#-------------------------------------------------------
# MAIN
#-------------------------------------------------------
wks = Ngl.open_wks("png","plot_mptick_10") #-- open workstation
#-----------------------------------
#-- first plot: Lambert Conformal
#-----------------------------------
#-- northern hemisphere
minlon = -90. #-- min lon to mask
maxlon = 40. #-- max lon to mask
minlat = 20. #-- min lat to mask
maxlat = 80. #-- max lat to mask
mpres = Ngl.Resources() #-- resource object
mpres.nglMaximize = True
mpres.nglDraw = False #-- turn off plot draw and frame advance. We will
mpres.nglFrame = False #-- do it later after adding subtitles.
mpres.mpFillOn = True #-- turn map fill on
mpres.mpOutlineOn = False #-- 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.tiMainString = "Adding lat/lon labels to a masked LC map~C~(northern hemisphere)"
mpres.tiMainOffsetYF = 0.05
mpres.tiMainFontHeightF = 0.016 #-- decrease font size
mpres.mpProjection = "LambertConformal"
def plot_oope_map(data, figname, size_class=None, percentage=1):
''' Draws 2D OOPE maps.
:param xarray.Dataset data: 2D OOPE array. Dims must be (y, x, comm, size)
:param str figname: Name of the figure file (must end by .png or .pdf)
:param list size_class: Size classes to output (in m)
:param float percentage: percentage used to saturate colorbar from percentile.
Colorbar is saturated from values of the (X) and (100 - X) percentile.
:return: None
'''
if size_class is None:
size_class = [1e-3, 1e-2, 1e-1, 1]
# sort size class in ascending order, and add 0 and infinity as size bounds
size_class = np.sort(size_class)
if size_class[0] != 0:
size_class = np.concatenate(([0], size_class), axis=0)
if size_class[-1] != np.Inf:
size_class = np.concatenate((size_class, [np.Inf]), axis=0)
# Check that the OOPE dataset has 4 dimensions (i.e. no time dimension)
ndims = len(data['OOPE'].dims)
if ndims != 4:
message = 'Data must have dimensions of size (lat, lon, comm, wei)'
print(message)
sys.exit(0)
# Recover data variables
length = data['length'].values
oope = data['OOPE'].values
lon = data['longitude'][:].values
lat = data['latitude'][:].values
comm = data['community'][:].values.astype(np.int)
# mask oope where land
oope = np.ma.masked_where(np.isnan(oope), oope)
comm_string = misc.extract_community_names(data)
if figname.endswith('png'):
form = 'png'
elif figname.endswith('pdf'):
form = 'pdf'
else:
message = 'Figure name should end with png or pdf'
print(message)
sys.exit(0)
# opens the document
wks = Ngl.open_wks(form, figname)
# set the document colormap
# resngl = Ngl.Resources()
# resngl.wkColorMap = 'precip2_15lev'
# Ngl.set_values(wks, resngl)
# Add gray to the workspace
Ngl.new_color(wks, 0.7, 0.7, 0.7)
# init the plot resources
# For a detailed description, see https://www.ncl.ucar.edu/Document/Graphics/Resources/
res = Ngl.Resources()
# not necessary, just a good habit
res.nglDraw = False
res.nglFrame = False
# Set map resources.
res.mpLimitMode = "LatLon" # limit map via lat/lon
res.mpMinLatF = lat.min() # map area
res.mpMaxLatF = lat.max() # latitudes
res.mpMinLonF = lon.min() # and
res.mpMaxLonF = lon.max() # longitudes
res.mpFillOn = True
res.mpLandFillColor = "LightGray"
res.mpOceanFillColor = -1
res.mpInlandWaterFillColor = "LightBlue"
res.mpGeophysicalLineThicknessF = 1 # thickness of coastlines
# coordinates for contour plots
res.sfXArray = lon
res.sfYArray = lat
res.cnFillOn = True # filled contour
res.cnLinesOn = False # no lines
res.cnLineLabelsOn = False # no labels
res.cnInfoLabelOn = False # no info about contours
res.cnFillMode = 'CellFill' # contourf=AreaFill, pcolor="CellFill" or "RasterFill"
res.lbOrientation = "Horizontal" # colorbar orientation
res.lbLabelFontHeightF = 0.012 # colorbar label fontsize
res.lbTitlePosition = "Bottom" # position of colorbar title
res.lbTitleFontHeightF = 0.012 # title font height
# res.cnFillPalette = "wgne15"
res.cnFillPalette = "WhiteBlueGreenYellowRed"
txres = Ngl.Resources()
txres.txJust = "BottomCenter"
txres.txFontHeightF = 0.02
res.cnLevelSelectionMode = 'ExplicitLevels'
res.cnMaxLevelCount = 41
res.mpGridAndLimbOn = True
# Equations are complicated with NCARG
# see https://www.ncl.ucar.edu/Applications/fcodes.shtml
res.lbTitleString = "OOPE (J.kg~S~-1~N~.m~S~-2~N~)" # title of colorbar
# Loop over communities
for icom in comm:
# Loop over size classes
for isize in xrange(0, len(size_class) - 1):
# Extract sizes comprised between the size class bound
iw = np.nonzero((length >= size_class[isize]) & (length < size_class[isize+1]))[0]
if iw.size == 0:
continue
# Integrate OOPE for the given community and given size class
temp = oope[:, :, icom, iw]
temp = np.sum(temp, axis=-1)
# Finds the colorbar limits
cmin, cmax = misc.find_percentile(temp, percentage=1)
# draw the contour maps
# defines the contour
res.cnLevels = np.linspace(cmin, cmax, res.cnMaxLevelCount)
mapplot = Ngl.contour_map(wks, temp, res)
# add title
title = 'Community=%s, %.2E m <= L < %.2E m' % (comm_string[icom], size_class[isize], size_class[isize + 1])
Ngl.text_ndc(wks, title, 0.5, 0.85, txres)
# draws the map
Ngl.draw(mapplot)
# add a page to the pdf output
Ngl.frame(wks)
def plot_rain_around_point(path, data_rain_tot_sum, hour_start, hour_end, point, stat_processing, member):
if stat_processing == 'max':
data_processed = (data_rain_tot_sum[hour_end, :, :] - data_rain_tot_sum[hour_start, :, :]).max(axis=0)
elif stat_processing == 'member_extract':
data_processed = data_rain_tot_sum[hour_end, member-1, :] - data_rain_tot_sum[hour_start, member-1, :]
########################################################################
if stat_processing == 'max':
member_text_filename = 'max'
elif stat_processing == 'member_extract':
member_text_filename = 'm{:02d}'.format(member)
plot_name = 'iconeueps_rain_{}_{:02d}Z-{:02d}Z_{}'.format(\
point['name'], hour_start+12, hour_end+12, member_text_filename)
