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05-py-assimilate.py
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05-py-assimilate.py
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from __future__ import division
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This script intended to be the main assimilation script
NEED TO CHECK:
> 1. Division by numbers 4/5 check not to divide
> 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__ = "moghimis@gmail.com"
#####################################################################
# Saeed Moghimi; moghimis@gmail.com
# Logs:
# 1.0 03/25/2013 02:14:41 PM
#
#
#
import os,sys
############################################
os.system('rm base_info.pyc' )
if 'base_info' in sys.modules:
del(sys.modules["base_info"])
import base_info
###########################################
import matplotlib
matplotlib.use('Agg')
import glob
import matplotlib.pyplot as plt
import numpy as np
import netCDF4
import netcdftime
from scipy.optimize import fsolve
from datetime import datetime
global g
import pickle
#
args = sys.argv
itr = int(args[1])
sys.path.append("py/")
#control vars
LOCALIZE = True
real_data = base_info.real_data
assim_wav = base_info.asim_wav
assim_cur = base_info.asim_sar
assim_swf = base_info.asim_swf
localize_len = base_info.Localize_len
radar_err_reduce = base_info.radar_err_reduction[itr]
sar_err_reduce = base_info.sar_err_reduction[itr]
swift_err_reduce = base_info.swift_err_reduction[itr]
##
#Set input parameters
#base_dir = '/home/shusin5/users/moghimi/assimilation/assim_local/real_data_10days_v2/01_main_ebb/'
#inp_dir = base_dir + '/inp/'
#scr_dir = '/home/server/pi/homes/moghimi/work/00-projs/01-muri/00-progs/cowast/project2/07-assim/scr_generations/set4_all/pypysim/'
#
base_dir = base_info.base_dir
scr_dir = base_info.scr_dir
prior = base_info.prior
final_grd = base_info.grd
#
######################################################
####################################################
#Set input parameters
#pysim_inp = base_dir + '/pysim_inp.txt'
#fdata1=open(pysim_inp)
#for line in fdata1.readlines():
# if 'itr' in line: itr = int (line.split()[-1])
#fdata1.close()
#
inp_dir = base_dir + '/inp/'
run_id = '/run_'+str(1000+itr)
assimilate_dir = base_dir+run_id+'/05_assimilate/'
#####################################################
#import calc_k
g = 9.8126
#sys.path.append(os.environ["INP_DIR"]+'/scr/py')
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')
#
# lump observations together
class comp_list:
def __init__(self):
self.x= np.zeros((0) ,dtype='float')
self.y= np.zeros((0) ,dtype='float')
self.data= np.zeros((0) ,dtype='float')
self.s= np.zeros((0) ,dtype='float')
self.f= np.zeros((0) ,dtype='float')
#
# compute model covariances
#
def mycov(a,b):
"""
Cab = myCov(a,b)
Computes sample covariance 'a' and 'b'. Inputs should have dimensions
as follows...
size(a) = [ M, N ]
size(b) = [ P, N ]
That is, each matrix consists of N samples of an Mx1 (or Px1) vector.
Note M and P need not be equal (unlike the builtin matlab function cov.m).
Output 'Cab' is the sample covariance, which has dimensions MxP.
a=2 * np.random.rand(3 ,5)
b=2.2* np.random.rand(4 ,5)
"""
if(a.shape[1] != b.shape[1]):
print 'inputs must have same number of samples (2nd dimension)'
[any,anx] = a.shape
[bny,bnx] = b.shape
a_mean = np.tile(a.mean(1),a.shape[1]).reshape (a.shape[1],a.shape[0]).T
b_mean = np.tile(b.mean(1),b.shape[1]).reshape (b.shape[1],b.shape[0]).T
