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Traktor/myenv/Lib/site-packages/numpy/lib/utils.py

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losowanie zdjec
2024-05-26 05:12:46 +02:00
import os
import sys
import textwrap
import types
import re
import warnings
import functools
import platform
from .._utils import set_module
from numpy.core.numerictypes import issubclass_, issubsctype, issubdtype
from numpy.core import ndarray, ufunc, asarray
import numpy as np
__all__ = [
'issubclass_', 'issubsctype', 'issubdtype', 'deprecate',
'deprecate_with_doc', 'get_include', 'info', 'source', 'who',
'lookfor', 'byte_bounds', 'safe_eval', 'show_runtime'
]
def show_runtime():
"""
Print information about various resources in the system
including available intrinsic support and BLAS/LAPACK library
in use
.. versionadded:: 1.24.0
See Also
--------
show_config : Show libraries in the system on which NumPy was built.
Notes
-----
1. Information is derived with the help of `threadpoolctl <https://pypi.org/project/threadpoolctl/>`_
library if available.
2. SIMD related information is derived from ``__cpu_features__``,
``__cpu_baseline__`` and ``__cpu_dispatch__``
"""
from numpy.core._multiarray_umath import (
__cpu_features__, __cpu_baseline__, __cpu_dispatch__
)
from pprint import pprint
config_found = [{
"numpy_version": np.__version__,
"python": sys.version,
"uname": platform.uname(),
}]
features_found, features_not_found = [], []
for feature in __cpu_dispatch__:
if __cpu_features__[feature]:
features_found.append(feature)
else:
features_not_found.append(feature)
config_found.append({
"simd_extensions": {
"baseline": __cpu_baseline__,
"found": features_found,
"not_found": features_not_found
}
})
try:
from threadpoolctl import threadpool_info
config_found.extend(threadpool_info())
except ImportError:
print("WARNING: `threadpoolctl` not found in system!"
" Install it by `pip install threadpoolctl`."
" Once installed, try `np.show_runtime` again"
" for more detailed build information")
pprint(config_found)
def get_include():
"""
Return the directory that contains the NumPy \\*.h header files.
Extension modules that need to compile against NumPy should use this
function to locate the appropriate include directory.
Notes
-----
When using ``distutils``, for example in ``setup.py``::
import numpy as np
...
Extension('extension_name', ...
include_dirs=[np.get_include()])
...
"""
import numpy
if numpy.show_config is None:
# running from numpy source directory
d = os.path.join(os.path.dirname(numpy.__file__), 'core', 'include')
else:
# using installed numpy core headers
import numpy.core as core
d = os.path.join(os.path.dirname(core.__file__), 'include')
return d
class _Deprecate:
"""
Decorator class to deprecate old functions.
Refer to `deprecate` for details.
See Also
--------
deprecate
"""
def __init__(self, old_name=None, new_name=None, message=None):
self.old_name = old_name
self.new_name = new_name
self.message = message
def __call__(self, func, *args, **kwargs):
"""
Decorator call. Refer to ``decorate``.
"""
old_name = self.old_name
new_name = self.new_name
message = self.message
if old_name is None:
old_name = func.__name__
if new_name is None:
depdoc = "`%s` is deprecated!" % old_name
else:
depdoc = "`%s` is deprecated, use `%s` instead!" % \
(old_name, new_name)
if message is not None:
depdoc += "\n" + message
@functools.wraps(func)
def newfunc(*args, **kwds):
warnings.warn(depdoc, DeprecationWarning, stacklevel=2)
return func(*args, **kwds)
newfunc.__name__ = old_name
doc = func.__doc__
if doc is None:
doc = depdoc
else:
lines = doc.expandtabs().split('\n')
indent = _get_indent(lines[1:])
if lines[0].lstrip():
# Indent the original first line to let inspect.cleandoc()
# dedent the docstring despite the deprecation notice.
doc = indent * ' ' + doc
else:
# Remove the same leading blank lines as cleandoc() would.
skip = len(lines[0]) + 1
for line in lines[1:]:
if len(line) > indent:
break
skip += len(line) + 1
doc = doc[skip:]
depdoc = textwrap.indent(depdoc, ' ' * indent)
doc = '\n\n'.join([depdoc, doc])
newfunc.__doc__ = doc
return newfunc
def _get_indent(lines):
"""
Determines the leading whitespace that could be removed from all the lines.
