-
Notifications
You must be signed in to change notification settings - Fork 1
/
05-pre_asim_wav.py
502 lines (458 loc) · 16.2 KB
/
05-pre_asim_wav.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
from __future__ import division
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Pre-process Radar data and put them in vector form.
- A little bit of cleaning as well
- Finally interpolated Radar and SWAN will be save in a netcdf file to read by assimilation routine
"""
__author__ = "Saeed Moghimi"
__copyright__ = "Copyright 2015, Oregon State University"
__license__ = "GPL"
__version__ = "0.1"
__email__ = "moghimis@gmail.com"
#####################################################################
# Saeed Moghimi; moghimis@gmail.com
# Logs:
# 1.0 03/25/2013 02:14:41 PM
#
#####################################################################
import os,sys
import glob
import numpy as np
import netCDF4
import multiprocessing
from datetime import datetime
import netcdftime
from scipy.optimize import fsolve
args = sys.argv
itr = int(args[1])
sys.path.append("py/")
from dispersion import *
#
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
real_wave = base_info.real_data
jumpp = base_info.jump_wav
####################################################
#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()
#####################################################
#import calc_k
g = 9.8126
run_id = '/run_'+str(1000+itr)
inp_dir = base_dir + '/inp/'
#### 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
wav_member_dir = base_dir+run_id+'/04_wav_adj'
data_dir = inp_dir+'/'
# #### Pickle name and check if exist !
namep = '/wave_jump-' + str(jumpp)+'__itr-'+str(itr)+'.p'
pick_name = wav_member_dir + namep
# if os.path.isfile(pick_name):
# sys.exit('Wave file exist !')
###################################
print 'Loading SWAN members '
###################################
memdir = wav_member_dir+'/member1*'
dirlist = np.array(glob.glob(memdir))
dirlist.sort()
nmemw = len(dirlist)
#create variables to be read in
# read SIZE from a sample
ncf = dirlist[0]+'/swout.nc'
nc = netCDF4.Dataset(ncf)
ncv = nc.variables
h = ncv['dep'][:]
xw = ncv['x'][:];xw1=xw[1,:]
yw = ncv['y'][:];yw1=yw[:,1]
[ny,nx]=np.squeeze(h).shape
nc.close()
#
uw = np.zeros((ny,nx,nmemw),dtype='float')
vw = np.zeros_like(uw)
dw = np.zeros_like(uw)
Hw = np.zeros_like(uw)
mw = np.zeros_like(uw)
dirw = np.zeros_like(uw)
#
failind=[]
for i in range(nmemw):
if np.mod(i,50)==0: print ' > WAV member read in ', (i*1.0/nmemw)*100.0, '%'
try:
ncf = dirlist[i]+'/swout.nc'
nc = netCDF4.Dataset(ncf)
ncv = nc.variables
uw[:,:,i] = np.squeeze(ncv['velx'][:,:])
vw[:,:,i] = np.squeeze(ncv['vely'][:,:])
dw[:,:,i] = np.squeeze(ncv['dep'][:,:])
mw[:,:,i] = np.squeeze(ncv['mask'][:,:])
Hw[:,:,i] = np.squeeze(ncv['hsig'][:,:])
dirw[:,:,i]=np.squeeze(ncv['dir'][:,:])
nc.close()
except:
failind.append(i)
