def draw_2d(self, varname, v):
res = Ngl.Resources()
plot_name = "wo_%s" % varname.lower()
lgr.info("plot name set to - %s" % plot_name)
wks = _get_wks(plot_name)
for t in range(3):
plot = Ngl.contour(wks, v[t, :, :], res)
return plot
def draw_2d(self, varname, v):
res = Ngl.Resources()
plot_name = 'wo_%s' % varname.lower()
lgr.info('plot name set to - %s' % plot_name)
wks = _get_wks(plot_name)
for t in range(3):
plot = Ngl.contour(wks, v[t, :, :], res)
return plot
def print_cape_for_timestamp(wrf_data, timestamp, filepath):
slp = pressure_lib.get_sea_level_pressure(wrf_data)
cinfo = getvar(wrf_data, "cape_2d", missing=0.0)
cape = cinfo[0, :, :].fillna(0)
lat, lon = latlon_coords(slp)
lat_normal = to_np(lat)
lon_normal = to_np(lon)
# rain sum
cape_res = get_pyngl(cinfo)
cape_res.nglDraw = False # don't draw plot
cape_res.nglFrame = False # don't advance frame
cape_res.cnFillOn = True # turn on contour fill
cape_res.cnLinesOn = False # turn off contour lines
cape_res.cnLineLabelsOn = False # turn off line labels
cape_res.cnFillMode = "RasterFill" # These two resources
cape_res.cnLevelSelectionMode = "ExplicitLevels"
cape_res.cnFillColors = numpy.array([ [255,255,255], [ 0,255, 0], [ 0,128, 0], \
[240,230,140], [255,255, 0], [255,140, 0], \
[255, 0, 0], [139, 0, 0], [186, 85,211],\
[153, 50,204], [139, 0,139], ],'f') / 255.
cape_res.cnLevels = numpy.array(
[.1, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500])
cape_res = geography_lib.initialize_geography(cape_res, "gray50")
cape_res.lbTitleString = "Convective available potential energy [CAPE] in (J/kg)"
cape_res.lbOrientation = "horizontal"
cape_res.lbTitleFontHeightF = 0.015
cape_res.lbLabelFontHeightF = 0.015
cape_res.tiMainString = "Thunderstorm probability (%s)" % timestamp.strftime(
"%b %d %Y %HUTC")
cape_res.trGridType = "TriangularMesh" # can speed up plotting.
cape_res.tfDoNDCOverlay = True # required for native projection
# pressure
p_res = pressure_lib.get_pressure_resource(lat_normal, lon_normal)
wk_res = Ngl.Resources()
wk_res.wkWidth = 2500
wk_res.wkHeight = 2500
output_path = "%scape_%s" % (filepath, timestamp.strftime("%Y_%m_%d_%H"))
wks_comp = Ngl.open_wks("png", output_path, wk_res)
# creating plots for the measurands
capeplot = Ngl.contour_map(wks_comp, cape, cape_res)
pplot = Ngl.contour(wks_comp, slp, p_res)
Ngl.overlay(capeplot, pplot)
Ngl.maximize_plot(wks_comp, capeplot)
Ngl.draw(capeplot)
Ngl.frame(wks_comp)
Ngl.delete_wks(wks_comp) # delete currently used workstation
def contour(A, **kwargs):
#The following allows an expert user to pass
#Ngl options directly to the plotter.
#ToDo: Note that
#options explicitly specified later will over-ride
#what the user specified. This should be fixed,
#by checking for things already specified. We should
#also allow for using this resource to specify a color
#map.
if 'resource' in kwargs.keys():
r = kwargs['resource']
else:
r = Dummy()
#
r.cnFillOn = True #Uses color fill
# Set axes if they have been specified
# as keyword arguments
if 'x' in kwargs.keys():
r.sfXArray = kwargs['x']
if 'y' in kwargs.keys():
r.sfYArray = kwargs['y']
#
# Now create the plot
rw = Dummy()
#Set the color map
if 'colors' in kwargs.keys():
if (kwargs['colors'] == 'gray') or (kwargs['colors'] == 'grey'):
#Set the default greyscale
rw.wkColorMap = 'gsdtol'
else:
rw.wkColorMap = kwargs['colors']
else:
#Default rainbow color table
rw.wkColorMap = "temp1"
w = Ngl.open_wks('x11', 'Climate Workbook', rw)
r.nglDraw = False
r.nglFrame = False
plot = Ngl.contour(w, A, r)
#Now draw the plot
r.nglDraw = True
Ngl.panel(w, [plot], [1, 1], r)
return plotObj(w, plot, rw) #So user can delete or save plot
def contour(A,**kwargs):
#The following allows an expert user to pass
#Ngl options directly to the plotter.
#ToDo: Note that
#options explicitly specified later will over-ride
#what the user specified. This should be fixed,
#by checking for things already specified. We should
#also allow for using this resource to specify a color
#map.
if 'resource' in kwargs.keys():
r = kwargs['resource']
else:
r = Dummy()
#
r.cnFillOn = True #Uses color fill
# Set axes if they have been specified
# as keyword arguments
if 'x' in kwargs.keys():
r.sfXArray = kwargs['x']
if 'y' in kwargs.keys():
r.sfYArray = kwargs['y']
#
# Now create the plot
rw = Dummy()
#Set the color map
if 'colors' in kwargs.keys():
if (kwargs['colors'] == 'gray') or (kwargs['colors'] == 'grey') :
#Set the default greyscale
rw.wkColorMap = 'gsdtol'
else:
rw.wkColorMap = kwargs['colors']
else:
#Default rainbow color table
rw.wkColorMap = "temp1"
w = Ngl.open_wks('x11','Climate Workbook',rw)
r.nglDraw = False
r.nglFrame = False
plot = Ngl.contour(w,A,r)
#Now draw the plot
r.nglDraw = True
Ngl.panel(w,[plot],[1,1],r)
return plotObj(w,plot,rw) #So user can delete or save plot
def get_3h_rainsum(previous_data, current_data, timestamp, filepath):
slp = pressure_lib.get_sea_level_pressure(current_data)
previous_sum, rain_con = get_cumulated_rain_sum(previous_data)
current_sum, rain_con = get_cumulated_rain_sum(current_data)
rain_sum = current_sum - previous_sum
lat, lon = latlon_coords(rain_con)
lat_normal = to_np(lat)
lon_normal = to_np(lon)
# rain sum
rr_res = initialize_rain_resource(rain_con)
rr_res.lbTitleString = "3h rainsum in (mm)"
rr_res.lbOrientation = "horizontal"
rr_res.lbTitleFontHeightF = 0.015
rr_res.lbLabelFontHeightF = 0.015
rr_res.tiMainString = "3h rainsum (%s)" % timestamp.strftime(
"%b %d %Y %HUTC")
rr_res.trGridType = "TriangularMesh" # can speed up plotting.
rr_res.tfDoNDCOverlay = True # required for native projection
# pressure
p_res = pressure_lib.get_pressure_resource(lat_normal, lon_normal)
wk_res = Ngl.Resources()
wk_res.wkWidth = 2500
wk_res.wkHeight = 2500
output_path = "%srain_3h_%s" % (filepath,
timestamp.strftime("%Y_%m_%d_%H"))
wks_comp = Ngl.open_wks("png", output_path, wk_res)
# creating plots for the measurands
rrplot = Ngl.contour_map(wks_comp, rain_sum, rr_res)
pplot = Ngl.contour(wks_comp, slp, p_res)
Ngl.overlay(rrplot, pplot)
Ngl.maximize_plot(wks_comp, rrplot)
Ngl.draw(rrplot)
Ngl.frame(wks_comp)
Ngl.delete_wks(wks_comp) # delete currently used workstation
bres.sfYArray = lat[:] base_plot = Ngl.contour_map(wks, unew, bres) # Set resources for contour plot only ores = set_common_resources() ores.cnFillOpacityF = 1.0 # Fully opaque ores.lbLabelBarOn = False # Turn off labelbar ores.sfMissingValueV = u._FillValue ores.sfXArray = lon['lon|-120:120'] ores.sfYArray = lat['lat|-30:30'] # Use PyNIO's selection syntax to subset the lat/lon area of interest overlay_plot = Ngl.contour(wks, u['time|i1 lat|-30:30 lon|-120:120'], ores) # Overlay the contour plot on the contour/map plot Ngl.overlay(base_plot, overlay_plot) # Drawing the base plot draws both plots Ngl.draw(base_plot) Ngl.frame(wks) # Create the contour/plot again, but with no transparency del bres.cnFillOpacityF plot = Ngl.contour_map(wks, u[1, :, :], bres) # Set resources for a partially transparent polygon. gnres = Ngl.Resources() gnres.gsFillOpacityF = 0.6 # mostly opaque
res.cnLineLabelsOn = True
res.cnLinesOn = True
res.cnMonoLineLabelFontColor = True
res.lbLabelFontHeightF = 0.01
res.cnLineDashPattern = 0
res.cnLineThicknessF = 5.0
res.cnLineColor = "steelblue1"
res.cnLevelSelectionMode = "ManualLevels"
res.cnMinLevelValF = -85.0
res.cnMaxLevelValF = 115.0
res.cnLevelSpacingF = 100.0
# plot ITD and overlay on colour contours
ws_plot2 = ngl.contour(wks, ws, res)
ngl.overlay(ws_plot, ws_plot2)
ngl.overlay(ws_plot, uv_plot)
ngl.maximize_plot(wks, ws_plot)
ngl.draw(ws_plot)
ngl.frame(wks)
ngl.destroy(wks)
del res
del vcres
###################################################################################################
# open forecast file
def plot_psirr(vpx=0.2, vpy=0.8, hei=0.4, lbar=True, xlab=1,ylab=1):
##
## Colors for hydrothermal stream functions
##
ld = 550.
