forked from pressel/pycles
-
Notifications
You must be signed in to change notification settings - Fork 0
/
NetCDFIO.pyx
361 lines (308 loc) · 12.7 KB
/
NetCDFIO.pyx
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
#!python
#cython: boundscheck=False
#cython: wraparound=False
#cython: initializedcheck=False
#cython: cdivision=True
import netCDF4 as nc
import os
import shutil
cimport ParallelMPI
cimport TimeStepping
cimport PrognosticVariables
cimport DiagnosticVariables
cimport Grid
import numpy as np
cimport numpy as np
import cython
cdef class NetCDFIO_Stats:
def __init__(self):
return
@cython.wraparound(True)
cpdef initialize(self, dict namelist, Grid.Grid Gr, ParallelMPI.ParallelMPI Pa):
self.last_output_time = 0.0
self.uuid = str(namelist['meta']['uuid'])
self.frequency = namelist['stats_io']['frequency']
# Setup the statistics output path
outpath = str(os.path.join(namelist['output'][
'output_root'] + 'Output.' + namelist['meta']['simname'] + '.' + self.uuid[-5:]))
self.stats_path = str(
os.path.join(
outpath,
namelist['stats_io']['stats_dir']))
self.path_plus_file = str(
self.stats_path +
'/' +
'Stats.' +
namelist['meta']['simname'] +
'.nc')
if Pa.rank == 0:
try:
os.mkdir(outpath)
except:
pass
try:
os.mkdir(self.stats_path)
except:
pass
shutil.copyfile(
os.path.join(
'./',
namelist['meta']['simname'] +
'.in'),
os.path.join(
outpath,
namelist['meta']['simname'] +
'.in'))
self.setup_stats_file(Gr, Pa)
return
cpdef setup_stats_file(self, Grid.Grid Gr, ParallelMPI.ParallelMPI Pa):
root_grp = nc.Dataset(self.path_plus_file, 'w', format='NETCDF4')
# Set profile dimensions
profile_grp = root_grp.createGroup('profiles')
profile_grp.createDimension('z', Gr.dims.n[2])
profile_grp.createDimension('t', None)
z = profile_grp.createVariable('z', 'f8', ('z'))
z[:] = np.array(Gr.z[Gr.dims.gw:-Gr.dims.gw])
profile_grp.createVariable('t', 'f8', ('t'))
del z
reference_grp = root_grp.createGroup('reference')
reference_grp.createDimension('z', Gr.dims.n[2])
z = reference_grp.createVariable('z', 'f8', ('z'))
z[:] = np.array(Gr.z[Gr.dims.gw:-Gr.dims.gw])
del z
ts_grp = root_grp.createGroup('timeseries')
ts_grp.createDimension('t', None)
ts_grp.createVariable('t', 'f8', ('t'))
root_grp.close()
return
cpdef add_profile(self, var_name, Grid.Grid Gr, ParallelMPI.ParallelMPI Pa):
if Pa.rank == 0:
root_grp = nc.Dataset(self.path_plus_file, 'r+', format='NETCDF4')
profile_grp = root_grp.groups['profiles']
new_var = profile_grp.createVariable(var_name, 'f8', ('t', 'z'))
root_grp.close()
return
cpdef add_reference_profile(self, var_name, Grid.Grid Gr, ParallelMPI.ParallelMPI Pa):
'''
Adds a profile to the reference group NetCDF Stats file.
