def main(blkdatfile,
saltfile,
lon,
lat,
lon2=None,
lat2=None,
sectionid=None,
dpi=180):
logger.info("Salinity file:%s" % saltfile)
m = re.match("(.*)_s([0-9]{2})_([0-9a-z]{4}\.nc)", saltfile)
if m:
tempfile = m.group(1) + "_t" + m.group(2) + "_" + m.group(3)
else:
msg = "Cant deduce temp file from salt file name"
logger.error(msg)
raise ValueError, msg
logger.info("Temp file:%s" % saltfile)
sig0 = modeltools.hycom.Sigma(0)
sig2 = modeltools.hycom.Sigma(2)
# output filename info from season value
if int(m.group(2)) > 0 and int(m.group(2)) < 13:
sinfo = m.group(2)
elif int(m.group(2)) == 0:
sinfo = "year"
elif int(m.group(2)) == 13:
sinfo = "jfm"
elif int(m.group(2)) == 14:
sinfo = "amj"
elif int(m.group(2)) == 15:
sinfo = "jas"
elif int(m.group(2)) == 16:
sinfo = "ond"
if sectionid: sinfo = sinfo + "_%s" % sectionid
s_ncid = netCDF4.Dataset(saltfile, "r")
t_ncid = netCDF4.Dataset(tempfile, "r")
s_lon = s_ncid.variables["lon"][:]
s_lat = s_ncid.variables["lat"][:]
dprof = s_ncid["depth"][:]
nz = dprof.size
#Closest point
if lon2 is None or lat2 is None:
section = False
i = numpy.argmin(numpy.abs(numpy.mod(s_lon - lon + 360., 360.)))
j = numpy.argmin(numpy.abs(s_lat - lat))
salprof = s_ncid["s_an"][0, :, j, i]
temprof = t_ncid["t_an"][0, :, j, i]
salprof = numpy.expand_dims(salprof, axis=1)
temprof = numpy.expand_dims(temprof, axis=1)
# Section
else:
section = True
lon2d, lat2d = numpy.meshgrid(s_lon, s_lat)
sec = gridxsec.Section([lon, lon2], [lat, lat2], lon2d, lat2d)
i, j = sec.grid_indexes
salprof = s_ncid["s_an"][0, :, :, :]
temprof = t_ncid["t_an"][0, :, :, :]
salprof = salprof[:, j, i]
temprof = temprof[:, j, i]
# MAsk salinity and temperature profiles
salprof = numpy.ma.masked_where(salprof == s_ncid["s_an"]._FillValue,
salprof)
temprof = numpy.ma.masked_where(temprof == t_ncid["t_an"]._FillValue,
temprof)
# Interface values dprofi
dprofi = numpy.zeros(nz + 1)
for k in range(dprof.size):
if k < dprof.size - 1:
dprofi[k] = 0.5 * (dprof[k] + dprof[k + 1])
else:
dprofi[k] = dprof[k]
# MAx depth in data. TODO. Get from profiles
maxd = numpy.zeros(salprof[0, :].shape)
for k in range(nz):
maxd[~salprof[k, :].mask] = dprof[k]
logger.info("MAx depth in data is %d m" % numpy.max(maxd))
# Open blkdat.input
bp = modeltools.hycom.BlkdatParser(blkdatfile)
dp0k = bp["dp0k"]
ds0k = bp["ds0k"]
sigma = bp["sigma"]
kdm = bp["kdm"]
nhybrd = bp["nhybrd"]
nsigma = bp["nsigma"]
thflag = bp["thflag"]
kapref = bp["kapref"]
isotop = bp["isotop"]
eqstate = modeltools.hycom.Sigma(thflag)
#eqstate = modeltools.hycom.Sigma12Term(thflag) #Test 12 term sigma
#eqstate = modeltools.hycom.Sigma17Term(thflag) #Test 17 term sigma
sigprof = eqstate.sig(temprof, salprof)
sigprof = numpy.ma.masked_where(salprof.mask, sigprof)
# Thermobaricity
if kapref <> 0:
if thflag == 2:
mykappa = modeltools.hycom.Kappa(kapref, 2000.0e4)
elif thflag == 0:
mykappa = modeltools.hycom.Kappa(kapref, 0.)
else:
raise ValueError, "Unknown value of thflag"
tmp = mykappa.kappaf(salprof.transpose(), temprof.transpose(),
sigprof.transpose(), dprof * 9806)
#print tmp.shape,sigprof.shape
sigprof_tbar = sigprof - tmp.transpose()
# Min thickness interface values
intf, masks = bp.intf_min_profile(numpy.array(maxd))
dp0 = intf[:, 1:] - intf[:, :-1]
intfmid = (intf[:, 1:] + intf[:, :-1]) * .5
#print dp0[numpy.argmax(maxd),:],dp0.shape
# We have interface values, now use designated layer sigma values and actual sigma
# values to find an approximate vertical coordinate setup.
newintf, intsig = modeltools.tools.isopycnal_coordinate_layers(
dprofi, sigprof, dp0, numpy.array(sigma), isotop)
# TODO: Integrate over temp and sal profiles for plotting these
# Find segments where its ok to place layer legend
import scipy.ndimage.measurements
lab, num_features = scipy.ndimage.measurements.label(
maxd > numpy.max(maxd) * .25)
feat_count = {}
for i in range(num_features):
feat_count[i + 1] = numpy.count_nonzero(lab == i + 1)
tmp = sorted(feat_count.items(), key=lambda x: x[1],
reverse=True) # Order by feature count
main_feature = tmp[0][0]
ft_ind = []
for i_enum, i in enumerate(tmp):
#logger.info( "Feature %03d: %d cells" %(i[0],i[1]))
tmp = numpy.where(lab == i[0])
ft_ind.append(int(tmp[0].mean()))
#print ft_ind
#tmp=numpy.where(lab==main_feature)
#print tmp
#sec_xpind = int (tmp[0].mean())
#print sec_xpind
cols = "b"
colt = "r"
cold = "m"
# Plot vertical profile and layers. Two cases: section or not
if section:
f, ax = plt.subplots(1, figsize=(10, 5))
ax.set_title(
"sigma-%d layers from lon=%6.2f, lat=%6.2f to lon=%6.2f, lat=%6.2f"
% (thflag, lon, lat, lon2, lat2))
x = sec.distance / 1000.
ymult = 1.
xlim = [x.min(), x.max()]
else:
f, ax = plt.subplots(1)
ax.set_title("Sigma-%d profile and layers at lon=%6.2f, lat=%6.2f" %
(thflag, lon, lat))
x = numpy.array([-100, 100])
ymult = numpy.ones((2))
ax.plot(sigprof, -dprof, lw=2, color=cold, label="Sigma-%d" % thflag)
xlim = ax.get_xlim()
#print ax.get_position()
ax.set_position([.1, .1, .7, .7])
ylim = [-numpy.max(maxd) + 10, 0.]
for k in range(kdm + 1):
if k < kdm:
ax.plot(x,
-newintf[:, k] * ymult,
lw=.1,
color=".5",
label="layer %d: %6.3f" % (k + 1, sigma[k]))
if section:
##ind = x.size/2
#ind = numpy.argmax(maxd)
#xp = x[ind]
#yp = -0.5*(newintf[ind,k] + newintf[ind,k+1])
##ax.text(xp,yp,"layer %d: %4.2f"%(k+1,sigma[k]),verticalalignment="center",horizontalalignment="left",fontsize=6)
#ax.text(xp,yp,"%d"%(k+1),verticalalignment="center",horizontalalignment="left",fontsize=6)
for ind in ft_ind:
xp = x[ind]
yp = -0.5 * (newintf[ind, k] + newintf[ind, k + 1])
#ax.text(xp,yp,"layer %d: %4.2f"%(k+1,sigma[k]),verticalalignment="center",horizontalalignment="left",fontsize=6)
ax.text(xp,
yp,
"%d" % (k + 1),
verticalalignment="center",
horizontalalignment="left",
fontsize=6)
# TODO
#ind = x.size-x.size/6
#xp = x[ind]
#yp = -0.5*(newintf[ind,k] + newintf[ind,k+1])
#ax.annotate("layer %d: %4.2f"%(k+1,sigma[k]),
# xy=(xp, yp), xycoords='data',
# xytext=(1.01,0.0-k*.03), textcoords='axes fraction',
# arrowprops=dict(facecolor='b', shrink=0.01,alpha=.5,ec="none",width=1,headwidth=3,frac=.03),
# horizontalalignment='left', verticalalignment='center',
# fontsize=6
# )
else:
xp = numpy.sum(xlim) / 2.
