def print_cape_for_timestamp(wrf_data, timestamp, filepath):
slp = pressure_lib.get_sea_level_pressure(wrf_data)
cinfo = getvar(wrf_data, "cape_2d", missing=0.0)
cape = cinfo[0, :, :].fillna(0)
lat, lon = latlon_coords(slp)
lat_normal = to_np(lat)
lon_normal = to_np(lon)
# rain sum
cape_res = get_pyngl(cinfo)
cape_res.nglDraw = False # don't draw plot
cape_res.nglFrame = False # don't advance frame
cape_res.cnFillOn = True # turn on contour fill
cape_res.cnLinesOn = False # turn off contour lines
cape_res.cnLineLabelsOn = False # turn off line labels
cape_res.cnFillMode = "RasterFill" # These two resources
cape_res.cnLevelSelectionMode = "ExplicitLevels"
cape_res.cnFillColors = numpy.array([ [255,255,255], [ 0,255, 0], [ 0,128, 0], \
[240,230,140], [255,255, 0], [255,140, 0], \
[255, 0, 0], [139, 0, 0], [186, 85,211],\
[153, 50,204], [139, 0,139], ],'f') / 255.
cape_res.cnLevels = numpy.array(
[.1, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500])
cape_res = geography_lib.initialize_geography(cape_res, "gray50")
cape_res.lbTitleString = "Convective available potential energy [CAPE] in (J/kg)"
cape_res.lbOrientation = "horizontal"
cape_res.lbTitleFontHeightF = 0.015
cape_res.lbLabelFontHeightF = 0.015
cape_res.tiMainString = "Thunderstorm probability (%s)" % timestamp.strftime(
"%b %d %Y %HUTC")
cape_res.trGridType = "TriangularMesh" # can speed up plotting.
cape_res.tfDoNDCOverlay = True # required for native projection
# pressure
p_res = pressure_lib.get_pressure_resource(lat_normal, lon_normal)
wk_res = Ngl.Resources()
wk_res.wkWidth = 2500
wk_res.wkHeight = 2500
output_path = "%scape_%s" % (filepath, timestamp.strftime("%Y_%m_%d_%H"))
wks_comp = Ngl.open_wks("png", output_path, wk_res)
# creating plots for the measurands
capeplot = Ngl.contour_map(wks_comp, cape, cape_res)
pplot = Ngl.contour(wks_comp, slp, p_res)
Ngl.overlay(capeplot, pplot)
Ngl.maximize_plot(wks_comp, capeplot)
Ngl.draw(capeplot)
Ngl.frame(wks_comp)
Ngl.delete_wks(wks_comp) # delete currently used workstation
def get_3h_rainsum(previous_data, current_data, timestamp, filepath):
slp = pressure_lib.get_sea_level_pressure(current_data)
previous_sum, rain_con = get_cumulated_rain_sum(previous_data)
current_sum, rain_con = get_cumulated_rain_sum(current_data)
rain_sum = current_sum - previous_sum
lat, lon = latlon_coords(rain_con)
lat_normal = to_np(lat)
lon_normal = to_np(lon)
# rain sum
rr_res = initialize_rain_resource(rain_con)
rr_res.lbTitleString = "3h rainsum in (mm)"
rr_res.lbOrientation = "horizontal"
rr_res.lbTitleFontHeightF = 0.015
rr_res.lbLabelFontHeightF = 0.015
rr_res.tiMainString = "3h rainsum (%s)" % timestamp.strftime(
"%b %d %Y %HUTC")
rr_res.trGridType = "TriangularMesh" # can speed up plotting.
rr_res.tfDoNDCOverlay = True # required for native projection
# pressure
p_res = pressure_lib.get_pressure_resource(lat_normal, lon_normal)
wk_res = Ngl.Resources()
wk_res.wkWidth = 2500
wk_res.wkHeight = 2500
output_path = "%srain_3h_%s" % (filepath,
timestamp.strftime("%Y_%m_%d_%H"))
wks_comp = Ngl.open_wks("png", output_path, wk_res)
# creating plots for the measurands
rrplot = Ngl.contour_map(wks_comp, rain_sum, rr_res)
pplot = Ngl.contour(wks_comp, slp, p_res)
Ngl.overlay(rrplot, pplot)
Ngl.maximize_plot(wks_comp, rrplot)
Ngl.draw(rrplot)
Ngl.frame(wks_comp)
Ngl.delete_wks(wks_comp) # delete currently used workstation
def plot_power(Pow, symmetry=("symm"), source="", var1="", plotpath="./", flim=0.5, nWavePlt=20, cmin=0.05, cmax=0.55,
cspc=0.05, plotxy=[1, 1], N=[1, 2]):
dims = Pow.shape
nplot = dims[0]
FillMode = "AreaFill"
# text labels
abc = list(string.ascii_lowercase)
# plot resources
wkstype = "png"
wks = ngl.open_wks(wkstype, plotpath + "SpaceTimePower_" + source + var1)
plots = []
# coherence2 plot resources
res = coh_resources(cmin, cmax, cspc, FillMode, flim, nWavePlt)
# phase arrow resources
resA = phase_resources(flim, nWavePlt)
# dispersion curve resources
dcres = ngl.Resources()
dcres.gsLineThicknessF = 2.0
dcres.gsLineDashPattern = 0
# text box resources
txres = ngl.Resources()
txres.txPerimOn = True
txres.txFontHeightF = 0.013
txres.txBackgroundFillColor = "Background"
# panel plot resources
resP = panel_resources(nplot, abc)
# plot contours and phase arrows
pp = 0
while pp < nplot:
if nplot == 1:
coh2 = Pow
else:
coh2 = Pow[pp, :, :]
if len(symmetry) == nplot:
Symmetry = symmetry[pp]
else:
Symmetry = symmetry
res.tiMainString = source + " log10( Power(" + var1 + ")) " + Symmetry
plot = ngl.contour(wks, coh2, res)
(matsuno_names, textlabels, textlocsX, textlocsY) = text_labels(Symmetry)
nlabel = len(textlocsX)
# generate matsuno mode dispersion curves
if Symmetry == "midlat":
He = [3000, 7000, 10000]
matsuno_modes = mp.matsuno_modes_wk_bg(he=He, n=N, latitude=0., max_wn=nWavePlt, n_wn=500, u=25)
else:
He = [12, 25, 50]
matsuno_modes = mp.matsuno_modes_wk(he=He, n=N, latitude=0., max_wn=nWavePlt, n_wn=500)
# add polylines for dispersion curves
for he in matsuno_modes:
df = matsuno_modes[he]
wn = df.index.values
for wavename in df:
for matsuno_name in matsuno_names:
if wavename == (matsuno_name + str(he) + "m)"):
wave = df[wavename].values
wnwave = wn[~np.isnan(wave)]
wave = wave[~np.isnan(wave)]
ngl.add_polyline(wks, plot, wnwave, wave, dcres)
# add text boxes
ll = 0
while ll < nlabel:
ngl.add_text(wks, plot, textlabels[ll], textlocsX[ll], textlocsY[ll], txres)
ll += 1
plots.append(plot)
pp += 1
# panel plots
ngl.panel(wks, plots, plotxy, resP)
ngl.delete_wks(wks)
#ngl.end()
return
res.cnMinLevelValF = -0.1#0.45#-0.1 #-- contour min. value
res.cnMaxLevelValF = 2.0#1.5#2.0 #-- contour max. value
res.cnLevelSpacingF = 0.1#0.05#0.1 #-- contour interval
colors00 = Ngl.read_colormap_file("MPL_RdBu")
ncolors00= colors00.shape[0]
colors0 = colors00[np.linspace(2,ncolors00-1,int((res.cnMaxLevelValF-res.cnMinLevelValF)/res.cnLevelSpacingF),dtype=int),:]
grey_color = np.expand_dims(np.array([0.85, 0.85, 0.85, 1]), axis=0)
colors = np.append(grey_color,colors0[::-1,:],axis=0)
res.cnFillPalette = colors#'BlWhRe' #'MPL_YlOrRd' #-- choose color map
plot = []
title = 'CESM all forcing vs '+sf+' foricng~C~FWI >= p'+str(pxx)+' risk ratio'
for pp in range(2,nperiods,1):
print('period '+str(pp+1))
res.tiMainString = str(pyear0[pp])+'-'+str(pyear1[pp])
riskratio_cyc, lon_cyc = Ngl.add_cyclic(riskratio_ens_agree_masked[pp,:,:], lon)
res.sfXArray = lon_cyc
p = Ngl.contour_map(wks,riskratio_cyc,res)
if pp==(nperiods-1):
for rr in range(0,n_reg,1):
poly_y = [df_reg['reg_N'][rr],df_reg['reg_S'][rr],df_reg['reg_S'][rr],df_reg['reg_N'][rr],df_reg['reg_N'][rr]]
poly_x = [df_reg['reg_E'][rr],df_reg['reg_E'][rr],df_reg['reg_W'][rr],df_reg['reg_W'][rr],df_reg['reg_E'][rr]]
poly_reg = Ngl.add_polygon(wks,p,poly_x,poly_y,gsres)
plot.append(p)
Ngl.panel(wks,plot,[4,1],pres)
Ngl.text_ndc(wks,title,0.5,0.92,txres)
Ngl.frame(wks)
Ngl.delete_wks(wks)
def make_test(wrf_file=None, fixed_case=None):
if wrf_file is not None:
ncfile = NetCDF(wrf_file)
lats, lons, proj_params = get_proj_params(ncfile)
proj = getproj(lats=lats, lons=lons, **proj_params)
name_suffix = basename(wrf_file)
elif fixed_case is not None:
