cfres.cnLinesOn = False #-- don't draw contour lines cfres.cnLineLabelsOn = False #-- turn off contour line labels cfres.lbOrientation = "horizontal" cfres.mpGridAndLimbOn = False # Turn off grid and limb lines. pmres.gsMarkerSizeF = 0.0015 pmres.gsMarkerThicknessF = 0.1 pmres.gsMarkerOpacityF = 1 txres = Ngl.Resources() txres.txJust = "BottomCenter" txres.txFontHeightF = 0.025 title = 'Time of emergegence of FWI>p95 frequency~C~1-sigma above all forcing '+str(pyear0[base_pp])+'-'+str(pyear1[base_pp]) diff_cyc, lon_cyc = Ngl.add_cyclic(toe_diff_median_masked,lon) cfres.sfXArray = lon_cyc cfres.tiMainString = sf_list[ss] plot = Ngl.contour_map(wks,diff_cyc,cfres) pmres.gsMarkerColor = colors[np.int(ncolors/4),:] pmres.gsMarkerIndex = 1 marker_all = Ngl.add_polymarker(wks,plot,lon_1d_all,lat_1d_all,pmres) pmres.gsMarkerColor = colors[np.int(ncolors/4)*3,:] pmres.gsMarkerIndex = 1 marker_sf = Ngl.add_polymarker(wks,plot,lon_1d_sf,lat_1d_sf,pmres) Ngl.draw(plot) Ngl.text_ndc(wks,title,0.5,0.87,txres) Ngl.frame(wks)
# Main code
#----------------------------------------------------------------------
# Open file and get variable. The lat/lon variables will be
# generated using fspan. This data came from another dataset
# that had lat/lon on the file, but lat/lon was nothing more
# than equally-spaced values which we can regenerate exactly.
#
f = Nio.open_file(os.path.join(Ngl.pynglpath("data"), "cdf", "hgt.nc"), "r")
hgt = f.variables["HGT"][:, :, :]
lat = Ngl.fspan(-90, 90, 73)
lon = Ngl.fspan(0, 357.5, 144)
# Add a cyclic point in the longitude dimension.
hgt0, lon = Ngl.add_cyclic(hgt[0, :, :], lon)
#
# Start graphics.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "spaghetti")
Ngl.define_colormap(wks, "default") # Change color map.
mpres = Ngl.Resources()
mpres.nglDraw = False # Do not draw until the end.
mpres.nglFrame = False # Do not automatically advance frame.
#
# Set contour resources first, because we'll make a copy of them
ffile))
print("You can get the files from the NCL website at:")
print("http://www.ncl.ucar.edu/Document/Manuals/NCL_User_Guide/Data/")
sys.exit()
minval = 250. #-- minimum contour level
maxval = 315 #-- maximum contour level
inc = 5. #-- contour level spacing
#-- open file and read variables
f = Nio.open_file(ffile, "r") #-- open data file
temp = f.variables["tsurf"][0, :, :] #-- first time step
lat = f.variables["lat"][:] #-- all latitudes
lon = f.variables["lon"][:] #-- all longitudes
tempac, lon = Ngl.add_cyclic(temp, lon)
#-- open a workstation
wkres = Ngl.Resources() #-- generate an res object
wks_type = "png" #-- output type
wks_name = os.path.basename(__file__).split(".")[0]
wks = Ngl.open_wks(wks_type, wks_name, wkres)
#-- set resources
res = Ngl.Resources() #-- generate an resource object for plot
if hasattr(f.variables["tsurf"], "long_name") and hasattr(
f.variables["tsurf"], "units"):
res.tiMainString = "{} ({})".format(
f.variables["tsurf"].long_name,
f.variables["tsurf"].units) #-- set main title
# # Add a text string explaining the lines. # text_res = Ngl.Resources() # text resources text_res.txFontHeightF = 0.03 # font height text_res.txFontColor = "Red" Ngl.text_ndc(wks,"dashed red line shows area with no data",0.5,0.17,text_res) Ngl.frame(wks) # Now advance frame. # # Add cyclic points. Since lat2d/lon2d are 2D arrays, make them # cyclic the same way you do the 2D data array. # u = Ngl.add_cyclic(urot) v = Ngl.add_cyclic(vrot) lon = Ngl.add_cyclic(lon2d) lat = Ngl.add_cyclic(lat2d) # # Specify new coordinate arrays for data. # resources.vfXArray = lon resources.vfYArray = lat resources.tiMainString = "Streamline plot with cyclic point added" plot = Ngl.streamline_map(wks,u,v,resources) #
pres.nglPanelLabelBar = True #False