mpi_file = nc.Dataset(path['base'] + path['grid'] + 'icon_grid_0028_R02B07_N02.nc', 'r')
vlat = mpi_file.variables['clat_vertices'][:].data * 180./np.pi
vlon = mpi_file.variables['clon_vertices'][:].data * 180./np.pi
clat = mpi_file.variables['clat'][:].data * 180./np.pi
clon = mpi_file.variables['clon'][:].data * 180./np.pi
mpi_file.close()
########################################################################
x_resolution = 800
y_resolution = 800
wks_res = Ngl.Resources()
wks_res.wkWidth = x_resolution
wks_res.wkHeight = y_resolution
wks_type = 'png'
wks = Ngl.open_wks(wks_type, path['base'] + path['plots'] + plot_name, wks_res)
resources = Ngl.Resources()
resources.mpProjection = 'Hammer'
resources.mpCenterLonF = point['lon']
resources.mpCenterLatF = point['lat']
radius = 500 # image radius in km around centered point
cutout_plot = dict(
lat_min = point['lat'] - radius / 111.2,
lat_max = point['lat'] + radius / 111.2,
lon_min = point['lon'] - radius / (111.2 * np.cos(point['lat']*np.pi/180)),
lon_max = point['lon'] + radius / (111.2 * np.cos(point['lat']*np.pi/180)),
)
resources.mpLimitMode = 'latlon'
resources.mpMinLonF = cutout_plot['lon_min']
resources.mpMaxLonF = cutout_plot['lon_max']
resources.mpMinLatF = cutout_plot['lat_min']
resources.mpMaxLatF = cutout_plot['lat_max']
resources.nglMaximize = False
resources.vpXF = 0.05
resources.vpYF = 0.95
resources.vpWidthF = 0.7
resources.vpHeightF = 0.7
########################################################################
# Turn on filled map areas:
resources.mpFillOn = True
# Set colors for [FillValue, Ocean, Land , InlandWater]:
resources.mpFillColors = ['pink','blue','white','blue']
resources.mpDataBaseVersion = 'MediumRes'
resources.mpDataSetName = 'Earth..4'
resources.mpOutlineBoundarySets = 'national'
resources.mpGeophysicalLineThicknessF = 7.0 * x_resolution / 1000
resources.mpNationalLineThicknessF = 7.0 * x_resolution / 1000
#resources.mpGridAndLimbDrawOrder = 'postdraw'
resources.mpGridAndLimbOn = False
#resources.mpLimbLineColor = 'black'
#resources.mpLimbLineThicknessF = 10
#resources.mpGridLineColor = 'black'
#resources.mpGridLineThicknessF = 1.0
#resources.mpGridSpacingF = 1
resources.mpPerimOn = True
resources.mpPerimLineColor = 'black'
resources.mpPerimLineThicknessF = 8.0 * x_resolution / 1000
resources.tmXBOn = False
resources.tmXTOn = False
resources.tmYLOn = False
resources.tmYROn = False
resources.sfDataArray = data_processed
resources.sfXArray = clon
resources.sfYArray = clat
resources.sfXCellBounds = vlon
resources.sfYCellBounds = vlat
resources.sfMissingValueV = 9999
resources.cnFillOn = True
resources.cnFillMode = 'CellFill'
resources.cnCellFillEdgeColor = 'black'
resources.cnMissingValFillColor = 'black'
resources.cnFillPalette = 'WhiteBlueGreenYellowRed'
resources.cnLevelSelectionMode = 'ManualLevels'
minlevel = 0.0
maxlevel = 50.0
numberoflevels = 250
resources.cnMinLevelValF = minlevel
resources.cnMaxLevelValF = maxlevel
resources.cnLevelSpacingF = (maxlevel - minlevel) / numberoflevels
# Turn off contour lines and labels:
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
# Set resources for a nice label bar
resources.lbLabelBarOn = True
resources.lbAutoManage = False
resources.lbOrientation = 'vertical'
resources.lbLabelOffsetF = 0.05
#resources.lbBoxMinorExtentF = 0.2
resources.lbLabelStride = 25
resources.lbLabelFontHeightF = 0.02
resources.lbBoxSeparatorLinesOn = False
resources.lbBoxLineThicknessF = 4.0
#resources.lbBoxEndCapStyle = 'TriangleBothEnds'
resources.lbLabelAlignment = 'BoxCenters'
resources.lbTitleString = 'mm'
resources.lbTitleFontHeightF = 0.02
resources.lbTitlePosition = 'Right'
resources.lbTitleDirection = 'Across'
resources.lbTitleAngleF = 90.0
resources.lbTitleExtentF = 0.1
resources.lbTitleOffsetF = 0.0
resources.nglFrame = False
plot = Ngl.contour_map(wks, data_processed, resources)
########################################################################
polymarker_res_1 = Ngl.Resources()
polymarker_res_1.gsMarkerColor = 'black'
polymarker_res_1.gsMarkerIndex = 16
polymarker_res_1.gsMarkerSizeF = 0.012
polymarker_res_1.gsMarkerThicknessF = 1
Ngl.polymarker(wks, plot, point['lon'], point['lat'], polymarker_res_1)
########################################################################
polymarker_res_2 = Ngl.Resources()
polymarker_res_2.gsMarkerColor = 'white'
polymarker_res_2.gsMarkerIndex = 16
polymarker_res_2.gsMarkerSizeF = 0.008
polymarker_res_2.gsMarkerThicknessF = 1
Ngl.polymarker(wks, plot, point['lon'], point['lat'], polymarker_res_2)
########################################################################
if stat_processing == 'max':
member_text = 'max member of each cell'
elif stat_processing == 'member_extract':
member_text = 'member {:d}'.format(member)
text = 'rain_gsp + rain_con, 07.08.19 {}Z-{}Z, {}'.format(hour_start+12, hour_end+12, member_text)
x = 0.1
y = 0.95
text_res_1 = Ngl.Resources()
text_res_1.txFontHeightF = 0.02
text_res_1.txJust = 'BottomLeft'
Ngl.text_ndc(wks, text, x, y, text_res_1)
Ngl.frame(wks)
Ngl.destroy(wks)
return
# http://www.ncl.ucar.edu/Document/Manuals/NCL_User_Guide/Data/
#
'''
Transition Guide Python Example: TRANS_xy_0.py
- Drawing a xy plot
2018-08-21 kmf
'''
import numpy
import Ngl
import os
#-- define x and y variables
x = [10., 20., 30., 40., 50., 60., 70., 80., 90.]
y = numpy.array([0., 0.7, 1., 0.7, 0., -0.7, -1., -0.7, 0.], numpy.float32)
#-- open a workstation
wkres = Ngl.Resources()
wks_type = "png" #-- output type of workstation
wks = Ngl.open_wks(wks_type, os.path.basename(__file__).split('.')[0])
#-- set resources
res = Ngl.Resources() #-- generate an res object for plot
#-- draw the plot
plot = Ngl.xy(wks, x, y, res)
#-- done
Ngl.end()
def rain_prf (ptype,cseason, ncases, cases, casenames, nsite, lats, lons, filepath, filepathobs,casedir,varis,cscale,chscale,pname):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = 'Red'
mkres.gsMarkerSizeF = 15.
