def map_lakepct(var,
lon,
lat,
levels,
title_str,
clrmap,
fig_name,
alone=True,
ax=False):
# figure creation
if alone:
fig, ax = plt.subplots(1,
1,
figsize=(13, 8),
subplot_kw={'projection': ccrs.PlateCarree()})
ax.add_feature(ctp.feature.OCEAN, color='gainsboro')
ax.coastlines(color='darkgrey')
#ax.coastlines(color="grey")
LON, LAT = mpu.infer_interval_breaks(lon, lat)
# add the data to the map (more info on colormaps: https://matplotlib.org/users/colormaps.html)
cmap, norm = mpu.from_levels_and_cmap(levels, clrmap)
# do not show zeros
var[var == 0] = np.nan
cmap.set_over(cmap(0.99))
h = ax.pcolormesh(LON, LAT, var, cmap=cmap, norm=norm)
# set the extent of the cartopy geoAxes to "global"
ax.set_global()
ax.set_title(title_str, pad=10, loc='right')
# remove the frame
ax.outline_patch.set_visible(False)
# plot the colorbar
cbar = mpu.colorbar(h, ax, orientation='horizontal', pad=0.1, extend='max')
cbar.ax.set_xlabel('Natural lake area fraction [%]', size=16)
cbar.set_ticks(np.arange(0, 110, 10)) # horizontal colorbar
#cbar.ax.set_xticklabels(['Low', 'Medium', 'High']) # horizontal colorbar
# if so, save the figure
if fig_name != 0 and alone:
plt.savefig(fig_name + '.jpeg', dpi=1000, bbox_inches='tight')
def plot_region_hc_map(var, region_props, lakes_path, indir_lakedata):
# get region specific info from dictionary
extent = region_props['extent']
continent_extent = region_props['continent_extent']
name = region_props['name']
name_str = region_props['name_str']
ax_location = region_props['ax_location']
levels = region_props['levels']
fig_size = region_props['fig_size']
cb_orientation = region_props['cb_orientation']
path_lakes = lakes_path + name + '.shp'
# settings
clb_label = 'Joule'
title_str = name_str + ' heat content anomaly'
fig_name = 'Heat_content_' + name
cmap = 'YlOrBr'
cmap, norm = mpu.from_levels_and_cmap(levels, cmap, extend='max')
lon, lat = get_lonlat(indir_lakedata)
LON, LAT = mpu.infer_interval_breaks(lon, lat)
lakes = gpd.read_file(path_lakes)
# plotting
fig, ax = plt.subplots(1,
1,
figsize=fig_size,
subplot_kw={'projection': ccrs.PlateCarree()})
ax.add_feature(ctp.feature.OCEAN, color='gainsboro')
ax.coastlines(color="grey")
# add the data to the map (more info on colormaps: https://matplotlib.org/users/colormaps.html)
h = ax.pcolormesh(LON, LAT, var, cmap=cmap, norm=norm)
# load the lake shapefile
lakes.plot(ax=ax, edgecolor='gray', facecolor='none')
# set grid lines
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=0.5,
color='gainsboro',
alpha=0.5)
gl.xlines = True
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabels_bottom = None
gl.ylabels_right = None
# set extent (in right way)
extent[1], extent[2] = extent[2], extent[1]
ax.set_extent(extent)
# create effect for map borders:
effect = Stroke(linewidth=1.5, foreground='darkgray')
# set effect for main ax
ax.outline_patch.set_path_effects([effect])
# Create an inset GeoAxes showing the location of the lakes region
#x0 y0 width height
sub_ax = fig.add_axes(ax_location, projection=ccrs.PlateCarree())
sub_ax.set_extent(continent_extent)
#lakes.plot(ax=sub_ax)
sub_ax.outline_patch.set_path_effects([effect])
extent_box = sgeom.box(extent[0], extent[2], extent[1], extent[3])
sub_ax.add_geometries([extent_box],
ccrs.PlateCarree(),
facecolor='none',
edgecolor='red',
linewidth=2)
# Add the land, coastlines and the extent of the inset axis
sub_ax.add_feature(cfeature.LAND, edgecolor='gray')
sub_ax.coastlines(color='gray')
extent_box = sgeom.box(extent[0], extent[2], extent[1], extent[3])
sub_ax.add_geometries([extent_box],
ccrs.PlateCarree(),
facecolor='none',
edgecolor='black',
linewidth=2)
# plot the colorbar
cbar = mpu.colorbar(h,
ax,
extend='max',
orientation=cb_orientation,
pad=0.05)
if cb_orientation == 'vertical':
cbar.ax.set_ylabel(clb_label, size=16)
elif cb_orientation == 'horizontal':
cbar.ax.set_xlabel(clb_label, size=16)
#ax.set_title(title_str, pad=10)
plotdir = '/home/inne/documents/phd/data/processed/isimip_lakeheat/plots/'
plt.savefig(plotdir + fig_name + '.png', dpi=500)
def plot_global_hc_map(name_str, var, lakes_path, indir_lakedata):
# get region specific info from dictionary
if name_str == 'global_absolute':
levels = np.arange(-1e17, 1.1e17, 0.1e17)
elif name_str == 'global':
levels = np.arange(-1e19, 1.1e19, 0.1e19)
cb_orientation = 'horizontal'
path_lakes = lakes_path + name_str + '.shp'
# settings
clb_label = 'Joule'
title_str = name_str + ' heat content anomaly'
fig_name = 'Heat_content_' + name_str
cmap = 'RdBu_r' #, 'YlOrBr'
cmap, norm = mpu.from_levels_and_cmap(levels, cmap, extend='max')
lon, lat = get_lonlat(indir_lakedata)
LON, LAT = mpu.infer_interval_breaks(lon, lat)
# plotting
fig, ax = plt.subplots(1,
1,
figsize=(13, 8),
subplot_kw={'projection': ccrs.PlateCarree()})
ax.add_feature(ctp.feature.OCEAN, color='gainsboro')
ax.coastlines(color="grey")
