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plot_mean_slope-burger.py
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plot_mean_slope-burger.py
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'''
Plot mean slope burger number in the model domain from the model output.
Made for the shelf transport paper.
'''
import numpy as np
import matplotlib.pyplot as plt
import glob
import netCDF4 as netCDF
import tracpy
import tracpy.plotting
import matplotlib as mpl
import pdb
import op
from matplotlib import ticker, colors
from matplotlib.mlab import find
import os
import gsw
import octant
from scipy import ndimage
mpl.rcParams.update({'font.size': 14})
mpl.rcParams['font.sans-serif'] = 'Arev Sans, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Helvetica, Avant Garde, sans-serif'
mpl.rcParams['mathtext.fontset'] = 'custom'
mpl.rcParams['mathtext.cal'] = 'cursive'
mpl.rcParams['mathtext.rm'] = 'sans'
mpl.rcParams['mathtext.tt'] = 'monospace'
mpl.rcParams['mathtext.it'] = 'sans:italic'
mpl.rcParams['mathtext.bf'] = 'sans:bold'
mpl.rcParams['mathtext.sf'] = 'sans'
mpl.rcParams['mathtext.fallback_to_cm'] = 'True'
g = 9.81
rho0 = 1023. # kg/m^3
whichtime = 'interannual' # 'seasonal' or 'interannual'
whichseason = 'summer' # 'winter' or 'summer'
# loc = 'http://barataria.tamu.edu:6060/thredds/dodsC/NcML/txla_nesting6.nc'
# grid = tracpy.inout.readgrid(loc, usebasemap=True)
grid_filename = '/atch/raid1/zhangxq/Projects/txla_nesting6/txla_grd_v4_new.nc'
vert_filename='/atch/raid1/zhangxq/Projects/txla_nesting6/ocean_his_0001.nc'
grid = tracpy.inout.readgrid(grid_filename, vert_filename=vert_filename, usebasemap=True)
fname1 = 'calcs/slope-burger/f-alpha.npz'
fname2 = 'calcs/slope-burger/' + whichtime + 'N.npz'
# Things that don't change in time
d = netCDF.Dataset(grid_filename)
f = d.variables['f'][:] # Coriolis
pm = d.variables['pm'][:]; pn = d.variables['pn'][:]
h = d.variables['h'][:]
h_smoothed = ndimage.filters.gaussian_filter(h, 20)
dhdy, dhdx = np.gradient(h_smoothed, 1/pn, 1/pm)
alpha = np.sqrt((dhdx)**2 + (dhdy)**2) # dimensional bottom slope, across-shelf
np.savez(fname1, f=f, alpha=alpha, xr=grid['xr'].T, yr=grid['yr'].T)
d.close()
Years = np.arange(2004,2015)
if whichtime == 'interannual':
if not os.path.exists(fname2):
N = np.zeros((Years.size,f.shape[0],f.shape[1])) # season x grid
for i,Year in enumerate(Years): # loop through years
if Year<=2012:
fname = glob.glob('/home/kthyng/shelf/' + str(Year) + '/ocean_his_????.nc')
d = netCDF.MFDataset(fname)
elif (Year==2013) or (Year==2014):
fname = glob.glob('/home/kthyng/shelf/' + str(Year) + '/ocean_his_*.nc')
d = netCDF.MFDataset(fname)
t = d.variables['ocean_time'][:]
units = d.variables['ocean_time'].units
dates = netCDF.num2date(t, units)
years = np.asarray([dates[j].year for j in xrange(len(dates))])
months = np.asarray([dates[j].month for j in xrange(len(dates))])
days = np.asarray([dates[j].day for j in xrange(len(dates))])
hours = np.asarray([dates[j].hour for j in xrange(len(dates))])
# pdb.set_trace()
# Summer
count = np.zeros(f.shape) # count of not-masked N values
tinds = find((Year==years) * ((months==7) + (months==8))) # loop through season