yp = -0.5 * (newintf[0, k] + newintf[0, k + 1])
#ax.text(xp,yp,"layer %d: %4.2f"%(k+1,sigma[k]),verticalalignment="center",horizontalalignment="left",fontsize=6)
ax.text(xp,
yp,
"%d" % (k + 1),
verticalalignment="center",
horizontalalignment="left",
fontsize=6)
isopyc = numpy.abs(intsig[:, k] - sigma[k]) < 1e-3
ax.fill_between(x,
-newintf[:, k + 1] * ymult,
-newintf[:, k] * ymult,
color="g",
alpha=".5",
where=isopyc * ymult)
else:
# Plot sea floor
#ax.plot(x,-newintf[:,k]*ymult,lw=2,color="k",label="Sea floor")
ax.fill_between(x,
-newintf[:, k] * ymult,
-10 * numpy.ones(x.shape) * maxd.max(),
color=".5")
# # Some dummy plots for the legend
# ax.plot([-100,100],[1e5,1e5],lw=.1,color=".5",label="Layer interface")
# Set up final grid
ax.grid(False)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_ylabel("Depth[m]")
if section:
ax.set_xlabel("Distance along section[km]")
else:
ax.set_xlabel("Sigma-%d [$kg / m^3$]" % thflag, color=cold)
for t in ax.get_xticklabels():
if not section: t.set_color(cold)
t.set_size(8)
t.set_rotation(-45)
#ax.legend(loc="lower left")
leg = ax.legend(bbox_to_anchor=(1.05, 1),
loc=2,
borderaxespad=0.,
labelspacing=-.5)
ltext = leg.get_texts()
plt.setp(ltext, fontsize=4)
#
fname = "layers_%s.png" % sinfo
logger.info("Layers in %s" % fname)
plt.gcf().savefig(fname, dpi=dpi)
#
ax.set_ylim(-750, 0)
fname = "layers750_%s.png" % sinfo
logger.info("Layers for top 750m in %s" % fname)
plt.gcf().savefig("layers750_%s.png" % sinfo, dpi=dpi)
#
ax.set_ylim(-100, 0)
fname = "layers100_%s.png" % sinfo
logger.info("Layers for top 100m in %s" % fname)
plt.gcf().savefig("layers100_%s.png" % sinfo, dpi=dpi)
# Plot density of original data
f, ax = plt.subplots(1, figsize=(10, 5))
if section:
P = ax.pcolormesh(x, -dprof, sigprof, cmap="Paired")
CS = ax.contour(x, -dprof, sigprof, sigma)
ax.clabel(CS, inline=1, fontsize=4)
ax.set_xlabel("x[m]")
ax.set_ylabel("Depth [m]")
ax.set_title("Sigma-%d along section [$kg / m^3$]" % (thflag))
f.colorbar(P, ax=ax)
else:
ax.plot(sigprof, -dprof, lw=2, color=cold, label="Sigma-%d" % thflag)
ax.set_ylabel("Depth[m]")
ax.set_xlabel("Sigma-%d Density [$kg / m^3$]" % thflag, color=cold)
ax.set_title("Sigma-%d density profile at %6.2fE,%6.2fN" %
(thflag, lon, lat))
for t in ax.get_xticklabels():
t.set_color(cold)
#t.set_size(8)
#t.set_rotation(-45)
ax.grid(True)
f.savefig("dens_data_%s.png" % sinfo, dpi=dpi)
# Plot density of original data
if kapref > 0:
f, ax = plt.subplots(1, figsize=(10, 5))
if section:
P = ax.pcolormesh(x, -dprof, sigprof_tbar, cmap="Paired")
CS = ax.contour(x, -dprof, sigprof_tbar, sigma)
ax.clabel(CS, inline=1, fontsize=4)
ax.set_xlabel("x[m]")
ax.set_ylabel("Depth [m]")
ax.set_title("In-situ Sigma-%d along section [$kg / m^3$]" %
(thflag))
f.colorbar(P, ax=ax)
else:
ax.plot(sigprof_tbar,
-dprof,
lw=2,
color=cold,
label="Sigma-%d" % thflag)
ax.set_ylabel("Depth[m]")
ax.set_xlabel("Sigma-%d Density [$kg / m^3$]" % thflag, color=cold)
ax.set_title("In-situ Sigma-%d density profile at %6.2fE,%6.2fN" %
(thflag, lon, lat))
for t in ax.get_xticklabels():
t.set_color(cold)
#t.set_size(8)
#t.set_rotation(-45)
ax.grid(True)
f.savefig("dens_data_kappa%d_%s.png" % (kapref, sinfo), dpi=dpi)
# Plot vertical density gradient of original data
#print sigprof.shape,dprof.shape
dsigprof = numpy.zeros(sigprof.shape)
for k in range(nz):
#print k,nz
if k < nz - 1:
dsigprof[k, :] = (sigprof[k + 1, :] - sigprof[k, :]) / dprof[k]
else:
dsigprof[k, :] = dsigprof[k - 1, :]
dsigprof = numpy.ma.masked_where(numpy.abs(dsigprof) > 1e10, dsigprof)
dsigprof = dsigprof * 100 # DS per 100 meters
f, ax = plt.subplots(1, figsize=(10, 5))
if section:
P = ax.pcolormesh(x,
-dprof,
dsigprof,
norm=matplotlib.colors.LogNorm(vmin=0.001, vmax=10),
cmap="Paired")
CS = ax.contour(x, -dprof, sigprof, sigma)
ax.clabel(CS, inline=1, fontsize=4)
ax.set_title("Delta rho [$kg / m^3$] per 100 meter in the vertical")
ax.set_ylabel("Depth[m]")
ax.set_xlabel("Distance along section")
f.colorbar(P, ax=ax)
else:
ax.plot(dsigprof, -dprof, lw=2, color=cold, label="Sigma-%d" % thflag)
ax.set_ylabel("Depth[m]")
ax.set_xlabel("Density Difference [$kg / m^3$]", color=cold)
ax.set_title(
"Delta rho per 100 meter in the vertical at %8.2fE , %8.2fN" %
(lon, lat))
for t in ax.get_xticklabels():
t.set_color(cold)
#t.set_size(8)
#t.set_rotation(-45)
ax.grid(True)
f.savefig("densgrad_data_%s.png" % sinfo, dpi=dpi)
# Plot Buoyancy frequency
Nsq = dsigprof / 100. * 9.81 / 1000.
f, ax = plt.subplots(1, figsize=(10, 5))
if section:
P = ax.pcolormesh(x,
-dprof,
Nsq,
norm=matplotlib.colors.LogNorm(vmin=1e-9, vmax=1e-4),
cmap="Paired")
#CS=ax.contour(x,-dprof,Nsq,sigma)
ax.clabel(CS, inline=1, fontsize=4)
f.colorbar(P, ax=ax)
ax.set_title("$N^2$ along section")
ax.set_ylabel("Depth[m]")
ax.set_xlabel("Distance along section")
else:
ax.plot(Nsq, -dprof, lw=2, color=cold, label="Sigma-%d" % thflag)
ax.set_ylabel("Depth[m]")
ax.set_xlabel("$N^2$[I always forget the units]", color=cold)
ax.set_title("$N^2$ at %6.2fE,%6.2fN" % (lon, lat))
ax.xaxis.set_major_formatter(FormatStrFormatter('%.2g'))
for t in ax.get_xticklabels():
t.set_color(cold)
#t.set_size(8)
#t.set_rotation(-45)
ax.grid(True)
f.savefig("Nsq_data_%s.png" % sinfo, dpi=dpi)
def main(region,experiment,tracer,month,axis,layer,cmap,clim,workdir,\
section,ijspace):
user = getpass.getuser()
if region == 'TP0' or region == 'TP2' or region == 'TP5' or region == 'NAT':
version = 'new'
elif region == 'TP4' or region == 'NA2':
version = 'old'
else:
print('wrong or not implemented domain is provided')
quit()
if axis == 'horizontal' or axis == 'vertical':
pass
else:
print('provide arguments "horizontal" or "vertical"')
quit()
if region == "TP0":
region = "TP0a1.00"
if region == "TP2":
region = "TP2a0.10"
if region == "TP4":
region = "TP4a0.12"
if region == "TP5":
region = "TP5a0.06"
if region == "NAT":
region = "NATa1.00"
if region == "NA2":
region = "NA2a0.80"
if tracer == "nitrate" or tracer == 'phosphate' or \
tracer == 'silicate' or tracer == "oxygen" or \
tracer == 'dic' or tracer == "alkalinity" or \
tracer == 'temperature' or tracer == "salinity" :
pass
else:
print('tracer name not correct')
print('choose: nitrate, phosphate, silicate, \
oxygen, dic, alkalinity, temperature, salinity')
quit()
units = "(mgC m$^{-2}$ s$^{-1}$), tracer converted to C"
key = "trc" # trc for tracers, below tem and sal is set if necessary
if version == "new":
if tracer == "nitrate":
name = "relax.ECO_no3"
elif tracer == "phosphate":
name = "relax.ECO_pho"
elif tracer == "silicate":
name = "relax.ECO_sil"
elif tracer == "oxygen":
name = "relax.ECO_oxy"
units = "mmol m$^{-3}$"
elif tracer == "dic":
name = "relax.CO2_dic"
units = "mmol m$^{-3}$"
elif tracer == "alkalinity":
name = "relax.CO2_alk"
units = "mEq m$^{-3}$"
elif tracer == "temperature":
name = "relax_tem"
key = "tem"
units = "$^{o}C$"
elif tracer == "salinity":
name = "relax_sal"
key = "sal"
units = "psu"
if version == "old":
if tracer == "nitrate":
name = "relax_nit"
elif tracer == "phosphate":
name = "relax_pho"
elif tracer == "silicate":
name = "relax_sil"
elif tracer == "oxygen":
name = "relax_oxy"
units = "mmol m$^{-3}$"
elif tracer == "dic":
name = "relax_dic"
units = "mmol m$^{-3}$"
elif tracer == "alkalinity":
name = "relax_alk"
units = "mEq m$^{-3}$"
elif tracer == "temperature":
name = "relax_tem"
key = "tem"
units = "$^{o}C$"
elif tracer == "salinity":
name = "relax_sal"
key = "sal"
units = "psu"
abgrid = abfile.ABFileGrid(workdir + user + "/" + \
region + "/topo/regional.grid","r")
plon = abgrid.read_field("plon")
plat = abgrid.read_field("plat")
jdm, idm = plon.shape
abdepth = abfile.ABFileBathy(workdir + user + "/" + \
region + "/relax/" + experiment + "/SCRATCH/regional.depth.b", \
"r",idm=idm,jdm=jdm)
depthm = abdepth.read_field("depth")
abrelax = abfile.ABFileRelax(workdir + user + "/" + \
region + "/relax/" + experiment + \
"/" + name + ".a","r")
# now plot
fig = plt.figure(figsize=(6, 5), facecolor='w')
ax = fig.add_subplot(1, 1, 1)
ax.set_position([0.01, 0.02, 0.865, 0.96])
cmap = plt.get_cmap(cmap)
ax.set_facecolor('xkcd:gray')
if axis == 'horizontal':
if layer is None:
print(" ")
print("provide the layer number to be plotted, e.g. --layer=1")
print("quitting ...")
