def plotit(ax, aa, sect_df, sn):
pfun.add_coast(ax)
pfun.dar(ax)
ax.axis(aa)
ax.grid(True)
x0 = sect_df.loc[sn,'x0']; x1 = sect_df.loc[sn,'x1']
y0 = sect_df.loc[sn,'y0']; y1 = sect_df.loc[sn,'y1']
xx = (x0+x1)/2; yy = (y0+y1)/2
if (xx > aa[0]) & (xx < aa[1]) & (yy > aa[2]) & (yy < aa[3]):
ax.plot([x0,x1], [y0,y1], '-b', linewidth=3, alpha=.5)
ax.text(xx, yy, sn, fontsize=12, color='r', fontweight='bold')
def P_carbon(in_dict):
# START
fig = plt.figure(figsize=figsize)
ds = nc.Dataset(in_dict['fn'])
vlims = in_dict['vlims'].copy()
out_dict['vlims'] = vlims
# PLOT CODE
# panel 1
t_str = 'Surface TIC'
ax = fig.add_subplot(121)
vn = 'TIC'
cs, out_dict['vlims'][vn] = pfun.add_map_field(ax,
ds,
vn,
vlims=vlims[vn],
cmap='YlOrBr')
fig.colorbar(cs)
pfun.add_bathy_contours(ax, ds)
pfun.add_coast(ax)
ax.axis(pfun.get_aa(ds))
pfun.dar(ax)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
ax.set_title(t_str + ' (' + pfun.get_units(ds, vn) + ')')
pfun.add_info(ax, in_dict['fn'])
pfun.add_windstress_flower(ax, ds)
# panel 2
t_str = 'Surface Alkalinity'
ax = fig.add_subplot(122)
vn = 'alkalinity'
cs, out_dict['vlims'][vn] = pfun.add_map_field(ax,
ds,
vn,
vlims=vlims[vn],
cmap='RdYlGn')
fig.colorbar(cs)
pfun.add_bathy_contours(ax, ds)
pfun.add_coast(ax)
ax.axis(pfun.get_aa(ds))
pfun.dar(ax)
ax.set_xlabel('Longitude')
ax.set_title(t_str + ' (' + pfun.get_units(ds, vn) + ')')
pfun.add_velocity_vectors(ax, ds, in_dict['fn'])
# FINISH
ds.close()
if len(in_dict['fn_out']) > 0:
plt.savefig(in_dict['fn_out'])
plt.close()
else:
plt.show()
return out_dict
def plotit(ax, aa, sect_df, sn):
pfun.add_coast(ax)
pfun.dar(ax)
ax.axis(aa)
ax.grid(True)
x0 = sect_df.loc[sn, 'x0']
x1 = sect_df.loc[sn, 'x1']
y0 = sect_df.loc[sn, 'y0']
y1 = sect_df.loc[sn, 'y1']
xx = (x0 + x1) / 2
yy = (y0 + y1) / 2
if (xx > aa[0]) & (xx < aa[1]) & (yy > aa[2]) & (yy < aa[3]):
ax.plot([x0, x1], [y0, y1], '-b', linewidth=3, alpha=.5)
ax.text(xx, yy, sn, fontsize=12, color='r', fontweight='bold')
lmix = np.log10(mmix+1e-8)
lmix_neap = np.log10(mmix_neap+1e-8)
lmix_spring = np.log10(mmix_spring+1e-8)
# PLOTTING
plt.close('all')
# MAPS OF MIXING per unit area
fig = plt.figure(figsize=(18,8))
vmin = -6; vmax = -2
# full region
ax = fig.add_subplot(131)
cs = ax.pcolormesh(xp, yp, lmix[1:-1, 1:-1], vmin=vmin, vmax=vmax, cmap='rainbow')
fig.colorbar(cs, ax=ax)
pfun.dar(ax)
pfun.add_coast(ax)
ax.axis(aa)
ax.set_title('$log_{10}[Average\ Mixing\ (psu^{2}\ m\ s^{-1})]$')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
# close up of Puget Sound
aa = [-123.25, -122, 47, 48.5]
ax = fig.add_subplot(132)
cs = ax.pcolormesh(xp, yp, lmix_neap[1:-1, 1:-1], vmin=vmin, vmax=vmax, cmap='rainbow')
pfun.dar(ax)
pfun.add_coast(ax)
ax.axis(aa)
ax.set_xlabel('Longitude')
ax.set_title('Close-up of Puget Sound: NEAP')
for add_stations in [True, False]:
fig = plt.figure(figsize=(16,11))
# -------------- FULL MAP -----------------------------------------------
ax = fig.add_subplot(121)
cs = ax.pcolormesh(lon[6:-6,6:],lat[6:-6,6:],v[6:-6,6:], cmap=cmap, vmin=vmin, vmax=vmax)
nudge_alpha = .1