# In case some members failed for whatever reason: discard them
if(len(failind) > 0):
print 'WARNING WARNING WARNING: the following members failed:'
for i in failind:
print 'member -> ', dirlist[i]
#
keepind=setdiff1d( range(nmemw),failind)
uw=uw[:,:,keepind]
vw=vw[:,:,keepind]
mw=mw[:,:,keepind]
dw=dw[:,:,keepind]
Hw=Hw[:,:,keepind]
dirw=dirw[:,:,keepind]
dirlistw=dirlist[keepind]
#mask out land points
uw = np.ma.masked_where(mw==0,uw)
vw = np.ma.masked_where(mw==0,vw)
dw = np.ma.masked_where(mw==0,dw)
Hw = np.ma.masked_where(mw==0,Hw)
dirw=np.ma.masked_where(mw==0,dirw)
maskm = mw[:,:,0]==0
[nyw,nxw,Nw]=dw.shape
print ' > Number of wave members >', Nw
#sys.exit('asdasdasdasda')
######################################
# Read Radar data
# load the data grid for currents
if real_wave:
###Real OBS
ncf_wdata = data_dir+'obs_wave/rad/cBathy.nc'
print 'real data obs file name > ', ncf_wdata
nc_wdata=netCDF4.Dataset(ncf_wdata)
ncv_wdata=nc_wdata.variables
xwd = np.squeeze(ncv_wdata['x'] [:])
ywd = np.squeeze(ncv_wdata['y'] [:])
fdwd = np.squeeze(ncv_wdata['fb'] [:])
kdwd = np.squeeze(ncv_wdata['k'] [:])
dirwd = np.squeeze(ncv_wdata['dir'] [:])
kd_err = np.squeeze(ncv_wdata['k_err'][:])
nc_wdata.close()
###
if jumpp > 1:
kxx = jumpp
kyy = jumpp
xwd = xwd [ ::kyy,::kxx]
ywd = ywd [ ::kyy,::kxx]
fdwd = fdwd [:,::kyy,::kxx]
kdwd = kdwd [:,::kyy,::kxx]
dirwd = dirwd [:,::kyy,::kxx]
kd_err = kd_err[:,::kyy,::kxx]
###
nkw,nyw,nxw = fdwd.shape
try:
mtmp = fdwd.mask
except:
fdwd = np.ma.masked_where(fdwd > 10, fdwd)
###
mmax = np.zeros((nyw,nxw))
kdmax = np.zeros((nyw,nxw))
fdmax = np.zeros((nyw,nxw))
dirmax = np.zeros((nyw,nxw))
kerrmax = np.zeros((nyw,nxw))
###
for ii in range(nxw):
for jj in range(nyw):
if not (fdwd[:,jj,ii].mask.all==True):
indw = np.argmax(fdwd[:,jj,ii])
fdmax [jj,ii] = fdwd [indw,jj,ii]
kdmax [jj,ii] = kdwd [indw,jj,ii]
dirmax [jj,ii] = dirwd[indw,jj,ii]
kerrmax[jj,ii] = kd_err[indw,jj,ii]
###
#Eliminate data points out side of the model area
fdmax[ (xwd<xw.min()) | (xwd > xw.max()) | (ywd<yw.min()) |(ywd > yw.max()) ]=np.NaN
fdmax = np.ma.masked_where(np.isnan(fdmax),fdmax )
kdmax = np.ma.masked_where(np.isnan(fdmax),kdmax )
dirmax = np.ma.masked_where(np.isnan(fdmax),dirmax )
kerrmax = np.ma.masked_where(np.isnan(fdmax),kerrmax)
maskw = fdmax.mask
#Matlab like flaten for comparison
xwcdf = xwd.flatten(1)
maskwf = maskw.flatten(1)
xwcd = xwcdf[~maskwf]
ywcdf = ywd.flatten(1)
ywcd = ywcdf[~maskwf]
fwdf = fdmax.flatten(1)
fwd = fwdf[~maskwf]
sig = 2 * np.pi * fwd
k_finf = kdmax.flatten(1)
k_fin = k_finf[~maskwf]
skwdf = kerrmax.flatten(1)
skwd = skwdf[~maskwf]
else:
###synthetic
ncf_wdata=data_dir+'obs_wave/syn/wav_syn_obs.nc'
print 'SYN data obs file name > ', ncf_wdata
nc_wdata=netCDF4.Dataset(ncf_wdata)
ncv_wdata=nc_wdata.variables
xwd = np.squeeze(ncv_wdata['x'] [::jumpp])
ywd = np.squeeze(ncv_wdata['y'] [::jumpp])