print(" ")
quit()
else:
relax = abrelax.read_field(key, np.int(layer), np.int(month) - 1)
abrelax.close()
pmesh = plt.pcolormesh(relax, cmap=cmap)
cb = ax.figure.colorbar(pmesh)
if clim is not None: pmesh.set_clim(clim)
if axis == 'vertical':
if section is None:
print(" ")
print("provide the section to be plotted")
print("--section='lon1,lon2,lat1,lat2'")
print("--section='-30.1,2.5,50.0,75.5'")
print("quitting ...")
print(" ")
quit()
else:
lon1 = section[0]
lon2 = section[1]
lat1 = section[2]
lat2 = section[3]
# pick up indexes
if ijspace:
sec = gridxsec.SectionIJSpace([lon1, lon2], [lat1, lat2], plon,
plat)
else:
sec = gridxsec.Section([lon1, lon2], [lat1, lat2], plon, plat)
I, J = sec.grid_indexes
dist = sec.distance
slon = sec.longitude
slat = sec.latitude
dpname = modeltools.hycom.layer_thickness_variable["archive"]
# get arbitrary relaxation thicknesses
dummy = sorted(fnmatch.filter(\
os.listdir(workdir + user + "/" + \
region + "/relax/" + experiment), \
'relax.0000_[012]*_00.a'))
dummyarch = workdir + user + "/" + \
region + "/relax/" + experiment + \
"/" + dummy[np.int(month)-1]
dummyfile = abfile.ABFileArchv(dummyarch, "r")
kdm = max(dummyfile.fieldlevels)
intfsec = np.zeros((kdm + 1, I.size))
datasec = np.zeros((kdm + 1, I.size))
for k in range(kdm):
dp2d = dummyfile.read_field(dpname, k + 1)
data2d = abrelax.read_field(key, k + 1, np.int(month) - 1)
dp2d = np.ma.filled(dp2d, 0.)
dp2d = dp2d / modeltools.hycom.onem
data2d = np.ma.filled(data2d, 1e30)
intfsec[k + 1, :] = intfsec[k, :] + dp2d[J, I]
if k == 0: datasec[k, :] = data2d[J, I]
datasec[k + 1, :] = data2d[J, I]
datasec = np.ma.masked_where(datasec > 0.5 * 1e30, datasec)
x = dist / 1000. # km
pmesh = ax.pcolormesh(x, -intfsec, datasec, cmap=cmap)
cb = ax.figure.colorbar(pmesh)
if clim is not None: pmesh.set_clim(clim)
axm = fig.add_subplot(212)
axm.set_position([0.85, 0.85, 0.15, 0.15])
axm.set_facecolor('xkcd:gray')
pmesh2 = plt.pcolormesh(dummyfile.read_field(dpname, 1) * 0.,
cmap=cmap)
pltsec = plt.plot(I, J, 'r', lw=2)
plt.xticks([])
plt.yticks([])
plt.text(0.015,1.05, "%s %s %s" %(tracer, "relaxation month:",\
month.zfill(2)),transform=ax.transAxes,FontSize=13)
if axis == 'horizontal':
plt.text(0.015,
1.005,
units + " @layer=" + layer,
transform=ax.transAxes,
FontSize=8)
else:
plt.text(0.015, 1.005, units, transform=ax.transAxes, FontSize=8)
# save figure
counter = 0
plottemp = workdir + user + "/" + \
region + "/relax/" + experiment + \
"/" + name + "_" + axis + "_%s" + ".png"
plotname = plottemp % (np.str(counter).zfill(3))
while os.path.isfile(plotname):
counter += 1
plotname = plottemp % (np.str(counter).zfill(3))
fig.canvas.print_figure(plotname, dpi=180)
print(" ")
print("figure: " + plotname)
print(" ")
def main(lon1,
lat1,
lon2,
lat2,
variable,
files,
filetype="archive",
clim=None,
sectionid="",
ijspace=False,
xaxis="distance",
section_map=False,
dens=False,
dpi=180):
logger.info("Filetype is %s" % filetype)
gfile = abf.ABFileGrid("regional.grid", "r")
plon = gfile.read_field("plon")
plat = gfile.read_field("plat")
# Set up section info
if ijspace:
sec = gridxsec.SectionIJSpace([lon1, lon2], [lat1, lat2], plon, plat)
else:
sec = gridxsec.Section([lon1, lon2], [lat1, lat2], plon, plat)
I, J = sec.grid_indexes
dist = sec.distance
print('dit.shae=', dist.shape)
slon = sec.longitude
slat = sec.latitude
logger.info("Min max I-index (starts from 0):%d %d" % (I.min(), I.max()))
logger.info("Min max J-index (starts from 0):%d %d" % (J.min(), J.max()))
#
#
if section_map:
ll_lon = slon.min() - 10.
ur_lon = slon.max() + 10.
ll_lat = np.maximum(-90., slat.min() - 10.)
ur_lat = np.minimum(90., slat.max() + 10.)
proj = ccrs.Stereographic(central_latitude=90.0,
central_longitude=-40.0)
pxy = proj.transform_points(ccrs.PlateCarree(), plon, plat)
px = pxy[:, :, 0]
py = pxy[:, :, 1]
x, y = np.meshgrid(np.arange(slon.shape[0]), np.arange(slat.shape[0]))
figure = plt.figure(figsize=(10, 8))
ax = figure.add_subplot(111)
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([-179, 179, 53, 85], ccrs.PlateCarree())
ax.add_feature(cfeature.GSHHSFeature('auto', edgecolor='grey'))
ax.add_feature(cfeature.GSHHSFeature('auto', facecolor='grey'))
ax.gridlines()
ax.plot(slon, slat, "r-", lw=1)
pos = ax.get_position()
asp = pos.height / pos.width
w = figure.get_figwidth()
h = asp * w
figure.set_figheight(h)
if sectionid:
figure.canvas.print_figure("map_%s.png" % sectionid, dpi=dpi)
else:
figure.canvas.print_figure("map.png", dpi=dpi)
# Get layer thickness variable used in hycom
dpname = modeltools.hycom.layer_thickness_variable[filetype]
logger.info("Filetype %s: layer thickness variable is %s" %
(filetype, dpname))
if xaxis == "distance":
x = dist / 1000.
xlab = "Distance along section[km]"
elif xaxis == "i":
x = I
xlab = "i-index"
elif xaxis == "j":
x = J
xlab = "j-index"
elif xaxis == "lon":
x = slon
xlab = "longitude"
elif xaxis == "lat":
x = slat
xlab = "latitude"
else:
logger.warning("xaxis must be i,j,lo,lat or distance")
x = dist / 1000.