dl = 50
res = Ngl.Resources()
res.wkColorMap = 'WhiteBlue'
#res.wkColorMap = 'BlueWhiteOrangeRed'
Ngl.set_values(wks,res)
##
## Reverse colormap
##
del res
res = Ngl.Resources()
cmap = Ngl.retrieve_colormap(wks)
cmap[2:,:] = cmap[-1:1:-1,:]
res.wkColorMap = cmap
Ngl.set_values(wks,res)
psi_levels = np.arange(-ld,0.,dl)
if(1):
if(1):
del res
res = Ngl.Resources()
res.nglMaximize = False
res.nglPaperOrientation = 'Portrait'
res.nglFrame = False
res.nglDraw = False
res.cnFillOn = True
res.cnLinesOn = True
res.cnLineLabelsOn = False
res.cnLineLabelDensityF = 2
res.cnLineLabelBackgroundColor = -1
res.cnLevelSelectionMode = 'ExplicitLevels'
res.cnLevels = psi_levels
if (lbar):
res.pmLabelBarSide = 'Top'
res.lbLabelBarOn = True
res.lbOrientation = 'Horizontal'
res.pmLabelBarDisplayMode = 'Always'
res.pmLabelBarWidthF = 0.6
res.pmLabelBarHeightF = 0.03
res.pmLabelBarOrthogonalPosF = 0.07
#res.pmLabelBarParallelPosF = 1.1
res.lbTitleString = 'Sv (10~S~9~N~kg/s)'
res.lbLabelFontHeightF = 0.012
res.lbTitleFontHeightF = 0.015
else:
res.pmLabelBarDisplayMode = 'Never'
res.sfXArray = vx
res.sfYArray = vy
res.trXMinF = -3
res.trXMaxF = 55
res.trYMinF = 250
res.trYMaxF = 360
res.tiXAxisString = 'Latent heat [kJ/kg]'
res.tiYAxisString = 'Dry static energy [kJ/kg]'
res.tiMainString = title
res.tiMainOffsetYF = -0.045
res.tiMainFontHeightF = 0.015
if (xlab == 1):
res.tiXAxisOn = True
res.tmXBLabelsOn = True
res.tiXAxisSide = 'Bottom'
elif (xlab == -1):
res.tiXAxisOn = True
res.tmXBLabelsOn = False
res.tmXTLabelsOn = True
res.tiXAxisSide = 'Top'
elif (xlab == 0):
res.tiXAxisOn = False
res.tmXBLabelsOn = False
res.tmXTLabelsOn = False
if (ylab == 1):
res.tiYAxisOn = True
res.tmYLLabelsOn = True
res.tmYRLabelsOn = False
res.tiYAxisSide = 'Left'
elif (ylab == -1):
res.tiYAxisOn = True
res.tmYLLabelsOn = False
res.tmYRLabelsOn = True
res.tiYAxisSide = 'Right'
elif (ylab == 0):
res.tiYAxisOn = False
res.tmYLLabelsOn = False
res.tmYRLabelsOn = False
res.vpWidthF = hei
res.vpHeightF = hei
res.vpYF = vpy
res.vpXF = vpx
lsmooth = False
if (lsmooth):
sigma = 2
order = 0
for ji in range(0,2):
ndimage.filters.gaussian_filter(zplot[:,:],sigma,order=order,\
output=zplot[:,:],\
mode='reflect', cval=0.0)
cont = Ngl.contour(wks,zplot[:,:],res)
## Clausius-Clapeyron line
vy_cc = np.linspace(240,360,101)
press = 101300.
vx_cc,es = cclap(vy_cc*1000/1004.,press)
vx_cc = 2.5 * 10**3 * vx_cc
#ccstring = 'C-C, RH=100%'
lres = Ngl.Resources()
lres.gsLineDashPattern = 15
lres.gsLineLabelFontHeightF = 0.009
lres.gsLineThicknessF = 2
#lres.gsLineLabelString = ccstring
line = Ngl.add_polyline(wks,cont,vx_cc,vy_cc,lres)
## MSE profile
lres = Ngl.Resources()
lres.gsLineDashPattern = 1
lres.gsLineThicknessF = 3
line = Ngl.add_polyline(wks,cont,vlh,vdse,lres)
Ngl.draw(cont)
mpres.mpMaskAreaSpecifiers = mask_specs # areas to mask (protect)
#
# This set of resources deals with a map labelbar.
#
# Note that this is different from a contour labelbar
# (which we will turn off below), and we have more
# control over it (i.e. we can set the number of boxes,
# the fill # colors, etc) than we do with a contour labelbar.
#
mpres.pmLabelBarDisplayMode = "Always" # Turn on a map labelbar
# Labelbar resources.
mpres.lbLabelStrings = ["Ocean","Land","< 0","0-10","10-20","20-30",\
"30-40","40-50","50-60","60-70","70-80", \
"80-90","90-100","> 100"]
mpres.lbFillColors = mp_fill_colrs + cn_fill_clrs
# Fill resources
cnres.cnFillColors = cn_fill_clrs
cnres.pmLabelBarDisplayMode = "Never" # turn off, b/c map has one turned on
contour = Ngl.contour(wks,z[:,:],cnres)
map = Ngl.map(wks,mpres)
Ngl.overlay(map,contour)
Ngl.draw(map)
Ngl.frame(wks)
Ngl.end()
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."
#
cnres.cnFillPatterns = [0, 0, 0, 2, 3, 6, 8, 10, -1, 9, 11, 12, 17, 16]
cnres.cnMonoFillScale = False # We want to use multiple fill scales.
