/usr/lib/python2.7/dist-packages/dipy/tracking/tests/test_streamline.py is in python-dipy 0.10.1-1.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 | from __future__ import print_function
import numpy as np
from numpy.linalg import norm
import numpy.testing as npt
from dipy.testing.memory import get_type_refcount
from nose.tools import assert_true, assert_equal, assert_almost_equal
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises, run_module_suite)
from dipy.tracking.streamline import (set_number_of_points,
length as ds_length,
relist_streamlines,
unlist_streamlines,
center_streamlines,
transform_streamlines,
select_random_set_of_streamlines,
compress_streamlines,
select_by_rois,
orient_by_rois)
streamline = np.array([[82.20181274, 91.36505890, 43.15737152],
[82.38442230, 91.79336548, 43.87036514],
[82.48710632, 92.27861023, 44.56298065],
[82.53310394, 92.78545380, 45.24635315],
[82.53793335, 93.26902008, 45.94785309],
[82.48797607, 93.75003815, 46.64939880],
[82.35533142, 94.25181580, 47.32533264],
[82.15484619, 94.76634216, 47.97451019],
[81.90982819, 95.28792572, 48.60244370],
[81.63336945, 95.78153229, 49.23971176],
[81.35479736, 96.24868011, 49.89558792],
[81.08713531, 96.69807434, 50.56812668],
[80.81504822, 97.14285278, 51.24193192],
[80.52591705, 97.56719971, 51.92168427],
[80.26599884, 97.98269653, 52.61848068],
[80.04635620, 98.38131714, 53.33855820],
[79.84691620, 98.77052307, 54.06955338],
[79.57667542, 99.13599396, 54.78985596],
[79.23351288, 99.43207550, 55.51065063],
[78.84815979, 99.64141846, 56.24016571],
[78.47383881, 99.77347565, 56.99299240],
[78.12837219, 99.81330872, 57.76969528],
[77.80438995, 99.85082245, 58.55574799],
[77.49439240, 99.88065338, 59.34777069],
[77.21414185, 99.85343933, 60.15090561],
[76.96416473, 99.82772827, 60.96406937],
[76.74712372, 99.80519104, 61.78676605],
[76.52263641, 99.79122162, 62.60765076],
[76.03757477, 100.08692169, 63.24152374],
[75.44867706, 100.35265350, 63.79513168],
[74.78033447, 100.57255554, 64.27278900],
[74.11605835, 100.77330780, 64.76428986],
[73.51222992, 100.98779297, 65.32373047],
[72.97387695, 101.23387146, 65.93502045],
[72.47355652, 101.49151611, 66.57343292],
[71.99834442, 101.72480774, 67.23979950],
[71.56909180, 101.98665619, 67.92664337],
[71.18083191, 102.29483795, 68.61888123],
[70.81879425, 102.63343048, 69.31127167],
[70.47422791, 102.98672485, 70.00532532],
[70.10092926, 103.28502655, 70.70999908],
[69.69512177, 103.51667023, 71.42147064],
[69.27423096, 103.71351624, 72.13452911],
[68.91260529, 103.81676483, 72.89796448],
[68.60788727, 103.81982422, 73.69258118],
[68.34162903, 103.76619720, 74.49915314],
[68.08542633, 103.70635223, 75.30856323],
[67.83590698, 103.60187531, 76.11553955],
[67.56822968, 103.44821930, 76.90870667],
[67.28399658, 103.25878906, 77.68825531],
[67.00117493, 103.03740692, 78.45989227],
[66.72718048, 102.80329895, 79.23099518],
[66.46197510, 102.54130554, 79.99622345],
[66.20803833, 102.22305298, 80.74387360],
[65.96872711, 101.88980865, 81.48987579],
[65.72864532, 101.59316254, 82.25085449],
[65.47808075, 101.33383942, 83.02194214],
[65.21841431, 101.11295319, 83.80186462],
[64.95678711, 100.94080353, 84.59326935],
[64.71759033, 100.82022095, 85.40114594],
[64.48053741, 100.73490143, 86.21411896],
[64.24304199, 100.65074158, 87.02709198],
[64.01773834, 100.55318451, 87.84204865],
[63.83801651, 100.41996765, 88.66333008],
[63.70982361, 100.25119019, 89.48779297],
[63.60707855, 100.06730652, 90.31262207],
[63.46164322, 99.91001892, 91.13648224],
[63.26287842, 99.78648376, 91.95485687],
[63.03713226, 99.68377686, 92.76905823],
[62.81192398, 99.56619263, 93.58140564],