xlab = "Distance along section[km]"
# Loop over archive files
figure = plt.figure()
ax = figure.add_subplot(111)
pos = ax.get_position()
for fcnt, myfile0 in enumerate(files):
# Remove [ab] ending if present
m = re.match("(.*)\.[ab]", myfile0)
if m:
myfile = m.group(1)
else:
myfile = myfile0
# Add more filetypes if needed. By def we assume archive
if filetype == "archive":
i_abfile = abf.ABFileArchv(myfile, "r")
elif filetype == "restart":
i_abfile = abf.ABFileRestart(myfile,
"r",
idm=gfile.idm,
jdm=gfile.jdm)
else:
raise NotImplementedError("Filetype %s not implemented" % filetype)
# kdm assumed to be max level in ab file
kdm = max(i_abfile.fieldlevels)
# Set up interface and daat arrays
xx = np.zeros((kdm + 1, I.size))
intfsec = np.zeros((kdm + 1, I.size))
datasec = np.zeros((kdm + 1, I.size))
if dens:
datasec_sal = np.zeros((kdm + 1, I.size))
sigma_sec = np.zeros((kdm + 1, I.size))
# Loop over layers in file.
logger.info("File %s" % (myfile))
for k in range(kdm):
logger.debug("File %s, layer %03d/%03d" % (myfile, k, kdm))
# Get 2D fields
dp2d = i_abfile.read_field(dpname, k + 1)
data2d = i_abfile.read_field(variable, k + 1)
dp2d = np.ma.filled(dp2d, 0.) / modeltools.hycom.onem
data2d = np.ma.filled(data2d, 1e30)
# Place data into section arrays
intfsec[k + 1, :] = intfsec[k, :] + dp2d[J, I]
if k == 0: datasec[k, :] = data2d[J, I]
datasec[k + 1, :] = data2d[J, I]
if dens:
data2d_sal = i_abfile.read_field('salin', k + 1)
data2d_sal = np.ma.filled(data2d_sal, 1e30)
datasec_sal[k + 1, :] = data2d_sal[J, I]
i_maxd = np.argmax(np.abs(intfsec[kdm, :]))
for k in range(kdm + 1):
xx[k, :] = x[:]
datasec = np.ma.masked_where(datasec > 0.5 * 1e30, datasec)
print("datasec min, max=", datasec.min(), datasec.max())
if dens:
datasec_sal = np.ma.masked_where(datasec_sal > 0.5 * 1e30,
datasec_sal)
print("datasec_sal min, max=", datasec_sal.min(),
datasec_sal.max())
sigma_sec = mod_hyc2plot.sig(datasec, datasec_sal)
sigma_sec = np.ma.masked_where(sigma_sec < 0.0, sigma_sec)
datasec = sigma_sec
# Set up section plot
datasec = np.ma.masked_where(datasec > 0.5 * 1e30, datasec)
print("min, max=", datasec.min(), datasec.max())
if clim is None:
clim = [datasec.min(), datasec.max()]
#clim=[0.0,13]
print("clim=", clim[0], clim[1])
if clim is not None:
lvls = MaxNLocator(nbins=70).tick_values(clim[0], clim[1])
mf = 'sawtooth_fc100.txt'
LinDic = mod_hyc2plot.cmap_dict(mf)
my_cmap = matplotlib.colors.LinearSegmentedColormap(
'my_colormap', LinDic)
cmap = my_cmap
norm = BoundaryNorm(lvls, ncolors=cmap.N, clip=True)
P = ax.contourf(xx, -intfsec, datasec, cmap=cmap, levels=lvls)
# Plot layer interfaces
for k in range(1, kdm + 1):
if k % 100 == 0:
PL = ax.plot(x, -intfsec[k, :], "-", color="k")
textx = x[i_maxd]
texty = -0.5 * (intfsec[k - 1, i_maxd] + intfsec[k, i_maxd])
ax.text(textx,
texty,
str(k),
verticalalignment="center",
horizontalalignment="center",
fontsize=6)
elif k % 5 == 0:
PL = ax.plot(x, -intfsec[k, :], "--", color="k", linewidth=0.5)
textx = x[i_maxd]
texty = -0.5 * (intfsec[k - 1, i_maxd] + intfsec[k, i_maxd])
ax.text(textx,
texty,
str(k),
verticalalignment="center",
horizontalalignment="center",
fontsize=6)
else:
if k > 2 and k % 2 == 0:
PL = ax.plot(x,
-intfsec[k, :],
"-",
color=".5",
linewidth=0.5)
textx = x[i_maxd]
texty = -0.5 * (intfsec[k - 1, i_maxd] +
intfsec[k, i_maxd])
ax.text(textx,
texty,
str(k),
verticalalignment="center",
horizontalalignment="center",
fontsize=6)
else:
continue
# Print figure
ax.set_facecolor('xkcd:gray')
aspect = 90
pad_fraction = 0.25
divider = make_axes_locatable(ax)
width = axes_size.AxesY(ax, aspect=1. / aspect)
pad = axes_size.Fraction(pad_fraction, width)
cax = divider.append_axes("right", size=width, pad=pad)
cb = ax.figure.colorbar(P, cax=cax)
if clim is not None: P.set_clim(clim)
if dens:
ax.set_title('[P. density ]: ' + myfile)
else:
ax.set_title('[' + variable + ']: ' + myfile)
ax.set_ylabel('Depth [m]')
ax.set_xlabel(xlab)
# Print in different y-lims
suff = os.path.basename(myfile)
if sectionid: suff = suff + "_" + sectionid
if dens: variable = "dens"
figure.canvas.print_figure("sec_%s_full_%s.png" % (variable, suff),
dpi=dpi)
ax.set_ylim(-1000, 0)
figure.canvas.print_figure("sec_%s_1000m_%s.png" % (variable, suff),
dpi=dpi)
# Close input file
i_abfile.close()
#
ax.clear()
cb.remove()
def main(lon1,lat1,lon2,lat2,variable,files,filetype="archive",clim=None,sectionid="",
ijspace=False,xaxis="distance",section_map=False,ncfiles="",dpi=180) :
#TP4Grd='/cluster/work/users/aal069/TP4a0.12/mfile/'
logger.info("Filetype is %s"% filetype)
gfile = abf.ABFileGrid("regional.grid","r")
plon=gfile.read_field("plon")
plat=gfile.read_field("plat")
# Set up section info
if ijspace :
sec = gridxsec.SectionIJSpace([lon1,lon2],[lat1,lat2],plon,plat)
else :
sec = gridxsec.Section([lon1,lon2],[lat1,lat2],plon,plat)
I,J=sec.grid_indexes
dist=sec.distance
print('dit.shae=',dist.shape)
slon=sec.longitude
slat=sec.latitude
# In testing
#J,I,slon,slat,case,dist=sec.find_intersection(qlon,qlat)
#print I,J
#raise NameError,"test"
logger.info("Min max I-index (starts from 0):%d %d"%(I.min(),I.max()))
logger.info("Min max J-index (starts from 0):%d %d"%(J.min(),J.max()))
#
#
if section_map :
ll_lon=slon.min()-10.
ur_lon=slon.max()+10.
ll_lat=np.maximum(-90.,slat.min()-10.)
ur_lat=np.minimum(90. ,slat.max()+10.)
proj=ccrs.Stereographic(central_latitude=90.0,central_longitude=-40.0)
#pxy = proj.transform_points(ccrs.PlateCarree(), plon, plat)
#px=pxy[:,:,0]
#py=pxy[:,:,1]
#x,y=np.meshgrid(np.arange(slon.shape[0]),np.arange(slat.shape[0]))
figure =plt.figure(figsize=(8,8))
ax=figure.add_subplot(111,projection=proj)
#ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([-179, 179, 53, 85],ccrs.PlateCarree())
#ax = plt.axes(projection=ccrs.Stereographic())
ax.add_feature(cfeature.GSHHSFeature('auto', edgecolor='grey'))
ax.add_feature(cfeature.GSHHSFeature('auto', facecolor='grey'))
ax.gridlines()
#ax.coastlines(resolution='110m')
ax.plot(slon,slat,"r-",lw=1,transform=ccrs.PlateCarree())
pos = ax.get_position()
asp=pos.height/pos.width
w=figure.get_figwidth()
h=asp*w
figure.set_figheight(h)
if sectionid :
figure.canvas.print_figure("map_%s.png"%sectionid,dpi=dpi,bbox_inches='tight')
else :
figure.canvas.print_figure("map.png",dpi=dpi,bbox_inches='tight')
# Get layer thickness variable used in hycom
dpname = modeltools.hycom.layer_thickness_variable[filetype]
logger.info("Filetype %s: layer thickness variable is %s"%(filetype,dpname))
if xaxis == "distance" :
x=dist/1000.
