526 lines
19 KiB
Python
526 lines
19 KiB
Python
|
import pytest
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from numpy.testing import (
|
||
|
|
assert_, assert_equal, assert_array_equal, assert_almost_equal,
|
||
|
|
assert_array_almost_equal, assert_raises, assert_raises_regex,
|
||
|
|
assert_warns
|
||
|
|
)
|
||
|
|
from numpy.lib.index_tricks import (
|
||
|
|
mgrid, ogrid, ndenumerate, fill_diagonal, diag_indices, diag_indices_from,
|
||
|
|
index_exp, ndindex, r_, s_, ix_
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class TestRavelUnravelIndex:
|
||
|
|
def test_basic(self):
|
||
|
|
assert_equal(np.unravel_index(2, (2, 2)), (1, 0))
|
||
|
|
|
||
|
|
# test backwards compatibility with older dims
|
||
|
|
# keyword argument; see Issue #10586
|
||
|
|
with assert_warns(DeprecationWarning):
|
||
|
|
# we should achieve the correct result
|
||
|
|
# AND raise the appropriate warning
|
||
|
|
# when using older "dims" kw argument
|
||
|
|
assert_equal(np.unravel_index(indices=2,
|
||
|
|
dims=(2, 2)),
|
||
|
|
(1, 0))
|
||
|
|
|
||
|
|
# test that new shape argument works properly
|
||
|
|
assert_equal(np.unravel_index(indices=2,
|
||
|
|
shape=(2, 2)),
|
||
|
|
(1, 0))
|
||
|
|
|
||
|
|
# test that an invalid second keyword argument
|
||
|
|
# is properly handled
|
||
|
|
with assert_raises(TypeError):
|
||
|
|
np.unravel_index(indices=2, hape=(2, 2))
|
||
|
|
|
||
|
|
with assert_raises(TypeError):
|
||
|
|
np.unravel_index(2, hape=(2, 2))
|
||
|
|
|
||
|
|
with assert_raises(TypeError):
|
||
|
|
np.unravel_index(254, ims=(17, 94))
|
||
|
|
|
||
|
|
assert_equal(np.ravel_multi_index((1, 0), (2, 2)), 2)
|
||
|
|
assert_equal(np.unravel_index(254, (17, 94)), (2, 66))
|
||
|
|
assert_equal(np.ravel_multi_index((2, 66), (17, 94)), 254)
|
||
|
|
assert_raises(ValueError, np.unravel_index, -1, (2, 2))
|
||
|
|
assert_raises(TypeError, np.unravel_index, 0.5, (2, 2))
|
||
|
|
assert_raises(ValueError, np.unravel_index, 4, (2, 2))
|
||
|
|
assert_raises(ValueError, np.ravel_multi_index, (-3, 1), (2, 2))
|
||
|
|
assert_raises(ValueError, np.ravel_multi_index, (2, 1), (2, 2))
|
||
|
|
assert_raises(ValueError, np.ravel_multi_index, (0, -3), (2, 2))
|
||
|
|
assert_raises(ValueError, np.ravel_multi_index, (0, 2), (2, 2))
|
||
|
|
assert_raises(TypeError, np.ravel_multi_index, (0.1, 0.), (2, 2))
|
||
|
|
|
||
|
|
assert_equal(np.unravel_index((2*3 + 1)*6 + 4, (4, 3, 6)), [2, 1, 4])
|
||
|
|
assert_equal(
|
||
|
|
np.ravel_multi_index([2, 1, 4], (4, 3, 6)), (2*3 + 1)*6 + 4)
|
||
|
|
|
||
|
|
arr = np.array([[3, 6, 6], [4, 5, 1]])
|
||
|
|
assert_equal(np.ravel_multi_index(arr, (7, 6)), [22, 41, 37])
|
||
|
|
assert_equal(
|
||
|
|
np.ravel_multi_index(arr, (7, 6), order='F'), [31, 41, 13])
|
||
|
|
assert_equal(
|
||
|
|
np.ravel_multi_index(arr, (4, 6), mode='clip'), [22, 23, 19])
