def spaghetti(self, t, lv, va, contour, wrfouts, outpath, da=0, dom=0):
"""
wrfouts : list of wrfout files
Only change dom if there are multiple domains.
"""
m, x, y = self.basemap_setup()
time_idx = self.W.get_time_idx(t)
colours = utils.generate_colours(M, len(wrfouts))
# import pdb; pdb.set_trace()
if lv == 2000:
lv_idx = None
else:
print("Only support surface right now")
raise Exception
lat_sl, lon_sl = self.get_limited_domain(da)
slices = {'t': time_idx, 'lv': lv_idx, 'la': lat_sl, 'lo': lon_sl}
# self.ax.set_color_cycle(colours)
ctlist = []
for n, wrfout in enumerate(wrfouts):
self.W = WRFOut(wrfout)
data = self.W.get(va, slices)[0, ...]
# m.contour(x,y,data,levels=[contour,])
ct = m.contour(x,
y,
data,
colors=[
colours[n],
],
levels=[
contour,
],
label=wrfout.split('/')[-2])
print(
("Plotting contour level {0} for {1} from file \n {2}".format(
contour, va, wrfout)))
# ctlist.append(ct)
# self.ax.legend()
# labels = [w.split('/')[-2] for w in wrfouts]
# print labels
# self.fig.legend(handles=ctlist)
# plt.legend(handles=ctlist,labels=labels)
#labels,ncol=3, loc=3,
# bbox_to_anchor=[0.5,1.5])
datestr = utils.string_from_time('output', t, tupleformat=0)
lv_na = utils.get_level_naming(va, lv)
naming = ['spaghetti', va, lv_na, datestr]
if dom:
naming.append(dom)
fname = self.create_fname(*naming)
self.save(outpath, fname)
def spaghetti(self,t,lv,va,contour,wrfouts,outpath,da=0,dom=0):
"""
wrfouts : list of wrfout files
Only change dom if there are multiple domains.
"""
m,x,y = self.basemap_setup()
time_idx = self.W.get_time_idx(t)
colours = utils.generate_colours(M,len(wrfouts))
# import pdb; pdb.set_trace()
if lv==2000:
lv_idx = None
else:
print("Only support surface right now")
raise Exception
lat_sl, lon_sl = self.get_limited_domain(da)
slices = {'t': time_idx, 'lv': lv_idx, 'la': lat_sl, 'lo': lon_sl}
# self.ax.set_color_cycle(colours)
ctlist = []
for n,wrfout in enumerate(wrfouts):
self.W = WRFOut(wrfout)
data = self.W.get(va,slices)[0,...]
# m.contour(x,y,data,levels=[contour,])
ct = m.contour(x,y,data,colors=[colours[n],],levels=[contour,],label=wrfout.split('/')[-2])
print("Plotting contour level {0} for {1} from file \n {2}".format(
contour,va,wrfout))
# ctlist.append(ct)
# self.ax.legend()
# labels = [w.split('/')[-2] for w in wrfouts]
# print labels
# self.fig.legend(handles=ctlist)
# plt.legend(handles=ctlist,labels=labels)
#labels,ncol=3, loc=3,
# bbox_to_anchor=[0.5,1.5])
datestr = utils.string_from_time('output',t,tupleformat=0)
lv_na = utils.get_level_naming(va,lv)
naming = ['spaghetti',va,lv_na,datestr]
if dom:
naming.append(dom)
fname = self.create_fname(*naming)
self.save(outpath,fname)
def composite_profile(self,
va,
plot_time,
plot_latlon,
wrfouts,
outpath,
dom=1,
mean=True,
std=True,
xlim=False,
ylim=False,
fig=False,
ax=False,
locname=0,
ml=-2):
"""
Loop over wrfout files.
Get profile of variable
Plot all members
Optional standard deviation
Optional mean
If ax, save image to that axis
ml : member level. negative number that corresponds to the
folder in absolute string for naming purposes.
