forked from hail-is/hail
-
Notifications
You must be signed in to change notification settings - Fork 0
/
matrixtable.py
4462 lines (3586 loc) · 166 KB
/
matrixtable.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
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
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
import itertools
from typing import Iterable, Optional, Dict, Tuple, Any, List
from collections import Counter
import hail as hl
from hail.expr.expressions import Expression, StructExpression, \
expr_struct, expr_any, expr_bool, analyze, Indices, \
construct_reference, construct_expr, extract_refs_by_indices, \
ExpressionException, TupleExpression, unify_all
from hail.expr.types import types_match, tarray, tset
from hail.expr.matrix_type import tmatrix
import hail.ir as ir
from hail.table import Table, ExprContainer, TableIndexKeyError
from hail.typecheck import typecheck, typecheck_method, dictof, anytype, \
anyfunc, nullable, sequenceof, oneof, numeric, lazy, enumeration
from hail.utils import storage_level, default_handler, deduplicate
from hail.utils.java import warning, Env, info
from hail.utils.misc import wrap_to_tuple, \
get_key_by_exprs, \
get_select_exprs, check_annotate_exprs, process_joins
import warnings
class GroupedMatrixTable(ExprContainer):
"""Matrix table grouped by row or column that can be aggregated into a new matrix table."""
def __init__(self,
parent: 'MatrixTable',
row_keys=None,
computed_row_key=None,
col_keys=None,
computed_col_key=None,
entry_fields=None,
row_fields=None,
col_fields=None,
partitions=None):
super(GroupedMatrixTable, self).__init__()
self._parent = parent
self._copy_fields_from(parent)
self._row_keys = row_keys
self._computed_row_key = computed_row_key
self._col_keys = col_keys
self._computed_col_key = computed_col_key
self._entry_fields = entry_fields
self._row_fields = row_fields
self._col_fields = col_fields
self._partitions = partitions
def _copy(self, *,
row_keys=None,
computed_row_key=None,
col_keys=None,
computed_col_key=None,
entry_fields=None,
row_fields=None,
col_fields=None,
partitions=None):
return GroupedMatrixTable(
parent=self._parent,
row_keys=row_keys if row_keys is not None else self._row_keys,
computed_row_key=computed_row_key if computed_row_key is not None else self._computed_row_key,
col_keys=col_keys if col_keys is not None else self._col_keys,
computed_col_key=computed_col_key if computed_col_key is not None else self._computed_col_key,
entry_fields=entry_fields if entry_fields is not None else self._entry_fields,
row_fields=row_fields if row_fields is not None else self._row_fields,
col_fields=col_fields if col_fields is not None else self._col_fields,
partitions=partitions if partitions is not None else self._partitions
)
def _fixed_indices(self):
if self._row_keys is None and self._col_keys is None:
return self._parent._entry_indices
if self._row_keys is not None and self._col_keys is None:
return self._parent._col_indices
if self._row_keys is None and self._col_keys is not None:
return self._parent._row_indices
return self._parent._global_indices
@typecheck_method(item=str)
def __getitem__(self, item):
return self._get_field(item)
def describe(self, handler=print):
"""Print information about grouped matrix table."""
if self._row_keys is None:
rowstr = ""
else:
rowstr = "\nRows: \n" + "\n ".join(["{}: {}".format(k, v._type) for k, v in self._row_keys.items()])
if self._col_keys is None:
colstr = ""
else:
colstr = "\nColumns: \n" + "\n ".join(["{}: {}".format(k, v) for k, v in self._col_keys.items()])
s = (f'----------------------------------------\n'
f'GroupedMatrixTable grouped by {rowstr}{colstr}\n'
f'----------------------------------------\n'
f'Parent MatrixTable:\n')
handler(s)
self._parent.describe(handler)
@typecheck_method(exprs=oneof(str, Expression),
named_exprs=expr_any)
def group_rows_by(self, *exprs, **named_exprs) -> 'GroupedMatrixTable':
"""Group rows.
