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
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])
plt.close('all')
fig = plt.figure(figsize=(17,9))
fs = 18 # fontsize
# map field
ax = fig.add_subplot(131)
lon = ds['lon_psi'][:]
lat = ds['lat_psi'][:]
v =ds[vn][0, -1, 1:-1, 1:-1]
fac=pinfo.fac_dict[vn]
vv = fac * v
vv[:, :6] = np.nan
vv[:6, :] = np.nan
cs = ax.pcolormesh(lon, lat, vv, vmin=vlims[0], vmax=vlims[1], cmap=cmap)
pfun.add_coast(ax)
ax.axis(pfun.get_aa(ds))
pfun.dar(ax)
ax.set_axis_off()
# add a box for the subplot
aa = [-123.5, -122.1, 47.03, 48.8]
pfun.draw_box(ax, aa, color='c', alpha=.5, linewidth=5, inset=.01)
ax.text(.95, .07, 'LiveOcean\nPHYTOPLANKTON\n'
+ datetime.strftime(T['tm'], '%m/%d/%Y'), fontsize=fs, color='k',
transform=ax.transAxes, horizontalalignment='center',
fontweight='bold')
# PS map
# map field
#ax = plt.subplot2grid((3,3), (1,2), rowspan=2)
ax = fig.add_subplot(132)
ds1.close()
ds2.close()
if testing:
# try plotting some results
import matplotlib.pyplot as plt
plt.close('all')
ds = nc.Dataset(out_fn)
plon = ds['lon_psi'][:]
plat = ds['lat_psi'][:]
mask = ds['mask_rho'][1:-1, 1:-1]
pth = os.path.abspath('../../plotting/')
if pth not in sys.path:
sys.path.append(pth)
import pfun
aa = pfun.get_aa(ds)
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 7))
vn_list = ['salt', 'temp', 'AKs', 'u', 'v', 'w']
rr = [0, 0, 0, 1, 1, 1]
cc = [0, 1, 2, 0, 1, 2]
ii = 0
for vn in vn_list:
ax = axes[rr[ii], cc[ii]]
vv = ds[vn][0, -1, 1:-1, 1:-1].squeeze()
vv[mask == 0] = np.nan
cs = ax.pcolormesh(plon, plat, vv, cmap='rainbow')
fig.colorbar(cs, ax=ax)
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
if False:
item = Lfun.choose_item(indir0)
else:
item = 'cas4_v2_lo6biom_2017.06.01_2017.07.01'
indir = indir0 + item + '/'
if False:
infile = Lfun.choose_item(indir, tag='.nc')
fn = indir + infile
else:
fn = indir + 'svar_hourly.nc'
ds = nc.Dataset(fn)
xp = ds['lon_psi'][:]
yp = ds['lat_psi'][:]
ot = ds['ocean_time'][:]
aa = pfun.get_aa(ds)
mix = ds['mix'][:]
mask= ds['mask_rho'][:] # 1 on water, 0 on land
DA = ds['DA'][:] # cell horizontal area
x = ds['lon_rho'][:]
y = ds['lat_rho'][:]
ds.close()
days = (ot - ot[0])/86400.
mdays = Lfun.modtime_to_mdate_vec(ot)
mdt = mdates.num2date(mdays) # list of datetimes of data
# neap time limits: centered on 2017.06.16
ii0_neap = 24*14
ii1_neap = 24*17
mmix_neap = mix[ii0_neap:ii1_neap,:,:].mean(axis=0)