-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy path05-pre_asim_cur.py
419 lines (361 loc) · 12.1 KB
/
05-pre_asim_cur.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
from __future__ import division
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Pre-process ROMS and velocity data and put them in vector form.
- A little bit of cleaning as well
- Finally interpolated data and model will be save in a netcdf file to read by assimilation routine
#
#
NEED TO CHECK:
> 1. Division by numbers 4/5 check not to devide
> 2. check cos and sin for degree or radian
> 3.
"""
__author__ = "Saeed Moghimi"
__copyright__ = "Copyright 2015, Oregon State University"
__license__ = "GPL"
__version__ = "0.1"
__email__ = "[email protected]"
#####################################################################
# Saeed Moghimi; [email protected]
# Logs:
# 1.0 03/25/2013 02:14:41 PM
#
#
#
import os,sys
import glob
import numpy as np
import netCDF4
from datetime import datetime
args = sys.argv
itr = int(args[1])
#
os.system('rm base_info.pyc' )
if 'base_info' in sys.modules:
del(sys.modules["base_info"])
import base_info
##############################################
base_dir = base_info.base_dir
inp_dir = base_info.inp_dir
scr_dir = base_info.scr_dir
prior = base_info.prior
final_grd = base_info.grd
jumpp = base_info.jump_cur
real_curr = base_info.real_data
#pysim_inp = base_dir + '/pysim_inp.txt'
####################################################
#Set input parameters
#fdata1 = open(pysim_inp)
#for line in fdata1.readlines():
# print line
# if 'itr' in line: itr = int(line.split()[-1])
#fdata1.close()
#####################################################
inp_dir = base_dir + '/inp/'
run_id = '/run_'+str(1000+itr)
#### Funcs
## interpolation
methodi='csa'
if methodi=='csa':
import octant.csa as csa
elif methodi=='tri':
from delaunay import triangulate
def interpg(x_old,y_old,data_old,x_new,y_new):
if methodi=='csa':
csa_interp = csa.CSA(x_old,y_old,data_old)
data_new = csa_interp(x_new,y_new)
return data_new
elif methodi=='grd':
from matplotlib.mlab import griddata
data_new = griddata(x_old.flatten(),y_old.flatten(),data_old.flatten(),x_new,y_new)
elif methodi=='tri':
tri=triangulate.Triangulation(x_old,y_old)
interp_b=tri.nn_extrapolator(data_old)
data_new = interp_b(x_new,y_new)
return data_new
class component:
def __init__(self,num):
self.x=np.zeros((num),dtype='float')
self.y=np.zeros((num),dtype='float')
self.data=np.zeros((num),dtype='float')
self.s=np.zeros((num),dtype='float')
#### End of Funcs
cur_member_dir = base_dir+run_id+'/04_mem_adj'
data_dir = inp_dir+'/'
#### Pickle name and check if exist !
namep = '/cur_jump-'+str(jumpp)+'__itr-'+str(itr)+'.p'
pick_name = cur_member_dir + namep
#if os.path.isfile(pick_name):
# sys.exit('CUR file exist !')
###########################################################
# Read circulation data
# load the data grid for currents
#ncf_cdata=data_dir+'obs/obs_every2ocean_avg_region.nc'
if real_curr:
ncf_cdata = data_dir+'obs/sar/uASAR.nc'
print "Real cur data > ", ncf_cdata
nc_cdata = netCDF4.Dataset(ncf_cdata)
ncv_cdata = nc_cdata.variables
xcd = np.squeeze(ncv_cdata['x'][:])
ycd = np.squeeze(ncv_cdata['y'][:])
ucd = np.squeeze(ncv_cdata['u'][0,:])
vcd = np.squeeze(ncv_cdata['v'][0,:])
sucd = 1.0 * np.squeeze(ncv_cdata['u_err'][:])
svcd = 1.0 * np.squeeze(ncv_cdata['v_err'][:])
#zcd=np.squeeze(ncv_cdata['z'][0,:])
#mcd=np.squeeze(ncv_cdata['mask'][:])
maskd = ucd > 1e5
ucd = np.ma.masked_array(ucd,maskd)
xcd = np.ma.masked_array(xcd,maskd)
ycd = np.ma.masked_array(ycd,maskd)
ucd = np.ma.masked_array(ucd,maskd)
vcd = np.ma.masked_array(vcd,maskd)
sucd = np.ma.masked_array(sucd,maskd)
