def gen_dummy_data(): minlat = 24 maxlat = 50 minlon = -125 maxlon = -67 #---Size of dummy data. Use smaller values (16x32) for faster code speed nlat = 64 nlon = 128 data = Ngl.generate_2d_array([nlat,nlon], 10, 19, 0., 100.) lat = Ngl.fspan(minlat,maxlat,nlat) lon = Ngl.fspan(minlon,maxlon,nlon) return data,lat,lon
def add_highlights(wks,plot,xmin,xmax,ymin,ymax,title):
nboxes = 10
xbox = Ngl.fspan(xmin,xmax,nboxes)
ybox = [ymin,ymin,ymax,ymax,ymin]
nboxes = xbox.shape[0]-1
#---Resources for filled purple boxes.
gnres = Ngl.Resources()
gnres.gsFillColor = "goldenrod" # "MediumPurple1"
gnres.gsFillOpacityF = 0.15
id = []
for n in range(0,nboxes-1,2):
id.append(Ngl.add_polygon(wks,plot,\
[xbox[n],xbox[n+1],xbox[n+1],xbox[n],xbox[n]],\
ybox,gnres))
#---Resources to outline box of interest
lnres = Ngl.Resources()
lnres.gsLineThicknessF = 3.0
border = Ngl.add_polyline(wks,plot,[xmin,xmax,xmax,xmin,xmin],\
[ymin,ymin,ymax,ymax,ymin],lnres)
txres = Ngl.Resources()
txres.txFontHeightF = 0.022
txres.txFont = "Helvetica-Bold"
txres.txJust = "TopCenter"
text = Ngl.add_text(wks,plot,title,(xmin+xmax)/2.,ymax-0.05,txres)
return([id,border,text])
# # Notes: # from __future__ import print_function import Ngl 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. #
#---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
res.lbLabelFontHeightF = 0.01
res.mpFillOn = False
res.mpGridAndLimbOn = False
res.sfXArray = lonCell # where to overlay contours
res.sfYArray = latCell
#
# It may be necessary to use RasterFill or CellFill for large grids.
#
# The default "AreaFill" can be too slow and/or will run out of
#
# Leave these both alone, regardless of what module you are testing.
#
import numpy
from utils import *
#
# Begin linmsg tests.
#
#
# Correct value for first set of tests.
#
npts = 101
x = Ngl.fspan(0., npts - 1, 101)
y = Ngl.fspan(0., npts - 1, 101)
xorig = Ngl.fspan(0., npts - 1, 101)
xnew = Ngl.linmsg(x)
test_values("linmsg (float)", xnew, xorig)
check_type(xnew, default_type)
del xnew
xnew = Ngl.linmsg(x, 0)
test_values("linmsg (float,int)", xnew, xorig)
check_type(xnew, default_type)
del xnew
xnew = Ngl.linmsg(x, 0, 0)
test_values("linmsg (float,int,int)", xnew, xorig)
skewtOpts.sktWindDirectionMissingV = -999. # Missing value for
# wind direction.
skewtOpts.sktColoredBandsOn = True # Default is False
skewtOpts.tiMainString = "Raob Data; No Winds"
skewt_bkgd = Ngl.skewt_bkg(wks, skewtOpts)
skewt_data = Ngl.skewt_plt(wks, skewt_bkgd, p, tc, tdc, z, \
wspd, wdir, skewtOpts)
Ngl.draw(skewt_bkgd)
Ngl.draw(skewt_data)
Ngl.frame(wks)
#
# Plot 2 - Create background skew-T and plot sounding and winds.
#
wspd = Ngl.fspan(0., 150., nlvl) # wind speed at each level.
wdir = Ngl.fspan(0., 360., nlvl) # wind direction.
#
# Create a few artificial "pibal" reports.
#
hght = numpy.array([1500., 6000., 10000., 15000.], 'f') # Meters
hspd = numpy.array([50., 27., 123., 13.], 'f')
hdir = numpy.array([315., 225., 45., 135.], 'f')
dataOpts = Ngl.Resources() # Options describing
# data and plotting.
dataOpts.sktHeightWindBarbsOn = True # Plot wind barbs at
# height levels.
dataOpts.sktPressureWindBarbComponents = "SpeedDirection" # Wind speed and
# dir [else: u,v].
#---Create dummy data for additional curve.
npts2 = 40
y2 = numpy.zeros([npts2], 'f')
for i in range(npts2):
y2[i] = 3.0 - 6. * random.random()
#---Force some of the y2 points be missing
y2 = ma.array(y2,mask=[0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,\
1,1,0,0,0,0,1,1,1,0,1,0,0,0,1,0,0,1,1,1])
#---Set some resources for the polyline
gsres = Ngl.Resources()
gsres.gsLineColor = "Blue"
gsres.gsLineThicknessF = 3.