Examples
--------
Aggregate to a matrix with genes as row keys, computing the number of
non-reference calls as an entry field:
>>> dataset_result = (dataset.group_rows_by(dataset.gene)
... .aggregate(n_non_ref = hl.agg.count_where(dataset.GT.is_non_ref())))
Notes
-----
All complex expressions must be passed as named expressions.
Parameters
----------
exprs : args of :class:`str` or :class:`.Expression`
Row fields to group by.
named_exprs : keyword args of :class:`.Expression`
Row-indexed expressions to group by.
Returns
-------
:class:`.GroupedMatrixTable`
Grouped matrix. Can be used to call :meth:`.GroupedMatrixTable.aggregate`.
"""
if self._row_keys is not None:
raise NotImplementedError("GroupedMatrixTable is already grouped by rows.")
if self._col_keys is not None:
raise NotImplementedError("GroupedMatrixTable is already grouped by cols; cannot also group by rows.")
caller = 'group_rows_by'
row_key, computed_key = get_key_by_exprs(caller,
exprs,
named_exprs,
self._parent._row_indices,
override_protected_indices={self._parent._global_indices,
self._parent._col_indices})
self._check_bindings(caller, computed_key, self._parent._row_indices)
return self._copy(row_keys=row_key, computed_row_key=computed_key)
@typecheck_method(exprs=oneof(str, Expression),
named_exprs=expr_any)
def group_cols_by(self, *exprs, **named_exprs) -> 'GroupedMatrixTable':
"""Group columns.
Examples
--------
Aggregate to a matrix with cohort as column keys, computing the call rate
as an entry field:
>>> dataset_result = (dataset.group_cols_by(dataset.cohort)
... .aggregate(call_rate = hl.agg.fraction(hl.is_defined(dataset.GT))))
Notes
-----
All complex expressions must be passed as named expressions.
Parameters
----------
exprs : args of :class:`str` or :class:`.Expression`
Column fields to group by.
named_exprs : keyword args of :class:`.Expression`
Column-indexed expressions to group by.
Returns
-------
:class:`.GroupedMatrixTable`
Grouped matrix, can be used to call :meth:`.GroupedMatrixTable.aggregate`.
"""
if self._row_keys is not None:
raise NotImplementedError("GroupedMatrixTable is already grouped by rows; cannot also group by cols.")
if self._col_keys is not None:
raise NotImplementedError("GroupedMatrixTable is already grouped by cols.")
caller = 'group_cols_by'
col_key, computed_key = get_key_by_exprs(caller,
exprs,
named_exprs,
self._parent._col_indices,
override_protected_indices={self._parent._global_indices,
self._parent._row_indices})
self._check_bindings(caller, computed_key, self._parent._col_indices)
return self._copy(col_keys=col_key, computed_col_key=computed_key)
def _check_bindings(self, caller, new_bindings, indices):
empty = []
def iter_option(o):
return o if o is not None else empty
if indices == self._parent._row_indices:
fixed_fields = [*self._parent.globals, *self._parent.col]
else:
assert indices == self._parent._col_indices
fixed_fields = [*self._parent.globals, *self._parent.row]
bound_fields = set(itertools.chain(
iter_option(self._row_keys),
iter_option(self._col_keys),
iter_option(self._col_fields),
iter_option(self._row_fields),
iter_option(self._entry_fields),
fixed_fields))
for k in new_bindings:
if k in bound_fields:
raise ExpressionException(f"{caller!r} cannot assign duplicate field {k!r}")
def partition_hint(self, n: int) -> 'GroupedMatrixTable':
"""Set the target number of partitions for aggregation.
Examples
--------
Use `partition_hint` in a :meth:`.MatrixTable.group_rows_by` /
:meth:`.GroupedMatrixTable.aggregate` pipeline:
>>> dataset_result = (dataset.group_rows_by(dataset.gene)
... .partition_hint(5)
... .aggregate(n_non_ref = hl.agg.count_where(dataset.GT.is_non_ref())))
Notes
-----
Until Hail's query optimizer is intelligent enough to sample records at all
stages of a pipeline, it can be necessary in some places to provide some
explicit hints.