# add the data to the map (more info on colormaps: https://matplotlib.org/users/colormaps.html)
h = ax.pcolormesh(LON, LAT, var, cmap=cmap, norm=norm)
# set grid lines
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=0.5,
color='gainsboro',
alpha=0.5)
gl.xlines = True
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabels_bottom = None
gl.ylabels_right = None
effect = Stroke(linewidth=1.5, foreground='darkgray')
# set effect for main ax
ax.outline_patch.set_path_effects([effect])
# plot the colorbar
cbar = mpu.colorbar(h,
ax,
extend='max',
orientation=cb_orientation,
pad=0.05)
if cb_orientation == 'vertical':
cbar.ax.set_ylabel(clb_label, size=16)
elif cb_orientation == 'horizontal':
cbar.ax.set_xlabel(clb_label, size=16)
#ax.set_title(title_str, pad=10)
plotdir = '/home/inne/documents/phd/data/processed/isimip_lakeheat/plots/'
plt.savefig(plotdir + fig_name + '.png')
f, ax = plt.subplots(1, 1, subplot_kw=dict(projection=ccrs.Robinson()))
h = data.plot.pcolormesh(ax=ax,
transform=ccrs.PlateCarree(),
vmin=-2,
vmax=2,
cmap="RdBu_r",
add_colorbar=False,
rasterized=True)
ax.coastlines()
ax.set_global()
ax.set_title("")
f.subplots_adjust(left=0.025, right=0.875, bottom=0.05, top=0.95)
mpu.colorbar(h, ax, extend='both')
#mpu.colorbar(h, ax, extend='max')
plt.draw()
plt.savefig(out_file, dpi=400, bbox_inches="tight")
#calculate BGP/BGC ratio
file_B17 = f'{in_dir}TSrec_{scen}_B17_{season}_850-2015sum.nc'
file_D18 = f'{in_dir}TSrec_{scen}_D18_{season}_850-2015sum.nc'
rec = (xr.open_dataset(file_B17).TSrec + xr.open_dataset(file_D18).TSrec) / 2.
BGP = rec.sum(dim=["conversion"])
ratio = (BGP / ts_map.where(ts_map > 0.01)) * 100.
#plot map of BGP/BGC ratio
data = ratio
def horizontal_map(variable_name, date, start_hour,
end_hour, pressure_level=False,
subset=False, initiation=False,
save=False, gif=False):
'''This function plots the chosen variable for the analysis
of the initiation environment on a horizontal (2D) map. Supported variables for plotting
procedure are updraft, reflectivity, helicity, pw, cape, cin, ctt, temperature_surface,
wind_shear, updraft_reflectivity, rh, omega, pvo, avo, theta_e, water_vapor, uv_wind and
divergence.'''
### Predefine some variables ###
# Get the list of all needed wrf files
data_dir = '/scratch3/thomasl/work/data/casestudy_baden/'
# Define save directory
save_dir = '/scratch3/thomasl/work/retrospective_part' '/casestudy_baden/horizontal_maps/'
# Change extent of plot
subset_extent = [6.2, 9.4, 46.5, 48.5]
# Set the location of the initiation of the thunderstorm
initiation_location = CoordPair(lat=47.25, lon=7.85)
# 2D variables:
if variable_name == 'updraft':
variable_name = 'W_UP_MAX'
title_name = 'Maximum Z-Wind Updraft'
colorbar_label = 'Max Z-Wind Updraft [$m$ $s^-$$^1$]'
save_name = 'updraft'
variable_min = 0
variable_max = 30
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'reflectivity':
variable_name = 'REFD_MAX'
title_name = 'Maximum Derived Radar Reflectivity'
colorbar_label = 'Maximum Derived Radar Reflectivity [$dBZ$]'
save_name = 'reflectivity'
variable_min = 0
variable_max = 75
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'helicity':
variable_name = 'UP_HELI_MAX'
title_name = 'Maximum Updraft Helicity'
colorbar_label = 'Maximum Updraft Helicity [$m^{2}$ $s^{-2}$]'
save_name = 'helicity'
variable_min = 0
variable_max = 140
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'pw':
title_name = 'Precipitable Water'
colorbar_label = 'Precipitable Water [$kg$ $m^{-2}$]'
save_name = 'pw'
variable_min = 0
variable_max = 50
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'cape':
variable_name = 'cape_2d'
title_name = 'CAPE'
colorbar_label = 'Convective Available Potential Energy' '[$J$ $kg^{-1}$]'
save_name = 'cape'
variable_min = 0
variable_max = 3000
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'cin':
variable_name = 'cape_2d'
title_name = 'CIN'
colorbar_label = 'Convective Inhibition [$J$ $kg^{-1}$]'
save_name = 'cin'
variable_min = 0
variable_max = 100
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'ctt':
title_name = 'Cloud Top Temperature'
colorbar_label = 'Cloud Top Temperature [$K$]'
save_name = 'cct'
variable_min = 210
variable_max = 300
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'temperature_surface':
variable_name = 'T2'
title_name = 'Temperature @ 2 m'
colorbar_label = 'Temperature [$K$]'
save_name = 'temperature_surface'
variable_min = 285
variable_max = 305
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'wind_shear':
variable_name = 'slp'
title_name = 'SLP, Wind @ 850hPa, Wind @ 500hPa\n' 'and 500-850hPa Vertical Wind Shear'
save_name = 'wind_shear'
variable_min = 1000
variable_max = 1020
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
elif variable_name == 'updraft_reflectivity':
variable_name = 'W_UP_MAX'
title_name = 'Updraft and Reflectivity'
colorbar_label = 'Max Z-Wind Updraft [$m$ $s^-$$^1$]'
save_name = 'updraft_reflectivity'