cnres.cnFillScales = [1., .5, .6, 1., .7, .5, .4, .9, .6, 1., 1., .8, .5, .9]
cnres.cnFillColors = [6, 8, 20, 22, 4, 2, 10, 1, 12, 15, 1, 13, 5, 7]
contour = Ngl.contour(wks, T, cnres)
Ngl.end()
resources.cnFillOn = True resources.cnFillMode = "RasterFill" resources.cnLinesOn = False resources.cnLineLabelsOn = False resources.cnMaxLevelCount = 22 resources.tiMainString = "2562 Element Geodesic grid" resources.lbBoxLinesOn = False resources.lbTitleString = "kinetic energy" resources.nglDraw = False # Just create the plot. Don't resources.nglFrame = False # draw it or advance the frame. contour = Ngl.contour(wks,ke,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 # Now we want to draw the plot resources.nglFrame = True # and advance the frame. resources.mpGridAndLimbOn = False resources.mpProjection = "Orthographic"
resources.tiMainString = "Depth of a subsurface stratum" resources.tiMainFont = "Times-Bold" resources.tiXAxisString = "x values" # X axis label. resources.tiYAxisString = "y values" # Y axis label. resources.cnFillOn = True # Turn on contour fill. resources.cnFillPalette = cmap # Set colors for contours. resources.cnInfoLabelOn = False # Turn off info label. resources.cnLineLabelsOn = False # Turn off line labels. resources.lbOrientation = "Horizontal" # Draw it horizontally. # label bar. resources.vpYF = 0.9 # Change Y location of plot. zt = numpy.transpose(zo) contour = Ngl.contour(xwks, zt, resources) #----------- Begin second plot ----------------------------------------- del resources resources = Ngl.Resources() resources.tiMainString = "~F26~slices" # Define a title. resources.xyLineColors = ["red", "cyan", "darkgreen", "darkorchid4"] resources.xyLineThicknessF = 3.0 # Define line thickness. resources.pmLegendDisplayMode = "Always" # Turn on the drawing resources.pmLegendZone = 0 # Change the location resources.pmLegendOrthogonalPosF = 0.31 # of the legend resources.lgJustification = "BottomRight" resources.pmLegendWidthF = 0.4 # Change width and
# resources = Ngl.Resources() resources.sfXArray = lat_t resources.sfYArray = z_t resources.sfMissingValueV = T._FillValue[0] resources.cnFillOn = True 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) # # Linearize the Y axis. # resources.tiMainString = "Y axis linearized" resources.nglYAxisType = "LinearAxis" plot = Ngl.contour(wks,T,resources) # # Linearize the X axis. # resources.tiMainString = "X and Y axes linearized" resources.nglXAxisType = "LinearAxis" plot = Ngl.contour(wks,T,resources)
res.cnLineLabelsOn = False #-- turn off line labels. res.cnInfoLabelOn = False #-- turn off info label. res.cnFillPalette = "BlueWhiteOrangeRed" #-- set color map. res.pmLabelBarOrthogonalPosF = -0.03 #-- move labelbar close to plot res.sfXArray = lon #-- scalar field x res.sfYArray = lev #-- scalar field y res.trYReverse = True #-- reverse the Y axis res.nglYAxisType = "LogAxis" #-- y axis log res.tiYAxisString = "%s (hPa)" % f.variables["lev"].long_name res.nglPointTickmarksOutward = True #-- point tickmarks out res.tmYLMode = "Explicit" #-- set y axis tickmark labels res.tmXBMode = "Explicit" #-- set x axis tickmark labels res.tmYLValues = levs res.tmXBValues = lons res.tmYLLabels = map(str,levs) res.tmXBLabels = nice_lon_labels(lons) res.tmXBLabelFontHeightF = 0.015 # - make font smaller res.tmYLLabelFontHeightF = 0.015 map = Ngl.contour(wks,t26,res) #-- draw contours #-- end Ngl.end()
tempsfc_res.tmYLLabelFont = 26 # Change the font. tempsfc_res.tmXBValues = taus tempsfc_res.tmXBLabels = taus tempsfc_res.tmXTOn = False # turn off the top tickmarks tempsfc_res.xyLineThicknesses = 2 tempsfc_res.xyLineColor = "red" tempsfc_res.nglDraw = False # Don't draw individual plot. tempsfc_res.nglFrame = False # Don't advance frame. tempsfc_res.nglMaximize = False # Do not maximize plot in frame # # Create the four plots (they won't get drawn here, because nglDraw # was set to False for all three of them. # rhfill = Ngl.contour(wks,smothrh,rh_res) templine = Ngl.contour(wks,smothtemp,temp_res) windlayer = Ngl.vector(wks,ugrid,vgrid,uv_res) rainhist = Ngl.xy(wks,taus,rain03,rain_res) # # For the rain plot, we want filled bars instead of a curve, so create # a dummy plot with all the Y values equal to rain_res.trYMinF. This # will insure no curve gets drawn. Then, add the bars and bar outlines # later with Ngl.add_polygon and Ngl.add_polyline. # # # Make dummy data equal to min Y axis value. # dummy = rain_res.trYMinF * Numeric.ones([len(rain03)],rain03.typecode())
resources = Ngl.Resources()
#
# Define a NumPy data array containing the temperature for the
# first time step and first level. This array does not have the
# attributes that are associated with the variable temp.
#
tempa = temp[0, 0, :, :]
tempa = tempa - 273.15
#
# Specify the main title base on the long_name attribute of temp.
#
if hasattr(temp, "long_name"):
resources.tiMainString = temp.long_name
plot = Ngl.contour(wks, tempa, resources)
# ----------- Begin second plot -----------------------------------------
resources.cnMonoLineColor = False # Allow multiple colors for contour lines.
resources.tiMainString = "Temperature (C)"
plot = Ngl.contour(wks, tempa, resources) # Draw a contour plot.
# ----------- Begin third plot -----------------------------------------
resources.cnFillOn = True # Turn on contour line fill.
resources.cnMonoFillPattern = False # Turn off using a single fill pattern.
resources.cnMonoFillColor = True
resources.cnMonoLineColor = True
def plot_psirr(wks, zplot, vx, vy, xlab = 'x', ylab = 'y', vpx=0.2, vpy=0.8, \
hei=0.4, cc='dse',lsmooth=False, frame=True):
"""
"""
##
## Colors for hydrothermal stream functions
##
ld = 450.
dl = 60
res = Ngl.Resources()
res.wkColorMap = 'WhiteBlue'
#res.wkColorMap = 'BlueWhiteOrangeRed'
Ngl.set_values(wks,res)
##
## Reverse colormap
##
del res
res = Ngl.Resources()
cmap = Ngl.retrieve_colormap(wks)
cmap[2:,:] = cmap[-1:1:-1,:]
res.wkColorMap = cmap
Ngl.set_values(wks,res)
psi_levels = np.arange(-ld,0.,dl)
##
## Positions of four subplots
##
print ' Plotting stream functions in (r,r) coordinates'
if(1):
if(1):
#vu = np.zeros(zplot[-1,:,:].shape)
#vv = np.zeros(zplot[-1,:,:].shape)
#vu[1:,:] = zplot[-1,0:-1,:] - zplot[-1,1:,:]
#vv[:,1:] = zplot[-1,:,1:] - zplot[-1,:,0:-1]
title = 'psi('+xlab+','+ylab+')'
print ' Amplitude, max. err : '+repr(np.max(np.abs(zplot[:,:]))),\
repr(np.max(np.abs(zplot[:,-1])))
del res
res = Ngl.Resources()
res.nglMaximize = False
res.nglPaperOrientation = 'Portrait'
res.nglFrame = False
res.nglDraw = False
res.cnFillOn = True
res.cnLinesOn = True
res.cnLineLabelsOn = False
res.cnLineLabelDensityF = 2
res.cnLineLabelBackgroundColor = -1
res.cnLevelSelectionMode = 'ExplicitLevels'
res.cnLevels = psi_levels
res.pmLabelBarSide = 'Right'
res.lbLabelBarOn = True
res.lbOrientation = 'Vertical'
res.pmLabelBarDisplayMode = 'Always'
res.pmLabelBarWidthF = 0.03
res.pmLabelBarHeightF = hei
res.lbTitleString = 'Sv (10~S~9~N~kg/s)'
res.lbLabelFontHeightF = 0.01
res.lbTitleFontHeightF = 0.01
res.sfXArray = vx
res.sfYArray = vy
res.tiXAxisString = xlab
res.tiYAxisString = ylab
res.vpWidthF = hei
res.vpHeightF = hei
res.vpYF = vpy
res.vpXF = vpx
if (lsmooth):
sigma = 2
order = 0
for ji in range(0,2):
ndimage.filters.gaussian_filter(zplot[:,:],sigma,order=order,\
output=zplot[:,:],\
mode='reflect', cval=0.0)
cont = Ngl.contour(wks,zplot[:,:],res)
wks,cont = plot_cc(wks,cont,x='lh',y=cc)
Ngl.draw(cont)
if(frame):
Ngl.frame(wks)
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 = "ps"
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."
