/
writer.py
340 lines (262 loc) · 11 KB
/
writer.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
# This file is part of the Open Data Cube, see https://opendatacube.org for more information
#
# Copyright (c) 2015-2024 ODC Contributors
# SPDX-License-Identifier: Apache-2.0
"""
Create netCDF4 Storage Units and write data to them
"""
import logging
import numbers
from datetime import datetime
from collections import namedtuple
import numpy
from datacube.utils.masking import describe_flags_def
from netCDF4 import Dataset
from odc.geo import CRS
from odc.geo.geom import box
from odc.geo.math import data_resolution_and_offset
from datacube import __version__
Variable = namedtuple('Variable', ('dtype', 'nodata', 'dims', 'units'))
_LOG = logging.getLogger(__name__)
DEFAULT_GRID_MAPPING = 'spatial_ref'
_STANDARD_COORDINATES = {
'longitude': {
'standard_name': 'longitude',
'long_name': 'longitude',
'axis': 'X'
},
'latitude': {
'standard_name': 'latitude',
'long_name': 'latitude',
'axis': 'Y'
},
'x': {
'standard_name': 'projection_x_coordinate',
'long_name': 'x coordinate of projection',
# 'axis': 'X' # this makes gdal (2.0.0) think x is longitude and it does bad things to it (subtract 360)
},
'y': {
'standard_name': 'projection_y_coordinate',
'long_name': 'y coordinate of projection',
# 'axis': 'Y' # see x's axis comment above
},
'time': {
'standard_name': 'time',
'long_name': 'Time, unix time-stamp',
'axis': 'T',
'calendar': 'standard'
}
}
def create_netcdf(netcdf_path, **kwargs):
"""
Create and return an empty NetCDF file
:param netcdf_path: File path to write to
:param kwargs: See :class:`Dataset` for more information
:return: open NetCDF Dataset
"""
nco = Dataset(netcdf_path, 'w', **kwargs)
nco.date_created = datetime.today().isoformat()
nco.setncattr('Conventions', 'CF-1.6, ACDD-1.3')
nco.history = ("NetCDF-CF file created by "
"datacube version '{}' at {:%Y%m%d}."
.format(__version__, datetime.utcnow()))
return nco
def append_netcdf(netcdf_path):
"""
Open a NetCDF file in append mode
:param netcdf_path:
:return: open NetCDF Dataset
"""
return Dataset(netcdf_path, 'a')
def create_coordinate(nco, name, labels, units):
"""
:type nco: netCDF4.Dataset
:type name: str
:type labels: numpy.array
:type units: str
:rtype: netCDF4.Variable
"""
labels = netcdfy_coord(labels)
nco.createDimension(name, labels.size)
var = nco.createVariable(name, labels.dtype, name)
var[:] = labels
var.units = units
for key, value in _STANDARD_COORDINATES.get(name, {}).items():
setattr(var, key, value)
return var
def create_variable(nco, name, var, grid_mapping=None, attrs=None, **kwargs):
"""
:param nco:
:param name:
:param datacube.model.Variable var:
:param kwargs:
:return:
"""
assert var.dtype.kind != 'U' # Creates Non CF-Compliant NetCDF File
def clamp_chunksizes(chunksizes, dim_names):
if chunksizes is None:
return None
maxsizes = [len(nco.dimensions[dim]) for dim in dim_names]
# pad chunksizes to new dimension length if too short
chunksizes = tuple(chunksizes) + tuple(maxsizes[len(chunksizes):])
# clamp
return [min(sz, maxsz) for sz, maxsz in zip(chunksizes, maxsizes)]
if var.dtype.kind == 'S' and var.dtype.itemsize > 1:
new_dim_name = name + '_nchar'
nco.createDimension(new_dim_name, size=var.dtype.itemsize)
dims = tuple(var.dims) + (new_dim_name,)
datatype = numpy.dtype('S1')
else:
dims = var.dims
datatype = var.dtype
chunksizes = clamp_chunksizes(kwargs.pop('chunksizes', None), dims)
data_var = nco.createVariable(varname=name,
datatype=datatype,
dimensions=dims,
fill_value=getattr(var, 'nodata', None),