infiles = ['' for x in range(ncases)]
ncdfs = ['' for x in range(ncases)]
nregions = nsite
varisobs = ['CC_ISBL', 'OMEGA','SHUM','CLWC_ISBL', 'THETA','RELHUM','U','CIWC_ISBL','T' ]
nvaris = len(varis)
cunits = ['%','mba/day','g/kg','g/kg','K', '%', 'm/s', 'g/kg', 'm/s', 'm/s','K','m' ]
cscaleobs = [100, 1, 1, 1000 , 1., 1, 1, 1000, 1,1,1,1,1,1,1]
obsdataset =['ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI','ERAI','ERAI']
plotrain=['' for x in range(nsite)]
for ire in range (0, nsite):
if not os.path.exists(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N'):
os.mkdir(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N')
plotname = casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N/'+pname+'_'+str(lons[ire])+'E_'+str(lats[ire])+'N_'+cseason
plotrain[ire] = pname+'_'+str(lons[ire])+'E_'+str(lats[ire])+'N_'+cseason
wks= Ngl.open_wks(ptype,plotname)
Ngl.define_colormap(wks,'GMT_paired')
plot = []
res = Ngl.Resources()
res.nglDraw = False
res.nglFrame = False
res.lgLabelFontHeightF = .02 # change font height
res.lgPerimOn = False # no box around
res.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
#res.vpXF = 0.04
# res.vpYF = 0.30
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tiMainFont = _Font
res.tmXBLabelFontHeightF = 0.015
res.tmXBLabelFontThicknessF = 1.0
# res.tmXBLabelAngleF = 45
res.xyMarkLineMode = 'Lines'
res.xyLineThicknesses = [3.0, 3.0, 3.0, 3.0, 3.0, 3.0,3.,3.,3.,3.,3,3,3,3,3,3,3]
res.xyDashPatterns = np.arange(0,24,1)
# res.xyMarkers = np.arange(16,40,1)
# res.xyMarkerSizeF = 0.005
pres = Ngl.Resources()
# pres.nglMaximize = True
pres.nglFrame = False
pres.txFont = _Font
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelTop = 0.88
pres.wkWidth = 2500
pres.wkHeight = 2500
for iv in range (0, nvaris):
if(iv == nvaris-1):
res.pmLegendDisplayMode = 'Never'
res.xyExplicitLegendLabels = casenames[:]
res.pmLegendSide = 'top'
res.pmLegendParallelPosF = 0.6
res.pmLegendOrthogonalPosF = -0.5
res.pmLegendWidthF = 0.10
res.pmLegendHeightF = 0.10
res.lgLabelFontHeightF = .02
res.lgLabelFontThicknessF = 1.5
res.lgPerimOn = False
else:
res.pmLegendDisplayMode = 'NEVER'
for im in range (0,ncases):
ncdfs[im] = './data/'+cases[im]+'_site_location.nc'
infiles[im]= filepath[im]+cases[im]+'/'+cases[im]+'_'+cseason+'_climo.nc'
inptrs = Dataset(infiles[im],'r') # pointer to file1
lat=inptrs.variables['lat'][:]
nlat=len(lat)
lon=inptrs.variables['lon'][:]
nlon=len(lon)
lev=inptrs.variables['lev'][:]
nlev=len(lev)
ncdf= Dataset(ncdfs[im],'r')
n =ncdf.variables['n'][:]
idx_cols=ncdf.variables['idx_cols'][:,:]
ncdf.close()
if (im ==0):
A_field = np.zeros((ncases,nlev),np.float32)
for subc in range( 0, n[ire]):
npoint=idx_cols[ire,n[subc]-1]-1
tmp=inptrs.variables[varis[iv]][0,:,npoint]
theunits=str(chscale[iv])+'x'+inptrs.variables[varis[iv]].units
A_field[im,:] = (A_field[im,:]+tmp[:]/n[ire]).astype(np.float32 )
A_field[im,:] = A_field[im,:] *cscale[iv]
inptrs.close()
res.tiMainString = varis[iv]+' '+theunits
# res.trXMinF = min(np.min(A_field[0, :]))
# res.trXMaxF = max(np.max(A_field[0, :]))
res.trYReverse = True
res.xyLineColors = np.arange(3,20,2)
res.xyMarkerColors = np.arange(2,20,2)
p = Ngl.xy(wks,A_field,lev,res)
# res.trYReverse = False
# res.xyLineColors = ['black']
# pt = Ngl.xy(wks,B,pre1,res)
# Ngl.overlay(p,pt)
plot.append(p)
pres.txString = pname+' at'+ str(lons[ire])+'E,'+str(lats[ire])+'N'
txres = Ngl.Resources()
txres.txFontHeightF = 0.020
txres.txFont = _Font
Ngl.text_ndc(wks,pname+' at'+ str(lons[ire])+'E,'+str(lats[ire])+'N',0.5,0.92+ncases*0.01,txres)
Common_functions.create_legend(wks,casenames,np.arange(3,20,2),0.1,0.89+ncases*0.01)
Ngl.panel(wks,plot[:],[nvaris/2,2],pres)
Ngl.frame(wks)
Ngl.destroy(wks)
return plotrain
def draw_e3sm_bgt (ptype,cseason, ncases, cases, casenames, nsite, lats, lons, filepath, filepathobs,casedir,dpsc):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = "Red"
mkres.gsMarkerSizeF = 15.