name_suffix = fixed_case
if fixed_case == "south_rot":
lats, lons, proj = nz_proj()
elif fixed_case == "arg_rot":
lats, lons, proj = argentina_proj()
elif fixed_case == "south_polar":
lats, lons, proj = south_polar_proj()
elif fixed_case == "north_polar":
lats, lons, proj = north_polar_proj()
elif fixed_case == "dateline_rot":
lats, lons, proj = dateline_rot_proj()
print("wrf proj4: {}".format(proj.proj4()))
if PYNGL:
# PyNGL plotting
wks_type = bytes("png")
wks = Ngl.open_wks(wks_type, bytes("pyngl_{}".format(name_suffix)))
mpres = proj.pyngl()
map = Ngl.map(wks, mpres)
Ngl.delete_wks(wks)
if BASEMAP:
# Basemap plotting
fig = plt.figure(figsize=(10, 10))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
# Define and plot the meridians and parallels
min_lat = np.amin(lats)
max_lat = np.amax(lats)
min_lon = np.amin(lons)
max_lon = np.amax(lons)
parallels = np.arange(np.floor(min_lat), np.ceil(max_lat),
(max_lat - min_lat) / 5.0)
meridians = np.arange(np.floor(min_lon), np.ceil(max_lon),
(max_lon - min_lon) / 5.0)
bm = proj.basemap()
bm.drawcoastlines(linewidth=.5)
#bm.drawparallels(parallels,labels=[1,1,1,1],fontsize=10)
#bm.drawmeridians(meridians,labels=[1,1,1,1],fontsize=10)
print("basemap proj4: {}".format(bm.proj4string))
plt.savefig("basemap_{}.png".format(name_suffix))
plt.close(fig)
if CARTOPY:
# Cartopy plotting
fig = plt.figure(figsize=(10, 10))
ax = plt.axes([0.1, 0.1, 0.8, 0.8], projection=proj.cartopy())
print("cartopy proj4: {}".format(proj.cartopy().proj4_params))
ax.coastlines('50m', linewidth=0.8)
#print proj.x_extents()
#print proj.y_extents()
ax.set_xlim(proj.cartopy_xlim())
ax.set_ylim(proj.cartopy_ylim())
ax.gridlines()
plt.savefig("cartopy_{}.png".format(name_suffix))
plt.close(fig)
def create_meteogram_for(filepath, filename, timestamp):
with open(filepath + filename) as f:
head, input = read_file(f)
# Create numpy 2D array
cdf = numpy.array(input)
# Collect meteorological data
pressure = cdf[:, 9] / 100
rain_cum = cdf[:, 16]
rain_expl = cdf[:, 17]
tempht = cdf[:, 5] - 273.15
hum = cdf[:, 6]
taus = cdf[:, 1]
u = cdf[:, 7]
v = cdf[:, 8]
rain_sum = numpy.add(rain_cum, rain_expl)
# set up a color map and open an output workstation.
colors = numpy.array([ \
[255,255,255], [255,255,255], [255,255,255], \
[240,255,240], [220,255,220], [190,255,190], \
[120,255,120], [ 80,255, 80], [ 50,200, 50], \
[ 20,150, 20] \
],'f') / 255.
wks_type = "png"
output_file = filepath + timestamp.strftime(
"%m_%d_%H") + "_meteogram_" + filename.split(".")[0]
wks = Ngl.open_wks(wks_type, output_file)
# calculate secondary data
count_xdata = taus[-1]
main_hours, sec_hours, labels = generate_xlegend(timestamp, count_xdata)
# dew points data and relative humidity
rel_hum = humidity_lib.calculate_relative_humidity(hum, tempht, pressure,
len(taus))
dew_point = temperature_lib.calculate_dewpoint(tempht, rel_hum, len(taus))
# 3 hour rain sums
rain3h_sum, rain3h_time = rain_lib.calculate_3hrain_data(rain_sum, taus)
# wind speed
wind_speed = wind_lib.calculate_windspeed(u, v, len(taus))
# wind direction
wind_direction = wind_lib.calculate_winddirection(u, v, len(taus))
# generate measurand resources
# pressure resource
sealevel_pressure = pressure_lib.reduce_pressure_to_sealevel(
pressure, cdf[:, 5], float(head[13]))
pres_res = pressure_lib.get_pressure_resource(count_xdata,
sealevel_pressure)
pres_res.tiMainString = format_title(head[0], timestamp)
pres_res = config_xaxis_legend(pres_res, main_hours, sec_hours, labels)
# relative humidity
relhum_res = humidity_lib.get_relhumidity_resource(count_xdata)
relhum_res = config_xaxis_legend(relhum_res, main_hours, sec_hours, labels)
# wind speed recource
wind_res = wind_lib.get_windspeed_resource(count_xdata, wind_speed)
wind_res = config_xaxis_legend(wind_res, main_hours, sec_hours, labels)
# wind direction recource
direction_res = wind_lib.get_winddirection_resource(
count_xdata, wind_speed)
direction_res = config_xaxis_legend(direction_res, main_hours, sec_hours,
labels)
# rain sum resources
rainsum_res = rain_lib.get_rainsum_resource(count_xdata)
rainsum_res = config_xaxis_legend(rainsum_res, main_hours, sec_hours,
labels)
# 3 hour rain sum bar charts
rain3h_res = rain_lib.get_3hrain_resource(rain3h_time)
rainhist = create_rain_bar_plot(wks, rain3h_time, rain3h_sum, rain3h_res)
# ground temperature resource
tempsfc_res = temperature_lib.get_temperature_resource(
count_xdata, tempht, dew_point)
tempsfc_res = config_xaxis_legend(tempsfc_res, main_hours, sec_hours,
labels)
# generate plot results
pressmsz = Ngl.xy(wks, taus, sealevel_pressure, pres_res)
relhummsz = Ngl.xy(wks, taus, rel_hum, relhum_res)
windmsz = Ngl.xy(wks, taus, wind_speed, wind_res)
dirmsz = Ngl.xy(wks, taus, wind_direction, direction_res)
rainsum = Ngl.xy(wks, taus, rain_sum, rainsum_res)
temptmsz = Ngl.xy(wks, taus, tempht, tempsfc_res)
tempsfc_res.xyLineColor = "blue" # line color for dew point
dewpmsz = Ngl.xy(wks, taus, dew_point, tempsfc_res)
Ngl.draw(pressmsz)
Ngl.draw(relhummsz)
Ngl.overlay(rainsum, rainhist)
Ngl.draw(rainsum)
Ngl.draw(windmsz)
Ngl.draw(dirmsz)
Ngl.overlay(temptmsz, dewpmsz)
Ngl.draw(temptmsz)
Ngl.frame(wks)
Ngl.delete_wks(wks) # delete currently used workstation
def ens_spread_map(var1var2):
recoverpath = dict(base = '/',
temp = 'data/additional_data/temp/{}/'.format(ex_op_str))
# load all dictionaries and strings from json file #
json_filename = 'ensemble_spread_maps_{}_dicts_strings.json'.format(var1var2)
with open(recoverpath['base'] + recoverpath['temp'] + json_filename, 'r') as f:
path, domain, variable1, variable2, model, run, hour = json.load(f)
# load numpy arrays #
numpyarrays_filename = 'ensemble_spread_maps_{}_ndarrays_outofloop.npz'.format(var1var2)
with open(path['base'] + path['temp'] + numpyarrays_filename, 'rb') as f:
with np.load(f) as loadedfile:
clat = loadedfile['clat']
clon = loadedfile['clon']
vlat = loadedfile['vlat']
vlon = loadedfile['vlon']
ll_lat = loadedfile['ll_lat']
ll_lon = loadedfile['ll_lon']
numpyarrays_filename = 'ensemble_spread_maps_{}_ndarrays_withinloop.npz'.format(var1var2)
with open(recoverpath['base'] + recoverpath['temp'] + numpyarrays_filename, 'rb') as f:
with np.load(f) as loadedfile:
data_array1 = loadedfile['data_array1']
data_array2 = loadedfile['data_array2']
#print('loaded all vars')
# example plot_name: icon-eu-eps_2020070512_mslp_mean_spread_europe_000h
plot_name = '{}_{:4d}{:02d}{:02d}{:02d}_{}_{}_{:03d}h'.format(
model, run['year'], run['month'], run['day'], run['hour'],
var1var2, domain['name'], hour)
# plot basic map with borders #
wks_res = Ngl.Resources()
wks_res.wkWidth = domain['plot_width']
wks_res.wkHeight = domain['plot_width'] # the whitespace above and below the plot will be cut afterwards
filename_colorpalette = 'colormap_WhiteBeigeGreenBlue_12.txt'
with open(path['base'] + path['colorpalette'] + filename_colorpalette, 'r') as f:
lines = f.readlines()
rgb_colors = []
rgb_colors.append([1, 1, 1])
rgb_colors.append([0, 0, 0])
rgb_colors.append([.5, .5, .5])
for line in lines:
rgb_colors.append([float(line[0:3])/255, float(line[4:7])/255, float(line[8:11])/255])
