imagewrapper.py 38.7 KB
Newer Older
1
2
3
4
5
6
#!/usr/bin/env python
#
# imagewrapper.py - The ImageWrapper class.
#
# Author: Paul McCarthy <pauldmccarthy@gmail.com>
#
7
8
"""This module provides the :class:`ImageWrapper` class, which can be used
to manage data access to ``nibabel`` NIFTI images.
9

10
11
12
13
14

Terminology
-----------


15
There are some confusing terms used in this module, so it may be useful to
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
get their definitions straight:

  - *Coverage*:  The portion of an image that has been covered in the data
                 range calculation. The ``ImageWrapper`` keeps track of
                 the coverage for individual volumes within a 4D image (or
                 slices in a 3D image).

  - *Slice*:     Portion of the image data which is being accessed. A slice
                 comprises either a tuple of ``slice`` objects (or integers),
                 or a sequence of ``(low, high)`` tuples, specifying the
                 index range into each image dimension that is covered by
                 the slice.

  - *Expansion*: A sequence of ``(low, high)`` tuples, specifying an
                 index range into each image dimension, that is used to
                 *expand* the *coverage* of an image, based on a given set of
                 *slices*.
33
34
35
"""


36
37
import logging
import collections
38
import itertools as it
39

40
41
import numpy     as np
import nibabel   as nib
42
43

import fsl.utils.notifier as notifier
44
import fsl.utils.async    as async
45
46
47
48
49
50


log = logging.getLogger(__name__)


class ImageWrapper(notifier.Notifier):
51
52
53
    """The ``ImageWrapper`` class is a convenience class which manages data
    access to ``nibabel`` NIFTI images. The ``ImageWrapper`` class can be
    used to:
54

55
56
57
    
      - Control whether the image is loaded into memory, or kept on disk
    
58
      - Incrementally update the known image data range, as more image
59
60
61
        data is read in.


62
63
64
    *In memory or on disk?*

    The image data will be kept on disk, and accessed through the
Paul McCarthy's avatar
Paul McCarthy committed
65
66
    ``nibabel.Nifti1Image.dataobj`` (or ``nibabel.Nifti2Image.dataobj``) array
    proxy, if:
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82

     - The ``loadData`` parameter to :meth:`__init__` is ``False``.
     - The :meth:`loadData` method never gets called.
     - The image data is not modified (via :meth:`__setitem__`.

    If any of these conditions do not hold, the image data will be loaded into
    memory and accessed directly, via the ``nibabel.Nifti1Image.get_data``
    method.


    *Image dimensionality*

    
    The ``ImageWrapper`` abstracts away trailing image dimensions of length 1.
    This means that if the header for a NIFTI image specifies that the image
    has four dimensions, but the fourth dimension is of length 1, you do not
83
84
85
86
    need to worry about indexing that fourth dimension. However, all NIFTI
    images will be presented as having at least three dimensions, so if your
    image header specifies a third dimension of length 1, you will still
    need provide an index of 0 for that dimensions, for all data accesses.
87
88
89
90
91
92
93
94


    *Data range*

    
    In order to avoid the computational overhead of calculating the image data
    range (its minimum/maximum values) when an image is first loaded in, an
    ``ImageWrapper`` incrementally updates the known image data range as data
95
    is accessed. The ``ImageWrapper`` keeps track of the image data *coverage*,
96
97
98
99
100
101
102
103
104
105
106
107
    the portion of the image which has already been considered in the data
    range calculation. When data from a region of the image not in the coverage
    is accessed, the coverage is expanded to include this region. The coverage
    is always expanded in a rectilinear manner, i.e. the coverage is always
    rectangular for a 2D image, or cuboid for a 3D image.

    
    For a 4D image, the ``ImageWrapper`` internally maintains a separate
    coverage and known data range for each 3D volume within the image. For a 3D
    image, separate coverages and data ranges are stored for each 2D slice.


