def setup(self):
self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
self.b = np.empty((3, 0, 5, 6))
self.complex_indices = ['skip', Ellipsis,
0,
# Boolean indices, up to 3-d for some special cases of eating up
# dimensions, also need to test all False
np.array([True, False, False]),
np.array([[True, False], [False, True]]),
np.array([[[False, False], [False, False]]]),
# Some slices:
slice(-5, 5, 2),
slice(1, 1, 100),
slice(4, -1, -2),
slice(None, None, -3),
# Some Fancy indexes:
np.empty((0, 1, 1), dtype=np.intp), # empty and can be broadcast
np.array([0, 1, -2]),
np.array([[2], [0], [1]]),
np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
np.array([2, -1], dtype=np.int8),
np.zeros([1]*31, dtype=int), # trigger too large array.
np.array([0., 1.])] # invalid datatype
# Some simpler indices that still cover a bit more
self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]),
'skip']
# Very simple ones to fill the rest:
self.fill_indices = [slice(None, None), 0]
def _unique1d(ar,
return_index=False,
return_inverse=False,
return_counts=False):
"""
Find the unique elements of an array, ignoring shape.
"""
ar = np.asanyarray(ar).flatten()
optional_indices = return_index or return_inverse
if optional_indices:
perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')
aux = ar[perm]
else:
ar.sort()
aux = ar
mask = np.empty(aux.shape, dtype=np.bool_)
mask[:1] = True
mask[1:] = aux[1:] != aux[:-1]
ret = (aux[mask], )
if return_index:
ret += (perm[mask], )
if return_inverse:
imask = np.cumsum(mask) - 1
inv_idx = np.empty(mask.shape, dtype=np.intp)
inv_idx[perm] = imask
ret += (inv_idx, )
if return_counts:
idx = np.concatenate(np.nonzero(mask) + ([mask.size], ))
ret += (np.diff(idx), )
return ret
def test_bad_out_shape(self):
a = array([1, 2])
b = array([3, 4])
assert_raises(ValueError, concatenate, (a, b), out=np.empty(5))
assert_raises(ValueError, concatenate, (a, b), out=np.empty((4,1)))
assert_raises(ValueError, concatenate, (a, b), out=np.empty((1,4)))
concatenate((a, b), out=np.empty(4))
def test_multiindex_exceptions(self):
a = np.empty(5, dtype=object)
assert_raises(IndexError, a.item, 20)
a = np.empty((5, 0), dtype=object)
assert_raises(IndexError, a.item, (0, 0))
a = np.empty(5, dtype=object)
assert_raises(IndexError, a.itemset, 20, 0)
a = np.empty((5, 0), dtype=object)
assert_raises(IndexError, a.itemset, (0, 0), 0)
def test_out_dtype(self):
out = np.empty(4, np.float32)
res = concatenate((array([1, 2]), array([3, 4])), out=out)
assert_(out is res)
out = np.empty(4, np.complex64)
res = concatenate((array([0.1, 0.2]), array([0.3, 0.4])), out=out)
assert_(out is res)
# invalid cast
out = np.empty(4, np.int32)
assert_raises(TypeError, concatenate,
(array([0.1, 0.2]), array([0.3, 0.4])), out=out)
def test_functionality(self):
s = (2, 3, 4, 5)
a = np.empty(s)
for axis in range(-5, 4):
b = expand_dims(a, axis)
assert_(b.shape[axis] == 1)
assert_(np.squeeze(b).shape == s)
def test_arraytypes_fasttake(self):
'take from a 0-length dimension'
x = np.empty((2, 3, 0, 4))
assert_raises(IndexError, x.take, [0], axis=2)
assert_raises(IndexError, x.take, [1], axis=2)
assert_raises(IndexError, x.take, [0], axis=2, mode='wrap')
assert_raises(IndexError, x.take, [0], axis=2, mode='clip')
def test_datetime_memoryview(self):
# gh-11656
# Values verified with v1.13.3, shape is not () as in test_scalar_dim
def as_dict(m):
return dict(strides=m.strides,
shape=m.shape,
itemsize=m.itemsize,
ndim=m.ndim,
format=m.format)
dt1 = np.datetime64('2016-01-01')
dt2 = np.datetime64('2017-01-01')
expected = {
'strides': (1, ),
'itemsize': 1,
'ndim': 1,
'shape': (8, ),
'format': 'B'
}
v = memoryview(dt1)
res = as_dict(v)
assert_equal(res, expected)
v = memoryview(dt2 - dt1)
res = as_dict(v)
assert_equal(res, expected)
dt = np.dtype([('a', 'uint16'), ('b', 'M8[s]')])
a = np.empty(1, dt)
# Fails to create a PEP 3118 valid buffer
assert_raises((ValueError, BufferError), memoryview, a[0])
def foo(t):
a = np.empty((4, 2, 3), t)
a.fill(1)
b = a.copy()
c = a.copy()
c.fill(0)
self._test_equal(a, b)
self._test_not_equal(c, b)
def test_recarrays(self):
"""Test record arrays."""