[62.57145309, 99.42708588, 94.38592529],
[62.32259369, 99.25592804, 95.18167114],
[62.07497787, 99.05770111, 95.97154236],
[61.82253647, 98.83877563, 96.75438690],
[61.59536743, 98.59293365, 97.53706360],
[61.46530151, 98.30503845, 98.32772827],
[61.39904785, 97.97928619, 99.11172485],
[61.33279419, 97.65353394, 99.89572906],
[61.26067352, 97.30914307, 100.67123413],
[61.19459534, 96.96743011, 101.44847107],
[61.19580460, 96.63417053, 102.23215485],
[61.26572037, 96.29887390, 103.01185608],
[61.39840698, 95.96297455, 103.78307343],
[61.57207870, 95.64262390, 104.55268097],
[61.78163528, 95.35540771, 105.32629395],
[62.06700134, 95.09746552, 106.08564758],
[62.39427185, 94.85724640, 106.83369446],
[62.74076462, 94.62278748, 107.57482147],
[63.11461639, 94.40107727, 108.30641937],
[63.53397751, 94.20418549, 109.02002716],
[64.00019836, 94.03809357, 109.71183777],
[64.43580627, 93.87523651, 110.42416382],
[64.84857941, 93.69993591, 111.14715576],
[65.26740265, 93.51858521, 111.86515808],
[65.69511414, 93.36718750, 112.58474731],
[66.10470581, 93.22719574, 113.31711578],
[66.45891571, 93.06028748, 114.07256317],
[66.78582001, 92.90560913, 114.84281921],
[67.11138916, 92.79004669, 115.62040710],
[67.44729614, 92.75711823, 116.40135193],
[67.75688171, 92.98265076, 117.16111755],
[68.02041626, 93.28012848, 117.91371155],
[68.25725555, 93.53466797, 118.69052124],
[68.46047974, 93.63263702, 119.51107788],
[68.62039948, 93.62007141, 120.34690094],
[68.76782227, 93.56475067, 121.18331909],
[68.90222168, 93.46326447, 122.01765442],
[68.99872589, 93.30039978, 122.84759521],
[69.04119873, 93.05428314, 123.66156769],
[69.05086517, 92.74394989, 124.45450592],
[69.02742004, 92.40427399, 125.23509979],
[68.95466614, 92.09059143, 126.02339935],
[68.84975433, 91.79674530, 126.81564331],
[68.72673798, 91.53726196, 127.61715698],
[68.60685730, 91.30300140, 128.42681885],
[68.50636292, 91.12481689, 129.25317383],
[68.39311218, 91.01572418, 130.08976746],
[68.25946808, 90.94654083, 130.92756653]],
dtype=np.float32)
streamline_64bit = streamline.astype(np.float64)
streamlines = [streamline[[0, 10]], streamline,
streamline[::2], streamline[::3],
streamline[::5], streamline[::6]]
streamlines_64bit = [streamline_64bit[[0, 10]], streamline_64bit,
streamline_64bit[::2], streamline_64bit[::3],
streamline_64bit[::4], streamline_64bit[::5]]
heterogeneous_streamlines = [streamline_64bit,
streamline_64bit.reshape((-1, 6)),
streamline_64bit.reshape((-1, 2))]
def length_python(xyz, along=False):
xyz = np.asarray(xyz, dtype=np.float64)
if xyz.shape[0] < 2:
if along:
return np.array([0])
return 0
dists = np.sqrt((np.diff(xyz, axis=0)**2).sum(axis=1))
if along:
return np.cumsum(dists)
return np.sum(dists)
def set_number_of_points_python(xyz, n_pols=3):
def _extrap(xyz, cumlen, distance):
''' Helper function for extrapolate '''
ind = np.where((cumlen-distance) > 0)[0][0]
len0 = cumlen[ind-1]
len1 = cumlen[ind]
Ds = distance-len0
Lambda = Ds/(len1-len0)
return Lambda*xyz[ind] + (1-Lambda)*xyz[ind-1]
cumlen = np.zeros(xyz.shape[0])
cumlen[1:] = length_python(xyz, along=True)
step = cumlen[-1] / (n_pols-1)
ar = np.arange(0, cumlen[-1], step)
if np.abs(ar[-1] - cumlen[-1]) < np.finfo('f4').eps:
ar = ar[:-1]
xyz2 = [_extrap(xyz, cumlen, distance) for distance in ar]
return np.vstack((np.array(xyz2), xyz[-1]))
def test_set_number_of_points():
# Test resampling of only one streamline
nb_points = 12
modified_streamline_cython = set_number_of_points(
streamline, nb_points)
modified_streamline_python = set_number_of_points_python(
streamline, nb_points)
assert_equal(len(modified_streamline_cython), nb_points)