108
109
110
111
    The ``ImageWrapper`` implements the :class:`.Notifier` interface.
    Listeners can register to be notified whenever the known image data range
    is updated. The data range can be accessed via the :attr:`dataRange`
    property.
112

113

114
115
116
117
118
119
120
    The ``ImageWrapper`` class uses the following functions (also defined in 
    this module) to keep track of the portion of the image that has currently
    been included in the data range calculation:

    .. autosummary::
       :nosignatures:

121
       naninfrange
122
123
       isValidFancySliceObj
       canonicalSliceObj
124
125
126
       sliceObjToSliceTuple
       sliceTupleToSliceObj
       sliceCovered
127
       calcExpansion
128
       adjustCoverage
129
130
    """

131
    
132
133
134
135
136
137
    def __init__(self,
                 image,
                 name=None,
                 loadData=False,
                 dataRange=None,
                 threaded=False):
138
        """Create an ``ImageWrapper``.
139

Paul McCarthy's avatar
Paul McCarthy committed
140
        :arg image:     A ``nibabel.Nifti1Image`` or ``nibabel.Nifti2Image``.
141

142
143
        :arg name:      A name for this ``ImageWrapper``, solely used for 
                        debug log messages.
144

145
146
147
148
149
150
        :arg loadData:  If ``True``, the image data is loaded into memory.
                        Otherwise it is kept on disk (and data access is
                        performed through the ``nibabel.Nifti1Image.dataobj``
                        array proxy).

        :arg dataRange: A tuple containing the initial ``(min, max)``  data
151
152
                        range to use. See the :meth:`reset` method for
                        important information about this parameter.
153
154
155
156

        :arg threaded:  If ``True``, the data range is updated on a
                        :class:`.TaskThread`. Otherwise (the default), the
                        data range is updated directly on reads/writes.
157
        """
158

159
160
161
        self.__image      = image
        self.__name       = name
        self.__taskThread = None
162

163
164
165
166
167
168
169
170
171
172
173
174
        # Save the number of 'real' dimensions,
        # that is the number of dimensions minus
        # any trailing dimensions of length 1
        self.__numRealDims = len(image.shape)
        for d in reversed(image.shape):
            if d == 1: self.__numRealDims -= 1
            else:      break

        # And save the number of
        # 'padding' dimensions too.
        self.__numPadDims = len(image.shape) - self.__numRealDims

175
176
177
178
179
180
181
182
183
        # Too many shapes! Figure out
        # what shape we should present
        # the data as (e.g. at least 3
        # dimensions). This is used in
        # __getitem__ to force the
        # result to have the correct
        # dimensionality.
        self.__canonicalShape = canonicalShape(image.shape)

184
185
186
187
188
189
190
191
192
193
194
195
196
        # The internal state is stored
        # in these attributes - they're
        # initialised in the reset method.
        self.__range     = None
        self.__coverage  = None
        self.__volRanges = None
        self.__covered   = False

        self.reset(dataRange)

        if loadData:
            self.loadData()

197
        if threaded:
198
            self.__taskThread = async.TaskThread()
199
            self.__taskThread.daemon = True
200
201
202
203
204
205
206
207
208
209
210
211
            self.__taskThread.start()


    def __del__(self):
        """If this ``ImageWrapper`` was created with ``threaded=True``,
        the :class:`.TaskThread` is stopped.
        """
        self.__image = None
        if self.__taskThread is not None:
            self.__taskThread.stop()
            self.__taskThraed = None

212
213
214
215
216
217
218
219
220
221
222

    def reset(self, dataRange=None):
        """Reset the internal state and known data range of this
        ``ImageWrapper``.

        
        :arg dataRange: A tuple containing the initial ``(min, max)``  data
                        range to use. See the :meth:`reset` method.


        .. note:: The ``dataRange`` parameter is intended for situations where
223
224
                  the image data range is known in advance (e.g. it was
                  calculated earlier, and the image is being re-loaded). If a
225
226
                  ``dataRange`` is passed in, it will *not* be overwritten by
                  any range calculated from the data, unless the calculated
227
                  data range is wider than the provided ``dataRange``.
228
229
230
231
232
        """
        
        if dataRange is None:
            dataRange = None, None

233
        image =             self.__image
234
235
236
        ndims =             self.__numRealDims - 1
        nvols = image.shape[self.__numRealDims - 1]

237
238
        # The current known image data range. This
        # gets updated as more image data gets read.
239
        self.__range = dataRange
240