rgb_colors.append(rgb_colors[-1])
wks_res.wkColorMap = np.array(rgb_colors)
if variable1['name'] == 'gph_500hPa' :
levels1 = np.arange(0, 12.1, 1)
elif variable1['name'] == 'mslp' :
levels1 = np.arange(0, 12.1, 1)
elif variable1['name'] == 't_850hPa' :
levels1 = np.arange(0, 6.1, 0.5)
wks_type = 'png'
wks = Ngl.open_wks(wks_type, path['base'] + path['plots'] + plot_name, wks_res)
mpres = Ngl.Resources()
mpres.mpProjection = domain['projection']
if domain['limits_type'] == 'radius':
mpres.mpLimitMode = 'LatLon'
mpres.mpCenterLonF = domain['centerlon']
mpres.mpCenterLatF = domain['centerlat']
cutout_plot = dict(
lat_min = float(np.where(domain['centerlat'] - domain['radius'] / 111.2 < -90,
-90, domain['centerlat'] - domain['radius'] / 111.2)),
lat_max = float(np.where(domain['centerlat'] + domain['radius'] / 111.2 > 90,
90, domain['centerlat'] + domain['radius'] / 111.2)),
)
cutout_plot['lon_min'] = float(np.where(cutout_plot['lat_min'] <= -90 or cutout_plot['lat_max'] >= 90,
0,
domain['centerlon'] - domain['radius'] \
/ (111.2 * np.cos(domain['centerlat']*np.pi/180))))
cutout_plot['lon_max'] = float(np.where(cutout_plot['lat_min'] <= -90 or cutout_plot['lat_max'] >= 90,
360,
domain['centerlon'] + domain['radius'] \
/ (111.2 * np.cos(domain['centerlat']*np.pi/180))))
mpres.mpMinLonF = cutout_plot['lon_min']
mpres.mpMaxLonF = cutout_plot['lon_max']
mpres.mpMinLatF = cutout_plot['lat_min']
mpres.mpMaxLatF = cutout_plot['lat_max']
elif domain['limits_type'] == 'deltalatlon':
mpres.mpLimitMode = 'LatLon'
mpres.mpCenterLonF = 0
mpres.mpCenterLatF = 0
mpres.mpMinLonF = domain['centerlon'] - domain['deltalon_deg']
mpres.mpMaxLonF = domain['centerlon'] + domain['deltalon_deg']
mpres.mpMinLatF = domain['centerlat'] - domain['deltalat_deg']
mpres.mpMaxLatF = domain['centerlat'] + domain['deltalat_deg']
elif domain['limits_type'] == 'angle':
mpres.mpLimitMode = 'Angles'
mpres.mpCenterLonF = domain['centerlon']
mpres.mpCenterLatF = domain['centerlat']
mpres.mpLeftAngleF = domain['angle']
mpres.mpRightAngleF = domain['angle']
mpres.mpTopAngleF = domain['angle']
mpres.mpBottomAngleF = domain['angle']
mpres.nglMaximize = False
mpres.vpXF = 0.001
mpres.vpYF = 1.00
mpres.vpWidthF = 0.88
mpres.vpHeightF = 1.00
mpres.mpMonoFillColor = 'True'
mpres.mpFillColor = -1
mpres.mpFillOn = True
mpres.mpGridAndLimbOn = False
mpres.mpGreatCircleLinesOn = False
mpres.mpDataBaseVersion = 'MediumRes'
mpres.mpDataSetName = 'Earth..4'
mpres.mpOutlineBoundarySets = 'national'
mpres.mpGeophysicalLineColor = 'black'
mpres.mpNationalLineColor = 'black'
mpres.mpGeophysicalLineThicknessF = 1.0 * domain['plot_width'] / 1000
mpres.mpNationalLineThicknessF = 1.0 * domain['plot_width'] / 1000
mpres.mpPerimOn = True
mpres.mpPerimLineColor = 'black'
mpres.mpPerimLineThicknessF = 6.0 * domain['plot_width'] / 1000
mpres.tmXBOn = False
mpres.tmXTOn = False
mpres.tmYLOn = False
mpres.tmYROn = False
mpres.nglDraw = False # don't draw plot
mpres.nglFrame = False # don't advance frame
# settings for variable1 / shading #
v1res = Ngl.Resources()
v1res.sfDataArray = data_array1
v1res.sfXArray = clon
v1res.sfYArray = clat
v1res.sfXCellBounds = vlon
v1res.sfYCellBounds = vlat
v1res.sfMissingValueV = 9999
v1res.cnMissingValFillColor = 'Gray80'
v1res.cnLinesOn = False # Turn off contour lines.
v1res.cnFillOn = True
v1res.cnFillMode = 'CellFill'
#v1res.cnFillOpacityF = 0.5
#v1res.cnFillDrawOrder = 'Predraw'
v1res.cnLevelSelectionMode = 'ExplicitLevels'
v1res.cnLevels = levels1
# set resources for a nice label bar #
v1res.lbLabelBarOn = True
v1res.lbAutoManage = False
v1res.lbOrientation = 'vertical'
v1res.lbLabelOffsetF = 0.04 # minor axis fraction: the distance between colorbar and numbers
v1res.lbBoxMinorExtentF = 0.20 # minor axis fraction: width of the color boxes when labelbar down
v1res.lbTopMarginF = 0.14 # make a little more space at top for the unit label
v1res.lbRightMarginF = 0.0
v1res.lbBottomMarginF = 0.0
v1res.lbLeftMarginF = -0.35
v1res.cnLabelBarEndStyle = 'ExcludeOuterBoxes'
#if variable1['name'] == 'prec_rate' :
# v1res.cnLabelBarEndStyle = 'IncludeOuterBoxes'
#v1res.cnExplicitLabelBarLabelsOn = True
#v1res.pmLabelBarDisplayMode = 'Always'
v1res.pmLabelBarWidthF = 0.10
#v1res.lbLabelStrings = label_str_list
v1res.lbLabelFontHeightF = 0.010
#v1res.lbBoxCount = 40
v1res.lbBoxSeparatorLinesOn = False
v1res.lbBoxLineThicknessF = 4
#v1res.lbBoxEndCapStyle = 'TriangleBothEnds'
v1res.lbLabelAlignment = 'ExternalEdges'
if variable2['name'] == 'mslp':
v1res.lbLabelStride = 1
elif variable2['name'] == 'gph_500hPa':
v1res.lbLabelStride = 1
elif variable2['name'] == 't_850hPa':
v1res.lbLabelStride = 2
v1res.nglFrame = False
v1res.nglDraw = False
# settings for variable2 / thin contourlines #
v2res = Ngl.Resources()
v2res.sfDataArray = data_array2
v2res.sfXArray = ll_lon
v2res.sfYArray = ll_lat
v2res.sfMissingValueV = 9999
#v2res.trGridType = 'TriangularMesh'
v2res.cnFillOn = False
v2res.cnLinesOn = True
v2res.cnLineLabelsOn = True
v2res.cnLineLabelInterval = 1
v2res.cnLineLabelPlacementMode = 'Constant'
v2res.cnLineDashSegLenF = 0.25
v2res.cnLabelDrawOrder = 'PostDraw' # necessary to make everything visible
v2res.cnLineThicknessF = 3.0 * domain['plot_width'] / 1000
v2res.cnInfoLabelOn = False
v2res.cnSmoothingOn = False
v2res.cnSmoothingDistanceF = 0.01
v2res.cnSmoothingTensionF = 0.1
if variable2['name'] == 'mslp':
v2res.cnLevelSpacingF = 2
v2res.cnLineLabelFontHeightF = 0.008
v2res.cnLineDashSegLenF = 0.18
v2res.cnLineLabelInterval = 2
elif variable2['name'] == 'gph_500hPa':
v2res.cnLevelSpacingF = 4
v2res.cnLineLabelFontHeightF = 0.008
elif variable2['name'] == 't_850hPa':
v2res.cnLevelSpacingF = 2
v2res.cnLineLabelFontHeightF = 0.009
v2res.cnLineDashSegLenF = 0.18
v2res.cnLevelSelectionMode = 'ManualLevels'
#v2res.cnLevels = levels2
#v2res.cnRasterSmoothingOn = True
v2res.nglFrame = False
v2res.nglDraw = False
# settings for variable2 / thick contourlines #
v3res = Ngl.Resources()
v3res.sfDataArray = data_array2
v3res.sfXArray = ll_lon
v3res.sfYArray = ll_lat
v3res.sfMissingValueV = 9999
#v3res.trGridType = 'TriangularMesh'
v3res.cnFillOn = False
v3res.cnLinesOn = True
v3res.cnLineLabelsOn = True
v3res.cnLineLabelInterval = 1
v3res.cnLineLabelPlacementMode = 'Constant'
v3res.cnLineDashSegLenF = 0.25
v3res.cnLabelDrawOrder = 'PostDraw' # necessary to make everything visible
v3res.cnLineThicknessF = 5.0 * domain['plot_width'] / 1000
v3res.cnLineLabelFontHeightF = 0.010
v3res.cnInfoLabelOn = False
v3res.cnSmoothingOn = False
#v3res.cnSmoothingDistanceF = 0.01
#v3res.cnSmoothingTensionF = 0.1
v3res.cnLevelSelectionMode = 'ManualLevels'
#v3res.cnLevels = levels2
#v3res.cnRasterSmoothingOn = True
if variable2['name'] == 'mslp':
v3res.cnLevelSpacingF = 10
v3res.cnLineLabelFontHeightF = 0.008
v3res.cnLineDashSegLenF = 0.18
v3res.cnLineLabelInterval = 2
elif variable2['name'] == 'gph_500hPa':
v3res.cnLevelSpacingF = 16
v3res.cnLineLabelFontHeightF = 0.008
v3res.cnLevelSelectionMode = 'ExplicitLevels'
v3res.cnLevels = np.arange(520, 600, 16)
elif variable2['name'] == 't_850hPa':
v3res.cnLevelSpacingF = 6
