def save(self, filename='plot'):
#'eps' workstation doesn't work reliably, but 'ps' is OK
weps = Ngl.open_wks('ps', filename, self.WorkstationResources)
Ngl.change_workstation(self.plot, weps)
Ngl.draw(self.plot)
Ngl.change_workstation(self.plot, self.workstation)
Ngl.destroy(weps)
def save(self,filename = 'plot'):
#'eps' workstation doesn't work reliably, but 'ps' is OK
weps = Ngl.open_wks('ps',filename,self.WorkstationResources)
Ngl.change_workstation(self.plot,weps)
Ngl.draw(self.plot)
Ngl.change_workstation(self.plot,self.workstation)
Ngl.destroy(weps)
def skewt(fname, p, tc, tdc, z, wspd, wdir, saveas='pdf', barbstride=20):
wks = Ngl.open_wks(saveas,fname)
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].
dataOpts.sktHeightWindBarbPositions = hght # height of wind reports
dataOpts.sktHeightWindBarbSpeeds = hspd # speed
# [or u components]
dataOpts.sktHeightWindBarbDirections = hdir # direction
# [or v components]
"""
skewtOpts = Ngl.Resources()
skewtOpts.sktHeightScaleOn = True # default is False
skewtOpts.sktHeightScaleUnits = "km" # default is "feet"
skewtOpts.sktColoredBandsOn = False # default is False
skewtOpts.sktGeopotentialWindBarbColor = "Red"
dataOpts.sktPressureWindBarbStride = barbstride
skewtOpts.tiMainString = "Graw Launch WBB - Env. Instr Lab 5"
# create a background
skewt_bkgd = Ngl.skewt_bkg(wks, skewtOpts)
# plot the darn profile...
skewt_data = Ngl.skewt_plt(wks, skewt_bkgd, p, tc, tdc, z, \
wspd, wdir, dataOpts)
Ngl.draw(skewt_bkgd)
Ngl.draw(skewt_data)
Ngl.frame(wks)
Ngl.end()
def hilo_valplot(lons, lats, highs, lows, cfg):
"""
Special case of having a value plot with a high and low value to
plot, which is common for some climate applications
"""
tmpfp = tempfile.mktemp()
cmap = numpy.array([[1., 1., 1.], [0., 0., 0.], [1., 0., 0.], \
[0., 0., 1.], [0., 1., 0.]], 'f')
rlist = Ngl.Resources()
rlist.wkColorMap = cmap
#rlist.wkOrientation = "landscape"
wks = Ngl.open_wks("ps", tmpfp, rlist)
res = iowa()
res.mpOutlineDrawOrder = "PreDraw"
plot = Ngl.map(wks, res)
for key in cfg.keys():
if key == 'wkColorMap' or key[0] == "_":
continue
setattr(res, key, cfg[key])
txres = Ngl.Resources()
txres.txFontHeightF = 0.016
txres.txFontColor = "red"
txres.txJust = "BottomRight"
for i in range(len(lons)):
Ngl.add_text(wks, plot, cfg["_format"] % highs[i],
lons[i], lats[i],txres)
txres = Ngl.Resources()
txres.txFontHeightF = 0.016
txres.txFontColor = "blue"
txres.txJust = "TopRight"
for i in range(len(lons)):
Ngl.add_text(wks, plot, cfg["_format"] % lows[i],
lons[i], lats[i],txres)
if cfg.has_key("_labels"):
txres = Ngl.Resources()
txres.txFontHeightF = 0.008
txres.txJust = "CenterLeft"
txres.txFontColor = 1
for i in range(len(lons)):
Ngl.add_text(wks, plot, cfg["_labels"][i],
lons[i], lats[i],txres)
watermark(wks)
manual_title(wks, cfg)
vpbox(wks)
Ngl.draw(plot)
Ngl.frame(wks)
del wks
return tmpfp
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 _draw_plots(self, plots, res):
"""Draw the provided plots."""
for plot in xrange(self.number_plots):
# It is OK for a plot to be None, it will just be skipped. This
# allows the user to have a lot of control over their panel plot.
if plots[plot] is not None:
# Set the position of the plot.
res_pos = Ngl.Resources()
res_pos.vpXF, res_pos.vpYF = self.plot_coordinates[plot]
Ngl.set_values(plots[plot], res_pos)
# Draw to plot.
Ngl.draw(plots[plot])
def __call__(self, *args):
"""Ngl graphics function with modifications applied."""
# Make a local copy of the resources arguments, preventing them
# from being modified in the calling namespace. These copied (and
# possibly modified) are used only inside this method.
res = list()
for i, arg in enumerate(args):
if isinstance(arg, Ngl.Resources):
res.append((copy(arg), i))
# Form the new argument list containing copies of the resource
# variables. We alse need to intercept nglDraw and nglFrame at the top
# level, making sure they are turned off while modifications are
# applied. After modifications we can check if drawing and frame
# advancing was requested and do so then.
new_args = list(args)
draw_on = frame_on = False
for r, i in res:
draw_on = getattr(r, 'nglDraw', True)
frame_on = getattr(r, 'nglFrame', True)
if draw_on:
setattr(r, 'nglDraw', False)
if frame_on:
setattr(r, 'nglFrame', False)
new_args[i] = r
# Call the modifier pre-plot methods.
special_resources = list()
for modifier in self.modifiers:
# Run the preplot method of each modifier.
modifier.preplot(*new_args)
special_resources += modifier.resource_names
# Go back and remove all special resources from resource variables
# before they are passed to the Ngl plotting routine.
for r, i in res:
for resource_name in special_resources:
try:
delattr(r, resource_name)
except AttributeError:
pass
# Make the plot.
plot = self.f(*new_args)
# Call the modifier post-plot methods.
wks = args[0]
for modifier in self.modifiers:
modifier.postplot(wks, plot)
# Check if the plot should be drawn and the frame advanced. Do so now
# if required.
if draw_on:
Ngl.draw(plot)
if frame_on:
Ngl.frame(wks)
# Return the modified plot.
return plot
def draw(data, lon, lat, dataSources, latRanges, lonRanges, imgTypes,
colorBarName, imgOutputPaths, img_output_names, mainTitle, subTitles,
unit, dataSources1):
'''
方法定义： 用于绘图。（画的什么图不清楚，建议名字优化一下）
@param data: 数据（是否是2D呢？？）
@param lon: 精度
@param lat: 纬度 （建议常用的查询要素封装成到一个类中去）
@param dataSources: 数据源
@param latRanges: 纬度范围
@param lonRanges: 经度
@param imgTypes: 图片格式
@param colorBarName: 颜色名称（到底是啥玩意）
@param imgOutputPaths: （图片输出路径）
@param img_output_names: （图片名称）
@param mainTitle: 主标题
@param subTitles: 副标题
@param unit: 单元（什么的单元？？？）
@param dataSources1: 数据源1（这个数据源1是干嘛用的，为啥叫1）
@return: 无返回值，只是用于画图，然后保存而已。
'''
#1.数据信息提取Begin
startLon = lonRanges[0]
endLon = lonRanges[1]
startLat = latRanges[0]
endLat = latRanges[1]
print(" Longitude starts with %f ,ends with %f " %
(startLon, endLon)) #获取经纬度起止值(此处建议引入日志配置，而不是简单的print函数)
print(" Latitude starts with %f ,ends with %f " % (startLon, endLon))
image_path = output_directory_absolute_path + img_output_names #指定输出文件 (常量不应该写死在方法内部，应该定义全局变量，或者由配置文件引入。)
#2.绘图环境配置Begin
wks = create_workstation(colorBarName, image_path)
# 3.绘制等值线图
plot = draw_contour(colorBarName, data, endLat, endLon, imgOutputPaths,
lat, lon, startLat, startLon, wks)
#4.绘制海岸线图
draw_sea_polyline(endLon, plot, wks)
Ngl.draw(plot)
Ngl.frame(wks)
Ngl.end()
#5.字体绘制
im = setting_font(dataSources1, image_path, mainTitle, subTitles, unit)
#6.加载ncc.png(不清楚这个作用是什么？ 背景色吗)
setting_bgImage(im)
print(image_path + ".png")
im.save(image_path + ".png")
def plot(filename, outputname):
# set up parameters
f = Nio.open_file(filename)
q = f.variables['q'][:, 0, :]
qY = f.variables['qY'][:, 0, :]
ae = 6.371229e6
r2d = 57.2957795
lat = f.variables['lat'][:, :] * r2d
lon = f.variables['lon'][:, :] * r2d
vertx = f.variables['vertx'][:, :, :] * r2d
verty = f.variables['verty'][:, :, :] * r2d
ncols = f.variables['nCols'][:]
# Open a workstation.
wks_type = "pdf"
wks = Ngl.open_wks(wks_type, outputname)
# Create contour and map resource lists.
for it in range(1, 2):
cnres = Ngl.Resources()
cnres.nglFrame = False
cnres.nglDraw = False
cnres.mpGridAndLimbOn = False
cnres.mpOutlineOn = False
cnres.mpPerimOn = False # ; turn off box around plot
cnres.nglMaximize = True
cnres.mpProjection = "Orthographic" # Change the map projection.