#---Add the line to existing plot
x = Ngl.fspan(min(years), max(years), npts2)
prim1 = Ngl.add_polyline(wks, plot, x, y2, gsres)
#---Change the main title of original plot.
srlist = Ngl.Resources()
srlist.tiMainString = "Adding a blue curve with missing values"
Ngl.set_values(plot, srlist)
#---Draw plot (and its attached line)
Ngl.draw(plot)
Ngl.frame(wks)
Ngl.end()
# # Output: # A single visualization with nine plots # from __future__ import print_function import numpy, os import Ngl, Nio #---Start the graphics section wks_type = "png" wks = Ngl.open_wks(wks_type, "newcolor4") #---Generate some dummy lat/lon data nlat = 64 nlon = 128 lat = Ngl.fspan(-90, 90, nlat) lon = Ngl.fspan(-178.5, 178.5, nlon) #---Values to use for contour labelbar dmins = [-20., -10., -21.] # Data mins dmaxs = [16., 10., 17.] # Data maxs dspas = [4., 1., 3.] # Data spacing # One color map per row of plots colormaps = ["wgne15", "StepSeq25", "BlueDarkRed18"] # Create resource list for customizing contour over maps res = Ngl.Resources() res.nglMaximize = False res.nglFrame = False
Ngl.wmsetp("col", 2) # Draw in red.
Ngl.wmsetp("wbs", .035) # Increase the size of the barb.
#
# For illustration use numpy arrays for storing the wind
# barb information for the wmbarbmap call (could more easily
# make individual calls in a loop in the case here).
#
lat = numpy.zeros([3, 10], 'f')
lon = numpy.zeros([3, 10], 'f')
u = 0. * numpy.ones([3, 10], 'f')
v = 90. * numpy.ones([3, 10], 'f')
lat[0, :] = 65.
lat[1, :] = 70.
lat[2, :] = 75.
lonp = Ngl.fspan(-40., 5., 10)
lon[0, :] = lonp
lon[1, :] = lonp
lon[2, :] = lonp
Ngl.wmbarbmap(wks, lat, lon, u, v)
#############################
# #
# Upper right quadrant. #
# #
#############################
#
# Plot some station model data for some cities in
# Kansas and Oklahoma.
#
# Change location of map in frame. Size is the same as before.
def draw_lines(): mkres = Ngl.Resources() # Marker resources lnres = Ngl.Resources() # Line resources xline = Ngl.fspan(0.15,0.85,80) yline1 = 0.025 * (numpy.cos(2.*PI*(Ngl.fspan(1,80,80)/20.))) + 0.9 yline2 = 0.045 * (numpy.sin(2.*PI*(Ngl.fspan(1,80,80)/20.))) + 0.8 yline3 = 0.045 * (numpy.sin(2.*PI*(Ngl.fspan(1,80,80)/10.))) + 0.7 # Draw 3 sets of lines. lnres.gsLineColor = "forestgreen" # Black is the default lnres.gsLineThicknessF = 4.0 # 1.0 is the default Ngl.polyline_ndc(wks,xline,yline1,lnres) lnres.gsLineColor = "brown4" lnres.gsLineThicknessF = 3.0 lnres.gsLineDashPattern = 2 # Default is a solid line (0) Ngl.polyline_ndc(wks,xline,yline2,lnres) lnres.gsLineColor = "NavyBlue" lnres.gsLineThicknessF = 2.0 lnres.gsLineDashPattern = 11 Ngl.polyline_ndc(wks,xline,yline3,lnres) # Draw markers at the end of each of the 3 lines. mkres.gsMarkerColor = "ForestGreen" mkres.gsMarkerIndex = 6 mkres.gsMarkerSizeF = 0.02 Ngl.polymarker_ndc(wks,[xline[0],xline[-1]],[yline1[0],yline1[-1]],mkres) mkres.gsMarkerColor = "brown4" mkres.gsMarkerIndex = 11 Ngl.polymarker_ndc(wks,[xline[0],xline[-1]],[yline2[0],yline2[-1]],mkres) mkres.gsMarkerColor = "NavyBlue" mkres.gsMarkerIndex = 16 mkres.gsMarkerSizeF = 0.01 Ngl.polymarker_ndc(wks,[xline[0],xline[-1]],[yline3[0],yline3[-1]],mkres) # # Draw a vertical line with some missing values. Include markers # so you can see where the missing values were. # lnres.gsLineColor = "blue4" lnres.gsLineThicknessF = 1.5 lnres.gsLineDashPattern = 0 vxline = ma.array(yline2[25::3] - .65) vyline = ma.array( xline[25::3] - .30) # # Draw the markers for each point. # mkres.gsMarkerColor = "mediumorchid4" mkres.gsMarkerIndex = 12 # Stars mkres.gsMarkerThicknessF = 2.0 # Default is 1.0 Ngl.polymarker_ndc(wks,vxline,vyline,mkres) # # Make vxline a masked array and set some values to missing # using the "mask" array. Note that if there's a single point # surrounded by missing