def plot_stuff(xe, ye, H, cmap, grid, shelf_depth, ax, levels=np.linspace(0,100,11), extend='max'):
'''
Do the main plotting stuff.
'''
XE, YE = np.meshgrid(op.resize(xe, 0), op.resize(ye, 0))
# Try with pcolor too
#pdb.set_trace()
mappable = ax.contourf(XE, YE, H, cmap=cmap, levels=levels, extend=extend)
ax.contour(grid['xr'], grid['yr'], grid['h'], [shelf_depth], colors='0.1', linewidth=3)
return mappable
tracpy.plotting.background(grid=grid, ax=ax, mers=np.arange(-100, -80, 2))
ax.set_title('Winter')
Files = glob.glob('tracks/20??-0[1,2]-*gc.nc')
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')
Files = glob.glob('tracks/20??-0[7,8]-*gc.nc')
if not os.path.exists(fname):
for File in Files:
# print File
d = netCDF.Dataset(File)
# pdb.set_trace()
U = d.variables['U'][:]; V = d.variables['V'][:]
d.close()
Stemp = np.sqrt(op.resize(U, 1)**2 + op.resize(V, 0)**2)
S[i,:,:] = S[i,:,:] + Stemp
# locator = ticker.MaxNLocator(11)
# locator.create_dummy_axis()
# locator.set_bounds(0, 1)
# levels = locator()
# extend = 'max'
# H = H/Hmax
Smax = 1.
else:
d = np.load(fname); S = d['S']; d.close()
Smax = S.max()
if howplot=='log':
# w = netCDF.Dataset('/atch/raid1/zhangxq/Projects/narr_txla/txla_blk_narr_' + str(year) + '.nc')
# Wind time period to use
unitsWind = (w.variables['time'].units).replace('/','-')
datesWind = netCDF.num2date(w.variables['time'][:], unitsWind)
# datesWind = datesModel
wdx = 25; wdy = 40 # in indices
##
## River forcing ##
r1 = netCDF.Dataset('/rho/raid/home/kthyng/txla/TXLA_river_4dyes_2012.nc') # use for through 2011
r2 = netCDF.Dataset('/rho/raid/home/kthyng/txla/TXLA_river_4dyes_2012_2014.nc') # use for 2012-2014
# River timing
tr1 = r1.variables['river_time']
tunitsr1 = tr1.units
# interpolate times for this data file since at the 12 hours mark instead of beginning of the day
tr1 = op.resize(tr1, 0)
datesr1 = netCDF.num2date(tr1[:], tunitsr1)
tr2 = r2.variables['river_time']
datesr2 = netCDF.num2date(tr2[:], tr2.units)
# all of river input
Q1 = np.abs(r1.variables['river_transport'][:]).sum(axis=1)*2.0/3.0
# interpolate this like for time
Q1 = op.resize(Q1, 0)
Q2 = np.abs(r2.variables['river_transport'][:]).sum(axis=1)*2.0/3.0
# Combine river info into one dataset
iend1 = find(datesr1<datetime(2012,1,1,0,0,0))[-1] # ending index for file 1
tRiver = np.concatenate((tr1[:iend1], tr2[:]), axis=0)
datesRiver = np.concatenate((datesr1[:iend1], datesr2))
R = np.concatenate((Q1[:iend1], Q2))
r1.close(); r2.close()
# start and end indices in time for river discharge
def readfields(tind,grid,nc,z0=None,zpar=None):
'''
readfields()
Kristen Thyng, March 2013
Reads in model output in order to calculate fluxes and z grid
properties to send into step.f95.
Should be called initially and then subsequently each time loop.
All arrays are changed to Fortran ordering (from Python ordering)
and to tracmass variables ordering from ROMS ordering
i.e. from [t,k,j,i] to [i,j,k,t]
right away after reading in.
Input:
tind Single time index for model output to read in
grid Dictionary containing all necessary time-independent grid fields
nc NetCDF object for relevant files
z0 (optional) if doing 2d isoslice, z0 contains string saying which kind
zpar (optional) if doing 2d isoslice, zpar is the depth/level/density
at which we are to get the level
Output:
uflux1 Zonal (x) flux at tind
vflux1 Meriodional (y) flux at tind
dzt Height of k-cells in 3 dim in meters on rho vertical grid. [imt,jmt,km]
zrt Time-dependent depths of cells on vertical rho grid (meters).
For the isoslice case, zrt ends up with 1 vertical level which contains
the depths for the vertical center of the cell for that level.
zwt Time-dependent depths of cells on vertical w grid (meters). zwt always
contains the depths at the vertical cell edges for the whole 3D grid
and the correct depths can be accessed using the drifter indices.
Array descriptions:
u,v Zonal (x) and meridional (y) velocities [imt,jmt,km] (m/s)
ssh Free surface [imt,jmt] (m)
dz Height of k-cells in 1 dim [km]
From coord.f95: z coordinates (z>0 going up) for layers in meters
bottom layer: k=0; surface layer: k=KM and zw=0
dz = layer thickness
zt Depths (negative) in meters of w vertical grid [imt,jmt,km+1]
dzt Height of k-cells in 3 dim in meters on rho vertical grid. [imt,jmt,km]
dzt0 Height of k-cells in 2 dim. [imt,jmt]
dzu Height of each u grid cell [imt-1,jmt,km]
dzv Height of each v grid cell [imt,jmt-1,km]
uflux1 Zonal (x) fluxes [imt-1,jmt,km] (m^3/s)?
vflux1 Meriodional (y) fluxes [imt,jmt-1,km] (m^3/s)?
'''
# tic_temp = time.time()
# Read in model output for index tind
if z0 == 's': # read in less model output to begin with, to save time
u = nc.variables['u'][tind,zpar,:,:]
v = nc.variables['v'][tind,zpar,:,:]
ssh = nc.variables['zeta'][tind,:,:] # [t,j,i], ssh in tracmass
else:
u = nc.variables['u'][tind,:,:,:]
v = nc.variables['v'][tind,:,:,:]
ssh = nc.variables['zeta'][tind,:,:] # [t,j,i], ssh in tracmass
# time_read = time.time()-tic_temp
# tic_temp = time.time()
# # make arrays in same order as is expected in the fortran code
# # ROMS expects [time x k x j x i] but tracmass is expecting [i x j x k x time]
# # change these arrays to be fortran-directioned instead of python
# # u = u.T.copy(order='f')
# # v = v.T.copy(order='f')
# # ssh = ssh.T.copy(order='f')
# # # Flip vertical dimension because in ROMS surface is at k=-1
# # # and in tracmass surface is at 1
# # u = np.flipud(u)
# # v = np.flipud(v)
# time_flip1 = time.time()-tic_temp
# This is code from tracmass itself, which is being replaced by Rob's octant code
# # Copy calculations from rutgersNWA/readfields.f95
# dzt = np.ones((grid['imt'],grid['jmt'],grid['km']))*np.nan
# dzu = np.ones((grid['imt']-1,grid['jmt'],grid['km']))*np.nan
# dzv = np.ones((grid['imt'],grid['jmt']-1,grid['km']))*np.nan
# for k in xrange(grid['km']):
# dzt0 = (grid['sc_r'][k]-grid['Cs_r'][k])*grid['hc'] \
# + grid['Cs_r'][k]*grid['h']
# dzt[:,:,k] = dzt0 + ssh*(1.+dzt0/grid['h'])
# dzt0 = dzt[:,:,grid['km']-1]
# dzt[:,:,0:grid['km']-1] = dzt[:,:,1:grid['km']] - dzt[:,:,0:grid['km']-1]
# dzt[:,:,grid['km']-1] = ssh - dzt0
# tic_temp = time.time()
h = grid['h'].T.copy(order='c')
# Use octant to calculate depths for the appropriate vertical grid parameters
# have to transform a few back to ROMS coordinates and python ordering for this
zwt = octant.depths.get_zw(1, 1, grid['km']+1, grid['theta_s'], grid['theta_b'],
h, grid['hc'], zeta=ssh, Hscale=3)
# Change dzt to tracmass/fortran ordering
# zwt = zwt.T.copy(order='f')
# dzt = zwt[:,:,1:] - zwt[:,:,:-1]
dzt = zwt[1:,:,:] - zwt[:-1,:,:]
# pdb.set_trace()
# time_zw = time.time()-tic_temp
# tic_temp = time.time()
# also want depths on rho grid
zrt = octant.depths.get_zrho(1, 1, grid['km'], grid['theta_s'], grid['theta_b'],
h, grid['hc'], zeta=ssh, Hscale=3)
# Change dzt to tracmass/fortran ordering
# zrt = zrt.T.copy(order='f')
# time_zr = time.time()-tic_temp
# tic_temp = time.time()
dzu = .5*(dzt[:,:,0:grid['imt']-1] + dzt[:,:,1:grid['imt']])
dzv = .5*(dzt[:,0:grid['jmt']-1,:] + dzt[:,1:grid['jmt'],:])
# dzu = .5*(dzt[0:grid['imt']-1,:,:] + dzt[1:grid['imt'],:,:])
# dzv = .5*(dzt[:,0:grid['jmt']-1,:] + dzt[:,1:grid['jmt'],:])
# dzu = dzt[0:grid['imt']-1,:,:]*0.5 + dzt[1:grid['imt'],:,:]*0.5
# dzv = dzt[:,0:grid['jmt']-1,:]*0.5 + dzt[:,1:grid['jmt'],:]*0.5
# dzu[0:grid['imt']-1,:,:] = dzt[0:grid['imt']-1,:,:]*0.5 + dzt[1:grid['imt'],:,:]*0.5
# dzv[:,0:grid['jmt']-1,:] = dzt[:,0:grid['jmt']-1,:]*0.5 + dzt[:,1:grid['jmt'],:]*0.5
# pdb.set_trace()
# time_interp = time.time()-tic_temp
# tic_temp = time.time()
# Change order back to ROMS/python for this calculation
# u = u.T.copy(order='c')
# v = v.T.copy(order='c')
# dzu = dzu.T.copy(order='c')
# dzv = dzv.T.copy(order='c')
# dzt = dzt.T.copy(order='c')
dyu = grid['dyu'].T.copy(order='c')
dxv = grid['dxv'].T.copy(order='c')
# zrt = zrt.T.copy(order='c')
# time_flip2 = time.time()-tic_temp
# pdb.set_trace()
# tic_temp = time.time()
# I think I can avoid this loop for the isoslice case
if z0 == None: # 3d case
uflux1 = u*dzu*dyu
vflux1 = v*dzv*dxv
elif z0 == 's': # want a specific s level zpar
uflux1 = u*dzu[zpar,:,:]*dyu
vflux1 = v*dzv[zpar,:,:]*dxv
dzt = dzt[zpar,:,:]
zrt = zrt[zpar,:,:]
elif z0 == 'rho' or z0 == 'salt' or z0 == 'temp':
# the vertical setup we're selecting an isovalue of
vert = nc.variables[z0][tind,:,:,:]
# pdb.set_trace()
# Calculate flux and then take slice
uflux1 = octant.tools.isoslice(u*dzu*dyu,op.resize(vert,2),zpar)
vflux1 = octant.tools.isoslice(v*dzv*dxv,op.resize(vert,1),zpar)
dzt = octant.tools.isoslice(dzt,vert,zpar)
zrt = octant.tools.isoslice(zrt,vert,zpar)
# pdb.set_trace()
elif z0 == 'z':
# Calculate flux and then take slice
uflux1 = octant.tools.isoslice(u*dzu*dyu,op.resize(zrt,2),zpar)
vflux1 = octant.tools.isoslice(v*dzv*dxv,op.resize(zrt,1),zpar)
dzt = octant.tools.isoslice(dzt,zrt,zpar)
zrt = np.ones(uflux1.shape)*zpar # array of the input desired depth
# time_flux = time.time()-tic_temp
# tic_temp = time.time()
# Change all back to tracmass/fortran ordering if being used again
# tic = time.time()
# uflux1 = uflux1.T.copy(order='f')
# vflux1 = vflux1.T.copy(order='f')
# dzt = dzt.T.copy(order='f')
# zrt = zrt.T.copy(order='f')
# ssh = ssh.T.copy(order='f')
# zwt = zwt.T.copy(order='f')
# print "copy time",time.time()-tic
# tic = time.time()
# This is faster than copying arrays
# Don't bother changing order of these arrays since I have to change it in
# run.py anyway (concatenate appears not to preserve ordering)
uflux1 = uflux1.T
vflux1 = vflux1.T
dzt = np.asfortranarray(dzt.T)
# uflux1 = np.asfortranarray(uflux1.T)
# vflux1 = np.asfortranarray(vflux1.T)
# dzt = np.asfortranarray(dzt.T)
zrt = np.asfortranarray(zrt.T)
ssh = np.asfortranarray(ssh.T)
zwt = np.asfortranarray(zwt.T)
# print "fortran time",time.time()-tic
# time_flip3 = time.time()-tic_temp
# tic_temp = time.time()
# make sure that all fluxes have a placeholder for depth
if is_string_like(z0):
uflux1 = uflux1.reshape(np.append(uflux1.shape,1))
vflux1 = vflux1.reshape(np.append(vflux1.shape,1))
dzt = dzt.reshape(np.append(dzt.shape,1))
zrt = zrt.reshape(np.append(zrt.shape,1))
# time_reshape = time.time()-tic_temp
# # Flip vertical dimension because in ROMS surface is at k=-1
# # and in tracmass surface is at 1
# # This is not true. In tracmass, surface is at k=KM
# uflux1 = uflux1[:,:,::-1]
# vflux1 = vflux1[:,:,::-1]
# dzt = dzt[:,:,::-1]
# uflux1 = np.flipud(uflux1)
# vflux1 = np.flipud(vflux1)
# dzt = np.flipud(dzt)
# print "time to read=",time_read
# # print "time to flip=",time_flip1
# print "time to get zw=",time_zw
# print "time to get zr=",time_zr
# print "time to interp=",time_interp
# print "time to flip2=",time_flip2
# print "time to flux=",time_flux
# print "time to flip3=",time_flip3
# print "time to reshape=",time_reshape
return uflux1, vflux1, dzt, zrt, zwt
def transport(name, fmod=None, Title=None, dmax=None, N=7, extraname=None,
llcrnrlon=-98.5, llcrnrlat=22.5, urcrnrlat=31.0, urcrnrlon=-87.5,
colormap='Blues'):
'''
Make plot of zoomed-in area near DWH spill of transport of drifters over
time.
