# -*- coding: utf8 -*-
from vcmq import cdms2, curve2, map2, data_sample, N, P
# Curve
f = cdms2.open(data_sample('mars3d.t.nc'))
sst = f('temp')
f.close()
c = curve2(sst, subplot=211, date_fmt='%b %d', date_locator='day', vmin=11.8,
date_minor_locator='hour/6', show=False)
# Add a letter for subfigures like "a)"
c.add_key(1)
# Add a simple text
c.add_text(0.5, 0.5, 'Simple text', shadow=True)
c.add_text('2008/01/08', 12.0, 'Simple text 2', size=15, weight='bold',
glow=True, transform='data')
c.add_text(0.1, 0.5, 'Simple text with box', rotation=90., va='center',
bbox=dict(boxstyle='round4,pad=0.5', facecolor='r', alpha=0.5, linewidth=2))
# Add a label to the right/left/top/bottom
c.add_right_label('Right label', pos=0.05, family='monospace')
# Add lon/lat/time label
c.add_lon_label(0.1, 0.9, -5.6, color='b')
imin = N.argmin(sst)
tmin = sst.getTime().asComponentTime()[imin]
vmin = sst[imin]
c.add_time_label(tmin, vmin, tmin, fmt='Min at%n%H:%M %d/%m/%Y', va='top', ha='center',
color='g', transform='data')
sst2d = f('TEMP', time=slice(0, 1), level=slice(-1, None), squeeze=1)
f.close()
# 1D
# - filtrages
sst1d_gen13 = generic1d(sst1d, 13)
sst1d_gen3 = generic1d(sst1d, [1., 1., 1.])
sst1d_sha = shapiro1d(sst1d) # -> SHAPIRO AVEC GENERIC1D
sst1d_bar13 = bartlett1d(sst1d, 13)
# -> TESTEZ GAUSSIEN SUR 13 HEURES
# - plots
curve2(sst1d, 'k', label='Original', show=False, figsize=(12, 5))
curve2(sst1d_gen13, 'r', label='Generic 13 pts', show=False)
curve2(sst1d_gen3, 'g', label='Generic 3 pts', show=False)
curve2(sst1d_sha, 'b', label='shapiro', show=False)
curve2(sst1d_bar13, 'm', label='Bartlett', legend=True)
# -> MASQUEZ UNE PARTIE DES DONNEES ET JOUEZ AVEC LE PARAMETRE MASK=
# -> LISEZ UN BLOCK 3D ET FILTREZ LE SUIVANT LE TEMPS
# 2D
# - filtrage
sst2d_gen13 = generic2d(sst2d, 13)
# - ouverture du fichier et infos temporelles
f = cdms2.open(data_sample('mars3d.xt.xe.nc'))
ctimes = f['xe'].getTime().asComponentTime()
# - passage par un accumulateur de stats
sa = StatAccum(tmean=True)
# Boucle sur des intervals journaliers
for itv in Intervals((ctimes[0], ctimes[-1], 'cc'),
'day'): # -> ESSAYER 2 JOURS
# Lecture
print itv
tmp = f('xe', time=itv)
# Accumulation
sa += tmp
del tmp
# Finalisation
# - restitution
xem = sa.get_tmean()
print N.ma.allclose(xem.asma(), f('xe').mean(axis=0))
curve2(xem)
del sa
# - fermeture
f.close()
-8,
va='center',
ha='center',
transform='data',
fmt='%g')
m.add_lat_label(-8, 47, 47, va='center', ha='center', transform='data')
m.add_time_label(.1, .9, '2000')
# Add place
m.add_place(-2, 49, 'My place', text_family='monospace', text_size=15)
# Add lines
m.add_line([-8, 46, -1, 50], color='b')
m.add_lines([-8, -1, -4, -8], [45, 45, 47, 45],
color='r',
linewidth=2,
shadow=True)
m.add_box([-8, 46, -1, 50], color='c')
m.savefig(figfile % 0)
m.close()
# Test with dates
var = MV2.array([3, 6])
time = create_time(['2000', '2005'], units='years since 2000')
var.setAxis(0, time)
c = curve2(var, show=False)
c.add_point('2001', 4, marker='+', size=100, linewidth=2)
c.savefig(figfile % 1)
m.close()
P.xlabel("Lag [hour]")
P.ylabel("Error [m s-1]")
add_key(1)
P.title("Linear lag error model")
# Interpolation
sph, speh = regrid1d(sp, taxo, method="cellerr", erri=spe, errl=-a, geterr=True)
# Time zoom for plot clarity
tzoom = (ct1.sub(7, cdtime.Hour), ctimesi[-1])
sp = sp(tzoom)
spe = spe(tzoom)
sph = sph(tzoom)
speh = speh(tzoom)