a = np.empty(2, [('floupi', float), ('floupa', float)])
a['floupi'] = [1, 2]
a['floupa'] = [1, 2]
b = a.copy()
self._test_equal(a, b)
c = np.empty(2, [('floupipi', float), ('floupa', float)])
c['floupipi'] = a['floupi'].copy()
c['floupa'] = a['floupa'].copy()
with suppress_warnings() as sup:
l = sup.record(FutureWarning, message="elementwise == ")
self._test_not_equal(c, b)
assert_equal(len(l), 1)
def test_deprecations(self):
# 2017-05-17, 1.13.0
s = (2, 3, 4, 5)
a = np.empty(s)
with warnings.catch_warnings():
warnings.simplefilter("always")
assert_warns(DeprecationWarning, expand_dims, a, -6)
assert_warns(DeprecationWarning, expand_dims, a, 5)
def test_empty(self):
# can't apply_along_axis when there's no chance to call the function
def never_call(x):
assert_(False) # should never be reached
a = np.empty((0, 0))
assert_raises(ValueError, np.apply_along_axis, never_call, 0, a)
assert_raises(ValueError, np.apply_along_axis, never_call, 1, a)
# but it's sometimes ok with some non-zero dimensions
def empty_to_1(x):
assert_(len(x) == 0)
return 1
a = np.empty((10, 0))
actual = np.apply_along_axis(empty_to_1, 1, a)
assert_equal(actual, np.ones(10))
assert_raises(ValueError, np.apply_along_axis, empty_to_1, 0, a)
def test_as_strided():
a = np.array([None])
a_view = as_strided(a)
expected = np.array([None])
assert_array_equal(a_view, np.array([None]))
a = np.array([1, 2, 3, 4])
a_view = as_strided(a, shape=(2, ), strides=(2 * a.itemsize, ))
expected = np.array([1, 3])
assert_array_equal(a_view, expected)
a = np.array([1, 2, 3, 4])
a_view = as_strided(a, shape=(3, 4), strides=(0, 1 * a.itemsize))
expected = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
assert_array_equal(a_view, expected)
# Regression test for gh-5081
dt = np.dtype([('num', 'i4'), ('obj', 'O')])
a = np.empty((4, ), dtype=dt)
a['num'] = np.arange(1, 5)
a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
expected_num = [[1, 2, 3, 4]] * 3
expected_obj = [[None] * 4] * 3
assert_equal(a_view.dtype, dt)
assert_array_equal(expected_num, a_view['num'])
assert_array_equal(expected_obj, a_view['obj'])
# Make sure that void types without fields are kept unchanged
a = np.empty((4, ), dtype='V4')
a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
assert_equal(a.dtype, a_view.dtype)
# Make sure that the only type that could fail is properly handled
dt = np.dtype({'names': [''], 'formats': ['V4']})
a = np.empty((4, ), dtype=dt)
a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
assert_equal(a.dtype, a_view.dtype)
# Custom dtypes should not be lost (gh-9161)
r = [rational(i) for i in range(4)]
a = np.array(r, dtype=rational)
a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
assert_equal(a.dtype, a_view.dtype)
assert_array_equal([r] * 3, a_view)
def test_contiguous_flags():
a = np.ones((4, 4, 1))[::2, :, :]
if NPY_RELAXED_STRIDES_CHECKING:
a.strides = a.strides[:2] + (-123, )
b = np.ones((2, 2, 1, 2, 2)).swapaxes(3, 4)
def check_contig(a, ccontig, fcontig):
assert_(a.flags.c_contiguous == ccontig)
assert_(a.flags.f_contiguous == fcontig)
# Check if new arrays are correct:
check_contig(a, False, False)
check_contig(b, False, False)
if NPY_RELAXED_STRIDES_CHECKING:
check_contig(np.empty((2, 2, 0, 2, 2)), True, True)
check_contig(np.array([[[1], [2]]], order='F'), True, True)
else:
check_contig(np.empty((2, 2, 0, 2, 2)), True, False)
check_contig(np.array([[[1], [2]]], order='F'), False, True)
check_contig(np.empty((2, 2)), True, False)
check_contig(np.empty((2, 2), order='F'), False, True)
# Check that np.array creates correct contiguous flags:
check_contig(np.array(a, copy=False), False, False)
check_contig(np.array(a, copy=False, order='C'), True, False)
check_contig(np.array(a, ndmin=4, copy=False, order='F'), False, True)
if NPY_RELAXED_STRIDES_CHECKING:
# Check slicing update of flags and :
check_contig(a[0], True, True)
check_contig(a[None, ::4, ..., None], True, True)
check_contig(b[0, 0, ...], False, True)
check_contig(b[:, :, 0:0, :, :], True, True)
else:
# Check slicing update of flags:
check_contig(a[0], True, False)
# Would be nice if this was C-Contiguous:
check_contig(a[None, 0, ..., None], False, False)
check_contig(b[0, 0, 0, ...], False, True)