# Using a 5 digits precision because of streamline is in float32.
assert_array_almost_equal(modified_streamline_cython,
modified_streamline_python, 5)
modified_streamline_cython = set_number_of_points(
streamline_64bit, nb_points)
modified_streamline_python = set_number_of_points_python(
streamline_64bit, nb_points)
assert_equal(len(modified_streamline_cython), nb_points)
assert_array_almost_equal(modified_streamline_cython,
modified_streamline_python)
res = []
simple_streamline = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]], 'f4')
for nb_points in range(2, 200):
modified_streamline_cython = set_number_of_points(
simple_streamline, nb_points)
res.append(nb_points - len(modified_streamline_cython))
assert_equal(np.sum(res), 0)
# Test resampling of multiple streamlines of different nb_points
nb_points = 12
modified_streamlines_cython = set_number_of_points(
streamlines, nb_points)
for i, s in enumerate(streamlines):
modified_streamline_python = set_number_of_points_python(s, nb_points)
# Using a 5 digits precision because of streamline is in float32.
assert_array_almost_equal(modified_streamlines_cython[i],
modified_streamline_python, 5)
modified_streamlines_cython = set_number_of_points(
streamlines_64bit, nb_points)
for i, s in enumerate(streamlines_64bit):
modified_streamline_python = set_number_of_points_python(s, nb_points)
assert_array_almost_equal(modified_streamlines_cython[i],
modified_streamline_python)
# Test streamlines with mixed dtype
streamlines_mixed_dtype = [streamline,
streamline.astype(np.float64),
streamline.astype(np.int32),
streamline.astype(np.int64)]
nb_points_mixed_dtype = [len(s) for s in set_number_of_points(
streamlines_mixed_dtype, nb_points)]
assert_array_equal(nb_points_mixed_dtype,
[nb_points] * len(streamlines_mixed_dtype))
# Test streamlines with different shape
modified_streamlines_cython = set_number_of_points(
heterogeneous_streamlines, nb_points)
for i, s in enumerate(heterogeneous_streamlines):
modified_streamline_python = set_number_of_points_python(s, nb_points)
assert_array_almost_equal(modified_streamlines_cython[i],
modified_streamline_python)
# Test streamline with integer dtype
modified_streamline = set_number_of_points(streamline.astype(np.int32))
assert_true(modified_streamline.dtype == np.float32)
modified_streamline = set_number_of_points(streamline.astype(np.int64))
assert_true(modified_streamline.dtype == np.float64)
# Test empty list
assert_equal(set_number_of_points([]), [])
# Test streamline having only one point
assert_raises(ValueError, set_number_of_points, np.array([[1, 2, 3]]))
# We do not support list of lists, it should be numpy ndarray.
streamline_unsupported = [[1, 2, 3], [4, 5, 5], [2, 1, 3], [4, 2, 1]]
assert_raises(AttributeError, set_number_of_points, streamline_unsupported)
# Test setting number of points of a numpy with flag WRITABLE=False
streamline_readonly = streamline.copy()
streamline_readonly.setflags(write=False)
assert_equal(len(set_number_of_points(streamline_readonly, nb_points=42)),
42)
# Test setting computing length of a numpy with flag WRITABLE=False
streamlines_readonly = []
for s in streamlines:
streamlines_readonly.append(s.copy())
streamlines_readonly[-1].setflags(write=False)
assert_equal(len(set_number_of_points(streamlines_readonly, nb_points=42)),
len(streamlines_readonly))
streamlines_readonly = []
for s in streamlines_64bit:
streamlines_readonly.append(s.copy())
streamlines_readonly[-1].setflags(write=False)
assert_equal(len(set_number_of_points(streamlines_readonly, nb_points=42)),
len(streamlines_readonly))
def test_set_number_of_points_memory_leaks():
# Test some dtypes
dtypes = [np.float32, np.float64, np.int32, np.int64]
for dtype in dtypes:
rng = np.random.RandomState(1234)
NB_STREAMLINES = 10000
streamlines = [rng.randn(rng.randint(10, 100), 3).astype(dtype) for _ in range(NB_STREAMLINES)]
list_refcount_before = get_type_refcount()["list"]
rstreamlines = set_number_of_points(streamlines, nb_points=2)
list_refcount_after = get_type_refcount()["list"]
del rstreamlines # Delete `rstreamlines` because it holds a reference to `list`.