# Main plot
curve2(sp, "o", err=spe.asma() / 2.0, markersize=2, ymin=-0.4, ymax=0.1, show=False, subplot=212, label="Original")
curve2(
sph,
"-r",
err=speh.asma() / 2.0,
linewidth=1.5,
show=False,
key=2,
savefigs=__file__,
label="Interpolated",
legend=True,
tight_layout=True,
legend_loc="lower right",
)
ncfile = "swan.four.nc"
lon0 = -5.1
lon1 = -4.9
lat0 = 48.35
lat1 = 48.55
# Imports
from vcmq import cdms2, data_sample, N, transect, curve2, code_file_name, os, \
add_map_lines, P, add_shadow
# Read data
f = cdms2.open(data_sample(ncfile))
hs = f('HS', squeeze=1)
f.close()
# Compute transect
hst, lons, lats = transect(hs, (lon0,lon1), (lat0,lat1), getcoords=True)
# Plot along time
s = curve2(hst, figsize=(8,3), title='Spatial transect on curved grid',
show=False, top=0.85)
# Add a small map to show the transect positions
o = add_map_lines(hs, lons, lats, map_bgcolor='w', map_bbox= [.6, .2, .3, .5], map_anchor='W')
# Save
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
s.savefig(figfile, pdf=True)
# -*- coding: utf8 -*-
# Read sea level at Brest
from vcmq import cdms2, P, curve2, savefigs, data_sample
f = cdms2.open(data_sample("tide.sealevel.BREST.mars.nc"))
sea_level = f('sea_level')
f.close()
# Filtering
from vacumm.tide.filters import demerliac
cotes, tide = demerliac(sea_level, get_tide=True)
# Plots
kwplot = dict(date_fmt='%d/%m', show=False, date_rotation=0)
# - tidal signal
curve2(sea_level, 'k', subplot=211, label='Original', title='Sea level at Brest',**kwplot)
curve2(tide, 'b', label='Tidal signal', **kwplot)
P.legend().legendPatch.set_alpha(.7)
# - surcotes/decotes
curve2(cotes, 'r', subplot=212, hspace=.3, label='Demerliac', **kwplot)
P.legend().legendPatch.set_alpha(.7)
savefigs(__file__, savefigs_pdf=True)
"""Test :func:`~vacumm.misc.plot.curve2` with an arbitrary axis""" # Imports from vcmq import MV2, code_file_name, os, code_file_name, curve2 # Init var = MV2.arange(5.) var.units = '$Kg$' var.long_name = 'Flour' axis = var.getAxis(0) axis.units = 'm' axis.long_name = 'Distance' # Plot figfile = code_file_name(ext='png') if os.path.exists(figfile): os.remove(figfile) curve2(var, savefig=figfile, show=False, close=True)
# Plot temperature
s = section2(
xtemp,
yaxis=xdepth,
ymin=-800,
fill='contourf',
nmax=20,
contour_linewidths=0.7,
bgcolor='0.5',
figsize=(7, 4),
cmap='thermal',
# cmap='vacumm_rnb2_hymex',
title='%(long_name)s (dens) along temp transect',
show=False)
# Plot MLD
curve2(xmld, 'w-', linewidth=2, show=False)
# Add a small map to show the transect positions
add_map_lines(temp[-1],
xlons,
xlats,
map_zoom=0.7,
color='k',
map_cmap='thermal')
# Save
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
s.savefig(figfile, pdf=True)
"""Test :func:`~vacumm.misc.plot.curve2` with a latitude axis""" # Imports from vcmq import MV2, code_file_name, os, code_file_name, curve2, create_lat # Init var = MV2.arange(5.) var.units = 'mm' var.long_name = 'Precipitation' create_lat(var.getAxis(0)) # Plot figfile = code_file_name(ext='png') if os.path.exists(figfile): os.remove(figfile) curve2(var, savefig=figfile, show=False, close=True, latex_units=True)
# Imports
from vcmq import cdms2, data_sample, N, transect, curve2, code_file_name, os, \
add_map_lines, P, add_shadow
# Read data
f = cdms2.open(data_sample(ncfile))
hs = f('HS', squeeze=1)
f.close()
# Compute transect
hst, lons, lats = transect(hs, (lon0, lon1), (lat0, lat1), getcoords=True)
