def get_xy_from_latlon(self,lat,lon):
"""
Return x and y coordinates for given lat/lon.
exactlat, exactlon : exact coordinates of closest x/y
"""
y,x,exactlat,exactlon = utils.getXY(self.W.lats1D,self.W.lons1D,lat,lon)
return x,y
def get_xy_from_latlon(self, lat, lon):
"""
Return x and y coordinates for given lat/lon.
exactlat, exactlon : exact coordinates of closest x/y
"""
y, x, exactlat, exactlon = utils.getXY(self.W.lats1D, self.W.lons1D,
lat, lon)
return x, y
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 plot_skewT(self,
plot_time,
plot_latlon,
dom,
outpath,
save_output=0,
save_plot=1):
# Defines the ranges of the plot, do not confuse with self.P_bot and self.P_top
self.barb_increments = {'half': 2.5, 'full': 5.0, 'flag': 25.0}
self.skewness = 37.5
self.P_bot = 100000.
self.P_top = 10000.
self.dp = 100.
self.plevs = N.arange(self.P_bot, self.P_top - 1, -self.dp)
# pdb.set_trace()
prof_lat, prof_lon = plot_latlon
datestr = utils.string_from_time('output', plot_time)
t_idx = self.W.get_time_idx(plot_time)
y, x, exact_lat, exact_lon = utils.getXY(self.W.lats1D, self.W.lons1D,
prof_lat, prof_lon)
# Create figure
if save_plot:
# height, width = (10,10)
# fig = plt.figure(figsize=(width,height))
self.isotherms()
self.isobars()
self.dry_adiabats()
self.moist_adiabats()
# P_slices = {'t': t_idx, 'la': y, 'lo': x}
# H_slices = {'t':t_idx, 'lv':0, 'la':y, 'lo':x}
# pdb.set_trace()
P = self.W.get('pressure', utc=t_idx, lats=y, lons=x)[0, :, 0, 0]
elev = self.W.get('HGT', utc=t_idx, level=0, lats=y, lons=x)
thin_locs = utils.thinned_barbs(P)
self.windbarbs(self.W.nc,
t_idx,
y,
x,
P,
thin_locs,
n=45,
color='blue')
self.temperature(self.W.nc,
t_idx,
y,
x,
P,
linestyle='solid',
color='blue')
self.dewpoint(self.W.nc,
t_idx,
y,
x,
P,
linestyle='dashed',
color='blue')
xticks = N.arange(-20, 51, 5)
yticks = N.arange(100000.0, self.P_top - 1, -10**4)
ytix = ["%4u" % (p / 100.0) for p in yticks]
self.ax.set_xticks(
xticks,
['' if tick % 10 != 0 else str(tick) for tick in xticks])
self.ax.set_yticks(yticks, ytix)
self.ax.axis([-20, 50, 105000.0, 20000.0])
self.ax.set_xlabel(r'Temperature ($^{\circ}$C) at 1000 hPa')
self.ax.set_xticks(
xticks,
['' if tick % 10 != 0 else str(tick) for tick in xticks])
self.ax.set_ylabel('Pressure (hPa)')
self.ax.set_yticks(yticks, ytix)
#yticks = N.arange(self.P_bot,P_t-1,-10**4)
#plt.yticks(yticks,yticks/100)
fname = '_'.join(('skewT', datestr, '{0:03d}'.format(x),
'{0:03d}'.format(y))) + '.png'
# utils.trycreate(self.path_to_output)
# fpath = os.path.join(self.path_to_output,fname)
# plt.savefig(fpath)
self.save(outpath, fname)
plt.close()
# For saving Skew T data
if save_output:
# Pickle files are saved here:
pickle_fname = '_'.join(
('WRFsounding', datestr, '{0:03d}'.format(x),
'{0:03d}'.format(y) + '.p'))
pickle_path = os.path.join(self.C.pickledir, pickle_fname)
u, v = self.return_data('wind', self.W.nc, t_idx, y, x, thin_locs)
T = self.return_data('temp',
self.W.nc,
t_idx,
y,
x,
thin_locs,
P=P)
Td = self.return_data('dwpt',
self.W.nc,
t_idx,
y,
x,
thin_locs,
P=P)
data_dict = {'u': u, 'v': v, 'T': T, 'Td': Td, 'P': P}
with open(pickle_path, 'wb') as p:
pickle.dump(data_dict, p)
print(("Saving data to {0}".format(pickle_path)))
return
else:
return
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_skewT(self,plot_time,plot_latlon,dom,outpath,save_output=0,save_plot=1):
# Defines the ranges of the plot, do not confuse with self.P_bot and self.P_top
self.barb_increments = {'half': 2.5,'full':5.0,'flag':25.0}
self.skewness = 37.5
self.P_bot = 100000.
