distributor_factory.py 100 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
# D2O
# Copyright (C) 2016  Theo Steininger
#
# Author: Theo Steininger
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
theos's avatar
theos committed
18

19

theos's avatar
theos committed
20
21
import numpy as np

theos's avatar
theos committed
22
23
from d2o.config import configuration as gc,\
                       dependency_injector as gdi
theos's avatar
theos committed
24
25
26
27
28
29
30
31
32

from distributed_data_object import distributed_data_object

from d2o_iter import d2o_slicing_iter,\
                     d2o_not_iter
from d2o_librarian import d2o_librarian
from dtype_converter import dtype_converter
from cast_axis_to_tuple import cast_axis_to_tuple
from translate_to_mpi_operator import op_translate_dict
33
from slicing_generator import slicing_generator
theos's avatar
theos committed
34
35
36
37
38
39
40
41

from strategies import STRATEGIES

MPI = gdi[gc['mpi_module']]
h5py = gdi.get('h5py')
pyfftw = gdi.get('pyfftw')


theos's avatar
theos committed
42
43
44
45
46
about_cstring = lambda z: z

from sys import stdout
about_infos_cprint = lambda z: stdout.write(z + "\n"); stdout.flush()

47

theos's avatar
theos committed
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
class _distributor_factory(object):

    def __init__(self):
        self.distributor_store = {}

    def parse_kwargs(self, distribution_strategy, comm,
                     global_data=None, global_shape=None,
                     local_data=None, local_shape=None,
                     alias=None, path=None,
                     dtype=None, skip_parsing=False, **kwargs):

        if skip_parsing:
            return_dict = {'comm': comm,
                           'dtype': dtype,
                           'name': distribution_strategy
                           }
            if distribution_strategy in STRATEGIES['global']:
                return_dict['global_shape'] = global_shape
            elif distribution_strategy in STRATEGIES['local']:
                return_dict['local_shape'] = local_shape
            return return_dict

        return_dict = {}

theos's avatar
theos committed
72
        expensive_checks = gc['mpi_init_checks']
theos's avatar
theos committed
73
74
75

        # Parse the MPI communicator
        if comm is None:
theos's avatar
theos committed
76
            raise ValueError(about_cstring(
theos's avatar
theos committed
77
78
79
80
81
82
83
                "ERROR: The distributor needs MPI-communicator object comm!"))
        else:
            return_dict['comm'] = comm

        if expensive_checks:
            # Check that all nodes got the same distribution_strategy
            strat_list = comm.allgather(distribution_strategy)
84
            if not all(x == strat_list[0] for x in strat_list):
theos's avatar
theos committed
85
                raise ValueError(about_cstring(
theos's avatar
theos committed
86
                    "ERROR: The distribution-strategy must be the same on " +
87
                    "all nodes! Got: %s") % distribution_strategy)
theos's avatar
theos committed
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111

        # Check for an hdf5 file and open it if given
        if 'h5py' in gdi and alias is not None:
            # set file path
            file_path = path if (path is not None) else alias
            # open hdf5 file
            if h5py.get_config().mpi and gc['mpi_module'] == 'MPI':
                f = h5py.File(file_path, 'r', driver='mpio', comm=comm)
            else:
                f = h5py.File(file_path, 'r')
            # open alias in file
            dset = f[alias]
        else:
            dset = None

        # Parse the datatype
        if distribution_strategy in ['not', 'equal', 'fftw'] and \
                (dset is not None):
            dtype = dset.dtype

        elif distribution_strategy in ['not', 'equal', 'fftw']:
            if dtype is None:
                if global_data is None:
                    dtype = np.dtype('float64')
112
                    about_infos_cprint('INFO: dtype was set to default.')
theos's avatar
theos committed
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
                else:
                    try:
                        dtype = global_data.dtype
                    except(AttributeError):
                        dtype = np.array(global_data).dtype
            else:
                dtype = np.dtype(dtype)

        elif distribution_strategy in STRATEGIES['local']:
            if dtype is None:
                if isinstance(global_data, distributed_data_object):
                    dtype = global_data.dtype
                elif local_data is not None:
                    try:
                        dtype = local_data.dtype
                    except(AttributeError):
                        dtype = np.array(local_data).dtype
                else:
                    dtype = np.dtype('float64')
theos's avatar
theos committed
132
                    about_infos_cprint('INFO: dtype set was set to default.')
theos's avatar
theos committed
133
134
135
136
137

            else:
                dtype = np.dtype(dtype)
        if expensive_checks:
            dtype_list = comm.allgather(dtype)
138
            if not all(x == dtype_list[0] for x in dtype_list):
theos's avatar
theos committed
139
                raise ValueError(about_cstring(
theos's avatar
theos committed
140
141
142
143
144
145
146
147
                    "ERROR: The given dtype must be the same on all nodes!"))
        return_dict['dtype'] = dtype

        # Parse the shape
        # Case 1: global-type slicer
        if distribution_strategy in STRATEGIES['global']:
            if dset is not None:
                global_shape = dset.shape
148
            elif global_data is not None and not np.isscalar(global_data):
theos's avatar
theos committed
149
150
151
                global_shape = global_data.shape
            elif global_shape is not None:
                global_shape = tuple(global_shape)
152
153
            elif global_data is not None:
                global_shape = ()
theos's avatar
theos committed
154
            else:
theos's avatar
theos committed
155
                raise ValueError(about_cstring(
156
                    "ERROR: Neither global_data nor " +
theos's avatar
theos committed
157
                    "global_shape nor hdf5 file supplied!"))
158
159
160
#            if global_shape == ():
#                raise ValueError(about_cstring(
#                    "ERROR: global_shape == () is not a valid shape!"))
theos's avatar
theos committed
161
162
163
164
165

            if expensive_checks:
                global_shape_list = comm.allgather(global_shape)
                if not all(x == global_shape_list[0]
                           for x in global_shape_list):
theos's avatar
theos committed
166
                    raise ValueError(about_cstring(
theos's avatar
theos committed
167
168
169
170
171
172
173
174
                        "ERROR: The global_shape must be the same on all " +
                        "nodes!"))
            return_dict['global_shape'] = global_shape

        # Case 2: local-type slicer
        elif distribution_strategy in ['freeform']:
            if isinstance(global_data, distributed_data_object):
                local_shape = global_data.local_shape
175
            elif local_data is not None and not np.isscalar(local_data):
theos's avatar
theos committed
176
177
178
179
                local_shape = local_data.shape
            elif local_shape is not None:
                local_shape = tuple(local_shape)
            else:
theos's avatar
theos committed
180
                raise ValueError(about_cstring(
theos's avatar
theos committed
181
182
183
                    "ERROR: Neither non-0-dimensional local_data nor " +
                    "local_shape nor global d2o supplied!"))
            if local_shape == ():
theos's avatar
theos committed
184
                raise ValueError(about_cstring(
theos's avatar
theos committed
185
186
187
188
189
190
191
                    "ERROR: local_shape == () is not a valid shape!"))

            if expensive_checks:
                local_shape_list = comm.allgather(local_shape[1:])
                cleared_set = set(local_shape_list)
                cleared_set.discard(())
                if len(cleared_set) > 1:
theos's avatar
theos committed
192
                    raise ValueError(about_cstring(
theos's avatar
theos committed
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
                        "ERROR: All but the first entry of local_shape " +
                        "must be the same on all nodes!"))
            return_dict['local_shape'] = local_shape

        # Add the name of the distributor if needed
        if distribution_strategy in ['equal', 'fftw', 'freeform']:
            return_dict['name'] = distribution_strategy

        # close the file-handle
        if dset is not None:
            f.close()

        return return_dict

    def hash_arguments(self, distribution_strategy, **kwargs):
        kwargs = kwargs.copy()

        comm = kwargs['comm']
        kwargs['comm'] = id(comm)

        if 'global_shape' in kwargs:
            kwargs['global_shape'] = kwargs['global_shape']
        if 'local_shape' in kwargs:
            local_shape = kwargs['local_shape']
            local_shape_list = comm.allgather(local_shape)
            kwargs['local_shape'] = tuple(local_shape_list)

        kwargs['dtype'] = self.dictionize_np(kwargs['dtype'])
        kwargs['distribution_strategy'] = distribution_strategy

        return frozenset(kwargs.items())

    def dictionize_np(self, x):
        dic = x.type.__dict__.items()
        if x is np.float:
            dic[24] = 0
            dic[29] = 0
            dic[37] = 0
        return frozenset(dic)

    def get_distributor(self, distribution_strategy, comm, **kwargs):
        # check if the distribution strategy is known
        if distribution_strategy not in STRATEGIES['all']:
theos's avatar
theos committed
236
            raise ValueError(about_cstring(
theos's avatar
theos committed
237
238
239
240
241
242
243
244
                "ERROR: Unknown distribution strategy supplied."))