xlab="Distance along section[km]"
elif xaxis == "i" :
x=I
xlab="i-index"
elif xaxis == "j" :
x=J
xlab="j-index"
elif xaxis == "lon" :
x=slon
xlab="longitude"
elif xaxis == "lat" :
x=slat
xlab="latitude"
else :
logger.warning("xaxis must be i,j,lo,lat or distance")
x=dist/1000.
xlab="Distance along section[km]"
# get kdm from the first file:
# Remove [ab] ending if present
print('firstfilw', files[0])
m=re.match("(.*)\.[ab]",files[0])
print('m=',m.group(1))
myf=m.group(1)
fi_abfile = abf.ABFileArchv(myf,"r")
kdm=max(fi_abfile.fieldlevels)
# Loop over archive files
figure = plt.figure()
ax=figure.add_subplot(111)
pos = ax.get_position()
count_sum=0
intfsec_sum=np.zeros((kdm+1,I.size))
datasec_sum=np.zeros((kdm+1,I.size))
for fcnt,myfile0 in enumerate(files) :
count_sum=count_sum+1
print('count_sum==', count_sum)
print('fcnt=', fcnt)
print('mfile0=', myfile0)
# Remove [ab] ending if present
m=re.match("(.*)\.[ab]",myfile0)
if m :
myfile=m.group(1)
else :
myfile=myfile0
# Add more filetypes if needed. By def we assume archive
if filetype == "archive" :
i_abfile = abf.ABFileArchv(myfile,"r")
elif filetype == "restart" :
i_abfile = abf.ABFileRestart(myfile,"r",idm=gfile.idm,jdm=gfile.jdm)
else :
raise NotImplementedError("Filetype %s not implemented"%filetype)
# kdm assumed to be max level in ab file
kdm=max(i_abfile.fieldlevels)
# Set up interface and daat arrays
xx=np.zeros((kdm+1,I.size))
intfsec=np.zeros((kdm+1,I.size))
datasec=np.zeros((kdm+1,I.size))
# Loop over layers in file.
logger.info("File %s"%(myfile))
for k in range(kdm) :
logger.debug("File %s, layer %03d/%03d"%(myfile,k,kdm))
# Get 2D fields
dp2d=i_abfile.read_field(dpname,k+1)
data2d=i_abfile.read_field(variable,k+1)
#print('---mn,mx data=', data2d.min(),data2d.max())
if (k%kdm==49):
print("---Reach bottom layer" )
dp2d=np.ma.filled(dp2d,0.)/modeltools.hycom.onem
data2d=np.ma.filled(data2d,1e30)
# Place data into section arrays
intfsec[k+1,:] = intfsec[k,:] + dp2d[J,I]
if k==0 : datasec[k,:] = data2d[J,I]
datasec[k+1,:] = data2d[J,I]
intfsec_sum=intfsec_sum + intfsec
datasec_sum=datasec_sum + datasec
#print 'prs_intafce=', np.transpose(intfsec[:,15])
i_abfile.close()
# end loop over files
intfsec_avg=intfsec_sum/count_sum
datasec_avg=datasec_sum/count_sum
if ncfiles :
MLDGS_sum=np.zeros((1,I.size))
count_sum=0
for fcnt,ncfile in enumerate(ncfiles) :
count_sum=count_sum+1
print('ncfile count_sum==', count_sum)
print('ncfile fcnt=', fcnt)
print('ncfilefile=', ncfile)
MLDGS=np.zeros((1,I.size))
ncfile0 = netCDF4.Dataset(ncfile,'r')
MLD_2D = ncfile0.variables['GS_MLD'][:]
#MLD_2D = ncfile0.variables['mlp'][:]
MLDGS[0,:]=MLD_2D[0,J,I]
MLDGS_sum= MLDGS_sum + MLDGS
ncfile0.close()
# end loop over files
MLDGS_avg=MLDGS_sum/count_sum
#
#-----------------------------------------------------------------
# read from clim mld TP5netcdf
if ncfiles :
if 'TP2' in files[0]:
fh=netCDF4.Dataset('mld_dr003_l3_modif_Interp_TP2grd.nc')
else:
fh=netCDF4.Dataset('mld_dr003_l3_modif_Interp_TP5grd.nc')
fhmldintrp = fh.variables['TP5mld'][:]
fh.close()
#fhMLDintrp_sum=np.zeros((760,800))
MLDclim_sum=np.zeros((1,I.size))
cunt_sum=0
for ii in range(12) :
cunt_sum=cunt_sum +1
MLDclim=np.zeros((1,I.size))
MLDclim[0,:]=fhmldintrp[ii,J,I]
MLDclim_sum= MLDclim_sum + MLDclim
print('clim count_sum==', cunt_sum)
MLDclim_avg=MLDclim_sum/cunt_sum
#-----------------------------------------------------------------
i_maxd=np.argmax(np.abs(intfsec_avg[kdm,:]))
#print i_maxd
for k in range(kdm+1) :
xx[k,:] = x[:]
# Set up section plot
#datasec = np.ma.masked_where(datasec==1e30,datasec)
datasec_avg = np.ma.masked_where(datasec_avg>0.5*1e30,datasec_avg)
#print datasec.min(),datasec.max()
#P=ax.pcolormesh(dist/1000.,-intfsec,datasec)
#print i_maxd
for k in range(kdm+1) :
xx[k,:] = x[:]
if clim is not None : lvls = MaxNLocator(nbins=30).tick_values(clim[0], clim[1])
#print 'levels=', lvls
mf='sawtooth_0-1.txt'
LinDic=mod_hyc2plot.cmap_dict(mf)
my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap',LinDic)
cmap=my_cmap
#cmap = matplotlib.pyplot.get_cmap('gist_rainbow_r')
norm = BoundaryNorm(lvls, ncolors=cmap.N, clip=True)
print('x.shape=' , x.shape)
print('x.min,xmax=' , x.min(),x.max())
print('xx.shape=' , xx.shape)
print('xx.min,xxmax=' , xx.min(),xx.max())
print('intfsec_avg.shape=', intfsec_avg.shape)
print('datasec_avg.shape=', datasec_avg.shape)
#P=ax.pcolormesh(x,-intfsec,datasec,cmap=cmap)
P=ax.contourf(xx,-intfsec_avg,datasec_avg,extend='both',cmap=cmap,levels=lvls)
if 'sal' in variable:
P1=ax.contour(xx,-intfsec_avg,datasec_avg,levels=[32.0,33.0,34.0,35.0,35.5],
colors=('k',),linestyles=('-',),linewidths=(1.5,))
else:
P1=ax.contour(xx,-intfsec_avg,datasec_avg,levels=[-1,0.0,2.0],
colors=('k',),linestyles=('-',),linewidths=(1.5,))
matplotlib.pyplot.clabel(P1, fmt = '%2.1d', colors = 'k', fontsize=10) #contour line labels
# Plot layer interfaces
for k in range(1,kdm+1) :
if k%100 == 0 :
PL=ax.plot(x,-intfsec_avg[k,:],"-",color="k")
elif k%5 == 0 and k <= 10:
PL=ax.plot(x,-intfsec_avg[k,:],"--",color="k", linewidth=0.5)
textx = x[i_maxd]
texty = -0.5*(intfsec_avg[k-1,i_maxd] + intfsec_avg[k,i_maxd])
ax.text(textx,texty,str(k),verticalalignment="center",horizontalalignment="center",fontsize=6)
elif k%2 and k > 10 :
PL=ax.plot(x,-intfsec_avg[k,:],"--",color="k", linewidth=0.5)
textx = x[i_maxd]
texty = -0.5*(intfsec_avg[k-1,i_maxd] + intfsec_avg[k,i_maxd])
ax.text(textx,texty,str(k),verticalalignment="center",horizontalalignment="center",fontsize=6)
if ncfiles :
PL=ax.plot(x,-MLDGS_avg[0,:],"-",color="w", linewidth=1.50)
PL=ax.plot(x,-MLDclim_avg[0,:],"--",color="r", linewidth=1.50)