"""
indent = sys.maxsize
for line in lines:
content = len(line.lstrip())
if content:
indent = min(indent, len(line) - content)
if indent == sys.maxsize:
indent = 0
return indent
def deprecate(*args, **kwargs):
"""
Issues a DeprecationWarning, adds warning to `old_name`'s
docstring, rebinds ``old_name.__name__`` and returns the new
function object.
This function may also be used as a decorator.
Parameters
----------
func : function
The function to be deprecated.
old_name : str, optional
The name of the function to be deprecated. Default is None, in
which case the name of `func` is used.
new_name : str, optional
The new name for the function. Default is None, in which case the
deprecation message is that `old_name` is deprecated. If given, the
deprecation message is that `old_name` is deprecated and `new_name`
should be used instead.
message : str, optional
Additional explanation of the deprecation. Displayed in the
docstring after the warning.
Returns
-------
old_func : function
The deprecated function.
Examples
--------
Note that ``olduint`` returns a value after printing Deprecation
Warning:
>>> olduint = np.deprecate(np.uint)
DeprecationWarning: `uint64` is deprecated! # may vary
>>> olduint(6)
6
"""
# Deprecate may be run as a function or as a decorator
# If run as a function, we initialise the decorator class
# and execute its __call__ method.
if args:
fn = args[0]
args = args[1:]
return _Deprecate(*args, **kwargs)(fn)
else:
return _Deprecate(*args, **kwargs)
def deprecate_with_doc(msg):
"""
Deprecates a function and includes the deprecation in its docstring.
This function is used as a decorator. It returns an object that can be
used to issue a DeprecationWarning, by passing the to-be decorated
function as argument, this adds warning to the to-be decorated function's
docstring and returns the new function object.
See Also
--------
deprecate : Decorate a function such that it issues a `DeprecationWarning`
Parameters
----------
msg : str
Additional explanation of the deprecation. Displayed in the
docstring after the warning.
Returns
-------
obj : object
"""
return _Deprecate(message=msg)
#--------------------------------------------
# Determine if two arrays can share memory
#--------------------------------------------
def byte_bounds(a):
"""
Returns pointers to the end-points of an array.
Parameters
----------
a : ndarray
Input array. It must conform to the Python-side of the array
interface.
Returns
-------
(low, high) : tuple of 2 integers
The first integer is the first byte of the array, the second
integer is just past the last byte of the array. If `a` is not
contiguous it will not use every byte between the (`low`, `high`)
values.
Examples
--------
>>> I = np.eye(2, dtype='f'); I.dtype
dtype('float32')
>>> low, high = np.byte_bounds(I)
>>> high - low == I.size*I.itemsize
True
>>> I = np.eye(2); I.dtype
dtype('float64')
>>> low, high = np.byte_bounds(I)
>>> high - low == I.size*I.itemsize
True
"""
ai = a.__array_interface__
a_data = ai['data'][0]
astrides = ai['strides']
ashape = ai['shape']
bytes_a = asarray(a).dtype.itemsize
a_low = a_high = a_data
if astrides is None:
# contiguous case
a_high += a.size * bytes_a
else:
for shape, stride in zip(ashape, astrides):
if stride < 0:
a_low += (shape-1)*stride
else:
a_high += (shape-1)*stride
a_high += bytes_a
return a_low, a_high
#-----------------------------------------------------------------------------
# Function for output and information on the variables used.
#-----------------------------------------------------------------------------
def who(vardict=None):
"""
Print the NumPy arrays in the given dictionary.
If there is no dictionary passed in or `vardict` is None then returns
NumPy arrays in the globals() dictionary (all NumPy arrays in the
namespace).
Parameters
----------
vardict : dict, optional
A dictionary possibly containing ndarrays. Default is globals().
Returns
-------
out : None
Returns 'None'.
Notes
-----
Prints out the name, shape, bytes and type of all of the ndarrays
present in `vardict`.