        # parse the kwargs
        parsed_kwargs = self.parse_kwargs(
            distribution_strategy=distribution_strategy,
            comm=comm,
            **kwargs)

245
246
247
248
249
        if parsed_kwargs.get('global_shape') == ():
            distribution_strategy = 'not'
            about_infos_cprint("WARNING: Distribution strategy was set to "
                               "'not' because of global_shape == ()")

theos's avatar
theos committed
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
        hashed_kwargs = self.hash_arguments(distribution_strategy,
                                            **parsed_kwargs)
        # check if the distributors has already been produced in the past
        if hashed_kwargs in self.distributor_store:
            return self.distributor_store[hashed_kwargs]
        else:
            # produce new distributor
            if distribution_strategy == 'not':
                produced_distributor = _not_distributor(**parsed_kwargs)

            elif distribution_strategy == 'equal':
                produced_distributor = _slicing_distributor(
                    slicer=_equal_slicer,
                    **parsed_kwargs)

            elif distribution_strategy == 'fftw':
                produced_distributor = _slicing_distributor(
                    slicer=_fftw_slicer,
                    **parsed_kwargs)
            elif distribution_strategy == 'freeform':
                produced_distributor = _slicing_distributor(
                    slicer=_freeform_slicer,
                    **parsed_kwargs)

            self.distributor_store[hashed_kwargs] = produced_distributor
            return self.distributor_store[hashed_kwargs]


distributor_factory = _distributor_factory()


def _infer_key_type(key):
    if key is None:
        return (None, None)
    found_boolean = False
    # Check which case we got:
    if isinstance(key, slice) or np.isscalar(key):
        found = 'slicetuple'
    elif isinstance(key, tuple) or isinstance(key, list):
        # Check if there is something different in the array than
        # scalars and slices
        scalarQ = np.array(map(np.isscalar, key))
        sliceQ = np.array(map(lambda z: isinstance(z, slice), key))
        if np.all(scalarQ + sliceQ):
            found = 'slicetuple'
        else:
            found = 'indexinglist'
    elif isinstance(key, np.ndarray):
        found = 'ndarray'
        found_boolean = (key.dtype.type == np.bool_)
    elif isinstance(key, distributed_data_object):
        found = 'd2o'
        found_boolean = (key.dtype == np.bool_)
    else:
theos's avatar
theos committed
304
        raise ValueError(about_cstring("ERROR: Unknown keytype!"))
theos's avatar
theos committed
305
306
307
308
309
    return (found, found_boolean)


class distributor(object):

theos's avatar
theos committed
310
311
312
313
314
315
316
317
318
319
320
    def distribute_data(self, data=None, alias=None,
                        path=None, copy=True, **kwargs):
        '''
        distribute data checks
        - whether the data is located on all nodes or only on node 0
        - that the shape of 'data' matches the global_shape
        '''

        if 'h5py' in gdi and alias is not None:
            data = self.load_data(alias=alias, path=path)

321
322
323
324
        h5py_dataset_Q = False
        if 'h5py' in gdi:
            h5py_dataset_Q = isinstance(data, h5py.Dataset)

theos's avatar
theos committed
325
326
327
328
329
        if data is None:
            return np.empty(self.local_shape, dtype=self.dtype)
        elif np.isscalar(data):
            return np.ones(self.local_shape, dtype=self.dtype)*data
        elif isinstance(data, np.ndarray) or \
330
331
                isinstance(data, distributed_data_object) or \
                h5py_dataset_Q:
theos's avatar
theos committed
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
            data = self.extract_local_data(data)

            if data.shape is not self.local_shape:
                copy = True

            if copy:
                result_data = np.empty(self.local_shape, dtype=self.dtype)
                result_data[:] = data
            else:
                result_data = data

            return result_data

        else:
            new_data = np.array(data)
            return new_data.astype(self.dtype,
                                   copy=copy).reshape(self.local_shape)

theos's avatar
theos committed
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
    def disperse_data(self, data, to_key, data_update, from_key=None,
                      local_keys=False, copy=True, **kwargs):
        # Check which keys we got:
        (to_found, to_found_boolean) = _infer_key_type(to_key)
        (from_found, from_found_boolean) = _infer_key_type(from_key)

        comm = self.comm
        if local_keys is False:
            return self._disperse_data_primitive(
                                         data=data,
                                         to_key=to_key,
                                         data_update=data_update,
                                         from_key=from_key,
                                         copy=copy,
                                         to_found=to_found,
                                         to_found_boolean=to_found_boolean,
                                         from_found=from_found,
                                         from_found_boolean=from_found_boolean,
                                         **kwargs)

        else:
            # assert that all to_keys are from same type
            to_found_list = comm.allgather(to_found)
            assert(all(x == to_found_list[0] for x in to_found_list))
            to_found_boolean_list = comm.allgather(to_found_boolean)
            assert(all(x == to_found_boolean_list[0] for x in
                       to_found_boolean_list))
            from_found_list = comm.allgather(from_found)
            assert(all(x == from_found_list[0] for x in from_found_list))
            from_found_boolean_list = comm.allgather(from_found_boolean)
            assert(all(x == from_found_boolean_list[0] for
                       x in from_found_boolean_list))

            # gather the local to_keys into a global to_key_list
            # Case 1: the to_keys are not distributed_data_objects
            # -> allgather does the job
            if to_found != 'd2o':
                to_key_list = comm.allgather(to_key)
            # Case 2: if the to_keys are distributed_data_objects, gather
            # the index of the array and build the to_key_list with help
            # from the librarian
            else:
                to_index_list = comm.allgather(to_key.index)
                to_key_list = map(lambda z: d2o_librarian[z], to_index_list)

            # gather the local from_keys. It is the same procedure as above
            if from_found != 'd2o':
                from_key_list = comm.allgather(from_key)
            else:
                from_index_list = comm.allgather(from_key.index)
                from_key_list = map(lambda z: d2o_librarian[z],
                                    from_index_list)

            local_data_update_is_scalar = np.isscalar(data_update)
            local_scalar_list = comm.allgather(local_data_update_is_scalar)
            for i in xrange(len(to_key_list)):
                if np.all(np.array(local_scalar_list) == True):
                    scalar_list = comm.allgather(data_update)
                    temp_data_update = scalar_list[i]
                elif isinstance(data_update, distributed_data_object):
                    data_update_index_list = comm.allgather(data_update.index)
                    data_update_list = map(lambda z: d2o_librarian[z],
                                           data_update_index_list)
                    temp_data_update = data_update_list[i]
                else:
                    # build a temporary freeform d2o which only contains data
                    # from node i
                    if comm.rank == i:
                        temp_shape = np.shape(data_update)
                        try:
                            temp_dtype = np.dtype(data_update.dtype)
                        except(TypeError):
                            temp_dtype = np.array(data_update).dtype
                    else:
                        temp_shape = None
                        temp_dtype = None
                    temp_shape = comm.bcast(temp_shape, root=i)
                    temp_dtype = comm.bcast(temp_dtype, root=i)

                    if comm.rank != i:
                        temp_shape = list(temp_shape)
                        temp_shape[0] = 0
                        temp_shape = tuple(temp_shape)
                        temp_data = np.empty(temp_shape, dtype=temp_dtype)
                    else:
                        temp_data = data_update
                    temp_data_update = distributed_data_object(
                                        local_data=temp_data,
                                        distribution_strategy='freeform',
                                        copy=False,
                                        comm=self.comm)
                # disperse the data one after another
                self._disperse_data_primitive(
                                      data=data,
                                      to_key=to_key_list[i],
                                      data_update=temp_data_update,
                                      from_key=from_key_list[i],
                                      copy=copy,
                                      to_found=to_found,
                                      to_found_boolean=to_found_boolean,
                                      from_found=from_found,
                                      from_found_boolean=from_found_boolean,
                                      **kwargs)
                i += 1

455
    def bincount(self, obj, length, weights=None, axis=None):
456
457
        if obj.shape == ():
            raise ValueError("object of too small depth for desired array")
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
        data = obj.get_local_data(copy=False)
        # this implementation fits all distribution strategies where the
        # axes of the global array correspond to the axes of the local data

        if weights is not None:
            local_weights = self.extract_local_data(weights)
        else:
            local_weights = None

        # if present, parse the axis keyword and transpose/reorder self.data
        # such that all affected axes follow each other. Only if they are in a
        # sequence flattening will be possible
        if axis is not None:
            # do the reordering
            ndim = len(self.global_shape)
            axis = sorted(cast_axis_to_tuple(axis, length=ndim))
            reordering = [x for x in xrange(ndim) if x not in axis]
            reordering += axis

            data = np.transpose(data, reordering)
            if local_weights is not None:
                local_weights = np.transpose(local_weights, reordering)

            reord_axis = range(ndim-len(axis), ndim)

            # semi-flatten the dimensions in `axis`, i.e. after reordering
            # the last ones.
            semi_flat_dim = reduce(lambda x, y: x*y,
                                   data.shape[ndim-len(reord_axis):])
            flat_shape = data.shape[:ndim-len(reord_axis)] + (semi_flat_dim, )
        else:
            flat_shape = (reduce(lambda x, y: x*y, data.shape), )

        data = np.ascontiguousarray(data.reshape(flat_shape))
        if local_weights is not None:
            local_weights = np.ascontiguousarray(
                                local_weights.reshape(flat_shape))

        # compute the local bincount results
        # -> prepare the local result array
        if local_weights is None:
            result_dtype = np.int
        else:
            result_dtype = np.float
        local_counts = np.empty(flat_shape[:-1] + (length, ),
                                dtype=result_dtype)
        # iterate over all entries in the surviving axes and compute the local
        # bincounts
        for slice_list in slicing_generator(flat_shape,
                                            axes=(len(flat_shape)-1, )):
theos's avatar
theos committed
508
509
510
511
512
513
514
515
            if local_weights is not None:
                current_weights = local_weights[slice_list]
            else:
                current_weights = None
            local_counts[slice_list] = np.bincount(
                                            data[slice_list],
                                            weights=current_weights,
                                            minlength=length)
516
517
518
519
520
521