### else :
### PL=ax.plot(x,-intfsec_avg[k,:],"-",color=".5")
# Print figure and remove wite space.
aspect = 50
pad_fraction = 0.25
divider = make_axes_locatable(ax)
width = axes_size.AxesY(ax, aspect=1./aspect)
pad = axes_size.Fraction(pad_fraction, width)
cax = divider.append_axes("right", size=width, pad=pad)
cb=ax.figure.colorbar(P,cax=cax,extend='both')
#cb=ax.figure.colorbar(P,extend='both')
if clim is not None : P.set_clim(clim)
#cb=ax.figure.colorbar(P,extend='both')
ax.set_title(variable+':'+myfile+'AVG-')
ax.set_ylabel('Depth [m]')
ax.set_xlabel(xlab)
#ax.set_position(pos)
#matplotlib.pyplot.tight_layout()
# Print in different y-lims
suff=os.path.basename(myfile)
if sectionid : suff=suff+"_"+sectionid
figure.canvas.print_figure("sec_AVG_%s_full_%s.png"%(variable,suff),dpi=dpi)
#ax.set_ylim(-1000,0)
if 'Fram' in sectionid or 'Svin' in sectionid:
print('sectionid=', sectionid)
ax.set_ylim(-600,0)
figure.canvas.print_figure("sec_AVG_%s_600m_%s.png"%(variable,suff),dpi=dpi)
else:
#ax.set_ylim(-2500,0)
#figure.canvas.print_figure("sec_AVG_%s_2500m_%s.png"%(variable,suff),dpi=dpi)
ax.set_ylim(-3000,0)
figure.canvas.print_figure("sec_AVG_%s_3000m_%s.png"%(variable,suff),dpi=dpi)
# Close input file
#i_abfile.close()
#
ax.clear()
cb.remove()
def main(lon1,lat1,lon2,lat2,variable,files,filetype="archive",clim=None,sectionid="",
ijspace=False,xaxis="distance",section_map=False,dpi=180) :
TP4Grd='/cluster/work/users/aal069/TP4a0.12/mfile/'
logger.info("Filetype is %s"% filetype)
gfile = abf.ABFileGrid("regional.grid","r")
plon=gfile.read_field("plon")
plat=gfile.read_field("plat")
# Set up section info
if ijspace :
sec = gridxsec.SectionIJSpace([lon1,lon2],[lat1,lat2],plon,plat)
else :
sec = gridxsec.Section([lon1,lon2],[lat1,lat2],plon,plat)
I,J=sec.grid_indexes
dist=sec.distance
print('dit.shae=',dist.shape)
slon=sec.longitude
slat=sec.latitude
# In testing
#J,I,slon,slat,case,dist=sec.find_intersection(qlon,qlat)
#print I,J
#raise NameError,"test"
logger.info("Min max I-index (starts from 0):%d %d"%(I.min(),I.max()))
logger.info("Min max J-index (starts from 0):%d %d"%(J.min(),J.max()))
#
#
if section_map :
ll_lon=slon.min()-10.
ur_lon=slon.max()+10.
ll_lat=np.maximum(-90.,slat.min()-10.)
ur_lat=np.minimum(90. ,slat.max()+10.)
proj=ccrs.Stereographic(central_latitude=90.0,central_longitude=-40.0)
#pxy = proj.transform_points(ccrs.PlateCarree(), plon, plat)
#px=pxy[:,:,0]
#py=pxy[:,:,1]
#x,y=np.meshgrid(np.arange(slon.shape[0]),np.arange(slat.shape[0]))
figure =plt.figure(figsize=(8,8))
ax=figure.add_subplot(111,projection=proj)
#ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([-179, 179, 53, 85],ccrs.PlateCarree())
#ax = plt.axes(projection=ccrs.Stereographic())
ax.add_feature(cfeature.GSHHSFeature('auto', edgecolor='grey'))
ax.add_feature(cfeature.GSHHSFeature('auto', facecolor='grey'))
ax.gridlines()
#ax.coastlines(resolution='110m')
ax.plot(slon,slat,"r-",lw=1,transform=ccrs.PlateCarree())
pos = ax.get_position()
asp=pos.height/pos.width
w=figure.get_figwidth()
h=asp*w
figure.set_figheight(h)
if sectionid :
figure.canvas.print_figure("map_%s.png"%sectionid,dpi=dpi,bbox_inches='tight')
else :
figure.canvas.print_figure("map.png",dpi=dpi,bbox_inches='tight')
# Get layer thickness variable used in hycom
dpname = modeltools.hycom.layer_thickness_variable[filetype]
logger.info("Filetype %s: layer thickness variable is %s"%(filetype,dpname))
if xaxis == "distance" :
x=dist/1000.
xlab="Distance along section[km]"
elif xaxis == "i" :
x=I
xlab="i-index"
elif xaxis == "j" :
x=J
xlab="j-index"
elif xaxis == "lon" :
x=slon
xlab="longitude"
elif xaxis == "lat" :
x=slat
xlab="latitude"
else :
logger.warning("xaxis must be i,j,lo,lat or distance")
x=dist/1000.
xlab="Distance along section[km]"
# get kdm from the first file:
# Remove [ab] ending if present
myfile0=files[0]
print( 'myfile0', myfile0)
m=re.match("(.*)\.[ab]",myfile0)
print('m=',m.group(1))
if m :
myfile=m.group(1)
else :
myfile=myfile0
dta_afile = abf.AFile(gfile.idm,gfile.jdm,myfile0,"r")
#intfl='../../../relax/010/relax_int.a'
intfl=myfile[:-3] + 'int.a'
int_afile = abf.AFile(gfile.idm,gfile.jdm,intfl,"r")
#
lyr=1
#record_num,xmn,xmx=dta_afile.get_record_number(variable,lyr)
#print 'record_num, variable, layer ===', record_num-1, variable, lyr
#print 'mn,mx=',xmn,xmx
# for record in record_num :
record_num=1
record_var=record_num-1
fld = dta_afile.read_record(record_var)
print('mn,mx data=',fld.min(),fld.max())
#np.testing.assert_approx_equal(xmn,fld.min(),significant=8)
#np.testing.assert_approx_equal(xmx,fld.max(),significant=8)
# presure interfc
#record_num,xmn,xmx=int_afile.get_record_number('int',lyr)
#print 'record_num, variable, layer ===', record_num-1, 'int', lyr
#print 'mn,mx=',xmn,xmx
# for record in record_num :
record_prs=record_num-1
fld = int_afile.read_record(record_prs)
print('mn,mx intface=',fld.min(),fld.max())
#np.testing.assert_approx_equal(xmn,fld.min(),significant=8)
#np.testing.assert_approx_equal(xmx,fld.max(),significant=8)
#
#kdm=max(fi_abfile.fieldlevels)
kdm=50
# Loop over archive files
figure = plt.figure()
ax=figure.add_subplot(111)
pos = ax.get_position()
count_sum=0
intfsec_sum=np.zeros((kdm+1,I.size))
datasec_sum=np.zeros((kdm+1,I.size))
#
for mnth in range(12) :
count_sum=count_sum+1
logger.info("Reading data and presure interface for month %s"%(mnth+1))
record_var_pnt=record_var + mnth*kdm
record_prs_pnt=record_prs + mnth*kdm
print('pointing at record num: record_var_pnt', record_var_pnt)
print('pointing at record num: record_prs_pnt', record_prs_pnt)
# Set up interface and daat arrays
xx=np.zeros((kdm+1,I.size))
intfsec=np.zeros((kdm+1,I.size))
datasec=np.zeros((kdm+1,I.size))