#
cnres.cnFillPatterns = [0, 0, 0, 2, 3, 6, 8,10,-1, 9, 11,12,17,16]
cnres.cnMonoFillScale = False # We want to use multiple fill scales.
cnres.cnFillScales = [1.,.5,.6,1.,.7,.5,.4,.9,.6,1.,1.,.8,.5,.9]
cnres.cnFillColors = [ 6, 8,20,22, 4, 2,10, 1,12,15, 1,13, 5, 7]
contour = Ngl.contour(wks,T,cnres)
Ngl.end()
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 cnres.tmYLValues = time_values cnres.tmXBLabels = lon_labels cnres.tmYLLabels = time_labels cnres.nglPointTickmarksOutward = True contour = Ngl.contour(wks,chi,cnres) # Create, but don't draw contour plot. neg_dash_contours(contour) # Dash the negative contours. Ngl.draw(contour) # Now draw contours and Ngl.frame(wks) # advance the frame. # # Set some new resources to control how the lines are labeled. # Use Ngl.set_values to apply these new resources to our existing # plot. # nrlist = Ngl.Resources() nrlist.cnLineLabelPlacementMode = "Computed"
resources = Ngl.Resources() # # Define a NumPy data array containing the temperature for the # first time step and first level. This array does not have the # attributes that are associated with the variable temp. # tempa = temp[0,0,:,:] tempa = tempa - 273.15 # # Specify the main title base on the long_name attribute of temp. # if hasattr(temp,"long_name"): resources.tiMainString = temp.long_name plot = Ngl.contour(wks,tempa,resources) #----------- Begin second plot ----------------------------------------- resources.cnMonoLineColor = False # Allow multiple colors for contour lines. resources.tiMainString = "Temperature (C)" plot = Ngl.contour(wks,tempa,resources) # Draw a contour plot. #----------- Begin third plot ----------------------------------------- resources.cnFillOn = True # Turn on contour line fill. resources.cnFillPalette = "default" # Set colors for contours resources.cnMonoFillPattern = False # Turn off using a single fill pattern. resources.cnMonoFillColor = True resources.cnMonoLineColor = True
# res.tiYAxisSide = 'Right' # res.tmYLLabelsOn = False # res.tmYRLabelsOn = True # res.pmLabelBarDisplayMode = 'Never' res.cnFillOn = True res.cnLinesOn = False res.cnLineLabelsOn = False res.cnLevels = vol_levels res.vpXF = 0.55#vxf[ja] res.vpWidthF = 0.3 res.vpYF = 0.7#vyf[ja] res.vpHeightF = 0.3 #res.tiMainString = title[ja] volplot = Ngl.contour(wks,zplot[ja,:,:],res) ## ## Draw hydrothermal stream lines on top ## res.pmLabelBarDisplayMode = 'Never' res.cnFillOn = False res.cnLinesOn = True res.cnLineLabelsOn = True res.cnInfoLabelOn = False res.cnLevels = psi_levels psi2 = psi * 10**(-9) psiplot = Ngl.contour(wks,psi2,res) Ngl.overlay(volplot,psiplot)
resources.sfXArray = lons # Portion of map on which to overlay resources.sfYArray = lats # contour plot. #resources.sfElementNodes = ele #resources.sfFirstNodeIndex = 1 resources.cnFillOn = True resources.cnLinesOn = False resources.cnLineLabelsOn = False # # This plot isn't very interesting because it isn't overlaid on a map. # We are only creating it so we can retrieve information that we need # to overlay it on a map plot later. You can turn off this plot # by setting the nglDraw and nglFrame resources to False. # contour = Ngl.contour(wks,to_plot,resources) # # The next set of resources will apply to the map plot. # resources.mpProjection = "Stereographic" # # Once the high resolution coastline data files have been # downloaded (see the Notes section above for details), to # access them you need to change the following resource # to "HighRes". # resources.mpDataBaseVersion = "MediumRes"
"250","200","150","100", "50"]
resources.tmYLMinorOn = False # No minor tick marks.
resources.cnFillOn = True # Turn on contour level fill.
resources.cnMonoFillColor = True # Use one fill color.
resources.cnMonoFillPattern = False # Use multiple fill patterns.
resources.cnLineLabelAngleF = 0. # Draw contour line labels right-side up.
resources.cnLevelSpacingF = 1.0
resources.nglDraw = False # Don't draw the plot or advance the
resources.nglFrame = False # frame in the call to Ngl.contour.
resources.nglMaximize = False
resources.pmLabelBarDisplayMode = "Never" # Turn off label bar.
contour = Ngl.contour(wks, unew, resources) # Create a contour plot.
levels = Ngl.get_float_array(contour,"cnLevels")
patterns = numpy.zeros((len(levels)+1),'i')
patterns[:] = -1
for i in xrange(len(levels)):
if (levels[i] <= -6.):
patterns[i] = 5
else:
if (levels[i] > 0.):
patterns[i] = 17.
patterns[-1] = 17 # last pattern
rlist = Ngl.Resources()
# 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
# print(vertx[0, :, 0],verty[0, :, 0])
for nc in range(ncols[it]):
Ngl.polyline(wks,contour,vertx[it, :, nc],verty[it, :, nc],gsres)
Ngl.frame(wks)
Ngl.end()
resources.lbLabelFont = "Helvetica"
resources.lbLabelFontHeightF = 0.015
resources.lbBottomMarginF = 0.4 # Move the label bar up.
resources.tmXBLabelFont = "Helvetica"
resources.tmXBLabelFontHeightF = 0.02
resources.tmYLLabelFont = "Helvetica"
resources.tmYLLabelFontHeightF = 0.02
# label bar.
resources.vpYF = 0.9 # Change Y location of plot.
#
# Contour
#
zt = numpy.transpose(zo)
contour = Ngl.contour(wks, zt, resources)
#
# Repeat the above, except disallow negative interpolated values.
#
Ngl.nnsetp("non", 1)
resources.tiMainString = "Negative interpolants not allowed"
zo = Ngl.natgrid(x, y, z, xo, yo)
zt = numpy.transpose(zo)
contour = Ngl.contour(wks, zt, resources)
#
# Retrieve the default value for the tautness factor "bJ".
#
bJ_value = Ngl.nngetp("bJ")
# print(bJ_value)
resources.tiMainString = "Depth of a subsurface stratum"
resources.tiMainFont = "Times-Bold"
resources.tiXAxisString = "x values" # X axis label.
resources.tiYAxisString = "y values" # Y axis label.
resources.cnFillOn = True # Turn on contour fill.
resources.cnInfoLabelOn = False # Turn off info label.
resources.cnLineLabelsOn = False # Turn off line labels.
resources.lbOrientation = "Horizontal" # Draw it horizontally.
# label bar.
resources.nglSpreadColors = False # Do not interpolate color space.
resources.vpYF = 0.9 # Change Y location of plot.
zt = Numeric.transpose(zo)
contour = Ngl.contour(xwks,zt,resources)
#----------- Begin second plot -----------------------------------------
del resources
resources = Ngl.Resources()
resources.tiMainString = ":F26:slices" # Define a title.
resources.xyLineColors = [2,8,10,14] # Define line colors.
resources.xyLineThicknessF = 3.0 # Define line thickness.
resources.pmLegendDisplayMode = "Always" # Turn on the drawing
resources.pmLegendZone = 0 # Change the location
resources.pmLegendOrthogonalPosF = 0.31 # of the legend
resources.lgJustification = "BottomRight"
resources.pmLegendWidthF = 0.4 # Change width and
res.cnLineLabelsOn = True
res.cnLinesOn = True
res.cnMonoLineLabelFontColor = True
res.lbLabelFontHeightF = 0.01
res.cnLineDashPattern = 11
res.cnLineThicknessF = 5.0
res.cnLineColor = "purple"
res.cnLevelSelectionMode = "ManualLevels"
res.cnMinLevelValF = -85.0
res.cnMaxLevelValF = 115.0
res.cnLevelSpacingF = 100.0
# plot ITD and overlay on colour contours
dp_plot2 = ngl.contour(wks, dewpoint, res)
ngl.overlay(dp_plot, dp_plot2)
ngl.maximize_plot(wks, dp_plot)
ngl.draw(dp_plot)
ngl.frame(wks)
ngl.destroy(wks)
del res
del dewpoint
###################################################################################################
# open forecast file
# # Set some font heights to make them slightly bigger than the default. # Turn off nglScale, because this resource wants to set the axes font # heights for you. # resources.nglScale = False resources.cnLineLabelFontHeightF = 0.025 resources.tiMainFontHeightF = 0.037 resources.lbLabelFontHeightF = 0.032 resources.tmXBLabelFontHeightF = 0.030 resources.tmYLLabelFontHeightF = 0.030 for i in range(0,nplots): resources.tiMainString = "Temperature at time = " + str(i) plot.append(Ngl.contour(wks,temp[i,:,:],resources)) Ngl.panel(wks,plot[0:4],[2,2]) # Draw 2 rows/2 columns of plots. # # Now add some extra white space around each plot. # panelres = Ngl.Resources() panelres.nglPanelYWhiteSpacePercent = 5. panelres.nglPanelXWhiteSpacePercent = 5. Ngl.panel(wks,plot[0:4],[2,2],panelres) # Draw 2 rows/2 columns of plots. # # This section will set resources for drawing contour plots over a map. #
"250","200","150","100", "50"]
resources.tmYLMinorOn = False # No minor tick marks.