        # restore the original ordering
        # place the bincount stuff at the location of the first `axis` entry
        if axis is not None:
            # axis has been sorted above
            insert_position = axis[0]
theos's avatar
theos committed
522
            new_ndim = len(local_counts.shape)
523
            return_order = (range(0, insert_position) +
theos's avatar
theos committed
524
525
                            [new_ndim-1, ] +
                            range(insert_position, new_ndim-1))
526
527
528
529
            local_counts = np.ascontiguousarray(
                                local_counts.transpose(return_order))
        return self._combine_local_bincount_counts(obj, local_counts, axis)

theos's avatar
theos committed
530
531
532
533
534
535
536
537
538
539
540

class _slicing_distributor(distributor):
    def __init__(self, slicer, name, dtype, comm, **remaining_parsed_kwargs):

        self.comm = comm
        self.distribution_strategy = name
        self.dtype = np.dtype(dtype)

        self._my_dtype_converter = dtype_converter

        if not self._my_dtype_converter.known_np_Q(self.dtype):
theos's avatar
theos committed
541
            raise TypeError(about_cstring(
theos's avatar
theos committed
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
                "ERROR: The datatype " + str(self.dtype.__repr__()) +
                " is not known to mpi4py."))

        self.mpi_dtype = self._my_dtype_converter.to_mpi(self.dtype)

        self.slicer = slicer
        self._local_size = self.slicer(comm=comm, **remaining_parsed_kwargs)
        self.local_start = self._local_size[0]
        self.local_end = self._local_size[1]
        self.global_shape = self._local_size[2]
        self.global_dim = reduce(lambda x, y: x*y, self.global_shape)

        self.local_length = self.local_end - self.local_start
        self.local_shape = (self.local_length,) + tuple(self.global_shape[1:])
        self.local_dim = np.product(self.local_shape)
        self.local_dim_list = np.empty(comm.size, dtype=np.int)
        comm.Allgather([np.array(self.local_dim, dtype=np.int), MPI.INT],
                       [self.local_dim_list, MPI.INT])
        self.local_dim_offset = np.sum(self.local_dim_list[0:comm.rank])

        self.local_slice = np.array([self.local_start, self.local_end,
                                     self.local_length, self.local_dim,
                                     self.local_dim_offset],
                                    dtype=np.int)
        # collect all local_slices
        self.all_local_slices = np.empty((comm.size, 5), dtype=np.int)
        comm.Allgather([np.array((self.local_slice,), dtype=np.int), MPI.INT],
                       [self.all_local_slices, MPI.INT])

    def initialize_data(self, global_data, local_data, alias, path, hermitian,
                        copy, **kwargs):
        if 'h5py' in gdi and alias is not None:
            local_data = self.load_data(alias=alias, path=path)
            return (local_data, hermitian)

        if self.distribution_strategy in ['equal', 'fftw']:
            if np.isscalar(global_data):
                local_data = np.empty(self.local_shape, dtype=self.dtype)
                local_data.fill(global_data)
                hermitian = True
            else:
                local_data = self.distribute_data(data=global_data,
                                                  copy=copy)
        elif self.distribution_strategy in ['freeform']:
            if isinstance(global_data, distributed_data_object):
                local_data = global_data.get_local_data(copy=copy)
            elif np.isscalar(local_data):
                temp_local_data = np.empty(self.local_shape,
                                           dtype=self.dtype)
                temp_local_data.fill(local_data)
                local_data = temp_local_data
                hermitian = True
            elif local_data is None:
                local_data = np.empty(self.local_shape, dtype=self.dtype)
            elif isinstance(local_data, np.ndarray):
                local_data = local_data.astype(
                               self.dtype, copy=copy).reshape(self.local_shape)
            else:
                local_data = np.array(local_data).astype(
                    self.dtype, copy=copy).reshape(self.local_shape)
        else:
theos's avatar
theos committed
603
            raise TypeError(about_cstring(
theos's avatar
theos committed
604
605
606
607
608
609
610
611
612
                "ERROR: Unknown istribution strategy"))
        return (local_data, hermitian)

    def globalize_flat_index(self, index):
        return int(index) + self.local_dim_offset

    def globalize_index(self, index):
        index = np.array(index, dtype=np.int).flatten()
        if index.shape != (len(self.global_shape),):
theos's avatar
theos committed
613
            raise TypeError(about_cstring("ERROR: Length\
theos's avatar
theos committed
614
615
616
617
618
619
620
621
622
                of index tuple does not match the array's shape!"))
        globalized_index = index
        globalized_index[0] = index[0] + self.local_start
        # ensure that the globalized index list is within the bounds
        global_index_memory = globalized_index
        globalized_index = np.clip(globalized_index,
                                   -np.array(self.global_shape),
                                   np.array(self.global_shape) - 1)
        if np.any(global_index_memory != globalized_index):
theos's avatar
theos committed
623
            about_infos_cprint("WARNING: Indices were clipped!")
theos's avatar
theos committed
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
        globalized_index = tuple(globalized_index)
        return globalized_index

    def _allgather(self, thing, comm=None):
        if comm is None:
            comm = self.comm
        gathered_things = comm.allgather(thing)
        return gathered_things

    def _Allreduce_helper(self, sendbuf, recvbuf, op):
        send_dtype = self._my_dtype_converter.to_mpi(sendbuf.dtype)
        recv_dtype = self._my_dtype_converter.to_mpi(recvbuf.dtype)
        self.comm.Allreduce([sendbuf, send_dtype],
                            [recvbuf, recv_dtype],
                            op=op)
        return recvbuf

    def _selective_allreduce(self, data, op, bufferQ=False):
        size = self.comm.size
        rank = self.comm.rank

        if size == 1:
            if data is None:
                raise ValueError("ERROR: No process with non-None data.")
            result_data = data
        else:

            # infer which data should be included in the allreduce and if its
            # array data
            if data is None:
                got_array = np.array([0])
            elif not isinstance(data, np.ndarray):
                got_array = np.array([2])
            elif reduce(lambda x, y: x*y, data.shape) == 0:
                got_array = np.array([1])
            elif np.issubdtype(data.dtype, np.complexfloating):
                # MPI.MAX and MPI.MIN do not support complex data types
                got_array = np.array([3])
            else:
                got_array = np.array([4])

            got_array_list = np.empty(size, dtype=np.int)
            self.comm.Allgather([got_array, MPI.INT],
                                [got_array_list, MPI.INT])

            if reduce(lambda x, y: x & y, got_array_list == 1):
                return data

            # get first node with non-None data
            try:
                start = next(i for i in xrange(size) if got_array_list[i] > 1)
            except(StopIteration):
                raise ValueError("ERROR: No process with non-None data.")

            # check if the Uppercase function can be used or not
            # -> check if op supports buffers and if we got real array-data
            if bufferQ and got_array_list[start] == 4:
                # Send the dtype and shape from the start process to the others
                (new_dtype,
                 new_shape) = self.comm.bcast((data.dtype,
                                               data.shape), root=start)
                mpi_dtype = self._my_dtype_converter.to_mpi(new_dtype)
                if rank == start:
                    result_data = data
                else:
                    result_data = np.empty(new_shape, dtype=new_dtype)

                self.comm.Bcast([result_data, mpi_dtype], root=start)

                for i in xrange(start+1, size):
                    if got_array_list[i] > 1:
                        if rank == i:
                            temp_data = data
                        else:
                            temp_data = np.empty(new_shape, dtype=new_dtype)
                        self.comm.Bcast([temp_data, mpi_dtype], root=i)
                        result_data = op(result_data, temp_data)

            else:
                result_data = self.comm.bcast(data, root=start)
                for i in xrange(start+1, size):
                    if got_array_list[i] > 1:
                        temp_data = self.comm.bcast(data, root=i)
                        result_data = op(result_data, temp_data)
        return result_data

    def contraction_helper(self, parent, function, allow_empty_contractions,
                           axis=None, **kwargs):
        if axis == ():
            return parent.copy()

        old_shape = parent.shape
716
        axis = cast_axis_to_tuple(axis, length=len(self.global_shape))
theos's avatar
theos committed
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
        if axis is None:
            new_shape = ()
        else:
            new_shape = tuple([old_shape[i] for i in xrange(len(old_shape))
                               if i not in axis])

        local_data = parent.data

        try:
            contracted_local_data = function(local_data, axis=axis, **kwargs)
        except(ValueError):
            contracted_local_data = None

        # check if additional contraction along the first axis must be done
        if axis is None or 0 in axis:
            (mpi_op, bufferQ) = op_translate_dict[function]
            contracted_global_data = self._selective_allreduce(
                                        contracted_local_data,
                                        mpi_op,
                                        bufferQ)
            new_dist_strategy = 'not'
        else:
            if contracted_local_data is None:
                # raise the exception implicitly
                function(local_data, axis=axis, **kwargs)
            contracted_global_data = contracted_local_data
            new_dist_strategy = parent.distribution_strategy

        if new_shape == ():
            result = contracted_global_data
        else:
            new_dtype = contracted_global_data.dtype
            # try to store the result in a distributed_data_object with the
            # distribution_strategy as parent
            result = parent.copy_empty(global_shape=new_shape,
                                       dtype=new_dtype,
                                       distribution_strategy=new_dist_strategy)