The default number of partitions for :meth:`.GroupedMatrixTable.aggregate` is
the number of partitions in the upstream dataset. If the aggregation greatly
reduces the size of the dataset, providing a hint for the target number of
partitions can accelerate downstream operations.
Parameters
----------
n : int
Number of partitions.
Returns
-------
:class:`.GroupedMatrixTable`
Same grouped matrix table with a partition hint.
"""
self._partitions = n
return self
@typecheck_method(named_exprs=expr_any)
def aggregate_cols(self, **named_exprs) -> 'GroupedMatrixTable':
"""Aggregate cols by group.
Examples
--------
Aggregate to a matrix with cohort as column keys, computing the mean height
per cohort as a new column field:
>>> dataset_result = (dataset.group_cols_by(dataset.cohort)
... .aggregate_cols(mean_height = hl.agg.mean(dataset.pheno.height))
... .result())
Notes
-----
The aggregation scope includes all column fields and global fields.
See Also
--------
:meth:`.result`
Parameters
----------
named_exprs : varargs of :class:`.Expression`
Aggregation expressions.
Returns
-------
:class:`.GroupedMatrixTable`
"""
if self._row_keys is not None:
raise NotImplementedError("GroupedMatrixTable is already grouped by rows. Cannot aggregate over cols.")
assert self._col_keys is not None
base = self._col_fields if self._col_fields is not None else hl.struct()
for k, e in named_exprs.items():
analyze('GroupedMatrixTable.aggregate_cols', e, self._parent._global_indices, {self._parent._col_axis})
self._check_bindings('aggregate_cols', named_exprs, self._parent._col_indices)
return self._copy(col_fields=base.annotate(**named_exprs))
@typecheck_method(named_exprs=expr_any)
def aggregate_rows(self, **named_exprs) -> 'GroupedMatrixTable':
"""Aggregate rows by group.
Examples
--------
Aggregate to a matrix with genes as row keys, collecting the functional
consequences per gene as a set as a new row field:
>>> dataset_result = (dataset.group_rows_by(dataset.gene)
... .aggregate_rows(consequences = hl.agg.collect_as_set(dataset.consequence))
... .result())
Notes
-----
The aggregation scope includes all row fields and global fields.
See Also
--------
:meth:`.result`
Parameters
----------
named_exprs : varargs of :class:`.Expression`
Aggregation expressions.
Returns
-------
:class:`.GroupedMatrixTable`
"""
if self._col_keys is not None:
raise NotImplementedError("GroupedMatrixTable is already grouped by cols. Cannot aggregate over rows.")
assert self._row_keys is not None
base = self._row_fields if self._row_fields is not None else hl.struct()
for k, e in named_exprs.items():
analyze('GroupedMatrixTable.aggregate_rows', e, self._parent._global_indices, {self._parent._row_axis})
self._check_bindings('aggregate_rows', named_exprs, self._parent._row_indices)
return self._copy(row_fields=base.annotate(**named_exprs))
@typecheck_method(named_exprs=expr_any)
def aggregate_entries(self, **named_exprs) -> 'GroupedMatrixTable':
"""Aggregate entries by group.
Examples
--------
Aggregate to a matrix with genes as row keys, computing the number of
non-reference calls as an entry field:
>>> dataset_result = (dataset.group_rows_by(dataset.gene)
... .aggregate_entries(n_non_ref = hl.agg.count_where(dataset.GT.is_non_ref()))
... .result())
See Also
--------
:meth:`.aggregate`, :meth:`.result`
Parameters
----------
named_exprs : varargs of :class:`.Expression`
Aggregation expressions.