ax.pcolormesh(lon[:,:6],lat[:,:6],v[:,:6], cmap=cmap, vmin=vmin, vmax=vmax, alpha=nudge_alpha)
ax.pcolormesh(lon[:6,:],lat[:6,:],v[:6,:], cmap=cmap, vmin=vmin, vmax=vmax, alpha=nudge_alpha)
ax.pcolormesh(lon[-6:,:],lat[-6:,:],v[-6:,:], cmap=cmap, vmin=vmin, vmax=vmax, alpha=nudge_alpha)
pfun.add_bathy_contours(ax, ds, txt=False)
pfun.add_coast(ax)
ax.axis(pfun.get_aa(ds))
pfun.dar(ax)
if add_stations:
add_sta_loc(ax, moor_df, ctd_df, tide_df, do_leg=True)
# Inset colorbar
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
cbaxes = inset_axes(ax, width="4%", height="40%", loc='lower left', borderpad=3)
cb = fig.colorbar(cs, cax=cbaxes, orientation='vertical')
ax.text(.08, .53, r'SST $[^\circ C]$', transform=ax.transAxes)
ax.text(.08, .03, dstr, size=fs2, transform=ax.transAxes, style='italic')
ax.set_xticks([-130, -126, -122])
ax.set_yticks([42, 44, 46, 48, 50, 52])
# create interpolant
upi = intp.interp2d(lonr_s[0, :], latr_s[:, 0], upd[t, :])
vpi = intp.interp2d(lonr_s[0, :], latr_s[:, 0], vpd[t, :])
uai = intp.interp2d(lonr_s[0, :], latr_s[:, 0], uad[t, :])
vai = intp.interp2d(lonr_s[0, :], latr_s[:, 0], vad[t, :])
rs = np.ma.masked_where(maskr == 0, dens)
p = ax0.pcolormesh(lonr,
latr,
rs,
vmin=-vmax,
vmax=vmax,
cmap='RdYlBu_r',
alpha=1.0)
ax0.axis(a_ll)
pfun.dar(ax0)
ax0.set_xlabel('Longitude', fontweight='bold')
ax0.set_ylabel('Latitude', fontweight='bold')
ax0.set_title(ax0_title, fontweight='bold')
cbar = plt.colorbar(p, orientation='horizontal', pad=0.1)
cbar.ax.set_xlabel(r'$kg/m^{3}$')
if t == 0:
# create regular grid
aaa = ax0.axis()
daax = aaa[1] - aaa[0]
daay = aaa[3] - aaa[2]
axrat = np.cos(np.deg2rad(aaa[2])) * daax / daay
x = np.linspace(aaa[0], aaa[1], int(round(nngrid * axrat)))
y = np.linspace(aaa[2], aaa[3], nngrid)
xx, yy = np.meshgrid(x, y)
ax1.axis(aa1)
ax1.grid(True)
ax1.set_ylabel('Transport $[1000\ m^{3}s^{-1}]$')
ax1.set_title(season.title() + ' ' + year_str)
ax1.fill([aa2[0], aa2[1], aa2[1], aa2[0], aa2[0]],
[aa2[2], aa2[2], aa2[3], aa2[3], aa2[2]],
'orange',
alpha=.3)
ax2.axis(aa2)
ax2.grid(True)
ax2.set_ylabel('Transport $[1000\ m^{3}s^{-1}]$')
pfun.add_coast(ax3, color='gray')
pfun.dar(ax3)
ax3.axis(aa3)
ax3.set_xticks([])
ax3.set_yticks([])
# just do these things once
for sn in sect_df.index:
x0, x1, y0, y1, landward = sect_df.loc[sn, :]
ax3.text(x1,
y1,
sn,
fontsize=.6 * fs,
color='k',
va='center',
ha='center',
rotation=45,
style='italic',
for sn in sect_list:
sn = sn.upper()
ax1.text(dist[counter], qin[counter]+10, sn, rotation=45, fontsize=8)
counter += 1
ax2.fill_between(dist, sout, y2=sin, color=lcol, alpha=.8)
ax2.set_xlim(0,distmax)
ax2.grid(True)
ax2.set_xlabel('Distance from Mouth (km)', fontsize=fs)
ax2.set_ylabel('$S_{in},\ S_{out}\ (psu)$', fontsize=fs)
#ax2.legend()
if lstr == 'JdF to South Sound':
aa = [-125.5, -122, 46.7, 50.3]
pfun.add_coast(ax3)
pfun.dar(ax3)
ax3.axis(aa)
ax3.grid(True)
plotit(ax3, sect_df, sect_list, lcol)
# add rivers
#%% get river info
ri_fn = Gr['ri_dir'] + 'river_info.csv'
df = pd.read_csv(ri_fn, index_col='rname')
for rn in df.index:
try:
fn_tr = Gr['ri_dir'] + 'tracks/' + rn + '.csv'