for tind in tinds:
fname = 'calcs/slope-burger/' + 'N-' + str(years[tind]) + '-' \
+ str(months[tind]).zfill(2) + '-' + str(days[tind]).zfill(2) \
+ 'T' + str(hours[tind]).zfill(2) + '.npz'
if not os.path.exists(fname):
salt = d.variables['salt'][tind,:,:,:]
temp = d.variables['temp'][tind,:,:,:]
rho = gsw.rho(salt, temp, 0)
zeta = d.variables['zeta'][tind,:,:]
zwt = octant.depths.get_zw(d.variables['Vtransform'][:][0], d.variables['Vstretching'][:][0],
salt.shape[0], d.variables['theta_s'][:][0], d.variables['theta_b'][:][0],
h, d.variables['hc'][:][0], zeta=zeta, Hscale=3)
Ntemp = np.ma.median(np.sqrt(-g/rho0 * ((rho[2:,:,:]-rho[:-2,:,:])/(zwt[2:,:,:]-zwt[:-2,:,:]))), axis=0) # just save median
Ntemp = Ntemp.filled()
np.savez(fname, N=Ntemp) # can't save masked arrays
else:
Ntemp = np.load(fname)['N']
ind = Ntemp[:,:]>500
Ntemp[ind] = np.nan
combined = np.dstack((N[i,:,:],Ntemp))
N[i,:,:] = np.nansum(combined, axis=2)
count = count + ~np.isnan(Ntemp)
N[i,:,:] = N[i,:,:]/count # finish mean
# pdb.set_trace()
S = N*alpha/f
# pdb.set_trace()
np.savez(fname2, N=N, S=S, f=f, alpha=alpha, xr=grid['xr'].T, yr=grid['yr'].T)
else:
S = np.load(fname2)['S']
# levels = np.linspace(0., 0.5, 11)
Smean = S.mean(axis=0)
lev_exp = np.linspace(np.log10(.01), np.log10(4.0), 11)
# lev_exp = np.linspace(np.log10(.001), np.log10(0.5),11)
# # lev_exp = np.linspace(np.log10(.05), 0.6,11)
# # lev_exp = np.linspace(np.log10(.01), np.ceil(np.log10(np.nanmax(S))),50)
# # lev_exp = np.linspace(np.ceil(np.log10(np.nanmin(S))), np.ceil(np.log10(np.nanmax(S))),11)
levels = np.power(10, lev_exp)
fig, axarr = plt.subplots(4,3)
fig.set_size_inches(8.7, 11.5)
fig.subplots_adjust(left=0.008, bottom=0.1, right=1.0, top=0.98, wspace=0.005, hspace=0.1)
for i, ax in enumerate(axarr.flatten()):
# Titles for subplots
if i==10:
tracpy.plotting.background(grid=grid, ax=ax, mers=np.arange(-100, -80, 3),
pars=np.arange(20, 36, 2), outline=False, parslabels=[0, 1, 0, 0])
ax.set_title(str(2004+i))
elif i==11:
ax.set_frame_on(False)
ax.set_axis_off()
else:
tracpy.plotting.background(grid=grid, ax=ax, mers=np.arange(-100, -80, 3),
pars=np.arange(20, 36, 2), outline=False,
merslabels=[0, 0, 0, 0], parslabels=[0, 0, 0, 0])
ax.set_title(str(2004+i))
if i!=11:
xr=grid['xr'].T; yr=grid['yr'].T
mappable = ax.contourf(xr, yr, S[i,:,:], cmap='Blues', levels=levels, norm=colors.LogNorm())#, extend='max')
# mappable = ax.contourf(xr, yr, S[i,:,:]-Smean, cmap='Blues', levels=levels, extend='max')
ax.set_frame_on(False)
# Horizontal colorbar below plot
cax = fig.add_axes([0.25, 0.05, 0.5, 0.02]) #colorbar axes
cb = plt.colorbar(mappable, cax=cax, orientation='horizontal')
cb.set_label('Mean slope Burger number')
cb.set_ticks([.01,.1,1])
# cb.set_ticks(levels[::2])
# cb.set_ticklabels(["%1.2f" % level for level in levels[::2]])
fig.savefig('figures/slope-burger/' + whichtime + '-' + whichseason + '_mean-log.png', bbox_inches='tight')
elif whichtime == 'seasonal':
if not os.path.exists(fname2):
N = np.zeros((2,f.shape[0],f.shape[1])) # season x grid
count = np.zeros((2,f.shape[0],f.shape[1])) # count of not-masked N values