FILL IN
Inputs:
name
U
V
lon0
lat0
T0
'''
# (name=None, U, V, lon0, lat0, T0, dmax, extraname, Title, N,
# llcrnrlon, llcrnrlat, urcrnrlat, urcrnrlon, colormap):
# Load in transport information
U, V, lon0, lat0, T0 = inout.loadtransport(name,fmod=fmod)
# Smaller basemap parameters.
loc = 'http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc'
grid = inout.readgrid(loc, llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat,
urcrnrlat=urcrnrlat, urcrnrlon=urcrnrlon)
S = np.sqrt(op.resize(U,1)**2+op.resize(V,0)**2)
Splot = (S/T0)*100
if dmax is None:
dmax = Splot.max()
else:
dmax = dmax
# from http://matplotlib.1069221.n5.nabble.com/question-about-contours-and-clim-td21111.html
locator = ticker.MaxNLocator(N) # if you want no more than 10 contours
locator.create_dummy_axis()
locator.set_bounds(0,dmax)#d.min(),d.max())
levs = locator()
fig = figure(figsize=(12,10))
background(grid=grid)
c = contourf(grid['xpsi'], grid['ypsi'], Splot,
cmap=colormap, extend='max', levels=levs)
title(Title)
# Add initial drifter location (all drifters start at the same location)
lon0 = lon0.mean()
lat0 = lat0.mean()
x0, y0 = grid['basemap'](lon0, lat0)
plot(x0, y0, 'go', markersize=10)
# Inlaid colorbar
cax = fig.add_axes([0.49, 0.25, 0.39, 0.02])
# cax = fig.add_axes([0.5, 0.2, 0.35, 0.02])
cb = colorbar(cax=cax,orientation='horizontal')
cb.set_label('Normalized drifter transport (%)')
if extraname is None:
savefig('figures/' + name + '/transport', bbox_inches='tight')
else:
savefig('figures/' + name + '/transport' + extraname, bbox_inches='tight')
def transport(name,
fmod=None,
Title=None,
dmax=None,
N=7,
extraname=None,
llcrnrlon=-98.5,
llcrnrlat=22.5,
urcrnrlat=31.0,
urcrnrlon=-87.5,
colormap='Blues',
fig=None,
ax=None):
'''
Make plot of zoomed-in area near DWH spill of transport of drifters over
time.
FILL IN
Inputs:
name
U
V
lon0
lat0
T0
'''
# (name=None, U, V, lon0, lat0, T0, dmax, extraname, Title, N,
# llcrnrlon, llcrnrlat, urcrnrlat, urcrnrlon, colormap):
# Load in transport information
U, V, lon0, lat0, T0 = inout.loadtransport(name, fmod=fmod)
# Smaller basemap parameters.
loc = 'http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc'
grid = inout.readgrid(loc,
llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat,
urcrnrlat=urcrnrlat,
urcrnrlon=urcrnrlon)
S = np.sqrt(op.resize(U, 1)**2 + op.resize(V, 0)**2)
Splot = (S / T0) * 100
if dmax is None:
dmax = Splot.max()
else:
dmax = dmax
# from http://matplotlib.1069221.n5.nabble.com/question-about-contours-and-clim-td21111.html
locator = ticker.MaxNLocator(N) # if you want no more than 10 contours
locator.create_dummy_axis()
locator.set_bounds(0, dmax) #d.min(),d.max())
levs = locator()
if fig is None:
fig = figure(figsize=(11, 10))
else:
fig = fig
background(grid=grid)
c = contourf(grid['xpsi'],
grid['ypsi'],
Splot,
cmap=colormap,
extend='max',
levels=levs)
title(Title)
# # Add initial drifter location (all drifters start at the same location)
# lon0 = lon0.mean()
# lat0 = lat0.mean()
# x0, y0 = grid['basemap'](lon0, lat0)
# plot(x0, y0, 'go', markersize=10)
if ax is None:
ax = gca()
else:
ax = ax
# Want colorbar at the given location relative to axis so this works regardless of # of subplots,
# so convert from axis to figure coordinates
# To do this, first convert from axis to display coords
# transformations: http://matplotlib.org/users/transforms_tutorial.html
ax_coords = [0.35, 0.25, 0.6,
0.02] # axis: [x_left, y_bottom, width, height]
disp_coords = ax.transAxes.transform([
(ax_coords[0], ax_coords[1]),
(ax_coords[0] + ax_coords[2], ax_coords[1] + ax_coords[3])
]) # display: [x_left,y_bottom,x_right,y_top]
inv = fig.transFigure.inverted(
) # inverter object to go from display coords to figure coords
fig_coords = inv.transform(
disp_coords) # figure: [x_left,y_bottom,x_right,y_top]
# actual desired figure coords. figure: [x_left, y_bottom, width, height]
fig_coords = [
fig_coords[0, 0], fig_coords[0,
1], fig_coords[1, 0] - fig_coords[0, 0],
fig_coords[1, 1] - fig_coords[0, 1]
]
# Inlaid colorbar
cax = fig.add_axes(fig_coords)
# cax = fig.add_axes([0.39, 0.25, 0.49, 0.02])
# cax = fig.add_axes([0.49, 0.25, 0.39, 0.02])
cb = colorbar(cax=cax, orientation='horizontal')
cb.set_label('Normalized drifter transport (%)')
if extraname is None:
savefig('figures/' + name + '/transport', bbox_inches='tight')
else:
savefig('figures/' + name + '/' + extraname + 'transport',
bbox_inches='tight')
latv = grid.variables['lat_v'][:] xv, yv = basemap(lonv,latv) hfull = grid.variables['h'][:] lonr = grid.variables['lon_rho'][:]#[1:-1,1:-1] latr = grid.variables['lat_rho'][:]#[1:-1,1:-1] xr, yr = basemap(lonr,latr) lonpsi = grid.variables['lon_psi'][:] latpsi = grid.variables['lat_psi'][:] xpsi, ypsi = basemap(lonpsi,latpsi) maskr = grid.variables['mask_rho'][:]#[1:-1,1:-1] X, Y = np.meshgrid(np.arange(xr.shape[1]),np.arange(yr.shape[0])) # grid in index coordinates, without ghost cells tri = delaunay.Triangulation(X.flatten(),Y.flatten()) # Angle on rho grid theta = grid.variables['angle'][:] # Interpolate theta to be on psi grid theta = op.resize(op.resize(theta,0),1) pm = grid.variables['pm'][:] # 1/dx pn = grid.variables['pn'][:] # 1/dy # The following are for setting up z array on u (v) grids for calculating u (v) fluxes # Want h only within domain in y (x) direction (no ghost cells) and interpolated onto cell # walls in x (y) direction hu = op.resize(grid.variables['h'][1:-1,:],1) hv = op.resize(grid.variables['h'][:,1:-1],0) loc = '/Users/kthyng/Documents/research/postdoc/' # for model outputs files = np.sort(glob.glob(loc + 'ocean_his_*.nc')) # sorted list of file names filesfull = np.sort(glob.glob(loc + 'ocean_his_*.nc')) #full path of files # Find the list of files that cover the desired time period for i,name in enumerate(files): # Loop through files nctemp = netCDF.Dataset(name)
# w = netCDF.Dataset('/atch/raid1/zhangxq/Projects/narr_txla/txla_blk_narr_' + str(year) + '.nc')
# Wind time period to use
unitsWind = (w.variables['time'].units).replace('/','-')
datesWind = netCDF.num2date(w.variables['time'][:], unitsWind)
# datesWind = datesModel
wdx = 25; wdy = 40 # in indices
##
## River forcing ##
r1 = netCDF.Dataset('/rho/raid/home/kthyng/txla/TXLA_river_4dyes_2012.nc') # use for through 2011
r2 = netCDF.Dataset('/rho/raid/home/kthyng/txla/TXLA_river_4dyes_2012_2014.nc') # use for 2012-2014
# River timing
tr1 = r1.variables['river_time']
tunitsr1 = tr1.units
# interpolate times for this data file since at the 12 hours mark instead of beginning of the day
tr1 = op.resize(tr1, 0)
datesr1 = netCDF.num2date(tr1[:], tunitsr1)
tr2 = r2.variables['river_time']
datesr2 = netCDF.num2date(tr2[:], tr2.units)
# all of river input
Q1 = np.abs(r1.variables['river_transport'][:]).sum(axis=1)*2.0/3.0
# interpolate this like for time
Q1 = op.resize(Q1, 0)
Q2 = np.abs(r2.variables['river_transport'][:]).sum(axis=1)*2.0/3.0
# Combine river info into one dataset
iend1 = find(datesr1<datetime(2012,1,1,0,0,0))[-1] # ending index for file 1
tRiver = np.concatenate((tr1[:iend1], tr2[:]), axis=0)
datesRiver = np.concatenate((datesr1[:iend1], datesr2))
R = np.concatenate((Q1[:iend1], Q2))
r1.close(); r2.close()
# start and end indices in time for river discharge
# interpolation constant for each time step
r2 = np.linspace(0, 1, t1[istart_dates1:].size)
r1 = 1. - r2
# Linearly combine model output in overlap region
# pdb.set_trace()
uin2[:iend_dates2] = (r1.reshape(r1.size,1,1)*uin1[istart_dates1:] + r2.reshape(r1.size,1,1)*uin2[:iend_dates2])
del(uin1)
# Cut off end part
uin2 = uin2[:istart_dates2]
t = t2[:istart_dates2]
# Update time dimension
nt = uin2.shape[0]
# u and v are on rho grid to start
uin2 = op.resize(uin2, 2); # onto staggered grid
uin2 = uin2.reshape((nt, 1, yu, xu)).repeat(zl,axis=1)
# Make new file
rootgrp = netCDF.Dataset('ocean_his_' + str(i+1).zfill(4) + '.nc','w',format='NETCDF3_64BIT')
rootgrp.createDimension('xu',xu)
rootgrp.createDimension('yu',yu)
rootgrp.createDimension('xv',xv)
rootgrp.createDimension('yv',yv)
rootgrp.createDimension('zl',zl)
# Change time dimension to be unlimited for aggregation later
rootgrp.createDimension('nt',None)
ocean_time = rootgrp.createVariable('ocean_time','f8',('nt')) # 64-bit floating point
u = rootgrp.createVariable('u','f4',('nt','zl','yu','xu')) # 64-bit floating point
v = rootgrp.createVariable('v','f4',('nt','zl','yv','xv')) # 64-bit floating point
u[:] = uin2; ocean_time[:] = t;
Smax = 0
for i,fmod in enumerate(fmods):
## Read in transport info ##
Files = glob.glob('tracks/' + fmod + '-*')
U = 0; V = 0
for File in Files:
d = netCDF.Dataset(File)
U += d.variables['U'][:]
V += d.variables['V'][:]
d.close
# S is at cell centers, minus ghost points
Stemp = np.sqrt(op.resize(U[:,1:-1],0)**2 + op.resize(V[1:-1,:],1)**2)
S.append(Stemp)
Smax = max((Smax,Stemp.max()))
## Plot ##
fig = plt.figure(figsize=(12,4.8))#, dpi=150)
fig.suptitle('Surface transport', fontsize=18)
for i in xrange(len(S)):
ax = fig.add_subplot(2,4,i+1)
ax.set_frame_on(False) # kind of like it without the box
if i==0:
def readfields(tind, grid, nc, z0=None, zpar=None, zparuv=None):
"""
readfields()
Kristen Thyng, March 2013
Reads in model output in order to calculate fluxes and z grid
properties to send into step.f95.
