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05-pre_asim_wav.py
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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)
nmemw = base_info.N
#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)
if n==0:
csa_interp = csa.CSA(xw[~maskm],yw[~maskm],dw[~maskm,n])
else:
csa_interp.zin = dw[~maskm,n]
hi[:,n] = csa_interp(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)
if n==0:
csa_interp = csa.CSA(xw[~maskm],yw[~maskm],uw[~maskm,n])
else:
csa_interp.zin = uw[~maskm,n]
ui[:,n] = csa_interp(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)
if n==0:
csa_interp = csa.CSA(xw[~maskm],yw[~maskm],vw[~maskm,n])
else:
csa_interp.zin = vw[~maskm,n]
vi[:,n] = csa_interp(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)
if n==0:
csa_interp = csa.CSA(xw[~maskm],yw[~maskm],Hw[~maskm,n])
else:
csa_interp.zin = Hw[~maskm,n]
Hi[:,n] = csa_interp(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)
if n==0:
csa_interp = csa.CSA(xw[~maskm],yw[~maskm],dirw[~maskm,n])
else:
csa_interp.zin = dirw[~maskm,n]
alphai[:,n] = csa_interp(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 > '