pres.nglPanelTop = 0.88
res.cnMinLevelValF = 1996 #-- contour min. value
res.cnMaxLevelValF = 2080 #-- contour max. value
res.cnLevelSpacingF = 4 #-- contour interval
colors00 = Ngl.read_colormap_file("MPL_YlOrRd")
ncolors00= colors00.shape[0]
colors0 = colors00[np.linspace(2,ncolors00-1,int((res.cnMaxLevelValF-res.cnMinLevelValF)/res.cnLevelSpacingF),dtype=int),:]
grey_color = np.expand_dims(np.array([0.85, 0.85, 0.85, 1]), axis=0)
colors = np.append(grey_color,colors0[::-1,:],axis=0)
res.cnFillPalette = colors #BlWhRe' #'MPL_YlOrRd' #-- choose color map
plot = []
title = 'Time of Emergence ~C~1 standard deviation ~C~ FWI > p95 Frequency'
toe_cyc, lon_cyc = Ngl.add_cyclic(toe_all_ens_agree_masked,lon)
res.sfXArray = lon_cyc
res.tiMainString = 'all forcing'
p = Ngl.contour_map(wks,toe_cyc,res)
plot.append(p)
Ngl.panel(wks,plot,[1,1],pres)
Ngl.text_ndc(wks,title,0.5,0.85,txres)
Ngl.frame(wks)
Ngl.delete_wks(wks)
print("You do not have the necessary file (%s) to run this example." % (diri+fname))
print("You can get the files from the NCL website at:")
print("http://www.ncl.ucar.edu/Document/Manuals/NCL_User_Guide/Data/")
sys.exit()
minval = 250. #-- minimum contour level
maxval = 315 #-- maximum contour level
inc = 5. #-- contour level spacing
#-- open file and read variables
f = Nio.open_file(diri + fname,"r") #-- open data file
temp = f.variables["tsurf"][0,:,:] #-- first time step
lat = f.variables["lat"][:] #-- all latitudes
lon = f.variables["lon"][:] #-- all longitudes
tempac,lon = Ngl.add_cyclic(temp,lon)
#-- open a workstation
wks_type = "png" #-- graphics output type
wkres = Ngl.Resources() #-- generate an res object
#-- for workstation
wkres.wkWidth = 2500 #-- plot res 2500 pixel width
wkres.wkHeight = 2500 #-- plot resolution 2500
wks = Ngl.open_wks(wks_type,"plot_contour_ngl",wkres) #-- open workstation
#-- set resources
res = Ngl.Resources() #-- generate an resource object for plot
if hasattr(f.variables["tsurf"],"long_name"):
res.tiMainString = f.variables["tsurf"].long_name #-- set main title
filea = "ice5g_21k_1deg.nc" fileb = "sstanom.robinsonproj.nc" a = Nio.open_file(os.path.join(dirc,"cdf",filea)) b = Nio.open_file(os.path.join(dirc,"cdf",fileb)) topo = a.variables["Topo"][:,:] Lat = a.variables["Lat"][:] Lon = a.variables["Lon"][:] icem = a.variables["Icemask"][:,:] # Keep topo where icem.eq.1 topo = numpy.ma.where(icem == 1, topo,1e20) # Add longitude cyclic point topo,Lon = Ngl.add_cyclic(topo,Lon) sst = b.variables["SST"][:,:] lat = b.variables["lat"][:] lon = b.variables["lon"][:] sst,lon = Ngl.add_cyclic(sst,lon) # # Start the graphics # wks_type = "ps" wks = Ngl.open_wks(wks_type,"multi_plot") Ngl.merge_colormaps(wks,"BlWhRe","default") #==========================================================================
if (not os.path.exists(diri + fname)):
print("You do not have the necessary file (%s) to run this example." %
(diri + fname))
print("You can get the files from the NCL website at:")
print("http://www.ncl.ucar.edu/Document/Manuals/NCL_User_Guide/Data/")
sys.exit()
#-- open file and read variables
f = Nio.open_file(diri + fname, "r") #-- open data file
t = f.variables["t"] #-- get whole "t" variable
t26 = t[0, :, 26, :] #-- variable at lat index 26