            # However, there are cases where the contracted data does not any
            # longer follow the prior distribution scheme.
            # Example: FFTW distribution on 4 MPI processes
            # Contracting (4, 4) to (4,).
            # (4, 4) was distributed (1, 4)...(1, 4)
            # (4, ) is not distributed like (1,)...(1,) but like (2,)(2,)()()!
761
762
763
            equalQ = (result.local_shape == contracted_global_data.shape)
            all_equalQ = np.all(self._allgather(equalQ))
            if not all_equalQ:
theos's avatar
theos committed
764
765
766
767
768
769
770
771
                result = parent.copy_empty(
                                    local_shape=contracted_global_data.shape,
                                    dtype=new_dtype,
                                    distribution_strategy='freeform')
            result.set_local_data(contracted_global_data, copy=False)

        return result

theos's avatar
theos committed
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
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
#    def distribute_data(self, data=None, alias=None,
#                        path=None, copy=True, **kwargs):
#        '''
#        distribute data checks
#        - whether the data is located on all nodes or only on node 0
#        - that the shape of 'data' matches the global_shape
#        '''
#
##        comm = self.comm
#
#        if 'h5py' in gdi and alias is not None:
#            data = self.load_data(alias=alias, path=path)
#
#        if data is None:
#            return np.empty(self.global_shape, dtype=self.dtype)
#        elif np.isscalar(data):
#            return np.ones(self.global_shape, dtype=self.dtype)*data
#            copy = False
#        elif isinstance(data, np.ndarray) or \
#                isinstance(data, distributed_data_object):
#            data = self.extract_local_data(data)
#
#            if data.shape is not self.local_shape:
#                copy = True
#
#            if copy:
#                result_data = np.empty(self.local_shape, dtype=self.dtype)
#                result_data[:] = data
#            else:
#                result_data = data
#
#            return result_data
#
#        else:
#            new_data = np.array(data)
#            return new_data.astype(self.dtype,
#                                   copy=copy).reshape(self.global_shape)
#
#
##        local_data_available_Q = (data is not None)
##        data_available_Q = np.array(comm.allgather(local_data_available_Q))
##
##        if np.all(data_available_Q == False):
##            return np.empty(self.local_shape, dtype=self.dtype, order='C')
##        # if all nodes got data, we assume that it is the right data and
##        # store it individually.
##        elif np.all(data_available_Q == True):
##            if isinstance(data, distributed_data_object):
##                temp_d2o = data.get_data((slice(self.local_start,
##                                                self.local_end),),
##                                         local_keys=True,
##                                         copy=copy)
##                return temp_d2o.get_local_data(copy=False).astype(self.dtype,
##                                                                  copy=False)
##            elif np.isscalar(data):
##                return np.ones(self.local_shape, dtype=self.dtype)*data
##            else:
##                return data[self.local_start:self.local_end].astype(
##                    self.dtype,
##                    copy=copy)
##        else:
##            raise ValueError(
##                "ERROR: distribute_data must get data on all nodes!")
theos's avatar
theos committed
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853

    def _disperse_data_primitive(self, data, to_key, data_update, from_key,
                                 copy, to_found, to_found_boolean, from_found,
                                 from_found_boolean, **kwargs):
        if np.isscalar(data_update):
            from_key = None

        # Case 1: to_key is a slice-tuple. Hence, the basic indexing/slicing
        # machinery will be used
        if to_found == 'slicetuple':
            if from_found == 'slicetuple':
                return self.disperse_data_to_slices(data=data,
                                                    to_slices=to_key,
                                                    data_update=data_update,
                                                    from_slices=from_key,
                                                    copy=copy,
                                                    **kwargs)
            else:
                if from_key is not None:
theos's avatar
theos committed
854
                    about_infos_cprint(
theos's avatar
theos committed
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
                        "INFO: Advanced injection is not available for this " +
                        "combination of to_key and from_key.")
                    prepared_data_update = data_update[from_key]
                else:
                    prepared_data_update = data_update

                return self.disperse_data_to_slices(
                                            data=data,
                                            to_slices=to_key,
                                            data_update=prepared_data_update,
                                            copy=copy,
                                            **kwargs)

        # Case 2: key is an array
        elif (to_found == 'ndarray' or to_found == 'd2o'):
            # Case 2.1: The array is boolean.
            if to_found_boolean:
                if from_key is not None:
theos's avatar
theos committed
873
                    about_infos_cprint(
theos's avatar
theos committed
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
                        "INFO: Advanced injection is not available for this " +
                        "combination of to_key and from_key.")
                    prepared_data_update = data_update[from_key]
                else:
                    prepared_data_update = data_update
                return self.disperse_data_to_bool(
                                              data=data,
                                              to_boolean_key=to_key,
                                              data_update=prepared_data_update,
                                              copy=copy,
                                              **kwargs)
            # Case 2.2: The array is not boolean. Only 1-dimensional
            # advanced slicing is supported.
            else:
                if len(to_key.shape) != 1:
theos's avatar
theos committed
889
                    raise ValueError(about_cstring(
theos's avatar
theos committed
890
891
892
893
894
895
896
897
898
899
900
901
                        "WARNING: Only one-dimensional advanced indexing " +
                        "is supported"))
                # Make a recursive call in order to trigger the 'list'-section
                return self.disperse_data(data=data, to_key=[to_key],
                                          data_update=data_update,
                                          from_key=from_key, copy=copy,
                                          **kwargs)

        # Case 3 : to_key is a list. This list is interpreted as
        # one-dimensional advanced indexing list.
        elif to_found == 'indexinglist':
            if from_key is not None:
theos's avatar
theos committed
902
                about_infos_cprint(
theos's avatar
theos committed
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
                    "INFO: Advanced injection is not available for this " +
                    "combination of to_key and from_key.")
                prepared_data_update = data_update[from_key]
            else:
                prepared_data_update = data_update
            return self.disperse_data_to_list(data=data,
                                              to_list_key=to_key,
                                              data_update=prepared_data_update,
                                              copy=copy,
                                              **kwargs)

    def disperse_data_to_list(self, data, to_list_key, data_update,
                              copy=True, **kwargs):

        if to_list_key == []:
            return data

        local_to_list_key = self._advanced_index_decycler(to_list_key)
        return self._disperse_data_to_list_and_bool_helper(
            data=data,
            local_to_key=local_to_list_key,
            data_update=data_update,
            copy=copy,
            **kwargs)

    def disperse_data_to_bool(self, data, to_boolean_key, data_update,
                              copy=True, **kwargs):
        # Extract the part of the to_boolean_key which corresponds to the
        # local data
        local_to_boolean_key = self.extract_local_data(to_boolean_key)
        return self._disperse_data_to_list_and_bool_helper(
            data=data,
            local_to_key=local_to_boolean_key,
            data_update=data_update,
            copy=copy,
            **kwargs)

    def _disperse_data_to_list_and_bool_helper(self, data, local_to_key,
                                               data_update, copy, **kwargs):
        comm = self.comm
        rank = comm.rank
        # Infer the length and offset of the locally affected data
        locally_affected_data = data[local_to_key]
        data_length = np.shape(locally_affected_data)[0]
        data_length_list = comm.allgather(data_length)
        data_length_offset_list = np.append([0],
                                            np.cumsum(data_length_list)[:-1])

        # Update the local data object with its very own portion
        o = data_length_offset_list
        l = data_length

        if isinstance(data_update, distributed_data_object):
            local_data_update = data_update.get_data(
                                          slice(o[rank], o[rank] + l),
                                          local_keys=True
                                          ).get_local_data(copy=False)
            data[local_to_key] = local_data_update.astype(self.dtype,
                                                          copy=False)
        elif np.isscalar(data_update):
            data[local_to_key] = data_update
        else:
            data[local_to_key] = np.array(data_update[o[rank]:o[rank] + l],
                                          copy=copy).astype(self.dtype,
                                                            copy=False)
        return data

    def disperse_data_to_slices(self, data, to_slices,
                                data_update, from_slices=None, copy=True):
        comm = self.comm
        (to_slices, sliceified) = self._sliceify(to_slices)

        # parse the to_slices object
        localized_to_start, localized_to_stop = self._backshift_and_decycle(
            to_slices[0], self.local_start, self.local_end,
            self.global_shape[0])
        local_to_slice = (slice(localized_to_start, localized_to_stop,
                                to_slices[0].step),) + to_slices[1:]
        local_to_slice_shape = data[local_to_slice].shape

        to_step = to_slices[0].step
        if to_step is None:
            to_step = 1
        elif to_step == 0:
theos's avatar
theos committed
987
            raise ValueError(about_cstring(
theos's avatar
theos committed
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
                "ERROR: to_step size == 0!"))

        # Compute the offset of the data the individual node will take.
        # The offset is free of stepsizes. It is the offset in terms of
        # the purely transported data. If to_step < 0, the offset will
        # be calculated in reverse order
        order = np.sign(to_step)

        local_affected_data_length = local_to_slice_shape[0]
        local_affected_data_length_list = np.empty(comm.size, dtype=np.int)
        comm.Allgather(
            [np.array(local_affected_data_length, dtype=np.int), MPI.INT],
            [local_affected_data_length_list, MPI.INT])
        local_affected_data_length_offset_list = np.append([0],
                                                           np.cumsum(
            local_affected_data_length_list[::order])[:-1])[::order]

        if np.isscalar(data_update):
            data[local_to_slice] = data_update
        else:
            # construct the locally adapted from_slice object
            r = comm.rank
            o = local_affected_data_length_offset_list
            l = local_affected_data_length

            data_update = self._enfold(data_update, sliceified)

            # parse the from_slices object
            if from_slices is None:
                from_slices = (slice(None, None, None),)
            (from_slices_start, from_slices_stop) = \
                self._backshift_and_decycle(
                                            slice_object=from_slices[0],
                                            shifted_start=0,
                                            shifted_stop=data_update.shape[0],
                                            global_length=data_update.shape[0])
            if from_slices_start is None:
theos's avatar
theos committed
1025
                raise ValueError(about_cstring(
theos's avatar
theos committed
1026
1027
1028
1029
1030
1031
1032
1033
                    "ERROR: _backshift_and_decycle should never return " +
                    "None for local_start!"))