x_c, y_c = qmoving(it * 24. * 3600., 12. * 24. * 3600., 0. * np.pi, ae)
cnres.mpCenterLonF = x_c * r2d
cnres.mpCenterLatF = y_c * r2d
cnres.mpOutlineOn = False
cnres.sfXArray = lon[it, 0:ncols[it]]
cnres.sfYArray = lat[it, 0:ncols[it]]
cnres.cnLinesOn = True
cnres.cnFillOn = False
cnres.cnLineLabelsOn = False
cnres.cnInfoLabelOn = False
cnres.cnLevelSelectionMode = "ExplicitLevels" # Set explicit contour levels
cnres.cnLevels = np.arange(0.1, 1., 0.1) # 0,5,10,...,70
cnres.cnLineThicknessF = 3.
cnres.pmTickMarkDisplayMode = "Never"
contour1 = Ngl.contour_map(wks, q[it, 0:ncols[it]], cnres)
cnres.cnLineColor = "red"
contour2 = Ngl.contour_map(wks, qY[it, 0:ncols[it]], cnres)
Ngl.draw(contour1)
Ngl.draw(contour2)
# Ngl.draw(vc)
Ngl.frame(wks)
Ngl.end()
def plot(filename, outputname, centerlon, centerlat):
# set up parameters
f = Nio.open_file(filename)
q = f.variables['q'][:, 0, :]
r2d = 57.2957795
lat = f.variables['lat'][:, :] * r2d
lon = f.variables['lon'][:, :] * r2d
vertx = f.variables['vertx'][:, :, :] * r2d
verty = f.variables['verty'][:, :, :] * r2d
ncols = f.variables['nCols'][:]
# Open a workstation.
wks_type = "pdf"
wks = Ngl.open_wks(wks_type, outputname)
# Create contour and map resource lists.
for it in range(13):
cnres = Ngl.Resources()
cnres.nglFrame = False
cnres.nglDraw = False
cnres.mpGridAndLimbOn = False
cnres.mpOutlineOn = False
cnres.mpPerimOn = False # ; turn off box around plot
cnres.nglMaximize = True
cnres.mpProjection = "Orthographic" # Change the map projection.
cnres.mpCenterLonF = centerlon
cnres.mpCenterLatF = centerlat
cnres.mpOutlineOn = False
cnres.sfXArray = lon[it, 0:ncols[it]]
cnres.sfYArray = lat[it, 0:ncols[it]]
cnres.cnLinesOn = True
cnres.cnFillOn = False
cnres.cnLineLabelsOn = False
cnres.cnInfoLabelOn = False
cnres.cnLevelSelectionMode = "ExplicitLevels" # Set explicit contour levels
cnres.cnLevels = np.arange(0.1, 1., 0.1) # 0,5,10,...,70
cnres.cnLineThicknessF = 3.
cnres.cnLineColor = "red"
cnres.pmTickMarkDisplayMode = "Never"
contour1 = Ngl.contour_map(wks, q[it, 0:ncols[it]], cnres)
gsres = Ngl.Resources()
gsres.gsLineColor = "Gray25"
gsres.gsLineThicknessF = 2.0
for nc in range(ncols[it]):
Ngl.polyline(wks, contour1, vertx[it, :, nc], verty[it, :, nc],
gsres)
Ngl.draw(contour1)
Ngl.frame(wks)
Ngl.end()
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
def plot_xy(vpx=0.2,vpy=0.8,hei=0.3,wth=0.4,xlab='',ylab='',legend=True, yrev=False, lab=''):
nprof = len(vy)
res = Ngl.Resources()
res.nglFrame = False
res.nglPaperOrientation = 'Portrait'
res.nglMaximize = False
res.nglDraw = False
res.xyLineColors = colors
res.xyLineThicknesses = thicknesses
res.tiYAxisString = ylab
res.tiXAxisString = xlab
res.tiMainString = lab
res.tiMainFontHeightF = 0.015
res.tiMainOffsetYF = -hei/4.7
res.tiMainOffsetXF = -wth * 2./5.
res.trXMinF = xmin
res.trXMaxF = xmax
res.trYMinF = ymin
res.trYMaxF = ymax
res.trYReverse = yrev
res.vpXF = vpx
res.vpYF = vpy
res.vpHeightF = hei
res.vpWidthF = wth
xy = Ngl.xy(wks,vx[0],vy[0],res)
for jn in range(1,nprof):
res = Ngl.Resources()
res.gsLineColor = colors[jn]
res.gsLineDashPattern = patterns[jn]
res.gsLineThicknessF = thicknesses[jn]
if ( isinstance(vx,list) and len(vx) >= len(vy) ):
tmpx = vx[jn]
elif (isinstance(vx,list) and len(vx)-1 < jn ):
tmpx = vx[0]
else:
tmpx = vx
line = Ngl.add_polyline(wks,xy,tmpx,vy[jn],res)
Ngl.draw(xy)
wth2 = 0.08
hei2 = 0.08
for jn in range(0,2):
if (jn == 0):
x0 = vpx+wth-(wth2)
y0 = vpy+hei2
st = 0
sp = 4
elif (jn == 1 and nprof > 4):
x0 = vpx+wth-(2*wth2+0.05)
y0 = vpy+hei2
st = 4
sp = nprof
lres = Ngl.Resources()
lres.vpWidthF = wth2
lres.vpHeightF = hei2
lres.lgLineColors = colors[st:sp]
lres.lgLineThicknesses = thicknesses[st:sp]
lres.lgDashIndexes = patterns[st:sp]
lres.lgLineLabelsOn = False
lres.lgLabelFontHeightF = 0.008
if(legend):
lg = Ngl.legend_ndc(wks,sp-st,titles1[st:sp],x0,y0,lres)
def main():
start_year = 1981
end_year = 2008
#mean alt
path_to_yearly = "alt_era_b1_yearly.nc"
ds = Dataset(path_to_yearly)
hm = ds.variables["alt"][:]
years = ds.variables["year"][:]
years_sel = np.where(( start_year <= years ) & (years <= end_year))[0]
print(years_sel)
hm = hm[np.array(years_sel),:,:]
print(hm.shape)
good_points = ~np.any(hm < 0, axis = 0)
hm2d = np.ma.masked_all(good_points.shape)
hm2d[good_points] = np.mean( hm[ : , good_points],
axis = 0)
#alt from climatology
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
# dm = CRCMDataManager(data_folder=sim_data_folder)
# hc = dm.get_alt_using_monthly_mean_climatology(xrange(start_year,end_year+1))
coord_file = os.path.join(sim_data_folder, "pmNorthAmerica_0.44deg_ERA40-Int_B1_200812_moyenne")
basemap, lons2d, lats2d = draw_regions.get_basemap_and_coords(resolution="c",
file_path = coord_file#, llcrnrlat=40.0, llcrnrlon=-145, urcrnrlon=-10
)
#basemap.transform_scalar()
#basemap = Basemap()
lons2d[lons2d > 180] -= 360
x, y = basemap(lons2d, lats2d)
print(x.min(), x.max())
permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 3)
#plot_utils.apply_plot_params(width_pt=None, width_cm=25,height_cm=35, font_size=12)
#fig = plt.figure()
#assert isinstance(fig, Figure)
h_max = 10
#cmap = cm.get_cmap("jet",10)
#cmap.set_over(cmap(1.0))
clevels = np.arange(0,h_max+1,1)
#gs = gridspec.GridSpec(1,1)
all_axes = []
all_img = []
#ax = fig.add_subplot(gs[0,0])
hm2d = np.ma.masked_where(mask_cond | (hm2d > h_max), hm2d)
wks_res = Ngl.Resources()
assert isinstance(wks_res, Ngl.Resources)
wks = Ngl.open_wks("x11", "ALT mean", wks_res)
wks_res.cnFillOn = True
wks_res.sfXArray = lons2d
wks_res.sfYArray = lats2d
#---Create contours over map.
map = Ngl.contour_map(wks,hm2d,wks_res)
#---Draw plot and advance frame. MRB outlines will be included.
Ngl.draw(map)
Ngl.frame(wks)
def plot_xy(wks,vx,data_vy,vpx=0.2,vpy=0.8,hei=0.3,xlab='',ylab='',\
prof=False,yrev=True,\
linecolors = ['red','blue','black','green','yellow'],\
linethicknesses=[1,1,1,1,1],
linepatterns=[0,0,0,0,0],\
titles=[''],\
xmin=0.,xmax=1000.,ymin=0.,ymax=100.,\
frame=True):
"""
"""
if ( isinstance(data_vy,list) ):
nprof = len(data_vy)
else:
data_vy = [data_vy]
nprof = 1
vx = fill_blanks(vx,nprof)
linecolors = fill_blanks(linecolors,nprof)
linepatterns = fill_blanks(linepatterns,nprof)
linethicknesses = fill_blanks(linethicknesses,nprof)
if (prof):
tmp = vx
vx = data_vy
data_vy = tmp
res = Ngl.Resources()
res.nglFrame = False
res.nglPaperOrientation = 'Portrait'
res.nglMaximize = False
res.nglDraw = False
res.xyLineColors = linecolors
res.tiYAxisString = ylab
res.tiXAxisString = xlab
res.trYReverse = yrev
res.trXMinF = xmin
res.trXMaxF = xmax
res.trYMinF = ymin
res.trYMaxF = ymax
if(prof):
wth = hei * 1./np.sqrt(2.)
else:
wth = hei * np.sqrt(2.)