# Test ravel and squeeze.
check_contig(a.ravel(), True, True)
check_contig(np.ones((1, 3, 1)).squeeze(), True, True)
def test_packbits_empty():
shapes = [
(0,), (10, 20, 0), (10, 0, 20), (0, 10, 20), (20, 0, 0), (0, 20, 0),
(0, 0, 20), (0, 0, 0),
]
for dt in '?bBhHiIlLqQ':
for shape in shapes:
a = np.empty(shape, dtype=dt)
b = np.packbits(a)
assert_equal(b.dtype, np.uint8)
assert_equal(b.shape, (0,))
def test_unpackbits_empty_with_axis():
# Lists of packed shapes for different axes and unpacked shapes.
shapes = [
([(0,)], (0,)),
([(2, 24, 0), (16, 3, 0), (16, 24, 0)], (16, 24, 0)),
([(2, 0, 24), (16, 0, 24), (16, 0, 3)], (16, 0, 24)),
([(0, 16, 24), (0, 2, 24), (0, 16, 3)], (0, 16, 24)),
([(3, 0, 0), (24, 0, 0), (24, 0, 0)], (24, 0, 0)),
([(0, 24, 0), (0, 3, 0), (0, 24, 0)], (0, 24, 0)),
([(0, 0, 24), (0, 0, 24), (0, 0, 3)], (0, 0, 24)),
([(0, 0, 0), (0, 0, 0), (0, 0, 0)], (0, 0, 0)),
]
for in_shapes, out_shape in shapes:
for ax, in_shape in enumerate(in_shapes):
a = np.empty(in_shape, dtype=np.uint8)
b = np.unpackbits(a, axis=ax)
assert_equal(b.dtype, np.uint8)
assert_equal(b.shape, out_shape)
def test_packbits_empty_with_axis():
# Original shapes and lists of packed shapes for different axes.
shapes = [
((0,), [(0,)]),
((10, 20, 0), [(2, 20, 0), (10, 3, 0), (10, 20, 0)]),
((10, 0, 20), [(2, 0, 20), (10, 0, 20), (10, 0, 3)]),
((0, 10, 20), [(0, 10, 20), (0, 2, 20), (0, 10, 3)]),
((20, 0, 0), [(3, 0, 0), (20, 0, 0), (20, 0, 0)]),
((0, 20, 0), [(0, 20, 0), (0, 3, 0), (0, 20, 0)]),
((0, 0, 20), [(0, 0, 20), (0, 0, 20), (0, 0, 3)]),
((0, 0, 0), [(0, 0, 0), (0, 0, 0), (0, 0, 0)]),
]
for dt in '?bBhHiIlLqQ':
for in_shape, out_shapes in shapes:
for ax, out_shape in enumerate(out_shapes):
a = np.empty(in_shape, dtype=dt)
b = np.packbits(a, axis=ax)
assert_equal(b.dtype, np.uint8)
assert_equal(b.shape, out_shape)
def _keep_fields(base, keep_names, usemask=True, asrecarray=False):
"""
Return a new array keeping only the fields in `keep_names`,
and preserving the order of those fields.
Parameters
----------
base : array
Input array
keep_names : string or sequence
String or sequence of strings corresponding to the names of the
fields to keep. Order of the names will be preserved.
usemask : {False, True}, optional
Whether to return a masked array or not.
asrecarray : string or sequence, optional
Whether to return a recarray or a mrecarray (`asrecarray=True`) or
a plain ndarray or masked array with flexible dtype. The default
is False.
