class is a subclass of a second class.
`issubclass_` is equivalent to the Python built-in ``issubclass``,
except that it returns False instead of raising a TypeError if one
of the arguments is not a class.
Parameters
----------
arg1 : class
Input class. True is returned if `arg1` is a subclass of `arg2`.
arg2 : class or tuple of classes.
Input class. If a tuple of classes, True is returned if `arg1` is a
subclass of any of the tuple elements.
Returns
-------
out : bool
Whether `arg1` is a subclass of `arg2` or not.
See Also
--------
issubsctype, issubdtype, issctype
Examples
--------
>>> np.issubclass_(np.int32, int)
False
>>> np.issubclass_(np.int32, float)
False
>>> np.issubclass_(np.float64, float)
True
"""
try:
return issubclass(arg1, arg2)
except TypeError:
return False
@set_module('numpy')
def issubsctype(arg1, arg2):
"""
Determine if the first argument is a subclass of the second argument.
Parameters
----------
arg1, arg2 : dtype or dtype specifier
Data-types.
Returns
-------
out : bool
The result.
See Also
--------
issctype, issubdtype, obj2sctype
Examples
--------
>>> np.issubsctype('S8', str)
False
>>> np.issubsctype(np.array([1]), int)
True
>>> np.issubsctype(np.array([1]), float)
False
"""
return issubclass(obj2sctype(arg1), obj2sctype(arg2))
@set_module('numpy')
def issubdtype(arg1, arg2):
r"""
Returns True if first argument is a typecode lower/equal in type hierarchy.
This is like the builtin :func:`issubclass`, but for `dtype`\ s.
Parameters
----------
arg1, arg2 : dtype_like
`dtype` or object coercible to one
Returns
-------
out : bool
See Also
--------
:ref:`arrays.scalars` : Overview of the numpy type hierarchy.
issubsctype, issubclass_
Examples
--------
`issubdtype` can be used to check the type of arrays:
>>> ints = np.array([1, 2, 3], dtype=np.int32)
>>> np.issubdtype(ints.dtype, np.integer)
True
>>> np.issubdtype(ints.dtype, np.floating)
False
>>> floats = np.array([1, 2, 3], dtype=np.float32)
>>> np.issubdtype(floats.dtype, np.integer)
False
>>> np.issubdtype(floats.dtype, np.floating)
True
Similar types of different sizes are not subdtypes of each other:
>>> np.issubdtype(np.float64, np.float32)
False
>>> np.issubdtype(np.float32, np.float64)
False
but both are subtypes of `floating`:
>>> np.issubdtype(np.float64, np.floating)
True
>>> np.issubdtype(np.float32, np.floating)
True
For convenience, dtype-like objects are allowed too:
>>> np.issubdtype('S1', np.string_)
True
>>> np.issubdtype('i4', np.signedinteger)
True
"""
if not issubclass_(arg1, generic):
arg1 = dtype(arg1).type
if not issubclass_(arg2, generic):
arg2 = dtype(arg2).type
return issubclass(arg1, arg2)
# This dictionary allows look up based on any alias for an array data-type
class _typedict(dict):
"""
Base object for a dictionary for look-up with any alias for an array dtype.
Instances of `_typedict` can not be used as dictionaries directly,
first they have to be populated.
"""
def __getitem__(self, obj):
return dict.__getitem__(self, obj2sctype(obj))
nbytes = _typedict()
_alignment = _typedict()
_maxvals = _typedict()
_minvals = _typedict()
def _construct_lookups():
for name, info in _concrete_typeinfo.items():
obj = info.type
nbytes[obj] = info.bits // 8
_alignment[obj] = info.alignment
if len(info) > 5:
_maxvals[obj] = info.max
_minvals[obj] = info.min
else:
_maxvals[obj] = None
_minvals[obj] = None
_construct_lookups()
@set_module('numpy')
def sctype2char(sctype):
"""
Return the string representation of a scalar dtype.
Parameters
----------
sctype : scalar dtype or object
If a scalar dtype, the corresponding string character is
returned. If an object, `sctype2char` tries to infer its scalar type
and then return the corresponding string character.
Returns
-------
typechar : str
The string character corresponding to the scalar type.
Raises
------
ValueError
If `sctype` is an object for which the type can not be inferred.
See Also
--------
obj2sctype, issctype, issubsctype, mintypecode
Examples
--------
>>> for sctype in [np.int32, np.double, np.complex_, np.string_, np.ndarray]:
... print(np.sctype2char(sctype))
l # may vary
d
D
S
O
>>> x = np.array([1., 2-1.j])
>>> np.sctype2char(x)
'D'
>>> np.sctype2char(list)
'O'
"""
sctype = obj2sctype(sctype)
if sctype is None:
raise ValueError("unrecognized type")
if sctype not in _concrete_types:
# for compatibility
raise KeyError(sctype)
return dtype(sctype).char
# Create dictionary of casting functions that wrap sequences
# indexed by type or type character
cast = _typedict()
for key in _concrete_types:
cast[key] = lambda x, k=key: array(x, copy=False).astype(k)
def _scalar_type_key(typ):
"""A ``key`` function for `sorted`."""
dt = dtype(typ)
return (dt.kind.lower(), dt.itemsize)
ScalarType = [int, float, complex, bool, bytes, str, memoryview]
ScalarType += sorted(_concrete_types, key=_scalar_type_key)
ScalarType = tuple(ScalarType)
# Now add the types we've determined to this module
for key in allTypes:
globals()[key] = allTypes[key]
__all__.append(key)
del key
typecodes = {'Character':'c',
'Integer':'bhilqp',
'UnsignedInteger':'BHILQP',
'Float':'efdg',
'Complex':'FDG',
'AllInteger':'bBhHiIlLqQpP',
'AllFloat':'efdgFDG',
'Datetime': 'Mm',
'All':'?bhilqpBHILQPefdgFDGSUVOMm'}
# backwards compatibility --- deprecated name
# Formal deprecation: Numpy 1.20.0, 2020-10-19 (see numpy/__init__.py)
typeDict = sctypeDict
# b -> boolean
# u -> unsigned integer
# i -> signed integer
# f -> floating point
# c -> complex
# M -> datetime
# m -> timedelta
# S -> string
# U -> Unicode string
# V -> record
# O -> Python object
_kind_list = ['b', 'u', 'i', 'f', 'c', 'S', 'U', 'V', 'O', 'M', 'm']
__test_types = '?'+typecodes['AllInteger'][:-2]+typecodes['AllFloat']+'O'
__len_test_types = len(__test_types)
# Keep incrementing until a common type both can be coerced to
# is found. Otherwise, return None
def _find_common_coerce(a, b):
if a > b:
return a
try:
thisind = __test_types.index(a.char)
except ValueError:
return None
return _can_coerce_all([a, b], start=thisind)
# Find a data-type that all data-types in a list can be coerced to
def _can_coerce_all(dtypelist, start=0):
N = len(dtypelist)
if N == 0:
return None
if N == 1:
return dtypelist[0]
thisind = start
while thisind < __len_test_types:
newdtype = dtype(__test_types[thisind])
numcoerce = len([x for x in dtypelist if newdtype >= x])
if numcoerce == N:
return newdtype
thisind += 1
return None
def _register_types():
numbers.Integral.register(integer)
numbers.Complex.register(inexact)
numbers.Real.register(floating)
numbers.Number.register(number)
_register_types()
@set_module('numpy')
def find_common_type(array_types, scalar_types):
"""
Determine common type following standard coercion rules.
Parameters
----------
array_types : sequence
A list of dtypes or dtype convertible objects representing arrays.
scalar_types : sequence
A list of dtypes or dtype convertible objects representing scalars.
Returns
-------
datatype : dtype
The common data type, which is the maximum of `array_types` ignoring
`scalar_types`, unless the maximum of `scalar_types` is of a
different kind (`dtype.kind`). If the kind is not understood, then
None is returned.
See Also
--------
dtype, common_type, can_cast, mintypecode
Examples
--------
>>> np.find_common_type([], [np.int64, np.float32, complex])
dtype('complex128')
>>> np.find_common_type([np.int64, np.float32], [])
dtype('float64')
The standard casting rules ensure that a scalar cannot up-cast an
array unless the scalar is of a fundamentally different kind of data
(i.e. under a different hierarchy in the data type hierarchy) then
the array:
>>> np.find_common_type([np.float32], [np.int64, np.float64])
dtype('float32')
Complex is of a different type, so it up-casts the float in the
`array_types` argument:
>>> np.find_common_type([np.float32], [complex])
dtype('complex128')
Type specifier strings are convertible to dtypes and can therefore
be used instead of dtypes:
>>> np.find_common_type(['f4', 'f4', 'i4'], ['c8'])
dtype('complex128')
"""
array_types = [dtype(x) for x in array_types]
scalar_types = [dtype(x) for x in scalar_types]
maxa = _can_coerce_all(array_types)
maxsc = _can_coerce_all(scalar_types)
if maxa is None:
return maxsc
if maxsc is None:
return maxa
try:
index_a = _kind_list.index(maxa.kind)
index_sc = _kind_list.index(maxsc.kind)
except ValueError:
return None
if index_sc > index_a:
return _find_common_coerce(maxsc, maxa)
else:
return maxa
| 17,271 | Python | 24.740686 | 81 | 0.584564 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_methods.py | """
Array methods which are called by both the C-code for the method
and the Python code for the NumPy-namespace function
"""
import warnings
from contextlib import nullcontext
from numpy.core import multiarray as mu
from numpy.core import umath as um
from numpy.core.multiarray import asanyarray
from numpy.core import numerictypes as nt
from numpy.core import _exceptions
from numpy._globals import _NoValue
from numpy.compat import pickle, os_fspath
# save those O(100) nanoseconds!
umr_maximum = um.maximum.reduce
umr_minimum = um.minimum.reduce
umr_sum = um.add.reduce
umr_prod = um.multiply.reduce
umr_any = um.logical_or.reduce
umr_all = um.logical_and.reduce
# Complex types to -> (2,)float view for fast-path computation in _var()
_complex_to_float = {
nt.dtype(nt.csingle) : nt.dtype(nt.single),
nt.dtype(nt.cdouble) : nt.dtype(nt.double),
}
# Special case for windows: ensure double takes precedence
if nt.dtype(nt.longdouble) != nt.dtype(nt.double):
_complex_to_float.update({
nt.dtype(nt.clongdouble) : nt.dtype(nt.longdouble),
})
# avoid keyword arguments to speed up parsing, saves about 15%-20% for very
# small reductions
def _amax(a, axis=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_maximum(a, axis, None, out, keepdims, initial, where)
def _amin(a, axis=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_minimum(a, axis, None, out, keepdims, initial, where)
def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_prod(a, axis, dtype, out, keepdims, initial, where)
def _any(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
# Parsing keyword arguments is currently fairly slow, so avoid it for now
if where is True:
return umr_any(a, axis, dtype, out, keepdims)
return umr_any(a, axis, dtype, out, keepdims, where=where)
def _all(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
# Parsing keyword arguments is currently fairly slow, so avoid it for now
if where is True:
return umr_all(a, axis, dtype, out, keepdims)
return umr_all(a, axis, dtype, out, keepdims, where=where)
def _count_reduce_items(arr, axis, keepdims=False, where=True):
# fast-path for the default case
if where is True:
# no boolean mask given, calculate items according to axis
if axis is None:
axis = tuple(range(arr.ndim))
elif not isinstance(axis, tuple):
axis = (axis,)
items = 1
for ax in axis:
items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)]
items = nt.intp(items)
else:
# TODO: Optimize case when `where` is broadcast along a non-reduction
# axis and full sum is more excessive than needed.
# guarded to protect circular imports
from numpy.lib.stride_tricks import broadcast_to
# count True values in (potentially broadcasted) boolean mask
items = umr_sum(broadcast_to(where, arr.shape), axis, nt.intp, None,
keepdims)
return items
# Numpy 1.17.0, 2019-02-24
# Various clip behavior deprecations, marked with _clip_dep as a prefix.
def _clip_dep_is_scalar_nan(a):
# guarded to protect circular imports
from numpy.core.fromnumeric import ndim
if ndim(a) != 0:
return False
try:
return um.isnan(a)
except TypeError:
return False
def _clip_dep_is_byte_swapped(a):
if isinstance(a, mu.ndarray):
return not a.dtype.isnative
return False
def _clip_dep_invoke_with_casting(ufunc, *args, out=None, casting=None, **kwargs):
# normal path
if casting is not None:
return ufunc(*args, out=out, casting=casting, **kwargs)
# try to deal with broken casting rules
try:
return ufunc(*args, out=out, **kwargs)
except _exceptions._UFuncOutputCastingError as e:
# Numpy 1.17.0, 2019-02-24
warnings.warn(
"Converting the output of clip from {!r} to {!r} is deprecated. "
"Pass `casting=\"unsafe\"` explicitly to silence this warning, or "
"correct the type of the variables.".format(e.from_, e.to),
DeprecationWarning,
stacklevel=2
)
return ufunc(*args, out=out, casting="unsafe", **kwargs)
def _clip(a, min=None, max=None, out=None, *, casting=None, **kwargs):
if min is None and max is None:
raise ValueError("One of max or min must be given")
# Numpy 1.17.0, 2019-02-24
# This deprecation probably incurs a substantial slowdown for small arrays,
# it will be good to get rid of it.
if not _clip_dep_is_byte_swapped(a) and not _clip_dep_is_byte_swapped(out):
using_deprecated_nan = False
if _clip_dep_is_scalar_nan(min):
min = -float('inf')
using_deprecated_nan = True
if _clip_dep_is_scalar_nan(max):
max = float('inf')
using_deprecated_nan = True
if using_deprecated_nan:
warnings.warn(
"Passing `np.nan` to mean no clipping in np.clip has always "
"been unreliable, and is now deprecated. "
"In future, this will always return nan, like it already does "
"when min or max are arrays that contain nan. "
"To skip a bound, pass either None or an np.inf of an "
"appropriate sign.",
DeprecationWarning,
stacklevel=2
)
if min is None:
return _clip_dep_invoke_with_casting(
um.minimum, a, max, out=out, casting=casting, **kwargs)
elif max is None:
return _clip_dep_invoke_with_casting(
um.maximum, a, min, out=out, casting=casting, **kwargs)
else:
return _clip_dep_invoke_with_casting(
um.clip, a, min, max, out=out, casting=casting, **kwargs)
def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
arr = asanyarray(a)
is_float16_result = False
rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
if rcount == 0 if where is True else umr_any(rcount == 0, axis=None):
warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None:
if issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
elif issubclass(arr.dtype.type, nt.float16):
dtype = mu.dtype('f4')
is_float16_result = True
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
if is_float16_result and out is None:
ret = arr.dtype.type(ret)
elif hasattr(ret, 'dtype'):
if is_float16_result:
ret = arr.dtype.type(ret / rcount)
else:
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
where=True):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
# Make this warning show up on top.
if ddof >= rcount if where is True else umr_any(ddof >= rcount, axis=None):
warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning,
stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
# Compute the mean.
# Note that if dtype is not of inexact type then arraymean will
# not be either.
arrmean = umr_sum(arr, axis, dtype, keepdims=True, where=where)
# The shape of rcount has to match arrmean to not change the shape of out
# in broadcasting. Otherwise, it cannot be stored back to arrmean.
if rcount.ndim == 0:
# fast-path for default case when where is True
div = rcount
else:
# matching rcount to arrmean when where is specified as array
div = rcount.reshape(arrmean.shape)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(arrmean, div, out=arrmean, casting='unsafe',
subok=False)
elif hasattr(arrmean, "dtype"):
arrmean = arrmean.dtype.type(arrmean / rcount)
else:
arrmean = arrmean / rcount
# Compute sum of squared deviations from mean
# Note that x may not be inexact and that we need it to be an array,
# not a scalar.
x = asanyarray(arr - arrmean)
if issubclass(arr.dtype.type, (nt.floating, nt.integer)):
x = um.multiply(x, x, out=x)
# Fast-paths for built-in complex types
elif x.dtype in _complex_to_float:
xv = x.view(dtype=(_complex_to_float[x.dtype], (2,)))
um.multiply(xv, xv, out=xv)
x = um.add(xv[..., 0], xv[..., 1], out=x.real).real
# Most general case; includes handling object arrays containing imaginary
# numbers and complex types with non-native byteorder
else:
x = um.multiply(x, um.conjugate(x), out=x).real
ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where)
# Compute degrees of freedom and make sure it is not negative.
rcount = um.maximum(rcount - ddof, 0)
# divide by degrees of freedom
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
where=True):
ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims, where=where)
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(um.sqrt(ret))
else:
ret = um.sqrt(ret)
return ret
def _ptp(a, axis=None, out=None, keepdims=False):
return um.subtract(
umr_maximum(a, axis, None, out, keepdims),
umr_minimum(a, axis, None, None, keepdims),
out
)
def _dump(self, file, protocol=2):
if hasattr(file, 'write'):
ctx = nullcontext(file)
else:
ctx = open(os_fspath(file), "wb")
with ctx as f:
pickle.dump(self, f, protocol=protocol)
def _dumps(self, protocol=2):
return pickle.dumps(self, protocol=protocol)
| 10,887 | Python | 36.034013 | 82 | 0.629834 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/_string_helpers.py | """
String-handling utilities to avoid locale-dependence.
Used primarily to generate type name aliases.
"""
# "import string" is costly to import!
# Construct the translation tables directly
# "A" = chr(65), "a" = chr(97)
_all_chars = [chr(_m) for _m in range(256)]
_ascii_upper = _all_chars[65:65+26]
_ascii_lower = _all_chars[97:97+26]
LOWER_TABLE = "".join(_all_chars[:65] + _ascii_lower + _all_chars[65+26:])
UPPER_TABLE = "".join(_all_chars[:97] + _ascii_upper + _all_chars[97+26:])
def english_lower(s):
""" Apply English case rules to convert ASCII strings to all lower case.
This is an internal utility function to replace calls to str.lower() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "I".lower() != "i" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
lowered : str
Examples
--------
>>> from numpy.core.numerictypes import english_lower
>>> english_lower('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz0123456789_'
>>> english_lower('')
''
"""
lowered = s.translate(LOWER_TABLE)
return lowered
def english_upper(s):
""" Apply English case rules to convert ASCII strings to all upper case.
This is an internal utility function to replace calls to str.upper() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "i".upper() != "I" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
uppered : str
Examples
--------
>>> from numpy.core.numerictypes import english_upper
>>> english_upper('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_'
>>> english_upper('')
''
"""
uppered = s.translate(UPPER_TABLE)
return uppered
def english_capitalize(s):
""" Apply English case rules to convert the first character of an ASCII
string to upper case.
This is an internal utility function to replace calls to str.capitalize()
such that we can avoid changing behavior with changing locales.
Parameters
----------
s : str
Returns
-------
capitalized : str
Examples
--------
>>> from numpy.core.numerictypes import english_capitalize
>>> english_capitalize('int8')
'Int8'
>>> english_capitalize('Int8')
'Int8'
>>> english_capitalize('')
''
"""
if s:
return english_upper(s[0]) + s[1:]
else:
return s
| 2,855 | Python | 27.277227 | 88 | 0.654291 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/npy_cpu.h | /*
* This set (target) cpu specific macros:
* - Possible values:
* NPY_CPU_X86
* NPY_CPU_AMD64
* NPY_CPU_PPC
* NPY_CPU_PPC64
* NPY_CPU_PPC64LE
* NPY_CPU_SPARC
* NPY_CPU_S390
* NPY_CPU_IA64
* NPY_CPU_HPPA
* NPY_CPU_ALPHA
* NPY_CPU_ARMEL
* NPY_CPU_ARMEB
* NPY_CPU_SH_LE
* NPY_CPU_SH_BE
* NPY_CPU_ARCEL
* NPY_CPU_ARCEB
* NPY_CPU_RISCV64
* NPY_CPU_LOONGARCH
* NPY_CPU_WASM
*/
#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_
#include "numpyconfig.h"
#if defined( __i386__ ) || defined(i386) || defined(_M_IX86)
/*
* __i386__ is defined by gcc and Intel compiler on Linux,
* _M_IX86 by VS compiler,
* i386 by Sun compilers on opensolaris at least
*/
#define NPY_CPU_X86
#elif defined(__x86_64__) || defined(__amd64__) || defined(__x86_64) || defined(_M_AMD64)
/*
* both __x86_64__ and __amd64__ are defined by gcc
* __x86_64 defined by sun compiler on opensolaris at least
* _M_AMD64 defined by MS compiler
*/
#define NPY_CPU_AMD64
#elif defined(__powerpc64__) && defined(__LITTLE_ENDIAN__)
#define NPY_CPU_PPC64LE
#elif defined(__powerpc64__) && defined(__BIG_ENDIAN__)
#define NPY_CPU_PPC64
#elif defined(__ppc__) || defined(__powerpc__) || defined(_ARCH_PPC)
/*
* __ppc__ is defined by gcc, I remember having seen __powerpc__ once,
* but can't find it ATM
* _ARCH_PPC is used by at least gcc on AIX
* As __powerpc__ and _ARCH_PPC are also defined by PPC64 check
* for those specifically first before defaulting to ppc
*/
#define NPY_CPU_PPC
#elif defined(__sparc__) || defined(__sparc)
/* __sparc__ is defined by gcc and Forte (e.g. Sun) compilers */
#define NPY_CPU_SPARC
#elif defined(__s390__)
#define NPY_CPU_S390
#elif defined(__ia64)
#define NPY_CPU_IA64
#elif defined(__hppa)
#define NPY_CPU_HPPA
#elif defined(__alpha__)
#define NPY_CPU_ALPHA
#elif defined(__arm__) || defined(__aarch64__) || defined(_M_ARM64)
/* _M_ARM64 is defined in MSVC for ARM64 compilation on Windows */
#if defined(__ARMEB__) || defined(__AARCH64EB__)
#if defined(__ARM_32BIT_STATE)
#define NPY_CPU_ARMEB_AARCH32
#elif defined(__ARM_64BIT_STATE)
#define NPY_CPU_ARMEB_AARCH64
#else
#define NPY_CPU_ARMEB
#endif
#elif defined(__ARMEL__) || defined(__AARCH64EL__) || defined(_M_ARM64)
#if defined(__ARM_32BIT_STATE)
#define NPY_CPU_ARMEL_AARCH32
#elif defined(__ARM_64BIT_STATE) || defined(_M_ARM64)
#define NPY_CPU_ARMEL_AARCH64
#else
#define NPY_CPU_ARMEL
#endif
#else
# error Unknown ARM CPU, please report this to numpy maintainers with \
information about your platform (OS, CPU and compiler)
#endif
#elif defined(__sh__) && defined(__LITTLE_ENDIAN__)
#define NPY_CPU_SH_LE
#elif defined(__sh__) && defined(__BIG_ENDIAN__)
#define NPY_CPU_SH_BE
#elif defined(__MIPSEL__)
#define NPY_CPU_MIPSEL
#elif defined(__MIPSEB__)
#define NPY_CPU_MIPSEB
#elif defined(__or1k__)
#define NPY_CPU_OR1K
#elif defined(__mc68000__)
#define NPY_CPU_M68K
#elif defined(__arc__) && defined(__LITTLE_ENDIAN__)
#define NPY_CPU_ARCEL
#elif defined(__arc__) && defined(__BIG_ENDIAN__)
#define NPY_CPU_ARCEB
#elif defined(__riscv) && defined(__riscv_xlen) && __riscv_xlen == 64
#define NPY_CPU_RISCV64
#elif defined(__loongarch__)
#define NPY_CPU_LOONGARCH
#elif defined(__EMSCRIPTEN__)
/* __EMSCRIPTEN__ is defined by emscripten: an LLVM-to-Web compiler */
#define NPY_CPU_WASM
#else
#error Unknown CPU, please report this to numpy maintainers with \
information about your platform (OS, CPU and compiler)
#endif
/*
* Except for the following architectures, memory access is limited to the natural
* alignment of data types otherwise it may lead to bus error or performance regression.
* For more details about unaligned access, see https://www.kernel.org/doc/Documentation/unaligned-memory-access.txt.
*/
#if defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64) || defined(__aarch64__) || defined(__powerpc64__)
#define NPY_ALIGNMENT_REQUIRED 0
#endif
#ifndef NPY_ALIGNMENT_REQUIRED
#define NPY_ALIGNMENT_REQUIRED 1
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_ */
| 4,603 | C | 34.415384 | 117 | 0.606126 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/old_defines.h | /* This header is deprecated as of NumPy 1.7 */
#ifndef NUMPY_CORE_INCLUDE_NUMPY_OLD_DEFINES_H_
#define NUMPY_CORE_INCLUDE_NUMPY_OLD_DEFINES_H_
#if defined(NPY_NO_DEPRECATED_API) && NPY_NO_DEPRECATED_API >= NPY_1_7_API_VERSION
#error The header "old_defines.h" is deprecated as of NumPy 1.7.
#endif
#define NDARRAY_VERSION NPY_VERSION
#define PyArray_MIN_BUFSIZE NPY_MIN_BUFSIZE
#define PyArray_MAX_BUFSIZE NPY_MAX_BUFSIZE
#define PyArray_BUFSIZE NPY_BUFSIZE
#define PyArray_PRIORITY NPY_PRIORITY
#define PyArray_SUBTYPE_PRIORITY NPY_PRIORITY
#define PyArray_NUM_FLOATTYPE NPY_NUM_FLOATTYPE
#define NPY_MAX PyArray_MAX
#define NPY_MIN PyArray_MIN
#define PyArray_TYPES NPY_TYPES
#define PyArray_BOOL NPY_BOOL
#define PyArray_BYTE NPY_BYTE
#define PyArray_UBYTE NPY_UBYTE
#define PyArray_SHORT NPY_SHORT
#define PyArray_USHORT NPY_USHORT
#define PyArray_INT NPY_INT
#define PyArray_UINT NPY_UINT
#define PyArray_LONG NPY_LONG
#define PyArray_ULONG NPY_ULONG
#define PyArray_LONGLONG NPY_LONGLONG
#define PyArray_ULONGLONG NPY_ULONGLONG
#define PyArray_HALF NPY_HALF
#define PyArray_FLOAT NPY_FLOAT
#define PyArray_DOUBLE NPY_DOUBLE
#define PyArray_LONGDOUBLE NPY_LONGDOUBLE
#define PyArray_CFLOAT NPY_CFLOAT
#define PyArray_CDOUBLE NPY_CDOUBLE
#define PyArray_CLONGDOUBLE NPY_CLONGDOUBLE
#define PyArray_OBJECT NPY_OBJECT
#define PyArray_STRING NPY_STRING
#define PyArray_UNICODE NPY_UNICODE
#define PyArray_VOID NPY_VOID
#define PyArray_DATETIME NPY_DATETIME
#define PyArray_TIMEDELTA NPY_TIMEDELTA
#define PyArray_NTYPES NPY_NTYPES
#define PyArray_NOTYPE NPY_NOTYPE
#define PyArray_CHAR NPY_CHAR
#define PyArray_USERDEF NPY_USERDEF
#define PyArray_NUMUSERTYPES NPY_NUMUSERTYPES
#define PyArray_INTP NPY_INTP
#define PyArray_UINTP NPY_UINTP
#define PyArray_INT8 NPY_INT8
#define PyArray_UINT8 NPY_UINT8
#define PyArray_INT16 NPY_INT16
#define PyArray_UINT16 NPY_UINT16
#define PyArray_INT32 NPY_INT32
#define PyArray_UINT32 NPY_UINT32
#ifdef NPY_INT64
#define PyArray_INT64 NPY_INT64
#define PyArray_UINT64 NPY_UINT64
#endif
#ifdef NPY_INT128
#define PyArray_INT128 NPY_INT128
#define PyArray_UINT128 NPY_UINT128
#endif
#ifdef NPY_FLOAT16
#define PyArray_FLOAT16 NPY_FLOAT16
#define PyArray_COMPLEX32 NPY_COMPLEX32
#endif
#ifdef NPY_FLOAT80
#define PyArray_FLOAT80 NPY_FLOAT80
#define PyArray_COMPLEX160 NPY_COMPLEX160
#endif
#ifdef NPY_FLOAT96
#define PyArray_FLOAT96 NPY_FLOAT96
#define PyArray_COMPLEX192 NPY_COMPLEX192
#endif
#ifdef NPY_FLOAT128
#define PyArray_FLOAT128 NPY_FLOAT128
#define PyArray_COMPLEX256 NPY_COMPLEX256
#endif
#define PyArray_FLOAT32 NPY_FLOAT32
#define PyArray_COMPLEX64 NPY_COMPLEX64
#define PyArray_FLOAT64 NPY_FLOAT64
#define PyArray_COMPLEX128 NPY_COMPLEX128
#define PyArray_TYPECHAR NPY_TYPECHAR
#define PyArray_BOOLLTR NPY_BOOLLTR
#define PyArray_BYTELTR NPY_BYTELTR
#define PyArray_UBYTELTR NPY_UBYTELTR
#define PyArray_SHORTLTR NPY_SHORTLTR
#define PyArray_USHORTLTR NPY_USHORTLTR
#define PyArray_INTLTR NPY_INTLTR
#define PyArray_UINTLTR NPY_UINTLTR
#define PyArray_LONGLTR NPY_LONGLTR
#define PyArray_ULONGLTR NPY_ULONGLTR
#define PyArray_LONGLONGLTR NPY_LONGLONGLTR
#define PyArray_ULONGLONGLTR NPY_ULONGLONGLTR
#define PyArray_HALFLTR NPY_HALFLTR
#define PyArray_FLOATLTR NPY_FLOATLTR
#define PyArray_DOUBLELTR NPY_DOUBLELTR
#define PyArray_LONGDOUBLELTR NPY_LONGDOUBLELTR
#define PyArray_CFLOATLTR NPY_CFLOATLTR
#define PyArray_CDOUBLELTR NPY_CDOUBLELTR
#define PyArray_CLONGDOUBLELTR NPY_CLONGDOUBLELTR
#define PyArray_OBJECTLTR NPY_OBJECTLTR
#define PyArray_STRINGLTR NPY_STRINGLTR
#define PyArray_STRINGLTR2 NPY_STRINGLTR2
#define PyArray_UNICODELTR NPY_UNICODELTR
#define PyArray_VOIDLTR NPY_VOIDLTR
#define PyArray_DATETIMELTR NPY_DATETIMELTR
#define PyArray_TIMEDELTALTR NPY_TIMEDELTALTR
#define PyArray_CHARLTR NPY_CHARLTR
#define PyArray_INTPLTR NPY_INTPLTR
#define PyArray_UINTPLTR NPY_UINTPLTR
#define PyArray_GENBOOLLTR NPY_GENBOOLLTR
#define PyArray_SIGNEDLTR NPY_SIGNEDLTR
#define PyArray_UNSIGNEDLTR NPY_UNSIGNEDLTR
#define PyArray_FLOATINGLTR NPY_FLOATINGLTR
#define PyArray_COMPLEXLTR NPY_COMPLEXLTR
#define PyArray_QUICKSORT NPY_QUICKSORT
#define PyArray_HEAPSORT NPY_HEAPSORT
#define PyArray_MERGESORT NPY_MERGESORT
#define PyArray_SORTKIND NPY_SORTKIND
#define PyArray_NSORTS NPY_NSORTS
#define PyArray_NOSCALAR NPY_NOSCALAR
#define PyArray_BOOL_SCALAR NPY_BOOL_SCALAR
#define PyArray_INTPOS_SCALAR NPY_INTPOS_SCALAR
#define PyArray_INTNEG_SCALAR NPY_INTNEG_SCALAR
#define PyArray_FLOAT_SCALAR NPY_FLOAT_SCALAR
#define PyArray_COMPLEX_SCALAR NPY_COMPLEX_SCALAR
#define PyArray_OBJECT_SCALAR NPY_OBJECT_SCALAR
#define PyArray_SCALARKIND NPY_SCALARKIND
#define PyArray_NSCALARKINDS NPY_NSCALARKINDS
#define PyArray_ANYORDER NPY_ANYORDER
#define PyArray_CORDER NPY_CORDER
#define PyArray_FORTRANORDER NPY_FORTRANORDER
#define PyArray_ORDER NPY_ORDER
#define PyDescr_ISBOOL PyDataType_ISBOOL
#define PyDescr_ISUNSIGNED PyDataType_ISUNSIGNED
#define PyDescr_ISSIGNED PyDataType_ISSIGNED
#define PyDescr_ISINTEGER PyDataType_ISINTEGER
#define PyDescr_ISFLOAT PyDataType_ISFLOAT
#define PyDescr_ISNUMBER PyDataType_ISNUMBER
#define PyDescr_ISSTRING PyDataType_ISSTRING
#define PyDescr_ISCOMPLEX PyDataType_ISCOMPLEX
#define PyDescr_ISPYTHON PyDataType_ISPYTHON
#define PyDescr_ISFLEXIBLE PyDataType_ISFLEXIBLE
#define PyDescr_ISUSERDEF PyDataType_ISUSERDEF
#define PyDescr_ISEXTENDED PyDataType_ISEXTENDED
#define PyDescr_ISOBJECT PyDataType_ISOBJECT
#define PyDescr_HASFIELDS PyDataType_HASFIELDS
#define PyArray_LITTLE NPY_LITTLE
#define PyArray_BIG NPY_BIG
#define PyArray_NATIVE NPY_NATIVE
#define PyArray_SWAP NPY_SWAP
#define PyArray_IGNORE NPY_IGNORE
#define PyArray_NATBYTE NPY_NATBYTE
#define PyArray_OPPBYTE NPY_OPPBYTE
#define PyArray_MAX_ELSIZE NPY_MAX_ELSIZE
#define PyArray_USE_PYMEM NPY_USE_PYMEM
#define PyArray_RemoveLargest PyArray_RemoveSmallest
#define PyArray_UCS4 npy_ucs4
#endif /* NUMPY_CORE_INCLUDE_NUMPY_OLD_DEFINES_H_ */
| 6,405 | C | 33.074468 | 82 | 0.766432 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/experimental_dtype_api.h | /*
* This header exports the new experimental DType API as proposed in
* NEPs 41 to 43. For background, please check these NEPs. Otherwise,
* this header also serves as documentation for the time being.
*
* Please do not hesitate to contact @seberg with questions. This is
* developed together with https://github.com/seberg/experimental_user_dtypes
* and those interested in experimenting are encouraged to contribute there.
*
* To use the functions defined in the header, call::
*
* if (import_experimental_dtype_api(version) < 0) {
* return NULL;
* }
*
* in your module init. (A version mismatch will be reported, just update
* to the correct one, this will alert you of possible changes.)
*
* The following lists the main symbols currently exported. Please do not
* hesitate to ask for help or clarification:
*
* - PyUFunc_AddLoopFromSpec:
*
* Register a new loop for a ufunc. This uses the `PyArrayMethod_Spec`
* which must be filled in (see in-line comments).
*
* - PyUFunc_AddWrappingLoop:
*
* Register a new loop which reuses an existing one, but modifies the
* result dtypes. Please search the internal NumPy docs for more info
* at this point. (Used for physical units dtype.)
*
* - PyUFunc_AddPromoter:
*
* Register a new promoter for a ufunc. A promoter is a function stored
* in a PyCapsule (see in-line comments). It is passed the operation and
* requested DType signatures and can mutate it to attempt a new search
* for a matching loop/promoter.
* I.e. for Numba a promoter could even add the desired loop.
*
* - PyArrayInitDTypeMeta_FromSpec:
*
* Initialize a new DType. It must currently be a static Python C type
* that is declared as `PyArray_DTypeMeta` and not `PyTypeObject`.
* Further, it must subclass `np.dtype` and set its type to
* `PyArrayDTypeMeta_Type` (before calling `PyType_Read()`).
*
* - PyArray_CommonDType:
*
* Find the common-dtype ("promotion") for two DType classes. Similar
* to `np.result_type`, but works on the classes and not instances.
*
* - PyArray_PromoteDTypeSequence:
*
* Same as CommonDType, but works with an arbitrary number of DTypes.
* This function is smarter and can often return successful and unambiguous
* results when `common_dtype(common_dtype(dt1, dt2), dt3)` would
* depend on the operation order or fail. Nevertheless, DTypes should
* aim to ensure that their common-dtype implementation is associative
* and commutative! (Mainly, unsigned and signed integers are not.)
*
* For guaranteed consistent results DTypes must implement common-Dtype
* "transitively". If A promotes B and B promotes C, than A must generally
* also promote C; where "promotes" means implements the promotion.
* (There are some exceptions for abstract DTypes)
*
* - PyArray_GetDefaultDescr:
*
* Given a DType class, returns the default instance (descriptor).
* This is an inline function checking for `singleton` first and only
* calls the `default_descr` function if necessary.
*
* - PyArray_DoubleDType, etc.:
*
* Aliases to the DType classes for the builtin NumPy DTypes.
*
* WARNING
* =======
*
* By using this header, you understand that this is a fully experimental
* exposure. Details are expected to change, and some options may have no
* effect. (Please contact @seberg if you have questions!)
* If the exposure stops working, please file a bug report with NumPy.
* Further, a DType created using this API/header should still be expected
* to be incompatible with some functionality inside and outside of NumPy.
* In this case crashes must be expected. Please report any such problems
* so that they can be fixed before final exposure.
* Furthermore, expect missing checks for programming errors which the final
* API is expected to have.
*
* Symbols with a leading underscore are likely to not be included in the
* first public version, if these are central to your use-case, please let
* us know, so that we can reconsider.
*
* "Array-like" consumer API not yet under considerations
* ======================================================
*
* The new DType API is designed in a way to make it potentially useful for
* alternative "array-like" implementations. This will require careful
* exposure of details and functions and is not part of this experimental API.
*
* Brief (incompatibility) changelog
* =================================
*
* 2. None (only additions).
* 3. New `npy_intp *view_offset` argument for `resolve_descriptors`.
* This replaces the `NPY_CAST_IS_VIEW` flag. It can be set to 0 if the
* operation is a view, and is pre-initialized to `NPY_MIN_INTP` indicating
* that the operation is not a view.
*/
#ifndef NUMPY_CORE_INCLUDE_NUMPY_EXPERIMENTAL_DTYPE_API_H_
#define NUMPY_CORE_INCLUDE_NUMPY_EXPERIMENTAL_DTYPE_API_H_
#include <Python.h>
#include "ndarraytypes.h"
/*
* There must be a better way?! -- Oh well, this is experimental
* (my issue with it, is that I cannot undef those helpers).
*/
#if defined(PY_ARRAY_UNIQUE_SYMBOL)
#define NPY_EXP_DTYPE_API_CONCAT_HELPER2(x, y) x ## y
#define NPY_EXP_DTYPE_API_CONCAT_HELPER(arg) NPY_EXP_DTYPE_API_CONCAT_HELPER2(arg, __experimental_dtype_api_table)
#define __experimental_dtype_api_table NPY_EXP_DTYPE_API_CONCAT_HELPER(PY_ARRAY_UNIQUE_SYMBOL)
#else
#define __experimental_dtype_api_table __experimental_dtype_api_table
#endif
/* Support for correct multi-file projects: */
#if defined(NO_IMPORT) || defined(NO_IMPORT_ARRAY)
extern void **__experimental_dtype_api_table;
#else
/*
* Just a hack so I don't forget importing as much myself, I spend way too
* much time noticing it the first time around :).
*/
static void
__not_imported(void)
{
printf("*****\nCritical error, dtype API not imported\n*****\n");
}
static void *__uninitialized_table[] = {
&__not_imported, &__not_imported, &__not_imported, &__not_imported,
&__not_imported, &__not_imported, &__not_imported, &__not_imported};
#if defined(PY_ARRAY_UNIQUE_SYMBOL)
void **__experimental_dtype_api_table = __uninitialized_table;
#else
static void **__experimental_dtype_api_table = __uninitialized_table;
#endif
#endif
/*
* DTypeMeta struct, the content may be made fully opaque (except the size).
* We may also move everything into a single `void *dt_slots`.
*/
typedef struct {
PyHeapTypeObject super;
PyArray_Descr *singleton;
int type_num;
PyTypeObject *scalar_type;
npy_uint64 flags;
void *dt_slots;
void *reserved[3];
} PyArray_DTypeMeta;
/*
* ******************************************************
* ArrayMethod API (Casting and UFuncs)
* ******************************************************
*/
/*
* NOTE: Expected changes:
* * invert logic of floating point error flag
* * probably split runtime and general flags into two
* * should possibly not use an enum for typedef for more stable ABI?
*/
typedef enum {
/* Flag for whether the GIL is required */
NPY_METH_REQUIRES_PYAPI = 1 << 1,
/*
* Some functions cannot set floating point error flags, this flag
* gives us the option (not requirement) to skip floating point error
* setup/check. No function should set error flags and ignore them
* since it would interfere with chaining operations (e.g. casting).
*/
NPY_METH_NO_FLOATINGPOINT_ERRORS = 1 << 2,
/* Whether the method supports unaligned access (not runtime) */
NPY_METH_SUPPORTS_UNALIGNED = 1 << 3,
/* All flags which can change at runtime */
NPY_METH_RUNTIME_FLAGS = (
NPY_METH_REQUIRES_PYAPI |
NPY_METH_NO_FLOATINGPOINT_ERRORS),
} NPY_ARRAYMETHOD_FLAGS;
/*
* The main object for creating a new ArrayMethod. We use the typical `slots`
* mechanism used by the Python limited API (see below for the slot defs).
*/
typedef struct {
const char *name;
int nin, nout;
NPY_CASTING casting;
NPY_ARRAYMETHOD_FLAGS flags;
PyArray_DTypeMeta **dtypes;
PyType_Slot *slots;
} PyArrayMethod_Spec;
typedef PyObject *_ufunc_addloop_fromspec_func(
PyObject *ufunc, PyArrayMethod_Spec *spec);
/*
* The main ufunc registration function. This adds a new implementation/loop
* to a ufunc. It replaces `PyUFunc_RegisterLoopForType`.
*/
#define PyUFunc_AddLoopFromSpec \
(*(_ufunc_addloop_fromspec_func *)(__experimental_dtype_api_table[0]))
/* Please see the NumPy definitions in `array_method.h` for details on these */
typedef int translate_given_descrs_func(int nin, int nout,
PyArray_DTypeMeta *wrapped_dtypes[],
PyArray_Descr *given_descrs[], PyArray_Descr *new_descrs[]);
typedef int translate_loop_descrs_func(int nin, int nout,
PyArray_DTypeMeta *new_dtypes[], PyArray_Descr *given_descrs[],
PyArray_Descr *original_descrs[], PyArray_Descr *loop_descrs[]);
typedef int _ufunc_wrapping_loop_func(PyObject *ufunc_obj,
PyArray_DTypeMeta *new_dtypes[], PyArray_DTypeMeta *wrapped_dtypes[],
translate_given_descrs_func *translate_given_descrs,
translate_loop_descrs_func *translate_loop_descrs);
#define PyUFunc_AddWrappingLoop \
(*(_ufunc_wrapping_loop_func *)(__experimental_dtype_api_table[7]))
/*
* Type of the C promoter function, which must be wrapped into a
* PyCapsule with name "numpy._ufunc_promoter".
*
* Note that currently the output dtypes are always NULL unless they are
* also part of the signature. This is an implementation detail and could
* change in the future. However, in general promoters should not have a
* need for output dtypes.
* (There are potential use-cases, these are currently unsupported.)
*/
typedef int promoter_function(PyObject *ufunc,
PyArray_DTypeMeta *op_dtypes[], PyArray_DTypeMeta *signature[],
PyArray_DTypeMeta *new_op_dtypes[]);
/*
* Function to register a promoter.
*
* @param ufunc The ufunc object to register the promoter with.
* @param DType_tuple A Python tuple containing DTypes or None matching the
* number of inputs and outputs of the ufunc.
* @param promoter A PyCapsule with name "numpy._ufunc_promoter" containing
* a pointer to a `promoter_function`.
*/
typedef int _ufunc_addpromoter_func(
PyObject *ufunc, PyObject *DType_tuple, PyObject *promoter);
#define PyUFunc_AddPromoter \
(*(_ufunc_addpromoter_func *)(__experimental_dtype_api_table[1]))
/*
* The resolve descriptors function, must be able to handle NULL values for
* all output (but not input) `given_descrs` and fill `loop_descrs`.
* Return -1 on error or 0 if the operation is not possible without an error
* set. (This may still be in flux.)
* Otherwise must return the "casting safety", for normal functions, this is
* almost always "safe" (or even "equivalent"?).
*
* `resolve_descriptors` is optional if all output DTypes are non-parametric.
*/
#define NPY_METH_resolve_descriptors 1
typedef NPY_CASTING (resolve_descriptors_function)(
/* "method" is currently opaque (necessary e.g. to wrap Python) */
PyObject *method,
/* DTypes the method was created for */
PyObject **dtypes,
/* Input descriptors (instances). Outputs may be NULL. */
PyArray_Descr **given_descrs,
/* Exact loop descriptors to use, must not hold references on error */
PyArray_Descr **loop_descrs,
npy_intp *view_offset);
/* NOT public yet: Signature needs adapting as external API. */
#define _NPY_METH_get_loop 2
/*
* Current public API to define fast inner-loops. You must provide a
* strided loop. If this is a cast between two "versions" of the same dtype
* you must also provide an unaligned strided loop.
* Other loops are useful to optimize the very common contiguous case.
*
* NOTE: As of now, NumPy will NOT use unaligned loops in ufuncs!
*/
#define NPY_METH_strided_loop 3
#define NPY_METH_contiguous_loop 4
#define NPY_METH_unaligned_strided_loop 5
#define NPY_METH_unaligned_contiguous_loop 6
typedef struct {
PyObject *caller; /* E.g. the original ufunc, may be NULL */
PyObject *method; /* The method "self". Currently an opaque object */
/* Operand descriptors, filled in by resolve_descriptors */
PyArray_Descr **descriptors;
/* Structure may grow (this is harmless for DType authors) */
} PyArrayMethod_Context;
typedef int (PyArrayMethod_StridedLoop)(PyArrayMethod_Context *context,
char *const *data, const npy_intp *dimensions, const npy_intp *strides,
NpyAuxData *transferdata);
/*
* ****************************
* DTYPE API
* ****************************
*/
#define NPY_DT_ABSTRACT 1 << 1
#define NPY_DT_PARAMETRIC 1 << 2
#define NPY_DT_discover_descr_from_pyobject 1
#define _NPY_DT_is_known_scalar_type 2
#define NPY_DT_default_descr 3
#define NPY_DT_common_dtype 4
#define NPY_DT_common_instance 5
#define NPY_DT_setitem 6
#define NPY_DT_getitem 7
// TODO: These slots probably still need some thought, and/or a way to "grow"?
typedef struct{
PyTypeObject *typeobj; /* type of python scalar or NULL */
int flags; /* flags, including parametric and abstract */
/* NULL terminated cast definitions. Use NULL for the newly created DType */
PyArrayMethod_Spec **casts;
PyType_Slot *slots;
/* Baseclass or NULL (will always subclass `np.dtype`) */
PyTypeObject *baseclass;
} PyArrayDTypeMeta_Spec;
#define PyArrayDTypeMeta_Type \
(*(PyTypeObject *)__experimental_dtype_api_table[2])
typedef int __dtypemeta_fromspec(
PyArray_DTypeMeta *DType, PyArrayDTypeMeta_Spec *dtype_spec);
/*
* Finalize creation of a DTypeMeta. You must ensure that the DTypeMeta is
* a proper subclass. The DTypeMeta object has additional fields compared to
* a normal PyTypeObject!
* The only (easy) creation of a new DType is to create a static Type which
* inherits `PyArray_DescrType`, sets its type to `PyArrayDTypeMeta_Type` and
* uses `PyArray_DTypeMeta` defined above as the C-structure.
*/
#define PyArrayInitDTypeMeta_FromSpec \
((__dtypemeta_fromspec *)(__experimental_dtype_api_table[3]))
/*
* *************************************
* WORKING WITH DTYPES
* *************************************
*/
typedef PyArray_DTypeMeta *__common_dtype(
PyArray_DTypeMeta *DType1, PyArray_DTypeMeta *DType2);
#define PyArray_CommonDType \
((__common_dtype *)(__experimental_dtype_api_table[4]))
typedef PyArray_DTypeMeta *__promote_dtype_sequence(
npy_intp num, PyArray_DTypeMeta *DTypes[]);
#define PyArray_PromoteDTypeSequence \
((__promote_dtype_sequence *)(__experimental_dtype_api_table[5]))
typedef PyArray_Descr *__get_default_descr(
PyArray_DTypeMeta *DType);
#define _PyArray_GetDefaultDescr \
((__get_default_descr *)(__experimental_dtype_api_table[6]))
static NPY_INLINE PyArray_Descr *
PyArray_GetDefaultDescr(PyArray_DTypeMeta *DType)
{
if (DType->singleton != NULL) {
Py_INCREF(DType->singleton);
return DType->singleton;
}
return _PyArray_GetDefaultDescr(DType);
}
/*
* NumPy's builtin DTypes:
*/
#define PyArray_BoolDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[10])
/* Integers */
#define PyArray_ByteDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[11])
#define PyArray_UByteDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[12])
#define PyArray_ShortDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[13])
#define PyArray_UShortDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[14])
#define PyArray_IntDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[15])
#define PyArray_UIntDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[16])
#define PyArray_LongDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[17])
#define PyArray_ULongDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[18])
#define PyArray_LongLongDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[19])
#define PyArray_ULongLongDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[20])
/* Integer aliases */
#define PyArray_Int8Type (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[21])
#define PyArray_UInt8DType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[22])
#define PyArray_Int16DType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[23])
#define PyArray_UInt16DType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[24])
#define PyArray_Int32DType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[25])
#define PyArray_UInt32DType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[26])
#define PyArray_Int64DType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[27])
#define PyArray_UInt64DType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[28])
#define PyArray_IntpDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[29])
#define PyArray_UIntpDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[30])
/* Floats */
#define PyArray_HalfType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[31])
#define PyArray_FloatDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[32])
#define PyArray_DoubleDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[33])
#define PyArray_LongDoubleDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[34])
/* Complex */
#define PyArray_CFloatDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[35])
#define PyArray_CDoubleDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[36])
#define PyArray_CLongDoubleDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[37])
/* String/Bytes */
#define PyArray_StringDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[38])
#define PyArray_UnicodeDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[39])
/* Datetime/Timedelta */
#define PyArray_DatetimeDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[40])
#define PyArray_TimedeltaDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[41])
/*
* ********************************
* Initialization
* ********************************
*
* Import the experimental API, the version must match the one defined in
* the header to ensure changes are taken into account. NumPy will further
* runtime-check this.
* You must call this function to use the symbols defined in this file.
*/
#if !defined(NO_IMPORT) && !defined(NO_IMPORT_ARRAY)
#define __EXPERIMENTAL_DTYPE_VERSION 4
static int
import_experimental_dtype_api(int version)
{
if (version != __EXPERIMENTAL_DTYPE_VERSION) {
PyErr_Format(PyExc_RuntimeError,
"DType API version %d did not match header version %d. Please "
"update the import statement and check for API changes.",
version, __EXPERIMENTAL_DTYPE_VERSION);
return -1;
}
if (__experimental_dtype_api_table != __uninitialized_table) {
/* already imported. */
return 0;
}
PyObject *multiarray = PyImport_ImportModule("numpy.core._multiarray_umath");
if (multiarray == NULL) {
return -1;
}
PyObject *api = PyObject_CallMethod(multiarray,
"_get_experimental_dtype_api", "i", version);
Py_DECREF(multiarray);
if (api == NULL) {
return -1;
}
__experimental_dtype_api_table = (void **)PyCapsule_GetPointer(api,
"experimental_dtype_api_table");
Py_DECREF(api);
if (__experimental_dtype_api_table == NULL) {
__experimental_dtype_api_table = __uninitialized_table;
return -1;
}
return 0;
}
#endif /* !defined(NO_IMPORT) && !defined(NO_IMPORT_ARRAY) */
#endif /* NUMPY_CORE_INCLUDE_NUMPY_EXPERIMENTAL_DTYPE_API_H_ */
| 19,915 | C | 38.673307 | 118 | 0.687974 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/__ufunc_api.h |
#ifdef _UMATHMODULE
extern NPY_NO_EXPORT PyTypeObject PyUFunc_Type;
extern NPY_NO_EXPORT PyTypeObject PyUFunc_Type;
NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndData \
(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int);
NPY_NO_EXPORT int PyUFunc_RegisterLoopForType \
(PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *);
NPY_NO_EXPORT int PyUFunc_GenericFunction \
(PyUFuncObject *NPY_UNUSED(ufunc), PyObject *NPY_UNUSED(args), PyObject *NPY_UNUSED(kwds), PyArrayObject **NPY_UNUSED(op));
NPY_NO_EXPORT void PyUFunc_f_f_As_d_d \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_d_d \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_f_f \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_g_g \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_F_F_As_D_D \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_F_F \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_D_D \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_G_G \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_O_O \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_ff_f_As_dd_d \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_ff_f \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_dd_d \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_gg_g \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_FF_F_As_DD_D \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_DD_D \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_FF_F \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_GG_G \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_OO_O \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_O_O_method \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_OO_O_method \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_On_Om \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT int PyUFunc_GetPyValues \
(char *, int *, int *, PyObject **);
NPY_NO_EXPORT int PyUFunc_checkfperr \
(int, PyObject *, int *);
NPY_NO_EXPORT void PyUFunc_clearfperr \
(void);
NPY_NO_EXPORT int PyUFunc_getfperr \
(void);
NPY_NO_EXPORT int PyUFunc_handlefperr \
(int, PyObject *, int, int *);
NPY_NO_EXPORT int PyUFunc_ReplaceLoopBySignature \
(PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *);
NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndDataAndSignature \
(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int, const char *);
NPY_NO_EXPORT int PyUFunc_SetUsesArraysAsData \
(void **NPY_UNUSED(data), size_t NPY_UNUSED(i));
NPY_NO_EXPORT void PyUFunc_e_e \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_e_e_As_f_f \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_e_e_As_d_d \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_ee_e \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_ee_e_As_ff_f \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT void PyUFunc_ee_e_As_dd_d \
(char **, npy_intp const *, npy_intp const *, void *);
NPY_NO_EXPORT int PyUFunc_DefaultTypeResolver \
(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **);
NPY_NO_EXPORT int PyUFunc_ValidateCasting \
(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr **);
NPY_NO_EXPORT int PyUFunc_RegisterLoopForDescr \
(PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *);
NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndDataAndSignatureAndIdentity \
(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *);
#else
#if defined(PY_UFUNC_UNIQUE_SYMBOL)
#define PyUFunc_API PY_UFUNC_UNIQUE_SYMBOL
#endif
#if defined(NO_IMPORT) || defined(NO_IMPORT_UFUNC)
extern void **PyUFunc_API;
#else
#if defined(PY_UFUNC_UNIQUE_SYMBOL)
void **PyUFunc_API;
#else
static void **PyUFunc_API=NULL;
#endif
#endif
#define PyUFunc_Type (*(PyTypeObject *)PyUFunc_API[0])
#define PyUFunc_FromFuncAndData \
(*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int)) \
PyUFunc_API[1])
#define PyUFunc_RegisterLoopForType \
(*(int (*)(PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *)) \
PyUFunc_API[2])
#define PyUFunc_GenericFunction \
(*(int (*)(PyUFuncObject *NPY_UNUSED(ufunc), PyObject *NPY_UNUSED(args), PyObject *NPY_UNUSED(kwds), PyArrayObject **NPY_UNUSED(op))) \
PyUFunc_API[3])
#define PyUFunc_f_f_As_d_d \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[4])
#define PyUFunc_d_d \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[5])
#define PyUFunc_f_f \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[6])
#define PyUFunc_g_g \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[7])
#define PyUFunc_F_F_As_D_D \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[8])
#define PyUFunc_F_F \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[9])
#define PyUFunc_D_D \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[10])
#define PyUFunc_G_G \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[11])
#define PyUFunc_O_O \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[12])
#define PyUFunc_ff_f_As_dd_d \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[13])
#define PyUFunc_ff_f \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[14])
#define PyUFunc_dd_d \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[15])
#define PyUFunc_gg_g \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[16])
#define PyUFunc_FF_F_As_DD_D \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[17])
#define PyUFunc_DD_D \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[18])
#define PyUFunc_FF_F \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[19])
#define PyUFunc_GG_G \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[20])
#define PyUFunc_OO_O \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[21])
#define PyUFunc_O_O_method \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[22])
#define PyUFunc_OO_O_method \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[23])
#define PyUFunc_On_Om \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[24])
#define PyUFunc_GetPyValues \
(*(int (*)(char *, int *, int *, PyObject **)) \
PyUFunc_API[25])
#define PyUFunc_checkfperr \
(*(int (*)(int, PyObject *, int *)) \
PyUFunc_API[26])
#define PyUFunc_clearfperr \
(*(void (*)(void)) \
PyUFunc_API[27])
#define PyUFunc_getfperr \
(*(int (*)(void)) \
PyUFunc_API[28])
#define PyUFunc_handlefperr \
(*(int (*)(int, PyObject *, int, int *)) \
PyUFunc_API[29])
#define PyUFunc_ReplaceLoopBySignature \
(*(int (*)(PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *)) \
PyUFunc_API[30])
#define PyUFunc_FromFuncAndDataAndSignature \
(*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int, const char *)) \
PyUFunc_API[31])
#define PyUFunc_SetUsesArraysAsData \
(*(int (*)(void **NPY_UNUSED(data), size_t NPY_UNUSED(i))) \
PyUFunc_API[32])
#define PyUFunc_e_e \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[33])
#define PyUFunc_e_e_As_f_f \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[34])
#define PyUFunc_e_e_As_d_d \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[35])
#define PyUFunc_ee_e \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[36])
#define PyUFunc_ee_e_As_ff_f \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[37])
#define PyUFunc_ee_e_As_dd_d \
(*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
PyUFunc_API[38])
#define PyUFunc_DefaultTypeResolver \
(*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **)) \
PyUFunc_API[39])
#define PyUFunc_ValidateCasting \
(*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr **)) \
PyUFunc_API[40])
#define PyUFunc_RegisterLoopForDescr \
(*(int (*)(PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *)) \
PyUFunc_API[41])
#define PyUFunc_FromFuncAndDataAndSignatureAndIdentity \
(*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *)) \
PyUFunc_API[42])
static NPY_INLINE int
_import_umath(void)
{
PyObject *numpy = PyImport_ImportModule("numpy.core._multiarray_umath");
PyObject *c_api = NULL;
if (numpy == NULL) {
PyErr_SetString(PyExc_ImportError,
"numpy.core._multiarray_umath failed to import");
return -1;
}
c_api = PyObject_GetAttrString(numpy, "_UFUNC_API");
Py_DECREF(numpy);
if (c_api == NULL) {
PyErr_SetString(PyExc_AttributeError, "_UFUNC_API not found");
return -1;
}
if (!PyCapsule_CheckExact(c_api)) {
PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is not PyCapsule object");
Py_DECREF(c_api);
return -1;
}
PyUFunc_API = (void **)PyCapsule_GetPointer(c_api, NULL);
Py_DECREF(c_api);
if (PyUFunc_API == NULL) {
PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is NULL pointer");
return -1;
}
return 0;
}
#define import_umath() \
do {\
UFUNC_NOFPE\
if (_import_umath() < 0) {\
PyErr_Print();\
PyErr_SetString(PyExc_ImportError,\
"numpy.core.umath failed to import");\
return NULL;\
}\
} while(0)
#define import_umath1(ret) \
do {\
UFUNC_NOFPE\
if (_import_umath() < 0) {\
PyErr_Print();\
PyErr_SetString(PyExc_ImportError,\
"numpy.core.umath failed to import");\
return ret;\
}\
} while(0)
#define import_umath2(ret, msg) \
do {\
UFUNC_NOFPE\
if (_import_umath() < 0) {\
PyErr_Print();\
PyErr_SetString(PyExc_ImportError, msg);\
return ret;\
}\
} while(0)
#define import_ufunc() \
do {\
UFUNC_NOFPE\
if (_import_umath() < 0) {\
PyErr_Print();\
PyErr_SetString(PyExc_ImportError,\
"numpy.core.umath failed to import");\
}\
} while(0)
#endif
| 12,614 | C | 39.432692 | 156 | 0.583954 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/arrayobject.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_
#define NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_
#define Py_ARRAYOBJECT_H
#include "ndarrayobject.h"
#include "npy_interrupt.h"
#ifdef NPY_NO_PREFIX
#include "noprefix.h"
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_ */
| 282 | C | 20.769229 | 53 | 0.758865 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/npy_endian.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_ENDIAN_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NPY_ENDIAN_H_
/*
* NPY_BYTE_ORDER is set to the same value as BYTE_ORDER set by glibc in
* endian.h
*/
#if defined(NPY_HAVE_ENDIAN_H) || defined(NPY_HAVE_SYS_ENDIAN_H)
/* Use endian.h if available */
#if defined(NPY_HAVE_ENDIAN_H)
#include <endian.h>
#elif defined(NPY_HAVE_SYS_ENDIAN_H)
#include <sys/endian.h>
#endif
#if defined(BYTE_ORDER) && defined(BIG_ENDIAN) && defined(LITTLE_ENDIAN)
#define NPY_BYTE_ORDER BYTE_ORDER
#define NPY_LITTLE_ENDIAN LITTLE_ENDIAN
#define NPY_BIG_ENDIAN BIG_ENDIAN
#elif defined(_BYTE_ORDER) && defined(_BIG_ENDIAN) && defined(_LITTLE_ENDIAN)
#define NPY_BYTE_ORDER _BYTE_ORDER
#define NPY_LITTLE_ENDIAN _LITTLE_ENDIAN
#define NPY_BIG_ENDIAN _BIG_ENDIAN
#elif defined(__BYTE_ORDER) && defined(__BIG_ENDIAN) && defined(__LITTLE_ENDIAN)
#define NPY_BYTE_ORDER __BYTE_ORDER
#define NPY_LITTLE_ENDIAN __LITTLE_ENDIAN
#define NPY_BIG_ENDIAN __BIG_ENDIAN
#endif
#endif
#ifndef NPY_BYTE_ORDER
/* Set endianness info using target CPU */
#include "npy_cpu.h"
#define NPY_LITTLE_ENDIAN 1234
#define NPY_BIG_ENDIAN 4321
#if defined(NPY_CPU_X86) \
|| defined(NPY_CPU_AMD64) \
|| defined(NPY_CPU_IA64) \
|| defined(NPY_CPU_ALPHA) \
|| defined(NPY_CPU_ARMEL) \
|| defined(NPY_CPU_ARMEL_AARCH32) \
|| defined(NPY_CPU_ARMEL_AARCH64) \
|| defined(NPY_CPU_SH_LE) \
|| defined(NPY_CPU_MIPSEL) \
|| defined(NPY_CPU_PPC64LE) \
|| defined(NPY_CPU_ARCEL) \
|| defined(NPY_CPU_RISCV64) \
|| defined(NPY_CPU_LOONGARCH) \
|| defined(NPY_CPU_WASM)
#define NPY_BYTE_ORDER NPY_LITTLE_ENDIAN
#elif defined(NPY_CPU_PPC) \
|| defined(NPY_CPU_SPARC) \
|| defined(NPY_CPU_S390) \
|| defined(NPY_CPU_HPPA) \
|| defined(NPY_CPU_PPC64) \
|| defined(NPY_CPU_ARMEB) \
|| defined(NPY_CPU_ARMEB_AARCH32) \
|| defined(NPY_CPU_ARMEB_AARCH64) \
|| defined(NPY_CPU_SH_BE) \
|| defined(NPY_CPU_MIPSEB) \
|| defined(NPY_CPU_OR1K) \
|| defined(NPY_CPU_M68K) \
|| defined(NPY_CPU_ARCEB)
#define NPY_BYTE_ORDER NPY_BIG_ENDIAN
#else
#error Unknown CPU: can not set endianness
#endif
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_ENDIAN_H_ */
| 2,786 | C | 34.730769 | 84 | 0.538047 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/_numpyconfig.h | #define NPY_SIZEOF_SHORT SIZEOF_SHORT
#define NPY_SIZEOF_INT SIZEOF_INT
#define NPY_SIZEOF_LONG SIZEOF_LONG
#define NPY_SIZEOF_FLOAT 4
#define NPY_SIZEOF_COMPLEX_FLOAT 8
#define NPY_SIZEOF_DOUBLE 8
#define NPY_SIZEOF_COMPLEX_DOUBLE 16
#define NPY_SIZEOF_LONGDOUBLE 8
#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 16
#define NPY_SIZEOF_PY_INTPTR_T 8
#define NPY_SIZEOF_OFF_T 4
#define NPY_SIZEOF_PY_LONG_LONG 8
#define NPY_SIZEOF_LONGLONG 8
#define NPY_NO_SIGNAL 1
#define NPY_NO_SMP 0
#define NPY_HAVE_DECL_ISNAN
#define NPY_HAVE_DECL_ISINF
#define NPY_HAVE_DECL_SIGNBIT
#define NPY_HAVE_DECL_ISFINITE
#define NPY_USE_C99_COMPLEX 1
#define NPY_USE_C99_FORMATS 1
#define NPY_VISIBILITY_HIDDEN
#define NPY_ABI_VERSION 0x01000009
#define NPY_API_VERSION 0x00000010
#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS 1
#endif
| 823 | C | 27.413792 | 40 | 0.787363 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/npy_math.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_MATH_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NPY_MATH_H_
#include <numpy/npy_common.h>
#include <math.h>
#ifdef __SUNPRO_CC
#include <sunmath.h>
#endif
/* By adding static inline specifiers to npy_math function definitions when
appropriate, compiler is given the opportunity to optimize */
#if NPY_INLINE_MATH
#define NPY_INPLACE NPY_INLINE static
#else
#define NPY_INPLACE
#endif
#ifdef __cplusplus
extern "C" {
#endif
/*
* NAN and INFINITY like macros (same behavior as glibc for NAN, same as C99
* for INFINITY)
*
* XXX: I should test whether INFINITY and NAN are available on the platform
*/
NPY_INLINE static float __npy_inff(void)
{
const union { npy_uint32 __i; float __f;} __bint = {0x7f800000UL};
return __bint.__f;
}
NPY_INLINE static float __npy_nanf(void)
{
const union { npy_uint32 __i; float __f;} __bint = {0x7fc00000UL};
return __bint.__f;
}
NPY_INLINE static float __npy_pzerof(void)
{
const union { npy_uint32 __i; float __f;} __bint = {0x00000000UL};
return __bint.__f;
}
NPY_INLINE static float __npy_nzerof(void)
{
const union { npy_uint32 __i; float __f;} __bint = {0x80000000UL};
return __bint.__f;
}
#define NPY_INFINITYF __npy_inff()
#define NPY_NANF __npy_nanf()
#define NPY_PZEROF __npy_pzerof()
#define NPY_NZEROF __npy_nzerof()
#define NPY_INFINITY ((npy_double)NPY_INFINITYF)
#define NPY_NAN ((npy_double)NPY_NANF)
#define NPY_PZERO ((npy_double)NPY_PZEROF)
#define NPY_NZERO ((npy_double)NPY_NZEROF)
#define NPY_INFINITYL ((npy_longdouble)NPY_INFINITYF)
#define NPY_NANL ((npy_longdouble)NPY_NANF)
#define NPY_PZEROL ((npy_longdouble)NPY_PZEROF)
#define NPY_NZEROL ((npy_longdouble)NPY_NZEROF)
/*
* Useful constants
*/
#define NPY_E 2.718281828459045235360287471352662498 /* e */
#define NPY_LOG2E 1.442695040888963407359924681001892137 /* log_2 e */
#define NPY_LOG10E 0.434294481903251827651128918916605082 /* log_10 e */
#define NPY_LOGE2 0.693147180559945309417232121458176568 /* log_e 2 */
#define NPY_LOGE10 2.302585092994045684017991454684364208 /* log_e 10 */
#define NPY_PI 3.141592653589793238462643383279502884 /* pi */
#define NPY_PI_2 1.570796326794896619231321691639751442 /* pi/2 */
#define NPY_PI_4 0.785398163397448309615660845819875721 /* pi/4 */
#define NPY_1_PI 0.318309886183790671537767526745028724 /* 1/pi */
#define NPY_2_PI 0.636619772367581343075535053490057448 /* 2/pi */
#define NPY_EULER 0.577215664901532860606512090082402431 /* Euler constant */
#define NPY_SQRT2 1.414213562373095048801688724209698079 /* sqrt(2) */
#define NPY_SQRT1_2 0.707106781186547524400844362104849039 /* 1/sqrt(2) */
#define NPY_Ef 2.718281828459045235360287471352662498F /* e */
#define NPY_LOG2Ef 1.442695040888963407359924681001892137F /* log_2 e */
#define NPY_LOG10Ef 0.434294481903251827651128918916605082F /* log_10 e */
#define NPY_LOGE2f 0.693147180559945309417232121458176568F /* log_e 2 */
#define NPY_LOGE10f 2.302585092994045684017991454684364208F /* log_e 10 */
#define NPY_PIf 3.141592653589793238462643383279502884F /* pi */
#define NPY_PI_2f 1.570796326794896619231321691639751442F /* pi/2 */
#define NPY_PI_4f 0.785398163397448309615660845819875721F /* pi/4 */
#define NPY_1_PIf 0.318309886183790671537767526745028724F /* 1/pi */
#define NPY_2_PIf 0.636619772367581343075535053490057448F /* 2/pi */
#define NPY_EULERf 0.577215664901532860606512090082402431F /* Euler constant */
#define NPY_SQRT2f 1.414213562373095048801688724209698079F /* sqrt(2) */
#define NPY_SQRT1_2f 0.707106781186547524400844362104849039F /* 1/sqrt(2) */
#define NPY_El 2.718281828459045235360287471352662498L /* e */
#define NPY_LOG2El 1.442695040888963407359924681001892137L /* log_2 e */
#define NPY_LOG10El 0.434294481903251827651128918916605082L /* log_10 e */
#define NPY_LOGE2l 0.693147180559945309417232121458176568L /* log_e 2 */
#define NPY_LOGE10l 2.302585092994045684017991454684364208L /* log_e 10 */
#define NPY_PIl 3.141592653589793238462643383279502884L /* pi */
#define NPY_PI_2l 1.570796326794896619231321691639751442L /* pi/2 */
#define NPY_PI_4l 0.785398163397448309615660845819875721L /* pi/4 */
#define NPY_1_PIl 0.318309886183790671537767526745028724L /* 1/pi */
#define NPY_2_PIl 0.636619772367581343075535053490057448L /* 2/pi */
#define NPY_EULERl 0.577215664901532860606512090082402431L /* Euler constant */
#define NPY_SQRT2l 1.414213562373095048801688724209698079L /* sqrt(2) */
#define NPY_SQRT1_2l 0.707106781186547524400844362104849039L /* 1/sqrt(2) */
/*
* Integer functions.