df_tr = pd.read_csv(fn_tr, index_col='ind')
x = df_tr['lon'].values
y = df_tr['lat'].values
linestyle='None')
#ax1.plot(lon0[:nt:24,tma],lat0[:nt:24,tma],'-k',alpha=0.2,linewidth=0.5)
ax1.set_title('Plan view', fontweight='bold')
ax2 = fig.add_subplot(1, 3, 3)
a_lz = [lonr.min() + lpad, 1.2, -60, 5]
p = ax2.pcolormesh(lonr[:NZ, :],
zr[:, midlat, :].squeeze(),
crosssalt,
vmin=0,
vmax=30,
cmap=cmap,
alpha=1.0)
plt.colorbar(p)
ax2.axis(a_lz)
pfun.dar(ax2)
ax2.set_aspect(1 / 80)
ax2.set_xlabel('Longitude', fontweight='bold')
ax2.set_ylabel('z (m)', fontweight='bold')
#plot particles
#ax2.plot(lon,z,'og',alpha=0.3)
ax2.plot(lon1,
z1,
marker='o',
color='black',
markerfacecolor='magenta',
alpha=1.0,
linestyle='None')
#ax2.plot(lon0[:nt:24,tma],z0[:nt:24,tma],'-k',alpha=0.2,linewidth=0.5)
ax2.set_title('Cross shelf section', fontweight='bold')
def P_nest_plot(in_dict):
# plots a field nested inside the corresponding HYCOM field
# - currently only works for salt and temp -
# START
fig = plt.figure(figsize=figsize)
ds = nc.Dataset(in_dict['fn'])
vlims = in_dict['vlims'].copy()
out_dict['vlims'] = vlims
# PLOT CODE
# ** choose the variable to plot **
which_var = 'temp'
# need to get Ldir, which means unpacking gtagex
fn = in_dict['fn']
gtagex = fn.split('/')[-3]
gtx_list = gtagex.split('_')
import Lfun
Ldir = Lfun.Lstart(gtx_list[0], gtx_list[1])
# Get HYCOM field
hyvar_dict = {'temp': 't3d', 'salt': 's3d'}
hvar = hyvar_dict[which_var]
f_string = fn.split('/')[-2]
fnhy = (Ldir['LOo'] + Ldir['gtag'] + '/' + f_string + '/ocn/Data/' + hvar +
'.nc')
ds = nc.Dataset(fnhy)
lon = ds.variables['lon'][:]
lat = ds.variables['lat'][:]
z = ds.variables['z'][:]
# make sure zlev is in the HYCOM z
zlev = zfun.find_nearest(z, in_dict['z_level'])
nz = list(z).index(zlev)
laym_hy = ds.variables[hvar + '_filt'][0, nz, :, :].squeeze()
vlims = pfun.auto_lims(laym_hy)
ds.close()
aa = [lon.min(), lon.max(), lat.min(), lat.max()]
# Get ROMS field
ds = nc.Dataset(fn)
zfull = pfun.get_zfull(ds, fn, 'rho')
laym = pfun.get_laym(ds, zfull, ds['mask_rho'][:], which_var, zlev)
# PLOTTING
cmap = plt.get_cmap(name='gist_ncar')
plt.close()
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=figsize, squeeze=False)
# panel 1
ax = axes[0, 0]
ax.set_title('HYCOM: z=' + str(zlev) + 'm, var=' + which_var)
cs = ax.pcolormesh(lon,
lat,
laym_hy,
vmin=vlims[0],
vmax=vlims[1],
cmap=cmap)
ax.axis(aa)
fig.colorbar(cs, ax=ax)
pfun.add_bathy_contours(ax, ds)
pfun.add_coast(ax)
pfun.dar(ax)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
# panel 2
ax = axes[0, 1]
ax.set_title('HYCOM + ROMS')
cs = ax.pcolormesh(lon,
lat,
laym_hy,
vmin=vlims[0],
vmax=vlims[1],
cmap=cmap)
ax.axis(aa)
laymd = laym.data
laymd[laym.mask] = 1e6
ax.pcolormesh(ds['lon_psi'][:],
ds['lat_psi'][:],
laymd[1:-1, 1:-1],
vmin=vlims[0],
vmax=vlims[1],
cmap=cmap)
fig.colorbar(cs, ax=ax)
pfun.add_bathy_contours(ax, ds)
pfun.add_coast(ax)
pfun.dar(ax)
ax.set_xlabel('Longitude')
# FINISH
ds.close()
if len(in_dict['fn_out']) > 0:
plt.savefig(in_dict['fn_out'])
plt.close()
else:
plt.show()
return out_dict
def P_basic(in_dict):