values, it will become a marker when # drawn with Ngl.polyline_ndc. # vxline = ma.array(vxline,mask=[0,1,0,1,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0]) Ngl.polyline_ndc(wks,vxline,vyline,lnres) # # Draw a text string describing the above line. # tres = Ngl.Resources() tres.txAngleF = -90. tres.txFontHeightF = 0.03 tres.txJust = "CenterCenter" Ngl.text_ndc(wks,"Line with msg vals",0.23,0.31,tres) # # Put a box around the missing value curve. # bres = Ngl.Resources() bres.gsLineColor = "lightsalmon4" bres.gsLineThicknessF = 3.0 Ngl.polyline_ndc(wks,[.09,.28,.28,.09,.09],[.04,.04,.57,.57,.04],bres)
cdf_file = Nio.open_file(os.path.join(dirc,"cdf","chi200_ud_smooth.nc")) chi = cdf_file.variables["CHI"] chi = chi[:,:]/1e6 lon = cdf_file.variables["lon"][:] time = cdf_file.variables["time"][:] # # Open a PS file. # wks_type = "ps" wks = Ngl.open_wks(wks_type,"contour3") # # Create arrays of longitude/time values and their corresponding labels. # lon_values = Ngl.fspan(0., 315., 8) lon_labels = ["0","45E","90E","135E","180","135W","90W","45W"] time_values = [ 0, 30, 61, 89, 120, 150, 181] time_labels = ["DEC","JAN","FEB","MAR","APR","MAY","JUN"] cnres = Ngl.Resources() cnres.nglDraw = False # Turn off draw and frame. cnres.nglFrame = False # We will do this later. cnres.sfXArray = lon cnres.sfYArray = time cnres.tmXBMode = "Explicit" # Label X and Y axes cnres.tmYLMode = "Explicit" # with explicit labels. cnres.tmXBValues = lon_values
# o Drawing lines and polygons in NDC space. # o Conversions between the three color spaces: RGB, HSV, YIQ. # # Output: # o Three color wheels are drawn using three different settings # for the "value" component in HSV space. # o Possible warnings to standard out if color conversion tests fail. # import Ngl import math import numpy # # Define the hues, and saturations, and values (the HSV) to be used. # hues = Ngl.fspan(0., 337.5, 16) saturations = Ngl.fspan(0., 1., 4) values = [0.50, 0.75, 1.00] # # Define the radian increment to be used for spacing the # color wedges around the wheels. # pi = 3.14159265 radian_inc = 2 * pi / float(len(hues)) # # Specify the Y-coordinates for the saturation labels. # slab_y = [.375, .292, .217]
cdf_file = Nio.open_file(os.path.join(dirc,"cdf","chi200_ud_smooth.nc")) chi = cdf_file.variables["CHI"] chi = chi[:,:]/1e6 lon = cdf_file.variables["lon"][:] time = cdf_file.variables["time"][:] # # Open a PNG file. # wks_type = "png" wks = Ngl.open_wks(wks_type,"contour3") # # Create arrays of longitude/time values and their corresponding labels. # lon_values = Ngl.fspan(0., 315., 8) lon_labels = ["0","45E","90E","135E","180","135W","90W","45W"] time_values = [ 0, 30, 61, 89, 120, 150, 181] time_labels = ["DEC","JAN","FEB","MAR","APR","MAY","JUN"] cnres = Ngl.Resources() cnres.nglDraw = False # Turn off draw and frame. cnres.nglFrame = False # We will do this later. cnres.sfXArray = lon cnres.sfYArray = time cnres.tmXBMode = "Explicit" # Label X and Y axes cnres.tmYLMode = "Explicit" # with explicit labels. cnres.tmXBValues = lon_values
# o Drawing lines and polygons in NDC space. # o Conversions between the three color spaces: RGB, HSV, YIQ. # # Output: # o Three color wheels are drawn using three different settings # for the "value" component in HSV space. # o Possible warnings to standard out if color conversion tests fail. # import Ngl import math import numpy # # Define the hues, and saturations, and values (the HSV) to be used. # hues = Ngl.fspan(0., 337.5, 16) saturations = Ngl.fspan(0., 1., 4) values = [0.50, 0.75, 1.00] # # Define the radian increment to be used for spacing the # color wedges around the wheels. # pi = 3.14159265 radian_inc = 2*pi/float(len(hues)) # # Specify the Y-coordinates for the saturation labels. # slab_y = [.375, .292, .217]