infiles = ["" for x in range(ncases)]
ncdfs = ["" for x in range(ncases)]
nregions = nsite
varis = [ "DCQ","DCCLDLIQ","DCCLDICE","PTEQ","PTTEND","DTCOND"]
nvaris = len(varis)
cscale = [1E8, 1E8, 1E8, 1E8, 1E4, 1E4]
chscale = ['1E-8', '1E-8', '1E-8', '1E-8', '1E-4', '1E-4']
plote3smbgt=["" for x in range(nsite*ncases)]
for ire in range (0, nsite):
for im in range (0,ncases):
if not os.path.exists(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N'):
os.mkdir(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N')
plotname = casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N/E3SM_Budgets_'+casenames[im]+"_"+str(lons[ire])+"E_"+str(lats[ire])+"N_"+cseason
plote3smbgt[im+ncases*ire] = 'E3SM_Budgets_'+casenames[im]+"_"+str(lons[ire])+"E_"+str(lats[ire])+"N_"+cseason
wks= Ngl.open_wks(ptype,plotname)
Ngl.define_colormap(wks,"radar")
plot = []
res = Ngl.Resources()
res.nglDraw = False
res.nglFrame = False
res.lgLabelFontHeightF = .012 # change font height
res.lgPerimOn = False # no box around
res.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
# res.txFontHeightF = .01
# res.vpXF = 0.04
# res.vpYF = 0.30
res.tmYLLabelFont = 12
res.tmXBLabelFont = 12
res.tmXBLabelFontHeightF = 0.01
res.tmXBLabelFontThicknessF = 1.0
res.xyMarkLineMode = "MarkLines"
res.xyLineThicknesses = [2.0, 2.0, 2.0, 2.0, 2.0, 2.0,2.,2.,2.,2.,2,2,2,2,2,2,2]
res.xyLineColors = np.arange(2,16,2)
res.xyDashPatterns = np.arange(0,24,1)
res.xyMarkers = np.arange(16,40,1)
res.xyMarkerSizeF = 0.005
res.xyMarkerColors = np.arange(2,16,2)
res.pmLegendDisplayMode = "ALWAYS"
res.pmLegendSide = "top" # Change location of
res.pmLegendParallelPosF = 0.6 # move units right
res.pmLegendOrthogonalPosF = -0.55 # more neg = down
res.pmLegendWidthF = 0.2 # Decrease width
res.pmLegendHeightF = 0.1 # Decrease height
res.lgBoxMinorExtentF = 0.1 # Shorten the legend lines
res.lgLabelFontHeightF = 0.015 # Change the font size
res.lgPerimOn = True
res.tiYAxisString = "PRESSURE"
res.trYReverse = True
pres = Ngl.Resources()
pres.nglMaximize = True
pres.wkWidth = 2000
pres.wkHeight = 2000
pres.nglFrame = False
pres.txFont = 12
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelTop = 0.93
for iv in range (0, nvaris):
if (varis[iv] == "DCQ" ):
if (dpsc[im] == "zm" ):
budget_ends = ["MPDQ", "RVMTEND_CLUBB","ZMDQ", "EVAPQZM"]
else:
budget_ends = ["MPDQ", "RVMTEND_CLUBB"]
nterms = len (budget_ends)
if (varis[iv] == "DTCOND" ):
if (dpsc[im] == "zm" ):
budget_ends = ["STEND_CLUBB", "MPDT", "DPDLFT","ZMDT", "EVAPTZM", "ZMMTT"]
else:
budget_ends = ["STEND_CLUBB", "MPDT", "DPDLFT"]
nterms = len (budget_ends)
if (varis[iv] == "PTTEND") :
budget_ends = ["DTCOND", "QRS", "QRL", "TTGW"]
nterms = len (budget_ends)
if (varis[iv] == "PTEQ") :
if (dpsc[im] == "zm" ):
budget_ends = ["MPDQ", "RVMTEND_CLUBB","ZMDQ", "EVAPQZM"]
else:
budget_ends = ["MPDQ", "RVMTEND_CLUBB"]
nterms = len (budget_ends)
if (varis[iv] == "DCCLDLIQ") :
if (dpsc[im] == "zm" ):
budget_ends = ["MPDLIQ", "RCMTEND_CLUBB", "DPDLFLIQ","ZMDLIQ"]
else:
budget_ends = ["MPDLIQ", "RCMTEND_CLUBB", "DPDLFLIQ"]
nterms = len (budget_ends)
if (varis[iv] == "DCCLDICE") :
if (dpsc[im] == "zm" ):
budget_ends = ["MPDICE", "RIMTEND_CLUBB", "DPDLFICE","ZMDICE"]
else:
budget_ends = ["MPDICE", "RIMTEND_CLUBB", "DPDLFICE"]
nterms = len (budget_ends)
ncdfs[im] = './data/'+cases[im]+'_site_location.nc'
infiles[im]= filepath[im]+cases[im]+'/'+cases[im]+'_'+cseason+'_climo.nc'
inptrs = Dataset(infiles[im],'r') # pointer to file1
lat=inptrs.variables['lat'][:]
nlat=len(lat)
lon=inptrs.variables['lon'][:]
nlon=len(lon)
ilev=inptrs.variables['lev'][:]
nilev=len(ilev)
ncdf= Dataset(ncdfs[im],'r')
n =ncdf.variables['n'][:]
idx_cols=ncdf.variables['idx_cols'][:,:]
ncdf.close()
A_field = np.zeros((nterms,nilev),np.float32)
theunits=str(chscale[iv])+"x"+inptrs.variables[varis[iv]].units
res.tiMainString = varis[iv]+" "+theunits
for it in range(0, nterms):
for subc in range( 0, n[ire]):
varis_bgt= budget_ends[it]
npoint=idx_cols[ire,n[subc]-1]-1
tmp=inptrs.variables[varis_bgt][0,:,npoint] #/n[ire]
tmp=tmp*cscale[iv]
if (varis_bgt == "MPDT" or varis_bgt == "STEND_CLUBB" ):
tmp=tmp/1004
A_field[it,:] = (A_field[it,:]+tmp[:]/n[ire]).astype(np.float32 )
inptrs.close()
res.xyExplicitLegendLabels = budget_ends[:]
p = Ngl.xy(wks,A_field,ilev,res)
plot.append(p)
xp=np.mod(iv,2)
yp=int(iv/2)
Ngl.panel(wks,plot[:],[nvaris/2,2],pres)
txres = Ngl.Resources()
txres.txFont = _Font
txres.txFontHeightF = 0.020
Ngl.text_ndc(wks,casenames[im]+" BUDGET at" +str(lons[ire])+"E,"+str(lats[ire])+"N",0.5,0.95,txres)
Ngl.frame(wks)
Ngl.destroy(wks)
return (plote3smbgt)
# Open a workstation.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "color5")
#
# Define our own color table going around
# all the hues in the HLS color space, starting
# with blue.
#
for k in range(0, niter):
h = 360 * float(k) / float(niter)
r, g, b = Ngl.hlsrgb(h, 50., 100.)
Ngl.set_color(wks, k, r, g, b)
#
# Create the set and draw the color cells.
#
poly_res = Ngl.Resources()
for j in range(ny):
for i in range(nx):
x, y = get_cell(i, j, xl, xr, yb, yt, nx, ny)
iter = convg(complex(x[0], y[0]), niter, 0.001, 10000)
poly_res.gsFillColor = iter - 1 # iter will be at least one.
for i in range(len(x)):
x[i], y[i] = user2ndc(x[i], y[i])
Ngl.polygon_ndc(wks, x, y, poly_res)
Ngl.frame(wks)
Ngl.end()
from __future__ import print_function import numpy,os # # Import Ngl,Nio support functions. # import Ngl, Nio wks_type = "png" wks = Ngl.open_wks (wks_type,"shapefile1") Ngl.define_colormap(wks,"rainbow+gray") # # Map resources. # res = Ngl.Resources() res.mpProjection = "LambertConformal" res.mpLambertParallel1F = 33 # two parallels res.mpLambertParallel2F = 45 res.mpLambertMeridianF = -98 # central meridian res.mpLimitMode = "Corners" # limit map via two opposite corners res.mpLeftCornerLatF = 22 # left corner res.mpLeftCornerLonF = -125 # left corner res.mpRightCornerLatF = 50 # right corner res.mpRightCornerLonF = -64 # right corner res.mpFillOn = True # Turn on fill for map areas. res.mpLandFillColor = "LightGray"
ncdf = Nio.open_file(os.path.join(dirc, "cdf", "scatter1.nc"), "r")
x = ncdf.variables["x"][:]
y = ncdf.variables["y"][:]
colors = ncdf.variables["colors"][:]
color_index = colors.astype('i')
#
# Open an output workstation.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "scatter1")
#
# Label the plot.
#
resources = Ngl.Resources()
resources.tiMainString = "2000 Mar 19 1040-1725Z"
resources.tiMainFont = "helvetica-bold"
resources.tiXAxisString = "Cloud Thickness (m)"
resources.tiXAxisFont = "helvetica-bold"
resources.tiYAxisString = "Liquid Water Path (mm)"
resources.tiYAxisFont = "helvetica-bold"
resources.tiXAxisFontHeightF = 0.025 # Change the font size.