chunksizes=chunksizes,
**kwargs)
if grid_mapping is not None:
data_var.grid_mapping = grid_mapping
if getattr(var, 'units', None):
data_var.units = var.units
data_var.set_auto_maskandscale(False)
return data_var
def _create_latlon_grid_mapping_variable(nco, crs, name=DEFAULT_GRID_MAPPING):
crs_var = nco.createVariable(name, 'i4')
crs_var.long_name = crs._crs.name # "Lon/Lat Coords in WGS84"
# also available as crs._crs.to_cf()['grid_mapping_name']
crs_var.grid_mapping_name = 'latitude_longitude'
crs_var.longitude_of_prime_meridian = 0.0
return crs_var
def _write_albers_params(crs_var, crs):
# http://spatialreference.org/ref/epsg/gda94-australian-albers/html/
# http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/cf-conventions.html#appendix-grid-mappings
cf = crs._crs.to_cf()
crs_var.grid_mapping_name = cf['grid_mapping_name']
crs_var.standard_parallel = tuple(cf['standard_parallel'])
crs_var.longitude_of_central_meridian = cf['longitude_of_central_meridian']
crs_var.latitude_of_projection_origin = cf['latitude_of_projection_origin']
def _write_sinusoidal_params(crs_var, crs):
cf = crs._crs.to_cf()
crs_var.grid_mapping_name = cf['grid_mapping_name']
crs_var.longitude_of_central_meridian = cf['longitude_of_projection_origin']
def _write_transverse_mercator_params(crs_var, crs):
cf = crs._crs.to_cf()
# http://spatialreference.org/ref/epsg/wgs-84-utm-zone-54s/
crs_var.grid_mapping_name = cf['grid_mapping_name']
crs_var.scale_factor_at_central_meridian = cf['scale_factor_at_central_meridian']
crs_var.longitude_of_central_meridian = cf['longitude_of_central_meridian']
crs_var.latitude_of_projection_origin = cf['latitude_of_projection_origin']
def _write_lcc2_params(crs_var, crs):
cf = crs._crs.to_cf()
# e.g. http://spatialreference.org/ref/sr-org/mexico-inegi-lambert-conformal-conic/
crs_var.grid_mapping_name = cf['grid_mapping_name']
crs_var.standard_parallel = cf['standard_parallel']
crs_var.latitude_of_projection_origin = cf['latitude_of_projection_origin']
crs_var.longitude_of_central_meridian = cf['longitude_of_central_meridian']
crs_var.false_easting = cf['false_easting']
crs_var.false_northing = cf['false_northing']
crs_var.semi_major_axis = crs.semi_major_axis
crs_var.semi_minor_axis = crs.semi_minor_axis
CRS_PARAM_WRITERS = {
'albers_conic_equal_area': _write_albers_params,
'albers_conical_equal_area': _write_albers_params,
'sinusoidal': _write_sinusoidal_params,
'transverse_mercator': _write_transverse_mercator_params,
'lambert_conformal_conic_2sp': _write_lcc2_params,
'lambert_conformal_conic': _write_lcc2_params,
}
def _create_projected_grid_mapping_variable(nco, crs, name=DEFAULT_GRID_MAPPING):
cf = crs._crs.to_cf()
grid_mapping_name = cf['grid_mapping_name']
if grid_mapping_name not in CRS_PARAM_WRITERS:
raise ValueError('{} CRS is not supported'.format(grid_mapping_name))
crs_var = nco.createVariable(name, 'i4')
CRS_PARAM_WRITERS[grid_mapping_name](crs_var, crs)
crs_var.false_easting = cf['false_easting']
crs_var.false_northing = cf['false_northing']
crs_var.long_name = crs._crs.name
return crs_var
def _write_geographical_extents_attributes(nco, extent):
geo_extents = extent.to_crs(CRS("EPSG:4326"))
nco.geospatial_bounds = geo_extents.wkt
nco.geospatial_bounds_crs = "EPSG:4326"
geo_bounds = geo_extents.boundingbox
nco.geospatial_lat_min = geo_bounds.bottom
nco.geospatial_lat_max = geo_bounds.top
nco.geospatial_lat_units = "degrees_north"
nco.geospatial_lon_min = geo_bounds.left
nco.geospatial_lon_max = geo_bounds.right
nco.geospatial_lon_units = "degrees_east"
