def create_map(wks): #---Map resources res = Ngl.Resources() # map resources res.nglDraw = False # don't draw map res.nglFrame = False # don't advance frame res.mpDataBaseVersion = "MediumRes" # better map outlines res.mpLimitMode = "LatLon" res.mpMaxLatF = -20 # select subregion res.mpMinLatF = -62 res.mpMinLonF = -78 res.mpMaxLonF = -25 res.mpOutlineOn = True res.mpGridAndLimbOn = False res.mpFillOn = True res.mpLandFillColor = "beige" res.mpOceanFillColor = "paleturquoise1" res.mpInlandWaterFillColor = "paleturquoise1" res.tmYROn = True # Turn on right tickmarks res.tmXTOn = True # Turn on top tickmarks res.tiMainString = "Trajectories colored by salinity (ppt)" res.tiMainFontHeightF = 0.02 map = Ngl.map(wks,res) # Draw map. return map
def hilo_valplot(lons, lats, highs, lows, cfg):
"""
Special case of having a value plot with a high and low value to
plot, which is common for some climate applications
"""
tmpfp = tempfile.mktemp()
cmap = numpy.array([[1., 1., 1.], [0., 0., 0.], [1., 0., 0.], \
[0., 0., 1.], [0., 1., 0.]], 'f')
rlist = Ngl.Resources()
rlist.wkColorMap = cmap
#rlist.wkOrientation = "landscape"
wks = Ngl.open_wks("ps", tmpfp, rlist)
res = iowa()
res.mpOutlineDrawOrder = "PreDraw"
plot = Ngl.map(wks, res)
for key in cfg.keys():
if key == 'wkColorMap' or key[0] == "_":
continue
setattr(res, key, cfg[key])
txres = Ngl.Resources()
txres.txFontHeightF = 0.016
txres.txFontColor = "red"
txres.txJust = "BottomRight"
for i in range(len(lons)):
Ngl.add_text(wks, plot, cfg["_format"] % highs[i],
lons[i], lats[i],txres)
txres = Ngl.Resources()
txres.txFontHeightF = 0.016
txres.txFontColor = "blue"
txres.txJust = "TopRight"
for i in range(len(lons)):
Ngl.add_text(wks, plot, cfg["_format"] % lows[i],
lons[i], lats[i],txres)
if cfg.has_key("_labels"):
txres = Ngl.Resources()
txres.txFontHeightF = 0.008
txres.txJust = "CenterLeft"
txres.txFontColor = 1
for i in range(len(lons)):
Ngl.add_text(wks, plot, cfg["_labels"][i],
lons[i], lats[i],txres)
watermark(wks)
manual_title(wks, cfg)
vpbox(wks)
Ngl.draw(plot)
Ngl.frame(wks)
del wks
return tmpfp
mpres.nglFrame = False #-- do it later after adding subtitles.
mpres.mpGridAndLimbOn = False #-- don't draw grid lines
mpres.mpGeophysicalLineThicknessF = 2. #-- map outlines thicker
#mpres.pmTitleDisplayMode = "Always"
#mpres.mpLimitMode = "LatLon" #-- map limit mode
#mpres.mpMinLonF = -60. #-- minimum lon
#mpres.mpMaxLonF = 60. #-- maximum lon
#mpres.mpMinLatF = 0. #-- minimum lat
#mpres.mpMaxLatF = 90. #-- maximum lat
mpres.tiMainString = "ICON data - gsSegments" #-- title string
#-- create map
map = Ngl.map(wks1,mpres)
Ngl.draw(map) #-- draw the empty map
#-- assign array which will hold the color indices of the cells
colors = range(3,ncol+3) #-- create color indices
gscolors = np.ones((ncells,),dtype=np.int) #-- assign array containing zeros
gscolors = -1*np.array(gscolors) #-- initialize to transparent color
#-- lowest level polygons
vlow = Ngl.ind(var < levels[0])
for k in xrange(0,len(vlow)-1):
if not np.all(var[vlow] == var._FillValue):
for kk in vlow:
gscolors[kk] = 3
print "finished level 0 -- " + str(len(vlow)) + " polygons considered - gscolors " + str(colors[0])
res.mpMinLonF = lontr
res.mpMaxLonF = lonbl
res.mpMinLatF = lattr
res.mpMaxLatF = latbl
res.mpOutlineBoundarySets = "AllBoundaries"
res.mpNationalLineColor = -1
res.mpNationalLineThicknessF = 2.0
res.mpGeophysicalLineColor = -1
res.mpGeophysicalLineThicknessF = 2.0
res.mpGridLatSpacingF = 5.0
res.mpGridLonSpacingF = 5.0
res.mpGridLineThicknessF = 2.0
res.mpGridLineColor = colour
res.cnMonoLineColor = True
grid_plot = ngl.map(wks, res)
ngl.maximize_plot(wks, grid_plot)
ngl.draw(grid_plot)
ngl.frame(wks)
ngl.destroy(wks)
del res
os.system('mogrify -trim grid_' + colour + '_' + region + '.png')
# if region == "WA" or region == "unknownWA":
# os.system('mogrify -trim grid_'+colour+'_'+region+'.png')
# os.system('mogrify -resize 886x600 grid_'+colour+'_'+region+'.png')
# elif region == "EA" or region == "unknownEA":
# os.system('mogrify -trim grid_'+colour+'_'+region+'.png')
# This example produces two visualizations:
# 1.) Cylindrical Equidistant projection
# 2.) Mollweide projection
#
# Notes:
#
import Ngl
wks_type = "ps"
wks = Ngl.open_wks(wks_type,"map1")
projections = ["CylindricalEquidistant","Mollweide"]
mpres = Ngl.Resources() # Indicate you want to set some
# resources.
mpres.mpShapeMode = "FreeAspect" # Allow the aspect ratio to be
# changed.
mpres.vpWidthF = 0.8 # Make the aspect ratio square.
