-
Notifications
You must be signed in to change notification settings - Fork 77
/
rioxarray.py
2010 lines (1749 loc) · 68.7 KB
/
rioxarray.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
# -- coding: utf-8 --
"""
This module is an extension for xarray to provide rasterio capabilities
to xarray datasets/dataarrays.
Credits: The `reproject` functionality was adopted from https://github.com/opendatacube/datacube-core # noqa
Source file:
- https://github.com/opendatacube/datacube-core/blob/084c84d78cb6e1326c7fbbe79c5b5d0bef37c078/datacube/api/geo_xarray.py # noqa
datacube is licensed under the Apache License, Version 2.0:
- https://github.com/opendatacube/datacube-core/blob/1d345f08a10a13c316f81100936b0ad8b1a374eb/LICENSE # noqa
"""
import copy
import math
from uuid import uuid4
import numpy as np
import pyproj
import rasterio.warp
import rasterio.windows
import xarray
from affine import Affine
from rasterio.crs import CRS
from rasterio.enums import Resampling
from rasterio.features import geometry_mask
from scipy.interpolate import griddata
from rioxarray.crs import crs_to_wkt
from rioxarray.exceptions import (
DimensionError,
DimensionMissingCoordinateError,
InvalidDimensionOrder,
MissingCRS,
NoDataInBounds,
OneDimensionalRaster,
RioXarrayError,
TooManyDimensions,
)
FILL_VALUE_NAMES = ("_FillValue", "missing_value", "fill_value", "nodata")
UNWANTED_RIO_ATTRS = ("nodatavals", "crs", "is_tiled", "res")
DEFAULT_GRID_MAP = "spatial_ref"
def affine_to_coords(affine, width, height, x_dim="x", y_dim="y"):
"""Generate 1d pixel centered coordinates from affine.
Based on code from the xarray rasterio backend.
Parameters
----------
affine: :obj:`affine.Affine`
The affine of the grid.
width: int
The width of the grid.
height: int
The height of the grid.
x_dim: str, optional
The name of the X dimension. Default is 'x'.
y_dim: str, optional
The name of the Y dimension. Default is 'y'.
Returns
-------
dict: x and y coordinate arrays.
"""
x_coords, _ = affine * (np.arange(width) + 0.5, np.zeros(width) + 0.5)
_, y_coords = affine * (np.zeros(height) + 0.5, np.arange(height) + 0.5)
return {y_dim: y_coords, x_dim: x_coords}
def _get_grid_map_name(src_data_array):
"""Get the grid map name of the variable."""
try:
return src_data_array.attrs["grid_mapping"]
except KeyError:
return DEFAULT_GRID_MAP
def _generate_attrs(src_data_array, dst_nodata):
# add original attributes
new_attrs = copy.deepcopy(src_data_array.attrs)
# remove all nodata information
for unwanted_attr in FILL_VALUE_NAMES + UNWANTED_RIO_ATTRS:
new_attrs.pop(unwanted_attr, None)
# add nodata information
fill_value = (
src_data_array.rio.nodata
if src_data_array.rio.nodata is not None
else dst_nodata
)
if src_data_array.rio.encoded_nodata is None and fill_value is not None:
new_attrs["_FillValue"] = fill_value
# add raster spatial information
new_attrs["grid_mapping"] = _get_grid_map_name(src_data_array)
return new_attrs
def add_xy_grid_meta(coords):
"""Add x,y metadata to coordinates"""
# add metadata to x,y coordinates
if "x" in coords:
x_coord_attrs = dict(coords["x"].attrs)
x_coord_attrs["long_name"] = "x coordinate of projection"
x_coord_attrs["standard_name"] = "projection_x_coordinate"
coords["x"].attrs = x_coord_attrs
elif "longitude" in coords:
x_coord_attrs = dict(coords["longitude"].attrs)
x_coord_attrs["long_name"] = "longitude"
x_coord_attrs["standard_name"] = "longitude"
coords["longitude"].attrs = x_coord_attrs
if "y" in coords:
y_coord_attrs = dict(coords["y"].attrs)
y_coord_attrs["long_name"] = "y coordinate of projection"
y_coord_attrs["standard_name"] = "projection_y_coordinate"
coords["y"].attrs = y_coord_attrs
elif "latitude" in coords:
x_coord_attrs = dict(coords["latitude"].attrs)
x_coord_attrs["long_name"] = "latitude"
x_coord_attrs["standard_name"] = "latitude"
coords["latitude"].attrs = x_coord_attrs
return coords
def add_spatial_ref(in_ds, dst_crs, grid_map_name):
in_ds.rio.write_crs(
input_crs=dst_crs, grid_mapping_name=grid_map_name, inplace=True
)
return in_ds
def _add_attrs_proj(new_data_array, src_data_array):
"""Make sure attributes and projection correct"""
# make sure dimension information is preserved
if new_data_array.rio._x_dim is None:
new_data_array.rio._x_dim = src_data_array.rio.x_dim
if new_data_array.rio._y_dim is None:
new_data_array.rio._y_dim = src_data_array.rio.y_dim
# make sure attributes preserved
new_attrs = _generate_attrs(src_data_array, None)