uwd = np.squeeze(ncv_wdata['velx'][0,::jumpp,::jumpp])
vwd = np.squeeze(ncv_wdata['vely'][0,::jumpp,::jumpp])
dwd = np.squeeze(ncv_wdata['depw'][0,::jumpp,::jumpp])
Hwd = np.squeeze(ncv_wdata['hsig'][0,::jumpp,::jumpp])
mwd = np.squeeze(ncv_wdata['mask'][0,::jumpp,::jumpp])
dirwd = np.squeeze(ncv_wdata['dir'] [0,::jumpp,::jumpp])
tm01 = np.squeeze(ncv_wdata['tm01'][0,::jumpp,::jumpp])
xwd,ywd = np.meshgrid(xwd, ywd)
# masking land point
# only in case of twin experiment or may be remote sensing data
mwd = np.ma.masked_where(mwd==0,mwd)
maskw = mwd.mask
if False:
# change obs data to vector
xwcd = xwd [~maskw].flatten(1)
ywcd = ywd [~maskw].flatten(1)
uwcd = uwd [~maskw].flatten(1)
vwcd = vwd [~maskw].flatten(1)
dwcd = dwd [~maskw].flatten(1)
Hwcd = Hwd [~maskw].flatten(1)
dirwcd = dirwd [~maskw].flatten(1)
tm01c = tm01 [~maskw].flatten(1)
else:
maskwd = maskw.flatten(1)
xwcd = xwd.flatten(1)
ywcd = ywd.flatten(1)
uwcd = uwd.flatten(1)
vwcd = vwd.flatten(1)
dwcd = dwd.flatten(1)
Hwcd = Hwd.flatten(1)
dirwcd = dirwd.flatten(1)
tm01c = tm01.flatten(1)
xwcd = xwcd [~maskwd]
ywcd = ywcd [~maskwd]
uwcd = uwcd [~maskwd]
vwcd = vwcd [~maskwd]
dwcd = dwcd [~maskwd]
Hwcd = Hwcd [~maskwd]
dirwcd = dirwcd [~maskwd]
tm01c = tm01c [~maskwd]
fwd = 1.0/(tm01c+0.0001)
sig = 2* np.pi * fwd
print ' > Wave relative direction to current assumed to be Nautical >> Hrad Coded'
alpha = 270-dirwcd
beta = np.rad2deg(np.arctan2(vwcd,uwcd))
gamma = np.deg2rad(beta-alpha) # angle btween wave and current
kguess = approxDispersion(sigma=sig,h=dwcd)
#Calculate wavenumber
if False:
#If Hwd = 0 then simple wave dispersion will be used
#test simple dispersion
Hwcd = np.zeros_like(Hwd)
uwcd = uwcd * 0.0
vwcd = vwcd * 0.0
k_fin = Dispersion(sigma = sig,dep = dwcd, u = uwcd ,
v = vwcd ,ang = gamma ,kguess = kguess ,
Hs = Hwcd)
#
# assign measurement uncertainty
skwd = np.ones(k_fin.shape) * np.random.randn(k_fin.shape[0]) * 1e-2
# Create container for each variables
ndata = len(xwcd)
k = component(num=ndata)
# Filling the containers
k.x = xwcd
k.y = ywcd
k.s = skwd
k.data = k_fin
k.f = fwd
meas = {}
meas.update({'k':k})
#sys.exit()
###############################################
print '> Interpolate ensemble to obs-points'
###############################################
for field in meas.keys():
obs=meas[field]
print ' > Interpolation of the members for > ',field
if field=='k':
nobs=len(obs.x)
hi=np.zeros((nobs,Nw),dtype='float')
ui=np.zeros_like(hi)
vi=np.zeros_like(hi)
Hi=np.zeros_like(hi)
alphai=np.zeros_like(hi)
for n in range(Nw):
hi[:,n]=interpg(xw[~maskm],yw[~maskm],dw[~maskm,n],obs.x,obs.y)
print ' > k1/5'
for n in range(Nw):
ui[:,n]=interpg(xw[~maskm],yw[~maskm],uw[~maskm,n],obs.x,obs.y)
print ' > k2/5'
for n in range(Nw):
vi[:,n]=interpg(xw[~maskm],yw[~maskm],vw[~maskm,n],obs.x,obs.y)
print ' > k3/5'
for n in range(Nw):
Hi[:,n]=interpg(xw[~maskm],yw[~maskm],Hw[~maskm,n],obs.x,obs.y)