mpres.vpHeightF = 0.8
mpres.pmTitleDisplayMode = "Always" # Force the title to appear.
for i in xrange(len(projections)):
mpres.mpProjection = projections[i]
mpres.tiMainString = projections[i]
map = Ngl.map(wks,mpres)
Ngl.end()
wks_type = "ps" wks = Ngl.open_wks(wks_type,"map2") mpres = Ngl.Resources() mpres.nglFrame = False # Don't advance frame after plot is drawn. mpres.nglDraw = False # Don't draw plot just yet. mpres.mpProjection = "Orthographic" mpres.mpCenterLatF = 30. mpres.mpCenterLonF = 160. mpres.mpCenterRotF = 60. mpres.mpLabelsOn = False mpres.mpPerimOn = False map = Ngl.map(wks,mpres) # Create map. gsres = Ngl.Resources() gsres.gsLineColor = "black" gsres.gsLineThicknessF = 2.0 gsres.gsLineLabelFont = "helvetica-bold" gsres.gsLineLabelFontHeightF = 0.02 gsres.gsLineLabelFontColor = "black" gsres.gsFillIndex = 0 gsres.gsEdgesOn = False # Tropical zone gsres.gsFillColor = 87
#-- set resources res = Ngl.Resources() res.nglDraw = False #-- don't draw the plot yet res.nglFrame = False #-- don't advance the frame yet res.mpFillOn = True res.mpOutlineOn = False res.mpOceanFillColor = "Transparent" res.mpLandFillColor = "Gray80" res.mpInlandWaterFillColor = "Gray80" res.mpGridAndLimbOn = False #-- don't draw grid lines res.pmTickMarkDisplayMode = "Always" #-- turn on map tickmark #-- create the map, but don't draw it yet map = Ngl.map(wks, res) #---------------------------------------------------------------- #-- polyline coordinates (Germany) #---------------------------------------------------------------- x = [6., 15., 15., 6., 6.] y = [47.5, 47.5, 54.5, 54.5, 47.5] #-- polyline resources plres = Ngl.Resources() plres.gsLineThicknessF = 2.0 #-- set line thickness plres.gsLineColor = "red" #-- set line color #-- add polyline to map box_1 = Ngl.add_polyline(wks, map, x, y, plres)
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)
setlist.wkColorMap = colormap #-- set color map to new colormap array
setlist.wkBackgroundColor = 'white' #-- has to be set when wkColorMap is set to colormap array
setlist.wkForegroundColor = 'black' #-- has to be set when wkColorMap is set to colormap array
Ngl.set_values(wks1,setlist)
#-- set map resources
mpres = Ngl.Resources()
mpres.nglDraw = False #-- turn off plot draw and frame advance. We will
mpres.nglFrame = False #-- do it later after adding subtitles.
mpres.mpGridAndLimbOn = False
mpres.mpGeophysicalLineThicknessF = 2.
mpres.pmTitleDisplayMode = 'Always'
mpres.tiMainString = 'PyNGL: unstructured grid ICON'
#-- create only a map
map = Ngl.map(wks1,mpres)
Ngl.draw(map)
#-- assign and initialize array which will hold the color indices of the cells
gscolors = -1*(np.ones((ncells,),dtype=np.int)) #-- assign array containing zeros; init to transparent: -1
#-- set color index of all cells in between levels
for m in xrange(0,nlevs):
vind = [] #-- empty list for color indices
for i in xrange(0,ncells-1):
if (varM[i] >= levels[m] and varM[i] < levels[m+1]):
gscolors[i] = colors[m+1]
vind.append(i)
print 'finished level %3d' % m , ' -- %5d ' % len(vind) , ' polygons considered - gscolors %3d' % colors[m]
del vind
res.mpOutlineOn = True res.mpLandFillColor = 2 res.mpOceanFillColor = 3 res.mpGridAndLimbOn = False res.vpXF = 0.05 res.vpYF = 0.95 res.vpWidthF = 0.8 res.vpHeightF = 0.8 res.tmXBTickStartF = -75 res.tmXBTickEndF = -25 res.tmYROn = True res.tmXTOn = True res.tmXBLabelFontHeightF = 0.02 res.tmYLLabelFontHeightF = 0.02 res.mpProjection = "CylindricalEquidistant" map = Ngl.map(wks,res) # Draw map. # # Main title. # txres = Ngl.Resources() txres.txFontHeightF = 0.025 txres.txFontColor = 1 txres.txFont = 22 Ngl.text_ndc(wks,"Trajectories colored by salinity (ppt)",0.52,0.90,txres) del txres # # Manually draw a labelbar using colors 4-12. #
respn.cnLowLabelsOn = True
respn.cnLowLabelFontHeightF = 0.01
respn.cnHighLabelFontHeightF = 0.01
respn.cnLowLabelPerimOn = False
respn.cnHighLabelPerimOn = False
respn.cnConpackParams = ["HLX:128,HLY:128"]
if model_name == "hrdps":
respn.cnConpackParams = ["HLX:256,HLY:256"]
respn.cnLowLabelBackgroundColor = "Transparent"
respn.cnHighLabelBackgroundColor = "Transparent"
respn.cnLevelSpacingF = 2
respn.cnLineLabelFontHeightF = 0.006
respn.cnLineLabelBackgroundColor = "Transparent"
respn.cnInfoLabelOn = False
map_plot = Ngl.map(wks, resmp)
plot_fill = Ngl.contour(wks, 3.6E6 * field[:, :, 0], resources)
#plt.hist(3.6E6*field[:,:,0].flatten(),range(1,20))
#plt.savefig('RC.png')
fst_data = read_fst(file, "PN", 0, fhour, yy)
field = fst_data[0]
lat = fst_data[1]
lon = fst_data[2]
respn.sfXArray = lon
respn.sfYArray = lat
plot_line = Ngl.contour(wks, field[:, :, 0], respn)
Ngl.overlay(map_plot, plot_fill)
res.nglDraw = False # Zoom in on France. res.mpLimitMode = "LatLon" res.mpMinLatF = 41 res.mpMaxLatF = 51.1 res.mpMinLonF = -5.15 res.mpMaxLonF = 9.6 res.mpFillOn = False res.mpOutlineOn = False res.tiMainString = filename res.tiMainFontHeightF = 0.015 # Make font slightly smaller. plot = Ngl.map(wks, res) # Just create map, don't draw it. #************************************************* # Section to add polylines to map. #************************************************* plres = Ngl.Resources() # resources for polylines plres.gsLineColor = "navyblue" plres.gsLineThicknessF = 2.0 # default is 1.0 plres.gsSegments = segments[:, 0] # This can be slow. Need to investigate. lnid = Ngl.add_polyline(wks, plot, lon, lat, plres) Ngl.draw(plot) # This draws map and attached polylines Ngl.frame(wks) # Advance frame.