Examples
--------
>>> a = np.arange(10)
>>> b = np.ones(20)
>>> np.who()
Name Shape Bytes Type
===========================================================
a 10 80 int64
b 20 160 float64
Upper bound on total bytes = 240
>>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str',
... 'idx':5}
>>> np.who(d)
Name Shape Bytes Type
===========================================================
x 2 16 float64
y 3 24 float64
Upper bound on total bytes = 40
"""
if vardict is None:
frame = sys._getframe().f_back
vardict = frame.f_globals
sta = []
cache = {}
for name in vardict.keys():
if isinstance(vardict[name], ndarray):
var = vardict[name]
idv = id(var)
if idv in cache.keys():
namestr = name + " (%s)" % cache[idv]
original = 0
else:
cache[idv] = name
namestr = name
original = 1
shapestr = " x ".join(map(str, var.shape))
bytestr = str(var.nbytes)
sta.append([namestr, shapestr, bytestr, var.dtype.name,
original])
maxname = 0
maxshape = 0
maxbyte = 0
totalbytes = 0
for val in sta:
if maxname < len(val[0]):
maxname = len(val[0])
if maxshape < len(val[1]):
maxshape = len(val[1])
if maxbyte < len(val[2]):
maxbyte = len(val[2])
if val[4]:
totalbytes += int(val[2])
if len(sta) > 0:
sp1 = max(10, maxname)
sp2 = max(10, maxshape)
sp3 = max(10, maxbyte)
prval = "Name %s Shape %s Bytes %s Type" % (sp1*' ', sp2*' ', sp3*' ')
print(prval + "\n" + "="*(len(prval)+5) + "\n")
for val in sta:
print("%s %s %s %s %s %s %s" % (val[0], ' '*(sp1-len(val[0])+4),
val[1], ' '*(sp2-len(val[1])+5),
val[2], ' '*(sp3-len(val[2])+5),
val[3]))
print("\nUpper bound on total bytes = %d" % totalbytes)
return
#-----------------------------------------------------------------------------
# NOTE: pydoc defines a help function which works similarly to this
# except it uses a pager to take over the screen.
# combine name and arguments and split to multiple lines of width
# characters. End lines on a comma and begin argument list indented with
# the rest of the arguments.
def _split_line(name, arguments, width):
firstwidth = len(name)
k = firstwidth
newstr = name
sepstr = ", "
arglist = arguments.split(sepstr)
for argument in arglist:
if k == firstwidth:
addstr = ""
else:
addstr = sepstr
k = k + len(argument) + len(addstr)
if k > width:
k = firstwidth + 1 + len(argument)
newstr = newstr + ",\n" + " "*(firstwidth+2) + argument
else:
newstr = newstr + addstr + argument
return newstr
_namedict = None
_dictlist = None
# Traverse all module directories underneath globals
# to see if something is defined
def _makenamedict(module='numpy'):
module = __import__(module, globals(), locals(), [])
thedict = {module.__name__:module.__dict__}
dictlist = [module.__name__]
totraverse = [module.__dict__]
while True:
if len(totraverse) == 0:
break
thisdict = totraverse.pop(0)
for x in thisdict.keys():
if isinstance(thisdict[x], types.ModuleType):
modname = thisdict[x].__name__
if modname not in dictlist:
moddict = thisdict[x].__dict__
dictlist.append(modname)
totraverse.append(moddict)
thedict[modname] = moddict
return thedict, dictlist
def _info(obj, output=None):
"""Provide information about ndarray obj.
Parameters
----------
obj : ndarray
Must be ndarray, not checked.
output
Where printed output goes.
Notes
-----
Copied over from the numarray module prior to its removal.
Adapted somewhat as only numpy is an option now.
Called by info.
"""
extra = ""
tic = ""
bp = lambda x: x
cls = getattr(obj, '__class__', type(obj))
nm = getattr(cls, '__name__', cls)
strides = obj.strides
endian = obj.dtype.byteorder
if output is None:
output = sys.stdout
print("class: ", nm, file=output)
print("shape: ", obj.shape, file=output)
print("strides: ", strides, file=output)
print("itemsize: ", obj.itemsize, file=output)
print("aligned: ", bp(obj.flags.aligned), file=output)
print("contiguous: ", bp(obj.flags.contiguous), file=output)
print("fortran: ", obj.flags.fortran, file=output)
print(
"data pointer: %s%s" % (hex(obj.ctypes._as_parameter_.value), extra),
file=output
)
print("byteorder: ", end=' ', file=output)
if endian in ['|', '=']:
print("%s%s%s" % (tic, sys.byteorder, tic), file=output)
byteswap = False
elif endian == '>':
print("%sbig%s" % (tic, tic), file=output)
byteswap = sys.byteorder != "big"
else:
print("%slittle%s" % (tic, tic), file=output)
byteswap = sys.byteorder != "little"
print("byteswap: ", bp(byteswap), file=output)
print("type: %s" % obj.dtype, file=output)
@set_module('numpy')
def info(object=None, maxwidth=76, output=None, toplevel='numpy'):
"""
Get help information for an array, function, class, or module.