# Calling `set_number_of_points` should increase the refcount of `list` by one
# since we kept the returned value.
assert_equal(list_refcount_after, list_refcount_before+1)
# Test mixed dtypes
rng = np.random.RandomState(1234)
NB_STREAMLINES = 10000
streamlines = []
for i in range(NB_STREAMLINES):
dtype = dtypes[i % len(dtypes)]
streamlines.append(rng.randn(rng.randint(10, 100), 3).astype(dtype))
list_refcount_before = get_type_refcount()["list"]
rstreamlines = set_number_of_points(streamlines, nb_points=2)
list_refcount_after = get_type_refcount()["list"]
# Calling `set_number_of_points` should increase the refcount of `list` by one
# since we kept the returned value.
assert_equal(list_refcount_after, list_refcount_before+1)
def test_length():
# Test length of only one streamline
length_streamline_cython = ds_length(streamline)
length_streamline_python = length_python(streamline)
assert_almost_equal(length_streamline_cython, length_streamline_python)
length_streamline_cython = ds_length(streamline_64bit)
length_streamline_python = length_python(streamline_64bit)
assert_almost_equal(length_streamline_cython, length_streamline_python)
# Test computing length of multiple streamlines of different nb_points
length_streamlines_cython = ds_length(streamlines)
for i, s in enumerate(streamlines):
length_streamline_python = length_python(s)
assert_array_almost_equal(length_streamlines_cython[i],
length_streamline_python)
length_streamlines_cython = ds_length(streamlines_64bit)
for i, s in enumerate(streamlines_64bit):
length_streamline_python = length_python(s)
assert_array_almost_equal(length_streamlines_cython[i],
length_streamline_python)
# Test streamlines having mixed dtype
streamlines_mixed_dtype = [streamline,
streamline.astype(np.float64),
streamline.astype(np.int32),
streamline.astype(np.int64)]
lengths_mixed_dtype = [ds_length(s)
for s in streamlines_mixed_dtype]
assert_array_equal(ds_length(streamlines_mixed_dtype),
lengths_mixed_dtype)
# Test streamlines with different shape
length_streamlines_cython = ds_length(
heterogeneous_streamlines)
for i, s in enumerate(heterogeneous_streamlines):
length_streamline_python = length_python(s)
assert_array_almost_equal(length_streamlines_cython[i],
length_streamline_python)
# Test streamline having integer dtype
length_streamline = ds_length(streamline.astype('int'))
assert_true(length_streamline.dtype == np.float64)
# Test empty list
assert_equal(ds_length([]), 0.0)
# Test streamline having only one point
assert_equal(ds_length(np.array([[1, 2, 3]])), 0.0)
# We do not support list of lists, it should be numpy ndarray.
streamline_unsupported = [[1, 2, 3], [4, 5, 5], [2, 1, 3], [4, 2, 1]]
assert_raises(AttributeError, ds_length,
streamline_unsupported)
# Test setting computing length of a numpy with flag WRITABLE=False
streamlines_readonly = []
for s in streamlines:
streamlines_readonly.append(s.copy())
streamlines_readonly[-1].setflags(write=False)
assert_array_almost_equal(ds_length(streamlines_readonly),
[length_python(s) for s in streamlines_readonly])
streamlines_readonly = []
for s in streamlines_64bit:
streamlines_readonly.append(s.copy())
streamlines_readonly[-1].setflags(write=False)
assert_array_almost_equal(ds_length(streamlines_readonly),
[length_python(s) for s in streamlines_readonly])
def test_length_memory_leaks():
# Test some dtypes
dtypes = [np.float32, np.float64, np.int32, np.int64]
for dtype in dtypes:
rng = np.random.RandomState(1234)
NB_STREAMLINES = 10000
streamlines = [rng.randn(rng.randint(10, 100), 3).astype(dtype) for _ in range(NB_STREAMLINES)]
list_refcount_before = get_type_refcount()["list"]
lengths = ds_length(streamlines)
list_refcount_after = get_type_refcount()["list"]