variable_min = 0
variable_max = 30
# Check if a certain pressure_level was defined.
if pressure_level != False:
sys.exit('The variable {} is a 2D variable. ' 'Definition of a pressure_level for ' 'plotting process is not required.'.format(variable_name))
# 3D variables:
elif variable_name == 'rh':
title_name = 'Relative Humidity'
colorbar_label = 'Relative Humidity [$pct$]'
save_name = 'rh'
variable_min = 0
variable_max = 100
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'omega':
title_name = 'Vertical Motion'
colorbar_label = 'Omega [$Pa$ $s^-$$^1$]'
save_name = 'omega'
variable_min = -50
variable_max = 50
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'pvo':
title_name = 'Potential Vorticity'
colorbar_label = 'Potential Vorticity [$PVU$]'
save_name = 'pvo'
variable_min = -1
variable_max = 9
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'avo':
title_name = 'Absolute Vorticity'
colorbar_label = 'Absolute Vorticity [$10^{-5}$' '$s^{-1}$]'
save_name = 'avo'
variable_min = -250
variable_max = 250
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'theta_e':
title_name = 'Theta-E'
colorbar_label = 'Theta-E [$K$]'
save_name = 'theta_e'
variable_min = 315
variable_max = 335
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'water_vapor':
variable_name = 'QVAPOR'
title_name = 'Water Vapor Mixing Ratio'
colorbar_label = 'Water Vapor Mixing Ratio [$g$ $kg^{-1}$]'
save_name = 'water_vapor'
variable_min = 5
variable_max = 15
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'uv_wind':
variable_name = 'wspd_wdir'
title_name = 'Wind Speed and Direction'
colorbar_label = 'Wind Speed [$m$ $s^{-1}$]'
save_name = 'uv_wind'
variable_min = 0
variable_max = 10
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
elif variable_name == 'divergence':
variable_name = 'ua'
title_name = 'Horizontal Wind Divergence'
colorbar_label = 'Divergence [$10^{-6}$ $s^{-1}$]'
save_name = 'divergence'
variable_min = -2.5
variable_max = 2.5
# Check if a certain pressure_level was defined.
if pressure_level == False:
sys.exit('The variable {} is a 3D variable. ' 'Definition of a pressure_level for ' 'plotting process is required.'.format(variable_name))
# Make a list of all wrf files in data directory
wrflist = list()
for (dirpath, dirnames, filenames) in os.walk(data_dir):
wrflist += [os.path.join(dirpath, file) for file in filenames]
### Plotting Iteration ###
# Iterate over a list of hourly timesteps
time = list()
for i in range(start_hour, end_hour):
time = str(i).zfill(2)
# Iterate over all 5 minutes steps of hour
for j in range(0, 60, 5):
minutes = str(j).zfill(2)
# Load the netCDF files out of the wrflist
ncfile = [Dataset(x) for x in wrflist
if x.endswith('{}_{}:{}:00'.format(date, time, minutes))]
# Load variable(s)
if title_name == 'CAPE':
variable = getvar(ncfile, variable_name)[0,:]
elif title_name == 'CIN':
variable = getvar(ncfile, variable_name)[1,:]
elif variable_name == 'ctt':
variable = getvar(ncfile, variable_name, units='K')
elif variable_name == 'wspd_wdir':
variable = getvar(ncfile, variable_name)[0,:]
elif variable_name == 'QVAPOR':
variable = getvar(ncfile, variable_name)*1000 # convert to g/kg
else:
variable = getvar(ncfile, variable_name)
if variable_name == 'slp':
slp = variable.squeeze()
ua = getvar(ncfile, 'ua')
va = getvar(ncfile, 'va')
p = getvar(ncfile, 'pressure')
u_wind850 = interplevel(ua, p, 850)
v_wind850 = interplevel(va, p, 850)
u_wind850 = u_wind850.squeeze()
v_wind850 = v_wind850.squeeze()
u_wind500 = interplevel(ua, p, 500)
v_wind500 = interplevel(va, p, 500)
u_wind500 = u_wind500.squeeze()
v_wind500 = v_wind500.squeeze()
slp = ndimage.gaussian_filter(slp, sigma=3, order=0)
# Interpolating 3d data to a horizontal pressure level
if pressure_level != False:
p = getvar(ncfile, 'pressure')
variable_pressure = interplevel(variable, p,
pressure_level)
variable = variable_pressure
if variable_name == 'wspd_wdir':
ua = getvar(ncfile, 'ua')
va = getvar(ncfile, 'va')
u_pressure = interplevel(ua, p, pressure_level)
v_pressure = interplevel(va, p, pressure_level)
elif title_name == 'Updraft and Reflectivity':
reflectivity = getvar(ncfile, 'REFD_MAX')
elif title_name == 'Difference in Theta-E values':
variable = getvar(ncfile, variable_name)
p = getvar(ncfile, 'pressure')
variable_pressure1 = interplevel(variable, p, '950')
variable_pressure2 = interplevel(variable, p, '950')
elif variable_name == 'ua':
va = getvar(ncfile, 'va')
p = getvar(ncfile, 'pressure')
v_pressure = interplevel(va, p, pressure_level)
u_wind = variable.squeeze()
v_wind = v_pressure.squeeze()
u_wind.attrs['units']='meters/second'
v_wind.attrs['units']='meters/second'
lats, lons = latlon_coords(variable)
lats = lats.squeeze()
lons = lons.squeeze()
dx, dy = mpcalc.lat_lon_grid_deltas(to_np(lons), to_np(lats))
divergence = mpcalc.divergence(u_wind, v_wind, dx, dy, dim_order='yx')
divergence = divergence*1e3
# Define cart projection
lats, lons = latlon_coords(variable)
cart_proj = ccrs.LambertConformal(central_longitude=8.722206,
central_latitude=46.73585)
bounds = geo_bounds(wrfin=ncfile)
# Create figure
fig = plt.figure(figsize=(15, 10))
if variable_name == 'slp':
fig.patch.set_facecolor('k')
ax = plt.axes(projection=cart_proj)
### Set map extent ###
domain_extent = [3.701088, 13.814863, 43.85472,49.49499]
if subset == True:
ax.set_extent([subset_extent[0],subset_extent[1],
subset_extent[2],subset_extent[3]],
ccrs.PlateCarree())
else:
ax.set_extent([domain_extent[0]+0.7,domain_extent[1]-0.7,