v3res.cnLineLabelFontHeightF = 0.009
v3res.cnLineDashSegLenF = 0.18
v3res.nglFrame = False
v3res.nglDraw = False
# settings for unit text #
text_str = variable1['unit']
text_res_1 = Ngl.Resources()
text_res_1.txFontColor = 'black'
text_res_1.txFontHeightF = 0.013
#text_x = 0.965
text_x = 0.972
text_y = 0.837 #0.865
# put everything together #
basic_map = Ngl.map(wks, mpres)
v1plot = Ngl.contour(wks, data_array1, v1res)
v2plot = Ngl.contour(wks, data_array2, v2res)
v3plot = Ngl.contour(wks, data_array2, v3res)
Ngl.overlay(basic_map, v1plot)
Ngl.overlay(basic_map, v2plot)
Ngl.overlay(basic_map, v3plot)
Ngl.draw(basic_map)
Ngl.text_ndc(wks, text_str, text_x, text_y, text_res_1)
Ngl.frame(wks)
Ngl.delete_wks(wks)
# cut top and bottom whitespace of plot #
im = Image.open(path['base'] + path['plots'] + plot_name + '.png')
image_array = np.asarray(im.convert('L'))
image_array = np.where(image_array < 255, 1, 0)
image_filter = np.amax(image_array, axis=1)
vmargins = [np.nonzero(image_filter)[0][0], np.nonzero(image_filter[::-1])[0][0]]
#print(vmargins)
#print(im.size)
im_cropped = Image.new('RGB',(im.size[0], im.size[1] - vmargins[0] - vmargins[1]), (255,255,255))
im_cropped.paste(im.crop((0, vmargins[0], im.size[0], im.size[1] - vmargins[1])), (0, 0))
#print(im_cropped.size)
im.close()
im_cropped.save(path['base'] + path['plots'] + plot_name + '.png', 'png')
im_cropped.close()
return
def double_contourplot(var1var2, model):
recoverpath = dict(base='/',
temp='data/additional_data/temp/{}/'.format(ex_op_str))
# load all dictionaries and strings from json file #
json_filename = 'deterministic_overview_maps_{}_{}_dicts_strings.json'.format(
model, var1var2)
with open(recoverpath['base'] + recoverpath['temp'] + json_filename,
'r') as f:
path, domain, variable1, variable2, run, hour = json.load(f)
# load numpy arrays #
if variable1['load_global_field']:
numpyarrays_filename = 'deterministic_overview_maps_{}_{}_ndarrays_outofloop.npz'.format(
model, var1var2)
if model == 'icon-eu-det':
with open(path['base'] + path['temp'] + numpyarrays_filename,
'rb') as f:
with np.load(f) as loadedfile:
clat = loadedfile['clat']
clon = loadedfile['clon']
elif model == 'icon-global-det':
if not variable1['load_global_field']:
with open(path['base'] + path['temp'] + numpyarrays_filename,
'rb') as f:
with np.load(f) as loadedfile:
ll_lat = loadedfile['ll_lat']
ll_lon = loadedfile['ll_lon']
elif variable2['name'] == '':
with open(path['base'] + path['temp'] + numpyarrays_filename,
'rb') as f:
with np.load(f) as loadedfile:
clat = loadedfile['clat']
clon = loadedfile['clon']
vlat = loadedfile['vlat']
vlon = loadedfile['vlon']
else:
with open(path['base'] + path['temp'] + numpyarrays_filename,
'rb') as f:
with np.load(f) as loadedfile:
clat = loadedfile['clat']
clon = loadedfile['clon']
vlat = loadedfile['vlat']
vlon = loadedfile['vlon']
ll_lat = loadedfile['ll_lat']
ll_lon = loadedfile['ll_lon']
numpyarrays_filename = 'deterministic_overview_maps_{}_{}_ndarrays_withinloop.npz'.format(
model, var1var2)
with open(
recoverpath['base'] + recoverpath['temp'] +
numpyarrays_filename, 'rb') as f:
with np.load(f) as loadedfile:
data_array1 = loadedfile['data_array1']
if variable2['name'] != '':
data_array2 = loadedfile['data_array2']
#print('loaded all vars')
if domain['limits_type'] == 'radius':
margin_deg = 20
elif domain['limits_type'] == 'deltalatlon' or domain[
'limits_type'] == 'angle':
margin_deg = 20
if model == 'icon-eu-det':
data_array1_cut, clat_cut, clon_cut, vlat_cut, vlon_cut = data_array1, clat, clon, None, None
elif model == 'icon-global-det':
data_array_dims = '2d'
[data_array1_cut], clat_cut, clon_cut, vlat_cut, vlon_cut = \
cut_by_domain(domain, variable1['grid'], data_array_dims,
[data_array1], clat, clon, vlat, vlon, margin_deg)
if variable2['name'] != '':
[data_array2_cut], ll_lat_cut, ll_lon_cut = \
cut_by_domain(domain, variable2['grid'], data_array_dims,
[data_array2], ll_lat, ll_lon, None, None, margin_deg)
#data_array2_cut, ll_lat_cut, ll_lon_cut = data_array2, ll_lat, ll_lon
else:
# if load_global_field == False
if variable1['name'][:2] == 'pv'\
and variable1['name'][-1:] == 'K':
iso_theta_value = float(variable1['name'][3:6])
data_array1_cut, clat_cut, clon_cut \
= calc_pv_on_theta(model, domain, variable1['grid'], run, hour, iso_theta_value)
elif variable1['name'][:5] == 'theta'\
and variable1['name'][-3:] == 'PVU':
iso_pv_value = float(variable1['name'][6:9])
data_array1_cut, clat_cut, clon_cut \
= calc_theta_on_pv(model, domain, variable1['grid'], run, hour, iso_pv_value)
# example plot_name: icon-global-det_2020070512_prec_rate_mslp_europe_000h.png
plot_name = '{}_{:4d}{:02d}{:02d}{:02d}_{}_{}_{:03d}h'.format(
model, run['year'], run['month'], run['day'], run['hour'], var1var2,
domain['name'], hour)
# plot basic map with borders #
if variable1['name'] == 'prec_rate'\
or variable1['name'] == 'prec_6h':
filename_colorpalette = 'colorscale_prec_rate.txt'
with open(path['base'] + path['colorpalette'] + filename_colorpalette,
'r') as f:
lines = f.readlines()
rgb_colors = []
rgb_colors.append([0, 0, 0,
0]) # color for under the lowest value, transparent
for i, line in enumerate(lines):
rgb_colors.append([
float(line[0:3]) / 255,
float(line[4:7]) / 255,
float(line[8:11]) / 255, 1
])
rgb_colors.append(
rgb_colors[-1]
) # add highest loaded color to color for over the highest value
levels1 = ([0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10, 20, 30, 50])
lbStride_value = 1
elif variable1['name'] == 't_850hPa':
rgb_colors = []
rgb_colors.append(
[0, 0, 0]) # placeholder for color for under the lowest value
for i in range(10):
rgb_colors.append(
list(
cmasher.take_cmap_colors('cmr.flamingo',
10,
cmap_range=(0.45, 0.88))[i]))
for i in range(2, 12):
rgb_colors.append(
list(
palettable.colorbrewer.sequential.Purples_9.
get_mpl_colormap(N=14)(i)[:3]))
rgb_colors.append(list(np.array([60, 53, 139]) / 255))
rgb_colors.append(list(np.array([65, 69, 171]) / 255))
rgb_colors.append(list(np.array([68, 86, 199]) / 255))
for i in range(2, 19):
rgb_colors.append(
list(
palettable.colorbrewer.diverging.Spectral_10.
get_mpl_colormap(N=19).reversed()(i)[:3]))
for i in range(1, 11):
rgb_colors.append(
list(
palettable.cartocolors.sequential.Burg_7.get_mpl_colormap(
N=11).reversed()(i)[:3]))
rgb_colors[0] = rgb_colors[
1] # copy lowest loaded color to color for under the lowest value
rgb_colors.append(
rgb_colors[-1]
) # add highest loaded color to color for over the highest value
rgb_colors[31] = list(np.array([255, 255, 160]) / 255)
rgb_colors[32] = list(np.array([247, 230, 128]) / 255)
levels1 = np.arange(-25, 25 + 1, 1)
lbStride_value = 5
elif variable1['name'] == 'theta_e_850hPa':
rgb_colors = []
rgb_colors.append(
[0, 0, 0]) # placeholder for color for under the lowest value
for i in range(5):
rgb_colors.append(
list(
cmasher.take_cmap_colors('cmr.flamingo',
5,
cmap_range=(0.65, 0.93))[i]))
for i in range(4, 13):
rgb_colors.append(
list(
palettable.colorbrewer.sequential.Purples_9.
get_mpl_colormap(N=16)(i)[:3]))
for i in range(26):
rgb_colors.append(
list(
palettable.colorbrewer.diverging.Spectral_10.