resources.cnFillOn = True # Turn on contour level fill.
resources.cnMonoFillColor = True # Use one fill color.
resources.cnMonoFillPattern = False # Use multiple fill patterns.
resources.cnLineLabelAngleF = 0. # Draw contour line labels right-side up.
resources.cnLevelSpacingF = 1.0
resources.nglDraw = False # Don't draw the plot or advance the
resources.nglFrame = False # frame in the call to Ngl.contour.
resources.nglMaximize = False
resources.pmLabelBarDisplayMode = "Never" # Turn off label bar.
contour = Ngl.contour(wks, unew, resources) # Create a contour plot.
levels = Ngl.get_float_array(contour,"cnLevels")
patterns = numpy.zeros((len(levels)+1),'i')
patterns[:] = -1
for i in xrange(len(levels)):
if (levels[i] <= -6.):
patterns[i] = 5
else:
if (levels[i] > 0.):
patterns[i] = 17.
patterns[-1] = 17 # last pattern
rlist = Ngl.Resources()
mpres.mpInlandWaterFillColor = "transparent" mpres.mpOceanFillColor = "transparent" mpres.mpLandFillColor = "tan" mpres.mpLabelsOn = False mpres.mpLeftCornerLatF = 10. mpres.mpLeftCornerLonF = -180. mpres.mpLimitMode = "corners" mpres.mpProjection = "Stereographic" mpres.mpRightCornerLatF = 10. mpres.mpRightCornerLonF = 0. mpres.tiMainFontHeightF = 0.02 mpres.tiMainString = "Busy graphic with contours and streamlines" stream = Ngl.streamline(wks, data[0, :, :], data[1, :, :], stres) contour = Ngl.contour(wks, data[2, :, :], cnres) map = Ngl.map(wks, mpres) Ngl.overlay(map, stream) Ngl.overlay(map, contour) # Since we overlaid 2 plots, we need to resize them to make sure # they fit in frame. Ngl.maximize_plot(wks, map) Ngl.draw(map) Ngl.frame(wks) del cnres.nglDraw del cnres.nglFrame del mpres.nglDraw
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()
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()
resources.sfXArray = lon # Portion of map on which to overlay resources.sfYArray = lat # contour plot. resources.sfElementNodes = ele resources.sfFirstNodeIndex = 1 resources.cnFillOn = True resources.cnLinesOn = False resources.cnLineLabelsOn = False # # This plot isn't very interesting because it isn't overlaid on a map. # We are only creating it so we can retrieve information that we need # to overlay it on a map plot later. You can turn off this plot # by setting the nglDraw and nglFrame resources to False. # contour = Ngl.contour(wks, depth, resources) # # The next set of resources will apply to the map plot. # resources.mpProjection = "CylindricalEquidistant" # # Once the high resolution coastline data files have been # downloaded (see the Notes section above for details), to # access them you need to change the following resource # to "HighRes". # resources.mpDataBaseVersion = "MediumRes" #
def plot_psirr_lines(vpx=0.2, vpy=0.8, hei=0.4, xlab=1,ylab=1):
psi_levels = np.array([-500,-50])
if(1):
if(1):
res = Ngl.Resources()
res.nglMaximize = False
res.nglPaperOrientation = 'Portrait'
res.nglFrame = False
res.nglDraw = False
res.cnFillOn = False
res.cnLinesOn = True
res.cnLineLabelsOn = True
res.cnLineLabelDensityF = 2
res.cnLineLabelBackgroundColor = -1
res.cnLevelSelectionMode = 'ExplicitLevels'
res.cnLevels = psi_levels
res.cnMonoLineColor = True
res.cnLineColor = 'blue'
res.cnLineThicknessF = 2
res.cnInfoLabelOn = False
res.sfXArray = vx
res.sfYArray = vy
res.trXMinF = -3
res.trXMaxF = 55
res.trYMinF = 250
res.trYMaxF = 360
res.tiXAxisString = 'Latent heat [kJ/kg]'
res.tiYAxisString = 'Dry static energy [kJ/kg]'
res.tiMainString = title
if (xlab == 1):
res.tiXAxisOn = True
res.tmXBLabelsOn = True
res.tiXAxisSide = 'Bottom'
elif (xlab == -1):
res.tiXAxisOn = True
res.tmXBLabelsOn = False
res.tmXTLabelsOn = True
res.tiXAxisSide = 'Top'
elif (xlab == 0):
res.tiXAxisOn = False
res.tmXBLabelsOn = False
res.tmXTLabelsOn = False
if (ylab == 1):
res.tiYAxisOn = True
res.tmYLLabelsOn = True
res.tmYRLabelsOn = False
res.tiYAxisSide = 'Left'
elif (ylab == -1):
res.tiYAxisOn = True
res.tmYLLabelsOn = False
res.tmYRLabelsOn = True
res.tiYAxisSide = 'Right'
elif (ylab == 0):
res.tiYAxisOn = False
res.tmYLLabelsOn = False
res.tmYRLabelsOn = False
res.vpWidthF = hei
res.vpHeightF = hei
res.vpYF = vpy
res.vpXF = vpx
lsmooth = False
if (lsmooth):
sigma = 2
order = 0
for ji in range(0,2):
ndimage.filters.gaussian_filter(zplot1[:,:],sigma,order=order,\
output=zplot[:,:],\
mode='reflect', cval=0.0)
cont1 = Ngl.contour(wks,zplot1[:,:],res)
## Clausius-Clapeyron line
vy_cc = np.linspace(240,360,101)
press = 101300.
vx_cc,es = cclap(vy_cc*1000/1004.,press)
vx_cc = 2.5 * 10**3 * vx_cc
#ccstring = 'C-C, RH=100%'
lres = Ngl.Resources()
lres.gsLineDashPattern = 15
lres.gsLineLabelFontHeightF = 0.009
lres.gsLineThicknessF = 3
#lres.gsLineLabelString = ccstring
line = Ngl.add_polyline(wks,cont1,vx_cc,vy_cc,lres)
## MSE profiles
lres = Ngl.Resources()
lres.gsLineDashPattern = 1
lres.gsLineThicknessF = 7
lres.gsLineColor = 'blue'
line = Ngl.add_polyline(wks,cont1,vlh1,vdse1,lres)
res.cnLineColor = 'red'
cont2 = Ngl.contour(wks,zplot2[:,:],res)
## MSE profiles
lres = Ngl.Resources()
lres.gsLineDashPattern = 1
lres.gsLineThicknessF = 7
lres.gsLineColor = 'red'
line = Ngl.add_polyline(wks,cont2,vlh2,vdse2,lres)
Ngl.overlay(cont2,cont1)
Ngl.draw(cont2)
mpres.mpMaskAreaSpecifiers = mask_specs # areas to mask (protect)