# remove fill value if it already exists in the encoding
# this is for data arrays pulling the encoding from a
# source data array instead of being generated anew.
if "_FillValue" in new_data_array.encoding:
new_attrs.pop("_FillValue", None)
new_data_array.rio.set_attrs(new_attrs, inplace=True)
# make sure projection added
add_xy_grid_meta(new_data_array.coords)
new_data_array = add_spatial_ref(
new_data_array, src_data_array.rio.crs, _get_grid_map_name(src_data_array)
)
new_data_array.rio.write_transform(inplace=True)
# make sure encoding added
new_data_array.encoding = src_data_array.encoding.copy()
return new_data_array
def _warp_spatial_coords(data_array, affine, width, height):
"""get spatial coords in new transform"""
new_spatial_coords = affine_to_coords(affine, width, height)
return {
"x": xarray.IndexVariable("x", new_spatial_coords["x"]),
"y": xarray.IndexVariable("y", new_spatial_coords["y"]),
}
def _get_nonspatial_coords(src_data_array):
coords = {}
for coord in set(src_data_array.coords) - {
src_data_array.rio.x_dim,
src_data_array.rio.y_dim,
DEFAULT_GRID_MAP,
}:
if src_data_array[coord].dims:
coords[coord] = xarray.IndexVariable(
src_data_array[coord].dims,
src_data_array[coord].values,
src_data_array[coord].attrs,
)
else:
coords[coord] = xarray.Variable(
src_data_array[coord].dims,
src_data_array[coord].values,
src_data_array[coord].attrs,
)
return coords
def _make_coords(src_data_array, dst_affine, dst_width, dst_height, dst_crs):
"""Generate the coordinates of the new projected `xarray.DataArray`"""
coords = _get_nonspatial_coords(src_data_array)
new_coords = _warp_spatial_coords(src_data_array, dst_affine, dst_width, dst_height)
new_coords.update(coords)
return add_xy_grid_meta(new_coords)
def _make_dst_affine(
src_data_array, src_crs, dst_crs, dst_resolution=None, dst_shape=None
):
"""Determine the affine of the new projected `xarray.DataArray`"""
src_bounds = src_data_array.rio.bounds()
src_height, src_width = src_data_array.rio.shape
dst_height, dst_width = dst_shape if dst_shape is not None else (None, None)
resolution_or_width_height = {
k: v
for k, v in [
("resolution", dst_resolution),
("dst_height", dst_height),
("dst_width", dst_width),
]
if v is not None
}
dst_affine, dst_width, dst_height = rasterio.warp.calculate_default_transform(
src_crs,
dst_crs,
src_width,
src_height,
*src_bounds,
**resolution_or_width_height,
)
return dst_affine, dst_width, dst_height
def _write_metatata_to_raster(raster_handle, xarray_dataset, tags):
"""
Write the metadata stored in the xarray object to raster metadata
"""
tags = xarray_dataset.attrs if tags is None else {**xarray_dataset.attrs, **tags}
# write scales and offsets
try:
raster_handle.scales = tags["scales"]
except KeyError:
try:
raster_handle.scales = (tags["scale_factor"],) * raster_handle.count
except KeyError:
pass
try:
raster_handle.offsets = tags["offsets"]
except KeyError:
try:
raster_handle.offsets = (tags["add_offset"],) * raster_handle.count
except KeyError:
pass
# filter out attributes that should be written in a different location
skip_tags = (
UNWANTED_RIO_ATTRS
+ FILL_VALUE_NAMES
+ ("transform", "scales", "scale_factor", "add_offset", "offsets")
)
# this is for when multiple values are used
# in this case, it will be stored in the raster description
if not isinstance(tags.get("long_name"), str):
skip_tags += ("long_name",)
tags = {key: value for key, value in tags.items() if key not in skip_tags}
raster_handle.update_tags(**tags)
# write band name information
long_name = xarray_dataset.attrs.get("long_name")
if isinstance(long_name, (tuple, list)):
if len(long_name) != raster_handle.count:
raise RioXarrayError(
"Number of names in the 'long_name' attribute does not equal "
"the number of bands."