domain_extent[2]+0.1,domain_extent[3]-0.1],
ccrs.PlateCarree())
# Plot contour of variables
levels_num = 11
levels = np.linspace(variable_min, variable_max, levels_num)
# Creating new colormap for diverging colormaps
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
new_cmap = LinearSegmentedColormap.from_list(
'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval,
b=maxval),
cmap(np.linspace(minval, maxval, n)))
return new_cmap
cmap = plt.get_cmap('RdYlBu')
if title_name == 'CIN':
cmap = ListedColormap(sns.cubehelix_palette(levels_num-1,
start=.5, rot=-.75, reverse=True))
variable_plot = plt.contourf(to_np(lons), to_np(lats), to_np(variable),
levels=levels, transform=ccrs.PlateCarree(), extend='max',
cmap=cmap)
initiation_color = 'r*'
elif variable_name == 'ctt':
cmap = ListedColormap(sns.cubehelix_palette(levels_num-1,
start=.5, rot=-.75, reverse=True))
variable_plot = plt.contourf(to_np(lons), to_np(lats), to_np(variable),
levels=levels, transform=ccrs.PlateCarree(), extend='both',
cmap=cmap)
initiation_color = 'r*'
elif variable_name == 'pvo':
cmap = plt.get_cmap('RdYlBu_r')
new_cmap = truncate_colormap(cmap, 0.05, 0.9)
new_norm = DivergingNorm(vmin=-1., vcenter=2., vmax=10)
variable_plot = plt.contourf(to_np(lons), to_np(lats), to_np(variable),
levels=levels, transform=ccrs.PlateCarree(),
cmap=new_cmap, extend='both', norm=new_norm)
initiation_color = 'k*'
elif variable_name == 'avo':
cmap = plt.get_cmap('RdYlBu_r')
new_cmap = truncate_colormap(cmap, 0.05, 0.9)
new_norm = DivergingNorm(vmin=variable_min, vcenter=0, vmax=variable_max)
variable_plot = plt.contourf(to_np(lons), to_np(lats), to_np(variable),
levels=levels, transform=ccrs.PlateCarree(),
cmap=new_cmap, extend='both', norm=new_norm)
initiation_color = 'k*'
elif variable_name == 'omega':
new_cmap = truncate_colormap(cmap, 0.05, 0.9)
new_norm = DivergingNorm(vmin=variable_min, vcenter=0, vmax=variable_max)
variable_plot = plt.contourf(to_np(lons), to_np(lats), to_np(variable),
levels=levels, transform=ccrs.PlateCarree(),
cmap=new_cmap, extend='both', norm=new_norm)
initiation_color = 'k*'
elif variable_name == 'ua':
new_cmap = truncate_colormap(cmap, 0.05, 0.9)
new_norm = DivergingNorm(vmin=variable_min, vcenter=0, vmax=variable_max)
variable_plot = plt.contourf(to_np(lons), to_np(lats), divergence,
levels=levels, transform=ccrs.PlateCarree(),
cmap=new_cmap, extend='both', norm=new_norm)
initiation_color = 'k*'
elif variable_name == 'UP_HELI_MAX' or variable_name == 'W_UP_MAX' or variable_name == 'QVAPOR':
cmap = ListedColormap(sns.cubehelix_palette(levels_num-1,
start=.5, rot=-.75))
variable_plot = plt.contourf(to_np(lons), to_np(lats),
to_np(variable), levels=levels, extend='max',
transform=ccrs.PlateCarree(),cmap=cmap)
initiation_color = 'r*'
elif variable_name == 'theta_e' or variable_name == 't2':
cmap = ListedColormap(sns.cubehelix_palette(levels_num-1,
start=.5, rot=-.75))
variable_plot = plt.contourf(to_np(lons), to_np(lats),
to_np(variable), levels=levels, extend='both',
transform=ccrs.PlateCarree(),cmap=cmap)
initiation_color = 'r*'
elif variable_name == 'REFD_MAX':
levels = np.arange(5., 75., 5.)
dbz_rgb = np.array([[4,233,231],
[1,159,244], [3,0,244],
[2,253,2], [1,197,1],
[0,142,0], [253,248,2],
[229,188,0], [253,149,0],
[253,0,0], [212,0,0],
[188,0,0],[248,0,253],
[152,84,198]], np.float32) / 255.0
dbz_cmap, dbz_norm = from_levels_and_colors(levels, dbz_rgb,
extend='max')
variable_plot = plt.contourf(to_np(lons), to_np(lats),
to_np(variable), levels=levels, extend='max',
transform=ccrs.PlateCarree(), cmap=dbz_cmap,
norm=dbz_norm)
initiation_color = 'r*'
elif variable_name == 'slp':
ax.background_patch.set_fill(False)
wslice = slice(1, None, 12)
# Plot 850-hPa wind vectors
vectors850 = ax.quiver(to_np(lons)[wslice, wslice],
to_np(lats)[wslice, wslice],
to_np(u_wind850)[wslice, wslice],
to_np(v_wind850)[wslice, wslice],
headlength=4, headwidth=3, scale=400, color='gold',
label='850mb wind', transform=ccrs.PlateCarree(),
zorder=2)
# Plot 500-hPa wind vectors
vectors500 = ax.quiver(to_np(lons)[wslice, wslice],
to_np(lats)[wslice, wslice],
to_np(u_wind500)[wslice, wslice],
to_np(v_wind500)[wslice, wslice],
headlength=4, headwidth=3, scale=400,
color='cornflowerblue', zorder=2,
label='500mb wind', transform=ccrs.PlateCarree())
# Plot 500-850 shear
shear = ax.quiver(to_np(lons[wslice, wslice]),
to_np(lats[wslice, wslice]),
to_np(u_wind500[wslice, wslice]) -
to_np(u_wind850[wslice, wslice]),
to_np(v_wind500[wslice, wslice]) -
to_np(v_wind850[wslice, wslice]),
headlength=4, headwidth=3, scale=400,
color='deeppink', zorder=2,
label='500-850mb shear', transform=ccrs.PlateCarree())
contour = ax.contour(to_np(lons), to_np(lats), slp, levels=levels,
colors='lime', linewidths=2, alpha=0.5, zorder=1,
transform=ccrs.PlateCarree())
ax.clabel(contour, fontsize=12, inline=1, inline_spacing=4, fmt='%i')
# Add a legend
ax.legend(('850mb wind', '500mb wind', '500-850mb shear'), loc=4)
# Manually set colors for legend
legend = ax.get_legend()
legend.legendHandles[0].set_color('gold')
legend.legendHandles[1].set_color('cornflowerblue')
legend.legendHandles[2].set_color('deeppink')
initiation_color = 'w*'
else:
cmap = ListedColormap(sns.cubehelix_palette(10,
start=.5, rot=-.75))
variable_plot = plt.contourf(to_np(lons), to_np(lats),
to_np(variable), levels=levels,
transform=ccrs.PlateCarree(),cmap=cmap)
initiation_color = 'r*'
# Plot reflectivity contours with colorbar
if title_name == 'Updraft and Reflectivity':
dbz_levels = np.arange(35., 75., 5.)