"""
# Set up figure
if isinstance(fig, M.figure.Figure):
self.fig = fig
self.ax = ax
else:
self.fig, self.ax = plt.subplots()
# Plot settings
if xlim:
xmin, xmax, xint = xlim
if ylim:
if ylim[1] > ylim[0]:
# top to bottom
P_top, P_bot, dp = [y * 100.0 for y in ylim]
else:
P_bot, P_top, dp = [y * 100.0 for y in ylim]
else:
P_bot = 100000.0
P_top = 20000.0
dp = 10000.0
# plevs = N.arange(P_bot,P_top,dp)
# Get wrfout prototype for information
W = WRFOut(wrfouts[0])
lat, lon = plot_latlon
datestr = utils.string_from_time('output', plot_time)
t_idx = W.get_time_idx(plot_time, )
y, x, exact_lat, exact_lon = utils.getXY(W.lats1D, W.lons1D, lat, lon)
slices = {'t': t_idx, 'la': y, 'lo': x}
#var_slices = {'t': t_idx, 'lv':0, 'la':y, 'lo':x}
# Initialise array
# Number of levels and ensembles:
nPlevs = W.z_dim
data = W.get(va, slices)
nvarlevs = data.shape[1]
nens = len(wrfouts)
# 2D: (profile,member)
profile_arr = N.zeros((nvarlevs, nens))
composite_P = N.zeros((nPlevs, nens))
# Set up legend
labels = []
colourlist = utils.generate_colours(M, nens)
# M.rcParams['axes.color_cycle'] = colourlist
# Collect profiles
for n, wrfout in enumerate(wrfouts):
W = WRFOut(wrfout)
# Get pressure levels
composite_P[:, n] = W.get('pressure', slices)[0, :, 0, 0]
#elev = self.W.get('HGT',H_slices)
#pdb.set_trace()
# Grab variable
profile_arr[:, n] = W.get(va, slices)[0, :, 0, 0]
# Plot variable on graph
self.ax.plot(profile_arr[:, n],
composite_P[:, n],
color=colourlist[n])
member = wrfout.split('/')[ml]
labels.append(member)
# if locname=='KOAX': pdb.set_trace()
# Compute mean, std etc
if mean:
profile_mean = N.mean(profile_arr, axis=1)
profile_mean_P = N.mean(composite_P, axis=1)
self.ax.plot(profile_mean, profile_mean_P, color='black')
if std:
# Assume mean P across ensemble is correct level
# to plot standard deviation
profile_std = N.std(profile_arr, axis=1)
std_upper = profile_mean + profile_std
std_lower = profile_mean - profile_std
self.ax.plot(std_upper, profile_mean_P, 'k--')
self.ax.plot(std_lower, profile_mean_P, 'k--')
if not locname:
fname = '_'.join(('profile_comp', va, datestr, '{0:03d}'.format(x),
'{0:03d}'.format(y))) + '.png'
else:
fname = '_'.join(('profile_comp', va, datestr, locname)) + '.png'
# Set semi-log graph
self.ax.set_yscale('log')
# Plot limits, ticks
yticks = N.arange(P_bot, P_top + dp * 100.0, -100 * 100.0)
# yticks = N.arange(P_bot,P_top+dp,-dp*100)
# self.ax.set_yticks(yticks)
ylabels = ["%4u" % (p / 100.0) for p in yticks]
self.ax.set_yticks(yticks)
self.ax.set_yticklabels(ylabels)
# import pdb; pdb.set_trace()
# self.ax.yaxis.tick_right()
#plt.axis([-20,50,105000.0,20000.0])
#plt.xlabel(r'Temperature ($^{\circ}$C) at 1000 hPa')
#plt.xticks(xticks,['' if tick%10!=0 else str(tick) for tick in xticks])
self.ax.set_ylabel('Pressure (hPa)')
#yticks = N.arange(self.P_bot,P_t-1,-10**4)
#plt.yticks(yticks,yticks/100)
if xlim:
self.ax.set_xlim([xmin, xmax])
xticks = N.arange(xmin, xmax + xint, xint)
self.ax.set_xticks(xticks)
# Flip y axis
# Limits are already set
self.ax.set_ylim([P_bot, P_top])
# plt.tight_layout(self.fig)
#plt.autoscale(enable=1,axis='x')
#ax = plt.gca()
#ax.relim()
#ax.autoscale_view()
#plt.draw()
self.ax.legend(labels, loc=2, fontsize=6)
self.save(outpath, fname)
plt.close(self.fig)
def composite_profile(self,va,plot_time,plot_latlon,wrfouts,outpath,
dom=1,mean=1,std=1,xlim=0,ylim=0,fig=0,ax=0,
locname=0,ml=-2):
"""
Loop over wrfout files.