)
for iii, band_description in enumerate(long_name):
raster_handle.set_band_description(iii + 1, band_description)
else:
band_description = long_name or xarray_dataset.name
if band_description:
for iii in range(raster_handle.count):
raster_handle.set_band_description(iii + 1, band_description)
def _get_data_var_message(obj):
"""
Get message for named data variables.
"""
try:
return f" Data variable: {obj.name}" if obj.name else ""
except AttributeError:
return ""
class XRasterBase(object):
"""This is the base class for the GIS extensions for xarray"""
def __init__(self, xarray_obj):
self._obj = xarray_obj
self._x_dim = None
self._y_dim = None
# Determine the spatial dimensions of the `xarray.DataArray`
if "x" in self._obj.dims and "y" in self._obj.dims:
self._x_dim = "x"
self._y_dim = "y"
elif "longitude" in self._obj.dims and "latitude" in self._obj.dims:
self._x_dim = "longitude"
self._y_dim = "latitude"
# properties
self._width = None
self._height = None
self._crs = None
@property
def crs(self):
""":obj:`rasterio.crs.CRS`:
Retrieve projection from `xarray.DataArray` or `xarray.Dataset`
"""
if self._crs is not None:
return None if self._crs is False else self._crs
# look in grid_mapping
grid_mapping_coord = self._obj.attrs.get("grid_mapping", DEFAULT_GRID_MAP)
try:
self.set_crs(
pyproj.CRS.from_cf(self._obj.coords[grid_mapping_coord].attrs),
inplace=True,
)
except (KeyError, pyproj.exceptions.CRSError):
try:
# look in attrs for 'crs'
self.set_crs(self._obj.attrs["crs"], inplace=True)
except KeyError:
self._crs = False
return None
return self._crs
def _get_obj(self, inplace):
"""
Get the object to modify.
Parameters
----------
inplace: bool
If True, returns self.
Returns
-------
xarray.Dataset or xarray.DataArray:
"""
if inplace:
return self._obj
obj_copy = self._obj.copy(deep=True)
# preserve attribute information
obj_copy.rio._x_dim = self._x_dim
obj_copy.rio._y_dim = self._y_dim
obj_copy.rio._width = self._width
obj_copy.rio._height = self._height
obj_copy.rio._crs = self._crs
return obj_copy
def set_crs(self, input_crs, inplace=True):
"""
Set the CRS value for the Dataset/DataArray without modifying
the dataset/data array.
Parameters
----------
input_crs: object
Anything accepted by `rasterio.crs.CRS.from_user_input`.
inplace: bool, optional
If True, it will write to the existing dataset. Default is False.
Returns
-------
xarray.Dataset or xarray.DataArray:
Dataset with crs attribute.
"""
crs = CRS.from_user_input(crs_to_wkt(input_crs))
obj = self._get_obj(inplace=inplace)
obj.rio._crs = crs
return obj
def write_crs(
self, input_crs=None, grid_mapping_name=DEFAULT_GRID_MAP, inplace=False
):
"""
Write the CRS to the dataset in a CF compliant manner.
Parameters
----------
input_crs: object
Anything accepted by `rasterio.crs.CRS.from_user_input`.
grid_mapping_name: str, optional
Name of the coordinate to store the CRS information in.
inplace: bool, optional
If True, it will write to the existing dataset. Default is False.
Returns
-------
xarray.Dataset or xarray.DataArray:
Modified dataset with CF compliant CRS information.