dbz_rgb = np.array([[253,248,2],
[229,188,0], [253,149,0],
[253,0,0], [212,0,0],
[188,0,0],[248,0,253],
[152,84,198]], np.float32) / 255.0
dbz_cmap, dbz_norm = from_levels_and_colors(dbz_levels, dbz_rgb,
extend='max')
contours = plt.contour(to_np(lons), to_np(lats),
to_np(reflectivity),
levels=dbz_levels,
transform=ccrs.PlateCarree(),
cmap=dbz_cmap, norm=dbz_norm,
linewidths=1)
cbar_refl = mpu.colorbar(contours, ax, orientation='horizontal', aspect=10,
shrink=.5, pad=0.05)
cbar_refl.set_label('Maximum Derived Radar Reflectivity' '[$dBZ$]', fontsize=12.5)
colorbar_lines = cbar_refl.ax.get_children()
colorbar_lines[0].set_linewidths([10]*5)
# Add wind quivers for every 10th data point
if variable_name == 'wspd_wdir':
plt.quiver(to_np(lons[::10,::10]), to_np(lats[::10,::10]),
to_np(u_pressure[::10, ::10]),
to_np(v_pressure[::10, ::10]),
transform=ccrs.PlateCarree())
# Plot colorbar
if variable_name == 'slp':
pass
else:
cbar = mpu.colorbar(variable_plot, ax, orientation='vertical', aspect=40,
shrink=.05, pad=0.05)
cbar.set_label(colorbar_label, fontsize=15)
cbar.set_ticks(levels)
# Add borders and coastlines
if variable_name == 'slp':
ax.add_feature(cfeature.BORDERS.with_scale('10m'),
edgecolor='white', linewidth=2)
ax.add_feature(cfeature.COASTLINE.with_scale('10m'),
edgecolor='white', linewidth=2)
else:
ax.add_feature(cfeature.BORDERS.with_scale('10m'),
linewidth=0.8)
ax.add_feature(cfeature.COASTLINE.with_scale('10m'),
linewidth=0.8)
### Add initiation location ###
if initiation == True:
ax.plot(initiation_location.lon, initiation_location.lat,
initiation_color, markersize=20, transform=ccrs.PlateCarree())
# Add gridlines
lon = np.arange(0, 20, 1)
lat = np.arange(40, 60, 1)
gl = ax.gridlines(xlocs=lon, ylocs=lat, zorder=3)
# Add tick labels
mpu.yticklabels(lat, ax=ax, fontsize=12.5)
mpu.xticklabels(lon, ax=ax, fontsize=12.5)
# Make nicetime
file_name = '{}wrfout_d02_{}_{}:{}:00'.format(data_dir,
date, time, minutes)
xr_file = xr.open_dataset(file_name)
nicetime = pd.to_datetime(xr_file.QVAPOR.isel(Time=0).XTIME.values)
nicetime = nicetime.strftime('%Y-%m-%d %H:%M')
# Add plot title
if pressure_level != False:
ax.set_title('{} @ {} hPa'.format(title_name, pressure_level),
loc='left', fontsize=15)
ax.set_title('Valid time: {} UTC'.format(nicetime),
loc='right', fontsize=15)
else:
if variable_name == 'slp':
ax.set_title(title_name, loc='left', fontsize=15, color='white')
ax.set_title('Valid time: {} UTC'.format(nicetime),
loc='right', fontsize=15, color='white')
else:
ax.set_title(title_name, loc='left', fontsize=20)
ax.set_title('Valid time: {} UTC'.format(nicetime),
loc='right', fontsize=15)
plt.show()
### Save figure ###
if save == True:
if pressure_level != False:
if subset == True:
fig.savefig('{}/{}/horizontal_map_{}_subset_{}_{}_{}:{}.png'.format(
save_dir, save_name, save_name, pressure_level, date, time,
minutes), bbox_inches='tight', dpi=300)
else:
fig.savefig('{}/{}/horizontal_map_{}_{}_{}_{}:{}.png'.format(
save_dir, save_name, save_name, pressure_level, date, time,
minutes), bbox_inches='tight', dpi=300)
else:
if subset == True:
fig.savefig('{}/{}/horizontal_map_{}_subset_{}_{}:{}.png'.format(
save_dir, save_name, save_name, date, time, minutes),
bbox_inches='tight', dpi=300, facecolor=fig.get_facecolor())
else:
fig.savefig('{}/{}/horizontal_map_{}_{}_{}:{}.png'.format(
save_dir, save_name, save_name, date, time, minutes),
bbox_inches='tight', dpi=300, facecolor=fig.get_facecolor())
### Make a GIF from the plots ###
if gif == True:
# Predifine some variables
gif_data_dir = save_dir + save_name
gif_save_dir = '{}gifs/'.format(save_dir)
gif_save_name = 'horizontal_map_{}.gif'.format(save_name)
# GIF creating procedure
os.chdir(gif_data_dir)
image_folder = os.fsencode(gif_data_dir)
filenames = []
for file in os.listdir(image_folder):
filename = os.fsdecode(file)
if filename.endswith( ('.png') ):
filenames.append(filename)
filenames.sort()
images = list(map(lambda filename: imageio.imread(filename),
filenames))
imageio.mimsave(os.path.join(gif_save_dir + gif_save_name),
images, duration = 0.50)
h.add_grid(increment=20)
# Add wind speed colored line
cmap = ListedColormap(sns.cubehelix_palette(10, start=.5, rot=-.75))
levels = np.linspace(0, 20, 11)
wind_speed_h = h.plot_colormapped(u,
v,
wind_speed,
intervals=levels,
cmap=cmap)
# Add color bar for hodograph
cbar_h = mpu.colorbar(wind_speed_h,
ax_hod,
orientation='vertical',
aspect=30,
shrink=.05,
pad=0.1,
extend='max')
cbar_h.set_label('Wind Speed [$kn$]', fontsize=10)
cbar_h.set_ticks(levels)
# Set label of axes
skew.ax.set_ylabel('Pressure [$hPa$]', fontsize=12.5)
skew.ax.set_xlabel('Temperature [$°C$]', fontsize=12.5)
# Make nicetime
nicetime = pd.to_datetime(date)
# Add title
skew.ax.set_title('')
def cross_section(variable_name,
date,
time,
start_lat,
start_lon,
end_lat,
end_lon,
save=False):
'''This function plots a vertical cross section of the chosen
variable. Supported variables for plotting procedure are
vertical_velocity, rh, omega, absolute_vorticity, theta_e and
reflectivity.'''