"""
newdtype = [(n, base.dtype[n]) for n in keep_names]
output = np.empty(base.shape, dtype=newdtype)
output = recursive_fill_fields(base, output)
return _fix_output(output, usemask=usemask, asrecarray=asrecarray)
def test_unpackbits_empty():
a = np.empty((0,), dtype=np.uint8)
b = np.unpackbits(a)
assert_equal(b.dtype, np.uint8)
assert_array_equal(b, np.empty((0,)))
import numpy1 as np
from numpy1.linalg.tests.test_linalg import (
LinalgCase, apply_tag, TestQR as _TestQR, LinalgTestCase,
_TestNorm2D, _TestNormDoubleBase, _TestNormSingleBase, _TestNormInt64Base,
SolveCases, InvCases, EigvalsCases, EigCases, SVDCases, CondCases,
PinvCases, DetCases, LstsqCases)
CASES = []
# square test cases
CASES += apply_tag('square', [
LinalgCase("0x0_matrix",
np.empty((0, 0), dtype=np.double).view(np.matrix),
np.empty((0, 1), dtype=np.double).view(np.matrix),
tags={'size-0'}),
LinalgCase("matrix_b_only",
np.array([[1., 2.], [3., 4.]]),
np.matrix([2., 1.]).T),
LinalgCase("matrix_a_and_b",
np.matrix([[1., 2.], [3., 4.]]),
np.matrix([2., 1.]).T),
])
# hermitian test-cases
CASES += apply_tag('hermitian', [
LinalgCase("hmatrix_a_and_b",
np.matrix([[1., 2.], [2., 1.]]),
None),
def ediff1d(ary, to_end=None, to_begin=None):
"""
The differences between consecutive elements of an array.
Parameters
----------
ary : array_like
If necessary, will be flattened before the differences are taken.
to_end : array_like, optional
Number(s) to append at the end of the returned differences.
to_begin : array_like, optional
Number(s) to prepend at the beginning of the returned differences.
Returns
-------
ediff1d : ndarray
The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``.
See Also
--------
diff, gradient
Notes
-----
When applied to masked arrays, this function drops the mask information
if the `to_begin` and/or `to_end` parameters are used.
Examples
--------
>>> x = np.array([1, 2, 4, 7, 0])
>>> np.ediff1d(x)
array([ 1, 2, 3, -7])
>>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))
array([-99, 1, 2, 3, -7, 88, 99])
The returned array is always 1D.
>>> y = [[1, 2, 4], [1, 6, 24]]
>>> np.ediff1d(y)
array([ 1, 2, -3, 5, 18])
"""
# force a 1d array
ary = np.asanyarray(ary).ravel()
# fast track default case
if to_begin is None and to_end is None:
return ary[1:] - ary[:-1]
if to_begin is None:
l_begin = 0
else:
to_begin = np.asanyarray(to_begin).ravel()
l_begin = len(to_begin)
if to_end is None:
l_end = 0
else:
to_end = np.asanyarray(to_end).ravel()
l_end = len(to_end)
# do the calculation in place and copy to_begin and to_end
l_diff = max(len(ary) - 1, 0)
result = np.empty(l_diff + l_begin + l_end, dtype=ary.dtype)
result = ary.__array_wrap__(result)
if l_begin > 0:
result[:l_begin] = to_begin
if l_end > 0:
result[l_begin + l_diff:] = to_end
np.subtract(ary[1:], ary[:-1], result[l_begin:l_begin + l_diff])
return result
def in1d(ar1, ar2, assume_unique=False, invert=False):
"""
Test whether each element of a 1-D array is also present in a second array.
Returns a boolean array the same length as `ar1` that is True
where an element of `ar1` is in `ar2` and False otherwise.
We recommend using :func:`isin` instead of `in1d` for new code.
Parameters
----------
ar1 : (M,) array_like
Input array.
ar2 : array_like
The values against which to test each value of `ar1`.
assume_unique : bool, optional
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
invert : bool, optional
If True, the values in the returned array are inverted (that is,
False where an element of `ar1` is in `ar2` and True otherwise).
Default is False. ``np.in1d(a, b, invert=True)`` is equivalent
to (but is faster than) ``np.invert(in1d(a, b))``.
.. versionadded:: 1.8.0
Returns
-------
in1d : (M,) ndarray, bool
The values `ar1[in1d]` are in `ar2`.
See Also
--------
isin : Version of this function that preserves the
shape of ar1.
numpy.lib.arraysetops : Module with a number of other functions for
performing set operations on arrays.
Notes
-----
`in1d` can be considered as an element-wise function version of the
python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly
equivalent to ``np.array([item in b for item in a])``.
However, this idea fails if `ar2` is a set, or similar (non-sequence)
container: As ``ar2`` is converted to an array, in those cases
``asarray(ar2)`` is an object array rather than the expected array of
contained values.