for i,Year in enumerate(Years): # loop through years
if Year<=2012:
fname = glob.glob('/home/kthyng/shelf/' + str(Year) + '/ocean_his_????.nc')
d = netCDF.MFDataset(fname)
elif (Year==2013) or (Year==2014):
fname = glob.glob('/home/kthyng/shelf/' + str(Year) + '/ocean_his_*.nc')
d = netCDF.MFDataset(fname)
t = d.variables['ocean_time'][:]
units = d.variables['ocean_time'].units
dates = netCDF.num2date(t, units)
years = np.asarray([dates[j].year for j in xrange(len(dates))])
months = np.asarray([dates[j].month for j in xrange(len(dates))])
days = np.asarray([dates[j].day for j in xrange(len(dates))])
hours = np.asarray([dates[j].hour for j in xrange(len(dates))])
# pdb.set_trace()
# Winter
i = 0
tinds = find((Year==years) * ((months==1) + (months==2))) # loop through season
for tind in tinds:
fname = 'calcs/slope-burger/' + 'N-' + str(years[tind]) + '-' \
+ str(months[tind]).zfill(2) + '-' + str(days[tind]).zfill(2) \
+ 'T' + str(hours[tind]).zfill(2) + '.npz'
if not os.path.exists(fname):
salt = d.variables['salt'][tind,:,:,:]
temp = d.variables['temp'][tind,:,:,:]
rho = gsw.rho(salt, temp, 0)
zeta = d.variables['zeta'][tind,:,:]
zwt = octant.depths.get_zw(d.variables['Vtransform'][:][0], d.variables['Vstretching'][:][0],
salt.shape[0], d.variables['theta_s'][:][0], d.variables['theta_b'][:][0],
h, d.variables['hc'][:][0], zeta=zeta, Hscale=3)
Ntemp = np.ma.median(np.sqrt(-g/rho0 * ((rho[2:,:,:]-rho[:-2,:,:])/(zwt[2:,:,:]-zwt[:-2,:,:]))), axis=0) # just save median
Ntemp = Ntemp.filled()
np.savez(fname, N=Ntemp) # can't save masked arrays
else:
Ntemp = np.load(fname)['N']
ind = Ntemp[:,:]>500
Ntemp[ind] = np.nan
combined = np.dstack((N[i,:,:],Ntemp))
N[i,:,:] = np.nansum(combined, axis=2)
count[i,:,:] = count[i,:,:] + ~np.isnan(Ntemp)
# N[i,:,:] = N[i,:,:]/count[i,:,:] # finish mean
# Summer
i = 1
tinds = find((Year==years) * ((months==7) + (months==8))) # loop through season
for tind in tinds:
fname = 'calcs/slope-burger/' + 'N-' + str(years[tind]) + '-' \
+ str(months[tind]).zfill(2) + '-' + str(days[tind]).zfill(2) \
+ 'T' + str(hours[tind]).zfill(2) + '.npz'
if not os.path.exists(fname):
salt = d.variables['salt'][tind,:,:,:]
temp = d.variables['temp'][tind,:,:,:]
rho = gsw.rho(salt, temp, 0)
zeta = d.variables['zeta'][tind,:,:]
zwt = octant.depths.get_zw(d.variables['Vtransform'][:][0], d.variables['Vstretching'][:][0],
salt.shape[0], d.variables['theta_s'][:][0], d.variables['theta_b'][:][0],
h, d.variables['hc'][:][0], zeta=zeta, Hscale=3)
Ntemp = np.ma.median(np.sqrt(-g/rho0 * ((rho[2:,:,:]-rho[:-2,:,:])/(zwt[2:,:,:]-zwt[:-2,:,:]))), axis=0) # just save median
Ntemp = Ntemp.filled()
np.savez(fname, N=Ntemp) # can't save masked arrays
else:
Ntemp = np.load(fname)['N']
ind = Ntemp[:,:]>500
Ntemp[ind] = np.nan
combined = np.dstack((N[i,:,:],Ntemp))
N[i,:,:] = np.nansum(combined, axis=2)
count[i,:,:] = count[i,:,:] + ~np.isnan(Ntemp)
N = N/count # finish mean
S = N*alpha/f
# pdb.set_trace()
np.savez(fname2, N=N, S=S, f=f, alpha=alpha, xr=grid['xr'].T, yr=grid['yr'].T)
else:
S = np.load(fname2)['S']
# Plot Winter and Summer
lev_exp = np.linspace(np.log10(.01), np.log10(4.0), 11)
