field.py 32.8 KB
Newer Older
csongor's avatar
csongor committed
1
2
3
from __future__ import division
import numpy as np

Theo Steininger's avatar
Theo Steininger committed
4
5
from keepers import Versionable,\
                    Loggable
Jait Dixit's avatar
Jait Dixit committed
6

7
from d2o import distributed_data_object,\
8
    STRATEGIES as DISTRIBUTION_STRATEGIES
csongor's avatar
csongor committed
9

10
from nifty.config import nifty_configuration as gc
csongor's avatar
csongor committed
11

12
from nifty.field_types import FieldType
13

14
from nifty.spaces.space import Space
15
from nifty.spaces.power_space import PowerSpace
csongor's avatar
csongor committed
16

csongor's avatar
csongor committed
17
import nifty.nifty_utilities as utilities
18
19
from nifty.random import Random

csongor's avatar
csongor committed
20

Jait Dixit's avatar
Jait Dixit committed
21
class Field(Loggable, Versionable, object):
Theo Steininger's avatar
Theo Steininger committed
22
    # ---Initialization methods---
23

Theo Steininger's avatar
Theo Steininger committed
24
    def __init__(self, domain=None, val=None, dtype=None, field_type=None,
25
                 distribution_strategy=None, copy=False):
csongor's avatar
csongor committed
26

27
        self.domain = self._parse_domain(domain=domain, val=val)
28
        self.domain_axes = self._get_axes_tuple(self.domain)
csongor's avatar
csongor committed
29

30
        self.field_type = self._parse_field_type(field_type, val=val)
31

Theo Steininger's avatar
Theo Steininger committed
32
33
34
35
36
37
        try:
            start = len(reduce(lambda x, y: x+y, self.domain_axes))
        except TypeError:
            start = 0
        self.field_type_axes = self._get_axes_tuple(self.field_type,
                                                    start=start)
38

Theo Steininger's avatar
Theo Steininger committed
39
        self.dtype = self._infer_dtype(dtype=dtype,
Jait Dixit's avatar
Jait Dixit committed
40
                                       val=val,
Theo Steininger's avatar
Theo Steininger committed
41
42
                                       domain=self.domain,
                                       field_type=self.field_type)
43

44
45
46
        self.distribution_strategy = self._parse_distribution_strategy(
                                distribution_strategy=distribution_strategy,
                                val=val)
csongor's avatar
csongor committed
47
48
49

        self.set_val(new_val=val, copy=copy)

50
    def _parse_domain(self, domain, val=None):
51
        if domain is None:
52
53
54
55
            if isinstance(val, Field):
                domain = val.domain
            else:
                domain = ()
56
        elif isinstance(domain, Space):
57
            domain = (domain,)
58
59
60
        elif not isinstance(domain, tuple):
            domain = tuple(domain)

csongor's avatar
csongor committed
61
        for d in domain:
62
            if not isinstance(d, Space):
63
64
65
                raise TypeError(
                    "Given domain contains something that is not a "
                    "nifty.space.")
csongor's avatar
csongor committed
66
67
        return domain

68
    def _parse_field_type(self, field_type, val=None):
69
        if field_type is None:
70
71
72
73
            if isinstance(val, Field):
                field_type = val.field_type
            else:
                field_type = ()
74
        elif isinstance(field_type, FieldType):
75
            field_type = (field_type,)
76
77
        elif not isinstance(field_type, tuple):
            field_type = tuple(field_type)
78
        for ft in field_type:
79
            if not isinstance(ft, FieldType):
80
81
                raise TypeError(
                    "Given object is not a nifty.FieldType.")
82
83
        return field_type

Theo Steininger's avatar
Theo Steininger committed
84
85
86
87
88
89
90
91
92
93
    def _get_axes_tuple(self, things_with_shape, start=0):
        i = start
        axes_list = []
        for thing in things_with_shape:
            l = []
            for j in range(len(thing.shape)):
                l += [i]
                i += 1
            axes_list += [tuple(l)]
        return tuple(axes_list)
94

95
    def _infer_dtype(self, dtype, val, domain, field_type):
csongor's avatar
csongor committed
96
        if dtype is None:
97
98
99
            if isinstance(val, Field) or \
               isinstance(val, distributed_data_object):
                dtype = val.dtype
Theo Steininger's avatar
Theo Steininger committed
100
101
102
103
104
105
106
            dtype_tuple = (np.dtype(gc['default_field_dtype']),)
        else:
            dtype_tuple = (np.dtype(dtype),)
        if domain is not None:
            dtype_tuple += tuple(np.dtype(sp.dtype) for sp in domain)
        if field_type is not None:
            dtype_tuple += tuple(np.dtype(ft.dtype) for ft in field_type)
csongor's avatar
csongor committed
107

Theo Steininger's avatar
Theo Steininger committed
108
        dtype = reduce(lambda x, y: np.result_type(x, y), dtype_tuple)
109