Get profile of variable
Plot all members
Optional standard deviation
Optional mean
If ax, save image to that axis
ml : member level. negative number that corresponds to the
folder in absolute string for naming purposes.
"""
# Set up figure
if isinstance(fig,M.figure.Figure):
self.fig = fig
self.ax = ax
else:
self.fig, self.ax = plt.subplots()
# Plot settings
if xlim:
xmin, xmax, xint = xlim
if ylim:
if ylim[1] > ylim[0]:
# top to bottom
P_top, P_bot, dp = [y*100.0 for y in ylim]
else:
P_bot, P_top, dp = [y*100.0 for y in ylim]
else:
P_bot = 100000.0
P_top = 20000.0
dp = 10000.0
# plevs = N.arange(P_bot,P_top,dp)
# Get wrfout prototype for information
W = WRFOut(wrfouts[0])
lat, lon = plot_latlon
datestr = utils.string_from_time('output',plot_time)
t_idx = W.get_time_idx(plot_time,)
y, x, exact_lat, exact_lon = utils.getXY(W.lats1D,W.lons1D,lat,lon)
slices = {'t': t_idx, 'la': y, 'lo': x}
#var_slices = {'t': t_idx, 'lv':0, 'la':y, 'lo':x}
# Initialise array
# Number of levels and ensembles:
nPlevs = W.z_dim
data = W.get(va,slices)
nvarlevs = data.shape[1]
nens = len(wrfouts)
# 2D: (profile,member)
profile_arr = N.zeros((nvarlevs,nens))
composite_P = N.zeros((nPlevs,nens))
# Set up legend
labels = []
colourlist = utils.generate_colours(M,nens)
# M.rcParams['axes.color_cycle'] = colourlist
# Collect profiles
for n,wrfout in enumerate(wrfouts):
W = WRFOut(wrfout)
# Get pressure levels
composite_P[:,n] = W.get('pressure',slices)[0,:,0,0]
#elev = self.W.get('HGT',H_slices)
#pdb.set_trace()
# Grab variable
profile_arr[:,n] = W.get(va,slices)[0,:,0,0]
# Plot variable on graph
self.ax.plot(profile_arr[:,n],composite_P[:,n],color=colourlist[n])
member = wrfout.split('/')[ml]
labels.append(member)
# if locname=='KOAX': pdb.set_trace()
# Compute mean, std etc
if mean:
profile_mean = N.mean(profile_arr,axis=1)
profile_mean_P = N.mean(composite_P,axis=1)
self.ax.plot(profile_mean,profile_mean_P,color='black')
if std:
# Assume mean P across ensemble is correct level
# to plot standard deviation
profile_std = N.std(profile_arr,axis=1)
std_upper = profile_mean + profile_std
std_lower = profile_mean - profile_std
self.ax.plot(std_upper,profile_mean_P,'k--')
self.ax.plot(std_lower,profile_mean_P,'k--')
if not locname:
fname = '_'.join(('profile_comp',va,datestr,'{0:03d}'.format(x),'{0:03d}'.format(y))) + '.png'
else:
fname = '_'.join(('profile_comp',va,datestr,locname)) + '.png'
# Set semi-log graph
self.ax.set_yscale('log')
# Plot limits, ticks
yticks = N.arange(P_bot,P_top+dp*100.0,-100*100.0)
# yticks = N.arange(P_bot,P_top+dp,-dp*100)
# self.ax.set_yticks(yticks)
ylabels = ["%4u" %(p/100.0) for p in yticks]
self.ax.set_yticks(yticks)
self.ax.set_yticklabels(ylabels)
# import pdb; pdb.set_trace()
# self.ax.yaxis.tick_right()
#plt.axis([-20,50,105000.0,20000.0])
#plt.xlabel(r'Temperature ($^{\circ}$C) at 1000 hPa')
#plt.xticks(xticks,['' if tick%10!=0 else str(tick) for tick in xticks])
self.ax.set_ylabel('Pressure (hPa)')
#yticks = N.arange(self.P_bot,P_t-1,-10**4)
#plt.yticks(yticks,yticks/100)
if xlim:
self.ax.set_xlim([xmin,xmax])
xticks = N.arange(xmin,xmax+xint,xint)
self.ax.set_xticks(xticks)
# Flip y axis
# Limits are already set
self.ax.set_ylim([P_bot,P_top])
# plt.tight_layout(self.fig)
#plt.autoscale(enable=1,axis='x')
#ax = plt.gca()
#ax.relim()
#ax.autoscale_view()
#plt.draw()
self.ax.legend(labels,loc=2,fontsize=6)
self.save(outpath,fname)
plt.close(self.fig)
def plot_error_growth(self,
ofname,
folder,
pfname,
sensitivity=0,
ylimits=0,
**kwargs):
"""Plots line graphs of DKE/DTE error growth
varying by a sensitivity - e.g. error growth involving
all members that use a certain parameterisation.