# TODO: broken anyway...
# nco.geospatial_lat_resolution = "{} degrees".format(abs(geobox.affine.e))
# nco.geospatial_lon_resolution = "{} degrees".format(abs(geobox.affine.a))
def create_grid_mapping_variable(nco, crs, name=DEFAULT_GRID_MAPPING):
if crs.geographic:
crs_var = _create_latlon_grid_mapping_variable(nco, crs, name)
elif crs.projected:
crs_var = _create_projected_grid_mapping_variable(nco, crs, name)
else:
raise ValueError('Unknown CRS')
# mark crs variable as a coordinate
coords = getattr(nco, 'coordinates', None)
coords = [] if coords is None else coords.split(',')
if name not in coords:
coords.append(name)
nco.coordinates = ','.join(coords)
crs_var.semi_major_axis = crs.semi_major_axis
crs_var.semi_minor_axis = crs.semi_minor_axis
crs_var.inverse_flattening = crs.inverse_flattening
crs_var.crs_wkt = crs.wkt
crs_var.spatial_ref = crs.wkt
dims = crs.dimensions
xres, xoff = data_resolution_and_offset(nco[dims[1]])
yres, yoff = data_resolution_and_offset(nco[dims[0]])
crs_var.GeoTransform = [xoff, xres, 0.0, yoff, 0.0, yres]
left, right = nco[dims[1]][0] - 0.5 * xres, nco[dims[1]][-1] + 0.5 * xres
bottom, top = nco[dims[0]][0] - 0.5 * yres, nco[dims[0]][-1] + 0.5 * yres
_write_geographical_extents_attributes(nco, box(left, bottom, right, top, crs=crs))
return crs_var
def write_flag_definition(variable, flags_definition):
# write bitflag info
# Functions for this are stored in Measurements
variable.QA_index = describe_flags_def(flags_def=flags_definition)
variable.flag_masks, variable.valid_range, variable.flag_meanings = flag_mask_meanings(flags_def=flags_definition)
def netcdfy_coord(data):
return netcdfy_data(data)
def netcdfy_data(data):
# NetCDF/CF Conventions only seem to allow storing ascii, not unicode
if data.dtype.kind == 'S' and data.dtype.itemsize > 1:
return data.view('S1').reshape(data.shape + (-1,))
if data.dtype.kind == 'M':
return data.astype('<M8[s]').astype('double')
else:
return data
def flag_mask_meanings(flags_def):
# Filter out any multi-bit mask values since we can't handle them yet
flags_def = {k: v for k, v in flags_def.items() if isinstance(v['bits'], numbers.Integral)}
max_bit = max([bit_def['bits'] for bit_def in flags_def.values()])
if max_bit >= 32:
# GDAL upto and including 2.0 can't support int64 attributes...
raise RuntimeError('Bit index too high: %s' % max_bit)
valid_range = numpy.array([0, (2 ** max_bit - 1) + 2 ** max_bit], dtype='int32')
masks = []
meanings = []
def by_bits(i):
_, v = i
return v['bits']
for name, bitdef in sorted(flags_def.items(), key=by_bits):
try:
true_value = bitdef['values'][1]
if true_value is True:
meaning = name
elif true_value is False:
meaning = 'no_' + name
else:
meaning = true_value
masks.append(2 ** bitdef['bits'])
meanings.append(str(meaning))
except KeyError:
continue
return numpy.array(masks, dtype='int32'), valid_range, ' '.join(meanings)