res.vpXF = vpx
res.vpYF = vpy
res.vpHeightF = hei
res.vpWidthF = wth
xy = Ngl.xy(wks,vx[0],data_vy[0],res)
for jn in range(1,nprof):
res = Ngl.Resources()
res.gsLineColor = linecolors[jn]
res.gsLineDashPattern = linepatterns[jn]
res.gsLineThicknessF = linethicknesses[jn]
line = Ngl.add_polyline(wks,xy,vx[jn],data_vy[jn],res)
Ngl.draw(xy)
wth2 = 0.15
hei2 = 0.08
for jn in range(0,2):
if (jn == 0):
x0 = vpx+wth-(wth2)
y0 = vpy+hei2
st = 0
sp = 4
elif (jn == 1):
x0 = vpx+wth-(2*wth2+0.05)
y0 = vpy+hei2
st = 4
sp = 7
lres = Ngl.Resources()
lres.vpWidthF = wth2
lres.vpHeightF = hei2
lres.lgLineColors = linecolors[st:sp]
lres.lgLineThicknesses = linethicknesses[st:sp]
lres.lgDashIndexes = linepatterns[st:sp]
lres.lgLineLabelsOn = False
lres.lgLabelFontHeightF = 0.01
lg = Ngl.legend_ndc(wks,sp-st,titles[st:sp],x0,y0,lres)
#tres = Ngl.Resources()
#tres.txFontHeightF = 0.015
#tres.txJust = 'CenterLeft'
#y = vpy + 0.03
#x0 = vpx
#dx = 0.15
#print nprof
#print titles
#for jn in range(0,nprof):
# tres.txFontColor = linecolors[jn]
# x = x0 + dx*jn
# Ngl.text_ndc(wks,titles[jn],x,y,tres)
if (frame):
Ngl.frame(wks)
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)
def plot_dots(wks,vx,vy,vpx=0.2,vpy=0.8,hei=0.3,xlab='',ylab='',\
prof=False,lreg=True,\
dotcolors = ['red','blue','black','green','yellow'],\
dotsizes=[1,1,1,1,1],
dotindices=[9,6,2,3,4,5],\
titles=[''],\
xmin=0.,xmax=1000.,ymin=0.,ymax=100.,\
frame=True):
"""
"""
ndots = len(vy)
print dotcolors
print dotindices
print dotsizes
dotcolors = fill_blanks(dotcolors,ndots)
dotindices = fill_blanks(dotindices,ndots)
dotsizes = fill_blanks(dotsizes,ndots)
res = Ngl.Resources()
res.nglFrame = False
res.nglPaperOrientation = 'Portrait'
res.nglMaximize = False
res.nglDraw = False
res.xyMarkLineMode = 'Markers'
res.xyMonoMarkerColor = False
res.xyMarkerColors = dotcolors
res.xyMarkerThicknesses = 2
res.xyMarkerSizes = np.array(dotsizes) * 0.01
res.xyMarkers = dotindices
res.tiYAxisString = ylab
res.tiXAxisString = xlab
res.trXMinF = xmin
res.trXMaxF = xmax
res.trYMinF = ymin
res.trYMaxF = ymax
if(prof):
wth = hei * 1./np.sqrt(2.)
else:
wth = hei * np.sqrt(2.)
res.vpXF = vpx
res.vpYF = vpy
res.vpHeightF = hei
res.vpWidthF = wth
print vx
print vy
print xmin,xmax,ymin,ymax
xy = Ngl.xy(wks,np.transpose(np.vstack((vx,vx))),np.transpose(np.vstack((vy,vy))),res)
if (lreg):
## Fit line
k,m,cor,sig,err = stats.linregress(vx,vy)
vx = np.linspace(xmin,xmax,10)
vy = vx * k + m
# Plot line
lres = Ngl.Resources()
lres.gsLineColor = 'black'
lres.gsLineDashPattern = 1
lres.gsLineThicknessF = 2
line = Ngl.add_polyline(wks,xy,vx,vy,lres)
# Print correlations
text = 'R = '+repr(cor)
if (sig*100. < 1.):
text = text + ' (99% sign.)'
elif (sig*100. < 5.):
text = text + ' (95% sign.)'
else:
text = text + ' (not sign.)'
tres = Ngl.Resources()
tres.txFontHeightF = 0.01
tres.txJust = 'BottomLeft'
tres.txFontColor = 'black'
Ngl.text_ndc(wks,text,vpx+wth,vpy-hei-0.03,tres)
Ngl.draw(xy)
#wth2 = 0.15
#hei2 = 0.08
#for jn in range(0,2):
# if (jn == 0):
# x0 = vpx+wth-(wth2)
# y0 = vpy+hei2
# st = 0
# sp = 4
# elif (jn == 1):
# x0 = vpx+wth-(2*wth2+0.05)
# y0 = vpy+hei2
# st = 4
# sp = 7
# lres = Ngl.Resources()
# lres.vpWidthF = wth2
# lres.vpHeightF = hei2
# lres.lgLineColors = linecolors[st:sp]
# lres.lgLineThicknesses = linethicknesses[st:sp]
# lres.lgDashIndexes = linepatterns[st:sp]
# lres.lgLineLabelsOn = False
# lres.lgLabelFontHeightF = 0.01
# lg = Ngl.legend_ndc(wks,sp-st,titles[st:sp],x0,y0,lres)
if (frame):
Ngl.frame(wks)
lbres.lbTitleOffsetF = 0.00 #---Create the labelbar labels = ["0","1","2","3"] lbid = Ngl.labelbar_ndc (wks,len(labels),labels,0.0,0.,lbres) # # Create some annotation resources indicating how we want to # attach the labelbar to the plot. The default is the center # of the plot. Below amOrthogonalPosF is set to move the # labelbar down and outside the plot. # amres = Ngl.Resources() amres.amOrthogonalPosF = 0.7 annoid = Ngl.add_annotation(plot,lbid,amres) txres = Ngl.Resources() txres.txFontHeightF = 0.01 txres.txJust = "TopRight" txres.txPerimOn = True txid = Ngl.add_text(wks,plot,flnm,res.mpMaxLonF,res.mpMaxLatF,txres) #---This will resize plot so it and the labelbar fit in the frame. Ngl.maximize_plot(wks, plot) #---Drawing the original map also draws the attached labelbar. Ngl.draw(plot) Ngl.frame(wks) Ngl.end()
res.trXMaxF = 20.0 res.trXMinF = 0.0 res.trYMinF = 0.0 # # Width, height, and position of X axis. Every time the # axis is redrawn, the vpYF resource will be changed to change # the position. # res.vpXF = 0.2 res.vpYF = 0.9 res.vpHeightF = 0.02 res.vpWidthF = 0.7 xy = Ngl.xy(wks, x, y, res) Ngl.draw(xy) # # Draw 9 different plots demonstrating control of the bottom x-axis # tickmark labels using the XBFormat string resource. See the description # of the Floating Point Format Specification scheme in the HLU reference # guide to learn about the semantics and syntax of the format string: # # http://www.ncl.ucar.edu/Document/Graphics/format_spec.shtml # # There are links to this description in the TickMark reference pages under # the entries for the format string resources (XBFormat, for example). # sres = Ngl.Resources()
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) # # Draw the map plot, which now contains the vectors and # filled/line contours. # Ngl.maximize_plot(wks,map_plot) # Maximize size of plot in frame. Ngl.draw(map_plot) Ngl.frame(wks) # # Change the title. # srlist.tiMainString = "line and filled contours" Ngl.set_values(map_plot,srlist) # # Remove the vector plot and redraw. We should now # just see the line contours and filled contours. # Ngl.remove_overlay(map_plot,vector_plot,0) Ngl.draw(map_plot) Ngl.frame(wks)
taus_above_zero = Numeric.take(taus,ind_above_zero) px[0::5] = (taus_above_zero - dx/2.).astype(taus.typecode()) px[1::5] = (taus_above_zero - dx/2.).astype(taus.typecode()) px[2::5] = (taus_above_zero + dx/2.).astype(taus.typecode()) px[3::5] = (taus_above_zero + dx/2.).astype(taus.typecode()) px[4::5] = (taus_above_zero - dx/2.).astype(taus.typecode()) py[0::5] = rain_res.trYMinF py[1::5] = Numeric.take(rain03,ind_above_zero) py[2::5] = Numeric.take(rain03,ind_above_zero) py[3::5] = rain_res.trYMinF py[4::5] = rain_res.trYMinF polyg = Ngl.add_polygon(wks,rainhist,px,py,pgres) # # For the outlines, we don't need the fifth point. # polyl = Ngl.add_polyline(wks,rainhist,px,py,pgres) temptmsz = Ngl.xy(wks,taus,tempht,tempsfc_res) # ---------------------- overlay, draw, and advance frame --------- Ngl.overlay(rhfill,templine) # Overlay temperature contour on rh plot. Ngl.overlay(rhfill,windlayer) # Overlay windbarbs on rh plot. Ngl.draw(rhfill) Ngl.draw(rainhist) Ngl.draw(temptmsz) Ngl.frame(wks) Ngl.end()
if Animate:
#
# Loop through time (the rightmost dimension of "T") and generate
# a contour/map plot at each time step.
#
for nt in range(1,T.shape[0]):
print "plotting day ",nt+1
#
# There's no need to recreate the contour/map plot since
# the only thing changing is the data and the title, so
# use Ngl.set_values to change the necessary two resources.
# Using Ngl.set_values will result in slightly faster code.
#
srlist2.sfDataArray = T[nt,:,:]
srlist1.lbTitleString = "Day " + str(nt+1)
Ngl.set_values(map.contour,srlist1) # changing labelbar string
Ngl.set_values(map.sffield,srlist2) # changing data
#
# Draw new contour/map plot and advance the frame.
#
Ngl.draw(map.base)
Ngl.frame(wks)
else:
print "Only one frame was generated."
print "Set 'Animate' to True if you want to generate all 31 frames."