# Plot along time
s = curve2(hst,
figsize=(8, 3),
title='Spatial transect on curved grid',
show=False,
top=0.85)
# Add a small map to show the transect positions
o = add_map_lines(hs,
lons,
lats,
map_bgcolor='w',
map_bbox=[.6, .2, .3, .5],
map_anchor='W')
# Save
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
s.savefig(figfile, pdf=True)
kwplot[att] = getattr(args, att)
for att in ('color', 'linewidth'):
val = getattr(args, att)
if val is not None:
kwplot[att] = val
kwplot['quiver_norm'] = args.qnorm
if len(vv)==2 and args.qonly:
kwplot['contour'] = kwplot['fill'] = False
if args.figsize:
kwplot['figsize'] = tuple([float(v) for v in args.figsize.split(',')])
if args.res=='off':
args.res = None
kwplot['res'] = args.res
#del kwplot['color']
#kwplot = {'color':None}
# Adaptative plot
if ndim==2:
if order in ['yx', 'xy']:
map2(vv, **kwplot)
elif 't' in order:
hov2(vv, **kwplot)
else:
plot2d(vv, **kwplot)
else:
if len(vv)>1:
stick2(*vv, **kwplot)
else:
curve2(vv, **kwplot)
kwplot[att] = getattr(args, att)
for att in ('color', 'linewidth'):
val = getattr(args, att)
if val is not None:
kwplot[att] = val
kwplot['quiver_norm'] = args.qnorm
if len(vv) == 2 and args.qonly:
kwplot['contour'] = kwplot['fill'] = False
if args.figsize:
kwplot['figsize'] = tuple([float(v) for v in args.figsize.split(',')])
if args.res == 'off':
args.res = None
kwplot['res'] = args.res
#del kwplot['color']
#kwplot = {'color':None}
# Adaptative plot
if ndim == 2:
if order in ['yx', 'xy']:
map2(vv, **kwplot)
elif 't' in order:
hov2(vv, **kwplot)
else:
plot2d(vv, **kwplot)
else:
if len(vv) > 1:
stick2(*vv, **kwplot)
else:
curve2(vv, **kwplot)
station = StationInfo('Brest')
print station.attributes()
print station.name, station.longitude
print 'Niveau moyen a Brest:', station.nm
# Read sea level at Brest
f = cdms2.open(data_sample("tide.sealevel.BREST.mars.nc"))
sea_level = f('sea_level')
f.close()
# Surcotes/decotes
cotes, tide = demerliac(sea_level, get_tide=True) # -> ESSAYER GODIN
kwp = dict(date_fmt='%b', date_locator='month')
curve2(sea_level, 'b', show=False, figsize=(15, 4), **kwp)
curve2(tide, 'r', **kwp)
curve2(cotes, figsize=(15, 4), **kwp)
# Extremas
slzoom1 = sea_level(('2006-10-01', '2006-10-02'))[::4] # Toutes les heures
bm, pm = extrema(slzoom1, spline=True, ref='mean') # -> SANS SPLINES
zz = zeros(slzoom1, ref='mean') # -> AUTRES REFERENCE ?
curve2(slzoom1, 'ko', markersize=3, figsize=(6, 4), show=False)
curve2(zz, 'go', linewidth=0, show=False, xstrict=False)
curve2(pm, 'ro', linewidth=0, show=False, xstrict=False)
curve2(bm, 'bo', linewidth=0, xstrict=False, title="Niveau de la mer")
# Outil marégraphique
# -*- coding: utf8 -*-
from vcmq import cdms2, curve2, map2, data_sample, N, P
# Curve
f = cdms2.open(data_sample('mars3d.t.nc'))
sst = f('temp')
f.close()
c = curve2(sst,
subplot=211,
date_fmt='%b %d',
date_locator='day',
vmin=11.8,
date_minor_locator='hour/6',
show=False)
# Add a letter for subfigures like "a)"
c.add_key(1)
# Add a simple text
c.add_text(0.5, 0.5, 'Simple text', shadow=True)
c.add_text('2008/01/08',
12.0,
'Simple text 2',
size=15,
weight='bold',
glow=True,
transform='data')
c.add_text(0.1,
0.5,
'Simple text with box',
rotation=90.,
f.close()
f = cdms2.open(data_sample('menor.nc'))
sst2d = f('TEMP', time=slice(0, 1), level=slice(-1, None), squeeze=1)
f.close()
# 1D
# - filtrages
sst1d_gen13 = generic1d(sst1d, 13)
sst1d_gen3 = generic1d(sst1d, [1., 1., 1.])