svcd = np.ma.masked_array(svcd,maskd)
xcdm = xcd.flatten(1)
ycdm = ycd.flatten(1)
ucdm = ucd.flatten(1)
vcdm = vcd.flatten(1)
sucdm = sucd.flatten(1)
svcdm = svcd.flatten(1)
xcdm = xcdm.compressed()
ycdm = ycdm.compressed()
ucdm = ucdm.compressed()
vcdm = vcdm.compressed()
sudm = sucdm.compressed()
svdm = svcdm.compressed()
else:
ncf_cdata=data_dir+'obs/syn/syn1nri_his.nc'
print "SYN cur data > ", ncf_cdata
nc_cdata=netCDF4.Dataset(ncf_cdata)
ncv_cdata=nc_cdata.variables
xcd = np.squeeze(ncv_cdata['x_rho'] [:])
ycd = np.squeeze(ncv_cdata['y_rho'] [:])
ucd = np.squeeze(ncv_cdata['u'] [0,:])
vcd = np.squeeze(ncv_cdata['v'] [0,:])
maskcd = np.squeeze(ncv_cdata['mask'][:])
maskd = (maskcd==0)
xcd,ycd = np.meshgrid(xcd, ycd)
sucd = 0.04 * np.ones(ucd.shape) * np.random.randn(ucd.shape[0],ucd.shape[1])
svcd = 0.04 * np.ones(vcd.shape) * np.random.randn(vcd.shape[0],vcd.shape[1])
#Matlab like fllaten for comparison
xcdf= xcd.flatten(1)
maskdf = maskd.flatten(1)
xcdm = xcdf[~maskdf]
ycdf= ycd.flatten(1)
ycdm = ycdf[~maskdf]
ucdf= ucd.flatten(1)
ucdm = ucdf[~maskdf]
vcdf= vcd.flatten(1)
vcdm = vcdf[~maskdf]
sucdf = sucd.flatten(1)
sudm = sucdf[~maskdf]
svcdf = svcd.flatten(1)
svdm = svcdf[~maskdf]
# # masking land point
if jumpp >1:
ucdm = ucdm[::jumpp]
vcdm = vcdm[::jumpp]
xcdm = xcdm[::jumpp]
ycdm = ycdm[::jumpp]
sudm = sudm[::jumpp]
svdm = svdm[::jumpp]
# Create container for each variables
ndata=len(xcd)
u=component(num=ndata)
v=component(num=ndata)
#z=component(num=ndata)
# Filling the containers
u.x = xcdm
u.y = ycdm
u.s = sudm
u.data = ucdm
v.x = xcdm
v.y = ycdm
v.s = svdm
v.data = vcdm
#z.x=xcd
#z.y=ycd
#z.s=szcd
#z.data=zcd
meas={}
meas['u']=u
meas['v']=v
######################
print 'Loading ROMS members '
######################
memdir = cur_member_dir+'/member1*'
dirlist = np.array(glob.glob(memdir))
dirlist.sort()
nmemc = len(dirlist)
#create variables to be read in
# read SIZE from a sample
#ncf = dirlist[0]+'/nri_his1000.nc'
ncf = glob.glob(dirlist[0]+'/nri_h*')[0]
nc = netCDF4.Dataset(ncf)
ncv = nc.variables
h = ncv['h'][:]
xc = ncv['x_rho'][:];xc1=xc[1,:]
yc = ncv['y_rho'][:];yc1=yc[:,1]
[ny,nx]=h.shape
nc.close()
uc = np.zeros((ny,nx,nmemc),dtype='float')
vc = np.zeros_like(uc)
zc = np.zeros_like(uc)
hc = np.zeros_like(uc)
mc = np.zeros_like(uc)
failind=[]
for i in range(nmemc):
if np.mod(i,50)==0: print ' > CUR member read in ', (i*1.0/nmemc)*100.0, '%'
try:
#ncf=dirlist[i]+'/nri_his1000.nc'
ncf = glob.glob(dirlist[i]+'/nri_h*')[0]
nc=netCDF4.Dataset(ncf)
ncv=nc.variables
hc[:,:,i]=ncv['h'][:,:]
uc[:,:,i]=ncv['ubar'][:,:]
vc[:,:,i]=ncv['vbar'][:,:]
zc[:,:,i]=ncv['zeta'][:,:]
mc[:,:,i]=ncv['mask'][:,:]
nc.close()
except IOError:
failind.append(i)
# In case some members failed for whatever reason: discard thempcol
if(len(failind) > 0):
print 'WARNING WARNING WARNING: the following members failed:'
for i in failind:
print 'member -> ', dirlist[i]
keepind=np.setdiff1d( range(nmemc),failind)
uc=uc[:,:,keepind]
vc=vc[:,:,keepind]
zc=zc[:,:,keepind]
mc=mc[:,:,keepind]
hc=hc[:,:,keepind]
dirlistc=dirlist[keepind]
#mask out land points
temp = uc.sum(2) + vc.sum(2) + hc.sum(2) +zc.sum(2)
uc = np.ma.masked_where(mc==0 | np.isnan(temp) , uc)
vc = np.ma.masked_where(mc==0 | np.isnan(temp) , vc)
hc = np.ma.masked_where(mc==0 | np.isnan(temp) , hc)
zc = np.ma.masked_where(mc==0 | np.isnan(temp) , zc)
maskm = mc[:,:,0]==0
[nyc,nxc,Nc]=hc.shape
print ' > Number of CUR members >', Nc
###############################################
print 'Interpolate ensemble to obs-points'
###############################################
for field in meas.keys():
obs=meas[field]
print ' > Interpolation of the members for > ',field
if field in ['u','v','z'] :
if field=='u' : data=uc