# Calling `ds_length` shouldn't increase the refcount of `list`
# since the return value is a numpy array.
assert_equal(list_refcount_after, list_refcount_before)
# Test mixed dtypes
rng = np.random.RandomState(1234)
NB_STREAMLINES = 10000
streamlines = []
for i in range(NB_STREAMLINES):
dtype = dtypes[i % len(dtypes)]
streamlines.append(rng.randn(rng.randint(10, 100), 3).astype(dtype))
list_refcount_before = get_type_refcount()["list"]
lengths = ds_length(streamlines)
list_refcount_after = get_type_refcount()["list"]
# Calling `ds_length` shouldn't increase the refcount of `list`
# since the return value is a numpy array.
assert_equal(list_refcount_after, list_refcount_before)
def test_unlist_relist_streamlines():
streamlines = [np.random.rand(10, 3),
np.random.rand(20, 3),
np.random.rand(5, 3)]
points, offsets = unlist_streamlines(streamlines)
assert_equal(offsets.dtype, np.dtype('i8'))
assert_equal(points.shape, (35, 3))
assert_equal(len(offsets), len(streamlines))
streamlines2 = relist_streamlines(points, offsets)
assert_equal(len(streamlines), len(streamlines2))
for i in range(len(streamlines)):
assert_array_equal(streamlines[i], streamlines2[i])
def test_center_and_transform():
A = np.array([[1, 2, 3], [1, 2, 3.]])
streamlines = [A for i in range(10)]
streamlines2, center = center_streamlines(streamlines)
B = np.zeros((2, 3))
assert_array_equal(streamlines2[0], B)
assert_array_equal(center, A[0])
affine = np.eye(4)
affine[0, 0] = 2
affine[:3, -1] = - np.array([2, 1, 1]) * center
streamlines3 = transform_streamlines(streamlines, affine)
assert_array_equal(streamlines3[0], B)
def test_select_random_streamlines():
streamlines = [np.random.rand(10, 3),
np.random.rand(20, 3),
np.random.rand(5, 3)]
new_streamlines = select_random_set_of_streamlines(streamlines, 2)
assert_equal(len(new_streamlines), 2)
new_streamlines = select_random_set_of_streamlines(streamlines, 4)
assert_equal(len(new_streamlines), 3)
def compress_streamlines_python(streamline, tol_error=0.01,
max_segment_length=10):
"""
Python version of the FiberCompression found on
https://github.com/scilus/FiberCompression.
"""
if streamline.shape[0] <= 2:
return streamline.copy()
# Euclidean distance
def segment_length(prev, next):
return np.sqrt(((prev-next)**2).sum())
# Projection of a 3D point on a 3D line, minimal distance
def dist_to_line(prev, next, curr):
return norm(np.cross(next-prev, curr-next)) / norm(next-prev)
nb_points = 0
compressed_streamline = np.zeros_like(streamline)
# Copy first point since it is always kept.
compressed_streamline[0, :] = streamline[0, :]
nb_points += 1
prev = streamline[0]
prev_id = 0
for next_id, next in enumerate(streamline[2:], start=2):
# Euclidean distance between last added point and current point.
if segment_length(prev, next) > max_segment_length:
compressed_streamline[nb_points, :] = streamline[next_id-1, :]
nb_points += 1
prev = streamline[next_id-1]
prev_id = next_id-1
continue
# Check that each point is not offset by more than `tol_error` mm.
for o, curr in enumerate(streamline[prev_id+1:next_id],
start=prev_id+1):
dist = dist_to_line(prev, next, curr)
if np.isnan(dist) or dist > tol_error:
compressed_streamline[nb_points, :] = streamline[next_id-1, :]
nb_points += 1
prev = streamline[next_id-1]