.. versionadded:: 1.4.0
Examples
--------
>>> test = np.array([0, 1, 2, 5, 0])
>>> states = [0, 2]
>>> mask = np.in1d(test, states)
>>> mask
array([ True, False, True, False, True])
>>> test[mask]
array([0, 2, 0])
>>> mask = np.in1d(test, states, invert=True)
>>> mask
array([False, True, False, True, False])
>>> test[mask]
array([1, 5])
"""
# Ravel both arrays, behavior for the first array could be different
ar1 = np.asarray(ar1).ravel()
ar2 = np.asarray(ar2).ravel()
# Check if one of the arrays may contain arbitrary objects
contains_object = ar1.dtype.hasobject or ar2.dtype.hasobject
# This code is run when
# a) the first condition is true, making the code significantly faster
# b) the second condition is true (i.e. `ar1` or `ar2` may contain
# arbitrary objects), since then sorting is not guaranteed to work
if len(ar2) < 10 * len(ar1)**0.145 or contains_object:
if invert:
mask = np.ones(len(ar1), dtype=bool)
for a in ar2:
mask &= (ar1 != a)
else:
mask = np.zeros(len(ar1), dtype=bool)
for a in ar2:
mask |= (ar1 == a)
return mask
# Otherwise use sorting
if not assume_unique:
ar1, rev_idx = np.unique(ar1, return_inverse=True)
ar2 = np.unique(ar2)
ar = np.concatenate((ar1, ar2))
# We need this to be a stable sort, so always use 'mergesort'
# here. The values from the first array should always come before
# the values from the second array.
order = ar.argsort(kind='mergesort')
sar = ar[order]
if invert:
bool_ar = (sar[1:] != sar[:-1])
else:
bool_ar = (sar[1:] == sar[:-1])
flag = np.concatenate((bool_ar, [invert]))
ret = np.empty(ar.shape, dtype=bool)
ret[order] = flag
if assume_unique:
return ret[:len(ar1)]
else:
return ret[rev_idx]
def test_unique_1d(self):
def check_all(a, b, i1, i2, c, dt):
base_msg = 'check {0} failed for type {1}'
msg = base_msg.format('values', dt)
v = unique(a)
assert_array_equal(v, b, msg)
msg = base_msg.format('return_index', dt)
v, j = unique(a, 1, 0, 0)
assert_array_equal(v, b, msg)
assert_array_equal(j, i1, msg)
msg = base_msg.format('return_inverse', dt)
v, j = unique(a, 0, 1, 0)
assert_array_equal(v, b, msg)
assert_array_equal(j, i2, msg)
msg = base_msg.format('return_counts', dt)
v, j = unique(a, 0, 0, 1)
assert_array_equal(v, b, msg)
assert_array_equal(j, c, msg)
msg = base_msg.format('return_index and return_inverse', dt)
v, j1, j2 = unique(a, 1, 1, 0)
assert_array_equal(v, b, msg)
assert_array_equal(j1, i1, msg)
assert_array_equal(j2, i2, msg)
msg = base_msg.format('return_index and return_counts', dt)
v, j1, j2 = unique(a, 1, 0, 1)
assert_array_equal(v, b, msg)
assert_array_equal(j1, i1, msg)
assert_array_equal(j2, c, msg)
msg = base_msg.format('return_inverse and return_counts', dt)
v, j1, j2 = unique(a, 0, 1, 1)
assert_array_equal(v, b, msg)
assert_array_equal(j1, i2, msg)
assert_array_equal(j2, c, msg)
msg = base_msg.format(('return_index, return_inverse '
'and return_counts'), dt)
v, j1, j2, j3 = unique(a, 1, 1, 1)
assert_array_equal(v, b, msg)
assert_array_equal(j1, i1, msg)
assert_array_equal(j2, i2, msg)
assert_array_equal(j3, c, msg)
a = [5, 7, 1, 2, 1, 5, 7] * 10
b = [1, 2, 5, 7]
i1 = [2, 3, 0, 1]
i2 = [2, 3, 0, 1, 0, 2, 3] * 10
c = np.multiply([2, 1, 2, 2], 10)
# test for numeric arrays
types = []
types.extend(np.typecodes['AllInteger'])
types.extend(np.typecodes['AllFloat'])
types.append('datetime64[D]')
types.append('timedelta64[D]')
for dt in types:
aa = np.array(a, dt)
bb = np.array(b, dt)
check_all(aa, bb, i1, i2, c, dt)
# test for object arrays
dt = 'O'
aa = np.empty(len(a), dt)
aa[:] = a
bb = np.empty(len(b), dt)
bb[:] = b
check_all(aa, bb, i1, i2, c, dt)
# test for structured arrays
dt = [('', 'i'), ('', 'i')]
aa = np.array(list(zip(a, a)), dt)
bb = np.array(list(zip(b, b)), dt)
check_all(aa, bb, i1, i2, c, dt)
# test for ticket #2799
aa = [1. + 0.j, 1 - 1.j, 1]
assert_array_equal(np.unique(aa), [1. - 1.j, 1. + 0.j])
# test for ticket #4785
a = [(1, 2), (1, 2), (2, 3)]
unq = [1, 2, 3]
inv = [0, 1, 0, 1, 1, 2]
a1 = unique(a)
assert_array_equal(a1, unq)
a2, a2_inv = unique(a, return_inverse=True)
assert_array_equal(a2, unq)
assert_array_equal(a2_inv, inv)
# test for chararrays with return_inverse (gh-5099)
a = np.chararray(5)
a[...] = ''
a2, a2_inv = np.unique(a, return_inverse=True)
assert_array_equal(a2_inv, np.zeros(5))
# test for ticket #9137
a = []
a1_idx = np.unique(a, return_index=True)[1]
a2_inv = np.unique(a, return_inverse=True)[1]
a3_idx, a3_inv = np.unique(a, return_index=True,
return_inverse=True)[1:]
assert_equal(a1_idx.dtype, np.intp)
assert_equal(a2_inv.dtype, np.intp)
assert_equal(a3_idx.dtype, np.intp)
assert_equal(a3_inv.dtype, np.intp)
def histogramdd(sample,
bins=10,
range=None,
normed=None,
weights=None,
density=None):
"""
Compute the multidimensional histogram of some data.