# Loop over layers in file.
logger.info("File %s"%(myfile))
logger.info("intfac_File %s"%(intfl))
#loop over 50 records
for k in range(kdm) :
logger.debug("File %s, layer %03d/%03d"%(myfile,k,kdm))
r_var=record_var_pnt+k
r_prs=record_prs_pnt+k
# Get 2D fields
dp2d = int_afile.read_record(r_prs)
data2d=dta_afile.read_record(r_var)
#print('reading rec num: r_var,r_dp=', r_var, r_prs,' ','data.min,data.max=',data2d.min(),data2d.max())
print('data: month,layer, range', (mnth+1),'',(r_var)%kdm,data2d.min(),data2d.max())
#dp2d=i_abfile.read_field(dpname,k+1)
#data2d=i_abfile.read_field(variable,k+1)
if ((r_var)%kdm==49):
print('mn,mx intface=',dp2d.min(),dp2d.max())
print('mn,mx data=', data2d.min(),data2d.max())
print( "Reach bottom layer" )
dp2d=np.ma.filled(dp2d,0.)/modeltools.hycom.onem
data2d=np.ma.filled(data2d,1e30)
# Place data into section arrays
#intfsec[k+1,:] = intfsec[k,:] + dp2d[J,I]
#print("data2d.shape=",data2d.shape)
#print("data2d[J,I].size=",data2d[J,I].size)
intfsec[k+1,:] = dp2d[J,I]
if k==0 : datasec[k,:] = data2d[J,I]
datasec[k+1,:] = data2d[J,I]
intfsec_sum=intfsec_sum + intfsec
datasec_sum=datasec_sum + datasec
#print 'prs_intafce=', np.transpose(intfsec[:,15])
dta_afile.close()
int_afile.close()
# end loop over files
print ('count_sum=',count_sum)
intfsec_avg=intfsec_sum/count_sum
datasec_avg=datasec_sum/count_sum
#
i_maxd=np.argmax(np.abs(intfsec_avg[kdm,:]))
#print i_maxd
for k in range(kdm+1) :
xx[k,:] = x[:]
# Set up section plot
#datasec = np.ma.masked_where(datasec==1e30,datasec)
datasec_avg = np.ma.masked_where(datasec_avg>0.5*1e30,datasec_avg)
#print datasec.min(),datasec.max()
#P=ax.pcolormesh(dist/1000.,-intfsec,datasec)
#print i_maxd
for k in range(kdm+1) :
xx[k,:] = x[:]
if clim is not None : lvls = MaxNLocator(nbins=30).tick_values(clim[0], clim[1])
#print 'levels=', lvls
mf='sawtooth_0-1.txt'
LinDic=mod_hyc2plot.cmap_dict(mf)
my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap',LinDic)
cmap=my_cmap
#cmap = plt.get_cmap('gist_rainbow_r')
norm = BoundaryNorm(lvls, ncolors=cmap.N, clip=True)
print('x.shape=' , x.shape)
print('x.min,xmax=' , x.min(),x.max())
print('xx.shape=' , xx.shape)
print('xx.min,xxmax=' , xx.min(),xx.max())
print('intfsec_avg.shape=', intfsec_avg.shape)
print('datasec_avg.shape=', datasec_avg.shape)
#P=ax.pcolormesh(x,-intfsec,datasec,cmap=cmap)
P=ax.contourf(xx,-intfsec_avg,datasec_avg,extend='both',cmap=cmap,levels=lvls)
if 'sal' in variable:
P1=ax.contour(xx,-intfsec_avg,datasec_avg,levels=[32.0,33.0,34.0,35.0,35.5],
colors=('k',),linestyles=('-',),linewidths=(1.5,))
else:
P1=ax.contour(xx,-intfsec_avg,datasec_avg,levels=[-1.0,0.0,2.0],
colors=('k',),linestyles=('-',),linewidths=(1.5,))
plt.clabel(P1, fmt = '%2.1d', colors = 'k', fontsize=10) #contour line labels
# Plot layer interfaces
for k in range(1,kdm+1) :
if k%100 == 0 :
PL=ax.plot(x,-intfsec_avg[k,:],"-",color="k")
elif k%5 == 0 and k <= 10:
PL=ax.plot(x,-intfsec_avg[k,:],"--",color="k", linewidth=0.5)
textx = x[i_maxd]
texty = -0.5*(intfsec_avg[k-1,i_maxd] + intfsec_avg[k,i_maxd])
ax.text(textx,texty,str(k),verticalalignment="center",horizontalalignment="center",fontsize=6)
elif k%2 == 0 and k > 10:
PL=ax.plot(x,-intfsec_avg[k,:],"--",color="k", linewidth=0.5)
textx = x[i_maxd]
texty = -0.5*(intfsec_avg[k-1,i_maxd] + intfsec_avg[k,i_maxd])
ax.text(textx,texty,str(k),verticalalignment="center",horizontalalignment="center",fontsize=6)
### else :
### PL=ax.plot(x,-intfsec_avg[k,:],"-",color=".5")
# Print figure and remove wite space.
aspect = 50
pad_fraction = 0.25
divider = make_axes_locatable(ax)
width = axes_size.AxesY(ax, aspect=1./aspect)
pad = axes_size.Fraction(pad_fraction, width)
cax = divider.append_axes("right", size=width, pad=pad)
cb=ax.figure.colorbar(P,cax=cax,extend='both')
#cb=ax.figure.colorbar(P,extend='both')
if clim is not None : P.set_clim(clim)
#cb=ax.figure.colorbar(P,extend='both')
ax.set_title(variable+':'+myfile+'AVG-')
ax.set_ylabel('Depth [m]')
ax.set_xlabel(xlab)
#ax.set_position(pos)
#plt.tight_layout()
# Print in different y-lims
suff=os.path.basename(myfile)
if sectionid : suff=suff+"_"+sectionid
figure.canvas.print_figure("sec_AVG_%s_full_%s.png"%(variable,suff),dpi=dpi)
if 'Fram' in sectionid or 'Svin' in sectionid:
ax.set_ylim(-600,0)
figure.canvas.print_figure("sec_AVG_%s_600m_%s.png"%(variable,suff),dpi=dpi)
else :
ax.set_ylim(-3000,0)
figure.canvas.print_figure("sec_AVG_%s_3000m_%s.png"%(variable,suff),dpi=dpi)
#ax.set_ylim(-600,0)
#figure.canvas.print_figure("sec_AVG_%s_600m_%s.png"%(variable,suff),dpi=dpi)
# Close input file
#i_abfile.close()
#
ax.clear()
cb.remove()
def main(lon1,lat1,lon2,lat2,variable,files,filetype="archive",clim=None,sectionid="",
ijspace=False,xaxis="distance",section_map=False,dpi=180) :
logger.info("Filetype is %s"% filetype)
gfile = abfile.ABFileGrid("regional.grid","r")
plon=gfile.read_field("plon")
plat=gfile.read_field("plat")
qlon=gfile.read_field("qlon")
qlat=gfile.read_field("qlat")
# Set up section info
if ijspace :
sec = gridxsec.SectionIJSpace([lon1,lon2],[lat1,lat2],plon,plat)
else :
sec = gridxsec.Section([lon1,lon2],[lat1,lat2],plon,plat)
I,J=sec.grid_indexes
dist=sec.distance
slon=sec.longitude
slat=sec.latitude
# In testing
#J,I,slon,slat,case,dist=sec.find_intersection(qlon,qlat)
#print I,J
#raise NameError,"test"
logger.info("Min max I-index (starts from 0):%d %d"%(I.min(),I.max()))
logger.info("Min max J-index (starts from 0):%d %d"%(J.min(),J.max()))
if section_map :
ll_lon=slon.min()-10.
ur_lon=slon.max()+10.
ll_lat=numpy.maximum(-90.,slat.min()-10.)
ur_lat=numpy.minimum(90. ,slat.max()+10.)
m = Basemap(projection='mill', llcrnrlon=ll_lon, llcrnrlat=ll_lat, urcrnrlon=ur_lon, urcrnrlat=ur_lat, resolution='l')
(x,y) = m(slon,slat)
figure = matplotlib.pyplot.figure()
ax=figure.add_subplot(111)
m.drawcoastlines()
#m.fillcontinents(color='coral',lake_color='aqua')
m.drawparallels(numpy.arange(-90.,120.,30.),labels=[1,0,0,0]) # draw parallels
m.drawmeridians(numpy.arange(0.,420.,60.),labels=[0,0,0,1]) # draw meridians
m.drawmapboundary() # draw a line around the map region
m.plot(x,y,"r",lw=3)
m.etopo()
#m.scatter(x,y,s=20,c=dist)
pos = ax.get_position()
#print pos
asp=pos.height/pos.width
#print asp
w=figure.get_figwidth()
#print w
h=asp*w
figure.set_figheight(h)
if sectionid :
figure.canvas.print_figure("map_%s.png"%sectionid,dpi=dpi)
else :
figure.canvas.print_figure("map.png",dpi=dpi)
# Get layer thickness variable used in hycom
dpname = modeltools.hycom.layer_thickness_variable[filetype]
logger.info("Filetype %s: layer thickness variable is %s"%(filetype,dpname))
if xaxis == "distance" :
x=dist/1000.
xlab="Distance along section[km]"
elif xaxis == "i" :
x=I
xlab="i-index"
elif xaxis == "j" :
x=J
xlab="j-index"
elif xaxis == "lon" :
x=slon
xlab="longitude"
elif xaxis == "lat" :
x=slat
xlab="latitude"
else :
logger.warning("xaxis must be i,j,lo,lat or distance")
x=dist/1000.