*/
NPY_INPLACE npy_uint npy_gcdu(npy_uint a, npy_uint b);
NPY_INPLACE npy_uint npy_lcmu(npy_uint a, npy_uint b);
NPY_INPLACE npy_ulong npy_gcdul(npy_ulong a, npy_ulong b);
NPY_INPLACE npy_ulong npy_lcmul(npy_ulong a, npy_ulong b);
NPY_INPLACE npy_ulonglong npy_gcdull(npy_ulonglong a, npy_ulonglong b);
NPY_INPLACE npy_ulonglong npy_lcmull(npy_ulonglong a, npy_ulonglong b);
NPY_INPLACE npy_int npy_gcd(npy_int a, npy_int b);
NPY_INPLACE npy_int npy_lcm(npy_int a, npy_int b);
NPY_INPLACE npy_long npy_gcdl(npy_long a, npy_long b);
NPY_INPLACE npy_long npy_lcml(npy_long a, npy_long b);
NPY_INPLACE npy_longlong npy_gcdll(npy_longlong a, npy_longlong b);
NPY_INPLACE npy_longlong npy_lcmll(npy_longlong a, npy_longlong b);
NPY_INPLACE npy_ubyte npy_rshiftuhh(npy_ubyte a, npy_ubyte b);
NPY_INPLACE npy_ubyte npy_lshiftuhh(npy_ubyte a, npy_ubyte b);
NPY_INPLACE npy_ushort npy_rshiftuh(npy_ushort a, npy_ushort b);
NPY_INPLACE npy_ushort npy_lshiftuh(npy_ushort a, npy_ushort b);
NPY_INPLACE npy_uint npy_rshiftu(npy_uint a, npy_uint b);
NPY_INPLACE npy_uint npy_lshiftu(npy_uint a, npy_uint b);
NPY_INPLACE npy_ulong npy_rshiftul(npy_ulong a, npy_ulong b);
NPY_INPLACE npy_ulong npy_lshiftul(npy_ulong a, npy_ulong b);
NPY_INPLACE npy_ulonglong npy_rshiftull(npy_ulonglong a, npy_ulonglong b);
NPY_INPLACE npy_ulonglong npy_lshiftull(npy_ulonglong a, npy_ulonglong b);
NPY_INPLACE npy_byte npy_rshifthh(npy_byte a, npy_byte b);
NPY_INPLACE npy_byte npy_lshifthh(npy_byte a, npy_byte b);
NPY_INPLACE npy_short npy_rshifth(npy_short a, npy_short b);
NPY_INPLACE npy_short npy_lshifth(npy_short a, npy_short b);
NPY_INPLACE npy_int npy_rshift(npy_int a, npy_int b);
NPY_INPLACE npy_int npy_lshift(npy_int a, npy_int b);
NPY_INPLACE npy_long npy_rshiftl(npy_long a, npy_long b);
NPY_INPLACE npy_long npy_lshiftl(npy_long a, npy_long b);
NPY_INPLACE npy_longlong npy_rshiftll(npy_longlong a, npy_longlong b);
NPY_INPLACE npy_longlong npy_lshiftll(npy_longlong a, npy_longlong b);
NPY_INPLACE uint8_t npy_popcountuhh(npy_ubyte a);
NPY_INPLACE uint8_t npy_popcountuh(npy_ushort a);
NPY_INPLACE uint8_t npy_popcountu(npy_uint a);
NPY_INPLACE uint8_t npy_popcountul(npy_ulong a);
NPY_INPLACE uint8_t npy_popcountull(npy_ulonglong a);
NPY_INPLACE uint8_t npy_popcounthh(npy_byte a);
NPY_INPLACE uint8_t npy_popcounth(npy_short a);
NPY_INPLACE uint8_t npy_popcount(npy_int a);
NPY_INPLACE uint8_t npy_popcountl(npy_long a);
NPY_INPLACE uint8_t npy_popcountll(npy_longlong a);
/*
* C99 double math funcs
*/
NPY_INPLACE double npy_sin(double x);
NPY_INPLACE double npy_cos(double x);
NPY_INPLACE double npy_tan(double x);
NPY_INPLACE double npy_sinh(double x);
NPY_INPLACE double npy_cosh(double x);
NPY_INPLACE double npy_tanh(double x);
NPY_INPLACE double npy_asin(double x);
NPY_INPLACE double npy_acos(double x);
NPY_INPLACE double npy_atan(double x);
NPY_INPLACE double npy_log(double x);
NPY_INPLACE double npy_log10(double x);
NPY_INPLACE double npy_exp(double x);
NPY_INPLACE double npy_sqrt(double x);
NPY_INPLACE double npy_cbrt(double x);
NPY_INPLACE double npy_fabs(double x);
NPY_INPLACE double npy_ceil(double x);
NPY_INPLACE double npy_fmod(double x, double y);
NPY_INPLACE double npy_floor(double x);
NPY_INPLACE double npy_expm1(double x);
NPY_INPLACE double npy_log1p(double x);
NPY_INPLACE double npy_hypot(double x, double y);
NPY_INPLACE double npy_acosh(double x);
NPY_INPLACE double npy_asinh(double xx);
NPY_INPLACE double npy_atanh(double x);
NPY_INPLACE double npy_rint(double x);
NPY_INPLACE double npy_trunc(double x);
NPY_INPLACE double npy_exp2(double x);
NPY_INPLACE double npy_log2(double x);
NPY_INPLACE double npy_atan2(double x, double y);
NPY_INPLACE double npy_pow(double x, double y);
NPY_INPLACE double npy_modf(double x, double* y);
NPY_INPLACE double npy_frexp(double x, int* y);
NPY_INPLACE double npy_ldexp(double n, int y);
NPY_INPLACE double npy_copysign(double x, double y);
double npy_nextafter(double x, double y);
double npy_spacing(double x);
/*
* IEEE 754 fpu handling. Those are guaranteed to be macros
*/
/* use builtins to avoid function calls in tight loops
* only available if npy_config.h is available (= numpys own build) */
#ifdef HAVE___BUILTIN_ISNAN
#define npy_isnan(x) __builtin_isnan(x)
#else
#ifndef NPY_HAVE_DECL_ISNAN
#define npy_isnan(x) ((x) != (x))
#else
#if defined(_MSC_VER) && (_MSC_VER < 1900)
#define npy_isnan(x) _isnan((x))
#else
#define npy_isnan(x) isnan(x)
#endif
#endif
#endif
/* only available if npy_config.h is available (= numpys own build) */
#ifdef HAVE___BUILTIN_ISFINITE
#define npy_isfinite(x) __builtin_isfinite(x)
#else
#ifndef NPY_HAVE_DECL_ISFINITE
#ifdef _MSC_VER
#define npy_isfinite(x) _finite((x))
#else
#define npy_isfinite(x) !npy_isnan((x) + (-x))
#endif
#else
#define npy_isfinite(x) isfinite((x))
#endif
#endif
/* only available if npy_config.h is available (= numpys own build) */
#ifdef HAVE___BUILTIN_ISINF
#define npy_isinf(x) __builtin_isinf(x)
#else
#ifndef NPY_HAVE_DECL_ISINF
#define npy_isinf(x) (!npy_isfinite(x) && !npy_isnan(x))
#else
#if defined(_MSC_VER) && (_MSC_VER < 1900)
#define npy_isinf(x) (!_finite((x)) && !_isnan((x)))
#else
#define npy_isinf(x) isinf((x))
#endif
#endif
#endif
#ifndef NPY_HAVE_DECL_SIGNBIT
int _npy_signbit_f(float x);
int _npy_signbit_d(double x);
int _npy_signbit_ld(long double x);
#define npy_signbit(x) \
(sizeof (x) == sizeof (long double) ? _npy_signbit_ld (x) \
: sizeof (x) == sizeof (double) ? _npy_signbit_d (x) \
: _npy_signbit_f (x))
#else
#define npy_signbit(x) signbit((x))
#endif
/*
* float C99 math functions
*/
NPY_INPLACE float npy_sinf(float x);
NPY_INPLACE float npy_cosf(float x);
NPY_INPLACE float npy_tanf(float x);
NPY_INPLACE float npy_sinhf(float x);
NPY_INPLACE float npy_coshf(float x);
NPY_INPLACE float npy_tanhf(float x);
NPY_INPLACE float npy_fabsf(float x);
NPY_INPLACE float npy_floorf(float x);
NPY_INPLACE float npy_ceilf(float x);
NPY_INPLACE float npy_rintf(float x);
NPY_INPLACE float npy_truncf(float x);
NPY_INPLACE float npy_sqrtf(float x);
NPY_INPLACE float npy_cbrtf(float x);
NPY_INPLACE float npy_log10f(float x);
NPY_INPLACE float npy_logf(float x);
NPY_INPLACE float npy_expf(float x);
NPY_INPLACE float npy_expm1f(float x);
NPY_INPLACE float npy_asinf(float x);
NPY_INPLACE float npy_acosf(float x);
NPY_INPLACE float npy_atanf(float x);
NPY_INPLACE float npy_asinhf(float x);
NPY_INPLACE float npy_acoshf(float x);
NPY_INPLACE float npy_atanhf(float x);
NPY_INPLACE float npy_log1pf(float x);
NPY_INPLACE float npy_exp2f(float x);
NPY_INPLACE float npy_log2f(float x);
NPY_INPLACE float npy_atan2f(float x, float y);
NPY_INPLACE float npy_hypotf(float x, float y);
NPY_INPLACE float npy_powf(float x, float y);
NPY_INPLACE float npy_fmodf(float x, float y);
NPY_INPLACE float npy_modff(float x, float* y);
NPY_INPLACE float npy_frexpf(float x, int* y);
NPY_INPLACE float npy_ldexpf(float x, int y);
NPY_INPLACE float npy_copysignf(float x, float y);
float npy_nextafterf(float x, float y);
float npy_spacingf(float x);
/*
* long double C99 math functions
*/
NPY_INPLACE npy_longdouble npy_sinl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_cosl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_tanl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_sinhl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_coshl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_tanhl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_fabsl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_floorl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_ceill(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_rintl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_truncl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_sqrtl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_cbrtl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_log10l(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_logl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_expl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_expm1l(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_asinl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_acosl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_atanl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_asinhl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_acoshl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_atanhl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_log1pl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_exp2l(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_log2l(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_atan2l(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_hypotl(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_powl(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_fmodl(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_modfl(npy_longdouble x, npy_longdouble* y);
NPY_INPLACE npy_longdouble npy_frexpl(npy_longdouble x, int* y);
NPY_INPLACE npy_longdouble npy_ldexpl(npy_longdouble x, int y);
NPY_INPLACE npy_longdouble npy_copysignl(npy_longdouble x, npy_longdouble y);
npy_longdouble npy_nextafterl(npy_longdouble x, npy_longdouble y);
npy_longdouble npy_spacingl(npy_longdouble x);
/*
* Non standard functions
*/
NPY_INPLACE double npy_deg2rad(double x);
NPY_INPLACE double npy_rad2deg(double x);
NPY_INPLACE double npy_logaddexp(double x, double y);
NPY_INPLACE double npy_logaddexp2(double x, double y);
NPY_INPLACE double npy_divmod(double x, double y, double *modulus);
NPY_INPLACE double npy_heaviside(double x, double h0);
NPY_INPLACE float npy_deg2radf(float x);
NPY_INPLACE float npy_rad2degf(float x);
NPY_INPLACE float npy_logaddexpf(float x, float y);
NPY_INPLACE float npy_logaddexp2f(float x, float y);
NPY_INPLACE float npy_divmodf(float x, float y, float *modulus);
NPY_INPLACE float npy_heavisidef(float x, float h0);
NPY_INPLACE npy_longdouble npy_deg2radl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_rad2degl(npy_longdouble x);
NPY_INPLACE npy_longdouble npy_logaddexpl(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_logaddexp2l(npy_longdouble x, npy_longdouble y);
NPY_INPLACE npy_longdouble npy_divmodl(npy_longdouble x, npy_longdouble y,
npy_longdouble *modulus);
NPY_INPLACE npy_longdouble npy_heavisidel(npy_longdouble x, npy_longdouble h0);
#define npy_degrees npy_rad2deg
#define npy_degreesf npy_rad2degf
#define npy_degreesl npy_rad2degl
#define npy_radians npy_deg2rad
#define npy_radiansf npy_deg2radf
#define npy_radiansl npy_deg2radl
/*
* Complex declarations
*/
/*
* C99 specifies that complex numbers have the same representation as
* an array of two elements, where the first element is the real part
* and the second element is the imaginary part.
*/
#define __NPY_CPACK_IMP(x, y, type, ctype) \
union { \
ctype z; \
type a[2]; \
} z1; \
\
z1.a[0] = (x); \
z1.a[1] = (y); \
\
return z1.z;
static NPY_INLINE npy_cdouble npy_cpack(double x, double y)
{
__NPY_CPACK_IMP(x, y, double, npy_cdouble);
}
static NPY_INLINE npy_cfloat npy_cpackf(float x, float y)
{
__NPY_CPACK_IMP(x, y, float, npy_cfloat);
}
static NPY_INLINE npy_clongdouble npy_cpackl(npy_longdouble x, npy_longdouble y)
{
__NPY_CPACK_IMP(x, y, npy_longdouble, npy_clongdouble);
}
#undef __NPY_CPACK_IMP
/*
* Same remark as above, but in the other direction: extract first/second
* member of complex number, assuming a C99-compatible representation
*
* Those are defineds as static inline, and such as a reasonable compiler would
* most likely compile this to one or two instructions (on CISC at least)
*/
#define __NPY_CEXTRACT_IMP(z, index, type, ctype) \
union { \
ctype z; \
type a[2]; \
} __z_repr; \
__z_repr.z = z; \
\
return __z_repr.a[index];
static NPY_INLINE double npy_creal(npy_cdouble z)
{
__NPY_CEXTRACT_IMP(z, 0, double, npy_cdouble);
}
static NPY_INLINE double npy_cimag(npy_cdouble z)
{
__NPY_CEXTRACT_IMP(z, 1, double, npy_cdouble);
}
static NPY_INLINE float npy_crealf(npy_cfloat z)
{
__NPY_CEXTRACT_IMP(z, 0, float, npy_cfloat);
}
static NPY_INLINE float npy_cimagf(npy_cfloat z)
{
__NPY_CEXTRACT_IMP(z, 1, float, npy_cfloat);
}
static NPY_INLINE npy_longdouble npy_creall(npy_clongdouble z)
{
__NPY_CEXTRACT_IMP(z, 0, npy_longdouble, npy_clongdouble);
}
static NPY_INLINE npy_longdouble npy_cimagl(npy_clongdouble z)
{
__NPY_CEXTRACT_IMP(z, 1, npy_longdouble, npy_clongdouble);
}
#undef __NPY_CEXTRACT_IMP
/*
* Double precision complex functions
*/
double npy_cabs(npy_cdouble z);
double npy_carg(npy_cdouble z);
npy_cdouble npy_cexp(npy_cdouble z);
npy_cdouble npy_clog(npy_cdouble z);
npy_cdouble npy_cpow(npy_cdouble x, npy_cdouble y);
npy_cdouble npy_csqrt(npy_cdouble z);
npy_cdouble npy_ccos(npy_cdouble z);
npy_cdouble npy_csin(npy_cdouble z);
npy_cdouble npy_ctan(npy_cdouble z);
npy_cdouble npy_ccosh(npy_cdouble z);
npy_cdouble npy_csinh(npy_cdouble z);
npy_cdouble npy_ctanh(npy_cdouble z);
npy_cdouble npy_cacos(npy_cdouble z);
npy_cdouble npy_casin(npy_cdouble z);
npy_cdouble npy_catan(npy_cdouble z);
npy_cdouble npy_cacosh(npy_cdouble z);
npy_cdouble npy_casinh(npy_cdouble z);
npy_cdouble npy_catanh(npy_cdouble z);
/*
* Single precision complex functions
*/
float npy_cabsf(npy_cfloat z);
float npy_cargf(npy_cfloat z);
npy_cfloat npy_cexpf(npy_cfloat z);
npy_cfloat npy_clogf(npy_cfloat z);
npy_cfloat npy_cpowf(npy_cfloat x, npy_cfloat y);
npy_cfloat npy_csqrtf(npy_cfloat z);
npy_cfloat npy_ccosf(npy_cfloat z);
npy_cfloat npy_csinf(npy_cfloat z);
npy_cfloat npy_ctanf(npy_cfloat z);
npy_cfloat npy_ccoshf(npy_cfloat z);
npy_cfloat npy_csinhf(npy_cfloat z);
npy_cfloat npy_ctanhf(npy_cfloat z);
npy_cfloat npy_cacosf(npy_cfloat z);
npy_cfloat npy_casinf(npy_cfloat z);
npy_cfloat npy_catanf(npy_cfloat z);
npy_cfloat npy_cacoshf(npy_cfloat z);
npy_cfloat npy_casinhf(npy_cfloat z);
npy_cfloat npy_catanhf(npy_cfloat z);
/*
* Extended precision complex functions
*/
npy_longdouble npy_cabsl(npy_clongdouble z);
npy_longdouble npy_cargl(npy_clongdouble z);
npy_clongdouble npy_cexpl(npy_clongdouble z);
npy_clongdouble npy_clogl(npy_clongdouble z);
npy_clongdouble npy_cpowl(npy_clongdouble x, npy_clongdouble y);
npy_clongdouble npy_csqrtl(npy_clongdouble z);
npy_clongdouble npy_ccosl(npy_clongdouble z);
npy_clongdouble npy_csinl(npy_clongdouble z);
npy_clongdouble npy_ctanl(npy_clongdouble z);
npy_clongdouble npy_ccoshl(npy_clongdouble z);
npy_clongdouble npy_csinhl(npy_clongdouble z);
npy_clongdouble npy_ctanhl(npy_clongdouble z);
npy_clongdouble npy_cacosl(npy_clongdouble z);
npy_clongdouble npy_casinl(npy_clongdouble z);
npy_clongdouble npy_catanl(npy_clongdouble z);
npy_clongdouble npy_cacoshl(npy_clongdouble z);
npy_clongdouble npy_casinhl(npy_clongdouble z);
npy_clongdouble npy_catanhl(npy_clongdouble z);
/*
* Functions that set the floating point error
* status word.
*/
/*
* platform-dependent code translates floating point
* status to an integer sum of these values
*/
#define NPY_FPE_DIVIDEBYZERO 1
#define NPY_FPE_OVERFLOW 2
#define NPY_FPE_UNDERFLOW 4
#define NPY_FPE_INVALID 8
int npy_clear_floatstatus_barrier(char*);
int npy_get_floatstatus_barrier(char*);
/*
* use caution with these - clang and gcc8.1 are known to reorder calls
* to this form of the function which can defeat the check. The _barrier
* form of the call is preferable, where the argument is
* (char*)&local_variable
*/
int npy_clear_floatstatus(void);
int npy_get_floatstatus(void);
void npy_set_floatstatus_divbyzero(void);
void npy_set_floatstatus_overflow(void);
void npy_set_floatstatus_underflow(void);
void npy_set_floatstatus_invalid(void);
#ifdef __cplusplus
}
#endif
#if NPY_INLINE_MATH
#include "npy_math_internal.h"
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_MATH_H_ */
| 21,370 | C | 34.618333 | 82 | 0.712307 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/npy_3kcompat.h | /*
* This is a convenience header file providing compatibility utilities
* for supporting different minor versions of Python 3.
* It was originally used to support the transition from Python 2,
* hence the "3k" naming.
*
* If you want to use this for your own projects, it's recommended to make a
* copy of it. Although the stuff below is unlikely to change, we don't provide
* strong backwards compatibility guarantees at the moment.
*/
#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_
#include <Python.h>
#include <stdio.h>
#ifndef NPY_PY3K
#define NPY_PY3K 1
#endif
#include "numpy/npy_common.h"
#include "numpy/ndarrayobject.h"
#ifdef __cplusplus
extern "C" {
#endif
/*
* PyInt -> PyLong
*/
/*
* This is a renamed copy of the Python non-limited API function _PyLong_AsInt. It is
* included here because it is missing from the PyPy API. It completes the PyLong_As*
* group of functions and can be useful in replacing PyInt_Check.
*/
static NPY_INLINE int
Npy__PyLong_AsInt(PyObject *obj)
{
int overflow;
long result = PyLong_AsLongAndOverflow(obj, &overflow);
/* INT_MAX and INT_MIN are defined in Python.h */
if (overflow || result > INT_MAX || result < INT_MIN) {
/* XXX: could be cute and give a different
message for overflow == -1 */
PyErr_SetString(PyExc_OverflowError,
"Python int too large to convert to C int");
return -1;
}
return (int)result;
}
#if defined(NPY_PY3K)
/* Return True only if the long fits in a C long */
static NPY_INLINE int PyInt_Check(PyObject *op) {
int overflow = 0;
if (!PyLong_Check(op)) {
return 0;
}
PyLong_AsLongAndOverflow(op, &overflow);
return (overflow == 0);
}
#define PyInt_FromLong PyLong_FromLong
#define PyInt_AsLong PyLong_AsLong
#define PyInt_AS_LONG PyLong_AsLong
#define PyInt_AsSsize_t PyLong_AsSsize_t
#define PyNumber_Int PyNumber_Long
/* NOTE:
*
* Since the PyLong type is very different from the fixed-range PyInt,
* we don't define PyInt_Type -> PyLong_Type.
*/
#endif /* NPY_PY3K */
/* Py3 changes PySlice_GetIndicesEx' first argument's type to PyObject* */
#ifdef NPY_PY3K
# define NpySlice_GetIndicesEx PySlice_GetIndicesEx
#else
# define NpySlice_GetIndicesEx(op, nop, start, end, step, slicelength) \
PySlice_GetIndicesEx((PySliceObject *)op, nop, start, end, step, slicelength)
#endif
#if PY_VERSION_HEX < 0x030900a4
/* Introduced in https://github.com/python/cpython/commit/d2ec81a8c99796b51fb8c49b77a7fe369863226f */
#define Py_SET_TYPE(obj, type) ((Py_TYPE(obj) = (type)), (void)0)
/* Introduced in https://github.com/python/cpython/commit/b10dc3e7a11fcdb97e285882eba6da92594f90f9 */
#define Py_SET_SIZE(obj, size) ((Py_SIZE(obj) = (size)), (void)0)
/* Introduced in https://github.com/python/cpython/commit/c86a11221df7e37da389f9c6ce6e47ea22dc44ff */
#define Py_SET_REFCNT(obj, refcnt) ((Py_REFCNT(obj) = (refcnt)), (void)0)
#endif
#define Npy_EnterRecursiveCall(x) Py_EnterRecursiveCall(x)
/* Py_SETREF was added in 3.5.2, and only if Py_LIMITED_API is absent */
#if PY_VERSION_HEX < 0x03050200
#define Py_SETREF(op, op2) \
do { \
PyObject *_py_tmp = (PyObject *)(op); \
(op) = (op2); \
Py_DECREF(_py_tmp); \
} while (0)
#endif
/* introduced in https://github.com/python/cpython/commit/a24107b04c1277e3c1105f98aff5bfa3a98b33a0 */
#if PY_VERSION_HEX < 0x030800A3
static NPY_INLINE PyObject *
_PyDict_GetItemStringWithError(PyObject *v, const char *key)
{
PyObject *kv, *rv;
kv = PyUnicode_FromString(key);
if (kv == NULL) {
return NULL;
}
rv = PyDict_GetItemWithError(v, kv);
Py_DECREF(kv);
return rv;
}
#endif
/*
* PyString -> PyBytes
*/
#if defined(NPY_PY3K)
#define PyString_Type PyBytes_Type
#define PyString_Check PyBytes_Check
#define PyStringObject PyBytesObject
#define PyString_FromString PyBytes_FromString
#define PyString_FromStringAndSize PyBytes_FromStringAndSize
#define PyString_AS_STRING PyBytes_AS_STRING
#define PyString_AsStringAndSize PyBytes_AsStringAndSize
#define PyString_FromFormat PyBytes_FromFormat
#define PyString_Concat PyBytes_Concat
#define PyString_ConcatAndDel PyBytes_ConcatAndDel
#define PyString_AsString PyBytes_AsString
#define PyString_GET_SIZE PyBytes_GET_SIZE
#define PyString_Size PyBytes_Size
#define PyUString_Type PyUnicode_Type
#define PyUString_Check PyUnicode_Check
#define PyUStringObject PyUnicodeObject
#define PyUString_FromString PyUnicode_FromString
#define PyUString_FromStringAndSize PyUnicode_FromStringAndSize
#define PyUString_FromFormat PyUnicode_FromFormat
#define PyUString_Concat PyUnicode_Concat2
#define PyUString_ConcatAndDel PyUnicode_ConcatAndDel
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
#define PyUString_Size PyUnicode_Size
#define PyUString_InternFromString PyUnicode_InternFromString
#define PyUString_Format PyUnicode_Format
#define PyBaseString_Check(obj) (PyUnicode_Check(obj))
#else
#define PyBytes_Type PyString_Type
#define PyBytes_Check PyString_Check
#define PyBytesObject PyStringObject
#define PyBytes_FromString PyString_FromString
#define PyBytes_FromStringAndSize PyString_FromStringAndSize
#define PyBytes_AS_STRING PyString_AS_STRING
#define PyBytes_AsStringAndSize PyString_AsStringAndSize
#define PyBytes_FromFormat PyString_FromFormat
#define PyBytes_Concat PyString_Concat
#define PyBytes_ConcatAndDel PyString_ConcatAndDel
#define PyBytes_AsString PyString_AsString
#define PyBytes_GET_SIZE PyString_GET_SIZE
#define PyBytes_Size PyString_Size
#define PyUString_Type PyString_Type
#define PyUString_Check PyString_Check
#define PyUStringObject PyStringObject
#define PyUString_FromString PyString_FromString
#define PyUString_FromStringAndSize PyString_FromStringAndSize
#define PyUString_FromFormat PyString_FromFormat
#define PyUString_Concat PyString_Concat
#define PyUString_ConcatAndDel PyString_ConcatAndDel
#define PyUString_GET_SIZE PyString_GET_SIZE
#define PyUString_Size PyString_Size
#define PyUString_InternFromString PyString_InternFromString
#define PyUString_Format PyString_Format
#define PyBaseString_Check(obj) (PyBytes_Check(obj) || PyUnicode_Check(obj))
#endif /* NPY_PY3K */
static NPY_INLINE void
PyUnicode_ConcatAndDel(PyObject **left, PyObject *right)
{
Py_SETREF(*left, PyUnicode_Concat(*left, right));
Py_DECREF(right);
}
static NPY_INLINE void
PyUnicode_Concat2(PyObject **left, PyObject *right)
{
Py_SETREF(*left, PyUnicode_Concat(*left, right));
}
/*
* PyFile_* compatibility
*/
/*
* Get a FILE* handle to the file represented by the Python object
*/
static NPY_INLINE FILE*
npy_PyFile_Dup2(PyObject *file, char *mode, npy_off_t *orig_pos)
{
int fd, fd2, unbuf;
Py_ssize_t fd2_tmp;
PyObject *ret, *os, *io, *io_raw;
npy_off_t pos;
FILE *handle;
/* For Python 2 PyFileObject, use PyFile_AsFile */
#if !defined(NPY_PY3K)
if (PyFile_Check(file)) {
return PyFile_AsFile(file);
}
#endif
/* Flush first to ensure things end up in the file in the correct order */
ret = PyObject_CallMethod(file, "flush", "");
if (ret == NULL) {
return NULL;
}
Py_DECREF(ret);
fd = PyObject_AsFileDescriptor(file);
if (fd == -1) {
return NULL;
}
/*
* The handle needs to be dup'd because we have to call fclose
* at the end
*/
os = PyImport_ImportModule("os");
if (os == NULL) {
return NULL;
}
ret = PyObject_CallMethod(os, "dup", "i", fd);
Py_DECREF(os);
if (ret == NULL) {
return NULL;
}
fd2_tmp = PyNumber_AsSsize_t(ret, PyExc_IOError);
Py_DECREF(ret);
if (fd2_tmp == -1 && PyErr_Occurred()) {
return NULL;
}
if (fd2_tmp < INT_MIN || fd2_tmp > INT_MAX) {
PyErr_SetString(PyExc_IOError,
"Getting an 'int' from os.dup() failed");
return NULL;
}
fd2 = (int)fd2_tmp;
/* Convert to FILE* handle */
#ifdef _WIN32
handle = _fdopen(fd2, mode);
#else
handle = fdopen(fd2, mode);
#endif
if (handle == NULL) {
PyErr_SetString(PyExc_IOError,
"Getting a FILE* from a Python file object failed");
return NULL;
}
/* Record the original raw file handle position */
*orig_pos = npy_ftell(handle);
if (*orig_pos == -1) {
/* The io module is needed to determine if buffering is used */
io = PyImport_ImportModule("io");
if (io == NULL) {
fclose(handle);
return NULL;
}
/* File object instances of RawIOBase are unbuffered */
io_raw = PyObject_GetAttrString(io, "RawIOBase");
Py_DECREF(io);
if (io_raw == NULL) {
fclose(handle);
return NULL;
}
unbuf = PyObject_IsInstance(file, io_raw);
Py_DECREF(io_raw);
if (unbuf == 1) {
/* Succeed if the IO is unbuffered */
return handle;
}
else {
PyErr_SetString(PyExc_IOError, "obtaining file position failed");
fclose(handle);
return NULL;
}
}
/* Seek raw handle to the Python-side position */
ret = PyObject_CallMethod(file, "tell", "");
if (ret == NULL) {
fclose(handle);
return NULL;
}
pos = PyLong_AsLongLong(ret);
Py_DECREF(ret);
if (PyErr_Occurred()) {
fclose(handle);
return NULL;
}
if (npy_fseek(handle, pos, SEEK_SET) == -1) {
PyErr_SetString(PyExc_IOError, "seeking file failed");
fclose(handle);
return NULL;
}
return handle;
}
/*
* Close the dup-ed file handle, and seek the Python one to the current position
*/
static NPY_INLINE int
npy_PyFile_DupClose2(PyObject *file, FILE* handle, npy_off_t orig_pos)
{
int fd, unbuf;
PyObject *ret, *io, *io_raw;
npy_off_t position;
/* For Python 2 PyFileObject, do nothing */
#if !defined(NPY_PY3K)
if (PyFile_Check(file)) {
return 0;
}
#endif
position = npy_ftell(handle);
/* Close the FILE* handle */
fclose(handle);
/*
* Restore original file handle position, in order to not confuse
* Python-side data structures
*/
fd = PyObject_AsFileDescriptor(file);
if (fd == -1) {
return -1;
}
if (npy_lseek(fd, orig_pos, SEEK_SET) == -1) {
/* The io module is needed to determine if buffering is used */
io = PyImport_ImportModule("io");
if (io == NULL) {
return -1;
}
/* File object instances of RawIOBase are unbuffered */
io_raw = PyObject_GetAttrString(io, "RawIOBase");
Py_DECREF(io);
if (io_raw == NULL) {
return -1;
}
unbuf = PyObject_IsInstance(file, io_raw);
Py_DECREF(io_raw);
if (unbuf == 1) {
/* Succeed if the IO is unbuffered */
return 0;
}
else {
PyErr_SetString(PyExc_IOError, "seeking file failed");
return -1;
}
}
if (position == -1) {
PyErr_SetString(PyExc_IOError, "obtaining file position failed");
return -1;
}
/* Seek Python-side handle to the FILE* handle position */
ret = PyObject_CallMethod(file, "seek", NPY_OFF_T_PYFMT "i", position, 0);
if (ret == NULL) {
return -1;
}
Py_DECREF(ret);
return 0;
}
static NPY_INLINE int
npy_PyFile_Check(PyObject *file)
{
int fd;
/* For Python 2, check if it is a PyFileObject */
#if !defined(NPY_PY3K)
if (PyFile_Check(file)) {
return 1;
}
#endif
fd = PyObject_AsFileDescriptor(file);
if (fd == -1) {
PyErr_Clear();
return 0;
}
return 1;
}
static NPY_INLINE PyObject*
npy_PyFile_OpenFile(PyObject *filename, const char *mode)
{
PyObject *open;
open = PyDict_GetItemString(PyEval_GetBuiltins(), "open");
if (open == NULL) {
return NULL;
}
return PyObject_CallFunction(open, "Os", filename, mode);
}
static NPY_INLINE int
npy_PyFile_CloseFile(PyObject *file)
{
PyObject *ret;
ret = PyObject_CallMethod(file, "close", NULL);
if (ret == NULL) {
return -1;
}
Py_DECREF(ret);
return 0;
}
/* This is a copy of _PyErr_ChainExceptions
*/
static NPY_INLINE void
npy_PyErr_ChainExceptions(PyObject *exc, PyObject *val, PyObject *tb)
{
if (exc == NULL)
return;
if (PyErr_Occurred()) {
/* only py3 supports this anyway */
#ifdef NPY_PY3K
PyObject *exc2, *val2, *tb2;
PyErr_Fetch(&exc2, &val2, &tb2);
PyErr_NormalizeException(&exc, &val, &tb);
if (tb != NULL) {
PyException_SetTraceback(val, tb);
Py_DECREF(tb);
}
Py_DECREF(exc);
PyErr_NormalizeException(&exc2, &val2, &tb2);
PyException_SetContext(val2, val);
PyErr_Restore(exc2, val2, tb2);
#endif
}
else {
PyErr_Restore(exc, val, tb);
}
}
/* This is a copy of _PyErr_ChainExceptions, with:
* - a minimal implementation for python 2
* - __cause__ used instead of __context__
*/
static NPY_INLINE void
npy_PyErr_ChainExceptionsCause(PyObject *exc, PyObject *val, PyObject *tb)
{
if (exc == NULL)
return;
if (PyErr_Occurred()) {
/* only py3 supports this anyway */
#ifdef NPY_PY3K
PyObject *exc2, *val2, *tb2;
PyErr_Fetch(&exc2, &val2, &tb2);
PyErr_NormalizeException(&exc, &val, &tb);
if (tb != NULL) {
PyException_SetTraceback(val, tb);
Py_DECREF(tb);
}
Py_DECREF(exc);
PyErr_NormalizeException(&exc2, &val2, &tb2);
PyException_SetCause(val2, val);
PyErr_Restore(exc2, val2, tb2);
#endif
}
else {
PyErr_Restore(exc, val, tb);
}
}
/*
* PyObject_Cmp
*/
#if defined(NPY_PY3K)
static NPY_INLINE int
PyObject_Cmp(PyObject *i1, PyObject *i2, int *cmp)
{
int v;
v = PyObject_RichCompareBool(i1, i2, Py_LT);
if (v == 1) {
*cmp = -1;
return 1;
}
else if (v == -1) {
return -1;
}
v = PyObject_RichCompareBool(i1, i2, Py_GT);
if (v == 1) {
*cmp = 1;
return 1;
}
else if (v == -1) {
return -1;
}
v = PyObject_RichCompareBool(i1, i2, Py_EQ);
if (v == 1) {
*cmp = 0;
return 1;
}
else {
*cmp = 0;
return -1;
}
}
#endif
/*
* PyCObject functions adapted to PyCapsules.
*
* The main job here is to get rid of the improved error handling
* of PyCapsules. It's a shame...
*/
static NPY_INLINE PyObject *
NpyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *))
{
PyObject *ret = PyCapsule_New(ptr, NULL, dtor);
if (ret == NULL) {
PyErr_Clear();
}
return ret;
}
static NPY_INLINE PyObject *
NpyCapsule_FromVoidPtrAndDesc(void *ptr, void* context, void (*dtor)(PyObject *))
{
PyObject *ret = NpyCapsule_FromVoidPtr(ptr, dtor);
if (ret != NULL && PyCapsule_SetContext(ret, context) != 0) {
PyErr_Clear();
Py_DECREF(ret);
ret = NULL;
}
return ret;
}
static NPY_INLINE void *
NpyCapsule_AsVoidPtr(PyObject *obj)
{
void *ret = PyCapsule_GetPointer(obj, NULL);
if (ret == NULL) {
PyErr_Clear();
}
return ret;
}
static NPY_INLINE void *
NpyCapsule_GetDesc(PyObject *obj)
{
return PyCapsule_GetContext(obj);
}
static NPY_INLINE int
NpyCapsule_Check(PyObject *ptr)
{
return PyCapsule_CheckExact(ptr);
}
#ifdef __cplusplus
}
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_ */
| 15,990 | C | 25.740803 | 105 | 0.627267 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/ndarraytypes.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_
#include "npy_common.h"
#include "npy_endian.h"
#include "npy_cpu.h"
#include "utils.h"
#define NPY_NO_EXPORT NPY_VISIBILITY_HIDDEN
/* Only use thread if configured in config and python supports it */
#if defined WITH_THREAD && !NPY_NO_SMP
#define NPY_ALLOW_THREADS 1
#else
#define NPY_ALLOW_THREADS 0
#endif
#ifndef __has_extension
#define __has_extension(x) 0
#endif
#if !defined(_NPY_NO_DEPRECATIONS) && \
((defined(__GNUC__)&& __GNUC__ >= 6) || \
__has_extension(attribute_deprecated_with_message))
#define NPY_ATTR_DEPRECATE(text) __attribute__ ((deprecated (text)))
#else
#define NPY_ATTR_DEPRECATE(text)
#endif
/*
* There are several places in the code where an array of dimensions
* is allocated statically. This is the size of that static
* allocation.
*
* The array creation itself could have arbitrary dimensions but all
* the places where static allocation is used would need to be changed
* to dynamic (including inside of several structures)
*/
#define NPY_MAXDIMS 32
#define NPY_MAXARGS 32
/* Used for Converter Functions "O&" code in ParseTuple */
#define NPY_FAIL 0
#define NPY_SUCCEED 1
/*
* Binary compatibility version number. This number is increased
* whenever the C-API is changed such that binary compatibility is
* broken, i.e. whenever a recompile of extension modules is needed.
*/
#define NPY_VERSION NPY_ABI_VERSION
/*
* Minor API version. This number is increased whenever a change is
* made to the C-API -- whether it breaks binary compatibility or not.
* Some changes, such as adding a function pointer to the end of the
* function table, can be made without breaking binary compatibility.
* In this case, only the NPY_FEATURE_VERSION (*not* NPY_VERSION)
* would be increased. Whenever binary compatibility is broken, both
* NPY_VERSION and NPY_FEATURE_VERSION should be increased.
*/
#define NPY_FEATURE_VERSION NPY_API_VERSION
enum NPY_TYPES { NPY_BOOL=0,
NPY_BYTE, NPY_UBYTE,
NPY_SHORT, NPY_USHORT,
NPY_INT, NPY_UINT,
NPY_LONG, NPY_ULONG,
NPY_LONGLONG, NPY_ULONGLONG,
NPY_FLOAT, NPY_DOUBLE, NPY_LONGDOUBLE,
NPY_CFLOAT, NPY_CDOUBLE, NPY_CLONGDOUBLE,
NPY_OBJECT=17,
NPY_STRING, NPY_UNICODE,
NPY_VOID,
/*
* New 1.6 types appended, may be integrated
* into the above in 2.0.
*/
NPY_DATETIME, NPY_TIMEDELTA, NPY_HALF,
NPY_NTYPES,
NPY_NOTYPE,
NPY_CHAR NPY_ATTR_DEPRECATE("Use NPY_STRING"),
NPY_USERDEF=256, /* leave room for characters */
/* The number of types not including the new 1.6 types */
NPY_NTYPES_ABI_COMPATIBLE=21
};
#if defined(_MSC_VER) && !defined(__clang__)
#pragma deprecated(NPY_CHAR)
#endif
/* basetype array priority */
#define NPY_PRIORITY 0.0
/* default subtype priority */
#define NPY_SUBTYPE_PRIORITY 1.0
/* default scalar priority */
#define NPY_SCALAR_PRIORITY -1000000.0
/* How many floating point types are there (excluding half) */
#define NPY_NUM_FLOATTYPE 3
/*
* These characters correspond to the array type and the struct
* module
*/
enum NPY_TYPECHAR {
NPY_BOOLLTR = '?',
NPY_BYTELTR = 'b',
NPY_UBYTELTR = 'B',
NPY_SHORTLTR = 'h',
NPY_USHORTLTR = 'H',
NPY_INTLTR = 'i',
NPY_UINTLTR = 'I',
NPY_LONGLTR = 'l',
NPY_ULONGLTR = 'L',
NPY_LONGLONGLTR = 'q',
NPY_ULONGLONGLTR = 'Q',
NPY_HALFLTR = 'e',
NPY_FLOATLTR = 'f',
NPY_DOUBLELTR = 'd',
NPY_LONGDOUBLELTR = 'g',
NPY_CFLOATLTR = 'F',
NPY_CDOUBLELTR = 'D',
NPY_CLONGDOUBLELTR = 'G',
NPY_OBJECTLTR = 'O',
NPY_STRINGLTR = 'S',
NPY_STRINGLTR2 = 'a',
NPY_UNICODELTR = 'U',
NPY_VOIDLTR = 'V',
NPY_DATETIMELTR = 'M',
NPY_TIMEDELTALTR = 'm',
NPY_CHARLTR = 'c',
/*
* No Descriptor, just a define -- this let's
* Python users specify an array of integers
* large enough to hold a pointer on the
* platform
*/
NPY_INTPLTR = 'p',
NPY_UINTPLTR = 'P',
/*
* These are for dtype 'kinds', not dtype 'typecodes'
* as the above are for.
*/
NPY_GENBOOLLTR ='b',
NPY_SIGNEDLTR = 'i',
NPY_UNSIGNEDLTR = 'u',
NPY_FLOATINGLTR = 'f',
NPY_COMPLEXLTR = 'c'
};
/*
* Changing this may break Numpy API compatibility
* due to changing offsets in PyArray_ArrFuncs, so be
* careful. Here we have reused the mergesort slot for
* any kind of stable sort, the actual implementation will
* depend on the data type.
*/
typedef enum {
NPY_QUICKSORT=0,
NPY_HEAPSORT=1,
NPY_MERGESORT=2,
NPY_STABLESORT=2,
} NPY_SORTKIND;
#define NPY_NSORTS (NPY_STABLESORT + 1)
typedef enum {
NPY_INTROSELECT=0
} NPY_SELECTKIND;
#define NPY_NSELECTS (NPY_INTROSELECT + 1)
typedef enum {
NPY_SEARCHLEFT=0,
NPY_SEARCHRIGHT=1
} NPY_SEARCHSIDE;
#define NPY_NSEARCHSIDES (NPY_SEARCHRIGHT + 1)
typedef enum {
NPY_NOSCALAR=-1,
NPY_BOOL_SCALAR,
NPY_INTPOS_SCALAR,
NPY_INTNEG_SCALAR,
NPY_FLOAT_SCALAR,
NPY_COMPLEX_SCALAR,
NPY_OBJECT_SCALAR
} NPY_SCALARKIND;
#define NPY_NSCALARKINDS (NPY_OBJECT_SCALAR + 1)
/* For specifying array memory layout or iteration order */
typedef enum {
/* Fortran order if inputs are all Fortran, C otherwise */
NPY_ANYORDER=-1,
/* C order */
NPY_CORDER=0,
/* Fortran order */
NPY_FORTRANORDER=1,
/* An order as close to the inputs as possible */
NPY_KEEPORDER=2
} NPY_ORDER;
/* For specifying allowed casting in operations which support it */
typedef enum {
_NPY_ERROR_OCCURRED_IN_CAST = -1,
/* Only allow identical types */
NPY_NO_CASTING=0,
/* Allow identical and byte swapped types */
NPY_EQUIV_CASTING=1,
/* Only allow safe casts */
NPY_SAFE_CASTING=2,
/* Allow safe casts or casts within the same kind */
NPY_SAME_KIND_CASTING=3,
/* Allow any casts */
NPY_UNSAFE_CASTING=4,
} NPY_CASTING;
typedef enum {
NPY_CLIP=0,
NPY_WRAP=1,
NPY_RAISE=2
} NPY_CLIPMODE;
typedef enum {
NPY_VALID=0,
NPY_SAME=1,
NPY_FULL=2
} NPY_CORRELATEMODE;
/* The special not-a-time (NaT) value */
#define NPY_DATETIME_NAT NPY_MIN_INT64
/*
* Upper bound on the length of a DATETIME ISO 8601 string
* YEAR: 21 (64-bit year)
* MONTH: 3
* DAY: 3
* HOURS: 3
* MINUTES: 3
* SECONDS: 3
* ATTOSECONDS: 1 + 3*6
* TIMEZONE: 5
* NULL TERMINATOR: 1
*/
#define NPY_DATETIME_MAX_ISO8601_STRLEN (21 + 3*5 + 1 + 3*6 + 6 + 1)
/* The FR in the unit names stands for frequency */
typedef enum {
/* Force signed enum type, must be -1 for code compatibility */
NPY_FR_ERROR = -1, /* error or undetermined */
/* Start of valid units */
NPY_FR_Y = 0, /* Years */
NPY_FR_M = 1, /* Months */
NPY_FR_W = 2, /* Weeks */
/* Gap where 1.6 NPY_FR_B (value 3) was */
NPY_FR_D = 4, /* Days */
NPY_FR_h = 5, /* hours */
NPY_FR_m = 6, /* minutes */
NPY_FR_s = 7, /* seconds */
NPY_FR_ms = 8, /* milliseconds */
NPY_FR_us = 9, /* microseconds */
NPY_FR_ns = 10, /* nanoseconds */
NPY_FR_ps = 11, /* picoseconds */
NPY_FR_fs = 12, /* femtoseconds */
NPY_FR_as = 13, /* attoseconds */
NPY_FR_GENERIC = 14 /* unbound units, can convert to anything */
} NPY_DATETIMEUNIT;
/*
* NOTE: With the NPY_FR_B gap for 1.6 ABI compatibility, NPY_DATETIME_NUMUNITS
* is technically one more than the actual number of units.
*/
#define NPY_DATETIME_NUMUNITS (NPY_FR_GENERIC + 1)
#define NPY_DATETIME_DEFAULTUNIT NPY_FR_GENERIC
/*
* Business day conventions for mapping invalid business
* days to valid business days.
*/
typedef enum {
/* Go forward in time to the following business day. */
NPY_BUSDAY_FORWARD,
NPY_BUSDAY_FOLLOWING = NPY_BUSDAY_FORWARD,
/* Go backward in time to the preceding business day. */
NPY_BUSDAY_BACKWARD,
NPY_BUSDAY_PRECEDING = NPY_BUSDAY_BACKWARD,
/*
* Go forward in time to the following business day, unless it
* crosses a month boundary, in which case go backward
*/
NPY_BUSDAY_MODIFIEDFOLLOWING,
/*
* Go backward in time to the preceding business day, unless it
* crosses a month boundary, in which case go forward.
*/
NPY_BUSDAY_MODIFIEDPRECEDING,
/* Produce a NaT for non-business days. */
NPY_BUSDAY_NAT,
/* Raise an exception for non-business days. */
NPY_BUSDAY_RAISE
} NPY_BUSDAY_ROLL;
/************************************************************
* NumPy Auxiliary Data for inner loops, sort functions, etc.
************************************************************/
/*
* When creating an auxiliary data struct, this should always appear
* as the first member, like this:
*
* typedef struct {
* NpyAuxData base;
* double constant;
* } constant_multiplier_aux_data;
*/
typedef struct NpyAuxData_tag NpyAuxData;
/* Function pointers for freeing or cloning auxiliary data */
typedef void (NpyAuxData_FreeFunc) (NpyAuxData *);
typedef NpyAuxData *(NpyAuxData_CloneFunc) (NpyAuxData *);
struct NpyAuxData_tag {
NpyAuxData_FreeFunc *free;
NpyAuxData_CloneFunc *clone;
/* To allow for a bit of expansion without breaking the ABI */
void *reserved[2];
};
/* Macros to use for freeing and cloning auxiliary data */
#define NPY_AUXDATA_FREE(auxdata) \
do { \
if ((auxdata) != NULL) { \
(auxdata)->free(auxdata); \
} \
} while(0)
#define NPY_AUXDATA_CLONE(auxdata) \
((auxdata)->clone(auxdata))
#define NPY_ERR(str) fprintf(stderr, #str); fflush(stderr);
#define NPY_ERR2(str) fprintf(stderr, str); fflush(stderr);
/*
* Macros to define how array, and dimension/strides data is
* allocated. These should be made private
*/
#define NPY_USE_PYMEM 1
#if NPY_USE_PYMEM == 1
/* use the Raw versions which are safe to call with the GIL released */
#define PyArray_malloc PyMem_RawMalloc
#define PyArray_free PyMem_RawFree
#define PyArray_realloc PyMem_RawRealloc
#else
#define PyArray_malloc malloc
#define PyArray_free free
#define PyArray_realloc realloc
#endif
/* Dimensions and strides */
#define PyDimMem_NEW(size) \
((npy_intp *)PyArray_malloc(size*sizeof(npy_intp)))
#define PyDimMem_FREE(ptr) PyArray_free(ptr)
#define PyDimMem_RENEW(ptr,size) \
((npy_intp *)PyArray_realloc(ptr,size*sizeof(npy_intp)))
/* forward declaration */
struct _PyArray_Descr;
/* These must deal with unaligned and swapped data if necessary */
typedef PyObject * (PyArray_GetItemFunc) (void *, void *);
typedef int (PyArray_SetItemFunc)(PyObject *, void *, void *);
typedef void (PyArray_CopySwapNFunc)(void *, npy_intp, void *, npy_intp,
npy_intp, int, void *);
typedef void (PyArray_CopySwapFunc)(void *, void *, int, void *);
typedef npy_bool (PyArray_NonzeroFunc)(void *, void *);
/*
* These assume aligned and notswapped data -- a buffer will be used
* before or contiguous data will be obtained
*/
typedef int (PyArray_CompareFunc)(const void *, const void *, void *);
typedef int (PyArray_ArgFunc)(void*, npy_intp, npy_intp*, void *);
typedef void (PyArray_DotFunc)(void *, npy_intp, void *, npy_intp, void *,
npy_intp, void *);
typedef void (PyArray_VectorUnaryFunc)(void *, void *, npy_intp, void *,
void *);
/*
* XXX the ignore argument should be removed next time the API version
* is bumped. It used to be the separator.
*/
typedef int (PyArray_ScanFunc)(FILE *fp, void *dptr,
char *ignore, struct _PyArray_Descr *);
typedef int (PyArray_FromStrFunc)(char *s, void *dptr, char **endptr,
struct _PyArray_Descr *);
typedef int (PyArray_FillFunc)(void *, npy_intp, void *);
typedef int (PyArray_SortFunc)(void *, npy_intp, void *);
typedef int (PyArray_ArgSortFunc)(void *, npy_intp *, npy_intp, void *);
typedef int (PyArray_PartitionFunc)(void *, npy_intp, npy_intp,
npy_intp *, npy_intp *,
void *);
typedef int (PyArray_ArgPartitionFunc)(void *, npy_intp *, npy_intp, npy_intp,
npy_intp *, npy_intp *,
void *);
typedef int (PyArray_FillWithScalarFunc)(void *, npy_intp, void *, void *);
typedef int (PyArray_ScalarKindFunc)(void *);
typedef void (PyArray_FastClipFunc)(void *in, npy_intp n_in, void *min,
void *max, void *out);
typedef void (PyArray_FastPutmaskFunc)(void *in, void *mask, npy_intp n_in,
void *values, npy_intp nv);
typedef int (PyArray_FastTakeFunc)(void *dest, void *src, npy_intp *indarray,
npy_intp nindarray, npy_intp n_outer,
npy_intp m_middle, npy_intp nelem,
NPY_CLIPMODE clipmode);
typedef struct {
npy_intp *ptr;
int len;
} PyArray_Dims;
typedef struct {
/*
* Functions to cast to most other standard types
* Can have some NULL entries. The types
* DATETIME, TIMEDELTA, and HALF go into the castdict
* even though they are built-in.
*/
PyArray_VectorUnaryFunc *cast[NPY_NTYPES_ABI_COMPATIBLE];
/* The next four functions *cannot* be NULL */
/*
* Functions to get and set items with standard Python types
* -- not array scalars
*/
PyArray_GetItemFunc *getitem;
PyArray_SetItemFunc *setitem;
/*
* Copy and/or swap data. Memory areas may not overlap
* Use memmove first if they might
*/
PyArray_CopySwapNFunc *copyswapn;
PyArray_CopySwapFunc *copyswap;
/*
* Function to compare items
* Can be NULL
*/
PyArray_CompareFunc *compare;
/*
* Function to select largest
* Can be NULL
*/
PyArray_ArgFunc *argmax;
/*
* Function to compute dot product
* Can be NULL
*/
PyArray_DotFunc *dotfunc;
/*
* Function to scan an ASCII file and
* place a single value plus possible separator
* Can be NULL
*/
PyArray_ScanFunc *scanfunc;
/*
* Function to read a single value from a string
* and adjust the pointer; Can be NULL
*/
PyArray_FromStrFunc *fromstr;
/*
* Function to determine if data is zero or not
* If NULL a default version is
* used at Registration time.
*/
PyArray_NonzeroFunc *nonzero;
/*
* Used for arange. Should return 0 on success
* and -1 on failure.
* Can be NULL.
*/
PyArray_FillFunc *fill;
/*
* Function to fill arrays with scalar values
* Can be NULL
*/
PyArray_FillWithScalarFunc *fillwithscalar;
/*
* Sorting functions
* Can be NULL
*/
PyArray_SortFunc *sort[NPY_NSORTS];
PyArray_ArgSortFunc *argsort[NPY_NSORTS];
/*
* Dictionary of additional casting functions
* PyArray_VectorUnaryFuncs
* which can be populated to support casting
* to other registered types. Can be NULL
*/
PyObject *castdict;
/*
* Functions useful for generalizing
* the casting rules.
* Can be NULL;
*/
PyArray_ScalarKindFunc *scalarkind;
int **cancastscalarkindto;
int *cancastto;
PyArray_FastClipFunc *fastclip;
PyArray_FastPutmaskFunc *fastputmask;
PyArray_FastTakeFunc *fasttake;
/*
* Function to select smallest
* Can be NULL
*/
PyArray_ArgFunc *argmin;
} PyArray_ArrFuncs;
/* The item must be reference counted when it is inserted or extracted. */
#define NPY_ITEM_REFCOUNT 0x01
/* Same as needing REFCOUNT */
#define NPY_ITEM_HASOBJECT 0x01
/* Convert to list for pickling */
#define NPY_LIST_PICKLE 0x02
/* The item is a POINTER */
#define NPY_ITEM_IS_POINTER 0x04
/* memory needs to be initialized for this data-type */
#define NPY_NEEDS_INIT 0x08
/* operations need Python C-API so don't give-up thread. */
#define NPY_NEEDS_PYAPI 0x10
/* Use f.getitem when extracting elements of this data-type */
#define NPY_USE_GETITEM 0x20
/* Use f.setitem when setting creating 0-d array from this data-type.*/
#define NPY_USE_SETITEM 0x40
/* A sticky flag specifically for structured arrays */
#define NPY_ALIGNED_STRUCT 0x80
/*
*These are inherited for global data-type if any data-types in the
* field have them
*/
#define NPY_FROM_FIELDS (NPY_NEEDS_INIT | NPY_LIST_PICKLE | \
NPY_ITEM_REFCOUNT | NPY_NEEDS_PYAPI)
#define NPY_OBJECT_DTYPE_FLAGS (NPY_LIST_PICKLE | NPY_USE_GETITEM | \
NPY_ITEM_IS_POINTER | NPY_ITEM_REFCOUNT | \
NPY_NEEDS_INIT | NPY_NEEDS_PYAPI)
#define PyDataType_FLAGCHK(dtype, flag) \
(((dtype)->flags & (flag)) == (flag))
#define PyDataType_REFCHK(dtype) \
PyDataType_FLAGCHK(dtype, NPY_ITEM_REFCOUNT)
typedef struct _PyArray_Descr {
PyObject_HEAD
/*
* the type object representing an
* instance of this type -- should not
* be two type_numbers with the same type
* object.