# This creates, and optionally saves, a basic plot of surface fields
# from a ROMS history file.
# INPUT a dict containing:
# fn: text string with the full path name of the history file to plot
# fn_out: text string with full path of output file name
# in_dict: a tuple with optional information to pass to the plot
# OUTPUT: either a screen image or a graphics file, and a dict of other
# information such as axis limits.
# START
fig = plt.figure(figsize=figsize)
ds = nc.Dataset(in_dict['fn'])
vlims = in_dict['vlims'].copy()
out_dict['vlims'] = vlims
# PLOT CODE
# panel 1
t_str = 'Surface Salinity'
ax = fig.add_subplot(121)
vn = 'salt'
cs, out_dict['vlims'][vn] = pfun.add_map_field(ax,
ds,
vn,
vlims=vlims[vn],
cmap='rainbow')
fig.colorbar(cs)
pfun.add_bathy_contours(ax, ds)
pfun.add_coast(ax)
ax.axis(pfun.get_aa(ds))
pfun.dar(ax)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
ax.set_title(t_str)
pfun.add_info(ax, in_dict['fn'])
pfun.add_windstress_flower(ax, ds)
# panel 2
t_str = 'Surface Temperature'
ax = fig.add_subplot(122)
vn = 'temp'
cs, out_dict['vlims'][vn] = pfun.add_map_field(ax,
ds,
vn,
vlims=vlims[vn],
cmap='bwr')
fig.colorbar(cs)
pfun.add_bathy_contours(ax, ds)
pfun.add_coast(ax)
ax.axis(pfun.get_aa(ds))
pfun.dar(ax)
ax.set_xlabel('Longitude')
ax.set_title(t_str + ' (' + pfun.get_units(ds, vn) + ')')
pfun.add_velocity_vectors(ax, ds, in_dict['fn'])
# FINISH
ds.close()
if len(in_dict['fn_out']) > 0:
plt.savefig(in_dict['fn_out'])
plt.close()
else:
plt.show()
return out_dict
def P_layer(in_dict):
# START
fig = plt.figure(figsize=figsize)
ds = nc.Dataset(in_dict['fn'])
vlims = in_dict['vlims'].copy()
out_dict['vlims'] = vlims
# PLOT CODE
zfull = pfun.get_zfull(ds, in_dict['fn'], 'rho')
# panel 1
t_str = 'Salinity'
ax = fig.add_subplot(121)
vn = 'salt'
laym = pfun.get_laym(ds, zfull, ds['mask_rho'][:], vn, in_dict['z_level'])
if len(vlims[vn]) == 0:
vlims[vn] = pfun.auto_lims(laym)
out_dict['vlims'][vn] = vlims[vn]
cs = ax.pcolormesh(ds['lon_psi'][:],
ds['lat_psi'][:],
laym[1:-1, 1:-1],
vmin=vlims[vn][0],
vmax=vlims[vn][1],
cmap='rainbow')
cb = fig.colorbar(cs)
cb.formatter.set_useOffset(False)
cb.update_ticks()
pfun.add_bathy_contours(ax, ds)
pfun.add_coast(ax)
ax.axis(pfun.get_aa(ds))
pfun.dar(ax)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
ax.set_title(t_str + ' on Z = ' + str(in_dict['z_level']) + ' m')
pfun.add_info(ax, in_dict['fn'])
pfun.add_windstress_flower(ax, ds)
# panel 2
t_str = 'Temperature'
ax = fig.add_subplot(122)
vn = 'temp'
laym = pfun.get_laym(ds, zfull, ds['mask_rho'][:], vn, in_dict['z_level'])
if len(vlims[vn]) == 0:
vlims[vn] = pfun.auto_lims(laym)
out_dict['vlims'][vn] = vlims[vn]
cs = ax.pcolormesh(ds['lon_psi'][:],
ds['lat_psi'][:],
laym[1:-1, 1:-1],
vmin=vlims[vn][0],
vmax=vlims[vn][1],
cmap='bwr')
cb = fig.colorbar(cs)
cb.formatter.set_useOffset(False)
cb.update_ticks()