|
||
|
|
assert_equal(np.ravel_multi_index(arr, (4, 4), mode=('clip', 'wrap')),
|
||
|
|
[12, 13, 13])
|
||
|
|
assert_equal(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9)), 1621)
|
||
|
|
|
||
|
|
assert_equal(np.unravel_index(np.array([22, 41, 37]), (7, 6)),
|
||
|
|
[[3, 6, 6], [4, 5, 1]])
|
||
|
|
assert_equal(
|
||
|
|
np.unravel_index(np.array([31, 41, 13]), (7, 6), order='F'),
|
||
|
|
[[3, 6, 6], [4, 5, 1]])
|
||
|
|
assert_equal(np.unravel_index(1621, (6, 7, 8, 9)), [3, 1, 4, 1])
|
||
|
|
|
||
|
|
def test_empty_indices(self):
|
||
|
|
msg1 = 'indices must be integral: the provided empty sequence was'
|
||
|
|
msg2 = 'only int indices permitted'
|
||
|
|
assert_raises_regex(TypeError, msg1, np.unravel_index, [], (10, 3, 5))
|
||
|
|
assert_raises_regex(TypeError, msg1, np.unravel_index, (), (10, 3, 5))
|
||
|
|
assert_raises_regex(TypeError, msg2, np.unravel_index, np.array([]),
|
||
|
|
(10, 3, 5))
|
||
|
|
assert_equal(np.unravel_index(np.array([],dtype=int), (10, 3, 5)),
|
||
|
|
[[], [], []])
|
||
|
|
assert_raises_regex(TypeError, msg1, np.ravel_multi_index, ([], []),
|
||
|
|
(10, 3))
|
||
|
|
assert_raises_regex(TypeError, msg1, np.ravel_multi_index, ([], ['abc']),
|
||
|
|
(10, 3))
|
||
|
|
assert_raises_regex(TypeError, msg2, np.ravel_multi_index,
|
||
|
|
(np.array([]), np.array([])), (5, 3))
|
||
|
|
assert_equal(np.ravel_multi_index(
|
||
|
|
(np.array([], dtype=int), np.array([], dtype=int)), (5, 3)), [])
|
||
|
|
assert_equal(np.ravel_multi_index(np.array([[], []], dtype=int),
|
||
|
|
(5, 3)), [])
|
||
|
|
|
||
|
|
def test_big_indices(self):
|
||
|
|
# ravel_multi_index for big indices (issue #7546)
|
||
|
|
if np.intp == np.int64:
|
||
|
|
arr = ([1, 29], [3, 5], [3, 117], [19, 2],
|
||
|
|
[2379, 1284], [2, 2], [0, 1])
|
||
|
|
assert_equal(
|
||
|
|
np.ravel_multi_index(arr, (41, 7, 120, 36, 2706, 8, 6)),
|
||
|
|
[5627771580, 117259570957])
|
||
|
|
|
||
|
|
# test unravel_index for big indices (issue #9538)
|
||
|
|
assert_raises(ValueError, np.unravel_index, 1, (2**32-1, 2**31+1))
|
||
|
|
|
||
|
|
# test overflow checking for too big array (issue #7546)
|
||
|
|
dummy_arr = ([0],[0])
|
||
|
|
half_max = np.iinfo(np.intp).max // 2
|
||
|
|
assert_equal(
|
||
|
|
np.ravel_multi_index(dummy_arr, (half_max, 2)), [0])
|
||
|
|
assert_raises(ValueError,
|
||
|
|
np.ravel_multi_index, dummy_arr, (half_max+1, 2))
|
||
|
|
assert_equal(
|
||
|
|
np.ravel_multi_index(dummy_arr, (half_max, 2), order='F'), [0])
|
||
|
|
assert_raises(ValueError,
|
||
|
|
np.ravel_multi_index, dummy_arr, (half_max+1, 2), order='F')
|
||
|
|
|
||
|
|
def test_dtypes(self):
|
||
|
|
# Test with different data types
|
||
|
|
for dtype in [np.int16, np.uint16, np.int32,
|
||
|
|
np.uint32, np.int64, np.uint64]:
|
||
|
|
