/usr/lib/python2.7/dist-packages/numpy/lib/tests/test_twodim_base.py is in python-numpy 1:1.12.1-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 | """Test functions for matrix module
"""
from __future__ import division, absolute_import, print_function
from numpy.testing import (
TestCase, run_module_suite, assert_equal, assert_array_equal,
assert_array_max_ulp, assert_array_almost_equal, assert_raises,
)
from numpy import (
arange, add, fliplr, flipud, zeros, ones, eye, array, diag,
histogram2d, tri, mask_indices, triu_indices, triu_indices_from,
tril_indices, tril_indices_from, vander,
)
import numpy as np
from numpy.compat import asbytes_nested
def get_mat(n):
data = arange(n)
data = add.outer(data, data)
return data
class TestEye(TestCase):
def test_basic(self):
assert_equal(eye(4),
array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]]))
assert_equal(eye(4, dtype='f'),
array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]], 'f'))
assert_equal(eye(3) == 1,
eye(3, dtype=bool))
def test_diag(self):
assert_equal(eye(4, k=1),
array([[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 0, 0, 0]]))
assert_equal(eye(4, k=-1),
array([[0, 0, 0, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]))
def test_2d(self):
assert_equal(eye(4, 3),
array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 0, 0]]))
assert_equal(eye(3, 4),
array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]))
def test_diag2d(self):
assert_equal(eye(3, 4, k=2),
array([[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 0, 0, 0]]))
assert_equal(eye(4, 3, k=-2),
array([[0, 0, 0],
[0, 0, 0],
[1, 0, 0],
[0, 1, 0]]))
def test_eye_bounds(self):
assert_equal(eye(2, 2, 1), [[0, 1], [0, 0]])
assert_equal(eye(2, 2, -1), [[0, 0], [1, 0]])
assert_equal(eye(2, 2, 2), [[0, 0], [0, 0]])
assert_equal(eye(2, 2, -2), [[0, 0], [0, 0]])
assert_equal(eye(3, 2, 2), [[0, 0], [0, 0], [0, 0]])
assert_equal(eye(3, 2, 1), [[0, 1], [0, 0], [0, 0]])
assert_equal(eye(3, 2, -1), [[0, 0], [1, 0], [0, 1]])
assert_equal(eye(3, 2, -2), [[0, 0], [0, 0], [1, 0]])
assert_equal(eye(3, 2, -3), [[0, 0], [0, 0], [0, 0]])
def test_strings(self):
assert_equal(eye(2, 2, dtype='S3'),
asbytes_nested([['1', ''], ['', '1']]))
def test_bool(self):
assert_equal(eye(2, 2, dtype=bool), [[True, False], [False, True]])
class TestDiag(TestCase):
def test_vector(self):
vals = (100 * arange(5)).astype('l')
b = zeros((5, 5))
for k in range(5):
b[k, k] = vals[k]
assert_equal(diag(vals), b)
b = zeros((7, 7))
c = b.copy()
for k in range(5):
b[k, k + 2] = vals[k]
c[k + 2, k] = vals[k]
assert_equal(diag(vals, k=2), b)
assert_equal(diag(vals, k=-2), c)
def test_matrix(self, vals=None):
if vals is None:
vals = (100 * get_mat(5) + 1).astype('l')
b = zeros((5,))
for k in range(5):
b[k] = vals[k, k]
assert_equal(diag(vals), b)
b = b * 0
for k in range(3):
b[k] = vals[k, k + 2]
assert_equal(diag(vals, 2), b[:3])
for k in range(3):
b[k] = vals[k + 2, k]
assert_equal(diag(vals, -2), b[:3])
def test_fortran_order(self):
vals = array((100 * get_mat(5) + 1), order='F', dtype='l')
self.test_matrix(vals)