#---Attach blank plot with special labels to map plot, and return
resBMP = Ngl.Resources()
resBMP.mpProjection = "CylindricalEquidistant" # Change the map projection.
resBMP.mpCenterLonF = 0. # Rotate the projection.
resBMP.mpGridAndLimbOn = False
resBMP.pmLabelBarDisplayMode = "Always"
resBMP.lbPerimOn = False
resBMP.nglDraw = False
resBMP.nglFrame = False
resBMP.mpOutlineBoundarySets = "AllBoundaries"
resBMP.mpLimitMode = "LatLon" # Limit the map view.
resBMP.mpMinLonF = plotstartlon
resBMP.mpMaxLonF = plotendlon
resBMP.mpMinLatF = plotstartlat
resBMP.mpMaxLatF = plotendlat
plot.append(Ngl.map(wks, resBMP))
else:
resMP = Ngl.Resources()
#resMP = initcontourplot(resMP,plotstartlat,plotstartlon,plotendlat,plotendlon,Tlats.values,Tlons.values)
#resMP.nglYAxisType = "LinearAxis"
#resMP.nglXAxisType = "LinearAxis"
resMP.cnFillOn = True
resMP.cnFillMode = "AreaFill"
resMP.gsnAddCyclic = True
resMP.MissingValFillColor = "white"
resMP.sfMissingValueV = -9999
resMP.mpGridMaskMode = "MaskLand"
resMP.nglDraw = False
resMP.nglFrame = False
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
res.mpLimitMode = "LatLon" # Limit map via lat/lon res.mpGridMaskMode = "MaskNotOcean" # Draw grid over ocean. res.mpOceanFillColor = -1 res.mpGridLineDashPattern = 7 res.tiMainFont = "helvetica" res.tiMainFontHeightF = 0.02 res.tiMainString = "Provinces of China" res.mpMinLatF = 17 res.mpMaxLatF = 55 res.mpMinLonF = 72 res.mpMaxLonF = 136 plot = Ngl.map(wks, res) # Create the map plot res.mpFillOn = True # Turn on map fill res.mpFillBoundarySets = "National" res.mpFillAreaSpecifiers = boundaries res.tiMainString = "States of India" res.mpMinLatF = 6 res.mpMaxLatF = 38 res.mpMinLonF = 65 res.mpMaxLonF = 100 Ngl.map(wks, res) Ngl.end()
'''
from __future__ import print_function
import Ngl
wks = Ngl.open_wks("png", "plot_mptick_3") #-- open workstation
mpres = Ngl.Resources() #-- resource object
mpres.mpFillOn = True #-- turn map fill on
mpres.mpOutlineOn = True #-- outline map
mpres.mpOceanFillColor = "Transparent" #-- set ocean fill color to transparent
mpres.mpLandFillColor = "Gray90" #-- set land fill color to gray
mpres.mpInlandWaterFillColor = "Gray90" #-- set inland water fill color to gray
mpres.mpProjection = "CylindricalEquidistant" #-- map projection
mpres.mpGridAndLimbOn = False #-- turn off grid lines
mpres.pmTickMarkDisplayMode = "Always" #-- turn on built-in tickmarks
mpres.mpLimitMode = "LatLon"
mpres.mpMinLonF = -65. #-- set min lon
mpres.mpMaxLonF = -63. #-- set max lon
mpres.mpMinLatF = 10. #-- set min lat
mpres.mpMaxLatF = 11. #-- set max lat
mpres.mpDataBaseVersion = "HighRes" #-- turn on highres coastline
mpres.tiMainString = "Map database HighRes (RANGS) and~C~Built-in tickmarks that include minutes"
map = Ngl.map(wks, mpres) #-- create the plot
Ngl.end()
res.mpOceanFillColor = "transparent" res.mpLandFillColor = "transparent" res.mpInlandWaterFillColor = "transparent" res.mpOutlineOn = True res.mpGridAndLimbOn = False res.pmTickMarkDisplayMode = "Never" res.tiMainFontHeightF = 0.012 res.tiMainString = " ~F22~Climate divisions colored by 3rd field~C~~F21~climdivcorr.24.8.154.75.267.22.53.59.data" res.nglDraw = False res.nglFrame = False map = Ngl.map(wks,res) # # Get all of the area names associated with Earth..3 and the # corresponding group numbers. The area names will be of the # format "Arkansas : 01", where "01" is the climate division number. # # The group numbers are essentially color index values that were # chosen such that adjacent areas will not have the same color # (if you choose to color each one randomly). # # For example, Arkansas has 9 climate divisions, with the following # groups: # Arkansas : 01, group 7 # Arkansas : 02, group 8 # Arkansas : 03, group 7