sst1d_sha = shapiro1d(sst1d) # -> SHAPIRO AVEC GENERIC1D
sst1d_bar13 = bartlett1d(sst1d, 13)
# -> TESTEZ GAUSSIEN SUR 13 HEURES
# - plots
curve2(sst1d, 'k', label='Original', show=False, figsize=(12, 5))
curve2(sst1d_gen13, 'r', label='Generic 13 pts', show=False)
curve2(sst1d_gen3, 'g', label='Generic 3 pts', show=False)
curve2(sst1d_sha, 'b', label='shapiro', show=False)
curve2(sst1d_bar13, 'm', label='Bartlett', legend=True)
# -> MASQUEZ UNE PARTIE DES DONNEES ET JOUEZ AVEC LE PARAMETRE MASK=
# -> LISEZ UN BLOCK 3D ET FILTREZ LE SUIVANT LE TEMPS
# 2D
# - filtrage
sst2d_gen13 = generic2d(sst2d, 13)
sst2d_gau13 = gaussian2d(sst2d, 13)
# Interpolations
for p in 'u', 'v', 'f', 'w':
var[p] = ag.interp(var['t'], 't', p, mode='extrap')
# Surface plots
vmin, vmax = minmax(*[var[p][-1] for p in ['u', 'v', 'f']])
kw = dict(show=False, res=None, vmin=vmin, vmax=vmax, colorbar=False, grid=False, cmap='jet')
m = map2(var['t'][-1], fill='pcolor',
title='Interpolations on an Arakawa C grid: T->U/V/F', **kw)
add_grid(var['t'], linestyle='-')
kw.update(fill='scatter', contour=False, fill_s=60)
markers = dict(u='>', v='^', f='D', t='o')
for p in 't', 'u', 'v', 'f':
m = map2(var[p][-1], fill_marker=markers[p], shadow=True, zorder=100, **kw)
m.savefig(code_file_name(ext='_1.png'))
m.close()
# Vertical plot
curve2(var['t'][:, 0, 0], 'o-b', ymax=0, show=False,
title='Interpolations on an Arakawa C grid: T->W')
curve2(var['w'][:, 0, 0], '^r', show=False, savefig=code_file_name(ext='_2.png'))
result = [
('assertEqual', [var['t'][0, 0, :2].mean(), var['u'][0, 0, 0]]),
('assertEqual', [var['t'][0, :2, 0].mean(), var['v'][0, 0, 0]]),
('assertEqual', [var['t'][0, :2, :2].mean(), var['f'][0, 0, 0]]),
('assertEqual', [var['t'][:2, 0, 0].mean(), var['w'][0, 0, 0]]),
]
# - ouverture du fichier et infos temporelles
f = cdms2.open(data_sample("mars3d.xt.xe.nc"))
ctimes = f["xe"].getTime().asComponentTime()
# - passage par un accumulateur de stats
sa = StatAccum(tmean=True)
# Boucle sur des intervals journaliers
for itv in Intervals((ctimes[0], ctimes[-1], "cc"), "day"): # -> ESSAYER 2 JOURS
# Lecture
print itv
tmp = f("xe", time=itv)
# Accumulation
sa += tmp
del tmp
# Finalisation
# - restitution
xem = sa.get_tmean()
print N.ma.allclose(xem.asma(), f("xe").mean(axis=0))
curve2(xem)
del sa
# - fermeture
f.close()
# Add text
m.add_text(0.1, 0.1, 'My text', color='r', shadow=True, weight='bold')
m.add_text(-2, 50.5, 'My text with transform', transform='data', ha='center',
bbox=dict(facecolor='y', alpha=.3))
m.add_lon_label(-8, 47.5, -8, va='center', ha='center', transform='data', fmt='%g')
m.add_lat_label(-8, 47, 47, va='center', ha='center', transform='data')
m.add_time_label(.1, .9, '2000')
# Add place
m.add_place(-2, 49, 'My place', text_family='monospace', text_size=15)
# Add lines
m.add_line([-8, 46, -1, 50], color='b')
m.add_lines([-8, -1, -4, -8], [45, 45, 47, 45], color='r', linewidth=2, shadow=True)
m.add_box([-8, 46, -1, 50], color='c')
m.savefig(figfile%0)
m.show()
m.close()
# Test with dates
var = MV2.array([3, 6])
time = create_time(['2000', '2005'], units='years since 2000')