Parameters
----------
object : object or str, optional
Input object or name to get information about. If `object` is
an `ndarray` instance, information about the array is printed.
If `object` is a numpy object, its docstring is given. If it is
a string, available modules are searched for matching objects.
If None, information about `info` itself is returned.
maxwidth : int, optional
Printing width.
output : file like object, optional
File like object that the output is written to, default is
``None``, in which case ``sys.stdout`` will be used.
The object has to be opened in 'w' or 'a' mode.
toplevel : str, optional
Start search at this level.
See Also
--------
source, lookfor
Notes
-----
When used interactively with an object, ``np.info(obj)`` is equivalent
to ``help(obj)`` on the Python prompt or ``obj?`` on the IPython
prompt.
Examples
--------
>>> np.info(np.polyval) # doctest: +SKIP
polyval(p, x)
Evaluate the polynomial p at x.
...
When using a string for `object` it is possible to get multiple results.
>>> np.info('fft') # doctest: +SKIP
*** Found in numpy ***
Core FFT routines
...
*** Found in numpy.fft ***
fft(a, n=None, axis=-1)
...
*** Repeat reference found in numpy.fft.fftpack ***
*** Total of 3 references found. ***
When the argument is an array, information about the array is printed.
>>> a = np.array([[1 + 2j, 3, -4], [-5j, 6, 0]], dtype=np.complex64)
>>> np.info(a)
class: ndarray
shape: (2, 3)
strides: (24, 8)
itemsize: 8
aligned: True
contiguous: True
fortran: False
data pointer: 0x562b6e0d2860 # may vary
byteorder: little
byteswap: False
type: complex64
"""
global _namedict, _dictlist
# Local import to speed up numpy's import time.
import pydoc
import inspect
if (hasattr(object, '_ppimport_importer') or
hasattr(object, '_ppimport_module')):
object = object._ppimport_module
elif hasattr(object, '_ppimport_attr'):
object = object._ppimport_attr
if output is None:
output = sys.stdout
if object is None:
info(info)
elif isinstance(object, ndarray):
_info(object, output=output)
elif isinstance(object, str):
if _namedict is None:
_namedict, _dictlist = _makenamedict(toplevel)
numfound = 0
objlist = []
for namestr in _dictlist:
try:
obj = _namedict[namestr][object]
if id(obj) in objlist:
print("\n "
"*** Repeat reference found in %s *** " % namestr,
file=output
)
else:
objlist.append(id(obj))
print(" *** Found in %s ***" % namestr, file=output)
info(obj)
print("-"*maxwidth, file=output)
numfound += 1
except KeyError:
pass
if numfound == 0:
print("Help for %s not found." % object, file=output)
else:
print("\n "
"*** Total of %d references found. ***" % numfound,
file=output
)
elif inspect.isfunction(object) or inspect.ismethod(object):
name = object.__name__
try:
arguments = str(inspect.signature(object))
except Exception:
arguments = "()"
if len(name+arguments) > maxwidth:
argstr = _split_line(name, arguments, maxwidth)
else:
argstr = name + arguments
print(" " + argstr + "\n", file=output)
print(inspect.getdoc(object), file=output)
elif inspect.isclass(object):
name = object.__name__
try:
arguments = str(inspect.signature(object))
except Exception:
arguments = "()"
if len(name+arguments) > maxwidth:
argstr = _split_line(name, arguments, maxwidth)
else:
argstr = name + arguments
print(" " + argstr + "\n", file=output)
doc1 = inspect.getdoc(object)
if doc1 is None:
if hasattr(object, '__init__'):
print(inspect.getdoc(object.__init__), file=output)
else:
print(inspect.getdoc(object), file=output)
methods = pydoc.allmethods(object)
public_methods = [meth for meth in methods if meth[0] != '_']
if public_methods:
print("\n\nMethods:\n", file=output)
for meth in public_methods:
thisobj = getattr(object, meth, None)
if thisobj is not None:
methstr, other = pydoc.splitdoc(
inspect.getdoc(thisobj) or "None"
)
print(" %s -- %s" % (meth, methstr), file=output)
elif hasattr(object, '__doc__'):
print(inspect.getdoc(object), file=output)
@set_module('numpy')
def source(object, output=sys.stdout):
"""
Print or write to a file the source code for a NumPy object.