#
# This set of resources deals with a map labelbar.
#
# Note that this is different from a contour labelbar
# (which we will turn off below), and we have more
# control over it (i.e. we can set the number of boxes,
# the fill # colors, etc) than we do with a contour labelbar.
#
mpres.pmLabelBarDisplayMode = "Always" # Turn on a map labelbar
# Labelbar resources.
mpres.lbLabelStrings = ["Ocean","Land","< 0","0-10","10-20","20-30",\
"30-40","40-50","50-60","60-70","70-80", \
"80-90","90-100","> 100"]
mpres.lbFillColors = mp_fill_colrs + cn_fill_clrs
# Fill resources
cnres.cnFillColors = cn_fill_clrs
cnres.pmLabelBarDisplayMode = "Never" # turn off, b/c map has one turned on
contour = Ngl.contour(wks, z[:, :], cnres)
map = Ngl.map(wks, mpres)
Ngl.overlay(map, contour)
Ngl.draw(map)
Ngl.frame(wks)
Ngl.end()
# 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) # # Linearize the Y axis. # resources.tiMainString = "Y axis linearized" resources.nglYAxisType = "LinearAxis" plot = Ngl.contour(wks,T,resources) # # Linearize the X axis. # resources.tiMainString = "X and Y axes linearized" resources.nglXAxisType = "LinearAxis" plot = Ngl.contour(wks,T,resources)
def plot_power(Pow, symmetry=("symm"), source="", var1="", plotpath="./", flim=0.5, nWavePlt=20, cmin=0.05, cmax=0.55,
cspc=0.05, plotxy=[1, 1], N=[1, 2]):
dims = Pow.shape
nplot = dims[0]
FillMode = "AreaFill"
# text labels
abc = list(string.ascii_lowercase)
# plot resources
wkstype = "png"
wks = ngl.open_wks(wkstype, plotpath + "SpaceTimePower_" + source + var1)
plots = []
# coherence2 plot resources
res = coh_resources(cmin, cmax, cspc, FillMode, flim, nWavePlt)
# phase arrow resources
resA = phase_resources(flim, nWavePlt)
# dispersion curve resources
dcres = ngl.Resources()
dcres.gsLineThicknessF = 2.0
dcres.gsLineDashPattern = 0
# text box resources
txres = ngl.Resources()
txres.txPerimOn = True
txres.txFontHeightF = 0.013
txres.txBackgroundFillColor = "Background"
# panel plot resources
resP = panel_resources(nplot, abc)
# plot contours and phase arrows
pp = 0
while pp < nplot:
if nplot == 1:
coh2 = Pow
else:
coh2 = Pow[pp, :, :]
if len(symmetry) == nplot:
Symmetry = symmetry[pp]
else:
Symmetry = symmetry
res.tiMainString = source + " log10( Power(" + var1 + ")) " + Symmetry
plot = ngl.contour(wks, coh2, res)
(matsuno_names, textlabels, textlocsX, textlocsY) = text_labels(Symmetry)
nlabel = len(textlocsX)
# generate matsuno mode dispersion curves
if Symmetry == "midlat":
He = [3000, 7000, 10000]
matsuno_modes = mp.matsuno_modes_wk_bg(he=He, n=N, latitude=0., max_wn=nWavePlt, n_wn=500, u=25)
else:
He = [12, 25, 50]
matsuno_modes = mp.matsuno_modes_wk(he=He, n=N, latitude=0., max_wn=nWavePlt, n_wn=500)
# add polylines for dispersion curves
for he in matsuno_modes:
df = matsuno_modes[he]
wn = df.index.values
for wavename in df:
for matsuno_name in matsuno_names:
if wavename == (matsuno_name + str(he) + "m)"):
wave = df[wavename].values
wnwave = wn[~np.isnan(wave)]
wave = wave[~np.isnan(wave)]
ngl.add_polyline(wks, plot, wnwave, wave, dcres)
# add text boxes
ll = 0
while ll < nlabel:
ngl.add_text(wks, plot, textlabels[ll], textlocsX[ll], textlocsY[ll], txres)
ll += 1
plots.append(plot)
pp += 1
# panel plots
ngl.panel(wks, plots, plotxy, resP)
ngl.delete_wks(wks)
#ngl.end()
return
jspn = numpy.power(xrange(-M / 2 + 5, M / 2 + 5), 2)
ispn = numpy.power(xrange(-N / 2 - 3, N / 2 - 3), 2)
for i in xrange(len(ispn)):
T[i, :] = ispn[i] + jspn
T = 100. - 8. * numpy.sqrt(T)
#
# Open a workstation and draw a contour plot.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "cns01p")
res = Ngl.Resources()
res.cnMonoLineDashPattern = False
plot1 = Ngl.contour(wks, T, res)
#
# Retrieve the automatically set line labels.
# These will be: ['-80', '-60', '-40', '-20', '0', '20', '40', '60', '80']
#
line_labels1 = Ngl.get_string_array(plot1, "cnLineLabelStrings")
#
# Set explicit line labels. Notice that the dash line
# setting carries over from the first plot.
#
res.cnExplicitLineLabelsOn = True
res.cnLineLabelStrings = [
"Lab1", "Lab2", "Lab3", "Lab4", "Lab5", "Lab6", "Lab7", "Lab8", "Lab9"
]
def plot_psiyr(wks,data,vx,vy,xlab='Latitude [deg N]',ylab = 'y',\
vsurf=[],hei=0.1,ylon=True,xon=False,lbar=False,flipy=False,\
vpx=0.15,vpy=0.8,lsmooth=False):
"""
Plots meridional overturning stream functions
Usage:
plot_psiyr(wks,data,vx,vy,xlab='Latitude',ylab='y',vsurf=np.array([]),\
hei=0.15,ylon=True,xon=True,lbar=True,flipy=False)
Input:
wks - Workstation ID
data - 2D matrix. Can be several matrices in a list
vx - Latitude vector
vy - y-vector. Can be several in a list
Optional:
xlab - Title for x-axis
ylab - Title for y-axis
vsurf - Vector of surface values to draw in
hei - Height of plot(s)
ylon - Turn on y labels on left side (else right)
xon - Turn on x labels on bottom
lbar - Turn on colorbar
Output:
None
"""
##
res = Ngl.Resources()
res.wkColorMap = 'ViBlGrWhYeOrRe'
Ngl.set_values(wks,res)
print ' Plotting overturning stream functions in (y,r) coordinates'
print lbar
if ( isinstance(data,list) ):
nplots = len(data)
else:
nplots = 1
data = [data]
vy = fill_blanks(vy,nplots)
ylab = fill_blanks(ylab,nplots)
vsurf = fill_blanks(vsurf,nplots)
ylon = fill_blanks(ylon,nplots)
xon = fill_blanks(xon,nplots)
lbar = fill_blanks(lbar,nplots)
lbar[0] = True
flipy = fill_blanks(flipy,nplots)
hei = max([0.8/(nplots+1),hei])
wth = min([0.7,hei * 4])
vpy = 0.9 - hei * np.arange(0,nplots+1)
##
## Color levels for y-r stream functions
##
colour_levels = np.arange(-190,210,20)
for jn in range(0,nplots):
zplot = data[jn]
res = Ngl.Resources()
res.nglMaximize = False
res.nglPaperOrientation = 'Portrait'
res.nglFrame = False
res.nglDraw = False
res.cnFillOn = True
res.cnLinesOn = True
res.cnLineLabelsOn = False
res.cnLineLabelDensityF = 2
res.cnLineLabelBackgroundColor = -1
res.cnLevelSelectionMode = 'ExplicitLevels'
res.cnLevels = colour_levels
res.sfXArray = vx
res.sfYArray = vy[jn]
res.vpXF = vpx
res.vpWidthF = wth
res.vpYF = vpy[jn]
res.vpHeightF = hei
##
## Axis
##
res.tiYAxisFontHeightF = 0.012
res.tiYAxisString = ylab[jn]
res.tiXAxisString = xlab
if( ylon[jn] == True ):
res.tiYAxisSide = 'Left'
res.tmYLLabelsOn = True
res.tmYRLabelsOn = False
res.tmYLLabelFontHeightF = 0.012
else:
res.tiYAxisSide = 'Right'
res.tmYLLabelsOn = False
res.tmYRLabelsOn = True
res.tmYRLabelFontHeightF = 0.012
if( xon[jn] == True ):
res.tiXAxisOn = True
res.tmXBLabelsOn = True
res.tiXAxisFontHeightF = 0.012
res.tmXBLabelFontHeightF = 0.012
else:
res.tiXAxisOn = False
res.tmXBLabelsOn = False
##
## Labelbar
##
if( lbar[jn] == True ):
res.lbTitleString = 'Sverdrup (10~S~9~N~kg/s)'
res.lbLabelFontHeightF = 0.012
res.lbTitleFontHeightF = 0.015
res.lbLabelBarOn = True
res.lbOrientation = 'Horizontal'
res.pmLabelBarSide = 'Top'
res.pmLabelBarDisplayMode = 'Always'
res.pmLabelBarWidthF = 0.65
res.pmLabelBarHeightF = 0.07
else:
res.lbLabelBarOn = False
##
## Flip y axis?
##
if( flipy[jn] == True ):
res.trYReverse = True
else:
res.trYReverse = False
## Smooth
if (lsmooth==True):
sigma = 2
order = 0
for ji in range(0,5):
ndimage.filters.gaussian_filter(zplot[:,:],sigma,order=order,\
output=zplot[:,:],\
mode='reflect', cval=0.0)
cont = Ngl.contour(wks,zplot,res)
##
## Also draw the surface values
##
if (vsurf[jn].shape[0] == vx.shape[0]):
lres = Ngl.Resources()
lres.gsLineThicknessF = 1.5
lres.gsLineColor = 'black'
lres.gsLineDashPattern = 1
line = Ngl.add_polyline(wks,cont,vx,vsurf[jn],lres)
else:
print ' Length of vx and vsurf do not agree '
## And show the max/min values
zmax = np.max( zplot.flatten() )
zmin = np.min( zplot.flatten() )
max_point = np.where(zplot == zmax)
min_point = np.where(zplot == zmin)
lmax = vx[max_point[1]][0]
ymax = vy[jn][max_point[0]][0]
print ' psimax = '+repr(zmax)
print ' at lat = '+repr(lmax)+', y = '+repr(ymax)
lmin = vx[min_point[1]][0]
ymin = vy[jn][min_point[0]][0]
print ' psimin = '+repr(zmin)
print ' at lat = '+repr(lmin)+', y = '+repr(ymin)
pres = Ngl.Resources()
pres.gsMarkerColor = 'black'
pres.gsMarkerThicknessF = 3.