Ngl.end()
def simple_contour(lons, lats, vals, cfg):
"""
Generate a simple contour plot, okay
"""
tmpfp = tempfile.mktemp()
rlist = Ngl.Resources()
if cfg.has_key("wkColorMap"):
rlist.wkColorMap = cfg['wkColorMap']
#rlist.wkOrientation = "landscape"
# Create Workstation
wks = Ngl.open_wks( "ps",tmpfp,rlist)
# Create Analysis
if cfg.has_key("_conus"):
analysis, res = grid_conus(lons, lats, vals)
elif cfg.get("_northeast", False):
analysis, res = grid_northeast(lons, lats, vals)
elif cfg.get("_midwest", False):
analysis, res = grid_midwest(lons, lats, vals)
else:
analysis, res = grid_iowa(lons, lats, vals)
analysis = numpy.transpose(analysis)
for key in cfg.keys():
if key == 'wkColorMap' or key[0] == "_":
continue
setattr(res, key, cfg[key])
if cfg.has_key("_MaskZero"):
mask = numpy.where( analysis <= 0.02, True, False)
analysis = numpy.ma.array(analysis, mask=mask)
# Generate Contour
if numpy.min(analysis) == numpy.max(analysis):
if cfg.has_key("_conus"):
res = conus()
elif cfg.has_key("_midwest"):
res = midwest()
else:
res = iowa()
contour = Ngl.map(wks, res)
else:
contour = Ngl.contour_map(wks,analysis,res)
if cfg.has_key("_showvalues") and cfg['_showvalues']:
txres = Ngl.Resources()
txres.txFontHeightF = 0.012
for i in range(len(lons)):
if cfg.has_key("_valuemask") and cfg['_valuemask'][i] is False:
continue
Ngl.add_text(wks, contour, cfg["_format"] % vals[i],
lons[i], lats[i],txres)
Ngl.draw( contour )
watermark(wks)
manual_title(wks, cfg)
vpbox(wks)
Ngl.frame(wks)
del wks
return tmpfp
def simple_grid_fill(xaxis, yaxis, grid, cfg):
"""
Generate a simple plot, but we already have the data!
"""
tmpfp = tempfile.mktemp()
rlist = Ngl.Resources()
if cfg.has_key("wkColorMap"):
rlist.wkColorMap = cfg['wkColorMap']
#rlist.wkOrientation = "landscape"
# Create Workstation
wks = Ngl.open_wks( "ps",tmpfp,rlist)
if cfg.has_key("_conus"):
res = conus()
elif cfg.get("_midwest", False):
res = midwest()
elif cfg.get("_louisiana", False):
res = louisiana2()
else:
res = iowa2()
if cfg.has_key("_MaskZero"):
mask = numpy.where( grid <= 0.01, True, False)
grid = numpy.ma.array(grid, mask=mask)
for key in cfg.keys():
if key == 'wkColorMap' or key[0] == "_":
continue
setattr(res, key, cfg[key])
res.sfXArray = xaxis
res.sfYArray = yaxis
# Generate Contour
contour = Ngl.contour_map(wks,grid,res)
# if cfg.has_key("_showvalues") and cfg['_showvalues']:
# txres = Ngl.Resources()
# txres.txFontHeightF = 0.012
# for i in range(len(xaxis)):
# if cfg.has_key("_valuemask") and cfg['_valuemask'][i] is False:
# continue
# Ngl.add_text(wks, contour, cfg["_format"] % vals[i],
# lons[i], lats[i],txres)
if cfg.has_key('_drawx'):
for lo, la in zip(cfg['_drawx'], cfg['_drawy']):
#print 'Adding Polygon!'
plres = Ngl.Resources()
plres.gsEdgesOn = True
plres.gsEdgeColor = "black"
plres.gsFillColor = -1
Ngl.add_polygon(wks, contour, lo, la, plres)
if cfg.get("_showvalues", False):
txres = Ngl.Resources()
txres.txFontHeightF = 0.012
(rows, cols) = numpy.shape(xaxis)
for row in range(rows):
for col in range(cols):
if xaxis[row,col] > res.mpMinLonF and xaxis[row,col] < res.mpMaxLonF and yaxis[row,col] > res.mpMinLatF and yaxis[row,col] < res.mpMaxLatF:
Ngl.add_text(wks, contour, cfg["_format"] % grid[row, col],
xaxis[row, col], yaxis[row, col], txres)
Ngl.draw(contour)
if cfg.get('_watermark', True):
watermark(wks)
manual_title(wks, cfg)
Ngl.frame(wks)
del wks
return tmpfp
def simple_valplot(lons, lats, vals, cfg):
"""
Generate a simple plot of values on a map!
"""
tmpfp = tempfile.mktemp()
rlist = Ngl.Resources()
if cfg.has_key("wkColorMap"):
rlist.wkColorMap = cfg['wkColorMap']
#rlist.wkOrientation = "landscape"
# Create Workstation
wks = Ngl.open_wks( "ps",tmpfp,rlist)
if cfg.has_key("_conus"):
res = conus()
elif cfg.get("_midwest", False):
res = midwest()
else:
res = iowa()
if cfg.has_key("_spatialDataLimiter"):
xmin, ymin, xmax, ymax = [ min(lons), min(lats),
max(lons), max(lats) ]
res.mpMinLonF = xmin - 0.25
res.mpMaxLonF = xmax + 0.25
res.mpMinLatF = ymin - 0.25
res.mpMaxLatF = ymax + 0.25
res.mpCenterLonF = (xmax + xmin)/2.0 # Central Longitude
res.mpCenterLatF = (ymax + ymin)/2.0 # Central Latitude
res.mpOutlineDrawOrder = "PreDraw"
res.mpUSStateLineColor = 10
res.mpNationalLineColor = 10
for key in cfg.keys():
if key == 'wkColorMap' or key[0] == "_":
continue
setattr(res, key, cfg[key])
plot = Ngl.map(wks, res)
if cfg.has_key("_stationplot"):
Ngl.wmsetp("col", 1)
Ngl.wmsetp("ezf",1)
if cfg.has_key("_removeskyc"):
Ngl.wmsetp("WBC", 0.001) # Get rid of sky coverage
Ngl.wmsetp("WBF", 0) # Put barb at center, no sky coverage
Ngl.wmsetp("WBR", 0.001) # Size of calm circle
#Ngl.wmsetp("WBL", 0.18) # Size of labels
#Ngl.wmsetp("WBS", 0.029) # Size of wind barb shaft
Ngl.wmstnm(wks, lats, lons, vals)
else:
txres = Ngl.Resources()
txres.txFontHeightF = 0.014
txres.txJust = "BottomCenter"
for i in range(len(lons)):
Ngl.add_text(wks, plot, cfg.get("_format",'%s') % vals[i],
lons[i], lats[i],txres)
if cfg.has_key("_labels"):
txres = Ngl.Resources()
txres.txFontHeightF = 0.008
txres.txJust = "TopCenter"
txres.txFontColor = 14
for i in range(len(lons)):
Ngl.add_text(wks, plot, cfg["_labels"][i],
lons[i], lats[i],txres)
watermark(wks)
manual_title(wks, cfg)
Ngl.draw(plot)
vpbox(wks)
Ngl.frame(wks)
del wks
return tmpfp
vpbox(wks)
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()
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()
rlist.cnFillPatterns = patterns
rlist.cnFillScaleF = 0.8
Ngl.set_values(contour,rlist)
Ngl.draw(contour) # Draw the contour plot.
txres = Ngl.Resources() # Annotate plot with some text.
txres.txFontHeightF = 0.015
Ngl.text_ndc(wks,"~F25~U Component", .270,.815,txres)
Ngl.text_ndc(wks,"~F25~(m-s~S~-1~N~)",.765,.815,txres)
txres.txFontHeightF = 0.025 # Label right Y axis.
txres.txAngleF = 90.
Ngl.text_ndc(wks,"~F25~Height (km)",.89,.5,txres)
Ngl.frame(wks) # Advance the frame.
#----------- Begin second plot -----------------------------------------
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "skewt2")
skewtOpts = Ngl.Resources()
skewtOpts.sktWindSpeedMissingV = -999. # Missing value for
# wind speed.
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')
if Animate:
#
# Loop through time (the rightmost dimension of "T") and generate
# a contour/map plot at each time step.
#
for nt in range(1, T.shape[0]):
print("plotting day {}".format(nt + 1))
#
# There's no need to recreate the contour/map plot since
# the only thing changing is the data and the title, so
# use Ngl.set_values to change the necessary two resources.
# Using Ngl.set_values will result in slightly faster code.
#
srlist2.sfDataArray = T[nt, :, :]
srlist1.lbTitleString = "Day {}".format(nt + 1)
Ngl.set_values(map.contour, srlist1) # changing labelbar string
Ngl.set_values(map.sffield, srlist2) # changing data
#
# Draw new contour/map plot and advance the frame.
#
Ngl.draw(map.base)
Ngl.frame(wks)
else:
print("Only one frame was generated.")
print("Set 'Animate' to True if you want to generate all 31 frames.")