# # TickMark resources. # res.tmBorderThicknessF = 5 # res.tmXBLabelFontAspectF = 2 res.tmXBLabelFontHeightF = 0.015 res.tmXBLabelFontThicknessF = 100 res.tmXBMajorThicknessF = 5 res.tmXBMajorLengthF = 0.01 res.tmYLLabelFontThicknessF = 100 res.tmYLMajorThicknessF = 5 plot = Ngl.map(wks, res) # Create the map plot, but don't advance frame. # # add specific out line # # attribute of polyline lnres = Ngl.Resources() lnres.gsLineColor = "red" lnres.gsLineThicknessF = 6.0 # 3x as thick lnres.gsLineDashPattern = 0 # china east lonCN = [112, 122] latCN = [22, 37] cnEast_bndry_lon = np.ravel([lonCN[0], lonCN[0], lonCN[1], lonCN[1], lonCN[0]]) cnEast_bndry_lat = np.ravel([latCN[0], latCN[1], latCN[1], latCN[0], latCN[0]])
def grid_order_contourplot(model, grid, first_varying_var, cut_domain):
# define paths #
path = dict(base='/', plots='data/plots/experimental/gridpoint_order/')
print('plotting model: {}'.format(model))
# load icosahedral grid information #
if grid == 'icosahedral':
clat, clon, vlat, vlon = read_grid_coordinates(model, grid)
data_array1 = np.arange(clat.shape[0], dtype='float32')
else:
clat, clon = read_grid_coordinates(model, grid)
clon = clon - 180
if first_varying_var == 'lon':
data_array1 = np.arange(clat.shape[0] * clon.shape[0],
dtype='float32').reshape(
(clat.shape[0], clon.shape[0]))
elif first_varying_var == 'lat':
data_array1 = np.arange(clat.shape[0] * clon.shape[0],
dtype='float32').reshape(
(clon.shape[0], clat.shape[0])).T
print('data_array1:', data_array1.shape, 'clat:', clat.shape, 'clon:',
clon.shape)
if grid == 'icosahedral':
print('vlat:', vlat.shape, 'vlon:', vlon.shape)
print('data_array1 min,max:', data_array1.min(), data_array1.max())
print('clat min,max:', clat.min(), clat.max())
print('clon min,max:', clon.min(), clon.max())
if grid == 'icosahedral':
print('vlat min,max:', vlat.min(), vlat.max())
print('vlon min,max:', vlon.min(), vlon.max())
print('------------------------------------------')
if cut_domain['name'] == 'uncut':
data_array1_cut = data_array1
else:
margin_deg = 0
if grid == 'icosahedral':
data_array1_cut, clat, clon, vlat, vlon \
= cut_by_domain(cut_domain, grid, data_array1, clat, clon, vlat, vlon, margin_deg)
else:
data_array1_cut, clat, clon \
= cut_by_domain(cut_domain, grid, data_array1, clat, clon, None, None, margin_deg)
print('data_array1:', data_array1_cut.shape, 'clat:', clat.shape, 'clon:',
clon.shape)
if grid == 'icosahedral':
print('vlat:', vlat.shape, 'vlon:', vlon.shape)
print('data_array1_cut min,max:', data_array1_cut.min(),
data_array1_cut.max())
print('clat min,max:', clat.min(), clat.max())
print('clon min,max:', clon.min(), clon.max())
if grid == 'icosahedral':
print('vlat min,max:', vlat.min(), vlat.max())
print('vlon min,max:', vlon.min(), vlon.max())
if grid == 'icosahedral':
plot_name = 'gridpoint_order_{}_{}_global_{}'.format(
model, grid, cut_domain['name'])
else:
plot_name = 'gridpoint_order_{}_{}_{}-varying-first_global_{}'.format(
model, grid, first_varying_var, cut_domain['name'])
# plot basic map with borders #
wks_res = Ngl.Resources()
x_resolution = 800
y_resolution = 800
wks_res.wkWidth = x_resolution
wks_res.wkHeight = y_resolution
wks_res.wkColorMap = 'BkBlAqGrYeOrReViWh200'
levels1 = np.linspace(data_array1.min(), data_array1.max(), 200)
wks_type = 'png'
wks = Ngl.open_wks(wks_type, path['base'] + path['plots'] + plot_name,
wks_res)
mpres = Ngl.Resources()
mpres.mpProjection = 'CylindricalEquidistant'
mpres.mpLimitMode = 'LatLon'
mpres.mpCenterLonF = 0.0
mpres.mpCenterLatF = 0.0
mpres.mpMinLonF = -180.
mpres.mpMaxLonF = 180.
mpres.mpMinLatF = -90.
mpres.mpMaxLatF = 90.