Theo Steininger's avatar
Theo Steininger committed
110
        return dtype
111

112
113
    def _parse_distribution_strategy(self, distribution_strategy, val):
        if distribution_strategy is None:
114
            if isinstance(val, distributed_data_object):
115
                distribution_strategy = val.distribution_strategy
116
            elif isinstance(val, Field):
117
                distribution_strategy = val.distribution_strategy
118
            else:
119
                self.logger.info("Datamodel set to default!")
120
                distribution_strategy = gc['default_distribution_strategy']
121
        elif distribution_strategy not in DISTRIBUTION_STRATEGIES['global']:
122
123
124
            raise ValueError(
                    "distribution_strategy must be a global-type "
                    "strategy.")
125
        return distribution_strategy
126
127

    # ---Factory methods---
128

129
130
    @classmethod
    def from_random(cls, random_type, domain=None, dtype=None, field_type=None,
131
                    distribution_strategy=None, **kwargs):
132
133
        # create a initially empty field
        f = cls(domain=domain, dtype=dtype, field_type=field_type,
134
                distribution_strategy=distribution_strategy)
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169

        # now use the processed input in terms of f in order to parse the
        # random arguments
        random_arguments = cls._parse_random_arguments(random_type=random_type,
                                                       f=f,
                                                       **kwargs)

        # extract the distributed_dato_object from f and apply the appropriate
        # random number generator to it
        sample = f.get_val(copy=False)
        generator_function = getattr(Random, random_type)
        sample.apply_generator(
            lambda shape: generator_function(dtype=f.dtype,
                                             shape=shape,
                                             **random_arguments))
        return f

    @staticmethod
    def _parse_random_arguments(random_type, f, **kwargs):

        if random_type == "pm1":
            random_arguments = {}

        elif random_type == "normal":
            mean = kwargs.get('mean', 0)
            std = kwargs.get('std', 1)
            random_arguments = {'mean': mean,
                                'std': std}

        elif random_type == "uniform":
            low = kwargs.get('low', 0)
            high = kwargs.get('high', 1)
            random_arguments = {'low': low,
                                'high': high}

csongor's avatar
csongor committed
170
        else:
171
172
            raise KeyError(
                "unsupported random key '" + str(random_type) + "'.")
csongor's avatar
csongor committed
173

174
        return random_arguments
csongor's avatar
csongor committed
175

176
177
178
179
180
181
182
183
184
    # ---Powerspectral methods---

    def power_analyze(self, spaces=None, log=False, nbin=None, binbounds=None,
                      real_signal=True):
        # assert that all spaces in `self.domain` are either harmonic or
        # power_space instances
        for sp in self.domain:
            if not sp.harmonic and not isinstance(sp, PowerSpace):
                raise AttributeError(
185
                    "Field has a space in `domain` which is neither "
186
187
188
                    "harmonic nor a PowerSpace.")

        # check if the `spaces` input is valid
189
190
191
192
193
        spaces = utilities.cast_axis_to_tuple(spaces, len(self.domain))
        if spaces is None:
            if len(self.domain) == 1:
                spaces = (0,)
            else:
194
195
196
                raise ValueError(
                    "Field has multiple spaces as domain "
                    "but `spaces` is None.")
197
198

        if len(spaces) == 0:
199
200
            raise ValueError(
                "No space for analysis specified.")
201
        elif len(spaces) > 1:
202
203
            raise ValueError(
                "Conversion of only one space at a time is allowed.")
204
205
206
207

        space_index = spaces[0]

        if not self.domain[space_index].harmonic:
208
209
            raise ValueError(
                "The analyzed space must be harmonic.")
210

211
212
213
214
215
216
        # Create the target PowerSpace instance:
        # If the associated signal-space field was real, we extract the
        # hermitian and anti-hermitian parts of `self` and put them
        # into the real and imaginary parts of the power spectrum.
        # If it was complex, all the power is put into a real power spectrum.

217
218
219
220
        distribution_strategy = \
            self.val.get_axes_local_distribution_strategy(
                self.domain_axes[space_index])

221
222
223
224
225
        if real_signal:
            power_dtype = np.dtype('complex')
        else:
            power_dtype = np.dtype('float')

226
227
        harmonic_domain = self.domain[space_index]
        power_domain = PowerSpace(harmonic_domain=harmonic_domain,
228
                                  distribution_strategy=distribution_strategy,
229
230
                                  log=log, nbin=nbin, binbounds=binbounds,
                                  dtype=power_dtype)
231

232
        # extract pindex and rho from power_domain
233
234
        pindex = power_domain.pindex
        rho = power_domain.rho
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252

        if real_signal:
            hermitian_part, anti_hermitian_part = \
                harmonic_domain.hermitian_decomposition(
                                            self.val,
                                            axes=self.domain_axes[space_index])

            [hermitian_power, anti_hermitian_power] = \
                [self._calculate_power_spectrum(
                                            x=part,
                                            pindex=pindex,
                                            rho=rho,
                                            axes=self.domain_axes[space_index])
                 for part in [hermitian_part, anti_hermitian_part]]

            power_spectrum = hermitian_power + 1j * anti_hermitian_power
        else:
            power_spectrum = self._calculate_power_spectrum(
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
                                            x=self.val,
                                            pindex=pindex,
                                            rho=rho,
                                            axes=self.domain_axes[space_index])