*/
PyTypeObject *typeobj;
/* kind for this type */
char kind;
/* unique-character representing this type */
char type;
/*
* '>' (big), '<' (little), '|'
* (not-applicable), or '=' (native).
*/
char byteorder;
/* flags describing data type */
char flags;
/* number representing this type */
int type_num;
/* element size (itemsize) for this type */
int elsize;
/* alignment needed for this type */
int alignment;
/*
* Non-NULL if this type is
* is an array (C-contiguous)
* of some other type
*/
struct _arr_descr *subarray;
/*
* The fields dictionary for this type
* For statically defined descr this
* is always Py_None
*/
PyObject *fields;
/*
* An ordered tuple of field names or NULL
* if no fields are defined
*/
PyObject *names;
/*
* a table of functions specific for each
* basic data descriptor
*/
PyArray_ArrFuncs *f;
/* Metadata about this dtype */
PyObject *metadata;
/*
* Metadata specific to the C implementation
* of the particular dtype. This was added
* for NumPy 1.7.0.
*/
NpyAuxData *c_metadata;
/* Cached hash value (-1 if not yet computed).
* This was added for NumPy 2.0.0.
*/
npy_hash_t hash;
} PyArray_Descr;
typedef struct _arr_descr {
PyArray_Descr *base;
PyObject *shape; /* a tuple */
} PyArray_ArrayDescr;
/*
* Memory handler structure for array data.
*/
/* The declaration of free differs from PyMemAllocatorEx */
typedef struct {
void *ctx;
void* (*malloc) (void *ctx, size_t size);
void* (*calloc) (void *ctx, size_t nelem, size_t elsize);
void* (*realloc) (void *ctx, void *ptr, size_t new_size);
void (*free) (void *ctx, void *ptr, size_t size);
/*
* This is the end of the version=1 struct. Only add new fields after
* this line
*/
} PyDataMemAllocator;
typedef struct {
char name[127]; /* multiple of 64 to keep the struct aligned */
uint8_t version; /* currently 1 */
PyDataMemAllocator allocator;
} PyDataMem_Handler;
/*
* The main array object structure.
*
* It has been recommended to use the inline functions defined below
* (PyArray_DATA and friends) to access fields here for a number of
* releases. Direct access to the members themselves is deprecated.
* To ensure that your code does not use deprecated access,
* #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
* (or NPY_1_8_API_VERSION or higher as required).
*/
/* This struct will be moved to a private header in a future release */
typedef struct tagPyArrayObject_fields {
PyObject_HEAD
/* Pointer to the raw data buffer */
char *data;
/* The number of dimensions, also called 'ndim' */
int nd;
/* The size in each dimension, also called 'shape' */
npy_intp *dimensions;
/*
* Number of bytes to jump to get to the
* next element in each dimension
*/
npy_intp *strides;
/*
* This object is decref'd upon
* deletion of array. Except in the
* case of WRITEBACKIFCOPY which has
* special handling.
*
* For views it points to the original
* array, collapsed so no chains of
* views occur.
*
* For creation from buffer object it
* points to an object that should be
* decref'd on deletion
*
* For WRITEBACKIFCOPY flag this is an
* array to-be-updated upon calling
* PyArray_ResolveWritebackIfCopy
*/
PyObject *base;
/* Pointer to type structure */
PyArray_Descr *descr;
/* Flags describing array -- see below */
int flags;
/* For weak references */
PyObject *weakreflist;
void *_buffer_info; /* private buffer info, tagged to allow warning */
/*
* For malloc/calloc/realloc/free per object
*/
PyObject *mem_handler;
} PyArrayObject_fields;
/*
* To hide the implementation details, we only expose
* the Python struct HEAD.
*/
#if !defined(NPY_NO_DEPRECATED_API) || \
(NPY_NO_DEPRECATED_API < NPY_1_7_API_VERSION)
/*
* Can't put this in npy_deprecated_api.h like the others.
* PyArrayObject field access is deprecated as of NumPy 1.7.
*/
typedef PyArrayObject_fields PyArrayObject;
#else
typedef struct tagPyArrayObject {
PyObject_HEAD
} PyArrayObject;
#endif
/*
* Removed 2020-Nov-25, NumPy 1.20
* #define NPY_SIZEOF_PYARRAYOBJECT (sizeof(PyArrayObject_fields))
*
* The above macro was removed as it gave a false sense of a stable ABI
* with respect to the structures size. If you require a runtime constant,
* you can use `PyArray_Type.tp_basicsize` instead. Otherwise, please
* see the PyArrayObject documentation or ask the NumPy developers for
* information on how to correctly replace the macro in a way that is
* compatible with multiple NumPy versions.
*/
/* Array Flags Object */
typedef struct PyArrayFlagsObject {
PyObject_HEAD
PyObject *arr;
int flags;
} PyArrayFlagsObject;
/* Mirrors buffer object to ptr */
typedef struct {
PyObject_HEAD
PyObject *base;
void *ptr;
npy_intp len;
int flags;
} PyArray_Chunk;
typedef struct {
NPY_DATETIMEUNIT base;
int num;
} PyArray_DatetimeMetaData;
typedef struct {
NpyAuxData base;
PyArray_DatetimeMetaData meta;
} PyArray_DatetimeDTypeMetaData;
/*
* This structure contains an exploded view of a date-time value.
* NaT is represented by year == NPY_DATETIME_NAT.
*/
typedef struct {
npy_int64 year;
npy_int32 month, day, hour, min, sec, us, ps, as;
} npy_datetimestruct;
/* This is not used internally. */
typedef struct {
npy_int64 day;
npy_int32 sec, us, ps, as;
} npy_timedeltastruct;
typedef int (PyArray_FinalizeFunc)(PyArrayObject *, PyObject *);
/*
* Means c-style contiguous (last index varies the fastest). The data
* elements right after each other.
*
* This flag may be requested in constructor functions.
* This flag may be tested for in PyArray_FLAGS(arr).
*/
#define NPY_ARRAY_C_CONTIGUOUS 0x0001
/*
* Set if array is a contiguous Fortran array: the first index varies
* the fastest in memory (strides array is reverse of C-contiguous
* array)
*
* This flag may be requested in constructor functions.
* This flag may be tested for in PyArray_FLAGS(arr).
*/
#define NPY_ARRAY_F_CONTIGUOUS 0x0002
/*
* Note: all 0-d arrays are C_CONTIGUOUS and F_CONTIGUOUS. If a
* 1-d array is C_CONTIGUOUS it is also F_CONTIGUOUS. Arrays with
* more then one dimension can be C_CONTIGUOUS and F_CONTIGUOUS
* at the same time if they have either zero or one element.
* A higher dimensional array always has the same contiguity flags as
* `array.squeeze()`; dimensions with `array.shape[dimension] == 1` are
* effectively ignored when checking for contiguity.
*/
/*
* If set, the array owns the data: it will be free'd when the array
* is deleted.
*
* This flag may be tested for in PyArray_FLAGS(arr).
*/
#define NPY_ARRAY_OWNDATA 0x0004
/*
* An array never has the next four set; they're only used as parameter
* flags to the various FromAny functions
*
* This flag may be requested in constructor functions.
*/
/* Cause a cast to occur regardless of whether or not it is safe. */
#define NPY_ARRAY_FORCECAST 0x0010
/*
* Always copy the array. Returned arrays are always CONTIGUOUS,
* ALIGNED, and WRITEABLE. See also: NPY_ARRAY_ENSURENOCOPY = 0x4000.
*
* This flag may be requested in constructor functions.
*/
#define NPY_ARRAY_ENSURECOPY 0x0020
/*
* Make sure the returned array is a base-class ndarray
*
* This flag may be requested in constructor functions.
*/
#define NPY_ARRAY_ENSUREARRAY 0x0040
#if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD
/*
* Dual use of the ENSUREARRAY flag, to indicate that this was converted
* from a python float, int, or complex.
* An array using this flag must be a temporary array that can never
* leave the C internals of NumPy. Even if it does, ENSUREARRAY is
* absolutely safe to abuse, since it already is a base class array :).
*/
#define _NPY_ARRAY_WAS_PYSCALAR 0x0040
#endif /* NPY_INTERNAL_BUILD */
/*
* Make sure that the strides are in units of the element size Needed
* for some operations with record-arrays.
*
* This flag may be requested in constructor functions.
*/
#define NPY_ARRAY_ELEMENTSTRIDES 0x0080
/*
* Array data is aligned on the appropriate memory address for the type
* stored according to how the compiler would align things (e.g., an
* array of integers (4 bytes each) starts on a memory address that's
* a multiple of 4)
*
* This flag may be requested in constructor functions.
* This flag may be tested for in PyArray_FLAGS(arr).
*/
#define NPY_ARRAY_ALIGNED 0x0100
/*
* Array data has the native endianness
*
* This flag may be requested in constructor functions.
*/
#define NPY_ARRAY_NOTSWAPPED 0x0200
/*
* Array data is writeable
*
* This flag may be requested in constructor functions.
* This flag may be tested for in PyArray_FLAGS(arr).
*/
#define NPY_ARRAY_WRITEABLE 0x0400
/*
* If this flag is set, then base contains a pointer to an array of
* the same size that should be updated with the current contents of
* this array when PyArray_ResolveWritebackIfCopy is called.
*
* This flag may be requested in constructor functions.
* This flag may be tested for in PyArray_FLAGS(arr).
*/
#define NPY_ARRAY_WRITEBACKIFCOPY 0x2000
/*
* No copy may be made while converting from an object/array (result is a view)
*
* This flag may be requested in constructor functions.
*/
#define NPY_ARRAY_ENSURENOCOPY 0x4000
/*
* NOTE: there are also internal flags defined in multiarray/arrayobject.h,
* which start at bit 31 and work down.
*/
#define NPY_ARRAY_BEHAVED (NPY_ARRAY_ALIGNED | \
NPY_ARRAY_WRITEABLE)
#define NPY_ARRAY_BEHAVED_NS (NPY_ARRAY_ALIGNED | \
NPY_ARRAY_WRITEABLE | \
NPY_ARRAY_NOTSWAPPED)
#define NPY_ARRAY_CARRAY (NPY_ARRAY_C_CONTIGUOUS | \
NPY_ARRAY_BEHAVED)
#define NPY_ARRAY_CARRAY_RO (NPY_ARRAY_C_CONTIGUOUS | \
NPY_ARRAY_ALIGNED)
#define NPY_ARRAY_FARRAY (NPY_ARRAY_F_CONTIGUOUS | \
NPY_ARRAY_BEHAVED)
#define NPY_ARRAY_FARRAY_RO (NPY_ARRAY_F_CONTIGUOUS | \
NPY_ARRAY_ALIGNED)
#define NPY_ARRAY_DEFAULT (NPY_ARRAY_CARRAY)
#define NPY_ARRAY_IN_ARRAY (NPY_ARRAY_CARRAY_RO)
#define NPY_ARRAY_OUT_ARRAY (NPY_ARRAY_CARRAY)
#define NPY_ARRAY_INOUT_ARRAY (NPY_ARRAY_CARRAY)
#define NPY_ARRAY_INOUT_ARRAY2 (NPY_ARRAY_CARRAY | \
NPY_ARRAY_WRITEBACKIFCOPY)
#define NPY_ARRAY_IN_FARRAY (NPY_ARRAY_FARRAY_RO)
#define NPY_ARRAY_OUT_FARRAY (NPY_ARRAY_FARRAY)
#define NPY_ARRAY_INOUT_FARRAY (NPY_ARRAY_FARRAY)
#define NPY_ARRAY_INOUT_FARRAY2 (NPY_ARRAY_FARRAY | \
NPY_ARRAY_WRITEBACKIFCOPY)
#define NPY_ARRAY_UPDATE_ALL (NPY_ARRAY_C_CONTIGUOUS | \
NPY_ARRAY_F_CONTIGUOUS | \
NPY_ARRAY_ALIGNED)
/* This flag is for the array interface, not PyArrayObject */
#define NPY_ARR_HAS_DESCR 0x0800
/*
* Size of internal buffers used for alignment Make BUFSIZE a multiple
* of sizeof(npy_cdouble) -- usually 16 so that ufunc buffers are aligned
*/
#define NPY_MIN_BUFSIZE ((int)sizeof(npy_cdouble))
#define NPY_MAX_BUFSIZE (((int)sizeof(npy_cdouble))*1000000)
#define NPY_BUFSIZE 8192
/* buffer stress test size: */
/*#define NPY_BUFSIZE 17*/
#define PyArray_MAX(a,b) (((a)>(b))?(a):(b))
#define PyArray_MIN(a,b) (((a)<(b))?(a):(b))
#define PyArray_CLT(p,q) ((((p).real==(q).real) ? ((p).imag < (q).imag) : \
((p).real < (q).real)))
#define PyArray_CGT(p,q) ((((p).real==(q).real) ? ((p).imag > (q).imag) : \
((p).real > (q).real)))
#define PyArray_CLE(p,q) ((((p).real==(q).real) ? ((p).imag <= (q).imag) : \
((p).real <= (q).real)))
#define PyArray_CGE(p,q) ((((p).real==(q).real) ? ((p).imag >= (q).imag) : \
((p).real >= (q).real)))
#define PyArray_CEQ(p,q) (((p).real==(q).real) && ((p).imag == (q).imag))
#define PyArray_CNE(p,q) (((p).real!=(q).real) || ((p).imag != (q).imag))
/*
* C API: consists of Macros and functions. The MACROS are defined
* here.
*/
#define PyArray_ISCONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_C_CONTIGUOUS)
#define PyArray_ISWRITEABLE(m) PyArray_CHKFLAGS((m), NPY_ARRAY_WRITEABLE)
#define PyArray_ISALIGNED(m) PyArray_CHKFLAGS((m), NPY_ARRAY_ALIGNED)
#define PyArray_IS_C_CONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_C_CONTIGUOUS)
#define PyArray_IS_F_CONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_F_CONTIGUOUS)
/* the variable is used in some places, so always define it */
#define NPY_BEGIN_THREADS_DEF PyThreadState *_save=NULL;
#if NPY_ALLOW_THREADS
#define NPY_BEGIN_ALLOW_THREADS Py_BEGIN_ALLOW_THREADS
#define NPY_END_ALLOW_THREADS Py_END_ALLOW_THREADS
#define NPY_BEGIN_THREADS do {_save = PyEval_SaveThread();} while (0);
#define NPY_END_THREADS do { if (_save) \
{ PyEval_RestoreThread(_save); _save = NULL;} } while (0);
#define NPY_BEGIN_THREADS_THRESHOLDED(loop_size) do { if ((loop_size) > 500) \
{ _save = PyEval_SaveThread();} } while (0);
#define NPY_BEGIN_THREADS_DESCR(dtype) \
do {if (!(PyDataType_FLAGCHK((dtype), NPY_NEEDS_PYAPI))) \
NPY_BEGIN_THREADS;} while (0);
#define NPY_END_THREADS_DESCR(dtype) \
do {if (!(PyDataType_FLAGCHK((dtype), NPY_NEEDS_PYAPI))) \
NPY_END_THREADS; } while (0);
#define NPY_ALLOW_C_API_DEF PyGILState_STATE __save__;
#define NPY_ALLOW_C_API do {__save__ = PyGILState_Ensure();} while (0);
#define NPY_DISABLE_C_API do {PyGILState_Release(__save__);} while (0);
#else
#define NPY_BEGIN_ALLOW_THREADS
#define NPY_END_ALLOW_THREADS
#define NPY_BEGIN_THREADS
#define NPY_END_THREADS
#define NPY_BEGIN_THREADS_THRESHOLDED(loop_size)
#define NPY_BEGIN_THREADS_DESCR(dtype)
#define NPY_END_THREADS_DESCR(dtype)
#define NPY_ALLOW_C_API_DEF
#define NPY_ALLOW_C_API
#define NPY_DISABLE_C_API
#endif
/**********************************
* The nditer object, added in 1.6
**********************************/
/* The actual structure of the iterator is an internal detail */
typedef struct NpyIter_InternalOnly NpyIter;
/* Iterator function pointers that may be specialized */
typedef int (NpyIter_IterNextFunc)(NpyIter *iter);
typedef void (NpyIter_GetMultiIndexFunc)(NpyIter *iter,
npy_intp *outcoords);
/*** Global flags that may be passed to the iterator constructors ***/
/* Track an index representing C order */
#define NPY_ITER_C_INDEX 0x00000001
/* Track an index representing Fortran order */
#define NPY_ITER_F_INDEX 0x00000002
/* Track a multi-index */
#define NPY_ITER_MULTI_INDEX 0x00000004
/* User code external to the iterator does the 1-dimensional innermost loop */
#define NPY_ITER_EXTERNAL_LOOP 0x00000008
/* Convert all the operands to a common data type */
#define NPY_ITER_COMMON_DTYPE 0x00000010
/* Operands may hold references, requiring API access during iteration */
#define NPY_ITER_REFS_OK 0x00000020
/* Zero-sized operands should be permitted, iteration checks IterSize for 0 */
#define NPY_ITER_ZEROSIZE_OK 0x00000040
/* Permits reductions (size-0 stride with dimension size > 1) */
#define NPY_ITER_REDUCE_OK 0x00000080
/* Enables sub-range iteration */
#define NPY_ITER_RANGED 0x00000100
/* Enables buffering */
#define NPY_ITER_BUFFERED 0x00000200
/* When buffering is enabled, grows the inner loop if possible */
#define NPY_ITER_GROWINNER 0x00000400
/* Delay allocation of buffers until first Reset* call */
#define NPY_ITER_DELAY_BUFALLOC 0x00000800
/* When NPY_KEEPORDER is specified, disable reversing negative-stride axes */
#define NPY_ITER_DONT_NEGATE_STRIDES 0x00001000
/*
* If output operands overlap with other operands (based on heuristics that
* has false positives but no false negatives), make temporary copies to
* eliminate overlap.
*/
#define NPY_ITER_COPY_IF_OVERLAP 0x00002000
/*** Per-operand flags that may be passed to the iterator constructors ***/
/* The operand will be read from and written to */
#define NPY_ITER_READWRITE 0x00010000
/* The operand will only be read from */
#define NPY_ITER_READONLY 0x00020000
/* The operand will only be written to */
#define NPY_ITER_WRITEONLY 0x00040000
/* The operand's data must be in native byte order */
#define NPY_ITER_NBO 0x00080000
/* The operand's data must be aligned */
#define NPY_ITER_ALIGNED 0x00100000
/* The operand's data must be contiguous (within the inner loop) */
#define NPY_ITER_CONTIG 0x00200000
/* The operand may be copied to satisfy requirements */
#define NPY_ITER_COPY 0x00400000
/* The operand may be copied with WRITEBACKIFCOPY to satisfy requirements */
#define NPY_ITER_UPDATEIFCOPY 0x00800000
/* Allocate the operand if it is NULL */
#define NPY_ITER_ALLOCATE 0x01000000
/* If an operand is allocated, don't use any subtype */
#define NPY_ITER_NO_SUBTYPE 0x02000000
/* This is a virtual array slot, operand is NULL but temporary data is there */
#define NPY_ITER_VIRTUAL 0x04000000
/* Require that the dimension match the iterator dimensions exactly */
#define NPY_ITER_NO_BROADCAST 0x08000000
/* A mask is being used on this array, affects buffer -> array copy */
#define NPY_ITER_WRITEMASKED 0x10000000
/* This array is the mask for all WRITEMASKED operands */
#define NPY_ITER_ARRAYMASK 0x20000000
/* Assume iterator order data access for COPY_IF_OVERLAP */
#define NPY_ITER_OVERLAP_ASSUME_ELEMENTWISE 0x40000000
#define NPY_ITER_GLOBAL_FLAGS 0x0000ffff
#define NPY_ITER_PER_OP_FLAGS 0xffff0000
/*****************************
* Basic iterator object
*****************************/
/* FWD declaration */
typedef struct PyArrayIterObject_tag PyArrayIterObject;
/*
* type of the function which translates a set of coordinates to a
* pointer to the data
*/
typedef char* (*npy_iter_get_dataptr_t)(
PyArrayIterObject* iter, const npy_intp*);
struct PyArrayIterObject_tag {
PyObject_HEAD
int nd_m1; /* number of dimensions - 1 */
npy_intp index, size;
npy_intp coordinates[NPY_MAXDIMS];/* N-dimensional loop */
npy_intp dims_m1[NPY_MAXDIMS]; /* ao->dimensions - 1 */
npy_intp strides[NPY_MAXDIMS]; /* ao->strides or fake */
npy_intp backstrides[NPY_MAXDIMS];/* how far to jump back */
npy_intp factors[NPY_MAXDIMS]; /* shape factors */
PyArrayObject *ao;
char *dataptr; /* pointer to current item*/
npy_bool contiguous;
npy_intp bounds[NPY_MAXDIMS][2];
npy_intp limits[NPY_MAXDIMS][2];
npy_intp limits_sizes[NPY_MAXDIMS];
npy_iter_get_dataptr_t translate;
} ;
/* Iterator API */
#define PyArrayIter_Check(op) PyObject_TypeCheck((op), &PyArrayIter_Type)
#define _PyAIT(it) ((PyArrayIterObject *)(it))
#define PyArray_ITER_RESET(it) do { \
_PyAIT(it)->index = 0; \
_PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \
memset(_PyAIT(it)->coordinates, 0, \
(_PyAIT(it)->nd_m1+1)*sizeof(npy_intp)); \
} while (0)
#define _PyArray_ITER_NEXT1(it) do { \
(it)->dataptr += _PyAIT(it)->strides[0]; \
(it)->coordinates[0]++; \
} while (0)
#define _PyArray_ITER_NEXT2(it) do { \
if ((it)->coordinates[1] < (it)->dims_m1[1]) { \
(it)->coordinates[1]++; \
(it)->dataptr += (it)->strides[1]; \
} \
else { \
(it)->coordinates[1] = 0; \
(it)->coordinates[0]++; \
(it)->dataptr += (it)->strides[0] - \
(it)->backstrides[1]; \
} \
} while (0)
#define PyArray_ITER_NEXT(it) do { \
_PyAIT(it)->index++; \
if (_PyAIT(it)->nd_m1 == 0) { \
_PyArray_ITER_NEXT1(_PyAIT(it)); \
} \
else if (_PyAIT(it)->contiguous) \
_PyAIT(it)->dataptr += PyArray_DESCR(_PyAIT(it)->ao)->elsize; \
else if (_PyAIT(it)->nd_m1 == 1) { \
_PyArray_ITER_NEXT2(_PyAIT(it)); \
} \
else { \
int __npy_i; \
for (__npy_i=_PyAIT(it)->nd_m1; __npy_i >= 0; __npy_i--) { \
if (_PyAIT(it)->coordinates[__npy_i] < \
_PyAIT(it)->dims_m1[__npy_i]) { \
_PyAIT(it)->coordinates[__npy_i]++; \
_PyAIT(it)->dataptr += \
_PyAIT(it)->strides[__npy_i]; \
break; \
} \
else { \
_PyAIT(it)->coordinates[__npy_i] = 0; \
_PyAIT(it)->dataptr -= \
_PyAIT(it)->backstrides[__npy_i]; \
} \
} \
} \
} while (0)
#define PyArray_ITER_GOTO(it, destination) do { \
int __npy_i; \
_PyAIT(it)->index = 0; \
_PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \
for (__npy_i = _PyAIT(it)->nd_m1; __npy_i>=0; __npy_i--) { \
if (destination[__npy_i] < 0) { \
destination[__npy_i] += \
_PyAIT(it)->dims_m1[__npy_i]+1; \
} \
_PyAIT(it)->dataptr += destination[__npy_i] * \
_PyAIT(it)->strides[__npy_i]; \
_PyAIT(it)->coordinates[__npy_i] = \
destination[__npy_i]; \
_PyAIT(it)->index += destination[__npy_i] * \
( __npy_i==_PyAIT(it)->nd_m1 ? 1 : \
_PyAIT(it)->dims_m1[__npy_i+1]+1) ; \
} \
} while (0)
#define PyArray_ITER_GOTO1D(it, ind) do { \
int __npy_i; \
npy_intp __npy_ind = (npy_intp)(ind); \
if (__npy_ind < 0) __npy_ind += _PyAIT(it)->size; \
_PyAIT(it)->index = __npy_ind; \
if (_PyAIT(it)->nd_m1 == 0) { \
_PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao) + \
__npy_ind * _PyAIT(it)->strides[0]; \
} \
else if (_PyAIT(it)->contiguous) \
_PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao) + \
__npy_ind * PyArray_DESCR(_PyAIT(it)->ao)->elsize; \
else { \
_PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \
for (__npy_i = 0; __npy_i<=_PyAIT(it)->nd_m1; \
__npy_i++) { \
_PyAIT(it)->coordinates[__npy_i] = \
(__npy_ind / _PyAIT(it)->factors[__npy_i]); \
_PyAIT(it)->dataptr += \
(__npy_ind / _PyAIT(it)->factors[__npy_i]) \
* _PyAIT(it)->strides[__npy_i]; \
__npy_ind %= _PyAIT(it)->factors[__npy_i]; \
} \
} \
} while (0)
#define PyArray_ITER_DATA(it) ((void *)(_PyAIT(it)->dataptr))
#define PyArray_ITER_NOTDONE(it) (_PyAIT(it)->index < _PyAIT(it)->size)
/*
* Any object passed to PyArray_Broadcast must be binary compatible
* with this structure.
*/
typedef struct {
PyObject_HEAD
int numiter; /* number of iters */
npy_intp size; /* broadcasted size */
npy_intp index; /* current index */
int nd; /* number of dims */
npy_intp dimensions[NPY_MAXDIMS]; /* dimensions */
PyArrayIterObject *iters[NPY_MAXARGS]; /* iterators */
} PyArrayMultiIterObject;
#define _PyMIT(m) ((PyArrayMultiIterObject *)(m))
#define PyArray_MultiIter_RESET(multi) do { \
int __npy_mi; \
_PyMIT(multi)->index = 0; \
for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \
PyArray_ITER_RESET(_PyMIT(multi)->iters[__npy_mi]); \
} \
} while (0)
#define PyArray_MultiIter_NEXT(multi) do { \
int __npy_mi; \
_PyMIT(multi)->index++; \
for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \
PyArray_ITER_NEXT(_PyMIT(multi)->iters[__npy_mi]); \
} \
} while (0)
#define PyArray_MultiIter_GOTO(multi, dest) do { \
int __npy_mi; \
for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \
PyArray_ITER_GOTO(_PyMIT(multi)->iters[__npy_mi], dest); \
} \
_PyMIT(multi)->index = _PyMIT(multi)->iters[0]->index; \
} while (0)
#define PyArray_MultiIter_GOTO1D(multi, ind) do { \
int __npy_mi; \
for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \
PyArray_ITER_GOTO1D(_PyMIT(multi)->iters[__npy_mi], ind); \
} \
_PyMIT(multi)->index = _PyMIT(multi)->iters[0]->index; \
} while (0)
#define PyArray_MultiIter_DATA(multi, i) \
((void *)(_PyMIT(multi)->iters[i]->dataptr))
#define PyArray_MultiIter_NEXTi(multi, i) \
PyArray_ITER_NEXT(_PyMIT(multi)->iters[i])
#define PyArray_MultiIter_NOTDONE(multi) \
(_PyMIT(multi)->index < _PyMIT(multi)->size)
/*
* Store the information needed for fancy-indexing over an array. The
* fields are slightly unordered to keep consec, dataptr and subspace
* where they were originally.
*/
typedef struct {
PyObject_HEAD
/*
* Multi-iterator portion --- needs to be present in this
* order to work with PyArray_Broadcast
*/
int numiter; /* number of index-array
iterators */
npy_intp size; /* size of broadcasted
result */
npy_intp index; /* current index */
int nd; /* number of dims */
npy_intp dimensions[NPY_MAXDIMS]; /* dimensions */
NpyIter *outer; /* index objects
iterator */
void *unused[NPY_MAXDIMS - 2];
PyArrayObject *array;
/* Flat iterator for the indexed array. For compatibility solely. */
PyArrayIterObject *ait;
/*
* Subspace array. For binary compatibility (was an iterator,
* but only the check for NULL should be used).
*/
PyArrayObject *subspace;
/*
* if subspace iteration, then this is the array of axes in
* the underlying array represented by the index objects
*/
int iteraxes[NPY_MAXDIMS];
npy_intp fancy_strides[NPY_MAXDIMS];
/* pointer when all fancy indices are 0 */
char *baseoffset;
/*
* after binding consec denotes at which axis the fancy axes
* are inserted.
*/
int consec;
char *dataptr;
int nd_fancy;
npy_intp fancy_dims[NPY_MAXDIMS];
/* Whether the iterator (any of the iterators) requires API */
int needs_api;
/*
* Extra op information.
*/
PyArrayObject *extra_op;
PyArray_Descr *extra_op_dtype; /* desired dtype */
npy_uint32 *extra_op_flags; /* Iterator flags */
NpyIter *extra_op_iter;
NpyIter_IterNextFunc *extra_op_next;
char **extra_op_ptrs;
/*
* Information about the iteration state.
*/
NpyIter_IterNextFunc *outer_next;
char **outer_ptrs;
npy_intp *outer_strides;
/*
* Information about the subspace iterator.
*/
NpyIter *subspace_iter;
NpyIter_IterNextFunc *subspace_next;
char **subspace_ptrs;
npy_intp *subspace_strides;
/* Count for the external loop (which ever it is) for API iteration */
npy_intp iter_count;
} PyArrayMapIterObject;
enum {
NPY_NEIGHBORHOOD_ITER_ZERO_PADDING,
NPY_NEIGHBORHOOD_ITER_ONE_PADDING,
NPY_NEIGHBORHOOD_ITER_CONSTANT_PADDING,
NPY_NEIGHBORHOOD_ITER_CIRCULAR_PADDING,
NPY_NEIGHBORHOOD_ITER_MIRROR_PADDING
};
typedef struct {
PyObject_HEAD
/*
* PyArrayIterObject part: keep this in this exact order
*/
int nd_m1; /* number of dimensions - 1 */
npy_intp index, size;
npy_intp coordinates[NPY_MAXDIMS];/* N-dimensional loop */
npy_intp dims_m1[NPY_MAXDIMS]; /* ao->dimensions - 1 */
npy_intp strides[NPY_MAXDIMS]; /* ao->strides or fake */
npy_intp backstrides[NPY_MAXDIMS];/* how far to jump back */
npy_intp factors[NPY_MAXDIMS]; /* shape factors */
PyArrayObject *ao;
char *dataptr; /* pointer to current item*/
npy_bool contiguous;
npy_intp bounds[NPY_MAXDIMS][2];
npy_intp limits[NPY_MAXDIMS][2];
npy_intp limits_sizes[NPY_MAXDIMS];
npy_iter_get_dataptr_t translate;
/*
* New members
*/
npy_intp nd;
/* Dimensions is the dimension of the array */
npy_intp dimensions[NPY_MAXDIMS];
/*
* Neighborhood points coordinates are computed relatively to the
* point pointed by _internal_iter
*/
PyArrayIterObject* _internal_iter;
/*
* To keep a reference to the representation of the constant value
* for constant padding
*/
char* constant;
int mode;
} PyArrayNeighborhoodIterObject;
/*
* Neighborhood iterator API
*/
/* General: those work for any mode */
static NPY_INLINE int
PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter);
static NPY_INLINE int
PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter);
#if 0
static NPY_INLINE int
PyArrayNeighborhoodIter_Next2D(PyArrayNeighborhoodIterObject* iter);
#endif
/*
* Include inline implementations - functions defined there are not
* considered public API
*/
#define NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_
#include "_neighborhood_iterator_imp.h"
#undef NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_
/* The default array type */
#define NPY_DEFAULT_TYPE NPY_DOUBLE
/*
* All sorts of useful ways to look into a PyArrayObject. It is recommended
* to use PyArrayObject * objects instead of always casting from PyObject *,
* for improved type checking.
*
* In many cases here the macro versions of the accessors are deprecated,
* but can't be immediately changed to inline functions because the
* preexisting macros accept PyObject * and do automatic casts. Inline
* functions accepting PyArrayObject * provides for some compile-time
* checking of correctness when working with these objects in C.
*/
#define PyArray_ISONESEGMENT(m) (PyArray_CHKFLAGS(m, NPY_ARRAY_C_CONTIGUOUS) || \
PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS))
#define PyArray_ISFORTRAN(m) (PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS) && \
(!PyArray_CHKFLAGS(m, NPY_ARRAY_C_CONTIGUOUS)))
#define PyArray_FORTRAN_IF(m) ((PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS) ? \
NPY_ARRAY_F_CONTIGUOUS : 0))
#if (defined(NPY_NO_DEPRECATED_API) && (NPY_1_7_API_VERSION <= NPY_NO_DEPRECATED_API))
/*
* Changing access macros into functions, to allow for future hiding
* of the internal memory layout. This later hiding will allow the 2.x series
* to change the internal representation of arrays without affecting
* ABI compatibility.
*/
static NPY_INLINE int
PyArray_NDIM(const PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->nd;
}
static NPY_INLINE void *
PyArray_DATA(PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->data;
}
static NPY_INLINE char *
PyArray_BYTES(PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->data;
}
static NPY_INLINE npy_intp *
PyArray_DIMS(PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->dimensions;
}
static NPY_INLINE npy_intp *
PyArray_STRIDES(PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->strides;
}
static NPY_INLINE npy_intp
PyArray_DIM(const PyArrayObject *arr, int idim)
{
return ((PyArrayObject_fields *)arr)->dimensions[idim];
}
static NPY_INLINE npy_intp
PyArray_STRIDE(const PyArrayObject *arr, int istride)
{
return ((PyArrayObject_fields *)arr)->strides[istride];
}
static NPY_INLINE NPY_RETURNS_BORROWED_REF PyObject *
PyArray_BASE(PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->base;
}
static NPY_INLINE NPY_RETURNS_BORROWED_REF PyArray_Descr *
PyArray_DESCR(PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->descr;
}
static NPY_INLINE int
PyArray_FLAGS(const PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->flags;
}
static NPY_INLINE npy_intp
PyArray_ITEMSIZE(const PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->descr->elsize;
}
static NPY_INLINE int
PyArray_TYPE(const PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->descr->type_num;
}
static NPY_INLINE int
PyArray_CHKFLAGS(const PyArrayObject *arr, int flags)
{
return (PyArray_FLAGS(arr) & flags) == flags;
}
static NPY_INLINE PyObject *
PyArray_GETITEM(const PyArrayObject *arr, const char *itemptr)
{
return ((PyArrayObject_fields *)arr)->descr->f->getitem(
(void *)itemptr, (PyArrayObject *)arr);
}
/*
* SETITEM should only be used if it is known that the value is a scalar
* and of a type understood by the arrays dtype.
* Use `PyArray_Pack` if the value may be of a different dtype.
*/
static NPY_INLINE int
PyArray_SETITEM(PyArrayObject *arr, char *itemptr, PyObject *v)
{
return ((PyArrayObject_fields *)arr)->descr->f->setitem(v, itemptr, arr);
}
#else
/* These macros are deprecated as of NumPy 1.7. */
#define PyArray_NDIM(obj) (((PyArrayObject_fields *)(obj))->nd)
#define PyArray_BYTES(obj) (((PyArrayObject_fields *)(obj))->data)
#define PyArray_DATA(obj) ((void *)((PyArrayObject_fields *)(obj))->data)
#define PyArray_DIMS(obj) (((PyArrayObject_fields *)(obj))->dimensions)
#define PyArray_STRIDES(obj) (((PyArrayObject_fields *)(obj))->strides)
#define PyArray_DIM(obj,n) (PyArray_DIMS(obj)[n])
#define PyArray_STRIDE(obj,n) (PyArray_STRIDES(obj)[n])
#define PyArray_BASE(obj) (((PyArrayObject_fields *)(obj))->base)
#define PyArray_DESCR(obj) (((PyArrayObject_fields *)(obj))->descr)
#define PyArray_FLAGS(obj) (((PyArrayObject_fields *)(obj))->flags)
#define PyArray_CHKFLAGS(m, FLAGS) \
((((PyArrayObject_fields *)(m))->flags & (FLAGS)) == (FLAGS))
#define PyArray_ITEMSIZE(obj) \
(((PyArrayObject_fields *)(obj))->descr->elsize)
#define PyArray_TYPE(obj) \
(((PyArrayObject_fields *)(obj))->descr->type_num)
#define PyArray_GETITEM(obj,itemptr) \
PyArray_DESCR(obj)->f->getitem((char *)(itemptr), \
(PyArrayObject *)(obj))
#define PyArray_SETITEM(obj,itemptr,v) \
PyArray_DESCR(obj)->f->setitem((PyObject *)(v), \
(char *)(itemptr), \
(PyArrayObject *)(obj))
#endif
static NPY_INLINE PyArray_Descr *
PyArray_DTYPE(PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->descr;
}
static NPY_INLINE npy_intp *
PyArray_SHAPE(PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->dimensions;
}
/*
* Enables the specified array flags. Does no checking,
* assumes you know what you're doing.
*/
static NPY_INLINE void
PyArray_ENABLEFLAGS(PyArrayObject *arr, int flags)
{
((PyArrayObject_fields *)arr)->flags |= flags;
}
/*
* Clears the specified array flags. Does no checking,
* assumes you know what you're doing.
*/
static NPY_INLINE void
PyArray_CLEARFLAGS(PyArrayObject *arr, int flags)
{
((PyArrayObject_fields *)arr)->flags &= ~flags;
}
static NPY_INLINE NPY_RETURNS_BORROWED_REF PyObject *
PyArray_HANDLER(PyArrayObject *arr)
{
return ((PyArrayObject_fields *)arr)->mem_handler;
}
#define PyTypeNum_ISBOOL(type) ((type) == NPY_BOOL)
#define PyTypeNum_ISUNSIGNED(type) (((type) == NPY_UBYTE) || \
((type) == NPY_USHORT) || \
((type) == NPY_UINT) || \
((type) == NPY_ULONG) || \
((type) == NPY_ULONGLONG))
#define PyTypeNum_ISSIGNED(type) (((type) == NPY_BYTE) || \
((type) == NPY_SHORT) || \
((type) == NPY_INT) || \
((type) == NPY_LONG) || \
((type) == NPY_LONGLONG))
#define PyTypeNum_ISINTEGER(type) (((type) >= NPY_BYTE) && \
((type) <= NPY_ULONGLONG))
#define PyTypeNum_ISFLOAT(type) ((((type) >= NPY_FLOAT) && \
((type) <= NPY_LONGDOUBLE)) || \
((type) == NPY_HALF))
#define PyTypeNum_ISNUMBER(type) (((type) <= NPY_CLONGDOUBLE) || \
((type) == NPY_HALF))
#define PyTypeNum_ISSTRING(type) (((type) == NPY_STRING) || \
((type) == NPY_UNICODE))
#define PyTypeNum_ISCOMPLEX(type) (((type) >= NPY_CFLOAT) && \
((type) <= NPY_CLONGDOUBLE))
#define PyTypeNum_ISPYTHON(type) (((type) == NPY_LONG) || \
((type) == NPY_DOUBLE) || \
((type) == NPY_CDOUBLE) || \
((type) == NPY_BOOL) || \
((type) == NPY_OBJECT ))
#define PyTypeNum_ISFLEXIBLE(type) (((type) >=NPY_STRING) && \
((type) <=NPY_VOID))
#define PyTypeNum_ISDATETIME(type) (((type) >=NPY_DATETIME) && \
((type) <=NPY_TIMEDELTA))
#define PyTypeNum_ISUSERDEF(type) (((type) >= NPY_USERDEF) && \
((type) < NPY_USERDEF+ \
NPY_NUMUSERTYPES))
#define PyTypeNum_ISEXTENDED(type) (PyTypeNum_ISFLEXIBLE(type) || \
PyTypeNum_ISUSERDEF(type))
#define PyTypeNum_ISOBJECT(type) ((type) == NPY_OBJECT)
#define PyDataType_ISBOOL(obj) PyTypeNum_ISBOOL(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISUNSIGNED(obj) PyTypeNum_ISUNSIGNED(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISSIGNED(obj) PyTypeNum_ISSIGNED(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISINTEGER(obj) PyTypeNum_ISINTEGER(((PyArray_Descr*)(obj))->type_num )
#define PyDataType_ISFLOAT(obj) PyTypeNum_ISFLOAT(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISNUMBER(obj) PyTypeNum_ISNUMBER(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISSTRING(obj) PyTypeNum_ISSTRING(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISCOMPLEX(obj) PyTypeNum_ISCOMPLEX(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISPYTHON(obj) PyTypeNum_ISPYTHON(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISFLEXIBLE(obj) PyTypeNum_ISFLEXIBLE(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISDATETIME(obj) PyTypeNum_ISDATETIME(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISUSERDEF(obj) PyTypeNum_ISUSERDEF(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISEXTENDED(obj) PyTypeNum_ISEXTENDED(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_ISOBJECT(obj) PyTypeNum_ISOBJECT(((PyArray_Descr*)(obj))->type_num)
#define PyDataType_HASFIELDS(obj) (((PyArray_Descr *)(obj))->names != NULL)
#define PyDataType_HASSUBARRAY(dtype) ((dtype)->subarray != NULL)
#define PyDataType_ISUNSIZED(dtype) ((dtype)->elsize == 0 && \
!PyDataType_HASFIELDS(dtype))
#define PyDataType_MAKEUNSIZED(dtype) ((dtype)->elsize = 0)
#define PyArray_ISBOOL(obj) PyTypeNum_ISBOOL(PyArray_TYPE(obj))
#define PyArray_ISUNSIGNED(obj) PyTypeNum_ISUNSIGNED(PyArray_TYPE(obj))
#define PyArray_ISSIGNED(obj) PyTypeNum_ISSIGNED(PyArray_TYPE(obj))
#define PyArray_ISINTEGER(obj) PyTypeNum_ISINTEGER(PyArray_TYPE(obj))
#define PyArray_ISFLOAT(obj) PyTypeNum_ISFLOAT(PyArray_TYPE(obj))
#define PyArray_ISNUMBER(obj) PyTypeNum_ISNUMBER(PyArray_TYPE(obj))
#define PyArray_ISSTRING(obj) PyTypeNum_ISSTRING(PyArray_TYPE(obj))
#define PyArray_ISCOMPLEX(obj) PyTypeNum_ISCOMPLEX(PyArray_TYPE(obj))
#define PyArray_ISPYTHON(obj) PyTypeNum_ISPYTHON(PyArray_TYPE(obj))
#define PyArray_ISFLEXIBLE(obj) PyTypeNum_ISFLEXIBLE(PyArray_TYPE(obj))
#define PyArray_ISDATETIME(obj) PyTypeNum_ISDATETIME(PyArray_TYPE(obj))
#define PyArray_ISUSERDEF(obj) PyTypeNum_ISUSERDEF(PyArray_TYPE(obj))
#define PyArray_ISEXTENDED(obj) PyTypeNum_ISEXTENDED(PyArray_TYPE(obj))
#define PyArray_ISOBJECT(obj) PyTypeNum_ISOBJECT(PyArray_TYPE(obj))
#define PyArray_HASFIELDS(obj) PyDataType_HASFIELDS(PyArray_DESCR(obj))
/*
* FIXME: This should check for a flag on the data-type that
* states whether or not it is variable length. Because the
* ISFLEXIBLE check is hard-coded to the built-in data-types.
*/
#define PyArray_ISVARIABLE(obj) PyTypeNum_ISFLEXIBLE(PyArray_TYPE(obj))
#define PyArray_SAFEALIGNEDCOPY(obj) (PyArray_ISALIGNED(obj) && !PyArray_ISVARIABLE(obj))
#define NPY_LITTLE '<'
#define NPY_BIG '>'
#define NPY_NATIVE '='
#define NPY_SWAP 's'
#define NPY_IGNORE '|'
#if NPY_BYTE_ORDER == NPY_BIG_ENDIAN
#define NPY_NATBYTE NPY_BIG
#define NPY_OPPBYTE NPY_LITTLE
#else
#define NPY_NATBYTE NPY_LITTLE
#define NPY_OPPBYTE NPY_BIG
#endif
#define PyArray_ISNBO(arg) ((arg) != NPY_OPPBYTE)
#define PyArray_IsNativeByteOrder PyArray_ISNBO
#define PyArray_ISNOTSWAPPED(m) PyArray_ISNBO(PyArray_DESCR(m)->byteorder)
#define PyArray_ISBYTESWAPPED(m) (!PyArray_ISNOTSWAPPED(m))
#define PyArray_FLAGSWAP(m, flags) (PyArray_CHKFLAGS(m, flags) && \
PyArray_ISNOTSWAPPED(m))
#define PyArray_ISCARRAY(m) PyArray_FLAGSWAP(m, NPY_ARRAY_CARRAY)
#define PyArray_ISCARRAY_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_CARRAY_RO)
#define PyArray_ISFARRAY(m) PyArray_FLAGSWAP(m, NPY_ARRAY_FARRAY)
#define PyArray_ISFARRAY_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_FARRAY_RO)
#define PyArray_ISBEHAVED(m) PyArray_FLAGSWAP(m, NPY_ARRAY_BEHAVED)
#define PyArray_ISBEHAVED_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_ALIGNED)
#define PyDataType_ISNOTSWAPPED(d) PyArray_ISNBO(((PyArray_Descr *)(d))->byteorder)
#define PyDataType_ISBYTESWAPPED(d) (!PyDataType_ISNOTSWAPPED(d))
/************************************************************
* A struct used by PyArray_CreateSortedStridePerm, new in 1.7.
************************************************************/
typedef struct {
npy_intp perm, stride;
} npy_stride_sort_item;
/************************************************************
* This is the form of the struct that's stored in the
* PyCapsule returned by an array's __array_struct__ attribute. See
* https://docs.scipy.org/doc/numpy/reference/arrays.interface.html for the full
* documentation.
************************************************************/
typedef struct {
int two; /*
* contains the integer 2 as a sanity
* check
*/
int nd; /* number of dimensions */
char typekind; /*
* kind in array --- character code of
* typestr
*/
int itemsize; /* size of each element */
int flags; /*
* how should be data interpreted. Valid
* flags are CONTIGUOUS (1), F_CONTIGUOUS (2),
* ALIGNED (0x100), NOTSWAPPED (0x200), and
* WRITEABLE (0x400). ARR_HAS_DESCR (0x800)
* states that arrdescr field is present in
* structure
*/
npy_intp *shape; /*
* A length-nd array of shape
* information
*/
npy_intp *strides; /* A length-nd array of stride information */
void *data; /* A pointer to the first element of the array */
PyObject *descr; /*
* A list of fields or NULL (ignored if flags
* does not have ARR_HAS_DESCR flag set)
*/
} PyArrayInterface;
/*
* This is a function for hooking into the PyDataMem_NEW/FREE/RENEW functions.
* See the documentation for PyDataMem_SetEventHook.
*/
typedef void (PyDataMem_EventHookFunc)(void *inp, void *outp, size_t size,
void *user_data);
/*
* PyArray_DTypeMeta related definitions.
*
* As of now, this API is preliminary and will be extended as necessary.
*/
#if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD
/*
* The Structures defined in this block are currently considered
* private API and may change without warning!
* Part of this (at least the size) is exepcted to be public API without
* further modifications.
*/
/* TODO: Make this definition public in the API, as soon as its settled */
NPY_NO_EXPORT extern PyTypeObject PyArrayDTypeMeta_Type;
/*
* While NumPy DTypes would not need to be heap types the plan is to
* make DTypes available in Python at which point they will be heap types.
* Since we also wish to add fields to the DType class, this looks like
* a typical instance definition, but with PyHeapTypeObject instead of
* only the PyObject_HEAD.
* This must only be exposed very extremely careful consideration, since
* it is a fairly complex construct which may be better to allow
* refactoring of.
*/
typedef struct {
PyHeapTypeObject super;
/*
* Most DTypes will have a singleton default instance, for the
* parametric legacy DTypes (bytes, string, void, datetime) this
* may be a pointer to the *prototype* instance?
*/
PyArray_Descr *singleton;
/* Copy of the legacy DTypes type number, usually invalid. */
int type_num;
/* The type object of the scalar instances (may be NULL?) */
PyTypeObject *scalar_type;
/*
* DType flags to signal legacy, parametric, or
* abstract. But plenty of space for additional information/flags.
*/
npy_uint64 flags;
/*
* Use indirection in order to allow a fixed size for this struct.
* A stable ABI size makes creating a static DType less painful
* while also ensuring flexibility for all opaque API (with one
* indirection due the pointer lookup).
*/
void *dt_slots;
void *reserved[3];
} PyArray_DTypeMeta;
#endif /* NPY_INTERNAL_BUILD */
/*
* Use the keyword NPY_DEPRECATED_INCLUDES to ensure that the header files
* npy_*_*_deprecated_api.h are only included from here and nowhere else.
*/
#ifdef NPY_DEPRECATED_INCLUDES
#error "Do not use the reserved keyword NPY_DEPRECATED_INCLUDES."
#endif
#define NPY_DEPRECATED_INCLUDES
#if !defined(NPY_NO_DEPRECATED_API) || \
(NPY_NO_DEPRECATED_API < NPY_1_7_API_VERSION)
#include "npy_1_7_deprecated_api.h"
#endif
/*
* There is no file npy_1_8_deprecated_api.h since there are no additional
* deprecated API features in NumPy 1.8.
*
* Note to maintainers: insert code like the following in future NumPy
* versions.
*
* #if !defined(NPY_NO_DEPRECATED_API) || \
* (NPY_NO_DEPRECATED_API < NPY_1_9_API_VERSION)
* #include "npy_1_9_deprecated_api.h"
* #endif
*/
#undef NPY_DEPRECATED_INCLUDES
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ */
| 69,078 | C | 34.154707 | 90 | 0.586439 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/npy_1_7_deprecated_api.h | #ifndef NPY_DEPRECATED_INCLUDES
#error "Should never include npy_*_*_deprecated_api directly."
#endif
#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_
/* Emit a warning if the user did not specifically request the old API */
#ifndef NPY_NO_DEPRECATED_API
#if defined(_WIN32)
#define _WARN___STR2__(x) #x
#define _WARN___STR1__(x) _WARN___STR2__(x)
#define _WARN___LOC__ __FILE__ "(" _WARN___STR1__(__LINE__) ") : Warning Msg: "
#pragma message(_WARN___LOC__"Using deprecated NumPy API, disable it with " \
"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION")
#else
#warning "Using deprecated NumPy API, disable it with " \
"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION"
#endif
#endif
/*
* This header exists to collect all dangerous/deprecated NumPy API
* as of NumPy 1.7.
*
* This is an attempt to remove bad API, the proliferation of macros,
* and namespace pollution currently produced by the NumPy headers.
*/
/* These array flags are deprecated as of NumPy 1.7 */
#define NPY_CONTIGUOUS NPY_ARRAY_C_CONTIGUOUS
#define NPY_FORTRAN NPY_ARRAY_F_CONTIGUOUS
/*
* The consistent NPY_ARRAY_* names which don't pollute the NPY_*
* namespace were added in NumPy 1.7.
*
* These versions of the carray flags are deprecated, but
* probably should only be removed after two releases instead of one.
*/
#define NPY_C_CONTIGUOUS NPY_ARRAY_C_CONTIGUOUS
#define NPY_F_CONTIGUOUS NPY_ARRAY_F_CONTIGUOUS
#define NPY_OWNDATA NPY_ARRAY_OWNDATA
#define NPY_FORCECAST NPY_ARRAY_FORCECAST
#define NPY_ENSURECOPY NPY_ARRAY_ENSURECOPY
#define NPY_ENSUREARRAY NPY_ARRAY_ENSUREARRAY
#define NPY_ELEMENTSTRIDES NPY_ARRAY_ELEMENTSTRIDES
#define NPY_ALIGNED NPY_ARRAY_ALIGNED
#define NPY_NOTSWAPPED NPY_ARRAY_NOTSWAPPED
#define NPY_WRITEABLE NPY_ARRAY_WRITEABLE
#define NPY_BEHAVED NPY_ARRAY_BEHAVED
#define NPY_BEHAVED_NS NPY_ARRAY_BEHAVED_NS
#define NPY_CARRAY NPY_ARRAY_CARRAY
#define NPY_CARRAY_RO NPY_ARRAY_CARRAY_RO
#define NPY_FARRAY NPY_ARRAY_FARRAY
#define NPY_FARRAY_RO NPY_ARRAY_FARRAY_RO
#define NPY_DEFAULT NPY_ARRAY_DEFAULT
#define NPY_IN_ARRAY NPY_ARRAY_IN_ARRAY
#define NPY_OUT_ARRAY NPY_ARRAY_OUT_ARRAY
#define NPY_INOUT_ARRAY NPY_ARRAY_INOUT_ARRAY
#define NPY_IN_FARRAY NPY_ARRAY_IN_FARRAY
#define NPY_OUT_FARRAY NPY_ARRAY_OUT_FARRAY
#define NPY_INOUT_FARRAY NPY_ARRAY_INOUT_FARRAY
#define NPY_UPDATE_ALL NPY_ARRAY_UPDATE_ALL
/* This way of accessing the default type is deprecated as of NumPy 1.7 */
#define PyArray_DEFAULT NPY_DEFAULT_TYPE
/* These DATETIME bits aren't used internally */
#define PyDataType_GetDatetimeMetaData(descr) \
((descr->metadata == NULL) ? NULL : \
((PyArray_DatetimeMetaData *)(PyCapsule_GetPointer( \
PyDict_GetItemString( \
descr->metadata, NPY_METADATA_DTSTR), NULL))))
/*
* Deprecated as of NumPy 1.7, this kind of shortcut doesn't
* belong in the public API.
*/
#define NPY_AO PyArrayObject
/*
* Deprecated as of NumPy 1.7, an all-lowercase macro doesn't
* belong in the public API.
*/
#define fortran fortran_
/*
* Deprecated as of NumPy 1.7, as it is a namespace-polluting
* macro.
*/
#define FORTRAN_IF PyArray_FORTRAN_IF
/* Deprecated as of NumPy 1.7, datetime64 uses c_metadata instead */
#define NPY_METADATA_DTSTR "__timeunit__"
/*
* Deprecated as of NumPy 1.7.
* The reasoning:
* - These are for datetime, but there's no datetime "namespace".
* - They just turn NPY_STR_<x> into "<x>", which is just
* making something simple be indirected.
*/
#define NPY_STR_Y "Y"
#define NPY_STR_M "M"
#define NPY_STR_W "W"
#define NPY_STR_D "D"
#define NPY_STR_h "h"
#define NPY_STR_m "m"
#define NPY_STR_s "s"
#define NPY_STR_ms "ms"
#define NPY_STR_us "us"
#define NPY_STR_ns "ns"
#define NPY_STR_ps "ps"
#define NPY_STR_fs "fs"
#define NPY_STR_as "as"
/*
* The macros in old_defines.h are Deprecated as of NumPy 1.7 and will be
* removed in the next major release.
