/
create_smoothed_fields.py
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/
create_smoothed_fields.py
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import numpy as np
import numpy.ma as ma
from netCDF4 import Dataset
#import micro_inverse_utils as mutils
import sys
sys.path.append('/home/anummel1/Projects/MicroInv/MicroInverse/')
from MicroInverse import MicroInverse_utils as mutils
#
import os
from joblib import Parallel, delayed
from joblib import load, dump
import tempfile
import shutil
import glob
#
#this script will first calculate smoothing weights and then apply them and create new set of smoothed files
#
calc_weights=False
apply_weights=True
model_data=False
smooth_highpass=False
#
n_weights=[2,4,6,8,10,15]
#
#load a dummy field
if not model_data:
filepath = '/datascope/hainegroup/anummel1/Projects/MicroInv/sst_data/amsr_avhrr/annual_files/'
wpath = '/datascope/hainegroup/anummel1/Projects/MicroInv/smoothing_weights/'
outpath = '/datascope/hainegroup/anummel1/Projects/MicroInv/sst_data/amsr_avhrr/'
elif not model_data and not smooth_highpass:
filepath = '/datascope/hainegroup/anummel1/Projects/MicroInv/sst_data/annual_files/'
wpath = '/datascope/hainegroup/anummel1/Projects/MicroInv/smoothing_weights/'
outpath = '/datascope/hainegroup/anummel1/Projects/MicroInv/sst_data/'
elif model_data:
#model_k='800'
var = 'sss'
print(var)
wpath = '/datascope/hainegroup/anummel1/Projects/MicroInv/model_smoothing_weights/'
outpath = '/datascope/hainegroup/anummel1/Projects/MicroInv/model_data/newCO2_control_'+model_k+'_daily_smooth_'+var+'/'
filepath = ''
fnames = glob.glob('/datascope/gnana_esms/newCO2_control_'+model_k+'_daily/history/*.ocean_daily.nc')
fnames.sort()
elif smooth_highpass:
print('smoothing highpass filtered data...')
filepath = '/datascope/hainegroup/anummel1/Projects/MicroInv/sst_data/smooth_annual_files_y5.0deg_x10.0deg/'
wpath = '/datascope/hainegroup/anummel1/Projects/MicroInv/smoothing_weights/'
outpath = '/datascope/hainegroup/anummel1/Projects/MicroInv/sst_data/'
else:
filepath = '/export/scratch/anummel1/sst_data/annual_files/'
wpath = '/export/scratch/anummel1/smoothing_weights/'
outpath = '/export/scratch/anummel1/sst_data/'
if not model_data:
var='sst'
lat_name='lat'
lon_name='lon'
fnames=os.listdir(filepath);
fnames.sort()
d1=Dataset(filepath+fnames[0])
sst=d1.variables[var][0,:,:].squeeze()
lat=d1.variables[lat_name][:]
lon=d1.variables[lon_name][:]
if model_data:
lat_name='yt_ocean'
lon_name='xt_ocean'
d1=Dataset(filepath+fnames[0])
sst=d1.variables[var][0,:,:]
lat=d1.variables[lat_name][:]
lon=d1.variables[lon_name][:]
lon[np.where(lon<-180)]=lon[np.where(lon<-180)]+360
lon[np.where(lon>180)]=lon[np.where(lon>180)]-360
lon,lat=np.meshgrid(lon,lat)
#first calculate weights
if calc_weights:
for n in n_weights:
print(n)
dum,weights_out=mutils.smooth2D_parallel(lon,lat,sst,n=n,num_cores=30,use_weights=True,weights_only=True,use_median=False,save_weights=True,save_path=wpath)
def save_smooth(lonin,latin,timein,data_smooth,var,outpath,outfile):
"""save data_smooth into a outfile """
tlen,ylen,xlen = data_smooth.shape
print(outpath+outfile)
ncfile = Dataset(outpath+outfile, 'w', format='NETCDF4')
#
t = ncfile.createDimension('time', None)
y = ncfile.createDimension('y', ylen)
x = ncfile.createDimension('x', xlen)
#
lat = ncfile.createVariable(lat_name,'f4',('y',))
lon = ncfile.createVariable(lon_name,'f4',('x',))
time = ncfile.createVariable('time','f8',('time',))
nc_var = ncfile.createVariable(var,'f4',('time','y','x',))
#
lat[:] = latin[:]
lon[:] = lonin[:]
time[:] = timein[:]
nc_var[:] = data_smooth[:]
#
ncfile.close()