self.P_top = 10000.
self.dp = 100.
self.plevs = N.arange(self.P_bot,self.P_top-1,-self.dp)
# pdb.set_trace()
prof_lat, prof_lon = plot_latlon
datestr = utils.string_from_time('output',plot_time)
t_idx = self.W.get_time_idx(plot_time)
y,x, exact_lat, exact_lon = utils.getXY(self.W.lats1D,self.W.lons1D,prof_lat,prof_lon)
# Create figure
if save_plot:
# height, width = (10,10)
# fig = plt.figure(figsize=(width,height))
self.isotherms()
self.isobars()
self.dry_adiabats()
self.moist_adiabats()
P_slices = {'t': t_idx, 'la': y, 'lo': x}
H_slices = {'t':t_idx, 'lv':0, 'la':y, 'lo':x}
# pdb.set_trace()
P = self.W.get('pressure',P_slices)[0,:,0,0]
elev = self.W.get('HGT',H_slices)
thin_locs = utils.thinned_barbs(P)
self.windbarbs(self.W.nc,t_idx,y,x,P,thin_locs,n=45,color='blue')
self.temperature(self.W.nc,t_idx,y,x,P,linestyle='solid',color='blue')
self.dewpoint(self.W.nc,t_idx,y,x,P,linestyle='dashed',color='blue')
xticks = N.arange(-20,51,5)
yticks = N.arange(100000.0,self.P_top-1,-10**4)
ytix = ["%4u" %(p/100.0) for p in yticks]
self.ax.set_xticks(xticks,['' if tick%10!=0 else str(tick) for tick in xticks])
self.ax.set_yticks(yticks,ytix)
self.ax.axis([-20,50,105000.0,20000.0])
self.ax.set_xlabel(r'Temperature ($^{\circ}$C) at 1000 hPa')
self.ax.set_xticks(xticks,['' if tick%10!=0 else str(tick) for tick in xticks])
self.ax.set_ylabel('Pressure (hPa)')
self.ax.set_yticks(yticks,ytix)
#yticks = N.arange(self.P_bot,P_t-1,-10**4)
#plt.yticks(yticks,yticks/100)
fname = '_'.join(('skewT',datestr,'{0:03d}'.format(x),'{0:03d}'.format(y))) + '.png'
# utils.trycreate(self.path_to_output)
# fpath = os.path.join(self.path_to_output,fname)
# plt.savefig(fpath)
self.save(outpath,fname)
plt.close()
# For saving Skew T data
if save_output:
# Pickle files are saved here:
pickle_fname = '_'.join(('WRFsounding',datestr,'{0:03d}'.format(x),
'{0:03d}'.format(y)+'.p'))
pickle_path = os.path.join(self.C.pickledir,pickle_fname)
u,v = self.return_data('wind',self.W.nc,t_idx,y,x,thin_locs)
T = self.return_data('temp',self.W.nc,t_idx,y,x,thin_locs,P=P)
Td = self.return_data('dwpt',self.W.nc,t_idx,y,x,thin_locs,P=P)
data_dict = {'u':u,'v':v,'T':T,'Td':Td,'P':P}
with open(pickle_path,'wb') as p:
pickle.dump(data_dict,p)
print("Saving data to {0}".format(pickle_path))
return
else:
return
def cold_pool_strength(self,
time,
wrf_sd=0,
wrf_nc=0,
out_sd=0,
swath_width=100,
dom=1,
twoplot=0,
fig=0,
axes=0,
dz=0):
"""
Pick A, B points on sim ref overlay
This sets the angle between north and line AB
Also sets the length in along-line direction
For every gridpt along line AB:
Locate gust front via shear
Starting at front, do 3-grid-pt-average in line-normal
direction
time : time (tuple or datenum) to plot
wrf_sd : string - subdirectory of wrfout file
wrf_nc : filename of wrf file requested.