        # create the result field and put power_spectrum into it
        result_domain = list(self.domain)
        result_domain[space_index] = power_domain

        result_field = self.copy_empty(domain=result_domain)
        result_field.set_val(new_val=power_spectrum, copy=False)

        return result_field

    def _calculate_power_spectrum(self, x, pindex, rho, axes=None):
        fieldabs = abs(x)
        fieldabs **= 2

        if axes is not None:
            pindex = self._shape_up_pindex(
                                    pindex=pindex,
                                    target_shape=x.shape,
                                    target_strategy=x.distribution_strategy,
                                    axes=axes)
        power_spectrum = pindex.bincount(weights=fieldabs,
                                         axis=axes)
        if axes is not None:
            new_rho_shape = [1, ] * len(power_spectrum.shape)
            new_rho_shape[axes[0]] = len(rho)
            rho = rho.reshape(new_rho_shape)
        power_spectrum /= rho

        power_spectrum **= 0.5
        return power_spectrum

    def _shape_up_pindex(self, pindex, target_shape, target_strategy, axes):
        if pindex.distribution_strategy not in \
                DISTRIBUTION_STRATEGIES['global']:
291
            raise ValueError("pindex's distribution strategy must be "
292
293
294
295
296
297
                             "global-type")

        if pindex.distribution_strategy in DISTRIBUTION_STRATEGIES['slicing']:
            if ((0 not in axes) or
                    (target_strategy is not pindex.distribution_strategy)):
                raise ValueError(
298
                    "A slicing distributor shall not be reshaped to "
299
300
301
302
303
304
305
306
307
308
309
310
311
                    "something non-sliced.")

        semiscaled_shape = [1, ] * len(target_shape)
        for i in axes:
            semiscaled_shape[i] = target_shape[i]
        local_data = pindex.get_local_data(copy=False)
        semiscaled_local_data = local_data.reshape(semiscaled_shape)
        result_obj = pindex.copy_empty(global_shape=target_shape,
                                       distribution_strategy=target_strategy)
        result_obj.set_full_data(semiscaled_local_data, copy=False)

        return result_obj

312
313
    def power_synthesize(self, spaces=None, real_signal=True,
                         mean=None, std=None):
314
        # assert that all spaces in `self.domain` are either of signal-type or
315
316
        # power_space instances
        for sp in self.domain:
317
            if not sp.harmonic and not isinstance(sp, PowerSpace):
318
                raise AttributeError(
319
                    "Field has a space in `domain` which is neither "
320
321
                    "harmonic nor a PowerSpace.")

322
323
324
325
326
327
        # check if the `spaces` input is valid
        spaces = utilities.cast_axis_to_tuple(spaces, len(self.domain))
        if spaces is None:
            if len(self.domain) == 1:
                spaces = (0,)
            else:
328
329
330
                raise ValueError(
                    "Field has multiple spaces as domain "
                    "but `spaces` is None.")
331
332

        if len(spaces) == 0:
333
334
            raise ValueError(
                "No space for synthesis specified.")
335
        elif len(spaces) > 1:
336
337
            raise ValueError(
                "Conversion of only one space at a time is allowed.")
338
339
340
341

        power_space_index = spaces[0]
        power_domain = self.domain[power_space_index]
        if not isinstance(power_domain, PowerSpace):
342
343
            raise ValueError(
                "A PowerSpace is needed for field synthetization.")
344
345
346
347
348
349
350
351
352
353
354
355
356
357

        # create the result domain
        result_domain = list(self.domain)
        harmonic_domain = power_domain.harmonic_domain
        result_domain[power_space_index] = harmonic_domain

        # create random samples: one or two, depending on whether the
        # power spectrum is real or complex

        if issubclass(power_domain.dtype.type, np.complexfloating):
            result_list = [None, None]
        else:
            result_list = [None]

358
359
        result_list = [self.__class__.from_random(
                             'normal',
360
361
362
                             mean=mean,
                             std=std,
                             domain=result_domain,
363
364
365
                             dtype=harmonic_domain.dtype,
                             field_type=self.field_type,
                             distribution_strategy=self.distribution_strategy)
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
                       for x in result_list]

        # from now on extract the values from the random fields for further
        # processing without killing the fields.
        # if the signal-space field should be real, hermitianize the field
        # components
        if real_signal:
            result_val_list = [harmonic_domain.hermitian_decomposition(
                                    x.val,
                                    axes=x.domain_axes[power_space_index])[0]
                               for x in result_list]
        else:
            result_val_list = [x.val for x in result_list]

        # weight the random fields with the power spectrum
        # therefore get the pindex from the power space
        pindex = power_domain.pindex
        # take the local data from pindex. This data must be compatible to the
        # local data of the field given the slice of the PowerSpace
        local_distribution_strategy = \
            result_list[0].val.get_axes_local_distribution_strategy(
                result_list[0].domain_axes[power_space_index])

        if pindex.distribution_strategy is not local_distribution_strategy:
390
            self.logger.warn(
391
                "The distribution_stragey of pindex does not fit the "
392
393
394
395
396
397
                "slice_local distribution strategy of the synthesized field.")