Ngl.end()
if(not os.path.exists(filename)):
print("You do not have the necessary shapefiles to run this example.")
print("The comments at the top of this script tell you how to get the files.")
sys.exit()
f = Nio.open_file(filename, "r")
#---Read data off shapefile
mrb_lon = f.variables["x"][:]
mrb_lat = f.variables["y"][:]
#---Add MRB outlines to map
lines = add_mrb_lines(wks,map,mrb_lat,mrb_lon)
#---Draw plot and advance frame. MRB outlines will be included.
Ngl.draw(map)
Ngl.frame(wks)
#
# Get the approximate index values that contain the area
# we don't want to mask.
#
# This will make our gc_inout loop below go faster, because we're
# not checking every single lat/lon point to see if it's inside
# or outside the area of interest.
#
min_mrb_lat = min(mrb_lat)
max_mrb_lat = max(mrb_lat)
min_mrb_lon = min(mrb_lon)
max_mrb_lon = max(mrb_lon)
# # Tropic of capricorn. Note: currently there is a limit on the # number of characters in a line label that prevents the '|' # character from being used between each letter in a label # of this length. # gsres.gsLineLabelString = "tr|o|p|ic of c|a|p|r|i|c|o|rn" py[0:2] = [-23.5, -23.5] Ngl.polyline(wks,map,px[:2],py[:2],gsres) # Arctic circle gsres.gsLineLabelString = "a|r|c|t|i|c c|i|r|c|l|e" py[0:2] = [66.5, 66.5] Ngl.polyline(wks,map,px[:2],py[:2],gsres) # Antarctic circle gsres.gsLineLabelString = "|a|n|t|a|r|c|t|i|c c|i|r|c|l|e" py[0:2] = [-66.5, -66.5] Ngl.polyline(wks,map,px[:2],py[:2],gsres) Ngl.draw(map) # Now draw the map and Ngl.frame(wks) # advance the frame. Ngl.end()
tstrings.append(Ngl.add_text(wks,plot,"CH~B~4~N~ + N~B~2~N~O",2,-.97,txres))
tstrings.append(Ngl.add_text(wks,plot,"Mineral Dust",3,-2.4,txres))
tstrings.append(Ngl.add_text(wks,plot,"Continental ice",4,-4.35,txres))
tstrings.append(Ngl.add_text(wks,plot,"+ sea level",4,-4.6,txres))
tstrings.append(Ngl.add_text(wks,plot,"Vegetation",5,-1.77,txres))
tstrings.append(Ngl.add_text(wks,plot,"Orbital",6,0.45,txres))
#
# Draw two vertical strings on left side of plot.
#
txres.txFontHeightF = 0.009
txres.txAngleF = 90.
Ngl.text_ndc(wks,"cooling",0.11,0.835,txres)
Ngl.text_ndc(wks,"warming",0.11,0.95,txres)
Ngl.draw(plot) # Draw the plot; all of the attached lines and
# text strings will also get drawn.
#==========================================================================
print "Beginning robinson map projection section"
Ngl.define_colormap(wks,"wh-bl-gr-ye-re")
mpres = Ngl.Resources() # New resource list for map plots.
mpres.nglMaximize = False
mpres.nglFrame = False
mpres.vpWidthF = 0.5
mpres.vpHeightF = 0.32
mpres.vpXF = .11
mpres.vpYF = .75
max_cont = 100.0 min_cont = 0.0 res.cnLevelSelectionMode = "ManualLevels" res.cnMinLevelValF = min_cont res.cnMaxLevelValF = max_cont res.cnLevelSpacingF = 2.5 res.cnLineThicknessF = 2.5 # create relative humidity plot for analysis data rh_plot = ngl.contour_map(wks, rh, res) ngl.maximize_plot(wks, rh_plot) ngl.draw(rh_plot) ngl.frame(wks) ngl.destroy(wks) del res ################################################################################################### # open forecast file f_fili = "GFS_forecast_%s_%s.nc" % (init_dt[:8], init_dt[8:10]) forecast = nio.open_file(diri + f_fili) # loop through forecast times for i in range(0, len(fore)):
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
f_fili = "GFS_forecast_%s_%s.nc" % (init_dt[:8], init_dt[8:10])
forecast = nio.open_file(diri + f_fili)
# loop through forecast times
#添加框框#
# 注意必须将frame和draw资源设置为手动
res = Ngl.Resources()
res.nglFrame = False # Don't draw plot or advance frame
res.nglDraw = False # until we add shapefile outlines.
# 注意必须先转换成一维的数组
lnres = Ngl.Resources()
lnres.gsLineColor="red"
lnres.gsLineThicknessF=5.0
lnres.gsLineDashPattern=0
lon=np.ravel([80,100,100,80,80])
lat=np.ravel([50,50,0,0,50])
lnid = Ngl.add_polyline(wks,plot,lon,lat,lnres)
# 手动画图和翻页
Ngl.draw(plot)
Ngl.frame(wks) # 翻页操作
vp # view port(vp) 和视觉有关的属性
# make plot bigger
res.vpXF = 0.1
res.vpYF = 0.9
res.vpWidthF = 0.8
res.vpHeightF = 0.8
# 画地图的等值线图
contour = Ngl.contour_map
Ngl.draw(contour) # 画到画布上
Ngl.frame(wks)
# 循环画图时特别重要, The maximum number of open PostScript workstations is fifteen.
bres.tmXBLabelFontHeightF = 0.015 # Make labels smaller. This
# will affect Y labels too.
#---Set the values and labels for the Y axis of blank plot.
bres.tmYLMode = "Explicit"
bres.tmYLValues = lat_values
bres.tmYLLabels = lat_labels
#---Align four corners of both plots, that is, don't do data transformation
bres.tfDoNDCOverlay = True
#---Create the blank plot.
blank = Ngl.blank_plot(wks,bres)
#---Draw blank plot for debugging purposes.
#Ngl.draw(blank)
#Ngl.frame(wks)
#---Overlay blank plot on existing map plot.
Ngl.overlay(map.base,blank)
#---Resize map so it fits within frame
Ngl.maximize_plot(wks, map)
#---Drawing the original map also draws the overlaid blank plot
Ngl.draw(map)
Ngl.frame(wks)
Ngl.end()
res.mpMinLatF = lattr
res.mpMaxLatF = latbl
res.mpOutlineBoundarySets = "AllBoundaries"
res.mpNationalLineColor = -1
res.mpNationalLineThicknessF = 2.0
res.mpGeophysicalLineColor = -1
res.mpGeophysicalLineThicknessF = 2.0
res.mpGridLatSpacingF = 5.0
res.mpGridLonSpacingF = 5.0
res.mpGridLineThicknessF = 2.0
res.mpGridLineColor = colour
res.cnMonoLineColor = True
grid_plot = ngl.map(wks, res)
ngl.maximize_plot(wks, grid_plot)
ngl.draw(grid_plot)
ngl.frame(wks)
ngl.destroy(wks)
del res
os.system('mogrify -trim grid_' + colour + '_' + region + '.png')
# if region == "WA" or region == "unknownWA":
# os.system('mogrify -trim grid_'+colour+'_'+region+'.png')
# os.system('mogrify -resize 886x600 grid_'+colour+'_'+region+'.png')
# elif region == "EA" or region == "unknownEA":
# os.system('mogrify -trim grid_'+colour+'_'+region+'.png')
# os.system('mogrify -resize 600x733 grid_'+colour+'_'+region+'.png')
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)
# and that the appropriate tickmarks were turned off in certain areas, # these two plots should look like one plot, but with two separate # curves and two separate Y axes. # # Also, change the main title of the first plot to reflect that # we have two vars here, and remove the main title from the second plot. # sres = Ngl.Resources() sres.tiMainString = "Two variables with individual scales" Ngl.set_values(plot1, sres) sres = Ngl.Resources() sres.tiMainOn = False Ngl.set_values(plot2, sres) Ngl.draw(plot1) Ngl.draw(plot2) Ngl.frame(wks) # # Just to show it can be done, add the second plot as an annotation # of the first plot. # sres = Ngl.Resources() sres.amZone = 0 # '0' means centered over base plot. sres.amResizeNotify = True anno = Ngl.add_annotation(plot1, plot2, sres) # # Change the main title to reflect this is an added annotation. #
def plot_dots(vpx=0.2,vpy=0.8,hei=0.3,wth=0.4,xlab='',ylab='',legend=True, yrev=False, lab=''):
res = Ngl.Resources()
res.nglFrame = False
res.nglPaperOrientation = 'Portrait'
res.nglMaximize = False
res.nglDraw = False
res.xyMarkLineMode = 'Markers'
res.xyMonoMarkerColor = True
res.xyMarkerColor = 'black'
res.xyMarkerThicknessF = 2
res.xyMarkerSizeF = 0.01
res.tiYAxisString = ylab
res.tiXAxisString = xlab
res.tiMainString = lab
res.tiMainFontHeightF = 0.015
res.tiMainOffsetYF = -hei/4.7
res.tiMainOffsetXF = -wth * 2./5.