241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
        # The coverage array is used to keep track of
        # the portions of the image which have been
        # considered in the data range calculation.
        # We use this coverage to avoid unnecessarily
        # re-calculating the data range on the same
        # part of the image.
        #
        # First of all, we're going to store a separate
        # 'coverage' for each 2D slice in the 3D image
        # (or 3D volume for 4D images). This effectively
        # means a seaprate coverage for each index in the
        # last 'real' image dimension (see above).
        # 
        # For each slice/volume, the the coverage is
        # stored as sequences of (low, high) indices, one
        # for each dimension in the slice/volume (e.g.
        # row/column for a slice, or row/column/depth
        # for a volume).
        #
260
        # All of these indices are stored in a numpy array:
261
262
        #   - first dimension:  low/high index
        #   - second dimension: image dimension
263
        #   - third dimension:  slice/volume index 
264
        self.__coverage = np.zeros((2, ndims, nvols), dtype=np.float32)
265

266
267
268
269
270
271
        # Internally, we calculate and store the
        # data range for each volume/slice/vector
        self.__volRanges = np.zeros((nvols, 2), dtype=np.float32)

        self.__coverage[ :] = np.nan
        self.__volRanges[:] = np.nan
272

273
274
275
276
277
        # This flag is set to true if/when the
        # full image data range becomes known
        # (i.e. when all data has been loaded in).
        self.__covered = False

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
        
    @property
    def dataRange(self):
        """Returns the currently known data range as a tuple of ``(min, max)``
        values.
        """
        # If no image data has been accessed, we
        # default to whatever is stored in the
        # header (which may or may not contain
        # useful values).
        low, high = self.__range
        hdr       = self.__image.get_header()

        if low  is None: low  = float(hdr['cal_min'])
        if high is None: high = float(hdr['cal_max'])

        return low, high

    
    @property
    def covered(self):
        """Returns ``True`` if this ``ImageWrapper`` has read the entire
        image data, ``False`` otherwise.
        """
        return self.__covered


    def coverage(self, vol):
        """Returns the current image data coverage for the specified volume
        (for a 4D image, slice for a 3D image, or vector for a 2D images).

        :arg vol: Index of the volume/slice/vector to return the coverage
                  for.

        :returns: The coverage for the specified volume, as a ``numpy``
                  array of shape ``(nd, 2)``, where ``nd`` is the number
                  of dimensions in the volume.
315
316
317

        .. note:: If the specified volume is not covered, the returned array
                  will contain ``np.nan`` values.
318
        """
319
        return np.array(self.__coverage[..., vol])
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336

    
    def loadData(self):
        """Forces all of the image data to be loaded into memory.

        .. note:: This method will be called by :meth:`__init__` if its
                  ``loadData`` parameter is ``True``.
        """

        # If the data is not already loaded, this will
        # cause nibabel to load it. By default, nibabel
        # will cache the numpy array that contains the
        # image data, so subsequent calls to this
        # method will not overwrite any changes that
        # have been made to the data array.
        self.__image.get_data()

337

338
    def __getData(self, sliceobj, isTuple=False):
339
        """Retrieves the image data at the location specified by ``sliceobj``.
340
341
342
343
344
345

        :arg sliceobj: Something which can be used to slice an array, or
                       a sequence of (low, high) index pairs.

        :arg isTuple:  Set to ``True`` if ``sliceobj`` is a sequence of
                       (low, high) index pairs.
346
347
        """

348
        if isTuple:
349
            sliceobj = sliceTupleToSliceObj(sliceobj)
350

351
        # If the image has not been loaded
352
        # into memory, we can use the nibabel
353
354
355
356
357
358
359
360
361
        # ArrayProxy. Otheriwse if it is in
        # memory, we can access it directly.
        #
        # Furthermore, if it is in memory and
        # has been modified, the ArrayProxy
        # will give us out-of-date values (as
        # the ArrayProxy reads from disk). So
        # we have to read from the in-memory
        # array to get changed values.
362
363
364
365
366
367
368
369
370
        #
        # Finally, note that if the caller has
        # given us a 'fancy' slice object (a
        # boolean numpy array), but the image
        # data is not in memory, we can't access
        # the data, as the nibabel ArrayProxy
        # (the dataobj attribute) cannot handle
        # fancy indexing. In this case an error
        # will be raised.
371
        if self.__image.in_memory: return self.__image.get_data()[sliceobj]
372
        else:                      return self.__image.dataobj[   sliceobj]
373
374