resources.tiYAxisFontHeightF = 0.025
resources.xyLineColors = "black" # Set the line colors.
resources.xyMonoLineThickness = True # Set the line colors.
resources.xyLineThicknessF = 3.0 # Triple the width.
resources.tmXBMajorLengthF = 0.02 # Force tickmarks to point
resources.tmXBMajorOutwardLengthF = 0.02 # out by making the outward
[ 0, 255, 255], "Cyan", \
[ 79, 148, 205], "SteelBlue3", \
[ 0, 0, 255], "Blue", \
[148, 0, 211], "DarkViolet", \
[255, 0, 255], "Magneta", \
[255, 255, 255], "White", \
[153, 153, 153], "Gray60", \
[102, 102, 102], "Gray40", \
[ 0, 0, 0], "Black" \
]
#
# Open a workstation with a default color table having
# background color "black" and foreground color "white".
#
rlist = Ngl.Resources()
rlist.wkColorMap = "default"
rlist.wkForegroundColor = "White"
rlist.wkBackgroundColor = "Black"
wks_type = "png"
wks = Ngl.open_wks(wks_type, "color4", rlist)
#
# Extract the colors and labels.
#
colors = colors_and_labels[0:len(colors_and_labels):2]
labels = colors_and_labels[1:len(colors_and_labels):2]
#
# Set up arrays and resource lists for drawing the boxes.
# Select "Helvetica-Bold" for all text.
for i in range(0, len(lon)):
lat2d[:, i] = lat
for i in range(0, len(lat)):
lon2d[i, :] = lon
# open workspace for forecast plots
wks_type = "png"
wks = ngl.open_wks(
wks_type,
"GFSanalysis_%s_%s_dewpoint_%shPa" % (region, init_dt[0:10], lev_hPa))
# define resources for forecast plots
res = ngl.Resources()
res.nglDraw = False
res.nglFrame = False
res.vpWidthF = 0.9
res.vpHeightF = 0.6
cmap = ngl.read_colormap_file("WhiteBlueGreenYellowRed")
res.mpGridAndLimbOn = False
res.cnFillPalette = cmap[:30:-1]
res.pmTickMarkDisplayMode = "Never"
res.cnInfoLabelOn = False
res.cnFillOn = True
res.cnLineLabelsOn = False
xo = numpy.arange(xl, xr + 0.0001, inc) # Create output X coordinate array.
# Add a small amount to xr to make
# sure xr is the final value in the
# range.
period = 3.0
yo = Ngl.ftcurvp(xi, yi, period, xo) # Interpolate.
yint = numpy.zeros(npts, 'f')
for i in xrange(npts):
yint[i] = Ngl.ftcurvpi(0., xo[i], period, xi, yi)
# Send graphics to PNG file
wks_type = "png"
wks = Ngl.open_wks(wks_type, "ngl07p")
txres = Ngl.Resources() # Set up variable for TextItem resources.
xyres = Ngl.Resources() # Set up variable for XyPlot resources.
gsres = Ngl.Resources() # Set up variable for GraphicStyle resources.
xyres.nglFrame = False # Don't advance the frame.
xyres.nglMaximize = False # Don't advance the frame.
xyres.tmXTBorderOn = False # Don't draw top axis.
xyres.tmXTOn = False # Don't draw top axis tick marks.
xyres.tmBorderThicknessF = 1.0 # Default thickness is 2.0
xyres.tmXBLabelFont = 21 # Change font and size of
xyres.tmXBLabelFontHeightF = 0.025 # X axis labels.
xyres.tmXBMajorLengthF = 0.015 # Default is 0.02.
xyres.tmXBMajorThicknessF = 1.0 # Default is 2.
xyres.tmXBMinorLengthF = 0.0075 # Default is 0.01.
# This example produces two visualizations:
# 1.) Cylindrical Equidistant projection
# 2.) Mollweide projection
#
# Notes:
#
from __future__ import print_function
import Ngl
wks_type = "png"
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 range(len(projections)):
mpres.mpProjection = projections[i]
mpres.tiMainString = projections[i]
map = Ngl.map(wks, mpres)
Ngl.end()
def add_labels_lcm(wks,map,dlat,dlon):
PI = 3.14159
RAD_TO_DEG = 180./PI
#-- determine whether we are in northern or southern hemisphere
if (float(minlat) >= 0. and float(maxlat) > 0.):
HEMISPHERE = "NH"
else:
HEMISPHERE = "SH"
#-- pick some "nice" values for the latitude labels.
lat_values = np.arange(int(minlat),int(maxlat),10)
lat_values = lat_values.astype(float)
nlat = len(lat_values)
#-- We need to get the slope of the left and right min/max longitude lines.
#-- Use NDC coordinates to do this.
lat1_ndc = 0.
lon1_ndc = 0.
lat2_ndc = 0.
lon2_ndc = 0.
lon1_ndc,lat1_ndc = Ngl.datatondc(map,minlon,lat_values[0])
lon2_ndc,lat2_ndc = Ngl.datatondc(map,minlon,lat_values[nlat-1])
slope_lft = (lat2_ndc-lat1_ndc)/(lon2_ndc-lon1_ndc)
lon1_ndc,lat1_ndc = Ngl.datatondc(map,maxlon,lat_values[0])
lon2_ndc,lat2_ndc = Ngl.datatondc(map,maxlon,lat_values[nlat-1])
slope_rgt = (lat2_ndc-lat1_ndc)/(lon2_ndc-lon1_ndc)
#-- set some text resources
txres = Ngl.Resources()
txres.txFontHeightF = 0.01
txres.txPosXF = 0.1
#-- Loop through lat values, and attach labels to the left and right edges of
#-- the masked LC plot. The labels will be rotated to fit the line better.
dum_lft = [] #-- assign arrays
dum_rgt = [] #-- assign arrays
lat_label_lft = [] #-- assign arrays
lat_label_rgt = [] #-- assign arrays
for n in range(0,nlat):
#-- left label
if(HEMISPHERE == "NH"):
rotate_val = -90.
direction = "N"
else:
rotate_val = 90.