res.trXMinF = xmin
res.trXMaxF = xmax
res.trYMinF = ymin
res.trYMaxF = ymax
res.trYReverse = yrev
res.vpXF = vpx
res.vpYF = vpy
res.vpHeightF = hei
res.vpWidthF = wth
xy = Ngl.xy(wks,np.transpose(np.vstack((vx,vx))),np.transpose(np.vstack((vy,vy))),res)
if(1):
## Fit line
k,m,cor,sig,err = stats.linregress(vx,vy)
vx2 = np.linspace(xmin,xmax,10)
vy2 = vx2 * k + m
# Plot line
lres = Ngl.Resources()
lres.gsLineColor = 'black'
lres.gsLineDashPattern = 1
lres.gsLineThicknessF = 2
line = Ngl.add_polyline(wks,xy,vx2,vy2,lres)
# Print correlations
text = 'k = %.2f R = %4.2f' % (k,cor)
if (sig*100. < 1.):
text = text + ' (99% sign.)'
elif (sig*100. < 5.):
text = text + ' (95% sign.)'
else:
text = text + ' (not sign.)'
tres = Ngl.Resources()
tres.txFontHeightF = 0.012
tres.txJust = 'CenterLeft'
tres.txFontColor = 'black'
Ngl.text_ndc(wks,text,vpx,vpy+0.02,tres)
Ngl.draw(xy)
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" nrlist.cnLineLabelDensityF = 3. # < 1.0 = less, > 1.0 = more nrlist.cnLineLabelInterval = 1 # Label every line if possible. Ngl.set_values(contour,nrlist)
vcres.vcLineArrowThicknessF = 2.0 # create vector plot for analysis data and overlay on colour contours level 1 uv_plot1 = ngl.vector(wks,u_1,v_1,vcres) # create vector plot for analysis data and overlay on colour contours level2 vcres.vcLineArrowColor = "Red" uv_plot2 = ngl.vector(wks,u_2,v_2,vcres) ngl.overlay(shear_plot,uv_plot1) ngl.overlay(shear_plot,uv_plot2) ngl.maximize_plot(wks, shear_plot) ngl.draw(shear_plot) ngl.frame(wks) ngl.destroy(wks) del res del vcres ################################################################################################### # open forecast file f_fili = "GFS_48h_forecast_%s_%s.nc" % (init_dt[:8], init_dt[8:10]) forecast = nio.open_file(diri+f_fili) # loop through forecast times
# Skew-T resource names decided on. # import Ngl wks_type = "ps" wks = Ngl.open_wks (wks_type, "skewt1") # # First plot - default background # skewtOpts = Ngl.Resources() skewtOpts.tiMainString = "Default Skew-T" skewt_bkgd = Ngl.skewt_bkg(wks, skewtOpts) Ngl.draw(skewt_bkgd) Ngl.frame(wks) del skewtOpts # # Second plot - fancier background # skewtOpts = Ngl.Resources() skewtOpts.sktColoredBandsOn = True # default is False skewtOpts.sktHeightScaleOn = True # default is False skewtOpts.tiMainString = "USAF Skew T, Log(p); DOD-WPC 9-16-1" skewt_bkgd = Ngl.skewt_bkg(wks, skewtOpts) Ngl.draw(skewt_bkgd) Ngl.frame(wks) del skewtOpts
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 gnres.gsFillColor = "white" lat_box = [-30, -30, 30, 30, -30] lon_box = [-120, 120, 120, -120, -120] # Add a partially opaque filled polygon box to the filled contours
def trj(wks, x, y, time, res=None):
''' Plots trajectories with arrow heads attached.
plot = trj(wks, x, y, time, res=None)
wks : The identifier returned from calling Ngl.open_wks.
x,y : The X and Y coordinates of the curve(s). These values can be
one-dimensional NumPy arrays, NumPy masked arrays or two-dimensional
NumPy arrays. If x and/or y are two-dimensional, then the leftmost
dimension determines the number of curves.
time : Time coordinates of the trajectories. These values can be
one-dimensional NumPy arrays, NumPy masked arrays
res: Resource list using following special resources:
Special resources:
trjArrowStep : Number of samples between arrow heads.
Default: 10
trjArrowOffsetF : Shift the position of the arrows along the curve.
Should be between 0. and 1.
Default: 0.5
trjArrowDirection : can be 'pos' or 'neg' to select direction of the
arrows.
Default: 'pos'
trjArrowXShapeF : Array containing the x NDC coordinates of the arrow
head relative to the heads centre.
Default: Equiliteral triangle
trjArrowYShapeF : Array containing the y NDC coordinates of the arrow
head relative to the heads centre.
Default: Equiliteral triangle
trjArrowXScaleF : Scales arrow head in X direction, befor rotation is
applied.
Default: 1.
trjArrowYScaleF : Scales arrow head in y direction, befor rotation is
applied.
Default: 1.
trjArrowSizeF : Scales the size of an arrow head.
Default: 0.02
The arrow heads are plotted with add_polygon, so all gs* attributes of the
resource list are applied to the arrow heads polygon.
'''
if not res:
res = ngl.Resources()
# set default values:
if not hasattr(res, 'trjArrowStep'):
res.trjArrowStep = 10
if not hasattr(res, 'trjArrowOffsetF'):
res.trjArrowOffsetF = .5
else:
res.trjArrowOffsetF -= np.floor(res.trjArrowOffsetF)
if not hasattr(res, 'trjArrowDirection'):
res.trjArrowDirection = 'pos'
if not hasattr(res, 'trjArrowXShapeF'):
res.trjArrowXShapeF = [np.sqrt(3) / 3.,
-np.sqrt(3) / 6.,
-np.sqrt(3) / 6.]
res.trjArrowXShapeF = np.asarray(res.trjArrowXShapeF)
if not hasattr(res, 'trjArrowYShapeF'):
res.trjArrowYShapeF = [0., .5, -.5]
res.trjArrowYShapeF = np.asarray(res.trjArrowYShapeF)
if not hasattr(res, 'trjArrowXScaleF'):
res.trjArrowXScaleF = 1.
if not hasattr(res, 'trjArrowYScaleF'):
res.trjArrowYScaleF = 1.
if not hasattr(res, 'trjArrowSizeF'):
res.trjArrowSizeF = .02
# check for draw and frame
if hasattr(res, "nglDraw"):
doDraw = res.nglDraw
else:
doDraw = True
res.nglDraw = False
if hasattr(res, "nglFrame"):
doFrame = res.nglFrame
else:
doFrame = True
res.nglFrame = False
# Convert to mask array
x = np.ma.asarray(x)
y = np.ma.asarray(y)
time = np.ma.asarray(time)
# check input data
if x.shape != y.shape:
raise ValueError("Inconsistend shape. x, y and time must have the "
+ "same shape.")
if x.ndim < 1 or x.ndim > 2:
raise ValueError("Input arrays x and y must be of rank 1 or 2.")
if np.rank(x) == 1:
# add singleton dimension to the begining
x = x[np.newaxis, ...]
y = y[np.newaxis, ...]
# Dimension of trajectories
dim = 0
# mask all missig values
np.ma.masked_where(y.mask, x, copy=False)
np.ma.masked_where(x.mask, y, copy=False)
# create line plot resource
res_lines = copy.deepcopy(res)
# remove trj* attributes from res_lines
for attr in dir(res_lines):
if attr[0:3] == "trj" or (attr[0:2] == "gs" and attr[2] != "n"):
delattr(res_lines, attr)
# create line plot
plot = ngl.xy(wks, x, y, res_lines)
# get axes length in data coordinates
xAxisLen, yAxisLen = [ngl.get_float(plot, "tr" + ax + "MaxF")
- ngl.get_float(plot, "tr" + ax + "MinF")
for ax in "XY"]
# get marker color
# to be implemented
# place Marker
marker_id = []
for t in xrange(x.shape[dim]):
xt = x[t, ...].compressed()
yt = y[t, ...].compressed()
tt = time[::res.trjArrowStep]
# shift time by offset
if res.trjArrowOffsetF != 0.:
tt = tt[:-1] + res.trjArrowOffsetF * (tt[1:] - tt[:-1])
# itterate over markers
for tm in tt:
# find nearest indices in time array
idx = (np.abs(time - tm)).argmin().min()
if time[idx] < tm:
idx1 = idx
idx2 = idx + 1
elif time[idx] > tm:
idx1 = idx - 1
idx2 = idx
else:
if idx == 0:
idx1 = idx
idx2 = idx + 1
else:
idx1 = idx - 1
idx2 = idx
if idx >= len(xt) - 1:
continue
# interpolate linearly to get coordinates
ds = (tm - time[idx1]) / (time[idx2] - time[idx1])
xm, ym = [coord[idx1] + ds * (coord[idx2] - coord[idx1])
for coord in [xt, yt]]
x1, y1 = ngl.datatondc(plot, xt[idx1], yt[idx1])
x2, y2 = ngl.datatondc(plot, xt[idx2], yt[idx2])
angle = np.arctan2(y2 - y1, x2 - x1)
# create marker resource
res_marker = copy.deepcopy(res)
# scale adjust marker scale
res_marker.trjArrowXScaleF = res.trjArrowXScaleF * xAxisLen
res_marker.trjArrowYScaleF = res.trjArrowYScaleF * yAxisLen
marker_id.append(
_plot_trj_marker(wks, plot, xm, ym, angle, res_marker))
if doDraw:
ngl.draw(plot)
res.nglDraw = True
if doFrame:
ngl.frame(wks)
res.nglFrame = True
return plot
res.mpGeophysicalLineColor = "gray40" res.mpGeophysicalLineThicknessF = 1.5 res.cnMonoLineColor = True res.cnLevelSelectionMode = "ManualLevels" res.cnMinLevelValF = 0.0 res.cnMaxLevelValF = 65.0 res.cnLevelSpacingF = 2.5 res.cnLineThicknessF = 2.5 # create PWAT plot for analysis data PWAT_plot = ngl.contour_map(wks, PWAT, res) ngl.maximize_plot(wks, PWAT_plot) ngl.draw(PWAT_plot) ngl.frame(wks) ngl.destroy(wks) del res del PWAT #del CIN ################################################################################################### # open forecast file f_fili = "GFS_forecast_%s_%s.nc" % (init_dt[:8], init_dt[8:10]) forecast = nio.open_file(diri + f_fili) # loop through forecast times
