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bottom_drag_func.py
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# Bottom drag calculation
#-----------------------------------------------------------------------
import numpy as np
import math
import scipy.io
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from scipy.ndimage import gaussian_filter1d
import resource
from netCDF4 import Dataset
from datetime import datetime
import window_func
import detrend_func
import calc_T_func
import calc_Tspatial_func
#-----------------------------------------------------------------------
# Functions used in the code
# Take x-derivative and average y-axis
def ddx(var,dx):
return (1./dx) * (var[:-1,:,:] - var[1:,:,:])
# Take y-derivative
def ddy(var,dy):
return (1./dy) * (var[:,:-1,:] - var[:,1:,:])
# Average specified dimension(s)
def avg_dim(var,axis,number):
for i in np.arange(number):
if axis == 'x':
var = 0.5 * (var[:-1,:,:] + var[1:,:,:])
if axis == 'y':
var = 0.5 * (var[:,:-1,:] + var[:,1:,:])
if axis == 'xy':
var = 0.5 * (var[:-1,:-1,:] + var[1:,1:,:])
return var
#-----------------------------------------------------------------------
# Constants used in this code
delta_ek = 2.0 #m
#-----------------------------------------------------------------------
def main(datapath,dataname,terms_dict):
### Load QGCM data
p_data = Dataset(datapath+dataname)
p = p_data.variables['p']
del p_data
p = np.transpose(np.squeeze(p,(2,1,0))) # dimensions: (x,y,time)
if terms_dict.get('print_stuff'):
print 'p.shape=',p.shape
# Take derivative of p
p_x = avg_dim(ddx(p,terms_dict.get('dx')[0]),'y',1)
p_y = avg_dim(ddy(p,terms_dict.get('dx')[1]),'x',1)
# Take second derivative of p
p_xx = avg_dim(ddx(p_x,terms_dict.get('dx')[0]),'y',1)
p_yy = avg_dim(ddy(p_y,terms_dict.get('dx')[1]),'x',1)
del p_x,p_y
# Calculate del2(p)
del2p = p_xx + p_yy
del p_xx,p_yy
# Ensure that dimensions are the same
p = avg_dim(p,'xy',3)
print 'Before calculating transfer ',datetime.now().time()
if terms_dict.get('spatial_flag'):
transfer_iso,kiso,ktiso = calc_T_func.main(p,del2p,terms_dict)
del kiso
else:
transfer_iso,kiso,ktiso = calc_T_func.main(p,del2p,terms_dict)
del p,del2p
# Multiply by the correct constants: delek / (abs(f0) * Htot)
fac = delta_ek/(abs(terms_dict.get('f0')*terms_dict.get('H')[3]))
transfer_iso = fac * transfer_iso
print 'After calculating transfer ',datetime.now().time()
if terms_dict.get('print_stuff'):
print 'Mem usage after transfer func =',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1000
# For correct units, I need to scale k and w
if not terms_dict.get('spatial_flag'):
kiso_plot = 1000*kiso
del kiso
ktiso_plot = 60*60*24*ktiso
del ktiso
if terms_dict.get('save_data'):
x1 = terms_dict.get('domain')[0]
x2 = terms_dict.get('domain')[1]
y1 = terms_dict.get('domain')[2]
y2 = terms_dict.get('domain')[3]
yrs = terms_dict.get('yrs')
save_name = terms_dict.get('save_name')
extra_name = terms_dict.get('extra_name')
if terms_dict.get('spatial_flag'):
#Tgrp = Dataset('/g/data/v45/pm2987/netcdf_transfers/bottomDrag_spatial_1yr_test_yr159_dg2_output037.nc', 'w', format='NETCDF3_CLASSIC')
Tgrp = Dataset('/g/data/v45/pm2987/netcdf_transfers/bottomDrag_spatial'+save_name+extra_name+'_'+str(x1)+'_'+str(x2)+'_'+str(y1)+'_'+str(y2)+'_'+str(yrs[0])+'_'+str(yrs[1])+'.nc', 'w', format='NETCDF3_CLASSIC')
Tgrp.createDimension('x',transfer_iso.shape[0])
Tgrp.createDimension('y',transfer_iso.shape[1])
Tgrp.createDimension('w',transfer_iso.shape[2])
T = Tgrp.createVariable('T','f4',('x','y','w'))
T[:,:,:] = transfer_iso
Tgrp.createDimension('ktiso_dim',len(ktiso_plot))
ktiso = Tgrp.createVariable('ktiso','f4',('ktiso_dim'))
ktiso[:] = ktiso_plot
Tgrp.close()
else:
#Tgrp = Dataset('/g/data/v45/pm2987/netcdf_transfers/buoyancy_1yr_test_layer1_yr159_dg2_output037.nc', 'w', format='NETCDF3_CLASSIC')
Tgrp = Dataset('/g/data/v45/pm2987/netcdf_transfers/bottomDrag_'+save_name+extra_name+'_'+str(x1)+'_'+str(x2)+'_'+str(y1)+'_'+str(y2)+'_'+str(yrs[0])+'_'+str(yrs[1])+'.nc', 'w', format='NETCDF3_CLASSIC')
Tgrp.createDimension('k',transfer_iso.shape[0])
Tgrp.createDimension('w',transfer_iso.shape[1])
T = Tgrp.createVariable('T','f4',('k','w'))
T[:,:] = transfer_iso
Tgrp.createDimension('kiso_dim',len(kiso_plot))
kiso = Tgrp.createVariable('kiso','f4',('kiso_dim'))
kiso[:] = kiso_plot
Tgrp.createDimension('ktiso_dim',len(ktiso_plot))
ktiso = Tgrp.createVariable('ktiso','f4',('ktiso_dim'))
ktiso[:] = ktiso_plot
Tgrp.close()
'''
if terms_dict.get('spatial_flag'):
# Average over spatial dimensions
transfer_iso = np.mean(np.mean(transfer_iso,axis=0),axis=0)
print 'transfer_iso.shape after averaging = ',transfer_iso.shape
# Plot (temporarily to test how it looks)
# Define dk and dw
if not terms_dict.get('spatial_flag'):
dk = kiso_plot[-1] - kiso_plot[-2]
dw = ktiso_plot[-1] - ktiso_plot[-2]
# Specify smaller font size for screen viewing
font = {'family' : 'normal',
'size' : 10}
matplotlib.rc('font', **font)
print 'Before TKE plots ',datetime.now().time()
if terms_dict.get('spatial_flag'):
# Create figure 2
plt.figure(num=2, figsize=(15,12))
# KEs
plt.plot(ktiso_plot,transfer_iso/dw,linewidth=5.0,color='m',label='PE12')
plt.axhline(0,color='k',linestyle='dotted',linewidth=3.0)
plt.xscale('log')
plt.axis('tight')
plt.title('Bottom Drag, integrated over wavenumber')
plt.xlabel('Frequency')
plt.ylabel('(nW/kg)/(rad/day)')
plt.legend()
plt.show()
'''