get_mpl_colormap(N=26).reversed()(i)[:3]))
for i in range(1, 11):
rgb_colors.append(
list(
palettable.cartocolors.sequential.Burg_7.get_mpl_colormap(
N=11).reversed()(i)[:3]))
for i in range(2, 2 + 5):
rgb_colors.append(
list(
palettable.scientific.sequential.Turku_10.get_mpl_colormap(
N=12).reversed()(i)[:3]))
rgb_colors[0] = rgb_colors[
1] # copy lowest loaded color to color for under the lowest value
rgb_colors.append(
rgb_colors[-1]
) # add highest loaded color to color for over the highest value
rgb_colors[27] = list(np.array([255, 255, 160]) / 255)
rgb_colors[28] = list(np.array([247, 237, 128]) / 255)
levels1 = np.arange(-20, 90 + 1, 2)
lbStride_value = 5
elif variable1['name'] == 'wind_300hPa':
rgb_colors = []
rgb_colors.append([0, 0, 0,
0]) # color for under the lowest value, transparent
for i in range(1, 8):
rgb_colors.append(
list(
palettable.cmocean.sequential.Ice_10.get_mpl_colormap(
N=10).reversed()(i)[:3]) + [1])
rgb_colors.append(
rgb_colors[-1]
) # add highest loaded color to color for over the highest value
levels1 = (list(range(120, 330 + 1, 30)))
lbStride_value = 1
elif variable1['name'] == 'cape_ml':
rgb_colors = []
rgb_colors.append([0, 0, 0,
0]) # color for under the lowest value, transparent
for i in range(2, 7):
rgb_colors.append(
list(
palettable.scientific.sequential.Tokyo_7.get_mpl_colormap(
N=7)(i)[:3]) + [1])
for i in range(1, 17, 3):
rgb_colors.append(
list(
palettable.cmocean.sequential.Thermal_20.get_mpl_colormap(
N=22).reversed()(i)[:3]) + [1])
for i in range(9, 18, 4):
rgb_colors.append(
list(
palettable.cmocean.sequential.Ice_20.get_mpl_colormap(
N=20)(i)[:3]) + [1])
rgb_colors.append(
rgb_colors[-1]
) # add highest loaded color to color for over the highest value
rgb_colors[1] = list(np.array([138, 98, 110]) / 255) + [1]
rgb_colors[10] = list(np.array([142, 69, 139]) / 255) + [1]
#levels1 = ([0,100,200,350,530,750,1000,1300,1630,2000,2400,2850,3330,3850,4400,5000]) # follows (wmax)**2
levels1 = ([
100, 200, 350, 500, 750, 1000, 1300, 1600, 2000, 2400, 2800, 3300,
3800, 4400, 5000
]) # rounded to be pretty
lbStride_value = 1
#rgb_arr = np.array(rgb_colors)
#np.savetxt("colorpalette_marco_cape_ml.txt", np.around(rgb_arr[3:-1]*255,0), fmt='%2d', delimiter = ",")
elif variable1['name'] == 'synth_bt_ir10.8':
filename_colorpalette = 'rainbowIRsummer.txt'
with open(path['base'] + path['colorpalette'] + filename_colorpalette,
'r') as f:
lines = f.readlines()
rgb_colors = []
rgb_colors.append(list(np.array([118, 39, 118]) / 255))
for i, line in enumerate(lines):
if i % 14 == 0 and i > 70:
rgb_colors.append(
[float(line[:10]),
float(line[11:21]),
float(line[22:32])])
rgb_colors.append(list(np.array([35, 244, 255]) / 255))
num_bw_colors = 30
for i in range(num_bw_colors + 1):
rgb_colors.append([
1 - i / num_bw_colors, 1 - i / num_bw_colors,
1 - i / num_bw_colors
])
rgb_colors.append(
rgb_colors[-1]
) # add highest loaded color to color for over the highest value
levels1 = (list(range(-90, -20, 1)) + list(range(-20, 40 + 1, 2)))
lbStride_value = 10
elif variable1['name'] == 'prec_24h'\
or variable1['name'] == 'prec_sum':
filename_colorpalette = 'colorscale_prec24h.txt'
with open(path['base'] + path['colorpalette'] + filename_colorpalette,
'r') as f:
lines = f.readlines()
rgb_colors = []
rgb_colors.append([0, 0, 0,
0]) # color for under the lowest value, transparent
for i, line in enumerate(lines):
rgb_colors.append([
float(line[0:3]) / 255,
float(line[4:7]) / 255,
float(line[8:11]) / 255, 1
])
rgb_colors.append(
rgb_colors[-1]
) # add highest loaded color to color for over the highest value
levels1 = ([
1, 10, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 250, 300, 400
])
lbStride_value = 1
elif variable1['name'] == 'vmax_10m':
filename_colorpalette = 'colorscale_vmax.txt'
with open(path['base'] + path['colorpalette'] + filename_colorpalette,
'r') as f:
lines = f.readlines()
rgb_colors = []
rgb_colors.append(
[0, 0, 0]) # placeholder for color for under the lowest value
for i in range(0, 11): # load colors from palettable
rgb_colors.append(
list(
cmasher.take_cmap_colors('cmr.chroma',
11,
cmap_range=(0.20,
0.95))[::-1][i]))
for i in range(0, 3): # load colors from palettable
rgb_colors.append(
list(
cmasher.take_cmap_colors('cmr.freeze',
3,
cmap_range=(0.40, 0.90))[i]))
for i in range(0, 2): # load colors from palettable
rgb_colors.append(
list(
cmasher.take_cmap_colors('cmr.flamingo',
2,
cmap_range=(0.55,
0.80))[::-1][i]))
rgb_colors[0] = rgb_colors[
1] # copy lowest loaded color to color for under the lowest value
rgb_colors.append(
rgb_colors[-1]
) # add highest loaded color to color for over the highest value
levels1 = ([
0, 6, 12, 20, 29, 39, 50, 62, 75, 89, 103, 118, 154, 178, 209, 251,
300
])
lbStride_value = 1
elif variable1['name'] == 'shear_200-850hPa':
filename_colorpalette = 'colorscale_shear_200-850hPa.txt'
with open(path['base'] + path['colorpalette'] + filename_colorpalette,
'r') as f:
lines = f.readlines()
rgb_colors = []
rgb_colors.append([1, 1, 1])
for i, line in enumerate(lines):
rgb_colors.append([
float(line[0:3]) / 255,
float(line[4:7]) / 255,
float(line[8:11]) / 255
])
rgb_colors.append([0, 0, 1])
levels1 = (list(np.arange(0, 30, 2.5)) + list(range(30, 50 + 1, 5)) +
[99])
lbStride_value = 1
elif variable1['name'][:2] == 'pv'\
and variable1['name'][-1:] == 'K':
filename_colorpalette = 'colorscale_ipv_eth.txt'
with open(path['base'] + path['colorpalette'] + filename_colorpalette,
'r') as f:
lines = f.readlines()
rgb_colors = []
rgb_colors.append(
[0, 0, 0]) # placeholder for color for under the lowest value
for i, line in enumerate(lines):
rgb_colors.append([
float(line[0:3]) / 255,
float(line[4:7]) / 255,
float(line[8:11]) / 255
])
rgb_colors[0] = rgb_colors[
1] # copy lowest loaded color to color for under the lowest value
rgb_colors.append(
rgb_colors[-1]
) # add highest loaded color to color for over the highest value
levels1 = ([-1, -0.5, 0.2, 0.7, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10])
lbStride_value = 1
elif variable1['name'][:5] == 'theta'\
and variable1['name'][-3:] == 'PVU':
rgb_colors = []
rgb_colors.append(
[0, 0, 0]) # placeholder for color for under the lowest value
for i in range(30):
rgb_colors.append(
list(
palettable.colorbrewer.diverging.RdYlBu_10.