ofname : output filename prefix
pfname : pickle filename
plotlist : list of folder names to loop over
ylim : tuple of min/max for y axis range
"""
DATA = self.load_data(folder, pfname, format='pickle')
for perm in DATA:
times = DATA[perm]['times']
break
times_tup = [time.gmtime(t) for t in times]
time_str = ["{2:02d}/{3:02d}".format(*t) for t in times_tup]
if sensitivity:
# Plot multiple line charts for each sensitivity
# Then a final chart with all the averages
# If data is 2D, sum over x/y to get one number
# Dictionary with average
AVE = {}
for sens in sensitivity:
ave_stack = 0
n_sens = len(sensitivity) - 1
colourlist = utils.generate_colours(M, n_sens)
M.rcParams['axes.color_cycle'] = colourlist
fig = plt.figure()
labels = []
#SENS['sens'] = {}
for perm in DATA:
f1 = DATA[perm]['file1']
f2 = DATA[perm]['file2']
if sens in f1:
f = f2
elif sens in f2:
f = f1
else:
f = 0
if f:
subdirs = f.split('/')
labels.append(subdirs[-2])
data = self.make_1D(DATA[perm]['values'])
plt.plot(times, data)
# pdb.set_trace()
ave_stack = utils.vstack_loop(N.asarray(data),
ave_stack)
else:
pass
# pdb.set_trace()
n_sens += 1
colourlist = utils.generate_colours(M, n_sens)
M.rcParams['axes.color_cycle'] = colourlist
AVE[sens] = N.average(ave_stack, axis=0)
labels.append('Average')
plt.plot(times, AVE[sens], 'k')
plt.legend(labels, loc=2, fontsize=9)
if ylimits:
plt.ylim(ylimits)
plt.gca().set_xticks(times[::2])
plt.gca().set_xticklabels(time_str[::2])
outdir = self.C.output_root
fname = '{0}_Growth_{1}.png'.format(ofname, sens)
fpath = os.path.join(outdir, fname)
fig.savefig(fpath)
plt.close()
print("Saved {0}.".format(fpath))
# Averages for each sensitivity
labels = []
fig = plt.figure()
ave_of_ave_stack = 0
for sens in AVE.keys():
plt.plot(times, AVE[sens])
labels.append(sens)
ave_of_ave_stack = utils.vstack_loop(AVE[sens],
ave_of_ave_stack)
labels.append('Average')
ave_of_ave = N.average(ave_of_ave_stack, axis=0)
plt.plot(times, ave_of_ave, 'k')
plt.legend(labels, loc=2, fontsize=9)
if ylimits:
plt.ylim(ylimits)
plt.gca().set_xticks(times[::2])
plt.gca().set_xticklabels(time_str[::2])
outdir = self.C.output_root
fname = '{0}_Growth_Averages.png'.format(ofname)
fpath = os.path.join(outdir, fname)
fig.savefig(fpath)
plt.close()
print("Saved {0}.".format(fpath))
#pdb.set_trace()
else:
fig = plt.figure()
ave_stack = 0
for perm in DATA:
data = self.make_1D(DATA[perm]['values'])
plt.plot(times, data, 'blue')
ave_stack = utils.vstack_loop(N.asarray(data), ave_stack)
total_ave = N.average(ave_stack, axis=0)
plt.plot(times, total_ave, 'black')
if ylimits:
plt.ylim(ylimits)
plt.gca().set_xticks(times[::2])
plt.gca().set_xticklabels(time_str[::2])
outdir = self.C.output_root
fname = '{0}_Growth_allmembers.png'.format(ofname)
fpath = os.path.join(outdir, fname)
fig.savefig(fpath)
plt.close()
print("Saved {0}.".format(fpath))