da = a - a_mean
db = b - b_mean
N = anx
cab = (1.0/(N-1.0)) * np.dot(da,db.T)
return cab
#
def omega_vec(Cpp,dist,L):
"""
Cpp = omega_vec(Cpp,dist,L)
helper function: decorrelation for long length scales (matrix to be
multiplied onto prior covariance, element by element
"""
if(L==0): return
a = np.sqrt(10.0/3.0)*L
b = dist
boa = b/a
c = 0*Cpp
ind = np.where ((0 <= b) & (b <= a))
c[ind] = \
-(1.0/4.0)*boa[ind]**5.0 \
+(1.0/2.0)*boa[ind]**4.0 \
+(5.0/8.0)*boa[ind]**3.0 \
-(5.0/3.0)*boa[ind]**2.0 \
+1.0
ind = np.where ((a < b) &(b <= 2.0 * a))
c[ind] = (1.0 /12.0)*boa[ind]**5.0 \
-(1.0/2.0 )*boa[ind]**4.0 \
+(5.0/8.0 )*boa[ind]**3.0 \
+(5.0/3.0 )*boa[ind]**2.0 \
- 5.0*boa[ind] \
+ 4.0 \
-(2.0/3.0)*boa[ind]**(-1.0);
Cpp=Cpp*c
return Cpp
#
allmeas = {}
if assim_wav:
print ' > Assim. WAV'
print ' > Reduce WAV ', radar_err_reduce
print ' > ','k'
#read wave dict
wav_member_dir = base_dir+run_id+'/04_wav_adj/'
namep = 'wav_k.nc'
wpick_name = wav_member_dir + namep
wavenc = netCDF4.Dataset(wpick_name)
wavv = wavenc.variables
wavnum = len(wavv['k_x'][:])
wmeas = component(wavnum)
wmeas.data = wavv['k_data'][:]
wmeas.x = wavv['k_x'][:]
wmeas.y = wavv['k_y'][:]
wmeas.s = wavv['k_s'][:] * radar_err_reduce
wmeas.f = wavv['k_f'][:]
wmeas.model = wavv['k_model'][:]
allmeas['k'] = wmeas
if assim_cur:
cur_member_dir = base_dir+run_id+'/04_mem_adj'
print ' > Assim. CUR'
print ' > Reduce CUR ', sar_err_reduce
for field in ['u','v']:
#for field in ['v']:
print ' > ',field
cpick_name = cur_member_dir+'/cur_'+field+'.nc'
curnc = netCDF4.Dataset(cpick_name)
curv = curnc.variables
curnum = len(curv[field+'_x'][:])
cmeas = component(curnum)
cmeas.data = curv[field+'_data'][:]
cmeas.x = curv[field+'_x'][:]
cmeas.y = curv[field+'_y'][:]
cmeas.s = curv[field+'_s'][:] * sar_err_reduce
cmeas.f = curv[field+'_f'][:]
cmeas.model = curv[field+'_model'][:]
allmeas[field] = cmeas
if base_info.sar_const_err is not None:
print ' > SAR const err =', base_info.sar_const_err
allmeas['u'].s = base_info.sar_const_err * np.ones_like(allmeas['u'].s)
allmeas['v'].s = base_info.sar_const_err * np.ones_like(allmeas['v'].s)
####################################################################################
# #SAR err correction when rad data is close
# #the idea is to increase sar error to decreas its effects when we have wave data close
if base_info.increase_sar_err_when_waves and assim_cur and assim_wav:
print ' > Increase SAR err close to Wav data points dist=',\
base_info.wav_cur_data_min_dist,' Coef= ', base_info.cur_data_err_increase_coef
for isar in range(len(allmeas['u'].x)):
dist2 = np.sqrt ( (allmeas['k'].x - allmeas['u'].x[isar])**2+\
(allmeas['k'].y - allmeas['u'].y[isar])**2 )
dist_lim = base_info.wav_cur_data_min_dist
coef = base_info.cur_data_err_increase_coef
dist2_min = max(dist2.min(),5)
if dist2_min < dist_lim :
allmeas['u'].s[isar] = allmeas['u'].s[isar] * coef * dist_lim / dist2_min
allmeas['v'].s[isar] = allmeas['v'].s[isar] * coef * dist_lim / dist2_min
####################################################################################
###
cur_member_dir = base_dir+run_id+'/04_mem_adj/'
namep = 'cur_members_prior.nc'
cpick_name = cur_member_dir + namep
curnc = netCDF4.Dataset(cpick_name)
curv = curnc.variables
hc = curv['h_mems'][:]
xc = curv['x_rho' ][:]
yc = curv['y_rho' ][:]
###
if assim_swf:
swf_member_dir = base_dir+run_id+'/04_swf_adj'
print ' > Assim. SWF'
print ' > Reduce SWF ', swift_err_reduce
for field in ['us','vs']:
#for field in ['v']:
print ' > ',field
cpick_name = swf_member_dir+'/swf_'+field+'.nc'
curnc = netCDF4.Dataset(cpick_name)
curv = curnc.variables
curnum = len(curv[field+'_x'][:])
cmeas = component(curnum)
cmeas.data = curv[field+'_data'][:]
cmeas.x = curv[field+'_x'][:]
cmeas.y = curv[field+'_y'][:]
cmeas.s = curv[field+'_s'][:] * swift_err_reduce
cmeas.f = curv[field+'_f'][:]
cmeas.model = curv[field+'_model'][:]
allmeas[field] = cmeas
####################################################################################
# #SWIFT err correction when SAR data is close
# #the idea is to increase sar error to decreas its effects when we have wave data close
if base_info.increase_swf_err_when_sar and assim_cur and assim_swf:
print ' > Increase SWIFT err close to SAR data points dist=',\
base_info.swf_cur_data_min_dist,' Coef= ', base_info.swf_data_err_increase_coef
for iswf in range(len(allmeas['us'].x)):
dist2 = np.sqrt ( (allmeas['u'].x - allmeas['us'].x[iswf])**2+\
(allmeas['u'].y - allmeas['us'].y[iswf])**2 )
dist_lim = base_info.swf_cur_data_min_dist
coef = base_info.swf_data_err_increase_coef
dist2_min = max(np.abs(dist2.min()),5)
if dist2_min < dist_lim :
allmeas['us'].s[iswf] = allmeas['us'].s[iswf] * coef * dist_lim / dist2_min
allmeas['vs'].s[iswf] = allmeas['vs'].s[iswf] * coef * dist_lim / dist2_min
####################################################################################
#nc_prior = netCDF4.Dataset(inp_dir+'/const/'+prior)
#ncv_prior = nc_prior.variables
#xc = np.squeeze(ncv_prior['x_rho'] [:])
#yc = np.squeeze(ncv_prior['y_rho'] [:])
#hc = np.squeeze(ncv_prior['h'] [:])
#nc_prior.close()
###
###
meas2 = comp_list()
for field in allmeas.keys():
#print field
obj=allmeas[field]
meas2.x = np.hstack((meas2.x , obj.x))
meas2.y = np.hstack((meas2.y , obj.y))
meas2.f = np.hstack((meas2.f , obj.f))
meas2.s = np.hstack((meas2.s , obj.s))
meas2.data = np.hstack((meas2.data , obj.data))
[nn] = meas2.data.shape
#plt.figure()
#plt.scatter(meas2.x,meas2.y,s=50,c=meas2.data,lw=0)
# Read members depth depth
# Assuming ROMS model area has the biggest coverage
[nyc,nxc,Nc] = hc.shape
#### construct model for all obs in dicts
meas2_model = np.zeros((nn,Nc) ,dtype='float')
for il in range(Nc):
test = np.zeros((0) ,dtype='float')
#print il
for field in allmeas.keys():
#print field
obj = allmeas[field]
inp = obj.model[:,il]
test = np.hstack((test,inp))
meas2_model[:,il] = test
meas2.model = meas2_model
measf = meas2
#sys.exit()
#-----------------------------------------
# assimilate
#-----------------------------------------
# We need some measures for observation errors
Cdd = np.diag(measf.s**2.0)
hvec = hc.reshape(nxc*nyc, Nc);
model = measf.model
print ' > Compute model covariances'
Chv = mycov(hvec ,model)
LCvvL = mycov(model,model)
#
xcf = xc.flatten()
ycf = yc.flatten()
ng = len(xcf)
n_meas =len(measf.x)
#
#
if LOCALIZE:
L = localize_len;
print ' > Localizing covariances L=', L, 'm'
if False:
from omegaf_vec import omegaf_vec
Chv ,distg = omegaf_vec( Chv , xcf , ycf , measf.x, measf.y, L, ng , n_meas )
Cdd ,distx = omegaf_vec( Cdd , measf.x, measf.y, measf.x, measf.y, L, n_meas, n_meas )
LCvvL,distx = omegaf_vec( LCvvL, measf.x, measf.y, measf.x, measf.y, L, n_meas, n_meas )
#Chv3 = omega_dist_vec( Chv, xcf , ycf, measf.x, measf.y, L)
#Cdd3 = omega_dist_vec( Cdd, measf.x, measf.y, measf.x, measf.y, L )
#LCvvL3 = omega_dist_vec( LCvvL,measf.x, measf.y, measf.x, measf.y, L )
#
else:
from distg import distg as dist