375
376
377
378
379
380
381
    def __imageIsCovered(self):
        """Returns ``True`` if all portions of the image have been covered
        in the data range calculation, ``False`` otherwise.
        """
        
        shape  = self.__image.shape
        slices = zip([0] * len(shape), shape)
382
        return sliceCovered(slices, self.__coverage)
383

384

385
386
387
    def __expandCoverage(self, slices):
        """Expands the current image data range and coverage to encompass the
        given ``slices``.
388
        """
389

390
391
        _, expansions = calcExpansion(slices, self.__coverage)
        expansions    = collapseExpansions(expansions, self.__numRealDims - 1)
392
393
394
395
396

        log.debug('Updating image {} data range [slice: {}] '
                  '(current range: [{}, {}]; '
                  'number of expansions: {}; '
                  'current coverage: {})'.format(
397
                      self.__name,
398
                      slices,
399
400
                      self.__range[0],
                      self.__range[1],
401
                      len(expansions),
402
                      self.__coverage))
403
404
405
406
407
408
409
410
411

        # As we access the data for each expansions,
        # we want it to have the same dimensionality
        # as the full image, so we can access data
        # for each volume in the image separately.
        # So we squeeze out the padding dimensions,
        # but not the volume dimension.
        squeezeDims = tuple(range(self.__numRealDims,
                                  self.__numRealDims + self.__numPadDims))
412
        
413
414
415
416
417
        # The calcExpansion function splits up the
        # expansions on volumes - here we calculate
        # the min/max per volume/expansion, and
        # iteratively update the stored per-volume
        # coverage and data range.
418
419
420
421
422
423
424
425
        for i, exp in enumerate(expansions):

            data = self.__getData(exp, isTuple=True)
            data = data.squeeze(squeezeDims)

            vlo, vhi = exp[self.__numRealDims - 1]

            for vi, vol in enumerate(range(vlo, vhi)):
426

427
428
                oldvlo, oldvhi = self.__volRanges[vol, :]
                voldata        = data[..., vi]
429

430
                newvlo, newvhi = naninfrange(voldata)
431

432
433
                if (not np.isnan(oldvlo)) and oldvlo < newvlo: newvlo = oldvlo
                if (not np.isnan(oldvhi)) and oldvhi > newvhi: newvhi = oldvhi
434

435
436
437
438
439
                # Update the stored range and
                # coverage for each volume 
                self.__volRanges[vol, :]  = newvlo, newvhi
                self.__coverage[..., vol] = adjustCoverage(
                    self.__coverage[..., vol], exp)
440

441
442
443
        # Calculate the new known data
        # range over the entire image
        # (i.e. over all volumes).
444
        newmin, newmax = naninfrange(self.__volRanges)
445

446
        oldmin, oldmax = self.__range
447
        self.__range   = (newmin, newmax)
448
449
        self.__covered = self.__imageIsCovered()

450
451
        if any((oldmin is None, oldmax is None)) or \
           not np.all(np.isclose([oldmin, oldmax], [newmin, newmax])):
452
453
454
455
456
457
            log.debug('Image {} range changed: [{}, {}] -> [{}, {}]'.format(
                self.__name,
                oldmin,
                oldmax,
                newmin,
                newmax))
458
459
            self.notify()

460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477

    def __updateDataRangeOnRead(self, slices, data):
        """Called by :meth:`__getitem__`. Calculates the minimum/maximum
        values of the given data (which has been extracted from the portion of
        the image specified by ``slices``), and updates the known data range
        of the image.

        :arg slices: A tuple of tuples, each tuple being a ``(low, high)``
                     index pair, one for each dimension in the image. 
        
        :arg data:   The image data at the given ``slices`` (as a ``numpy``
                     array).
        """

        # TODO You could do something with
        #      the provided data to avoid
        #      reading it in again.

478
479
480
481
482
        if self.__taskThread is None:
            self.__expandCoverage(slices)
        else:
            name = '{}_read_{}'.format(id(self), slices)
            if not self.__taskThread.isQueued(name):
483
                self.__taskThread.enqueue(
484
                    self.__expandCoverage, slices, taskName=name)
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
    def __updateDataRangeOnWrite(self, slices, data):
        """Called by :meth:`__setitem__`. Assumes that the image data has
        been changed (the data at ``slices`` has been replaced with ``data``.
        Updates the image data coverage, and known data range accordingly.

