diff options
Diffstat (limited to 'numpy/lib/utils.py')
| -rw-r--r-- | numpy/lib/utils.py | 118 |
1 files changed, 86 insertions, 32 deletions
diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py index d511c2a40..c74ee127d 100644 --- a/numpy/lib/utils.py +++ b/numpy/lib/utils.py @@ -193,7 +193,32 @@ def deprecate(*args, **kwargs): else: return _Deprecate(*args, **kwargs) -deprecate_with_doc = lambda msg: _Deprecate(message=msg) + +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) #-------------------------------------------- @@ -326,8 +351,7 @@ def who(vardict=None): maxshape = 0 maxbyte = 0 totalbytes = 0 - for k in range(len(sta)): - val = sta[k] + for val in sta: if maxname < len(val[0]): maxname = len(val[0]) if maxshape < len(val[1]): @@ -344,8 +368,7 @@ def who(vardict=None): prval = "Name %s Shape %s Bytes %s Type" % (sp1*' ', sp2*' ', sp3*' ') print(prval + "\n" + "="*(len(prval)+5) + "\n") - for k in range(len(sta)): - val = sta[k] + 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), @@ -406,7 +429,7 @@ def _makenamedict(module='numpy'): return thedict, dictlist -def _info(obj, output=sys.stdout): +def _info(obj, output=None): """Provide information about ndarray obj. Parameters @@ -432,6 +455,9 @@ def _info(obj, output=sys.stdout): 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) @@ -458,7 +484,7 @@ def _info(obj, output=sys.stdout): @set_module('numpy') -def info(object=None, maxwidth=76, output=sys.stdout, toplevel='numpy'): +def info(object=None, maxwidth=76, output=None, toplevel='numpy'): """ Get help information for a function, class, or module. @@ -473,7 +499,8 @@ def info(object=None, maxwidth=76, output=sys.stdout, toplevel='numpy'): Printing width. output : file like object, optional File like object that the output is written to, default is - ``stdout``. The object has to be opened in 'w' or 'a' mode. + ``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. @@ -518,6 +545,9 @@ def info(object=None, maxwidth=76, output=sys.stdout, toplevel='numpy'): 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): @@ -587,11 +617,11 @@ def info(object=None, maxwidth=76, output=sys.stdout, toplevel='numpy'): print(inspect.getdoc(object), file=output) methods = pydoc.allmethods(object) - if methods != []: + + public_methods = [meth for meth in methods if meth[0] != '_'] + if public_methods: print("\n\nMethods:\n", file=output) - for meth in methods: - if meth[0] == '_': - continue + for meth in public_methods: thisobj = getattr(object, meth, None) if thisobj is not None: methstr, other = pydoc.splitdoc( @@ -879,7 +909,7 @@ def _lookfor_generate_cache(module, import_modules, regenerate): sys.stdout = old_stdout sys.stderr = old_stderr # Catch SystemExit, too - except BaseException: + except (Exception, SystemExit): continue for n, v in _getmembers(item): @@ -979,7 +1009,7 @@ def safe_eval(source): return ast.literal_eval(source) -def _median_nancheck(data, result, axis, out): +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. @@ -987,34 +1017,58 @@ def _median_nancheck(data, result, axis, out): Parameters ---------- data : array - Input data to median function + Sorted input data to median function result : Array or MaskedArray - Result of median function - axis : {int, sequence of int, None}, optional - Axis or axes along which the median was computed. - out : ndarray, optional - Output array in which to place the result. + Result of median function. + axis : int + Axis along which the median was computed. + Returns ------- - median : scalar or ndarray - Median or NaN in axes which contained NaN in the input. + 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 - data = np.moveaxis(data, axis, -1) - n = np.isnan(data[..., -1]) + n = np.isnan(data.take(-1, axis=axis)) # masked NaN values are ok if np.ma.isMaskedArray(n): n = n.filled(False) - if result.ndim == 0: - if n == True: - if out is not None: - out[...] = data.dtype.type(np.nan) - result = out - else: - result = data.dtype.type(np.nan) - elif np.count_nonzero(n.ravel()) > 0: + if np.count_nonzero(n.ravel()) > 0: + # 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 data.dtype.type(np.nan) + result[n] = np.nan 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 #----------------------------------------------------------------------------- |