        # Now use numpy advanced indexing in order to put the entries of the
        # power spectrum into the appropriate places of the pindex array.
        # Do this for every 'pindex-slice' in parallel using the 'slice(None)'s
        local_pindex = pindex.get_local_data(copy=False)
398
        full_spec = self.val.get_full_data()
399
400
401
402
403

        local_blow_up = [slice(None)]*len(self.shape)
        local_blow_up[self.domain_axes[power_space_index][0]] = local_pindex

        # here, the power_spectrum is distributed into the new shape
404
        local_rescaler = full_spec[local_blow_up]
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425

        # apply the rescaler to the random fields
        result_val_list[0].apply_scalar_function(
                                            lambda x: x * local_rescaler.real,
                                            inplace=True)

        if issubclass(power_domain.dtype.type, np.complexfloating):
            result_val_list[1].apply_scalar_function(
                                            lambda x: x * local_rescaler.imag,
                                            inplace=True)

        # store the result into the fields
        [x.set_val(new_val=y, copy=False) for x, y in
            zip(result_list, result_val_list)]

        if issubclass(power_domain.dtype.type, np.complexfloating):
            result = result_list[0] + 1j*result_list[1]
        else:
            result = result_list[0]

        return result
426

Theo Steininger's avatar
Theo Steininger committed
427
    # ---Properties---
428

Theo Steininger's avatar
Theo Steininger committed
429
    def set_val(self, new_val=None, copy=False):
430
431
        new_val = self.cast(new_val)
        if copy:
Theo Steininger's avatar
Theo Steininger committed
432
433
            new_val = new_val.copy()
        self._val = new_val
434
        return self
csongor's avatar
csongor committed
435

436
437
    def get_val(self, copy=False):
        if copy:
Theo Steininger's avatar
Theo Steininger committed
438
            return self._val.copy()
439
        else:
Theo Steininger's avatar
Theo Steininger committed
440
            return self._val
csongor's avatar
csongor committed
441

Theo Steininger's avatar
Theo Steininger committed
442
443
444
    @property
    def val(self):
        return self._val
csongor's avatar
csongor committed
445

Theo Steininger's avatar
Theo Steininger committed
446
447
448
    @val.setter
    def val(self, new_val):
        self._val = self.cast(new_val)
csongor's avatar
csongor committed
449

450
451
    @property
    def shape(self):
452
453
454
455
456
457
458
        shape_tuple = ()
        shape_tuple += tuple(sp.shape for sp in self.domain)
        shape_tuple += tuple(ft.shape for ft in self.field_type)
        try:
            global_shape = reduce(lambda x, y: x + y, shape_tuple)
        except TypeError:
            global_shape = ()
csongor's avatar
csongor committed
459

460
        return global_shape
csongor's avatar
csongor committed
461

462
463
    @property
    def dim(self):
Theo Steininger's avatar
Theo Steininger committed
464
465
466
467
468
469
470
        dim_tuple = ()
        dim_tuple += tuple(sp.dim for sp in self.domain)
        dim_tuple += tuple(ft.dim for ft in self.field_type)
        try:
            return reduce(lambda x, y: x * y, dim_tuple)
        except TypeError:
            return 0
csongor's avatar
csongor committed
471

472
473
    @property
    def dof(self):
Theo Steininger's avatar
Theo Steininger committed
474
475
476
477
478
479
480
481
        dof = self.dim
        if issubclass(self.dtype.type, np.complexfloating):
            dof *= 2
        return dof

    @property
    def total_volume(self):
        volume_tuple = tuple(sp.total_volume for sp in self.domain)
482
        try:
Theo Steininger's avatar
Theo Steininger committed
483
            return reduce(lambda x, y: x * y, volume_tuple)
484
        except TypeError:
Theo Steininger's avatar
Theo Steininger committed
485
            return 0
486

Theo Steininger's avatar
Theo Steininger committed
487
    # ---Special unary/binary operations---
488

csongor's avatar
csongor committed
489
490
491
    def cast(self, x=None, dtype=None):
        if dtype is None:
            dtype = self.dtype
492
493
        else:
            dtype = np.dtype(dtype)
494

495
496
        casted_x = x

497
        for ind, sp in enumerate(self.domain):
498
            casted_x = sp.pre_cast(casted_x,
499
500
501
502
503
504
505
                                   axes=self.domain_axes[ind])

        for ind, ft in enumerate(self.field_type):
            casted_x = ft.pre_cast(casted_x,
                                   axes=self.field_type_axes[ind])

        casted_x = self._actual_cast(casted_x, dtype=dtype)
506
507

        for ind, sp in enumerate(self.domain):
508
509
            casted_x = sp.post_cast(casted_x,
                                    axes=self.domain_axes[ind])
510
511

        for ind, ft in enumerate(self.field_type):
512
513
            casted_x = ft.post_cast(casted_x,
                                    axes=self.field_type_axes[ind])
514
515

        return casted_x
csongor's avatar
csongor committed
516

Theo Steininger's avatar
Theo Steininger committed
517
    def _actual_cast(self, x, dtype=None):
518
        if isinstance(x, Field):
csongor's avatar
csongor committed
519
520
521
522
523
            x = x.get_val()