def test_diag_bounds(self):
A = [[1, 2], [3, 4], [5, 6]]
assert_equal(diag(A, k=2), [])
assert_equal(diag(A, k=1), [2])
assert_equal(diag(A, k=0), [1, 4])
assert_equal(diag(A, k=-1), [3, 6])
assert_equal(diag(A, k=-2), [5])
assert_equal(diag(A, k=-3), [])
def test_failure(self):
self.assertRaises(ValueError, diag, [[[1]]])
class TestFliplr(TestCase):
def test_basic(self):
self.assertRaises(ValueError, fliplr, ones(4))
a = get_mat(4)
b = a[:, ::-1]
assert_equal(fliplr(a), b)
a = [[0, 1, 2],
[3, 4, 5]]
b = [[2, 1, 0],
[5, 4, 3]]
assert_equal(fliplr(a), b)
class TestFlipud(TestCase):
def test_basic(self):
a = get_mat(4)
b = a[::-1, :]
assert_equal(flipud(a), b)
a = [[0, 1, 2],
[3, 4, 5]]
b = [[3, 4, 5],
[0, 1, 2]]
assert_equal(flipud(a), b)
class TestHistogram2d(TestCase):
def test_simple(self):
x = array(
[0.41702200, 0.72032449, 1.1437481e-4, 0.302332573, 0.146755891])
y = array(
[0.09233859, 0.18626021, 0.34556073, 0.39676747, 0.53881673])
xedges = np.linspace(0, 1, 10)
yedges = np.linspace(0, 1, 10)
H = histogram2d(x, y, (xedges, yedges))[0]
answer = array(
[[0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 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, 0, 0, 0]])
assert_array_equal(H.T, answer)
H = histogram2d(x, y, xedges)[0]
assert_array_equal(H.T, answer)
H, xedges, yedges = histogram2d(list(range(10)), list(range(10)))
assert_array_equal(H, eye(10, 10))
assert_array_equal(xedges, np.linspace(0, 9, 11))
assert_array_equal(yedges, np.linspace(0, 9, 11))
def test_asym(self):
x = array([1, 1, 2, 3, 4, 4, 4, 5])
y = array([1, 3, 2, 0, 1, 2, 3, 4])
H, xed, yed = histogram2d(
x, y, (6, 5), range=[[0, 6], [0, 5]], normed=True)
answer = array(
[[0., 0, 0, 0, 0],
[0, 1, 0, 1, 0],
[0, 0, 1, 0, 0],
[1, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 1]])
assert_array_almost_equal(H, answer/8., 3)
assert_array_equal(xed, np.linspace(0, 6, 7))
assert_array_equal(yed, np.linspace(0, 5, 6))
def test_norm(self):
x = array([1, 2, 3, 1, 2, 3, 1, 2, 3])
y = array([1, 1, 1, 2, 2, 2, 3, 3, 3])
H, xed, yed = histogram2d(
x, y, [[1, 2, 3, 5], [1, 2, 3, 5]], normed=True)
answer = array([[1, 1, .5],
[1, 1, .5],
[.5, .5, .25]])/9.
assert_array_almost_equal(H, answer, 3)
def test_all_outliers(self):
r = np.random.rand(100) + 1. + 1e6 # histogramdd rounds by decimal=6
H, xed, yed = histogram2d(r, r, (4, 5), range=([0, 1], [0, 1]))
assert_array_equal(H, 0)
def test_empty(self):
a, edge1, edge2 = histogram2d([], [], bins=([0, 1], [0, 1]))
assert_array_max_ulp(a, array([[0.]]))
a, edge1, edge2 = histogram2d([], [], bins=4)
assert_array_max_ulp(a, np.zeros((4, 4)))
def test_binparameter_combination(self):
x = array(
[0, 0.09207008, 0.64575234, 0.12875982, 0.47390599,
0.59944483, 1])
y = array(
[0, 0.14344267, 0.48988575, 0.30558665, 0.44700682,
0.15886423, 1])
edges = (0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
H, xe, ye = histogram2d(x, y, (edges, 4))
answer = array(
[[ 2., 0., 0., 0.],
[ 0., 1., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 1., 0., 0.],
[ 1., 0., 0., 0.],
[ 0., 1., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 1.]])