skewtOpts.sktWindDirectionMissingV = -999. # Missing value for
# wind direction.
skewtOpts.sktColoredBandsOn = True # Default is False
skewtOpts.tiMainString = "Raob Data; No Winds"
skewt_bkgd = Ngl.skewt_bkg(wks, skewtOpts)
skewt_data = Ngl.skewt_plt(wks, skewt_bkgd, p, tc, tdc, z, \
wspd, wdir, skewtOpts)
Ngl.draw(skewt_bkgd)
Ngl.draw(skewt_data)
Ngl.frame(wks)
#
# Plot 2 - Create background skew-T and plot sounding and winds.
#
wspd = Ngl.fspan(0., 150., nlvl) # wind speed at each level.
wdir = Ngl.fspan(0., 360., nlvl) # wind direction.
#
# Create a few artificial "pibal" reports.
#
hght = numpy.array([1500., 6000., 10000., 15000.], 'f') # Meters
hspd = numpy.array([ 50., 27., 123., 13.], 'f')
hdir = numpy.array([ 315., 225., 45., 135.], 'f')
dataOpts = Ngl.Resources() # Options describing
# data and plotting.
dataOpts.sktHeightWindBarbsOn = True # Plot wind barbs at
# height levels.
dataOpts.sktPressureWindBarbComponents = "SpeedDirection" # Wind speed and
# dir [else: u,v].
[0.,0.,res.trYMinF,res.trYMinF,0.],fillres))
#
# Draw some boxes and bars on the XY plot using calls to Ngl.add_polyline.
#
# First draw a thick line at Y = 0.
#
lineres = Ngl.Resources()
lineres.gsLineThicknessF = 3.
zero_line = Ngl.add_polyline(wks,plot,[res.trXMinF,res.trXMaxF],
[0.,0.],lineres)
#
# Add the vertical green "error" bars.
#
time = Ngl.fspan(1,5,5)
lineres.gsLineThicknessF = 2.5
lineres.gsLineColor = "DarkGreen"
vlines = []
for ii in range(5):
ii1 = ii + 1
vlines.append(Ngl.add_polyline(wks,plot,[ii1,ii1],
[minval[ii],maxval[ii]],lineres))
vlines.append(Ngl.add_polyline(wks,plot,[ii1-.08,ii1+.08], \
[maxval[ii],maxval[ii]],lineres))
vlines.append(Ngl.add_polyline(wks,plot,[ii1-.08,ii1+.08], \
[minval[ii],minval[ii]],lineres))
#
# Draw boxes going up or down from Y=0 line.
#
[0.,0.,res.trYMinF,res.trYMinF,0.],fillres))
#
# Draw some boxes and bars on the XY plot using calls to Ngl.add_polyline.
#
# First draw a thick line at Y = 0.
#
lineres = Ngl.Resources()
lineres.gsLineThicknessF = 3.