coords = np.array(
|
||
|
|
[[1, 0, 1, 2, 3, 4], [1, 6, 1, 3, 2, 0]], dtype=dtype)
|
||
|
|
shape = (5, 8)
|
||
|
|
uncoords = 8*coords[0]+coords[1]
|
||
|
|
assert_equal(np.ravel_multi_index(coords, shape), uncoords)
|
||
|
|
assert_equal(coords, np.unravel_index(uncoords, shape))
|
||
|
|
uncoords = coords[0]+5*coords[1]
|
||
|
|
assert_equal(
|
||
|
|
np.ravel_multi_index(coords, shape, order='F'), uncoords)
|
||
|
|
assert_equal(coords, np.unravel_index(uncoords, shape, order='F'))
|
||
|
|
|
||
|
|
coords = np.array(
|
||
|
|
[[1, 0, 1, 2, 3, 4], [1, 6, 1, 3, 2, 0], [1, 3, 1, 0, 9, 5]],
|
||
|
|
dtype=dtype)
|
||
|
|
shape = (5, 8, 10)
|
||
|
|
uncoords = 10*(8*coords[0]+coords[1])+coords[2]
|
||
|
|
assert_equal(np.ravel_multi_index(coords, shape), uncoords)
|
||
|
|
assert_equal(coords, np.unravel_index(uncoords, shape))
|
||
|
|
uncoords = coords[0]+5*(coords[1]+8*coords[2])
|
||
|
|
assert_equal(
|
||
|
|
np.ravel_multi_index(coords, shape, order='F'), uncoords)
|
||
|
|
assert_equal(coords, np.unravel_index(uncoords, shape, order='F'))
|
||
|
|
|
||
|
|
def test_clipmodes(self):
|
||
|
|
# Test clipmodes
|
||
|
|
assert_equal(
|
||
|
|
np.ravel_multi_index([5, 1, -1, 2], (4, 3, 7, 12), mode='wrap'),
|
||
|
|
np.ravel_multi_index([1, 1, 6, 2], (4, 3, 7, 12)))
|
||
|
|
assert_equal(np.ravel_multi_index([5, 1, -1, 2], (4, 3, 7, 12),
|
||
|
|
mode=(
|
||
|
|
'wrap', 'raise', 'clip', 'raise')),
|
||
|
|
np.ravel_multi_index([1, 1, 0, 2], (4, 3, 7, 12)))
|
||
|
|
assert_raises(
|
||
|
|
ValueError, np.ravel_multi_index, [5, 1, -1, 2], (4, 3, 7, 12))
|
||
|
|
|
||
|
|
def test_writeability(self):
|
||
|
|
# See gh-7269
|
||
|
|
x, y = np.unravel_index([1, 2, 3], (4, 5))
|
||
|
|
assert_(x.flags.writeable)
|
||
|
|
assert_(y.flags.writeable)
|
||
|
|
|
||
|
|
def test_0d(self):
|
||
|
|
# gh-580
|
||
|
|
x = np.unravel_index(0, ())
|
||
|
|
assert_equal(x, ())
|
||
|
|
|
||
|
|
assert_raises_regex(ValueError, "0d array", np.unravel_index, [0], ())
|
||
|
|
assert_raises_regex(
|
||
|
|
ValueError, "out of bounds", np.unravel_index, [1], ())
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("mode", ["clip", "wrap", "raise"])
|
||
|
|
def test_empty_array_ravel(self, mode):
|
||
|
|
res = np.ravel_multi_index(
|
||
|
|
np.zeros((3, 0), dtype=np.intp), (2, 1, 0), mode=mode)
|
||
|
|
assert(res.shape == (0,))
|
||
|
|
|
||
|
|
with assert_raises(ValueError):
|
||
|
|
np.ravel_multi_index(
|
||
|
|
np.zeros((3, 1), dtype=np.intp), (2, 1, 0), mode=mode)
|
||
|
|
|
||
|
|
def test_empty_array_unravel(self):
|
||
|
|
res = np.unravel_index(np.zeros(0, dtype=np.intp), (2, 1, 0))
|
||
|
|
# res is a tuple of three empty arrays
|
||
|
|
assert(len(res) == 3)
|
||
|
|
assert(all(a.shape == (0,) for a in res))
|
||
|
|
|
||
|
|
with assert_raises(ValueError):
|
||
|
|