### Predefine some variables ###
# Define data filename
data_dir = '/scratch3/thomasl/work/data/casestudy_baden/'
filename = '{}wrfout_d02_{}_{}:00'.format(data_dir, date, time)
# Define save directory
save_dir = '/scratch3/thomasl/work/retrospective_part/' 'casestudy_baden/cross_section/'
# Create the start point and end point for the cross section
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
### Start plotting procedure ###
# Open NetCDF file
ncfile = Dataset(filename)
# Extract the model height, terrain height and variables
ht = getvar(ncfile, 'z') / 1000 # change to km
ter = getvar(ncfile, 'ter') / 1000
if variable_name == 'vertical_velocity':
variable = getvar(ncfile, 'wa', units='kt')
title_name = 'Vertical Velocity'
colorbar_label = 'Vertical Velocity [$kn$]'
variable_min = -2
variable_max = 2
elif variable_name == 'rh':
variable = getvar(ncfile, 'rh')
title_name = 'Relative Humidity'
colorbar_label = 'Relative Humidity [$pct$]'
variable_min = 0
variable_max = 100
elif variable_name == 'omega':
variable = getvar(ncfile, 'omega')
title_name = 'Vertical Motion (Omega)'
colorbar_label = 'Omega [$Pa$ $s^-$$^1$]'
variable_min = -5
variable_max = 5
elif variable_name == 'absolute_vorticity':
variable = getvar(ncfile, 'avo')
title_name = 'Absolute Vorticity'
colorbar_label = 'Absolute Vorticity [$10^{-5}$' '$s^{-1}$]'
variable_min = -50
variable_max = 100
elif variable_name == 'theta_e':
variable = getvar(ncfile, 'theta_e')
title_name = 'Theta-E'
colorbar_label = 'Theta-E [$K$]'
variable_min = 315
variable_max = 335
elif variable_name == 'reflectivity':
variable = getvar(ncfile, 'dbz') #, timeidx=-1
title_name = 'Reflectivity'
colorbar_label = 'Reflectivity [$dBZ$]'
variable_min = 5
variable_max = 75
# Linear Z for interpolation
Z = 10**(variable / 10)
# Compute the vertical cross-section interpolation
z_cross = vertcross(Z,
ht,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
# Convert back after interpolation
variable_cross = 10.0 * np.log10(z_cross)
# Make a copy of the z cross data
variable_cross_filled = np.ma.copy(to_np(variable_cross))
# For each cross section column, find the first index with
# non-missing values and copy these to the missing elements below
for i in range(variable_cross_filled.shape[-1]):
column_vals = variable_cross_filled[:, i]
first_idx = int(np.transpose((column_vals > -200).nonzero())[0])
variable_cross_filled[0:first_idx,
i] = variable_cross_filled[first_idx, i]
ter_line = interpline(ter,
wrfin=ncfile,
start_point=start_point,
end_point=end_point)
# Get latitude and longitude points
lats, lons = latlon_coords(variable)
# Create the figure
fig = plt.figure(figsize=(15, 10))
ax = plt.axes()
ys = to_np(variable_cross.coords['vertical'])
xs = np.arange(0, variable_cross.shape[-1], 1)
# Make contour plot
if variable_name == 'reflectivity':
levels = np.arange(variable_min, variable_max, 5)