Should be called initially and then subsequently each time loop.
All arrays are changed to Fortran ordering (from Python ordering)
and to tracmass variables ordering from ROMS ordering
i.e. from [t,k,j,i] to [i,j,k,t]
right away after reading in.
Args:
tind: Single time index for model output to read in
grid: Dictionary containing all necessary time-independent grid
fields
nc: NetCDF object for relevant files
z0 (Optional): if doing 2d isoslice, z0 contains string saying which
kind
zpar (Optional): if doing 2d isoslice, zpar is the
depth/level/density at which we are to get the level
zparuv (Optional): Use this if the k index for the model output
fields (e.g, u, v) is different from the k index in the grid. This
might happen if, for example, only the surface current were saved,
but the model run originally did have many layers. This parameter
represents the k index for the u and v output, not for the grid.
Returns:
* uflux1 - Zonal (x) flux at tind
* vflux1 - Meriodional (y) flux at tind
* dzt - Height of k-cells in 3 dim in meters on rho vertical grid.
[imt,jmt,km]
* zrt - Time-dependent depths of cells on vertical rho grid (meters).
For the isoslice case, zrt ends up with 1 vertical level which
contains the depths for the vertical center of the cell for that
level.
* zwt - Time-dependent depths of cells on vertical w grid (meters).
zwt always contains the depths at the vertical cell edges for the
whole 3D grid and the correct depths can be accessed using the
drifter indices.
Array descriptions:
* u,v - Zonal (x) and meridional (y) velocities [imt,jmt,km] (m/s)
* ssh - Free surface [imt,jmt] (m)
* dz - Height of k-cells in 1 dim [km]
From coord.f95: z coordinates (z>0 going up) for layers in meters
bottom layer: k=0; surface layer: k=KM and zw=0
dz = layer thickness
* zt - Depths (negative) in meters of w vertical grid [imt,jmt,km+1]
* dzt - Height of k-cells in 3 dim in meters on rho vertical grid.
[imt,jmt,km]
* dzt0 - Height of k-cells in 2 dim. [imt,jmt]
* dzu - Height of each u grid cell [imt-1,jmt,km]
* dzv - Height of each v grid cell [imt,jmt-1,km]
* uflux1 - Zonal (x) fluxes [imt-1,jmt,km] (m^3/s)?
* vflux1 - Meriodional (y) fluxes [imt,jmt-1,km] (m^3/s)?
"""
# this parameter is in case there is less model output available
# vertically than was actually run on the grid
if zparuv is None:
zparuv = zpar
# tic_temp = time.time()
# Read in model output for index tind
if z0 == 's': # read in less model output to begin with, to save time
u = nc.variables['u'][tind, zparuv, :, :]
v = nc.variables['v'][tind, zparuv, :, :]
if 'zeta' in nc.variables:
# [t,j,i], ssh in tracmass
ssh = nc.variables['zeta'][tind, :, :]
sshread = True
else:
sshread = False
else:
u = nc.variables['u'][tind, :, :, :]
v = nc.variables['v'][tind, :, :, :]
if 'zeta' in nc.variables:
# [t,j,i], ssh in tracmass
ssh = nc.variables['zeta'][tind, :, :]
sshread = True
else:
sshread = False
# Use octant to calculate depths for the appropriate vertical grid
# parameters have to transform a few back to ROMS coordinates and python
# ordering for this
if sshread:
zwt = octant.depths.get_zw(grid.Vtransform, grid.Vstretching,
grid.km+1, grid.theta_s,
grid.theta_b, grid.h, grid.hc, zeta=ssh,
Hscale=3)
else: # if ssh isn't available, approximate as 0
zwt = octant.depths.get_zw(grid.Vtransform, grid.Vstretching,
grid.km+1, grid.theta_s,
grid.theta_b, grid.h, grid.hc, zeta=0,
Hscale=3)
# Change dzt to tracmass/fortran ordering
dzt = zwt[1:, :, :] - zwt[:-1, :, :]
# also want depths on rho grid
if sshread:
zrt = octant.depths.get_zrho(grid.Vtransform, grid.Vstretching,
grid.km, grid.theta_s,
grid.theta_b, grid.h, grid.hc,
zeta=ssh, Hscale=3)
else:
zrt = octant.depths.get_zrho(grid.Vtransform, grid.Vstretching,
grid.km, grid.theta_s,
grid.theta_b, grid.h, grid.hc, zeta=0,
Hscale=3)
dzu = .5*(dzt[:, :, 0:grid.imt-1] + dzt[:, :, 1:grid.imt])
dzv = .5*(dzt[:, 0:grid.jmt-1, :] + dzt[:, 1:grid.jmt, :])
# I think I can avoid this loop for the isoslice case
if z0 is None: # 3d case
uflux1 = u*dzu*grid.dyu
vflux1 = v*dzv*grid.dxv
elif z0 == 's': # want a specific s level zpar
uflux1 = u*dzu[zpar, :, :]*grid.dyu
vflux1 = v*dzv[zpar, :, :]*grid.dxv
dzt = dzt[zpar, :, :]
zrt = zrt[zpar, :, :]
elif z0 == 'rho' or z0 == 'salt' or z0 == 'temp':
# the vertical setup we're selecting an isovalue of
vert = nc.variables[z0][tind, :, :, :]
# Calculate flux and then take slice
uflux1 = octant.tools.isoslice(u*dzu*grid.dyu, op.resize(vert, 2), zpar)
vflux1 = octant.tools.isoslice(v*dzv*grid.dxv, op.resize(vert, 1), zpar)
dzt = octant.tools.isoslice(dzt, vert, zpar)
zrt = octant.tools.isoslice(zrt, vert, zpar)
elif z0 == 'z':
# Calculate flux and then take slice
uflux1 = octant.tools.isoslice(u*dzu*grid.dyu, op.resize(zrt, 2), zpar)
vflux1 = octant.tools.isoslice(v*dzv*grid.dxv, op.resize(zrt, 1), zpar)
dzt = octant.tools.isoslice(dzt, zrt, zpar)
zrt = np.ones(uflux1.shape)*zpar # array of the input desired depth
# make sure that all fluxes have a placeholder for depth
if isinstance(z0, str):
uflux1 = uflux1.reshape(np.append(1, uflux1.shape))
vflux1 = vflux1.reshape(np.append(1, vflux1.shape))
dzt = dzt.reshape(np.append(1, dzt.shape))
zrt = zrt.reshape(np.append(1, zrt.shape))
return uflux1, vflux1, dzt, zrt, zwt
ax1b.plot([x[n], 891975], [y[n], 146818], '-', color='0.2', lw=0.5)
ax1b.text(891975-10000, 146818-10000, 'Ship track', color='0.1')
if gradient:
cax1b = fig.add_axes([0.38, 0.94, 0.065, 0.015]) # colorbar axes
else:
cax1b = fig.add_axes([0.55, 0.94, 0.1, 0.015]) # colorbar axes
cb1b = fig.colorbar(map1b, cax=cax1b, orientation='horizontal')
cb1b.set_label('Free surface [m]', color='0.2')
cb1b.ax.tick_params(labelsize=10, length=2, color='0.2', labelcolor='0.2')
cb1b.set_ticks(np.arange(-0.1, 0.2, 0.1))
if gradient:
## TXLA Plot of top half of domain - gradients
grad1 = (ssh[1:,:] - ssh[:-1,:])/(yr[1:,:] - yr[:-1,:])
grad2 = (ssh[:,1:] - ssh[:,:-1])/(xr[:,1:] - xr[:,:-1])
grad = abs(np.sqrt(op.resize(grad1,1)**2 + op.resize(grad2,0)**2))
tracpy.plotting.background(grid, ax=ax1c, outline=[0, 0, 0, 0], mers=np.arange(-96, -86, 2), parslabels=[0,0,0,0], hlevs=[0])
map1c = ax1c.pcolormesh(xr, yr, grad, cmap=cmo.matter, vmin=0, vmax=0.000008)
ax1c.set_frame_on(False) # kind of like it without the box
ax1c.text(0.9, 0.01, 'c', color='0.1', transform=ax1c.transAxes, fontsize=12) # Label subplot
# Temperature, in jet
cmap = cm.jet
data = d[:, var.index('temp')]
# http://matplotlib.org/1.2.1/examples/pylab_examples/hist_colormapped.html
# we need to normalize the data to 0..1 for the full range of the colormap
fracs = data[1:][iup] # /data[1:][iup].max()
norm = colors.Normalize(fracs.min(), fracs.max())
# loop through the voronoi regions by data point and associated color fraction
for frac, point_region in zip(fracs, vor.point_region):
color = cmap(norm(frac)) # which color in cmap to use
# 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)
proj = tracpy.tools.make_proj('nwgom', usebasemap=True)
# grid = tracpy.inout.readgrid('../../grid.nc', proj)
grid = tracpy.inout.readgrid('grid.nc', proj)
if whichtime == 'seasonal':
# Load in histogram
d = np.load('figures/cross/seasonal100H.npz')
H = d['H']
X, Y = np.meshgrid(op.resize(d['xe'],0), op.resize(d['ye'],0))
fh = grid['trir'].nn_interpolator(grid['h'].flatten())
depths = fh(X,Y)
ishallow = depths < shelf_depth
ideep = depths > shelf_depth
# winter
offshore = np.nansum(H[0,ishallow])/ishallow.sum() # likelihood per histogram bin
onshore = np.nansum(H[0,ideep])/ideep.sum()
elif whichtime == 'interannual':
if whichseason == 'winter':
lonv = grid.variables['lon_v'][:,1:-1]
latv = grid.variables['lat_v'][:,1:-1]
xv, yv = basemap(lonv,latv)
hfull = grid.variables['h'][:]
lonr = grid.variables['lon_rho'][1:-1,1:-1]
latr = grid.variables['lat_rho'][1:-1,1:-1]
xr, yr = basemap(lonr,latr)
lonpsi = grid.variables['lon_psi'][:]
latpsi = grid.variables['lat_psi'][:]
xpsi, ypsi = basemap(lonpsi,latpsi)
X, Y = np.meshgrid(np.arange(latpsi.shape[0]),np.arange(latpsi.shape[1]))
tri = delaunay.Triangulation(X.flatten(),Y.flatten())
# Angle on rho grid
theta = grid.variables['angle'][:]