lev = f.variables["lev"][:] * 0.01 #-- all levels, convert to hPa
lat = f.variables["lat"][:] #-- reverse latitudes
lon = f.variables["lon"][:] #-- all longitudes
t26, lon = Ngl.add_cyclic(t26, lon)
strlat26 = lat[26] #-- retrieve data of lat index 26
#-- get the minimum and maximum of the data
minval = int(np.amin(t[:])) #-- minimum value
maxval = int(np.amax(t[:])) #-- maximum value
inc = 5 #-- contour level spacing
#-- values on which to place tickmarks on X and Y axis
lons = np.arange(-180, 240, 60)
levs = [1000, 700, 500, 400, 300, 200, 150, 100, 70, 50, 30, 10]
wks = Ngl.open_wks("png", "plot_rectilinear_slice_ngl")
#-- open workstation
#-- set resources
#-- define variables
diri = "$HOME/NCL/general/data/new_data/" #-- data directory
fname = "rectilinear_grid_2D.nc" #-- data file name
minval = 250. #-- minimum contour level
maxval = 315 #-- maximum contour level
inc = 5. #-- contour level spacing
ncn = (maxval - minval) / inc + 1 #-- number of contour levels.
#-- open file and read variables
f = Nio.open_file(diri + fname, "r") #-- open data file
temp = f.variables["tsurf"][0, ::-1, :] #-- first time step, reverse latitude
lat = f.variables["lat"][::-1] #-- reverse latitudes
lon = f.variables["lon"][:] #-- all longitudes
tempac = Ngl.add_cyclic(temp[:, :])
#-- open a workstation
wkres = Ngl.Resources() #-- generate an res object for workstation
wkres.wkColorMap = "rainbow" #-- choose colormap
wks_type = "x11" #-- graphics output type
wks = Ngl.open_wks(wks_type, "plot_contour_PyNGL", wkres) #-- open workstation
#-- set resources
res = Ngl.Resources() #-- generate an resource object for plot
if hasattr(f.variables["tsurf"], "long_name"):
res.tiMainString = f.variables["tsurf"].long_name #-- set main title
res.vpXF = 0.1 #-- start x-position of viewport
res.vpYF = 0.9 #-- start y-position of viewport
colors00 = Ngl.read_colormap_file("MPL_RdBu")
ncolors00 = colors00.shape[0]
colors0 = colors00[np.linspace(2,
ncolors00 - 1,
int((res.cnMaxLevelValF - res.cnMinLevelValF) /
res.cnLevelSpacingF),
dtype=int), :]
grey_color = np.expand_dims(np.array([0.85, 0.85, 0.85, 1]), axis=0)
colors = np.append(grey_color, colors0[::-1, :], axis=0)
res.cnFillPalette = colors #'BlWhRe' #'MPL_YlOrRd' #-- choose color map
plot = []
title = 'CESM all forcing vs all forcing without ' + sf + ' effect ~C~FWI >= p' + str(
pxx) + ' risk ratio'
for pp in range(5, nperiods, 1):
for vv in range(0, n_vars, 1):
print('period ' + str(pp + 1))
# res.tiMainString = var[vv]+' '+str(pyear0[pp])+'-'+str(pyear1[pp])
res.tiMainString = ''
riskratio_cyc, lon_cyc = Ngl.add_cyclic(
riskratio_ens_agree_masked[vv, pp, :, :], lon)
res.sfXArray = lon_cyc
p = Ngl.contour_map(wks, riskratio_cyc, res)
plot.append(p)
Ngl.panel(wks, plot, [n_vars, 1], pres)
#Ngl.text_ndc(wks,title,0.5,0.92,txres)
Ngl.frame(wks)
Ngl.delete_wks(wks)
def ngl_plot(wks,data2d,lon,lat,title,longname,units,
projection,clev,cmap,scrip_file):
cellbounds=False
if os.path.isfile(scrip_file):
infile = Nio.open_file(scrip_file,"r")
clat = infile.variables["grid_corner_lat"][:,:]
clon = infile.variables["grid_corner_lon"][:,:]
if clon.shape[0] == len(lat):
cellbounds=True
res = Ngl.Resources()
if cmap!=None:
res.cnFillPalette = cmap
if len(clev)==1:
res.cnLevelSelectionMode = "AutomaticLevels"
res.cnMaxLevelCount = clev[0]
else:
res.cnLevelSelectionMode = "ManualLevels"
res.cnMinLevelValF=clev[0]
res.cnMaxLevelValF=clev[1]
res.cnLevelSpacingF=clev[2]
# defaults. some projection options might change:
res.cnFillOn = True # Turn on contour fill.