direction = "S"
#-- add extra white space to labels
lat_label_lft.append("{}~S~o~N~{} ".format(str(np.abs(lat_values[n])),direction))
lat_label_rgt.append(" {}~S~o~N~{}".format(str(np.abs(lat_values[n])),direction))
txres.txAngleF = RAD_TO_DEG * np.arctan(slope_lft) + rotate_val
dum_lft.append(Ngl.add_text(wks,map,lat_label_lft[n],minlon,lat_values[n],txres))
#-- right label
if(HEMISPHERE == "NH"):
rotate_val = 90
else:
rotate_val = -90
txres.txAngleF = RAD_TO_DEG * np.arctan(slope_rgt) + rotate_val
dum_rgt.append(Ngl.add_text(wks,map,lat_label_rgt[n],maxlon,lat_values[n],txres))
#----------------------------------------------------------------------
# Now do longitude labels. These are harder because we're not adding
# them to a straight line.
# Loop through lon values, and attach labels to the bottom edge for
# northern hemisphere, or top edge for southern hemisphere.
#----------------------------------------------------------------------
del(txres.txPosXF)
txres.txPosYF = -5.0
#-- pick some "nice" values for the longitude labels
lon_values = np.arange(int(minlon+10),int(maxlon-10),10).astype(float)
lon_values = np.where(lon_values > 180, 360-lon_values, lon_values)
nlon = lon_values.size
dum_bot = [] #-- assign arrays
lon_labels = [] #-- assign arrays
if(HEMISPHERE == "NH"):
lat_val = minlat
else:
lat_val = maxlat
ctrl = "~C~"
for n in range(0,nlon):
if(lon_values[n] < 0):
if(HEMISPHERE == "NH"):
lon_labels.append("{}~S~o~N~W{}".format(str(np.abs(lon_values[n])),ctrl))
else:
lon_labels.append("{}{}~S~o~N~W".format(ctrl,str(np.abs(lon_values[n]))))
elif(lon_values[n] > 0):
if(HEMISPHERE == "NH"):
lon_labels.append("{}~S~o~N~E{}".format(str(lon_values[n]),ctrl))
else:
lon_labels.append("{}{}~S~o~N~E".format(ctrl,str(lon_values[n])))
else:
if(HEMISPHERE == "NH"):
lon_labels.append("{}0~S~o~N~{}".format(ctrl,ctrl))
else:
lon_labels.append("{}0~S~o~N~{}".format(ctrl,ctrl))
#-- For each longitude label, we need to figure out how much to rotate
#-- it, so get the approximate slope at that point.
if(HEMISPHERE == "NH"): #-- add labels to bottom of LC plot
lon1_ndc,lat1_ndc = Ngl.datatondc(map, lon_values[n]-0.5, minlat)
lon2_ndc,lat2_ndc = Ngl.datatondc(map, lon_values[n]+0.5, minlat)
txres.txJust = "TopCenter"
else: #-- add labels to top of LC plot
lon1_ndc,lat1_ndc = Ngl.datatondc(map, lon_values[n]+0.5, maxlat)
lon2_ndc,lat2_ndc = Ngl.datatondc(map, lon_values[n]-0.5, maxlat)
txres.txJust = "BottomCenter"
slope_bot = (lat1_ndc-lat2_ndc)/(lon1_ndc-lon2_ndc)
txres.txAngleF = RAD_TO_DEG * np.arctan(slope_bot)
#-- attach to map
dum_bot.append(Ngl.add_text(wks, map, str(lon_labels[n]), \
lon_values[n], lat_val, txres))
return
t = f.t[0, 0, :, :].values
rhum = f.rhumidity[0, 0, :, :].values
lat = f.lat.values
lon = f.lon.values
###f = Nio.open_file("rectilinear_grid_3D.nc", "r")
###t = f.variables["t"][0,0,:,:]
###rhum = f.variables["rhumidity"][0,0,:,:]
###lat = f.variables["lat"][:]
###lon = f.variables["lon"][:]
#-- define workstation
wks = Ngl.open_wks("png", os.path.basename(__file__).split('.')[0])
#-- plot resources for the map
mpres = Ngl.Resources()
mpres.nglDraw = False #-- don't draw plot
mpres.nglFrame = False #-- don't advance frame
mpres.mpOutlineOn = True #-- turn on map outlines
mpres.mpGeophysicalLineColor = "gray50" #-- map outline color
mpres.mpLimitMode = "LatLon" #-- limit map via lat/lon
mpres.mpMinLatF = 20.0 #-- min lat
mpres.mpMaxLatF = 60.0 #-- max lat
mpres.mpMinLonF = -10.0 #-- min lon
mpres.mpMaxLonF = 40.0 #-- max lon
#-- plot resources for the temperature plot
tres = Ngl.Resources()
tres.nglDraw = False #-- don't draw plot
tres.nglFrame = False #-- don't advance frame
tres.cnFillOn = True #-- turn on color fill
def plot_Robinson_pyngl(var,
lon,
lat,
wks_name='plot',
clim=None,
cspace=None,
cmap=None):
#
# Select a colormap and open a workstation.
#
rlist = Ngl.Resources()
wks_type = "png"
wks = Ngl.open_wks(wks_type, wks_name, rlist)
if cmap is None:
mycmap = 'BlAqGrYeOrReVi200'
Ngl.define_colormap(wks, mycmap)
else:
try:
mycmap = '/Users/frederic/python/cmap/' + cmap
mycmap = np.loadtxt(mycmap)
except:
mycmap = cmap
try:
Ngl.define_colormap(wks, mycmap)
except:
raise Warning, 'Unknown colormap'
#
# The next set of resources will apply to the contour plot and the labelbar.
#
resources = Ngl.Resources()
resources.sfXArray = lon
resources.sfYArray = lat
resources.gsnMaximize = True # use full page
resources.cnFillOn = True
resources.cnFillMode = "RasterFill"
resources.cnMaxLevelCount = 255
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
if clim is not None:
resources.cnLevelSelectionMode = "ManualLevels" # set manual contour levels
resources.cnMinLevelValF = clim[0] # set min contour level
resources.cnMaxLevelValF = clim[1] # set max contour level
if cspace is None:
LevelSpacing = (clim[1] - clim[0]) / 100.
resources.cnLevelSpacingF = LevelSpacing # set contour spacing
else:
resources.cnLevelSpacingF = cspace # set contour spacing
resources.lbOrientation = "Horizontal" # Default is vertical.
resources.lbBoxLinesOn = False
resources.lbLabelFontHeightF = 0.01 # label font height
resources.lbBoxMinorExtentF = 0.15
#
# The contour plot is not very interesting, so don't draw it.
#
resources.nglDraw = False
resources.nglFrame = False
contour = Ngl.contour(wks, var, resources)
#
# Retrieve the actual lat/lon end points of the scalar array so
# we know where to overlay on map.
#
xs = Ngl.get_float(contour.sffield, "sfXCActualStartF")
xe = Ngl.get_float(contour.sffield, "sfXCActualEndF")
ys = Ngl.get_float(contour.sffield, "sfYCActualStartF")
ye = Ngl.get_float(contour.sffield, "sfYCActualEndF")
resources.nglDraw = True # Turn these resources back on.