# Interpolate theta to be on psi grid
theta = op.resize(op.resize(theta,0),1)
grid.close()
nc = netCDF.Dataset('/Users/kthyng/Documents/research/postdoc/ocean_his_0150.nc')
t = nc.variables['ocean_time'][0:0+2]
# Some grid metrics
s = nc.variables['s_w'][:] # sigma coords, 31 layers
cs = nc.variables['Cs_w'][:] # stretching curve in sigma coords, 31 layers
nc.close()
xl = xpsi.shape[1]
yl = xpsi.shape[0]
zl = len(cs)-1
tl = 3 # three time outputs
dt = 4*3600 # 4 hours between outputs
# pdb.set_trace()
# w = netCDF.Dataset('/atch/raid1/zhangxq/Projects/narr_txla/txla_blk_narr_' + str(year) + '.nc')
# Wind time period to use
unitsWind = (w.variables['time'].units).replace('/','-')
datesWind = netCDF.num2date(w.variables['time'][:], unitsWind)
# datesWind = datesModel
wdx = 25; wdy = 30 # in indices
##
## River forcing ##
r1 = netCDF.Dataset('/rho/raid/home/kthyng/txla/TXLA_river_4dyes_2012.nc') # use for through 2011
r2 = netCDF.Dataset('/rho/raid/home/kthyng/txla/TXLA_river_4dyes_2012_2014.nc') # use for 2012-2014
# River timing
tr1 = r1.variables['river_time']
tunitsr1 = tr1.units
# interpolate times for this data file since at the 12 hours mark instead of beginning of the day
tr1 = op.resize(tr1, 0)
datesr1 = netCDF.num2date(tr1[:], tunitsr1)
tr2 = r2.variables['river_time']
datesr2 = netCDF.num2date(tr2[:], tr2.units)
# all of river input
Q1 = np.abs(r1.variables['river_transport'][:]).sum(axis=1)*2.0/3.0
# interpolate this like for time
Q1 = op.resize(Q1, 0)
Q2 = np.abs(r2.variables['river_transport'][:]).sum(axis=1)*2.0/3.0
# Combine river info into one dataset
iend1 = find(datesr1<datetime(2012,1,1,0,0,0))[-1] # ending index for file 1
tRiver = np.concatenate((tr1[:iend1], tr2[:]), axis=0)
datesRiver = np.concatenate((datesr1[:iend1], datesr2))
R = np.concatenate((Q1[:iend1], Q2))
r1.close(); r2.close()
# start and end indices in time for river discharge
def readfields(tind,grid,nc,z0=None, zpar=None, zparuv=None):
'''
readfields()
Kristen Thyng, March 2013
Reads in model output in order to calculate fluxes and z grid
properties to send into step.f95.
Should be called initially and then subsequently each time loop.
All arrays are changed to Fortran ordering (from Python ordering)
and to tracmass variables ordering from ROMS ordering
i.e. from [t,k,j,i] to [i,j,k,t]
right away after reading in.
Input:
tind Single time index for model output to read in
grid Dictionary containing all necessary time-independent grid fields
nc NetCDF object for relevant files
z0 (optional) if doing 2d isoslice, z0 contains string saying which kind
zpar (optional) if doing 2d isoslice, zpar is the depth/level/density
at which we are to get the level
zparuv (optional) Use this if the k index for the model output fields (e.g, u, v) is different
from the k index in the grid. This might happen if, for example, only the surface current
were saved, but the model run originally did have many layers. This parameter
represents the k index for the u and v output, not for the grid.
Output:
uflux1 Zonal (x) flux at tind
vflux1 Meriodional (y) flux at tind
dzt Height of k-cells in 3 dim in meters on rho vertical grid. [imt,jmt,km]
zrt Time-dependent depths of cells on vertical rho grid (meters).
For the isoslice case, zrt ends up with 1 vertical level which contains
the depths for the vertical center of the cell for that level.
zwt Time-dependent depths of cells on vertical w grid (meters). zwt always
contains the depths at the vertical cell edges for the whole 3D grid
and the correct depths can be accessed using the drifter indices.
Array descriptions:
u,v Zonal (x) and meridional (y) velocities [imt,jmt,km] (m/s)
ssh Free surface [imt,jmt] (m)
dz Height of k-cells in 1 dim [km]
From coord.f95: z coordinates (z>0 going up) for layers in meters
bottom layer: k=0; surface layer: k=KM and zw=0
dz = layer thickness
zt Depths (negative) in meters of w vertical grid [imt,jmt,km+1]
dzt Height of k-cells in 3 dim in meters on rho vertical grid. [imt,jmt,km]
dzt0 Height of k-cells in 2 dim. [imt,jmt]
dzu Height of each u grid cell [imt-1,jmt,km]
dzv Height of each v grid cell [imt,jmt-1,km]
uflux1 Zonal (x) fluxes [imt-1,jmt,km] (m^3/s)?
vflux1 Meriodional (y) fluxes [imt,jmt-1,km] (m^3/s)?
'''
# this parameter is in case there is less model output available vertically than
# was actually run on the grid
# pdb.set_trace()
if zparuv is None:
zparuv = zpar
# tic_temp = time.time()
# Read in model output for index tind
if z0 == 's': # read in less model output to begin with, to save time
u = nc.variables['u'][tind,zparuv,:,:]
v = nc.variables['v'][tind,zparuv,:,:]
if 'zeta' in nc.variables:
ssh = nc.variables['zeta'][tind,:,:] # [t,j,i], ssh in tracmass
sshread = True
else:
sshread = False
else:
u = nc.variables['u'][tind,:,:,:]
v = nc.variables['v'][tind,:,:,:]
if 'zeta' in nc.variables:
ssh = nc.variables['zeta'][tind,:,:] # [t,j,i], ssh in tracmass
sshread = True
else:
sshread = False
h = grid['h'].T.copy(order='c')
# Use octant to calculate depths for the appropriate vertical grid parameters
# have to transform a few back to ROMS coordinates and python ordering for this
if sshread:
zwt = octant.depths.get_zw(grid['Vtransform'], grid['Vstretching'], grid['km']+1, grid['theta_s'], grid['theta_b'],
h, grid['hc'], zeta=ssh, Hscale=3)
else: # if ssh isn't available, approximate as 0
zwt = octant.depths.get_zw(grid['Vtransform'], grid['Vstretching'], grid['km']+1, grid['theta_s'], grid['theta_b'],
h, grid['hc'], zeta=0, Hscale=3)
# Change dzt to tracmass/fortran ordering
dzt = zwt[1:,:,:] - zwt[:-1,:,:]
# also want depths on rho grid
if sshread:
zrt = octant.depths.get_zrho(grid['Vtransform'], grid['Vstretching'], grid['km'], grid['theta_s'], grid['theta_b'],
h, grid['hc'], zeta=ssh, Hscale=3)
else:
zrt = octant.depths.get_zrho(grid['Vtransform'], grid['Vstretching'], grid['km'], grid['theta_s'], grid['theta_b'],
h, grid['hc'], zeta=0, Hscale=3)
dzu = .5*(dzt[:,:,0:grid['imt']-1] + dzt[:,:,1:grid['imt']])
dzv = .5*(dzt[:,0:grid['jmt']-1,:] + dzt[:,1:grid['jmt'],:])
# Change order back to ROMS/python for this calculation
dyu = grid['dyu'].T.copy(order='c')
dxv = grid['dxv'].T.copy(order='c')
# I think I can avoid this loop for the isoslice case
if z0 == None: # 3d case
uflux1 = u*dzu*dyu
vflux1 = v*dzv*dxv
elif z0 == 's': # want a specific s level zpar
uflux1 = u*dzu[zpar,:,:]*dyu
vflux1 = v*dzv[zpar,:,:]*dxv
dzt = dzt[zpar,:,:]
zrt = zrt[zpar,:,:]
elif z0 == 'rho' or z0 == 'salt' or z0 == 'temp':
# the vertical setup we're selecting an isovalue of
vert = nc.variables[z0][tind,:,:,:]
# Calculate flux and then take slice
uflux1 = octant.tools.isoslice(u*dzu*dyu,op.resize(vert,2),zpar)
vflux1 = octant.tools.isoslice(v*dzv*dxv,op.resize(vert,1),zpar)
dzt = octant.tools.isoslice(dzt,vert,zpar)
zrt = octant.tools.isoslice(zrt,vert,zpar)
elif z0 == 'z':
# Calculate flux and then take slice
uflux1 = octant.tools.isoslice(u*dzu*dyu,op.resize(zrt,2),zpar)
vflux1 = octant.tools.isoslice(v*dzv*dxv,op.resize(zrt,1),zpar)
dzt = octant.tools.isoslice(dzt,zrt,zpar)
zrt = np.ones(uflux1.shape)*zpar # array of the input desired depth
# Change all back to tracmass/fortran ordering if being used again
# This is faster than copying arrays
# Don't bother changing order of these arrays since I have to change it in
# run.py anyway (concatenate appears not to preserve ordering)
uflux1 = uflux1.T
vflux1 = vflux1.T
dzt = np.asfortranarray(dzt.T)
zrt = np.asfortranarray(zrt.T)
if sshread:
ssh = np.asfortranarray(ssh.T)
zwt = np.asfortranarray(zwt.T)
# make sure that all fluxes have a placeholder for depth
if is_string_like(z0):
uflux1 = uflux1.reshape(np.append(uflux1.shape,1))
vflux1 = vflux1.reshape(np.append(vflux1.shape,1))
dzt = dzt.reshape(np.append(dzt.shape,1))
zrt = zrt.reshape(np.append(zrt.shape,1))
return uflux1, vflux1, dzt, zrt, zwt
.isel(s_rho=-1, eta_rho=slice(1, -1), xi_rho=slice(1, -1))
.data.mean(axis=0)
)
salt = np.asarray(salt).mean(axis=0)
# salt = np.squeeze(m.variables['salt'][itmodel,-1,1:-1,1:-1])
# Surface currents over domain, use psi grid for common locations
u = []
for year in years:
u.append(ds["u"].loc[str(year) + "-" + start : str(year) + "-" + stop].isel(s_rho=-1).data.mean(axis=0))