distg = dist(xcf , ycf, measf.x, measf.y, ng , n_meas )
distx = dist(measf.x , measf.y, measf.x, measf.y, n_meas, n_meas )
Chv = omega_vec (Chv ,distg,L);
Cdd = omega_vec (Cdd ,distx,L);
LCvvL = omega_vec (LCvvL,distx,L);
###########################################################################
[ndata]=measf.data.shape
noise=np.zeros((ndata,Nc))
for n in range (Nc):
noise[:,n] = measf.s * np.random.randn(ndata)
dh=np.zeros_like(hvec)
# assimilate for posterior ensemble. Add random noise to
# observations to ensure correct posterior ensemble covariance
print ' > Assimilating for posterior ensemble'
ChvinvC = np.dot( Chv , np.linalg.inv( LCvvL + Cdd ))
for n in range(Nc):
dh[:,n]= np.dot(ChvinvC,(measf.data+noise[:,n]-model[:,n]))
hpost = hvec+dh
outh = hpost.mean(1).reshape(nyc,nxc)
if False:
print ' > Pickle outputs'
out_pick={'hpost':hpost,'hpri':hvec,'dh':dh,'Chv':Chv, \
'LCvvL':LCvvL,'Cdd':Cdd, 'measf':measf,\
'xc':xc, 'yc':yc}
import cPickle as pickle
pick_name = 'assimilate_out.p'
pickle.dump( out_pick, open(pick_name , "wb" ) )
###########################################################
hpost = hpost.reshape(nyc,nxc,Nc)
hprio = hvec.reshape (nyc,nxc,Nc)
maskh = (hc <-5.0)
hpost = np.ma.masked_array(hpost,maskh)
hprio = np.ma.masked_array(hprio,maskh)
hpost_stdv = hpost.std(2)
hprio_stdv = hprio.std(2)
file_sufix = '_real_data-' + str (base_info.real_data)
file_sufix += '_cur-' + str (base_info.asim_sar)
file_sufix += '_wav-' + str (base_info.asim_wav)
file_sufix += '_swf-' + str (base_info.asim_swf)
file_sufix += '_curJ-' + str (base_info.jump_cur)
file_sufix += '_wavJ-' + str (base_info.jump_wav)
file_sufix += '_CurErr-' + str (sar_err_reduce)
file_sufix += '_WavErr-' + str (radar_err_reduce)
file_sufix += '_SwfErr-' + str (swift_err_reduce)
file_sufix += '_L-' + str (localize_len)
namep = 'assimilate_out'+file_sufix+'.nc'
out_name = assimilate_dir + namep
outnc = netCDF4.Dataset(out_name,'w',format='NETCDF3_CLASSIC')
outnc.createDimension('nx' ,nxc )
outnc.createDimension('ny' ,nyc )
outnc.createDimension('nmem' ,Nc )
outnc.createDimension('nobs' ,len(measf.data))
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_post','f8',('ny','nx','nmem'))
p2.missing_value = -9999.0
p2[:] = hpost
p3 = outnc.createVariable('h_prior','f8',('ny','nx','nmem'))
p3.missing_value = -9999.0
p3[:] = hprio
p4 = outnc.createVariable('h_post_std','f8',('ny','nx'))
p4.missing_value = -9999.0
p4[:] = hpost_stdv
p5 = outnc.createVariable('h_prio_std','f8',('ny','nx'))
p5.missing_value = -9999.0
p5[:] = hprio_stdv
p20 = outnc.createVariable('obs_x','f8',('nobs',))
p20.missing_value = -9999.0
p20[:] = measf.x
p21 = outnc.createVariable('obs_y','f8',('nobs',))
p21.missing_value = -9999.0
p21[:] = measf.y
p22 = outnc.createVariable('obs_s','f8',('nobs',))
p22.missing_value = -9999.0
p22[:] = measf.s
p23 = outnc.createVariable('obs_data','f8',('nobs',))
p23.missing_value = -9999.0
p23[:] = measf.data
p24 = outnc.createVariable('obs_f','f8',('nobs',))
p24.missing_value = -9999.0
p24[:] = measf.f
p25 = outnc.createVariable('obs_model','f8',('nobs','nmem',))
p25.missing_value = -9999.0
p25[:] = measf.model
readme = ' \n localiz_length=' + str (localize_len)
readme += ' \n member_num=' + str(Nc)
readme += ' \n base_dir =' + base_dir
readme += ' \n itr =' + str(itr)
readme += ' \n inp_dir =' + inp_dir
readme += ' \n real_data=' + str(base_info.real_data)
readme += ' \n current_opt =' + str (base_info.asim_sar)
readme += ' \n wave_model=' + str (base_info.asim_wav)