            # parse the step sizes
            from_step = from_slices[0].step
            if from_step is None:
                from_step = 1
            elif from_step == 0:
theos's avatar
theos committed
1034
                raise ValueError(about_cstring(
theos's avatar
theos committed
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
                    "ERROR: from_step size == 0!"))

            localized_from_start = from_slices_start + from_step * o[r]
            localized_from_stop = localized_from_start + from_step * l
            if localized_from_stop < 0:
                localized_from_stop = None

            localized_from_slice = (slice(localized_from_start,
                                          localized_from_stop,
                                          from_step),)

            update_slice = localized_from_slice + from_slices[1:]

            if isinstance(data_update, distributed_data_object):
                selected_update = data_update.get_data(
                                 key=update_slice,
1051
1052
                                 local_keys=True,
                                 copy=copy)
theos's avatar
theos committed
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
                local_data_update = selected_update.get_local_data(copy=False)
                local_data_update = local_data_update.astype(self.dtype,
                                                             copy=False)
                if np.prod(np.shape(local_data_update)) != 0:
                    data[local_to_slice] = local_data_update
            # elif np.isscalar(data_update):
            #    data[local_to_slice] = data_update
            else:
                local_data_update = np.array(data_update)[update_slice]
                if np.prod(np.shape(local_data_update)) != 0:
                    data[local_to_slice] = np.array(
                                                local_data_update,
                                                copy=copy).astype(self.dtype,
                                                                  copy=False)

    def collect_data(self, data, key, local_keys=False, copy=True, **kwargs):
        # collect_data supports three types of keys
        # Case 1: key is a slicing/index tuple
        # Case 2: key is a boolean-array of the same shape as self
        # Case 3: key is a list of shape (n,), where n is
        #         0<n<len(self.shape). The entries of the list must be a
        #         scalar/list/tuple/ndarray. If not scalar the length must be
        #         the same for all of the lists. This is essentially
        #         numpy advanced indexing in one dimension, only.

        # Check which case we got:
        (found, found_boolean) = _infer_key_type(key)
        comm = self.comm
        if local_keys is False:
            return self._collect_data_primitive(data, key, found,
                                                found_boolean, copy=copy,
                                                **kwargs)
        else:
            # assert that all keys are from same type
            found_list = comm.allgather(found)
            assert(all(x == found_list[0] for x in found_list))
            found_boolean_list = comm.allgather(found_boolean)
            assert(all(x == found_boolean_list[0] for x in found_boolean_list))

            # gather the local_keys into a global key_list
            # Case 1: the keys are no distributed_data_objects
            # -> allgather does the job
            if found != 'd2o':
                key_list = comm.allgather(key)
            # Case 2: if the keys are distributed_data_objects, gather
            # the index of the array and build the key_list with help
            # from the librarian
            else:
                index_list = comm.allgather(key.index)
                key_list = map(lambda z: d2o_librarian[z], index_list)
            i = 0
            for temp_key in key_list:
                # build the locally fed d2o
                temp_d2o = self._collect_data_primitive(data, temp_key, found,
                                                        found_boolean,
                                                        copy=copy, **kwargs)
                # collect the data stored in the d2o to the individual target
                # rank
                temp_data = temp_d2o.get_full_data(target_rank=i)
                if comm.rank == i:
                    individual_data = temp_data
                i += 1
            return_d2o = distributed_data_object(
                            local_data=individual_data,
                            distribution_strategy='freeform',
                            copy=False,
                            comm=self.comm)
            return return_d2o

    def _collect_data_primitive(self, data, key, found, found_boolean,
                                copy=True, **kwargs):

        # Case 1: key is a slice-tuple. Hence, the basic indexing/slicing
        # machinery will be used
        if found == 'slicetuple':
            return self.collect_data_from_slices(data=data,
                                                 slice_objects=key,
                                                 copy=copy,
                                                 **kwargs)
        # Case 2: key is an array
        elif (found == 'ndarray' or found == 'd2o'):
            # Case 2.1: The array is boolean.
            if found_boolean:
                return self.collect_data_from_bool(data=data,
                                                   boolean_key=key,
                                                   copy=copy,
                                                   **kwargs)
            # Case 2.2: The array is not boolean. Only 1-dimensional
            # advanced slicing is supported.
            else:
                if len(key.shape) != 1:
theos's avatar
theos committed
1144
                    raise ValueError(about_cstring(
theos's avatar
theos committed
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
                        "WARNING: Only one-dimensional advanced indexing " +
                        "is supported"))
                # Make a recursive call in order to trigger the 'list'-section
                return self.collect_data(data=data, key=[key], copy=copy,
                                         **kwargs)

        # Case 3 : key is a list. This list is interpreted as one-dimensional
        # advanced indexing list.
        elif found == 'indexinglist':
            return self.collect_data_from_list(data=data,
                                               list_key=key,
                                               copy=copy,
                                               **kwargs)

    def collect_data_from_list(self, data, list_key, copy=True, **kwargs):
        if list_key == []:
theos's avatar
theos committed
1161
            raise ValueError(about_cstring(
theos's avatar
theos committed
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
                "ERROR: key == [] is an unsupported key!"))
        local_list_key = self._advanced_index_decycler(list_key)
        local_result = data[local_list_key]
        global_result = distributed_data_object(
                                            local_data=local_result,
                                            distribution_strategy='freeform',
                                            copy=copy,
                                            comm=self.comm)
        return global_result

    def _advanced_index_decycler(self, from_list_key):
        global_length = self.global_shape[0]
        local_length = self.local_length
        shift = self.local_start
        rank = self.comm.rank

        zeroth_key = from_list_key[0]
        # Check if from_list_key is a scalar
        if np.isscalar(zeroth_key):
            # decycle negative index
            if zeroth_key < 0:
                zeroth_key += global_length
            # if the index is still negative, or it is greater than
            # global_length the index is ill-choosen
            if zeroth_key < 0 or zeroth_key >= global_length:
theos's avatar
theos committed
1187
                raise ValueError(about_cstring(
theos's avatar
theos committed
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
                    "ERROR: Index out of bounds!"))
            # shift the index
            local_zeroth_key = zeroth_key - shift
            # if the index lies within the local nodes' data-range
            # take the shifted index, combined with rest of from_list_key
            result = [local_zeroth_key]
            for ii in xrange(1, len(from_list_key)):
                current = from_list_key[ii]
                if isinstance(current, distributed_data_object):
                    result.append(current.get_full_data())
                else:
                    result.append(current)
            if (local_zeroth_key < 0) or (local_zeroth_key >= local_length):
                result = (np.array([], dtype=np.dtype('int')),) * \
                    len(from_list_key)

        elif isinstance(zeroth_key, distributed_data_object):
            zeroth_key = zeroth_key.copy()
            # decycle negative indices
            zeroth_key[zeroth_key < 0] = zeroth_key[zeroth_key < 0] + \
                global_length
            # if there are still negative indices, or indices greater than
            # global_length the indices are ill-choosen
            if (zeroth_key < 0).any() or (zeroth_key >= global_length).any():
theos's avatar
theos committed
1212
                raise ValueError(about_cstring(
theos's avatar
theos committed
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
                    "ERROR: Index out of bounds!"))
            # shift the indices according to shift
            shift_list = self.comm.allgather(shift)
            local_zeroth_key_list = map(lambda z: zeroth_key - z, shift_list)
            # discard all entries where the indices are negative or larger
            # than local_length
            greater_than_lower_list = map(lambda z: z >= 0,
                                          local_zeroth_key_list)
            # -> build up a list with the local selection d2o's
            local_length_list = self.comm.allgather(local_length)
            less_than_upper_list = map(lambda z, zz: z < zz,
                                       local_zeroth_key_list,
                                       local_length_list)
            local_selection_list = map(lambda z, zz: z * zz,
                                       less_than_upper_list,
                                       greater_than_lower_list)

            for j in xrange(len(local_zeroth_key_list)):
                temp_result = local_zeroth_key_list[j].\
                    get_data(local_selection_list[j]).\
                    get_full_data(target_rank=j)
                if j == rank:
                    result = temp_result
# TODO: Implement fast check!
#            if not all(result[i] <= result[i + 1]
#                       for i in xrange(len(result) - 1)):
theos's avatar
theos committed
1239
#                raise ValueError(about_cstring(
theos's avatar
theos committed
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
#                   "ERROR: The first dimemnsion of list_key must be sorted!"))