The source code is only returned for objects written in Python. Many
functions and classes are defined in C and will therefore not return
useful information.
Parameters
----------
object : numpy object
Input object. This can be any object (function, class, module,
...).
output : file object, optional
If `output` not supplied then source code is printed to screen
(sys.stdout). File object must be created with either write 'w' or
append 'a' modes.
See Also
--------
lookfor, info
Examples
--------
>>> np.source(np.interp) #doctest: +SKIP
In file: /usr/lib/python2.6/dist-packages/numpy/lib/function_base.py
def interp(x, xp, fp, left=None, right=None):
\"\"\".... (full docstring printed)\"\"\"
if isinstance(x, (float, int, number)):
return compiled_interp([x], xp, fp, left, right).item()
else:
return compiled_interp(x, xp, fp, left, right)
The source code is only returned for objects written in Python.
>>> np.source(np.array) #doctest: +SKIP
Not available for this object.
"""
# Local import to speed up numpy's import time.
import inspect
try:
print("In file: %s\n" % inspect.getsourcefile(object), file=output)
print(inspect.getsource(object), file=output)
except Exception:
print("Not available for this object.", file=output)
# Cache for lookfor: {id(module): {name: (docstring, kind, index), ...}...}
# where kind: "func", "class", "module", "object"
# and index: index in breadth-first namespace traversal
_lookfor_caches = {}
# regexp whose match indicates that the string may contain a function
# signature
_function_signature_re = re.compile(r"[a-z0-9_]+\(.*[,=].*\)", re.I)
@set_module('numpy')
def lookfor(what, module=None, import_modules=True, regenerate=False,
output=None):
"""
Do a keyword search on docstrings.
A list of objects that matched the search is displayed,
sorted by relevance. All given keywords need to be found in the
docstring for it to be returned as a result, but the order does
not matter.
Parameters
----------
what : str
String containing words to look for.
module : str or list, optional
Name of module(s) whose docstrings to go through.
import_modules : bool, optional
Whether to import sub-modules in packages. Default is True.
regenerate : bool, optional
Whether to re-generate the docstring cache. Default is False.
output : file-like, optional
File-like object to write the output to. If omitted, use a pager.
See Also
--------
source, info
Notes
-----
Relevance is determined only roughly, by checking if the keywords occur
in the function name, at the start of a docstring, etc.
Examples
--------
>>> np.lookfor('binary representation') # doctest: +SKIP
Search results for 'binary representation'
------------------------------------------
numpy.binary_repr
Return the binary representation of the input number as a string.
numpy.core.setup_common.long_double_representation
Given a binary dump as given by GNU od -b, look for long double
numpy.base_repr
Return a string representation of a number in the given base system.
...
"""
import pydoc
# Cache
cache = _lookfor_generate_cache(module, import_modules, regenerate)
# Search
# XXX: maybe using a real stemming search engine would be better?
found = []
whats = str(what).lower().split()
if not whats:
return
for name, (docstring, kind, index) in cache.items():
if kind in ('module', 'object'):
# don't show modules or objects
continue
doc = docstring.lower()
if all(w in doc for w in whats):
found.append(name)
# Relevance sort
# XXX: this is full Harrison-Stetson heuristics now,
# XXX: it probably could be improved
kind_relevance = {'func': 1000, 'class': 1000,
'module': -1000, 'object': -1000}
def relevance(name, docstr, kind, index):
r = 0
# do the keywords occur within the start of the docstring?
first_doc = "\n".join(docstr.lower().strip().split("\n")[:3])
r += sum([200 for w in whats if w in first_doc])
# do the keywords occur in the function name?
r += sum([30 for w in whats if w in name])
# is the full name long?
r += -len(name) * 5
# is the object of bad type?
r += kind_relevance.get(kind, -1000)
# is the object deep in namespace hierarchy?
r += -name.count('.') * 10
r += max(-index / 100, -100)
return r
def relevance_value(a):
return relevance(a, *cache[a])
found.sort(key=relevance_value)
# Pretty-print
s = "Search results for '%s'" % (' '.join(whats))
help_text = [s, "-"*len(s)]
for name in found[::-1]:
doc, kind, ix = cache[name]
doclines = [line.strip() for line in doc.strip().split("\n")
if line.strip()]
# find a suitable short description
try:
first_doc = doclines[0].strip()
if _function_signature_re.search(first_doc):
first_doc = doclines[1].strip()
except IndexError:
first_doc = ""
help_text.append("%s\n %s" % (name, first_doc))
if not found:
help_text.append("Nothing found.")