res.cnLinesOn = False # Turn off contour lines
res.cnLineLabelsOn = False # Turn off line labels.
res.cnInfoLabelOn = False # Turn off info label.
res.mpOutlineOn = True
res.mpFillOn = False
res.mpGridAndLimbOn = False # dont draw grid lines
#res.mpShapeMode = "FreeAspect"
if projection == "latlon" :
res.mpProjection = "CylindricalEquidistant"
res.mpLimitMode = "MaximalArea"
# res.mpMinLatF = -90.
# res.mpMaxLatF = 90.
# res.mpMinLonF = -180.
# res.mpMaxLonF = 180.
elif projection == "US1":
res.mpProjection = "CylindricalEquidistant"
res.mpLimitMode = "LatLon"
res.mpMinLatF = -30
res.mpMaxLatF = 75
res.mpMinLonF = -180
res.mpMaxLonF = 0
elif projection == "andes":
res.mpProjection = "CylindricalEquidistant"
res.mpLimitMode = "LatLon"
res.mpMinLatF = -40.
res.mpMaxLatF = 15.
res.mpMinLonF = -100.
res.mpMaxLonF = -40.
elif projection == "andes2":
res.mpProjection = "CylindricalEquidistant"
res.mpLimitMode = "LatLon"
res.mpMinLatF = -17.
res.mpMaxLatF = 3.
res.mpMinLonF = -85.
res.mpMaxLonF = -65.
elif projection == "himalaya":
res.mpProjection = "CylindricalEquidistant"
res.mpLimitMode = "LatLon"
res.mpMinLatF = 0.
res.mpMaxLatF = 60.
res.mpMinLonF = 50.
res.mpMaxLonF = 110.
elif projection == "oro":
res.mpProjection = "Orthographic"
res.mpCenterLatF = 45.
res.mpCenterLonF = -45.
elif projection == "oro-debug1":
res.mpProjection = "Orthographic"
res.mpLimitMode="Angles"
hdeg=10
res.mpLeftAngleF=hdeg
res.mpRightAngleF=hdeg
res.mpBottomAngleF=hdeg
res.mpTopAngleF=hdeg
elif projection == "debug1":
res.mpProjection = "CylindricalEquidistant"
res.mpLimitMode = "LatLon"
res.mpMinLatF = 30.
res.mpMaxLatF = 50.
res.mpMinLonF = 85.
res.mpMaxLonF = 105.
elif projection == "debug2":
res.mpProjection = "CylindricalEquidistant"
res.mpLimitMode = "LatLon"
res.mpMinLatF = -10.
res.mpMaxLatF = 75.
res.mpMinLonF = 45.
res.mpMaxLonF = 175.
elif projection == "baroclinic":
res.mpProjection = "CylindricalEquidistant"
res.mpLimitMode = "LatLon"
res.mpCenterLonF = 100.
res.mpMinLatF = 25.
res.mpMaxLatF = 75.
res.mpMinLonF = 25.
res.mpMaxLonF = 175.
res.cnLinesOn = True # Turn off contour lines
res.mpOutlineOn = False
else:
print("Bad projection argument: ",projection)
sys.exit(3)
res.nglFrame = False # Don't advance frame.