            result = [result]
            for ii in xrange(1, len(from_list_key)):
                current = from_list_key[ii]
                if np.isscalar(current):
                    result.append(current)
                elif isinstance(current, distributed_data_object):
                    result.append(current.get_data(
                                   local_selection_list[rank],
                                   local_keys=True).get_local_data(copy=False))
                else:
                    for j in xrange(len(local_selection_list)):
                        temp_select = local_selection_list[j].\
                            get_full_data(target_rank=j)
                        if j == rank:
                            temp_result = current[temp_select]
                    result.append(temp_result)

        else:
            zeroth_key = zeroth_key.copy()
            # decycle negative indices
            zeroth_key[zeroth_key < 0] = zeroth_key[zeroth_key < 0] + \
                global_length
            # if there are still negative indices, or indices greater than
            # global_length the indices are ill-choosen
            if (zeroth_key < 0).any() or (zeroth_key >= global_length).any():
theos's avatar
theos committed
1267
                raise ValueError(about_cstring(
theos's avatar
theos committed
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
                    "ERROR: Index out of bounds!"))
            # shift the indices according to shift
            local_zeroth_key = zeroth_key - shift
            # discard all entries where the indices are negative or larger
            # than local_length
            greater_than_lower = (local_zeroth_key >= 0)
            less_than_upper = (local_zeroth_key < local_length)
            local_selection = greater_than_lower * less_than_upper

            result = [local_zeroth_key[local_selection]]
# TODO: Implement fast check!
#            if not all(result[0][i] <= result[0][i + 1]
#                       for i in xrange(len(result[0]) - 1)):
theos's avatar
theos committed
1281
#                raise ValueError(about_cstring(
theos's avatar
theos committed
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
#                   "ERROR: The first dimemnsion of list_key must be sorted!"))

            for ii in xrange(1, len(from_list_key)):
                current = from_list_key[ii]
                if np.isscalar(current):
                    result.append(current)
                elif isinstance(current, distributed_data_object):
                    result.append(current.get_data(
                                   local_selection,
                                   local_keys=True).get_local_data(copy=False))
                else:
                    result.append(current[local_selection])

        return result

    def collect_data_from_bool(self, data, boolean_key, copy=True, **kwargs):
        local_boolean_key = self.extract_local_data(boolean_key)
        local_result = data[local_boolean_key]
        global_result = distributed_data_object(
                                            local_data=local_result,
                                            distribution_strategy='freeform',
                                            copy=copy,
                                            comm=self.comm)
        return global_result

    def _invert_mpi_data_ordering(self, data):
        data = np.ascontiguousarray(data)

        comm = self.comm
        s = comm.size
        r = comm.rank
        if s == 1:
            return data

        partner = s - 1 - r

        new_shape = comm.sendrecv(sendobj=data.shape,
                                  dest=partner,
                                  source=partner)
        new_data = np.empty(new_shape,
                            dtype=self.dtype)

        comm.Sendrecv(sendbuf=[data, self.mpi_dtype],
                      recvbuf=[new_data, self.mpi_dtype],
                      dest=partner,
                      source=partner)

        return new_data

    def collect_data_from_slices(self, data, slice_objects, copy=True,
                                 target_rank='all'):

        (slice_objects, sliceified) = self._sliceify(slice_objects)

        localized_start, localized_stop = self._backshift_and_decycle(
            slice_objects[0],
            self.local_start,
            self.local_end,
            self.global_shape[0])
        first_step = slice_objects[0].step
        local_slice = (slice(localized_start,
                             localized_stop,
                             first_step),) + slice_objects[1:]

        local_result = data[local_slice]
        if (first_step is not None) and (first_step < 0):
            local_result = self._invert_mpi_data_ordering(local_result)

        global_result = distributed_data_object(
                                            local_data=local_result,
                                            distribution_strategy='freeform',
                                            copy=copy,
                                            comm=self.comm)

        return self._defold(global_result, sliceified)

    def _backshift_and_decycle(self, slice_object, shifted_start, shifted_stop,
                               global_length):

        # Reformulate negative indices
        if slice_object.start < 0 and slice_object.start is not None:
            temp_start = slice_object.start + global_length
            if temp_start < 0:
                temp_start = 0

            slice_object = slice(temp_start, slice_object.stop,
                                 slice_object.step)

        if slice_object.stop < 0 and slice_object.stop is not None:
            temp_stop = slice_object.stop + global_length
            if temp_stop < 0:
                temp_stop = None

            slice_object = slice(slice_object.start, temp_stop,
                                 slice_object.step)

        # initialize the step
        if slice_object.step is None:
            step = 1
        else:
            step = slice_object.step

        # compute local_length
        local_length = shifted_stop - shifted_start
        if step > 0:
            shift = shifted_start
            # calculate the start index
            if slice_object.start is None:
                local_start = (-shift) % step  # step size compensation
            else:
                local_start = slice_object.start - shift
                # if the local_start is negative, pull it up to zero
                local_start = \
                    local_start % step if local_start < 0 else local_start

            if local_start >= local_length:
                return (0, 0)

            # calculate the stop index
            if slice_object.stop is None:
                local_stop = None
            else:
                local_stop = slice_object.stop - shift
                # if local_stop is negative, the slice is empty
                if local_stop < 0:
                    return (0, 0)
                if local_stop > local_length:
                    local_stop = None

        else:  # if step < 0
            step = -step
            # calculate the start index. (Here, local_start > local_stop!)
            if slice_object.start is None:
                local_start = (local_length - 1) -\
                    (-(global_length - shifted_stop)
                     ) % step  # stepsize compensation
                # if local_start becomes negative here, it means, that the
                # step size is bigger than the length of the local slice and
                # that no relevant data is in this slice
                if local_start < 0:
                    return (0, 0)
            else:
                if slice_object.start > global_length - 1:
                    slice_object = slice(global_length - 1,
                                         slice_object.stop,
                                         slice_object.step)
                local_start = slice_object.start - shifted_start
                # if the local_start is negative, immediately return the
                # values for an empty slice
                if local_start < 0:
                    return (0, 0)

                # if the local_start is greater than the local length, pull
                # it down
                if local_start > local_length - 1:
                    overhead = local_start - (local_length - 1)
                    overhead = overhead - overhead % (-step)
                    local_start = local_start - overhead
                    # if local_start becomes negative here, it means, that the
                    # step size is bigger than the length of the localslice and
                    # that no relevant data is in this slice
                    if local_start < 0:
                        return (0, 0)

            # calculate the stop index
            if slice_object.stop is None:
                local_stop = None
            else:
                local_stop = slice_object.stop - shifted_start
                # if local_stop is negative, pull it up to None
                local_stop = None if local_stop < 0 else local_stop
        # Note: if start or stop are greater than the array length,
        # numpy will automatically cut the index value down into the
        # array's range
#        if local_start > local_length:
#            local_start = local_length
#        if local_stop > local_length:
#            local_stop = local_length
        return (local_start, local_stop)

    def extract_local_data(self, data_object):
1463
1464
1465
        h5py_dataset_Q = False
        if 'h5py' in gdi:
            h5py_dataset_Q = isinstance(data_object, h5py.Dataset)
theos's avatar
theos committed
1466
1467
        # if data_object is not a ndarray or a d2o, cast it to a ndarray
        if not (isinstance(data_object, np.ndarray) or
1468
1469
                isinstance(data_object, distributed_data_object) or
                h5py_dataset_Q):
theos's avatar
theos committed
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
            data_object = np.array(data_object)
        # check if the shapes are remotely compatible, reshape if possible
        # and determine which dimensions match only via broadcasting
        try:
            (data_object, matching_dimensions) = \
                self._reshape_foreign_data(data_object)
        # if the shape-casting fails, try to fix things via local data
        # matching
        except(ValueError):
            # Check if all the local shapes match the supplied data
            local_matchQ = (self.local_shape == data_object.shape)
            global_matchQ = self._allgather(local_matchQ)
            # if the local shapes match, simply return the data_object
            if np.all(global_matchQ):
                extracted_data = data_object[:]
            else:
theos's avatar
theos committed
1486
                raise ValueError(about_cstring(
theos's avatar
theos committed
1487
1488
1489
1490
1491
                    "ERROR: supplied shapes do neither match globally " +
                    "nor locally"))

        # if shape-casting was successfull, extract the data
        else:
theos's avatar
theos committed
1492
1493
1494
            if isinstance(data_object, distributed_data_object):
                # If the first dimension matches only via broadcasting...
                # Case 1: ...do broadcasting.
1495
                if not matching_dimensions[0]:
theos's avatar
theos committed
1496
1497
1498
1499
1500
1501
1502
1503
1504
                    extracted_data = data_object.get_full_data()
                    extracted_data = extracted_data[0]
                else:
                    # Case 2: First dimension fits directly and data_object is
                    # a d2o
                    # Check if both d2os have the same slicing
                    # If the distributor is exactly the same, extract the data
                    if self is data_object.distributor:
                        # Simply take the local data
theos's avatar
theos committed
1505
                        extracted_data = data_object.data
theos's avatar
theos committed
1506
1507
1508
1509
1510
1511
1512
1513
1514
                    # If the distributor is not exactly the same, check if the
                    # geometry matches if it is a slicing distributor
                    # -> comm and local shapes
                    elif (isinstance(data_object.distributor,
                                     _slicing_distributor) and
                          (self.comm is data_object.distributor.comm) and
                          (np.all(self.all_local_slices ==
                                  data_object.distributor.all_local_slices))):
                            extracted_data = data_object.data
theos's avatar
theos committed
1515

theos's avatar
theos committed
1516
                    else:
1517
1518
                        # Case 2: no.
                        # All nodes extract their local slice from the
theos's avatar
theos committed
1519
1520
1521
1522
1523
1524
                        # data_object
                        extracted_data =\
                            data_object.get_data(slice(self.local_start,
                                                       self.local_end),
                                                 local_keys=True)
                        extracted_data = extracted_data.get_local_data()
theos's avatar
theos committed
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544