xlab="Distance along section[km]"
# Loop over archive files
figure = matplotlib.pyplot.figure()
ax=figure.add_subplot(111)
pos = ax.get_position()
for fcnt,myfile0 in enumerate(files) :
# Remove [ab] ending if present
m=re.match("(.*)\.[ab]",myfile0)
if m :
myfile=m.group(1)
else :
myfile=myfile0
# Add more filetypes if needed. By def we assume archive
if filetype == "archive" :
i_abfile = abfile.ABFileArchv(myfile,"r")
elif filetype == "restart" :
i_abfile = abfile.ABFileRestart(myfile,"r",idm=gfile.idm,jdm=gfile.jdm)
else :
raise NotImplementedError,"Filetype %s not implemented"%filetype
# kdm assumed to be max level in ab file
kdm=max(i_abfile.fieldlevels)
# Set up interface and daat arrays
intfsec=numpy.zeros((kdm+1,I.size))
datasec=numpy.zeros((kdm+1,I.size))
# Loop over layers in file.
logger.info("File %s"%(myfile))
for k in range(kdm) :
logger.debug("File %s, layer %03d/%03d"%(myfile,k,kdm))
# Get 2D fields
dp2d=i_abfile.read_field(dpname,k+1)
data2d=i_abfile.read_field(variable,k+1)
dp2d=numpy.ma.filled(dp2d,0.)/modeltools.hycom.onem
data2d=numpy.ma.filled(data2d,1e30)
# Place data into section arrays
intfsec[k+1,:] = intfsec[k,:] + dp2d[J,I]
if k==0 : datasec[k,:] = data2d[J,I]
datasec[k+1,:] = data2d[J,I]
i_maxd=numpy.argmax(numpy.abs(intfsec[kdm,:]))
#print i_maxd
# Set up section plot
#datasec = numpy.ma.masked_where(datasec==1e30,datasec)
datasec = numpy.ma.masked_where(datasec>0.5*1e30,datasec)
#print datasec.min(),datasec.max()
#figure = matplotlib.pyplot.figure()
#ax=figure.add_subplot(111)
#P=ax.pcolormesh(dist/1000.,-intfsec,datasec)
P=ax.pcolormesh(x,-intfsec,datasec,cmap="jet")
if clim is not None : P.set_clim(clim)
# Plot layer interfaces
for k in range(1,kdm+1) :
if k%10 == 0 :
PL=ax.plot(x,-intfsec[k,:],"--",color="k",lw=.5)
elif k%5 == 0 :
PL=ax.plot(x,-intfsec[k,:],"--",color="k",lw=.5)
else :
PL=ax.plot(x,-intfsec[k,:],"--",color=".5",lw=.5)
textx = x[i_maxd]
texty = -0.5*(intfsec[k-1,i_maxd] + intfsec[k,i_maxd])
ax.text(textx,texty,str(k),verticalalignment="center",horizontalalignment="center",fontsize=6)
cb=ax.figure.colorbar(P)
ax.set_title(myfile)
ax.set_ylabel(variable)
ax.set_xlabel(xlab)
#ax.set_position(pos)
#matplotlib.pyplot.tight_layout()
# Print in different y-lims
suff=os.path.basename(myfile)
if sectionid : suff=suff+"_"+sectionid
figure.canvas.print_figure("sec_%s_full_%s.png"%(variable,suff),dpi=dpi)
ax.set_ylim(-1000,0)
figure.canvas.print_figure("sec_%s_1000m_%s.png"%(variable,suff),dpi=dpi)
ax.set_ylim(-300,0)
figure.canvas.print_figure("sec_%s_300m_%s.png"%(variable,suff),dpi=dpi)
# Close input file
i_abfile.close()
#
ax.clear()
cb.remove()
def main(lon1,
lat1,
lon2,
lat2,
variable,
files,
filetype="archive",
clim=None,
sectionid="",
ijspace=False,
xaxis="distance",
section_map=False,
dpi=180):
logger.info("Filetype is %s" % filetype)
gfile = abf.ABFileGrid("regional.grid", "r")
plon = gfile.read_field("plon")
plat = gfile.read_field("plat")
# Set up section info
if ijspace:
sec = gridxsec.SectionIJSpace([lon1, lon2], [lat1, lat2], plon, plat)
else:
sec = gridxsec.Section([lon1, lon2], [lat1, lat2], plon, plat)
I, J = sec.grid_indexes
dist = sec.distance
slon = sec.longitude
slat = sec.latitude
print(slon.shape)
print(slat.shape)
logger.info("Min max I-index (starts from 0):%d %d" % (I.min(), I.max()))
logger.info("Min max J-index (starts from 0):%d %d" % (J.min(), J.max()))
if section_map:
ll_lon = slon.min() - 10.
ur_lon = slon.max() + 10.
ll_lat = np.maximum(-90., slat.min() - 10.)
ur_lat = np.minimum(90., slat.max() + 10.)
proj = ccrs.Stereographic(central_latitude=90.0,
central_longitude=-40.0)
pxy = proj.transform_points(ccrs.PlateCarree(), plon, plat)
px = pxy[:, :, 0]
py = pxy[:, :, 1]
x, y = np.meshgrid(np.arange(slon.shape[0]), np.arange(slat.shape[0]))
figure = plt.figure(figsize=(10, 8))
ax = figure.add_subplot(111)
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([-179, 179, 53, 85], ccrs.PlateCarree())
ax.add_feature(cfeature.GSHHSFeature('auto', edgecolor='grey'))
ax.add_feature(cfeature.GSHHSFeature('auto', facecolor='grey'))
ax.gridlines()
ax.plot(slon, slat, "r-", lw=1)
pos = ax.get_position()
asp = pos.height / pos.width
w = figure.get_figwidth()
h = asp * w
figure.set_figheight(h)
if sectionid:
figure.canvas.print_figure("map_%s.png" % sectionid, dpi=dpi)
else:
figure.canvas.print_figure("map.png", dpi=dpi)
# Get layer thickness variable used in hycom
dpname = modeltools.hycom.layer_thickness_variable[filetype]
logger.info("Filetype %s: layer thickness variable is %s" %
(filetype, dpname))
if xaxis == "distance":
x = dist / 1000.
xlab = "Distance along section[km]"
elif xaxis == "i":
x = I
xlab = "i-index"
elif xaxis == "j":
x = J
xlab = "j-index"
elif xaxis == "lon":
x = slon
xlab = "longitude"
elif xaxis == "lat":
x = slat
xlab = "latitude"
else:
logger.warning("xaxis must be i,j,lo,lat or distance")
x = dist / 1000.
xlab = "Distance along section[km]"
# Loop over archive files
figure = plt.figure()
ax = figure.add_subplot(111)
pos = ax.get_position()
for fcnt, myfile0 in enumerate(files):
# Remove [ab] ending if present
m = re.match("(.*)\.[ab]", myfile0)
if m:
myfile = m.group(1)
else:
myfile = myfile0
# Add more filetypes if needed. By def we assume archive
if filetype == "archive":
i_abfile = abf.ABFileArchv(myfile, "r")
elif filetype == "restart":
i_abfile = abf.ABFileRestart(myfile,
"r",
idm=gfile.idm,
jdm=gfile.jdm)
else:
raise NotImplementedError("Filetype %s not implemented" % filetype)
# kdm assumed to be max level in ab file
kdm = max(i_abfile.fieldlevels)
# Set up interface and daat arrays
intfsec = np.zeros((kdm + 1, I.size))
datasec = np.zeros((kdm + 1, I.size))
# Loop over layers in file.
logger.info("File %s" % (myfile))
for k in range(kdm):
logger.debug("File %s, layer %03d/%03d" % (myfile, k, kdm))
# Get 2D fields
dp2d = i_abfile.read_field(dpname, k + 1)
data2d = i_abfile.read_field(variable, k + 1)
dp2d = np.ma.filled(dp2d, 0.) / modeltools.hycom.onem
data2d = np.ma.filled(data2d, 1e30)
# Place data into section arrays
intfsec[k + 1, :] = intfsec[k, :] + dp2d[J, I]
if k == 0: datasec[k, :] = data2d[J, I]
datasec[k + 1, :] = data2d[J, I]
i_maxd = np.argmax(np.abs(intfsec[kdm, :]))
# Set up section plot
datasec = np.ma.masked_where(datasec > 0.5 * 1e30, datasec)
P = plt.pcolormesh(x, -intfsec, datasec, cmap="jet", shading='auto')
if clim is not None: P.set_clim(clim)
# Plot layer interfaces
for k in range(1, kdm + 1):
if k % 10 == 0:
PL = ax.plot(x, -intfsec[k, :], "--", color="k", lw=.5)
elif k % 5 == 0:
PL = ax.plot(x, -intfsec[k, :], "--", color="k", lw=.5)
else:
PL = ax.plot(x, -intfsec[k, :], "--", color=".5", lw=.5)
textx = x[i_maxd]
texty = -0.5 * (intfsec[k - 1, i_maxd] + intfsec[k, i_maxd])
ax.text(textx,
texty,
str(k),
verticalalignment="center",
horizontalalignment="center",
fontsize=6)
cb = ax.figure.colorbar(P)
ax.set_title(myfile)
ax.set_ylabel(variable)
ax.set_xlabel(xlab)
# Print in different y-lims
suff = os.path.basename(myfile)
if sectionid: suff = suff + "_" + sectionid
figure.canvas.print_figure("sec_%s_full_%s.png" % (variable, suff),
dpi=dpi)
ax.set_ylim(-1000, 0)
figure.canvas.print_figure("sec_%s_1000m_%s.png" % (variable, suff),
dpi=dpi)
ax.set_ylim(-300, 0)
figure.canvas.print_figure("sec_%s_300m_%s.png" % (variable, suff),
dpi=dpi)
# Close input file
i_abfile.close()
#
ax.clear()
cb.remove()