*/
#include "old_defines.h"
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_ */
| 4,327 | C | 33.624 | 79 | 0.677837 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/arrayscalars.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_ARRAYSCALARS_H_
#define NUMPY_CORE_INCLUDE_NUMPY_ARRAYSCALARS_H_
#ifndef _MULTIARRAYMODULE
typedef struct {
PyObject_HEAD
npy_bool obval;
} PyBoolScalarObject;
#endif
typedef struct {
PyObject_HEAD
signed char obval;
} PyByteScalarObject;
typedef struct {
PyObject_HEAD
short obval;
} PyShortScalarObject;
typedef struct {
PyObject_HEAD
int obval;
} PyIntScalarObject;
typedef struct {
PyObject_HEAD
long obval;
} PyLongScalarObject;
typedef struct {
PyObject_HEAD
npy_longlong obval;
} PyLongLongScalarObject;
typedef struct {
PyObject_HEAD
unsigned char obval;
} PyUByteScalarObject;
typedef struct {
PyObject_HEAD
unsigned short obval;
} PyUShortScalarObject;
typedef struct {
PyObject_HEAD
unsigned int obval;
} PyUIntScalarObject;
typedef struct {
PyObject_HEAD
unsigned long obval;
} PyULongScalarObject;
typedef struct {
PyObject_HEAD
npy_ulonglong obval;
} PyULongLongScalarObject;
typedef struct {
PyObject_HEAD
npy_half obval;
} PyHalfScalarObject;
typedef struct {
PyObject_HEAD
float obval;
} PyFloatScalarObject;
typedef struct {
PyObject_HEAD
double obval;
} PyDoubleScalarObject;
typedef struct {
PyObject_HEAD
npy_longdouble obval;
} PyLongDoubleScalarObject;
typedef struct {
PyObject_HEAD
npy_cfloat obval;
} PyCFloatScalarObject;
typedef struct {
PyObject_HEAD
npy_cdouble obval;
} PyCDoubleScalarObject;
typedef struct {
PyObject_HEAD
npy_clongdouble obval;
} PyCLongDoubleScalarObject;
typedef struct {
PyObject_HEAD
PyObject * obval;
} PyObjectScalarObject;
typedef struct {
PyObject_HEAD
npy_datetime obval;
PyArray_DatetimeMetaData obmeta;
} PyDatetimeScalarObject;
typedef struct {
PyObject_HEAD
npy_timedelta obval;
PyArray_DatetimeMetaData obmeta;
} PyTimedeltaScalarObject;
typedef struct {
PyObject_HEAD
char obval;
} PyScalarObject;
#define PyStringScalarObject PyBytesObject
typedef struct {
/* note that the PyObject_HEAD macro lives right here */
PyUnicodeObject base;
Py_UCS4 *obval;
char *buffer_fmt;
} PyUnicodeScalarObject;
typedef struct {
PyObject_VAR_HEAD
char *obval;
PyArray_Descr *descr;
int flags;
PyObject *base;
void *_buffer_info; /* private buffer info, tagged to allow warning */
} PyVoidScalarObject;
/* Macros
Py<Cls><bitsize>ScalarObject
Py<Cls><bitsize>ArrType_Type
are defined in ndarrayobject.h
*/
#define PyArrayScalar_False ((PyObject *)(&(_PyArrayScalar_BoolValues[0])))
#define PyArrayScalar_True ((PyObject *)(&(_PyArrayScalar_BoolValues[1])))
#define PyArrayScalar_FromLong(i) \
((PyObject *)(&(_PyArrayScalar_BoolValues[((i)!=0)])))
#define PyArrayScalar_RETURN_BOOL_FROM_LONG(i) \
return Py_INCREF(PyArrayScalar_FromLong(i)), \
PyArrayScalar_FromLong(i)
#define PyArrayScalar_RETURN_FALSE \
return Py_INCREF(PyArrayScalar_False), \
PyArrayScalar_False
#define PyArrayScalar_RETURN_TRUE \
return Py_INCREF(PyArrayScalar_True), \
PyArrayScalar_True
#define PyArrayScalar_New(cls) \
Py##cls##ArrType_Type.tp_alloc(&Py##cls##ArrType_Type, 0)
#define PyArrayScalar_VAL(obj, cls) \
((Py##cls##ScalarObject *)obj)->obval
#define PyArrayScalar_ASSIGN(obj, cls, val) \
PyArrayScalar_VAL(obj, cls) = val
#endif /* NUMPY_CORE_INCLUDE_NUMPY_ARRAYSCALARS_H_ */
| 3,818 | C | 19.868852 | 79 | 0.650079 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/__multiarray_api.h |
#if defined(_MULTIARRAYMODULE) || defined(WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE)
typedef struct {
PyObject_HEAD
npy_bool obval;
} PyBoolScalarObject;
extern NPY_NO_EXPORT PyTypeObject PyArrayMapIter_Type;
extern NPY_NO_EXPORT PyTypeObject PyArrayNeighborhoodIter_Type;
extern NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2];
NPY_NO_EXPORT unsigned int PyArray_GetNDArrayCVersion \
(void);
extern NPY_NO_EXPORT PyTypeObject PyBigArray_Type;
extern NPY_NO_EXPORT PyTypeObject PyArray_Type;
extern NPY_NO_EXPORT PyArray_DTypeMeta PyArrayDescr_TypeFull;
#define PyArrayDescr_Type (*(PyTypeObject *)(&PyArrayDescr_TypeFull))
extern NPY_NO_EXPORT PyTypeObject PyArrayFlags_Type;
extern NPY_NO_EXPORT PyTypeObject PyArrayIter_Type;
extern NPY_NO_EXPORT PyTypeObject PyArrayMultiIter_Type;
extern NPY_NO_EXPORT int NPY_NUMUSERTYPES;
extern NPY_NO_EXPORT PyTypeObject PyBoolArrType_Type;
extern NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2];
extern NPY_NO_EXPORT PyTypeObject PyGenericArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyNumberArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyIntegerArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PySignedIntegerArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyUnsignedIntegerArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyInexactArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyFloatingArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyComplexFloatingArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyFlexibleArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyCharacterArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyByteArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyShortArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyIntArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyLongArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyLongLongArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyUByteArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyUShortArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyUIntArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyULongArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyULongLongArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyFloatArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyDoubleArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyLongDoubleArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyCFloatArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyCDoubleArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyCLongDoubleArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyObjectArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyStringArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyUnicodeArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyVoidArrType_Type;
NPY_NO_EXPORT int PyArray_SetNumericOps \
(PyObject *);
NPY_NO_EXPORT PyObject * PyArray_GetNumericOps \
(void);
NPY_NO_EXPORT int PyArray_INCREF \
(PyArrayObject *);
NPY_NO_EXPORT int PyArray_XDECREF \
(PyArrayObject *);
NPY_NO_EXPORT void PyArray_SetStringFunction \
(PyObject *, int);
NPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromType \
(int);
NPY_NO_EXPORT PyObject * PyArray_TypeObjectFromType \
(int);
NPY_NO_EXPORT char * PyArray_Zero \
(PyArrayObject *);
NPY_NO_EXPORT char * PyArray_One \
(PyArrayObject *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_CastToType \
(PyArrayObject *, PyArray_Descr *, int);
NPY_NO_EXPORT int PyArray_CastTo \
(PyArrayObject *, PyArrayObject *);
NPY_NO_EXPORT int PyArray_CastAnyTo \
(PyArrayObject *, PyArrayObject *);
NPY_NO_EXPORT int PyArray_CanCastSafely \
(int, int);
NPY_NO_EXPORT npy_bool PyArray_CanCastTo \
(PyArray_Descr *, PyArray_Descr *);
NPY_NO_EXPORT int PyArray_ObjectType \
(PyObject *, int);
NPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromObject \
(PyObject *, PyArray_Descr *);
NPY_NO_EXPORT PyArrayObject ** PyArray_ConvertToCommonType \
(PyObject *, int *);
NPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromScalar \
(PyObject *);
NPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromTypeObject \
(PyObject *);
NPY_NO_EXPORT npy_intp PyArray_Size \
(PyObject *);
NPY_NO_EXPORT PyObject * PyArray_Scalar \
(void *, PyArray_Descr *, PyObject *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromScalar \
(PyObject *, PyArray_Descr *);
NPY_NO_EXPORT void PyArray_ScalarAsCtype \
(PyObject *, void *);
NPY_NO_EXPORT int PyArray_CastScalarToCtype \
(PyObject *, void *, PyArray_Descr *);
NPY_NO_EXPORT int PyArray_CastScalarDirect \
(PyObject *, PyArray_Descr *, void *, int);
NPY_NO_EXPORT PyObject * PyArray_ScalarFromObject \
(PyObject *);
NPY_NO_EXPORT PyArray_VectorUnaryFunc * PyArray_GetCastFunc \
(PyArray_Descr *, int);
NPY_NO_EXPORT PyObject * PyArray_FromDims \
(int NPY_UNUSED(nd), int *NPY_UNUSED(d), int NPY_UNUSED(type));
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(3) PyObject * PyArray_FromDimsAndDataAndDescr \
(int NPY_UNUSED(nd), int *NPY_UNUSED(d), PyArray_Descr *, char *NPY_UNUSED(data));
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromAny \
(PyObject *, PyArray_Descr *, int, int, int, PyObject *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(1) PyObject * PyArray_EnsureArray \
(PyObject *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(1) PyObject * PyArray_EnsureAnyArray \
(PyObject *);
NPY_NO_EXPORT PyObject * PyArray_FromFile \
(FILE *, PyArray_Descr *, npy_intp, char *);
NPY_NO_EXPORT PyObject * PyArray_FromString \
(char *, npy_intp, PyArray_Descr *, npy_intp, char *);
NPY_NO_EXPORT PyObject * PyArray_FromBuffer \
(PyObject *, PyArray_Descr *, npy_intp, npy_intp);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromIter \
(PyObject *, PyArray_Descr *, npy_intp);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(1) PyObject * PyArray_Return \
(PyArrayObject *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_GetField \
(PyArrayObject *, PyArray_Descr *, int);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) int PyArray_SetField \
(PyArrayObject *, PyArray_Descr *, int, PyObject *);
NPY_NO_EXPORT PyObject * PyArray_Byteswap \
(PyArrayObject *, npy_bool);
NPY_NO_EXPORT PyObject * PyArray_Resize \
(PyArrayObject *, PyArray_Dims *, int, NPY_ORDER NPY_UNUSED(order));
NPY_NO_EXPORT int PyArray_MoveInto \
(PyArrayObject *, PyArrayObject *);
NPY_NO_EXPORT int PyArray_CopyInto \
(PyArrayObject *, PyArrayObject *);
NPY_NO_EXPORT int PyArray_CopyAnyInto \
(PyArrayObject *, PyArrayObject *);
NPY_NO_EXPORT int PyArray_CopyObject \
(PyArrayObject *, PyObject *);
NPY_NO_EXPORT PyObject * PyArray_NewCopy \
(PyArrayObject *, NPY_ORDER);
NPY_NO_EXPORT PyObject * PyArray_ToList \
(PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_ToString \
(PyArrayObject *, NPY_ORDER);
NPY_NO_EXPORT int PyArray_ToFile \
(PyArrayObject *, FILE *, char *, char *);
NPY_NO_EXPORT int PyArray_Dump \
(PyObject *, PyObject *, int);
NPY_NO_EXPORT PyObject * PyArray_Dumps \
(PyObject *, int);
NPY_NO_EXPORT int PyArray_ValidType \
(int);
NPY_NO_EXPORT void PyArray_UpdateFlags \
(PyArrayObject *, int);
NPY_NO_EXPORT PyObject * PyArray_New \
(PyTypeObject *, int, npy_intp const *, int, npy_intp const *, void *, int, int, PyObject *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_NewFromDescr \
(PyTypeObject *, PyArray_Descr *, int, npy_intp const *, npy_intp const *, void *, int, PyObject *);
NPY_NO_EXPORT PyArray_Descr * PyArray_DescrNew \
(PyArray_Descr *);
NPY_NO_EXPORT PyArray_Descr * PyArray_DescrNewFromType \
(int);
NPY_NO_EXPORT double PyArray_GetPriority \
(PyObject *, double);
NPY_NO_EXPORT PyObject * PyArray_IterNew \
(PyObject *);
NPY_NO_EXPORT PyObject* PyArray_MultiIterNew \
(int, ...);
NPY_NO_EXPORT int PyArray_PyIntAsInt \
(PyObject *);
NPY_NO_EXPORT npy_intp PyArray_PyIntAsIntp \
(PyObject *);
NPY_NO_EXPORT int PyArray_Broadcast \
(PyArrayMultiIterObject *);
NPY_NO_EXPORT void PyArray_FillObjectArray \
(PyArrayObject *, PyObject *);
NPY_NO_EXPORT int PyArray_FillWithScalar \
(PyArrayObject *, PyObject *);
NPY_NO_EXPORT npy_bool PyArray_CheckStrides \
(int, int, npy_intp, npy_intp, npy_intp const *, npy_intp const *);
NPY_NO_EXPORT PyArray_Descr * PyArray_DescrNewByteorder \
(PyArray_Descr *, char);
NPY_NO_EXPORT PyObject * PyArray_IterAllButAxis \
(PyObject *, int *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_CheckFromAny \
(PyObject *, PyArray_Descr *, int, int, int, PyObject *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromArray \
(PyArrayObject *, PyArray_Descr *, int);
NPY_NO_EXPORT PyObject * PyArray_FromInterface \
(PyObject *);
NPY_NO_EXPORT PyObject * PyArray_FromStructInterface \
(PyObject *);
NPY_NO_EXPORT PyObject * PyArray_FromArrayAttr \
(PyObject *, PyArray_Descr *, PyObject *);
NPY_NO_EXPORT NPY_SCALARKIND PyArray_ScalarKind \
(int, PyArrayObject **);
NPY_NO_EXPORT int PyArray_CanCoerceScalar \
(int, int, NPY_SCALARKIND);
NPY_NO_EXPORT PyObject * PyArray_NewFlagsObject \
(PyObject *);
NPY_NO_EXPORT npy_bool PyArray_CanCastScalar \
(PyTypeObject *, PyTypeObject *);
NPY_NO_EXPORT int PyArray_CompareUCS4 \
(npy_ucs4 const *, npy_ucs4 const *, size_t);
NPY_NO_EXPORT int PyArray_RemoveSmallest \
(PyArrayMultiIterObject *);
NPY_NO_EXPORT int PyArray_ElementStrides \
(PyObject *);
NPY_NO_EXPORT void PyArray_Item_INCREF \
(char *, PyArray_Descr *);
NPY_NO_EXPORT void PyArray_Item_XDECREF \
(char *, PyArray_Descr *);
NPY_NO_EXPORT PyObject * PyArray_FieldNames \
(PyObject *);
NPY_NO_EXPORT PyObject * PyArray_Transpose \
(PyArrayObject *, PyArray_Dims *);
NPY_NO_EXPORT PyObject * PyArray_TakeFrom \
(PyArrayObject *, PyObject *, int, PyArrayObject *, NPY_CLIPMODE);
NPY_NO_EXPORT PyObject * PyArray_PutTo \
(PyArrayObject *, PyObject*, PyObject *, NPY_CLIPMODE);
NPY_NO_EXPORT PyObject * PyArray_PutMask \
(PyArrayObject *, PyObject*, PyObject*);
NPY_NO_EXPORT PyObject * PyArray_Repeat \
(PyArrayObject *, PyObject *, int);
NPY_NO_EXPORT PyObject * PyArray_Choose \
(PyArrayObject *, PyObject *, PyArrayObject *, NPY_CLIPMODE);
NPY_NO_EXPORT int PyArray_Sort \
(PyArrayObject *, int, NPY_SORTKIND);
NPY_NO_EXPORT PyObject * PyArray_ArgSort \
(PyArrayObject *, int, NPY_SORTKIND);
NPY_NO_EXPORT PyObject * PyArray_SearchSorted \
(PyArrayObject *, PyObject *, NPY_SEARCHSIDE, PyObject *);
NPY_NO_EXPORT PyObject * PyArray_ArgMax \
(PyArrayObject *, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_ArgMin \
(PyArrayObject *, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Reshape \
(PyArrayObject *, PyObject *);
NPY_NO_EXPORT PyObject * PyArray_Newshape \
(PyArrayObject *, PyArray_Dims *, NPY_ORDER);
NPY_NO_EXPORT PyObject * PyArray_Squeeze \
(PyArrayObject *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_View \
(PyArrayObject *, PyArray_Descr *, PyTypeObject *);
NPY_NO_EXPORT PyObject * PyArray_SwapAxes \
(PyArrayObject *, int, int);
NPY_NO_EXPORT PyObject * PyArray_Max \
(PyArrayObject *, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Min \
(PyArrayObject *, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Ptp \
(PyArrayObject *, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Mean \
(PyArrayObject *, int, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Trace \
(PyArrayObject *, int, int, int, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Diagonal \
(PyArrayObject *, int, int, int);
NPY_NO_EXPORT PyObject * PyArray_Clip \
(PyArrayObject *, PyObject *, PyObject *, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Conjugate \
(PyArrayObject *, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Nonzero \
(PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Std \
(PyArrayObject *, int, int, PyArrayObject *, int);
NPY_NO_EXPORT PyObject * PyArray_Sum \
(PyArrayObject *, int, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_CumSum \
(PyArrayObject *, int, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Prod \
(PyArrayObject *, int, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_CumProd \
(PyArrayObject *, int, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_All \
(PyArrayObject *, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Any \
(PyArrayObject *, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Compress \
(PyArrayObject *, PyObject *, int, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyArray_Flatten \
(PyArrayObject *, NPY_ORDER);
NPY_NO_EXPORT PyObject * PyArray_Ravel \
(PyArrayObject *, NPY_ORDER);
NPY_NO_EXPORT npy_intp PyArray_MultiplyList \
(npy_intp const *, int);
NPY_NO_EXPORT int PyArray_MultiplyIntList \
(int const *, int);
NPY_NO_EXPORT void * PyArray_GetPtr \
(PyArrayObject *, npy_intp const*);
NPY_NO_EXPORT int PyArray_CompareLists \
(npy_intp const *, npy_intp const *, int);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(5) int PyArray_AsCArray \
(PyObject **, void *, npy_intp *, int, PyArray_Descr*);
NPY_NO_EXPORT int PyArray_As1D \
(PyObject **NPY_UNUSED(op), char **NPY_UNUSED(ptr), int *NPY_UNUSED(d1), int NPY_UNUSED(typecode));
NPY_NO_EXPORT int PyArray_As2D \
(PyObject **NPY_UNUSED(op), char ***NPY_UNUSED(ptr), int *NPY_UNUSED(d1), int *NPY_UNUSED(d2), int NPY_UNUSED(typecode));
NPY_NO_EXPORT int PyArray_Free \
(PyObject *, void *);
NPY_NO_EXPORT int PyArray_Converter \
(PyObject *, PyObject **);
NPY_NO_EXPORT int PyArray_IntpFromSequence \
(PyObject *, npy_intp *, int);
NPY_NO_EXPORT PyObject * PyArray_Concatenate \
(PyObject *, int);
NPY_NO_EXPORT PyObject * PyArray_InnerProduct \
(PyObject *, PyObject *);
NPY_NO_EXPORT PyObject * PyArray_MatrixProduct \
(PyObject *, PyObject *);
NPY_NO_EXPORT PyObject * PyArray_CopyAndTranspose \
(PyObject *);
NPY_NO_EXPORT PyObject * PyArray_Correlate \
(PyObject *, PyObject *, int);
NPY_NO_EXPORT int PyArray_TypestrConvert \
(int, int);
NPY_NO_EXPORT int PyArray_DescrConverter \
(PyObject *, PyArray_Descr **);
NPY_NO_EXPORT int PyArray_DescrConverter2 \
(PyObject *, PyArray_Descr **);
NPY_NO_EXPORT int PyArray_IntpConverter \
(PyObject *, PyArray_Dims *);
NPY_NO_EXPORT int PyArray_BufferConverter \
(PyObject *, PyArray_Chunk *);
NPY_NO_EXPORT int PyArray_AxisConverter \
(PyObject *, int *);
NPY_NO_EXPORT int PyArray_BoolConverter \
(PyObject *, npy_bool *);
NPY_NO_EXPORT int PyArray_ByteorderConverter \
(PyObject *, char *);
NPY_NO_EXPORT int PyArray_OrderConverter \
(PyObject *, NPY_ORDER *);
NPY_NO_EXPORT unsigned char PyArray_EquivTypes \
(PyArray_Descr *, PyArray_Descr *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(3) PyObject * PyArray_Zeros \
(int, npy_intp const *, PyArray_Descr *, int);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(3) PyObject * PyArray_Empty \
(int, npy_intp const *, PyArray_Descr *, int);
NPY_NO_EXPORT PyObject * PyArray_Where \
(PyObject *, PyObject *, PyObject *);
NPY_NO_EXPORT PyObject * PyArray_Arange \
(double, double, double, int);
NPY_NO_EXPORT PyObject * PyArray_ArangeObj \
(PyObject *, PyObject *, PyObject *, PyArray_Descr *);
NPY_NO_EXPORT int PyArray_SortkindConverter \
(PyObject *, NPY_SORTKIND *);
NPY_NO_EXPORT PyObject * PyArray_LexSort \
(PyObject *, int);
NPY_NO_EXPORT PyObject * PyArray_Round \
(PyArrayObject *, int, PyArrayObject *);
NPY_NO_EXPORT unsigned char PyArray_EquivTypenums \
(int, int);
NPY_NO_EXPORT int PyArray_RegisterDataType \
(PyArray_Descr *);
NPY_NO_EXPORT int PyArray_RegisterCastFunc \
(PyArray_Descr *, int, PyArray_VectorUnaryFunc *);
NPY_NO_EXPORT int PyArray_RegisterCanCast \
(PyArray_Descr *, int, NPY_SCALARKIND);
NPY_NO_EXPORT void PyArray_InitArrFuncs \
(PyArray_ArrFuncs *);
NPY_NO_EXPORT PyObject * PyArray_IntTupleFromIntp \
(int, npy_intp const *);
NPY_NO_EXPORT int PyArray_TypeNumFromName \
(char const *);
NPY_NO_EXPORT int PyArray_ClipmodeConverter \
(PyObject *, NPY_CLIPMODE *);
NPY_NO_EXPORT int PyArray_OutputConverter \
(PyObject *, PyArrayObject **);
NPY_NO_EXPORT PyObject * PyArray_BroadcastToShape \
(PyObject *, npy_intp *, int);
NPY_NO_EXPORT void _PyArray_SigintHandler \
(int);
NPY_NO_EXPORT void* _PyArray_GetSigintBuf \
(void);
NPY_NO_EXPORT int PyArray_DescrAlignConverter \
(PyObject *, PyArray_Descr **);
NPY_NO_EXPORT int PyArray_DescrAlignConverter2 \
(PyObject *, PyArray_Descr **);
NPY_NO_EXPORT int PyArray_SearchsideConverter \
(PyObject *, void *);
NPY_NO_EXPORT PyObject * PyArray_CheckAxis \
(PyArrayObject *, int *, int);
NPY_NO_EXPORT npy_intp PyArray_OverflowMultiplyList \
(npy_intp const *, int);
NPY_NO_EXPORT int PyArray_CompareString \
(const char *, const char *, size_t);
NPY_NO_EXPORT PyObject* PyArray_MultiIterFromObjects \
(PyObject **, int, int, ...);
NPY_NO_EXPORT int PyArray_GetEndianness \
(void);
NPY_NO_EXPORT unsigned int PyArray_GetNDArrayCFeatureVersion \
(void);
NPY_NO_EXPORT PyObject * PyArray_Correlate2 \
(PyObject *, PyObject *, int);
NPY_NO_EXPORT PyObject* PyArray_NeighborhoodIterNew \
(PyArrayIterObject *, const npy_intp *, int, PyArrayObject*);
extern NPY_NO_EXPORT PyTypeObject PyTimeIntegerArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyDatetimeArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyTimedeltaArrType_Type;
extern NPY_NO_EXPORT PyTypeObject PyHalfArrType_Type;
extern NPY_NO_EXPORT PyTypeObject NpyIter_Type;
NPY_NO_EXPORT void PyArray_SetDatetimeParseFunction \
(PyObject *NPY_UNUSED(op));
NPY_NO_EXPORT void PyArray_DatetimeToDatetimeStruct \
(npy_datetime NPY_UNUSED(val), NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_datetimestruct *);
NPY_NO_EXPORT void PyArray_TimedeltaToTimedeltaStruct \
(npy_timedelta NPY_UNUSED(val), NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_timedeltastruct *);
NPY_NO_EXPORT npy_datetime PyArray_DatetimeStructToDatetime \
(NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_datetimestruct *NPY_UNUSED(d));
NPY_NO_EXPORT npy_datetime PyArray_TimedeltaStructToTimedelta \
(NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_timedeltastruct *NPY_UNUSED(d));
NPY_NO_EXPORT NpyIter * NpyIter_New \
(PyArrayObject *, npy_uint32, NPY_ORDER, NPY_CASTING, PyArray_Descr*);
NPY_NO_EXPORT NpyIter * NpyIter_MultiNew \
(int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **);
NPY_NO_EXPORT NpyIter * NpyIter_AdvancedNew \
(int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **, int, int **, npy_intp *, npy_intp);
NPY_NO_EXPORT NpyIter * NpyIter_Copy \
(NpyIter *);
NPY_NO_EXPORT int NpyIter_Deallocate \
(NpyIter *);
NPY_NO_EXPORT npy_bool NpyIter_HasDelayedBufAlloc \
(NpyIter *);
NPY_NO_EXPORT npy_bool NpyIter_HasExternalLoop \
(NpyIter *);
NPY_NO_EXPORT int NpyIter_EnableExternalLoop \
(NpyIter *);
NPY_NO_EXPORT npy_intp * NpyIter_GetInnerStrideArray \
(NpyIter *);
NPY_NO_EXPORT npy_intp * NpyIter_GetInnerLoopSizePtr \
(NpyIter *);
NPY_NO_EXPORT int NpyIter_Reset \
(NpyIter *, char **);
NPY_NO_EXPORT int NpyIter_ResetBasePointers \
(NpyIter *, char **, char **);
NPY_NO_EXPORT int NpyIter_ResetToIterIndexRange \
(NpyIter *, npy_intp, npy_intp, char **);
NPY_NO_EXPORT int NpyIter_GetNDim \
(NpyIter *);
NPY_NO_EXPORT int NpyIter_GetNOp \
(NpyIter *);
NPY_NO_EXPORT NpyIter_IterNextFunc * NpyIter_GetIterNext \
(NpyIter *, char **);
NPY_NO_EXPORT npy_intp NpyIter_GetIterSize \
(NpyIter *);
NPY_NO_EXPORT void NpyIter_GetIterIndexRange \
(NpyIter *, npy_intp *, npy_intp *);
NPY_NO_EXPORT npy_intp NpyIter_GetIterIndex \
(NpyIter *);
NPY_NO_EXPORT int NpyIter_GotoIterIndex \
(NpyIter *, npy_intp);
NPY_NO_EXPORT npy_bool NpyIter_HasMultiIndex \
(NpyIter *);
NPY_NO_EXPORT int NpyIter_GetShape \
(NpyIter *, npy_intp *);
NPY_NO_EXPORT NpyIter_GetMultiIndexFunc * NpyIter_GetGetMultiIndex \
(NpyIter *, char **);
NPY_NO_EXPORT int NpyIter_GotoMultiIndex \
(NpyIter *, npy_intp const *);
NPY_NO_EXPORT int NpyIter_RemoveMultiIndex \
(NpyIter *);
NPY_NO_EXPORT npy_bool NpyIter_HasIndex \
(NpyIter *);
NPY_NO_EXPORT npy_bool NpyIter_IsBuffered \
(NpyIter *);
NPY_NO_EXPORT npy_bool NpyIter_IsGrowInner \
(NpyIter *);
NPY_NO_EXPORT npy_intp NpyIter_GetBufferSize \
(NpyIter *);
NPY_NO_EXPORT npy_intp * NpyIter_GetIndexPtr \
(NpyIter *);
NPY_NO_EXPORT int NpyIter_GotoIndex \
(NpyIter *, npy_intp);
NPY_NO_EXPORT char ** NpyIter_GetDataPtrArray \
(NpyIter *);
NPY_NO_EXPORT PyArray_Descr ** NpyIter_GetDescrArray \
(NpyIter *);
NPY_NO_EXPORT PyArrayObject ** NpyIter_GetOperandArray \
(NpyIter *);
NPY_NO_EXPORT PyArrayObject * NpyIter_GetIterView \
(NpyIter *, npy_intp);
NPY_NO_EXPORT void NpyIter_GetReadFlags \
(NpyIter *, char *);
NPY_NO_EXPORT void NpyIter_GetWriteFlags \
(NpyIter *, char *);
NPY_NO_EXPORT void NpyIter_DebugPrint \
(NpyIter *);
NPY_NO_EXPORT npy_bool NpyIter_IterationNeedsAPI \
(NpyIter *);
NPY_NO_EXPORT void NpyIter_GetInnerFixedStrideArray \
(NpyIter *, npy_intp *);
NPY_NO_EXPORT int NpyIter_RemoveAxis \
(NpyIter *, int);
NPY_NO_EXPORT npy_intp * NpyIter_GetAxisStrideArray \
(NpyIter *, int);
NPY_NO_EXPORT npy_bool NpyIter_RequiresBuffering \
(NpyIter *);
NPY_NO_EXPORT char ** NpyIter_GetInitialDataPtrArray \
(NpyIter *);
NPY_NO_EXPORT int NpyIter_CreateCompatibleStrides \
(NpyIter *, npy_intp, npy_intp *);
NPY_NO_EXPORT int PyArray_CastingConverter \
(PyObject *, NPY_CASTING *);
NPY_NO_EXPORT npy_intp PyArray_CountNonzero \
(PyArrayObject *);
NPY_NO_EXPORT PyArray_Descr * PyArray_PromoteTypes \
(PyArray_Descr *, PyArray_Descr *);
NPY_NO_EXPORT PyArray_Descr * PyArray_MinScalarType \
(PyArrayObject *);
NPY_NO_EXPORT PyArray_Descr * PyArray_ResultType \
(npy_intp, PyArrayObject *arrs[], npy_intp, PyArray_Descr *descrs[]);
NPY_NO_EXPORT npy_bool PyArray_CanCastArrayTo \
(PyArrayObject *, PyArray_Descr *, NPY_CASTING);
NPY_NO_EXPORT npy_bool PyArray_CanCastTypeTo \
(PyArray_Descr *, PyArray_Descr *, NPY_CASTING);
NPY_NO_EXPORT PyArrayObject * PyArray_EinsteinSum \
(char *, npy_intp, PyArrayObject **, PyArray_Descr *, NPY_ORDER, NPY_CASTING, PyArrayObject *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(3) PyObject * PyArray_NewLikeArray \
(PyArrayObject *, NPY_ORDER, PyArray_Descr *, int);
NPY_NO_EXPORT int PyArray_GetArrayParamsFromObject \
(PyObject *NPY_UNUSED(op), PyArray_Descr *NPY_UNUSED(requested_dtype), npy_bool NPY_UNUSED(writeable), PyArray_Descr **NPY_UNUSED(out_dtype), int *NPY_UNUSED(out_ndim), npy_intp *NPY_UNUSED(out_dims), PyArrayObject **NPY_UNUSED(out_arr), PyObject *NPY_UNUSED(context));
NPY_NO_EXPORT int PyArray_ConvertClipmodeSequence \
(PyObject *, NPY_CLIPMODE *, int);
NPY_NO_EXPORT PyObject * PyArray_MatrixProduct2 \
(PyObject *, PyObject *, PyArrayObject*);
NPY_NO_EXPORT npy_bool NpyIter_IsFirstVisit \
(NpyIter *, int);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) int PyArray_SetBaseObject \
(PyArrayObject *, PyObject *);
NPY_NO_EXPORT void PyArray_CreateSortedStridePerm \
(int, npy_intp const *, npy_stride_sort_item *);
NPY_NO_EXPORT void PyArray_RemoveAxesInPlace \
(PyArrayObject *, const npy_bool *);
NPY_NO_EXPORT void PyArray_DebugPrint \
(PyArrayObject *);
NPY_NO_EXPORT int PyArray_FailUnlessWriteable \
(PyArrayObject *, const char *);
NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) int PyArray_SetUpdateIfCopyBase \
(PyArrayObject *, PyArrayObject *);
NPY_NO_EXPORT void * PyDataMem_NEW \
(size_t);
NPY_NO_EXPORT void PyDataMem_FREE \
(void *);
NPY_NO_EXPORT void * PyDataMem_RENEW \
(void *, size_t);
NPY_NO_EXPORT PyDataMem_EventHookFunc * PyDataMem_SetEventHook \
(PyDataMem_EventHookFunc *, void *, void **);
extern NPY_NO_EXPORT NPY_CASTING NPY_DEFAULT_ASSIGN_CASTING;
NPY_NO_EXPORT void PyArray_MapIterSwapAxes \
(PyArrayMapIterObject *, PyArrayObject **, int);
NPY_NO_EXPORT PyObject * PyArray_MapIterArray \
(PyArrayObject *, PyObject *);
NPY_NO_EXPORT void PyArray_MapIterNext \
(PyArrayMapIterObject *);
NPY_NO_EXPORT int PyArray_Partition \
(PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND);
NPY_NO_EXPORT PyObject * PyArray_ArgPartition \
(PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND);
NPY_NO_EXPORT int PyArray_SelectkindConverter \
(PyObject *, NPY_SELECTKIND *);
NPY_NO_EXPORT void * PyDataMem_NEW_ZEROED \
(size_t, size_t);
NPY_NO_EXPORT int PyArray_CheckAnyScalarExact \
(PyObject *);
NPY_NO_EXPORT PyObject * PyArray_MapIterArrayCopyIfOverlap \
(PyArrayObject *, PyObject *, int, PyArrayObject *);
NPY_NO_EXPORT int PyArray_ResolveWritebackIfCopy \
(PyArrayObject *);
NPY_NO_EXPORT int PyArray_SetWritebackIfCopyBase \
(PyArrayObject *, PyArrayObject *);
NPY_NO_EXPORT PyObject * PyDataMem_SetHandler \
(PyObject *);
NPY_NO_EXPORT PyObject * PyDataMem_GetHandler \
(void);
extern NPY_NO_EXPORT PyObject* PyDataMem_DefaultHandler;
#else
#if defined(PY_ARRAY_UNIQUE_SYMBOL)
#define PyArray_API PY_ARRAY_UNIQUE_SYMBOL
#endif
#if defined(NO_IMPORT) || defined(NO_IMPORT_ARRAY)
extern void **PyArray_API;
#else
#if defined(PY_ARRAY_UNIQUE_SYMBOL)
void **PyArray_API;
#else
static void **PyArray_API=NULL;
#endif
#endif
#define PyArray_GetNDArrayCVersion \
(*(unsigned int (*)(void)) \
PyArray_API[0])
#define PyBigArray_Type (*(PyTypeObject *)PyArray_API[1])
#define PyArray_Type (*(PyTypeObject *)PyArray_API[2])
#define PyArrayDescr_Type (*(PyTypeObject *)PyArray_API[3])
#define PyArrayFlags_Type (*(PyTypeObject *)PyArray_API[4])
#define PyArrayIter_Type (*(PyTypeObject *)PyArray_API[5])
#define PyArrayMultiIter_Type (*(PyTypeObject *)PyArray_API[6])
#define NPY_NUMUSERTYPES (*(int *)PyArray_API[7])
#define PyBoolArrType_Type (*(PyTypeObject *)PyArray_API[8])
#define _PyArrayScalar_BoolValues ((PyBoolScalarObject *)PyArray_API[9])
#define PyGenericArrType_Type (*(PyTypeObject *)PyArray_API[10])
#define PyNumberArrType_Type (*(PyTypeObject *)PyArray_API[11])
#define PyIntegerArrType_Type (*(PyTypeObject *)PyArray_API[12])
#define PySignedIntegerArrType_Type (*(PyTypeObject *)PyArray_API[13])
#define PyUnsignedIntegerArrType_Type (*(PyTypeObject *)PyArray_API[14])
#define PyInexactArrType_Type (*(PyTypeObject *)PyArray_API[15])
#define PyFloatingArrType_Type (*(PyTypeObject *)PyArray_API[16])
#define PyComplexFloatingArrType_Type (*(PyTypeObject *)PyArray_API[17])
#define PyFlexibleArrType_Type (*(PyTypeObject *)PyArray_API[18])
#define PyCharacterArrType_Type (*(PyTypeObject *)PyArray_API[19])
#define PyByteArrType_Type (*(PyTypeObject *)PyArray_API[20])
#define PyShortArrType_Type (*(PyTypeObject *)PyArray_API[21])
#define PyIntArrType_Type (*(PyTypeObject *)PyArray_API[22])
#define PyLongArrType_Type (*(PyTypeObject *)PyArray_API[23])
#define PyLongLongArrType_Type (*(PyTypeObject *)PyArray_API[24])
#define PyUByteArrType_Type (*(PyTypeObject *)PyArray_API[25])
#define PyUShortArrType_Type (*(PyTypeObject *)PyArray_API[26])
#define PyUIntArrType_Type (*(PyTypeObject *)PyArray_API[27])
#define PyULongArrType_Type (*(PyTypeObject *)PyArray_API[28])
#define PyULongLongArrType_Type (*(PyTypeObject *)PyArray_API[29])
#define PyFloatArrType_Type (*(PyTypeObject *)PyArray_API[30])
#define PyDoubleArrType_Type (*(PyTypeObject *)PyArray_API[31])
#define PyLongDoubleArrType_Type (*(PyTypeObject *)PyArray_API[32])
#define PyCFloatArrType_Type (*(PyTypeObject *)PyArray_API[33])
#define PyCDoubleArrType_Type (*(PyTypeObject *)PyArray_API[34])
#define PyCLongDoubleArrType_Type (*(PyTypeObject *)PyArray_API[35])
#define PyObjectArrType_Type (*(PyTypeObject *)PyArray_API[36])
#define PyStringArrType_Type (*(PyTypeObject *)PyArray_API[37])
#define PyUnicodeArrType_Type (*(PyTypeObject *)PyArray_API[38])
#define PyVoidArrType_Type (*(PyTypeObject *)PyArray_API[39])
#define PyArray_SetNumericOps \
(*(int (*)(PyObject *)) \
PyArray_API[40])
#define PyArray_GetNumericOps \
(*(PyObject * (*)(void)) \
PyArray_API[41])
#define PyArray_INCREF \
(*(int (*)(PyArrayObject *)) \
PyArray_API[42])
#define PyArray_XDECREF \
(*(int (*)(PyArrayObject *)) \
PyArray_API[43])
#define PyArray_SetStringFunction \
(*(void (*)(PyObject *, int)) \
PyArray_API[44])
#define PyArray_DescrFromType \
(*(PyArray_Descr * (*)(int)) \
PyArray_API[45])
#define PyArray_TypeObjectFromType \
(*(PyObject * (*)(int)) \
PyArray_API[46])
#define PyArray_Zero \
(*(char * (*)(PyArrayObject *)) \
PyArray_API[47])
#define PyArray_One \
(*(char * (*)(PyArrayObject *)) \
PyArray_API[48])
#define PyArray_CastToType \
(*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \
PyArray_API[49])
#define PyArray_CastTo \
(*(int (*)(PyArrayObject *, PyArrayObject *)) \
PyArray_API[50])
#define PyArray_CastAnyTo \
(*(int (*)(PyArrayObject *, PyArrayObject *)) \
PyArray_API[51])
#define PyArray_CanCastSafely \
(*(int (*)(int, int)) \
PyArray_API[52])
#define PyArray_CanCastTo \
(*(npy_bool (*)(PyArray_Descr *, PyArray_Descr *)) \
PyArray_API[53])
#define PyArray_ObjectType \
(*(int (*)(PyObject *, int)) \
PyArray_API[54])
#define PyArray_DescrFromObject \
(*(PyArray_Descr * (*)(PyObject *, PyArray_Descr *)) \
PyArray_API[55])
#define PyArray_ConvertToCommonType \
(*(PyArrayObject ** (*)(PyObject *, int *)) \
PyArray_API[56])
#define PyArray_DescrFromScalar \
(*(PyArray_Descr * (*)(PyObject *)) \
PyArray_API[57])
#define PyArray_DescrFromTypeObject \
(*(PyArray_Descr * (*)(PyObject *)) \
PyArray_API[58])
#define PyArray_Size \
(*(npy_intp (*)(PyObject *)) \
PyArray_API[59])
#define PyArray_Scalar \
(*(PyObject * (*)(void *, PyArray_Descr *, PyObject *)) \
PyArray_API[60])
#define PyArray_FromScalar \
(*(PyObject * (*)(PyObject *, PyArray_Descr *)) \
PyArray_API[61])
#define PyArray_ScalarAsCtype \
(*(void (*)(PyObject *, void *)) \
PyArray_API[62])
#define PyArray_CastScalarToCtype \
(*(int (*)(PyObject *, void *, PyArray_Descr *)) \
PyArray_API[63])
#define PyArray_CastScalarDirect \
(*(int (*)(PyObject *, PyArray_Descr *, void *, int)) \
PyArray_API[64])
#define PyArray_ScalarFromObject \
(*(PyObject * (*)(PyObject *)) \
PyArray_API[65])
#define PyArray_GetCastFunc \
(*(PyArray_VectorUnaryFunc * (*)(PyArray_Descr *, int)) \
PyArray_API[66])
#define PyArray_FromDims \
(*(PyObject * (*)(int NPY_UNUSED(nd), int *NPY_UNUSED(d), int NPY_UNUSED(type))) \
PyArray_API[67])
#define PyArray_FromDimsAndDataAndDescr \
(*(PyObject * (*)(int NPY_UNUSED(nd), int *NPY_UNUSED(d), PyArray_Descr *, char *NPY_UNUSED(data))) \
PyArray_API[68])
#define PyArray_FromAny \
(*(PyObject * (*)(PyObject *, PyArray_Descr *, int, int, int, PyObject *)) \
PyArray_API[69])
#define PyArray_EnsureArray \
(*(PyObject * (*)(PyObject *)) \
PyArray_API[70])
#define PyArray_EnsureAnyArray \
(*(PyObject * (*)(PyObject *)) \
PyArray_API[71])
#define PyArray_FromFile \
(*(PyObject * (*)(FILE *, PyArray_Descr *, npy_intp, char *)) \
PyArray_API[72])
#define PyArray_FromString \
(*(PyObject * (*)(char *, npy_intp, PyArray_Descr *, npy_intp, char *)) \
PyArray_API[73])
#define PyArray_FromBuffer \
(*(PyObject * (*)(PyObject *, PyArray_Descr *, npy_intp, npy_intp)) \
PyArray_API[74])
#define PyArray_FromIter \
(*(PyObject * (*)(PyObject *, PyArray_Descr *, npy_intp)) \
PyArray_API[75])
#define PyArray_Return \
(*(PyObject * (*)(PyArrayObject *)) \
PyArray_API[76])
#define PyArray_GetField \
(*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \
PyArray_API[77])
#define PyArray_SetField \
(*(int (*)(PyArrayObject *, PyArray_Descr *, int, PyObject *)) \
PyArray_API[78])
#define PyArray_Byteswap \
(*(PyObject * (*)(PyArrayObject *, npy_bool)) \
PyArray_API[79])
#define PyArray_Resize \
(*(PyObject * (*)(PyArrayObject *, PyArray_Dims *, int, NPY_ORDER NPY_UNUSED(order))) \
PyArray_API[80])
#define PyArray_MoveInto \
(*(int (*)(PyArrayObject *, PyArrayObject *)) \
PyArray_API[81])
#define PyArray_CopyInto \
(*(int (*)(PyArrayObject *, PyArrayObject *)) \
PyArray_API[82])
#define PyArray_CopyAnyInto \
(*(int (*)(PyArrayObject *, PyArrayObject *)) \
PyArray_API[83])
#define PyArray_CopyObject \
(*(int (*)(PyArrayObject *, PyObject *)) \
PyArray_API[84])
#define PyArray_NewCopy \
(*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \
PyArray_API[85])
#define PyArray_ToList \
(*(PyObject * (*)(PyArrayObject *)) \
PyArray_API[86])
#define PyArray_ToString \
(*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \
PyArray_API[87])
#define PyArray_ToFile \
(*(int (*)(PyArrayObject *, FILE *, char *, char *)) \
PyArray_API[88])
#define PyArray_Dump \
(*(int (*)(PyObject *, PyObject *, int)) \
PyArray_API[89])
#define PyArray_Dumps \
(*(PyObject * (*)(PyObject *, int)) \
PyArray_API[90])
#define PyArray_ValidType \
(*(int (*)(int)) \
PyArray_API[91])
#define PyArray_UpdateFlags \
(*(void (*)(PyArrayObject *, int)) \
PyArray_API[92])
#define PyArray_New \
(*(PyObject * (*)(PyTypeObject *, int, npy_intp const *, int, npy_intp const *, void *, int, int, PyObject *)) \
PyArray_API[93])
#define PyArray_NewFromDescr \
(*(PyObject * (*)(PyTypeObject *, PyArray_Descr *, int, npy_intp const *, npy_intp const *, void *, int, PyObject *)) \
PyArray_API[94])
#define PyArray_DescrNew \
(*(PyArray_Descr * (*)(PyArray_Descr *)) \
PyArray_API[95])
#define PyArray_DescrNewFromType \
(*(PyArray_Descr * (*)(int)) \
PyArray_API[96])
#define PyArray_GetPriority \
(*(double (*)(PyObject *, double)) \
PyArray_API[97])
#define PyArray_IterNew \
(*(PyObject * (*)(PyObject *)) \
PyArray_API[98])
#define PyArray_MultiIterNew \
(*(PyObject* (*)(int, ...)) \
PyArray_API[99])
#define PyArray_PyIntAsInt \
(*(int (*)(PyObject *)) \
PyArray_API[100])
#define PyArray_PyIntAsIntp \
(*(npy_intp (*)(PyObject *)) \
PyArray_API[101])
#define PyArray_Broadcast \
(*(int (*)(PyArrayMultiIterObject *)) \
PyArray_API[102])
#define PyArray_FillObjectArray \
(*(void (*)(PyArrayObject *, PyObject *)) \
PyArray_API[103])
#define PyArray_FillWithScalar \
(*(int (*)(PyArrayObject *, PyObject *)) \
PyArray_API[104])
#define PyArray_CheckStrides \
(*(npy_bool (*)(int, int, npy_intp, npy_intp, npy_intp const *, npy_intp const *)) \
PyArray_API[105])
#define PyArray_DescrNewByteorder \
(*(PyArray_Descr * (*)(PyArray_Descr *, char)) \
PyArray_API[106])
#define PyArray_IterAllButAxis \
(*(PyObject * (*)(PyObject *, int *)) \
PyArray_API[107])
#define PyArray_CheckFromAny \
(*(PyObject * (*)(PyObject *, PyArray_Descr *, int, int, int, PyObject *)) \
PyArray_API[108])
#define PyArray_FromArray \
(*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \
PyArray_API[109])
#define PyArray_FromInterface \
(*(PyObject * (*)(PyObject *)) \
PyArray_API[110])
#define PyArray_FromStructInterface \
(*(PyObject * (*)(PyObject *)) \
PyArray_API[111])
#define PyArray_FromArrayAttr \
(*(PyObject * (*)(PyObject *, PyArray_Descr *, PyObject *)) \
PyArray_API[112])
#define PyArray_ScalarKind \
(*(NPY_SCALARKIND (*)(int, PyArrayObject **)) \
PyArray_API[113])
#define PyArray_CanCoerceScalar \
(*(int (*)(int, int, NPY_SCALARKIND)) \
PyArray_API[114])
#define PyArray_NewFlagsObject \
(*(PyObject * (*)(PyObject *)) \
PyArray_API[115])
#define PyArray_CanCastScalar \
(*(npy_bool (*)(PyTypeObject *, PyTypeObject *)) \
PyArray_API[116])
#define PyArray_CompareUCS4 \
(*(int (*)(npy_ucs4 const *, npy_ucs4 const *, size_t)) \
PyArray_API[117])
#define PyArray_RemoveSmallest \
(*(int (*)(PyArrayMultiIterObject *)) \
PyArray_API[118])
#define PyArray_ElementStrides \
(*(int (*)(PyObject *)) \
PyArray_API[119])
#define PyArray_Item_INCREF \
(*(void (*)(char *, PyArray_Descr *)) \
PyArray_API[120])
#define PyArray_Item_XDECREF \
(*(void (*)(char *, PyArray_Descr *)) \
PyArray_API[121])
#define PyArray_FieldNames \
(*(PyObject * (*)(PyObject *)) \
PyArray_API[122])
#define PyArray_Transpose \
(*(PyObject * (*)(PyArrayObject *, PyArray_Dims *)) \
PyArray_API[123])
#define PyArray_TakeFrom \
(*(PyObject * (*)(PyArrayObject *, PyObject *, int, PyArrayObject *, NPY_CLIPMODE)) \
PyArray_API[124])
#define PyArray_PutTo \
(*(PyObject * (*)(PyArrayObject *, PyObject*, PyObject *, NPY_CLIPMODE)) \
PyArray_API[125])
#define PyArray_PutMask \
(*(PyObject * (*)(PyArrayObject *, PyObject*, PyObject*)) \
PyArray_API[126])
#define PyArray_Repeat \
(*(PyObject * (*)(PyArrayObject *, PyObject *, int)) \
PyArray_API[127])
#define PyArray_Choose \
(*(PyObject * (*)(PyArrayObject *, PyObject *, PyArrayObject *, NPY_CLIPMODE)) \
PyArray_API[128])
#define PyArray_Sort \
(*(int (*)(PyArrayObject *, int, NPY_SORTKIND)) \
PyArray_API[129])
#define PyArray_ArgSort \
(*(PyObject * (*)(PyArrayObject *, int, NPY_SORTKIND)) \
PyArray_API[130])
#define PyArray_SearchSorted \
(*(PyObject * (*)(PyArrayObject *, PyObject *, NPY_SEARCHSIDE, PyObject *)) \
PyArray_API[131])
#define PyArray_ArgMax \
(*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
PyArray_API[132])
#define PyArray_ArgMin \
(*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
PyArray_API[133])
#define PyArray_Reshape \
(*(PyObject * (*)(PyArrayObject *, PyObject *)) \
PyArray_API[134])
#define PyArray_Newshape \
(*(PyObject * (*)(PyArrayObject *, PyArray_Dims *, NPY_ORDER)) \
PyArray_API[135])
#define PyArray_Squeeze \
(*(PyObject * (*)(PyArrayObject *)) \
PyArray_API[136])
#define PyArray_View \
(*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, PyTypeObject *)) \
PyArray_API[137])
#define PyArray_SwapAxes \
(*(PyObject * (*)(PyArrayObject *, int, int)) \
PyArray_API[138])
#define PyArray_Max \
(*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
PyArray_API[139])
#define PyArray_Min \
(*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
PyArray_API[140])
#define PyArray_Ptp \
(*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
PyArray_API[141])
#define PyArray_Mean \
(*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \
PyArray_API[142])
#define PyArray_Trace \
(*(PyObject * (*)(PyArrayObject *, int, int, int, int, PyArrayObject *)) \
PyArray_API[143])
#define PyArray_Diagonal \
(*(PyObject * (*)(PyArrayObject *, int, int, int)) \
PyArray_API[144])
#define PyArray_Clip \
(*(PyObject * (*)(PyArrayObject *, PyObject *, PyObject *, PyArrayObject *)) \
PyArray_API[145])
#define PyArray_Conjugate \
(*(PyObject * (*)(PyArrayObject *, PyArrayObject *)) \
PyArray_API[146])
#define PyArray_Nonzero \
(*(PyObject * (*)(PyArrayObject *)) \
PyArray_API[147])
#define PyArray_Std \
(*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *, int)) \
PyArray_API[148])
#define PyArray_Sum \
(*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \
PyArray_API[149])
#define PyArray_CumSum \
(*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \
PyArray_API[150])
#define PyArray_Prod \
(*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \
PyArray_API[151])
#define PyArray_CumProd \
(*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \
PyArray_API[152])
#define PyArray_All \
(*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
PyArray_API[153])
#define PyArray_Any \
(*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
PyArray_API[154])
#define PyArray_Compress \
(*(PyObject * (*)(PyArrayObject *, PyObject *, int, PyArrayObject *)) \
PyArray_API[155])
#define PyArray_Flatten \
(*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \
PyArray_API[156])
#define PyArray_Ravel \
(*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \
PyArray_API[157])
#define PyArray_MultiplyList \
(*(npy_intp (*)(npy_intp const *, int)) \
PyArray_API[158])
#define PyArray_MultiplyIntList \
(*(int (*)(int const *, int)) \
PyArray_API[159])
#define PyArray_GetPtr \
(*(void * (*)(PyArrayObject *, npy_intp const*)) \
PyArray_API[160])
#define PyArray_CompareLists \
(*(int (*)(npy_intp const *, npy_intp const *, int)) \
PyArray_API[161])
#define PyArray_AsCArray \
(*(int (*)(PyObject **, void *, npy_intp *, int, PyArray_Descr*)) \
PyArray_API[162])
#define PyArray_As1D \
(*(int (*)(PyObject **NPY_UNUSED(op), char **NPY_UNUSED(ptr), int *NPY_UNUSED(d1), int NPY_UNUSED(typecode))) \
PyArray_API[163])
#define PyArray_As2D \
(*(int (*)(PyObject **NPY_UNUSED(op), char ***NPY_UNUSED(ptr), int *NPY_UNUSED(d1), int *NPY_UNUSED(d2), int NPY_UNUSED(typecode))) \
PyArray_API[164])
#define PyArray_Free \
(*(int (*)(PyObject *, void *)) \
PyArray_API[165])
#define PyArray_Converter \
(*(int (*)(PyObject *, PyObject **)) \
PyArray_API[166])
#define PyArray_IntpFromSequence \
(*(int (*)(PyObject *, npy_intp *, int)) \
PyArray_API[167])
#define PyArray_Concatenate \
(*(PyObject * (*)(PyObject *, int)) \
PyArray_API[168])
#define PyArray_InnerProduct \
(*(PyObject * (*)(PyObject *, PyObject *)) \
PyArray_API[169])
#define PyArray_MatrixProduct \
(*(PyObject * (*)(PyObject *, PyObject *)) \
PyArray_API[170])
#define PyArray_CopyAndTranspose \
(*(PyObject * (*)(PyObject *)) \
PyArray_API[171])
#define PyArray_Correlate \
(*(PyObject * (*)(PyObject *, PyObject *, int)) \
PyArray_API[172])
#define PyArray_TypestrConvert \
(*(int (*)(int, int)) \
PyArray_API[173])
#define PyArray_DescrConverter \
(*(int (*)(PyObject *, PyArray_Descr **)) \
PyArray_API[174])
#define PyArray_DescrConverter2 \
(*(int (*)(PyObject *, PyArray_Descr **)) \
PyArray_API[175])
#define PyArray_IntpConverter \
(*(int (*)(PyObject *, PyArray_Dims *)) \
PyArray_API[176])
#define PyArray_BufferConverter \
(*(int (*)(PyObject *, PyArray_Chunk *)) \
PyArray_API[177])
#define PyArray_AxisConverter \
(*(int (*)(PyObject *, int *)) \
PyArray_API[178])
#define PyArray_BoolConverter \
(*(int (*)(PyObject *, npy_bool *)) \
PyArray_API[179])
#define PyArray_ByteorderConverter \
(*(int (*)(PyObject *, char *)) \
PyArray_API[180])
#define PyArray_OrderConverter \
(*(int (*)(PyObject *, NPY_ORDER *)) \
PyArray_API[181])
#define PyArray_EquivTypes \
(*(unsigned char (*)(PyArray_Descr *, PyArray_Descr *)) \
PyArray_API[182])
#define PyArray_Zeros \
(*(PyObject * (*)(int, npy_intp const *, PyArray_Descr *, int)) \
PyArray_API[183])
#define PyArray_Empty \
(*(PyObject * (*)(int, npy_intp const *, PyArray_Descr *, int)) \
PyArray_API[184])
#define PyArray_Where \
(*(PyObject * (*)(PyObject *, PyObject *, PyObject *)) \
PyArray_API[185])
#define PyArray_Arange \
(*(PyObject * (*)(double, double, double, int)) \
PyArray_API[186])
#define PyArray_ArangeObj \
(*(PyObject * (*)(PyObject *, PyObject *, PyObject *, PyArray_Descr *)) \
PyArray_API[187])
#define PyArray_SortkindConverter \
(*(int (*)(PyObject *, NPY_SORTKIND *)) \
PyArray_API[188])
#define PyArray_LexSort \
(*(PyObject * (*)(PyObject *, int)) \
PyArray_API[189])
#define PyArray_Round \
(*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
PyArray_API[190])
#define PyArray_EquivTypenums \
(*(unsigned char (*)(int, int)) \
PyArray_API[191])
#define PyArray_RegisterDataType \
(*(int (*)(PyArray_Descr *)) \
PyArray_API[192])
#define PyArray_RegisterCastFunc \
(*(int (*)(PyArray_Descr *, int, PyArray_VectorUnaryFunc *)) \
PyArray_API[193])
#define PyArray_RegisterCanCast \
(*(int (*)(PyArray_Descr *, int, NPY_SCALARKIND)) \
PyArray_API[194])
#define PyArray_InitArrFuncs \
(*(void (*)(PyArray_ArrFuncs *)) \
PyArray_API[195])
#define PyArray_IntTupleFromIntp \
(*(PyObject * (*)(int, npy_intp const *)) \
PyArray_API[196])
#define PyArray_TypeNumFromName \
(*(int (*)(char const *)) \
PyArray_API[197])
#define PyArray_ClipmodeConverter \
(*(int (*)(PyObject *, NPY_CLIPMODE *)) \
PyArray_API[198])
#define PyArray_OutputConverter \
(*(int (*)(PyObject *, PyArrayObject **)) \
PyArray_API[199])
#define PyArray_BroadcastToShape \
(*(PyObject * (*)(PyObject *, npy_intp *, int)) \
PyArray_API[200])
#define _PyArray_SigintHandler \
(*(void (*)(int)) \
PyArray_API[201])
#define _PyArray_GetSigintBuf \
(*(void* (*)(void)) \
PyArray_API[202])
#define PyArray_DescrAlignConverter \
(*(int (*)(PyObject *, PyArray_Descr **)) \
PyArray_API[203])
#define PyArray_DescrAlignConverter2 \
(*(int (*)(PyObject *, PyArray_Descr **)) \
PyArray_API[204])
#define PyArray_SearchsideConverter \
(*(int (*)(PyObject *, void *)) \
PyArray_API[205])
#define PyArray_CheckAxis \
(*(PyObject * (*)(PyArrayObject *, int *, int)) \
PyArray_API[206])
#define PyArray_OverflowMultiplyList \
(*(npy_intp (*)(npy_intp const *, int)) \
PyArray_API[207])
#define PyArray_CompareString \
(*(int (*)(const char *, const char *, size_t)) \
PyArray_API[208])
#define PyArray_MultiIterFromObjects \
(*(PyObject* (*)(PyObject **, int, int, ...)) \
PyArray_API[209])
#define PyArray_GetEndianness \
(*(int (*)(void)) \
PyArray_API[210])
#define PyArray_GetNDArrayCFeatureVersion \
(*(unsigned int (*)(void)) \
PyArray_API[211])
#define PyArray_Correlate2 \
(*(PyObject * (*)(PyObject *, PyObject *, int)) \
PyArray_API[212])
#define PyArray_NeighborhoodIterNew \
(*(PyObject* (*)(PyArrayIterObject *, const npy_intp *, int, PyArrayObject*)) \
PyArray_API[213])
#define PyTimeIntegerArrType_Type (*(PyTypeObject *)PyArray_API[214])
#define PyDatetimeArrType_Type (*(PyTypeObject *)PyArray_API[215])
#define PyTimedeltaArrType_Type (*(PyTypeObject *)PyArray_API[216])
#define PyHalfArrType_Type (*(PyTypeObject *)PyArray_API[217])
#define NpyIter_Type (*(PyTypeObject *)PyArray_API[218])
#define PyArray_SetDatetimeParseFunction \
(*(void (*)(PyObject *NPY_UNUSED(op))) \
PyArray_API[219])
#define PyArray_DatetimeToDatetimeStruct \
(*(void (*)(npy_datetime NPY_UNUSED(val), NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_datetimestruct *)) \
PyArray_API[220])
#define PyArray_TimedeltaToTimedeltaStruct \
(*(void (*)(npy_timedelta NPY_UNUSED(val), NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_timedeltastruct *)) \
PyArray_API[221])
#define PyArray_DatetimeStructToDatetime \
(*(npy_datetime (*)(NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_datetimestruct *NPY_UNUSED(d))) \
PyArray_API[222])
#define PyArray_TimedeltaStructToTimedelta \
(*(npy_datetime (*)(NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_timedeltastruct *NPY_UNUSED(d))) \
PyArray_API[223])
#define NpyIter_New \
(*(NpyIter * (*)(PyArrayObject *, npy_uint32, NPY_ORDER, NPY_CASTING, PyArray_Descr*)) \
PyArray_API[224])
#define NpyIter_MultiNew \
(*(NpyIter * (*)(int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **)) \
PyArray_API[225])
#define NpyIter_AdvancedNew \
(*(NpyIter * (*)(int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **, int, int **, npy_intp *, npy_intp)) \
PyArray_API[226])
#define NpyIter_Copy \
(*(NpyIter * (*)(NpyIter *)) \
PyArray_API[227])
#define NpyIter_Deallocate \
(*(int (*)(NpyIter *)) \
PyArray_API[228])
#define NpyIter_HasDelayedBufAlloc \
(*(npy_bool (*)(NpyIter *)) \
PyArray_API[229])
#define NpyIter_HasExternalLoop \
(*(npy_bool (*)(NpyIter *)) \
PyArray_API[230])
#define NpyIter_EnableExternalLoop \
(*(int (*)(NpyIter *)) \
PyArray_API[231])
#define NpyIter_GetInnerStrideArray \
(*(npy_intp * (*)(NpyIter *)) \
PyArray_API[232])
#define NpyIter_GetInnerLoopSizePtr \
(*(npy_intp * (*)(NpyIter *)) \
PyArray_API[233])
#define NpyIter_Reset \
(*(int (*)(NpyIter *, char **)) \
PyArray_API[234])
#define NpyIter_ResetBasePointers \
(*(int (*)(NpyIter *, char **, char **)) \
PyArray_API[235])
#define NpyIter_ResetToIterIndexRange \
(*(int (*)(NpyIter *, npy_intp, npy_intp, char **)) \
PyArray_API[236])
#define NpyIter_GetNDim \
(*(int (*)(NpyIter *)) \
PyArray_API[237])
#define NpyIter_GetNOp \
(*(int (*)(NpyIter *)) \
PyArray_API[238])
#define NpyIter_GetIterNext \
(*(NpyIter_IterNextFunc * (*)(NpyIter *, char **)) \
PyArray_API[239])
#define NpyIter_GetIterSize \
(*(npy_intp (*)(NpyIter *)) \
PyArray_API[240])
#define NpyIter_GetIterIndexRange \
(*(void (*)(NpyIter *, npy_intp *, npy_intp *)) \
PyArray_API[241])
#define NpyIter_GetIterIndex \
(*(npy_intp (*)(NpyIter *)) \
PyArray_API[242])
#define NpyIter_GotoIterIndex \
(*(int (*)(NpyIter *, npy_intp)) \
PyArray_API[243])
#define NpyIter_HasMultiIndex \
(*(npy_bool (*)(NpyIter *)) \
PyArray_API[244])
#define NpyIter_GetShape \
(*(int (*)(NpyIter *, npy_intp *)) \
PyArray_API[245])
#define NpyIter_GetGetMultiIndex \
(*(NpyIter_GetMultiIndexFunc * (*)(NpyIter *, char **)) \
PyArray_API[246])
#define NpyIter_GotoMultiIndex \
(*(int (*)(NpyIter *, npy_intp const *)) \
PyArray_API[247])
#define NpyIter_RemoveMultiIndex \
(*(int (*)(NpyIter *)) \
PyArray_API[248])
#define NpyIter_HasIndex \
(*(npy_bool (*)(NpyIter *)) \
PyArray_API[249])
#define NpyIter_IsBuffered \
(*(npy_bool (*)(NpyIter *)) \
PyArray_API[250])
#define NpyIter_IsGrowInner \
(*(npy_bool (*)(NpyIter *)) \
PyArray_API[251])
#define NpyIter_GetBufferSize \
(*(npy_intp (*)(NpyIter *)) \
PyArray_API[252])
#define NpyIter_GetIndexPtr \
(*(npy_intp * (*)(NpyIter *)) \
PyArray_API[253])
#define NpyIter_GotoIndex \
(*(int (*)(NpyIter *, npy_intp)) \
PyArray_API[254])
#define NpyIter_GetDataPtrArray \
(*(char ** (*)(NpyIter *)) \
PyArray_API[255])
#define NpyIter_GetDescrArray \
(*(PyArray_Descr ** (*)(NpyIter *)) \
PyArray_API[256])
#define NpyIter_GetOperandArray \
(*(PyArrayObject ** (*)(NpyIter *)) \
PyArray_API[257])
#define NpyIter_GetIterView \
(*(PyArrayObject * (*)(NpyIter *, npy_intp)) \
PyArray_API[258])
#define NpyIter_GetReadFlags \
(*(void (*)(NpyIter *, char *)) \
PyArray_API[259])
#define NpyIter_GetWriteFlags \
(*(void (*)(NpyIter *, char *)) \
PyArray_API[260])
#define NpyIter_DebugPrint \
(*(void (*)(NpyIter *)) \
PyArray_API[261])
#define NpyIter_IterationNeedsAPI \
(*(npy_bool (*)(NpyIter *)) \
PyArray_API[262])
#define NpyIter_GetInnerFixedStrideArray \
(*(void (*)(NpyIter *, npy_intp *)) \
PyArray_API[263])
#define NpyIter_RemoveAxis \
(*(int (*)(NpyIter *, int)) \
PyArray_API[264])
#define NpyIter_GetAxisStrideArray \
(*(npy_intp * (*)(NpyIter *, int)) \
PyArray_API[265])
#define NpyIter_RequiresBuffering \
(*(npy_bool (*)(NpyIter *)) \
PyArray_API[266])
#define NpyIter_GetInitialDataPtrArray \
(*(char ** (*)(NpyIter *)) \
PyArray_API[267])
#define NpyIter_CreateCompatibleStrides \
(*(int (*)(NpyIter *, npy_intp, npy_intp *)) \
PyArray_API[268])
#define PyArray_CastingConverter \
(*(int (*)(PyObject *, NPY_CASTING *)) \
PyArray_API[269])
#define PyArray_CountNonzero \
(*(npy_intp (*)(PyArrayObject *)) \
PyArray_API[270])
#define PyArray_PromoteTypes \
(*(PyArray_Descr * (*)(PyArray_Descr *, PyArray_Descr *)) \
PyArray_API[271])
#define PyArray_MinScalarType \
(*(PyArray_Descr * (*)(PyArrayObject *)) \
PyArray_API[272])
#define PyArray_ResultType \
(*(PyArray_Descr * (*)(npy_intp, PyArrayObject *arrs[], npy_intp, PyArray_Descr *descrs[])) \
PyArray_API[273])
#define PyArray_CanCastArrayTo \
(*(npy_bool (*)(PyArrayObject *, PyArray_Descr *, NPY_CASTING)) \
PyArray_API[274])
#define PyArray_CanCastTypeTo \
(*(npy_bool (*)(PyArray_Descr *, PyArray_Descr *, NPY_CASTING)) \
PyArray_API[275])
#define PyArray_EinsteinSum \
(*(PyArrayObject * (*)(char *, npy_intp, PyArrayObject **, PyArray_Descr *, NPY_ORDER, NPY_CASTING, PyArrayObject *)) \
PyArray_API[276])
#define PyArray_NewLikeArray \
(*(PyObject * (*)(PyArrayObject *, NPY_ORDER, PyArray_Descr *, int)) \
PyArray_API[277])
#define PyArray_GetArrayParamsFromObject \
(*(int (*)(PyObject *NPY_UNUSED(op), PyArray_Descr *NPY_UNUSED(requested_dtype), npy_bool NPY_UNUSED(writeable), PyArray_Descr **NPY_UNUSED(out_dtype), int *NPY_UNUSED(out_ndim), npy_intp *NPY_UNUSED(out_dims), PyArrayObject **NPY_UNUSED(out_arr), PyObject *NPY_UNUSED(context))) \
PyArray_API[278])
#define PyArray_ConvertClipmodeSequence \
(*(int (*)(PyObject *, NPY_CLIPMODE *, int)) \
PyArray_API[279])
#define PyArray_MatrixProduct2 \
(*(PyObject * (*)(PyObject *, PyObject *, PyArrayObject*)) \
PyArray_API[280])
#define NpyIter_IsFirstVisit \
(*(npy_bool (*)(NpyIter *, int)) \
PyArray_API[281])
#define PyArray_SetBaseObject \
(*(int (*)(PyArrayObject *, PyObject *)) \
PyArray_API[282])
#define PyArray_CreateSortedStridePerm \
(*(void (*)(int, npy_intp const *, npy_stride_sort_item *)) \
PyArray_API[283])
#define PyArray_RemoveAxesInPlace \
(*(void (*)(PyArrayObject *, const npy_bool *)) \
PyArray_API[284])
#define PyArray_DebugPrint \
(*(void (*)(PyArrayObject *)) \
PyArray_API[285])
#define PyArray_FailUnlessWriteable \
(*(int (*)(PyArrayObject *, const char *)) \
PyArray_API[286])
#define PyArray_SetUpdateIfCopyBase \
(*(int (*)(PyArrayObject *, PyArrayObject *)) \
PyArray_API[287])
#define PyDataMem_NEW \
(*(void * (*)(size_t)) \
PyArray_API[288])
#define PyDataMem_FREE \
(*(void (*)(void *)) \
PyArray_API[289])
#define PyDataMem_RENEW \
(*(void * (*)(void *, size_t)) \
PyArray_API[290])
#define PyDataMem_SetEventHook \
(*(PyDataMem_EventHookFunc * (*)(PyDataMem_EventHookFunc *, void *, void **)) \
PyArray_API[291])
#define NPY_DEFAULT_ASSIGN_CASTING (*(NPY_CASTING *)PyArray_API[292])
#define PyArray_MapIterSwapAxes \
(*(void (*)(PyArrayMapIterObject *, PyArrayObject **, int)) \
PyArray_API[293])
#define PyArray_MapIterArray \
(*(PyObject * (*)(PyArrayObject *, PyObject *)) \
PyArray_API[294])
#define PyArray_MapIterNext \
(*(void (*)(PyArrayMapIterObject *)) \
PyArray_API[295])
#define PyArray_Partition \
(*(int (*)(PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND)) \
PyArray_API[296])
#define PyArray_ArgPartition \
(*(PyObject * (*)(PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND)) \
PyArray_API[297])
#define PyArray_SelectkindConverter \
(*(int (*)(PyObject *, NPY_SELECTKIND *)) \
PyArray_API[298])
#define PyDataMem_NEW_ZEROED \
(*(void * (*)(size_t, size_t)) \
PyArray_API[299])
#define PyArray_CheckAnyScalarExact \
(*(int (*)(PyObject *)) \
PyArray_API[300])
#define PyArray_MapIterArrayCopyIfOverlap \
(*(PyObject * (*)(PyArrayObject *, PyObject *, int, PyArrayObject *)) \
PyArray_API[301])
#define PyArray_ResolveWritebackIfCopy \
(*(int (*)(PyArrayObject *)) \
PyArray_API[302])
#define PyArray_SetWritebackIfCopyBase \
(*(int (*)(PyArrayObject *, PyArrayObject *)) \
PyArray_API[303])
#define PyDataMem_SetHandler \
(*(PyObject * (*)(PyObject *)) \
PyArray_API[304])
#define PyDataMem_GetHandler \
(*(PyObject * (*)(void)) \
PyArray_API[305])
#define PyDataMem_DefaultHandler (*(PyObject* *)PyArray_API[306])
#if !defined(NO_IMPORT_ARRAY) && !defined(NO_IMPORT)
static int
_import_array(void)
{
int st;
PyObject *numpy = PyImport_ImportModule("numpy.core._multiarray_umath");
PyObject *c_api = NULL;
if (numpy == NULL) {
return -1;
}
c_api = PyObject_GetAttrString(numpy, "_ARRAY_API");
Py_DECREF(numpy);
if (c_api == NULL) {
PyErr_SetString(PyExc_AttributeError, "_ARRAY_API not found");
return -1;
}
if (!PyCapsule_CheckExact(c_api)) {
PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is not PyCapsule object");
Py_DECREF(c_api);
return -1;
}
PyArray_API = (void **)PyCapsule_GetPointer(c_api, NULL);
Py_DECREF(c_api);
if (PyArray_API == NULL) {
PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is NULL pointer");
return -1;
}
/* Perform runtime check of C API version */
if (NPY_VERSION != PyArray_GetNDArrayCVersion()) {
PyErr_Format(PyExc_RuntimeError, "module compiled against "\
"ABI version 0x%x but this version of numpy is 0x%x", \
(int) NPY_VERSION, (int) PyArray_GetNDArrayCVersion());
return -1;
}
if (NPY_FEATURE_VERSION > PyArray_GetNDArrayCFeatureVersion()) {
PyErr_Format(PyExc_RuntimeError, "module compiled against "\
"API version 0x%x but this version of numpy is 0x%x", \
(int) NPY_FEATURE_VERSION, (int) PyArray_GetNDArrayCFeatureVersion());
return -1;
}
/*
* Perform runtime check of endianness and check it matches the one set by
* the headers (npy_endian.h) as a safeguard
*/
st = PyArray_GetEndianness();
if (st == NPY_CPU_UNKNOWN_ENDIAN) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as unknown endian");
return -1;
}
#if NPY_BYTE_ORDER == NPY_BIG_ENDIAN
if (st != NPY_CPU_BIG) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as "\
"big endian, but detected different endianness at runtime");
return -1;
}
#elif NPY_BYTE_ORDER == NPY_LITTLE_ENDIAN
if (st != NPY_CPU_LITTLE) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as "\
"little endian, but detected different endianness at runtime");
return -1;
}
#endif
return 0;
}
#define import_array() {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return NULL; } }
#define import_array1(ret) {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return ret; } }
#define import_array2(msg, ret) {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, msg); return ret; } }
#endif
#endif
| 62,314 | C | 39.099743 | 289 | 0.634079 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/noprefix.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_NOPREFIX_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NOPREFIX_H_
/*
* You can directly include noprefix.h as a backward
* compatibility measure
*/
#ifndef NPY_NO_PREFIX
#include "ndarrayobject.h"
#include "npy_interrupt.h"
#endif
#define SIGSETJMP NPY_SIGSETJMP
#define SIGLONGJMP NPY_SIGLONGJMP
#define SIGJMP_BUF NPY_SIGJMP_BUF
#define MAX_DIMS NPY_MAXDIMS
#define longlong npy_longlong
#define ulonglong npy_ulonglong
#define Bool npy_bool
#define longdouble npy_longdouble
#define byte npy_byte
#ifndef _BSD_SOURCE
#define ushort npy_ushort
#define uint npy_uint
#define ulong npy_ulong
#endif
#define ubyte npy_ubyte
#define ushort npy_ushort
#define uint npy_uint
#define ulong npy_ulong
#define cfloat npy_cfloat
#define cdouble npy_cdouble
#define clongdouble npy_clongdouble
#define Int8 npy_int8
#define UInt8 npy_uint8
#define Int16 npy_int16
#define UInt16 npy_uint16
#define Int32 npy_int32
#define UInt32 npy_uint32
#define Int64 npy_int64
#define UInt64 npy_uint64
#define Int128 npy_int128
#define UInt128 npy_uint128
#define Int256 npy_int256
#define UInt256 npy_uint256
#define Float16 npy_float16
#define Complex32 npy_complex32
#define Float32 npy_float32
#define Complex64 npy_complex64
#define Float64 npy_float64
#define Complex128 npy_complex128
#define Float80 npy_float80
#define Complex160 npy_complex160
#define Float96 npy_float96
#define Complex192 npy_complex192
#define Float128 npy_float128
#define Complex256 npy_complex256
#define intp npy_intp
#define uintp npy_uintp
#define datetime npy_datetime
#define timedelta npy_timedelta
#define SIZEOF_LONGLONG NPY_SIZEOF_LONGLONG
#define SIZEOF_INTP NPY_SIZEOF_INTP
#define SIZEOF_UINTP NPY_SIZEOF_UINTP
#define SIZEOF_HALF NPY_SIZEOF_HALF
#define SIZEOF_LONGDOUBLE NPY_SIZEOF_LONGDOUBLE
#define SIZEOF_DATETIME NPY_SIZEOF_DATETIME
#define SIZEOF_TIMEDELTA NPY_SIZEOF_TIMEDELTA
#define LONGLONG_FMT NPY_LONGLONG_FMT
#define ULONGLONG_FMT NPY_ULONGLONG_FMT
#define LONGLONG_SUFFIX NPY_LONGLONG_SUFFIX
#define ULONGLONG_SUFFIX NPY_ULONGLONG_SUFFIX
#define MAX_INT8 127
#define MIN_INT8 -128
#define MAX_UINT8 255
#define MAX_INT16 32767
#define MIN_INT16 -32768
#define MAX_UINT16 65535
#define MAX_INT32 2147483647
#define MIN_INT32 (-MAX_INT32 - 1)
#define MAX_UINT32 4294967295U
#define MAX_INT64 LONGLONG_SUFFIX(9223372036854775807)
#define MIN_INT64 (-MAX_INT64 - LONGLONG_SUFFIX(1))
#define MAX_UINT64 ULONGLONG_SUFFIX(18446744073709551615)
#define MAX_INT128 LONGLONG_SUFFIX(85070591730234615865843651857942052864)
#define MIN_INT128 (-MAX_INT128 - LONGLONG_SUFFIX(1))
#define MAX_UINT128 ULONGLONG_SUFFIX(170141183460469231731687303715884105728)
#define MAX_INT256 LONGLONG_SUFFIX(57896044618658097711785492504343953926634992332820282019728792003956564819967)
#define MIN_INT256 (-MAX_INT256 - LONGLONG_SUFFIX(1))
#define MAX_UINT256 ULONGLONG_SUFFIX(115792089237316195423570985008687907853269984665640564039457584007913129639935)
#define MAX_BYTE NPY_MAX_BYTE
#define MIN_BYTE NPY_MIN_BYTE
#define MAX_UBYTE NPY_MAX_UBYTE
#define MAX_SHORT NPY_MAX_SHORT
#define MIN_SHORT NPY_MIN_SHORT
#define MAX_USHORT NPY_MAX_USHORT
#define MAX_INT NPY_MAX_INT
#define MIN_INT NPY_MIN_INT
#define MAX_UINT NPY_MAX_UINT
#define MAX_LONG NPY_MAX_LONG
#define MIN_LONG NPY_MIN_LONG
#define MAX_ULONG NPY_MAX_ULONG
#define MAX_LONGLONG NPY_MAX_LONGLONG
#define MIN_LONGLONG NPY_MIN_LONGLONG
#define MAX_ULONGLONG NPY_MAX_ULONGLONG
#define MIN_DATETIME NPY_MIN_DATETIME
#define MAX_DATETIME NPY_MAX_DATETIME
#define MIN_TIMEDELTA NPY_MIN_TIMEDELTA
#define MAX_TIMEDELTA NPY_MAX_TIMEDELTA
#define BITSOF_BOOL NPY_BITSOF_BOOL
#define BITSOF_CHAR NPY_BITSOF_CHAR
#define BITSOF_SHORT NPY_BITSOF_SHORT
#define BITSOF_INT NPY_BITSOF_INT
#define BITSOF_LONG NPY_BITSOF_LONG
#define BITSOF_LONGLONG NPY_BITSOF_LONGLONG
#define BITSOF_HALF NPY_BITSOF_HALF
#define BITSOF_FLOAT NPY_BITSOF_FLOAT
#define BITSOF_DOUBLE NPY_BITSOF_DOUBLE
#define BITSOF_LONGDOUBLE NPY_BITSOF_LONGDOUBLE
#define BITSOF_DATETIME NPY_BITSOF_DATETIME
#define BITSOF_TIMEDELTA NPY_BITSOF_TIMEDELTA
#define _pya_malloc PyArray_malloc
#define _pya_free PyArray_free
#define _pya_realloc PyArray_realloc
#define BEGIN_THREADS_DEF NPY_BEGIN_THREADS_DEF
#define BEGIN_THREADS NPY_BEGIN_THREADS
#define END_THREADS NPY_END_THREADS
#define ALLOW_C_API_DEF NPY_ALLOW_C_API_DEF
#define ALLOW_C_API NPY_ALLOW_C_API
#define DISABLE_C_API NPY_DISABLE_C_API
#define PY_FAIL NPY_FAIL
#define PY_SUCCEED NPY_SUCCEED
#ifndef TRUE
#define TRUE NPY_TRUE
#endif
#ifndef FALSE
#define FALSE NPY_FALSE
#endif
#define LONGDOUBLE_FMT NPY_LONGDOUBLE_FMT
#define CONTIGUOUS NPY_CONTIGUOUS
#define C_CONTIGUOUS NPY_C_CONTIGUOUS
#define FORTRAN NPY_FORTRAN
#define F_CONTIGUOUS NPY_F_CONTIGUOUS
#define OWNDATA NPY_OWNDATA
#define FORCECAST NPY_FORCECAST
#define ENSURECOPY NPY_ENSURECOPY
#define ENSUREARRAY NPY_ENSUREARRAY
#define ELEMENTSTRIDES NPY_ELEMENTSTRIDES
#define ALIGNED NPY_ALIGNED
#define NOTSWAPPED NPY_NOTSWAPPED
#define WRITEABLE NPY_WRITEABLE
#define WRITEBACKIFCOPY NPY_ARRAY_WRITEBACKIFCOPY
#define ARR_HAS_DESCR NPY_ARR_HAS_DESCR
#define BEHAVED NPY_BEHAVED
#define BEHAVED_NS NPY_BEHAVED_NS
#define CARRAY NPY_CARRAY
#define CARRAY_RO NPY_CARRAY_RO
#define FARRAY NPY_FARRAY
#define FARRAY_RO NPY_FARRAY_RO
#define DEFAULT NPY_DEFAULT
#define IN_ARRAY NPY_IN_ARRAY
#define OUT_ARRAY NPY_OUT_ARRAY
#define INOUT_ARRAY NPY_INOUT_ARRAY
#define IN_FARRAY NPY_IN_FARRAY
#define OUT_FARRAY NPY_OUT_FARRAY
#define INOUT_FARRAY NPY_INOUT_FARRAY
#define UPDATE_ALL NPY_UPDATE_ALL
#define OWN_DATA NPY_OWNDATA
#define BEHAVED_FLAGS NPY_BEHAVED
#define BEHAVED_FLAGS_NS NPY_BEHAVED_NS
#define CARRAY_FLAGS_RO NPY_CARRAY_RO
#define CARRAY_FLAGS NPY_CARRAY
#define FARRAY_FLAGS NPY_FARRAY
#define FARRAY_FLAGS_RO NPY_FARRAY_RO
#define DEFAULT_FLAGS NPY_DEFAULT
#define UPDATE_ALL_FLAGS NPY_UPDATE_ALL_FLAGS
#ifndef MIN
#define MIN PyArray_MIN
#endif
#ifndef MAX
#define MAX PyArray_MAX
#endif
#define MAX_INTP NPY_MAX_INTP
#define MIN_INTP NPY_MIN_INTP
#define MAX_UINTP NPY_MAX_UINTP
#define INTP_FMT NPY_INTP_FMT
#ifndef PYPY_VERSION
#define REFCOUNT PyArray_REFCOUNT
#define MAX_ELSIZE NPY_MAX_ELSIZE
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NOPREFIX_H_ */
| 6,830 | C | 31.221698 | 116 | 0.736896 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/npy_interrupt.h | /*
* This API is only provided because it is part of publicly exported
* headers. Its use is considered DEPRECATED, and it will be removed
* eventually.