u = np.asarray(u).mean(axis=0)
v = []
for year in years:
v.append(ds["v"].loc[str(year) + "-" + start : str(year) + "-" + stop].isel(s_rho=-1).data.mean(axis=0))
v = np.asarray(v).mean(axis=0)
u = op.resize(u, 0)
v = op.resize(v, 1)
anglev = ds["angle"].data
u, v = rot2d(u, v, op.resize(op.resize(anglev, 0), 1))
if var == "speed":
salt = np.sqrt(u ** 2 + v ** 2)
# wind stress
sustr = []
for year in years:
sustr.append(ds["sustr"].loc[str(year) + "-" + start : str(year) + "-" + stop].data.mean(axis=0))
sustr = np.asarray(sustr).mean(axis=0)
svstr = []
for year in years:
svstr.append(ds["svstr"].loc[str(year) + "-" + start : str(year) + "-" + stop].data.mean(axis=0))
# reading in a subset of indices from the beginning is prohibitively slow
xg = d.variables['xg'][:]; xg = xg[ind[::ddi],:]
yg = d.variables['yg'][:]; yg = yg[ind[::ddi],:]
xp, yp, _ = tracpy.tools.interpolate2d(xg, yg, grid, 'm_ij2xy')
nind = xg==-1
del(xg,yg)
# xg[nind] = np.nan; yg[nind] = np.nan
xp[nind] = np.nan; yp[nind] = np.nan
d.close()
# Calculate and accumulate histograms of starting locations of drifters that cross shelf
# Htemp, xe, ye = np.histogram2d(xg.flatten(), yg.flatten(), bins=bins, range=[[XGrange[0], XGrange[1]], [YGrange[0], YGrange[1]]])
Htemp, xe, ye = np.histogram2d(xp.flatten(), yp.flatten(), bins=bins, range=[[XPrange[0], XPrange[1]], [YPrange[0], YPrange[1]]])
H = np.nansum( np.vstack((H[np.newaxis,:,:], Htemp[np.newaxis,:,:])), axis=0)
XE, YE = np.meshgrid(op.resize(xe, 0), op.resize(ye, 0))
hist, bin_edges = np.histogram(H.flat, bins=100) # find # of occurrences of histogram bin values
n = np.cumsum(hist)
Hmax = bin_edges[find(n<(n.max()-n.min())*.8+n.min())[-1]] # take the 80% of histogram occurrences as the max instead of actual max since too high
locator = ticker.MaxNLocator(11)
locator.create_dummy_axis()
locator.set_bounds(0, 1)
levels = locator()
extend = 'max'
H = H/Hmax
mappable = ax.contourf(XE, YE, H.T, cmap=cmap, levels=levels, extend=extend)
ax.contour(grid['xr'], grid['yr'], grid['h'], [shelf_depth], colors='0.1', linewidth=3)
# outline the area where drifters started
d = np.load('calcs/winter-contour-pts.npz')
ax.plot(d['x'], d['y'], 'k', lw=3)
nc = netCDF.Dataset(loc) # for wind
t = nc.variables['ocean_time']
datesw = netCDF.num2date(t[:], t.units)
# Surface stress
sustr = nc.variables['sustr']; svstr = nc.variables['svstr']
## River forcing ##
# r1 = netCDF.Dataset('/atch/raid1/zhangxq/Projects/txla_nesting6/TXLA_river_4dyes_2011.nc')
r1 = netCDF.Dataset('/rho/raid/home/kthyng/txla/TXLA_river_4dyes_2012.nc') # use for through 2011
r2 = netCDF.Dataset('/rho/raid/home/kthyng/txla/TXLA_river_4dyes_2012_2014.nc') # use for 2012-2014
# River timing
tr1 = r1.variables['river_time']
tunitsr1 = tr1.units
# interpolate times for this data file since at the 12 hours mark instead of beginning of the day
tr1 = op.resize(tr1, 0)
datesr1 = netCDF.num2date(tr1[:], tunitsr1)
tr2 = r2.variables['river_time']
datesr2 = netCDF.num2date(tr2[:], tr2.units)
# all of river input
Q1 = np.abs(r1.variables['river_transport'][:]).sum(axis=1)*2.0/3.0
# interpolate this like for time
Q1 = op.resize(Q1, 0)
Q2 = np.abs(r2.variables['river_transport'][:]).sum(axis=1)*2.0/3.0
# pdb.set_trace()
# Combine river info into one dataset
iend1 = find(datesr1<datetime(2012,1,1,0,0,0))[-1] # ending index for file 1
tr = np.concatenate((tr1[:iend1], tr2[:]), axis=0)
datesr = np.concatenate((datesr1[:iend1], datesr2))
Q = np.concatenate((Q1[:iend1], Q2))
for i, File in enumerate(Files):
datestr = File.split('/')[-1].split('gc')[0]
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('Simulation starting ' + datestr)
if not os.path.exists(fname):
d = netCDF.Dataset(File)
U = d.variables['U'][:]; V = d.variables['V'][:]
d.close()
S[i,:,:] = np.sqrt(op.resize(U, 1)**2 + op.resize(V, 0)**2)
# S[i,:,:] = S[i,:,:] + Stemp
# locator = ticker.MaxNLocator(11)
# locator.create_dummy_axis()
# locator.set_bounds(0, 1)
# levels = locator()
# extend = 'max'
# H = H/Hmax
Smax = 1.
else:
d = np.load(fname); S = d['S']; d.close()
Smax = S.max()
if howplot=='log':
def transport(
name,
fmod=None,
Title=None,
dmax=None,
N=7,
extraname=None,
llcrnrlon=-98.5,
llcrnrlat=22.5,
urcrnrlat=31.0,
urcrnrlon=-87.5,
colormap="Blues",
fig=None,
ax=None,
):
"""
Make plot of zoomed-in area near DWH spill of transport of drifters over
time.
FILL IN
Args:
name
U
V
lon0
lat0
T0
"""
# Load in transport information
U, V, lon0, lat0, T0 = inout.loadtransport(name, fmod=fmod)
# Smaller basemap parameters.
loc = "http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc"
grid = inout.readgrid(loc, llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, urcrnrlon=urcrnrlon)
S = np.sqrt(op.resize(U, 1) ** 2 + op.resize(V, 0) ** 2)
Splot = (S / T0) * 100
if dmax is None:
dmax = Splot.max()
else:
dmax = dmax
# from http://matplotlib.1069221.n5.nabble.com/question-about-contours-and-clim-td21111.html
locator = ticker.MaxNLocator(N) # if you want no more than 10 contours
locator.create_dummy_axis()
locator.set_bounds(0, dmax) # d.min(),d.max())
levs = locator()
if fig is None:
fig = plt.figure(figsize=(11, 10))
else:
fig = fig
background(grid=grid)
c = fig.contourf(grid.xpsi, grid.ypsi, Splot, cmap=colormap, extend="max", levels=levs)
plt.title(Title)
# # Add initial drifter location (all drifters start at the same location)
# lon0 = lon0.mean()
# lat0 = lat0.mean()
# x0, y0 = grid['basemap'](lon0, lat0)
# plot(x0, y0, 'go', markersize=10)
if ax is None:
ax = plt.gca()
else:
ax = ax
# Want colorbar at the given location relative to axis so this works
# regardless of # of subplots,
# so convert from axis to figure coordinates
# To do this, first convert from axis to display coords
# transformations: http://matplotlib.org/users/transforms_tutorial.html
ax_coords = [0.35, 0.25, 0.6, 0.02] # axis: [x_left, y_bottom, width, height]
# display: [x_left,y_bottom,x_right,y_top]
disp_coords = ax.transAxes.transform(
[(ax_coords[0], ax_coords[1]), (ax_coords[0] + ax_coords[2], ax_coords[1] + ax_coords[3])]
)
# inverter object to go from display coords to figure coords
inv = fig.transFigure.inverted()
# figure: [x_left,y_bottom,x_right,y_top]
fig_coords = inv.transform(disp_coords)
# actual desired figure coords. figure: [x_left, y_bottom, width, height]
fig_coords = [
fig_coords[0, 0],
fig_coords[0, 1],
fig_coords[1, 0] - fig_coords[0, 0],
fig_coords[1, 1] - fig_coords[0, 1],
]
# Inlaid colorbar
cax = fig.add_axes(fig_coords)
# cax = fig.add_axes([0.39, 0.25, 0.49, 0.02])
# cax = fig.add_axes([0.49, 0.25, 0.39, 0.02])
cb = fig.colorbar(cax=cax, orientation="horizontal")
cb.set_label("Normalized drifter transport (%)")
if extraname is None:
fig.savefig("figures/" + name + "/transport", bbox_inches="tight")
else:
fig.savefig("figures/" + name + "/" + extraname + "transport", bbox_inches="tight")
def hist(lonp, latp, fname, tind='final', which='contour', vmax=None, fig=None, ax=None, \
bins=(40,40), N=10, grid=None, xlims=None, ylims=None, C=None, Title=None,
weights=None, Label='Final drifter location (%)', isll=True, binscale=None):
"""
Plot histogram of given track data at time index tind.
Inputs:
lonp,latp Drifter track positions in lon/lat [time x ndrifters]
fname Plot name to save
tind (optional) Default is 'final', in which case the final
position of each drifter in the array is found
and plotted. Alternatively, a time index
can be input and drifters at that time will be plotted.
Note that once drifters hit the outer numerical boundary,
they are nan'ed out so this may miss some drifters.
which (optional) 'contour', 'pcolor', 'hexbin', 'hist2d'
for type of plot used. Default 'hexbin'.
bins (optional) Number of bins used in histogram. Default (15,25).
N (optional) Number of contours to make. Default 10.
grid (optional) grid as read in by inout.readgrid()
xlims (optional) value limits on the x axis
ylims (optional) value limits on the y axis
isll Default True. Inputs are in lon/lat. If False, assume they
are in projected coords.
Note: Currently assuming we are plotting the final location
of each drifter regardless of tind.