assert_array_equal(H, answer)
assert_array_equal(ye, array([0., 0.25, 0.5, 0.75, 1]))
H, xe, ye = histogram2d(x, y, (4, edges))
answer = array(
[[ 1., 1., 0., 1., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
[ 0., 1., 0., 0., 1., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])
assert_array_equal(H, answer)
assert_array_equal(xe, array([0., 0.25, 0.5, 0.75, 1]))
class TestTri(TestCase):
def test_dtype(self):
out = array([[1, 0, 0],
[1, 1, 0],
[1, 1, 1]])
assert_array_equal(tri(3), out)
assert_array_equal(tri(3, dtype=bool), out.astype(bool))
def test_tril_triu_ndim2():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.ones((2, 2), dtype=dtype)
b = np.tril(a)
c = np.triu(a)
yield assert_array_equal, b, [[1, 0], [1, 1]]
yield assert_array_equal, c, b.T
# should return the same dtype as the original array
yield assert_equal, b.dtype, a.dtype
yield assert_equal, c.dtype, a.dtype
def test_tril_triu_ndim3():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.array([
[[1, 1], [1, 1]],
[[1, 1], [1, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_tril_desired = np.array([
[[1, 0], [1, 1]],
[[1, 0], [1, 0]],
[[1, 0], [0, 0]],
], dtype=dtype)
a_triu_desired = np.array([
[[1, 1], [0, 1]],
[[1, 1], [0, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_triu_observed = np.triu(a)
a_tril_observed = np.tril(a)
yield assert_array_equal, a_triu_observed, a_triu_desired
yield assert_array_equal, a_tril_observed, a_tril_desired
yield assert_equal, a_triu_observed.dtype, a.dtype
yield assert_equal, a_tril_observed.dtype, a.dtype
def test_tril_triu_with_inf():
# Issue 4859
arr = np.array([[1, 1, np.inf],
[1, 1, 1],
[np.inf, 1, 1]])
out_tril = np.array([[1, 0, 0],
[1, 1, 0],
[np.inf, 1, 1]])
out_triu = out_tril.T
assert_array_equal(np.triu(arr), out_triu)
assert_array_equal(np.tril(arr), out_tril)
def test_tril_triu_dtype():
# Issue 4916
# tril and triu should return the same dtype as input
for c in np.typecodes['All']:
if c == 'V':
continue
arr = np.zeros((3, 3), dtype=c)
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
# check special cases
arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'],
['2004-01-01T12:00', '2003-01-03T13:45']],
dtype='datetime64')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
arr = np.zeros((3,3), dtype='f4,f4')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
def test_mask_indices():
# simple test without offset
iu = mask_indices(3, np.triu)
a = np.arange(9).reshape(3, 3)
yield (assert_array_equal, a[iu], array([0, 1, 2, 4, 5, 8]))
# Now with an offset
iu1 = mask_indices(3, np.triu, 1)
yield (assert_array_equal, a[iu1], array([1, 2, 5]))
def test_tril_indices():
# indices without and with offset
il1 = tril_indices(4)
il2 = tril_indices(4, k=2)
il3 = tril_indices(4, m=5)
il4 = tril_indices(4, k=2, m=5)
a = np.array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16]])
b = np.arange(1, 21).reshape(4, 5)
# indexing:
yield (assert_array_equal, a[il1],
array([1, 5, 6, 9, 10, 11, 13, 14, 15, 16]))
yield (assert_array_equal, b[il3],
array([1, 6, 7, 11, 12, 13, 16, 17, 18, 19]))
# And for assigning values:
a[il1] = -1
yield (assert_array_equal, a,