        if dtype is None:
            dtype = self.dtype

524
        return_x = distributed_data_object(
525
526
527
                            global_shape=self.shape,
                            dtype=dtype,
                            distribution_strategy=self.distribution_strategy)
528
529
        return_x.set_full_data(x, copy=False)
        return return_x
Theo Steininger's avatar
Theo Steininger committed
530
531

    def copy(self, domain=None, dtype=None, field_type=None,
532
             distribution_strategy=None):
Theo Steininger's avatar
Theo Steininger committed
533
        copied_val = self.get_val(copy=True)
534
535
536
537
538
        new_field = self.copy_empty(
                                domain=domain,
                                dtype=dtype,
                                field_type=field_type,
                                distribution_strategy=distribution_strategy)
Theo Steininger's avatar
Theo Steininger committed
539
540
        new_field.set_val(new_val=copied_val, copy=False)
        return new_field
csongor's avatar
csongor committed
541

Theo Steininger's avatar
Theo Steininger committed
542
    def copy_empty(self, domain=None, dtype=None, field_type=None,
543
                   distribution_strategy=None):
Theo Steininger's avatar
Theo Steininger committed
544
545
        if domain is None:
            domain = self.domain
csongor's avatar
csongor committed
546
        else:
Theo Steininger's avatar
Theo Steininger committed
547
            domain = self._parse_domain(domain)
csongor's avatar
csongor committed
548

Theo Steininger's avatar
Theo Steininger committed
549
550
551
552
        if dtype is None:
            dtype = self.dtype
        else:
            dtype = np.dtype(dtype)
csongor's avatar
csongor committed
553

Theo Steininger's avatar
Theo Steininger committed
554
555
556
557
        if field_type is None:
            field_type = self.field_type
        else:
            field_type = self._parse_field_type(field_type)
csongor's avatar
csongor committed
558

559
560
        if distribution_strategy is None:
            distribution_strategy = self.distribution_strategy
csongor's avatar
csongor committed
561

Theo Steininger's avatar
Theo Steininger committed
562
563
564
565
566
567
568
569
570
571
572
573
574
575
        fast_copyable = True
        try:
            for i in xrange(len(self.domain)):
                if self.domain[i] is not domain[i]:
                    fast_copyable = False
                    break
            for i in xrange(len(self.field_type)):
                if self.field_type[i] is not field_type[i]:
                    fast_copyable = False
                    break
        except IndexError:
            fast_copyable = False

        if (fast_copyable and dtype == self.dtype and
576
                distribution_strategy == self.distribution_strategy):
Theo Steininger's avatar
Theo Steininger committed
577
578
579
580
581
            new_field = self._fast_copy_empty()
        else:
            new_field = Field(domain=domain,
                              dtype=dtype,
                              field_type=field_type,
582
                              distribution_strategy=distribution_strategy)
Theo Steininger's avatar
Theo Steininger committed
583
        return new_field
csongor's avatar
csongor committed
584

Theo Steininger's avatar
Theo Steininger committed
585
586
587
588
589
590
591
    def _fast_copy_empty(self):
        # make an empty field
        new_field = EmptyField()
        # repair its class
        new_field.__class__ = self.__class__
        # copy domain, codomain and val
        for key, value in self.__dict__.items():
592
            if key != '_val':
Theo Steininger's avatar
Theo Steininger committed
593
594
595
596
597
598
                new_field.__dict__[key] = value
            else:
                new_field.__dict__[key] = self.val.copy_empty()
        return new_field

    def weight(self, power=1, inplace=False, spaces=None):
599
        if inplace:
csongor's avatar
csongor committed
600
601
602
603
            new_field = self
        else:
            new_field = self.copy_empty()

604
        new_val = self.get_val(copy=False)
csongor's avatar
csongor committed
605

csongor's avatar
csongor committed
606
        if spaces is None:
Theo Steininger's avatar
Theo Steininger committed
607
608
609
            spaces = range(len(self.domain))
        else:
            spaces = utilities.cast_axis_to_tuple(spaces, len(self.domain))
csongor's avatar
csongor committed
610

611
        for ind, sp in enumerate(self.domain):
Theo Steininger's avatar
Theo Steininger committed
612
613
614
615
616
            if ind in spaces:
                new_val = sp.weight(new_val,
                                    power=power,
                                    axes=self.domain_axes[ind],
                                    inplace=inplace)
617
618

        new_field.set_val(new_val=new_val, copy=False)
csongor's avatar
csongor committed
619
620
        return new_field

Theo Steininger's avatar
Theo Steininger committed
621
622
623
624
625
626
627
628
629
    def dot(self, x=None, bare=False):
        if isinstance(x, Field):
            try:
                assert len(x.domain) == len(self.domain)
                for index in xrange(len(self.domain)):
                    assert x.domain[index] == self.domain[index]
                for index in xrange(len(self.field_type)):
                    assert x.field_type[index] == self.field_type[index]
            except AssertionError:
630
631
                raise ValueError(
                    "domains are incompatible.")
Theo Steininger's avatar
Theo Steininger committed
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
            # extract the data from x and try to dot with this
            x = x.get_val(copy=False)

        # Compute the dot respecting the fact of discrete/continous spaces
        if bare:
            y = self
        else:
            y = self.weight(power=1)

        y = y.get_val(copy=False)

        # Cast the input in order to cure dtype and shape differences
        x = self.cast(x)

        dotted = x.conjugate() * y

        return dotted.sum()

650
    def norm(self, q=2):
csongor's avatar
csongor committed
651
652
653
654
655
656
657
658
659
660
661
662
663
664
        """
            Computes the Lq-norm of the field values.