mpres.nglMaximize = False
mpres.vpXF = 0.003
mpres.vpYF = 1.00
mpres.vpWidthF = 0.86
mpres.vpHeightF = 1.00
mpres.mpMonoFillColor = 'True'
mpres.mpFillColor = -1
#Ngl.set_values(wks,mpres)
mpres.mpFillOn = True
#resources.cnFillDrawOrder = 'Predraw' # draw contours first
mpres.mpGridAndLimbOn = False
mpres.mpGreatCircleLinesOn = False
#mpres.mpOutlineDrawOrder = 'PreDraw'
mpres.mpDataBaseVersion = 'LowRes'
mpres.mpDataSetName = 'Earth..4'
mpres.mpOutlineBoundarySets = 'national'
mpres.mpGeophysicalLineColor = 'black'
mpres.mpNationalLineColor = 'black'
mpres.mpGeophysicalLineThicknessF = 2. * x_resolution / 1000
mpres.mpNationalLineThicknessF = 2. * x_resolution / 1000
mpres.mpPerimOn = True
mpres.mpPerimLineColor = 'black'
mpres.mpPerimLineThicknessF = 8.0 * x_resolution / 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
basic_map = Ngl.map(wks, mpres)
# plot variable1 in shading/contours #
v1res = Ngl.Resources()
v1res.sfDataArray = data_array1_cut
v1res.sfXArray = clon
v1res.sfYArray = clat
if grid == 'icosahedral':
v1res.cnFillMode = 'CellFill'
v1res.sfXCellBounds = vlon
v1res.sfYCellBounds = vlat
else:
v1res.cnFillMode = 'RasterFill'
v1res.sfMissingValueV = 9999
v1res.cnLinesOn = False # Turn off contour lines.
v1res.cnFillOn = True
#v1res.cnFillOpacityF = 0.5
#v1res.cnFillDrawOrder = 'Predraw'
v1res.cnLineLabelsOn = False
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 = 0.05 # 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'
#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 = 'InteriorEdges'
v1res.lbLabelStride = 1
v1res.nglFrame = False
v1res.nglDraw = False
v1plot = Ngl.contour(wks, data_array1_cut, v1res)
# plot label bar unit #
text_str = 'Index'
text_res_1 = Ngl.Resources()
text_res_1.txFontColor = 'black'
text_x = 0.975
text_y = 0.72
text_res_1.txFontHeightF = 0.012
Ngl.overlay(basic_map, v1plot)
Ngl.draw(basic_map)
Ngl.text_ndc(wks, text_str, text_x, text_y, text_res_1)
Ngl.frame(wks)
Ngl.destroy(wks)
#Ngl.end()
# 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()
del data_array1, data_array1_cut, clat, clon
if grid == 'icosahedral':
del vlat, vlon
return
wks = Ngl.open_wks("png", "plot_scatter_3")
res = Ngl.Resources()
res.nglFrame = False #-- don't advance the frame
res.nglMaximize = True #-- maximize plot
res.tiMainString = "Locations of stations" #-- add title
res.mpLimitMode = "LatLon"
res.mpMinLatF = lat.min()
res.mpMaxLatF = lat.max()
res.mpMinLonF = lon.min()
res.mpMaxLonF = lon.max()
#-- first plot
map = Ngl.map(wks, res) #-- create the map plot, don't draw
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = "Brown"
Ngl.polymarker(wks, map, lon, lat, mkres)
Ngl.frame(wks)
#-- second plot
res.mpMinLatF = 22.
res.mpMaxLatF = 60.
res.mpMinLonF = -125.
res.mpMaxLonF = -65.
res.mpMaskAreaSpecifiers = ["Conterminous US"] # don't fill here res.mpGridAndLimbOn = False # no lat/lon lines # # Increase the viewport size (make the map bigger). # res.vpWidthF = 0.93 res.vpHeightF = 0.93 res.vpXF = 0.05 res.vpYF = 0.99 res.nglDraw = False # don't draw the plots now res.nglFrame = False # or advance the frame res.mpProjection = "LambertConformal" plot = Ngl.map(wks,res) # create the map plot # # Polygon resources. # res_poly = Ngl.Resources() res_poly.gsEdgesOn = True # draw border around polygons. res_poly.gsEdgeColor = "black" # # Prepare to draw the polygons. # random.seed(10) # set a seed for the random number generator # # Get the polygon data and fill polygons with random colors in
res.mpGridAndLimbOn = True
res.pmTickMarkDisplayMode = "Never"
res.mpProjection = "CylindricalEquidistant"
res.mpLimitMode = "LatLon" # Limit the map view.