array([[-1, 2, 3, 4],
[-1, -1, 7, 8],
[-1, -1, -1, 12],
[-1, -1, -1, -1]]))
b[il3] = -1
yield (assert_array_equal, b,
array([[-1, 2, 3, 4, 5],
[-1, -1, 8, 9, 10],
[-1, -1, -1, 14, 15],
[-1, -1, -1, -1, 20]]))
# These cover almost the whole array (two diagonals right of the main one):
a[il2] = -10
yield (assert_array_equal, a,
array([[-10, -10, -10, 4],
[-10, -10, -10, -10],
[-10, -10, -10, -10],
[-10, -10, -10, -10]]))
b[il4] = -10
yield (assert_array_equal, b,
array([[-10, -10, -10, 4, 5],
[-10, -10, -10, -10, 10],
[-10, -10, -10, -10, -10],
[-10, -10, -10, -10, -10]]))
class TestTriuIndices(object):
def test_triu_indices(self):
iu1 = triu_indices(4)
iu2 = triu_indices(4, k=2)
iu3 = triu_indices(4, m=5)
iu4 = triu_indices(4, k=2, m=5)
a = np.array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16]])
b = np.arange(1, 21).reshape(4, 5)
# Both for indexing:
yield (assert_array_equal, a[iu1],
array([1, 2, 3, 4, 6, 7, 8, 11, 12, 16]))
yield (assert_array_equal, b[iu3],
array([1, 2, 3, 4, 5, 7, 8, 9, 10, 13, 14, 15, 19, 20]))
# And for assigning values:
a[iu1] = -1
yield (assert_array_equal, a,
array([[-1, -1, -1, -1],
[5, -1, -1, -1],
[9, 10, -1, -1],
[13, 14, 15, -1]]))
b[iu3] = -1
yield (assert_array_equal, b,
array([[-1, -1, -1, -1, -1],
[6, -1, -1, -1, -1],
[11, 12, -1, -1, -1],
[16, 17, 18, -1, -1]]))
# These cover almost the whole array (two diagonals right of the
# main one):
a[iu2] = -10
yield (assert_array_equal, a,
array([[-1, -1, -10, -10],
[5, -1, -1, -10],
[9, 10, -1, -1],
[13, 14, 15, -1]]))
b[iu4] = -10
yield (assert_array_equal, b,
array([[-1, -1, -10, -10, -10],
[6, -1, -1, -10, -10],
[11, 12, -1, -1, -10],
[16, 17, 18, -1, -1]]))
class TestTrilIndicesFrom(object):
def test_exceptions(self):
assert_raises(ValueError, tril_indices_from, np.ones((2,)))
assert_raises(ValueError, tril_indices_from, np.ones((2, 2, 2)))
# assert_raises(ValueError, tril_indices_from, np.ones((2, 3)))
class TestTriuIndicesFrom(object):
def test_exceptions(self):
assert_raises(ValueError, triu_indices_from, np.ones((2,)))
assert_raises(ValueError, triu_indices_from, np.ones((2, 2, 2)))
# assert_raises(ValueError, triu_indices_from, np.ones((2, 3)))
class TestVander(object):
def test_basic(self):
c = np.array([0, 1, -2, 3])
v = vander(c)
powers = np.array([[0, 0, 0, 0, 1],
[1, 1, 1, 1, 1],
[16, -8, 4, -2, 1],
[81, 27, 9, 3, 1]])
# Check default value of N:
yield (assert_array_equal, v, powers[:, 1:])
# Check a range of N values, including 0 and 5 (greater than default)
m = powers.shape[1]
for n in range(6):
v = vander(c, N=n)
yield (assert_array_equal, v, powers[:, m-n:m])
def test_dtypes(self):
c = array([11, -12, 13], dtype=np.int8)
v = vander(c)
expected = np.array([[121, 11, 1],
[144, -12, 1],
[169, 13, 1]])
yield (assert_array_equal, v, expected)
c = array([1.0+1j, 1.0-1j])
v = vander(c, N=3)
expected = np.array([[2j, 1+1j, 1],
[-2j, 1-1j, 1]])
# The data is floating point, but the values are small integers,
# so assert_array_equal *should* be safe here (rather than, say,
# assert_array_almost_equal).
yield (assert_array_equal, v, expected)
if __name__ == "__main__":
run_module_suite()
|