#                # Check if the distributor and the comm match
#                # the own ones. Checking equality via 'is' is ok, as the
#                # distributor factory caches simmilar distributors
#                if self is data_object.distributor and\
#                        self.comm is data_object.distributor.comm:
#                    # Case 1: yes. Simply take the local data
#                    extracted_data = data_object.data
#                # If the distributors do not match directly, check
#                else:
#                    # Case 2: no. All nodes extract their local slice from the
#                    # data_object
#                    extracted_data =\
#                        data_object.get_data(slice(self.local_start,
#                                                   self.local_end),
#                                             local_keys=True)
#                    extracted_data = extracted_data.get_local_data()
#
#
theos's avatar
theos committed
1545
1546
1547
            # Case 2: np-array
            # If the first dimension matches only via broadcasting
            # ...do broadcasting.
1548
            elif not matching_dimensions[0]:
theos's avatar
theos committed
1549
                extracted_data = data_object[0:1]
theos's avatar
theos committed
1550
            # Case 3: First dimension fits directly and data_object is an
1551
            # generic array or h5py dataset
theos's avatar
theos committed
1552
1553
1554
1555
1556
1557
1558
            else:
                extracted_data =\
                    data_object[self.local_start:self.local_end]

        return extracted_data

    def _reshape_foreign_data(self, foreign):
theos's avatar
theos committed
1559

theos's avatar
theos committed
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
        # Case 1:
        # check if the shapes match directly
        if self.global_shape == foreign.shape:
            matching_dimensions = [True, ] * len(self.global_shape)
            return (foreign, matching_dimensions)
        # Case 2:
        # if not, try to reshape the input data.
        # In particular, this will fail when foreign is a d2o as long as
        # reshaping is not implemented
        try:
            output = foreign.reshape(self.global_shape)
            matching_dimensions = [True, ] * len(self.global_shape)
            return (output, matching_dimensions)
        except(ValueError, AttributeError):
            pass
        # Case 3:
        # if this does not work, try to broadcast the shape
        # check if the dimensions match
        if len(self.global_shape) != len(foreign.shape):
            raise ValueError(
theos's avatar
theos committed
1580
                about_cstring("ERROR: unequal number of dimensions!"))
theos's avatar
theos committed
1581
1582
1583
1584
1585
1586
1587
1588
1589
        # check direct matches
        direct_match = (np.array(self.global_shape) == np.array(foreign.shape))
        # check broadcast compatibility
        broadcast_match = (np.ones(len(self.global_shape), dtype=int) ==
                           np.array(foreign.shape))
        # combine the matches and assert that all are true
        combined_match = (direct_match | broadcast_match)
        if not np.all(combined_match):
            raise ValueError(
theos's avatar
theos committed
1590
                about_cstring("ERROR: incompatible shapes!"))
theos's avatar
theos committed
1591
1592
1593
1594
1595
1596
1597
1598
1599
        matching_dimensions = tuple(direct_match)
        return (foreign, matching_dimensions)

    def consolidate_data(self, data, target_rank='all', comm=None):
        if comm is None:
            comm = self.comm
        _gathered_data = np.empty(self.global_shape, dtype=self.dtype)
        _dim_list = self.all_local_slices[:, 3]
        _dim_offset_list = self.all_local_slices[:, 4]
1600
        data = np.ascontiguousarray(data)
theos's avatar
theos committed
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
        if target_rank == 'all':
            comm.Allgatherv([data, self.mpi_dtype],
                            [_gathered_data, _dim_list, _dim_offset_list,
                             self.mpi_dtype])
        else:
            comm.Gatherv([data, self.mpi_dtype],
                         [_gathered_data, _dim_list, _dim_offset_list,
                          self.mpi_dtype],
                         root=target_rank)
        return _gathered_data

    def flatten(self, data, inplace=False):
        if inplace:
            return data.ravel()
        else:
            return data.flatten()

    def where(self, data):
        # compute np.where result from the node's local data
        local_where = list(np.where(data))
        # shift the first dimension
        local_where[0] = local_where[0] + self.local_start
        local_where = tuple(local_where)

        global_where = map(lambda z: distributed_data_object(
                                             local_data=z,
                                             distribution_strategy='freeform'),
                           local_where)
        return global_where

    def unique(self, data):
        # if the size of the MPI communicator is equal to 1, the
        # reduce operator will not be applied. -> Cover this case directly.
        comm = self.comm
        size = self.comm.size
        rank = self.comm.rank
        if size == 1:
            global_unique_data = np.unique(data)
        else:
            local_unique_data = np.unique(data)
            local_data_length = np.array([local_unique_data.shape[0]])
            local_data_length_list = np.empty(size, dtype=np.int)
            comm.Allgather([local_data_length, MPI.INT],
                           [local_data_length_list, MPI.INT])

            global_unique_data = np.array([], dtype=self.dtype)
            for i in xrange(size):
                # broadcast data to the other nodes
                # prepare the recv array
                if rank != i:
                    work_shape = local_data_length_list[i]
                    work_array = np.empty(work_shape, dtype=self.dtype)
                else:
                    work_array = local_unique_data
                # do the actual broadcasting
                comm.Bcast([work_array, self.mpi_dtype], root=i)
                global_unique_data = np.unique(
                                        np.concatenate([work_array.flatten(),
                                                        global_unique_data]))
        return global_unique_data

1662
1663
1664
1665
1666
1667
1668
    def _combine_local_bincount_counts(self, obj, local_counts, axis):
        if axis is None or 0 in axis:
            global_counts = np.empty_like(local_counts)
            self._Allreduce_helper(local_counts, global_counts, MPI.SUM)
            result_object = obj.copy_empty(global_shape=global_counts.shape,
                                           dtype=global_counts.dtype,
                                           distribution_strategy='not')
1669
1670
            result_object.set_local_data(global_counts, copy=False)

theos's avatar
theos committed
1671
        else:
1672
            global_counts = local_counts
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
            global_shape = list(global_counts.shape)
            global_shape[0] = self.global_shape[0]

            # Try to preserve the distribution_strategy if possible.
            # Choose the local distribution_strategy if...
            # -> if the distribution_strategy was local in the beginning
            # -> if the slicing of the first axis changes even though
            #    the bincounting wasn't performed on it. This can be the case
            #    if axis=0 is extremely small and collapsing axis=1,2,3...
            #    forces fftw to use a different slicing.
            # Both cases are triggered by an ValueError exception

            try:
                if self.distribution_strategy not in STRATEGIES['global']:
                    raise ValueError

                result_object = obj.copy_empty(global_shape=global_shape,
                                               dtype=global_counts.dtype)
                result_object.set_local_data(global_counts, copy=False)

            except ValueError:
                result_object = obj.copy_empty(
                                           local_shape=global_counts.shape,
1696
1697
1698
                                           dtype=global_counts.dtype,
                                           distribution_strategy='freeform')
        return result_object
theos's avatar
theos committed
1699

1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717

#    def bincount(self, local_data, local_weights, minlength, axis=None):
#        if local_weights is None:
#            result_dtype = np.int
#        else:
#            result_dtype = np.float
#
#        local_counts = np.bincount(local_data,
#                                   weights=local_weights,
#                                   minlength=minlength)
#
#        # cast the local_counts to the right dtype while avoiding copying
#        local_counts = np.array(local_counts, copy=False, dtype=result_dtype)
#        global_counts = np.empty_like(local_counts)
#        self._Allreduce_helper(local_counts,
#                               global_counts,
#                               MPI.SUM)
#        return global_counts
theos's avatar
theos committed
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756

    def cumsum(self, parent, axis):
        data = parent.data
        # compute the local np.cumsum
        local_cumsum = np.cumsum(data, axis=axis)
        if axis is None or axis == 0:
            # communicate the highest value from node to node
            rank = self.comm.rank
            if local_cumsum.shape[0] == 0:
                local_shift = np.zeros((), dtype=local_cumsum.dtype)
            else:
                local_shift = local_cumsum[-1]
            local_shift_list = self.comm.allgather(local_shift)
            local_sum_of_shift = np.sum(local_shift_list[:rank],
                                        axis=0)
            local_cumsum += local_sum_of_shift

        # create the return d2o
        if axis is None:
            # try to preserve the distribution_strategy
            flat_global_shape = (self.global_dim, )
            flat_local_shape = np.shape(local_cumsum)
            result_d2o = parent.copy_empty(global_shape=flat_global_shape,
                                           local_shape=flat_local_shape)
            # check if the original distribution strategy yielded a suitable
            # local_shape
            if result_d2o.local_shape != flat_local_shape:
                # if it does not fit, construct a freeform d2o
                result_d2o = parent.copy_empty(
                                            global_shape=flat_global_shape,
                                            local_shape=flat_local_shape,
                                            distribution_strategy='freeform')
        else:
            result_d2o = parent.copy_empty()

        result_d2o.set_local_data(local_cumsum, copy=False)

        return result_d2o

1757
1758
1759
1760
1761
    def searchsorted(self, obj, v, side='left'):
        a = obj.get_local_data(copy=False)
        local_searched = np.searchsorted(a=a, v=v, side=side)
        global_searched = np.empty_like(local_searched)

theos's avatar
theos committed
1762
1763
1764
1765
1766
1767
#        if side is 'left':
#            op = MPI.MAX
#        elif side is 'right':
#            op = MPI.MIN
#        else:
#            raise ValueError
1768
1769
1770

        self.comm.Allreduce([local_searched, MPI.INT],
                            [global_searched, MPI.INT],
theos's avatar
theos committed
1771
                            op=MPI.SUM)
1772
1773
1774
1775
        if global_searched.shape == ():
            global_searched = global_searched[()]
        return global_searched

theos's avatar
theos committed
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
    def _sliceify(self, inp):
        sliceified = []
        result = []
        if isinstance(inp, tuple):
            x = inp
        elif isinstance(inp, list):
            x = tuple(inp)
        else:
            x = (inp, )

        for i in range(len(x)):
            if isinstance(x[i], slice):
                result += [x[i], ]
                sliceified += [False, ]
            else:
                if x[i] >= self.global_shape[i]:
                    raise IndexError('Index out of bounds!')
                if x[i] == -1:
                    result += [slice(-1, None)]
                else:
                    result += [slice(x[i], x[i] + 1), ]
                sliceified += [True, ]

        return (tuple(result), sliceified)

    def _enfold(self, in_data, sliceified):
        # TODO: Implement a reshape functionality in order to avoid this
        # low level mess!!

