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Fluxes_Transient.py
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import numpy as np
import os
from scipy.io import netcdf as scipy_netcdf
from matplotlib import pyplot as plt
from scipy.signal import firwin, filtfilt, lfilter
from scipy.fftpack import fft, fftfreq, fftshift, ifft, fft2, ifft2
baseDir = '/home/chris/GOLD/RandTopoSmooth/'
outputDirBaseString = 'output'
startYear = 39
endYear = 59
outputDirs = []
for iYear in range(startYear, endYear+1):
outputDirs.append(outputDirBaseString + "%03d" % iYear + '/')
print outputDirs
fileStartingString = 'ave_prog__'
uvFilesList = []
for iYear in range(len(outputDirs)):
#print baseDir + outputDirs[iYear]
for file in os.listdir(baseDir + outputDirs[iYear]):
if os.path.isfile(baseDir + outputDirs[iYear] + file) and file.startswith(fileStartingString):
uvFilesList.append(baseDir + outputDirs[iYear] + file)
uvFilesList = sorted(uvFilesList)
#print uvFilesList
timeVarName = 'Time'
xhName = 'xh'
xqName = 'xq'
yhName = 'yh'
yqName = 'yq'
zlName = 'zl'
ziName = 'zi'
#get number of time steps
testFile = scipy_netcdf.netcdf_file(uvFilesList[0], 'r')
xh = testFile.variables[xhName][:]
xq = testFile.variables[xqName][:]
yh = testFile.variables[yhName][:]
yq = testFile.variables[yqName][:]
zl = testFile.variables[zlName][:]
zi = testFile.variables[ziName][:]
time_singleFile = testFile.variables[timeVarName][:]
nT_singleFile = time_singleFile.shape[0]
nX_h = xh.shape[0]
nX_q = xq.shape[0]
nY_h = yh.shape[0]
nY_q = yq.shape[0]
nZ_l = zl.shape[0]
nZ_i = zi.shape[0]
deltaT = time_singleFile[1]-time_singleFile[0]
testFile.close()
nT_total = nT_singleFile * len(uvFilesList)
print nT_singleFile
print nT_total
#print uvFilesList
tileSizeX = 10
tileSizeY = 10
n_Xslice = 32
xqSliceSize = nX_q / n_Xslice
xhSliceSize = nX_h / n_Xslice
time = np.zeros(nT_total,dtype='float64')
u = np.zeros((nT_total,nZ_l,xqSliceSize+1),dtype='float64')
v = np.zeros((nT_total,nZ_l,2,xhSliceSize),dtype='float64')
e = np.zeros((nT_total,nZ_i,xhSliceSize),dtype='float64')
uLowPass = np.zeros((nT_total,nZ_l,xqSliceSize),dtype='float64')
vLowPass = np.zeros((nT_total,nZ_l,xhSliceSize),dtype='float64')
uTransientEddy = np.zeros((nT_total,nZ_l,xqSliceSize),dtype='float64')
vTransientEddy = np.zeros((nT_total,nZ_l,xhSliceSize),dtype='float64')
eTransientEddy = np.zeros((nT_total,nZ_i,xqSliceSize),dtype='float64')
uTimeMean = np.zeros((nZ_l,nY_h-1,nX_q),dtype='float64')
vTimeMean = np.zeros((nZ_l,nY_q-1,nX_h),dtype='float64')
eTimeMean = np.zeros((nZ_i,nY_q-1,nX_h),dtype='float64')
uTotalFluxTimeMean = np.zeros((nZ_l,nY_h-1,nX_q),dtype='float64')
vTotalFluxTimeMean = np.zeros((nZ_l,nY_h-1,nX_q),dtype='float64')
uEddyFluxTimeMean = np.zeros((nZ_l,nY_h-1,nX_q),dtype='float64')
vEddyFluxTimeMean = np.zeros((nZ_l,nY_h-1,nX_q),dtype='float64')
eEddyTimeMean = np.zeros((nZ_i,nY_h-1,nX_q),dtype='float64')
cutOffPeriod = 180.0
cutOffFreq = 1.0/cutOffPeriod
#Compute kernal length of the filter
transitionWidth = 2.0/(deltaT)
#print transitionWidth
kernalLength = int(4.0 / transitionWidth)
#Nyquist Frequency
nyquistFrequency = 0.5/deltaT
#print nyquistFrequency
tapWeights = firwin(kernalLength, cutOffFreq, window='blackman', nyq=nyquistFrequency)
for iY in range(1,nY_h):
for iX in range(0,n_Xslice):
fileCounter = 0
for iFileName in uvFilesList:
fileObject = scipy_netcdf.netcdf_file(iFileName, 'r')
startXIndex = iX*xqSliceSize
if iX < n_Xslice-1:
#endXIndex = ((iX+1) * xqSliceSize)+1
xSliceIndicies = range(startXIndex,((iX+1) * xqSliceSize)+1,1)
else:
endXIndex = ((iX+1) * xqSliceSize)
xSliceIndicies = range(startXIndex,((iX+1) * xqSliceSize),1)
xSliceIndicies.append(0)
endXIndex = (iX+1)*xqSliceSize
u[fileCounter*nT_singleFile:(fileCounter+1)*nT_singleFile,:,:] = fileObject.variables['u'][:,:,iY,xSliceIndicies]
v[fileCounter*nT_singleFile:(fileCounter+1)*nT_singleFile,:,:,:] = fileObject.variables['v'][:,:,iY-1:iY+1,startXIndex:endXIndex]
e[fileCounter*nT_singleFile:(fileCounter+1)*nT_singleFile,:,:] = fileObject.variables['e'][:,:,iY,startXIndex:endXIndex]
fileCounter = fileCounter + 1
fileObject.close()
#Interpolate to 'h' grid nodes
u_h = 0.5*(u[:,:,1::]+u[:,:,0:-1])
v_h = 0.5*(np.squeeze(v[:,:,1::,:]+v[:,:,0:-1,:]))
#print "filtering"
uLowPass = lfilter(tapWeights,[1.0],u_h, axis=0)
vLowPass = lfilter(tapWeights,[1.0],v_h, axis=0)
eLowPass = lfilter(tapWeights,[1.0],e, axis=0)
#print "finished filtering"
uTransientEddy = u_h - uLowPass
vTransientEddy = v_h - vLowPass
eTransientEddy = e - eLowPass
#print vTimeMean[:,iY-1,iX*xqSliceSize:(iX+1)*xqSliceSize].shape
#print np.squeeze(v_h.mean(axis=0)).shape
uTimeMean[:,iY-1,iX*xqSliceSize:(iX+1)*xqSliceSize] = np.squeeze(u_h.mean(axis=0))
vTimeMean[:,iY-1,iX*xqSliceSize:(iX+1)*xqSliceSize] = np.squeeze(v_h.mean(axis=0))
eTimeMean[:,iY-1,iX*xqSliceSize:(iX+1)*xqSliceSize] = np.squeeze(e.mean(axis=0))
#print uTransientEddy.shape
#print eTransientEddy[1::,...].shape
uFlux = uTransientEddy*eTransientEddy[:,1::,...]