prev_id = next_id-1
break
# Copy last point since it is always kept.
compressed_streamline[nb_points, :] = streamline[-1, :]
nb_points += 1
# Make sure the array have the correct size
return compressed_streamline[:nb_points]
def test_compress_streamlines():
for compress_func in [compress_streamlines_python, compress_streamlines]:
# Small streamlines (less than two points) are uncompressable.
for small_streamline in [np.array([[]]),
np.array([[1, 1, 1]]),
np.array([[1, 1, 1], [2, 2, 2]])]:
c_streamline = compress_func(small_streamline)
assert_equal(len(c_streamline), len(small_streamline))
assert_array_equal(c_streamline, small_streamline)
# Compressing a straight streamline that is less than 10mm long
# should output a two points streamline.
linear_streamline = np.linspace(0, 5, 100*3).reshape((100, 3))
c_streamline = compress_func(linear_streamline)
assert_equal(len(c_streamline), 2)
assert_array_equal(c_streamline, [linear_streamline[0],
linear_streamline[-1]])
# The distance of consecutive points must be less or equal than some
# value.
max_segment_length = 10
linear_streamline = np.linspace(0, 100, 100*3).reshape((100, 3))
linear_streamline[:, 1:] = 0.
c_streamline = compress_func(linear_streamline,
max_segment_length=max_segment_length)
segments_length = np.sqrt((np.diff(c_streamline,
axis=0)**2).sum(axis=1))
assert_true(np.all(segments_length <= max_segment_length))
assert_equal(len(c_streamline), 12)
assert_array_equal(c_streamline, linear_streamline[::9])
# A small `max_segment_length` should keep all points.
c_streamline = compress_func(linear_streamline,
max_segment_length=0.01)
assert_array_equal(c_streamline, linear_streamline)
# Test we can set `max_segment_length` to infinity
# (like the C++ version)
compress_func(streamline, max_segment_length=np.inf)
# Uncompressable streamline when `tol_error` == 1.
simple_streamline = np.array([[0, 0, 0],
[1, 1, 0],
[1.5, np.inf, 0],
[2, 2, 0],
[2.5, 20, 0],
[3, 3, 0]])
# Because of np.inf, compressing that streamline causes a warning.
with np.errstate(invalid='ignore'):
c_streamline = compress_func(simple_streamline, tol_error=1)
assert_array_equal(c_streamline, simple_streamline)
# Create a special streamline where every other point is increasingly
# farther from a straigth line formed by the streamline endpoints.
tol_errors = np.linspace(0, 10, 21)
orthogonal_line = np.array([[-np.sqrt(2)/2, np.sqrt(2)/2, 0]],
dtype=np.float32)
special_streamline = np.array([range(len(tol_errors)*2+1)] * 3,
dtype=np.float32).T
special_streamline[1::2] += orthogonal_line * tol_errors[:, None]
# # Uncomment to see the streamline.
# import pylab as plt
# plt.plot(special_streamline[:, 0], special_streamline[:, 1], '.-')
# plt.axis('equal'); plt.show()
# Test different values for `tol_error`.
for i, tol_error in enumerate(tol_errors):
cspecial_streamline = compress_streamlines(special_streamline,
tol_error=tol_error+1e-4,
max_segment_length=np.inf)
# First and last points should always be the same as the original ones.
assert_array_equal(cspecial_streamline[0], special_streamline[0])
assert_array_equal(cspecial_streamline[-1], special_streamline[-1])
assert_equal(len(cspecial_streamline),
len(special_streamline)-((i*2)+1))
# Make sure Cython and Python versions are the same.
cstreamline_python = compress_streamlines_python(
special_streamline,
tol_error=tol_error+1e-4,
max_segment_length=np.inf)
assert_equal(len(cspecial_streamline), len(cstreamline_python))
assert_array_almost_equal(cspecial_streamline, cstreamline_python)
def test_compress_streamlines_memory_leaks():
# Test some dtypes
dtypes = [np.float32, np.float64, np.int32, np.int64]
for dtype in dtypes:
rng = np.random.RandomState(1234)
NB_STREAMLINES = 10000
streamlines = [rng.randn(rng.randint(10, 100), 3).astype(dtype) for _ in range(NB_STREAMLINES)]
list_refcount_before = get_type_refcount()["list"]
cstreamlines = compress_streamlines(streamlines)
list_refcount_after = get_type_refcount()["list"]
del cstreamlines # Delete `cstreamlines` because it holds a reference to `list`.