np.unravel_index([1], (2, 1, 0))
|
||
|
|
|
||
|
|
class TestGrid:
|
||
|
|
def test_basic(self):
|
||
|
|
a = mgrid[-1:1:10j]
|
||
|
|
b = mgrid[-1:1:0.1]
|
||
|
|
assert_(a.shape == (10,))
|
||
|
|
assert_(b.shape == (20,))
|
||
|
|
assert_(a[0] == -1)
|
||
|
|
assert_almost_equal(a[-1], 1)
|
||
|
|
assert_(b[0] == -1)
|
||
|
|
assert_almost_equal(b[1]-b[0], 0.1, 11)
|
||
|
|
assert_almost_equal(b[-1], b[0]+19*0.1, 11)
|
||
|
|
assert_almost_equal(a[1]-a[0], 2.0/9.0, 11)
|
||
|
|
|
||
|
|
def test_linspace_equivalence(self):
|
||
|
|
y, st = np.linspace(2, 10, retstep=True)
|
||
|
|
assert_almost_equal(st, 8/49.0)
|
||
|
|
assert_array_almost_equal(y, mgrid[2:10:50j], 13)
|
||
|
|
|
||
|
|
def test_nd(self):
|
||
|
|
c = mgrid[-1:1:10j, -2:2:10j]
|
||
|
|
d = mgrid[-1:1:0.1, -2:2:0.2]
|
||
|
|
assert_(c.shape == (2, 10, 10))
|
||
|
|
assert_(d.shape == (2, 20, 20))
|
||
|
|
assert_array_equal(c[0][0, :], -np.ones(10, 'd'))
|
||
|
|
assert_array_equal(c[1][:, 0], -2*np.ones(10, 'd'))
|
||
|
|
assert_array_almost_equal(c[0][-1, :], np.ones(10, 'd'), 11)
|
||
|
|
assert_array_almost_equal(c[1][:, -1], 2*np.ones(10, 'd'), 11)
|
||
|
|
assert_array_almost_equal(d[0, 1, :] - d[0, 0, :],
|
||
|
|
0.1*np.ones(20, 'd'), 11)
|
||
|
|
assert_array_almost_equal(d[1, :, 1] - d[1, :, 0],
|
||
|
|
0.2*np.ones(20, 'd'), 11)
|
||
|
|
|
||
|
|
def test_sparse(self):
|
||
|
|
grid_full = mgrid[-1:1:10j, -2:2:10j]
|
||
|
|
grid_sparse = ogrid[-1:1:10j, -2:2:10j]
|
||
|
|
|
||
|
|
# sparse grids can be made dense by broadcasting
|
||
|
|
grid_broadcast = np.broadcast_arrays(*grid_sparse)
|
||
|
|
for f, b in zip(grid_full, grid_broadcast):
|
||
|
|
assert_equal(f, b)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("start, stop, step, expected", [
|
||
|
|
(None, 10, 10j, (200, 10)),
|
||
|
|
(-10, 20, None, (1800, 30)),
|
||
|
|
])
|
||
|
|
def test_mgrid_size_none_handling(self, start, stop, step, expected):
|
||
|
|
# regression test None value handling for
|
||
|
|
# start and step values used by mgrid;
|
||
|
|
# internally, this aims to cover previously
|
||
|
|
# unexplored code paths in nd_grid()
|
||
|
|
grid = mgrid[start:stop:step, start:stop:step]
|
||
|
|
# need a smaller grid to explore one of the
|
||
|
|
# untested code paths
|
||
|
|
grid_small = mgrid[start:stop:step]
|
||
|
|
assert_equal(grid.size, expected[0])
|
||
|
|
assert_equal(grid_small.size, expected[1])
|
||
|
|
|
||
|
|
def test_accepts_npfloating(self):
|
||
|
|
# regression test for #16466
|
||
|
|
grid64 = mgrid[0.1:0.33:0.1, ]
|
||
|
|
grid32 = mgrid[np.float32(0.1):np.float32(0.33):np.float32(0.1), ]
|
||
|
|
assert_(grid32.dtype == np.float64)
|
||
|
|
assert_array_almost_equal(grid64, grid32)
|
||
|
|
|
||
|
|
# different code path for single slice
|
||
|
|
grid64 = mgrid[0.1:0.33:0.1]
|
||
|
|