#res.tiXAxisString = "~F25~longitude"
#res.tiYAxisString = "~F25~latitude"
res.nglPointTickmarksOutward = True
if cellbounds:
res.cnFillMode = 'CellFill'
res.sfXCellBounds = clon
res.sfYCellBounds = clat
else:
#res.cnFillMode = "AreaFill"
res.cnFillMode = "RasterFill"
res.cnRasterSmoothingOn = True
res.sfXArray = lon[:]
res.sfYArray = lat[:]
#res.sfCopyData = False
# "not a valid resource in contour at this time...
# if wks_type == "pdf":
# res.gsnMaximize = True
# res.gsnPaperOrientation = "portrait"
res.lbLabelAutoStride = True # Clean up labelbar labels.
#res.lbAutoManage = True
#res.lbLabelStride = 10
res.lbBoxLinesOn = False # Turn of labelbar box lines.
res.lbOrientation = "horizontal"
res.tiMainString = title
print("Title: ",res.tiMainString)
print("data min/max=",numpy.amin(data2d),numpy.amax(data2d))
if res.cnLevelSelectionMode == "ManualLevels":
nlevels=(res.cnMaxLevelValF-res.cnMinLevelValF)/res.cnLevelSpacingF
print("contour levels: manual [",res.cnMinLevelValF,",",\
res.cnMaxLevelValF,"] spacing=",res.cnLevelSpacingF)
print("number of contour levels:",nlevels)
else:
print("contour levels: auto. number of levels:",res.cnMaxLevelCount)
nlevels=res.cnMaxLevelCount
if nlevels>20:
res.lbLabelStride = nlevels/8
# for lat/lon plots, add cyclic point:
res2=res
if hasattr(res,"sfXArray"):
if len(res.sfXArray)*len(res.sfYArray) == numpy.prod(data2d.shape):
print("NGL structured data plot. Adding cyclic point")
data2d,lon2= Ngl.add_cyclic(data2d[:,:],res.sfXArray[:])
res2.sfXArray=lon2
else:
print("NGL unstructered plot with internal triangulation")
elif hasattr(res,"sfXCellBounds"):
print("NGL unstructered plot with cell bounds")
else:
print("Error with resource coordinate data")
# plot:
map = Ngl.contour_map(wks,data2d,res2)
print("Contour done.")
del res2
#-- write variable long_name and units to the plot
txres = Ngl.Resources()
txres.txFontHeightF = 0.016
Ngl.text_ndc(wks,longname,0.14,0.82,txres)
Ngl.text_ndc(wks,units, 0.95,0.82,txres)
del txres
Ngl.frame(wks) # advance frame
return map
# # Send graphics to PNG # wks_type = "png" wks = Ngl.open_wks(wks_type, "vector_pop") # # Get the u/v and lat/lon variables. # urot = file.variables["urot"] vrot = file.variables["vrot"] lat2d = file.variables["lat2d"] lon2d = file.variables["lon2d"] t = file.variables["t"] u = Ngl.add_cyclic(urot[290:]) v = Ngl.add_cyclic(vrot[290:]) lon = Ngl.add_cyclic(lon2d[290:]) lat = Ngl.add_cyclic(lat2d[290:]) temp = Ngl.add_cyclic(t[290:]) # # Set up resource list. # # First we're going to draw a contour plot with the grid cells outlined # so you can see what the POP grid looks like. # cnres = Ngl.Resources() cnres.nglFrame = False #
#
wks_type = "png"
wks = Ngl.open_wks(wks_type,"ngl11p")
dirc = Ngl.pynglpath("data")
data = Ngl.asciiread(os.path.join(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.