If no wrfout file is explicitly specified, the
netCDF file in that folder is chosen if unambiguous.
out_sd : subdirectory of output .png.
swath_width : length in gridpoints in cross-section-normal direction
dom : domain number
return2 : return two figures. cold pool strength and cref/cross-section.
axes : if two-length tuple, this is the first and second axes for
cross-section/cref and cold pool strength, respectively
dz : plot height of cold pool only.
"""
# Initialise
self.W = self.get_wrfout(wrf_sd, wrf_nc, dom=dom)
outpath = self.get_outpath(out_sd)
# keyword arguments for plots
line_kwargs = {}
cps_kwargs = {}
# Create two-panel figure
if twoplot:
P2 = Figure(self.C, self.W, plotn=(1, 2))
line_kwargs['ax'] = P2.ax.flat[0]
line_kwargs['fig'] = P2.fig
P2.ax.flat[0].set_size_inches(3, 3)
cps_kwargs['ax'] = P2.ax.flat[1]
cps_kwargs['fig'] = P2.fig
P2.ax.flat[1].set_size_inches(6, 6)
elif isinstance(axes, tuple) and len(axes) == 2:
line_kwargs['ax'] = axes[0]
line_kwargs['fig'] = fig
cps_kwargs['ax'] = axes[1]
cps_kwargs['fig'] = fig
return_ax = 1
# Plot sim ref, send basemap axis to clicker function
F = BirdsEye(self.C, self.W)
self.data = F.plot2D('cref',
time,
2000,
dom,
outpath,
save=0,
return_data=1)
C = Clicker(self.C, self.W, data=self.data, **line_kwargs)
# C.fig.tight_layout()
# Line from front to back of system
C.draw_line()
# C.draw_box()
lon0, lat0 = C.bmap(C.x0, C.y0, inverse=True)
lon1, lat1 = C.bmap(C.x1, C.y1, inverse=True)
# Pick location for environmental dpt
# C.click_x_y()
# Here, it is the end of the cross-section
lon_env, lat_env = C.bmap(C.x1, C.y1, inverse=True)
y_env, x_env, exactlat, exactlon = utils.getXY(self.W.lats1D,
self.W.lons1D, lat_env,
lon_env)
# Create the cross-section object
X = CrossSection(self.C, self.W, lat0, lon0, lat1, lon1)
# Ask user the line-normal box width (self.km)
#C.set_box_width(X)
# Compute the grid (DX x DY)
cps = self.W.cold_pool_strength(X,
time,
swath_width=swath_width,
env=(x_env, y_env),
dz=dz)
# import pdb; pdb.set_trace()
# Plot this array
CPfig = BirdsEye(self.C, self.W, **cps_kwargs)
tstr = utils.string_from_time('output', time)
if dz:
fprefix = 'ColdPoolDepth_'
else:
fprefix = 'ColdPoolStrength_'
fname = fprefix + tstr
pdb.set_trace()
# imfig,imax = plt.subplots(1)
# imax.imshow(cps)
# plt.show(imfig)
# CPfig.plot_data(cps,'contourf',outpath,fname,time,V=N.arange(5,105,5))
mplcommand = 'contour'
plotkwargs = {}
if dz:
clvs = N.arange(100, 5100, 100)
else:
clvs = N.arange(10, 85, 2.5)
if mplcommand[:7] == 'contour':
plotkwargs['levels'] = clvs
plotkwargs['cmap'] = plt.cm.ocean_r
cf2 = CPfig.plot_data(cps, mplcommand, outpath, fname, time,
**plotkwargs)
# CPfig.fig.tight_layout()
plt.close(fig)
if twoplot:
P2.save(outpath, fname + "_twopanel")
if return_ax:
return C.cf, cf2