mark = Ngl.add_polymarker(wks,cont,lmax,ymax,pres)
mark = Ngl.add_polymarker(wks,cont,lmin,ymin,pres)
Ngl.draw(cont)
#Ngl.frame(wks)
flux_label = ['LH flux [PW]',\
'DSE flux [PW]',\
'MSE flux [PW]',\
'Volume flux [m3/s]',
'Pressure flux [hPa]',\
'Sensible heat flux [PW]']
jj = 0
if(1):
vy2 = np.zeros((nplots,vx.shape[0]))
for jc in range(0,nplots):
zplot = data[jc][:,:]
chi = 0.5 * (vy[jc][1:] + vy[jc][:-1])
for jj in range(0,vx.shape[0]):
dpsi = zplot[1:,jj] - zplot[:-1,jj]
vy2[jc,jj] = np.sum(dpsi * chi)
vy2 = 10**(-3) * (-1) * vy2[:,:] # W->PW and change sign
linecolors = ['blue','red','black','green','yellow','purple']
del res
res = Ngl.Resources()
res.nglMaximize = False
res.nglPaperOrientation = 'Portrait'
res.nglFrame = False
res.nglDraw = True
res.xyLineColors = linecolors
#res.xyLineThicknesses = [2,2,2]
#res.xyLabelMode = 'Custom'
#res.xyExplicitLabels = ['LH','DSE','MSE']
#
#if( np.mod(jc,2) == 0):
# res.tiYAxisSide = 'Left'
# res.tmYLLabelsOn = True
# res.tmYRLabelsOn = False
#else:
# res.tiYAxisSide = 'Right'
# res.tmYLLabelsOn = False
# res.tmYRLabelsOn = True
#
#if( jc == icoord-1):
# res.tiXAxisOn = True
# res.tmXBLabelsOn = True
#else:
# res.tiXAxisOn = False
# res.tmXBLabelsOn = False
## Axis
res.tiXAxisString = 'Latitude'
res.tiYAxisString = '[PW]'
res.tiXAxisFontHeightF = 0.012
res.tiYAxisFontHeightF = 0.012
res.tmXBLabelFontHeightF = 0.012
res.tmYLLabelFontHeightF = 0.012
res.trYMaxF = 6.
res.trYMinF = -6.
res.vpXF = vpx
res.vpWidthF = wth
res.vpYF = vpy[-1]
res.vpHeightF = hei
cont = Ngl.xy(wks,vx,vy2[0:3],res)
Ngl.frame(wks)
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
resources.cnLevelSelectionMode = "ExplicitLevels"
resources.cnLevels = [ 1.,2.,3.,4.,5.,6.,7.,8.,9.,10.,15.,20.,
25.,30.,35.,40.,45.,50.,100.,200.,300.,400.,
500.,600.,700.,800.,900.,1000.,1250.,1500.,
1750.,2000.,2250.,2500.,2750.,3000.,3250.,
3500.,3750.,4000.,4250.,4500.,4750.,5000.]
resources.tiMainString = "North Carolina Coast (depth in meters)"
resources.tiMainFontHeightF = 0.015
resources.nglDraw = False
resources.nglFrame = False
contour = Ngl.contour(wks,depth,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")
#
# The next set of resources will apply to the map plot.
#
resources.mpProjection = "CylindricalEquidistant"
resources.cnFillOn = True resources.cnFillMode = "AreaFill" resources.cnLinesOn = False resources.cnLineLabelsOn = False resources.lbBoxLinesOn = False resources.lbLabelFontHeightF = 0.015 resources.tiMainString = "HOMME grid - surface pressure (mb)" # # The contour plot is not very interesting, so don't draw it. # resources.nglDraw = False resources.nglFrame = False contour = Ngl.contour(wks,ps,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 = "Orthographic" resources.mpDataBaseVersion = "MediumRes"
#
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)
wks_type = "png"
wks = Ngl.open_wks(wks_type,"contour1")
cnres = Ngl.Resources()
cnres.sfYCStartV = 10 # Define limits for Y axis.
cnres.sfYCEndV = 1000
cnres.tiXAxisString = "linear" # Label for X axis.
cnres.tiYAxisString = "linear" # Label for Y axis.
contour = Ngl.contour(wks,T,cnres) # Create and draw contour plot.
cnres.tiYAxisString = "log" # Label for Y axis.
cnres.trYLog = True # Log scaling for Y axis
contour = Ngl.contour(wks,T,cnres) # Create and draw contour plot.
Ngl.end()
res.cnFillOn = True #-- turn on contour fill. res.cnLineLabelsOn = False #-- turn off line labels. res.cnInfoLabelOn = False #-- turn off info label. res.cnFillPalette = "BlueWhiteOrangeRed" #-- set color map. res.pmLabelBarOrthogonalPosF = -0.03 #-- move labelbar close to plot res.sfXArray = lon #-- scalar field x res.sfYArray = lev #-- scalar field y res.trYReverse = True #-- reverse the Y axis res.nglYAxisType = "LogAxis" #-- y axis log res.tiYAxisString = "%s (hPa)" % f.variables["lev"].long_name res.nglPointTickmarksOutward = True #-- point tickmarks out res.tmYLMode = "Explicit" #-- set y axis tickmark labels res.tmXBMode = "Explicit" #-- set x axis tickmark labels res.tmYLValues = levs res.tmXBValues = lons res.tmYLLabels = map(str, levs) res.tmXBLabels = nice_lon_labels(lons) res.tmXBLabelFontHeightF = 0.015 # - make font smaller res.tmYLLabelFontHeightF = 0.015 map = Ngl.contour(wks, t26, res) #-- draw contours #-- end Ngl.end()
# to their defaults. # import Ngl import Numeric M=29 N=25 T = Numeric.zeros([N,M]) # # create a mound as a test data set # jspn = Numeric.power(xrange(-M/2+5,M/2+5),2) ispn = Numeric.power(xrange(-N/2-3,N/2-3),2) for i in xrange(len(ispn)): T[i,:] = ispn[i] + jspn T = 100. - 8.*Numeric.sqrt(T) # # Open a workstation and draw the contour plot. # wks_type = "ps" wks = Ngl.open_wks(wks_type,"cns01p") res = Ngl.Resources() res.cnMonoLineDashPattern = False Ngl.contour(wks,T,res) Ngl.end()
res.cnLineLabelsOn = True res.cnLinesOn = True res.cnMonoLineLabelFontColor = True res.lbLabelFontHeightF = 0.0075 res.cnLineThicknessF = 2.5 res.cnInfoLabelOn = True res.cnInfoLabelString = "CIN Contours at -50, -100 and -250 J/Kg" res.cnInfoLabelOrthogonalPosF = -0.06 res.cnInfoLabelParallelPosF = 0.505 res.cnLevelSelectionMode = "ExplicitLevels" res.cnLevels = [-250.0, -100.0, -50.0] # plot CIN and overlay on colour contours CIN_plot = ngl.contour(wks,CIN,res) ngl.overlay(CAPE_plot,CIN_plot) ngl.maximize_plot(wks, CAPE_plot) ngl.draw(CAPE_plot) ngl.frame(wks) ngl.destroy(wks) del res del CAPE del CIN ################################################################################################### # open forecast file
jspn = numpy.power(xrange(-M/2+5,M/2+5),2)
ispn = numpy.power(xrange(-N/2-3,N/2-3),2)
for i in xrange(len(ispn)):
T[i,:] = ispn[i] + jspn
T = 100. - 8.*numpy.sqrt(T)