* (This includes the _PyArray_SigintHandler and _PyArray_GetSigintBuf
* functions which are however, public API, and not headers.)
*
* Instead of using these non-threadsafe macros consider periodically
* querying `PyErr_CheckSignals()` or `PyOS_InterruptOccurred()` will work.
* Both of these require holding the GIL, although cpython could add a
* version of `PyOS_InterruptOccurred()` which does not. Such a version
* actually exists as private API in Python 3.10, and backported to 3.9 and 3.8,
* see also https://bugs.python.org/issue41037 and
* https://github.com/python/cpython/pull/20599).
*/
#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_INTERRUPT_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NPY_INTERRUPT_H_
#ifndef NPY_NO_SIGNAL
#include <setjmp.h>
#include <signal.h>
#ifndef sigsetjmp
#define NPY_SIGSETJMP(arg1, arg2) setjmp(arg1)
#define NPY_SIGLONGJMP(arg1, arg2) longjmp(arg1, arg2)
#define NPY_SIGJMP_BUF jmp_buf
#else
#define NPY_SIGSETJMP(arg1, arg2) sigsetjmp(arg1, arg2)
#define NPY_SIGLONGJMP(arg1, arg2) siglongjmp(arg1, arg2)
#define NPY_SIGJMP_BUF sigjmp_buf
#endif
# define NPY_SIGINT_ON { \
PyOS_sighandler_t _npy_sig_save; \
_npy_sig_save = PyOS_setsig(SIGINT, _PyArray_SigintHandler); \
if (NPY_SIGSETJMP(*((NPY_SIGJMP_BUF *)_PyArray_GetSigintBuf()), \
1) == 0) { \
# define NPY_SIGINT_OFF } \
PyOS_setsig(SIGINT, _npy_sig_save); \
}
#else /* NPY_NO_SIGNAL */
#define NPY_SIGINT_ON
#define NPY_SIGINT_OFF
#endif /* HAVE_SIGSETJMP */
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_INTERRUPT_H_ */
| 1,948 | C | 33.192982 | 84 | 0.63039 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/utils.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_
#define NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_
#ifndef __COMP_NPY_UNUSED
#if defined(__GNUC__)
#define __COMP_NPY_UNUSED __attribute__ ((__unused__))
#elif defined(__ICC)
#define __COMP_NPY_UNUSED __attribute__ ((__unused__))
#elif defined(__clang__)
#define __COMP_NPY_UNUSED __attribute__ ((unused))
#else
#define __COMP_NPY_UNUSED
#endif
#endif
#if defined(__GNUC__) || defined(__ICC) || defined(__clang__)
#define NPY_DECL_ALIGNED(x) __attribute__ ((aligned (x)))
#elif defined(_MSC_VER)
#define NPY_DECL_ALIGNED(x) __declspec(align(x))
#else
#define NPY_DECL_ALIGNED(x)
#endif
/* Use this to tag a variable as not used. It will remove unused variable
* warning on support platforms (see __COM_NPY_UNUSED) and mangle the variable
* to avoid accidental use */
#define NPY_UNUSED(x) __NPY_UNUSED_TAGGED ## x __COMP_NPY_UNUSED
#define NPY_EXPAND(x) x
#define NPY_STRINGIFY(x) #x
#define NPY_TOSTRING(x) NPY_STRINGIFY(x)
#define NPY_CAT__(a, b) a ## b
#define NPY_CAT_(a, b) NPY_CAT__(a, b)
#define NPY_CAT(a, b) NPY_CAT_(a, b)
#endif /* NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_ */
| 1,185 | C | 30.210526 | 78 | 0.640506 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/ndarrayobject.h | /*
* DON'T INCLUDE THIS DIRECTLY.
*/
#ifndef NUMPY_CORE_INCLUDE_NUMPY_NDARRAYOBJECT_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NDARRAYOBJECT_H_
#ifdef __cplusplus
extern "C" {
#endif
#include <Python.h>
#include "ndarraytypes.h"
/* Includes the "function" C-API -- these are all stored in a
list of pointers --- one for each file
The two lists are concatenated into one in multiarray.
They are available as import_array()
*/
#include "__multiarray_api.h"
/* C-API that requires previous API to be defined */
#define PyArray_DescrCheck(op) PyObject_TypeCheck(op, &PyArrayDescr_Type)
#define PyArray_Check(op) PyObject_TypeCheck(op, &PyArray_Type)
#define PyArray_CheckExact(op) (((PyObject*)(op))->ob_type == &PyArray_Type)
#define PyArray_HasArrayInterfaceType(op, type, context, out) \
((((out)=PyArray_FromStructInterface(op)) != Py_NotImplemented) || \
(((out)=PyArray_FromInterface(op)) != Py_NotImplemented) || \
(((out)=PyArray_FromArrayAttr(op, type, context)) != \
Py_NotImplemented))
#define PyArray_HasArrayInterface(op, out) \
PyArray_HasArrayInterfaceType(op, NULL, NULL, out)
#define PyArray_IsZeroDim(op) (PyArray_Check(op) && \
(PyArray_NDIM((PyArrayObject *)op) == 0))
#define PyArray_IsScalar(obj, cls) \
(PyObject_TypeCheck(obj, &Py##cls##ArrType_Type))
#define PyArray_CheckScalar(m) (PyArray_IsScalar(m, Generic) || \
PyArray_IsZeroDim(m))
#define PyArray_IsPythonNumber(obj) \
(PyFloat_Check(obj) || PyComplex_Check(obj) || \
PyLong_Check(obj) || PyBool_Check(obj))
#define PyArray_IsIntegerScalar(obj) (PyLong_Check(obj) \
|| PyArray_IsScalar((obj), Integer))
#define PyArray_IsPythonScalar(obj) \
(PyArray_IsPythonNumber(obj) || PyBytes_Check(obj) || \
PyUnicode_Check(obj))
#define PyArray_IsAnyScalar(obj) \
(PyArray_IsScalar(obj, Generic) || PyArray_IsPythonScalar(obj))
#define PyArray_CheckAnyScalar(obj) (PyArray_IsPythonScalar(obj) || \
PyArray_CheckScalar(obj))
#define PyArray_GETCONTIGUOUS(m) (PyArray_ISCONTIGUOUS(m) ? \
Py_INCREF(m), (m) : \
(PyArrayObject *)(PyArray_Copy(m)))
#define PyArray_SAMESHAPE(a1,a2) ((PyArray_NDIM(a1) == PyArray_NDIM(a2)) && \
PyArray_CompareLists(PyArray_DIMS(a1), \
PyArray_DIMS(a2), \
PyArray_NDIM(a1)))
#define PyArray_SIZE(m) PyArray_MultiplyList(PyArray_DIMS(m), PyArray_NDIM(m))
#define PyArray_NBYTES(m) (PyArray_ITEMSIZE(m) * PyArray_SIZE(m))
#define PyArray_FROM_O(m) PyArray_FromAny(m, NULL, 0, 0, 0, NULL)
#define PyArray_FROM_OF(m,flags) PyArray_CheckFromAny(m, NULL, 0, 0, flags, \
NULL)
#define PyArray_FROM_OT(m,type) PyArray_FromAny(m, \
PyArray_DescrFromType(type), 0, 0, 0, NULL)
#define PyArray_FROM_OTF(m, type, flags) \
PyArray_FromAny(m, PyArray_DescrFromType(type), 0, 0, \
(((flags) & NPY_ARRAY_ENSURECOPY) ? \
((flags) | NPY_ARRAY_DEFAULT) : (flags)), NULL)
#define PyArray_FROMANY(m, type, min, max, flags) \
PyArray_FromAny(m, PyArray_DescrFromType(type), min, max, \
(((flags) & NPY_ARRAY_ENSURECOPY) ? \
(flags) | NPY_ARRAY_DEFAULT : (flags)), NULL)
#define PyArray_ZEROS(m, dims, type, is_f_order) \
PyArray_Zeros(m, dims, PyArray_DescrFromType(type), is_f_order)
#define PyArray_EMPTY(m, dims, type, is_f_order) \
PyArray_Empty(m, dims, PyArray_DescrFromType(type), is_f_order)
#define PyArray_FILLWBYTE(obj, val) memset(PyArray_DATA(obj), val, \
PyArray_NBYTES(obj))
#ifndef PYPY_VERSION
#define PyArray_REFCOUNT(obj) (((PyObject *)(obj))->ob_refcnt)
#define NPY_REFCOUNT PyArray_REFCOUNT
#endif
#define NPY_MAX_ELSIZE (2 * NPY_SIZEOF_LONGDOUBLE)
#define PyArray_ContiguousFromAny(op, type, min_depth, max_depth) \
PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \
max_depth, NPY_ARRAY_DEFAULT, NULL)
#define PyArray_EquivArrTypes(a1, a2) \
PyArray_EquivTypes(PyArray_DESCR(a1), PyArray_DESCR(a2))
#define PyArray_EquivByteorders(b1, b2) \
(((b1) == (b2)) || (PyArray_ISNBO(b1) == PyArray_ISNBO(b2)))
#define PyArray_SimpleNew(nd, dims, typenum) \
PyArray_New(&PyArray_Type, nd, dims, typenum, NULL, NULL, 0, 0, NULL)
#define PyArray_SimpleNewFromData(nd, dims, typenum, data) \
PyArray_New(&PyArray_Type, nd, dims, typenum, NULL, \
data, 0, NPY_ARRAY_CARRAY, NULL)
#define PyArray_SimpleNewFromDescr(nd, dims, descr) \
PyArray_NewFromDescr(&PyArray_Type, descr, nd, dims, \
NULL, NULL, 0, NULL)
#define PyArray_ToScalar(data, arr) \
PyArray_Scalar(data, PyArray_DESCR(arr), (PyObject *)arr)
/* These might be faster without the dereferencing of obj
going on inside -- of course an optimizing compiler should
inline the constants inside a for loop making it a moot point
*/
#define PyArray_GETPTR1(obj, i) ((void *)(PyArray_BYTES(obj) + \
(i)*PyArray_STRIDES(obj)[0]))
#define PyArray_GETPTR2(obj, i, j) ((void *)(PyArray_BYTES(obj) + \
(i)*PyArray_STRIDES(obj)[0] + \
(j)*PyArray_STRIDES(obj)[1]))
#define PyArray_GETPTR3(obj, i, j, k) ((void *)(PyArray_BYTES(obj) + \
(i)*PyArray_STRIDES(obj)[0] + \
(j)*PyArray_STRIDES(obj)[1] + \
(k)*PyArray_STRIDES(obj)[2]))
#define PyArray_GETPTR4(obj, i, j, k, l) ((void *)(PyArray_BYTES(obj) + \
(i)*PyArray_STRIDES(obj)[0] + \
(j)*PyArray_STRIDES(obj)[1] + \
(k)*PyArray_STRIDES(obj)[2] + \
(l)*PyArray_STRIDES(obj)[3]))
static NPY_INLINE void
PyArray_DiscardWritebackIfCopy(PyArrayObject *arr)
{
PyArrayObject_fields *fa = (PyArrayObject_fields *)arr;
if (fa && fa->base) {
if (fa->flags & NPY_ARRAY_WRITEBACKIFCOPY) {
PyArray_ENABLEFLAGS((PyArrayObject*)fa->base, NPY_ARRAY_WRITEABLE);
Py_DECREF(fa->base);
fa->base = NULL;
PyArray_CLEARFLAGS(arr, NPY_ARRAY_WRITEBACKIFCOPY);
}
}
}
#define PyArray_DESCR_REPLACE(descr) do { \
PyArray_Descr *_new_; \
_new_ = PyArray_DescrNew(descr); \
Py_XDECREF(descr); \
descr = _new_; \
} while(0)
/* Copy should always return contiguous array */
#define PyArray_Copy(obj) PyArray_NewCopy(obj, NPY_CORDER)
#define PyArray_FromObject(op, type, min_depth, max_depth) \
PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \
max_depth, NPY_ARRAY_BEHAVED | \
NPY_ARRAY_ENSUREARRAY, NULL)
#define PyArray_ContiguousFromObject(op, type, min_depth, max_depth) \
PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \
max_depth, NPY_ARRAY_DEFAULT | \
NPY_ARRAY_ENSUREARRAY, NULL)
#define PyArray_CopyFromObject(op, type, min_depth, max_depth) \
PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \
max_depth, NPY_ARRAY_ENSURECOPY | \
NPY_ARRAY_DEFAULT | \
NPY_ARRAY_ENSUREARRAY, NULL)
#define PyArray_Cast(mp, type_num) \
PyArray_CastToType(mp, PyArray_DescrFromType(type_num), 0)
#define PyArray_Take(ap, items, axis) \
PyArray_TakeFrom(ap, items, axis, NULL, NPY_RAISE)
#define PyArray_Put(ap, items, values) \
PyArray_PutTo(ap, items, values, NPY_RAISE)
/* Compatibility with old Numeric stuff -- don't use in new code */
#define PyArray_FromDimsAndData(nd, d, type, data) \
PyArray_FromDimsAndDataAndDescr(nd, d, PyArray_DescrFromType(type), \
data)
/*
Check to see if this key in the dictionary is the "title"
entry of the tuple (i.e. a duplicate dictionary entry in the fields
dict).
*/
static NPY_INLINE int
NPY_TITLE_KEY_check(PyObject *key, PyObject *value)
{
PyObject *title;
if (PyTuple_Size(value) != 3) {
return 0;
}
title = PyTuple_GetItem(value, 2);
if (key == title) {
return 1;
}
#ifdef PYPY_VERSION
/*
* On PyPy, dictionary keys do not always preserve object identity.
* Fall back to comparison by value.
*/
if (PyUnicode_Check(title) && PyUnicode_Check(key)) {
return PyUnicode_Compare(title, key) == 0 ? 1 : 0;
}
#endif
return 0;
}
/* Macro, for backward compat with "if NPY_TITLE_KEY(key, value) { ..." */
#define NPY_TITLE_KEY(key, value) (NPY_TITLE_KEY_check((key), (value)))
#define DEPRECATE(msg) PyErr_WarnEx(PyExc_DeprecationWarning,msg,1)
#define DEPRECATE_FUTUREWARNING(msg) PyErr_WarnEx(PyExc_FutureWarning,msg,1)
#ifdef __cplusplus
}
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NDARRAYOBJECT_H_ */
| 10,191 | C | 39.444444 | 79 | 0.546266 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/npy_os.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_
#if defined(linux) || defined(__linux) || defined(__linux__)
#define NPY_OS_LINUX
#elif defined(__FreeBSD__) || defined(__NetBSD__) || \
defined(__OpenBSD__) || defined(__DragonFly__)
#define NPY_OS_BSD
#ifdef __FreeBSD__
#define NPY_OS_FREEBSD
#elif defined(__NetBSD__)
#define NPY_OS_NETBSD
#elif defined(__OpenBSD__)
#define NPY_OS_OPENBSD
#elif defined(__DragonFly__)
#define NPY_OS_DRAGONFLY
#endif
#elif defined(sun) || defined(__sun)
#define NPY_OS_SOLARIS
#elif defined(__CYGWIN__)
#define NPY_OS_CYGWIN
#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32)
#define NPY_OS_WIN32
#elif defined(_WIN64) || defined(__WIN64__) || defined(WIN64)
#define NPY_OS_WIN64
#elif defined(__MINGW32__) || defined(__MINGW64__)
#define NPY_OS_MINGW
#elif defined(__APPLE__)
#define NPY_OS_DARWIN
#else
#define NPY_OS_UNKNOWN
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_ */
| 1,070 | C | 29.599999 | 61 | 0.624299 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/numpyconfig.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_
#include "_numpyconfig.h"
/*
* On Mac OS X, because there is only one configuration stage for all the archs
* in universal builds, any macro which depends on the arch needs to be
* hardcoded
*/
#ifdef __APPLE__
#undef NPY_SIZEOF_LONG
#undef NPY_SIZEOF_PY_INTPTR_T
#ifdef __LP64__
#define NPY_SIZEOF_LONG 8
#define NPY_SIZEOF_PY_INTPTR_T 8
#else
#define NPY_SIZEOF_LONG 4
#define NPY_SIZEOF_PY_INTPTR_T 4
#endif
#undef NPY_SIZEOF_LONGDOUBLE
#undef NPY_SIZEOF_COMPLEX_LONGDOUBLE
#if defined(__arm64__)
#define NPY_SIZEOF_LONGDOUBLE 8
#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 16
#elif defined(__x86_64)
#define NPY_SIZEOF_LONGDOUBLE 16
#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32
#elif defined (__i386)
#define NPY_SIZEOF_LONGDOUBLE 12
#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 24
#elif defined(__ppc__) || defined (__ppc64__)
#define NPY_SIZEOF_LONGDOUBLE 16
#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32
#else
#error "unknown architecture"
#endif
#endif
/**
* To help with the NPY_NO_DEPRECATED_API macro, we include API version
* numbers for specific versions of NumPy. To exclude all API that was
* deprecated as of 1.7, add the following before #including any NumPy
* headers:
* #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
*/
#define NPY_1_7_API_VERSION 0x00000007
#define NPY_1_8_API_VERSION 0x00000008
#define NPY_1_9_API_VERSION 0x00000008
#define NPY_1_10_API_VERSION 0x00000008
#define NPY_1_11_API_VERSION 0x00000008
#define NPY_1_12_API_VERSION 0x00000008
#define NPY_1_13_API_VERSION 0x00000008
#define NPY_1_14_API_VERSION 0x00000008
#define NPY_1_15_API_VERSION 0x00000008
#define NPY_1_16_API_VERSION 0x00000008
#define NPY_1_17_API_VERSION 0x00000008
#define NPY_1_18_API_VERSION 0x00000008
#define NPY_1_19_API_VERSION 0x00000008
#define NPY_1_20_API_VERSION 0x0000000e
#define NPY_1_21_API_VERSION 0x0000000e
#define NPY_1_22_API_VERSION 0x0000000f
#define NPY_1_23_API_VERSION 0x00000010
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_ */
| 2,284 | C | 32.115942 | 79 | 0.694396 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/ufuncobject.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_
#define NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_
#include <numpy/npy_math.h>
#include <numpy/npy_common.h>
#ifdef __cplusplus
extern "C" {
#endif
/*
* The legacy generic inner loop for a standard element-wise or
* generalized ufunc.
*/
typedef void (*PyUFuncGenericFunction)
(char **args,
npy_intp const *dimensions,
npy_intp const *strides,
void *innerloopdata);
/*
* The most generic one-dimensional inner loop for
* a masked standard element-wise ufunc. "Masked" here means that it skips
* doing calculations on any items for which the maskptr array has a true
* value.
*/
typedef void (PyUFunc_MaskedStridedInnerLoopFunc)(
char **dataptrs, npy_intp *strides,
char *maskptr, npy_intp mask_stride,
npy_intp count,
NpyAuxData *innerloopdata);
/* Forward declaration for the type resolver and loop selector typedefs */
struct _tagPyUFuncObject;
/*
* Given the operands for calling a ufunc, should determine the
* calculation input and output data types and return an inner loop function.
* This function should validate that the casting rule is being followed,
* and fail if it is not.
*
* For backwards compatibility, the regular type resolution function does not
* support auxiliary data with object semantics. The type resolution call
* which returns a masked generic function returns a standard NpyAuxData
* object, for which the NPY_AUXDATA_FREE and NPY_AUXDATA_CLONE macros
* work.
*
* ufunc: The ufunc object.
* casting: The 'casting' parameter provided to the ufunc.
* operands: An array of length (ufunc->nin + ufunc->nout),
* with the output parameters possibly NULL.
* type_tup: Either NULL, or the type_tup passed to the ufunc.
* out_dtypes: An array which should be populated with new
* references to (ufunc->nin + ufunc->nout) new
* dtypes, one for each input and output. These
* dtypes should all be in native-endian format.
*
* Should return 0 on success, -1 on failure (with exception set),
* or -2 if Py_NotImplemented should be returned.
*/
typedef int (PyUFunc_TypeResolutionFunc)(
struct _tagPyUFuncObject *ufunc,
NPY_CASTING casting,
PyArrayObject **operands,
PyObject *type_tup,
PyArray_Descr **out_dtypes);
/*
* Legacy loop selector. (This should NOT normally be used and we can expect
* that only the `PyUFunc_DefaultLegacyInnerLoopSelector` is ever set).
* However, unlike the masked version, it probably still works.
*
* ufunc: The ufunc object.
* dtypes: An array which has been populated with dtypes,
* in most cases by the type resolution function
* for the same ufunc.
* out_innerloop: Should be populated with the correct ufunc inner
* loop for the given type.
* out_innerloopdata: Should be populated with the void* data to
* be passed into the out_innerloop function.
* out_needs_api: If the inner loop needs to use the Python API,
* should set the to 1, otherwise should leave
* this untouched.
*/
typedef int (PyUFunc_LegacyInnerLoopSelectionFunc)(
struct _tagPyUFuncObject *ufunc,
PyArray_Descr **dtypes,
PyUFuncGenericFunction *out_innerloop,
void **out_innerloopdata,
int *out_needs_api);
typedef struct _tagPyUFuncObject {
PyObject_HEAD
/*
* nin: Number of inputs
* nout: Number of outputs
* nargs: Always nin + nout (Why is it stored?)
*/
int nin, nout, nargs;
/*
* Identity for reduction, any of PyUFunc_One, PyUFunc_Zero
* PyUFunc_MinusOne, PyUFunc_None, PyUFunc_ReorderableNone,
* PyUFunc_IdentityValue.
*/
int identity;
/* Array of one-dimensional core loops */
PyUFuncGenericFunction *functions;
/* Array of funcdata that gets passed into the functions */
void **data;
/* The number of elements in 'functions' and 'data' */
int ntypes;
/* Used to be unused field 'check_return' */
int reserved1;
/* The name of the ufunc */
const char *name;
/* Array of type numbers, of size ('nargs' * 'ntypes') */
char *types;
/* Documentation string */
const char *doc;
void *ptr;
PyObject *obj;
PyObject *userloops;
/* generalized ufunc parameters */
/* 0 for scalar ufunc; 1 for generalized ufunc */
int core_enabled;
/* number of distinct dimension names in signature */
int core_num_dim_ix;
/*
* dimension indices of input/output argument k are stored in
* core_dim_ixs[core_offsets[k]..core_offsets[k]+core_num_dims[k]-1]
*/
/* numbers of core dimensions of each argument */
int *core_num_dims;
/*
* dimension indices in a flatted form; indices
* are in the range of [0,core_num_dim_ix)
*/
int *core_dim_ixs;
/*
* positions of 1st core dimensions of each
* argument in core_dim_ixs, equivalent to cumsum(core_num_dims)
*/
int *core_offsets;
/* signature string for printing purpose */
char *core_signature;
/*
* A function which resolves the types and fills an array
* with the dtypes for the inputs and outputs.
*/
PyUFunc_TypeResolutionFunc *type_resolver;
/*
* A function which returns an inner loop written for
* NumPy 1.6 and earlier ufuncs. This is for backwards
* compatibility, and may be NULL if inner_loop_selector
* is specified.
*/
PyUFunc_LegacyInnerLoopSelectionFunc *legacy_inner_loop_selector;
/*
* This was blocked off to be the "new" inner loop selector in 1.7,
* but this was never implemented. (This is also why the above
* selector is called the "legacy" selector.)
*/
#ifndef Py_LIMITED_API
vectorcallfunc vectorcall;
#else
void *vectorcall;
#endif
/* Was previously the `PyUFunc_MaskedInnerLoopSelectionFunc` */
void *_always_null_previously_masked_innerloop_selector;
/*
* List of flags for each operand when ufunc is called by nditer object.
* These flags will be used in addition to the default flags for each
* operand set by nditer object.
*/
npy_uint32 *op_flags;
/*
* List of global flags used when ufunc is called by nditer object.
* These flags will be used in addition to the default global flags
* set by nditer object.
*/
npy_uint32 iter_flags;
/* New in NPY_API_VERSION 0x0000000D and above */
/*
* for each core_num_dim_ix distinct dimension names,
* the possible "frozen" size (-1 if not frozen).
*/
npy_intp *core_dim_sizes;
/*
* for each distinct core dimension, a set of UFUNC_CORE_DIM* flags
*/
npy_uint32 *core_dim_flags;
/* Identity for reduction, when identity == PyUFunc_IdentityValue */
PyObject *identity_value;
/* New in NPY_API_VERSION 0x0000000F and above */
/* New private fields related to dispatching */
void *_dispatch_cache;
/* A PyListObject of `(tuple of DTypes, ArrayMethod/Promoter)` */
PyObject *_loops;
} PyUFuncObject;
#include "arrayobject.h"
/* Generalized ufunc; 0x0001 reserved for possible use as CORE_ENABLED */
/* the core dimension's size will be determined by the operands. */
#define UFUNC_CORE_DIM_SIZE_INFERRED 0x0002
/* the core dimension may be absent */
#define UFUNC_CORE_DIM_CAN_IGNORE 0x0004
/* flags inferred during execution */
#define UFUNC_CORE_DIM_MISSING 0x00040000
#define UFUNC_ERR_IGNORE 0
#define UFUNC_ERR_WARN 1
#define UFUNC_ERR_RAISE 2
#define UFUNC_ERR_CALL 3
#define UFUNC_ERR_PRINT 4
#define UFUNC_ERR_LOG 5
/* Python side integer mask */
#define UFUNC_MASK_DIVIDEBYZERO 0x07
#define UFUNC_MASK_OVERFLOW 0x3f
#define UFUNC_MASK_UNDERFLOW 0x1ff
#define UFUNC_MASK_INVALID 0xfff
#define UFUNC_SHIFT_DIVIDEBYZERO 0
#define UFUNC_SHIFT_OVERFLOW 3
#define UFUNC_SHIFT_UNDERFLOW 6
#define UFUNC_SHIFT_INVALID 9
#define UFUNC_OBJ_ISOBJECT 1
#define UFUNC_OBJ_NEEDS_API 2
/* Default user error mode */
#define UFUNC_ERR_DEFAULT \
(UFUNC_ERR_WARN << UFUNC_SHIFT_DIVIDEBYZERO) + \
(UFUNC_ERR_WARN << UFUNC_SHIFT_OVERFLOW) + \
(UFUNC_ERR_WARN << UFUNC_SHIFT_INVALID)
#if NPY_ALLOW_THREADS
#define NPY_LOOP_BEGIN_THREADS do {if (!(loop->obj & UFUNC_OBJ_NEEDS_API)) _save = PyEval_SaveThread();} while (0);
#define NPY_LOOP_END_THREADS do {if (!(loop->obj & UFUNC_OBJ_NEEDS_API)) PyEval_RestoreThread(_save);} while (0);
#else
#define NPY_LOOP_BEGIN_THREADS
#define NPY_LOOP_END_THREADS
#endif
/*
* UFunc has unit of 0, and the order of operations can be reordered
* This case allows reduction with multiple axes at once.
*/
#define PyUFunc_Zero 0
/*
* UFunc has unit of 1, and the order of operations can be reordered
* This case allows reduction with multiple axes at once.
*/
#define PyUFunc_One 1
/*
* UFunc has unit of -1, and the order of operations can be reordered
* This case allows reduction with multiple axes at once. Intended for
* bitwise_and reduction.
*/
#define PyUFunc_MinusOne 2
/*
* UFunc has no unit, and the order of operations cannot be reordered.
* This case does not allow reduction with multiple axes at once.
*/
#define PyUFunc_None -1
/*
* UFunc has no unit, and the order of operations can be reordered
* This case allows reduction with multiple axes at once.
*/
#define PyUFunc_ReorderableNone -2
/*
* UFunc unit is an identity_value, and the order of operations can be reordered
* This case allows reduction with multiple axes at once.
*/
#define PyUFunc_IdentityValue -3
#define UFUNC_REDUCE 0
#define UFUNC_ACCUMULATE 1
#define UFUNC_REDUCEAT 2
#define UFUNC_OUTER 3
typedef struct {
int nin;
int nout;
PyObject *callable;
} PyUFunc_PyFuncData;
/* A linked-list of function information for
user-defined 1-d loops.
*/
typedef struct _loop1d_info {
PyUFuncGenericFunction func;
void *data;
int *arg_types;
struct _loop1d_info *next;
int nargs;
PyArray_Descr **arg_dtypes;
} PyUFunc_Loop1d;
#include "__ufunc_api.h"
#define UFUNC_PYVALS_NAME "UFUNC_PYVALS"
/*
* THESE MACROS ARE DEPRECATED.
* Use npy_set_floatstatus_* in the npymath library.
*/
#define UFUNC_FPE_DIVIDEBYZERO NPY_FPE_DIVIDEBYZERO
#define UFUNC_FPE_OVERFLOW NPY_FPE_OVERFLOW
#define UFUNC_FPE_UNDERFLOW NPY_FPE_UNDERFLOW
#define UFUNC_FPE_INVALID NPY_FPE_INVALID
#define generate_divbyzero_error() npy_set_floatstatus_divbyzero()
#define generate_overflow_error() npy_set_floatstatus_overflow()
/* Make sure it gets defined if it isn't already */
#ifndef UFUNC_NOFPE
/* Clear the floating point exception default of Borland C++ */
#if defined(__BORLANDC__)
#define UFUNC_NOFPE _control87(MCW_EM, MCW_EM);
#else
#define UFUNC_NOFPE
#endif
#endif
#ifdef __cplusplus
}
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_ */
| 11,895 | C | 32.22905 | 115 | 0.632282 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/npy_common.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_
#define NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_
/* need Python.h for npy_intp, npy_uintp */
#include <Python.h>
/* numpconfig.h is auto-generated */
#include "numpyconfig.h"
#ifdef HAVE_NPY_CONFIG_H
#include <npy_config.h>
#endif
/*
* using static inline modifiers when defining npy_math functions
* allows the compiler to make optimizations when possible
*/
#ifndef NPY_INLINE_MATH
#if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD
#define NPY_INLINE_MATH 1
#else
#define NPY_INLINE_MATH 0
#endif
#endif
/*
* gcc does not unroll even with -O3
* use with care, unrolling on modern cpus rarely speeds things up
*/
#ifdef HAVE_ATTRIBUTE_OPTIMIZE_UNROLL_LOOPS
#define NPY_GCC_UNROLL_LOOPS \
__attribute__((optimize("unroll-loops")))
#else
#define NPY_GCC_UNROLL_LOOPS
#endif
/* highest gcc optimization level, enabled autovectorizer */
#ifdef HAVE_ATTRIBUTE_OPTIMIZE_OPT_3
#define NPY_GCC_OPT_3 __attribute__((optimize("O3")))
#else
#define NPY_GCC_OPT_3
#endif
/* compile target attributes */
#if defined HAVE_ATTRIBUTE_TARGET_AVX && defined HAVE_LINK_AVX
#define NPY_GCC_TARGET_AVX __attribute__((target("avx")))
#else
#define NPY_GCC_TARGET_AVX
#endif
#if defined HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS
#define HAVE_ATTRIBUTE_TARGET_FMA
#define NPY_GCC_TARGET_FMA __attribute__((target("avx2,fma")))
#endif
#if defined HAVE_ATTRIBUTE_TARGET_AVX2 && defined HAVE_LINK_AVX2
#define NPY_GCC_TARGET_AVX2 __attribute__((target("avx2")))
#else
#define NPY_GCC_TARGET_AVX2
#endif
#if defined HAVE_ATTRIBUTE_TARGET_AVX512F && defined HAVE_LINK_AVX512F
#define NPY_GCC_TARGET_AVX512F __attribute__((target("avx512f")))
#elif defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS
#define NPY_GCC_TARGET_AVX512F __attribute__((target("avx512f")))
#else
#define NPY_GCC_TARGET_AVX512F
#endif
#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX && defined HAVE_LINK_AVX512_SKX
#define NPY_GCC_TARGET_AVX512_SKX __attribute__((target("avx512f,avx512dq,avx512vl,avx512bw,avx512cd")))
#elif defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS
#define NPY_GCC_TARGET_AVX512_SKX __attribute__((target("avx512f,avx512dq,avx512vl,avx512bw,avx512cd")))
#else
#define NPY_GCC_TARGET_AVX512_SKX
#endif
/*
* mark an argument (starting from 1) that must not be NULL and is not checked
* DO NOT USE IF FUNCTION CHECKS FOR NULL!! the compiler will remove the check
*/
#ifdef HAVE_ATTRIBUTE_NONNULL
#define NPY_GCC_NONNULL(n) __attribute__((nonnull(n)))
#else
#define NPY_GCC_NONNULL(n)
#endif
#if defined HAVE_XMMINTRIN_H && defined HAVE__MM_LOAD_PS
#define NPY_HAVE_SSE_INTRINSICS
#endif
#if defined HAVE_EMMINTRIN_H && defined HAVE__MM_LOAD_PD
#define NPY_HAVE_SSE2_INTRINSICS
#endif
#if defined HAVE_IMMINTRIN_H && defined HAVE_LINK_AVX2
#define NPY_HAVE_AVX2_INTRINSICS
#endif
#if defined HAVE_IMMINTRIN_H && defined HAVE_LINK_AVX512F
#define NPY_HAVE_AVX512F_INTRINSICS
#endif
/*
* give a hint to the compiler which branch is more likely or unlikely
* to occur, e.g. rare error cases:
*
* if (NPY_UNLIKELY(failure == 0))
* return NULL;
*
* the double !! is to cast the expression (e.g. NULL) to a boolean required by
* the intrinsic
*/
#ifdef HAVE___BUILTIN_EXPECT
#define NPY_LIKELY(x) __builtin_expect(!!(x), 1)
#define NPY_UNLIKELY(x) __builtin_expect(!!(x), 0)
#else
#define NPY_LIKELY(x) (x)
#define NPY_UNLIKELY(x) (x)
#endif
#ifdef HAVE___BUILTIN_PREFETCH
/* unlike _mm_prefetch also works on non-x86 */
#define NPY_PREFETCH(x, rw, loc) __builtin_prefetch((x), (rw), (loc))
#else
#ifdef HAVE__MM_PREFETCH
/* _MM_HINT_ET[01] (rw = 1) unsupported, only available in gcc >= 4.9 */
#define NPY_PREFETCH(x, rw, loc) _mm_prefetch((x), loc == 0 ? _MM_HINT_NTA : \
(loc == 1 ? _MM_HINT_T2 : \
(loc == 2 ? _MM_HINT_T1 : \
(loc == 3 ? _MM_HINT_T0 : -1))))
#else
#define NPY_PREFETCH(x, rw,loc)
#endif
#endif
#if defined(_MSC_VER) && !defined(__clang__)
#define NPY_INLINE __inline
/* clang included here to handle clang-cl on Windows */
#elif defined(__GNUC__) || defined(__clang__)
#if defined(__STRICT_ANSI__)
#define NPY_INLINE __inline__
#else
#define NPY_INLINE inline
#endif
#else
#define NPY_INLINE
#endif
#ifdef _MSC_VER
#define NPY_FINLINE static __forceinline
#elif defined(__GNUC__)
#define NPY_FINLINE static NPY_INLINE __attribute__((always_inline))
#else
#define NPY_FINLINE static
#endif
#ifdef HAVE___THREAD
#define NPY_TLS __thread
#else
#ifdef HAVE___DECLSPEC_THREAD_
#define NPY_TLS __declspec(thread)
#else
#define NPY_TLS
#endif
#endif
#ifdef WITH_CPYCHECKER_RETURNS_BORROWED_REF_ATTRIBUTE
#define NPY_RETURNS_BORROWED_REF \
__attribute__((cpychecker_returns_borrowed_ref))
#else
#define NPY_RETURNS_BORROWED_REF
#endif
#ifdef WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE
#define NPY_STEALS_REF_TO_ARG(n) \
__attribute__((cpychecker_steals_reference_to_arg(n)))
#else
#define NPY_STEALS_REF_TO_ARG(n)
#endif
/* 64 bit file position support, also on win-amd64. Ticket #1660 */
#if defined(_MSC_VER) && defined(_WIN64) && (_MSC_VER > 1400) || \
defined(__MINGW32__) || defined(__MINGW64__)
#include <io.h>
#define npy_fseek _fseeki64
#define npy_ftell _ftelli64
#define npy_lseek _lseeki64
#define npy_off_t npy_int64
#if NPY_SIZEOF_INT == 8
#define NPY_OFF_T_PYFMT "i"
#elif NPY_SIZEOF_LONG == 8
#define NPY_OFF_T_PYFMT "l"
#elif NPY_SIZEOF_LONGLONG == 8
#define NPY_OFF_T_PYFMT "L"
#else
#error Unsupported size for type off_t
#endif
#else
#ifdef HAVE_FSEEKO
#define npy_fseek fseeko
#else
#define npy_fseek fseek
#endif
#ifdef HAVE_FTELLO
#define npy_ftell ftello
#else
#define npy_ftell ftell
#endif
#include <sys/types.h>
#define npy_lseek lseek
#define npy_off_t off_t
#if NPY_SIZEOF_OFF_T == NPY_SIZEOF_SHORT
#define NPY_OFF_T_PYFMT "h"
#elif NPY_SIZEOF_OFF_T == NPY_SIZEOF_INT
#define NPY_OFF_T_PYFMT "i"
#elif NPY_SIZEOF_OFF_T == NPY_SIZEOF_LONG
#define NPY_OFF_T_PYFMT "l"
#elif NPY_SIZEOF_OFF_T == NPY_SIZEOF_LONGLONG
#define NPY_OFF_T_PYFMT "L"
#else
#error Unsupported size for type off_t
#endif
#endif
/* enums for detected endianness */
enum {
NPY_CPU_UNKNOWN_ENDIAN,
NPY_CPU_LITTLE,
NPY_CPU_BIG
};
/*
* This is to typedef npy_intp to the appropriate pointer size for this
* platform. Py_intptr_t, Py_uintptr_t are defined in pyport.h.
*/
typedef Py_intptr_t npy_intp;
typedef Py_uintptr_t npy_uintp;
/*
* Define sizes that were not defined in numpyconfig.h.
*/
#define NPY_SIZEOF_CHAR 1
#define NPY_SIZEOF_BYTE 1
#define NPY_SIZEOF_DATETIME 8
#define NPY_SIZEOF_TIMEDELTA 8
#define NPY_SIZEOF_INTP NPY_SIZEOF_PY_INTPTR_T
#define NPY_SIZEOF_UINTP NPY_SIZEOF_PY_INTPTR_T
#define NPY_SIZEOF_HALF 2
#define NPY_SIZEOF_CFLOAT NPY_SIZEOF_COMPLEX_FLOAT
#define NPY_SIZEOF_CDOUBLE NPY_SIZEOF_COMPLEX_DOUBLE
#define NPY_SIZEOF_CLONGDOUBLE NPY_SIZEOF_COMPLEX_LONGDOUBLE
#ifdef constchar
#undef constchar
#endif
#define NPY_SSIZE_T_PYFMT "n"
#define constchar char
/* NPY_INTP_FMT Note:
* Unlike the other NPY_*_FMT macros, which are used with PyOS_snprintf,
* NPY_INTP_FMT is used with PyErr_Format and PyUnicode_FromFormat. Those
* functions use different formatting codes that are portably specified
* according to the Python documentation. See issue gh-2388.
*/
#if NPY_SIZEOF_PY_INTPTR_T == NPY_SIZEOF_INT
#define NPY_INTP NPY_INT
#define NPY_UINTP NPY_UINT
#define PyIntpArrType_Type PyIntArrType_Type
#define PyUIntpArrType_Type PyUIntArrType_Type
#define NPY_MAX_INTP NPY_MAX_INT
#define NPY_MIN_INTP NPY_MIN_INT
#define NPY_MAX_UINTP NPY_MAX_UINT
#define NPY_INTP_FMT "d"
#elif NPY_SIZEOF_PY_INTPTR_T == NPY_SIZEOF_LONG
#define NPY_INTP NPY_LONG
#define NPY_UINTP NPY_ULONG
#define PyIntpArrType_Type PyLongArrType_Type
#define PyUIntpArrType_Type PyULongArrType_Type
#define NPY_MAX_INTP NPY_MAX_LONG
#define NPY_MIN_INTP NPY_MIN_LONG
#define NPY_MAX_UINTP NPY_MAX_ULONG
#define NPY_INTP_FMT "ld"
#elif defined(PY_LONG_LONG) && (NPY_SIZEOF_PY_INTPTR_T == NPY_SIZEOF_LONGLONG)
#define NPY_INTP NPY_LONGLONG
#define NPY_UINTP NPY_ULONGLONG
#define PyIntpArrType_Type PyLongLongArrType_Type
#define PyUIntpArrType_Type PyULongLongArrType_Type
#define NPY_MAX_INTP NPY_MAX_LONGLONG
#define NPY_MIN_INTP NPY_MIN_LONGLONG
#define NPY_MAX_UINTP NPY_MAX_ULONGLONG
#define NPY_INTP_FMT "lld"
#endif
/*
* We can only use C99 formats for npy_int_p if it is the same as
* intp_t, hence the condition on HAVE_UNITPTR_T
*/
#if (NPY_USE_C99_FORMATS) == 1 \
&& (defined HAVE_UINTPTR_T) \
&& (defined HAVE_INTTYPES_H)
#include <inttypes.h>
#undef NPY_INTP_FMT
#define NPY_INTP_FMT PRIdPTR
#endif
/*
* Some platforms don't define bool, long long, or long double.
* Handle that here.
*/
#define NPY_BYTE_FMT "hhd"
#define NPY_UBYTE_FMT "hhu"
#define NPY_SHORT_FMT "hd"
#define NPY_USHORT_FMT "hu"
#define NPY_INT_FMT "d"
#define NPY_UINT_FMT "u"
#define NPY_LONG_FMT "ld"
#define NPY_ULONG_FMT "lu"
#define NPY_HALF_FMT "g"
#define NPY_FLOAT_FMT "g"
#define NPY_DOUBLE_FMT "g"
#ifdef PY_LONG_LONG
typedef PY_LONG_LONG npy_longlong;
typedef unsigned PY_LONG_LONG npy_ulonglong;
# ifdef _MSC_VER
# define NPY_LONGLONG_FMT "I64d"
# define NPY_ULONGLONG_FMT "I64u"
# else
# define NPY_LONGLONG_FMT "lld"
# define NPY_ULONGLONG_FMT "llu"
# endif
# ifdef _MSC_VER
# define NPY_LONGLONG_SUFFIX(x) (x##i64)
# define NPY_ULONGLONG_SUFFIX(x) (x##Ui64)
# else
# define NPY_LONGLONG_SUFFIX(x) (x##LL)
# define NPY_ULONGLONG_SUFFIX(x) (x##ULL)
# endif
#else
typedef long npy_longlong;
typedef unsigned long npy_ulonglong;
# define NPY_LONGLONG_SUFFIX(x) (x##L)
# define NPY_ULONGLONG_SUFFIX(x) (x##UL)
#endif
typedef unsigned char npy_bool;
#define NPY_FALSE 0
#define NPY_TRUE 1
/*
* `NPY_SIZEOF_LONGDOUBLE` isn't usually equal to sizeof(long double).
* In some certain cases, it may forced to be equal to sizeof(double)
* even against the compiler implementation and the same goes for
* `complex long double`.
*
* Therefore, avoid `long double`, use `npy_longdouble` instead,
* and when it comes to standard math functions make sure of using
* the double version when `NPY_SIZEOF_LONGDOUBLE` == `NPY_SIZEOF_DOUBLE`.
* For example:
* npy_longdouble *ptr, x;
* #if NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE
* npy_longdouble r = modf(x, ptr);
* #else
* npy_longdouble r = modfl(x, ptr);
* #endif
*
* See https://github.com/numpy/numpy/issues/20348
*/
#if NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE
#define NPY_LONGDOUBLE_FMT "g"
typedef double npy_longdouble;
#else
#define NPY_LONGDOUBLE_FMT "Lg"
typedef long double npy_longdouble;
#endif
#ifndef Py_USING_UNICODE
#error Must use Python with unicode enabled.
#endif
typedef signed char npy_byte;
typedef unsigned char npy_ubyte;
typedef unsigned short npy_ushort;
typedef unsigned int npy_uint;
typedef unsigned long npy_ulong;
/* These are for completeness */
typedef char npy_char;
typedef short npy_short;
typedef int npy_int;
typedef long npy_long;
typedef float npy_float;
typedef double npy_double;
typedef Py_hash_t npy_hash_t;
#define NPY_SIZEOF_HASH_T NPY_SIZEOF_INTP
/*
* Disabling C99 complex usage: a lot of C code in numpy/scipy rely on being
* able to do .real/.imag. Will have to convert code first.
*/
#if 0
#if defined(NPY_USE_C99_COMPLEX) && defined(NPY_HAVE_COMPLEX_DOUBLE)
typedef complex npy_cdouble;
#else
typedef struct { double real, imag; } npy_cdouble;
#endif
#if defined(NPY_USE_C99_COMPLEX) && defined(NPY_HAVE_COMPLEX_FLOAT)
typedef complex float npy_cfloat;
#else
typedef struct { float real, imag; } npy_cfloat;
#endif
#if defined(NPY_USE_C99_COMPLEX) && defined(NPY_HAVE_COMPLEX_LONG_DOUBLE)
typedef complex long double npy_clongdouble;
#else
typedef struct {npy_longdouble real, imag;} npy_clongdouble;
#endif
#endif
#if NPY_SIZEOF_COMPLEX_DOUBLE != 2 * NPY_SIZEOF_DOUBLE
#error npy_cdouble definition is not compatible with C99 complex definition ! \
Please contact NumPy maintainers and give detailed information about your \
compiler and platform
#endif
typedef struct { double real, imag; } npy_cdouble;
#if NPY_SIZEOF_COMPLEX_FLOAT != 2 * NPY_SIZEOF_FLOAT
#error npy_cfloat definition is not compatible with C99 complex definition ! \
Please contact NumPy maintainers and give detailed information about your \
compiler and platform
#endif
typedef struct { float real, imag; } npy_cfloat;
#if NPY_SIZEOF_COMPLEX_LONGDOUBLE != 2 * NPY_SIZEOF_LONGDOUBLE
#error npy_clongdouble definition is not compatible with C99 complex definition ! \
Please contact NumPy maintainers and give detailed information about your \
compiler and platform
#endif
typedef struct { npy_longdouble real, imag; } npy_clongdouble;
/*
* numarray-style bit-width typedefs
*/
#define NPY_MAX_INT8 127
#define NPY_MIN_INT8 -128
#define NPY_MAX_UINT8 255
#define NPY_MAX_INT16 32767
#define NPY_MIN_INT16 -32768
#define NPY_MAX_UINT16 65535
#define NPY_MAX_INT32 2147483647
#define NPY_MIN_INT32 (-NPY_MAX_INT32 - 1)
#define NPY_MAX_UINT32 4294967295U
#define NPY_MAX_INT64 NPY_LONGLONG_SUFFIX(9223372036854775807)
#define NPY_MIN_INT64 (-NPY_MAX_INT64 - NPY_LONGLONG_SUFFIX(1))
#define NPY_MAX_UINT64 NPY_ULONGLONG_SUFFIX(18446744073709551615)
#define NPY_MAX_INT128 NPY_LONGLONG_SUFFIX(85070591730234615865843651857942052864)
#define NPY_MIN_INT128 (-NPY_MAX_INT128 - NPY_LONGLONG_SUFFIX(1))
#define NPY_MAX_UINT128 NPY_ULONGLONG_SUFFIX(170141183460469231731687303715884105728)
#define NPY_MAX_INT256 NPY_LONGLONG_SUFFIX(57896044618658097711785492504343953926634992332820282019728792003956564819967)
#define NPY_MIN_INT256 (-NPY_MAX_INT256 - NPY_LONGLONG_SUFFIX(1))
#define NPY_MAX_UINT256 NPY_ULONGLONG_SUFFIX(115792089237316195423570985008687907853269984665640564039457584007913129639935)
#define NPY_MIN_DATETIME NPY_MIN_INT64
#define NPY_MAX_DATETIME NPY_MAX_INT64
#define NPY_MIN_TIMEDELTA NPY_MIN_INT64
#define NPY_MAX_TIMEDELTA NPY_MAX_INT64
/* Need to find the number of bits for each type and
make definitions accordingly.
C states that sizeof(char) == 1 by definition
So, just using the sizeof keyword won't help.
It also looks like Python itself uses sizeof(char) quite a
bit, which by definition should be 1 all the time.