"""
if input_crs is not None:
data_obj = self.set_crs(input_crs, inplace=inplace)
else:
data_obj = self._get_obj(inplace=inplace)
# get original transform
transform = self._cached_transform()
# remove old grid maping coordinate if exists
try:
del data_obj.coords[grid_mapping_name]
except KeyError:
pass
if data_obj.rio.crs is None:
raise MissingCRS(
"CRS not found. Please set the CRS with 'set_crs()' or 'write_crs()'."
)
# add grid mapping coordinate
data_obj.coords[grid_mapping_name] = xarray.Variable((), 0)
grid_map_attrs = pyproj.CRS.from_user_input(data_obj.rio.crs).to_cf()
# spatial_ref is for compatibility with GDAL
crs_wkt = crs_to_wkt(data_obj.rio.crs)
grid_map_attrs["spatial_ref"] = crs_wkt
grid_map_attrs["crs_wkt"] = crs_wkt
if transform is not None:
grid_map_attrs["GeoTransform"] = " ".join(
[str(item) for item in transform.to_gdal()]
)
data_obj.coords[grid_mapping_name].rio.set_attrs(grid_map_attrs, inplace=True)
# add grid mapping attribute to variables
if hasattr(data_obj, "data_vars"):
for var in data_obj.data_vars:
if (
self.x_dim in data_obj[var].dims
and self.y_dim in data_obj[var].dims
):
data_obj[var].rio.update_attrs(
dict(grid_mapping=grid_mapping_name), inplace=True
).rio.set_spatial_dims(
x_dim=self.x_dim, y_dim=self.y_dim, inplace=True
)
return data_obj.rio.update_attrs(
dict(grid_mapping=grid_mapping_name), inplace=True
)
def _cached_transform(self):
"""
Get the transform from:
1. The GeoTransform metatada property in the grid mapping
2. The transform attribute.
"""
try:
# look in grid_mapping
grid_mapping_coord = self._obj.attrs.get("grid_mapping", DEFAULT_GRID_MAP)
return Affine.from_gdal(
*np.fromstring(
self._obj.coords[grid_mapping_coord].attrs["GeoTransform"], sep=" "
)
)
except KeyError:
try:
return Affine(*self._obj.attrs["transform"][:6])
except KeyError:
pass
return None
def write_transform(
self, transform=None, grid_mapping_name=DEFAULT_GRID_MAP, inplace=False
):
"""
.. versionadded:: 0.0.30
Write the GeoTransform to the dataset where GDAL can read it in.
https://gdal.org/drivers/raster/netcdf.html#georeference
Parameters
----------
transform: affine.Affine, optional
The transform of the dataset. If not provided, it will be calculated.
grid_mapping_name: str, optional
Name of the coordinate to store the CRS information in.
inplace: bool, optional
If True, it will write to the existing dataset. Default is False.
Returns
-------
xarray.Dataset or xarray.DataArray:
Modified dataset with Geo Transform written.
"""
transform = transform or self.transform(recalc=True)
data_obj = self._get_obj(inplace=inplace)
# delete the old attribute to prevent confusion
data_obj.attrs.pop("transform", None)
try:
grid_map_attrs = data_obj.coords[grid_mapping_name].attrs.copy()
except KeyError:
data_obj.coords[grid_mapping_name] = xarray.Variable((), 0)
grid_map_attrs = data_obj.coords[grid_mapping_name].attrs.copy()
grid_map_attrs["GeoTransform"] = " ".join(
[str(item) for item in transform.to_gdal()]
)
data_obj.coords[grid_mapping_name].rio.set_attrs(grid_map_attrs, inplace=True)
return data_obj
def set_attrs(self, new_attrs, inplace=False):
"""
Set the attributes of the dataset/dataarray and reset
rioxarray properties to re-search for them.
Parameters
----------
new_attrs: dict
A dictionary of new attributes.
inplace: bool, optional
If True, it will write to the existing dataset. Default is False.
Returns
-------
xarray.Dataset or xarray.DataArray:
Modified dataset with new attributes.
"""
data_obj = self._get_obj(inplace=inplace)
# set the attributes
data_obj.attrs = new_attrs
# reset rioxarray properties depending
# on attributes to be generated
data_obj.rio._nodata = None
data_obj.rio._crs = None
return data_obj
def update_attrs(self, new_attrs, inplace=False):
"""
Update the attributes of the dataset/dataarray and reset
rioxarray properties to re-search for them.