get_mpl_colormap(N=30)(i)[:3]))
rgb_colors[0] = rgb_colors[
1] # copy lowest loaded color to color for under the lowest value
rgb_colors.append(
rgb_colors[-1]
) # add highest loaded color to color for over the highest value
levels1 = np.arange(270, 420 + 1, 5)
#levels1 = np.arange(300, 360+1, 20)
lbStride_value = 2
mpres = Ngl.Resources()
mpres.mpProjection = domain['projection']
if domain['limits_type'] == 'radius':
mpres.mpLimitMode = 'LatLon'
mpres.mpCenterLonF = domain['centerlon']
mpres.mpCenterLatF = domain['centerlat']
cutout_plot = dict(
lat_min=float(
np.where(domain['centerlat'] - domain['radius'] / 111.2 < -90,
-90, domain['centerlat'] - domain['radius'] / 111.2)),
lat_max=float(
np.where(domain['centerlat'] + domain['radius'] / 111.2 > 90,
90, domain['centerlat'] + domain['radius'] / 111.2)),
)
cutout_plot['lon_min'] = float(np.where(cutout_plot['lat_min'] <= -90 or cutout_plot['lat_max'] >= 90,
0,
domain['centerlon'] - domain['radius'] \
/ (111.2 * np.cos(domain['centerlat']*np.pi/180))))
cutout_plot['lon_max'] = float(np.where(cutout_plot['lat_min'] <= -90 or cutout_plot['lat_max'] >= 90,
360,
domain['centerlon'] + domain['radius'] \
/ (111.2 * np.cos(domain['centerlat']*np.pi/180))))
mpres.mpMinLonF = cutout_plot['lon_min']
mpres.mpMaxLonF = cutout_plot['lon_max']
mpres.mpMinLatF = cutout_plot['lat_min']
mpres.mpMaxLatF = cutout_plot['lat_max']
elif domain['limits_type'] == 'deltalatlon':
mpres.mpLimitMode = 'LatLon'
mpres.mpCenterLonF = 0
mpres.mpCenterLatF = 0
mpres.mpMinLonF = domain['centerlon'] - domain['deltalon_deg']
mpres.mpMaxLonF = domain['centerlon'] + domain['deltalon_deg']
mpres.mpMinLatF = domain['centerlat'] - domain['deltalat_deg']
mpres.mpMaxLatF = domain['centerlat'] + domain['deltalat_deg']
elif domain['limits_type'] == 'angle':
mpres.mpLimitMode = 'Angles'
mpres.mpCenterLonF = domain['centerlon']
mpres.mpCenterLatF = domain['centerlat']
mpres.mpLeftAngleF = domain['angle']
mpres.mpRightAngleF = domain['angle']
mpres.mpTopAngleF = domain['angle']
mpres.mpBottomAngleF = domain['angle']
mpres.nglMaximize = False
mpres.vpXF = 0.001
mpres.vpYF = 1.00
mpres.vpWidthF = 0.88
mpres.vpHeightF = 1.00
mpres.mpLandFillColor = list(np.array([255, 225, 171]) / 255)
mpres.mpOceanFillColor = [1, 1, 1]
mpres.mpInlandWaterFillColor = [1, 1, 1]
mpres.mpFillOn = True
mpres.mpGridAndLimbOn = False
mpres.mpGreatCircleLinesOn = False
mpres.mpDataBaseVersion = 'MediumRes'
mpres.mpDataSetName = 'Earth..4'
mpres.mpOutlineBoundarySets = 'national'
mpres.mpGeophysicalLineColor = 'black'
mpres.mpNationalLineColor = 'black'
mpres.mpGeophysicalLineThicknessF = 1.5 * domain['plot_width'] / 1000
mpres.mpNationalLineThicknessF = 1.5 * domain['plot_width'] / 1000
mpres.mpPerimOn = True
mpres.mpPerimLineColor = 'black'
mpres.mpPerimLineThicknessF = 8.0 * domain['plot_width'] / 1000
mpres.tmXBOn = False
mpres.tmXTOn = False
mpres.tmYLOn = False
mpres.tmYROn = False
mpres.nglDraw = False #-- don't draw plot
mpres.nglFrame = False #-- don't advance frame
# settings for variable1 / shading #
v1res = Ngl.Resources()
v1res.nglFrame = False
v1res.nglDraw = False
v1res.sfDataArray = data_array1_cut
v1res.sfXArray = clon_cut
v1res.sfYArray = clat_cut
v1res.sfMissingValueV = 9999
#v1res.cnFillBackgroundColor = 'transparent'
v1res.cnMissingValFillColor = 'Gray80'
v1res.cnFillColors = np.array(rgb_colors)
v1res.cnLinesOn = False
v1res.cnFillOn = True
v1res.cnLineLabelsOn = False
if not variable1['load_global_field']:
v1res.cnFillMode = 'RasterFill'
elif model == 'icon-eu-det':
v1res.cnFillMode = 'RasterFill'
elif model == 'icon-global-det':
v1res.cnFillMode = 'CellFill'
v1res.sfXCellBounds = vlon_cut
v1res.sfYCellBounds = vlat_cut
#v1res.cnFillOpacityF = 0.5
#v1res.cnFillDrawOrder = 'Predraw'
v1res.cnLevelSelectionMode = 'ExplicitLevels'
v1res.cnLevels = levels1
v1res.lbLabelBarOn = True
v1res.lbAutoManage = False
v1res.lbOrientation = 'vertical'
v1res.lbLabelOffsetF = 0.04 # minor axis fraction: the distance between colorbar and numbers
v1res.lbBoxMinorExtentF = 0.20 # minor axis fraction: width of the color boxes when labelbar down
v1res.lbTopMarginF = 0.2 # make a little more space at top for the unit label
v1res.lbRightMarginF = 0.0
v1res.lbBottomMarginF = 0.05
v1res.lbLeftMarginF = -0.35
v1res.cnLabelBarEndStyle = 'ExcludeOuterBoxes'
#if variable1['name'] == 'prec_rate' :
# v1res.cnLabelBarEndStyle = 'IncludeOuterBoxes'
#v1res.cnExplicitLabelBarLabelsOn = True
#v1res.pmLabelBarDisplayMode = 'Always'
v1res.pmLabelBarWidthF = 0.10
#v1res.lbLabelStrings = label_str_list
v1res.lbLabelFontHeightF = 0.010
#v1res.lbBoxCount = 40
v1res.lbBoxSeparatorLinesOn = False
v1res.lbBoxLineThicknessF = 4
#v1res.lbBoxEndCapStyle = 'TriangleBothEnds'
v1res.lbLabelAlignment = 'ExternalEdges'
v1res.lbLabelStride = lbStride_value
# settings for variable2 / contourlines #
if variable2['name'] != '':
v2res = Ngl.Resources()
v2res.nglFrame = False
v2res.nglDraw = False
v2res.sfDataArray = data_array2_cut
v2res.sfXArray = ll_lon_cut #ll_lon_cyclic
v2res.sfYArray = ll_lat_cut
v2res.sfMissingValueV = 9999
#v2res.trGridType = 'TriangularMesh'
v2res.cnFillOn = False
v2res.cnLinesOn = True
v2res.cnLineLabelsOn = True
v2res.cnLineLabelPlacementMode = 'Constant'
v2res.cnLabelDrawOrder = 'PostDraw'
v2res.cnInfoLabelOn = False
v2res.cnSmoothingOn = False
v2res.cnLevelSelectionMode = 'ExplicitLevels'
v2res.cnLineThicknessF = 3.0 * domain['plot_width'] / 1000
v2res.cnLineLabelFontHeightF = 0.008
v2res.cnLineDashSegLenF = 0.25
v3res = Ngl.Resources()
v3res.nglFrame = False
v3res.nglDraw = False
v3res.sfDataArray = data_array2_cut
v3res.sfXArray = ll_lon_cut #ll_lon_cyclic
v3res.sfYArray = ll_lat_cut
v3res.sfMissingValueV = 9999
#v3res.trGridType = 'TriangularMesh'
v3res.cnFillOn = False
v3res.cnLinesOn = True
v3res.cnLineLabelsOn = True
v3res.cnLineLabelPlacementMode = 'Constant'
v3res.cnLabelDrawOrder = 'PostDraw'
v3res.cnInfoLabelOn = False
v3res.cnSmoothingOn = False
v3res.cnLevelSelectionMode = 'ExplicitLevels'
v3res.cnLineThicknessF = 5.0 * domain['plot_width'] / 1000
v3res.cnLineLabelFontHeightF = 0.008
v3res.cnLineDashSegLenF = 0.25
if variable2['name'] == 'mslp':
v2res.cnLevels = np.arange(900, 1100, 2)
v2res.cnLineLabelInterval = 1
v3res.cnLevels = np.arange(900, 1100, 10)
v3res.cnLineLabelInterval = 1
elif variable2['name'] == 'gph_300hPa':
v2res.cnLevels = np.arange(780, 1000, 6)
v2res.cnLineLabelInterval = 1
v3res.cnLevels = np.arange(780, 1000, 24)
v3res.cnLineLabelInterval = 1
elif variable2['name'] == 'gph_850hPa':
v2res.cnLevels = np.arange(100, 180, 2)
v2res.cnLineLabelInterval = 2
v3res.cnLevels = np.arange(100, 180, 10)
v3res.cnLineLabelInterval = 2
elif variable2['name'] == 'gph_700hPa':
v2res.cnLevels = np.arange(472, 633, 2)
v2res.cnLineLabelInterval = 1
v3res.cnLevels = np.arange(472, 633, 8)
v3res.cnLineLabelInterval = 1
elif variable2['name'] == 'gph_500hPa':
v2res.cnLevels = np.arange(472, 633, 4)
v2res.cnLineLabelInterval = 1
v3res.cnLevels = np.arange(472, 633, 16)
v3res.cnLineLabelInterval = 1
elif variable2['name'] == 'shear_0-6km':
v4res = Ngl.Resources()
v4res.nglFrame = False
v4res.nglDraw = False
v4res.sfDataArray = data_array2_cut
v4res.sfXArray = ll_lon_cut #ll_lon_cyclic
v4res.sfYArray = ll_lat_cut
v4res.sfMissingValueV = 9999
#v4res.trGridType = 'TriangularMesh'
v4res.cnFillOn = False
v4res.cnLinesOn = True
v4res.cnLineLabelsOn = True
v4res.cnLineLabelPlacementMode = 'Constant'
v4res.cnLabelDrawOrder = 'PostDraw'
v4res.cnInfoLabelOn = False
v4res.cnSmoothingOn = False
v4res.cnLevelSelectionMode = 'ExplicitLevels'
v4res.cnLineLabelFontHeightF = 0.008
v4res.cnLineDashSegLenF = 0.25
v5res = Ngl.Resources()
v5res.nglFrame = False
v5res.nglDraw = False
v5res.sfDataArray = data_array2_cut
v5res.sfXArray = ll_lon_cut #ll_lon_cyclic
v5res.sfYArray = ll_lat_cut
v5res.sfMissingValueV = 9999
#54res.trGridType = 'TriangularMesh'
v5res.cnFillOn = False
v5res.cnLinesOn = True
v5res.cnLineLabelsOn = True
v5res.cnLineLabelPlacementMode = 'Constant'
v5res.cnLabelDrawOrder = 'PostDraw'
v5res.cnInfoLabelOn = False
v5res.cnSmoothingOn = False
v5res.cnLevelSelectionMode = 'ExplicitLevels'
v5res.cnLineLabelFontHeightF = 0.008
v5res.cnLineDashSegLenF = 0.25