# Output
if output is not None:
output.write("\n".join(help_text))
elif len(help_text) > 10:
pager = pydoc.getpager()
pager("\n".join(help_text))
else:
print("\n".join(help_text))
def _lookfor_generate_cache(module, import_modules, regenerate):
"""
Generate docstring cache for given module.
Parameters
----------
module : str, None, module
Module for which to generate docstring cache
import_modules : bool
Whether to import sub-modules in packages.
regenerate : bool
Re-generate the docstring cache
Returns
-------
cache : dict {obj_full_name: (docstring, kind, index), ...}
Docstring cache for the module, either cached one (regenerate=False)
or newly generated.
"""
# Local import to speed up numpy's import time.
import inspect
from io import StringIO
if module is None:
module = "numpy"
if isinstance(module, str):
try:
__import__(module)
except ImportError:
return {}
module = sys.modules[module]
elif isinstance(module, list) or isinstance(module, tuple):
cache = {}
for mod in module:
cache.update(_lookfor_generate_cache(mod, import_modules,
regenerate))
return cache
if id(module) in _lookfor_caches and not regenerate:
return _lookfor_caches[id(module)]
# walk items and collect docstrings
cache = {}
_lookfor_caches[id(module)] = cache
seen = {}
index = 0
stack = [(module.__name__, module)]
while stack:
name, item = stack.pop(0)
if id(item) in seen:
continue
seen[id(item)] = True
index += 1
kind = "object"
if inspect.ismodule(item):
kind = "module"
try:
_all = item.__all__
except AttributeError:
_all = None
# import sub-packages
if import_modules and hasattr(item, '__path__'):
for pth in item.__path__:
for mod_path in os.listdir(pth):
this_py = os.path.join(pth, mod_path)
init_py = os.path.join(pth, mod_path, '__init__.py')
if (os.path.isfile(this_py) and
mod_path.endswith('.py')):
to_import = mod_path[:-3]
elif os.path.isfile(init_py):
to_import = mod_path
else:
continue
if to_import == '__init__':
continue
try:
old_stdout = sys.stdout
old_stderr = sys.stderr
try:
sys.stdout = StringIO()
sys.stderr = StringIO()
__import__("%s.%s" % (name, to_import))
finally:
sys.stdout = old_stdout
sys.stderr = old_stderr
except KeyboardInterrupt:
# Assume keyboard interrupt came from a user
raise
except BaseException:
# Ignore also SystemExit and pytests.importorskip
# `Skipped` (these are BaseExceptions; gh-22345)
continue
for n, v in _getmembers(item):
try:
item_name = getattr(v, '__name__', "%s.%s" % (name, n))
mod_name = getattr(v, '__module__', None)
except NameError:
# ref. SWIG's global cvars
# NameError: Unknown C global variable
item_name = "%s.%s" % (name, n)
mod_name = None
if '.' not in item_name and mod_name:
item_name = "%s.%s" % (mod_name, item_name)
if not item_name.startswith(name + '.'):
# don't crawl "foreign" objects
if isinstance(v, ufunc):
# ... unless they are ufuncs
pass
else:
continue
elif not (inspect.ismodule(v) or _all is None or n in _all):
continue
stack.append(("%s.%s" % (name, n), v))
elif inspect.isclass(item):
kind = "class"
for n, v in _getmembers(item):
stack.append(("%s.%s" % (name, n), v))
elif hasattr(item, "__call__"):
kind = "func"
try:
doc = inspect.getdoc(item)
except NameError:
# ref SWIG's NameError: Unknown C global variable
doc = None
if doc is not None:
cache[name] = (doc, kind, index)
return cache
def _getmembers(item):
import inspect
try:
members = inspect.getmembers(item)
except Exception:
members = [(x, getattr(item, x)) for x in dir(item)
if hasattr(item, x)]
return members
def safe_eval(source):
"""
Protected string evaluation.
Evaluate a string containing a Python literal expression without
allowing the execution of arbitrary non-literal code.
.. warning::
This function is identical to :py:meth:`ast.literal_eval` and
has the same security implications. It may not always be safe
to evaluate large input strings.