Parameters
----------
sample : (N, D) array, or (D, N) array_like
The data to be histogrammed.
Note the unusual interpretation of sample when an array_like:
* When an array, each row is a coordinate in a D-dimensional space -
such as ``histogramgramdd(np.array([p1, p2, p3]))``.
* When an array_like, each element is the list of values for single
coordinate - such as ``histogramgramdd((X, Y, Z))``.
The first form should be preferred.
bins : sequence or int, optional
The bin specification:
* A sequence of arrays describing the bin edges along each dimension.
* The number of bins for each dimension (nx, ny, ... =bins)
* The number of bins for all dimensions (nx=ny=...=bins).
range : sequence, optional
A sequence of length D, each an optional (lower, upper) tuple giving
the outer bin edges to be used if the edges are not given explicitly in
`bins`.
An entry of None in the sequence results in the minimum and maximum
values being used for the corresponding dimension.
The default, None, is equivalent to passing a tuple of D None values.
density : bool, optional
If False, the default, returns the number of samples in each bin.
If True, returns the probability *density* function at the bin,
``bin_count / sample_count / bin_volume``.
normed : bool, optional
An alias for the density argument that behaves identically. To avoid
confusion with the broken normed argument to `histogram`, `density`
should be preferred.
weights : (N,) array_like, optional
An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.
Weights are normalized to 1 if normed is True. If normed is False,
the values of the returned histogram are equal to the sum of the
weights belonging to the samples falling into each bin.
Returns
-------
H : ndarray
The multidimensional histogram of sample x. See normed and weights
for the different possible semantics.
edges : list
A list of D arrays describing the bin edges for each dimension.
See Also
--------
histogram: 1-D histogram
histogram2d: 2-D histogram
Examples
--------
>>> r = np.random.randn(100,3)
>>> H, edges = np.histogramdd(r, bins = (5, 8, 4))
>>> H.shape, edges[0].size, edges[1].size, edges[2].size
((5, 8, 4), 6, 9, 5)
"""
try:
# Sample is an ND-array.
N, D = sample.shape
except (AttributeError, ValueError):
# Sample is a sequence of 1D arrays.
sample = np.atleast_2d(sample).T
N, D = sample.shape
nbin = np.empty(D, int)
edges = D * [None]
dedges = D * [None]
if weights is not None:
weights = np.asarray(weights)
try:
M = len(bins)
if M != D:
raise ValueError(
'The dimension of bins must be equal to the dimension of the '
' sample x.')
except TypeError:
# bins is an integer
bins = D * [bins]
# normalize the range argument
if range is None:
range = (None, ) * D
elif len(range) != D:
raise ValueError('range argument must have one entry per dimension')
# Create edge arrays
for i in _range(D):
if np.ndim(bins[i]) == 0:
if bins[i] < 1:
raise ValueError(
'`bins[{}]` must be positive, when an integer'.format(i))
smin, smax = _get_outer_edges(sample[:, i], range[i])
edges[i] = np.linspace(smin, smax, bins[i] + 1)
elif np.ndim(bins[i]) == 1:
edges[i] = np.asarray(bins[i])
if np.any(edges[i][:-1] > edges[i][1:]):
raise ValueError(
'`bins[{}]` must be monotonically increasing, when an array'
.format(i))
else:
raise ValueError(
'`bins[{}]` must be a scalar or 1d array'.format(i))
nbin[i] = len(edges[i]) + 1 # includes an outlier on each end
dedges[i] = np.diff(edges[i])