resources.nglFrame = True
resources.mpProjection = "Robinson"
resources.mpCenterLonF = 270
resources.mpCenterLatF = 0
resources.mpGeophysicalLineThicknessF = 2
map = Ngl.contour_map(wks, var, resources)
Ngl.end()
import numpy wks_type = "png" wks = Ngl.open_wks(wks_type, "format") # # Create some dummy data for an XY plot. We are only doing this # to create an object that has tickmarks. # x = Ngl.fspan(0, 1, 20) y = x # # Set some resouces for a dummy XY plot. # res = Ngl.Resources() res.nglMaximize = False res.nglDraw = False res.nglFrame = False res.xyLineColor = -1 # Transparent line # # Resources for main title and Y axis title. # res.tiMainFont = "Helvetica-Bold" res.tiMainFontHeightF = 0.015 res.tiMainJust = "CenterLeft" res.tiMainPosition = "Left" res.tiYAxisAngleF = 0.0 res.tiYAxisFont = "Courier-Bold"
lat2d = np.zeros((len(lat),len(lon))) lon2d = np.zeros((len(lat),len(lon))) for i in range(0, len(lon)): lat2d[:,i] = lat for i in range(0, len(lat)): lon2d[i,:] = lon # open workspace for analysis plot imagename = "GFSanalysis_%s_%s_CAPECIN_SNGL" % (region, init_dt[0:10]) wks_type = "png" wks_res = ngl.Resources() wks_res.wkBackgroundOpacityF = 0.0 wks = ngl.open_wks(wks_type, imagename, wks_res) # define resources for analysis plot res = ngl.Resources() res.nglDraw = False res.nglFrame = False #res.tmXBOn = False #res.tmXTOn = False #res.tmYLOn = False #res.tmYROn = False res.vpWidthF = 0.9
N = 25
T = numpy.zeros((N, N), 'f')
jspn = numpy.power(numpy.arange(-12, 13), 2)
ispn = numpy.power(numpy.arange(-12, 13), 2)
for i in range(0, len(ispn)):
T[i, :] = (jspn + ispn[i]).astype('f')
T = 100.0 - numpy.sqrt(64 * T)
#
# Open the workstation and change the color map.
#
rlist = Ngl.Resources()
rlist.wkColorMap = "default"
wks_type = "png"
wks = Ngl.open_wks(wks_type, "contour2", rlist)
cnres = Ngl.Resources()
cnres.cnFillOn = True # Turn on contour level fill.
cnres.cnMonoFillPattern = False # Indicate you want multiple fill patterns.
#
# Set cnFillPatterns and cnFillColors to various indexes representing
# fill patterns and colors. A fill pattern index of "0" is solid fill.
# If you don't want any fill for a particular contour level, set it
# to "-1," which means "transparent."
#
del latVertex, lonVertex, verticesOnCell
#---Debug prints
print("This MPAS file has {} cells.".format(nCells))
print_min_max(t2m, "t2m")
print_min_max(latCell, "latCell")
print_min_max(lonCell, "lonCell")
print_min_max(latvoc, "latvoc")
print_min_max(lonvoc, "lonvoc")
#---Start the graphics
wks_type = "png"
wks = Ngl.open_wks(wks_type, "mpas2")
res = Ngl.Resources() # Plot mods desired.
res.nglDraw = False # Turn off plot draw and frame advance. We will
res.nglFrame = False # do it later after adding subtitles.
res.cnFillOn = True # color plot desired
res.cnFillPalette = "ncl_default"
res.cnLinesOn = False # turn off contour lines
res.cnLineLabelsOn = False # turn off contour labels
res.cnLevelSelectionMode = "ExplicitLevels"
res.cnLevels = Ngl.fspan(min(t2m), max(t2m),
253) # 253 levels (hence 254 colors)
res.lbOrientation = "Horizontal" # vertical by default
res.lbBoxLinesOn = False # turn off labelbar boxes
def __init__(self, grid, title):
"""
Initialize an instance of MapPlots.
Plotting of maps is powered by PyNGL.
:type grid: :class:`.Grid`
:arg grid: The Grid object describing the map data to be plotted.
:arg str title: The title describing the map data.
"""
self.grid = grid
"""(Grid) - The Grid object describing the map data to be plotted."""
self.title = title
"""(string) - The title describing the map data."""
# Initialize list to store data sets and titles
self.map_data = []
"""(list) - The list storing map data and titles."""
# Also for multiple maps
self.map_mult_data = []
"""(list) - The list storing map data and titles for multiple maps."""
#
# Adjust PyNGL settings, fine tuning can be done externally
#
# Set PyNGL resources
resources = Ngl.Resources()
# Define grid
resources.sfXArray = self.grid.lon_sequence()
resources.sfYArray = self.grid.lat_sequence()
# Change the map projection
resources.mpProjection = "Robinson"
# Rotate the projection
# Center in the middle of lonMin and lonMax
# resources.mpRelativeCenterLon = "true"
# Set plot limits
resources.mpLimitMode = "LatLon"
resources.mpMinLonF = self.grid.boundaries()["lon_min"]
resources.mpMaxLonF = self.grid.boundaries()["lon_max"]
resources.mpMinLatF = self.grid.boundaries()["lat_min"]
resources.mpMaxLatF = self.grid.boundaries()["lat_max"]
# Change the color map
resources.wkColorMap = "wh-bl-gr-ye-re"
# Change thickness of geophysical lines
resources.mpGeophysicalLineThicknessF = 2.0
# Configure the legend
resources.lbAutoManage = False
resources.lbOrientation = "Horizontal"
resources.lbLabelFont = "Helvetica"
resources.lbLabelFontHeightF = 0.0075
resources.lbTitleFontHeightF = 0.01
# Larger font for regional networks
# resources.lbLabelFontHeightF = 0.014
# resources.lbTitleFontHeightF = 0.02
# Configure the contour plots
resources.cnFillOn = True
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
resources.cnInfoLabelOn = False
resources.cnMaxLevelCount = 22
resources.cnFillMode = "RasterFill"
# Make resources object accessible from outside
self.resources = resources
"""The PyNGL resources allow fine tuning of plotting options."""