            Parameters
            ----------
            q : scalar
                Parameter q of the Lq-norm (default: 2).

            Returns
            -------
            norm : scalar
                The Lq-norm of the field values.

        """
665
        if q == 2:
666
            return (self.dot(x=self)) ** (1 / 2)
csongor's avatar
csongor committed
667
        else:
668
            return self.dot(x=self ** (q - 1)) ** (1 / q)
csongor's avatar
csongor committed
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684

    def conjugate(self, inplace=False):
        """
            Computes the complex conjugate of the field.

            Returns
            -------
            cc : field
                The complex conjugated field.

        """
        if inplace:
            work_field = self
        else:
            work_field = self.copy_empty()

685
        new_val = self.get_val(copy=False)
Theo Steininger's avatar
Theo Steininger committed
686
        new_val = new_val.conjugate()
687
        work_field.set_val(new_val=new_val, copy=False)
csongor's avatar
csongor committed
688
689
690

        return work_field

Theo Steininger's avatar
Theo Steininger committed
691
    # ---General unary/contraction methods---
692

Theo Steininger's avatar
Theo Steininger committed
693
694
    def __pos__(self):
        return self.copy()
695

Theo Steininger's avatar
Theo Steininger committed
696
697
698
699
    def __neg__(self):
        return_field = self.copy_empty()
        new_val = -self.get_val(copy=False)
        return_field.set_val(new_val, copy=False)
csongor's avatar
csongor committed
700
701
        return return_field

Theo Steininger's avatar
Theo Steininger committed
702
703
704
705
706
    def __abs__(self):
        return_field = self.copy_empty()
        new_val = abs(self.get_val(copy=False))
        return_field.set_val(new_val, copy=False)
        return return_field
csongor's avatar
csongor committed
707

Theo Steininger's avatar
Theo Steininger committed
708
709
710
711
712
713
    def _contraction_helper(self, op, spaces, types):
        # build a list of all axes
        if spaces is None:
            spaces = xrange(len(self.domain))
        else:
            spaces = utilities.cast_axis_to_tuple(spaces, len(self.domain))
csongor's avatar
csongor committed
714

Theo Steininger's avatar
Theo Steininger committed
715
716
717
718
        if types is None:
            types = xrange(len(self.field_type))
        else:
            types = utilities.cast_axis_to_tuple(types, len(self.field_type))
719

Theo Steininger's avatar
Theo Steininger committed
720
721
722
723
        axes_list = ()
        axes_list += tuple(self.domain_axes[sp_index] for sp_index in spaces)
        axes_list += tuple(self.field_type_axes[ft_index] for
                           ft_index in types)
724
        try:
Theo Steininger's avatar
Theo Steininger committed
725
            axes_list = reduce(lambda x, y: x+y, axes_list)
726
        except TypeError:
Theo Steininger's avatar
Theo Steininger committed
727
            axes_list = ()
csongor's avatar
csongor committed
728

Theo Steininger's avatar
Theo Steininger committed
729
730
731
        # perform the contraction on the d2o
        data = self.get_val(copy=False)
        data = getattr(data, op)(axis=axes_list)
csongor's avatar
csongor committed
732

Theo Steininger's avatar
Theo Steininger committed
733
734
735
        # check if the result is scalar or if a result_field must be constr.
        if np.isscalar(data):
            return data
csongor's avatar
csongor committed
736
        else:
Theo Steininger's avatar
Theo Steininger committed
737
738
739
740
741
742
743
744
745
746
747
            return_domain = tuple(self.domain[i]
                                  for i in xrange(len(self.domain))
                                  if i not in spaces)
            return_field_type = tuple(self.field_type[i]
                                      for i in xrange(len(self.field_type))
                                      if i not in types)
            return_field = Field(domain=return_domain,
                                 val=data,
                                 field_type=return_field_type,
                                 copy=False)
            return return_field
csongor's avatar
csongor committed
748

Theo Steininger's avatar
Theo Steininger committed
749
750
    def sum(self, spaces=None, types=None):
        return self._contraction_helper('sum', spaces, types)
csongor's avatar
csongor committed
751

Theo Steininger's avatar
Theo Steininger committed
752
753
    def prod(self, spaces=None, types=None):
        return self._contraction_helper('prod', spaces, types)
csongor's avatar
csongor committed
754

Theo Steininger's avatar
Theo Steininger committed
755
756
    def all(self, spaces=None, types=None):
        return self._contraction_helper('all', spaces, types)
csongor's avatar
csongor committed
757

Theo Steininger's avatar
Theo Steininger committed
758
759
    def any(self, spaces=None, types=None):
        return self._contraction_helper('any', spaces, types)
csongor's avatar
csongor committed
760