FileMA['QFLX'] / rhow) # convert from m/s to mm/day
varN = 1000.0 * 60.0 * 60.0 * 24.0 * np.squeeze(
FileNA['QFLX'] / rhow)
elif var in ["SHFLX"]:
a1, a2, adiff = -30, 30.0, 6.0
b1, b2, bdiff = -10.0, 10.0, 2.0
varM = np.squeeze(FileMA[var]) # convert from m/s to mm/day
varN = np.squeeze(FileNA[var])
lons = FileMA['lon'].values
lats = FileMA['lat'].values
varM = np.where(np.isnan(varM), -9999, varM)
varN = np.where(np.isnan(varN), -9999, varN)
varM, lons = Ngl.add_cyclic(varM, lons)
varN = Ngl.add_cyclic(varN)
elif var in ["PRECTZM"]:
varM = 1000.0 * 60.0 * 60.0 * 24.0 * np.squeeze(
FileMA['PRECT'].mean(dim='lon')) # convert from m/s to mm/day
varN = 1000.0 * 60.0 * 60.0 * 24.0 * np.squeeze(
FileNA['PRECT'].mean(dim='lon'))
lats = FileMA['lat'].values
elif var == "FSNSC":
a1, a2, adiff = 50, 400, 35
b1, b2, bdiff = 0.0, 10.0, 5.0
varM = np.squeeze(FileMA['FSNSC'])
landfrac = np.squeeze(FileMA['LANDFRAC'])
import Nio
import numpy
import os
dirc=Ngl.pynglpath("data")
file=Nio.open_file(os.path.join(dirc,"cdf","pop.nc"))
wks_type="png"
wks=Ngl.open_wks(wks_type,"map2")
urot=file.variables["urot"]
t=file.variables["t"]
lat2d=file.variables["lat2d"]
lon2d=file.variables["lon2d"]
u=Ngl.add_cyclic(urot[:])
temp=Ngl.add_cyclic(t[0:])
lon=Ngl.add_cyclic(lon2d[0:])
lat=Ngl.add_cyclic(lat2d[0:])
resource=Ngl.Resources()
resource.vfXArray=lon
resource.vfYArray=lat
resource.mpProjection="Stereographic"
resource.mpFillOn=True
resource.mpInlandWaterFillColor="SkyBlue"
resource.mpProjection = "Stereographic"
resource.mpEllipticalBoundary = True
filea = "ice5g_21k_1deg.nc"
fileb = "sstanom.robinsonproj.nc"
a = Nio.open_file(os.path.join(dirc, "cdf", filea))
b = Nio.open_file(os.path.join(dirc, "cdf", fileb))
topo = a.variables["Topo"][:, :]
Lat = a.variables["Lat"][:]
Lon = a.variables["Lon"][:]
icem = a.variables["Icemask"][:, :]
# Keep topo where icem.eq.1
topo = numpy.ma.where(icem == 1, topo, 1e20)
# Add longitude cyclic point
topo, Lon = Ngl.add_cyclic(topo, Lon)
sst = b.variables["SST"][:, :]
lat = b.variables["lat"][:]
lon = b.variables["lon"][:]
sst, lon = Ngl.add_cyclic(sst, lon)
#
# Start the graphics
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "multi_plot")
Ngl.merge_colormaps(wks, "BlWhRe", "default")
#==========================================================================
print("Beginning bar chart section")
#---Test if file exists
if(not os.path.exists(diri+fname)):
print("You do not have the necessary file (%s) to run this example." % (diri+fname))
print("You can get the files from the NCL website at:")
print("http://www.ncl.ucar.edu/Document/Manuals/NCL_User_Guide/Data/")
sys.exit()
#-- open file and read variables
f = Nio.open_file(diri + fname,"r") #-- open data file
t = f.variables["t"] #-- get whole "t" variable
t26 = t[0,:,26,:] #-- variable at lat index 26
lev = f.variables["lev"][:]*0.01 #-- all levels, convert to hPa
lat = f.variables["lat"][:] #-- reverse latitudes
lon = f.variables["lon"][:] #-- all longitudes
t26,lon = Ngl.add_cyclic(t26,lon)