res.mpMinLonF = lontr
res.mpMaxLonF = lonbl
res.mpMinLatF = lattr
res.mpMaxLatF = latbl
res.mpOutlineBoundarySets = "AllBoundaries"
res.mpNationalLineColor = colour
res.mpNationalLineThicknessF = 2.0
res.mpGeophysicalLineColor = colour
res.mpGeophysicalLineThicknessF = 2.0
res.mpGridLatSpacingF = 5.0
res.mpGridLonSpacingF = 5.0
res.mpGridLineThicknessF = 2.0
res.cnMonoLineColor = True
map_plot = ngl.map(wks,res)
ngl.maximize_plot(wks, map_plot)
ngl.draw(map_plot)
ngl.frame(wks)
ngl.destroy(wks)
del res
os.system('mogrify -trim map_'+colour+'_'+region+'.png')
# os.system('mogrify -resize 1350x900 map_'+colour+'_'+region+'.png')
if args.grid_only:
print('Plot Grid only')
config = Ngl.Resources()
config.nglFrame = False
config.nglDraw = False
config.mpProjection = "Orthographic"
config.mpLimitMode = "LatLon"
config.mpMinLonF = 0
config.mpMaxLonF = 18
config.mpMinLatF = 43
config.mpMaxLatF = 50
map = Ngl.map(wks,config)
ecx = numpy.ravel( vlon[ edge_vertices-1 ], order='F' )
ecy = numpy.ravel( vlat[ edge_vertices-1 ], order='F' )
lines_cfg = Ngl.Resources()
lines_cfg.gsSegments = numpy.arange(0, edge_vertices.size, 2)
poly = Ngl.add_polyline(wks, map, ecx, ecy, lines_cfg)
Ngl.draw(map)
Ngl.frame(wks)
Ngl.end()
sys.exit()
# regular plot
# Main code #---------------------------------------------------------------------- lon_spacing = 40 lat_spacing = 20 wks_type = "png" wks = Ngl.open_wks(wks_type,"map3") #---Create and draw map; note how lat/lon spacing doesn't line up with tickmarks res = Ngl.Resources() res.mpGridLatSpacingF = lat_spacing res.mpGridLonSpacingF = lat_spacing res.tiMainString = "Default map tickmarks and grid lines" map = Ngl.map(wks,res) del res #---Create map again, but turn off grid lines and tickmarks res = Ngl.Resources() res.nglDraw = False # Don't draw map res.nglFrame = False # Don't advance frame res.mpGridAndLimbOn = False # Will add own grid lines res.pmTickMarkDisplayMode = "Never" # Will add own labels res.tiMainString = "Custom map labels and grid lines" map = Ngl.map(wks,res) # Won't get drawn just yet #---Add custom tickmarks and grid lines labels = add_map_tickmarks(wks,map,lat_spacing,lon_spacing)
#---Open file for graphics
wks_type = "png"
wks = Ngl.open_wks(wks_type, "wmstnm03")
#
# Draw a world map.
#
mpres = Ngl.Resources()
mpres.nglFrame = False
mpres.mpSatelliteDistF = 1.3
mpres.mpOutlineBoundarySets = "USStates"
mpres.mpCenterLatF = 40.
mpres.mpCenterLonF = -97.
mpres.mpCenterRotF = 35.
mpres.mpProjection = "Satellite"
map = Ngl.map(wks, "Satellite", mpres)
#
# In the middle of Nebraska, draw a wind barb for a north wind
# with a magnitude of 15 knots.
#
Ngl.wmbarbmap(wks, 42., -99., 0., -15.)
#
# Draw the station model data at the selected cities. The call
# to wmsetp informs wmstnm that the wind barbs will be drawn over
# a map. To illustrate the adjustment for plotting the model
# data over a map, all winds are from the north.
#
Ngl.wmsetp("ezf", 1)
Ngl.wmstnm(wks, city_lats, city_lons, imdat)
def simple_valplot(lons, lats, vals, cfg):
"""
Generate a simple plot of values on a map!
"""
tmpfp = tempfile.mktemp()
rlist = Ngl.Resources()
if cfg.has_key("wkColorMap"):
rlist.wkColorMap = cfg['wkColorMap']
#rlist.wkOrientation = "landscape"
# Create Workstation
wks = Ngl.open_wks( "ps",tmpfp,rlist)
if cfg.has_key("_conus"):
res = conus()
elif cfg.get("_midwest", False):
res = midwest()
else:
res = iowa()
if cfg.has_key("_spatialDataLimiter"):
xmin, ymin, xmax, ymax = [ min(lons), min(lats),
max(lons), max(lats) ]
res.mpMinLonF = xmin - 0.25
res.mpMaxLonF = xmax + 0.25
res.mpMinLatF = ymin - 0.25
res.mpMaxLatF = ymax + 0.25
res.mpCenterLonF = (xmax + xmin)/2.0 # Central Longitude
res.mpCenterLatF = (ymax + ymin)/2.0 # Central Latitude
res.mpOutlineDrawOrder = "PreDraw"
res.mpUSStateLineColor = 10
res.mpNationalLineColor = 10
for key in cfg.keys():
if key == 'wkColorMap' or key[0] == "_":
continue
setattr(res, key, cfg[key])
plot = Ngl.map(wks, res)
if cfg.has_key("_stationplot"):
Ngl.wmsetp("col", 1)
Ngl.wmsetp("ezf",1)
if cfg.has_key("_removeskyc"):
Ngl.wmsetp("WBC", 0.001) # Get rid of sky coverage
Ngl.wmsetp("WBF", 0) # Put barb at center, no sky coverage
Ngl.wmsetp("WBR", 0.001) # Size of calm circle
#Ngl.wmsetp("WBL", 0.18) # Size of labels
#Ngl.wmsetp("WBS", 0.029) # Size of wind barb shaft
Ngl.wmstnm(wks, lats, lons, vals)
else:
txres = Ngl.Resources()
txres.txFontHeightF = 0.014
txres.txJust = "BottomCenter"
for i in range(len(lons)):
Ngl.add_text(wks, plot, cfg.get("_format",'%s') % vals[i],