# Compute the bin number each sample falls into.
Ncount = tuple(
# avoid np.digitize to work around gh-11022
np.searchsorted(edges[i], sample[:, i], side='right')
for i in _range(D))
# Using digitize, values that fall on an edge are put in the right bin.
# For the rightmost bin, we want values equal to the right edge to be
# counted in the last bin, and not as an outlier.
for i in _range(D):
# Find which points are on the rightmost edge.
on_edge = (sample[:, i] == edges[i][-1])
# Shift these points one bin to the left.
Ncount[i][on_edge] -= 1
# Compute the sample indices in the flattened histogram matrix.
# This raises an error if the array is too large.
xy = np.ravel_multi_index(Ncount, nbin)
# Compute the number of repetitions in xy and assign it to the
# flattened histmat.
hist = np.bincount(xy, weights, minlength=nbin.prod())
# Shape into a proper matrix
hist = hist.reshape(nbin)
# This preserves the (bad) behavior observed in gh-7845, for now.
hist = hist.astype(float, casting='safe')
# Remove outliers (indices 0 and -1 for each dimension).
core = D * (slice(1, -1), )
hist = hist[core]
# handle the aliasing normed argument
if normed is None:
if density is None:
density = False
elif density is None:
# an explicit normed argument was passed, alias it to the new name
density = normed
else:
raise TypeError("Cannot specify both 'normed' and 'density'")
if density:
# calculate the probability density function
s = hist.sum()
for i in _range(D):
shape = np.ones(D, int)
shape[i] = nbin[i] - 2
hist = hist / dedges[i].reshape(shape)
hist /= s
if (hist.shape != nbin - 2).any():
raise RuntimeError("Internal Shape Error")
return hist, edges
def test_fieldless_structured(self):
# gh-10366
no_fields = np.dtype([])
arr_no_fields = np.empty(4, dtype=no_fields)
assert_equal(repr(arr_no_fields), 'array([(), (), (), ()], dtype=[])')
def test_polyvalfromroots(self):
# check exception for broadcasting x values over root array with
# too few dimensions
assert_raises(ValueError,
poly.polyvalfromroots, [1], [1],
tensor=False)
# check empty input
assert_equal(poly.polyvalfromroots([], [1]).size, 0)
assert_(poly.polyvalfromroots([], [1]).shape == (0, ))
# check empty input + multidimensional roots
assert_equal(poly.polyvalfromroots([], [[1] * 5]).size, 0)
assert_(poly.polyvalfromroots([], [[1] * 5]).shape == (5, 0))
# check scalar input
assert_equal(poly.polyvalfromroots(1, 1), 0)
assert_(poly.polyvalfromroots(1, np.ones((3, 3))).shape == (3, ))
# check normal input)
x = np.linspace(-1, 1)
y = [x**i for i in range(5)]
for i in range(1, 5):
tgt = y[i]
res = poly.polyvalfromroots(x, [0] * i)
assert_almost_equal(res, tgt)
tgt = x * (x - 1) * (x + 1)
res = poly.polyvalfromroots(x, [-1, 0, 1])
assert_almost_equal(res, tgt)
# check that shape is preserved
for i in range(3):
dims = [2] * i
x = np.zeros(dims)
assert_equal(poly.polyvalfromroots(x, [1]).shape, dims)
assert_equal(poly.polyvalfromroots(x, [1, 0]).shape, dims)
assert_equal(poly.polyvalfromroots(x, [1, 0, 0]).shape, dims)
# check compatibility with factorization
ptest = [15, 2, -16, -2, 1]
r = poly.polyroots(ptest)
x = np.linspace(-1, 1)
assert_almost_equal(poly.polyval(x, ptest),
poly.polyvalfromroots(x, r))
# check multidimensional arrays of roots and values
# check tensor=False
rshape = (3, 5)
x = np.arange(-3, 2)
r = np.random.randint(-5, 5, size=rshape)
res = poly.polyvalfromroots(x, r, tensor=False)
tgt = np.empty(r.shape[1:])
for ii in range(tgt.size):
tgt[ii] = poly.polyvalfromroots(x[ii], r[:, ii])
assert_equal(res, tgt)
# check tensor=True
x = np.vstack([x, 2 * x])
res = poly.polyvalfromroots(x, r, tensor=True)
tgt = np.empty(r.shape[1:] + x.shape)
for ii in range(r.shape[1]):
for jj in range(x.shape[0]):
tgt[ii, jj, :] = poly.polyvalfromroots(x[jj], r[:, ii])
assert_equal(res, tgt)
def drop_fields(base, drop_names, usemask=True, asrecarray=False):
"""
Return a new array with fields in `drop_names` dropped.
Nested fields are supported.