strlat26 = lat[26] #-- retrieve data of lat index 26
#-- get the minimum and maximum of the data
minval = int(np.amin(t[:])) #-- minimum value
maxval = int(np.amax(t[:])) #-- maximum value
inc = 5 #-- contour level spacing
#-- values on which to place tickmarks on X and Y axis
lons = np.arange(-180,240,60)
levs = [1000,700,500,400,300,200,150,100,70,50,30,10]
wks = Ngl.open_wks("png","plot_rectilinear_slice_ngl")
#-- open workstation
#-- set resources
res = Ngl.Resources()
res.lbLabelBarOn = False
# Filled contour/labels/labelinfos
res.cnLinesOn = False
res.cnFillOn = True
res.cnLineLabelsOn = False
res.cnInfoLabelOn = False
res.mpGridAndLimbOn = False
#anotherway to define colormap (overlay the predefined color map)
# cmap = Ngl.read_colormap_file("WhiteBlueGreenYellowRed")
#Level selection
# res.lbBoxLinesOn = False
res.cnLevelSelectionMode = "ExplicitLevels" # Set explicit contour levels
res.cnLevels = np.arange(1e-5, 8.e-4, 4e-5) # 0,5,10,...,70
# maximize the plot
res.nglMaximize = True
res.nglFrame = False
# coordinate settings
NewTracerPlot, LonPlot = Ngl.add_cyclic(NewTracer_AI_Low[it, lev, :, :],
lon_low)
res.sfXArray = LonPlot
res.sfYArray = lat_low[:]
res.vpWidthF = 1
res.vpHeightF = 0.5
Ngl.contour_map(wks, NewTracerPlot, res)
Ngl.frame(wks)
# Ngl.Draw(wks)
Ngl.destroy(wks)
Ngl.end()
cmap = numpy.zeros((2,3),'f')
cmap[0] = [1.,1.,1.]
cmap[1] = [0.,0.,0.]
rlist = Ngl.Resources()
rlist.wkColorMap = cmap
wks_type = "ps"
wks = Ngl.open_wks(wks_type,"ngl11p",rlist)
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.
maxval[k] = int(np.amax(temp[k]))
i = i + 1
wks_type = "png"
wks = Ngl.open_wks(wks_type, "U_winter")
res = set_common_resources()
# set resource for the base plot
bres = set_common_resources()
bres.mpFillOn = False
bres.tiMainFontHeightF = 0.018
bres.cnFillOpacityF = 0.5
# for 850 hPa in spring
U_850, lonnew = Ngl.add_cyclic(temp["winter"][0, 0, 2, :, :], lons[:])
bres.tiMainString = "DJF Mean U Wind (m/s) at 850 hPa from 1979 to 2009"
bres.sfXArray = lonnew[:]
bres.sfYArray = lats[:]
bres.cnMinLevelValF = minval["winter"]
bres.cnMaxLevelValF = maxval["winter"]
bres.cnLevelSpacingF = 3
plot1 = Ngl.contour_map(wks, U_850, bres)
#for 500 hPa
U_500, lonnew = Ngl.add_cyclic(temp["winter"][0, 0, 5, :, :], lons[:])
bres.tiMainString = "DJF Mean U Wind (m/s) at 500 hPa from 1979 to 2009"
bres.cnLevelSpacingF = 3
plot2 = Ngl.contour_map(wks, U_500, bres)
[0.50,0.00,1.00], [1.00,0.50,0.00], [0.00,0.50,1.00], \
[0.50,1.00,0.00], [0.50,0.00,0.50], [0.50,1.00,0.50], \
[1.00,0.50,1.00], [0.00,0.50,0.00], [0.50,0.50,1.00], \
[1.00,0.00,0.50], [0.50,0.50,0.00], [0.00,0.50,0.50], \
[1.00,0.50,0.50], [0.00,1.00,0.50], [0.50,0.50,0.50], \
[0.625,0.625,0.625] ],dtype=float)
rlist = Ngl.Resources()
rlist.wkColorMap = cmap
wks_type = "ps"
wks = Ngl.open_wks(wks_type,"ngl09p",rlist) # Open a workstation.
resources = Ngl.Resources()
resources.sfMissingValueV = fill_value
icemonnew,hlonnew = Ngl.add_cyclic(icemon[0:nsub+1,:],hlon[:])
resources.sfXArray = hlonnew # Necessary for overlay on a map.