        :arg slices: A tuple of tuples, each tuple being a ``(low, high)``
                     index pair, one for each dimension in the image. 
        
        :arg data:   The image data at the given ``slices`` (as a ``numpy``
                     array). 
        """

        overlap = sliceOverlap(slices, self.__coverage)

        # If there's no overlap between the written
        # area and the current coverage, then it's
        # easy - we just expand the coverage to
        # include the newly written area.
        if overlap in (OVERLAP_SOME, OVERLAP_ALL):

            # If there is overlap between the written
            # area and the current coverage, things are
            # more complicated, because the portion of
            # the image that has been written over may
            # have contained the currently known data
            # minimum/maximum. We have no way of knowing
            # this, so we have to reset the coverage (on
            # the affected volumes), and recalculate the
            # data range.

            # TODO Could you store the location of the
            #      data minimum/maximum (in each volume),
            #      so you know whether resetting the
            #      coverage is necessary?
            lowVol, highVol = slices[self.__numRealDims - 1]

523
524
525
526
527
528
529
            log.debug('Image {} data written - clearing known data '
                      'range on volumes {} - {} (write slice: {})'.format(
                          self.__name,
                          lowVol,
                          highVol,
                          slices))

530
            for vol in range(lowVol, highVol):
531
532
                self.__coverage[:, :, vol]    = np.nan
                self.__volRanges[     vol, :] = np.nan
533

534
535
536
537
538
539

        if self.__taskThread is None:
            self.__expandCoverage(slices)
        else:
            name = '{}_write_{}'.format(id(self), slices)
            if not self.__taskThread.isQueued(name):
540
                self.__taskThread.enqueue(
541
                    self.__expandCoverage, slices, taskName=name)
542

543
            
544
    def __getitem__(self, sliceobj):
545
546
547
548
        """Returns the image data for the given ``sliceobj``, and updates
        the known image data range if necessary.

        :arg sliceobj: Something which can slice the image data.
549
550
        """

551
        log.debug('Getting image data: {}'.format(sliceobj))
552
553
554
        
        image = self.__image
        shape = image.shape
555
556
        ndims = len(shape)

557
        fancy = isValidFancySliceObj(sliceobj, shape)
558

559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
        if fancy:
            expNdims = ndims
        else:
            
            if not isinstance(sliceobj, tuple):
                sliceobj = (sliceobj,)

            # Figure out the number of dimensions
            # that the result should have, given
            # this slice object.
            expNdims = len(self.__canonicalShape) - \
                       len([s for s in sliceobj if isinstance(s, int)])

            # Truncate some dimensions from the
            # slice object if it has too many
            # (e.g. trailing dims of length 1).
            if len(sliceobj) > ndims:
                sliceobj = sliceobj[:ndims]
                
578
        # TODO Cache 3D images for large 4D volumes, 
579
        #      so you don't have to hit the disk?
580

581
        sliceobj = canonicalSliceObj(sliceobj, shape)
582
        data     = self.__getData(sliceobj)
583

584
        if not self.__covered:
585

586
            slices = sliceObjToSliceTuple(sliceobj, shape)
587

588
            if not sliceCovered(slices, self.__coverage):
589
                self.__updateDataRangeOnRead(slices, data)
590

591
592
593
        # Make sure that the result has the
        # shape that the caller is expecting.
        if not fancy and ndims != expNdims:
594
595
            data = data.reshape(list(data.shape) + [1] * (expNdims - ndims))

596
        return data
597
598


599
600
601
602
603
604
605
606
607
608
609
610
    def __setitem__(self, sliceobj, values):
        """Writes the given ``values`` to the image at the given ``sliceobj``.

        
        :arg sliceobj: Something which can be used to slice the array.
        :arg values:   Data to write to the image.