pfun.add_bathy_contours(ax, ds)
pfun.add_coast(ax)
ax.axis(pfun.get_aa(ds))
pfun.dar(ax)
ax.set_xlabel('Longitude')
ax.set_title(t_str + ' (' + pfun.get_units(ds, vn) + ')')
# FINISH
ds.close()
if len(in_dict['fn_out']) > 0:
plt.savefig(in_dict['fn_out'])
plt.close()
else:
plt.show()
return out_dict
def P_bio(in_dict):
# START
ds = nc.Dataset(in_dict['fn'])
vlims = in_dict['vlims'].copy()
out_dict['vlims'] = vlims
vn_list = ['NO3', 'phytoplankton', 'zooplankton', 'oxygen']
cmap_list = ['Oranges', 'Greens', 'BuPu', 'Spectral']
cmap_dict = dict(zip(vn_list, cmap_list))
NP = len(vn_list)
if False:
NR = np.maximum(1, np.ceil(np.sqrt(NP)).astype(int))
NC = np.ceil(np.sqrt(NP)).astype(int)
figsize = (16, 16)
else:
NR = 1
NC = NP
figsize = (23, 7)
fig, axes = plt.subplots(nrows=NR,
ncols=NC,
figsize=figsize,
squeeze=False)
cc = 0
for vn in vn_list:
ir = int(np.floor(cc / NC))
ic = int(cc - NC * ir)
# PLOT CODE
ax = axes[ir, ic]
t_str = vn
try:
vlims[vn]
except KeyError:
vlims[vn] = ()
if vn == 'oxygen':
cs, out_dict['vlims'][vn] = pfun.add_map_field(ax,
ds,
vn,
vlims=vlims[vn],
cmap=cmap_dict[vn],
slev=0)
ax.text(.95,
.04,
'BOTTOM',
horizontalalignment='right',
transform=ax.transAxes)
else:
cs, out_dict['vlims'][vn] = pfun.add_map_field(ax,
ds,
vn,
vlims=vlims[vn],
cmap=cmap_dict[vn])
fig.colorbar(cs, ax=ax)
pfun.add_bathy_contours(ax, ds)
pfun.add_coast(ax)
ax.axis(pfun.get_aa(ds))
pfun.dar(ax)
ax.set_title(t_str + ' (' + pfun.get_units(ds, vn) + ')')
if ir == NR - 1:
ax.set_xlabel('Longitude')
if ic == 0:
ax.set_ylabel('Latitude')
ax.set_title(t_str + ' (' + pfun.get_units(ds, vn) + ')')
if cc == 0:
pfun.add_info(ax, in_dict['fn'])
cc += 1
# FINISH
ds.close()
if len(in_dict['fn_out']) > 0:
plt.savefig(in_dict['fn_out'])
plt.close()
else:
plt.show()
return out_dict
ax3.quiver(sx,sy, vq0, vq1, scale=scl, scale_units='height',
linewidths=1, color=cq, alpha=aq)
if sx < x00 or sy > y11:
ax3.text(sx, sy+.04, str(int(np.abs(Q))), ha='center', va='center', size=ffs,
weight='bold', color=cq, alpha=1)
if sx > x00 and sy < y11:
ax4.quiver(sx,sy, vq0, vq1, scale=scl, scale_units='height',
linewidths=1, color=cq, alpha=aq)
ax4.text(sx, sy+.02, str(int(np.abs(Q))), ha='center', va='center', size=ffs,
weight='bold', color=cq, alpha=1)
pfun.add_coast(ax1, color='gray')
ax1.axis(aaS)
pfun.dar(ax1)
ax1.set_xticklabels([])
ax1.tick_params(labelsize=.8*fs)
ax1.text(.95,.9,'(a)', size=fs, transform=ax1.transAxes, ha='right', weight='bold')
ax1.set_ylabel('Latitude', size=.8*fs)
ax1.set_xticks([-125, -124, -123, -122])
ax1.text(.05,.05,'Tidal energy Flux $[MW]$', size=.8*fs, transform=ax1.transAxes,
weight='bold', color=cf)
pfun.add_coast(ax2, color='gray')
ax2.axis(aaP)
pfun.dar(ax2)
ax2.set_xticklabels([])
ax2.set_yticklabels([])
ax2.tick_params(labelsize=.8*fs)
ax2.text(.95,.9,'(b)', size=fs, transform=ax2.transAxes, ha='right', weight='bold')