# Create the dbz color table found on NWS pages.
dbz_rgb = np.array(
[[4, 233, 231], [1, 159, 244], [3, 0, 244], [2, 253, 2],
[1, 197, 1], [0, 142, 0], [253, 248, 2], [229, 188, 0],
[253, 149, 0], [253, 0, 0], [212, 0, 0], [188, 0, 0],
[248, 0, 253], [152, 84, 198]], np.float32) / 255.0
dbz_cmap, dbz_norm = from_levels_and_colors(levels,
dbz_rgb,
extend='max')
else:
levels = np.linspace(variable_min, variable_max, 11)
if variable_name == 'omega' or variable_name == 'vertical_velocity':
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
new_cmap = colors.LinearSegmentedColormap.from_list(
'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name,
a=minval,
b=maxval),
cmap(np.linspace(minval, maxval, n)))
return new_cmap
old_cmap = plt.get_cmap('RdYlBu')
cmap = truncate_colormap(old_cmap, 0.05, 0.9)
norm = colors.DivergingNorm(vmin=variable_min,
vcenter=0,
vmax=variable_max)
else:
cmap = ListedColormap(sns.cubehelix_palette(20, start=.5, rot=-.75))
if variable_name == 'omega' or variable_name == 'vertical_velocity':
variable_contours = ax.contourf(xs,
ys,
to_np(variable_cross_filled),
levels=levels,
cmap=cmap,
extend='both',
norm=norm)
elif variable_name == 'rh':
variable_contours = ax.contourf(xs,
ys,
to_np(variable_cross_filled),
levels=levels,
cmap=cmap,
extend='max')
elif variable_name == 'reflectivity':
variable_contours = ax.contourf(xs,
ys,
to_np(variable_cross_filled),
levels=levels,
cmap=dbz_cmap,
norm=dbz_norm,
extend='both')
else:
variable_contours = ax.contourf(xs,
ys,
to_np(variable_cross_filled),
levels=levels,
cmap=cmap,
extend='both')
# Plot wind barbs
if variable_name == 'vertical_velocity':
u = getvar(ncfile, 'ua', units='kt')
U = 10**(u / 10)
u_cross = vertcross(U,
ht,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
u_cross = 10.0 * np.log10(u_cross)
u_cross_filled = np.ma.copy(to_np(u_cross))
for i in range(u_cross_filled.shape[-1]):
column_vals = u_cross_filled[:, i]
first_idx = int(np.transpose((column_vals > -200).nonzero())[0])
u_cross_filled[0:first_idx, i] = u_cross_filled[first_idx, i]
ax.barbs(xs[::3],
ys[::3],
to_np(u_cross_filled[::3, ::3]),
to_np(variable_cross_filled[::3, ::3]),
length=7,
zorder=1)
if variable_name == 'omega':
u = getvar(ncfile, 'ua', units='kt')
U = 10**(u / 10)
u_cross = vertcross(U,
ht,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
u_cross = 10.0 * np.log10(u_cross)
u_cross_filled = np.ma.copy(to_np(u_cross))
for i in range(u_cross_filled.shape[-1]):
column_vals = u_cross_filled[:, i]
first_idx = int(np.transpose((column_vals > -200).nonzero())[0])
u_cross_filled[0:first_idx, i] = u_cross_filled[first_idx, i]
w = getvar(ncfile, 'wa', units='kt')
W = 10**(w / 10)
w_cross = vertcross(W,
ht,
wrfin=ncfile,
start_point=start_point,
end_point=end_point,
latlon=True,
meta=True)
w_cross = 10.0 * np.log10(w_cross)
w_cross_filled = np.ma.copy(to_np(w_cross))
for i in range(w_cross_filled.shape[-1]):
column_vals = w_cross_filled[:, i]
first_idx = int(np.transpose((column_vals > -200).nonzero())[0])
w_cross_filled[0:first_idx, i] = w_cross_filled[first_idx, i]
ax.barbs(xs[::3],
ys[::3],
to_np(u_cross_filled[::3, ::3]),
to_np(w_cross_filled[::3, ::3]),
length=7,
zorder=1,
color='grey')
# Add color bar
cbar = mpu.colorbar(variable_contours,
ax,
orientation='vertical',
aspect=40,
shrink=.05,
pad=0.05)
cbar.set_label(colorbar_label, fontsize=15)
cbar.set_ticks(levels)
# Set x-ticks to use latitude and longitude labels
coord_pairs = to_np(variable_cross.coords['xy_loc'])
x_ticks = np.arange(coord_pairs.shape[0])
x_labels = [
pair.latlon_str(fmt='{:.2f}, {:.2f}') for pair in to_np(coord_pairs)
]
# Set desired number of x ticks below
num_ticks = 5
thin = int((len(x_ticks) / num_ticks) + .5)
ax.set_xticks(x_ticks[::thin])
ax.set_xticklabels(x_labels[::thin], rotation=45, fontsize=10)
ax.set_xlim(x_ticks[0], x_ticks[-1])
# Set y-ticks and limit the height
ax.set_yticks(np.linspace(0, 12, 13))
ax.set_ylim(0, 12)
# Set x-axis and y-axis labels
ax.set_xlabel('Latitude, Longitude', fontsize=12.5)
ax.set_ylabel('Height [$km$]', fontsize=12.5)
# Fill in mountian area
ht_fill = ax.fill_between(xs,
0,
to_np(ter_line),
facecolor='saddlebrown',
zorder=2)
# Make nicetime
xr_file = xr.open_dataset(filename)
nicetime = pd.to_datetime(xr_file.QVAPOR.isel(Time=0).XTIME.values)
# Add title
ax.set_title('Vertical Cross-Section of {}'.format(title_name),
fontsize=20,
loc='left')
ax.set_title('Valid time: {} {}'.format(
nicetime.strftime('%Y-%m-%d %H:%M'), 'UTC'),
fontsize=15,
loc='right')
# Add grid for y axis
ax.grid(axis='y', linestyle='--', color='grey')
plt.show()
### Save figure ###
if save == True:
fig.savefig('{}cross_section_{}.png'.format(save_dir, variable_name),
bbox_inches='tight',
dpi=300)
def lagranto_plotting(traj_variable_name,
start_time,
end_time,
trajs_bunch_level='all',
subset=False,
save=False):
'''This function plots the chosen variables for the trajectories
calculated with Lagranto. Supported variables for plotting procedure
are water_vapor, updraft and height.'''