        
        .. note:: Modifying image data will cause the entire image to be
                  loaded into memory. 
        """

611
612
        sliceobj = canonicalSliceObj(   sliceobj, self.__image.shape)
        slices   = sliceObjToSliceTuple(sliceobj, self.__image.shape)
613
614
615
616
617
618
619
620
621
622
623

        # The image data has to be in memory
        # for the data to be changed. If it's
        # already in memory, this call won't
        # have any effect.
        self.loadData()

        self.__image.get_data()[sliceobj] = values
        self.__updateDataRangeOnWrite(slices, values)


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
def naninfrange(data):
    """Returns the minimum and maximum values in the given ``numpy`` array,
    ignoring ``nan`` and ``inf`` values.

    The ``numpy.nanmin``/``numpy.nanmax`` functions do not handle
    positive/negative infinity, so if such values are in the data, we need to
    use an alternate approach to calculating the minimum/maximum.
    """

    if not np.issubdtype(data.dtype, np.float):
        return data.min(), data.max()

    # But np.nanmin/nanmax are substantially
    # faster than the alternate, so we try it
    # first.
    dmin = np.nanmin(data)
    dmax = np.nanmax(data)

    # If there are no nans/infs in the data,
    # we can just use nanmin/nanmax
    if np.isfinite(dmin) and np.isfinite(dmax):
        return dmin, dmax

    # The entire array contains nans
    if np.isnan(dmin):
        return dmin, dmin

    # Otherwise we need to calculate min/max
    # only on finite values. This is the slow
    # option.

    # Find all finite values
    finite = np.isfinite(data)

    # Try to calculate min/max on those values.
    # An error will be raised if there are no
    # finite values in the array
    try:
        return data[finite].min(), data[finite].max()
    except:
        return np.nan, np.nan


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
def isValidFancySliceObj(sliceobj, shape):
    """Returns ``True`` if the given ``sliceobj`` is a valid and fancy slice
    object.

    ``nibabel`` refers to slice objects as "fancy" if they comprise anything
    but tuples of simple ``slice`` objects. The ``ImageWrapper`` class
    supports one type of "fancy" slicing, where the ``sliceobj`` is a boolean
    ``numpy`` array of the same shape as the image.

    This function returns ``True`` if the given ``sliceobj`` adheres to these
    requirements, ``False`` otherwise.
    """

    # We only support boolean numpy arrays
    # which have the same shape as the image
    return (isinstance(sliceobj, np.ndarray) and
            sliceobj.dtype == np.bool        and
            sliceobj.shape == shape)


def canonicalSliceObj(sliceobj, shape):
    """Returns a canonical version of the given ``sliceobj``. See the
    ``nibabel.fileslice.canonical_slicers` function.
    """

    if not isValidFancySliceObj(sliceobj, shape):
        sliceobj = nib.fileslice.canonical_slicers(sliceobj, shape)

    return sliceobj
    

698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
def canonicalShape(shape):
    """Calculates a *canonical* shape, how the given ``shape`` should
    be presented. The shape is forced to be at least three dimensions,
    with any other trailing dimensions of length 1 ignored.
    """

    shape = list(shape)

    # Squeeze out empty dimensions, as
    # 3D image can sometimes be listed
    # as having 4 or more dimensions 
    for i in reversed(range(len(shape))):
        if shape[i] == 1: shape = shape[:i]
        else:             break

    # But make sure the shape 
    # has at 3 least dimensions
    if len(shape) < 3:
        shape = shape + [1] * (3 - len(shape))

    return shape


721
722
723
def sliceObjToSliceTuple(sliceobj, shape):
    """Turns an array slice object into a tuple of (low, high) index
    pairs, one pair for each dimension in the given shape
724
725
726
727
728

    :arg sliceobj: Something which can be used to slice an array of shape
                   ``shape``.

    :arg shape:    Shape of the array being sliced.
729
730
    """

731
732
733
    if isValidFancySliceObj(sliceobj, shape):
        return tuple((0, s) for s in shape)

734
735
    indices = []

736
737
    # The sliceobj could be a single sliceobj
    # or integer, instead of a tuple
738
739
740
    if not isinstance(sliceobj, collections.Sequence):
        sliceobj = [sliceobj]

741
    # Turn e.g. array[6] into array[6, :, :]
742
743
744
745
    if len(sliceobj) != len(shape):
        missing  = len(shape) - len(sliceobj)
        sliceobj = list(sliceobj) + [slice(None) for i in range(missing)]