# lev_exp = np.linspace(np.log10(.001), np.log10(0.5),11)
# # lev_exp = np.linspace(np.log10(.05), 0.6,11)
# # lev_exp = np.linspace(np.log10(.01), np.ceil(np.log10(np.nanmax(S))),50)
# # lev_exp = np.linspace(np.ceil(np.log10(np.nanmin(S))), np.ceil(np.log10(np.nanmax(S))),11)
levels = np.power(10, lev_exp)
# levels = np.array([0.001, 0.01, 0.1])
fig, axarr = plt.subplots(1,2)
fig.set_size_inches(13.675, 6.6125)
fig.subplots_adjust(left=0.04, bottom=0.15, right=1.0, top=0.96, wspace=0.07, hspace=0.04)
for i, ax in enumerate(axarr):
# Titles for subplots
if i==0:
tracpy.plotting.background(grid=grid, ax=ax, mers=np.arange(-100, -80, 2))
ax.set_title('Winter')
# ind = zetaw<0
# ax.contourf(grid['xr'].T, grid['yr'].T, zetaw[ind].mean(axis=0), cmap='Blues', levels=levels, extend='min')
# ax.contourf(grid['xr'].T, grid['yr'].T, abs(zetaw).mean(axis=0), cmap='Reds', levels=levels, extend='max')
# ax.contourf(grid['xr'].T, grid['yr'].T, zetaw.mean(axis=0), cmap='RdBu_r', levels=levels, extend='both')
# ax.contourf(grid['xr'].T, grid['yr'].T, S[i,:,:], cmap='Blues', levels=levels, extend='max')
mappable = ax.contourf(grid['xr'].T, grid['yr'].T, S[i,:,:], cmap='Blues', levels=levels, norm=colors.LogNorm())#, extend='max')
elif i==1:
tracpy.plotting.background(grid=grid, ax=ax, parslabels=[0,0,0,0], mers=np.arange(-100, -80, 2))
ax.set_title('Summer')
# ind = zetas<0
# mappable = ax.contourf(grid['xr'].T, grid['yr'].T, zetas[ind].mean(axis=0), cmap='Reds', levels=levels, extend='min')
# mappable = ax.contourf(grid['xr'].T, grid['yr'].T, abs(zetas).mean(axis=0), cmap='Reds', levels=levels, extend='max')
# mappable = ax.contourf(grid['xr'].T, grid['yr'].T, zetas.mean(axis=0), cmap='RdBu_r', levels=levels, extend='both')
# mappable = ax.contourf(grid['xr'].T, grid['yr'].T, S[i,:,:], cmap='Blues', levels=levels, extend='max')
mappable = ax.contourf(grid['xr'].T, grid['yr'].T, S[i,:,:], cmap='Blues', levels=levels, norm=colors.LogNorm())#, extend='max')
# zmax = np.nanmax((zmaxw,zmaxs))
# Horizontal colorbar below plot
cax = fig.add_axes([0.25, 0.075, 0.5, 0.02]) #colorbar axes
cb = plt.colorbar(mappable, cax=cax, orientation='horizontal')
cb.set_label('Mean slope Burger number')
# cb.set_ticks(levels[::2])
cb.set_ticks([.001, .01, .1, 1.0])
# cb.set_ticklabels(["%1.4f" % level for level in levels[::2]])
fig.savefig('figures/slope-burger/' + whichtime + '_mean-log.png', bbox_inches='tight')
# # Plot seasonal difference
# levels = np.linspace(0,3.0,11)
# fig = plt.figure(figsize=(6.8375, 6.6125))
# fig.subplots_adjust(left=0.04, bottom=0.15, right=1.0, top=0.96, wspace=0.07, hspace=0.04)
# ax = fig.add_subplot(111)
# tracpy.plotting.background(grid=grid, ax=ax, mers=np.arange(-100, -80, 2))
# ax.set_title('Winter-Summer Slope Burger Number')
# mappable = ax.contourf(grid['xr'].T, grid['yr'].T, S[1,:,:]-S[0,:,:], cmap='Blues', levels=levels, extend='max')
# # Horizontal colorbar below plot
# cax = fig.add_axes([0.25, 0.075, 0.5, 0.02]) #colorbar axes
# cb = plt.colorbar(mappable, cax=cax, orientation='horizontal')
# # cb.set_label('')
# fig.text(0.125, 0.075, 'Summer', color='#cc0027')
# fig.text(0.760, 0.075, 'Winter', color='#1b72b7')