:param var_name: name of variable
:param Gr: Grid class
:param Pa: ParallelMPI class
:return:
'''
if Pa.rank == 0:
root_grp = nc.Dataset(self.path_plus_file, 'r+', format='NETCDF4')
reference_grp = root_grp.groups['reference']
new_var = reference_grp.createVariable(var_name, 'f8', ('z',))
root_grp.close()
return
cpdef add_ts(self, var_name, Grid.Grid Gr, ParallelMPI.ParallelMPI Pa):
if Pa.rank == 0:
root_grp = nc.Dataset(self.path_plus_file, 'r+', format='NETCDF4')
ts_grp = root_grp.groups['timeseries']
new_var = ts_grp.createVariable(var_name, 'f8', ('t',))
root_grp.close()
return
cpdef write_profile(self, var_name, double[:] data, ParallelMPI.ParallelMPI Pa):
if Pa.rank == 0:
root_grp = nc.Dataset(self.path_plus_file, 'r+', format='NETCDF4')
profile_grp = root_grp.groups['profiles']
var = profile_grp.variables[var_name]
var[-1, :] = np.array(data)
root_grp.close()
return
cpdef write_reference_profile(self, var_name, double[:] data, ParallelMPI.ParallelMPI Pa):
'''
Writes a profile to the reference group NetCDF Stats file. The variable must have already been
added to the NetCDF file using add_reference_profile
:param var_name: name of variables
:param data: data to be written to file
:param Pa: ParallelMPI class
:return:
'''
if Pa.rank == 0:
root_grp = nc.Dataset(self.path_plus_file, 'r+', format='NETCDF4')
reference_grp = root_grp.groups['reference']
var = reference_grp.variables[var_name]
var[:] = np.array(data)
root_grp.close()
return
@cython.wraparound(True)
cpdef write_ts(self, var_name, double data, ParallelMPI.ParallelMPI Pa):
if Pa.rank == 0:
root_grp = nc.Dataset(self.path_plus_file, 'r+', format='NETCDF4')
ts_grp = root_grp.groups['timeseries']
var = ts_grp.variables[var_name]
var[-1] = data
root_grp.close()
return
cpdef write_simulation_time(self, double t, ParallelMPI.ParallelMPI Pa):
if Pa.rank == 0:
root_grp = nc.Dataset(self.path_plus_file, 'r+', format='NETCDF4')
profile_grp = root_grp.groups['profiles']
ts_grp = root_grp.groups['timeseries']
# Write to profiles group
profile_t = profile_grp.variables['t']
profile_t[profile_t.shape[0]] = t
# Write to timeseries group
ts_t = ts_grp.variables['t']
ts_t[ts_t.shape[0]] = t
root_grp.close()
return
cdef class NetCDFIO_Fields:
def __init__(self):
return
@cython.wraparound(True)
cpdef initialize(self, dict namelist, ParallelMPI.ParallelMPI Pa):
self.last_output_time = 0.0
self.uuid = str(namelist['meta']['uuid'])
self.frequency = namelist['fields_io']['frequency']
self.diagnostic_fields = namelist['fields_io']['diagnostic_fields']
# Setup the statistics output path
outpath = str(os.path.join(namelist['output'][
'output_root'] + 'Output.' + namelist['meta']['simname'] + '.' + self.uuid[-5:]))
self.fields_path = str(
os.path.join(
outpath,
namelist['fields_io']['fields_dir']))
if Pa.rank == 0:
try:
os.mkdir(outpath)
except:
pass
try:
os.mkdir(self.fields_path)
except:
pass
shutil.copyfile(
os.path.join(
'./',
namelist['meta']['simname'] +
'.in'),
os.path.join(
outpath,
namelist['meta']['simname'] +
'.in'))
return
cpdef update(self, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV, TimeStepping.TimeStepping TS, ParallelMPI.ParallelMPI Pa):
new_dir = os.path.join(
self.fields_path, str(int(self.last_output_time)))
if Pa.rank == 0 and not os.path.exists(new_dir):
try:
os.mkdir(new_dir)
except:
print('Problem creating fields output dir')
Pa.barrier()
self.output_path = str(new_dir)
self.path_plus_file = str(
os.path.join(
self.output_path, str(
Pa.rank) + '.nc'))
self.create_fields_file(Gr, Pa)
self.do_output = True
return