"""
if grid is None:
loc = 'http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc'
grid = inout.readgrid(loc)
if isll: # if inputs are in lon/lat, change to projected x/y
# Change positions from lon/lat to x/y
xp, yp = grid['basemap'](lonp, latp)
# Need to retain nan's since basemap changes them to values
ind = np.isnan(lonp)
xp[ind] = np.nan
yp[ind] = np.nan
else:
xp = lonp
yp = latp
if fig is None:
fig = figure(figsize=(11, 10))
else:
fig = fig
background(grid) # Plot coastline and such
# pdb.set_trace()
if tind == 'final':
# Find final positions of drifters
xpc, ypc = tools.find_final(xp, yp)
elif is_numlike(tind):
xpc = xp[:, tind]
ypc = yp[:, tind]
else: # just plot what is input if some other string
xpc = xp.flatten()
ypc = yp.flatten()
if which == 'contour':
# Info for 2d histogram
H, xedges, yedges = np.histogram2d(xpc, ypc,
range=[[grid['xr'].min(), \
grid['xr'].max()], \
[grid['yr'].min(), \
grid['yr'].max()]],
bins=bins)
# Contour Plot
XE, YE = np.meshgrid(op.resize(xedges, 0), op.resize(yedges, 0))
d = (H / H.sum()) * 100
# # from http://matplotlib.1069221.n5.nabble.com/question-about-contours-and-clim-td21111.html
# locator = ticker.MaxNLocator(50) # if you want no more than 10 contours
# locator.create_dummy_axis()
# locator.set_bounds(0,1)#d.min(),d.max())
# levs = locator()
con = contourf(XE, YE, d.T,
N) #,levels=levs)#(0,15,30,45,60,75,90,105,120))
con.set_cmap('YlOrRd')
if Title is not None:
set_title(Title)
# Horizontal colorbar below plot
cax = fig.add_axes([0.3725, 0.25, 0.48, 0.02]) #colorbar axes
cb = colorbar(con, cax=cax, orientation='horizontal')
cb.set_label('Final drifter location (percent)')
# Save figure into a local directory called figures. Make directory if it doesn't exist.
if not os.path.exists('figures'):
os.makedirs('figures')
savefig('figures/' + fname + 'histcon.png', bbox_inches='tight')
# savefig('figures/' + fname + 'histcon.pdf',bbox_inches='tight')
elif which == 'pcolor':
# Info for 2d histogram
H, xedges, yedges = np.histogram2d(xpc, ypc,
range=[[grid['xr'].min(), \
grid['xr'].max()], \
[grid['yr'].min(), \
grid['yr'].max()]],
bins=bins, weights=weights)
# print H.T.max()
# pdb.set_trace()
# Pcolor plot
# C is the z value plotted, and is normalized by the total number of drifters
if C is None:
C = (H.T / H.sum()) * 100
else:
# or, provide some other weighting
C = (H.T / C) * 100
p = pcolor(xedges, yedges, C, cmap='YlOrRd')
if Title is not None:
set_title(Title)
# Set x and y limits
# pdb.set_trace()
if xlims is not None:
xlim(xlims)
if ylims is not None:
ylim(ylims)
# Horizontal colorbar below plot
cax = fig.add_axes([0.3775, 0.25, 0.48, 0.02]) #colorbar axes
cb = colorbar(p, cax=cax, orientation='horizontal')
cb.set_label('Final drifter location (percent)')
# Save figure into a local directory called figures. Make directory if it doesn't exist.
if not os.path.exists('figures'):
os.makedirs('figures')
savefig('figures/' + fname + 'histpcolor.png', bbox_inches='tight')
# savefig('figures/' + fname + 'histpcolor.pdf',bbox_inches='tight')
elif which == 'hexbin':
if ax is None:
ax = gca()
else:
ax = ax
if C is None:
# C with the reduce_C_function as sum is what makes it a percent
C = np.ones(len(xpc)) * (1. / len(xpc)) * 100
else:
C = C * np.ones(len(xpc)) * 100
hb = hexbin(xpc,
ypc,
C=C,
cmap='YlOrRd',
gridsize=bins[0],
extent=(grid['xpsi'].min(), grid['xpsi'].max(),
grid['ypsi'].min(), grid['ypsi'].max()),
reduce_C_function=sum,
vmax=vmax,
axes=ax,
bins=binscale)
# Set x and y limits
# pdb.set_trace()
if xlims is not None:
xlim(xlims)
if ylims is not None:
ylim(ylims)
if Title is not None:
ax.set_title(Title)
# Want colorbar at the given location relative to axis so this works regardless of # of subplots,
# so convert from axis to figure coordinates
# To do this, first convert from axis to display coords
# transformations: http://matplotlib.org/users/transforms_tutorial.html
ax_coords = [0.35, 0.25, 0.6,
0.02] # axis: [x_left, y_bottom, width, height]
disp_coords = ax.transAxes.transform([
(ax_coords[0], ax_coords[1]),
(ax_coords[0] + ax_coords[2], ax_coords[1] + ax_coords[3])
]) # display: [x_left,y_bottom,x_right,y_top]
inv = fig.transFigure.inverted(
) # inverter object to go from display coords to figure coords
fig_coords = inv.transform(
disp_coords) # figure: [x_left,y_bottom,x_right,y_top]
# actual desired figure coords. figure: [x_left, y_bottom, width, height]
fig_coords = [
fig_coords[0, 0], fig_coords[0, 1],
fig_coords[1, 0] - fig_coords[0, 0],
fig_coords[1, 1] - fig_coords[0, 1]
]
# Inlaid colorbar
cax = fig.add_axes(fig_coords)
# # Horizontal colorbar below plot
# cax = fig.add_axes([0.3775, 0.25, 0.48, 0.02]) #colorbar axes
cb = colorbar(cax=cax, orientation='horizontal')
cb.set_label(Label)
# pdb.set_trace()
# Save figure into a local directory called figures. Make directory if it doesn't exist.
if not os.path.exists('figures'):
os.makedirs('figures')
savefig('figures/' + fname + 'histhexbin.png', bbox_inches='tight')
# savefig('figures/' + fname + 'histhexbin.pdf',bbox_inches='tight')
elif which == 'hist2d':
# pdb.set_trace()
hist2d(xpc,
ypc,
bins=40,
range=[[grid['xr'].min(), grid['xr'].max()],
[grid['yr'].min(), grid['yr'].max()]],
normed=True)
set_cmap('YlOrRd')
# Set x and y limits
# pdb.set_trace()
if xlims is not None:
xlim(xlims)
if ylims is not None:
ylim(ylims)
# Horizontal colorbar below plot
cax = fig.add_axes([0.3775, 0.25, 0.48, 0.02]) #colorbar axes
cb = colorbar(cax=cax, orientation='horizontal')
cb.set_label('Final drifter location (percent)')
# Save figure into a local directory called figures. Make directory if it doesn't exist.
if not os.path.exists('figures'):
os.makedirs('figures')
savefig('figures/' + fname + 'hist2d.png', bbox_inches='tight')
def hist(
lonp,
latp,
fname,
tind="final",
which="contour",
vmax=None,
fig=None,
ax=None,
bins=(40, 40),
N=10,
grid=None,
xlims=None,
ylims=None,
C=None,
Title=None,
weights=None,
Label="Final drifter location (%)",
isll=True,
binscale=None,
):
"""
Plot histogram of given track data at time index tind.
Args:
lonp,latp: Drifter track positions in lon/lat [time x ndrifters]
fname: Plot name to save
tind (Optional): Default is 'final', in which case the final
position of each drifter in the array is found and plotted.
Alternatively, a time index can be input and drifters at that time
will be plotted. Note that once drifters hit the outer numerical
boundary, they are nan'ed out so this may miss some drifters.
which (Optional[str]): 'contour', 'pcolor', 'hexbin', 'hist2d' for
type of plot used. Default 'hexbin'.
bins (Optional): Number of bins used in histogram. Default (15,25).
N (Optional[int]): Number of contours to make. Default 10.
grid (Optional): grid as read in by inout.readgrid()
xlims (Optional): value limits on the x axis
ylims (Optional): value limits on the y axis
isll: Default True. Inputs are in lon/lat. If False, assume they
are in projected coords.
Note:
Currently assuming we are plotting the final location of each drifter
regardless of tind.
"""
if grid is None:
loc = "http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc"
grid = inout.readgrid(loc)
if isll: # if inputs are in lon/lat, change to projected x/y
# Change positions from lon/lat to x/y
xp, yp = grid.proj(lonp, latp)
# Need to retain nan's since basemap changes them to values
ind = np.isnan(lonp)
xp[ind] = np.nan
yp[ind] = np.nan
else:
xp = lonp
yp = latp
if fig is None:
fig = plt.figure(figsize=(11, 10))
else:
fig = fig
background(grid) # Plot coastline and such
if tind == "final":
# Find final positions of drifters
xpc, ypc = tools.find_final(xp, yp)
elif isinstance(tind, int):
xpc = xp[:, tind]
ypc = yp[:, tind]
else: # just plot what is input if some other string
xpc = xp.flatten()
ypc = yp.flatten()
if which == "contour":
# Info for 2d histogram
H, xedges, yedges = np.histogram2d(
xpc, ypc, range=[[grid.x_rho.min(), grid.x_rho.max()], [grid.y_rho.min(), grid.y_rho.max()]], bins=bins
)
# Contour Plot
XE, YE = np.meshgrid(op.resize(xedges, 0), op.resize(yedges, 0))
d = (H / H.sum()) * 100
# # from http://matplotlib.1069221.n5.nabble.com/question-about-contours-and-clim-td21111.html
# locator = ticker.MaxNLocator(50) # if you want no more than 10 contours
# locator.create_dummy_axis()
# locator.set_bounds(0,1)#d.min(),d.max())
# levs = locator()
con = fig.contourf(XE, YE, d.T, N) # ,levels=levs)#(0,15,30,45,60,75,90,105,120))
con.set_cmap("YlOrRd")
if Title is not None:
plt.set_title(Title)
# Horizontal colorbar below plot
cax = fig.add_axes([0.3725, 0.25, 0.48, 0.02]) # colorbar axes
cb = fig.colorbar(con, cax=cax, orientation="horizontal")
cb.set_label("Final drifter location (percent)")
# Save figure into a local directory called figures. Make directory
# if it doesn't exist.
if not os.path.exists("figures"):
os.makedirs("figures")
fig.savefig("figures/" + fname + "histcon.png", bbox_inches="tight")
elif which == "pcolor":
# Info for 2d histogram
H, xedges, yedges = np.histogram2d(
xpc,
ypc,
range=[[grid.x_rho.min(), grid.x_rho.max()], [grid.y_rho.min(), grid.y_rho.max()]],
bins=bins,
weights=weights,
)
# Pcolor plot
# C is the z value plotted, and is normalized by the total number of
# drifters
if C is None:
C = (H.T / H.sum()) * 100
else:
# or, provide some other weighting
C = (H.T / C) * 100
p = plt.pcolor(xedges, yedges, C, cmap="YlOrRd")
if Title is not None:
plt.set_title(Title)
# Set x and y limits
if xlims is not None:
plt.xlim(xlims)
if ylims is not None:
plt.ylim(ylims)
# Horizontal colorbar below plot
cax = fig.add_axes([0.3775, 0.25, 0.48, 0.02]) # colorbar axes
cb = fig.colorbar(p, cax=cax, orientation="horizontal")
cb.set_label("Final drifter location (percent)")
# Save figure into a local directory called figures. Make directory
# if it doesn't exist.
if not os.path.exists("figures"):
os.makedirs("figures")
fig.savefig("figures/" + fname + "histpcolor.png", bbox_inches="tight")
# savefig('figures/' + fname + 'histpcolor.pdf',bbox_inches='tight')
elif which == "hexbin":
if ax is None:
ax = plt.gca()
else:
ax = ax
if C is None:
# C with the reduce_C_function as sum is what makes it a percent
C = np.ones(len(xpc)) * (1.0 / len(xpc)) * 100
else:
C = C * np.ones(len(xpc)) * 100
hb = plt.hexbin(
xpc,
ypc,
C=C,
cmap="YlOrRd",
gridsize=bins[0],
extent=(grid.x_psi.min(), grid.x_psi.max(), grid.y_psi.min(), grid.y_psi.max()),
reduce_C_function=sum,
vmax=vmax,
axes=ax,
bins=binscale,
)
# Set x and y limits
if xlims is not None:
plt.xlim(xlims)
if ylims is not None:
plt.ylim(ylims)
if Title is not None:
ax.set_title(Title)
# Want colorbar at the given location relative to axis so this works
# regardless of # of subplots, so convert from axis to figure
# coordinates. To do this, first convert from axis to display coords
# transformations:
# http://matplotlib.org/users/transforms_tutorial.html
# axis: [x_left, y_bottom, width, height]
ax_coords = [0.35, 0.25, 0.6, 0.02]
# display: [x_left,y_bottom,x_right,y_top]
disp_coords = ax.transAxes.transform(
[(ax_coords[0], ax_coords[1]), (ax_coords[0] + ax_coords[2], ax_coords[1] + ax_coords[3])]
)
# inverter object to go from display coords to figure coords
inv = fig.transFigure.inverted()
# figure: [x_left,y_bottom,x_right,y_top]
fig_coords = inv.transform(disp_coords)
# actual desired figure coords. figure:
# [x_left, y_bottom, width, height]
fig_coords = [
fig_coords[0, 0],
fig_coords[0, 1],
fig_coords[1, 0] - fig_coords[0, 0],
fig_coords[1, 1] - fig_coords[0, 1],
]
# Inlaid colorbar
cax = fig.add_axes(fig_coords)
# # Horizontal colorbar below plot
# cax = fig.add_axes([0.3775, 0.25, 0.48, 0.02]) # colorbar axes
cb = fig.colorbar(hb, cax=cax, orientation="horizontal")
cb.set_label(Label)