Returns
-------
:class:`.GroupedMatrixTable`
"""
assert self._row_keys is not None or self._col_keys is not None
base = self._entry_fields if self._entry_fields is not None else hl.struct()
for k, e in named_exprs.items():
analyze('GroupedMatrixTable.aggregate_entries', e, self._fixed_indices(), {self._parent._row_axis, self._parent._col_axis})
self._check_bindings('aggregate_entries', named_exprs,
self._parent._col_indices if self._col_keys is not None else self._parent._row_indices)
return self._copy(entry_fields=base.annotate(**named_exprs))
def result(self) -> 'MatrixTable':
"""Return the result of aggregating by group.
Examples
--------
Aggregate to a matrix with genes as row keys, collecting the functional
consequences per gene as a row field and computing the number of
non-reference calls as an entry field:
>>> dataset_result = (dataset.group_rows_by(dataset.gene)
... .aggregate_rows(consequences = hl.agg.collect_as_set(dataset.consequence))
... .aggregate_entries(n_non_ref = hl.agg.count_where(dataset.GT.is_non_ref()))
... .result())
Aggregate to a matrix with cohort as column keys, computing the mean height
per cohort as a column field and computing the number of non-reference calls
as an entry field:
>>> dataset_result = (dataset.group_cols_by(dataset.cohort)
... .aggregate_cols(mean_height = hl.agg.stats(dataset.pheno.height).mean)
... .aggregate_entries(n_non_ref = hl.agg.count_where(dataset.GT.is_non_ref()))
... .result())
See Also
--------
:meth:`.aggregate`
Returns
-------
:class:`.MatrixTable`
Aggregated matrix table.
"""
assert self._row_keys is not None or self._col_keys is not None
defined_exprs = []
for e in [self._row_fields, self._col_fields, self._entry_fields]:
if e is not None:
defined_exprs.append(e)
for e in [self._computed_row_key, self._computed_col_key]:
if e is not None:
defined_exprs.extend(e.values())
def promote_none(e):
return hl.struct() if e is None else e
entry_exprs = promote_none(self._entry_fields)
if len(entry_exprs) == 0:
warning("'GroupedMatrixTable.result': No entry fields were defined.")
base, cleanup = self._parent._process_joins(*defined_exprs)
if self._col_keys is not None:
cck = self._computed_col_key or {}
computed_key_uids = {k: Env.get_uid() for k in cck}
modified_keys = [computed_key_uids.get(k, k) for k in self._col_keys]
mt = MatrixTable(ir.MatrixAggregateColsByKey(
ir.MatrixMapCols(
base._mir,
self._parent.col.annotate(**{computed_key_uids[k]: v for k, v in cck.items()})._ir,
modified_keys),
entry_exprs._ir,
promote_none(self._col_fields)._ir))
if cck:
mt = mt.rename({v: k for k, v in computed_key_uids.items()})
else:
cck = self._computed_row_key or {}
computed_key_uids = {k: Env.get_uid() for k in cck}
modified_keys = [computed_key_uids.get(k, k) for k in self._row_keys]
mt = MatrixTable(ir.MatrixAggregateRowsByKey(
ir.MatrixKeyRowsBy(
ir.MatrixMapRows(
ir.MatrixKeyRowsBy(base._mir, []),
self._parent._rvrow.annotate(**{computed_key_uids[k]: v for k, v in cck.items()})._ir),
modified_keys),
entry_exprs._ir,
promote_none(self._row_fields)._ir))
if cck:
mt = mt.rename({v: k for k, v in computed_key_uids.items()})
return cleanup(mt)
@typecheck_method(named_exprs=expr_any)
def aggregate(self, **named_exprs) -> 'MatrixTable':
"""Aggregate entries by group, used after :meth:`.MatrixTable.group_rows_by`
or :meth:`.MatrixTable.group_cols_by`.
Examples
--------
Aggregate to a matrix with genes as row keys, computing the number of
non-reference calls as an entry field:
>>> dataset_result = (dataset.group_rows_by(dataset.gene)
... .aggregate(n_non_ref = hl.agg.count_where(dataset.GT.is_non_ref())))
Notes
-----
Alias for :meth:`aggregate_entries`, :meth:`result`.