print ' > k4/5'
for n in range(Nw):
alphai[:,n]=interpg(xw[~maskm],yw[~maskm],dirw[~maskm,n],obs.x,obs.y)
print ' > k5/5'
print ' > Wave relative direction to current assumed to be Nautical >> Hrad Coded'
alphai = 270-alphai
beta = np.rad2deg(np.arctan2(vi,ui))
gamma = np.deg2rad(beta-alphai) # angle btwn wave and curr
ui [np.isnan(ui)]=0
vi [np.isnan(vi)]=0
Hi [np.isnan(Hi)]=0
gamma[np.isnan(gamma)]=np.pi/2.0
sigma2 = np.repeat(a=sig, repeats=Nw, axis=0).reshape(sig.shape[0],Nw)
print ' >>> in approxDispersion() '
kguess = approxDispersion(sigma=sigma2,h=hi)
# discard locations for which no ensemble wavenumber estimate is
# possible
if True:
hi[hi>1e3]=np.nan
[ind]=np.where(~np.isnan(sigma2.sum(1)*hi.sum(1)*kguess.sum(1)) & ~(hi.sum(1)<=0.0) & ~(kguess.sum(1)<=0.0))
#ind=find(~isnan(sigma.*sum(hi,2).*sum(kguess,2)) & sum(hi<0.1,2)==0 & sum(kguess<0,2)==0); #matlab orig ones
tmp=obs
obs.x = tmp.x[ind]
obs.y = tmp.y[ind]
obs.s = tmp.s[ind]
obs.f = tmp.f[ind]
obs.data = tmp.data[ind]
sigma2 = sigma2[ind]
hi = hi[ind]
ui = ui[ind]
vi = vi[ind]
kguess = kguess[ind]
gamma = gamma[ind]
Hs = Hi[ind]
if False:
#test simple dispersion
Hs = np.zeros_like(gamma)
#Hs = Hi
ui = ui * 0.0
vi = vi * 0.0
#[nyi,nxi]=k_all.shape
#obs.model=k_all.reshape(nyi*nxi)
print ' >>> call calc_k()'
start_calc_k = datetime.now()
multi = True
if multi:
npoint,nmemw = ui.shape
nbr_chunks = 10
chunk_size = npoint / nbr_chunks
chunks = [(sigma2[x*chunk_size:(x+1)*chunk_size,:], \
kguess[x*chunk_size:(x+1)*chunk_size,:], \
hi [x*chunk_size:(x+1)*chunk_size,:], \
ui [x*chunk_size:(x+1)*chunk_size,:], \
vi [x*chunk_size:(x+1)*chunk_size,:], \
gamma [x*chunk_size:(x+1)*chunk_size,:], \
Hs [x*chunk_size:(x+1)*chunk_size,:],)\
for x in xrange(nbr_chunks)]
p = multiprocessing.Pool()
po = p.map_async(calc_k, chunks)
results = po.get()
obs.model=np.zeros_like(ui)
x=0
for res in results:
obs.model[x*chunk_size:(x+1)*chunk_size,:]= res
x+=1
else:
chunks=(sigma2,kguess,hi,ui,vi,gamma,Hs)
obs.model = calc_k(chunks)
end_calc_k=datetime.now()
delta_calc_k=end_calc_k-start_calc_k
print 'total_second= ', delta_calc_k.total_seconds()
meas['k'] = 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,:]
##################################################
# prepare output
##################################################
for field in meas.keys():
obs = meas[field]
namep = '/wav_'+ field+'.nc'
out_name = wav_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 = 'moghimis@gmail.com wave data structure for assimilation '+datetime.now().isoformat()
#outnc.att= infile[-12:]
outnc.close()
if False:
import cPickle as pickle
pickle.dump( meas, open(pick_name , "wb" ) )
print 'Wave pickle is ready at > ', pick_name
args = sys.argv
scr_name = args[0]
scr_dir1 = os.getcwd()
os.system('cp -fr ' + scr_name + ' ' + wav_member_dir)
os.system('cp -fr base_info.py ' + wav_member_dir)
print ' END > '