#
# Open a workstation and draw a contour plot.
#
wks_type = "ps"
wks = Ngl.open_wks(wks_type,"cns01p")
res = Ngl.Resources()
res.cnMonoLineDashPattern = False
plot1 = Ngl.contour(wks,T,res)
#
# Retrieve the automatically set line labels.
# These will be: ['-80', '-60', '-40', '-20', '0', '20', '40', '60', '80']
#
line_labels1 = Ngl.get_string_array(plot1,"cnLineLabelStrings")
#
# Set explicit line labels. Notice that the dash line
# setting carries over from the first plot.
#
res.cnExplicitLineLabelsOn = True
res.cnLineLabelStrings = ["Lab1", "Lab2", "Lab3", "Lab4", "Lab5",
"Lab6", "Lab7", "Lab8", "Lab9"]
plot2 = Ngl.contour(wks,T,res)
resources.cnFillOn = True # Turn on contour fill. resources.cnInfoLabelOn = False # Turn off info label. resources.cnLineLabelsOn = False # Turn off line labels. resources.lbOrientation = "Horizontal" # Draw labelbar horizontally. resources.nglSpreadColors = False # Do not interpolate color space. resources.vpYF = 0.9 # Change Y location of plot. # # Transpose the array so we can plot it, because Natgrid returns it # as an X x Y array, rather than Y x X which PyNGL expects. # zt = numpy.transpose(zo) contour = Ngl.contour(wks,zt,resources) # # Add some polymarkers showing the original locations of the X,Y points. # poly_res = Ngl.Resources() poly_res.gsMarkerIndex = 16 poly_res.gsMarkerSizeF = 0.015 Ngl.add_polymarker(wks,contour,x,y,poly_res) Ngl.draw(contour) Ngl.frame(wks) # # Now turn on RasterFill for the contours and compare.
cnres.nglMaximize = True # Maximize plot in frame cnres.nglDraw = False # Don't draw plot cnres.nglFrame = False # Don't advance the frame cnres.cnFillOn = True # Turn on contour fill cnres.cnFillPalette = cmap[:-3, :] cnres.cnLevelSelectionMode = "ManualLevels" cnres.cnLevelSpacingF = nice_spc cnres.cnMinLevelValF = nice_min cnres.cnMaxLevelValF = nice_max cnres.lbOrientation = "Vertical" # Default is horizontal cnres.tiMainString = "This is a title" cnres.tiXAxisString = "X axis" cnres.tiYAxisString = "Y axis" contourplot = Ngl.contour(wks, data, cnres) # Draw plot with viewport and bounding boxes. Ngl.draw(contourplot) draw_bb_box(wks, contourplot) draw_vp_box(wks, contourplot) # Advance frame. Ngl.frame(wks) Ngl.end()
resources.cnLineLabelsOn = False # Turn off contour labels
#
# Set some font heights to make them slightly bigger than the default.
# Turn off nglScale, because this resource wants to set the axes font
# heights for you.
#
resources.nglScale = False
resources.tiMainFontHeightF = 0.037
resources.lbLabelFontHeightF = 0.032
resources.tmXBLabelFontHeightF = 0.030
resources.tmYLLabelFontHeightF = 0.030
for i in range(0, nplots):
resources.tiMainString = "Temperature at time = {}".format(i)
plot.append(Ngl.contour(wks, temp[i, :, :], resources))
Ngl.panel(wks, plot[0:4], [2, 2]) # Draw 2 rows/2 columns of plots.
#
# Now add some extra white space around each plot.
#
panelres = Ngl.Resources()
panelres.nglPanelYWhiteSpacePercent = 5.
panelres.nglPanelXWhiteSpacePercent = 5.
Ngl.panel(wks, plot[0:4], [2, 2], panelres) # Draw 2 rows/2 columns of plots.
#
# This section will set resources for drawing contour plots over a map.
#
res.sfXArray = FEDU_Alpha
res.sfYArray = energy_lev
res.cnExplicitLabelBarLabelsOn = True
res.lbLabelStrings = ("1","10","100","1e3","1e4","1e5")
res.cnLevelSelectionMode = 'ExplicitLevels'
#res.cnMinLevelValF = 0.
#res.cnMaxLevelValF = 1.e8
res.cnLevels = (1., 10., 100., 1000., 10000., 100000.)
#res.cnLevels = ( 1, 1e2, 1e3, 1e4, 1e5, 1e6)
#res.cnLevels = np.logspace(1, 6, num=12)
res.cnFillMode = 'CellFill'
res.tmXBMode = 'Explicit'
res.tmXBValues = (8, 25, 41, 57, 74, 90, 106, 123, 139, 155, 172)
res.tmXBLabels = ("8", "25", "41", "57", "74", "90", "106", "123", "139", "155", "172")
Ngl.contour(wks,FEDU_plot[0:20,:],res)
# ------------------------------------------
# Plot RBSP for 8 degree pitch
# ------------------------------------------
wks_type = "ps"
wks2 = Ngl.open_wks(wks_type,"RBSP_8degree")
res2 = Ngl.Resources()
res2.cnFillOn = True # turn on color fill
res2.cnLineLabelsOn = False # turn off contour line labels
res2.lbLabelFontHeightF = 0.018 # make labelbar labels smaller
#res2.sfXArray = Epoch
res2.sfYArray = energy_lev
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
cfres.lbOrientation = "Horizontal" # horizontal labelbar cfres.lbLabelFontHeightF = 0.012 # Decrease font size. cfres.pmLabelBarOrthogonalPosF = -0.05 # Move labelbar up. clres.cnLineLabelsOn = False # Turn off contour line labels. clres.cnLineDashPatterns = 3 # dashed contour lines clres.cnLineThicknessF = 3. # triple thick contour lines clres.cnInfoLabelOrthogonalPosF = -0.15 # Move info label up. # # Create the various plots. They will not get drawn because # nglDraw is set to False for all of them. # map_plot = Ngl.map(wks,mpres) vector_plot = Ngl.vector(wks,ua,va,vcres) line_contour_plot = Ngl.contour(wks,pa,clres) fill_contour_plot = Ngl.contour(wks,ta,cfres) # # Overlay everything on the map plot. # Ngl.overlay(map_plot,fill_contour_plot) Ngl.overlay(map_plot,line_contour_plot) Ngl.overlay(map_plot,vector_plot) # # Change the title. # srlist = Ngl.Resources() srlist.tiMainString = "vectors, line, and filled contours" Ngl.set_values(map_plot,srlist)
resources.cnFillMode = "AreaFill" resources.cnFillPalette = "BlAqGrYeOrReVi200" resources.cnLinesOn = False resources.cnLineLabelsOn = False resources.lbBoxLinesOn = False resources.lbLabelFontHeightF = 0.015 resources.tiMainString = "HOMME grid - surface pressure (mb)" # # The contour plot is not very interesting, so don't draw it. # resources.nglDraw = False resources.nglFrame = False contour = Ngl.contour(wks, ps, 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 = "Orthographic" resources.mpDataBaseVersion = "MediumRes"
rres = Ngl.Resources()
rres.nglDraw = False #-- don't draw plot
rres.nglFrame = False #-- don't advance frame
rres.cnInfoLabelOrthogonalPosF = 0.13 #-- move info label upward
rres.sfXArray = lon
rres.sfYArray = lat
#-- generate tplot, but don't draw it yet
print("-- map --")
map = Ngl.map(wks, mpres)
#-- generate tplot, but don't draw it yet
print("-- tplot --")
tplot = Ngl.contour(wks, t, tres)
#-- generate plot2, but don't draw it yet
print("-- rplot --")
rplot = Ngl.contour(wks, rhum, rres)
#-- overlay rplot on tplot
print("-- overlay tplot --")
Ngl.overlay(map, tplot)
print("-- overlay rplot --")
Ngl.overlay(map, rplot)
#-- draw the plot
Ngl.draw(map)
#-- write variable long_name and units to the plot
# Drawing a contour visualization using all defaults. # # Output: # A single visualization is produced. # # import Ngl import numpy M=29 N=25 T = numpy.zeros([N,M]) # # create a mound as a test data set # jspn = numpy.power(xrange(-M/2+5,M/2+5),2) ispn = numpy.power(xrange(-N/2-3,N/2-3),2) for i in xrange(len(ispn)): T[i,:] = ispn[i] + jspn T = 100. - 8.*numpy.sqrt(T) # # Open a workstation and draw the contour plot. # wks_type = "ps" wks = Ngl.open_wks(wks_type,"cn01p") Ngl.contour(wks,T) Ngl.end()