# Calling `compress_streamlines` should increase the refcount of `list` by one
# since we kept the returned value.
assert_equal(list_refcount_after, list_refcount_before+1)
# Test mixed dtypes
rng = np.random.RandomState(1234)
NB_STREAMLINES = 10000
streamlines = []
for i in range(NB_STREAMLINES):
dtype = dtypes[i % len(dtypes)]
streamlines.append(rng.randn(rng.randint(10, 100), 3).astype(dtype))
list_refcount_before = get_type_refcount()["list"]
cstreamlines = compress_streamlines(streamlines)
list_refcount_after = get_type_refcount()["list"]
# Calling `compress_streamlines` should increase the refcount of `list` by one
# since we kept the returned value.
assert_equal(list_refcount_after, list_refcount_before+1)
def test_select_by_rois():
streamlines = [np.array([[0, 0., 0.9],
[1.9, 0., 0.]]),
np.array([[0.1, 0., 0],
[0, 1., 1.],
[0, 2., 2.]]),
np.array([[2, 2, 2],
[3, 3, 3]])]
# Make two ROIs:
mask1 = np.zeros((4, 4, 4), dtype=bool)
mask2 = np.zeros_like(mask1)
mask1[0, 0, 0] = True
mask2[1, 0, 0] = True
selection = select_by_rois(streamlines, [mask1], [True],
tol=1)
npt.assert_array_equal(list(selection), [streamlines[0],
streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
tol=1)
npt.assert_array_equal(list(selection), [streamlines[0],
streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, False])
npt.assert_array_equal(list(selection), [streamlines[1]])
# Setting tolerance too low gets overridden:
selection = select_by_rois(streamlines, [mask1, mask2], [True, False],
tol=0.1)
npt.assert_array_equal(list(selection), [streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
tol=0.87)
npt.assert_array_equal(list(selection), [streamlines[1]])
mask3 = np.zeros_like(mask1)
mask3[0, 2, 2] = 1
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False], tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0]])
# Select using only one ROI
selection = select_by_rois(streamlines, [mask1], [True], tol=0.87)
npt.assert_array_equal(list(selection), [streamlines[1]])
selection = select_by_rois(streamlines, [mask1], [True], tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0],
streamlines[1]])
# Use different modes:
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="all",
tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0]])
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="either_end",
tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0]])
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="both_end",
tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0]])
mask2[0, 2, 2] = True
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="both_end",
tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0],
streamlines[1]])
# Test with generator input:
def generate_sl(streamlines):
for sl in streamlines:
yield sl
selection = select_by_rois(generate_sl(streamlines), [mask1], [True],
tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0],
streamlines[1]])
def test_orient_by_rois():
streamlines = [np.array([[0, 0., 0],
[1, 0., 0.],
[2, 0., 0.]]),
np.array([[2, 0., 0.],
[1, 0., 0],
[0, 0, 0.]])]
# Make two ROIs:
mask1_vol = np.zeros((4, 4, 4), dtype=bool)
mask2_vol = np.zeros_like(mask1_vol)
mask1_vol[0, 0, 0] = True
mask2_vol[1, 0, 0] = True
mask1_coords = np.array(np.where(mask1_vol)).T
mask2_coords = np.array(np.where(mask2_vol)).T
# If there is an affine, we'll use it:
affine = np.eye(4)
affine[:, 3] = [-1, 100, -20, 1]
# Transform the streamlines:
x_streamlines = [sl + affine[:3, 3] for sl in streamlines]
for copy in [True, False]:
for sl, affine in zip([streamlines, x_streamlines], [None, affine]):
for mask1, mask2 in \
zip([mask1_vol, mask1_coords], [mask2_vol, mask2_coords]):
new_streamlines = orient_by_rois(sl, mask1, mask2,
affine=affine, copy=copy)
if copy:
flipped_sl = [sl[0], sl[1][::-1]]
else:
flipped_sl = [np.array([[0, 0., 0],
[1, 0., 0.],
[2, 0., 0.]]),
np.array([[0, 0., 0.],
[1, 0., 0],
[2, 0, 0.]])]
if affine is not None:
flipped_sl = [s + affine[:3, 3] for s in flipped_sl]
npt.assert_equal(new_streamlines, flipped_sl)
if __name__ == '__main__':
run_module_suite()
|