        if isinstance(in_data, distributed_data_object):
            local_data = in_data.get_local_data(copy=False)
1807
        elif not isinstance(in_data, np.ndarray):
theos's avatar
theos committed
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
            local_data = np.array(in_data, copy=False)
            in_data = local_data
        else:
            local_data = in_data

        temp_local_shape = ()
        temp_global_shape = ()
        j = 0
        for i in sliceified:
            if i is False:
                try:
                    temp_local_shape += (local_data.shape[j],)
                    temp_global_shape += (in_data.shape[j],)
                except(IndexError):
                    temp_local_shape += (1,)
                    temp_global_shape += (1,)
                j += 1
            else:
                temp_local_shape += (1,)
                temp_global_shape += (1,)
                try:
                    if in_data.shape[j] == 1:
                        j += 1
                except(IndexError):
                    pass

        # take into account that the sliceified tuple may be too short,
        # because of a non-exaustive list of slices
        for i in range(len(local_data.shape) - j):
            temp_local_shape += (local_data.shape[j],)
            temp_global_shape += (in_data.shape[j],)
            j += 1

        if isinstance(in_data, distributed_data_object):
            # in case of leading scalars, indenify the node with data
            # and broadcast the shape to the others
            if sliceified[0]:
                # Case 1: The in_data d2o has more than one dimension
                if len(in_data.shape) > 1 and \
                  in_data.distribution_strategy in STRATEGIES['slicing']:
                    local_in_data = in_data.get_local_data(copy=False)
                    local_has_data = (np.prod(local_in_data.shape) != 0)
                    local_has_data_list = np.array(
                                       self.comm.allgather(local_has_data))
                    nodes_with_data = np.where(local_has_data_list)[0]
                    if np.shape(nodes_with_data)[0] > 1:
                        raise ValueError(
                            "ERROR: scalar index on first dimension, " +
                            "but more than one node has data!")
                    elif np.shape(nodes_with_data)[0] == 1:
                        node_with_data = nodes_with_data[0]
                    else:
                        node_with_data = -1

                    if node_with_data == -1:
                        broadcasted_shape = (0,) * len(temp_local_shape)
                    else:
                        broadcasted_shape = self.comm.bcast(
                                                    temp_local_shape,
                                                    root=node_with_data)
                    if self.comm.rank != node_with_data:
                        temp_local_shape = np.array(broadcasted_shape)
                        temp_local_shape[0] = 0
                        temp_local_shape = tuple(temp_local_shape)

                # Case 2: The in_data d2o is only onedimensional
                else:
                    # The data contained in the d2o must be stored on one
                    # single node at the end. Hence it is ok to consolidate
                    # the data and to make a recursive call.
                    temp_data = in_data.get_full_data()
                    return self._enfold(temp_data, sliceified)

            if in_data.distribution_strategy in STRATEGIES['global']:
                new_data = in_data.copy_empty(global_shape=temp_global_shape)
                new_data.set_local_data(local_data, copy=False)
            elif in_data.distribution_strategy in STRATEGIES['local']:
                reshaped_data = local_data.reshape(temp_local_shape)
                new_data = distributed_data_object(
                           local_data=reshaped_data,
                           distribution_strategy=in_data.distribution_strategy,
                           copy=False,
                           comm=self.comm)
            return new_data
        else:
            return local_data.reshape(temp_local_shape)

    def _defold(self, in_data, sliceified):
        # TODO: Implement a reshape functionality in order to avoid this
        # low level mess!!
        if isinstance(in_data, distributed_data_object):
            local_data = in_data.data
1900
        elif not isinstance(in_data, np.ndarray):
theos's avatar
theos committed
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
            local_data = np.array(in_data, copy=False)
            in_data = local_data
        else:
            local_data = in_data
        temp_local_shape = ()
        temp_global_shape = ()
        j = 0
        for i in sliceified:
            if i is False:
                try:
                    temp_local_shape += (local_data.shape[j],)
                    temp_global_shape += (in_data.shape[j],)
                except(IndexError):
                    temp_local_shape += (1,)
                    temp_global_shape += (1,)
            j += 1

        # take into account that the sliceified tuple may be too short,
        # because of a non-exaustive list of slices
        for i in range(len(local_data.shape) - j):
            temp_local_shape += (local_data.shape[j],)
            temp_global_shape += (in_data.shape[j],)
            j += 1

        if isinstance(in_data, distributed_data_object):
            if temp_global_shape == ():
                new_data = in_data.get_full_data().flatten()[0]
            elif in_data.distribution_strategy != 'freeform':
                new_data = in_data.copy_empty(global_shape=temp_global_shape)
                if np.any(np.array(local_data.shape)[np.array(sliceified)] ==
                          0):
                    new_data.data[:] = np.empty((0,) * len(temp_local_shape),
                                                dtype=in_data.dtype)
                else:
                    new_data.data[:] = local_data.reshape(temp_local_shape)
            else:
theos's avatar
theos committed
1937
1938
1939
                if np.any(
                    np.array(local_data.shape)[
                        np.where(np.array(sliceified))] == 0):
theos's avatar
theos committed
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
                    temp = np.array(temp_local_shape)
                    temp[0] = 0
                    temp_local_shape = tuple(temp)
                    reshaped_data = np.empty(temp_local_shape,
                                             dtype=in_data.dtype)
                else:
                    reshaped_data = local_data.reshape(temp_local_shape)

                new_data = distributed_data_object(
                                           local_data=reshaped_data,
                                           distribution_strategy='freeform',
                                           copy=False,
                                           comm=self.comm)
            return new_data
        else:
            if temp_global_shape == ():
                return local_data.flatten()[0]
            else:
                return local_data.reshape(temp_local_shape)

Theo Steininger's avatar
Theo Steininger committed
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
    def save_data(self, data, alias, path=None, overwriteQ=True):
        comm = self.comm
        h5py_parallel = h5py.get_config().mpi
        if comm.size > 1 and not h5py_parallel:
            raise RuntimeError("ERROR: Programm is run with MPI " +
                               "size > 1 but non-parallel version of " +
                               "h5py is loaded.")
        # if no path and therefore no filename was given, use the alias
        # as filename
        use_path = alias if path is None else path

        # create the file-handle
        if h5py_parallel and gc['mpi_module'] == 'MPI':
            f = h5py.File(use_path, 'a', driver='mpio', comm=comm)
        else:
            f = h5py.File(use_path, 'a')
        # check if dataset with name == alias already exists
        try:
            f[alias]
            # if yes, and overwriteQ is set to False, raise an Error
            if overwriteQ is False:
                raise ValueError(about_cstring(
                    "ERROR: overwriteQ is False, but alias already " +
                    "in use!"))
            else:  # if yes, remove the existing dataset
                del f[alias]
        except(KeyError):
            pass
1988

Theo Steininger's avatar
Theo Steininger committed
1989
1990
1991
1992
1993
1994
1995
1996
        # create dataset
        dset = f.create_dataset(alias,
                                shape=self.global_shape,
                                dtype=self.dtype)
        # write the data
        dset[self.local_start:self.local_end] = data
        # close the file
        f.close()
1997

Theo Steininger's avatar
Theo Steininger committed
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
    def load_data(self, alias, path):
        comm = self.comm
        # parse the path
        file_path = path if (path is not None) else alias
        # create the file-handle
        if h5py.get_config().mpi and gc['mpi_module'] == 'MPI':
            f = h5py.File(file_path, 'r', driver='mpio', comm=comm)
        else:
            f = h5py.File(file_path, 'r')
        dset = f[alias]
        # check shape
        if dset.shape != self.global_shape:
            raise TypeError(about_cstring(
                "ERROR: The shape of the given dataset does not match " +
                "the distributed_data_object."))
        # check dtype
        if dset.dtype != self.dtype:
            raise TypeError(about_cstring(
                "ERROR: The datatype of the given dataset does not " +
                "match the one of the distributed_data_object."))
        # if everything seems to fit, load the data
        data = dset[self.local_start:self.local_end]
        # close the file
        f.close()
        return data
theos's avatar
theos committed
2023

Theo Steininger's avatar
Theo Steininger committed
2024
2025
    def _data_to_hdf5(self, hdf5_dataset, data):
        hdf5_dataset[self.local_start:self.local_end] = data
2026

theos's avatar
theos committed
2027
2028
2029
    def get_iter(self, d2o):
        return d2o_slicing_iter(d2o)

2030
2031
    def get_axes_local_distribution_strategy(self, axes):
        if 0 in axes:
2032
2033
2034
2035
2036
2037
            if self.distribution_strategy in STRATEGIES['local']:
                raise ValueError(about_cstring(
                    "ERROR: axes_local_distribution_strategy is not uniquely "
                    "defined for local-type distribution strategies."))
            else:
                return self.distribution_strategy