Idea: Make Use of CHAR_BIT which should tell us how many
BITS per CHARACTER
*/
/* Include platform definitions -- These are in the C89/90 standard */
#include <limits.h>
#define NPY_MAX_BYTE SCHAR_MAX
#define NPY_MIN_BYTE SCHAR_MIN
#define NPY_MAX_UBYTE UCHAR_MAX
#define NPY_MAX_SHORT SHRT_MAX
#define NPY_MIN_SHORT SHRT_MIN
#define NPY_MAX_USHORT USHRT_MAX
#define NPY_MAX_INT INT_MAX
#ifndef INT_MIN
#define INT_MIN (-INT_MAX - 1)
#endif
#define NPY_MIN_INT INT_MIN
#define NPY_MAX_UINT UINT_MAX
#define NPY_MAX_LONG LONG_MAX
#define NPY_MIN_LONG LONG_MIN
#define NPY_MAX_ULONG ULONG_MAX
#define NPY_BITSOF_BOOL (sizeof(npy_bool) * CHAR_BIT)
#define NPY_BITSOF_CHAR CHAR_BIT
#define NPY_BITSOF_BYTE (NPY_SIZEOF_BYTE * CHAR_BIT)
#define NPY_BITSOF_SHORT (NPY_SIZEOF_SHORT * CHAR_BIT)
#define NPY_BITSOF_INT (NPY_SIZEOF_INT * CHAR_BIT)
#define NPY_BITSOF_LONG (NPY_SIZEOF_LONG * CHAR_BIT)
#define NPY_BITSOF_LONGLONG (NPY_SIZEOF_LONGLONG * CHAR_BIT)
#define NPY_BITSOF_INTP (NPY_SIZEOF_INTP * CHAR_BIT)
#define NPY_BITSOF_HALF (NPY_SIZEOF_HALF * CHAR_BIT)
#define NPY_BITSOF_FLOAT (NPY_SIZEOF_FLOAT * CHAR_BIT)
#define NPY_BITSOF_DOUBLE (NPY_SIZEOF_DOUBLE * CHAR_BIT)
#define NPY_BITSOF_LONGDOUBLE (NPY_SIZEOF_LONGDOUBLE * CHAR_BIT)
#define NPY_BITSOF_CFLOAT (NPY_SIZEOF_CFLOAT * CHAR_BIT)
#define NPY_BITSOF_CDOUBLE (NPY_SIZEOF_CDOUBLE * CHAR_BIT)
#define NPY_BITSOF_CLONGDOUBLE (NPY_SIZEOF_CLONGDOUBLE * CHAR_BIT)
#define NPY_BITSOF_DATETIME (NPY_SIZEOF_DATETIME * CHAR_BIT)
#define NPY_BITSOF_TIMEDELTA (NPY_SIZEOF_TIMEDELTA * CHAR_BIT)
#if NPY_BITSOF_LONG == 8
#define NPY_INT8 NPY_LONG
#define NPY_UINT8 NPY_ULONG
typedef long npy_int8;
typedef unsigned long npy_uint8;
#define PyInt8ScalarObject PyLongScalarObject
#define PyInt8ArrType_Type PyLongArrType_Type
#define PyUInt8ScalarObject PyULongScalarObject
#define PyUInt8ArrType_Type PyULongArrType_Type
#define NPY_INT8_FMT NPY_LONG_FMT
#define NPY_UINT8_FMT NPY_ULONG_FMT
#elif NPY_BITSOF_LONG == 16
#define NPY_INT16 NPY_LONG
#define NPY_UINT16 NPY_ULONG
typedef long npy_int16;
typedef unsigned long npy_uint16;
#define PyInt16ScalarObject PyLongScalarObject
#define PyInt16ArrType_Type PyLongArrType_Type
#define PyUInt16ScalarObject PyULongScalarObject
#define PyUInt16ArrType_Type PyULongArrType_Type
#define NPY_INT16_FMT NPY_LONG_FMT
#define NPY_UINT16_FMT NPY_ULONG_FMT
#elif NPY_BITSOF_LONG == 32
#define NPY_INT32 NPY_LONG
#define NPY_UINT32 NPY_ULONG
typedef long npy_int32;
typedef unsigned long npy_uint32;
typedef unsigned long npy_ucs4;
#define PyInt32ScalarObject PyLongScalarObject
#define PyInt32ArrType_Type PyLongArrType_Type
#define PyUInt32ScalarObject PyULongScalarObject
#define PyUInt32ArrType_Type PyULongArrType_Type
#define NPY_INT32_FMT NPY_LONG_FMT
#define NPY_UINT32_FMT NPY_ULONG_FMT
#elif NPY_BITSOF_LONG == 64
#define NPY_INT64 NPY_LONG
#define NPY_UINT64 NPY_ULONG
typedef long npy_int64;
typedef unsigned long npy_uint64;
#define PyInt64ScalarObject PyLongScalarObject
#define PyInt64ArrType_Type PyLongArrType_Type
#define PyUInt64ScalarObject PyULongScalarObject
#define PyUInt64ArrType_Type PyULongArrType_Type
#define NPY_INT64_FMT NPY_LONG_FMT
#define NPY_UINT64_FMT NPY_ULONG_FMT
#define MyPyLong_FromInt64 PyLong_FromLong
#define MyPyLong_AsInt64 PyLong_AsLong
#elif NPY_BITSOF_LONG == 128
#define NPY_INT128 NPY_LONG
#define NPY_UINT128 NPY_ULONG
typedef long npy_int128;
typedef unsigned long npy_uint128;
#define PyInt128ScalarObject PyLongScalarObject
#define PyInt128ArrType_Type PyLongArrType_Type
#define PyUInt128ScalarObject PyULongScalarObject
#define PyUInt128ArrType_Type PyULongArrType_Type
#define NPY_INT128_FMT NPY_LONG_FMT
#define NPY_UINT128_FMT NPY_ULONG_FMT
#endif
#if NPY_BITSOF_LONGLONG == 8
# ifndef NPY_INT8
# define NPY_INT8 NPY_LONGLONG
# define NPY_UINT8 NPY_ULONGLONG
typedef npy_longlong npy_int8;
typedef npy_ulonglong npy_uint8;
# define PyInt8ScalarObject PyLongLongScalarObject
# define PyInt8ArrType_Type PyLongLongArrType_Type
# define PyUInt8ScalarObject PyULongLongScalarObject
# define PyUInt8ArrType_Type PyULongLongArrType_Type
#define NPY_INT8_FMT NPY_LONGLONG_FMT
#define NPY_UINT8_FMT NPY_ULONGLONG_FMT
# endif
# define NPY_MAX_LONGLONG NPY_MAX_INT8
# define NPY_MIN_LONGLONG NPY_MIN_INT8
# define NPY_MAX_ULONGLONG NPY_MAX_UINT8
#elif NPY_BITSOF_LONGLONG == 16
# ifndef NPY_INT16
# define NPY_INT16 NPY_LONGLONG
# define NPY_UINT16 NPY_ULONGLONG
typedef npy_longlong npy_int16;
typedef npy_ulonglong npy_uint16;
# define PyInt16ScalarObject PyLongLongScalarObject
# define PyInt16ArrType_Type PyLongLongArrType_Type
# define PyUInt16ScalarObject PyULongLongScalarObject
# define PyUInt16ArrType_Type PyULongLongArrType_Type
#define NPY_INT16_FMT NPY_LONGLONG_FMT
#define NPY_UINT16_FMT NPY_ULONGLONG_FMT
# endif
# define NPY_MAX_LONGLONG NPY_MAX_INT16
# define NPY_MIN_LONGLONG NPY_MIN_INT16
# define NPY_MAX_ULONGLONG NPY_MAX_UINT16
#elif NPY_BITSOF_LONGLONG == 32
# ifndef NPY_INT32
# define NPY_INT32 NPY_LONGLONG
# define NPY_UINT32 NPY_ULONGLONG
typedef npy_longlong npy_int32;
typedef npy_ulonglong npy_uint32;
typedef npy_ulonglong npy_ucs4;
# define PyInt32ScalarObject PyLongLongScalarObject
# define PyInt32ArrType_Type PyLongLongArrType_Type
# define PyUInt32ScalarObject PyULongLongScalarObject
# define PyUInt32ArrType_Type PyULongLongArrType_Type
#define NPY_INT32_FMT NPY_LONGLONG_FMT
#define NPY_UINT32_FMT NPY_ULONGLONG_FMT
# endif
# define NPY_MAX_LONGLONG NPY_MAX_INT32
# define NPY_MIN_LONGLONG NPY_MIN_INT32
# define NPY_MAX_ULONGLONG NPY_MAX_UINT32
#elif NPY_BITSOF_LONGLONG == 64
# ifndef NPY_INT64
# define NPY_INT64 NPY_LONGLONG
# define NPY_UINT64 NPY_ULONGLONG
typedef npy_longlong npy_int64;
typedef npy_ulonglong npy_uint64;
# define PyInt64ScalarObject PyLongLongScalarObject
# define PyInt64ArrType_Type PyLongLongArrType_Type
# define PyUInt64ScalarObject PyULongLongScalarObject
# define PyUInt64ArrType_Type PyULongLongArrType_Type
#define NPY_INT64_FMT NPY_LONGLONG_FMT
#define NPY_UINT64_FMT NPY_ULONGLONG_FMT
# define MyPyLong_FromInt64 PyLong_FromLongLong
# define MyPyLong_AsInt64 PyLong_AsLongLong
# endif
# define NPY_MAX_LONGLONG NPY_MAX_INT64
# define NPY_MIN_LONGLONG NPY_MIN_INT64
# define NPY_MAX_ULONGLONG NPY_MAX_UINT64
#elif NPY_BITSOF_LONGLONG == 128
# ifndef NPY_INT128
# define NPY_INT128 NPY_LONGLONG
# define NPY_UINT128 NPY_ULONGLONG
typedef npy_longlong npy_int128;
typedef npy_ulonglong npy_uint128;
# define PyInt128ScalarObject PyLongLongScalarObject
# define PyInt128ArrType_Type PyLongLongArrType_Type
# define PyUInt128ScalarObject PyULongLongScalarObject
# define PyUInt128ArrType_Type PyULongLongArrType_Type
#define NPY_INT128_FMT NPY_LONGLONG_FMT
#define NPY_UINT128_FMT NPY_ULONGLONG_FMT
# endif
# define NPY_MAX_LONGLONG NPY_MAX_INT128
# define NPY_MIN_LONGLONG NPY_MIN_INT128
# define NPY_MAX_ULONGLONG NPY_MAX_UINT128
#elif NPY_BITSOF_LONGLONG == 256
# define NPY_INT256 NPY_LONGLONG
# define NPY_UINT256 NPY_ULONGLONG
typedef npy_longlong npy_int256;
typedef npy_ulonglong npy_uint256;
# define PyInt256ScalarObject PyLongLongScalarObject
# define PyInt256ArrType_Type PyLongLongArrType_Type
# define PyUInt256ScalarObject PyULongLongScalarObject
# define PyUInt256ArrType_Type PyULongLongArrType_Type
#define NPY_INT256_FMT NPY_LONGLONG_FMT
#define NPY_UINT256_FMT NPY_ULONGLONG_FMT
# define NPY_MAX_LONGLONG NPY_MAX_INT256
# define NPY_MIN_LONGLONG NPY_MIN_INT256
# define NPY_MAX_ULONGLONG NPY_MAX_UINT256
#endif
#if NPY_BITSOF_INT == 8
#ifndef NPY_INT8
#define NPY_INT8 NPY_INT
#define NPY_UINT8 NPY_UINT
typedef int npy_int8;
typedef unsigned int npy_uint8;
# define PyInt8ScalarObject PyIntScalarObject
# define PyInt8ArrType_Type PyIntArrType_Type
# define PyUInt8ScalarObject PyUIntScalarObject
# define PyUInt8ArrType_Type PyUIntArrType_Type
#define NPY_INT8_FMT NPY_INT_FMT
#define NPY_UINT8_FMT NPY_UINT_FMT
#endif
#elif NPY_BITSOF_INT == 16
#ifndef NPY_INT16
#define NPY_INT16 NPY_INT
#define NPY_UINT16 NPY_UINT
typedef int npy_int16;
typedef unsigned int npy_uint16;
# define PyInt16ScalarObject PyIntScalarObject
# define PyInt16ArrType_Type PyIntArrType_Type
# define PyUInt16ScalarObject PyIntUScalarObject
# define PyUInt16ArrType_Type PyIntUArrType_Type
#define NPY_INT16_FMT NPY_INT_FMT
#define NPY_UINT16_FMT NPY_UINT_FMT
#endif
#elif NPY_BITSOF_INT == 32
#ifndef NPY_INT32
#define NPY_INT32 NPY_INT
#define NPY_UINT32 NPY_UINT
typedef int npy_int32;
typedef unsigned int npy_uint32;
typedef unsigned int npy_ucs4;
# define PyInt32ScalarObject PyIntScalarObject
# define PyInt32ArrType_Type PyIntArrType_Type
# define PyUInt32ScalarObject PyUIntScalarObject
# define PyUInt32ArrType_Type PyUIntArrType_Type
#define NPY_INT32_FMT NPY_INT_FMT
#define NPY_UINT32_FMT NPY_UINT_FMT
#endif
#elif NPY_BITSOF_INT == 64
#ifndef NPY_INT64
#define NPY_INT64 NPY_INT
#define NPY_UINT64 NPY_UINT
typedef int npy_int64;
typedef unsigned int npy_uint64;
# define PyInt64ScalarObject PyIntScalarObject
# define PyInt64ArrType_Type PyIntArrType_Type
# define PyUInt64ScalarObject PyUIntScalarObject
# define PyUInt64ArrType_Type PyUIntArrType_Type
#define NPY_INT64_FMT NPY_INT_FMT
#define NPY_UINT64_FMT NPY_UINT_FMT
# define MyPyLong_FromInt64 PyLong_FromLong
# define MyPyLong_AsInt64 PyLong_AsLong
#endif
#elif NPY_BITSOF_INT == 128
#ifndef NPY_INT128
#define NPY_INT128 NPY_INT
#define NPY_UINT128 NPY_UINT
typedef int npy_int128;
typedef unsigned int npy_uint128;
# define PyInt128ScalarObject PyIntScalarObject
# define PyInt128ArrType_Type PyIntArrType_Type
# define PyUInt128ScalarObject PyUIntScalarObject
# define PyUInt128ArrType_Type PyUIntArrType_Type
#define NPY_INT128_FMT NPY_INT_FMT
#define NPY_UINT128_FMT NPY_UINT_FMT
#endif
#endif
#if NPY_BITSOF_SHORT == 8
#ifndef NPY_INT8
#define NPY_INT8 NPY_SHORT
#define NPY_UINT8 NPY_USHORT
typedef short npy_int8;
typedef unsigned short npy_uint8;
# define PyInt8ScalarObject PyShortScalarObject
# define PyInt8ArrType_Type PyShortArrType_Type
# define PyUInt8ScalarObject PyUShortScalarObject
# define PyUInt8ArrType_Type PyUShortArrType_Type
#define NPY_INT8_FMT NPY_SHORT_FMT
#define NPY_UINT8_FMT NPY_USHORT_FMT
#endif
#elif NPY_BITSOF_SHORT == 16
#ifndef NPY_INT16
#define NPY_INT16 NPY_SHORT
#define NPY_UINT16 NPY_USHORT
typedef short npy_int16;
typedef unsigned short npy_uint16;
# define PyInt16ScalarObject PyShortScalarObject
# define PyInt16ArrType_Type PyShortArrType_Type
# define PyUInt16ScalarObject PyUShortScalarObject
# define PyUInt16ArrType_Type PyUShortArrType_Type
#define NPY_INT16_FMT NPY_SHORT_FMT
#define NPY_UINT16_FMT NPY_USHORT_FMT
#endif
#elif NPY_BITSOF_SHORT == 32
#ifndef NPY_INT32
#define NPY_INT32 NPY_SHORT
#define NPY_UINT32 NPY_USHORT
typedef short npy_int32;
typedef unsigned short npy_uint32;
typedef unsigned short npy_ucs4;
# define PyInt32ScalarObject PyShortScalarObject
# define PyInt32ArrType_Type PyShortArrType_Type
# define PyUInt32ScalarObject PyUShortScalarObject
# define PyUInt32ArrType_Type PyUShortArrType_Type
#define NPY_INT32_FMT NPY_SHORT_FMT
#define NPY_UINT32_FMT NPY_USHORT_FMT
#endif
#elif NPY_BITSOF_SHORT == 64
#ifndef NPY_INT64
#define NPY_INT64 NPY_SHORT
#define NPY_UINT64 NPY_USHORT
typedef short npy_int64;
typedef unsigned short npy_uint64;
# define PyInt64ScalarObject PyShortScalarObject
# define PyInt64ArrType_Type PyShortArrType_Type
# define PyUInt64ScalarObject PyUShortScalarObject
# define PyUInt64ArrType_Type PyUShortArrType_Type
#define NPY_INT64_FMT NPY_SHORT_FMT
#define NPY_UINT64_FMT NPY_USHORT_FMT
# define MyPyLong_FromInt64 PyLong_FromLong
# define MyPyLong_AsInt64 PyLong_AsLong
#endif
#elif NPY_BITSOF_SHORT == 128
#ifndef NPY_INT128
#define NPY_INT128 NPY_SHORT
#define NPY_UINT128 NPY_USHORT
typedef short npy_int128;
typedef unsigned short npy_uint128;
# define PyInt128ScalarObject PyShortScalarObject
# define PyInt128ArrType_Type PyShortArrType_Type
# define PyUInt128ScalarObject PyUShortScalarObject
# define PyUInt128ArrType_Type PyUShortArrType_Type
#define NPY_INT128_FMT NPY_SHORT_FMT
#define NPY_UINT128_FMT NPY_USHORT_FMT
#endif
#endif
#if NPY_BITSOF_CHAR == 8
#ifndef NPY_INT8
#define NPY_INT8 NPY_BYTE
#define NPY_UINT8 NPY_UBYTE
typedef signed char npy_int8;
typedef unsigned char npy_uint8;
# define PyInt8ScalarObject PyByteScalarObject
# define PyInt8ArrType_Type PyByteArrType_Type
# define PyUInt8ScalarObject PyUByteScalarObject
# define PyUInt8ArrType_Type PyUByteArrType_Type
#define NPY_INT8_FMT NPY_BYTE_FMT
#define NPY_UINT8_FMT NPY_UBYTE_FMT
#endif
#elif NPY_BITSOF_CHAR == 16
#ifndef NPY_INT16
#define NPY_INT16 NPY_BYTE
#define NPY_UINT16 NPY_UBYTE
typedef signed char npy_int16;
typedef unsigned char npy_uint16;
# define PyInt16ScalarObject PyByteScalarObject
# define PyInt16ArrType_Type PyByteArrType_Type
# define PyUInt16ScalarObject PyUByteScalarObject
# define PyUInt16ArrType_Type PyUByteArrType_Type
#define NPY_INT16_FMT NPY_BYTE_FMT
#define NPY_UINT16_FMT NPY_UBYTE_FMT
#endif
#elif NPY_BITSOF_CHAR == 32
#ifndef NPY_INT32
#define NPY_INT32 NPY_BYTE
#define NPY_UINT32 NPY_UBYTE
typedef signed char npy_int32;
typedef unsigned char npy_uint32;
typedef unsigned char npy_ucs4;
# define PyInt32ScalarObject PyByteScalarObject
# define PyInt32ArrType_Type PyByteArrType_Type
# define PyUInt32ScalarObject PyUByteScalarObject
# define PyUInt32ArrType_Type PyUByteArrType_Type
#define NPY_INT32_FMT NPY_BYTE_FMT
#define NPY_UINT32_FMT NPY_UBYTE_FMT
#endif
#elif NPY_BITSOF_CHAR == 64
#ifndef NPY_INT64
#define NPY_INT64 NPY_BYTE
#define NPY_UINT64 NPY_UBYTE
typedef signed char npy_int64;
typedef unsigned char npy_uint64;
# define PyInt64ScalarObject PyByteScalarObject
# define PyInt64ArrType_Type PyByteArrType_Type
# define PyUInt64ScalarObject PyUByteScalarObject
# define PyUInt64ArrType_Type PyUByteArrType_Type
#define NPY_INT64_FMT NPY_BYTE_FMT
#define NPY_UINT64_FMT NPY_UBYTE_FMT
# define MyPyLong_FromInt64 PyLong_FromLong
# define MyPyLong_AsInt64 PyLong_AsLong
#endif
#elif NPY_BITSOF_CHAR == 128
#ifndef NPY_INT128
#define NPY_INT128 NPY_BYTE
#define NPY_UINT128 NPY_UBYTE
typedef signed char npy_int128;
typedef unsigned char npy_uint128;
# define PyInt128ScalarObject PyByteScalarObject
# define PyInt128ArrType_Type PyByteArrType_Type
# define PyUInt128ScalarObject PyUByteScalarObject
# define PyUInt128ArrType_Type PyUByteArrType_Type
#define NPY_INT128_FMT NPY_BYTE_FMT
#define NPY_UINT128_FMT NPY_UBYTE_FMT
#endif
#endif
#if NPY_BITSOF_DOUBLE == 32
#ifndef NPY_FLOAT32
#define NPY_FLOAT32 NPY_DOUBLE
#define NPY_COMPLEX64 NPY_CDOUBLE
typedef double npy_float32;
typedef npy_cdouble npy_complex64;
# define PyFloat32ScalarObject PyDoubleScalarObject
# define PyComplex64ScalarObject PyCDoubleScalarObject
# define PyFloat32ArrType_Type PyDoubleArrType_Type
# define PyComplex64ArrType_Type PyCDoubleArrType_Type
#define NPY_FLOAT32_FMT NPY_DOUBLE_FMT
#define NPY_COMPLEX64_FMT NPY_CDOUBLE_FMT
#endif
#elif NPY_BITSOF_DOUBLE == 64
#ifndef NPY_FLOAT64
#define NPY_FLOAT64 NPY_DOUBLE
#define NPY_COMPLEX128 NPY_CDOUBLE
typedef double npy_float64;
typedef npy_cdouble npy_complex128;
# define PyFloat64ScalarObject PyDoubleScalarObject
# define PyComplex128ScalarObject PyCDoubleScalarObject
# define PyFloat64ArrType_Type PyDoubleArrType_Type
# define PyComplex128ArrType_Type PyCDoubleArrType_Type
#define NPY_FLOAT64_FMT NPY_DOUBLE_FMT
#define NPY_COMPLEX128_FMT NPY_CDOUBLE_FMT
#endif
#elif NPY_BITSOF_DOUBLE == 80
#ifndef NPY_FLOAT80
#define NPY_FLOAT80 NPY_DOUBLE
#define NPY_COMPLEX160 NPY_CDOUBLE
typedef double npy_float80;
typedef npy_cdouble npy_complex160;
# define PyFloat80ScalarObject PyDoubleScalarObject
# define PyComplex160ScalarObject PyCDoubleScalarObject
# define PyFloat80ArrType_Type PyDoubleArrType_Type
# define PyComplex160ArrType_Type PyCDoubleArrType_Type
#define NPY_FLOAT80_FMT NPY_DOUBLE_FMT
#define NPY_COMPLEX160_FMT NPY_CDOUBLE_FMT
#endif
#elif NPY_BITSOF_DOUBLE == 96
#ifndef NPY_FLOAT96
#define NPY_FLOAT96 NPY_DOUBLE
#define NPY_COMPLEX192 NPY_CDOUBLE
typedef double npy_float96;
typedef npy_cdouble npy_complex192;
# define PyFloat96ScalarObject PyDoubleScalarObject
# define PyComplex192ScalarObject PyCDoubleScalarObject
# define PyFloat96ArrType_Type PyDoubleArrType_Type
# define PyComplex192ArrType_Type PyCDoubleArrType_Type
#define NPY_FLOAT96_FMT NPY_DOUBLE_FMT
#define NPY_COMPLEX192_FMT NPY_CDOUBLE_FMT
#endif
#elif NPY_BITSOF_DOUBLE == 128
#ifndef NPY_FLOAT128
#define NPY_FLOAT128 NPY_DOUBLE
#define NPY_COMPLEX256 NPY_CDOUBLE
typedef double npy_float128;
typedef npy_cdouble npy_complex256;
# define PyFloat128ScalarObject PyDoubleScalarObject
# define PyComplex256ScalarObject PyCDoubleScalarObject
# define PyFloat128ArrType_Type PyDoubleArrType_Type
# define PyComplex256ArrType_Type PyCDoubleArrType_Type
#define NPY_FLOAT128_FMT NPY_DOUBLE_FMT
#define NPY_COMPLEX256_FMT NPY_CDOUBLE_FMT
#endif
#endif
#if NPY_BITSOF_FLOAT == 32
#ifndef NPY_FLOAT32
#define NPY_FLOAT32 NPY_FLOAT
#define NPY_COMPLEX64 NPY_CFLOAT
typedef float npy_float32;
typedef npy_cfloat npy_complex64;
# define PyFloat32ScalarObject PyFloatScalarObject
# define PyComplex64ScalarObject PyCFloatScalarObject
# define PyFloat32ArrType_Type PyFloatArrType_Type
# define PyComplex64ArrType_Type PyCFloatArrType_Type
#define NPY_FLOAT32_FMT NPY_FLOAT_FMT
#define NPY_COMPLEX64_FMT NPY_CFLOAT_FMT
#endif
#elif NPY_BITSOF_FLOAT == 64
#ifndef NPY_FLOAT64
#define NPY_FLOAT64 NPY_FLOAT
#define NPY_COMPLEX128 NPY_CFLOAT
typedef float npy_float64;
typedef npy_cfloat npy_complex128;
# define PyFloat64ScalarObject PyFloatScalarObject
# define PyComplex128ScalarObject PyCFloatScalarObject
# define PyFloat64ArrType_Type PyFloatArrType_Type
# define PyComplex128ArrType_Type PyCFloatArrType_Type
#define NPY_FLOAT64_FMT NPY_FLOAT_FMT
#define NPY_COMPLEX128_FMT NPY_CFLOAT_FMT
#endif
#elif NPY_BITSOF_FLOAT == 80
#ifndef NPY_FLOAT80
#define NPY_FLOAT80 NPY_FLOAT
#define NPY_COMPLEX160 NPY_CFLOAT
typedef float npy_float80;
typedef npy_cfloat npy_complex160;
# define PyFloat80ScalarObject PyFloatScalarObject
# define PyComplex160ScalarObject PyCFloatScalarObject
# define PyFloat80ArrType_Type PyFloatArrType_Type
# define PyComplex160ArrType_Type PyCFloatArrType_Type
#define NPY_FLOAT80_FMT NPY_FLOAT_FMT
#define NPY_COMPLEX160_FMT NPY_CFLOAT_FMT
#endif
#elif NPY_BITSOF_FLOAT == 96
#ifndef NPY_FLOAT96
#define NPY_FLOAT96 NPY_FLOAT
#define NPY_COMPLEX192 NPY_CFLOAT
typedef float npy_float96;
typedef npy_cfloat npy_complex192;
# define PyFloat96ScalarObject PyFloatScalarObject
# define PyComplex192ScalarObject PyCFloatScalarObject
# define PyFloat96ArrType_Type PyFloatArrType_Type
# define PyComplex192ArrType_Type PyCFloatArrType_Type
#define NPY_FLOAT96_FMT NPY_FLOAT_FMT
#define NPY_COMPLEX192_FMT NPY_CFLOAT_FMT
#endif
#elif NPY_BITSOF_FLOAT == 128
#ifndef NPY_FLOAT128
#define NPY_FLOAT128 NPY_FLOAT
#define NPY_COMPLEX256 NPY_CFLOAT
typedef float npy_float128;
typedef npy_cfloat npy_complex256;
# define PyFloat128ScalarObject PyFloatScalarObject
# define PyComplex256ScalarObject PyCFloatScalarObject
# define PyFloat128ArrType_Type PyFloatArrType_Type
# define PyComplex256ArrType_Type PyCFloatArrType_Type
#define NPY_FLOAT128_FMT NPY_FLOAT_FMT
#define NPY_COMPLEX256_FMT NPY_CFLOAT_FMT
#endif
#endif
/* half/float16 isn't a floating-point type in C */
#define NPY_FLOAT16 NPY_HALF
typedef npy_uint16 npy_half;
typedef npy_half npy_float16;
#if NPY_BITSOF_LONGDOUBLE == 32
#ifndef NPY_FLOAT32
#define NPY_FLOAT32 NPY_LONGDOUBLE
#define NPY_COMPLEX64 NPY_CLONGDOUBLE
typedef npy_longdouble npy_float32;
typedef npy_clongdouble npy_complex64;
# define PyFloat32ScalarObject PyLongDoubleScalarObject
# define PyComplex64ScalarObject PyCLongDoubleScalarObject
# define PyFloat32ArrType_Type PyLongDoubleArrType_Type
# define PyComplex64ArrType_Type PyCLongDoubleArrType_Type
#define NPY_FLOAT32_FMT NPY_LONGDOUBLE_FMT
#define NPY_COMPLEX64_FMT NPY_CLONGDOUBLE_FMT
#endif
#elif NPY_BITSOF_LONGDOUBLE == 64
#ifndef NPY_FLOAT64
#define NPY_FLOAT64 NPY_LONGDOUBLE
#define NPY_COMPLEX128 NPY_CLONGDOUBLE
typedef npy_longdouble npy_float64;
typedef npy_clongdouble npy_complex128;
# define PyFloat64ScalarObject PyLongDoubleScalarObject
# define PyComplex128ScalarObject PyCLongDoubleScalarObject
# define PyFloat64ArrType_Type PyLongDoubleArrType_Type
# define PyComplex128ArrType_Type PyCLongDoubleArrType_Type
#define NPY_FLOAT64_FMT NPY_LONGDOUBLE_FMT
#define NPY_COMPLEX128_FMT NPY_CLONGDOUBLE_FMT
#endif
#elif NPY_BITSOF_LONGDOUBLE == 80
#ifndef NPY_FLOAT80
#define NPY_FLOAT80 NPY_LONGDOUBLE
#define NPY_COMPLEX160 NPY_CLONGDOUBLE
typedef npy_longdouble npy_float80;
typedef npy_clongdouble npy_complex160;
# define PyFloat80ScalarObject PyLongDoubleScalarObject
# define PyComplex160ScalarObject PyCLongDoubleScalarObject
# define PyFloat80ArrType_Type PyLongDoubleArrType_Type
# define PyComplex160ArrType_Type PyCLongDoubleArrType_Type
#define NPY_FLOAT80_FMT NPY_LONGDOUBLE_FMT
#define NPY_COMPLEX160_FMT NPY_CLONGDOUBLE_FMT
#endif
#elif NPY_BITSOF_LONGDOUBLE == 96
#ifndef NPY_FLOAT96
#define NPY_FLOAT96 NPY_LONGDOUBLE
#define NPY_COMPLEX192 NPY_CLONGDOUBLE
typedef npy_longdouble npy_float96;
typedef npy_clongdouble npy_complex192;
# define PyFloat96ScalarObject PyLongDoubleScalarObject
# define PyComplex192ScalarObject PyCLongDoubleScalarObject
# define PyFloat96ArrType_Type PyLongDoubleArrType_Type
# define PyComplex192ArrType_Type PyCLongDoubleArrType_Type
#define NPY_FLOAT96_FMT NPY_LONGDOUBLE_FMT
#define NPY_COMPLEX192_FMT NPY_CLONGDOUBLE_FMT
#endif
#elif NPY_BITSOF_LONGDOUBLE == 128
#ifndef NPY_FLOAT128
#define NPY_FLOAT128 NPY_LONGDOUBLE
#define NPY_COMPLEX256 NPY_CLONGDOUBLE
typedef npy_longdouble npy_float128;
typedef npy_clongdouble npy_complex256;
# define PyFloat128ScalarObject PyLongDoubleScalarObject
# define PyComplex256ScalarObject PyCLongDoubleScalarObject
# define PyFloat128ArrType_Type PyLongDoubleArrType_Type
# define PyComplex256ArrType_Type PyCLongDoubleArrType_Type
#define NPY_FLOAT128_FMT NPY_LONGDOUBLE_FMT
#define NPY_COMPLEX256_FMT NPY_CLONGDOUBLE_FMT
#endif
#elif NPY_BITSOF_LONGDOUBLE == 256
#define NPY_FLOAT256 NPY_LONGDOUBLE
#define NPY_COMPLEX512 NPY_CLONGDOUBLE
typedef npy_longdouble npy_float256;
typedef npy_clongdouble npy_complex512;
# define PyFloat256ScalarObject PyLongDoubleScalarObject
# define PyComplex512ScalarObject PyCLongDoubleScalarObject
# define PyFloat256ArrType_Type PyLongDoubleArrType_Type
# define PyComplex512ArrType_Type PyCLongDoubleArrType_Type
#define NPY_FLOAT256_FMT NPY_LONGDOUBLE_FMT
#define NPY_COMPLEX512_FMT NPY_CLONGDOUBLE_FMT
#endif
/* datetime typedefs */
typedef npy_int64 npy_timedelta;
typedef npy_int64 npy_datetime;
#define NPY_DATETIME_FMT NPY_INT64_FMT
#define NPY_TIMEDELTA_FMT NPY_INT64_FMT
/* End of typedefs for numarray style bit-width names */
#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_ */
| 39,015 | C | 33.742654 | 124 | 0.74861 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/oldnumeric.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_OLDNUMERIC_H_
#define NUMPY_CORE_INCLUDE_NUMPY_OLDNUMERIC_H_
/* FIXME -- this file can be deleted? */
#include "arrayobject.h"
#ifndef PYPY_VERSION
#ifndef REFCOUNT
# define REFCOUNT NPY_REFCOUNT
# define MAX_ELSIZE 16
#endif
#endif
#define PyArray_UNSIGNED_TYPES
#define PyArray_SBYTE NPY_BYTE
#define PyArray_CopyArray PyArray_CopyInto
#define _PyArray_multiply_list PyArray_MultiplyIntList
#define PyArray_ISSPACESAVER(m) NPY_FALSE
#define PyScalarArray_Check PyArray_CheckScalar
#define CONTIGUOUS NPY_CONTIGUOUS
#define OWN_DIMENSIONS 0
#define OWN_STRIDES 0
#define OWN_DATA NPY_OWNDATA
#define SAVESPACE 0
#define SAVESPACEBIT 0
#undef import_array
#define import_array() { if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); } }
#endif /* NUMPY_CORE_INCLUDE_NUMPY_OLDNUMERIC_H_ */
| 899 | C | 26.272726 | 147 | 0.773081 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/halffloat.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_HALFFLOAT_H_
#define NUMPY_CORE_INCLUDE_NUMPY_HALFFLOAT_H_
#include <Python.h>
#include <numpy/npy_math.h>
#ifdef __cplusplus
extern "C" {
#endif
/*
* Half-precision routines
*/
/* Conversions */
float npy_half_to_float(npy_half h);
double npy_half_to_double(npy_half h);
npy_half npy_float_to_half(float f);
npy_half npy_double_to_half(double d);
/* Comparisons */
int npy_half_eq(npy_half h1, npy_half h2);
int npy_half_ne(npy_half h1, npy_half h2);
int npy_half_le(npy_half h1, npy_half h2);
int npy_half_lt(npy_half h1, npy_half h2);
int npy_half_ge(npy_half h1, npy_half h2);
int npy_half_gt(npy_half h1, npy_half h2);
/* faster *_nonan variants for when you know h1 and h2 are not NaN */
int npy_half_eq_nonan(npy_half h1, npy_half h2);
int npy_half_lt_nonan(npy_half h1, npy_half h2);
int npy_half_le_nonan(npy_half h1, npy_half h2);
/* Miscellaneous functions */
int npy_half_iszero(npy_half h);
int npy_half_isnan(npy_half h);
int npy_half_isinf(npy_half h);
int npy_half_isfinite(npy_half h);
int npy_half_signbit(npy_half h);
npy_half npy_half_copysign(npy_half x, npy_half y);
npy_half npy_half_spacing(npy_half h);
npy_half npy_half_nextafter(npy_half x, npy_half y);
npy_half npy_half_divmod(npy_half x, npy_half y, npy_half *modulus);
/*
* Half-precision constants
*/
#define NPY_HALF_ZERO (0x0000u)
#define NPY_HALF_PZERO (0x0000u)
#define NPY_HALF_NZERO (0x8000u)
#define NPY_HALF_ONE (0x3c00u)
#define NPY_HALF_NEGONE (0xbc00u)
#define NPY_HALF_PINF (0x7c00u)
#define NPY_HALF_NINF (0xfc00u)
#define NPY_HALF_NAN (0x7e00u)
#define NPY_MAX_HALF (0x7bffu)
/*
* Bit-level conversions
*/
npy_uint16 npy_floatbits_to_halfbits(npy_uint32 f);
npy_uint16 npy_doublebits_to_halfbits(npy_uint64 d);
npy_uint32 npy_halfbits_to_floatbits(npy_uint16 h);
npy_uint64 npy_halfbits_to_doublebits(npy_uint16 h);
#ifdef __cplusplus
}
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_HALFFLOAT_H_ */
| 1,959 | C | 26.605633 | 69 | 0.715161 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/_neighborhood_iterator_imp.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_
#error You should not include this header directly
#endif
/*
* Private API (here for inline)
*/
static NPY_INLINE int
_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter);
/*
* Update to next item of the iterator
*
* Note: this simply increment the coordinates vector, last dimension
* incremented first , i.e, for dimension 3
* ...
* -1, -1, -1
* -1, -1, 0
* -1, -1, 1
* ....
* -1, 0, -1
* -1, 0, 0
* ....
* 0, -1, -1
* 0, -1, 0
* ....
*/
#define _UPDATE_COORD_ITER(c) \
wb = iter->coordinates[c] < iter->bounds[c][1]; \
if (wb) { \
iter->coordinates[c] += 1; \
return 0; \
} \
else { \
iter->coordinates[c] = iter->bounds[c][0]; \
}
static NPY_INLINE int
_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter)
{
npy_intp i, wb;
for (i = iter->nd - 1; i >= 0; --i) {
_UPDATE_COORD_ITER(i)
}
return 0;
}
/*
* Version optimized for 2d arrays, manual loop unrolling
*/
static NPY_INLINE int
_PyArrayNeighborhoodIter_IncrCoord2D(PyArrayNeighborhoodIterObject* iter)
{
npy_intp wb;
_UPDATE_COORD_ITER(1)
_UPDATE_COORD_ITER(0)
return 0;
}
#undef _UPDATE_COORD_ITER
/*
* Advance to the next neighbour
*/
static NPY_INLINE int
PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter)
{
_PyArrayNeighborhoodIter_IncrCoord (iter);
iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates);
return 0;
}
/*
* Reset functions
*/
static NPY_INLINE int
PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter)
{
npy_intp i;
for (i = 0; i < iter->nd; ++i) {
iter->coordinates[i] = iter->bounds[i][0];
}
iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates);
return 0;
}
| 1,877 | C | 19.637362 | 81 | 0.633458 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/npy_no_deprecated_api.h | /*
* This include file is provided for inclusion in Cython *.pyd files where
* one would like to define the NPY_NO_DEPRECATED_API macro. It can be
* included by
*
* cdef extern from "npy_no_deprecated_api.h": pass
*
*/
#ifndef NPY_NO_DEPRECATED_API
/* put this check here since there may be multiple includes in C extensions. */
#if defined(NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_) || \
defined(NUMPY_CORE_INCLUDE_NUMPY_NPY_DEPRECATED_API_H) || \
defined(NUMPY_CORE_INCLUDE_NUMPY_OLD_DEFINES_H_)
#error "npy_no_deprecated_api.h" must be first among numpy includes.
#else
#define NPY_NO_DEPRECATED_API NPY_API_VERSION
#endif
#endif /* NPY_NO_DEPRECATED_API */
| 678 | C | 31.333332 | 79 | 0.724189 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/libdivide/libdivide.h | // libdivide.h - Optimized integer division
// https://libdivide.com
//
// Copyright (C) 2010 - 2019 ridiculous_fish, <[email protected]>
// Copyright (C) 2016 - 2019 Kim Walisch, <[email protected]>
//
// libdivide is dual-licensed under the Boost or zlib licenses.
// You may use libdivide under the terms of either of these.
// See LICENSE.txt for more details.
#ifndef NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_
#define NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_
#define LIBDIVIDE_VERSION "3.0"
#define LIBDIVIDE_VERSION_MAJOR 3
#define LIBDIVIDE_VERSION_MINOR 0
#include <stdint.h>
#if defined(__cplusplus)
#include <cstdlib>
#include <cstdio>
#include <type_traits>
#else
#include <stdlib.h>
#include <stdio.h>
#endif
#if defined(LIBDIVIDE_AVX512)
#include <immintrin.h>
#elif defined(LIBDIVIDE_AVX2)
#include <immintrin.h>
#elif defined(LIBDIVIDE_SSE2)
#include <emmintrin.h>
#endif
#if defined(_MSC_VER)
#include <intrin.h>
// disable warning C4146: unary minus operator applied
// to unsigned type, result still unsigned
#pragma warning(disable: 4146)
#define LIBDIVIDE_VC
#endif
#if !defined(__has_builtin)
#define __has_builtin(x) 0
#endif
#if defined(__SIZEOF_INT128__)
#define HAS_INT128_T
// clang-cl on Windows does not yet support 128-bit division
#if !(defined(__clang__) && defined(LIBDIVIDE_VC))
#define HAS_INT128_DIV
#endif
#endif
#if defined(__x86_64__) || defined(_M_X64)
#define LIBDIVIDE_X86_64
#endif
#if defined(__i386__)
#define LIBDIVIDE_i386
#endif
#if defined(__GNUC__) || defined(__clang__)
#define LIBDIVIDE_GCC_STYLE_ASM
#endif
#if defined(__cplusplus) || defined(LIBDIVIDE_VC)
#define LIBDIVIDE_FUNCTION __FUNCTION__
#else
#define LIBDIVIDE_FUNCTION __func__
#endif
#define LIBDIVIDE_ERROR(msg) \
do { \
fprintf(stderr, "libdivide.h:%d: %s(): Error: %s\n", \
__LINE__, LIBDIVIDE_FUNCTION, msg); \
abort(); \
} while (0)
#if defined(LIBDIVIDE_ASSERTIONS_ON)
#define LIBDIVIDE_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "libdivide.h:%d: %s(): Assertion failed: %s\n", \
__LINE__, LIBDIVIDE_FUNCTION, #x); \
abort(); \
} \
} while (0)
#else
#define LIBDIVIDE_ASSERT(x)
#endif
#ifdef __cplusplus
namespace libdivide {
#endif
// pack divider structs to prevent compilers from padding.
// This reduces memory usage by up to 43% when using a large
// array of libdivide dividers and improves performance
// by up to 10% because of reduced memory bandwidth.
#pragma pack(push, 1)
struct libdivide_u32_t {
uint32_t magic;
uint8_t more;
};
struct libdivide_s32_t {
int32_t magic;
uint8_t more;
};
struct libdivide_u64_t {
uint64_t magic;
uint8_t more;
};
struct libdivide_s64_t {
int64_t magic;
uint8_t more;
};
struct libdivide_u32_branchfree_t {
uint32_t magic;
uint8_t more;
};
struct libdivide_s32_branchfree_t {
int32_t magic;
uint8_t more;
};
struct libdivide_u64_branchfree_t {
uint64_t magic;
uint8_t more;
};
struct libdivide_s64_branchfree_t {
int64_t magic;
uint8_t more;
};
#pragma pack(pop)
// Explanation of the "more" field:
//
// * Bits 0-5 is the shift value (for shift path or mult path).
// * Bit 6 is the add indicator for mult path.
// * Bit 7 is set if the divisor is negative. We use bit 7 as the negative
// divisor indicator so that we can efficiently use sign extension to
// create a bitmask with all bits set to 1 (if the divisor is negative)
// or 0 (if the divisor is positive).
//
// u32: [0-4] shift value
// [5] ignored
// [6] add indicator
// magic number of 0 indicates shift path
//
// s32: [0-4] shift value
// [5] ignored
// [6] add indicator
// [7] indicates negative divisor
// magic number of 0 indicates shift path
//
// u64: [0-5] shift value
// [6] add indicator
// magic number of 0 indicates shift path
//
// s64: [0-5] shift value
// [6] add indicator
// [7] indicates negative divisor
// magic number of 0 indicates shift path
//
// In s32 and s64 branchfree modes, the magic number is negated according to
// whether the divisor is negated. In branchfree strategy, it is not negated.
enum {
LIBDIVIDE_32_SHIFT_MASK = 0x1F,
LIBDIVIDE_64_SHIFT_MASK = 0x3F,
LIBDIVIDE_ADD_MARKER = 0x40,
LIBDIVIDE_NEGATIVE_DIVISOR = 0x80
};
static inline struct libdivide_s32_t libdivide_s32_gen(int32_t d);
static inline struct libdivide_u32_t libdivide_u32_gen(uint32_t d);
static inline struct libdivide_s64_t libdivide_s64_gen(int64_t d);
static inline struct libdivide_u64_t libdivide_u64_gen(uint64_t d);
static inline struct libdivide_s32_branchfree_t libdivide_s32_branchfree_gen(int32_t d);
static inline struct libdivide_u32_branchfree_t libdivide_u32_branchfree_gen(uint32_t d);
static inline struct libdivide_s64_branchfree_t libdivide_s64_branchfree_gen(int64_t d);
static inline struct libdivide_u64_branchfree_t libdivide_u64_branchfree_gen(uint64_t d);
static inline int32_t libdivide_s32_do(int32_t numer, const struct libdivide_s32_t *denom);
static inline uint32_t libdivide_u32_do(uint32_t numer, const struct libdivide_u32_t *denom);
static inline int64_t libdivide_s64_do(int64_t numer, const struct libdivide_s64_t *denom);
static inline uint64_t libdivide_u64_do(uint64_t numer, const struct libdivide_u64_t *denom);
static inline int32_t libdivide_s32_branchfree_do(int32_t numer, const struct libdivide_s32_branchfree_t *denom);
static inline uint32_t libdivide_u32_branchfree_do(uint32_t numer, const struct libdivide_u32_branchfree_t *denom);
static inline int64_t libdivide_s64_branchfree_do(int64_t numer, const struct libdivide_s64_branchfree_t *denom);
static inline uint64_t libdivide_u64_branchfree_do(uint64_t numer, const struct libdivide_u64_branchfree_t *denom);
static inline int32_t libdivide_s32_recover(const struct libdivide_s32_t *denom);
static inline uint32_t libdivide_u32_recover(const struct libdivide_u32_t *denom);
static inline int64_t libdivide_s64_recover(const struct libdivide_s64_t *denom);
static inline uint64_t libdivide_u64_recover(const struct libdivide_u64_t *denom);
static inline int32_t libdivide_s32_branchfree_recover(const struct libdivide_s32_branchfree_t *denom);
static inline uint32_t libdivide_u32_branchfree_recover(const struct libdivide_u32_branchfree_t *denom);
static inline int64_t libdivide_s64_branchfree_recover(const struct libdivide_s64_branchfree_t *denom);
static inline uint64_t libdivide_u64_branchfree_recover(const struct libdivide_u64_branchfree_t *denom);
//////// Internal Utility Functions
static inline uint32_t libdivide_mullhi_u32(uint32_t x, uint32_t y) {
uint64_t xl = x, yl = y;
uint64_t rl = xl * yl;
return (uint32_t)(rl >> 32);
}
static inline int32_t libdivide_mullhi_s32(int32_t x, int32_t y) {
int64_t xl = x, yl = y;
int64_t rl = xl * yl;
// needs to be arithmetic shift
return (int32_t)(rl >> 32);
}
static inline uint64_t libdivide_mullhi_u64(uint64_t x, uint64_t y) {
#if defined(LIBDIVIDE_VC) && \
defined(LIBDIVIDE_X86_64)
return __umulh(x, y);
#elif defined(HAS_INT128_T)
__uint128_t xl = x, yl = y;
__uint128_t rl = xl * yl;
return (uint64_t)(rl >> 64);
#else
// full 128 bits are x0 * y0 + (x0 * y1 << 32) + (x1 * y0 << 32) + (x1 * y1 << 64)
uint32_t mask = 0xFFFFFFFF;
uint32_t x0 = (uint32_t)(x & mask);
uint32_t x1 = (uint32_t)(x >> 32);
uint32_t y0 = (uint32_t)(y & mask);
uint32_t y1 = (uint32_t)(y >> 32);
uint32_t x0y0_hi = libdivide_mullhi_u32(x0, y0);
uint64_t x0y1 = x0 * (uint64_t)y1;
uint64_t x1y0 = x1 * (uint64_t)y0;
uint64_t x1y1 = x1 * (uint64_t)y1;
uint64_t temp = x1y0 + x0y0_hi;
uint64_t temp_lo = temp & mask;
uint64_t temp_hi = temp >> 32;
return x1y1 + temp_hi + ((temp_lo + x0y1) >> 32);
#endif
}
static inline int64_t libdivide_mullhi_s64(int64_t x, int64_t y) {
#if defined(LIBDIVIDE_VC) && \
defined(LIBDIVIDE_X86_64)
return __mulh(x, y);
#elif defined(HAS_INT128_T)
__int128_t xl = x, yl = y;
__int128_t rl = xl * yl;
return (int64_t)(rl >> 64);
#else
// full 128 bits are x0 * y0 + (x0 * y1 << 32) + (x1 * y0 << 32) + (x1 * y1 << 64)
uint32_t mask = 0xFFFFFFFF;
uint32_t x0 = (uint32_t)(x & mask);
uint32_t y0 = (uint32_t)(y & mask);
int32_t x1 = (int32_t)(x >> 32);
int32_t y1 = (int32_t)(y >> 32);
uint32_t x0y0_hi = libdivide_mullhi_u32(x0, y0);
int64_t t = x1 * (int64_t)y0 + x0y0_hi;
int64_t w1 = x0 * (int64_t)y1 + (t & mask);
return x1 * (int64_t)y1 + (t >> 32) + (w1 >> 32);
#endif
}
static inline int32_t libdivide_count_leading_zeros32(uint32_t val) {
#if defined(__GNUC__) || \
__has_builtin(__builtin_clz)
// Fast way to count leading zeros
return __builtin_clz(val);
#elif defined(LIBDIVIDE_VC)
unsigned long result;
if (_BitScanReverse(&result, val)) {
return 31 - result;
}
return 0;
#else
if (val == 0)
return 32;
int32_t result = 8;
uint32_t hi = 0xFFU << 24;
while ((val & hi) == 0) {
hi >>= 8;
result += 8;
}
while (val & hi) {
result -= 1;
hi <<= 1;
}
return result;
#endif
}
static inline int32_t libdivide_count_leading_zeros64(uint64_t val) {
#if defined(__GNUC__) || \
__has_builtin(__builtin_clzll)
// Fast way to count leading zeros
return __builtin_clzll(val);
#elif defined(LIBDIVIDE_VC) && defined(_WIN64)
unsigned long result;
if (_BitScanReverse64(&result, val)) {
return 63 - result;
}
return 0;
#else
uint32_t hi = val >> 32;
uint32_t lo = val & 0xFFFFFFFF;
if (hi != 0) return libdivide_count_leading_zeros32(hi);
return 32 + libdivide_count_leading_zeros32(lo);
#endif
}
// libdivide_64_div_32_to_32: divides a 64-bit uint {u1, u0} by a 32-bit
// uint {v}. The result must fit in 32 bits.
// Returns the quotient directly and the remainder in *r
static inline uint32_t libdivide_64_div_32_to_32(uint32_t u1, uint32_t u0, uint32_t v, uint32_t *r) {
#if (defined(LIBDIVIDE_i386) || defined(LIBDIVIDE_X86_64)) && \
defined(LIBDIVIDE_GCC_STYLE_ASM)
uint32_t result;
__asm__("divl %[v]"
: "=a"(result), "=d"(*r)
: [v] "r"(v), "a"(u0), "d"(u1)
);
return result;
#else
uint64_t n = ((uint64_t)u1 << 32) | u0;
uint32_t result = (uint32_t)(n / v);
*r = (uint32_t)(n - result * (uint64_t)v);
return result;
#endif
}
// libdivide_128_div_64_to_64: divides a 128-bit uint {u1, u0} by a 64-bit
// uint {v}. The result must fit in 64 bits.
// Returns the quotient directly and the remainder in *r
static uint64_t libdivide_128_div_64_to_64(uint64_t u1, uint64_t u0, uint64_t v, uint64_t *r) {
#if defined(LIBDIVIDE_X86_64) && \
defined(LIBDIVIDE_GCC_STYLE_ASM)
uint64_t result;
__asm__("divq %[v]"
: "=a"(result), "=d"(*r)
: [v] "r"(v), "a"(u0), "d"(u1)
);
return result;
#elif defined(HAS_INT128_T) && \
defined(HAS_INT128_DIV)
__uint128_t n = ((__uint128_t)u1 << 64) | u0;
uint64_t result = (uint64_t)(n / v);
*r = (uint64_t)(n - result * (__uint128_t)v);
return result;
#else
// Code taken from Hacker's Delight:
// http://www.hackersdelight.org/HDcode/divlu.c.
// License permits inclusion here per:
// http://www.hackersdelight.org/permissions.htm
const uint64_t b = (1ULL << 32); // Number base (32 bits)
uint64_t un1, un0; // Norm. dividend LSD's
uint64_t vn1, vn0; // Norm. divisor digits
uint64_t q1, q0; // Quotient digits
uint64_t un64, un21, un10; // Dividend digit pairs
uint64_t rhat; // A remainder
int32_t s; // Shift amount for norm
// If overflow, set rem. to an impossible value,
// and return the largest possible quotient
if (u1 >= v) {
*r = (uint64_t) -1;
return (uint64_t) -1;
}
// count leading zeros
s = libdivide_count_leading_zeros64(v);
if (s > 0) {
// Normalize divisor
v = v << s;
un64 = (u1 << s) | (u0 >> (64 - s));
un10 = u0 << s; // Shift dividend left
} else {
// Avoid undefined behavior of (u0 >> 64).
// The behavior is undefined if the right operand is
// negative, or greater than or equal to the length
// in bits of the promoted left operand.
un64 = u1;
un10 = u0;
}
// Break divisor up into two 32-bit digits
vn1 = v >> 32;
vn0 = v & 0xFFFFFFFF;
// Break right half of dividend into two digits
un1 = un10 >> 32;
un0 = un10 & 0xFFFFFFFF;
// Compute the first quotient digit, q1
q1 = un64 / vn1;
rhat = un64 - q1 * vn1;
while (q1 >= b || q1 * vn0 > b * rhat + un1) {
q1 = q1 - 1;
rhat = rhat + vn1;
if (rhat >= b)
break;
}
// Multiply and subtract
un21 = un64 * b + un1 - q1 * v;
// Compute the second quotient digit
q0 = un21 / vn1;
rhat = un21 - q0 * vn1;
while (q0 >= b || q0 * vn0 > b * rhat + un0) {
q0 = q0 - 1;
rhat = rhat + vn1;
if (rhat >= b)
break;
}
*r = (un21 * b + un0 - q0 * v) >> s;
return q1 * b + q0;
#endif
}
// Bitshift a u128 in place, left (signed_shift > 0) or right (signed_shift < 0)
static inline void libdivide_u128_shift(uint64_t *u1, uint64_t *u0, int32_t signed_shift) {
if (signed_shift > 0) {
uint32_t shift = signed_shift;
*u1 <<= shift;
*u1 |= *u0 >> (64 - shift);
*u0 <<= shift;
}
else if (signed_shift < 0) {
uint32_t shift = -signed_shift;
*u0 >>= shift;
*u0 |= *u1 << (64 - shift);
*u1 >>= shift;
}
}
// Computes a 128 / 128 -> 64 bit division, with a 128 bit remainder.
static uint64_t libdivide_128_div_128_to_64(uint64_t u_hi, uint64_t u_lo, uint64_t v_hi, uint64_t v_lo, uint64_t *r_hi, uint64_t *r_lo) {
#if defined(HAS_INT128_T) && \
defined(HAS_INT128_DIV)
__uint128_t ufull = u_hi;
__uint128_t vfull = v_hi;
ufull = (ufull << 64) | u_lo;
vfull = (vfull << 64) | v_lo;
uint64_t res = (uint64_t)(ufull / vfull);
__uint128_t remainder = ufull - (vfull * res);
*r_lo = (uint64_t)remainder;
*r_hi = (uint64_t)(remainder >> 64);
return res;
#else
// Adapted from "Unsigned Doubleword Division" in Hacker's Delight
// We want to compute u / v
typedef struct { uint64_t hi; uint64_t lo; } u128_t;
u128_t u = {u_hi, u_lo};
u128_t v = {v_hi, v_lo};
if (v.hi == 0) {
// divisor v is a 64 bit value, so we just need one 128/64 division
// Note that we are simpler than Hacker's Delight here, because we know
// the quotient fits in 64 bits whereas Hacker's Delight demands a full
// 128 bit quotient
*r_hi = 0;
return libdivide_128_div_64_to_64(u.hi, u.lo, v.lo, r_lo);
}
// Here v >= 2**64
// We know that v.hi != 0, so count leading zeros is OK
// We have 0 <= n <= 63
uint32_t n = libdivide_count_leading_zeros64(v.hi);
// Normalize the divisor so its MSB is 1
u128_t v1t = v;
libdivide_u128_shift(&v1t.hi, &v1t.lo, n);
uint64_t v1 = v1t.hi; // i.e. v1 = v1t >> 64
// To ensure no overflow
u128_t u1 = u;
libdivide_u128_shift(&u1.hi, &u1.lo, -1);
// Get quotient from divide unsigned insn.
uint64_t rem_ignored;
uint64_t q1 = libdivide_128_div_64_to_64(u1.hi, u1.lo, v1, &rem_ignored);
// Undo normalization and division of u by 2.
u128_t q0 = {0, q1};
libdivide_u128_shift(&q0.hi, &q0.lo, n);
libdivide_u128_shift(&q0.hi, &q0.lo, -63);
// Make q0 correct or too small by 1
// Equivalent to `if (q0 != 0) q0 = q0 - 1;`
if (q0.hi != 0 || q0.lo != 0) {
q0.hi -= (q0.lo == 0); // borrow
q0.lo -= 1;
}
// Now q0 is correct.
// Compute q0 * v as q0v
// = (q0.hi << 64 + q0.lo) * (v.hi << 64 + v.lo)
// = (q0.hi * v.hi << 128) + (q0.hi * v.lo << 64) +
// (q0.lo * v.hi << 64) + q0.lo * v.lo)
// Each term is 128 bit
// High half of full product (upper 128 bits!) are dropped
u128_t q0v = {0, 0};
q0v.hi = q0.hi*v.lo + q0.lo*v.hi + libdivide_mullhi_u64(q0.lo, v.lo);
q0v.lo = q0.lo*v.lo;
// Compute u - q0v as u_q0v
// This is the remainder
u128_t u_q0v = u;
u_q0v.hi -= q0v.hi + (u.lo < q0v.lo); // second term is borrow
u_q0v.lo -= q0v.lo;
// Check if u_q0v >= v
// This checks if our remainder is larger than the divisor
if ((u_q0v.hi > v.hi) ||
(u_q0v.hi == v.hi && u_q0v.lo >= v.lo)) {
// Increment q0
q0.lo += 1;
q0.hi += (q0.lo == 0); // carry
// Subtract v from remainder
u_q0v.hi -= v.hi + (u_q0v.lo < v.lo);
u_q0v.lo -= v.lo;
}
*r_hi = u_q0v.hi;
*r_lo = u_q0v.lo;
LIBDIVIDE_ASSERT(q0.hi == 0);
return q0.lo;
#endif
}
////////// UINT32
static inline struct libdivide_u32_t libdivide_internal_u32_gen(uint32_t d, int branchfree) {
if (d == 0) {
LIBDIVIDE_ERROR("divider must be != 0");
}
struct libdivide_u32_t result;
uint32_t floor_log_2_d = 31 - libdivide_count_leading_zeros32(d);
// Power of 2
if ((d & (d - 1)) == 0) {
// We need to subtract 1 from the shift value in case of an unsigned
// branchfree divider because there is a hardcoded right shift by 1
// in its division algorithm. Because of this we also need to add back
// 1 in its recovery algorithm.
result.magic = 0;
result.more = (uint8_t)(floor_log_2_d - (branchfree != 0));
} else {
uint8_t more;
uint32_t rem, proposed_m;
proposed_m = libdivide_64_div_32_to_32(1U << floor_log_2_d, 0, d, &rem);
LIBDIVIDE_ASSERT(rem > 0 && rem < d);
const uint32_t e = d - rem;
// This power works if e < 2**floor_log_2_d.
if (!branchfree && (e < (1U << floor_log_2_d))) {
// This power works
more = floor_log_2_d;
} else {
// We have to use the general 33-bit algorithm. We need to compute
// (2**power) / d. However, we already have (2**(power-1))/d and
// its remainder. By doubling both, and then correcting the
// remainder, we can compute the larger division.
// don't care about overflow here - in fact, we expect it
proposed_m += proposed_m;
const uint32_t twice_rem = rem + rem;
if (twice_rem >= d || twice_rem < rem) proposed_m += 1;
more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;
}
result.magic = 1 + proposed_m;
result.more = more;
// result.more's shift should in general be ceil_log_2_d. But if we
// used the smaller power, we subtract one from the shift because we're
// using the smaller power. If we're using the larger power, we
// subtract one from the shift because it's taken care of by the add
// indicator. So floor_log_2_d happens to be correct in both cases.
}
return result;
}
struct libdivide_u32_t libdivide_u32_gen(uint32_t d) {
return libdivide_internal_u32_gen(d, 0);
}
struct libdivide_u32_branchfree_t libdivide_u32_branchfree_gen(uint32_t d) {
if (d == 1) {
LIBDIVIDE_ERROR("branchfree divider must be != 1");
}
struct libdivide_u32_t tmp = libdivide_internal_u32_gen(d, 1);
struct libdivide_u32_branchfree_t ret = {tmp.magic, (uint8_t)(tmp.more & LIBDIVIDE_32_SHIFT_MASK)};
return ret;
}
uint32_t libdivide_u32_do(uint32_t numer, const struct libdivide_u32_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
return numer >> more;
}
else {
uint32_t q = libdivide_mullhi_u32(denom->magic, numer);
if (more & LIBDIVIDE_ADD_MARKER) {
uint32_t t = ((numer - q) >> 1) + q;
return t >> (more & LIBDIVIDE_32_SHIFT_MASK);
}
else {
// All upper bits are 0,
// don't need to mask them off.
return q >> more;
}
}
}
uint32_t libdivide_u32_branchfree_do(uint32_t numer, const struct libdivide_u32_branchfree_t *denom) {
uint32_t q = libdivide_mullhi_u32(denom->magic, numer);
uint32_t t = ((numer - q) >> 1) + q;
return t >> denom->more;
}
uint32_t libdivide_u32_recover(const struct libdivide_u32_t *denom) {
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
if (!denom->magic) {
return 1U << shift;
} else if (!(more & LIBDIVIDE_ADD_MARKER)) {
// We compute q = n/d = n*m / 2^(32 + shift)
// Therefore we have d = 2^(32 + shift) / m
// We need to ceil it.