def smooth_file(ff,n,filepath,fname,outpath,t_inds2,weights_out,var='sst',model_data=None,smooth_highpass=None):
#print(outpath+'smooth_highpass_y5deg_x10deg_annual_files_y'+str(2*n*0.25)+'deg_x'+str(2*2*n*0.25)+'deg/','year'+str(ff).zfill(2)+'_'+str(2*n*0.25)+'deg.nc')
#print n, fname
print(smooth_highpass)
f0=Dataset(filepath+fname)
data=f0.variables[var][:].squeeze()
if smooth_highpass:
filepath1='/datascope/hainegroup/anummel1/Projects/MicroInv/sst_data/amsr_avhrr/annual_files/'
#'/datascope/hainegroup/anummel1/Projects/MicroInv/sst_data/annual_files/'
fnames1=os.listdir(filepath1);
fnames1.sort()
f1=Dataset(filepath1+fnames1[ff])
data1=f1.variables[var][:]
data=data1-data
#
data_smooth=ma.masked_array(np.zeros(data.shape),mask=data.mask)
jind,iind=ma.where(1-data[0,:,:].mask);
for j in range(data.shape[0]):
if n>10:
#spread the calculation so less memory is used
nn=len(jind)
data_smooth[j,jind[:nn/4],iind[:nn/4]]=ma.sum(data[j,:,:].ravel()[list(t_inds2[:nn/4,:])]*weights_out[:nn/4,:,0],-1)
data_smooth[j,jind[nn/4:nn/2],iind[nn/4:nn/2]]=ma.sum(data[j,:,:].ravel()[list(t_inds2[nn/4:nn/2])]*weights_out[nn/4:nn/2,:,0],-1)
data_smooth[j,jind[nn/2:3*nn/4],iind[nn/2:3*nn/4]]=ma.sum(data[j,:,:].ravel()[list(t_inds2[nn/2:3*nn/4])]*weights_out[nn/2:3*nn/4,:,0],-1)
data_smooth[j,jind[3*nn/4:],iind[3*nn/4:]]=ma.sum(data[j,:,:].ravel()[list(t_inds2[3*nn/4:])]*weights_out[3*nn/4:,:,0],-1)
else:
data_smooth[j,jind,iind]=ma.sum(data[j,:,:].ravel()[list(t_inds2)]*weights_out[:,:,0],-1)
#
if not model_data and not smooth_highpass:
save_smooth(f0.variables[lon_name][:],f0.variables[lat_name][:],f0.variables['time'][:],data_smooth,var,outpath+'smooth_annual_files_y'+str(2*n*0.25)+'deg_x'+str(2*2*n*0.25)+'deg/',fname[:-3]+'_'+str(2*n*0.25)+'deg.nc')
elif smooth_highpass:
'saving...'
#this is for smoothing highpass filtered data
save_smooth(f0.variables[lon_name][:],f0.variables[lat_name][:],f0.variables['time'][:],data_smooth,var,outpath+'smooth_highpass_y5deg_x10deg_annual_files_y'+str(2*n*0.25)+'deg_x'+str(2*2*n*0.25)+'deg/','year'+str(ff).zfill(2)+'_'+str(2*n*0.25)+'deg.nc')
else:
save_smooth(f0.variables[lon_name][:],f0.variables[lat_name][:],f0.variables['time'][:],data_smooth,var,outpath+'smooth_annual_files_y'+str(2*n*0.25)+'deg_x'+str(2*2*n*0.25)+'deg/','year'+str(ff).zfill(2)+'_'+str(2*n*0.25)+'deg.nc')
f0.close()
if apply_weights:
for n in n_weights:
print(n)
if n<6:
n_cores=12
elif n<10:
n_cores=8
elif n<14:
n_cores=4
else:
n_cores=4
#
d2=np.load(wpath+str(n)+'_degree_smoothing_weights_coslat_y'+str(n)+'_x'+str(2*n)+'.npz')
t_inds = ma.reshape(np.arange(sst.ravel().shape[0]),(sst.shape[0],sst.shape[1]))
#
folder1 = tempfile.mkdtemp()
path1 = os.path.join(folder1, 'weights_out.mmap')
path2 = os.path.join(folder1, 't_inds2.mmap')
#
weights_out = np.memmap(path1, dtype=float, shape=d2['weights_out'].shape, mode='w+')
t_inds2 = np.memmap(path2, dtype=int, shape=d2['weights_out'].shape[:2], mode='w+')
#
weights_out[:] = d2['weights_out'][:]
t_inds2[:] = t_inds[weights_out[:,:,1].astype('int'),weights_out[:,:,2].astype('int')]
#this will give you [len(inds),n**2] shaped array of indices matching to corresponding points in data.ravel()
if n==15:
Parallel(n_jobs=n_cores)(delayed(smooth_file)(ff,n,filepath,fname,outpath,t_inds2,weights_out,var=var,model_data=model_data,smooth_highpass=smooth_highpass) for ff,fname in enumerate(fnames[10:]))
else:
Parallel(n_jobs=n_cores)(delayed(smooth_file)(ff,n,filepath,fname,outpath,t_inds2,weights_out,var=var,model_data=model_data,smooth_highpass=smooth_highpass) for ff,fname in enumerate(fnames))
try:
shutil.rmtree(folder1)
except OSError:
pass