zero_line = Ngl.add_polyline(wks, plot, [res.trXMinF, res.trXMaxF], [0., 0.],
lineres)
#
# Add the vertical green "error" bars.
#
time = Ngl.fspan(1, 5, 5)
lineres.gsLineThicknessF = 2.5
lineres.gsLineColor = "DarkGreen"
vlines = []
for ii in range(5):
ii1 = ii + 1
vlines.append(
Ngl.add_polyline(wks, plot, [ii1, ii1], [minval[ii], maxval[ii]],
lineres))
vlines.append(Ngl.add_polyline(wks,plot,[ii1-.08,ii1+.08], \
[maxval[ii],maxval[ii]],lineres))
vlines.append(Ngl.add_polyline(wks,plot,[ii1-.08,ii1+.08], \
[minval[ii],minval[ii]],lineres))
#
# Draw boxes going up or down from Y=0 line.
import Ngl, numpy
from numpy import ma
from utils import *
npts = 400
x = Ngl.fspan(100., npts - 1, npts)
y = 500. + x * numpy.sin(0.031415926535898 * x)
wks = Ngl.open_wks("ps", "datandc")
xy = Ngl.xy(wks, x, y)
#
# Test with all in-range values and no missing values present.
#
x_dat = x
y_dat = numpy.absolute(y)
x_ndc, y_ndc = Ngl.datatondc(xy, x_dat, y_dat)
x_dat2, y_dat2 = Ngl.ndctodata(xy, x_ndc, y_ndc)
check_type(x_ndc)
check_type(y_ndc)
check_type(x_dat2)
check_type(y_dat2)
test_values("no oor or msg data: datatondc/ndctodata",
x_dat,
x_dat2,
delta=1e-4)
test_values("no oor or msg data: datatondc/ndctodata",
return
#----------------------------------------------------------------------
# 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.
# A single visualization with four plots is produced.
#
# Notes:
#
from __future__ import print_function
import Ngl
import math
import numpy
#
# Create dummy data for XY plot.
#
npts = 501
xspan = Ngl.fspan(0, npts - 1, npts)
x = numpy.zeros([npts], 'f')
y = numpy.zeros([npts], 'f')
for i in range(0, npts):
x[i] = 500. + .9 * xspan[i] * math.cos(0.031415926535898 * xspan[i])
y[i] = 500. + .9 * xspan[i] * math.sin(0.031415926535898 * xspan[i])
wks_type = "png"
wks = Ngl.open_wks(wks_type, "xy1")
#
# Even though some resources are the same, create four different
# resource lists for each XY plot we plan to create.
#
xyres1 = Ngl.Resources()
#
import Ngl
#
# Open the ASCII file containing the climate division numbers
# and additional data.
#
dirc = Ngl.pynglpath("data")
filename = os.path.join(dirc,"asc","climdivcorr.txt")
cldata = Ngl.asciiread(filename,[345,3],"float")
clmin = min(cldata[:,2])
clmax = max(cldata[:,2])
# Group each datum into 1 of 20 equally-spaced bins
bins = Ngl.fspan(clmin,clmax,20)
lencl = len(cldata[:,0])
databin = numpy.zeros([lencl],'i')
for i in range(lencl):
ii = numpy.greater_equal(bins,cldata[i,2])
for j in range(len(ii)):
if ii[j]:
databin[i] = j + 1
break
#
# Start the graphics.
#
rlist = Ngl.Resources()
rlist.wkColorMap = "gui_default"
wks_type = "ps"
wks = Ngl.open_wks (wks_type,"clmdiv2",rlist)
res.nglMaximize = True
res.nglFrame = False
# res.nglDraw = False
# map background
# res.mpGridAndLimbOn = False
# res.mpGeophysicalLineColor = "Black"
# coordinate settings
res.sfXArray = lon[it, 0:ncols[it]]
res.sfYArray = lat[it, 0:ncols[it]]
res.vpWidthF = 1
res.vpHeightF = 0.5
# Set resources necessary to get map projection correct.
# res.mpCenterLonF = 180
# label settings
lon_values = Ngl.fspan(np.min(lon), np.max(lon), 5)
lon_labels = ["0","90","180","270","360"]
lat_values = Ngl.fspan(np.min(lat), np.max(lat), 5)
lat_labels = ["-90", "-45", "0", "45", "90"]
res.tmXBMode = "Explicit" # Label X and Y axes
res.tmYLMode = "Explicit" # with explicit labels.