See Also
--------
:meth:`aggregate_entries`, :meth:`result`
Parameters
----------
named_exprs : varargs of :class:`.Expression`
Aggregation expressions.
Returns
-------
:class:`.MatrixTable`
Aggregated matrix table.
"""
return self.aggregate_entries(**named_exprs).result()
matrix_table_type = lazy()
class MatrixTable(ExprContainer):
"""Hail's distributed implementation of a structured matrix.
Use :func:`.read_matrix_table` to read a matrix table that was written with
:meth:`.MatrixTable.write`.
Examples
--------
Add annotations:
>>> dataset = dataset.annotate_globals(pli = {'SCN1A': 0.999, 'SONIC': 0.014},
... populations = ['AFR', 'EAS', 'EUR', 'SAS', 'AMR', 'HIS'])
>>> dataset = dataset.annotate_cols(pop = dataset.populations[hl.int(hl.rand_unif(0, 6))],
... sample_gq = hl.agg.mean(dataset.GQ),
... sample_dp = hl.agg.mean(dataset.DP))
>>> dataset = dataset.annotate_rows(variant_gq = hl.agg.mean(dataset.GQ),
... variant_dp = hl.agg.mean(dataset.GQ),
... sas_hets = hl.agg.count_where(dataset.GT.is_het()))
>>> dataset = dataset.annotate_entries(gq_by_dp = dataset.GQ / dataset.DP)
Filter:
>>> dataset = dataset.filter_cols(dataset.pop != 'EUR')
>>> datasetm = dataset.filter_rows((dataset.variant_gq > 10) & (dataset.variant_dp > 5))
>>> dataset = dataset.filter_entries(dataset.gq_by_dp > 1)
Query:
>>> col_stats = dataset.aggregate_cols(hl.struct(pop_counts=hl.agg.counter(dataset.pop),
... high_quality=hl.agg.fraction((dataset.sample_gq > 10) & (dataset.sample_dp > 5))))
>>> print(col_stats.pop_counts)
>>> print(col_stats.high_quality)
>>> het_dist = dataset.aggregate_rows(hl.agg.stats(dataset.sas_hets))
>>> print(het_dist)
>>> entry_stats = dataset.aggregate_entries(hl.struct(call_rate=hl.agg.fraction(hl.is_defined(dataset.GT)),
... global_gq_mean=hl.agg.mean(dataset.GQ)))
>>> print(entry_stats.call_rate)
>>> print(entry_stats.global_gq_mean)
"""
@staticmethod
def _from_java(jmir):
return MatrixTable(ir.JavaMatrix(jmir))
@staticmethod
@typecheck(
globals=nullable(dictof(str, anytype)),
rows=nullable(dictof(str, sequenceof(anytype))),
cols=nullable(dictof(str, sequenceof(anytype))),
entries=nullable(dictof(str, sequenceof(sequenceof(anytype)))),
)
def from_parts(
globals: Optional[Dict[str, Any]] = None,
rows: Optional[Dict[str, Iterable[Any]]] = None,
cols: Optional[Dict[str, Iterable[Any]]] = None,
entries: Optional[Dict[str, Iterable[Iterable[Any]]]] = None
) -> 'MatrixTable':
"""Create a `MatrixTable` from its component parts.
Example
-------
>>> mt = hl.MatrixTable.from_parts(
... globals={'hello':'world'},
... rows={'foo':[1, 2]},
... cols={'bar':[3, 4]},
... entries={'baz':[[1, 2],[3, 4]]}
... )
>>> mt.describe()
----------------------------------------
Global fields:
'hello': str
----------------------------------------
Column fields:
'col_idx': int32
'bar': int32
----------------------------------------
Row fields:
'row_idx': int32
'foo': int32
----------------------------------------
Entry fields:
'baz': int32
----------------------------------------
Column key: ['col_idx']
Row key: ['row_idx']
----------------------------------------
>>> mt.row.show()
+---------+-------+
| row_idx | foo |
+---------+-------+
| int32 | int32 |
+---------+-------+
| 0 | 1 |
| 1 | 2 |
+---------+-------+
>>> mt.col.show()
+---------+-------+
| col_idx | bar |
+---------+-------+
| int32 | int32 |
+---------+-------+
| 0 | 3 |
| 1 | 4 |
+---------+-------+
>>> mt.entry.show()
+---------+-------+-------+
| row_idx | 0.baz | 1.baz |
+---------+-------+-------+
| int32 | int32 | int32 |
+---------+-------+-------+
| 0 | 1 | 2 |
| 1 | 3 | 4 |
+---------+-------+-------+
Notes
-----
- Matrix dimensions are inferred from input data.