grid32 = mgrid[np.float32(0.1):np.float32(0.33):np.float32(0.1)]
|
||
|
|
assert_(grid32.dtype == np.float64)
|
||
|
|
assert_array_almost_equal(grid64, grid32)
|
||
|
|
|
||
|
|
def test_accepts_npcomplexfloating(self):
|
||
|
|
# Related to #16466
|
||
|
|
assert_array_almost_equal(
|
||
|
|
mgrid[0.1:0.3:3j, ], mgrid[0.1:0.3:np.complex64(3j), ]
|
||
|
|
)
|
||
|
|
|
||
|
|
# different code path for single slice
|
||
|
|
assert_array_almost_equal(
|
||
|
|
mgrid[0.1:0.3:3j], mgrid[0.1:0.3:np.complex64(3j)]
|
||
|
|
)
|
||
|
|
|
||
|
|
class TestConcatenator:
|
||
|
|
def test_1d(self):
|
||
|
|
assert_array_equal(r_[1, 2, 3, 4, 5, 6], np.array([1, 2, 3, 4, 5, 6]))
|
||
|
|
b = np.ones(5)
|
||
|
|
c = r_[b, 0, 0, b]
|
||
|
|
assert_array_equal(c, [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1])
|
||
|
|
|
||
|
|
def test_mixed_type(self):
|
||
|
|
g = r_[10.1, 1:10]
|
||
|
|
assert_(g.dtype == 'f8')
|
||
|
|
|
||
|
|
def test_more_mixed_type(self):
|
||
|
|
g = r_[-10.1, np.array([1]), np.array([2, 3, 4]), 10.0]
|
||
|
|
assert_(g.dtype == 'f8')
|
||
|
|
|
||
|
|
def test_complex_step(self):
|
||
|
|
# Regression test for #12262
|
||
|
|
g = r_[0:36:100j]
|
||
|
|
assert_(g.shape == (100,))
|
||
|
|
|
||
|
|
# Related to #16466
|
||
|
|
g = r_[0:36:np.complex64(100j)]
|
||
|
|
assert_(g.shape == (100,))
|
||
|
|
|
||
|
|
def test_2d(self):
|
||
|
|
b = np.random.rand(5, 5)
|
||
|
|
c = np.random.rand(5, 5)
|
||
|
|
d = r_['1', b, c] # append columns
|
||
|
|
assert_(d.shape == (5, 10))
|
||
|
|
assert_array_equal(d[:, :5], b)
|
||
|
|
assert_array_equal(d[:, 5:], c)
|
||
|
|
d = r_[b, c]
|
||
|
|
assert_(d.shape == (10, 5))
|
||
|
|
assert_array_equal(d[:5, :], b)
|
||
|
|
assert_array_equal(d[5:, :], c)
|
||
|
|
|
||
|
|
def test_0d(self):
|
||
|
|
assert_equal(r_[0, np.array(1), 2], [0, 1, 2])
|
||
|
|
assert_equal(r_[[0, 1, 2], np.array(3)], [0, 1, 2, 3])
|
||
|
|
assert_equal(r_[np.array(0), [1, 2, 3]], [0, 1, 2, 3])
|
||
|
|
|
||
|
|
|
||
|
|
class TestNdenumerate:
|
||
|
|
def test_basic(self):
|
||
|
|
a = np.array([[1, 2], [3, 4]])
|
||
|
|
assert_equal(list(ndenumerate(a)),
|
||
|
|
[((0, 0), 1), ((0, 1), 2), ((1, 0), 3), ((1, 1), 4)])
|
||
|
|
|
||
|
|
|
||
|
|
class TestIndexExpression:
|
||
|
|
def test_regression_1(self):
|
||
|
|
# ticket #1196
|
||
|
|
a = np.arange(2)
|
||
|
|
assert_equal(a[:-1], a[s_[:-1]])
|
||
|
|
assert_equal(a[:-1], a[index_exp[:-1]])
|
||
|
|
|
||
|
|
def test_simple_1(self):
|
||
|
|
a = np.random.rand(4, 5, 6)
|
||
|
|
|
||
|
|
assert_equal(a[:, :3, [1, 2]], a[index_exp[:, :3, [1, 2]]])
|
||
|
|
assert_equal(a[:, :3, [1, 2]], a[s_[:, :3, [1, 2]]])
|
||
|
|
|
||
|
|
|
||
|
|
class TestIx_:
|
||
|
|
def test_regression_1(self):
|
||
|
|
# Test empty untyped inputs create outputs of indexing type, gh-5804
|
||
|