v2res.cnLevels = [10]
v2res.cnLineLabelInterval = 1
v2res.cnLineDashSegLenF = 0.15
v2res.cnLineThicknessF = 2.4 * domain['plot_width'] / 1000
v3res.cnLevels = [15]
v3res.cnLineLabelInterval = 1
v3res.cnLineThicknessF = 4.5 * domain['plot_width'] / 1000
v3res.cnLineDashSegLenF = 0.15
v3res.cnLineThicknessF = 3.6 * domain['plot_width'] / 1000
v4res.cnLevels = [20]
v4res.cnLineLabelInterval = 1
v4res.cnLineDashSegLenF = 0.15
v4res.cnLineThicknessF = 5.4 * domain['plot_width'] / 1000
v5res.cnLevels = [25]
v5res.cnLineLabelInterval = 1
v5res.cnLineDashSegLenF = 0.15
v5res.cnLineThicknessF = 8.0 * domain['plot_width'] / 1000
''' these are settings for some experiment with lows/highs min/max pressure labels:
v2res.cnLineLabelsOn = True
v2res.cnLabelDrawOrder = 'PostDraw'
v2res.cnLineLabelPerimOn = False
v2res.cnLineLabelBackgroundColor = 'transparent'
v2res.cnLineLabelPlacementMode = 'Computed'
v2res.cnLineLabelDensityF = 0.2
v2res.cnLineLabelFontHeightF = 0.010
#v2res.cnLineDashSegLenF = 0.25
v2res.cnLowLabelsOn = True
#v2res.cnLowLabelPerimOn = False
v2res.cnLowLabelString = '$ZDV$hPa'
#v2res.cnLowLabelFontColor = 'black'
v2res.cnLowLabelBackgroundColor = 'white'''
# settings for unit text #
if variable1['name'][:2] == 'pv'\
and (domain['name'] == 'southern_south_america'\
or domain['name'] == 'south_pole'):
text_str = variable1['unit'] + ' * -1'
else:
text_str = variable1['unit']
text_res_1 = Ngl.Resources()
text_res_1.txFontColor = 'black'
text_res_1.txFontHeightF = 0.013
text_x = 0.965
text_y = domain['text_y']
# settings for description label #
res_text_descr = Ngl.Resources()
res_text_descr.txJust = 'CenterLeft'
res_text_descr.txFontHeightF = 0.01
res_text_descr.txFontColor = 'black'
res_text_descr.txBackgroundFillColor = 'white'
res_text_descr.txPerimOn = True
res_text_descr.txPerimColor = 'black'
text_descr_str1 = 'ICON-Global-Deterministic from {:02d}.{:02d}.{:4d} {:02d}UTC +{:d}h, '.format(
run['day'], run['month'], run['year'], run['hour'], hour)
if variable2['name'] == '':
text_descr_str2 = 'Contours: {} in {}'.format(variable1['name'],
variable1['unit'])
else:
text_descr_str2 = 'Contours: {} in {}, Lines: {} in {}'.format(
variable1['name'], variable1['unit'], variable2['name'],
variable2['unit'])
text_descr_x = 0.02
text_descr_y = domain['text_y'] - 0.005
# override settings for specific cases #
if domain['name'] == 'mediterranean':
mpres.mpPerimLineThicknessF /= 1.5
mpres.mpGeophysicalLineThicknessF /= 1.5
mpres.mpNationalLineThicknessF /= 1.5
mpres.vpWidthF = 0.94
v1res.lbLabelFontHeightF = 0.005
v1res.pmLabelBarWidthF = 0.04
v1res.lbLeftMarginF = -0.50
if variable2['name'] != '':
v2res.cnLineThicknessF /= 1.5
v2res.cnLineLabelFontHeightF /= 1.5
v3res.cnLineThicknessF /= 1.5
v3res.cnLineLabelFontHeightF /= 1.5
v2res.cnLineDashSegLenF = 0.12
v3res.cnLineDashSegLenF = 0.12
text_res_1.txFontHeightF = 0.007
text_x = 0.980
res_text_descr.txFontHeightF = 0.007
text_descr_x = 0.005
elif domain['name'] == 'north_pole' or domain['name'] == 'south_pole':
if variable2['name'] == 'mslp':
v2res.cnLevels = np.arange(900, 1100, 4)
v3res.cnLevels = np.arange(900, 1100, 20)
elif variable2['name'] == 'gph_300hPa':
v2res.cnLevels = np.arange(780, 1000, 12)
v3res.cnLevels = np.arange(780, 1000, 48)
elif variable2['name'] == 'gph_500hPa':
v2res.cnLevels = np.arange(472, 633, 8)
v3res.cnLevels = np.arange(472, 633, 32)
elif variable2['name'] == 'gph_700hPa':
v2res.cnLevels = np.arange(472, 633, 8)
v3res.cnLevels = np.arange(472, 633, 32)
elif variable2['name'] == 'gph_850hPa':
v2res.cnLevels = np.arange(102, 180, 4)
v3res.cnLevels = np.arange(102, 180, 20)
elif domain['name'] == 'Argentina_Central' or domain[
'name'] == 'Argentina_Central_cerca':
if variable2['name'] == 'gph_850hPa':
v2res.cnLevels = np.arange(100, 180, 1)
v3res.cnLevels = np.arange(100, 180, 5)
v2res.cnLineLabelInterval = 1
wks_res = Ngl.Resources()
wks_res.wkWidth = domain['plot_width']
wks_res.wkHeight = domain[
'plot_width'] # the whitespace above and below the plot will be cut afterwards
#wks_res.wkColorMap = np.array(rgb_colors)
wks_type = 'png'
wks = Ngl.open_wks(wks_type, path['base'] + path['plots'] + plot_name,
wks_res)
basic_map = Ngl.map(wks, mpres)
# plot subnational borders for some domains #
'''shp_filenames = []
if domain['name'] == 'southern_south_america'\
or domain['name'] == 'arg_uru_braz'\
or domain['name'] == 'argentina_central'\
or domain['name'] == 'argentina_central_cerca':
shp_filenames.append(['gadm36_ARG_1.shp', 0.3])
shp_filenames.append(['gadm36_BRA_1.shp', 0.3])
shp_filenames.append(['gadm36_CHL_1.shp', 0.3])
elif domain['name'] == 'north_america':
shp_filenames.append(['gadm36_USA_1.shp', 0.3])
shp_filenames.append(['gadm36_CAN_1.shp', 0.3])
shp_filenames.append(['gadm36_MEX_1.shp', 0.3])
elif domain['name'] == 'eastern_asia':
shp_filenames.append(['gadm36_CHN_1.shp', 0.3])
for shp_filename, lineThickness in shp_filenames:
shpf = Nio.open_file(path['base'] + path['shapefiles'] + shp_filename, 'r')
shpf_lon = np.ravel(shpf.variables['x'][:])
shpf_lat = np.ravel(shpf.variables['y'][:])
shpf_segments = shpf.variables['segments'][:, 0]
plres = Ngl.Resources()
plres.gsLineColor = 'black'
plres.gsLineThicknessF = lineThickness
plres.gsSegments = shpf_segments
Ngl.add_polyline(wks, basic_map, shpf_lon, shpf_lat, plres)'''
# put everything together #
contourshades_plot = Ngl.contour(wks, data_array1_cut, v1res)
if variable2['name'] != '':
contourlines_minor_plot = Ngl.contour(wks, data_array2_cut, v2res)
contourlines_major_plot = Ngl.contour(wks, data_array2_cut, v3res)
if variable2['name'] == 'shear_0-6km':
contourlines_major2_plot = Ngl.contour(wks, data_array2_cut, v4res)
contourlines_major3_plot = Ngl.contour(wks, data_array2_cut, v5res)
Ngl.overlay(basic_map, contourshades_plot)
if variable2['name'] != '':
Ngl.overlay(basic_map, contourlines_minor_plot)
Ngl.overlay(basic_map, contourlines_major_plot)
if variable2['name'] == 'shear_0-6km':
Ngl.overlay(basic_map, contourlines_major2_plot)
Ngl.overlay(basic_map, contourlines_major3_plot)
Ngl.draw(basic_map)
Ngl.text_ndc(wks, text_str, text_x, text_y, text_res_1)
#Ngl.text_ndc(wks, text_descr_str1 + text_descr_str2, text_descr_x, text_descr_y, res_text_descr)
Ngl.frame(wks)
Ngl.delete_wks(wks)
# cut top and bottom whitespace of plot #
im = Image.open(path['base'] + path['plots'] + plot_name + '.png')
image_array = np.asarray(im.convert('L'))
image_array = np.where(image_array < 255, 1, 0)
image_filter = np.amax(image_array, axis=1)
vmargins = [
np.nonzero(image_filter)[0][0],
np.nonzero(image_filter[::-1])[0][0]
]
#print(vmargins)
#print(im.size)
im_cropped = Image.new(
'RGB', (im.size[0], im.size[1] - vmargins[0] - vmargins[1]),
(255, 255, 255))
im_cropped.paste(
im.crop((0, vmargins[0], im.size[0], im.size[1] - vmargins[1])),
(0, 0))
#print(im_cropped.size)
im.close()
im_cropped.save(path['base'] + path['plots'] + plot_name + '.png', 'png')
im_cropped.close()
return
def popsicle_diagram(restartfile, param_file, pftcolors, stemcolor=159, file=None, title=None, xtitle=None, ytitle=None, yrange=None, colormap=None, title_charsize=0.75, aspect_ratio=None, makepng=False, png_dens=pngdens, use_wks=None, showjupyter=False):
if use_wks == None:
plot_type = get_workstation_type(file)
wks_res = Ngl.Resources()
if plot_type == 'x11':
wks_res.wkPause = False
elif plot_type == 'png':
wks_res.wkWidth = page_width * 100
wks_res.wkHeight = page_height * 100
else:
wks_res.wkPaperWidthF = page_width
wks_res.wkPaperHeightF = page_height
wks_res.wkOrientation = "portrait"
if not colormap == None:
colormap = parse_colormap(colormap)
wks_res.wkColorMap = colormap
wks = Ngl.open_wks(plot_type,file,wks_res)
if wks < 0 and plot_type == "x11":
clear_oldest_x11_window()
wks = Ngl.open_wks(plot_type,file,wks_res)
else:
wks = use_wks
resources = Ngl.Resources()
#cdkif plot_type != 'png':
resources.nglDraw = False
#cdkif plot_type != 'png':
resources.nglFrame = False
if title != None:
resources.tiMainString = title
if xtitle != None:
resources.tiXAxisString = xtitle
if ytitle != None:
resources.tiYAxisString = ytitle
if yrange != None:
resources.trYMinF = np.min(yrange)
resources.trYMaxF = np.max(yrange)
resources.trXMinF = 0.