- You must provide row and column dimensions by specifying rows or
entries (inclusive) and cols or entries (inclusive).
- The respective dimensions of rows, cols and entries must match should
you provide rows and entries or cols and entries (inclusive).
Parameters
----------
globals : :class:`dict` from :class:`str` to :obj:`any`
Global fields by name.
rows: :class:`dict` from :class:`str` to :class:`list` of :obj:`any`
Row fields by name.
cols: :class:`dict` from :class:`str` to :class:`list` of :obj:`any`
Column fields by name.
entries: :class:`dict` from :class:`str` to :class:`list` of :class:`list` of :obj:`any`
Matrix entries by name in the form `entry[row_idx][col_idx]`.
Returns
-------
:class:`.MatrixTable`
A MatrixTable assembled from inputs whose rows are keyed by `row_idx`
and columns are keyed by `col_idx`.
"""
# General idea: build a `Table` representation matching that returned by
# `MatrixTable.localize_entries` and then call `_unlocalize_entries`. In
# this form, the column table is bundled with the globals and the entries
# for each row is stored on the row.
def raise_when_mismatched_property_dimensions(kvs: Dict[str, Iterable[Any]]):
def value_len(entry):
return len(entry[1])
kvs = sorted(kvs.items(), key=value_len)
dims = itertools.groupby(kvs, value_len)
dims = {size: [k for k, _ in group] for size, group in dims}
if len(dims) > 1:
raise ValueError(f"property matrix dimensions do not match: {dims}.")
def transpose(kvs: Dict[str, Iterable[Any]]) -> List[Dict[str, Any]]:
raise_when_mismatched_property_dimensions(kvs)
return [dict(zip(kvs, vs)) for vs in zip(*kvs.values())]
def anyval(kvs):
return next(iter(kvs.values()))
# In the case rows or cols aren't specified, we need to infer the
# matrix dimensions from *an* entry. Which one isn't important as we
# enforce congruence among input dimensions.
assert not ((rows is None or cols is None) and (entries is None))
cols = transpose(cols) if cols else [{} for _ in anyval(entries)[0]]
for i, _ in enumerate(cols):
cols[i] = hl.struct(col_idx=i, **cols[i])
if globals is None:
globals = {}
cols_field_name = Env.get_uid()
globals[cols_field_name] = cols
rows = transpose(rows) if rows else [{} for _ in anyval(entries)]
entries = [transpose(e) for e in transpose(entries)
] if entries else [[{} for _ in cols] for _ in rows]
if len(rows) != len(entries) or len(cols) != len(entries[0]):
raise ValueError((
"mismatched matrix dimensions: "
"number of rows and cols does not match entry dimensions."