Parameters
----------
base : array
Input array
drop_names : string or sequence
String or sequence of strings corresponding to the names of the
fields to drop.
usemask : {False, True}, optional
Whether to return a masked array or not.
asrecarray : string or sequence, optional
Whether to return a recarray or a mrecarray (`asrecarray=True`) or
a plain ndarray or masked array with flexible dtype. The default
is False.
Examples
--------
>>> from numpy1.lib import recfunctions as rfn
>>> a = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
... dtype=[('a', int), ('b', [('ba', float), ('bb', int)])])
>>> rfn.drop_fields(a, 'a')
array([((2.0, 3),), ((5.0, 6),)],
dtype=[('b', [('ba', '<f8'), ('bb', '<i4')])])
>>> rfn.drop_fields(a, 'ba')
array([(1, (3,)), (4, (6,))],
dtype=[('a', '<i4'), ('b', [('bb', '<i4')])])
>>> rfn.drop_fields(a, ['ba', 'bb'])
array([(1,), (4,)],
dtype=[('a', '<i4')])
"""
if _is_string_like(drop_names):
drop_names = [drop_names]
else:
drop_names = set(drop_names)
def _drop_descr(ndtype, drop_names):
names = ndtype.names
newdtype = []
for name in names:
current = ndtype[name]
if name in drop_names:
continue
if current.names:
descr = _drop_descr(current, drop_names)
if descr:
newdtype.append((name, descr))
else:
newdtype.append((name, current))
return newdtype
newdtype = _drop_descr(base.dtype, drop_names)
if not newdtype:
return None
output = np.empty(base.shape, dtype=newdtype)
output = recursive_fill_fields(base, output)
return _fix_output(output, usemask=usemask, asrecarray=asrecarray)
def test_out_dtype_error(self):
for f in self.nanfuncs:
for dtype in [np.bool_, np.int_, np.object_]:
out = np.empty(_ndat.shape[0], dtype=dtype)
assert_raises(TypeError, f, _ndat, axis=1, out=out)
def as_series(alist, trim=True):
"""
Return argument as a list of 1-d arrays.
The returned list contains array(s) of dtype double, complex double, or
object. A 1-d argument of shape ``(N,)`` is parsed into ``N`` arrays of
size one; a 2-d argument of shape ``(M,N)`` is parsed into ``M`` arrays
of size ``N`` (i.e., is "parsed by row"); and a higher dimensional array
raises a Value Error if it is not first reshaped into either a 1-d or 2-d
array.
Parameters
----------
alist : array_like
A 1- or 2-d array_like
trim : boolean, optional
When True, trailing zeros are removed from the inputs.
When False, the inputs are passed through intact.
Returns
-------
[a1, a2,...] : list of 1-D arrays
A copy of the input data as a list of 1-d arrays.
Raises
------
ValueError
Raised when `as_series` cannot convert its input to 1-d arrays, or at
least one of the resulting arrays is empty.
Examples
--------
>>> from numpy1.polynomial import polyutils as pu
>>> a = np.arange(4)
>>> pu.as_series(a)
[array([ 0.]), array([ 1.]), array([ 2.]), array([ 3.])]
>>> b = np.arange(6).reshape((2,3))
>>> pu.as_series(b)
[array([ 0., 1., 2.]), array([ 3., 4., 5.])]
>>> pu.as_series((1, np.arange(3), np.arange(2, dtype=np.float16)))
[array([ 1.]), array([ 0., 1., 2.]), array([ 0., 1.])]
>>> pu.as_series([2, [1.1, 0.]])
[array([ 2.]), array([ 1.1])]
>>> pu.as_series([2, [1.1, 0.]], trim=False)
[array([ 2.]), array([ 1.1, 0. ])]
"""
arrays = [np.array(a, ndmin=1, copy=0) for a in alist]
if min([a.size for a in arrays]) == 0:
raise ValueError("Coefficient array is empty")
if any([a.ndim != 1 for a in arrays]):
raise ValueError("Coefficient array is not 1-d")
if trim:
arrays = [trimseq(a) for a in arrays]
if any([a.dtype == np.dtype(object) for a in arrays]):
ret = []
for a in arrays:
if a.dtype != np.dtype(object):
tmp = np.empty(len(a), dtype=np.dtype(object))
tmp[:] = a[:]
ret.append(tmp)
else:
ret.append(a.copy())
else:
try:
dtype = np.common_type(*arrays)
except Exception:
raise ValueError("Coefficient arrays have no common type")
ret = [np.array(a, copy=1, dtype=dtype) for a in arrays]
return ret
def _get_fieldmask(self):
mdescr = [(n, '|b1') for n in self.dtype.names]
fdmask = np.empty(self.shape, dtype=mdescr)
fdmask.flat = tuple([False] * len(mdescr))
return fdmask