var.setAxis(0, time)
c = curve2(var, show=False)
c.add_point('2001', 4, marker='+', size=100, linewidth=2)
c.savefig(figfile%1)
m.close()
"""Test :func:`~vacumm.misc.plot.curve2` with a time axis""" # Imports from vcmq import MV2, code_file_name, os, code_file_name, curve2 # Init var = MV2.arange(5.) var.units = 'W' var.long_name = 'Power' axis = var.getAxis(0) axis.units = 'months since 2013-01-01' axis.long_name = 'Time' axis.axis = 'T' # Plot figfile = code_file_name(ext='png') if os.path.exists(figfile): os.remove(figfile) curve2(var, savefig=figfile, show=True, close=True)
a, b, _, _, _ = linregress(lags, els)
# - plot
P.figure(figsize=(6, 6))
P.subplot(211)
P.plot(lags, els, 'o')
P.plot([0, lags[-1]], [b, a*lags[-1]+b], 'g')
P.axhline(b, color='0.8', ls='--')
P.ylim(ymin=0)
P.xlabel('Lag [hour]')
P.ylabel('Error [m s-1]')
add_key(1)
P.title('Linear lag error model')
# Interpolation
sph, speh = regrid1d(sp, taxo, method='cellerr', erri=spe, errl=-a, geterr=True)
# Time zoom for plot clarity
tzoom = (ct1.sub(7, cdtime.Hour), ctimesi[-1])
sp = sp(tzoom)
spe = spe(tzoom)
sph = sph(tzoom)
speh = speh(tzoom)
# Main plot
curve2(sp, 'o', err=spe.asma()/2., markersize=2, ymin=-.4, ymax=.1,
show=False, subplot=212, label='Original')
curve2(sph, '-r', err=speh.asma()/2., linewidth=1.5, show=False, key=2,
savefigs=__file__, label='Interpolated', legend=True, tight_layout=True,
legend_loc='lower right')
errl=-a,
geterr=True)
# Time zoom for plot clarity
tzoom = (ct1.sub(7, cdtime.Hour), ctimesi[-1])
sp = sp(tzoom)
spe = spe(tzoom)
sph = sph(tzoom)
speh = speh(tzoom)
# Main plot
curve2(sp,
'o',
err=spe.asma() / 2.,
markersize=2,
ymin=-.4,
ymax=.1,
show=False,
subplot=212,
label='Original')
curve2(sph,
'-r',
err=speh.asma() / 2.,
linewidth=1.5,
show=False,
key=2,
savefigs=__file__,
label='Interpolated',
legend=True,
tight_layout=True,
legend_loc='lower right',
"""Test :func:`~vacumm.misc.plot.curve2` with a call to :func:`~vacumm.misc.plot.hlitvs`"""
# Imports
from vcmq import MV2, code_file_name, os, code_file_name, curve2
# Init
var = MV2.arange(5.0)
var.units = "W"
var.long_name = "Power"
axis = var.getAxis(0)
axis.units = "days since 2013-01-01"
axis.long_name = "Time"
axis.axis = "T"
# Plot
figfile = code_file_name(ext="png")
if os.path.exists(figfile):
os.remove(figfile)
curve2(var, savefig=figfile, show=True, close=True, hlitvs=True)
# Stations
station = StationInfo('Brest')
print station.attributes()
print station.name, station.longitude
print 'Niveau moyen a Brest:', station.nm
# Read sea level at Brest
f = cdms2.open(data_sample("tide.sealevel.BREST.mars.nc"))
sea_level = f('sea_level')
f.close()
# Surcotes/decotes
cotes, tide = demerliac(sea_level, get_tide=True) # -> ESSAYER GODIN
kwp = dict(date_fmt='%b', date_locator='month')
curve2(sea_level, 'b', show=False, figsize=(15, 4), **kwp)
curve2(tide, 'r', **kwp)
curve2(cotes, figsize=(15, 4), **kwp)
# Extremas
slzoom1 = sea_level(('2006-10-01', '2006-10-02'))[::4] # Toutes les heures
bm, pm = extrema(slzoom1, spline=True, ref='mean') # -> SANS SPLINES
zz = zeros(slzoom1, ref='mean') # -> AUTRES REFERENCE ?