))
entries_field_name = Env.get_uid()
for i, (row_props, entry_props) in enumerate(zip(rows, entries)):
row_entries = [hl.struct(**kvs) for kvs in entry_props]
rows[i] = hl.Struct(row_idx=i, **row_props, **{entries_field_name: row_entries})
ht = Table.parallelize(rows, key='row_idx', globals=hl.struct(**globals))
return ht._unlocalize_entries(entries_field_name, cols_field_name, col_key=['col_idx'])
def __init__(self, mir):
super(MatrixTable, self).__init__()
self._mir = mir
self._globals = None
self._col_values = None
self._row_axis = 'row'
self._col_axis = 'column'
self._global_indices = Indices(self, set())
self._row_indices = Indices(self, {self._row_axis})
self._col_indices = Indices(self, {self._col_axis})
self._entry_indices = Indices(self, {self._row_axis, self._col_axis})
self._type = self._mir.typ
self._global_type = self._type.global_type
self._col_type = self._type.col_type
self._row_type = self._type.row_type
self._entry_type = self._type.entry_type
self._globals = construct_reference('global', self._global_type,
indices=self._global_indices)
self._rvrow = construct_reference('va',
self._type.row_type,
indices=self._row_indices)
self._row = hl.struct(**{k: self._rvrow[k] for k in self._row_type.keys()})
self._col = construct_reference('sa', self._col_type,
indices=self._col_indices)
self._entry = construct_reference('g', self._entry_type,
indices=self._entry_indices)
self._indices_from_ref = {'global': self._global_indices,
'va': self._row_indices,
'sa': self._col_indices,
'g': self._entry_indices}
self._row_key = hl.struct(
**{k: self._row[k] for k in self._type.row_key})
self._partition_key = self._row_key
self._col_key = hl.struct(
**{k: self._col[k] for k in self._type.col_key})
self._num_samples = None
for k, v in itertools.chain(self._globals.items(),
self._row.items(),
self._col.items(),
self._entry.items()):
self._set_field(k, v)
@property
def _schema(self) -> tmatrix:
return tmatrix(
self._global_type,
self._col_type, list(self._col_key),
self._row_type, list(self._row_key),
self._entry_type)
def __getitem__(self, item):
invalid_usage = TypeError("MatrixTable.__getitem__: invalid index argument(s)\n"
" Usage 1: field selection: mt['field']\n"
" Usage 2: Entry joining: mt[mt2.row_key, mt2.col_key]\n\n"
" To join row or column fields, use one of the following:\n"
" rows:\n"
" mt.index_rows(mt2.row_key)\n"
" mt.rows().index(mt2.row_key)\n"
" mt.rows()[mt2.row_key]\n"
" cols:\n"
" mt.index_cols(mt2.col_key)\n"
" mt.cols().index(mt2.col_key)\n"
" mt.cols()[mt2.col_key]")
if isinstance(item, str):
return self._get_field(item)
if isinstance(item, tuple) and len(item) == 2:
# this is the join path
exprs = item
row_key = wrap_to_tuple(exprs[0])
col_key = wrap_to_tuple(exprs[1])
try:
return self.index_entries(row_key, col_key)
except TypeError as e:
raise invalid_usage from e
raise invalid_usage
@property
def _col_key_types(self):
return [v.dtype for _, v in self.col_key.items()]
@property
def _row_key_types(self):
return [v.dtype for _, v in self.row_key.items()]
@property
def col_key(self) -> 'StructExpression':
"""Column key struct.
Examples
--------
Get the column key field names:
>>> list(dataset.col_key)
['s']
Returns
-------
:class:`.StructExpression`
"""
return self._col_key
@property
def row_key(self) -> 'StructExpression':
"""Row key struct.
Examples
--------
Get the row key field names:
>>> list(dataset.row_key)
['locus', 'alleles']
Returns
-------
:class:`.StructExpression`
"""
return self._row_key
@property
def globals(self) -> 'StructExpression':
"""Returns a struct expression including all global fields.
Returns
-------
:class:`.StructExpression`
"""
return self._globals
@property
def row(self) -> 'StructExpression':
"""Returns a struct expression of all row-indexed fields, including keys.
Examples
--------
Get the first five row field names:
>>> list(dataset.row)[:5]
['locus', 'alleles', 'rsid', 'qual', 'filters']
Returns
-------
:class:`.StructExpression`
Struct of all row fields.
"""
return self._row
@property
def row_value(self) -> 'StructExpression':
"""Returns a struct expression including all non-key row-indexed fields.