resources.trXMaxF = 1.
resources.tiMainFontHeightF = 0.025 * title_charsize
resources.tmXBMajorLengthF = 0.01
resources.tmXBMajorOutwardLengthF = 0.01
resources.tmYLMajorLengthF = 0.01
resources.tmYLMajorOutwardLengthF = 0.01
resources.tmXBMinorOn = False
resources.tmYLMinorOn = False
resources.tmXTOn = False
resources.tmYROn = False
resources.tmXBOn = False
resources.tmXBLabelsOn = False
if aspect_ratio != None:
if aspect_ratio > 1.:
resources.vpHeightF = 0.6 / aspect_ratio
else:
resources.vpWidthF = 0.6 * aspect_ratio
resources.trXMinF = 0.
resources.trXMaxF = 1.
if yrange == None:
yrange = [0, np.ma.max(restartfile.variables['fates_height'][:])]
resources.trYMinF = np.min(yrange)
resources.trYMaxF = np.max(yrange)
dummies = np.ma.masked_all(3)
plot = Ngl.xy(wks, dummies, dummies, resources)
#### the main logic all goes here. ####
# first calculate the crown areas of each cohort
cohort_crownareas = carea_allom(restartfile, param_file)
max_coh_per_patch = 100
max_cohorts = len(restartfile.variables['fates_CohortsPerPatch'][:])
max_patches = max_cohorts / max_coh_per_patch
maxcanlev = restartfile.variables['fates_canopy_layer'][:].max()
cohort_rhs = np.zeros(maxcanlev)
## iterate over patches
patch_area_sofar = 0.
vline_res = Ngl.Resources
vline_res.gsLineThicknessF = .01
#
crown_res = Ngl.Resources()
crown_res.gsLineColor = "black"
crown_res.gsLineThicknessF = .01
crown_res.gsEdgesOn = True
crown_res.gsFillOpacityF = 0.5
#
shadow_res = Ngl.Resources()
shadow_res.gsFillColor = "black"
shadow_res.gsEdgesOn = False
#
stem_res1 = Ngl.Resources()
stem_res1.gsLineThicknessF = 1.
stem_res1.gsEdgesOn = True
stem_res1.gsLineColor = "black"
#
stem_res2 = Ngl.Resources()
stem_res2.gsEdgesOn = False
stem_res2.gsFillOpacityF = 1.
#
for i in range(max_patches):
## draw thin vertical line at patch lower boundary edge to delineate patches
patch_lower_edge = 1.-patch_area_sofar - restartfile.variables['fates_area'][i*max_coh_per_patch]/1e4
zlx = [patch_lower_edge, patch_lower_edge]
zly = [0., 1000.]
Ngl.add_polyline(wks,plot, zlx, zly, vline_res)
#
## iterate over cohorts
for l in range(maxcanlev-1,-1,-1):
shadow_res.gsFillOpacity = l * 0.5
cohort_rhs = 1. - patch_area_sofar
if restartfile.variables['fates_CohortsPerPatch'][i*max_coh_per_patch] > 0:
for j in range(restartfile.variables['fates_CohortsPerPatch'][i*max_coh_per_patch]-1,-1,-1):
cindx = i * max_coh_per_patch + j
if restartfile.variables['fates_canopy_layer'][cindx]-1 == l:
rhs = cohort_rhs
lhs = rhs - cohort_crownareas[cindx]/1e4
ctop = restartfile.variables['fates_height'][cindx]
cbot = ctop * 0.6
crown_res.gsFillColor = pftcolors[restartfile.variables['fates_pft'][cindx]-1]
px = [rhs,rhs,lhs,lhs,rhs]
py = [ctop,cbot,cbot,ctop,ctop]
#
stem_center = (lhs + rhs) /2.
stem_width = .00001 * restartfile.variables['fates_dbh'][cindx]
stem_res2.gsFillColor = stemcolor
sx = [stem_center+stem_width,stem_center+stem_width,stem_center-stem_width,stem_center-stem_width,stem_center+stem_width]
sy = [cbot,0.,0.,cbot,cbot]
Ngl.add_polyline(wks,plot,sx,sy,stem_res1)
Ngl.add_polygon(wks,plot,sx,sy,stem_res2)
#
if l > 0:
Ngl.add_polygon(wks,plot,px,py,shadow_res)
#
Ngl.add_polygon(wks,plot,px,py,crown_res)
#
cohort_rhs = lhs
#if lhs < patch_lower_edge:
# raise Exception
#
## get cohort properties: height, upper horizontal edge, crown area, pft, canopy layer
#
## draw cohort as popsicle
#
patch_area_sofar = patch_area_sofar + restartfile.variables['fates_area'][i*max_coh_per_patch]/1e4
if use_wks == None:
Ngl.draw(plot)
Ngl.frame(wks)
if not file==None:
Ngl.delete_wks(wks)
#
if makepng or showjupyter:
pdf_to_png(file, density=png_dens)
if jupyter_avail and showjupyter:
print(' ')
print('showing file '+file)
display(Image(file+'.png'))
else:
x11_window_list.append(wks)
else:
return plot
def print_comp_for_timestamp(wrf_data, timestamp, filepath):
slp = pressure_lib.get_sea_level_pressure(wrf_data)
temperature = getvar(wrf_data,"tc")
u = get_latitude_wind(wrf_data)
v = get_longitude_wind(wrf_data)
lat, lon = latlon_coords(temperature)
lat_normal = to_np(lat)
lon_normal = to_np(lon)
# temperature
t_res = get_pyngl(temperature)
t_res.nglDraw = False # don't draw plot
t_res.nglFrame = False # don't advance frame
t_res.cnFillOn = True # turn on contour fill
t_res.cnLinesOn = False # turn off contour lines
t_res.cnLineLabelsOn = False # turn off line labels
t_res.cnFillMode = "RasterFill" # These two resources
t_res.cnFillPalette = "BlAqGrYeOrReVi200"
t_res.cnLevelSelectionMode = "ManualLevels"
t_res.cnMinLevelValF = -25.0 # min. temperature for composite
t_res.cnMaxLevelValF = 45.0 # max. temperature for composite
t_res.cnLevelSpacingF = 1 # increment
t_res = geography_lib.initialize_geography(t_res, "gray25")
t_res.lbTitleString = "%s in (%s)" % (temperature.description,temperature.units)
t_res.lbOrientation = "horizontal"
t_res.lbTitleFontHeightF = 0.015
t_res.lbLabelFontHeightF = 0.015
t_res.tiMainString = "Composite (%s)" % timestamp.strftime("%b %d %Y %HUTC")
t_res.trGridType = "TriangularMesh" # can speed up plotting.
t_res.tfDoNDCOverlay = True # required for native projection
# pressure
p_res = pressure_lib.get_pressure_resource(lat_normal, lon_normal)
# wind
uv_res = Ngl.Resources()
uv_res.nglDraw = False # don't draw plot
uv_res.nglFrame = False # don't advance frame
uv_res.vcFillArrowsOn = True
uv_res.vcRefMagnitudeF = 15.0
uv_res.vcRefLengthF = 0.03
uv_res.vcMinDistanceF = 0.02
uv_res.vcRefAnnoFontHeightF = 0.01
uv_res.vcRefAnnoString1 = "$VMG$ m/s"
uv_res.vcRefAnnoString2 = "ground windspeed (m/s) reference"
uv_res.vfXArray = lon_normal
uv_res.vfYArray = lat_normal
wk_res = Ngl.Resources()
wk_res.wkWidth = 2500
wk_res.wkHeight = 2500
output_path = "%scomp_%s" % (filepath, timestamp.strftime("%Y_%m_%d_%H"))
wks_comp = Ngl.open_wks("png", output_path, wk_res)
# creating plots for the measurands
tplot = Ngl.contour_map(wks_comp,temperature[0,:,:],t_res)
pplot = Ngl.contour(wks_comp,slp,p_res)
vector = Ngl.vector(wks_comp,u,v,uv_res)
Ngl.overlay(tplot, vector)
Ngl.overlay(tplot, pplot)
Ngl.maximize_plot(wks_comp, tplot)
Ngl.draw(tplot)
Ngl.frame(wks_comp)
Ngl.delete_wks(wks_comp) # delete currently used workstation