# 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. # resources.tiMainString = "Interpolation to a 2D grid (RasterFill)" resources.cnFillMode = 'RasterFill' contour = Ngl.contour(wks,zt,resources) # # Draw polymarkers again. # Ngl.add_polymarker(wks,contour,x,y,poly_res) Ngl.draw(contour)
def draw_colormap(wks, cmap):
#---Read as RGBA array
cmapr = Ngl.read_colormap_file(cmap)
#---Set some options
orientation = "horizontal" # "horizontal" or "vertical"
lbres = Ngl.Resources()
if (orientation == "vertical"):
width = 0.2
height = 1.0 # full height of viewport
xpos = 0.5 - width / 2. # centered
ypos = 1.0 # flush w/top of viewport
else:
height = 0.2
width = 1.0 # full width of viewport
xpos = 0.0 # flush right
ypos = 0.5 + height / 2. # centered
lbres.vpWidthF = width
lbres.vpHeightF = height
lbres.lbOrientation = orientation
lbres.lbLabelsOn = False
lbres.lbBoxLinesOn = False
#---Make sure labelbar fills the viewport region we specified
lbres.lbBoxMinorExtentF = 1.0
lbres.lbTopMarginF = 0.0
lbres.lbBottomMarginF = 0.0
lbres.lbRightMarginF = 0.0
lbres.lbLeftMarginF = 0.0
#---Necessary so we get all solid fill
lbres.lbMonoFillPattern = True
lbres.lbFillColors = cmapr
cmaplen = cmapr.shape[0]
labels = ["" for x in range(cmaplen)]
lbid = Ngl.labelbar_ndc(wks, cmaplen, labels, xpos, ypos, lbres)
Ngl.draw(lbid)
#---Draw a box around the labelbar
xbox = [xpos, xpos + width, xpos + width, xpos, xpos]
ybox = [ypos, ypos, ypos - height, ypos - height, ypos]
lnres = Ngl.Resources()
lnres.gsLineThicknessF = 1.5
Ngl.polyline_ndc(wks, xbox, ybox, lnres)
lnres = Ngl.Resources()
lnres.gsLineThicknessF = 1.5
Ngl.polyline_ndc(wks, xbox, ybox, lnres)
#---Add name of the color map
txres = Ngl.Resources()
txres.txFontHeightF = 0.02
if (orientation == "vertical"):
txres.txJust = "TopRight"
Ngl.text_ndc(wks, cmap, xpos, 1.0, txres)
else:
txres.txJust = "BottomCenter"
Ngl.text_ndc(wks, cmap, 0.5, ypos, txres)
Ngl.frame(wks) # Advance the frame
res3.tmYLLabelsOn = False res3.tmYLMinorOn = False # # Turn on the right Y labels and tickmarks and move the axis string to # the right. # res3.tmYRLabelsOn = False res3.tmYROn = False res3.tmYUseLeft = False res3.tmYRFormat = "f" # Gets rid of unnecessary trailing zeros plot3 = Ngl.xy(wks,x2,y2,res3) # Two plots sres = Ngl.Resources() Ngl.set_values(plot1,sres) sres = Ngl.Resources() Ngl.set_values(plot2,sres) sres = Ngl.Resources() Ngl.set_values(plot3,sres) Ngl.draw(plot1) Ngl.draw(plot2) Ngl.draw(plot3) Ngl.frame(wks)
res.mpGeophysicalLineColor = "gray40" res.mpGeophysicalLineThicknessF = 1.5 res.cnMonoLineColor = True res.cnLevelSelectionMode = "ManualLevels" res.cnMinLevelValF = 1000.0 res.cnMaxLevelValF = 6000.0 res.cnLevelSpacingF = 250.0 res.cnLineThicknessF = 2.5 # create PWAT plot for analysis data MD_plot = ngl.contour_map(wks,MD,res) ngl.maximize_plot(wks, MD_plot) ngl.draw(MD_plot) ngl.frame(wks) ngl.destroy(wks) del res del MD #del CIN ################################################################################################### # open forecast file f_fili = "GFS_forecast_%s_%s.nc" % (init_dt[:8], init_dt[8:10]) forecast = nio.open_file(diri+f_fili) # loop through forecast times
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
res.tiMainFontColor = "Navy" res.tiMainOffsetYF = 0.02 res.tiMainFontHeightF = 0.035 res.tiYAxisString = "Left Y axis string" res.stMonoLineColor = False # Use multiple colors for streamlines. res.stLineThicknessF = 2.0 # Twice as thick res.lbOrientation = "Horizontal" res.pmLabelBarOrthogonalPosF = -0.02 res.pmLabelBarHeightF = 0.1 res.pmLabelBarWidthF = 0.6 plot = Ngl.streamline_scalar(wks, u, v, spd, res) # Create streamline plot. txres = Ngl.Resources() # Text resources desired txres.txFontColor = "OrangeRed" subtitles(wks, plot, "Left string", "Center string", "Right string", txres) del txres.txFontColor # Go back to foreground color (black) txres.txFontHeightF = 0.029 right_axis(wks, plot, "Right Y axis string", txres) Ngl.maximize_plot(wks, plot) Ngl.draw(plot) # Drawing the plot will draw the three subtitles attached. Ngl.frame(wks) Ngl.end()
res.trXMaxF = 20.0 res.trXMinF = 0.0 res.trYMinF = 0.0 # # Width, height, and position of X axis. Every time the # axis is redrawn, the vpYF resource will be changed to change # the position. # res.vpXF = 0.2 res.vpYF = 0.9 res.vpHeightF = 0.02 res.vpWidthF = 0.7 xy = Ngl.xy(wks, x, y, res) Ngl.draw(xy) # # Draw 9 different plots demonstrating control of the bottom x-axis # tickmark labels using the XBFormat string resource. See the description # of the Floating Point Format Specification scheme in the HLU reference # guide to learn about the semantics and syntax of the format string: # # http://www.ncl.ucar.edu/Document/Graphics/format_spec.shtml # # There are links to this description in the TickMark reference pages under # the entries for the format string resources (XBFormat, for example). # sres = Ngl.Resources()
res.mpFillOn = False #-- don't draw map outlines
res.mpGridAndLimbOn = False #-- don't draw grid lines
res.cnFillMode = "CellFill" #-- set fill mode
res.sfXArray = x #-- transform x to mesh scalar field
res.sfYArray = y #-- transform y to mesh scalar field
res.sfXCellBounds = vlon #-- needed if set cnFillMode = "CellFill"
res.sfYCellBounds = vlat #-- needed if set cnFillMode = "CellFill"
res.tiMainString = "ICON grid - raster fill"
res.tiMainOffsetYF = 0.03 #-- move main title towards plot
#-- create the contour plot to retrieve levels and colors
plot = Ngl.contour_map(wks,var,res)
Ngl.draw(plot)
Ngl.frame(wks)
del(levels)
#========== second plot ============
#-- retrieve the colors and levels from contour plot
levels = list(Ngl.get_float_array(plot.contour,"cnLevels"))
labels = list(Ngl.get_string_array(plot.contour,"cnLevels"))
nlevels = len(levels)
#-- define colormap (RGB value range 0-255)
colormap255 = np.array([[ 0, 0, 0], [255, 255, 255], [ 80, 255, 255], \
[ 27, 44, 98], [ 45, 102, 175], [ 66, 133, 196], \
[ 90, 166, 217], [119, 189, 230], [148, 211, 243], \
[215, 239, 249], [237, 248, 252], [255, 255, 255], \
# Make sure map plot is same size as contour/map plot. mpres.nglMaximize = False # this must be turned off otherwise plot will be resized! mpres.vpXF = bres.vpx mpres.vpYF = bres.vpy mpres.vpWidthF = bres.vpw mpres.vpHeightF = bres.vph # Turn off since they are already drawn in contour/map plot. mpres.pmTickMarkDisplayMode = "Never" # Make the ocean white and land transparent. This will mask out the contour fill over ocean. mpres.mpOceanFillColor = "white" mpres.mpInlandWaterFillColor = "white" mpres.mpLandFillColor = "transparent" # Create a map plot with the white ocean and transparent land. map_plot = Ngl.map(wks, mpres) # # If you need to resize the plots later---for example, to use in # a panel---then it's easier to make one plot an annotation of # the other. # annoid = Ngl.add_annotation(contour_map_plot, map_plot) Ngl.draw(contour_map_plot) # This draws both plots Ngl.frame(wks) Ngl.end()
resources.vpYF = 0.90 # Change size and location of plot. resources.vpXF = 0.18 resources.vpWidthF = 0.74 resources.vpHeightF = 0.74 resources.trYMaxF = 980 # Set the maximum value for the Y axes. resources.tiYAxisString = "Depth of a subsurface stratum" xy = Ngl.xy(xwks, xo, zt[2:9:2, :], resources) # Draw an XY plot. #----------- Draw to other workstations ------------------------------ Ngl.change_workstation(contour, cgmwks) # Change the workstation that the Ngl.change_workstation(xy, cgmwks) # contour and XY plot is drawn to. Ngl.draw(contour) # Draw the contour plot to the new Ngl.frame(cgmwks) # workstation and advance the frame. Ngl.draw(xy) # Draw the XY plot to the new Ngl.frame(cgmwks) # workstation and advance the frame. Ngl.change_workstation(contour, pswks) # Do the same for the PostScript Ngl.change_workstation(xy, pswks) # workstation. Ngl.draw(contour) Ngl.frame(pswks) Ngl.draw(xy) Ngl.frame(pswks) Ngl.change_workstation(contour, pdfwks) # And for the PDF workstation... Ngl.change_workstation(xy, pdfwks) Ngl.draw(contour) Ngl.frame(pdfwks)
dirc = Ngl.pynglpath("data")
f = Nio.open_file(os.path.join(dirc,"cdf","uv300.nc"))
u = f.variables["U"][0,:,8]
lat = f.variables["lat"][:]
#---Start the graphics section
wks_type = "png"
wks = Ngl.open_wks (wks_type,"newcolor2") # Open "newcolor2.png" for graphics
#---Set some resources.