cpdef create_fields_file(self, Grid.Grid Gr, ParallelMPI.ParallelMPI Pa):
rootgrp = nc.Dataset(self.path_plus_file, 'w', format='NETCDF4')
dimgrp = rootgrp.createGroup('dims')
fieldgrp = rootgrp.createGroup('fields')
fieldgrp.createDimension('nl', np.int(Gr.dims.npl))
dimgrp.createDimension('d1', 1)
nl_0 = dimgrp.createVariable('nl_0', 'i4', ('d1'))
nl_1 = dimgrp.createVariable('nl_1', 'i4', ('d1'))
nl_2 = dimgrp.createVariable('nl_2', 'i4', ('d1'))
n_0 = dimgrp.createVariable('n_0', 'i4', ('d1'))
n_1 = dimgrp.createVariable('n_1', 'i4', ('d1'))
n_2 = dimgrp.createVariable('n_2', 'i4', ('d1'))
indx_lo_0 = dimgrp.createVariable('indx_lo_0', 'i4', ('d1'))
indx_lo_1 = dimgrp.createVariable('indx_lo_1', 'i4', ('d1'))
indx_lo_2 = dimgrp.createVariable('indx_lo_2', 'i4', ('d1'))
ng = dimgrp.createVariable('ng', 'i4', ('d1'))
# Dimension of equivalent 3d array owned by this rank
nl_0[:] = Gr.dims.nl[0]
nl_1[:] = Gr.dims.nl[1]
nl_2[:] = Gr.dims.nl[2]
n_0[:] = Gr.dims.n[0]
n_1[:] = Gr.dims.n[1]
n_2[:] = Gr.dims.n[2]
# Lower Left has point in global 3d array of the equivalent 3d array
# owned by this processor
indx_lo_0[:] = Gr.dims.indx_lo[0]
indx_lo_1[:] = Gr.dims.indx_lo[1]
indx_lo_2[:] = Gr.dims.indx_lo[2]
ng[:] = Gr.dims.npd
rootgrp.close()
return
cpdef dump_prognostic_variables(self, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV):
cdef:
Py_ssize_t i, j, k, ijk, ishift, jshift
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t imin = Gr.dims.gw
Py_ssize_t jmin = Gr.dims.gw
Py_ssize_t kmin = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0] - Gr.dims.gw
Py_ssize_t jmax = Gr.dims.nlg[1] - Gr.dims.gw
Py_ssize_t kmax = Gr.dims.nlg[2] - Gr.dims.gw
Py_ssize_t var_shift
double[:] data = np.empty((Gr.dims.npl,), dtype=np.double, order='c')
Py_ssize_t count
for name in PV.name_index.keys():
self.add_field(name)
var_shift = PV.get_varshift(Gr, name)
count = 0
with nogil:
for i in range(imin, imax):
ishift = i * istride
for j in range(jmin, jmax):
jshift = j * jstride
for k in range(kmin, kmax):
ijk = ishift + jshift + k
data[count] = PV.values[var_shift + ijk]
count += 1
self.write_field(name, data)
return
cpdef dump_diagnostic_variables(self, Grid.Grid Gr, DiagnosticVariables.DiagnosticVariables DV, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t i, j, k, ijk, ishift, jshift
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t imin = Gr.dims.gw
Py_ssize_t jmin = Gr.dims.gw
Py_ssize_t kmin = Gr.dims.gw
Py_ssize_t imax = Gr.dims.nlg[0] - Gr.dims.gw
Py_ssize_t jmax = Gr.dims.nlg[1] - Gr.dims.gw
Py_ssize_t kmax = Gr.dims.nlg[2] - Gr.dims.gw
Py_ssize_t var_shift
double[:] data = np.empty((Gr.dims.npl,), dtype=np.double, order='c')
Py_ssize_t count
for name in self.diagnostic_fields:
try:
self.add_field(name)
var_shift = DV.get_varshift(Gr, str(name))
count = 0
with nogil:
for i in range(imin, imax):
ishift = i * istride
for j in range(jmin, jmax):
jshift = j * jstride
for k in range(kmin, kmax):
ijk = ishift + jshift + k
data[count] = DV.values[var_shift + ijk]
count += 1
self.write_field(str(name), data)
except:
Pa.root_print('Could not output DiagnosticVariable Field: ' + name )
return
cpdef add_field(self, name):
rootgrp = nc.Dataset(self.path_plus_file, 'r+', format='NETCDF4')
fieldgrp = rootgrp.groups['fields']
fieldgrp.createVariable(name, 'f8', ('nl'))
rootgrp.close()
return
cpdef write_field(self, name, double[:] data):
rootgrp = nc.Dataset(self.path_plus_file, 'r+', format='NETCDF4')
fieldgrp = rootgrp.groups['fields']
var = fieldgrp.variables[name]
var[:] = np.array(data)
rootgrp.close()
return