curve2(slzoom1, 'ko', markersize=3, figsize=(6, 4), show=False)
curve2(zz, 'go', linewidth=0, show=False, xstrict=False)
curve2(pm, 'ro', linewidth=0, show=False, xstrict=False)
curve2(bm, 'bo', linewidth=0, xstrict=False, title="Niveau de la mer")
# Outil marégraphique
slzoom2 = sea_level(('2006-10', '2006-11'))[::4] # Toutes les heures
mg = Marigraph(slzoom2, verbose=True)
cmap='jet')
m = map2(var['t'][-1],
fill='pcolor',
title='Interpolations on an Arakawa C grid: T->U/V/F',
**kw)
add_grid(var['t'], linestyle='-')
kw.update(fill='scatter', contour=False, fill_s=60)
markers = dict(u='>', v='^', f='D', t='o')
for p in 't', 'u', 'v', 'f':
m = map2(var[p][-1], fill_marker=markers[p], shadow=True, zorder=100, **kw)
m.savefig(code_file_name(ext='_1.png'))
m.close()
# Vertical plot
curve2(var['t'][:, 0, 0],
'o-b',
ymax=0,
show=False,
title='Interpolations on an Arakawa C grid: T->W')
curve2(var['w'][:, 0, 0],
'^r',
show=False,
savefig=code_file_name(ext='_2.png'))
result = [
('assertEqual', [var['t'][0, 0, :2].mean(), var['u'][0, 0, 0]]),
('assertEqual', [var['t'][0, :2, 0].mean(), var['v'][0, 0, 0]]),
('assertEqual', [var['t'][0, :2, :2].mean(), var['f'][0, 0, 0]]),
('assertEqual', [var['t'][:2, 0, 0].mean(), var['w'][0, 0, 0]]),
]
# -*- coding: utf8 -*-
# Read sea level at Brest
from vcmq import cdms2, P, curve2, savefigs, data_sample
f = cdms2.open(data_sample("tide.sealevel.BREST.mars.nc"))
sea_level = f('sea_level')
f.close()
# Filtering
from vacumm.tide.filters import demerliac
cotes, tide = demerliac(sea_level, get_tide=True)
# Plots
kwplot = dict(date_fmt='%d/%m', show=False, date_rotation=0)
# - tidal signal
curve2(sea_level,
'k',
subplot=211,
label='Original',
title='Sea level at Brest',
**kwplot)
curve2(tide, 'b', label='Tidal signal', **kwplot)
P.legend().legendPatch.set_alpha(.7)
# - surcotes/decotes
curve2(cotes, 'r', subplot=212, hspace=.3, label='Demerliac', **kwplot)
P.legend().legendPatch.set_alpha(.7)
savefigs(__file__, savefigs_pdf=True)
P.close()
mld = mixed_layer_depth(dens, depth=depth, mode='deltadens', format_axes=True)
del dens
# Compute transect
tlons = (lon0,lon1)
tlats = (lat0,lat1)
tlons = N.concatenate([N.linspace(lon0,lon1,100.),N.linspace(lon1,lon1,100.)])
tlats = N.concatenate([N.linspace(lat0,lat1,100.),N.linspace(lat1,lat0,100.)])
xtemp, xlons, xlats = transect(temp, tlons, tlats, getcoords=True, outaxis='dist')
xdepth = transect(depth, tlons, tlats)
xmld = transect(mld, tlons, tlats)
xmld[:]*=-1
# Plot temperature
s = section2(xtemp, yaxis=xdepth, ymin=-800, fill='contourf', nmax=20,
contour_linewidths=0.7, bgcolor='0.5', figsize=(7,4),
cmap='vacumm_rnb2_hymex',
title='%(long_name)s (dens) along temp transect', show=False)
# Plot MLD
curve2(xmld, 'w-', linewidth=2, show=False)
# Add a small map to show the transect positions
add_map_lines(temp[-1], xlons, xlats, map_zoom=0.7, color='k')
# Save
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
s.savefig(figfile, pdf=True)