res = Ngl.Resources()
res.nglDraw = False
res.nglFrame = False
plot = Ngl.xy (wks,lat,u,res) # Create plot
txres = Ngl.Resources() # text mods desired
txres.txFont = 30
txres.txFontHeightF = 0.04 # font smaller. default big
txres.txFontOpacityF = 0.10 # highly transparent
txres.txAngleF = 45.
txid = Ngl.add_text(wks,plot,"Preliminary Data",10,15,txres)
Ngl.draw(plot) # Drawing plot will draw attached text
Ngl.frame(wks) # Advance frame
Ngl.end()
resources.vpYF = 0.90 # Change size and location of plot. resources.vpXF = 0.18 resources.vpWidthF = 0.74 resources.vpHeightF = 0.74 resources.trYMaxF = 980 # Set the maximum value for the Y axes. resources.tiYAxisString = "Depth of a subsurface stratum" xy = Ngl.xy(xwks,xo,zt[2:9:2,:],resources) # Draw an XY plot. #----------- Draw to other workstations ------------------------------ Ngl.change_workstation(contour,cgmwks) # Change the workstation that the Ngl.change_workstation(xy,cgmwks) # contour and XY plot is drawn to. Ngl.draw(contour) # Draw the contour plot to the new Ngl.frame(cgmwks) # workstation and advance the frame. Ngl.draw(xy) # Draw the XY plot to the new Ngl.frame(cgmwks) # workstation and advance the frame. Ngl.change_workstation(contour,pswks) # Do the same for the PostScript Ngl.change_workstation(xy,pswks) # workstation. Ngl.draw(contour) Ngl.frame(pswks) Ngl.draw(xy) Ngl.frame(pswks) Ngl.change_workstation(contour,pdfwks) # And for the PDF workstation... Ngl.change_workstation(xy,pdfwks) Ngl.draw(contour) Ngl.frame(pdfwks)
#
wks_type = "ps"
wks = Ngl.open_wks(wks_type, "skewt2")
skewtOpts = Ngl.Resources()
skewtOpts.sktWindSpeedMissingV = -999. # Missing value for
# wind speed.
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')
plres.gsFillColor = colors[2]
if (unemp[i] >= 0.04):
plres.gsFillColor = colors[3]
startSegment = geometry[i][geom_segIndex]
numSegments = geometry[i][geom_numSegs]
lines = []
for seg in range(startSegment, startSegment+numSegments):
startPT = segments[seg, segs_xyzIndex]
endPT = startPT + segments[seg, segs_numPnts] - 1
lines.append(Ngl.add_polygon(wks, plot, lon[startPT:endPT], \
lat[startPT:endPT], plres))
segNum = segNum + 1
Ngl.draw(plot)
# Make a labelbar...
labels = [ "1", "2", "3", "4" ]
lres = Ngl.Resources()
lres.vpWidthF = 0.50 # location
lres.vpHeightF = 0.05 # " "
lres.lbPerimOn = False # Turn off perimeter.
lres.lbOrientation = "Horizontal" # Default is vertical.
lres.lbLabelAlignment = "BoxCenters" # Default is "BoxCenters".
lres.lbFillColors = colors
lres.lbMonoFillPattern = True # Fill them all solid.
lres.lbLabelFontHeightF = 0.012 # label font height
lres.lbTitleString = "percent" # title
lres.lbTitlePosition = "Bottom" # location of title
def plot_oope_map(data, figname, size_class=None, percentage=1):
''' Draws 2D OOPE maps.
:param xarray.Dataset data: 2D OOPE array. Dims must be (y, x, comm, size)
:param str figname: Name of the figure file (must end by .png or .pdf)
:param list size_class: Size classes to output (in m)
:param float percentage: percentage used to saturate colorbar from percentile.
Colorbar is saturated from values of the (X) and (100 - X) percentile.
:return: None
'''
if size_class is None:
size_class = [1e-3, 1e-2, 1e-1, 1]
# sort size class in ascending order, and add 0 and infinity as size bounds
size_class = np.sort(size_class)
if size_class[0] != 0:
size_class = np.concatenate(([0], size_class), axis=0)
if size_class[-1] != np.Inf:
size_class = np.concatenate((size_class, [np.Inf]), axis=0)
# Check that the OOPE dataset has 4 dimensions (i.e. no time dimension)
ndims = len(data['OOPE'].dims)
if ndims != 4:
message = 'Data must have dimensions of size (lat, lon, comm, wei)'
print(message)
sys.exit(0)
# Recover data variables
length = data['length'].values
oope = data['OOPE'].values
lon = data['longitude'][:].values
lat = data['latitude'][:].values
comm = data['community'][:].values.astype(np.int)
# mask oope where land
oope = np.ma.masked_where(np.isnan(oope), oope)
comm_string = misc.extract_community_names(data)
if figname.endswith('png'):
form = 'png'
elif figname.endswith('pdf'):
form = 'pdf'
else:
message = 'Figure name should end with png or pdf'
print(message)
sys.exit(0)
# opens the document
wks = Ngl.open_wks(form, figname)
# set the document colormap
# resngl = Ngl.Resources()
# resngl.wkColorMap = 'precip2_15lev'
# Ngl.set_values(wks, resngl)
# Add gray to the workspace
Ngl.new_color(wks, 0.7, 0.7, 0.7)
# init the plot resources
# For a detailed description, see https://www.ncl.ucar.edu/Document/Graphics/Resources/
res = Ngl.Resources()
# not necessary, just a good habit
res.nglDraw = False
res.nglFrame = False
# Set map resources.
res.mpLimitMode = "LatLon" # limit map via lat/lon
res.mpMinLatF = lat.min() # map area
res.mpMaxLatF = lat.max() # latitudes
res.mpMinLonF = lon.min() # and
res.mpMaxLonF = lon.max() # longitudes
res.mpFillOn = True
res.mpLandFillColor = "LightGray"
res.mpOceanFillColor = -1
res.mpInlandWaterFillColor = "LightBlue"
res.mpGeophysicalLineThicknessF = 1 # thickness of coastlines
# coordinates for contour plots
res.sfXArray = lon
res.sfYArray = lat
res.cnFillOn = True # filled contour
res.cnLinesOn = False # no lines
res.cnLineLabelsOn = False # no labels
res.cnInfoLabelOn = False # no info about contours
res.cnFillMode = 'CellFill' # contourf=AreaFill, pcolor="CellFill" or "RasterFill"
res.lbOrientation = "Horizontal" # colorbar orientation
res.lbLabelFontHeightF = 0.012 # colorbar label fontsize
res.lbTitlePosition = "Bottom" # position of colorbar title
res.lbTitleFontHeightF = 0.012 # title font height
# res.cnFillPalette = "wgne15"
res.cnFillPalette = "WhiteBlueGreenYellowRed"
txres = Ngl.Resources()
txres.txJust = "BottomCenter"
txres.txFontHeightF = 0.02
res.cnLevelSelectionMode = 'ExplicitLevels'
res.cnMaxLevelCount = 41
res.mpGridAndLimbOn = True
# Equations are complicated with NCARG
# see https://www.ncl.ucar.edu/Applications/fcodes.shtml
res.lbTitleString = "OOPE (J.kg~S~-1~N~.m~S~-2~N~)" # title of colorbar
# Loop over communities
for icom in comm:
# Loop over size classes
for isize in xrange(0, len(size_class) - 1):
# Extract sizes comprised between the size class bound
iw = np.nonzero((length >= size_class[isize]) & (length < size_class[isize+1]))[0]
if iw.size == 0:
continue
# Integrate OOPE for the given community and given size class
temp = oope[:, :, icom, iw]
temp = np.sum(temp, axis=-1)
# Finds the colorbar limits
cmin, cmax = misc.find_percentile(temp, percentage=1)
# draw the contour maps
# defines the contour
res.cnLevels = np.linspace(cmin, cmax, res.cnMaxLevelCount)
mapplot = Ngl.contour_map(wks, temp, res)
# add title
title = 'Community=%s, %.2E m <= L < %.2E m' % (comm_string[icom], size_class[isize], size_class[isize + 1])
Ngl.text_ndc(wks, title, 0.5, 0.85, txres)
# draws the map
Ngl.draw(mapplot)
# add a page to the pdf output
Ngl.frame(wks)