vFlux = vTransientEddy*eTransientEddy[:,1::,...]
uEddyFluxTimeMean[:,iY-1,iX*xqSliceSize:(iX+1)*xqSliceSize] = np.squeeze(uFlux.mean(axis=0))
vEddyFluxTimeMean[:,iY-1,iX*xqSliceSize:(iX+1)*xqSliceSize] = np.squeeze(vFlux.mean(axis=0))
uFlux = u_h * e[:,1::,...]
vFlux = v_h * e[:,1::,...]
uTotalFluxTimeMean[:,iY-1,iX*xqSliceSize:(iX+1)*xqSliceSize] = np.squeeze(uFlux.mean(axis=0))
vTotalFluxTimeMean[:,iY-1,iX*xqSliceSize:(iX+1)*xqSliceSize] = np.squeeze(vFlux.mean(axis=0))
eEddyTimeMean[:,iY-1,iX*xqSliceSize:(iX+1)*xqSliceSize] = np.squeeze(eTransientEddy.mean(axis=0))
print "row", iY, 'of ', nY_h, 'done'
postProcessedOutput = scipy_netcdf.netcdf_file('transient_fluxes_randTopoSmooth.nc', 'w')
xDim = postProcessedOutput.createDimension('x', xh.shape[0])
yDim = postProcessedOutput.createDimension('y', yh.shape[0]-1)
zlDim = postProcessedOutput.createDimension('zl', zl.shape[0])
ziDim = postProcessedOutput.createDimension('zi', zi.shape[0])
xVar = postProcessedOutput.createVariable('x','f4',('x',))
yVar = postProcessedOutput.createVariable('y','f4',('y',))
zlVar = postProcessedOutput.createVariable('zl','f4',('zl',))
ziVar = postProcessedOutput.createVariable('zi','f4',('zi',))
xVar[:] = xh
yVar[:] = yh[1::]
zlVar[:] = zl
ziVar[:] = zi
uTimeMeanVar = postProcessedOutput.createVariable('u_time_mean','f4',('zl','y','x'))
vTimeMeanVar = postProcessedOutput.createVariable('v_time_mean','f4',('zl','y','x'))
eTimeMeanVar = postProcessedOutput.createVariable('e_time_mean','f4',('zi','y','x'))
eEddyTimeMeanVar = postProcessedOutput.createVariable('e_eddy_time_mean','f4',('zi','y','x'))
uFluxTotalVar = postProcessedOutput.createVariable('uTotalFlux','f4',('zl','y','x'))
vFluxTotalVar = postProcessedOutput.createVariable('vTotalFlux','f4',('zl','y','x'))
uFluxEddyVar = postProcessedOutput.createVariable('uEddyFlux','f4',('zl','y','x'))
vFluxEddyVar = postProcessedOutput.createVariable('vEddyFlux','f4',('zl','y','x'))
print uTimeMean.shape
uTimeMeanVar[:,:,:] = uTimeMean
vTimeMeanVar[:,:,:] = vTimeMean
eTimeMeanVar[:,:,:] = eTimeMean
eEddyTimeMeanVar[:,:,:] = eEddyTimeMean
uFluxTotalVar[:,:,:] = uTotalFluxTimeMean
vFluxTotalVar[:,:,:] = vTotalFluxTimeMean
uFluxEddyVar[:,:,:] = uEddyFluxTimeMean
vFluxEddyVar[:,:,:] = vEddyFluxTimeMean
postProcessedOutput.close()
layerToPlot = 4
plt.figure(1)
plt.contourf(xh,yh[1::],uTimeMean[layerToPlot,:,:],15,cmap=plt.cm.jet)
plt.figure(2)
plt.contourf(xh,yh[1::],vTimeMean[layerToPlot,:,:],15,cmap=plt.cm.jet)
plt.figure(3)
plt.contourf(xh,yh[1::],uFluxVar[layerToPlot,:,:],15,cmap=plt.cm.jet)
plt.figure(4)
plt.contourf(xh,yh[1::],vFluxVar[layerToPlot,:,:],15,cmap=plt.cm.jet)
plt.show()