|
a, = np.ix_(range(0))
|
||
|
|
assert_equal(a.dtype, np.intp)
|
||
|
|
|
||
|
|
a, = np.ix_([])
|
||
|
|
assert_equal(a.dtype, np.intp)
|
||
|
|
|
||
|
|
# but if the type is specified, don't change it
|
||
|
|
a, = np.ix_(np.array([], dtype=np.float32))
|
||
|
|
assert_equal(a.dtype, np.float32)
|
||
|
|
|
||
|
|
def test_shape_and_dtype(self):
|
||
|
|
sizes = (4, 5, 3, 2)
|
||
|
|
# Test both lists and arrays
|
||
|
|
for func in (range, np.arange):
|
||
|
|
arrays = np.ix_(*[func(sz) for sz in sizes])
|
||
|
|
for k, (a, sz) in enumerate(zip(arrays, sizes)):
|
||
|
|
assert_equal(a.shape[k], sz)
|
||
|
|
assert_(all(sh == 1 for j, sh in enumerate(a.shape) if j != k))
|
||
|
|
assert_(np.issubdtype(a.dtype, np.integer))
|
||
|
|
|
||
|
|
def test_bool(self):
|
||
|
|
bool_a = [True, False, True, True]
|
||
|
|
int_a, = np.nonzero(bool_a)
|
||
|
|
assert_equal(np.ix_(bool_a)[0], int_a)
|
||
|
|
|
||
|
|
def test_1d_only(self):
|
||
|
|
idx2d = [[1, 2, 3], [4, 5, 6]]
|
||
|
|
assert_raises(ValueError, np.ix_, idx2d)
|
||
|
|
|
||
|
|
def test_repeated_input(self):
|
||
|
|
length_of_vector = 5
|
||
|
|
x = np.arange(length_of_vector)
|
||
|
|
out = ix_(x, x)
|
||
|
|
assert_equal(out[0].shape, (length_of_vector, 1))
|
||
|
|
assert_equal(out[1].shape, (1, length_of_vector))
|
||
|
|
# check that input shape is not modified
|
||
|
|
assert_equal(x.shape, (length_of_vector,))
|
||
|
|
|
||
|
|
|
||
|
|
def test_c_():
|
||
|
|
a = np.c_[np.array([[1, 2, 3]]), 0, 0, np.array([[4, 5, 6]])]
|
||
|
|
assert_equal(a, [[1, 2, 3, 0, 0, 4, 5, 6]])
|
||
|
|
|
||
|
|
|
||
|
|
class TestFillDiagonal:
|
||
|
|
def test_basic(self):
|
||
|
|
a = np.zeros((3, 3), int)
|
||
|
|
fill_diagonal(a, 5)
|
||
|
|
assert_array_equal(
|
||
|
|
a, np.array([[5, 0, 0],
|
||
|
|
[0, 5, 0],
|
||
|
|
[0, 0, 5]])
|
||
|
|
)
|
||
|
|
|
||
|
|
def test_tall_matrix(self):
|
||
|
|
a = np.zeros((10, 3), int)
|
||
|
|
fill_diagonal(a, 5)
|
||
|
|
assert_array_equal(
|
||
|
|
a, np.array([[5, 0, 0],
|
||
|
|
[0, 5, 0],
|
||
|
|
[0, 0, 5],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 0, 0]])
|
||
|
|
)
|
||
|
|
|
||
|
|
def test_tall_matrix_wrap(self):
|
||
|
|
a = np.zeros((10, 3), int)
|
||
|
|
fill_diagonal(a, 5, True)
|
||
|
|
assert_array_equal(
|
||
|
|
a, np.array([[5, 0, 0],
|
||
|
|
[0, 5, 0],
|
||
|
|
[0, 0, 5],
|
||
|
|
[0, 0, 0],
|
||
|
|
[5, 0, 0],
|
||
|
|
[0, 5, 0],
|
||
|
|
[0, 0, 5],
|
||
|
|
[0, 0, 0],
|
||
|
|
[5, 0, 0],
|
||
|
|
[0, 5, 0]])
|
||
|
|
)
|
||
|
|
|
||
|
|
def test_wide_matrix(self):
|
||
|
|
a = np.zeros((3, 10), int)
|
||
|
|
fill_diagonal(a, 5)
|
||
|
|
assert_array_equal(
|
||
|
|
a, np.array([[5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 5, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 5, 0, 0, 0, 0, 0, 0, 0]])
|
||
|
|
)
|
||
|
|
|
||