Theo Steininger's avatar
Theo Steininger committed
761
762
    def min(self, spaces=None, types=None):
        return self._contraction_helper('min', spaces, types)
csongor's avatar
csongor committed
763

Theo Steininger's avatar
Theo Steininger committed
764
765
    def nanmin(self, spaces=None, types=None):
        return self._contraction_helper('nanmin', spaces, types)
csongor's avatar
csongor committed
766

Theo Steininger's avatar
Theo Steininger committed
767
768
    def max(self, spaces=None, types=None):
        return self._contraction_helper('max', spaces, types)
csongor's avatar
csongor committed
769

Theo Steininger's avatar
Theo Steininger committed
770
771
    def nanmax(self, spaces=None, types=None):
        return self._contraction_helper('nanmax', spaces, types)
csongor's avatar
csongor committed
772

Theo Steininger's avatar
Theo Steininger committed
773
774
    def mean(self, spaces=None, types=None):
        return self._contraction_helper('mean', spaces, types)
csongor's avatar
csongor committed
775

Theo Steininger's avatar
Theo Steininger committed
776
777
    def var(self, spaces=None, types=None):
        return self._contraction_helper('var', spaces, types)
csongor's avatar
csongor committed
778

Theo Steininger's avatar
Theo Steininger committed
779
780
    def std(self, spaces=None, types=None):
        return self._contraction_helper('std', spaces, types)
csongor's avatar
csongor committed
781

Theo Steininger's avatar
Theo Steininger committed
782
    # ---General binary methods---
csongor's avatar
csongor committed
783

Theo Steininger's avatar
Theo Steininger committed
784
    def _binary_helper(self, other, op, inplace=False):
csongor's avatar
csongor committed
785
        # if other is a field, make sure that the domains match
786
        if isinstance(other, Field):
Theo Steininger's avatar
Theo Steininger committed
787
788
789
790
            try:
                assert len(other.domain) == len(self.domain)
                for index in xrange(len(self.domain)):
                    assert other.domain[index] == self.domain[index]
791
                assert len(other.field_type) == len(self.field_type)
Theo Steininger's avatar
Theo Steininger committed
792
793
794
                for index in xrange(len(self.field_type)):
                    assert other.field_type[index] == self.field_type[index]
            except AssertionError:
795
796
                raise ValueError(
                    "domains are incompatible.")
Theo Steininger's avatar
Theo Steininger committed
797
            other = other.get_val(copy=False)
csongor's avatar
csongor committed
798

Theo Steininger's avatar
Theo Steininger committed
799
800
        self_val = self.get_val(copy=False)
        return_val = getattr(self_val, op)(other)
csongor's avatar
csongor committed
801
802
803
804

        if inplace:
            working_field = self
        else:
805
            working_field = self.copy_empty(dtype=return_val.dtype)
csongor's avatar
csongor committed
806

Theo Steininger's avatar
Theo Steininger committed
807
        working_field.set_val(return_val, copy=False)
csongor's avatar
csongor committed
808
809
810
        return working_field

    def __add__(self, other):
Theo Steininger's avatar
Theo Steininger committed
811
        return self._binary_helper(other, op='__add__')
812

813
    def __radd__(self, other):
Theo Steininger's avatar
Theo Steininger committed
814
        return self._binary_helper(other, op='__radd__')
csongor's avatar
csongor committed
815
816

    def __iadd__(self, other):
Theo Steininger's avatar
Theo Steininger committed
817
        return self._binary_helper(other, op='__iadd__', inplace=True)
csongor's avatar
csongor committed
818
819

    def __sub__(self, other):
Theo Steininger's avatar
Theo Steininger committed
820
        return self._binary_helper(other, op='__sub__')
csongor's avatar
csongor committed
821
822

    def __rsub__(self, other):
Theo Steininger's avatar
Theo Steininger committed
823
        return self._binary_helper(other, op='__rsub__')
csongor's avatar
csongor committed
824
825

    def __isub__(self, other):
Theo Steininger's avatar
Theo Steininger committed
826
        return self._binary_helper(other, op='__isub__', inplace=True)
csongor's avatar
csongor committed
827
828

    def __mul__(self, other):
Theo Steininger's avatar
Theo Steininger committed
829
        return self._binary_helper(other, op='__mul__')
830

831
    def __rmul__(self, other):
Theo Steininger's avatar
Theo Steininger committed
832
        return self._binary_helper(other, op='__rmul__')
csongor's avatar
csongor committed
833
834

    def __imul__(self, other):
Theo Steininger's avatar
Theo Steininger committed
835
        return self._binary_helper(other, op='__imul__', inplace=True)
csongor's avatar
csongor committed
836
837

    def __div__(self, other):
Theo Steininger's avatar
Theo Steininger committed
838
        return self._binary_helper(other, op='__div__')
csongor's avatar
csongor committed
839
840

    def __rdiv__(self, other):
Theo Steininger's avatar
Theo Steininger committed
841
        return self._binary_helper(other, op='__rdiv__')
csongor's avatar
csongor committed
842
843

    def __idiv__(self, other):
Theo Steininger's avatar
Theo Steininger committed
844
        return self._binary_helper(other, op='__idiv__', inplace=True)
845

