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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)