|
|
def test_operate_4d_array(self):
|
||
|
|
a = np.zeros((3, 3, 3, 3), int)
|
||
|
|
fill_diagonal(a, 4)
|
||
|
|
i = np.array([0, 1, 2])
|
||
|
|
assert_equal(np.where(a != 0), (i, i, i, i))
|
||
|
|
|
||
|
|
def test_low_dim_handling(self):
|
||
|
|
# raise error with low dimensionality
|
||
|
|
a = np.zeros(3, int)
|
||
|
|
with assert_raises_regex(ValueError, "at least 2-d"):
|
||
|
|
fill_diagonal(a, 5)
|
||
|
|
|
||
|
|
def test_hetero_shape_handling(self):
|
||
|
|
# raise error with high dimensionality and
|
||
|
|
# shape mismatch
|
||
|
|
a = np.zeros((3,3,7,3), int)
|
||
|
|
with assert_raises_regex(ValueError, "equal length"):
|
||
|
|
fill_diagonal(a, 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_diag_indices():
|
||
|
|
di = diag_indices(4)
|
||
|
|
a = np.array([[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12],
|
||
|
|
[13, 14, 15, 16]])
|
||
|
|
a[di] = 100
|
||
|
|
assert_array_equal(
|
||
|
|
a, np.array([[100, 2, 3, 4],
|
||
|
|
[5, 100, 7, 8],
|
||
|
|
[9, 10, 100, 12],
|
||
|
|
[13, 14, 15, 100]])
|
||
|
|
)
|
||
|
|
|
||
|
|
# Now, we create indices to manipulate a 3-d array:
|
||
|
|
d3 = diag_indices(2, 3)
|
||
|
|
|
||
|
|
# And use it to set the diagonal of a zeros array to 1:
|
||
|
|
a = np.zeros((2, 2, 2), int)
|
||
|
|
a[d3] = 1
|
||
|
|
assert_array_equal(
|
||
|
|
a, np.array([[[1, 0],
|
||
|
|
[0, 0]],
|
||
|
|
[[0, 0],
|
||
|
|
[0, 1]]])
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class TestDiagIndicesFrom:
|
||
|
|
|
||
|
|
def test_diag_indices_from(self):
|
||
|
|
x = np.random.random((4, 4))
|
||
|
|
r, c = diag_indices_from(x)
|
||
|
|
assert_array_equal(r, np.arange(4))
|
||
|
|
assert_array_equal(c, np.arange(4))
|
||
|
|
|
||
|
|
def test_error_small_input(self):
|
||
|
|
x = np.ones(7)
|
||
|
|
with assert_raises_regex(ValueError, "at least 2-d"):
|
||
|
|
diag_indices_from(x)
|
||
|
|
|
||
|
|
def test_error_shape_mismatch(self):
|
||
|
|
x = np.zeros((3, 3, 2, 3), int)
|
||
|
|
with assert_raises_regex(ValueError, "equal length"):
|
||
|
|
diag_indices_from(x)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ndindex():
|
||
|
|
x = list(ndindex(1, 2, 3))
|
||
|
|
expected = [ix for ix, e in ndenumerate(np.zeros((1, 2, 3)))]
|
||
|
|
assert_array_equal(x, expected)
|
||
|
|
|
||
|
|
x = list(ndindex((1, 2, 3)))
|
||
|
|
assert_array_equal(x, expected)
|
||
|
|
|
||
|
|
# Test use of scalars and tuples
|
||
|
|
x = list(ndindex((3,)))
|
||
|
|
assert_array_equal(x, list(ndindex(3)))
|
||
|
|
|
||
|
|
# Make sure size argument is optional
|
||
|
|
x = list(ndindex())
|
||
|
|
assert_equal(x, [()])
|
||
|
|
|
||
|
|
x = list(ndindex(()))
|
||
|
|
assert_equal(x, [()])
|
||
|
|
|
||
|
|
# Make sure 0-sized ndindex works correctly
|
||
|
|
x = list(ndindex(*[0]))
|
||
|
|
assert_equal(x, [])
|