# Save figure into a local directory called figures. Make directory
# if it doesn't exist.
if not os.path.exists("figures"):
os.makedirs("figures")
fig.savefig("figures/" + fname + "histhexbin.png", bbox_inches="tight")
# savefig('figures/' + fname + 'histhexbin.pdf',bbox_inches='tight')
elif which == "hist2d":
plt.hist2d(
xpc,
ypc,
bins=40,
range=[[grid.x_rho.min(), grid.x_rho.max()], [grid.y_rho.min(), grid.y_rho.max()]],
normed=True,
)
plt.set_cmap("YlOrRd")
# Set x and y limits
if xlims is not None:
xlim(xlims)
if ylims is not None:
ylim(ylims)
# Horizontal colorbar below plot
cax = fig.add_axes([0.3775, 0.25, 0.48, 0.02]) # colorbar axes
cb = fig.colorbar(cax=cax, orientation="horizontal")
cb.set_label("Final drifter location (percent)")
# Save figure into a local directory called figures. Make directory
# if it doesn't exist.
if not os.path.exists("figures"):
os.makedirs("figures")
fig.savefig("figures/" + fname + "hist2d.png", bbox_inches="tight")
def hist(lonp, latp, fname, tind='final', which='contour', \
bins=(40,40), N=10, grid=None, xlims=None, ylims=None):
"""
Plot histogram of given track data at time index tind.
Inputs:
lonp,latp Drifter track positions in lon/lat [time x ndrifters]
fname Plot name to save
tind (optional) Default is 'final', in which case the final
position of each drifter in the array is found
and plotted. Alternatively, a time index
can be input and drifters at that time will be plotted.
Note that once drifters hit the outer numerical boundary,
they are nan'ed out so this may miss some drifters.
which (optional) 'contour', 'pcolor', 'hexbin', 'hist2d'
for type of plot used. Default 'hexbin'.
bins (optional) Number of bins used in histogram. Default (15,25).
N (optional) Number of contours to make. Default 10.
grid (optional) grid as read in by inout.readgrid()
xlims (optional) value limits on the x axis
ylims (optional) value limits on the y axis
Note: Currently assuming we are plotting the final location
of each drifter regardless of tind.
"""
if grid is None:
loc = 'http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc'
grid = inout.readgrid(loc)
# Change positions from lon/lat to x/y
xp, yp = grid['basemap'](lonp, latp)
# Need to retain nan's since basemap changes them to values
ind = np.isnan(lonp)
xp[ind] = np.nan
yp[ind] = np.nan
fig = figure(figsize=(12,10))
background(grid) # Plot coastline and such
# pdb.set_trace()
if tind == 'final':
# Find final positions of drifters
xpc, ypc = tools.find_final(xp, yp)
elif is_numlike(tind):
xpc = xp[:,tind]
ypc = yp[:,tind]
else: # just plot what is input if some other string
xpc = xp.flatten()
ypc = yp.flatten()
if which == 'contour':
# Info for 2d histogram
H, xedges, yedges = np.histogram2d(xpc, ypc,
range=[[grid['xr'].min(), \
grid['xr'].max()], \
[grid['yr'].min(), \
grid['yr'].max()]],
bins=bins)
# Contour Plot
XE, YE = np.meshgrid(op.resize(xedges,0), op.resize(yedges,0))
d = (H/H.sum())*100
# # from http://matplotlib.1069221.n5.nabble.com/question-about-contours-and-clim-td21111.html
# locator = ticker.MaxNLocator(50) # if you want no more than 10 contours
# locator.create_dummy_axis()
# locator.set_bounds(0,1)#d.min(),d.max())
# levs = locator()
con = contourf(XE, YE, d.T, N)#,levels=levs)#(0,15,30,45,60,75,90,105,120))
con.set_cmap('YlOrRd')
# Horizontal colorbar below plot
cax = fig.add_axes([0.3725, 0.25, 0.48, 0.02]) #colorbar axes
cb = colorbar(con, cax=cax, orientation='horizontal')
cb.set_label('Final drifter location (percent)')
# Save figure into a local directory called figures. Make directory if it doesn't exist.
if not os.path.exists('figures'):
os.makedirs('figures')
savefig('figures/' + fname + 'histcon.png',bbox_inches='tight')
# savefig('figures/' + fname + 'histcon.pdf',bbox_inches='tight')
elif which == 'pcolor':
# Info for 2d histogram
H, xedges, yedges = np.histogram2d(xpc, ypc,
range=[[grid['xr'].min(), \
grid['xr'].max()], \
[grid['yr'].min(), \
grid['yr'].max()]],
bins=bins)
# Pcolor plot
p = pcolor(xedges, yedges, (H.T/H.sum())*100, cmap='YlOrRd')
# Set x and y limits
# pdb.set_trace()
if xlims is not None:
xlim(xlims)
if ylims is not None:
ylim(ylims)
# Horizontal colorbar below plot
cax = fig.add_axes([0.3775, 0.25, 0.48, 0.02]) #colorbar axes
cb = colorbar(p, cax=cax, orientation='horizontal')
cb.set_label('Final drifter location (percent)')
# Save figure into a local directory called figures. Make directory if it doesn't exist.
if not os.path.exists('figures'):
os.makedirs('figures')
savefig('figures/' + fname + 'histpcolor.png', bbox_inches='tight')
# savefig('figures/' + fname + 'histpcolor.pdf',bbox_inches='tight')
elif which == 'hexbin':
# C with the reduce_C_function as sum is what makes it a percent
C = np.ones(len(xpc))*(1./len(xpc))*100
hb = hexbin(xpc, ypc, C=C, cmap='YlOrRd', gridsize=bins[0],
extent=(grid['xr'].min(), grid['xr'].max(),
grid['yr'].min(), grid['yr'].max()),
reduce_C_function=sum)
# Set x and y limits
# pdb.set_trace()
if xlims is not None:
xlim(xlims)
if ylims is not None:
ylim(ylims)
# Horizontal colorbar below plot
cax = fig.add_axes([0.3775, 0.25, 0.48, 0.02]) #colorbar axes
cb = colorbar(cax=cax, orientation='horizontal')
cb.set_label('Final drifter location (percent)')
# pdb.set_trace()
# Save figure into a local directory called figures. Make directory if it doesn't exist.
if not os.path.exists('figures'):
os.makedirs('figures')
savefig('figures/' + fname + 'histhexbin.png', bbox_inches='tight')
# savefig('figures/' + fname + 'histhexbin.pdf',bbox_inches='tight')
elif which == 'hist2d':
# pdb.set_trace()
hist2d(xpc, ypc, bins=40,
range=[[grid['xr'].min(), grid['xr'].max()],
[grid['yr'].min(), grid['yr'].max()]], normed=True)
set_cmap('YlOrRd')
# Set x and y limits
# pdb.set_trace()
if xlims is not None:
xlim(xlims)
if ylims is not None:
ylim(ylims)
# Horizontal colorbar below plot
cax = fig.add_axes([0.3775, 0.25, 0.48, 0.02]) #colorbar axes
cb = colorbar(cax=cax,orientation='horizontal')
cb.set_label('Final drifter location (percent)')
# Save figure into a local directory called figures. Make directory if it doesn't exist.
if not os.path.exists('figures'):
os.makedirs('figures')
savefig('figures/' + fname + 'hist2d.png',bbox_inches='tight')
def readfields(tind, grid, nc, z0=None, zpar=None, zparuv=None):
"""
readfields()
Kristen Thyng, March 2013
Reads in model output in order to calculate fluxes and z grid
properties to send into step.f95.
Should be called initially and then subsequently each time loop.
All arrays are changed to Fortran ordering (from Python ordering)
and to tracmass variables ordering from ROMS ordering
i.e. from [t,k,j,i] to [i,j,k,t]
right away after reading in.
Args:
tind: Single time index for model output to read in
grid: Dictionary containing all necessary time-independent grid
fields
nc: NetCDF object for relevant files
z0 (Optional): if doing 2d isoslice, z0 contains string saying which
kind
zpar (Optional): if doing 2d isoslice, zpar is the
depth/level/density at which we are to get the level
zparuv (Optional): Use this if the k index for the model output
fields (e.g, u, v) is different from the k index in the grid. This
might happen if, for example, only the surface current were saved,
but the model run originally did have many layers. This parameter
represents the k index for the u and v output, not for the grid.
Returns:
* uflux1 - Zonal (x) flux at tind
* vflux1 - Meriodional (y) flux at tind
* dzt - Height of k-cells in 3 dim in meters on rho vertical grid.
[imt,jmt,km]
* zrt - Time-dependent depths of cells on vertical rho grid (meters).
For the isoslice case, zrt ends up with 1 vertical level which
contains the depths for the vertical center of the cell for that
level.
* zwt - Time-dependent depths of cells on vertical w grid (meters).
zwt always contains the depths at the vertical cell edges for the
whole 3D grid and the correct depths can be accessed using the
drifter indices.
Array descriptions:
* u,v - Zonal (x) and meridional (y) velocities [imt,jmt,km] (m/s)
* ssh - Free surface [imt,jmt] (m)
* dz - Height of k-cells in 1 dim [km]
From coord.f95: z coordinates (z>0 going up) for layers in meters
bottom layer: k=0; surface layer: k=KM and zw=0
dz = layer thickness
* zt - Depths (negative) in meters of w vertical grid [imt,jmt,km+1]
* dzt - Height of k-cells in 3 dim in meters on rho vertical grid.
[imt,jmt,km]
* dzt0 - Height of k-cells in 2 dim. [imt,jmt]
* dzu - Height of each u grid cell [imt-1,jmt,km]
* dzv - Height of each v grid cell [imt,jmt-1,km]
* uflux1 - Zonal (x) fluxes [imt-1,jmt,km] (m^3/s)?
* vflux1 - Meriodional (y) fluxes [imt,jmt-1,km] (m^3/s)?
