diff options
Diffstat (limited to 'numpy/core/multiarray.py')
| -rw-r--r-- | numpy/core/multiarray.py | 158 |
1 files changed, 114 insertions, 44 deletions
diff --git a/numpy/core/multiarray.py b/numpy/core/multiarray.py index df0ed2df4..c0fcc10ff 100644 --- a/numpy/core/multiarray.py +++ b/numpy/core/multiarray.py @@ -7,7 +7,9 @@ by importing from the extension module. """ import functools +import sys import warnings +import sys from . import overrides from . import _multiarray_umath @@ -15,7 +17,7 @@ import numpy as np from numpy.core._multiarray_umath import * from numpy.core._multiarray_umath import ( _fastCopyAndTranspose, _flagdict, _insert, _reconstruct, _vec_string, - _ARRAY_API, _monotonicity + _ARRAY_API, _monotonicity, _get_ndarray_c_version ) __all__ = [ @@ -30,15 +32,21 @@ __all__ = [ 'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data', 'digitize', 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype', 'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat', - 'frombuffer', 'fromfile', 'fromiter', 'fromstring', 'getbuffer', 'inner', + 'frombuffer', 'fromfile', 'fromiter', 'fromstring', 'inner', 'int_asbuffer', 'interp', 'interp_complex', 'is_busday', 'lexsort', 'matmul', 'may_share_memory', 'min_scalar_type', 'ndarray', 'nditer', - 'nested_iters', 'newbuffer', 'normalize_axis_index', 'packbits', + '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', 'test_interrupt', 'tracemalloc_domain', 'typeinfo', 'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros'] +if sys.version_info.major < 3: + __all__ += ['newbuffer', 'getbuffer'] + +# For backward compatibility, make sure pickle imports these functions from here +_reconstruct.__module__ = 'numpy.core.multiarray' +scalar.__module__ = 'numpy.core.multiarray' arange.__module__ = 'numpy' @@ -67,9 +75,9 @@ array_function_from_c_func_and_dispatcher = functools.partial( @array_function_from_c_func_and_dispatcher(_multiarray_umath.empty_like) -def empty_like(prototype, dtype=None, order=None, subok=None): +def empty_like(prototype, dtype=None, order=None, subok=None, shape=None): """ - empty_like(prototype, dtype=None, order='K', subok=True) + 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. @@ -93,6 +101,12 @@ def empty_like(prototype, dtype=None, order=None, subok=None): 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 ------- @@ -117,11 +131,11 @@ def empty_like(prototype, dtype=None, order=None, subok=None): -------- >>> a = ([1,2,3], [4,5,6]) # a is array-like >>> np.empty_like(a) - array([[-1073741821, -1073741821, 3], #random + 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],#random + array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000], # uninitialized [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]]) """ @@ -211,9 +225,11 @@ def concatenate(arrays, axis=None, out=None): fill_value=999999) """ - for array in arrays: - yield array - yield out + 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) @@ -286,8 +302,8 @@ def inner(a, b): An example where `b` is a scalar: >>> np.inner(np.eye(2), 7) - array([[ 7., 0.], - [ 0., 7.]]) + array([[7., 0.], + [0., 7.]]) """ return (a, b) @@ -421,8 +437,8 @@ def lexsort(keys, axis=None): >>> 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 - >>> print(ind) - [2 0 4 6 5 3 1] + >>> 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)] @@ -483,11 +499,15 @@ def can_cast(from_, to, casting=None): Notes ----- - Starting in NumPy 1.9, can_cast function now returns False in 'safe' - casting mode for integer/float dtype and string dtype if the string dtype - length is not long enough to store the max integer/float value converted - to a string. Previously can_cast in 'safe' mode returned True for - integer/float dtype and a string dtype of any length. + .. 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 -------- @@ -1101,9 +1121,9 @@ def putmask(a, mask, values): @array_function_from_c_func_and_dispatcher(_multiarray_umath.packbits) -def packbits(myarray, axis=None): +def packbits(a, axis=None, bitorder='big'): """ - packbits(myarray, axis=None) + packbits(a, axis=None, bitorder='big') Packs the elements of a binary-valued array into bits in a uint8 array. @@ -1111,12 +1131,19 @@ def packbits(myarray, axis=None): Parameters ---------- - myarray : array_like + 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 ------- @@ -1139,34 +1166,56 @@ def packbits(myarray, axis=None): ... [0,0,1]]]) >>> b = np.packbits(a, axis=-1) >>> b - array([[[160],[64]],[[192],[32]]], dtype=uint8) + 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 (myarray,) + return (a,) @array_function_from_c_func_and_dispatcher(_multiarray_umath.unpackbits) -def unpackbits(myarray, axis=None): +def unpackbits(a, axis=None, count=None, bitorder='big'): """ - unpackbits(myarray, axis=None) + unpackbits(a, axis=None, count=None, bitorder='big') Unpacks elements of a uint8 array into a binary-valued output array. - Each element of `myarray` 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. + 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 ---------- - myarray : ndarray, uint8 type + 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 ------- @@ -1175,8 +1224,8 @@ def unpackbits(myarray, axis=None): See Also -------- - packbits : Packs the elements of a binary-valued array into bits in a uint8 - array. + packbits : Packs the elements of a binary-valued array into bits in + a uint8 array. Examples -------- @@ -1190,9 +1239,27 @@ def unpackbits(myarray, axis=None): 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 (myarray,) + return (a,) @array_function_from_c_func_and_dispatcher(_multiarray_umath.shares_memory) @@ -1329,7 +1396,7 @@ def is_busday(dates, weekmask=None, holidays=None, busdaycal=None, out=None): >>> # 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], dtype='bool') + array([False, False, True]) """ return (dates, weekmask, holidays, out) @@ -1403,27 +1470,27 @@ def busday_offset(dates, offsets, roll=None, weekmask=None, holidays=None, -------- >>> # First business day in October 2011 (not accounting for holidays) ... np.busday_offset('2011-10', 0, roll='forward') - numpy.datetime64('2011-10-03','D') + 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','D') + 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','D') + 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','D') + 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','D') + numpy.datetime64('2011-03-21') >>> np.busday_offset('2011-03-22', 0, roll='forward') - numpy.datetime64('2011-03-22','D') + 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','D') + numpy.datetime64('2011-03-21') >>> np.busday_offset('2011-03-22', 1, roll='backward') - numpy.datetime64('2011-03-23','D') + numpy.datetime64('2011-03-23') """ return (dates, offsets, weekmask, holidays, out) @@ -1487,7 +1554,7 @@ def busday_count(begindates, enddates, weekmask=None, holidays=None, ... np.busday_count('2011-01', '2011-02') 21 >>> # Number of weekdays in 2011 - ... np.busday_count('2011', '2012') + >>> np.busday_count('2011', '2012') 260 >>> # Number of Saturdays in 2011 ... np.busday_count('2011', '2012', weekmask='Sat') @@ -1525,6 +1592,7 @@ def datetime_as_string(arr, unit=None, timezone=None, casting=None): 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', @@ -1555,6 +1623,8 @@ def datetime_as_string(arr, unit=None, timezone=None, casting=None): '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' """ |