csongor's avatar
csongor committed
846
    def __pow__(self, other):
Theo Steininger's avatar
Theo Steininger committed
847
        return self._binary_helper(other, op='__pow__')
csongor's avatar
csongor committed
848
849

    def __rpow__(self, other):
Theo Steininger's avatar
Theo Steininger committed
850
        return self._binary_helper(other, op='__rpow__')
csongor's avatar
csongor committed
851
852

    def __ipow__(self, other):
Theo Steininger's avatar
Theo Steininger committed
853
        return self._binary_helper(other, op='__ipow__', inplace=True)
csongor's avatar
csongor committed
854
855

    def __lt__(self, other):
Theo Steininger's avatar
Theo Steininger committed
856
        return self._binary_helper(other, op='__lt__')
csongor's avatar
csongor committed
857
858

    def __le__(self, other):
Theo Steininger's avatar
Theo Steininger committed
859
        return self._binary_helper(other, op='__le__')
csongor's avatar
csongor committed
860
861
862
863
864

    def __ne__(self, other):
        if other is None:
            return True
        else:
Theo Steininger's avatar
Theo Steininger committed
865
            return self._binary_helper(other, op='__ne__')
csongor's avatar
csongor committed
866
867
868
869
870

    def __eq__(self, other):
        if other is None:
            return False
        else:
Theo Steininger's avatar
Theo Steininger committed
871
            return self._binary_helper(other, op='__eq__')
csongor's avatar
csongor committed
872
873

    def __ge__(self, other):
Theo Steininger's avatar
Theo Steininger committed
874
        return self._binary_helper(other, op='__ge__')
csongor's avatar
csongor committed
875
876

    def __gt__(self, other):
Theo Steininger's avatar
Theo Steininger committed
877
878
879
880
881
882
883
884
885
886
887
888
889
        return self._binary_helper(other, op='__gt__')

    def __repr__(self):
        return "<nifty_core.field>"

    def __str__(self):
        minmax = [self.min(), self.max()]
        mean = self.mean()
        return "nifty_core.field instance\n- domain      = " + \
               repr(self.domain) + \
               "\n- val         = " + repr(self.get_val()) + \
               "\n  - min.,max. = " + str(minmax) + \
               "\n  - mean = " + str(mean)
csongor's avatar
csongor committed
890

Jait Dixit's avatar
Jait Dixit committed
891
892
893
    # ---Serialization---

    def _to_hdf5(self, hdf5_group):
Theo Steininger's avatar
Theo Steininger committed
894
895
896
897
        hdf5_group.attrs['dtype'] = self.dtype.name
        hdf5_group.attrs['distribution_strategy'] = self.distribution_strategy
        hdf5_group.attrs['field_type_axes'] = str(self.field_type_axes)
        hdf5_group.attrs['domain_axes'] = str(self.domain_axes)
898
899
        hdf5_group['num_domain'] = len(self.domain)
        hdf5_group['num_ft'] = len(self.field_type)
Jait Dixit's avatar
Jait Dixit committed
900

Theo Steininger's avatar
Theo Steininger committed
901
        ret_dict = {'val': self.val}
Jait Dixit's avatar
Jait Dixit committed
902
903
904
905
906
907
908
909
910
911

        for i in range(len(self.domain)):
            ret_dict['s_' + str(i)] = self.domain[i]

        for i in range(len(self.field_type)):
            ret_dict['ft_' + str(i)] = self.field_type[i]

        return ret_dict

    @classmethod
Theo Steininger's avatar
Theo Steininger committed
912
    def _from_hdf5(cls, hdf5_group, repository):
Jait Dixit's avatar
Jait Dixit committed
913
914
915
916
917
918
        # create empty field
        new_field = EmptyField()
        # reset class
        new_field.__class__ = cls
        # set values
        temp_domain = []
919
        for i in range(hdf5_group['num_domain'][()]):
Theo Steininger's avatar
Theo Steininger committed
920
            temp_domain.append(repository.get('s_' + str(i), hdf5_group))
Jait Dixit's avatar
Jait Dixit committed
921
922
923
        new_field.domain = tuple(temp_domain)

        temp_ft = []
924
        for i in range(hdf5_group['num_ft'][()]):
Theo Steininger's avatar
Theo Steininger committed
925
            temp_domain.append(repository.get('ft_' + str(i), hdf5_group))
Jait Dixit's avatar
Jait Dixit committed
926
927
        new_field.field_type = tuple(temp_ft)

Theo Steininger's avatar
Theo Steininger committed
928
929
930
931
932
933
934
        exec('new_field.domain_axes = ' + hdf5_group.attrs['domain_axes'])
        exec('new_field.field_type_axes = ' +
             hdf5_group.attrs['field_type_axes'])
        new_field._val = repository.get('val', hdf5_group)
        new_field.dtype = np.dtype(hdf5_group.attrs['dtype'])
        new_field.distribution_strategy =\
            hdf5_group.attrs['distribution_strategy']
Jait Dixit's avatar
Jait Dixit committed
935
936

        return new_field
937

Theo Steininger's avatar
Theo Steininger committed
938

939
class EmptyField(Field):
csongor's avatar
csongor committed
940
941
    def __init__(self):
        pass