Parameters
----------
new_attrs: dict
A dictionary of new attributes to update with.
inplace: bool, optional
If True, it will write to the existing dataset. Default is False.
Returns
-------
xarray.Dataset or xarray.DataArray:
Modified dataset with updated attributes.
"""
data_attrs = dict(self._obj.attrs)
data_attrs.update(**new_attrs)
return self.set_attrs(data_attrs, inplace=inplace)
def set_spatial_dims(self, x_dim, y_dim, inplace=True):
"""
This sets the spatial dimensions of the dataset.
Parameters
----------
x_dim: str
The name of the x dimension.
y_dim: str
The name of the y dimension.
inplace: bool, optional
If True, it will modify the dataframe in place.
Otherwise it will return a modified copy.
Returns
-------
xarray.Dataset or xarray.DataArray:
Dataset with spatial dimensions set.
"""
def set_dims(obj, in_x_dim, in_y_dim):
if in_x_dim in obj.dims:
obj.rio._x_dim = x_dim
else:
raise DimensionError(
f"x dimension ({x_dim}) not found.{_get_data_var_message(obj)}"
)
if y_dim in obj.dims:
obj.rio._y_dim = y_dim
else:
raise DimensionError(
f"y dimension ({x_dim}) not found.{_get_data_var_message(obj)}"
)
data_obj = self._get_obj(inplace=inplace)
set_dims(data_obj, x_dim, y_dim)
return data_obj
@property
def x_dim(self):
"""str: The dimension for the X-axis."""
if self._x_dim is not None:
return self._x_dim
raise DimensionError(
"x dimension not found. 'set_spatial_dims()' can address this."
f"{_get_data_var_message(self._obj)}"
)
@property
def y_dim(self):
"""str: The dimension for the Y-axis."""
if self._y_dim is not None:
return self._y_dim
raise DimensionError(
"x dimension not found. 'set_spatial_dims()' can address this."
f"{_get_data_var_message(self._obj)}"
)
@property
def width(self):
"""int: Returns the width of the dataset (x dimension size)"""
if self._width is not None:
return self._width
self._width = self._obj[self.x_dim].size
return self._width
@property
def height(self):
"""int: Returns the height of the dataset (y dimension size)"""
if self._height is not None:
return self._height
self._height = self._obj[self.y_dim].size
return self._height
@property
def shape(self):
"""tuple(int, int): Returns the shape (height, width)"""
return (self.height, self.width)
def isel_window(self, window):
"""
Use a rasterio.window.Window to select a subset of the data.
.. warning:: Float indices are converted to integers.
Parameters
----------
window: :class:`rasterio.window.Window`
The window of the dataset to read.
Returns
-------
:obj:`xarray.Dataset` | :obj:`xarray.DataArray`: The data in the window.
"""
(row_start, row_stop), (col_start, col_stop) = window.toranges()
row_start = math.ceil(row_start) if row_start < 0 else math.floor(row_start)
row_stop = math.floor(row_stop) if row_stop < 0 else math.ceil(row_stop)
col_start = math.ceil(col_start) if col_start < 0 else math.floor(col_start)
col_stop = math.floor(col_stop) if col_stop < 0 else math.ceil(col_stop)
row_slice = slice(int(row_start), int(row_stop))
col_slice = slice(int(col_start), int(col_stop))
return (
self._obj.isel({self.y_dim: row_slice, self.x_dim: col_slice})
.copy() # this is to prevent sharing coordinates with the original dataset
.rio.set_spatial_dims(x_dim=self.x_dim, y_dim=self.y_dim, inplace=True)
.rio.write_transform(
transform=rasterio.windows.transform(
rasterio.windows.Window.from_slices(
rows=row_slice,
cols=col_slice,
width=self.width,
height=self.height,
),
self.transform(recalc=True),
),
inplace=True,
)
)
@xarray.register_dataarray_accessor("rio")
class RasterArray(XRasterBase):
"""This is the GIS extension for :class:`xarray.DataArray`"""
def __init__(self, xarray_obj):
super(RasterArray, self).__init__(xarray_obj)
# properties
self._nodata = None
self._count = None
def set_nodata(self, input_nodata, inplace=True):
"""
Set the nodata value for the DataArray without modifying
the data array.
Parameters
----------
input_nodata: object
Valid nodata for dtype.
inplace: bool, optional
If True, it will write to the existing dataset. Default is False.