746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
    for dim, s in enumerate(sliceobj):

        # each element in the slices tuple should 
        # be a slice object or an integer
        if isinstance(s, slice): i = [s.start, s.stop]
        else:                    i = [s,       s + 1]

        if i[0] is None: i[0] = 0
        if i[1] is None: i[1] = shape[dim]

        indices.append(tuple(i))

    return tuple(indices)


761
762
def sliceTupleToSliceObj(slices):
    """Turns a sequence of (low, high) index pairs into a tuple of array
763
    ``slice`` objects.
764
765

    :arg slices: A sequence of (low, high) index pairs.
766
767
768
769
770
771
772
773
774
775
    """

    sliceobj = []

    for lo, hi in slices:
        sliceobj.append(slice(lo, hi, 1))

    return tuple(sliceobj)


776
777
778
779
def adjustCoverage(oldCoverage, slices): 
    """Adjusts/expands the given ``oldCoverage`` so that it covers the
    given set of ``slices``.

780
    :arg oldCoverage: A ``numpy`` array of shape ``(2, n)`` containing
781
782
                      the (low, high) index pairs for ``n`` dimensions of
                      a single slice/volume in the image.
783
    
784
785
786
787
    :arg slices:      A sequence of (low, high) index pairs. If ``slices``
                      contains more dimensions than are specified in
                      ``oldCoverage``, the trailing dimensions are ignored.

788
    :return: A ``numpy`` array containing the adjusted/expanded coverage.
789
790
    """

791
    newCoverage = np.zeros(oldCoverage.shape, dtype=np.uint32)
792

793
    for dim in range(oldCoverage.shape[1]):
794

795
796
        low,      high      = slices[        dim]
        lowCover, highCover = oldCoverage[:, dim]
797

798
799
        if np.isnan(lowCover)  or low  < lowCover:  lowCover  = low
        if np.isnan(highCover) or high > highCover: highCover = high
800

801
        newCoverage[:, dim] = lowCover, highCover
802
803
804

    return newCoverage

805

806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
OVERLAP_ALL = 0
"""Indicates that the slice is wholly contained within the coverage.  This is
a return code for the :func:`sliceOverlap` function.
"""


OVERLAP_SOME = 1
"""Indicates that the slice partially overlaps with the coverage. This is a
return code for the :func:`sliceOverlap` function.
"""


OVERLAP_NONE = 2
"""Indicates that the slice does not overlap with the coverage. This is a
return code for the :func:`sliceOverlap` function.
"""


def sliceOverlap(slices, coverage):
    """Determines whether the given ``slices`` overlap with the given
    ``coverage``.

    :arg slices:    A sequence of (low, high) index pairs, assumed to cover
                    all image dimensions.
    :arg coverage:  A ``numpy`` array of shape ``(2, nd, nv)`` (where ``nd``
                    is the number of dimensions being covered, and ``nv`` is
                    the number of volumes (or vectors/slices) in the image,
                    which contains the (low, high) index pairs describing
                    the current image coverage.

    :returns: One of the following codes:
Paul McCarthy's avatar
Paul McCarthy committed
837
    
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
              .. autosummary::

              OVERLAP_ALL
              OVERLAP_SOME
              OVERLAP_NONE
    """

    numDims         = coverage.shape[1]
    lowVol, highVol = slices[numDims]

    # Overlap state is calculated for each volume
    overlapStates = np.zeros(highVol - lowVol)

    for i, vol in enumerate(range(lowVol, highVol)):

        state = OVERLAP_ALL

        for dim in range(numDims):

            lowCover, highCover = coverage[:, dim, vol]
            lowSlice, highSlice = slices[     dim] 

            # No coverage
            if np.isnan(lowCover) or np.isnan(highCover):
                state = OVERLAP_NONE
                break

            # The slice is contained within the
            # coverage on this dimension - check
            # the other dimensions.
            if lowSlice >= lowCover and highSlice <= highCover:
                continue

            # The slice does not overlap at all
            # with the coverage on this dimension
            # (or at all). No overlap - no need
            # to check the other dimensions.
            if lowSlice >= highCover or highSlice <= lowCover:
                state = OVERLAP_NONE
                break