"""
# this parameter is in case there is less model output available
# vertically than was actually run on the grid
if zparuv is None:
zparuv = zpar
# tic_temp = time.time()
# Read in model output for index tind
if z0 == 's': # read in less model output to begin with, to save time
if nc.variables['u'].ndim == 4:
u = nc.variables['u'][tind, zparuv, :, :]
v = nc.variables['v'][tind, zparuv, :, :]
elif nc.variables['u'].ndim == 3:
u = nc.variables['u'][tind, :, :]
v = nc.variables['v'][tind, :, :]
if 'zeta' in nc.variables:
# [t,j,i], ssh in tracmass
ssh = nc.variables['zeta'][tind, :, :]
sshread = True
else:
sshread = False
else:
u = nc.variables['u'][tind, :, :, :]
v = nc.variables['v'][tind, :, :, :]
if 'zeta' in nc.variables:
# [t,j,i], ssh in tracmass
ssh = nc.variables['zeta'][tind, :, :]
sshread = True
else:
sshread = False
# Use octant to calculate depths for the appropriate vertical grid
# parameters have to transform a few back to ROMS coordinates and python
# ordering for this
if sshread:
zwt = octant.depths.get_zw(grid.Vtransform,
grid.Vstretching,
grid.km + 1,
grid.theta_s,
grid.theta_b,
grid.h,
grid.hc,
zeta=ssh,
Hscale=3)
else: # if ssh isn't available, approximate as 0
zwt = octant.depths.get_zw(grid.Vtransform,
grid.Vstretching,
grid.km + 1,
grid.theta_s,
grid.theta_b,
grid.h,
grid.hc,
zeta=0,
Hscale=3)
# Change dzt to tracmass/fortran ordering
dzt = zwt[1:, :, :] - zwt[:-1, :, :]
# also want depths on rho grid
if sshread:
zrt = octant.depths.get_zrho(grid.Vtransform,
grid.Vstretching,
grid.km,
grid.theta_s,
grid.theta_b,
grid.h,
grid.hc,
zeta=ssh,
Hscale=3)
else:
zrt = octant.depths.get_zrho(grid.Vtransform,
grid.Vstretching,
grid.km,
grid.theta_s,
grid.theta_b,
grid.h,
grid.hc,
zeta=0,
Hscale=3)
dzu = .5 * (dzt[:, :, 0:grid.imt - 1] + dzt[:, :, 1:grid.imt])
dzv = .5 * (dzt[:, 0:grid.jmt - 1, :] + dzt[:, 1:grid.jmt, :])
# I think I can avoid this loop for the isoslice case
if z0 is None: # 3d case
uflux1 = u * dzu * grid.dyu
vflux1 = v * dzv * grid.dxv
elif z0 == 's': # want a specific s level zpar
uflux1 = u * dzu[zpar, :, :] * grid.dyu
vflux1 = v * dzv[zpar, :, :] * grid.dxv
dzt = dzt[zpar, :, :]
zrt = zrt[zpar, :, :]
elif z0 == 'rho' or z0 == 'salt' or z0 == 'temp':
# the vertical setup we're selecting an isovalue of
vert = nc.variables[z0][tind, :, :, :]
# Calculate flux and then take slice
uflux1 = octant.tools.isoslice(u * dzu * grid.dyu, op.resize(vert, 2),
zpar)
vflux1 = octant.tools.isoslice(v * dzv * grid.dxv, op.resize(vert, 1),
zpar)
dzt = octant.tools.isoslice(dzt, vert, zpar)
zrt = octant.tools.isoslice(zrt, vert, zpar)
elif z0 == 'z':
# Calculate flux and then take slice
uflux1 = octant.tools.isoslice(u * dzu * grid.dyu, op.resize(zrt, 2),
zpar)
vflux1 = octant.tools.isoslice(v * dzv * grid.dxv, op.resize(zrt, 1),
zpar)
dzt = octant.tools.isoslice(dzt, zrt, zpar)
zrt = np.ones(uflux1.shape) * zpar # array of the input desired depth
# make sure that all fluxes have a placeholder for depth
if isinstance(z0, str):
uflux1 = uflux1.reshape(np.append(1, uflux1.shape))
vflux1 = vflux1.reshape(np.append(1, vflux1.shape))
dzt = dzt.reshape(np.append(1, dzt.shape))
zrt = zrt.reshape(np.append(1, zrt.shape))
return uflux1, vflux1, dzt, zrt, zwt
def readfields(tind,grid,nc,z0=None, zpar=None, zparuv=None):
'''
readfields()
Kristen Thyng, March 2013
Reads in model output in order to calculate fluxes and z grid
properties to send into step.f95.
Should be called initially and then subsequently each time loop.
All arrays are changed to Fortran ordering (from Python ordering)
and to tracmass variables ordering from ROMS ordering
i.e. from [t,k,j,i] to [i,j,k,t]
right away after reading in.
Input:
tind Single time index for model output to read in
grid Dictionary containing all necessary time-independent grid fields
nc NetCDF object for relevant files
z0 (optional) if doing 2d isoslice, z0 contains string saying which kind
zpar (optional) if doing 2d isoslice, zpar is the depth/level/density
at which we are to get the level
zparuv (optional) Use this if the k index for the model output fields (e.g, u, v) is different
from the k index in the grid. This might happen if, for example, only the surface current
were saved, but the model run originally did have many layers. This parameter
represents the k index for the u and v output, not for the grid.
Output:
uflux1 Zonal (x) flux at tind
vflux1 Meriodional (y) flux at tind
dzt Height of k-cells in 3 dim in meters on rho vertical grid. [imt,jmt,km]
zrt Time-dependent depths of cells on vertical rho grid (meters).
For the isoslice case, zrt ends up with 1 vertical level which contains
the depths for the vertical center of the cell for that level.
zwt Time-dependent depths of cells on vertical w grid (meters). zwt always
contains the depths at the vertical cell edges for the whole 3D grid
and the correct depths can be accessed using the drifter indices.
Array descriptions:
u,v Zonal (x) and meridional (y) velocities [imt,jmt,km] (m/s)
ssh Free surface [imt,jmt] (m)
dz Height of k-cells in 1 dim [km]
From coord.f95: z coordinates (z>0 going up) for layers in meters
bottom layer: k=0; surface layer: k=KM and zw=0
dz = layer thickness
zt Depths (negative) in meters of w vertical grid [imt,jmt,km+1]
dzt Height of k-cells in 3 dim in meters on rho vertical grid. [imt,jmt,km]
dzt0 Height of k-cells in 2 dim. [imt,jmt]
dzu Height of each u grid cell [imt-1,jmt,km]
dzv Height of each v grid cell [imt,jmt-1,km]
uflux1 Zonal (x) fluxes [imt-1,jmt,km] (m^3/s)?
vflux1 Meriodional (y) fluxes [imt,jmt-1,km] (m^3/s)?
'''
# this parameter is in case there is less model output available vertically than
# was actually run on the grid
# pdb.set_trace()
if zparuv is None:
zparuv = zpar
# tic_temp = time.time()
# Read in model output for index tind
if z0 == 's': # read in less model output to begin with, to save time
u = nc.variables['u'][tind,zparuv,:,:]
v = nc.variables['v'][tind,zparuv,:,:]
if 'zeta' in nc.variables:
ssh = nc.variables['zeta'][tind,:,:] # [t,j,i], ssh in tracmass
sshread = True
else:
sshread = False
else:
u = nc.variables['u'][tind,:,:,:]
v = nc.variables['v'][tind,:,:,:]
if 'zeta' in nc.variables:
ssh = nc.variables['zeta'][tind,:,:] # [t,j,i], ssh in tracmass
sshread = True
else:
sshread = False
h = grid['h'].T.copy(order='c')
# Use octant to calculate depths for the appropriate vertical grid parameters
# have to transform a few back to ROMS coordinates and python ordering for this
if sshread:
zwt = octant.depths.get_zw(grid['Vtransform'], grid['Vstretching'], grid['km']+1, grid['theta_s'], grid['theta_b'],
h, grid['hc'], zeta=ssh, Hscale=3)
else: # if ssh isn't available, approximate as 0
zwt = octant.depths.get_zw(grid['Vtransform'], grid['Vstretching'], grid['km']+1, grid['theta_s'], grid['theta_b'],
h, grid['hc'], zeta=0, Hscale=3)
# Change dzt to tracmass/fortran ordering
dzt = zwt[1:,:,:] - zwt[:-1,:,:]
# also want depths on rho grid
if sshread:
zrt = octant.depths.get_zrho(grid['Vtransform'], grid['Vstretching'], grid['km'], grid['theta_s'], grid['theta_b'],
h, grid['hc'], zeta=ssh, Hscale=3)
else:
zrt = octant.depths.get_zrho(grid['Vtransform'], grid['Vstretching'], grid['km'], grid['theta_s'], grid['theta_b'],
h, grid['hc'], zeta=0, Hscale=3)
dzu = .5*(dzt[:,:,0:grid['imt']-1] + dzt[:,:,1:grid['imt']])
dzv = .5*(dzt[:,0:grid['jmt']-1,:] + dzt[:,1:grid['jmt'],:])
# Change order back to ROMS/python for this calculation
dyu = grid['dyu'].T.copy(order='c')
dxv = grid['dxv'].T.copy(order='c')
# I think I can avoid this loop for the isoslice case
if z0 == None: # 3d case
uflux1 = u*dzu*dyu
vflux1 = v*dzv*dxv
elif z0 == 's': # want a specific s level zpar
uflux1 = u*dzu[zpar,:,:]*dyu
vflux1 = v*dzv[zpar,:,:]*dxv
dzt = dzt[zpar,:,:]
zrt = zrt[zpar,:,:]
elif z0 == 'rho' or z0 == 'salt' or z0 == 'temp':
# the vertical setup we're selecting an isovalue of
vert = nc.variables[z0][tind,:,:,:]
# Calculate flux and then take slice
uflux1 = octant.tools.isoslice(u*dzu*dyu,op.resize(vert,2),zpar)
vflux1 = octant.tools.isoslice(v*dzv*dxv,op.resize(vert,1),zpar)
dzt = octant.tools.isoslice(dzt,vert,zpar)
zrt = octant.tools.isoslice(zrt,vert,zpar)
elif z0 == 'z':
# Calculate flux and then take slice
uflux1 = octant.tools.isoslice(u*dzu*dyu,op.resize(zrt,2),zpar)
vflux1 = octant.tools.isoslice(v*dzv*dxv,op.resize(zrt,1),zpar)
dzt = octant.tools.isoslice(dzt,zrt,zpar)
zrt = np.ones(uflux1.shape)*zpar # array of the input desired depth
# Change all back to tracmass/fortran ordering if being used again
# This is faster than copying arrays
# Don't bother changing order of these arrays since I have to change it in
# run.py anyway (concatenate appears not to preserve ordering)
uflux1 = uflux1.T
vflux1 = vflux1.T
dzt = np.asfortranarray(dzt.T)
zrt = np.asfortranarray(zrt.T)
if sshread:
ssh = np.asfortranarray(ssh.T)
zwt = np.asfortranarray(zwt.T)
# make sure that all fluxes have a placeholder for depth
if is_string_like(z0):
uflux1 = uflux1.reshape(np.append(uflux1.shape,1))
vflux1 = vflux1.reshape(np.append(vflux1.shape,1))
dzt = dzt.reshape(np.append(dzt.shape,1))
zrt = zrt.reshape(np.append(zrt.shape,1))
return uflux1, vflux1, dzt, zrt, zwt
import pdb
import op
seed = "C"
loccalcs = "calcs/"
loctracks = "tracks/"
# Loop through tracks files and run calculations
Files = glob.glob(loctracks + seed + "*.nc")
# Highest res case for comparison
dc = netCDF.Dataset(loctracks + seed + "_dx250_V25.nc") # control case
Uc = dc.variables["U"][:]
Vc = dc.variables["V"][:]
Sc = np.sqrt(op.resize(Uc, 1) ** 2 + op.resize(Vc, 0) ** 2)
dc.close()
# lonpc = dc.variables['lonp'][:]; latpc = dc.variables['latp'][:]; tpc = dc.variables['tp'][:];
loc = "http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc"
grid = tracpy.inout.readgrid(loc) # grid file, Gulf model domain
# grid = tracpy.inout.readgrid(loc[1], vert_filename=loc[0], llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
# llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat) # grid file, Gulf model domain
for File in Files:
print File
# Read in tracks
d = netCDF.Dataset(File)