res.tmXBValues = lon_values
res.tmYLValues = lat_values
res.tmXBLabels = lon_labels
res.tmYLLabels = lat_labels
contour = Ngl.contour(wks,q[it, 0:ncols[it]],res)
gsres = Ngl.Resources()
gsres.gsLineColor = "Gray25"
gsres.gsLineThicknessF = 3.0
# A single visualization with several tickmark objects is produced. # # Notes: # import Ngl import numpy wks_type = "ps" 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. #
def draw_lines():
mkres = Ngl.Resources() # Marker resources
lnres = Ngl.Resources() # Line resources
xline = Ngl.fspan(0.15, 0.85, 80)
yline1 = 0.025 * (numpy.cos(2.0 * PI * (Ngl.fspan(1, 80, 80) / 20.0))) + 0.9
yline2 = 0.045 * (numpy.sin(2.0 * PI * (Ngl.fspan(1, 80, 80) / 20.0))) + 0.8
yline3 = 0.045 * (numpy.sin(2.0 * PI * (Ngl.fspan(1, 80, 80) / 10.0))) + 0.7
# Draw 3 sets of lines.
lnres.gsLineColor = 21 # Color index 1 is the default
lnres.gsLineThicknessF = 1.5 # 1.0 is the default
Ngl.polyline_ndc(wks, xline, yline1, lnres)
lnres.gsLineColor = 85
lnres.gsLineThicknessF = 2.0
lnres.gsLineDashPattern = 2 # Default is a solid line (0)
Ngl.polyline_ndc(wks, xline, yline2, lnres)
lnres.gsLineColor = 42
lnres.gsLineThicknessF = 3.5
lnres.gsLineDashPattern = 11
Ngl.polyline_ndc(wks, xline, yline3, lnres)
# Draw markers at the end of each of the 3 lines.
mkres.gsMarkerColor = 21
mkres.gsMarkerIndex = 6
mkres.gsMarkerSizeF = 0.02
Ngl.polymarker_ndc(wks, [xline[0], xline[-1]], [yline1[0], yline1[-1]], mkres)
mkres.gsMarkerColor = 85
mkres.gsMarkerIndex = 11
Ngl.polymarker_ndc(wks, [xline[0], xline[-1]], [yline2[0], yline2[-1]], mkres)
mkres.gsMarkerColor = 42
mkres.gsMarkerIndex = 16
mkres.gsMarkerSizeF = 0.01
Ngl.polymarker_ndc(wks, [xline[0], xline[-1]], [yline3[0], yline3[-1]], mkres)
#
# Draw a vertical line with some missing values. Include markers
# so you can see where the missing values were.
#
lnres.gsLineColor = 80
lnres.gsLineThicknessF = 1.5
lnres.gsLineDashPattern = 0
vxline = ma.array(yline2[25::3] - 0.65)
vyline = ma.array(xline[25::3] - 0.30)
#
# Draw the markers for each point.
#
mkres.gsMarkerColor = 15
mkres.gsMarkerIndex = 12 # Stars
mkres.gsMarkerThicknessF = 2.0 # Default is 1.0
Ngl.polymarker_ndc(wks, vxline, vyline, mkres)
#
# Make vxline a masked array and set some values to missing
# using the "mask" array. Note that if there's a single point
# surrounded by missing values, it will become a marker when
# drawn with Ngl.polyline_ndc.
#
vxline = ma.array(vxline, mask=[0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0])
Ngl.polyline_ndc(wks, vxline, vyline, lnres)
#
# Draw a text string describing the above line.
#
tres = Ngl.Resources()
tres.txAngleF = -90.0
tres.txFontHeightF = 0.03
tres.txJust = "CenterCenter"
Ngl.text_ndc(wks, "Line with msg vals", 0.23, 0.31, tres)
#
# Put a box around the missing value curve.
#
bres = Ngl.Resources()
bres.gsLineColor = 79
bres.gsLineThicknessF = 3.0
Ngl.polyline_ndc(wks, [0.09, 0.28, 0.28, 0.09, 0.09], [0.04, 0.04, 0.57, 0.57, 0.04], bres)
# Import Ngl support functions.
#
import Ngl
#
# Open the ASCII file containing the climate division numbers
# and additional data.
#
dirc = Ngl.pynglpath("data")
filename = os.path.join(dirc, "asc", "climdivcorr.txt")
cldata = Ngl.asciiread(filename, [345, 3], "float")
clmin = min(cldata[:, 2])
clmax = max(cldata[:, 2])
# Group each datum into 1 of 20 equally-spaced bins
bins = Ngl.fspan(clmin, clmax, 20)
lencl = len(cldata[:, 0])
databin = numpy.zeros([lencl], 'i')
for i in range(lencl):
ii = numpy.greater_equal(bins, cldata[i, 2])
for j in range(len(ii)):
if ii[j]:
databin[i] = j + 1
break
#
# Start the graphics.
#
rlist = Ngl.Resources()
rlist.wkColorMap = "gui_default"
wks_type = "png"
wks = Ngl.open_wks(wks_type, "clmdiv2", rlist)