// We know d is not a power of 2, so m is not a power of 2,
// so we can just add 1 to the floor
uint32_t hi_dividend = 1U << shift;
uint32_t rem_ignored;
return 1 + libdivide_64_div_32_to_32(hi_dividend, 0, denom->magic, &rem_ignored);
} else {
// Here we wish to compute d = 2^(32+shift+1)/(m+2^32).
// Notice (m + 2^32) is a 33 bit number. Use 64 bit division for now
// Also note that shift may be as high as 31, so shift + 1 will
// overflow. So we have to compute it as 2^(32+shift)/(m+2^32), and
// then double the quotient and remainder.
uint64_t half_n = 1ULL << (32 + shift);
uint64_t d = (1ULL << 32) | denom->magic;
// Note that the quotient is guaranteed <= 32 bits, but the remainder
// may need 33!
uint32_t half_q = (uint32_t)(half_n / d);
uint64_t rem = half_n % d;
// We computed 2^(32+shift)/(m+2^32)
// Need to double it, and then add 1 to the quotient if doubling th
// remainder would increase the quotient.
// Note that rem<<1 cannot overflow, since rem < d and d is 33 bits
uint32_t full_q = half_q + half_q + ((rem<<1) >= d);
// We rounded down in gen (hence +1)
return full_q + 1;
}
}
uint32_t libdivide_u32_branchfree_recover(const struct libdivide_u32_branchfree_t *denom) {
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
if (!denom->magic) {
return 1U << (shift + 1);
} else {
// Here we wish to compute d = 2^(32+shift+1)/(m+2^32).
// Notice (m + 2^32) is a 33 bit number. Use 64 bit division for now
// Also note that shift may be as high as 31, so shift + 1 will
// overflow. So we have to compute it as 2^(32+shift)/(m+2^32), and
// then double the quotient and remainder.
uint64_t half_n = 1ULL << (32 + shift);
uint64_t d = (1ULL << 32) | denom->magic;
// Note that the quotient is guaranteed <= 32 bits, but the remainder
// may need 33!
uint32_t half_q = (uint32_t)(half_n / d);
uint64_t rem = half_n % d;
// We computed 2^(32+shift)/(m+2^32)
// Need to double it, and then add 1 to the quotient if doubling th
// remainder would increase the quotient.
// Note that rem<<1 cannot overflow, since rem < d and d is 33 bits
uint32_t full_q = half_q + half_q + ((rem<<1) >= d);
// We rounded down in gen (hence +1)
return full_q + 1;
}
}
/////////// UINT64
static inline struct libdivide_u64_t libdivide_internal_u64_gen(uint64_t d, int branchfree) {
if (d == 0) {
LIBDIVIDE_ERROR("divider must be != 0");
}
struct libdivide_u64_t result;
uint32_t floor_log_2_d = 63 - libdivide_count_leading_zeros64(d);
// Power of 2
if ((d & (d - 1)) == 0) {
// We need to subtract 1 from the shift value in case of an unsigned
// branchfree divider because there is a hardcoded right shift by 1
// in its division algorithm. Because of this we also need to add back
// 1 in its recovery algorithm.
result.magic = 0;
result.more = (uint8_t)(floor_log_2_d - (branchfree != 0));
} else {
uint64_t proposed_m, rem;
uint8_t more;
// (1 << (64 + floor_log_2_d)) / d
proposed_m = libdivide_128_div_64_to_64(1ULL << floor_log_2_d, 0, d, &rem);
LIBDIVIDE_ASSERT(rem > 0 && rem < d);
const uint64_t e = d - rem;
// This power works if e < 2**floor_log_2_d.
if (!branchfree && e < (1ULL << floor_log_2_d)) {
// This power works
more = floor_log_2_d;
} else {
// We have to use the general 65-bit algorithm. We need to compute
// (2**power) / d. However, we already have (2**(power-1))/d and
// its remainder. By doubling both, and then correcting the
// remainder, we can compute the larger division.
// don't care about overflow here - in fact, we expect it
proposed_m += proposed_m;
const uint64_t twice_rem = rem + rem;
if (twice_rem >= d || twice_rem < rem) proposed_m += 1;
more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;
}
result.magic = 1 + proposed_m;
result.more = more;
// result.more's shift should in general be ceil_log_2_d. But if we
// used the smaller power, we subtract one from the shift because we're
// using the smaller power. If we're using the larger power, we
// subtract one from the shift because it's taken care of by the add
// indicator. So floor_log_2_d happens to be correct in both cases,
// which is why we do it outside of the if statement.
}
return result;
}
struct libdivide_u64_t libdivide_u64_gen(uint64_t d) {
return libdivide_internal_u64_gen(d, 0);
}
struct libdivide_u64_branchfree_t libdivide_u64_branchfree_gen(uint64_t d) {
if (d == 1) {
LIBDIVIDE_ERROR("branchfree divider must be != 1");
}
struct libdivide_u64_t tmp = libdivide_internal_u64_gen(d, 1);
struct libdivide_u64_branchfree_t ret = {tmp.magic, (uint8_t)(tmp.more & LIBDIVIDE_64_SHIFT_MASK)};
return ret;
}
uint64_t libdivide_u64_do(uint64_t numer, const struct libdivide_u64_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
return numer >> more;
}
else {
uint64_t q = libdivide_mullhi_u64(denom->magic, numer);
if (more & LIBDIVIDE_ADD_MARKER) {
uint64_t t = ((numer - q) >> 1) + q;
return t >> (more & LIBDIVIDE_64_SHIFT_MASK);
}
else {
// All upper bits are 0,
// don't need to mask them off.
return q >> more;
}
}
}
uint64_t libdivide_u64_branchfree_do(uint64_t numer, const struct libdivide_u64_branchfree_t *denom) {
uint64_t q = libdivide_mullhi_u64(denom->magic, numer);
uint64_t t = ((numer - q) >> 1) + q;
return t >> denom->more;
}
uint64_t libdivide_u64_recover(const struct libdivide_u64_t *denom) {
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
if (!denom->magic) {
return 1ULL << shift;
} else if (!(more & LIBDIVIDE_ADD_MARKER)) {
// We compute q = n/d = n*m / 2^(64 + shift)
// Therefore we have d = 2^(64 + shift) / m
// We need to ceil it.
// We know d is not a power of 2, so m is not a power of 2,
// so we can just add 1 to the floor
uint64_t hi_dividend = 1ULL << shift;
uint64_t rem_ignored;
return 1 + libdivide_128_div_64_to_64(hi_dividend, 0, denom->magic, &rem_ignored);
} else {
// Here we wish to compute d = 2^(64+shift+1)/(m+2^64).
// Notice (m + 2^64) is a 65 bit number. This gets hairy. See
// libdivide_u32_recover for more on what we do here.
// TODO: do something better than 128 bit math
// Full n is a (potentially) 129 bit value
// half_n is a 128 bit value
// Compute the hi half of half_n. Low half is 0.
uint64_t half_n_hi = 1ULL << shift, half_n_lo = 0;
// d is a 65 bit value. The high bit is always set to 1.
const uint64_t d_hi = 1, d_lo = denom->magic;
// Note that the quotient is guaranteed <= 64 bits,
// but the remainder may need 65!
uint64_t r_hi, r_lo;
uint64_t half_q = libdivide_128_div_128_to_64(half_n_hi, half_n_lo, d_hi, d_lo, &r_hi, &r_lo);
// We computed 2^(64+shift)/(m+2^64)
// Double the remainder ('dr') and check if that is larger than d
// Note that d is a 65 bit value, so r1 is small and so r1 + r1
// cannot overflow
uint64_t dr_lo = r_lo + r_lo;
uint64_t dr_hi = r_hi + r_hi + (dr_lo < r_lo); // last term is carry
int dr_exceeds_d = (dr_hi > d_hi) || (dr_hi == d_hi && dr_lo >= d_lo);
uint64_t full_q = half_q + half_q + (dr_exceeds_d ? 1 : 0);
return full_q + 1;
}
}
uint64_t libdivide_u64_branchfree_recover(const struct libdivide_u64_branchfree_t *denom) {
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
if (!denom->magic) {
return 1ULL << (shift + 1);
} else {
// Here we wish to compute d = 2^(64+shift+1)/(m+2^64).
// Notice (m + 2^64) is a 65 bit number. This gets hairy. See
// libdivide_u32_recover for more on what we do here.
// TODO: do something better than 128 bit math
// Full n is a (potentially) 129 bit value
// half_n is a 128 bit value
// Compute the hi half of half_n. Low half is 0.
uint64_t half_n_hi = 1ULL << shift, half_n_lo = 0;
// d is a 65 bit value. The high bit is always set to 1.
const uint64_t d_hi = 1, d_lo = denom->magic;
// Note that the quotient is guaranteed <= 64 bits,
// but the remainder may need 65!
uint64_t r_hi, r_lo;
uint64_t half_q = libdivide_128_div_128_to_64(half_n_hi, half_n_lo, d_hi, d_lo, &r_hi, &r_lo);
// We computed 2^(64+shift)/(m+2^64)
// Double the remainder ('dr') and check if that is larger than d
// Note that d is a 65 bit value, so r1 is small and so r1 + r1
// cannot overflow
uint64_t dr_lo = r_lo + r_lo;
uint64_t dr_hi = r_hi + r_hi + (dr_lo < r_lo); // last term is carry
int dr_exceeds_d = (dr_hi > d_hi) || (dr_hi == d_hi && dr_lo >= d_lo);
uint64_t full_q = half_q + half_q + (dr_exceeds_d ? 1 : 0);
return full_q + 1;
}
}
/////////// SINT32
static inline struct libdivide_s32_t libdivide_internal_s32_gen(int32_t d, int branchfree) {
if (d == 0) {
LIBDIVIDE_ERROR("divider must be != 0");
}
struct libdivide_s32_t result;
// If d is a power of 2, or negative a power of 2, we have to use a shift.
// This is especially important because the magic algorithm fails for -1.
// To check if d is a power of 2 or its inverse, it suffices to check
// whether its absolute value has exactly one bit set. This works even for
// INT_MIN, because abs(INT_MIN) == INT_MIN, and INT_MIN has one bit set
// and is a power of 2.
uint32_t ud = (uint32_t)d;
uint32_t absD = (d < 0) ? -ud : ud;
uint32_t floor_log_2_d = 31 - libdivide_count_leading_zeros32(absD);
// check if exactly one bit is set,
// don't care if absD is 0 since that's divide by zero
if ((absD & (absD - 1)) == 0) {
// Branchfree and normal paths are exactly the same
result.magic = 0;
result.more = floor_log_2_d | (d < 0 ? LIBDIVIDE_NEGATIVE_DIVISOR : 0);
} else {
LIBDIVIDE_ASSERT(floor_log_2_d >= 1);
uint8_t more;
// the dividend here is 2**(floor_log_2_d + 31), so the low 32 bit word
// is 0 and the high word is floor_log_2_d - 1
uint32_t rem, proposed_m;
proposed_m = libdivide_64_div_32_to_32(1U << (floor_log_2_d - 1), 0, absD, &rem);
const uint32_t e = absD - rem;
// We are going to start with a power of floor_log_2_d - 1.
// This works if works if e < 2**floor_log_2_d.
if (!branchfree && e < (1U << floor_log_2_d)) {
// This power works
more = floor_log_2_d - 1;
} else {
// We need to go one higher. This should not make proposed_m
// overflow, but it will make it negative when interpreted as an
// int32_t.
proposed_m += proposed_m;
const uint32_t twice_rem = rem + rem;
if (twice_rem >= absD || twice_rem < rem) proposed_m += 1;
more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;
}
proposed_m += 1;
int32_t magic = (int32_t)proposed_m;
// Mark if we are negative. Note we only negate the magic number in the
// branchfull case.
if (d < 0) {
more |= LIBDIVIDE_NEGATIVE_DIVISOR;
if (!branchfree) {
magic = -magic;
}
}
result.more = more;
result.magic = magic;
}
return result;
}
struct libdivide_s32_t libdivide_s32_gen(int32_t d) {
return libdivide_internal_s32_gen(d, 0);
}
struct libdivide_s32_branchfree_t libdivide_s32_branchfree_gen(int32_t d) {
struct libdivide_s32_t tmp = libdivide_internal_s32_gen(d, 1);
struct libdivide_s32_branchfree_t result = {tmp.magic, tmp.more};
return result;
}
int32_t libdivide_s32_do(int32_t numer, const struct libdivide_s32_t *denom) {
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
if (!denom->magic) {
uint32_t sign = (int8_t)more >> 7;
uint32_t mask = (1U << shift) - 1;
uint32_t uq = numer + ((numer >> 31) & mask);
int32_t q = (int32_t)uq;
q >>= shift;
q = (q ^ sign) - sign;
return q;
} else {
uint32_t uq = (uint32_t)libdivide_mullhi_s32(denom->magic, numer);
if (more & LIBDIVIDE_ADD_MARKER) {
// must be arithmetic shift and then sign extend
int32_t sign = (int8_t)more >> 7;
// q += (more < 0 ? -numer : numer)
// cast required to avoid UB
uq += ((uint32_t)numer ^ sign) - sign;
}
int32_t q = (int32_t)uq;
q >>= shift;
q += (q < 0);
return q;
}
}
int32_t libdivide_s32_branchfree_do(int32_t numer, const struct libdivide_s32_branchfree_t *denom) {
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
// must be arithmetic shift and then sign extend
int32_t sign = (int8_t)more >> 7;
int32_t magic = denom->magic;
int32_t q = libdivide_mullhi_s32(magic, numer);
q += numer;
// If q is non-negative, we have nothing to do
// If q is negative, we want to add either (2**shift)-1 if d is a power of
// 2, or (2**shift) if it is not a power of 2
uint32_t is_power_of_2 = (magic == 0);
uint32_t q_sign = (uint32_t)(q >> 31);
q += q_sign & ((1U << shift) - is_power_of_2);
// Now arithmetic right shift
q >>= shift;
// Negate if needed
q = (q ^ sign) - sign;
return q;
}
int32_t libdivide_s32_recover(const struct libdivide_s32_t *denom) {
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
if (!denom->magic) {
uint32_t absD = 1U << shift;
if (more & LIBDIVIDE_NEGATIVE_DIVISOR) {
absD = -absD;
}
return (int32_t)absD;
} else {
// Unsigned math is much easier
// We negate the magic number only in the branchfull case, and we don't
// know which case we're in. However we have enough information to
// determine the correct sign of the magic number. The divisor was
// negative if LIBDIVIDE_NEGATIVE_DIVISOR is set. If ADD_MARKER is set,
// the magic number's sign is opposite that of the divisor.
// We want to compute the positive magic number.
int negative_divisor = (more & LIBDIVIDE_NEGATIVE_DIVISOR);
int magic_was_negated = (more & LIBDIVIDE_ADD_MARKER)
? denom->magic > 0 : denom->magic < 0;
// Handle the power of 2 case (including branchfree)
if (denom->magic == 0) {
int32_t result = 1U << shift;
return negative_divisor ? -result : result;
}
uint32_t d = (uint32_t)(magic_was_negated ? -denom->magic : denom->magic);
uint64_t n = 1ULL << (32 + shift); // this shift cannot exceed 30
uint32_t q = (uint32_t)(n / d);
int32_t result = (int32_t)q;
result += 1;
return negative_divisor ? -result : result;
}
}
int32_t libdivide_s32_branchfree_recover(const struct libdivide_s32_branchfree_t *denom) {
return libdivide_s32_recover((const struct libdivide_s32_t *)denom);
}
///////////// SINT64
static inline struct libdivide_s64_t libdivide_internal_s64_gen(int64_t d, int branchfree) {
if (d == 0) {
LIBDIVIDE_ERROR("divider must be != 0");
}
struct libdivide_s64_t result;
// If d is a power of 2, or negative a power of 2, we have to use a shift.
// This is especially important because the magic algorithm fails for -1.
// To check if d is a power of 2 or its inverse, it suffices to check
// whether its absolute value has exactly one bit set. This works even for
// INT_MIN, because abs(INT_MIN) == INT_MIN, and INT_MIN has one bit set
// and is a power of 2.
uint64_t ud = (uint64_t)d;
uint64_t absD = (d < 0) ? -ud : ud;
uint32_t floor_log_2_d = 63 - libdivide_count_leading_zeros64(absD);
// check if exactly one bit is set,
// don't care if absD is 0 since that's divide by zero
if ((absD & (absD - 1)) == 0) {
// Branchfree and non-branchfree cases are the same
result.magic = 0;
result.more = floor_log_2_d | (d < 0 ? LIBDIVIDE_NEGATIVE_DIVISOR : 0);
} else {
// the dividend here is 2**(floor_log_2_d + 63), so the low 64 bit word
// is 0 and the high word is floor_log_2_d - 1
uint8_t more;
uint64_t rem, proposed_m;
proposed_m = libdivide_128_div_64_to_64(1ULL << (floor_log_2_d - 1), 0, absD, &rem);
const uint64_t e = absD - rem;
// We are going to start with a power of floor_log_2_d - 1.
// This works if works if e < 2**floor_log_2_d.
if (!branchfree && e < (1ULL << floor_log_2_d)) {
// This power works
more = floor_log_2_d - 1;
} else {
// We need to go one higher. This should not make proposed_m
// overflow, but it will make it negative when interpreted as an
// int32_t.
proposed_m += proposed_m;
const uint64_t twice_rem = rem + rem;
if (twice_rem >= absD || twice_rem < rem) proposed_m += 1;
// note that we only set the LIBDIVIDE_NEGATIVE_DIVISOR bit if we
// also set ADD_MARKER this is an annoying optimization that
// enables algorithm #4 to avoid the mask. However we always set it
// in the branchfree case
more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;
}
proposed_m += 1;
int64_t magic = (int64_t)proposed_m;
// Mark if we are negative
if (d < 0) {
more |= LIBDIVIDE_NEGATIVE_DIVISOR;
if (!branchfree) {
magic = -magic;
}
}
result.more = more;
result.magic = magic;
}
return result;
}
struct libdivide_s64_t libdivide_s64_gen(int64_t d) {
return libdivide_internal_s64_gen(d, 0);
}
struct libdivide_s64_branchfree_t libdivide_s64_branchfree_gen(int64_t d) {
struct libdivide_s64_t tmp = libdivide_internal_s64_gen(d, 1);
struct libdivide_s64_branchfree_t ret = {tmp.magic, tmp.more};
return ret;
}
int64_t libdivide_s64_do(int64_t numer, const struct libdivide_s64_t *denom) {
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
if (!denom->magic) { // shift path
uint64_t mask = (1ULL << shift) - 1;
uint64_t uq = numer + ((numer >> 63) & mask);
int64_t q = (int64_t)uq;
q >>= shift;
// must be arithmetic shift and then sign-extend
int64_t sign = (int8_t)more >> 7;
q = (q ^ sign) - sign;
return q;
} else {
uint64_t uq = (uint64_t)libdivide_mullhi_s64(denom->magic, numer);
if (more & LIBDIVIDE_ADD_MARKER) {
// must be arithmetic shift and then sign extend
int64_t sign = (int8_t)more >> 7;
// q += (more < 0 ? -numer : numer)
// cast required to avoid UB
uq += ((uint64_t)numer ^ sign) - sign;
}
int64_t q = (int64_t)uq;
q >>= shift;
q += (q < 0);
return q;
}
}
int64_t libdivide_s64_branchfree_do(int64_t numer, const struct libdivide_s64_branchfree_t *denom) {
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
// must be arithmetic shift and then sign extend
int64_t sign = (int8_t)more >> 7;
int64_t magic = denom->magic;
int64_t q = libdivide_mullhi_s64(magic, numer);
q += numer;
// If q is non-negative, we have nothing to do.
// If q is negative, we want to add either (2**shift)-1 if d is a power of
// 2, or (2**shift) if it is not a power of 2.
uint64_t is_power_of_2 = (magic == 0);
uint64_t q_sign = (uint64_t)(q >> 63);
q += q_sign & ((1ULL << shift) - is_power_of_2);
// Arithmetic right shift
q >>= shift;
// Negate if needed
q = (q ^ sign) - sign;
return q;
}
int64_t libdivide_s64_recover(const struct libdivide_s64_t *denom) {
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
if (denom->magic == 0) { // shift path
uint64_t absD = 1ULL << shift;
if (more & LIBDIVIDE_NEGATIVE_DIVISOR) {
absD = -absD;
}
return (int64_t)absD;
} else {
// Unsigned math is much easier
int negative_divisor = (more & LIBDIVIDE_NEGATIVE_DIVISOR);
int magic_was_negated = (more & LIBDIVIDE_ADD_MARKER)
? denom->magic > 0 : denom->magic < 0;
uint64_t d = (uint64_t)(magic_was_negated ? -denom->magic : denom->magic);
uint64_t n_hi = 1ULL << shift, n_lo = 0;
uint64_t rem_ignored;
uint64_t q = libdivide_128_div_64_to_64(n_hi, n_lo, d, &rem_ignored);
int64_t result = (int64_t)(q + 1);
if (negative_divisor) {
result = -result;
}
return result;
}
}
int64_t libdivide_s64_branchfree_recover(const struct libdivide_s64_branchfree_t *denom) {
return libdivide_s64_recover((const struct libdivide_s64_t *)denom);
}
#if defined(LIBDIVIDE_AVX512)
static inline __m512i libdivide_u32_do_vector(__m512i numers, const struct libdivide_u32_t *denom);
static inline __m512i libdivide_s32_do_vector(__m512i numers, const struct libdivide_s32_t *denom);
static inline __m512i libdivide_u64_do_vector(__m512i numers, const struct libdivide_u64_t *denom);
static inline __m512i libdivide_s64_do_vector(__m512i numers, const struct libdivide_s64_t *denom);
static inline __m512i libdivide_u32_branchfree_do_vector(__m512i numers, const struct libdivide_u32_branchfree_t *denom);
static inline __m512i libdivide_s32_branchfree_do_vector(__m512i numers, const struct libdivide_s32_branchfree_t *denom);
static inline __m512i libdivide_u64_branchfree_do_vector(__m512i numers, const struct libdivide_u64_branchfree_t *denom);
static inline __m512i libdivide_s64_branchfree_do_vector(__m512i numers, const struct libdivide_s64_branchfree_t *denom);
//////// Internal Utility Functions
static inline __m512i libdivide_s64_signbits(__m512i v) {;
return _mm512_srai_epi64(v, 63);
}
static inline __m512i libdivide_s64_shift_right_vector(__m512i v, int amt) {
return _mm512_srai_epi64(v, amt);
}
// Here, b is assumed to contain one 32-bit value repeated.
static inline __m512i libdivide_mullhi_u32_vector(__m512i a, __m512i b) {
__m512i hi_product_0Z2Z = _mm512_srli_epi64(_mm512_mul_epu32(a, b), 32);
__m512i a1X3X = _mm512_srli_epi64(a, 32);
__m512i mask = _mm512_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0);
__m512i hi_product_Z1Z3 = _mm512_and_si512(_mm512_mul_epu32(a1X3X, b), mask);
return _mm512_or_si512(hi_product_0Z2Z, hi_product_Z1Z3);
}
// b is one 32-bit value repeated.
static inline __m512i libdivide_mullhi_s32_vector(__m512i a, __m512i b) {
__m512i hi_product_0Z2Z = _mm512_srli_epi64(_mm512_mul_epi32(a, b), 32);
__m512i a1X3X = _mm512_srli_epi64(a, 32);
__m512i mask = _mm512_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0);
__m512i hi_product_Z1Z3 = _mm512_and_si512(_mm512_mul_epi32(a1X3X, b), mask);
return _mm512_or_si512(hi_product_0Z2Z, hi_product_Z1Z3);
}
// Here, y is assumed to contain one 64-bit value repeated.
// https://stackoverflow.com/a/28827013
static inline __m512i libdivide_mullhi_u64_vector(__m512i x, __m512i y) {
__m512i lomask = _mm512_set1_epi64(0xffffffff);
__m512i xh = _mm512_shuffle_epi32(x, (_MM_PERM_ENUM) 0xB1);
__m512i yh = _mm512_shuffle_epi32(y, (_MM_PERM_ENUM) 0xB1);
__m512i w0 = _mm512_mul_epu32(x, y);
__m512i w1 = _mm512_mul_epu32(x, yh);
__m512i w2 = _mm512_mul_epu32(xh, y);
__m512i w3 = _mm512_mul_epu32(xh, yh);
__m512i w0h = _mm512_srli_epi64(w0, 32);
__m512i s1 = _mm512_add_epi64(w1, w0h);
__m512i s1l = _mm512_and_si512(s1, lomask);
__m512i s1h = _mm512_srli_epi64(s1, 32);
__m512i s2 = _mm512_add_epi64(w2, s1l);
__m512i s2h = _mm512_srli_epi64(s2, 32);
__m512i hi = _mm512_add_epi64(w3, s1h);
hi = _mm512_add_epi64(hi, s2h);
return hi;
}
// y is one 64-bit value repeated.
static inline __m512i libdivide_mullhi_s64_vector(__m512i x, __m512i y) {
__m512i p = libdivide_mullhi_u64_vector(x, y);
__m512i t1 = _mm512_and_si512(libdivide_s64_signbits(x), y);
__m512i t2 = _mm512_and_si512(libdivide_s64_signbits(y), x);
p = _mm512_sub_epi64(p, t1);
p = _mm512_sub_epi64(p, t2);
return p;
}
////////// UINT32
__m512i libdivide_u32_do_vector(__m512i numers, const struct libdivide_u32_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
return _mm512_srli_epi32(numers, more);
}
else {
__m512i q = libdivide_mullhi_u32_vector(numers, _mm512_set1_epi32(denom->magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// uint32_t t = ((numer - q) >> 1) + q;
// return t >> denom->shift;
uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
__m512i t = _mm512_add_epi32(_mm512_srli_epi32(_mm512_sub_epi32(numers, q), 1), q);
return _mm512_srli_epi32(t, shift);
}
else {
return _mm512_srli_epi32(q, more);
}
}
}
__m512i libdivide_u32_branchfree_do_vector(__m512i numers, const struct libdivide_u32_branchfree_t *denom) {
__m512i q = libdivide_mullhi_u32_vector(numers, _mm512_set1_epi32(denom->magic));
__m512i t = _mm512_add_epi32(_mm512_srli_epi32(_mm512_sub_epi32(numers, q), 1), q);
return _mm512_srli_epi32(t, denom->more);
}
////////// UINT64
__m512i libdivide_u64_do_vector(__m512i numers, const struct libdivide_u64_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
return _mm512_srli_epi64(numers, more);
}
else {
__m512i q = libdivide_mullhi_u64_vector(numers, _mm512_set1_epi64(denom->magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// uint32_t t = ((numer - q) >> 1) + q;
// return t >> denom->shift;
uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
__m512i t = _mm512_add_epi64(_mm512_srli_epi64(_mm512_sub_epi64(numers, q), 1), q);
return _mm512_srli_epi64(t, shift);
}
else {
return _mm512_srli_epi64(q, more);
}
}
}
__m512i libdivide_u64_branchfree_do_vector(__m512i numers, const struct libdivide_u64_branchfree_t *denom) {
__m512i q = libdivide_mullhi_u64_vector(numers, _mm512_set1_epi64(denom->magic));
__m512i t = _mm512_add_epi64(_mm512_srli_epi64(_mm512_sub_epi64(numers, q), 1), q);
return _mm512_srli_epi64(t, denom->more);
}
////////// SINT32
__m512i libdivide_s32_do_vector(__m512i numers, const struct libdivide_s32_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
uint32_t mask = (1U << shift) - 1;
__m512i roundToZeroTweak = _mm512_set1_epi32(mask);
// q = numer + ((numer >> 31) & roundToZeroTweak);
__m512i q = _mm512_add_epi32(numers, _mm512_and_si512(_mm512_srai_epi32(numers, 31), roundToZeroTweak));
q = _mm512_srai_epi32(q, shift);
__m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
// q = (q ^ sign) - sign;
q = _mm512_sub_epi32(_mm512_xor_si512(q, sign), sign);
return q;
}
else {
__m512i q = libdivide_mullhi_s32_vector(numers, _mm512_set1_epi32(denom->magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// must be arithmetic shift
__m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
// q += ((numer ^ sign) - sign);
q = _mm512_add_epi32(q, _mm512_sub_epi32(_mm512_xor_si512(numers, sign), sign));
}
// q >>= shift
q = _mm512_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK);
q = _mm512_add_epi32(q, _mm512_srli_epi32(q, 31)); // q += (q < 0)
return q;
}
}
__m512i libdivide_s32_branchfree_do_vector(__m512i numers, const struct libdivide_s32_branchfree_t *denom) {
int32_t magic = denom->magic;
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
// must be arithmetic shift
__m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
__m512i q = libdivide_mullhi_s32_vector(numers, _mm512_set1_epi32(magic));
q = _mm512_add_epi32(q, numers); // q += numers
// If q is non-negative, we have nothing to do
// If q is negative, we want to add either (2**shift)-1 if d is
// a power of 2, or (2**shift) if it is not a power of 2
uint32_t is_power_of_2 = (magic == 0);
__m512i q_sign = _mm512_srai_epi32(q, 31); // q_sign = q >> 31
__m512i mask = _mm512_set1_epi32((1U << shift) - is_power_of_2);
q = _mm512_add_epi32(q, _mm512_and_si512(q_sign, mask)); // q = q + (q_sign & mask)
q = _mm512_srai_epi32(q, shift); // q >>= shift
q = _mm512_sub_epi32(_mm512_xor_si512(q, sign), sign); // q = (q ^ sign) - sign
return q;
}
////////// SINT64
__m512i libdivide_s64_do_vector(__m512i numers, const struct libdivide_s64_t *denom) {
uint8_t more = denom->more;
int64_t magic = denom->magic;
if (magic == 0) { // shift path
uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
uint64_t mask = (1ULL << shift) - 1;
__m512i roundToZeroTweak = _mm512_set1_epi64(mask);
// q = numer + ((numer >> 63) & roundToZeroTweak);
__m512i q = _mm512_add_epi64(numers, _mm512_and_si512(libdivide_s64_signbits(numers), roundToZeroTweak));
q = libdivide_s64_shift_right_vector(q, shift);
__m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
// q = (q ^ sign) - sign;
q = _mm512_sub_epi64(_mm512_xor_si512(q, sign), sign);
return q;
}
else {
__m512i q = libdivide_mullhi_s64_vector(numers, _mm512_set1_epi64(magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// must be arithmetic shift
__m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
// q += ((numer ^ sign) - sign);
q = _mm512_add_epi64(q, _mm512_sub_epi64(_mm512_xor_si512(numers, sign), sign));
}
// q >>= denom->mult_path.shift
q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK);
q = _mm512_add_epi64(q, _mm512_srli_epi64(q, 63)); // q += (q < 0)
return q;
}
}
__m512i libdivide_s64_branchfree_do_vector(__m512i numers, const struct libdivide_s64_branchfree_t *denom) {
int64_t magic = denom->magic;
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
// must be arithmetic shift
__m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
// libdivide_mullhi_s64(numers, magic);
__m512i q = libdivide_mullhi_s64_vector(numers, _mm512_set1_epi64(magic));
q = _mm512_add_epi64(q, numers); // q += numers
// If q is non-negative, we have nothing to do.
// If q is negative, we want to add either (2**shift)-1 if d is
// a power of 2, or (2**shift) if it is not a power of 2.
uint32_t is_power_of_2 = (magic == 0);
__m512i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63
__m512i mask = _mm512_set1_epi64((1ULL << shift) - is_power_of_2);
q = _mm512_add_epi64(q, _mm512_and_si512(q_sign, mask)); // q = q + (q_sign & mask)
q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift
q = _mm512_sub_epi64(_mm512_xor_si512(q, sign), sign); // q = (q ^ sign) - sign
return q;
}
#elif defined(LIBDIVIDE_AVX2)
static inline __m256i libdivide_u32_do_vector(__m256i numers, const struct libdivide_u32_t *denom);
static inline __m256i libdivide_s32_do_vector(__m256i numers, const struct libdivide_s32_t *denom);
static inline __m256i libdivide_u64_do_vector(__m256i numers, const struct libdivide_u64_t *denom);
static inline __m256i libdivide_s64_do_vector(__m256i numers, const struct libdivide_s64_t *denom);
static inline __m256i libdivide_u32_branchfree_do_vector(__m256i numers, const struct libdivide_u32_branchfree_t *denom);
static inline __m256i libdivide_s32_branchfree_do_vector(__m256i numers, const struct libdivide_s32_branchfree_t *denom);
static inline __m256i libdivide_u64_branchfree_do_vector(__m256i numers, const struct libdivide_u64_branchfree_t *denom);
static inline __m256i libdivide_s64_branchfree_do_vector(__m256i numers, const struct libdivide_s64_branchfree_t *denom);
//////// Internal Utility Functions
// Implementation of _mm256_srai_epi64(v, 63) (from AVX512).
static inline __m256i libdivide_s64_signbits(__m256i v) {
__m256i hiBitsDuped = _mm256_shuffle_epi32(v, _MM_SHUFFLE(3, 3, 1, 1));
__m256i signBits = _mm256_srai_epi32(hiBitsDuped, 31);
return signBits;
}
// Implementation of _mm256_srai_epi64 (from AVX512).
static inline __m256i libdivide_s64_shift_right_vector(__m256i v, int amt) {
const int b = 64 - amt;
__m256i m = _mm256_set1_epi64x(1ULL << (b - 1));
__m256i x = _mm256_srli_epi64(v, amt);
__m256i result = _mm256_sub_epi64(_mm256_xor_si256(x, m), m);
return result;
}
// Here, b is assumed to contain one 32-bit value repeated.
static inline __m256i libdivide_mullhi_u32_vector(__m256i a, __m256i b) {
__m256i hi_product_0Z2Z = _mm256_srli_epi64(_mm256_mul_epu32(a, b), 32);
__m256i a1X3X = _mm256_srli_epi64(a, 32);
__m256i mask = _mm256_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0);
__m256i hi_product_Z1Z3 = _mm256_and_si256(_mm256_mul_epu32(a1X3X, b), mask);
return _mm256_or_si256(hi_product_0Z2Z, hi_product_Z1Z3);
}
// b is one 32-bit value repeated.
static inline __m256i libdivide_mullhi_s32_vector(__m256i a, __m256i b) {
__m256i hi_product_0Z2Z = _mm256_srli_epi64(_mm256_mul_epi32(a, b), 32);
__m256i a1X3X = _mm256_srli_epi64(a, 32);
__m256i mask = _mm256_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0);
__m256i hi_product_Z1Z3 = _mm256_and_si256(_mm256_mul_epi32(a1X3X, b), mask);
return _mm256_or_si256(hi_product_0Z2Z, hi_product_Z1Z3);
}
// Here, y is assumed to contain one 64-bit value repeated.
// https://stackoverflow.com/a/28827013
static inline __m256i libdivide_mullhi_u64_vector(__m256i x, __m256i y) {
__m256i lomask = _mm256_set1_epi64x(0xffffffff);
__m256i xh = _mm256_shuffle_epi32(x, 0xB1); // x0l, x0h, x1l, x1h
__m256i yh = _mm256_shuffle_epi32(y, 0xB1); // y0l, y0h, y1l, y1h
__m256i w0 = _mm256_mul_epu32(x, y); // x0l*y0l, x1l*y1l
__m256i w1 = _mm256_mul_epu32(x, yh); // x0l*y0h, x1l*y1h
__m256i w2 = _mm256_mul_epu32(xh, y); // x0h*y0l, x1h*y0l
__m256i w3 = _mm256_mul_epu32(xh, yh); // x0h*y0h, x1h*y1h
__m256i w0h = _mm256_srli_epi64(w0, 32);
__m256i s1 = _mm256_add_epi64(w1, w0h);
__m256i s1l = _mm256_and_si256(s1, lomask);
__m256i s1h = _mm256_srli_epi64(s1, 32);
__m256i s2 = _mm256_add_epi64(w2, s1l);
__m256i s2h = _mm256_srli_epi64(s2, 32);
__m256i hi = _mm256_add_epi64(w3, s1h);
hi = _mm256_add_epi64(hi, s2h);
return hi;
}
// y is one 64-bit value repeated.
static inline __m256i libdivide_mullhi_s64_vector(__m256i x, __m256i y) {
__m256i p = libdivide_mullhi_u64_vector(x, y);
__m256i t1 = _mm256_and_si256(libdivide_s64_signbits(x), y);
__m256i t2 = _mm256_and_si256(libdivide_s64_signbits(y), x);
p = _mm256_sub_epi64(p, t1);
p = _mm256_sub_epi64(p, t2);
return p;
}
////////// UINT32
__m256i libdivide_u32_do_vector(__m256i numers, const struct libdivide_u32_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
return _mm256_srli_epi32(numers, more);
}
else {
__m256i q = libdivide_mullhi_u32_vector(numers, _mm256_set1_epi32(denom->magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// uint32_t t = ((numer - q) >> 1) + q;
// return t >> denom->shift;
uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
__m256i t = _mm256_add_epi32(_mm256_srli_epi32(_mm256_sub_epi32(numers, q), 1), q);
return _mm256_srli_epi32(t, shift);
}
else {
return _mm256_srli_epi32(q, more);
}
}
}
__m256i libdivide_u32_branchfree_do_vector(__m256i numers, const struct libdivide_u32_branchfree_t *denom) {
__m256i q = libdivide_mullhi_u32_vector(numers, _mm256_set1_epi32(denom->magic));
__m256i t = _mm256_add_epi32(_mm256_srli_epi32(_mm256_sub_epi32(numers, q), 1), q);
return _mm256_srli_epi32(t, denom->more);
}
////////// UINT64
__m256i libdivide_u64_do_vector(__m256i numers, const struct libdivide_u64_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
return _mm256_srli_epi64(numers, more);
}
else {
__m256i q = libdivide_mullhi_u64_vector(numers, _mm256_set1_epi64x(denom->magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// uint32_t t = ((numer - q) >> 1) + q;
// return t >> denom->shift;
uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
__m256i t = _mm256_add_epi64(_mm256_srli_epi64(_mm256_sub_epi64(numers, q), 1), q);
return _mm256_srli_epi64(t, shift);
}
else {
return _mm256_srli_epi64(q, more);
}
}
}
__m256i libdivide_u64_branchfree_do_vector(__m256i numers, const struct libdivide_u64_branchfree_t *denom) {
__m256i q = libdivide_mullhi_u64_vector(numers, _mm256_set1_epi64x(denom->magic));
__m256i t = _mm256_add_epi64(_mm256_srli_epi64(_mm256_sub_epi64(numers, q), 1), q);
return _mm256_srli_epi64(t, denom->more);
}
////////// SINT32
__m256i libdivide_s32_do_vector(__m256i numers, const struct libdivide_s32_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
uint32_t mask = (1U << shift) - 1;
__m256i roundToZeroTweak = _mm256_set1_epi32(mask);
// q = numer + ((numer >> 31) & roundToZeroTweak);
__m256i q = _mm256_add_epi32(numers, _mm256_and_si256(_mm256_srai_epi32(numers, 31), roundToZeroTweak));
q = _mm256_srai_epi32(q, shift);
__m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
// q = (q ^ sign) - sign;
q = _mm256_sub_epi32(_mm256_xor_si256(q, sign), sign);
return q;
}
else {
__m256i q = libdivide_mullhi_s32_vector(numers, _mm256_set1_epi32(denom->magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// must be arithmetic shift
__m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
// q += ((numer ^ sign) - sign);
q = _mm256_add_epi32(q, _mm256_sub_epi32(_mm256_xor_si256(numers, sign), sign));
}
// q >>= shift
q = _mm256_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK);
q = _mm256_add_epi32(q, _mm256_srli_epi32(q, 31)); // q += (q < 0)
return q;
}
}
__m256i libdivide_s32_branchfree_do_vector(__m256i numers, const struct libdivide_s32_branchfree_t *denom) {
int32_t magic = denom->magic;
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
// must be arithmetic shift
__m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
__m256i q = libdivide_mullhi_s32_vector(numers, _mm256_set1_epi32(magic));
q = _mm256_add_epi32(q, numers); // q += numers
// If q is non-negative, we have nothing to do
// If q is negative, we want to add either (2**shift)-1 if d is
// a power of 2, or (2**shift) if it is not a power of 2
uint32_t is_power_of_2 = (magic == 0);
__m256i q_sign = _mm256_srai_epi32(q, 31); // q_sign = q >> 31
__m256i mask = _mm256_set1_epi32((1U << shift) - is_power_of_2);
q = _mm256_add_epi32(q, _mm256_and_si256(q_sign, mask)); // q = q + (q_sign & mask)
q = _mm256_srai_epi32(q, shift); // q >>= shift
q = _mm256_sub_epi32(_mm256_xor_si256(q, sign), sign); // q = (q ^ sign) - sign
return q;
}
////////// SINT64
__m256i libdivide_s64_do_vector(__m256i numers, const struct libdivide_s64_t *denom) {
uint8_t more = denom->more;
int64_t magic = denom->magic;
if (magic == 0) { // shift path
uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
uint64_t mask = (1ULL << shift) - 1;
__m256i roundToZeroTweak = _mm256_set1_epi64x(mask);
// q = numer + ((numer >> 63) & roundToZeroTweak);
__m256i q = _mm256_add_epi64(numers, _mm256_and_si256(libdivide_s64_signbits(numers), roundToZeroTweak));
q = libdivide_s64_shift_right_vector(q, shift);
__m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
// q = (q ^ sign) - sign;
q = _mm256_sub_epi64(_mm256_xor_si256(q, sign), sign);
return q;
}
else {
__m256i q = libdivide_mullhi_s64_vector(numers, _mm256_set1_epi64x(magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// must be arithmetic shift
__m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
// q += ((numer ^ sign) - sign);
q = _mm256_add_epi64(q, _mm256_sub_epi64(_mm256_xor_si256(numers, sign), sign));
}
// q >>= denom->mult_path.shift
q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK);
q = _mm256_add_epi64(q, _mm256_srli_epi64(q, 63)); // q += (q < 0)
return q;
}
}
__m256i libdivide_s64_branchfree_do_vector(__m256i numers, const struct libdivide_s64_branchfree_t *denom) {
int64_t magic = denom->magic;
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
// must be arithmetic shift
__m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
// libdivide_mullhi_s64(numers, magic);
__m256i q = libdivide_mullhi_s64_vector(numers, _mm256_set1_epi64x(magic));
q = _mm256_add_epi64(q, numers); // q += numers
// If q is non-negative, we have nothing to do.
// If q is negative, we want to add either (2**shift)-1 if d is
// a power of 2, or (2**shift) if it is not a power of 2.
uint32_t is_power_of_2 = (magic == 0);
__m256i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63
__m256i mask = _mm256_set1_epi64x((1ULL << shift) - is_power_of_2);
q = _mm256_add_epi64(q, _mm256_and_si256(q_sign, mask)); // q = q + (q_sign & mask)
q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift
q = _mm256_sub_epi64(_mm256_xor_si256(q, sign), sign); // q = (q ^ sign) - sign
return q;
}
#elif defined(LIBDIVIDE_SSE2)
static inline __m128i libdivide_u32_do_vector(__m128i numers, const struct libdivide_u32_t *denom);
static inline __m128i libdivide_s32_do_vector(__m128i numers, const struct libdivide_s32_t *denom);
static inline __m128i libdivide_u64_do_vector(__m128i numers, const struct libdivide_u64_t *denom);
static inline __m128i libdivide_s64_do_vector(__m128i numers, const struct libdivide_s64_t *denom);
static inline __m128i libdivide_u32_branchfree_do_vector(__m128i numers, const struct libdivide_u32_branchfree_t *denom);
static inline __m128i libdivide_s32_branchfree_do_vector(__m128i numers, const struct libdivide_s32_branchfree_t *denom);
static inline __m128i libdivide_u64_branchfree_do_vector(__m128i numers, const struct libdivide_u64_branchfree_t *denom);
static inline __m128i libdivide_s64_branchfree_do_vector(__m128i numers, const struct libdivide_s64_branchfree_t *denom);
//////// Internal Utility Functions
// Implementation of _mm_srai_epi64(v, 63) (from AVX512).
static inline __m128i libdivide_s64_signbits(__m128i v) {
__m128i hiBitsDuped = _mm_shuffle_epi32(v, _MM_SHUFFLE(3, 3, 1, 1));
__m128i signBits = _mm_srai_epi32(hiBitsDuped, 31);
return signBits;
}
// Implementation of _mm_srai_epi64 (from AVX512).
static inline __m128i libdivide_s64_shift_right_vector(__m128i v, int amt) {
const int b = 64 - amt;
__m128i m = _mm_set1_epi64x(1ULL << (b - 1));
__m128i x = _mm_srli_epi64(v, amt);
__m128i result = _mm_sub_epi64(_mm_xor_si128(x, m), m);
return result;
}
// Here, b is assumed to contain one 32-bit value repeated.
static inline __m128i libdivide_mullhi_u32_vector(__m128i a, __m128i b) {
__m128i hi_product_0Z2Z = _mm_srli_epi64(_mm_mul_epu32(a, b), 32);
__m128i a1X3X = _mm_srli_epi64(a, 32);
__m128i mask = _mm_set_epi32(-1, 0, -1, 0);
__m128i hi_product_Z1Z3 = _mm_and_si128(_mm_mul_epu32(a1X3X, b), mask);
return _mm_or_si128(hi_product_0Z2Z, hi_product_Z1Z3);
}
// SSE2 does not have a signed multiplication instruction, but we can convert
// unsigned to signed pretty efficiently. Again, b is just a 32 bit value
// repeated four times.
static inline __m128i libdivide_mullhi_s32_vector(__m128i a, __m128i b) {
__m128i p = libdivide_mullhi_u32_vector(a, b);
// t1 = (a >> 31) & y, arithmetic shift
__m128i t1 = _mm_and_si128(_mm_srai_epi32(a, 31), b);
__m128i t2 = _mm_and_si128(_mm_srai_epi32(b, 31), a);
p = _mm_sub_epi32(p, t1);
p = _mm_sub_epi32(p, t2);
return p;
}
// Here, y is assumed to contain one 64-bit value repeated.
// https://stackoverflow.com/a/28827013
static inline __m128i libdivide_mullhi_u64_vector(__m128i x, __m128i y) {
__m128i lomask = _mm_set1_epi64x(0xffffffff);
__m128i xh = _mm_shuffle_epi32(x, 0xB1); // x0l, x0h, x1l, x1h
__m128i yh = _mm_shuffle_epi32(y, 0xB1); // y0l, y0h, y1l, y1h
__m128i w0 = _mm_mul_epu32(x, y); // x0l*y0l, x1l*y1l
__m128i w1 = _mm_mul_epu32(x, yh); // x0l*y0h, x1l*y1h
__m128i w2 = _mm_mul_epu32(xh, y); // x0h*y0l, x1h*y0l
__m128i w3 = _mm_mul_epu32(xh, yh); // x0h*y0h, x1h*y1h
__m128i w0h = _mm_srli_epi64(w0, 32);
__m128i s1 = _mm_add_epi64(w1, w0h);
__m128i s1l = _mm_and_si128(s1, lomask);
__m128i s1h = _mm_srli_epi64(s1, 32);
__m128i s2 = _mm_add_epi64(w2, s1l);
__m128i s2h = _mm_srli_epi64(s2, 32);
__m128i hi = _mm_add_epi64(w3, s1h);
hi = _mm_add_epi64(hi, s2h);
return hi;
}
// y is one 64-bit value repeated.
static inline __m128i libdivide_mullhi_s64_vector(__m128i x, __m128i y) {
__m128i p = libdivide_mullhi_u64_vector(x, y);
__m128i t1 = _mm_and_si128(libdivide_s64_signbits(x), y);
__m128i t2 = _mm_and_si128(libdivide_s64_signbits(y), x);
p = _mm_sub_epi64(p, t1);
p = _mm_sub_epi64(p, t2);
return p;
}
////////// UINT32
__m128i libdivide_u32_do_vector(__m128i numers, const struct libdivide_u32_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
return _mm_srli_epi32(numers, more);
}
else {
__m128i q = libdivide_mullhi_u32_vector(numers, _mm_set1_epi32(denom->magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// uint32_t t = ((numer - q) >> 1) + q;
// return t >> denom->shift;
uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
__m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_sub_epi32(numers, q), 1), q);
return _mm_srli_epi32(t, shift);
}
else {
return _mm_srli_epi32(q, more);
}
}
}
__m128i libdivide_u32_branchfree_do_vector(__m128i numers, const struct libdivide_u32_branchfree_t *denom) {
__m128i q = libdivide_mullhi_u32_vector(numers, _mm_set1_epi32(denom->magic));
__m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_sub_epi32(numers, q), 1), q);
return _mm_srli_epi32(t, denom->more);
}
////////// UINT64
__m128i libdivide_u64_do_vector(__m128i numers, const struct libdivide_u64_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
return _mm_srli_epi64(numers, more);
}
else {
__m128i q = libdivide_mullhi_u64_vector(numers, _mm_set1_epi64x(denom->magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// uint32_t t = ((numer - q) >> 1) + q;
// return t >> denom->shift;
uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
__m128i t = _mm_add_epi64(_mm_srli_epi64(_mm_sub_epi64(numers, q), 1), q);
return _mm_srli_epi64(t, shift);
}
else {
return _mm_srli_epi64(q, more);
}
}
}
__m128i libdivide_u64_branchfree_do_vector(__m128i numers, const struct libdivide_u64_branchfree_t *denom) {
__m128i q = libdivide_mullhi_u64_vector(numers, _mm_set1_epi64x(denom->magic));
__m128i t = _mm_add_epi64(_mm_srli_epi64(_mm_sub_epi64(numers, q), 1), q);
return _mm_srli_epi64(t, denom->more);
}
////////// SINT32
__m128i libdivide_s32_do_vector(__m128i numers, const struct libdivide_s32_t *denom) {
uint8_t more = denom->more;
if (!denom->magic) {
uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
uint32_t mask = (1U << shift) - 1;
__m128i roundToZeroTweak = _mm_set1_epi32(mask);
// q = numer + ((numer >> 31) & roundToZeroTweak);
__m128i q = _mm_add_epi32(numers, _mm_and_si128(_mm_srai_epi32(numers, 31), roundToZeroTweak));
q = _mm_srai_epi32(q, shift);
__m128i sign = _mm_set1_epi32((int8_t)more >> 7);
// q = (q ^ sign) - sign;
q = _mm_sub_epi32(_mm_xor_si128(q, sign), sign);
return q;
}
else {
__m128i q = libdivide_mullhi_s32_vector(numers, _mm_set1_epi32(denom->magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// must be arithmetic shift
__m128i sign = _mm_set1_epi32((int8_t)more >> 7);
// q += ((numer ^ sign) - sign);
q = _mm_add_epi32(q, _mm_sub_epi32(_mm_xor_si128(numers, sign), sign));
}
// q >>= shift
q = _mm_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK);
q = _mm_add_epi32(q, _mm_srli_epi32(q, 31)); // q += (q < 0)
return q;
}
}
__m128i libdivide_s32_branchfree_do_vector(__m128i numers, const struct libdivide_s32_branchfree_t *denom) {
int32_t magic = denom->magic;
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
// must be arithmetic shift
__m128i sign = _mm_set1_epi32((int8_t)more >> 7);
__m128i q = libdivide_mullhi_s32_vector(numers, _mm_set1_epi32(magic));
q = _mm_add_epi32(q, numers); // q += numers
// If q is non-negative, we have nothing to do
// If q is negative, we want to add either (2**shift)-1 if d is
// a power of 2, or (2**shift) if it is not a power of 2
uint32_t is_power_of_2 = (magic == 0);
__m128i q_sign = _mm_srai_epi32(q, 31); // q_sign = q >> 31
__m128i mask = _mm_set1_epi32((1U << shift) - is_power_of_2);
q = _mm_add_epi32(q, _mm_and_si128(q_sign, mask)); // q = q + (q_sign & mask)
q = _mm_srai_epi32(q, shift); // q >>= shift
q = _mm_sub_epi32(_mm_xor_si128(q, sign), sign); // q = (q ^ sign) - sign
return q;
}
////////// SINT64
__m128i libdivide_s64_do_vector(__m128i numers, const struct libdivide_s64_t *denom) {
uint8_t more = denom->more;
int64_t magic = denom->magic;
if (magic == 0) { // shift path
uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
uint64_t mask = (1ULL << shift) - 1;
__m128i roundToZeroTweak = _mm_set1_epi64x(mask);
// q = numer + ((numer >> 63) & roundToZeroTweak);
__m128i q = _mm_add_epi64(numers, _mm_and_si128(libdivide_s64_signbits(numers), roundToZeroTweak));
q = libdivide_s64_shift_right_vector(q, shift);
__m128i sign = _mm_set1_epi32((int8_t)more >> 7);
// q = (q ^ sign) - sign;
q = _mm_sub_epi64(_mm_xor_si128(q, sign), sign);
return q;
}
else {
__m128i q = libdivide_mullhi_s64_vector(numers, _mm_set1_epi64x(magic));
if (more & LIBDIVIDE_ADD_MARKER) {
// must be arithmetic shift
__m128i sign = _mm_set1_epi32((int8_t)more >> 7);
// q += ((numer ^ sign) - sign);
q = _mm_add_epi64(q, _mm_sub_epi64(_mm_xor_si128(numers, sign), sign));
}
// q >>= denom->mult_path.shift
q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK);
q = _mm_add_epi64(q, _mm_srli_epi64(q, 63)); // q += (q < 0)
return q;
}
}
__m128i libdivide_s64_branchfree_do_vector(__m128i numers, const struct libdivide_s64_branchfree_t *denom) {
int64_t magic = denom->magic;
uint8_t more = denom->more;
uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
// must be arithmetic shift
__m128i sign = _mm_set1_epi32((int8_t)more >> 7);
// libdivide_mullhi_s64(numers, magic);
__m128i q = libdivide_mullhi_s64_vector(numers, _mm_set1_epi64x(magic));
q = _mm_add_epi64(q, numers); // q += numers
// If q is non-negative, we have nothing to do.
// If q is negative, we want to add either (2**shift)-1 if d is
// a power of 2, or (2**shift) if it is not a power of 2.
uint32_t is_power_of_2 = (magic == 0);
__m128i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63
__m128i mask = _mm_set1_epi64x((1ULL << shift) - is_power_of_2);
q = _mm_add_epi64(q, _mm_and_si128(q_sign, mask)); // q = q + (q_sign & mask)
q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift
q = _mm_sub_epi64(_mm_xor_si128(q, sign), sign); // q = (q ^ sign) - sign
return q;
}
#endif
/////////// C++ stuff
#ifdef __cplusplus
// The C++ divider class is templated on both an integer type
// (like uint64_t) and an algorithm type.
// * BRANCHFULL is the default algorithm type.
// * BRANCHFREE is the branchfree algorithm type.
enum {
BRANCHFULL,
BRANCHFREE
};
#if defined(LIBDIVIDE_AVX512)
#define LIBDIVIDE_VECTOR_TYPE __m512i
#elif defined(LIBDIVIDE_AVX2)
#define LIBDIVIDE_VECTOR_TYPE __m256i
#elif defined(LIBDIVIDE_SSE2)
#define LIBDIVIDE_VECTOR_TYPE __m128i
#endif
#if !defined(LIBDIVIDE_VECTOR_TYPE)
#define LIBDIVIDE_DIVIDE_VECTOR(ALGO)
#else
#define LIBDIVIDE_DIVIDE_VECTOR(ALGO) \
LIBDIVIDE_VECTOR_TYPE divide(LIBDIVIDE_VECTOR_TYPE n) const { \
return libdivide_##ALGO##_do_vector(n, &denom); \
}
#endif
// The DISPATCHER_GEN() macro generates C++ methods (for the given integer
// and algorithm types) that redirect to libdivide's C API.
#define DISPATCHER_GEN(T, ALGO) \
libdivide_##ALGO##_t denom; \
dispatcher() { } \
dispatcher(T d) \
: denom(libdivide_##ALGO##_gen(d)) \
{ } \
T divide(T n) const { \
return libdivide_##ALGO##_do(n, &denom); \
} \
LIBDIVIDE_DIVIDE_VECTOR(ALGO) \
T recover() const { \
return libdivide_##ALGO##_recover(&denom); \
}
// The dispatcher selects a specific division algorithm for a given
// type and ALGO using partial template specialization.
template<bool IS_INTEGRAL, bool IS_SIGNED, int SIZEOF, int ALGO> struct dispatcher { };
template<> struct dispatcher<true, true, sizeof(int32_t), BRANCHFULL> { DISPATCHER_GEN(int32_t, s32) };
template<> struct dispatcher<true, true, sizeof(int32_t), BRANCHFREE> { DISPATCHER_GEN(int32_t, s32_branchfree) };
template<> struct dispatcher<true, false, sizeof(uint32_t), BRANCHFULL> { DISPATCHER_GEN(uint32_t, u32) };
template<> struct dispatcher<true, false, sizeof(uint32_t), BRANCHFREE> { DISPATCHER_GEN(uint32_t, u32_branchfree) };
template<> struct dispatcher<true, true, sizeof(int64_t), BRANCHFULL> { DISPATCHER_GEN(int64_t, s64) };
template<> struct dispatcher<true, true, sizeof(int64_t), BRANCHFREE> { DISPATCHER_GEN(int64_t, s64_branchfree) };
template<> struct dispatcher<true, false, sizeof(uint64_t), BRANCHFULL> { DISPATCHER_GEN(uint64_t, u64) };
template<> struct dispatcher<true, false, sizeof(uint64_t), BRANCHFREE> { DISPATCHER_GEN(uint64_t, u64_branchfree) };
// This is the main divider class for use by the user (C++ API).
// The actual division algorithm is selected using the dispatcher struct
// based on the integer and algorithm template parameters.
template<typename T, int ALGO = BRANCHFULL>
class divider {
public:
// We leave the default constructor empty so that creating
// an array of dividers and then initializing them
// later doesn't slow us down.
divider() { }
// Constructor that takes the divisor as a parameter
divider(T d) : div(d) { }
// Divides n by the divisor
T divide(T n) const {
return div.divide(n);
}
// Recovers the divisor, returns the value that was
// used to initialize this divider object.
T recover() const {
return div.recover();
}
bool operator==(const divider<T, ALGO>& other) const {
return div.denom.magic == other.denom.magic &&
div.denom.more == other.denom.more;
}
bool operator!=(const divider<T, ALGO>& other) const {
return !(*this == other);
}
#if defined(LIBDIVIDE_VECTOR_TYPE)
// Treats the vector as packed integer values with the same type as
// the divider (e.g. s32, u32, s64, u64) and divides each of
// them by the divider, returning the packed quotients.
LIBDIVIDE_VECTOR_TYPE divide(LIBDIVIDE_VECTOR_TYPE n) const {
return div.divide(n);
}
#endif
private:
// Storage for the actual divisor
dispatcher<std::is_integral<T>::value,
std::is_signed<T>::value, sizeof(T), ALGO> div;
};
// Overload of operator / for scalar division
template<typename T, int ALGO>
T operator/(T n, const divider<T, ALGO>& div) {
return div.divide(n);
}
// Overload of operator /= for scalar division
template<typename T, int ALGO>
T& operator/=(T& n, const divider<T, ALGO>& div) {
n = div.divide(n);
return n;
}
#if defined(LIBDIVIDE_VECTOR_TYPE)
// Overload of operator / for vector division
template<typename T, int ALGO>
LIBDIVIDE_VECTOR_TYPE operator/(LIBDIVIDE_VECTOR_TYPE n, const divider<T, ALGO>& div) {
return div.divide(n);
}
// Overload of operator /= for vector division
template<typename T, int ALGO>
LIBDIVIDE_VECTOR_TYPE& operator/=(LIBDIVIDE_VECTOR_TYPE& n, const divider<T, ALGO>& div) {
n = div.divide(n);
return n;
}
#endif
// libdivdie::branchfree_divider<T>
template <typename T>
using branchfree_divider = divider<T, BRANCHFREE>;
} // namespace libdivide
#endif // __cplusplus
#endif // NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_
| 80,138 | C | 37.528365 | 137 | 0.597694 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/random/distributions.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_
#define NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_
#ifdef __cplusplus
extern "C" {
#endif
#include <Python.h>
#include "numpy/npy_common.h"
#include <stddef.h>
#include <stdbool.h>
#include <stdint.h>
#include "numpy/npy_math.h"
#include "numpy/random/bitgen.h"
/*
* RAND_INT_TYPE is used to share integer generators with RandomState which
* used long in place of int64_t. If changing a distribution that uses
* RAND_INT_TYPE, then the original unmodified copy must be retained for
* use in RandomState by copying to the legacy distributions source file.