#
cmap = numpy.array([[1.00,.000,.000],\
[.950,.010,.000],[.870,.050,.000],[.800,.090,.000],\
[.700,.090,.000],[.700,.120,.000],[.700,.180,.000],\
[.700,.260,.000],[.700,.285,.000],[.680,.330,.000],\
[.570,.420,.000],[.560,.530,.000],[.550,.550,.000],\
[.130,.570,.000],[.060,.680,.000],[.000,.690,.000],\
[.000,.700,.100],[.000,.600,.300],[.000,.500,.500],\
[.000,.400,.700],[.000,.300,.700],[.000,.200,.700],\
[.000,.100,.700],[.000,.000,.700],[.100,.100,.700],\
[.200,.200,.700],[.300,.300,.700],[.420,.400,.700],\
[.560,.500,.700],[.610,.600,.700],[.700,.700,.700]],
'f')
#--- Indicate where to send graphics
rlist = Ngl.Resources()
wks_type = "png"
wks = Ngl.open_wks(wks_type, "panel1", rlist)
#
# Turn off draw for the individual plots, since we are going to
# panel them later.
#
resources = Ngl.Resources()
resources.nglDraw = False
resources.nglFrame = False
#
# Loop through the timesteps and create each plot, titling each
# one according to which timestep it is.
#
# A single visualization with two XY curves
#
import numpy,os
import Ngl,Nio
dirc = Ngl.pynglpath("data")
f = Nio.open_file(os.path.join(dirc,"cdf","uv300.nc"))
u = f.variables["U"][0,:,8]
lat = f.variables["lat"][:]
#---Start the graphics section
wks_type = "png"
wks = Ngl.open_wks (wks_type,"newcolor2") # Open "newcolor2.png" for graphics
#---Set some resources.
res = Ngl.Resources()
res.nglDraw = False
res.nglFrame = False
plot = Ngl.xy (wks,lat,u,res) # Create plot
txres = Ngl.Resources() # text mods desired
txres.txFont = 30
txres.txFontHeightF = 0.04 # font smaller. default big
txres.txFontOpacityF = 0.10 # highly transparent
txres.txAngleF = 45.
txid = Ngl.add_text(wks,plot,"Preliminary Data",10,15,txres)
Ngl.draw(plot) # Drawing plot will draw attached text
Ngl.frame(wks) # Advance frame
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
#
# Import PyNGL support functions.
#
import Ngl
import os, string, types
# Read data off file.
filename = os.path.join(Ngl.pynglpath("data"), "asc", "fcover.dat")
data = Ngl.asciiread(filename, [3, 73, 73], "float")
# Send graphics to PNG file
wks_type = "png"
wks = Ngl.open_wks(wks_type, "streamline2")
stres = Ngl.Resources()
cnres = Ngl.Resources()
mpres = Ngl.Resources()
cnres.nglDraw = False
cnres.nglFrame = False
mpres.nglDraw = False
mpres.nglFrame = False
stres.nglDraw = False
stres.nglFrame = False
# Set sf/vf resources to indicate where on map to overlay.
cnres.sfXCStartV = -180.
cnres.sfXCEndV = 180.
cnres.sfYCStartV = -90.
cnres.sfYCEndV = 90.
#-- information
print("")
print("Cell points: ", nv)
print("Cells: ", str(ncells))
print("Variable ta min/max: %.2f " % np.min(var) + "/" +
" %.2f" % np.max(var))
print("")
#-- open a workstation
wks_type = "x11" #-- graphics output type
wks = Ngl.open_wks(wks_type,
"plot_contour_unstructured_PyNGL") #-- open a workstation
#-- set resources
res = Ngl.Resources() #-- plot mods desired.
res.nglDraw = False #-- turn off plot draw and frame advance. We will
res.nglFrame = False #-- do it later after adding subtitles.
res.cnFillOn = True #-- color plot desired
res.cnFillMode = "CellFill" #-- set fill mode
res.cnFillPalette = "BlueWhiteOrangeRed" #-- choose colormap
res.cnLinesOn = False #-- turn off contour lines
res.cnLineLabelsOn = False #-- turn off contour labels
res.cnLevelSelectionMode = "ExplicitLevels" #-- use explicit levels
res.cnLevels = levels #-- set levels
res.lbOrientation = "Horizontal" #-- vertical by default
res.lbBoxLinesOn = False #-- turn off labelbar boxes
res.lbLabelFontHeightF = 0.01 #-- labelbar label font size
# ---Values to use for contour labelbar
if plotType == 4:
varMin = -0.5 # Data mins
elif plotType == 6:
varMin = -1.5 # Data mins
varMax = 4 # Data maxs
varInt = 0.5 # Data spacing
levels = np.arange(varMin, varMax + 0.0001, varInt)
# list(np.arange(varMin,varMax,varInt))
nlevs = len(levels) # -- number of levels
# -- convert list of floats to list of strings
labels = ['{:.1f}'.format(x) for x in levels]
labels = str(labels)
# Create resource list for customizing contour over maps
res = Ngl.Resources()
res.nglMaximize = False
res.nglFrame = False
res.nglDraw = False
res.sfXArray = lon
res.sfYArray = lat
res.sfMissingValueV = -99999
# Title
res.tiMainFont = "Helvetica-Bold"
res.tiMainFontHeightF = 0.015
# res.tiMainOffsetYF = 0.02
# Map
- Drawing a map
- Drawing polylines
- Drawing polygons
- Drawing all available (16) polymarkers
2018-09-11 kmf
'''
import Ngl
import os
#-- open a workstation and define colormap
wks = Ngl.open_wks("png", os.path.basename(__file__).split('.')[0])
#-- 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)
cdf_file = Nio.open_file(os.path.join(data_dir, "cdf", "ocean.nc"))
T = ma.masked_values(cdf_file.variables["T"][:, :],
cdf_file.variables["T"]._FillValue)
lat_t = cdf_file.variables["lat_t"][:]
z_t = cdf_file.variables["z_t"][:] / 100. # convert cm to m
#
# Open a workstation.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "irregular")
#
# Set up a resource list for plot options.
#
resources = Ngl.Resources()
resources.sfXArray = lat_t
resources.sfYArray = z_t
resources.cnFillOn = True
resources.cnFillPalette = "gui_default"
resources.cnLineLabelsOn = False
resources.tiMainString = "Default axes" # main title
#
# Draw the plot as is, with no transformations in place.
#
plot = Ngl.contour(wks, T, resources)
#
wks_type = "png"
wks = Ngl.open_wks(wks_type,"ngl11p")
dirc = Ngl.pynglpath("data")
data = Ngl.asciiread(dirc+"/asc/u.cocos",(39,14),"float")
pressure = data[:,0] # First column of data is pressure (mb).
height = data[:,1] # Second column is height (km).
u = data[:,2:14] # Rest of columns are climatological zonal winds
# (u: m/s)
unew = Ngl.add_cyclic(u) # Add cyclic points to u.
#----------- Begin first plot -----------------------------------------
resources = Ngl.Resources()
resources.tiMainString = "~F26~Cocos Island" # Main title.
resources.tiYAxisString = "~F25~Pressure (mb)" # Y axes label.
resources.sfYCStartV = float(max(pressure)) # Indicate start and end of left
resources.sfYCEndV = float(min(pressure)) # Y axes values.
resources.trYReverse = True # Reverse the Y values.
resources.trYLog = True # Use log scale.
resources.tmXBMode = "Explicit" # Define your own tick mark labels.
resources.tmXBLabelFont = "times-roman" # Change font of labels.
resources.tmXBLabelFontHeightF = 0.015 # Change font height of labels.
resources.tmXBMinorOn = False # No minor tick marks.
resources.tmXBValues = range(0,13,1) # Location to put tick mark labels