Returns
-------
xarray.DataArray: Dataset with nodata attribute set.
"""
obj = self._get_obj(inplace=inplace)
obj.rio._nodata = input_nodata
return obj
def write_nodata(self, input_nodata, inplace=False):
"""
Write the nodata to the DataArray in a CF compliant manner.
Parameters
----------
input_nodata: object
Nodata value for the DataArray.
If input_nodata is None, it will remove the _FillValue attribute.
inplace: bool, optional
If True, it will write to the existing DataArray. Default is False.
Returns
-------
xarray.DataArray: Modified DataArray with CF compliant nodata information.
"""
data_obj = self._get_obj(inplace=inplace)
input_nodata = False if input_nodata is None else input_nodata
if input_nodata is not False:
data_obj.rio.update_attrs(dict(_FillValue=input_nodata), inplace=True)
else:
new_vars = dict(data_obj.attrs)
new_vars.pop("_FillValue", None)
data_obj.rio.set_attrs(new_vars, inplace=True)
data_obj.rio.set_nodata(input_nodata, inplace=True)
return data_obj
@property
def encoded_nodata(self):
"""Return the encoded nodata value for the dataset if encoded."""
return self._obj.encoding.get("_FillValue")
@property
def nodata(self):
"""Get the nodata value for the dataset."""
if self._nodata is not None:
return None if self._nodata is False else self._nodata
if self.encoded_nodata is not None:
self._nodata = np.nan
else:
self._nodata = self._obj.attrs.get(
"_FillValue",
self._obj.attrs.get(
"missing_value",
self._obj.attrs.get("fill_value", self._obj.attrs.get("nodata")),
),
)
# look in places used by `xarray.open_rasterio`
if self._nodata is None:
try:
self._nodata = self._obj._file_obj.acquire().nodata
except AttributeError:
try:
self._nodata = self._obj.attrs["nodatavals"][0]
except (KeyError, IndexError):
pass
if self._nodata is None:
self._nodata = False
return None
return self._nodata
def resolution(self, recalc=False):
"""Determine the resolution of the `xarray.DataArray`
Parameters
----------
recalc: bool, optional
Will force the resolution to be recalculated instead of using the
transform attribute.
"""
transform = self._cached_transform()
if (
not recalc or self.width == 1 or self.height == 1
) and transform is not None:
resolution_x = transform.a
resolution_y = transform.e
return resolution_x, resolution_y
# if the coordinates of the spatial dimensions are missing
# use the cached transform resolution
try:
left, bottom, right, top = self._internal_bounds()
except DimensionMissingCoordinateError:
if transform is None:
raise
resolution_x = transform.a
resolution_y = transform.e
return resolution_x, resolution_y
if self.width == 1 or self.height == 1:
raise OneDimensionalRaster(
"Only 1 dimenional array found. Cannot calculate the resolution."
f"{_get_data_var_message(self._obj)}"
)
resolution_x = (right - left) / (self.width - 1)
resolution_y = (bottom - top) / (self.height - 1)
return resolution_x, resolution_y
def _internal_bounds(self):
"""Determine the internal bounds of the `xarray.DataArray`"""
if self.x_dim not in self._obj.coords:
raise DimensionMissingCoordinateError(f"{self.x_dim} missing coordinates.")
elif self.y_dim not in self._obj.coords:
raise DimensionMissingCoordinateError(f"{self.y_dim} missing coordinates.")
try:
left = float(self._obj[self.x_dim][0])
right = float(self._obj[self.x_dim][-1])
top = float(self._obj[self.y_dim][0])
bottom = float(self._obj[self.y_dim][-1])
except IndexError:
raise NoDataInBounds(
"Unable to determine bounds from coordinates."
f"{_get_data_var_message(self._obj)}"
)
return left, bottom, right, top
def _check_dimensions(self):
"""
This function validates that the dimensions 2D/3D and
they are are in the proper order.
Returns
-------
str or None: Name extra dimension.