Parameters
----------
source : str
The string to evaluate.
Returns
-------
obj : object
The result of evaluating `source`.
Raises
------
SyntaxError
If the code has invalid Python syntax, or if it contains
non-literal code.
Examples
--------
>>> np.safe_eval('1')
1
>>> np.safe_eval('[1, 2, 3]')
[1, 2, 3]
>>> np.safe_eval('{"foo": ("bar", 10.0)}')
{'foo': ('bar', 10.0)}
>>> np.safe_eval('import os')
Traceback (most recent call last):
...
SyntaxError: invalid syntax
>>> np.safe_eval('open("/home/user/.ssh/id_dsa").read()')
Traceback (most recent call last):
...
ValueError: malformed node or string: <_ast.Call object at 0x...>
"""
# Local import to speed up numpy's import time.
import ast
return ast.literal_eval(source)
def _median_nancheck(data, result, axis):
"""
Utility function to check median result from data for NaN values at the end
and return NaN in that case. Input result can also be a MaskedArray.
Parameters
----------
data : array
Sorted input data to median function
result : Array or MaskedArray
Result of median function.
axis : int
Axis along which the median was computed.
Returns
-------
result : scalar or ndarray
Median or NaN in axes which contained NaN in the input. If the input
was an array, NaN will be inserted in-place. If a scalar, either the
input itself or a scalar NaN.
"""
if data.size == 0:
return result
potential_nans = data.take(-1, axis=axis)
n = np.isnan(potential_nans)
# masked NaN values are ok, although for masked the copyto may fail for
# unmasked ones (this was always broken) when the result is a scalar.
if np.ma.isMaskedArray(n):
n = n.filled(False)
if not n.any():
return result
# Without given output, it is possible that the current result is a
# numpy scalar, which is not writeable. If so, just return nan.
if isinstance(result, np.generic):
return potential_nans
# Otherwise copy NaNs (if there are any)
np.copyto(result, potential_nans, where=n)
return result
def _opt_info():
"""
Returns a string contains the supported CPU features by the current build.
The string format can be explained as follows:
- dispatched features that are supported by the running machine
end with `*`.
- dispatched features that are "not" supported by the running machine
end with `?`.
- remained features are representing the baseline.
"""
from numpy.core._multiarray_umath import (
__cpu_features__, __cpu_baseline__, __cpu_dispatch__
)
if len(__cpu_baseline__) == 0 and len(__cpu_dispatch__) == 0:
return ''
enabled_features = ' '.join(__cpu_baseline__)
for feature in __cpu_dispatch__:
if __cpu_features__[feature]:
enabled_features += f" {feature}*"
else:
enabled_features += f" {feature}?"
return enabled_features
def drop_metadata(dtype, /):
"""
Returns the dtype unchanged if it contained no metadata or a copy of the
dtype if it (or any of its structure dtypes) contained metadata.
This utility is used by `np.save` and `np.savez` to drop metadata before
saving.
.. note::
Due to its limitation this function may move to a more appropriate
home or change in the future and is considered semi-public API only.
.. warning::
This function does not preserve more strange things like record dtypes
and user dtypes may simply return the wrong thing. If you need to be
sure about the latter, check the result with:
``np.can_cast(new_dtype, dtype, casting="no")``.
"""
if dtype.fields is not None:
found_metadata = dtype.metadata is not None
names = []
formats = []
offsets = []
titles = []
for name, field in dtype.fields.items():
field_dt = drop_metadata(field[0])
if field_dt is not field[0]:
found_metadata = True
names.append(name)
formats.append(field_dt)
offsets.append(field[1])
titles.append(None if len(field) < 3 else field[2])
if not found_metadata:
return dtype
structure = dict(
names=names, formats=formats, offsets=offsets, titles=titles,
itemsize=dtype.itemsize)
# NOTE: Could pass (dtype.type, structure) to preserve record dtypes...
return np.dtype(structure, align=dtype.isalignedstruct)
elif dtype.subdtype is not None:
# subarray dtype
subdtype, shape = dtype.subdtype
new_subdtype = drop_metadata(subdtype)
if dtype.metadata is None and new_subdtype is subdtype:
return dtype
return np.dtype((new_subdtype, shape))
else:
# Normal unstructured dtype
if dtype.metadata is None:
return dtype
# Note that `dt.str` doesn't round-trip e.g. for user-dtypes.
return np.dtype(dtype.str)
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