lons[i], lats[i],txres)
if cfg.has_key("_labels"):
txres = Ngl.Resources()
txres.txFontHeightF = 0.008
txres.txJust = "TopCenter"
txres.txFontColor = 14
for i in range(len(lons)):
Ngl.add_text(wks, plot, cfg["_labels"][i],
lons[i], lats[i],txres)
watermark(wks)
manual_title(wks, cfg)
Ngl.draw(plot)
vpbox(wks)
Ngl.frame(wks)
del wks
return tmpfp
vpbox(wks)
# Zoom in on South America. res.mpLimitMode = "LatLon" res.mpMinLatF = -60 res.mpMaxLatF = 15 res.mpMinLonF = -90 res.mpMaxLonF = -30 res.mpFillOn = True res.mpLandFillColor = "NavajoWhite" res.mpOceanFillColor = "SlateGray2" res.tiMainString = "Stream network data for South America" res.tiMainFontHeightF = 0.015 # Make font slightly smaller. plot = Ngl.map(wks, res) # Draw map, but don't advance frame. #************************************************* # Section to add polylines to map. #************************************************* f = Nio.open_file(filename, "r") # Open shapefile lon = numpy.ravel(f.variables["x"][:]) lat = numpy.ravel(f.variables["y"][:]) segments = f.variables["segments"][:, 0] plres = Ngl.Resources() # resources for polylines plres.gsLineColor = "blue" plres.gsSegments = segments id = Ngl.add_polyline(wks, plot, lon, lat, plres)
def simple_contour(lons, lats, vals, cfg):
"""
Generate a simple contour plot, okay
"""
tmpfp = tempfile.mktemp()
rlist = Ngl.Resources()
if cfg.has_key("wkColorMap"):
rlist.wkColorMap = cfg['wkColorMap']
#rlist.wkOrientation = "landscape"
# Create Workstation
wks = Ngl.open_wks( "ps",tmpfp,rlist)
# Create Analysis
if cfg.has_key("_conus"):
analysis, res = grid_conus(lons, lats, vals)
elif cfg.get("_northeast", False):
analysis, res = grid_northeast(lons, lats, vals)
elif cfg.get("_midwest", False):
analysis, res = grid_midwest(lons, lats, vals)
else:
analysis, res = grid_iowa(lons, lats, vals)
analysis = numpy.transpose(analysis)
for key in cfg.keys():
if key == 'wkColorMap' or key[0] == "_":
continue
setattr(res, key, cfg[key])
if cfg.has_key("_MaskZero"):
mask = numpy.where( analysis <= 0.02, True, False)
analysis = numpy.ma.array(analysis, mask=mask)
# Generate Contour
if numpy.min(analysis) == numpy.max(analysis):
if cfg.has_key("_conus"):
res = conus()
elif cfg.has_key("_midwest"):
res = midwest()
else:
res = iowa()
contour = Ngl.map(wks, res)
else:
contour = Ngl.contour_map(wks,analysis,res)
if cfg.has_key("_showvalues") and cfg['_showvalues']:
txres = Ngl.Resources()
txres.txFontHeightF = 0.012
for i in range(len(lons)):
if cfg.has_key("_valuemask") and cfg['_valuemask'][i] is False:
continue
Ngl.add_text(wks, contour, cfg["_format"] % vals[i],
lons[i], lats[i],txres)
Ngl.draw( contour )
watermark(wks)
manual_title(wks, cfg)
vpbox(wks)
Ngl.frame(wks)
del wks
return tmpfp
cfres.cnLinesOn = False # Turn off contour lines. cfres.cnLineLabelsOn = False # Turn off contour line labels. cfres.lbOrientation = "Horizontal" # horizontal labelbar cfres.lbLabelFontHeightF = 0.012 # Decrease font size. cfres.pmLabelBarOrthogonalPosF = -0.05 # Move labelbar up. clres.cnLineLabelsOn = False # Turn off contour line labels. clres.cnLineDashPatterns = 3 # dashed contour lines clres.cnLineThicknessF = 3. # triple thick contour lines clres.cnInfoLabelOrthogonalPosF = -0.15 # Move info label up. # # Create the various plots. They will not get drawn because # nglDraw is set to False for all of them. # map_plot = Ngl.map(wks,mpres) vector_plot = Ngl.vector(wks,ua,va,vcres) line_contour_plot = Ngl.contour(wks,pa,clres) fill_contour_plot = Ngl.contour(wks,ta,cfres) # # Overlay everything on the map plot. # Ngl.overlay(map_plot,fill_contour_plot) Ngl.overlay(map_plot,line_contour_plot) Ngl.overlay(map_plot,vector_plot) # # Change the title. # srlist = Ngl.Resources()
wks_type = "png" wks = Ngl.open_wks(wks_type, "map2") mpres = Ngl.Resources() mpres.nglFrame = False # Don't advance frame after plot is drawn. mpres.nglDraw = False # Don't draw plot just yet. mpres.mpProjection = "Orthographic" mpres.mpCenterLatF = 30. mpres.mpCenterLonF = 160. mpres.mpCenterRotF = 60. mpres.mpLabelsOn = False mpres.mpPerimOn = False map = Ngl.map(wks, mpres) # Create map. gsres = Ngl.Resources() gsres.gsLineColor = "black" gsres.gsLineThicknessF = 2.0 gsres.gsLineLabelFont = "helvetica-bold" gsres.gsLineLabelFontHeightF = 0.02 gsres.gsLineLabelFontColor = "black" gsres.gsFillIndex = 0 gsres.gsEdgesOn = False # Tropical zone gsres.gsFillColor = 87
tres.sfXArray = lon
tres.sfYArray = lat
#-- plot resources for the rhumidity plot
rres = Ngl.Resources()