"""
extra_dims = list(set(list(self._obj.dims)) - set([self.x_dim, self.y_dim]))
if len(extra_dims) > 1:
raise TooManyDimensions(
"Only 2D and 3D data arrays supported."
f"{_get_data_var_message(self._obj)}"
)
elif extra_dims and self._obj.dims != (extra_dims[0], self.y_dim, self.x_dim):
raise InvalidDimensionOrder(
"Invalid dimension order. Expected order: {0}. "
"You can use `DataArray.transpose{0}`"
" to reorder your dimensions.".format(
(extra_dims[0], self.y_dim, self.x_dim)
)
+ f"{_get_data_var_message(self._obj)}"
)
elif not extra_dims and self._obj.dims != (self.y_dim, self.x_dim):
raise InvalidDimensionOrder(
"Invalid dimension order. Expected order: {0}"
"You can use `DataArray.transpose{0}` "
"to reorder your dimensions.".format((self.y_dim, self.x_dim))
+ f"{_get_data_var_message(self._obj)}"
)
return extra_dims[0] if extra_dims else None
@property
def count(self):
if self._count is not None:
return self._count
extra_dim = self._check_dimensions()
self._count = 1
if extra_dim is not None:
self._count = self._obj[extra_dim].size
return self._count
def bounds(self, recalc=False):
"""Determine the bounds of the `xarray.DataArray`
Parameters
----------
recalc: bool, optional
Will force the bounds to be recalculated instead of using the
transform attribute.
Returns
-------
left, bottom, right, top: float
Outermost coordinates.
"""
resolution_x, resolution_y = self.resolution(recalc=recalc)
try:
# attempt to get bounds from xarray coordinate values
left, bottom, right, top = self._internal_bounds()
left -= resolution_x / 2.0
right += resolution_x / 2.0
top -= resolution_y / 2.0
bottom += resolution_y / 2.0
except DimensionMissingCoordinateError:
transform = self._cached_transform()
left = transform.c
top = transform.f
right = left + resolution_x * self.width
bottom = top + resolution_y * self.height
return left, bottom, right, top
def transform_bounds(self, dst_crs, densify_pts=21, recalc=False):
"""Transform bounds from src_crs to dst_crs.
Optionally densifying the edges (to account for nonlinear transformations
along these edges) and extracting the outermost bounds.
Note: this does not account for the antimeridian.
Parameters
----------
dst_crs: str, :obj:`rasterio.crs.CRS`, or dict
Target coordinate reference system.
densify_pts: uint, optional
Number of points to add to each edge to account for nonlinear
edges produced by the transform process. Large numbers will produce
worse performance. Default: 21 (gdal default).
recalc: bool, optional
Will force the bounds to be recalculated instead of using the transform
attribute.
Returns
-------
left, bottom, right, top: float
Outermost coordinates in target coordinate reference system.
"""
return rasterio.warp.transform_bounds(
self.crs, dst_crs, *self.bounds(recalc=recalc), densify_pts=densify_pts
)
def transform(self, recalc=False):
"""
Parameters
----------
recalc: bool, optional
If True, it will re-calculate the transform instead of using
the cached transform.
Returns
-------
affine.Afffine: The affine of the `xarray.DataArray`
"""
try:
src_left, _, _, src_top = self.bounds(recalc=recalc)
src_resolution_x, src_resolution_y = self.resolution(recalc=recalc)
except DimensionMissingCoordinateError:
return Affine.identity()
return Affine.translation(src_left, src_top) * Affine.scale(
src_resolution_x, src_resolution_y
)
def reproject(
self,
dst_crs,
resolution=None,
shape=None,
transform=None,
resampling=Resampling.nearest,
):
"""
Reproject :class:`xarray.DataArray` objects
Powered by `rasterio.warp.reproject`
.. note:: Only 2D/3D arrays with dimensions 'x'/'y' are currently supported.
Requires either a grid mapping variable with 'spatial_ref' or
a 'crs' attribute to be set containing a valid CRS.
If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.
.. versionadded:: 0.0.27 shape
.. versionadded:: 0.0.28 transform
Parameters
----------
dst_crs: str
OGC WKT string or Proj.4 string.
resolution: float or tuple(float, float), optional
Size of a destination pixel in destination projection units
(e.g. degrees or metres).
shape: tuple(int, int), optional
Shape of the destination in pixels (dst_height, dst_width). Cannot be used
together with resolution.
transform, optional
The destination transform.
resampling: Resampling method, optional
See rasterio.warp.reproject for more details.
Returns
-------
:class:`xarray.DataArray`: A reprojected DataArray.