*/
#ifdef NP_RANDOM_LEGACY
#define RAND_INT_TYPE long
#define RAND_INT_MAX LONG_MAX
#else
#define RAND_INT_TYPE int64_t
#define RAND_INT_MAX INT64_MAX
#endif
#ifdef _MSC_VER
#define DECLDIR __declspec(dllexport)
#else
#define DECLDIR extern
#endif
#ifndef MIN
#define MIN(x, y) (((x) < (y)) ? x : y)
#define MAX(x, y) (((x) > (y)) ? x : y)
#endif
#ifndef M_PI
#define M_PI 3.14159265358979323846264338328
#endif
typedef struct s_binomial_t {
int has_binomial; /* !=0: following parameters initialized for binomial */
double psave;
RAND_INT_TYPE nsave;
double r;
double q;
double fm;
RAND_INT_TYPE m;
double p1;
double xm;
double xl;
double xr;
double c;
double laml;
double lamr;
double p2;
double p3;
double p4;
} binomial_t;
DECLDIR float random_standard_uniform_f(bitgen_t *bitgen_state);
DECLDIR double random_standard_uniform(bitgen_t *bitgen_state);
DECLDIR void random_standard_uniform_fill(bitgen_t *, npy_intp, double *);
DECLDIR void random_standard_uniform_fill_f(bitgen_t *, npy_intp, float *);
DECLDIR int64_t random_positive_int64(bitgen_t *bitgen_state);
DECLDIR int32_t random_positive_int32(bitgen_t *bitgen_state);
DECLDIR int64_t random_positive_int(bitgen_t *bitgen_state);
DECLDIR uint64_t random_uint(bitgen_t *bitgen_state);
DECLDIR double random_standard_exponential(bitgen_t *bitgen_state);
DECLDIR float random_standard_exponential_f(bitgen_t *bitgen_state);
DECLDIR void random_standard_exponential_fill(bitgen_t *, npy_intp, double *);
DECLDIR void random_standard_exponential_fill_f(bitgen_t *, npy_intp, float *);
DECLDIR void random_standard_exponential_inv_fill(bitgen_t *, npy_intp, double *);
DECLDIR void random_standard_exponential_inv_fill_f(bitgen_t *, npy_intp, float *);
DECLDIR double random_standard_normal(bitgen_t *bitgen_state);
DECLDIR float random_standard_normal_f(bitgen_t *bitgen_state);
DECLDIR void random_standard_normal_fill(bitgen_t *, npy_intp, double *);
DECLDIR void random_standard_normal_fill_f(bitgen_t *, npy_intp, float *);
DECLDIR double random_standard_gamma(bitgen_t *bitgen_state, double shape);
DECLDIR float random_standard_gamma_f(bitgen_t *bitgen_state, float shape);
DECLDIR double random_normal(bitgen_t *bitgen_state, double loc, double scale);
DECLDIR double random_gamma(bitgen_t *bitgen_state, double shape, double scale);
DECLDIR float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale);
DECLDIR double random_exponential(bitgen_t *bitgen_state, double scale);
DECLDIR double random_uniform(bitgen_t *bitgen_state, double lower, double range);
DECLDIR double random_beta(bitgen_t *bitgen_state, double a, double b);
DECLDIR double random_chisquare(bitgen_t *bitgen_state, double df);
DECLDIR double random_f(bitgen_t *bitgen_state, double dfnum, double dfden);
DECLDIR double random_standard_cauchy(bitgen_t *bitgen_state);
DECLDIR double random_pareto(bitgen_t *bitgen_state, double a);
DECLDIR double random_weibull(bitgen_t *bitgen_state, double a);
DECLDIR double random_power(bitgen_t *bitgen_state, double a);
DECLDIR double random_laplace(bitgen_t *bitgen_state, double loc, double scale);
DECLDIR double random_gumbel(bitgen_t *bitgen_state, double loc, double scale);
DECLDIR double random_logistic(bitgen_t *bitgen_state, double loc, double scale);
DECLDIR double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma);
DECLDIR double random_rayleigh(bitgen_t *bitgen_state, double mode);
DECLDIR double random_standard_t(bitgen_t *bitgen_state, double df);
DECLDIR double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,
double nonc);
DECLDIR double random_noncentral_f(bitgen_t *bitgen_state, double dfnum,
double dfden, double nonc);
DECLDIR double random_wald(bitgen_t *bitgen_state, double mean, double scale);
DECLDIR double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa);
DECLDIR double random_triangular(bitgen_t *bitgen_state, double left, double mode,
double right);
DECLDIR RAND_INT_TYPE random_poisson(bitgen_t *bitgen_state, double lam);
DECLDIR RAND_INT_TYPE random_negative_binomial(bitgen_t *bitgen_state, double n,
double p);
DECLDIR int64_t random_binomial(bitgen_t *bitgen_state, double p,
int64_t n, binomial_t *binomial);
DECLDIR int64_t random_logseries(bitgen_t *bitgen_state, double p);
DECLDIR int64_t random_geometric(bitgen_t *bitgen_state, double p);
DECLDIR RAND_INT_TYPE random_geometric_search(bitgen_t *bitgen_state, double p);
DECLDIR RAND_INT_TYPE random_zipf(bitgen_t *bitgen_state, double a);
DECLDIR int64_t random_hypergeometric(bitgen_t *bitgen_state,
int64_t good, int64_t bad, int64_t sample);
DECLDIR uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max);
/* Generate random uint64 numbers in closed interval [off, off + rng]. */
DECLDIR uint64_t random_bounded_uint64(bitgen_t *bitgen_state, uint64_t off,
uint64_t rng, uint64_t mask,
bool use_masked);
/* Generate random uint32 numbers in closed interval [off, off + rng]. */
DECLDIR uint32_t random_buffered_bounded_uint32(bitgen_t *bitgen_state,
uint32_t off, uint32_t rng,
uint32_t mask, bool use_masked,
int *bcnt, uint32_t *buf);
DECLDIR uint16_t random_buffered_bounded_uint16(bitgen_t *bitgen_state,
uint16_t off, uint16_t rng,
uint16_t mask, bool use_masked,
int *bcnt, uint32_t *buf);
DECLDIR uint8_t random_buffered_bounded_uint8(bitgen_t *bitgen_state, uint8_t off,
uint8_t rng, uint8_t mask,
bool use_masked, int *bcnt,
uint32_t *buf);
DECLDIR npy_bool random_buffered_bounded_bool(bitgen_t *bitgen_state, npy_bool off,
npy_bool rng, npy_bool mask,
bool use_masked, int *bcnt,
uint32_t *buf);
DECLDIR void random_bounded_uint64_fill(bitgen_t *bitgen_state, uint64_t off,
uint64_t rng, npy_intp cnt,
bool use_masked, uint64_t *out);
DECLDIR void random_bounded_uint32_fill(bitgen_t *bitgen_state, uint32_t off,
uint32_t rng, npy_intp cnt,
bool use_masked, uint32_t *out);
DECLDIR void random_bounded_uint16_fill(bitgen_t *bitgen_state, uint16_t off,
uint16_t rng, npy_intp cnt,
bool use_masked, uint16_t *out);
DECLDIR void random_bounded_uint8_fill(bitgen_t *bitgen_state, uint8_t off,
uint8_t rng, npy_intp cnt,
bool use_masked, uint8_t *out);
DECLDIR void random_bounded_bool_fill(bitgen_t *bitgen_state, npy_bool off,
npy_bool rng, npy_intp cnt,
bool use_masked, npy_bool *out);
DECLDIR void random_multinomial(bitgen_t *bitgen_state, RAND_INT_TYPE n, RAND_INT_TYPE *mnix,
double *pix, npy_intp d, binomial_t *binomial);
/* multivariate hypergeometric, "count" method */
DECLDIR int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state,
int64_t total,
size_t num_colors, int64_t *colors,
int64_t nsample,
size_t num_variates, int64_t *variates);
/* multivariate hypergeometric, "marginals" method */
DECLDIR void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state,
int64_t total,
size_t num_colors, int64_t *colors,
int64_t nsample,
size_t num_variates, int64_t *variates);
/* Common to legacy-distributions.c and distributions.c but not exported */
RAND_INT_TYPE random_binomial_btpe(bitgen_t *bitgen_state,
RAND_INT_TYPE n,
double p,
binomial_t *binomial);
RAND_INT_TYPE random_binomial_inversion(bitgen_t *bitgen_state,
RAND_INT_TYPE n,
double p,
binomial_t *binomial);
double random_loggam(double x);
static NPY_INLINE double next_double(bitgen_t *bitgen_state) {
return bitgen_state->next_double(bitgen_state->state);
}
#ifdef __cplusplus
}
#endif
#endif /* NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_ */
| 9,865 | C | 45.980952 | 93 | 0.623315 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/include/numpy/random/bitgen.h | #ifndef NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_
#define NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_
#pragma once
#include <stddef.h>
#include <stdbool.h>
#include <stdint.h>
/* Must match the declaration in numpy/random/<any>.pxd */
typedef struct bitgen {
void *state;
uint64_t (*next_uint64)(void *st);
uint32_t (*next_uint32)(void *st);
double (*next_double)(void *st);
uint64_t (*next_raw)(void *st);
} bitgen_t;
#endif /* NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_ */
| 488 | C | 22.285713 | 58 | 0.690574 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/tests/test_simd.py | # NOTE: Please avoid the use of numpy.testing since NPYV intrinsics
# may be involved in their functionality.
import pytest, math, re
import itertools
from numpy.core._simd import targets
from numpy.core._multiarray_umath import __cpu_baseline__
class _Test_Utility:
# submodule of the desired SIMD extension, e.g. targets["AVX512F"]
npyv = None
# the current data type suffix e.g. 's8'
sfx = None
# target name can be 'baseline' or one or more of CPU features
target_name = None
def __getattr__(self, attr):
"""
To call NPV intrinsics without the attribute 'npyv' and
auto suffixing intrinsics according to class attribute 'sfx'
"""
return getattr(self.npyv, attr + "_" + self.sfx)
def _data(self, start=None, count=None, reverse=False):
"""
Create list of consecutive numbers according to number of vector's lanes.
"""
if start is None:
start = 1
if count is None:
count = self.nlanes
rng = range(start, start + count)
if reverse:
rng = reversed(rng)
if self._is_fp():
return [x / 1.0 for x in rng]
return list(rng)
def _is_unsigned(self):
return self.sfx[0] == 'u'
def _is_signed(self):
return self.sfx[0] == 's'
def _is_fp(self):
return self.sfx[0] == 'f'
def _scalar_size(self):
return int(self.sfx[1:])
def _int_clip(self, seq):
if self._is_fp():
return seq
max_int = self._int_max()
min_int = self._int_min()
return [min(max(v, min_int), max_int) for v in seq]
def _int_max(self):
if self._is_fp():
return None
max_u = self._to_unsigned(self.setall(-1))[0]
if self._is_signed():
return max_u // 2
return max_u
def _int_min(self):
if self._is_fp():
return None
if self._is_unsigned():
return 0
return -(self._int_max() + 1)
def _true_mask(self):
max_unsig = getattr(self.npyv, "setall_u" + self.sfx[1:])(-1)
return max_unsig[0]
def _to_unsigned(self, vector):
if isinstance(vector, (list, tuple)):
return getattr(self.npyv, "load_u" + self.sfx[1:])(vector)
else:
sfx = vector.__name__.replace("npyv_", "")
if sfx[0] == "b":
cvt_intrin = "cvt_u{0}_b{0}"
else:
cvt_intrin = "reinterpret_u{0}_{1}"
return getattr(self.npyv, cvt_intrin.format(sfx[1:], sfx))(vector)
def _pinfinity(self):
v = self.npyv.setall_u32(0x7f800000)
return self.npyv.reinterpret_f32_u32(v)[0]
def _ninfinity(self):
v = self.npyv.setall_u32(0xff800000)
return self.npyv.reinterpret_f32_u32(v)[0]
def _nan(self):
v = self.npyv.setall_u32(0x7fc00000)
return self.npyv.reinterpret_f32_u32(v)[0]
def _cpu_features(self):
target = self.target_name
if target == "baseline":
target = __cpu_baseline__
else:
target = target.split('__') # multi-target separator
return ' '.join(target)
class _SIMD_BOOL(_Test_Utility):
"""
To test all boolean vector types at once
"""
def _data(self, start=None, count=None, reverse=False):
nlanes = getattr(self.npyv, "nlanes_u" + self.sfx[1:])
true_mask = self._true_mask()
rng = range(nlanes)
if reverse:
rng = reversed(rng)
return [true_mask if x % 2 else 0 for x in rng]
def _load_b(self, data):
len_str = self.sfx[1:]
load = getattr(self.npyv, "load_u" + len_str)
cvt = getattr(self.npyv, f"cvt_b{len_str}_u{len_str}")
return cvt(load(data))
def test_operators_logical(self):
"""
Logical operations for boolean types.
Test intrinsics:
npyv_xor_##SFX, npyv_and_##SFX, npyv_or_##SFX, npyv_not_##SFX
"""
data_a = self._data()
data_b = self._data(reverse=True)
vdata_a = self._load_b(data_a)
vdata_b = self._load_b(data_b)
data_and = [a & b for a, b in zip(data_a, data_b)]
vand = getattr(self, "and")(vdata_a, vdata_b)
assert vand == data_and
data_or = [a | b for a, b in zip(data_a, data_b)]
vor = getattr(self, "or")(vdata_a, vdata_b)
assert vor == data_or
data_xor = [a ^ b for a, b in zip(data_a, data_b)]
vxor = getattr(self, "xor")(vdata_a, vdata_b)
assert vxor == data_xor
vnot = getattr(self, "not")(vdata_a)
assert vnot == data_b
def test_tobits(self):
data2bits = lambda data: sum([int(x != 0) << i for i, x in enumerate(data, 0)])
for data in (self._data(), self._data(reverse=True)):
vdata = self._load_b(data)
data_bits = data2bits(data)
tobits = bin(self.tobits(vdata))
assert tobits == bin(data_bits)
class _SIMD_INT(_Test_Utility):
"""
To test all integer vector types at once
"""
def test_operators_shift(self):
if self.sfx in ("u8", "s8"):
return
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
for count in range(self._scalar_size()):
# load to cast
data_shl_a = self.load([a << count for a in data_a])
# left shift
shl = self.shl(vdata_a, count)
assert shl == data_shl_a
# load to cast
data_shr_a = self.load([a >> count for a in data_a])
# right shift
shr = self.shr(vdata_a, count)
assert shr == data_shr_a
# shift by zero or max or out-range immediate constant is not applicable and illogical
for count in range(1, self._scalar_size()):
# load to cast
data_shl_a = self.load([a << count for a in data_a])
# left shift by an immediate constant
shli = self.shli(vdata_a, count)
assert shli == data_shl_a
# load to cast
data_shr_a = self.load([a >> count for a in data_a])
# right shift by an immediate constant
shri = self.shri(vdata_a, count)
assert shri == data_shr_a
def test_arithmetic_subadd_saturated(self):
if self.sfx in ("u32", "s32", "u64", "s64"):
return
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
data_adds = self._int_clip([a + b for a, b in zip(data_a, data_b)])
adds = self.adds(vdata_a, vdata_b)
assert adds == data_adds
data_subs = self._int_clip([a - b for a, b in zip(data_a, data_b)])
subs = self.subs(vdata_a, vdata_b)
assert subs == data_subs
def test_math_max_min(self):
data_a = self._data()
data_b = self._data(self.nlanes)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
data_max = [max(a, b) for a, b in zip(data_a, data_b)]
simd_max = self.max(vdata_a, vdata_b)
assert simd_max == data_max
data_min = [min(a, b) for a, b in zip(data_a, data_b)]
simd_min = self.min(vdata_a, vdata_b)
assert simd_min == data_min
class _SIMD_FP32(_Test_Utility):
"""
To only test single precision
"""
def test_conversions(self):
"""
Round to nearest even integer, assume CPU control register is set to rounding.
Test intrinsics:
npyv_round_s32_##SFX
"""
features = self._cpu_features()
if not self.npyv.simd_f64 and re.match(r".*(NEON|ASIMD)", features):
# very costly to emulate nearest even on Armv7
# instead we round halves to up. e.g. 0.5 -> 1, -0.5 -> -1
_round = lambda v: int(v + (0.5 if v >= 0 else -0.5))
else:
_round = round
vdata_a = self.load(self._data())
vdata_a = self.sub(vdata_a, self.setall(0.5))
data_round = [_round(x) for x in vdata_a]
vround = self.round_s32(vdata_a)
assert vround == data_round
class _SIMD_FP64(_Test_Utility):
"""
To only test double precision
"""
def test_conversions(self):
"""
Round to nearest even integer, assume CPU control register is set to rounding.
Test intrinsics:
npyv_round_s32_##SFX
"""
vdata_a = self.load(self._data())
vdata_a = self.sub(vdata_a, self.setall(0.5))
vdata_b = self.mul(vdata_a, self.setall(-1.5))
data_round = [round(x) for x in list(vdata_a) + list(vdata_b)]
vround = self.round_s32(vdata_a, vdata_b)
assert vround == data_round
class _SIMD_FP(_Test_Utility):
"""
To test all float vector types at once
"""
def test_arithmetic_fused(self):
vdata_a, vdata_b, vdata_c = [self.load(self._data())]*3
vdata_cx2 = self.add(vdata_c, vdata_c)
# multiply and add, a*b + c
data_fma = self.load([a * b + c for a, b, c in zip(vdata_a, vdata_b, vdata_c)])
fma = self.muladd(vdata_a, vdata_b, vdata_c)
assert fma == data_fma
# multiply and subtract, a*b - c
fms = self.mulsub(vdata_a, vdata_b, vdata_c)
data_fms = self.sub(data_fma, vdata_cx2)
assert fms == data_fms
# negate multiply and add, -(a*b) + c
nfma = self.nmuladd(vdata_a, vdata_b, vdata_c)
data_nfma = self.sub(vdata_cx2, data_fma)
assert nfma == data_nfma
# negate multiply and subtract, -(a*b) - c
nfms = self.nmulsub(vdata_a, vdata_b, vdata_c)
data_nfms = self.mul(data_fma, self.setall(-1))
assert nfms == data_nfms
def test_abs(self):
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
data = self._data()
vdata = self.load(self._data())
abs_cases = ((-0, 0), (ninf, pinf), (pinf, pinf), (nan, nan))
for case, desired in abs_cases:
data_abs = [desired]*self.nlanes
vabs = self.abs(self.setall(case))
assert vabs == pytest.approx(data_abs, nan_ok=True)
vabs = self.abs(self.mul(vdata, self.setall(-1)))
assert vabs == data
def test_sqrt(self):
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
data = self._data()
vdata = self.load(self._data())
sqrt_cases = ((-0.0, -0.0), (0.0, 0.0), (-1.0, nan), (ninf, nan), (pinf, pinf))
for case, desired in sqrt_cases:
data_sqrt = [desired]*self.nlanes
sqrt = self.sqrt(self.setall(case))
assert sqrt == pytest.approx(data_sqrt, nan_ok=True)
data_sqrt = self.load([math.sqrt(x) for x in data]) # load to truncate precision
sqrt = self.sqrt(vdata)
assert sqrt == data_sqrt
def test_square(self):
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
data = self._data()
vdata = self.load(self._data())
# square
square_cases = ((nan, nan), (pinf, pinf), (ninf, pinf))
for case, desired in square_cases:
data_square = [desired]*self.nlanes
square = self.square(self.setall(case))
assert square == pytest.approx(data_square, nan_ok=True)
data_square = [x*x for x in data]
square = self.square(vdata)
assert square == data_square
@pytest.mark.parametrize("intrin, func", [("ceil", math.ceil),
("trunc", math.trunc), ("floor", math.floor), ("rint", round)])
def test_rounding(self, intrin, func):
"""
Test intrinsics:
npyv_rint_##SFX
npyv_ceil_##SFX
npyv_trunc_##SFX
npyv_floor##SFX
"""
intrin_name = intrin
intrin = getattr(self, intrin)
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
# special cases
round_cases = ((nan, nan), (pinf, pinf), (ninf, ninf))
for case, desired in round_cases:
data_round = [desired]*self.nlanes
_round = intrin(self.setall(case))
assert _round == pytest.approx(data_round, nan_ok=True)
for x in range(0, 2**20, 256**2):
for w in (-1.05, -1.10, -1.15, 1.05, 1.10, 1.15):
data = self.load([(x+a)*w for a in range(self.nlanes)])
data_round = [func(x) for x in data]
_round = intrin(data)
assert _round == data_round
# signed zero
if intrin_name == "floor":
data_szero = (-0.0,)
else:
data_szero = (-0.0, -0.25, -0.30, -0.45, -0.5)
for w in data_szero:
_round = self._to_unsigned(intrin(self.setall(w)))
data_round = self._to_unsigned(self.setall(-0.0))
assert _round == data_round
def test_max(self):
"""
Test intrinsics:
npyv_max_##SFX
npyv_maxp_##SFX
"""
data_a = self._data()
data_b = self._data(self.nlanes)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
data_max = [max(a, b) for a, b in zip(data_a, data_b)]
_max = self.max(vdata_a, vdata_b)
assert _max == data_max
maxp = self.maxp(vdata_a, vdata_b)
assert maxp == data_max
# test IEEE standards
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
max_cases = ((nan, nan, nan), (nan, 10, 10), (10, nan, 10),
(pinf, pinf, pinf), (pinf, 10, pinf), (10, pinf, pinf),
(ninf, ninf, ninf), (ninf, 10, 10), (10, ninf, 10),
(10, 0, 10), (10, -10, 10))
for case_operand1, case_operand2, desired in max_cases:
data_max = [desired]*self.nlanes
vdata_a = self.setall(case_operand1)
vdata_b = self.setall(case_operand2)
maxp = self.maxp(vdata_a, vdata_b)
assert maxp == pytest.approx(data_max, nan_ok=True)
if nan in (case_operand1, case_operand2, desired):
continue
_max = self.max(vdata_a, vdata_b)
assert _max == data_max
def test_min(self):
"""
Test intrinsics:
npyv_min_##SFX
npyv_minp_##SFX
"""
data_a = self._data()
data_b = self._data(self.nlanes)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
data_min = [min(a, b) for a, b in zip(data_a, data_b)]
_min = self.min(vdata_a, vdata_b)
assert _min == data_min
minp = self.minp(vdata_a, vdata_b)
assert minp == data_min
# test IEEE standards
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
min_cases = ((nan, nan, nan), (nan, 10, 10), (10, nan, 10),
(pinf, pinf, pinf), (pinf, 10, 10), (10, pinf, 10),
(ninf, ninf, ninf), (ninf, 10, ninf), (10, ninf, ninf),
(10, 0, 0), (10, -10, -10))
for case_operand1, case_operand2, desired in min_cases:
data_min = [desired]*self.nlanes
vdata_a = self.setall(case_operand1)
vdata_b = self.setall(case_operand2)
minp = self.minp(vdata_a, vdata_b)
assert minp == pytest.approx(data_min, nan_ok=True)
if nan in (case_operand1, case_operand2, desired):
continue
_min = self.min(vdata_a, vdata_b)
assert _min == data_min
def test_reciprocal(self):
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
data = self._data()
vdata = self.load(self._data())
recip_cases = ((nan, nan), (pinf, 0.0), (ninf, -0.0), (0.0, pinf), (-0.0, ninf))
for case, desired in recip_cases:
data_recip = [desired]*self.nlanes
recip = self.recip(self.setall(case))
assert recip == pytest.approx(data_recip, nan_ok=True)
data_recip = self.load([1/x for x in data]) # load to truncate precision
recip = self.recip(vdata)
assert recip == data_recip
def test_special_cases(self):
"""
Compare Not NaN. Test intrinsics:
npyv_notnan_##SFX
"""
nnan = self.notnan(self.setall(self._nan()))
assert nnan == [0]*self.nlanes
class _SIMD_ALL(_Test_Utility):
"""
To test all vector types at once
"""
def test_memory_load(self):
data = self._data()
# unaligned load
load_data = self.load(data)
assert load_data == data
# aligned load
loada_data = self.loada(data)
assert loada_data == data
# stream load
loads_data = self.loads(data)
assert loads_data == data
# load lower part
loadl = self.loadl(data)
loadl_half = list(loadl)[:self.nlanes//2]
data_half = data[:self.nlanes//2]
assert loadl_half == data_half
assert loadl != data # detect overflow
def test_memory_store(self):
data = self._data()
vdata = self.load(data)
# unaligned store
store = [0] * self.nlanes
self.store(store, vdata)
assert store == data
# aligned store
store_a = [0] * self.nlanes
self.storea(store_a, vdata)
assert store_a == data
# stream store
store_s = [0] * self.nlanes
self.stores(store_s, vdata)
assert store_s == data
# store lower part
store_l = [0] * self.nlanes
self.storel(store_l, vdata)
assert store_l[:self.nlanes//2] == data[:self.nlanes//2]
assert store_l != vdata # detect overflow
# store higher part
store_h = [0] * self.nlanes
self.storeh(store_h, vdata)
assert store_h[:self.nlanes//2] == data[self.nlanes//2:]
assert store_h != vdata # detect overflow
def test_memory_partial_load(self):
if self.sfx in ("u8", "s8", "u16", "s16"):
return
data = self._data()
lanes = list(range(1, self.nlanes + 1))
lanes += [self.nlanes**2, self.nlanes**4] # test out of range
for n in lanes:
load_till = self.load_till(data, n, 15)
data_till = data[:n] + [15] * (self.nlanes-n)
assert load_till == data_till
load_tillz = self.load_tillz(data, n)
data_tillz = data[:n] + [0] * (self.nlanes-n)
assert load_tillz == data_tillz
def test_memory_partial_store(self):
if self.sfx in ("u8", "s8", "u16", "s16"):
return
data = self._data()
data_rev = self._data(reverse=True)
vdata = self.load(data)
lanes = list(range(1, self.nlanes + 1))
lanes += [self.nlanes**2, self.nlanes**4]
for n in lanes:
data_till = data_rev.copy()
data_till[:n] = data[:n]
store_till = self._data(reverse=True)
self.store_till(store_till, n, vdata)
assert store_till == data_till
def test_memory_noncont_load(self):
if self.sfx in ("u8", "s8", "u16", "s16"):
return
for stride in range(1, 64):
data = self._data(count=stride*self.nlanes)
data_stride = data[::stride]
loadn = self.loadn(data, stride)
assert loadn == data_stride
for stride in range(-64, 0):
data = self._data(stride, -stride*self.nlanes)
data_stride = self.load(data[::stride]) # cast unsigned
loadn = self.loadn(data, stride)
assert loadn == data_stride
def test_memory_noncont_partial_load(self):
if self.sfx in ("u8", "s8", "u16", "s16"):
return
lanes = list(range(1, self.nlanes + 1))
lanes += [self.nlanes**2, self.nlanes**4]
for stride in range(1, 64):
data = self._data(count=stride*self.nlanes)
data_stride = data[::stride]
for n in lanes:
data_stride_till = data_stride[:n] + [15] * (self.nlanes-n)
loadn_till = self.loadn_till(data, stride, n, 15)
assert loadn_till == data_stride_till
data_stride_tillz = data_stride[:n] + [0] * (self.nlanes-n)
loadn_tillz = self.loadn_tillz(data, stride, n)
assert loadn_tillz == data_stride_tillz
for stride in range(-64, 0):
data = self._data(stride, -stride*self.nlanes)
data_stride = list(self.load(data[::stride])) # cast unsigned
for n in lanes:
data_stride_till = data_stride[:n] + [15] * (self.nlanes-n)
loadn_till = self.loadn_till(data, stride, n, 15)
assert loadn_till == data_stride_till
data_stride_tillz = data_stride[:n] + [0] * (self.nlanes-n)
loadn_tillz = self.loadn_tillz(data, stride, n)
assert loadn_tillz == data_stride_tillz
def test_memory_noncont_store(self):
if self.sfx in ("u8", "s8", "u16", "s16"):
return
vdata = self.load(self._data())
for stride in range(1, 64):
data = [15] * stride * self.nlanes
data[::stride] = vdata
storen = [15] * stride * self.nlanes
storen += [127]*64
self.storen(storen, stride, vdata)
assert storen[:-64] == data
assert storen[-64:] == [127]*64 # detect overflow
for stride in range(-64, 0):
data = [15] * -stride * self.nlanes
data[::stride] = vdata
storen = [127]*64
storen += [15] * -stride * self.nlanes
self.storen(storen, stride, vdata)
assert storen[64:] == data
assert storen[:64] == [127]*64 # detect overflow
def test_memory_noncont_partial_store(self):
if self.sfx in ("u8", "s8", "u16", "s16"):
return
data = self._data()
vdata = self.load(data)
lanes = list(range(1, self.nlanes + 1))
lanes += [self.nlanes**2, self.nlanes**4]
for stride in range(1, 64):
for n in lanes:
data_till = [15] * stride * self.nlanes
data_till[::stride] = data[:n] + [15] * (self.nlanes-n)
storen_till = [15] * stride * self.nlanes
storen_till += [127]*64
self.storen_till(storen_till, stride, n, vdata)
assert storen_till[:-64] == data_till
assert storen_till[-64:] == [127]*64 # detect overflow
for stride in range(-64, 0):
for n in lanes:
data_till = [15] * -stride * self.nlanes
data_till[::stride] = data[:n] + [15] * (self.nlanes-n)
storen_till = [127]*64
storen_till += [15] * -stride * self.nlanes
self.storen_till(storen_till, stride, n, vdata)
assert storen_till[64:] == data_till
assert storen_till[:64] == [127]*64 # detect overflow
@pytest.mark.parametrize("intrin, table_size, elsize", [
("self.lut32", 32, 32),
("self.lut16", 16, 64)
])
def test_lut(self, intrin, table_size, elsize):
"""
Test lookup table intrinsics:
npyv_lut32_##sfx
npyv_lut16_##sfx
"""
if elsize != self._scalar_size():
return
intrin = eval(intrin)
idx_itrin = getattr(self.npyv, f"setall_u{elsize}")
table = range(0, table_size)
for i in table:
broadi = self.setall(i)
idx = idx_itrin(i)
lut = intrin(table, idx)
assert lut == broadi
def test_misc(self):
broadcast_zero = self.zero()
assert broadcast_zero == [0] * self.nlanes
for i in range(1, 10):
broadcasti = self.setall(i)
assert broadcasti == [i] * self.nlanes
data_a, data_b = self._data(), self._data(reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
# py level of npyv_set_* don't support ignoring the extra specified lanes or
# fill non-specified lanes with zero.
vset = self.set(*data_a)
assert vset == data_a
# py level of npyv_setf_* don't support ignoring the extra specified lanes or
# fill non-specified lanes with the specified scalar.
vsetf = self.setf(10, *data_a)
assert vsetf == data_a
# We're testing the sanity of _simd's type-vector,
# reinterpret* intrinsics itself are tested via compiler
# during the build of _simd module
sfxes = ["u8", "s8", "u16", "s16", "u32", "s32", "u64", "s64", "f32"]
if self.npyv.simd_f64:
sfxes.append("f64")
for sfx in sfxes:
vec_name = getattr(self, "reinterpret_" + sfx)(vdata_a).__name__
assert vec_name == "npyv_" + sfx
# select & mask operations
select_a = self.select(self.cmpeq(self.zero(), self.zero()), vdata_a, vdata_b)
assert select_a == data_a
select_b = self.select(self.cmpneq(self.zero(), self.zero()), vdata_a, vdata_b)
assert select_b == data_b
# cleanup intrinsic is only used with AVX for
# zeroing registers to avoid the AVX-SSE transition penalty,
# so nothing to test here
self.npyv.cleanup()
def test_reorder(self):
data_a, data_b = self._data(), self._data(reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
# lower half part
data_a_lo = data_a[:self.nlanes//2]
data_b_lo = data_b[:self.nlanes//2]
# higher half part
data_a_hi = data_a[self.nlanes//2:]
data_b_hi = data_b[self.nlanes//2:]
# combine two lower parts
combinel = self.combinel(vdata_a, vdata_b)
assert combinel == data_a_lo + data_b_lo
# combine two higher parts
combineh = self.combineh(vdata_a, vdata_b)
assert combineh == data_a_hi + data_b_hi
# combine x2
combine = self.combine(vdata_a, vdata_b)
assert combine == (data_a_lo + data_b_lo, data_a_hi + data_b_hi)
# zip(interleave)
data_zipl = [v for p in zip(data_a_lo, data_b_lo) for v in p]
data_ziph = [v for p in zip(data_a_hi, data_b_hi) for v in p]
vzip = self.zip(vdata_a, vdata_b)
assert vzip == (data_zipl, data_ziph)
def test_reorder_rev64(self):
# Reverse elements of each 64-bit lane
ssize = self._scalar_size()
if ssize == 64:
return
data_rev64 = [
y for x in range(0, self.nlanes, 64//ssize)
for y in reversed(range(x, x + 64//ssize))
]
rev64 = self.rev64(self.load(range(self.nlanes)))
assert rev64 == data_rev64
def test_operators_comparison(self):
if self._is_fp():
data_a = self._data()
else:
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
mask_true = self._true_mask()
def to_bool(vector):
return [lane == mask_true for lane in vector]
# equal
data_eq = [a == b for a, b in zip(data_a, data_b)]
cmpeq = to_bool(self.cmpeq(vdata_a, vdata_b))
assert cmpeq == data_eq
# not equal
data_neq = [a != b for a, b in zip(data_a, data_b)]
cmpneq = to_bool(self.cmpneq(vdata_a, vdata_b))
assert cmpneq == data_neq
# greater than
data_gt = [a > b for a, b in zip(data_a, data_b)]
cmpgt = to_bool(self.cmpgt(vdata_a, vdata_b))
assert cmpgt == data_gt
# greater than and equal
data_ge = [a >= b for a, b in zip(data_a, data_b)]
cmpge = to_bool(self.cmpge(vdata_a, vdata_b))
assert cmpge == data_ge
# less than
data_lt = [a < b for a, b in zip(data_a, data_b)]
cmplt = to_bool(self.cmplt(vdata_a, vdata_b))
assert cmplt == data_lt
# less than and equal
data_le = [a <= b for a, b in zip(data_a, data_b)]
cmple = to_bool(self.cmple(vdata_a, vdata_b))
assert cmple == data_le
def test_operators_logical(self):
if self._is_fp():
data_a = self._data()
else:
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
if self._is_fp():
data_cast_a = self._to_unsigned(vdata_a)
data_cast_b = self._to_unsigned(vdata_b)
cast, cast_data = self._to_unsigned, self._to_unsigned
else:
data_cast_a, data_cast_b = data_a, data_b
cast, cast_data = lambda a: a, self.load
data_xor = cast_data([a ^ b for a, b in zip(data_cast_a, data_cast_b)])
vxor = cast(self.xor(vdata_a, vdata_b))
assert vxor == data_xor
data_or = cast_data([a | b for a, b in zip(data_cast_a, data_cast_b)])
vor = cast(getattr(self, "or")(vdata_a, vdata_b))
assert vor == data_or
data_and = cast_data([a & b for a, b in zip(data_cast_a, data_cast_b)])
vand = cast(getattr(self, "and")(vdata_a, vdata_b))
assert vand == data_and
data_not = cast_data([~a for a in data_cast_a])
vnot = cast(getattr(self, "not")(vdata_a))
assert vnot == data_not
def test_conversion_boolean(self):
bsfx = "b" + self.sfx[1:]
to_boolean = getattr(self.npyv, "cvt_%s_%s" % (bsfx, self.sfx))
from_boolean = getattr(self.npyv, "cvt_%s_%s" % (self.sfx, bsfx))
false_vb = to_boolean(self.setall(0))
true_vb = self.cmpeq(self.setall(0), self.setall(0))
assert false_vb != true_vb
false_vsfx = from_boolean(false_vb)
true_vsfx = from_boolean(true_vb)
assert false_vsfx != true_vsfx
def test_conversion_expand(self):
"""
Test expand intrinsics:
npyv_expand_u16_u8
npyv_expand_u32_u16
"""
if self.sfx not in ("u8", "u16"):
return
totype = self.sfx[0]+str(int(self.sfx[1:])*2)
expand = getattr(self.npyv, f"expand_{totype}_{self.sfx}")
# close enough from the edge to detect any deviation
data = self._data(self._int_max() - self.nlanes)
vdata = self.load(data)
edata = expand(vdata)
# lower half part
data_lo = data[:self.nlanes//2]
# higher half part
data_hi = data[self.nlanes//2:]
assert edata == (data_lo, data_hi)
def test_arithmetic_subadd(self):
if self._is_fp():
data_a = self._data()
else:
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
# non-saturated
data_add = self.load([a + b for a, b in zip(data_a, data_b)]) # load to cast
add = self.add(vdata_a, vdata_b)
assert add == data_add
data_sub = self.load([a - b for a, b in zip(data_a, data_b)])
sub = self.sub(vdata_a, vdata_b)
assert sub == data_sub
def test_arithmetic_mul(self):
if self.sfx in ("u64", "s64"):
return
if self._is_fp():
data_a = self._data()
else:
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
data_mul = self.load([a * b for a, b in zip(data_a, data_b)])
mul = self.mul(vdata_a, vdata_b)
assert mul == data_mul
def test_arithmetic_div(self):
if not self._is_fp():
return
data_a, data_b = self._data(), self._data(reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
# load to truncate f64 to precision of f32
data_div = self.load([a / b for a, b in zip(data_a, data_b)])
div = self.div(vdata_a, vdata_b)
assert div == data_div
def test_arithmetic_intdiv(self):
"""
Test integer division intrinsics:
npyv_divisor_##sfx
npyv_divc_##sfx
"""
if self._is_fp():
return
int_min = self._int_min()
def trunc_div(a, d):
"""
Divide towards zero works with large integers > 2^53,
and wrap around overflow similar to what C does.
"""
if d == -1 and a == int_min:
return a
sign_a, sign_d = a < 0, d < 0
if a == 0 or sign_a == sign_d:
return a // d
return (a + sign_d - sign_a) // d + 1
data = [1, -int_min] # to test overflow
data += range(0, 2**8, 2**5)
data += range(0, 2**8, 2**5-1)
bsize = self._scalar_size()
if bsize > 8:
data += range(2**8, 2**16, 2**13)
data += range(2**8, 2**16, 2**13-1)
if bsize > 16:
data += range(2**16, 2**32, 2**29)
data += range(2**16, 2**32, 2**29-1)
if bsize > 32:
data += range(2**32, 2**64, 2**61)
data += range(2**32, 2**64, 2**61-1)
# negate
data += [-x for x in data]
for dividend, divisor in itertools.product(data, data):
divisor = self.setall(divisor)[0] # cast
if divisor == 0:
continue
dividend = self.load(self._data(dividend))
data_divc = [trunc_div(a, divisor) for a in dividend]
divisor_parms = self.divisor(divisor)
divc = self.divc(dividend, divisor_parms)
assert divc == data_divc
def test_arithmetic_reduce_sum(self):
"""
Test reduce sum intrinsics:
npyv_sum_##sfx
"""
if self.sfx not in ("u32", "u64", "f32", "f64"):
return
# reduce sum
data = self._data()
vdata = self.load(data)
data_sum = sum(data)
vsum = self.sum(vdata)
assert vsum == data_sum
def test_arithmetic_reduce_sumup(self):
"""
Test extend reduce sum intrinsics:
npyv_sumup_##sfx
"""
if self.sfx not in ("u8", "u16"):
return
rdata = (0, self.nlanes, self._int_min(), self._int_max()-self.nlanes)
for r in rdata:
data = self._data(r)
vdata = self.load(data)
data_sum = sum(data)
vsum = self.sumup(vdata)
assert vsum == data_sum
def test_mask_conditional(self):
"""
Conditional addition and subtraction for all supported data types.
Test intrinsics:
npyv_ifadd_##SFX, npyv_ifsub_##SFX
"""
vdata_a = self.load(self._data())
vdata_b = self.load(self._data(reverse=True))
true_mask = self.cmpeq(self.zero(), self.zero())
false_mask = self.cmpneq(self.zero(), self.zero())
data_sub = self.sub(vdata_b, vdata_a)
ifsub = self.ifsub(true_mask, vdata_b, vdata_a, vdata_b)
assert ifsub == data_sub
ifsub = self.ifsub(false_mask, vdata_a, vdata_b, vdata_b)
assert ifsub == vdata_b
data_add = self.add(vdata_b, vdata_a)
ifadd = self.ifadd(true_mask, vdata_b, vdata_a, vdata_b)
assert ifadd == data_add
ifadd = self.ifadd(false_mask, vdata_a, vdata_b, vdata_b)
assert ifadd == vdata_b
bool_sfx = ("b8", "b16", "b32", "b64")
int_sfx = ("u8", "s8", "u16", "s16", "u32", "s32", "u64", "s64")
fp_sfx = ("f32", "f64")
all_sfx = int_sfx + fp_sfx
tests_registry = {
bool_sfx: _SIMD_BOOL,
int_sfx : _SIMD_INT,
fp_sfx : _SIMD_FP,
("f32",): _SIMD_FP32,
("f64",): _SIMD_FP64,
all_sfx : _SIMD_ALL
}
for target_name, npyv in targets.items():
simd_width = npyv.simd if npyv else ''
pretty_name = target_name.split('__') # multi-target separator
if len(pretty_name) > 1:
# multi-target
pretty_name = f"({' '.join(pretty_name)})"
else:
pretty_name = pretty_name[0]
skip = ""
skip_sfx = dict()
if not npyv:
skip = f"target '{pretty_name}' isn't supported by current machine"
elif not npyv.simd:
skip = f"target '{pretty_name}' isn't supported by NPYV"
elif not npyv.simd_f64:
skip_sfx["f64"] = f"target '{pretty_name}' doesn't support double-precision"
for sfxes, cls in tests_registry.items():
for sfx in sfxes:
skip_m = skip_sfx.get(sfx, skip)
inhr = (cls,)
attr = dict(npyv=targets[target_name], sfx=sfx, target_name=target_name)
tcls = type(f"Test{cls.__name__}_{simd_width}_{target_name}_{sfx}", inhr, attr)
if skip_m:
pytest.mark.skip(reason=skip_m)(tcls)
globals()[tcls.__name__] = tcls
| 37,478 | Python | 36.107921 | 94 | 0.531565 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/tests/test_errstate.py | import pytest
import sysconfig
import numpy as np
from numpy.testing import assert_, assert_raises
# The floating point emulation on ARM EABI systems lacking a hardware FPU is
# known to be buggy. This is an attempt to identify these hosts. It may not
# catch all possible cases, but it catches the known cases of gh-413 and
# gh-15562.
hosttype = sysconfig.get_config_var('HOST_GNU_TYPE')
arm_softfloat = False if hosttype is None else hosttype.endswith('gnueabi')
class TestErrstate:
@pytest.mark.skipif(arm_softfloat,
reason='platform/cpu issue with FPU (gh-413,-15562)')
def test_invalid(self):
with np.errstate(all='raise', under='ignore'):
a = -np.arange(3)
# This should work
with np.errstate(invalid='ignore'):
np.sqrt(a)
# While this should fail!
with assert_raises(FloatingPointError):
np.sqrt(a)
@pytest.mark.skipif(arm_softfloat,
reason='platform/cpu issue with FPU (gh-15562)')
def test_divide(self):
with np.errstate(all='raise', under='ignore'):
a = -np.arange(3)
# This should work
with np.errstate(divide='ignore'):
a // 0
# While this should fail!
with assert_raises(FloatingPointError):
a // 0
# As should this, see gh-15562
with assert_raises(FloatingPointError):
a // a
def test_errcall(self):
def foo(*args):
print(args)
olderrcall = np.geterrcall()
with np.errstate(call=foo):
assert_(np.geterrcall() is foo, 'call is not foo')
with np.errstate(call=None):
assert_(np.geterrcall() is None, 'call is not None')
assert_(np.geterrcall() is olderrcall, 'call is not olderrcall')
def test_errstate_decorator(self):
@np.errstate(all='ignore')
def foo():
a = -np.arange(3)
a // 0
foo()
| 2,066 | Python | 33.449999 | 77 | 0.57454 |
omniverse-code/kit/exts/omni.kit.pip_archive/pip_prebundle/numpy/core/tests/test_records.py | import collections.abc
import textwrap
from io import BytesIO
from os import path
from pathlib import Path
import pytest
import numpy as np
from numpy.testing import (
assert_, assert_equal, assert_array_equal, assert_array_almost_equal,
assert_raises, temppath,
)
from numpy.compat import pickle
class TestFromrecords:
def test_fromrecords(self):
r = np.rec.fromrecords([[456, 'dbe', 1.2], [2, 'de', 1.3]],
names='col1,col2,col3')
assert_equal(r[0].item(), (456, 'dbe', 1.2))
assert_equal(r['col1'].dtype.kind, 'i')
assert_equal(r['col2'].dtype.kind, 'U')
assert_equal(r['col2'].dtype.itemsize, 12)
assert_equal(r['col3'].dtype.kind, 'f')
def test_fromrecords_0len(self):
""" Verify fromrecords works with a 0-length input """
dtype = [('a', float), ('b', float)]
r = np.rec.fromrecords([], dtype=dtype)
assert_equal(r.shape, (0,))
def test_fromrecords_2d(self):
data = [
[(1, 2), (3, 4), (5, 6)],
[(6, 5), (4, 3), (2, 1)]
]
expected_a = [[1, 3, 5], [6, 4, 2]]
expected_b = [[2, 4, 6], [5, 3, 1]]
# try with dtype
r1 = np.rec.fromrecords(data, dtype=[('a', int), ('b', int)])
assert_equal(r1['a'], expected_a)
assert_equal(r1['b'], expected_b)
# try with names
r2 = np.rec.fromrecords(data, names=['a', 'b'])
assert_equal(r2['a'], expected_a)
assert_equal(r2['b'], expected_b)
assert_equal(r1, r2)
def test_method_array(self):
r = np.rec.array(b'abcdefg' * 100, formats='i2,a3,i4', shape=3, byteorder='big')
assert_equal(r[1].item(), (25444, b'efg', 1633837924))
def test_method_array2(self):
r = np.rec.array([(1, 11, 'a'), (2, 22, 'b'), (3, 33, 'c'), (4, 44, 'd'), (5, 55, 'ex'),
(6, 66, 'f'), (7, 77, 'g')], formats='u1,f4,a1')
assert_equal(r[1].item(), (2, 22.0, b'b'))
def test_recarray_slices(self):
r = np.rec.array([(1, 11, 'a'), (2, 22, 'b'), (3, 33, 'c'), (4, 44, 'd'), (5, 55, 'ex'),
(6, 66, 'f'), (7, 77, 'g')], formats='u1,f4,a1')
assert_equal(r[1::2][1].item(), (4, 44.0, b'd'))
def test_recarray_fromarrays(self):
x1 = np.array([1, 2, 3, 4])
x2 = np.array(['a', 'dd', 'xyz', '12'])
x3 = np.array([1.1, 2, 3, 4])
r = np.rec.fromarrays([x1, x2, x3], names='a,b,c')
assert_equal(r[1].item(), (2, 'dd', 2.0))
x1[1] = 34
assert_equal(r.a, np.array([1, 2, 3, 4]))
def test_recarray_fromfile(self):
data_dir = path.join(path.dirname(__file__), 'data')
filename = path.join(data_dir, 'recarray_from_file.fits')
fd = open(filename, 'rb')
fd.seek(2880 * 2)
r1 = np.rec.fromfile(fd, formats='f8,i4,a5', shape=3, byteorder='big')
fd.seek(2880 * 2)
r2 = np.rec.array(fd, formats='f8,i4,a5', shape=3, byteorder='big')
fd.seek(2880 * 2)
bytes_array = BytesIO()
bytes_array.write(fd.read())
bytes_array.seek(0)
r3 = np.rec.fromfile(bytes_array, formats='f8,i4,a5', shape=3, byteorder='big')
fd.close()
assert_equal(r1, r2)
assert_equal(r2, r3)
def test_recarray_from_obj(self):
count = 10
a = np.zeros(count, dtype='O')
b = np.zeros(count, dtype='f8')
c = np.zeros(count, dtype='f8')
for i in range(len(a)):
a[i] = list(range(1, 10))
mine = np.rec.fromarrays([a, b, c], names='date,data1,data2')
for i in range(len(a)):
assert_((mine.date[i] == list(range(1, 10))))
assert_((mine.data1[i] == 0.0))
assert_((mine.data2[i] == 0.0))
def test_recarray_repr(self):
a = np.array([(1, 0.1), (2, 0.2)],
dtype=[('foo', '<i4'), ('bar', '<f8')])
a = np.rec.array(a)
assert_equal(
repr(a),
textwrap.dedent("""\
rec.array([(1, 0.1), (2, 0.2)],
dtype=[('foo', '<i4'), ('bar', '<f8')])""")
)
# make sure non-structured dtypes also show up as rec.array
a = np.array(np.ones(4, dtype='f8'))
assert_(repr(np.rec.array(a)).startswith('rec.array'))
# check that the 'np.record' part of the dtype isn't shown
a = np.rec.array(np.ones(3, dtype='i4,i4'))
assert_equal(repr(a).find('numpy.record'), -1)
a = np.rec.array(np.ones(3, dtype='i4'))
assert_(repr(a).find('dtype=int32') != -1)
def test_0d_recarray_repr(self):
arr_0d = np.rec.array((1, 2.0, '2003'), dtype='<i4,<f8,<M8[Y]')
assert_equal(repr(arr_0d), textwrap.dedent("""\
rec.array((1, 2., '2003'),
dtype=[('f0', '<i4'), ('f1', '<f8'), ('f2', '<M8[Y]')])"""))
record = arr_0d[()]
assert_equal(repr(record), "(1, 2., '2003')")
# 1.13 converted to python scalars before the repr
try:
np.set_printoptions(legacy='1.13')
assert_equal(repr(record), '(1, 2.0, datetime.date(2003, 1, 1))')
finally:
np.set_printoptions(legacy=False)
def test_recarray_from_repr(self):
a = np.array([(1,'ABC'), (2, "DEF")],
dtype=[('foo', int), ('bar', 'S4')])
recordarr = np.rec.array(a)
recarr = a.view(np.recarray)
recordview = a.view(np.dtype((np.record, a.dtype)))
recordarr_r = eval("numpy." + repr(recordarr), {'numpy': np})
recarr_r = eval("numpy." + repr(recarr), {'numpy': np})
recordview_r = eval("numpy." + repr(recordview), {'numpy': np})
assert_equal(type(recordarr_r), np.recarray)
assert_equal(recordarr_r.dtype.type, np.record)
assert_equal(recordarr, recordarr_r)
assert_equal(type(recarr_r), np.recarray)
assert_equal(recarr_r.dtype.type, np.record)
assert_equal(recarr, recarr_r)
assert_equal(type(recordview_r), np.ndarray)
assert_equal(recordview.dtype.type, np.record)
assert_equal(recordview, recordview_r)
def test_recarray_views(self):
a = np.array([(1,'ABC'), (2, "DEF")],
dtype=[('foo', int), ('bar', 'S4')])
b = np.array([1,2,3,4,5], dtype=np.int64)
#check that np.rec.array gives right dtypes
assert_equal(np.rec.array(a).dtype.type, np.record)
assert_equal(type(np.rec.array(a)), np.recarray)
assert_equal(np.rec.array(b).dtype.type, np.int64)
assert_equal(type(np.rec.array(b)), np.recarray)
#check that viewing as recarray does the same
assert_equal(a.view(np.recarray).dtype.type, np.record)
assert_equal(type(a.view(np.recarray)), np.recarray)
assert_equal(b.view(np.recarray).dtype.type, np.int64)
assert_equal(type(b.view(np.recarray)), np.recarray)
#check that view to non-structured dtype preserves type=np.recarray
r = np.rec.array(np.ones(4, dtype="f4,i4"))
rv = r.view('f8').view('f4,i4')
assert_equal(type(rv), np.recarray)
assert_equal(rv.dtype.type, np.record)
#check that getitem also preserves np.recarray and np.record
r = np.rec.array(np.ones(4, dtype=[('a', 'i4'), ('b', 'i4'),
('c', 'i4,i4')]))
assert_equal(r['c'].dtype.type, np.record)
assert_equal(type(r['c']), np.recarray)
#and that it preserves subclasses (gh-6949)
class C(np.recarray):
pass
c = r.view(C)
assert_equal(type(c['c']), C)
# check that accessing nested structures keep record type, but
# not for subarrays, non-void structures, non-structured voids
test_dtype = [('a', 'f4,f4'), ('b', 'V8'), ('c', ('f4',2)),
('d', ('i8', 'i4,i4'))]
r = np.rec.array([((1,1), b'11111111', [1,1], 1),
((1,1), b'11111111', [1,1], 1)], dtype=test_dtype)
assert_equal(r.a.dtype.type, np.record)
assert_equal(r.b.dtype.type, np.void)
assert_equal(r.c.dtype.type, np.float32)
assert_equal(r.d.dtype.type, np.int64)
# check the same, but for views
r = np.rec.array(np.ones(4, dtype='i4,i4'))
assert_equal(r.view('f4,f4').dtype.type, np.record)
assert_equal(r.view(('i4',2)).dtype.type, np.int32)
assert_equal(r.view('V8').dtype.type, np.void)
assert_equal(r.view(('i8', 'i4,i4')).dtype.type, np.int64)
#check that we can undo the view
arrs = [np.ones(4, dtype='f4,i4'), np.ones(4, dtype='f8')]
for arr in arrs:
rec = np.rec.array(arr)
# recommended way to view as an ndarray:
arr2 = rec.view(rec.dtype.fields or rec.dtype, np.ndarray)
assert_equal(arr2.dtype.type, arr.dtype.type)
assert_equal(type(arr2), type(arr))
def test_recarray_from_names(self):
ra = np.rec.array([
(1, 'abc', 3.7000002861022949, 0),
(2, 'xy', 6.6999998092651367, 1),
(0, ' ', 0.40000000596046448, 0)],
names='c1, c2, c3, c4')
pa = np.rec.fromrecords([
(1, 'abc', 3.7000002861022949, 0),
(2, 'xy', 6.6999998092651367, 1),
(0, ' ', 0.40000000596046448, 0)],
names='c1, c2, c3, c4')
assert_(ra.dtype == pa.dtype)
assert_(ra.shape == pa.shape)
for k in range(len(ra)):
assert_(ra[k].item() == pa[k].item())
def test_recarray_conflict_fields(self):
ra = np.rec.array([(1, 'abc', 2.3), (2, 'xyz', 4.2),
(3, 'wrs', 1.3)],
names='field, shape, mean')
ra.mean = [1.1, 2.2, 3.3]
assert_array_almost_equal(ra['mean'], [1.1, 2.2, 3.3])
assert_(type(ra.mean) is type(ra.var))
ra.shape = (1, 3)
assert_(ra.shape == (1, 3))
ra.shape = ['A', 'B', 'C']
assert_array_equal(ra['shape'], [['A', 'B', 'C']])
ra.field = 5
assert_array_equal(ra['field'], [[5, 5, 5]])
assert_(isinstance(ra.field, collections.abc.Callable))
def test_fromrecords_with_explicit_dtype(self):
a = np.rec.fromrecords([(1, 'a'), (2, 'bbb')],
dtype=[('a', int), ('b', object)])
assert_equal(a.a, [1, 2])
assert_equal(a[0].a, 1)
assert_equal(a.b, ['a', 'bbb'])
assert_equal(a[-1].b, 'bbb')
#
ndtype = np.dtype([('a', int), ('b', object)])
a = np.rec.fromrecords([(1, 'a'), (2, 'bbb')], dtype=ndtype)
assert_equal(a.a, [1, 2])
assert_equal(a[0].a, 1)
assert_equal(a.b, ['a', 'bbb'])
assert_equal(a[-1].b, 'bbb')
def test_recarray_stringtypes(self):
# Issue #3993
a = np.array([('abc ', 1), ('abc', 2)],
dtype=[('foo', 'S4'), ('bar', int)])
a = a.view(np.recarray)
assert_equal(a.foo[0] == a.foo[1], False)
def test_recarray_returntypes(self):
qux_fields = {'C': (np.dtype('S5'), 0), 'D': (np.dtype('S5'), 6)}
a = np.rec.array([('abc ', (1,1), 1, ('abcde', 'fgehi')),
('abc', (2,3), 1, ('abcde', 'jklmn'))],
dtype=[('foo', 'S4'),
('bar', [('A', int), ('B', int)]),
('baz', int), ('qux', qux_fields)])
assert_equal(type(a.foo), np.ndarray)
assert_equal(type(a['foo']), np.ndarray)
assert_equal(type(a.bar), np.recarray)
assert_equal(type(a['bar']), np.recarray)
assert_equal(a.bar.dtype.type, np.record)
assert_equal(type(a['qux']), np.recarray)
assert_equal(a.qux.dtype.type, np.record)
assert_equal(dict(a.qux.dtype.fields), qux_fields)
assert_equal(type(a.baz), np.ndarray)
assert_equal(type(a['baz']), np.ndarray)
assert_equal(type(a[0].bar), np.record)
assert_equal(type(a[0]['bar']), np.record)
assert_equal(a[0].bar.A, 1)
assert_equal(a[0].bar['A'], 1)
assert_equal(a[0]['bar'].A, 1)
assert_equal(a[0]['bar']['A'], 1)
assert_equal(a[0].qux.D, b'fgehi')
assert_equal(a[0].qux['D'], b'fgehi')
assert_equal(a[0]['qux'].D, b'fgehi')
assert_equal(a[0]['qux']['D'], b'fgehi')
def test_zero_width_strings(self):
# Test for #6430, based on the test case from #1901
cols = [['test'] * 3, [''] * 3]
rec = np.rec.fromarrays(cols)
assert_equal(rec['f0'], ['test', 'test', 'test'])
assert_equal(rec['f1'], ['', '', ''])
dt = np.dtype([('f0', '|S4'), ('f1', '|S')])
rec = np.rec.fromarrays(cols, dtype=dt)
assert_equal(rec.itemsize, 4)
assert_equal(rec['f0'], [b'test', b'test', b'test'])
assert_equal(rec['f1'], [b'', b'', b''])
class TestPathUsage:
# Test that pathlib.Path can be used
def test_tofile_fromfile(self):
with temppath(suffix='.bin') as path:
path = Path(path)
np.random.seed(123)
a = np.random.rand(10).astype('f8,i4,a5')
a[5] = (0.5,10,'abcde')
with path.open("wb") as fd:
a.tofile(fd)
x = np.core.records.fromfile(path,
formats='f8,i4,a5',
shape=10)
assert_array_equal(x, a)
class TestRecord:
def setup_method(self):
self.data = np.rec.fromrecords([(1, 2, 3), (4, 5, 6)],
dtype=[("col1", "<i4"),
("col2", "<i4"),
("col3", "<i4")])
def test_assignment1(self):
a = self.data
assert_equal(a.col1[0], 1)
a[0].col1 = 0
assert_equal(a.col1[0], 0)
def test_assignment2(self):
a = self.data
assert_equal(a.col1[0], 1)
a.col1[0] = 0
assert_equal(a.col1[0], 0)
def test_invalid_assignment(self):
a = self.data
def assign_invalid_column(x):
x[0].col5 = 1
assert_raises(AttributeError, assign_invalid_column, a)
def test_nonwriteable_setfield(self):
# gh-8171
r = np.rec.array([(0,), (1,)], dtype=[('f', 'i4')])
r.flags.writeable = False
with assert_raises(ValueError):
r.f = [2, 3]
with assert_raises(ValueError):
r.setfield([2,3], *r.dtype.fields['f'])
def test_out_of_order_fields(self):
# names in the same order, padding added to descr
x = self.data[['col1', 'col2']]
assert_equal(x.dtype.names, ('col1', 'col2'))
assert_equal(x.dtype.descr,
[('col1', '<i4'), ('col2', '<i4'), ('', '|V4')])
# names change order to match indexing, as of 1.14 - descr can't
# represent that
y = self.data[['col2', 'col1']]
assert_equal(y.dtype.names, ('col2', 'col1'))
assert_raises(ValueError, lambda: y.dtype.descr)
def test_pickle_1(self):
# Issue #1529
a = np.array([(1, [])], dtype=[('a', np.int32), ('b', np.int32, 0)])
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
assert_equal(a, pickle.loads(pickle.dumps(a, protocol=proto)))
assert_equal(a[0], pickle.loads(pickle.dumps(a[0],
protocol=proto)))
def test_pickle_2(self):
a = self.data
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
assert_equal(a, pickle.loads(pickle.dumps(a, protocol=proto)))
assert_equal(a[0], pickle.loads(pickle.dumps(a[0],
protocol=proto)))
def test_pickle_3(self):
# Issue #7140
a = self.data
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
pa = pickle.loads(pickle.dumps(a[0], protocol=proto))
assert_(pa.flags.c_contiguous)
assert_(pa.flags.f_contiguous)
assert_(pa.flags.writeable)
assert_(pa.flags.aligned)
def test_pickle_void(self):
# issue gh-13593
dt = np.dtype([('obj', 'O'), ('int', 'i')])
a = np.empty(1, dtype=dt)
data = (bytearray(b'eman'),)
a['obj'] = data
a['int'] = 42
ctor, args = a[0].__reduce__()
# check the constructor is what we expect before interpreting the arguments
assert ctor is np.core.multiarray.scalar
dtype, obj = args
# make sure we did not pickle the address
assert not isinstance(obj, bytes)
assert_raises(RuntimeError, ctor, dtype, 13)
# Test roundtrip:
dump = pickle.dumps(a[0])
unpickled = pickle.loads(dump)
assert a[0] == unpickled
# Also check the similar (impossible) "object scalar" path:
with pytest.warns(DeprecationWarning):
assert ctor(np.dtype("O"), data) is data
def test_objview_record(self):
# https://github.com/numpy/numpy/issues/2599
dt = np.dtype([('foo', 'i8'), ('bar', 'O')])
r = np.zeros((1,3), dtype=dt).view(np.recarray)
r.foo = np.array([1, 2, 3]) # TypeError?
# https://github.com/numpy/numpy/issues/3256
ra = np.recarray((2,), dtype=[('x', object), ('y', float), ('z', int)])
ra[['x','y']] # TypeError?
def test_record_scalar_setitem(self):
# https://github.com/numpy/numpy/issues/3561
rec = np.recarray(1, dtype=[('x', float, 5)])
rec[0].x = 1
assert_equal(rec[0].x, np.ones(5))
def test_missing_field(self):
# https://github.com/numpy/numpy/issues/4806
arr = np.zeros((3,), dtype=[('x', int), ('y', int)])
assert_raises(KeyError, lambda: arr[['nofield']])
def test_fromarrays_nested_structured_arrays(self):
arrays = [
np.arange(10),
np.ones(10, dtype=[('a', '<u2'), ('b', '<f4')]),
]
arr = np.rec.fromarrays(arrays) # ValueError?
@pytest.mark.parametrize('nfields', [0, 1, 2])
def test_assign_dtype_attribute(self, nfields):
dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)][:nfields])
data = np.zeros(3, dt).view(np.recarray)
# the original and resulting dtypes differ on whether they are records
assert data.dtype.type == np.record
assert dt.type != np.record
# ensure that the dtype remains a record even when assigned
data.dtype = dt
assert data.dtype.type == np.record
@pytest.mark.parametrize('nfields', [0, 1, 2])
def test_nested_fields_are_records(self, nfields):
""" Test that nested structured types are treated as records too """
dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)][:nfields])
dt_outer = np.dtype([('inner', dt)])
data = np.zeros(3, dt_outer).view(np.recarray)
assert isinstance(data, np.recarray)
assert isinstance(data['inner'], np.recarray)
data0 = data[0]
assert isinstance(data0, np.record)
assert isinstance(data0['inner'], np.record)
def test_nested_dtype_padding(self):
""" test that trailing padding is preserved """
# construct a dtype with padding at the end
dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)])
dt_padded_end = dt[['a', 'b']]
assert dt_padded_end.itemsize == dt.itemsize
dt_outer = np.dtype([('inner', dt_padded_end)])
data = np.zeros(3, dt_outer).view(np.recarray)
assert_equal(data['inner'].dtype, dt_padded_end)
data0 = data[0]
assert_equal(data0['inner'].dtype, dt_padded_end)
def test_find_duplicate():
l1 = [1, 2, 3, 4, 5, 6]
assert_(np.rec.find_duplicate(l1) == [])
l2 = [1, 2, 1, 4, 5, 6]
assert_(np.rec.find_duplicate(l2) == [1])
l3 = [1, 2, 1, 4, 1, 6, 2, 3]
assert_(np.rec.find_duplicate(l3) == [1, 2])
l3 = [2, 2, 1, 4, 1, 6, 2, 3]
assert_(np.rec.find_duplicate(l3) == [2, 1])
| 20,269 | Python | 37.90595 | 96 | 0.516898 |
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