### Predefine some variables ###
traj_data_dir = '/scratch3/thomasl/work/retrospective_part/lagranto/' 'traj_baden_10000_every1000_area_1200.ll'
save_dir = '/scratch3/thomasl/work/retrospective_part' '/casestudy_baden/lagranto/'
start_locations = 'area' # distinction between single and multiple
# starting points of trajectories
number_trajs_plot = 1
# Location of Initiation
lat = 47.25
lon = 7.85
initiation = CoordPair(lat, lon)
# Change extent of plot
subset_extent = [7, 9, 47, 48]
# Variables for getting PBL height of WRF model data
date = '2018-05-30'
time = '16:10'
wrf_filename = '/scratch3/thomasl/work/data/casestudy_baden/' 'wrfout_d02_{}_{}:00'.format(
date, time)
# Variables:
if traj_variable_name == 'water_vapor':
traj_variable_name = 'QVAPOR'
title_name = 'Trajectories of Water Vapor'
colorbar_label_trajs = 'Water Vapor Mixing Ratio [$g$ $kg^{-1}$]'
save_name = 'trajectory_water_vapor_{}_{}'.format(start_time, end_time)
traj_variable_min = 0
traj_variable_max = 15
elif traj_variable_name == 'updraft':
traj_variable_name = 'W_UP_MAX'
title_name = 'Trajectories of Updraft'
colorbar_label_trajs = 'Max Z-Wind Updraft [$m$ $s^-$$^1$]'
save_name = 'trajectory_updraft_{}_{}'.format(start_time, end_time)
traj_variable_min = 0
traj_variable_max = 10
elif traj_variable_name == 'height':
traj_variable_name = 'z'
title_name = 'Height of Trajectories'
colorbar_label_trajs = 'Height of Trajectories [$m$]'
save_name = 'trajectory_height_{}_{}'.format(start_time, end_time)
traj_variable_min = 0
traj_variable_max = 10000
### Plotting procedure ###
trajs = Tra()
trajs.load_ascii(traj_data_dir)
# Get PBL out of WRF model data
ncfile = Dataset(wrf_filename)
wrf_file = xr.open_dataset(wrf_filename)
data = wrf_file.PBLH
location = ll_to_xy(ncfile, lat, lon)
data_point = data.sel(west_east=location[0], south_north=location[1])
pbl = data_point.values
# Separate trajectories in 3 vertical bunches
# Trajectories of pbl (according to pbl height of WRF model data)
trajspbl = []
for t in trajs:
if (t['z'][0] <= pbl):
trajspbl.append(t)
# Trajectories between pbl and 5000 m
trajs5 = []
for t in trajs:
if (t['z'][0] > pbl and t['z'][0] < 5000):
trajs5.append(t)
# Trajectories between 5000 m and 10000 m
trajs10 = []
for t in trajs:
if (t['z'][0] > 5000 and t['z'][0] <= 10000):
trajs10.append(t)
if trajs_bunch_level == 'pbl':
trajs_bunch = trajspbl
elif trajs_bunch_level == '5':
trajs_bunch = trajs5
elif trajs_bunch_level == '10':
trajs_bunch = trajs10
elif trajs_bunch_level == 'all':
trajs_bunch = trajs
# Get terrain height
terrain_height = getvar(ncfile, 'HGT') / 1000 # change to km
# Define cart projection
lats, lons = latlon_coords(terrain_height)
cart_proj = ccrs.LambertConformal(central_longitude=8.722206,
central_latitude=46.73585)
# Create figure
fig = plt.figure(figsize=(15, 10))
ax = plt.axes(projection=cart_proj)
### Set map extent ###
domain_extent = [3.701088, 13.814863, 43.85472, 49.49499]
if subset == True:
ax.set_extent([
subset_extent[0], subset_extent[1], subset_extent[2],
subset_extent[3]
], ccrs.PlateCarree())
else:
ax.set_extent([
domain_extent[0] + 0.7, domain_extent[1] - 0.7,
domain_extent[2] + 0.1, domain_extent[3] - 0.1
], ccrs.PlateCarree())
# Plot trajectories
levels_trajs = np.linspace(traj_variable_min, traj_variable_max, 11)
rounded_levels_trajs = [round(elem, 1) for elem in levels_trajs]
cmap = ListedColormap(sns.cubehelix_palette(
10,
start=.5,
rot=-.75,
))
plt_trajs = plot_trajs(ax,
trajs_bunch[0::number_trajs_plot],
traj_variable_name,
linewidth=2,
levels=rounded_levels_trajs,
cmap=cmap)
# Plot the terrain height with colorbar
levels = np.linspace(0, 4, 21)
terrain = plt.contourf(to_np(lons),
to_np(lats),
to_np(terrain_height),
levels=levels,
transform=ccrs.PlateCarree(),
cmap=get_cmap('Greys'),
alpha=0.75)
cbar = mpu.colorbar(terrain,
ax,
orientation='horizontal',
aspect=40,
shrink=.05,
pad=0.075)
cbar.set_label('Terrain Height [$km$]', fontsize=15)
cbar.set_ticks(levels)
# Make only every second color bar tick label visible
for label in cbar.ax.xaxis.get_ticklabels()[1::2]:
label.set_visible(False)
# Add color bar for trajectory variable
if traj_variable_name == 'QVAPOR':
extend = 'both'
else:
extend = 'max'
cbar_trajs = mpu.colorbar(plt_trajs,
ax,
orientation='vertical',
aspect=40,
shrink=.05,
pad=0.05,
extend=extend)
cbar_trajs.set_label(colorbar_label_trajs, fontsize=15)
cbar_trajs.set_ticks(rounded_levels_trajs)
# Add borders and coastlines
ax.add_feature(cfeature.BORDERS.with_scale('10m'), linewidth=0.8)
ax.add_feature(cfeature.COASTLINE.with_scale('10m'), linewidth=0.8)
# Add cross for initiation location
for t in trajs:
ax.plot(t['lon'][0], t['lat'][0], 'kx', transform=ccrs.PlateCarree())
# Add gridlines
lon = np.arange(0, 20, 1)
lat = np.arange(40, 60, 1)
gl = ax.gridlines(xlocs=lon, ylocs=lat, zorder=3)
# Add tick labels
mpu.yticklabels(lat, ax=ax, fontsize=12.5)
mpu.xticklabels(lon, ax=ax, fontsize=12.5)
# Set title
ax.set_title(title_name, loc='left', fontsize=20)
ax.set_title('Time Range: {} - {} UTC'.format(start_time, end_time),
loc='right',
fontsize=15)
plt.show()
### Save figure ###
if save == True:
if subset == True:
if trajs_bunch_level == 'all':
fig.savefig('{}{}_subset_{}.png'.format(
save_dir, save_name, start_locations),
bbox_inches='tight',
dpi=300)
else:
fig.savefig('{}{}_subset_{}_{}.png'.format(
save_dir, save_name, trajs_bunch_level, start_locations),
bbox_inches='tight',
dpi=300)
else:
if trajs_bunch_level == 'all':
fig.savefig('{}{}_{}.png'.format(save_dir, save_name,
start_locations),
bbox_inches='tight',
dpi=300)
else:
fig.savefig('{}{}_{}_{}.png'.format(save_dir, save_name,
trajs_bunch_level,
start_locations),
bbox_inches='tight',
dpi=300)