Examples
--------
Get the first five non-key row field names:
>>> list(dataset.row_value)[:5]
['rsid', 'qual', 'filters', 'info', 'use_as_marker']
Returns
-------
:class:`.StructExpression`
Struct of all row fields, minus keys.
"""
return self._row.drop(*self.row_key)
@property
def col(self) -> 'StructExpression':
"""Returns a struct expression of all column-indexed fields, including keys.
Examples
--------
Get all column field names:
>>> list(dataset.col) # doctest: +SKIP_OUTPUT_CHECK
['s', 'sample_qc', 'is_case', 'pheno', 'cov', 'cov1', 'cov2', 'cohorts', 'pop']
Returns
-------
:class:`.StructExpression`
Struct of all column fields.
"""
return self._col
@property
def col_value(self) -> 'StructExpression':
"""Returns a struct expression including all non-key column-indexed fields.
Examples
--------
Get all non-key column field names:
>>> list(dataset.col_value) # doctest: +SKIP_OUTPUT_CHECK
['sample_qc', 'is_case', 'pheno', 'cov', 'cov1', 'cov2', 'cohorts', 'pop']
Returns
-------
:class:`.StructExpression`
Struct of all column fields, minus keys.
"""
return self._col.drop(*self.col_key)
@property
def entry(self) -> 'StructExpression':
"""Returns a struct expression including all row-and-column-indexed fields.
Examples
--------
Get all entry field names:
>>> list(dataset.entry)
['GT', 'AD', 'DP', 'GQ', 'PL']
Returns
-------
:class:`.StructExpression`
Struct of all entry fields.
"""
return self._entry
@typecheck_method(keys=oneof(str, Expression),
named_keys=expr_any)
def key_cols_by(self, *keys, **named_keys) -> 'MatrixTable':
"""Key columns by a new set of fields.
See :meth:`.Table.key_by` for more information on defining a key.
Parameters
----------
keys : varargs of :class:`str` or :class:`.Expression`.
Column fields to key by.
named_keys : keyword args of :class:`.Expression`.
Column fields to key by.
Returns
-------
:class:`.MatrixTable`
"""
key_fields, computed_keys = get_key_by_exprs("MatrixTable.key_cols_by", keys, named_keys, self._col_indices)
if not computed_keys:
return MatrixTable(ir.MatrixMapCols(self._mir, self._col._ir, key_fields))
else:
new_col = self.col.annotate(**computed_keys)
base, cleanup = self._process_joins(new_col)
return cleanup(MatrixTable(
ir.MatrixMapCols(
base._mir,
new_col._ir,
key_fields
)))
@typecheck_method(new_key=str)
def _key_rows_by_assert_sorted(self, *new_key):
rk_names = list(self.row_key)
i = 0
while (i < min(len(new_key), len(rk_names))):
if new_key[i] != rk_names[i]:
break
i += 1
if i < 1:
raise ValueError(
f'cannot implement an unsafe sort with no shared key:\n new key: {new_key}\n old key: {rk_names}')
return MatrixTable(ir.MatrixKeyRowsBy(self._mir, list(new_key), is_sorted=True))
@typecheck_method(keys=oneof(str, Expression),
named_keys=expr_any)
def key_rows_by(self, *keys, **named_keys) -> 'MatrixTable':
"""Key rows by a new set of fields.
Examples
--------
>>> dataset_result = dataset.key_rows_by('locus')
>>> dataset_result = dataset.key_rows_by(dataset['locus'])
>>> dataset_result = dataset.key_rows_by(**dataset.row_key.drop('alleles'))
All of these expressions key the dataset by the 'locus' field, dropping
the 'alleles' field from the row key.
>>> dataset_result = dataset.key_rows_by(contig=dataset['locus'].contig,
... position=dataset['locus'].position,
... alleles=dataset['alleles'])
This keys the dataset by the newly defined fields, 'contig' and 'position',
and the 'alleles' field. The old row key field, 'locus', is preserved as
a non-key field.
Notes
-----
See :meth:`.Table.key_by` for more information on defining a key.