rres.nglDraw = False #-- don't draw plot
rres.nglFrame = False #-- don't advance frame
rres.cnInfoLabelOrthogonalPosF = 0.13 #-- move info label upward
rres.sfXArray = lon
rres.sfYArray = lat
#-- generate tplot, but don't draw it yet
print("-- map --")
map = Ngl.map(wks, mpres)
#-- generate tplot, but don't draw it yet
print("-- tplot --")
tplot = Ngl.contour(wks, t, tres)
#-- generate plot2, but don't draw it yet
print("-- rplot --")
rplot = Ngl.contour(wks, rhum, rres)
#-- overlay rplot on tplot
print("-- overlay tplot --")
Ngl.overlay(map, tplot)
print("-- overlay rplot --")
Ngl.overlay(map, rplot)
def draw_gh_500hPa(sel_year, sel_month):
print("darwing 500hPa gh plot for " + str(sel_month).zfill(2) + " " +
str(sel_year))
files_cli = sorted(
glob.glob(
os.path.join(
'/home/alley/work/Dong/mongo/seasonal_analysis/data/data/download_from_mongo/cli/',
'gh_500_*.grb')))
f_cli = xr.open_mfdataset(files_cli,
concat_dim="time",
combine="nested",
engine="cfgrib",
parallel=True)
h = f_cli["z"]
h = h.mean(dim="time")
h_cli = h / 98
lat = f_cli["latitude"].values
lon = f_cli["longitude"].values
file_cur = "/home/alley/work/Dong/mongo/seasonal_analysis/data/data/download_from_mongo/cur/gh_500_" + str(
sel_year) + str(sel_month).zfill(2) + ".grb"
# print(file_cur)
f_cur = xr.open_mfdataset(file_cur, engine="cfgrib", parallel=True)
h_cur = f_cur["z"] / 98
h_ano = h_cur - h_cli
leftString = "500hPa Height & Anomaly for " + str(sel_month) + str(
sel_year)
rightString = "dagpm"
wks_type = 'png'
wks = Ngl.open_wks(
wks_type,
'/home/alley/work/Dong/mongo/seasonal_analysis/images/gh_500hPa_' +
str(sel_year) + str(sel_month).zfill(2) + '.png')
res = Ngl.Resources()
res.nglFrame = False
res.nglDraw = False
res.mpLimitMode = "LatLon"
res.mpMinLonF = 60
res.mpMaxLonF = 180
res.mpMinLatF = 0
res.mpMaxLatF = 60
plot_map = Ngl.map(wks, res)
fres = Ngl.Resources()
fres.nglFrame = False
fres.nglDraw = False
fres.cnFillOn = True
fres.sfXArray = lon
fres.sfYArray = lat
fres.lbOrientation = "Horizontal" # horizontal labelbar
fres.cnLinesOn = False
fres.cnLineLabelsOn = False
fres.cnLevelSelectionMode = "ExplicitLevels"
fres.cnFillPalette = "BlueDarkRed18"
fres.cnLevels = np.arange(-5, 6, 1)
plot_cn = Ngl.contour(wks, h_ano, fres)
cnres = Ngl.Resources()
cnres.nglDraw = False
cnres.nglFrame = False
cnres.cnFillOn = False
cnres.cnLinesOn = True
cnres.cnLineLabelsOn = True
cnres.cnInfoLabelOn = False
cnres.sfXArray = lon
cnres.sfYArray = lat
cnres.cnLevelSelectionMode = "ExplicitLevels"
cnres.cnLevels = np.arange(500, 600, 4)
cnres.cnLineColor = "black"
cnres.cnLineThicknessF = 5
plot2 = Ngl.contour(wks, h_cur, cnres)
Ngl.overlay(plot_map, plot_cn)
Ngl.overlay(plot_map, plot2)
#
txres = Ngl.Resources()
txres.txFontHeightF = 0.024
#
Ngl.text_ndc(wks, leftString, 0.3, 0.83, txres)
Ngl.text_ndc(wks, rightString, 0.85, 0.83, txres)
#
Ngl.maximize_plot(wks, plot_map)
Ngl.draw(plot_map)
Ngl.frame(wks)
Ngl.end()
print("Finish darwing 500hPa gh plot plot for " + str(sel_month).zfill(2) +
" " + str(sel_year))
res.mpRightCornerLatF = 50 # right corner
res.mpRightCornerLonF = -64 # right corner
res.mpFillOn = True # Turn on fill for map areas.
res.mpLandFillColor = "LightGray"
res.mpOceanFillColor = "Cyan"
res.mpInlandWaterFillColor = "Cyan"
res.pmTickMarkDisplayMode = "Always" # Turn on map tickmarks
res.tiMainString = "Percentage unemployment, by state"
res.nglDraw = False # don't draw the plots now
res.nglFrame = False # or advance the frame
plot = Ngl.map(wks,res) # create the map plot
#
# Read data off shapefile. Must have states.shp, states.dbf,
# and states.prj file in this directory.
#
dirc = Ngl.pynglpath("data")
f = Nio.open_file(os.path.join(dirc,"shp","states.shp"), "r") # Open shapefile
segments = f.variables["segments"][:]
geometry = f.variables["geometry"][:]
segsDims = segments.shape
geomDims = geometry.shape
#
# Read global attributes
#
res.mpLimitMode = "LatLon" # Limit map via lat/lon res.mpGridMaskMode = "MaskNotOcean" # Draw grid over ocean. res.mpOceanFillColor = -1 res.mpGridLineDashPattern = 7 res.tiMainFont = "helvetica" res.tiMainFontHeightF = 0.02 res.tiMainString = "Provinces of China" res.mpMinLatF = 17 res.mpMaxLatF = 55 res.mpMinLonF = 72 res.mpMaxLonF = 136 plot = Ngl.map(wks, res) # Create the map plot res.mpFillOn = True # Turn on map fill res.mpFillBoundarySets = "National" res.mpFillAreaSpecifiers = boundaries res.tiMainString = "States of India" res.mpMinLatF = 6 res.mpMaxLatF = 38 res.mpMinLonF = 65 res.mpMaxLonF = 100 Ngl.map(wks, res) Ngl.end()