            # There is some overlap between the
            # slice and coverage on this dimension
            # - check the other dimensions.
            state = OVERLAP_SOME
            
        overlapStates[i] = state

    if   np.any(overlapStates == OVERLAP_SOME): return OVERLAP_SOME
    elif np.all(overlapStates == OVERLAP_NONE): return OVERLAP_NONE
    elif np.all(overlapStates == OVERLAP_ALL):  return OVERLAP_ALL


def sliceCovered(slices, coverage):
892
893
    """Returns ``True`` if the portion of the image data calculated by
    the given ``slices` has already been calculated, ``False`` otherwise.
894

895
896
897
898
899
900
901
    :arg slices:    A sequence of (low, high) index pairs, assumed to cover
                    all image dimensions.
    :arg coverage:  A ``numpy`` array of shape ``(2, nd, nv)`` (where ``nd``
                    is the number of dimensions being covered, and ``nv`` is
                    the number of volumes (or vectors/slices) in the image,
                    which contains the (low, high) index pairs describing
                    the current image coverage.
902
903
    """

904
905
    numDims         = coverage.shape[1]
    lowVol, highVol = slices[numDims]
906
907
908

    for vol in range(lowVol, highVol):

909
        for dim in range(numDims):
910

911
912
            lowCover, highCover = coverage[:, dim, vol]
            lowSlice, highSlice = slices[     dim] 
913

914
            if np.isnan(lowCover) or np.isnan(highCover):
915
916
917
918
919
920
921
922
                return False

            if lowSlice  < lowCover:  return False
            if highSlice > highCover: return False

    return True


923
def calcExpansion(slices, coverage):
924
925
926
927
928
929
930
931
    """Calculates a series of *expansion* slices, which can be used to expand
    the given ``coverage`` so that it includes the given ``slices``.

    :arg slices:   Slices that the coverage needs to be expanded to cover.
    :arg coverage: Current image coverage.

    :returns: A list of volume indices, and a corresponding list of
              expansions.
932
933
    """

934
935
936
    numDims         = coverage.shape[1]
    padDims         = len(slices) - numDims - 1
    lowVol, highVol = slices[numDims] 
937
938

    expansions = []
939
    volumes    = []
940

941
942
943
944
945
946
947
948
949
950
    # Finish off an expansion by
    # adding indices for the vector/
    # slice/volume dimension, and for
    # 'padding' dimensions of size 1.
    def finishExpansion(exp, vol):
        exp.append((vol, vol + 1))
        for i in range(padDims):
            exp.append((0, 1))
        return exp
    
951
    for vol in range(lowVol, highVol):
952

953
954
955
        # No coverage of this volume - 
        # we need the whole slice.
        if np.any(np.isnan(coverage[:, :, vol])):
956
957
            exp = [(s[0], s[1]) for s in slices[:numDims]]
            exp = finishExpansion(exp, vol)
958
            volumes   .append(vol)
959
            expansions.append(exp)
960
961
            continue

962
963
964
965
966
967
968
969
        # First we'll figure out the index
        # range for each dimension that
        # needs to be added to the coverage.
        # We build a list of required ranges,
        # where each entry is a tuple
        # containing:
        #   (dimension, lowIndex, highIndex)
        reqRanges = []
970

971
        for dim in range(numDims):
972

973
            lowCover, highCover = coverage[:, dim, vol]
974
            lowSlice, highSlice = slices[     dim]
975

976
977
978
            # The slice covers a region
            # below the current coverage
            if lowCover - lowSlice > 0:
979
                reqRanges.append((dim, int(lowSlice), int(lowCover)))
980
981
982
983
                
            # The slice covers a region
            # above the current coverage
            if highCover - highSlice < 0:
984
                reqRanges.append((dim, int(highCover), int(highSlice)))
985
986

        # Now we generate an expansion for
987
988
        # each of those ranges.
        volExpansions = []
989
990
        for dimx, xlo, xhi in reqRanges:

991
            expansion = [[np.nan, np.nan] for d in range(numDims)]
992
993
994
995
996
997
998
999

            # The expansion for each
            # dimension will span the range
            # for that dimension...
            expansion[dimx][0] = xlo
            expansion[dimx][1] = xhi
                
            # And will span the union of
1000
            # the coverage, and calculated
For faster browsing, not all history is shown. View entire blame