resources.sfYArray = hlat[0:nsub+1]
resources.nglSpreadColors = False # Do not interpolate color space.
resources.tiMainString = "CSM Y00-99 Mean Ice Fraction Month =" + str(month)
resources.pmTickMarkDisplayMode = "Never"
map = Ngl.contour_map(wks,icemonnew,resources) # Draw a contour
# over a map.
nmos = 12 # Specify the number of months in the loop (max 120).
for nmo in range(1,nmos):
month = nmo+1
for i in xrange(fice_masked.shape[0]):
#-- define variables diri = "$HOME/NCL/general/data/new_data/" #-- data directory fname = "rectilinear_grid_2D.nc" #-- data file name minval = 250. #-- minimum contour level maxval = 315 #-- maximum contour level inc = 5. #-- contour level spacing ncn = (maxval-minval)/inc + 1 #-- number of contour levels. #-- open file and read variables f = Nio.open_file(diri + fname,"r") #-- open data file temp = f.variables["tsurf"][0,::-1,:] #-- first time step, reverse latitude lat = f.variables["lat"][::-1] #-- reverse latitudes lon = f.variables["lon"][:] #-- all longitudes tempac = Ngl.add_cyclic(temp[:,:]) #-- open a workstation wkres = Ngl.Resources() #-- generate an res object for workstation wkres.wkColorMap = "rainbow" #-- choose colormap wks_type = "x11" #-- graphics output type wks = Ngl.open_wks(wks_type,"plot_contour_PyNGL",wkres) #-- open workstation #-- set resources res = Ngl.Resources() #-- generate an resource object for plot if hasattr(f.variables["tsurf"],"long_name"): res.tiMainString = f.variables["tsurf"].long_name #-- set main title res.vpXF = 0.1 #-- start x-position of viewport res.vpYF = 0.9 #-- start y-position of viewport
# rlist = Ngl.Resources() rlist.wkColorMap = ["White","Black","Tan1","SkyBlue","Red"] wks_type = "ps" wks = Ngl.open_wks(wks_type,"vector_pop",rlist) # # Get the u/v and lat/lon variables. # urot = file.variables["urot"] vrot = file.variables["vrot"] lat2d = file.variables["lat2d"] lon2d = file.variables["lon2d"] t = file.variables["t"] u = Ngl.add_cyclic(urot[290:]) v = Ngl.add_cyclic(vrot[290:]) lon = Ngl.add_cyclic(lon2d[290:]) lat = Ngl.add_cyclic(lat2d[290:]) temp = Ngl.add_cyclic(t[290:]) # # Set up resource list. # # First we're going to draw a contour plot with the grid cells outlined # so you can see what the POP grid looks like. # cnres = Ngl.Resources() cnres.nglFrame = False #
nsub = 16 # Subscript location of northernmost hlat to be plotted.
cmap = numpy.array([ \
[1.00,1.00,0.50], [0.00,0.00,0.50], [0.50,1.00,1.00], \
[0.50,0.00,0.00], [1.00,0.00,1.00], [0.00,1.00,1.00], \
[1.00,1.00,0.00], [0.00,0.00,1.00], [0.00,1.00,0.00], \
[1.00,0.00,0.00] ],dtype=float)
wks_type = "png"
wks = Ngl.open_wks(wks_type,"ngl09p")
resources = Ngl.Resources()
# Add a longitude cyclic point
icemonnew,hlonnew = Ngl.add_cyclic(icemon[0:nsub+1,:],hlon[:])
resources.sfMissingValueV = fill_value
resources.cnFillPalette = cmap
resources.sfXArray = hlonnew # Necessary for overlay on a map.
resources.sfYArray = hlat[0:nsub+1]
resources.tiMainString = "CSM Y00-99 Mean Ice Fraction Month =" + str(month)
resources.pmTickMarkDisplayMode = "Never"
map = Ngl.contour_map(wks,icemonnew,resources) # Draw a contour
# over a map.
nmos = 12 # Specify the number of months in the loop (max 120).
for nmo in range(1,nmos):
month = nmo+1