if field=='v' : data=vc
if field=='z' : data=zc
nobs=len(obs.x)
datai=np.zeros((nobs,Nc),dtype='float')
#xy = np.array(zip(xc[~maskm],yc[~maskm]))
#tmp,ind = np.unique(xy, return_index=True)
#xcu = xc[~maskm][ind]
#ycu = yc[~maskm][ind]
for n in range(Nc):
datai[:,n]=interpg(xc[~maskm],yc[~maskm],data[~maskm,n],obs.x,obs.y)
obs.model=datai
meas[field]=obs
# remove any nan possibly created during interpolation
for field in meas.keys():
obs=meas[field]
failind = np.array(np.where(np.isnan(obs.model.sum(1)))).squeeze()
keepind = np.setdiff1d(range(len(obs.x)),failind)
obs.x = obs.x [keepind]
obs.y = obs.y [keepind]
obs.s = obs.s [keepind]
obs.data = obs.data [keepind]
obs.model = obs.model[keepind,:]
###################################################
#adding a dummy var to make it similar to wave dictionary
for field in ['u','v']: #,'z']:
obs=meas[field]
obs.f=np.zeros_like(obs.x)
meas[field]=obs
##################################################
# prepare output
##################################################
for field in ['u','v']: #,'z']:
obs = meas[field]
namep = '/cur_'+ field+'.nc'
out_name = cur_member_dir + namep
outnc = netCDF4.Dataset(out_name,'w',format='NETCDF3_CLASSIC')
dim_data = field+'_data_num'
dim_model = field+'_model_num'
num_data,num_model = obs.model.shape
outnc.createDimension(dim_data , num_data )
outnc.createDimension(dim_model , num_model)
p0 = outnc.createVariable(field+'_x','f8',(dim_data,))
p0.missing_value = -9999.0
p0[:] = obs.x
p1 = outnc.createVariable(field+'_y','f8',(dim_data,))
p1.missing_value = -9999.0
p1[:] = obs.y
p2 = outnc.createVariable(field+'_s','f8',(dim_data,))
p2.missing_value = -9999.0
p2[:] = obs.s
p3 = outnc.createVariable(field+'_data','f8',(dim_data,))
p3.missing_value = -9999.0
p3[:] = obs.data
p4 = outnc.createVariable(field+'_f','f8',(dim_data,))
p4.missing_value = -9999.0
p4[:] = obs.f
p5 = outnc.createVariable(field+'_model','f8',(dim_data,dim_model))
p5.missing_value = -9999.0
p5[:] = obs.model
outnc.history = '[email protected] current structure for assimilation '+datetime.now().isoformat()
#outnc.att= infile[-12:]
outnc.close()
# Member prior info out
namep = '/cur_members_prior.nc'
out_name = cur_member_dir + namep
outnc = netCDF4.Dataset(out_name,'w',format='NETCDF3_CLASSIC')
ny,nx,nmem = hc.shape
outnc.createDimension('ny' , ny )
outnc.createDimension('nx' , nx )
outnc.createDimension('nmem' , nmem )
p0 = outnc.createVariable('x_rho','f8',('ny','nx',))
p0.missing_value = -9999.0
p0[:] = xc
p1 = outnc.createVariable('y_rho','f8',('ny','nx',))
p1.missing_value = -9999.0
p1[:] = yc
p2 = outnc.createVariable('h_mems','f8',('ny','nx','nmem',))
p2.missing_value = -9999.0
p2[:] = hc
p3 = outnc.createVariable('u_mems','f8',('ny','nx','nmem',))
p3.missing_value = -9999.0
p3[:] = uc
p4 = outnc.createVariable('v_mems','f8',('ny','nx','nmem',))
p4.missing_value = -9999.0
p4[:] = vc
outnc.history = '[email protected], mems prior for assimilation '+datetime.now().isoformat()
#outnc.att= infile[-12:]
outnc.close()
if False:
import cPickle as pickle
###
namep = '/cur_jump-'+str(jumpp)+'__itr-'+str(itr)+'.p'
pick_name = cur_member_dir + namep
pickle.dump( meas, open(pick_name , "wb" ) )
print 'Circulation pickle is ready at > ', pick_name
###
out_pick={'xc':xc,'yc':yc,'hc':hc}
namep = '/hp_jump-'+str(jumpp)+'__itr-'+str(itr)+'.p'
pick_name = cur_member_dir + namep
pickle.dump( out_pick, open(pick_name , "wb" ) )
args = sys.argv
scr_name = args[0]
scr_dir1 = os.getcwd()
os.system('cp -fr ' + scr_name + ' ' + cur_member_dir)
os.system('cp -fr base_info.py ' + cur_member_dir)
print 'Members CUR data is ready at > ', out_name
## end
##################################################