readme += ' \n roms2 =' + str (base_info.asim_sar2)
readme += ' \n sar_err_reduction=' + str (base_info.sar_err_reduction)
readme += ' \n radar_err_reduction=' + str (base_info.radar_err_reduction)
readme += ' \n swift=' + str (base_info.asim_swf)
readme += ' \n roms1_data_jump=' + str (base_info.jump_cur)
readme += ' \n wave_data_jump=' + str (base_info.jump_wav)
readme += ' \n curve_grid4uv=' + str (base_info.uv_curv)
readme += ' \n SAR_err_reduce=' + str (sar_err_reduce)
readme += ' \n WAV_err_reduce=' + str (radar_err_reduce)
readme += ' \n SWF_err_reduce=' + str (swift_err_reduce)
outnc.history = 'moghimis@gmail.com wave data structure for assimilation '+datetime.now().isoformat() + readme
#outnc.att= infile[-12:]
outnc.close()
args = sys.argv
scr_name = args[0]
scr_dir1 = os.getcwd()
os.system('cp -fr ' + scr_name + ' ' + assimilate_dir)
os.system('cp -fr base_info.py ' + assimilate_dir)
if True:
figname = assimilate_dir + '/pic_'+file_sufix
figname1 = figname+'.png'
figname2 = figname+'_prior.png'
##### posterior
plt.figure()
plt.pcolor(xc,yc,outh)
plt.clim(-1,7)
plt.colorbar()
plt.contour(xc,yc,outh,colors='k',levels=np.linspace(-2, 8, 12))
plt.savefig(figname1,dpi=450)
##### prior
plt.figure()
plt.pcolor(xc,yc,hvec.mean(1).reshape(nyc,nxc))
plt.clim(-1,7)
plt.colorbar()
plt.contour(xc,yc,hvec.mean(1).reshape(nyc,nxc),colors='k',levels=np.linspace(-2, 8, 12))
plt.savefig(figname2,dpi=450)
print ' END > '
# ################################################################3
# if False:
# xc1 = [0. , 100. , 200., 400.]
#
# yc1 = 2 * xc1
# xc2, yc2 = np.meshgrid(xc1, yc1)
# xc2f = xc2.flatten()
# yc2f = yc2.flatten()
#
#
# mod1 = (xc2f + yc2f + 100)/(xc2f+0.5* yc2f+100)
#
# mod = tile ( (mod1 + np.random.randn(len(mod1)) * mod1.mean()),5).reshape (len(mod1),5)
#
# LCvvL2 = mycov(mod,mod)
#
# plt.figure()
# plt.pcolor(LCvvL2)
# plt.colorbar()
#
# L=1000
# n_meas = len(xc2f)
# LCvvL3,distx = omegaf_vec( LCvvL2, xc2f , yc2f , xc2f , yc2f , L , n_meas, n_meas )
# plt.figure()
# plt.pcolor(LCvvL3)
# plt.colorbar()
#
#
# if False:
# plt.figure()
# plt.imshow(np.flipud(distg))
# plt.title('distg')
# plt.colorbar()
#
# plt.figure()
# plt.imshow(np.flipud(distx))
# plt.title('distx')
# plt.colorbar()
#
# plt.figure()
# plt.imshow(np.flipud(Chv[::2,::2]))
# plt.title('Chv')
# plt.clim(-0.0001,0.0001)
# plt.colorbar()
#
#
#
# plt.figure()
# plt.imshow(np.flipud(Chv3))
# plt.title('Chv loc')
# plt.clim(-0.5,0.5)
# plt.colorbar()
#
#
# plt.figure()
# plt.imshow(np.flipud(LCvvL))
# plt.title('LCvvL')
# plt.clim(-0.0001,0.0001)
# plt.colorbar()
#
# plt.figure()
# plt.imshow(np.flipud(LCvvL2))
# plt.title('LCvvL locf')
# plt.clim(-0.0001,0.0001)
# plt.colorbar()
#
# plt.figure()
# plt.imshow(np.flipud(LCvvL2))
# plt.title('LCvvL locp')
# plt.clim(-0.0001,0.0001)
# plt.colorbar()
# #close('all')
#
# #if False:
# plt.figure()
# plt.imshow(np.flipud(ChvinvC))
# plt.title('ChvinvC')
# plt.clim(-1,1)
# plt.colorbar()
#
# plt.figure()
# plt.imshow(np.flipud(ChvinvC2))
# plt.title('ChvinvC loc')
# plt.clim(-1,1)
# plt.colorbar()
#
# plt.figure()
# plt.imshow(np.flipud(( LCvvL + Cdd )))
# plt.title('( LCvvL + Cdd )')
# plt.clim(-0.01,0.01)
# plt.colorbar()
#
# plt.figure()
# plt.imshow(np.flipud(inv_LCvvL_Cdd))
# plt.title('inv(LCvvL+Cdd) ')
# plt.clim(-1000,1000)
# plt.colorbar()
#
# plt.figure()
# plt.imshow(np.flipud(inv_LCvvL_Cdd2))
# plt.title('inv(LCvvL+Cdd) loc')
# plt.clim(-1000,1000)
# plt.colorbar()